# AppliedXL > AppliedXL builds intelligence infrastructure for the information industry. Its editorial algorithms turn the public record — clinical trial registries, regulatory filings, and other institutional sources — into source-linked signals, indicators, forecasts, and market resolution, delivered before events become news. AppliedXL was founded by computational journalists. Its first vertical is biopharma: machine-scale monitoring of clinical trials and regulatory activity, probability-of-success forecasts, and authoritative event resolution for prediction markets. Contact: support@appliedxl.com. Sitemaps: [Site](https://www.appliedxl.com/sitemap-index.xml) · [News index](https://appliedxl.com/news/sitemap-index.xml) · [News](https://appliedxl.com/news/news-sitemap.xml) ## Platform - [Intelligence Infrastructure for the Information Industry](https://www.appliedxl.com/): AppliedXL turns the public record into verified signals, indicators, and forecasts that enable newsrooms, exchanges, and financial desktops to lead the AI era. - [AXL Core](https://www.appliedxl.com/platform): One system, four layers. The Atlas maps a domain. Four products compute on the map. The trust chain verifies every output. Delivery puts it in your product. - [Turn your domain expertise into AI-ready intelligence.](https://www.appliedxl.com/platform/atlas): AXL Atlas turns the judgment of an expert desk into a structured map of sources, subjects, events, signals, actions, outcomes, and evidence. - [An Atlas in action.](https://www.appliedxl.com/platform/atlas-in-action): Every field and event type in AppliedXL's clinical reference build. An interactive map of the Pharma Industry Atlas: seven layers, from sources to outcomes. - [Every product, in your infrastructure.](https://www.appliedxl.com/platform/delivery): Signals, indicators, forecasts, and resolution delivered through the systems you already run: feeds, APIs, and MCP — point-in-time correct and source-linked. - [Trust is the product.](https://www.appliedxl.com/platform/trust-chain): Computational journalism applied to commercial intelligence: verification before delivery, corrections on the record, and a visible chain from source to signal. ## Products - [Event probability, learned from history.](https://www.appliedxl.com/forecasts): Forecasts learns which patterns precede which outcomes, and turns today's events into tomorrow's probabilities. - [Recurring events, turned into benchmarks.](https://www.appliedxl.com/indicators): Indicators turns the events Signals detects into measures your clients can't get anywhere else: averages, rates, rankings, and indices. - [Outcomes resolved against the official record.](https://www.appliedxl.com/resolution): Resolution monitors predefined events, verifies the evidence, and produces documented outcome analysis for exchange settlement. - [Material change, detected before consensus.](https://www.appliedxl.com/signals): Signals reads the official records in your domain and turns material change into the alerts, briefings, and news your product delivers. ## Solutions - [Built for the companies that create intelligence.](https://www.appliedxl.com/solutions): Turn primary evidence into proprietary signals your clients can trust, act on, and feed into machines. - [Benchmarks from institutional events.](https://www.appliedxl.com/solutions/exchanges): Benchmarks and indices built from institutional events: AppliedXL turns verified public-record changes into computable benchmarks for exchanges and index providers. - [Specialized feeds your terminal doesn't have yet.](https://www.appliedxl.com/solutions/financial-desktops): Specialized event feeds for financial desktops and terminals: source-linked biopharma signals, indicators, and forecasts delivered ahead of the news cycle. - [Your beats, monitored at machine scale.](https://www.appliedxl.com/solutions/newsrooms): AppliedXL monitors your beats at machine scale: verified, source-linked event detection that gives newsroom reporters a head start on the public record. - [More markets. Authoritative resolution.](https://www.appliedxl.com/solutions/prediction-markets): More markets with authoritative resolution: AppliedXL supplies event definitions, live probabilities, and source-linked settlement for prediction markets. - [Risk, detected before the balance sheet.](https://www.appliedxl.com/solutions/ratings-risk): Risk detected before the balance sheet: AppliedXL surfaces early, source-linked risk signals for ratings and risk intelligence providers. - [Deep coverage without linear analyst growth.](https://www.appliedxl.com/solutions/specialist-providers): Deep vertical coverage without linear analyst growth: AppliedXL event detection expands specialist information products at machine scale. ## Themes - [Where new information markets appear first.](https://www.appliedxl.com/themes): When a change is coming, the record moves before the market does. AppliedXL maps the sources, entities, events, and outcomes behind that change. - [Infrastructure intelligence for the AI economy.](https://www.appliedxl.com/themes/ai-infrastructure): AppliedXL tracks power, land, interconnection, and data-center buildout records, giving investors verified early signals on AI infrastructure capacity. - [Supply intelligence for strategic capacity.](https://www.appliedxl.com/themes/critical-materials): AppliedXL monitors mining, processing, and supply-chain records for critical materials, surfacing verified signals on the industrial buildout ahead of the news. - [Project intelligence for the energy transition.](https://www.appliedxl.com/themes/energy-transition): AppliedXL monitors permits, grid filings, and project records across the energy transition, turning fragmented public data into verified market signals. - [Clinical intelligence for medical innovation.](https://www.appliedxl.com/themes/human-health): AppliedXL tracks clinical trials, FDA decisions, and drug-development milestones at machine scale, delivering verified biopharma signals before the news cycle. - [Institutional intelligence for emerging risk.](https://www.appliedxl.com/themes/systemic-risk): AppliedXL detects enforcement actions, sanctions, cyber incidents, and trade controls in the public record, delivering early source-linked signals on emerging systemic risk. ## Research - [Research and Insights](https://www.appliedxl.com/research): Research and insights from AppliedXL: theses, benchmarks, and case studies on computational journalism, event detection, and forecasting for the information industry. - [AppliedXL and Bain: A New Pharma Operating Discipline](https://www.appliedxl.com/research/appliedxl-bain-operating-discipline): A combined framework that pairs real-time AI monitoring with strategic analysis, turning clinical-trial execution into a measurable operational advantage. - [AppliedXL Partners with Bloomberg](https://www.appliedxl.com/research/appliedxl-bloomberg-partnership): Bloomberg Terminal users now have access to AppliedXL's AI-generated coverage of clinical trials, built to surface catalyst events in the pharmaceutical pipeline. - [AppliedXL and Kalshi Partner on Biopharma Prediction Markets](https://www.appliedxl.com/research/appliedxl-kalshi-partnership): The program introduces contracts tied to selected clinical trial outcomes and FDA decisions, supported by structured public evidence and predefined resolution criteria. - [Vertical AI vs. General AI: Biopharma Benchmark](https://www.appliedxl.com/research/appliedxl-vs-claude-chatgpt-perplexity-biopharma): A head-to-head benchmark of AppliedXL against Claude, ChatGPT, and Perplexity on real biopharma research tasks, measuring depth, domain reasoning, and accuracy. - [Biopharma's Public Probability](https://www.appliedxl.com/research/biopharma-public-probability): A new report from Kalshi and AppliedXL on the state and future of prediction markets in drug development — and what happens when expectations become public prices. - [Biopharma's Public Probability — AppliedXL & Kalshi](https://www.appliedxl.com/research/biopharma-public-probability-report): The State and Future of Prediction Markets in Drug Development. The full web edition of the joint AppliedXL and Kalshi report on biopharma prediction markets. - [Clinical Trial Data as Alpha: Biotech Quant Model](https://www.appliedxl.com/research/clinical-trial-alpha-quant-model): AppliedXL built a biotech quant investing model on point-in-time clinical trial data enriched with SEC filings, press releases, and AI agents. - [State-of-the-Art Clinical Trial Prediction](https://www.appliedxl.com/research/clinical-trial-prediction): Clinical trials don't just succeed or fail. They pass through five sequential checkpoints, and failure can concentrate at any one of them. - [FDA Drug Repurposing: 5 Drugs Without a Champion](https://www.appliedxl.com/research/fda-drug-repurposing-intelligence): The FDA opened a public docket requesting input on drug repurposing opportunities where commercial incentives are insufficient to drive new applications. - [The Hidden Signals That Decide Drug Success or Failure](https://www.appliedxl.com/research/hidden-signals-drug-success-failure): AppliedXL decodes subtle shifts in clinical trials flagging risks and opportunities before they hit the news cycle. - [From Public Record to Market Resolution](https://www.appliedxl.com/research/how-a-market-resolves): How a fact becomes a settlement: an illustrative walkthrough of AppliedXL's resolution process for event markets — public-record monitoring, curation, contract terms, evidence review, human sign-off, and the exchange's independent outcome review. - [Original Intelligence](https://www.appliedxl.com/research/original-intelligence): For two centuries, information companies were paid for two things at once: originating what is true, and delivering it. AI has split the bundle and priced the halves at opposite extremes. Delivery is becoming a commodity. Origination is becoming the whole business. - [Prediction Model: Six Clinical Trial Case Studies](https://www.appliedxl.com/research/prediction-model-six-clinical-trial-case-studies): Six clinical trials tracked from first filing to outcome. - [Seeing Risk Before It Becomes News](https://www.appliedxl.com/research/seeing-risk-before-news): Execution failures rarely appear suddenly. They build quietly inside clinical programs until they spill into earnings calls, regulatory filings, or headlines. - [Spillover Risk and Readthrough Alpha](https://www.appliedxl.com/research/spillover-risk-readthrough-alpha): Registry anomalies precede press releases by 48-72 hours. - [Systematic Trading Strategies in Biotech](https://www.appliedxl.com/research/systematic-trading-strategies-biotech): Exploratory research on using structured clinical trial event data for alpha generation: early instability detection, mechanistic readthrough, and more. - [Who Will Monetize Truth? A Thesis For the Future of Information](https://www.appliedxl.com/research/who-will-monetize-truth): A thesis for the future of the information business: the news industry is being repriced around companies that sell awareness versus those that sell action. ## Company - [Important change is visible before it is obvious.](https://www.appliedxl.com/about): AppliedXL brings the standards of a newsroom to the scale of a machine — reading the public record to see material change before the market prices it. - [The Science of First](https://www.appliedxl.com/book): A book about seeing what others miss. Francesco Marconi shows how early signals in data, behavior, and narrative reveal the shifts that matter before they become news. - [Power new intelligence.](https://www.appliedxl.com/contact): Tell us the domain you want to move on, and we will scope a working sample on your records, delivered through your product, under your brand. ## Legal - [Privacy Policy](https://www.appliedxl.com/privacy): How Applied X Lab, Inc. collects, uses, and shares personal information through our website, platform, and related services. - [Terms of Use](https://www.appliedxl.com/terms): The Terms of Use governing access to AppliedXL.com and AppliedXL’s website content, operated by Applied X Lab, Inc. ## Other - [Intelligence Strategy Assessment](https://www.appliedxl.com/assessment): Take the 3-minute assessment and we'll build your customized intelligence strategy playbook — an executive report on turning your domain expertise into original information no competitor has. - [AI Editorial Policy](https://www.appliedxl.com/editorial-policy): AppliedXL's AI Editorial Policy: the standards that govern our editorial systems — curated primary sources, traceable provenance, algorithmic accuracy checks, human oversight, and editorial independence. - [FAQ — Biopharma Prediction Markets](https://www.appliedxl.com/faq-resolution-event-contracts): Frequently asked questions about the Kalshi–AppliedXL partnership, biopharma prediction markets, how resolution works, and the safeguards behind the markets. - [Going Live — See results in days. Fully launched in weeks.](https://www.appliedxl.com/going-live): Not a six-month integration. The system maps your domain to an Atlas, encodes the editorial judgment for your audience, and delivers verified product through the trust chain — a working use case in days, production in weeks. - [Media & Partnerships](https://www.appliedxl.com/media): Press inquiries, partnership proposals, and speaking requests for AppliedXL. Reach the team directly. - [Site Map](https://www.appliedxl.com/sitemap): A complete index of every page on appliedxl.com — platform, products, solutions, themes, research, and company pages. ## Live Exploration - [Biopharma News](https://appliedxl.com/news): The wire — AI-generated clinical trial and regulatory coverage, in production. - [Pharma Forecast Platform](https://appliedxl.ai/): Clinical trial probability of success and regulatory approval forecasts, live. --- # Intelligence Infrastructure for the Information Industry URL: https://www.appliedxl.com/ New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → ## Intelligence Infrastructurefor the Information Industry AppliedXL turns the public record into verified signals, indicators, and forecasts that enable newsrooms, exchanges, and financial desktops to lead the AI era. Get startedExplore the platformTrusted byProductsSignalsMaterial change, detected before consensus.IndicatorsRecurring events, turned into benchmarks.ForecastsEvent probability, learned from history.ResolutionOutcomes resolved against the official record. ### Important change is visible in the record before it is obvious in the market. The platform reads it the moment it appears, verifies it against the source, and puts it in your product first. See how it worksSolutionsFinancial Desktop Providers→Newsrooms→Specialist Information Providers→Exchanges & Index Providers→Prediction Markets→Ratings & Risk Intelligence→CoverageHuman Health & Life SciencesEnergy Transition & Climate MarketsAI Infrastructure & Compute BuildoutIndustrial Buildout & Critical MaterialsSecurity, Regulation & Systemic Risk ### The record moves first. Built by computational journalists, to a newsroom's standard of accuracy. Verified back to the source of truth. Get startedSee the trust chain ↑ --- # Important change is visible before it is obvious. URL: https://www.appliedxl.com/about New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / ABOUT A trial is updated. A permit is delayed. A regulator acts. A filing appears. A registry moves. ## Important change is visible before it is obvious. Before a market reprices, a record changes. The evidence is public — but it is fragmented, technical, and unreadable at scale. We exist to close that gap. WHERE WE COME FROM The standards of a newsroom. The scale of a machine. AppliedXL began in the newsroom. Our methods come from computational journalism — the discipline of turning primary documents into verified fact. Read the record. Check every claim against its source. Publish only what you can prove. We took that standard and built it to run continuously, at machine scale, across the institutional records that move regulated markets. The judgment is a journalist's. The reach is a system's. That combination is the whole company. WHAT WE BELIEVE ### Standards don't scale by accident. Four convictions decide how the platform is built — and what we refuse to ship without. 01 #### The record comes first. Markets react to narrative. We start from the primary record — the filing, the trial update, the permit — not the headline written about it. 02 #### Nothing is true until it traces to source. Every output carries the document it came from, with lineage and correction history intact. Verification isn't a feature. It's the standard. 03 #### Early is only useful if it's verifiable. We surface material change as it appears in the record — not by guessing ahead of it, and not by waiting for someone else to confirm it first. 04 #### Machines give scale. People set the standard. The more of a domain's sources and materiality logic we encode, the sharper the signal. Judgment is encoded, never abdicated. The most valuable window in intelligence is the time between a record changing and the market understanding why. AppliedXL is built for that window. FOUNDED 2020 HQ Brooklyn, New York ORIGIN Computational journalism BACKED BY Hearst Ventures · Communitas Capital · Montage · Newlab · Dow Jones · Tuesday Capital IN THE PRESS The New York Times The Rise of the Robot Reporter Fortune Is A.I. the end of journalism or its savior? The Wall Street Journal AI Can Almost Write Like A Human and More Advances Are Coming ### Work with us. Name what you need to understand before the market does. The Atlas maps the records that reveal it. Get in touch Media & Partnerships ↑ --- # Intelligence Strategy Assessment URL: https://www.appliedxl.com/assessment New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → Intelligence strategy assessment ## Get your customized intelligence strategy playbook. Leverage AI to turn your domain expertise into original information no competitor has, and use it to grow, diversify, and strengthen your position. Take the 3-minute assessment and we’ll build your customized executive report. Start assessment Common questions Which companies qualify? This is built for information companies: financial terminals, newsrooms, specialist data and research providers, exchanges and index providers, prediction markets, and ratings and risk businesses. We focus here for one reason: as AI makes distributing information nearly free, the durable advantage moves to what AI can’t replicate: original intelligence drawn straight from primary sources. Each of these businesses already has the expertise to know which facts matter; we help turn the public record into information that’s theirs alone. What’s in the report? A source-level playbook built from your answers: where you’re already defensible, where AI is commoditizing you, the primary records you’re underusing, the original information your expertise could produce, one product you could build and who pays for it, and your first three moves. How does the assessment work? It starts from a simple idea: the public record already holds signals in your field before they surface as news. From your answers, we map the primary sources in your domain (filings, disclosures, institutional records), then identify the original intelligence your expertise could turn them into, the products it could support, and where to begin. It’s the same method behind our own work, and it’s an analysis first, not a pitch. How long does it take? The assessment itself takes about three minutes. Your report is delivered within five business days, and we walk you through it live in a 30-minute session. Is there a cost? No. The report and the walkthrough are free, with no obligation. It’s yours to keep whether we end up working together or not. Why do you review each submission? Every report names real sources in your specific domain and is analyzed by our team, not auto-generated. That’s real work per company, so we point it at the businesses we can genuinely help. How is it delivered? As a written PDF, plus a 30-minute session with our team to walk through it and answer your questions. What do you do with my information? Your details are used only to prepare and deliver your report and arrange the session. No lists, no marketing. ↑ --- # The Science of First URL: https://www.appliedxl.com/book New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → RESEARCH / THE SCIENCE OF FIRST ## The Science of First A book about seeing what others miss. Get the Book It shows how early signals in data, behavior, and narrative reveal the shifts that matter long before they become news. Learn to read the patterns that shape what happens next. The future sends warnings. Those who notice first, get ahead. ### Why It Matters Information moves faster than you can keep up. If you wait for headlines, expert commentary, and research reports, you are already late. The Science of First shows you how to spot the first signs of change, before markets, stories, or competitors react. Seeing early is the new advantage. See shifts early Catch emerging moves before the market prices them in, before the story breaks, and before competitors react. Act before the crowd Act with clarity when others are still guessing, reducing risk and gaining timing advantage. Build an information edge Turn early-signal detection into a daily habit using a disciplined method for spotting real signals in the flood of AI-driven noise. Signals move faster than news. Francesco Marconi, co-founder and CEO of AppliedXL, shows how to read weak signals with precision in an environment overwhelmed by AI-driven noise. ### The Early Signal Era Signals surface first, in data, behavior, and networks, long before headlines, reports, or experts catch up. These early shifts are what drive the events that follow. The Science of First teaches the skill every decision-maker now needs: spotting those earliest deviations that reveal what is coming before anyone else sees it. For journalists, investors, and executives who compete on timing, this book is the operating manual for staying ahead in the early-signal era. Get the Book on Amazon ↑ --- # Power new intelligence. URL: https://www.appliedxl.com/contact New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → GET STARTED ## Power new intelligence. Tell us the domain you want to move on, and we will scope a working sample on your records, delivered through your product, under your brand. First name *Last name *Business email *Phone numberJob title *Company *What is your type of company? *Select...Financial Desktop ProvidersNewsroomsSpecialist Information ProvidersExchanges & Index ProvidersPrediction MarketsRatings & Risk IntelligenceCorporationAsset ManagerOtherTell us about your focusSubmit See our Privacy Policy for how we handle this information. ↑ --- # AI Editorial Policy URL: https://www.appliedxl.com/editorial-policy New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → AI EDITORIAL POLICY ## AI Editorial Policy AppliedXL is an information company built for the age of AI. We use computational systems to do what journalism has always done (monitor institutions, verify facts, and report what matters) at a scale and speed no human newsroom can match. We treat this not as a departure from journalistic standards but as their extension: the discipline of sourcing, verification, and accountability, encoded into software and applied continuously. VERSION JULY 15, 2026Contents01Curated primary sources02Clear citations and traceable provenance03Algorithmic checks for accuracy, bias, and objectivity04Humans in the loop05Clear disclosure of AI use06Uncertainty is labeled, not hidden07Corrections and reader revision requests08Editorial independence09What our systems are designed not to do10Continuous review11Scope and legal notice ### 00Our view on AI and news We are candid about what these systems can and cannot do. AI is effective at reading, structuring, and monitoring large volumes of institutional records. It is not a source of truth. Truth originates in the record: the filing, the registry entry, the docket. Our systems are designed to detect and contextualize facts from verifiable primary sources, not to generate them. Where confidence is low, we label it or hold publication. This policy describes the standards that govern our editorial systems and the people responsible for them. ### 01Curated primary sources Editorial outputs draw from a curated corpus of primary, authoritative sources: government registries, regulatory filings, official dockets, and institutional disclosures. We do not use open web search, social media, or unvetted aggregators as editorial inputs. Sources are admitted through a documented review of authority, provenance, and reliability; the corpus is versioned so outputs can be traced to the inputs available when they were produced. ### 02Clear citations and traceable provenance Outputs are designed to be attributable to specific documents in specific sources, with that attribution traveling alongside the output. Provenance is a technical requirement of our pipelines, not an editorial courtesy: claims that cannot be linked to an underlying record are flagged and withheld from publication. ### 03Algorithmic checks for accuracy, bias, and objectivity Automated quality controls run continuously across our systems: accuracy checks that validate extracted facts against source documents; bias and objectivity checks that test whether outputs systematically misrepresent the underlying record or drift from neutral framing; benchmarking against human-verified datasets, tracked across model and pipeline versions; and anomaly detection that routes irregular outputs to human review before publication. Audit findings are documented internally, and material changes in system performance are communicated to affected clients. ### 04Humans in the loop Automation concentrates accountability rather than diminishing it. Editorial responsibility rests with people, not models. Human oversight operates at the system level: our teams conduct regular audits of the pipelines and their outputs on a defined cadence, review flagged anomalies, adjudicate cases the systems cannot resolve, and approve material changes to sources, models, or editorial logic before they reach production. Humans do not review every individual output; they verify that the systems producing them meet our standards, and intervene where the checks in this policy indicate they do not. ### 05Clear disclosure of AI use We disclose where AI is involved in what we publish. Automated outputs are labeled as such, and we distinguish where human judgment has reviewed or approved them. Our disclosures aim to be specific enough to be meaningful: what the system did, from what sources, and under what oversight. ### 06Uncertainty is labeled, not hidden Verified facts drawn from the record are distinguished from probabilistic outputs such as forecasts, projections, and early signals, which are labeled as such. We prefer a narrower claim on solid ground to a broader one on hedged foundations. Forecasts and indicators are analytical products; they are not investment, legal, or medical advice, and should not be relied on as the sole basis for any decision. ### 07Corrections and reader revision requests Readers and clients can submit revision requests on any published output. Requests are logged and reviewed by a human editor. When we identify an error, we correct it, annotate the correction visibly, and trace it to its root cause in the pipeline. Corrections are treated as system feedback: a single flagged error can trigger review of related outputs produced under the same conditions. ### 08Editorial independence Our systems serve the record. Company shareholders and investors do not direct what our systems detect, how facts are characterized, or which findings are published. Clients and partners define the scope of what we monitor for them, never what the record shows or how it is reported. Where a conflict of interest could reasonably be perceived, we disclose it. ### 09What our systems are designed not to do We define the boundaries of automation as deliberately as its capabilities. Our systems are built not to generate claims unsupported by primary records, speculate about motive or intent, produce content designed to persuade rather than inform, or publish personal information beyond what appears in the public record and is editorially necessary. ### 10Continuous review This policy is reviewed regularly and whenever we make material changes to our systems. Revisions are versioned and dated. Our standards are stable; our implementation of them is expected to improve. ### 11Scope and legal notice This policy describes AppliedXL's editorial practices and aspirations. It is a statement of standards, not a contract, warranty, or guarantee of accuracy, completeness, or fitness for any purpose, and it does not create rights enforceable by any third party. Our products are provided subject to the terms of the applicable client agreements, which govern in the event of any conflict with this policy. Errors can occur in any information system, including ours; our commitment is to detect, disclose, and correct them under the processes described here. Questions and revision requests: support@appliedxl.com ↑ --- # FAQ — Biopharma Prediction Markets URL: https://www.appliedxl.com/faq-resolution-event-contracts New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → FAQ ## Kalshi, AppliedXL, and biopharma prediction markets Answers to the most common questions about the partnership, how markets are resolved against the public record, and the safeguards behind them. VERSION JULY 16, 2026Contents01The basics02Ethics and potential impact03Understanding the markets04How resolution analysis works05Market integrity and safeguards06About AppliedXL07Get involved and learn more ### 01The basics What is the partnership between Kalshi and AppliedXL? Kalshi operates regulated event-contract markets tied to selected biopharma outcomes, including clinical trial readouts and FDA regulatory decisions. AppliedXL provides independent resolution analysis for these markets. Our systems monitor the public sources identified in each contract, organize the relevant evidence, and assess whether the contract’s predefined conditions appear to have been met. AppliedXL submits its analysis and supporting evidence to Kalshi. Kalshi operates the exchange and remains the sole and final adjudicator of every contract under its rules. Read the announcement. What information is used to resolve a contract? Resolution is based exclusively on publicly available sources identified in the contract terms before trading begins. Depending on the market, these may include ClinicalTrials.gov records, FDA documents and databases, advisory committee records, other public institutional records, or an official company disclosure expressly named in the contract. AppliedXL does not use private or identifiable patient-level data, confidential clinical trial records, material nonpublic information, analyst commentary, rumors, private communications, or undisclosed information provided by a sponsor or another third party. Public records may contain aggregate clinical results, but AppliedXL does not access or evaluate identifiable patient information as part of the resolution process. Why create these markets? Expectations about drug development are produced by pharmaceutical companies, banks, investors, researchers, and specialist analysts. Many of those estimates remain private, while parts of the public clinical record are incomplete or delayed. In April 2026, the FDA reported that 29.6% of studies it considered highly likely to be subject to mandatory reporting requirements had no results information submitted to ClinicalTrials.gov. This figure applies to a defined category of overdue studies, rather than all clinical trials. A publicly traded contract can provide a visible, continuously updated market-implied probability for a defined event. That price reflects current trading activity. It should not be treated as scientific consensus, clinical evidence, or a prediction guaranteed to be accurate. ### 02Ethics and potential impact What ethical and social risks did you consider? Biopharma prediction markets raise questions that extend beyond trading. A visible market price could affect how patients, physicians, researchers, or investors perceive a clinical trial. People with access to material nonpublic information may also have an unfair advantage. AppliedXL and Kalshi consulted clinicians, bioethicists, academics, investors, and biopharma R&D and strategy professionals to examine risks including: insider trading and unequal access to information; possible effects on patient enrollment or physician referrals; the treatment of vulnerable patient populations; market manipulation or attempts to influence an outcome; ambiguous or unreliable contract design; the possibility that market prices could be mistaken for medical or scientific evidence. These risks cannot be eliminated entirely. The pilot therefore begins with a limited scope and includes predefined outcomes, named public sources, employment verification, participation restrictions, human review, and a clear separation between AppliedXL’s analysis and Kalshi’s final adjudication. The research, expert perspectives, and proposed safeguards are documented in the joint report, Biopharma’s Public Probability: The State and Future of Prediction Markets in Drug Development. Could these markets affect patients or clinical research? Potentially. A market price could influence perceptions of a treatment or trial, even though it does not establish whether a treatment is safe, effective, or appropriate for an individual patient. To reduce this risk, the initial pilot focuses on selected late-stage trials with publicly registered endpoints and generally considers clinical trial markets only after enrollment has closed. This is intended to reduce the possibility that a visible price could influence recruitment or physician referrals. Market prices should never be used to make treatment decisions or determine whether someone should join, remain in, or leave a clinical trial. Those decisions should be made with qualified medical professionals using appropriate clinical information. AppliedXL and Kalshi plan to review the pilot’s effects and update the framework as they receive feedback from experts, patients, and other stakeholders. What kinds of biopharma events are appropriate for a market? The initial pilot focuses on selected late-stage clinical trial outcomes and FDA regulatory decisions that can be evaluated against clearly identified public sources. A potential market must have: an objectively defined question; criteria established before trading begins; a sufficiently reliable public record; a source, or hierarchy of sources, named in the contract terms; a level of ethical and integrity risk considered appropriate for the pilot. Some events may be unsuitable because the outcome is ambiguous, the relevant information is unlikely to become public, the event can be influenced by a small group, or the contract could create unacceptable risks for patients or clinical research. Late-stage status alone does not make a market appropriate. Each candidate must still be assessed for verifiability, integrity, and potential impact. Who decides which markets are listed? AppliedXL may identify potential events and evaluate whether they appear suitable for clear and reliable resolution. Kalshi independently decides which markets to list and is responsible for the contract terms, participant rules, market operation, and final settlement. Members of the public may also submit market suggestions through Kalshi. A suggestion is reviewed by Kalshi and is not guaranteed to be listed. ### 03Understanding the markets What kinds of events can be traded? Markets may cover clearly defined events such as: whether a clinical trial meets a specified primary endpoint; whether the FDA approves a named drug by a specified date; the outcome of an FDA advisory committee vote; another clinical or regulatory milestone documented in an identified public record. The precise question, deadline, source, and resolution criteria are defined in the official contract terms. Why might these markets be useful to investors? A biopharma contract focuses on a specific, predefined event, such as whether a trial meets its primary endpoint or whether the FDA approves a drug by a particular date. That differs from owning a company’s stock, whose price can be affected by management, financing, other pipeline assets, market conditions, and many additional factors. A contract can make expectations around one event more visible, but it does not perfectly isolate the underlying science. Prices may also reflect liquidity, trader participation, timing, sentiment, and the design of the contract. Does AppliedXL provide investment advice? No. AppliedXL does not provide investment, legal, or medical advice. Its data, models, and resolution analysis are not recommendations to trade any contract or security or to make a medical decision. Prediction-market prices and modeled probabilities are estimates of uncertain future outcomes. They are not statements of fact about the safety, effectiveness, or likelihood of approval of any drug. What is the difference between AppliedXL’s probability of success and a Kalshi market price? They are produced differently and serve different purposes. AppliedXL’s probability of success is a model output derived from specified clinical, regulatory, and operational data using a defined methodology. A Kalshi market price is produced through trading and reflects the prices at which participants are currently willing to buy or sell the contract. One is model-driven and the other is market-driven. Neither is a statement of scientific fact, and AppliedXL’s probability model does not determine how a Kalshi contract settles. ### 04How resolution analysis works What does “resolution infrastructure” mean? Resolution infrastructure refers to the technical and editorial systems used to: monitor the public sources identified in a contract; detect potentially relevant disclosures; organize the underlying evidence; compare that evidence with the contract’s predefined terms; produce a documented and reviewable analysis. AppliedXL’s infrastructure supports the resolution process. It does not replace Kalshi’s authority to determine and finalize the outcome. Most binary event contracts are designed to settle as YES or NO. The official contract terms may also address delays, corrections, ambiguity, cancellation, or other exceptional circumstances. Learn more on the Resolution page. Why does biopharma require specialized resolution analysis? Clinical and regulatory outcomes are often disclosed across trial registries, FDA records, advisory committee materials, regulatory filings, scientific publications, and company announcements. A trial may meet a statistical endpoint without establishing clinical significance. A company may announce positive results while the underlying public record remains incomplete. A successful trial also does not necessarily result in regulatory approval. Evaluating a contract therefore requires domain knowledge, systematic monitoring, and close adherence to the contract’s exact language and identified sources. How does AppliedXL analyze a contract’s outcome? Before trading begins, the contract terms identify the question, deadline, resolution criteria, and source, or hierarchy of sources, that Kalshi will use to determine the outcome. Depending on the contract, those sources may include: a ClinicalTrials.gov record; an FDA action document or public database; an FDA advisory committee vote record; another official regulatory or institutional record; an official company disclosure, when expressly identified in the contract terms. AppliedXL monitors the named public sources, flags disclosures relevant to open contracts, and compares the available evidence with the contract’s predefined conditions. A human reviewer examines the evidence and documents the basis for the analysis. Cases involving incomplete, corrected, delayed, or conflicting information receive additional review. AppliedXL does not rely on rumors, analyst interpretations, confidential communications, private clinical records, or material nonpublic information. The analysis is limited to the public record identified in the contract terms. AppliedXL then submits its analysis and supporting evidence to Kalshi. Kalshi independently reviews that information and remains the sole and final adjudicator under its exchange rules. See a worked example: From Public Record to Market Resolution. Who decides the final outcome, AppliedXL or Kalshi? Kalshi does. AppliedXL provides independent analysis, a recommended determination, and the public evidence supporting it. Kalshi has sole authority to determine and finalize the outcome under its rules. Does the market price affect how a contract settles? No. Trading activity determines the market price, but it does not determine the contract’s outcome. Settlement is based on the contract’s predefined terms and identified public sources. What happens if public sources conflict or change? AppliedXL documents the conflicting information and evaluates it according to the contract’s predefined terms and source hierarchy. AppliedXL does not change the criteria after the outcome becomes known or select a source based on which side of the market benefits. Kalshi determines how the applicable contract terms address delays, corrections, conflicting records, or ambiguity. The official terms and exchange rules govern in every case. How does AppliedXL use AI in the process? AI assists with monitoring, document classification, information extraction, and matching new public disclosures with open contracts. No contract is finally adjudicated solely on the basis of an automated reading. AppliedXL applies human review to its resolution analysis, and Kalshi independently makes and finalizes the settlement determination. The analysis is limited to publicly available information identified in the contract terms. AppliedXL does not use identifiable patient data, confidential trial records, material nonpublic information, or private communications to analyze an outcome. ### 05Market integrity and safeguards What safeguards does Kalshi apply? Kalshi requires participants in these biopharma markets to complete employment verification as an additional integrity measure. On Kalshi’s side, its rules also restrict trading by people who possess material nonpublic information relevant to a contract or who have the ability to influence its outcome. For a given market, the categories of persons Kalshi does not allow to trade may include: Officers, directors, employees, contractors, and consultants of the drug’s manufacturer, including its subsidiaries, affiliates, joint-venture partners, co-development partners, and licensees involved in the clinical development program or the trial. Members of any Data Safety Monitoring Board (DSMB), Independent Data Monitoring Committee (IDMC), clinical endpoint adjudication committee, or equivalent independent oversight body with access to unblinded data for the trial. Principal investigators, sub-investigators, clinical research coordinators, and site staff at sites enrolling or treating patients in the trial, to the extent they have access to unblinded or aggregated efficacy or safety data. Employees, contractors, and consultants of contract research organizations (CROs), central laboratories, biostatistical firms, and data-management vendors engaged by the manufacturer to conduct, analyze, or monitor the trial. Officers, directors, employees, and review staff of the FDA, EMA, PMDA, or other regulatory authority with access to non-public submissions, review documents, or communications relating to the trial or the drug. Members of institutional review boards (IRBs) or ethics committees reviewing safety or efficacy data for the trial. Employees of investment banks, financial advisors, or other advisors engaged by the manufacturer or its partners with access to non-public clinical data for the trial or knowledge of the timing of its disclosure. Persons who have entered into confidentiality, consulting, or expert-network agreements with the manufacturer or its agents that provide access to non-public clinical data, timelines, or results for the trial. Immediate family members (spouses, domestic partners, parents, children, and siblings) and household members of any of the above. Any person who has obtained material non-public information regarding the timing, content, or outcome of the trial’s results through any means, including inadvertent disclosure, misappropriation, or breach of a confidentiality obligation. The precise eligibility restrictions and prohibited categories are governed by Kalshi’s rules and the terms applicable to each market. Kalshi is responsible for exchange surveillance, investigation, enforcement, and regulatory reporting. Traders are responsible for reviewing and complying with the applicable rules. Does employment verification eliminate insider-trading risk? No. Employment verification is one preventive control within a broader compliance and surveillance framework. A person may obtain material nonpublic information through consulting work, vendors, professional relationships, household members, or other channels that are not apparent from an employer’s name alone. Employment verification must therefore operate alongside trading restrictions, surveillance, investigation, enforcement, and reporting procedures. Does AppliedXL trade in these markets? No. AppliedXL employees are prohibited by company policy from participating in prediction markets, not only the contracts for which AppliedXL provides analysis. AppliedXL does not take a financial position in the outcome of a contract it supports. What happens if suspicious trading is detected? Kalshi is responsible for exchange surveillance, investigation, enforcement, and regulatory reporting. AppliedXL does not conduct exchange surveillance or determine whether a participant has violated Kalshi’s rules. Are these markets legal? Kalshi operates as a federally regulated designated contract market in the United States and is responsible for listing and operating its contracts under the applicable regulatory framework. AppliedXL does not provide legal opinions concerning Kalshi’s contracts or an individual’s eligibility to trade. Questions about contract legality, regulatory status, participant eligibility, or trading restrictions should be directed to Kalshi or qualified legal counsel. ### 06About AppliedXL Is AppliedXL now a prediction-markets company? No. AppliedXL is a public-intelligence company that monitors institutional records and turns them into structured signals, forecasts, and verified outcomes. Prediction-market resolution analysis is one application of that infrastructure. How is the resolution service different from other AppliedXL products? The resolution service is powered by the same underlying technology but uses a defined subset of AppliedXL’s public-record infrastructure. It tracks upcoming clinical and regulatory catalysts and documents how they resolve, including trial success or failure and regulatory approval or rejection. Does AppliedXL support resolution in areas beyond biopharma? The underlying capability can apply to other regulated domains where outcomes can be evaluated against clearly defined and authoritative public records. Any future application would require domain-specific criteria, source standards, conflicts policies, and human expertise. Safeguards developed for biopharma should not automatically be assumed to apply to another field. ### 07Get involved and learn more Can outside experts contribute expertise or data? AppliedXL may work with subject-matter experts, academics, newsrooms, and other organizations on methodology, public-source interpretation, and the broader study of resolution systems. Any contribution to a specific resolution process must be subject to appropriate conflict checks. Contributors must not provide material nonpublic information, trade on information obtained through the process, or control the final adjudication. Any evidence used to analyze a contract must come from the publicly available sources identified in its terms. Kalshi remains the final decision-maker for every contract. How can I learn more? For trial and regulatory coverage, see Biopharma News or the AppliedXL platform. Kalshi and AppliedXL have also published: Biopharma’s Public Probability: The State and Future of Prediction Markets in Drug Development; From Public Record to Market Resolution, a step-by-step resolution example; the official partnership announcement. Disclaimer: This document is provided for general informational purposes only. AppliedXL does not provide investment, legal, or medical advice. Nothing in this FAQ is a recommendation to trade a contract or security, participate in a clinical trial, select a medical treatment, or make any other financial or medical decision. Prediction-market prices and modeled probabilities are estimates of uncertain future events. Prices may be affected by liquidity, participation, sentiment, market structure, and other factors. They are not clinical evidence, scientific consensus, or statements of fact about a drug, trial, company, or regulatory outcome. AppliedXL’s resolution analysis is based exclusively on the publicly available sources identified in the applicable contract terms. AppliedXL does not use identifiable patient data, confidential clinical trial records, material nonpublic information, rumors, analyst commentary, or private communications to determine whether a contract’s conditions appear to have been met. AppliedXL provides independent resolution analysis but does not operate the exchange or finally adjudicate contracts. Kalshi remains solely responsible for listing, operating, determining, and settling its markets under its rules. The official Kalshi contract terms, exchange rules, and applicable law control. If this FAQ conflicts with those materials, the official materials govern. Questions concerning the markets, participant eligibility, exchange rules, settlement, or regulatory status should be directed to Kalshi. ↑ --- # Event probability, learned from history. URL: https://www.appliedxl.com/forecasts New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / FORECASTS ## Event probability, learned from history. Forecasts learns which patterns precede which outcomes, and turns today's events into tomorrow's probabilities. ### Price what hasn't happened yet. Outcome Probability The odds an event lands the way it might: a regulatory approval clearing, a clinical trial reading out positive, a decision going one way or the other. Timing & Date Forecasts When a catalyst is likely to occur, not just whether. Expected dates and windows that move as the record does. ### Every revision has a reason. PricedA probability from everything known at that moment, and nothing known later.RevisedA new event lands; the probability updates.ExplainedEach revision links to the event that caused it. Your analysts can see why, and override.ScoredThe outcome arrives from Resolution and grades the forecast. Every prediction joins the calibration record. ### New verticals in weeks, not years. Request a Forecasts sampleSee the trust chain ↑ --- # Going Live — See results in days. Fully launched in weeks. URL: https://www.appliedxl.com/going-live New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / GOING LIVE ## See results in days. Fully launched in weeks. Not a six-month integration. The system maps your domain to an Atlas, encodes the editorial judgment for your audience, and delivers verified product through the trust chain — a working use case in days, production in weeks. Get started Explore the platform ### How it works ### Each product is verified with the tests that fit it. 01Signals Verification test runs Accuracy benchmarking Latency testing 02Indicators Point-in-time backtest Methodology validation Committee-ready docs 03Forecasts Calibration testing Lead-time measurement Backtested hit rates 04Resolution Rule-and-source testing Edge-case suite Evidence-package check ### Bring a domain. Leave with a launched product. A scoped use case in days, a verified feed in your stack in weeks. Get started See the trust chain ↑ --- # Recurring events, turned into benchmarks. URL: https://www.appliedxl.com/indicators New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / INDICATORS ## Recurring events, turned into benchmarks. Indicators turns the events Signals detects into measures your clients can't get anywhere else: averages, rates, rankings, and indices. ### Own measures no one else has. Event Analytics Every verified event rolled into consistent measures: counts, rates, durations, and averages across every entity you cover. Risk Scores Composite scores that turn a history of events into a single, defensible read on where an entity stands and where it's heading. Company Rankings Peer-relative rankings on the methodology you control, so your clients see who leads and who lags on the measures that matter. Benchmark Indices Index-grade series built from verified inputs, versioned and documented, ready to license, track, or trade against. Ratings Inputs Clean, source-linked measures your ratings and research teams can feed straight into their own models and decisions. ### A number your committee can defend. AccumulatedEach event enters with its source and timestamp. Verified inputs, or no measure at all.ComputedThe methodology runs identically every time. Written once, applied without exception.VersionedEvery methodology change is dated and documented. A trail your index committee can defend.PublishedThe measure ships to your product, full backfill behind it. ### New verticals in weeks, not years. Request an Indicators sampleSee the trust chain ↑ --- # Media & Partnerships URL: https://www.appliedxl.com/media New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → MEDIA & PARTNERSHIPS ## Let’s talk. Press inquiries, partnership proposals, speaking requests — tell us what you have in mind and it goes straight to the team. Name *Email *OrganizationWhat’s on your mind? *Submit ↑ --- # AXL Core URL: https://www.appliedxl.com/platform New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM ## AXL Core One system, four layers. The Atlas maps a domain. Four products compute on the map. The trust chain verifies every output. Delivery puts it in your product. 01FOUNDATION ### The Atlas Before anything computes, the domain is mapped: which records are authoritative, which entities they touch, which changes are material, what counts as an outcome. The Atlas grounds every product in the record instead of the pattern. Explore the Atlas02PRODUCTS ### Four products Signals detect. Indicators measure. Forecasts project. Resolution settles. Each runs on the Atlas and feeds verified outputs into your product. SignalsMaterial change, detected before consensus.IndicatorsRecurring events, turned into benchmarks.ForecastsEvent probability, learned from history.ResolutionOutcomes resolved against the official record.03VERIFICATION ### The trust chain Six steps from record to product. Every output carries its source, timestamp, and lineage. See the trust chain04DELIVERY ### Into your product Verified outputs arrive entity-resolved and point-in-time correct, through APIs, streaming feeds, bulk history, MCP endpoints, or white-label modules. Feeds, APIs & MCP ↑ --- # Turn your domain expertise into AI-ready intelligence. URL: https://www.appliedxl.com/platform/atlas New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / ATLAS ## Turn your domain expertise into AI-ready intelligence. AXL Atlas turns the judgment of an expert desk into a structured map of sources, subjects, events, signals, actions, outcomes, and evidence. See an Atlas in actionMap your domain ### Generic AI produces fluent noise Language models can read and generate. They cannot tell which records carry authority, which changes deserve attention, or what counts as a verified outcome. That judgment lives inside expert teams. The Atlas makes it computable. ### What the Atlas contains Seven structured layers. Each one encodes a different kind of domain knowledge. EVIDENCE The source, timestamp, lineage, and correction history behind every object. ### How an Atlas is built A domain's sources, rules, and outcome criteria are encoded once. From there the platform runs. New domains come online in weeks. Encode the domain The sources, subjects, event types, materiality rules, and outcome criteria that define the domain are captured as an explicit map. Structure the record Records are extracted, classified, timestamped, and normalized into events, signals, permitted actions, forecasts, and outcomes. Ground the intelligence layer Copilots, feeds, and workflows run on the Atlas, with source lineage, permissions, and correction history attached to every output. ### Your product runs on the foundation AppliedXL maintains the public-record layer. Your rules, your clients, and your intelligence products are built on top of it. Every output traces back to the record it came from. ### Map your domain. See how the AXL Atlas turns your sources, rules, and expertise into AI-ready intelligence. Map your domainSee the trust chain ↑ --- # An Atlas in action. URL: https://www.appliedxl.com/platform/atlas-in-action New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / ATLAS / IN ACTION ## Pharma Industry Atlas Every field and event type in AppliedXL's clinical reference build. Click a layer; every object carries its definition. 7 LAYERS 28 GROUPS 129 FIELDS & EVENTS 500k+ TRIALS MONITORED 7k+ UPDATES / DAY Reads from the base: evidence grounds the sources, sources describe the subjects, events fire signals, signals trigger actions, and actions produce the outcomes that become new ground truth. Every AppliedXL product runs on this map: Signals drafts from it, Indicators computes on it, Forecasts prices with it, Resolution settles against it. This is one domain's Atlas; yours is a mapping exercise away. Map your domain Explore the Atlas ↑ --- # Every product, in your infrastructure. URL: https://www.appliedxl.com/platform/delivery New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / DELIVERY ## Every product, in your infrastructure. Signals, Indicators, Forecasts, and Resolution ship through the systems you already run. Point-in-time correct, source-linked, and entity-resolved against your identifiers. ### However your product consumes and distributes intelligence Online platform A hosted interface for your team to explore entities, events, forecasts, and resolutions, source document attached. Available as a standalone workspace or as white-label modules embedded inside your own product. APIs Two feed types on demand, JSON, versioned, and paginated. Content Delivery Feeds stream finished narrative intelligence as records change, through webhooks or a subscription stream. Structured Data Feeds deliver point-in-time structured records, entities, events, and outcomes keyed to your identifiers, for backtesting, model training, and joins with your existing data. Specialized AI agents A live MCP endpoint your copilots and agents call directly, returning verified, source-linked intelligence in the moment. In production today on clinical records. ### Built to sit inside a production system Mapped to the identifiers you already use Every object arrives keyed to standard identifiers, so it joins your existing data with no reconciliation layer. Custom identifier mappings are part of the Atlas build. Latency Detection to delivery in minutes for real-time feeds; batch windows on request. Reliability Versioned APIs, backward compatibility, and status monitoring. SLA terms set per agreement. Corrections Restatements and corrections propagate through the same channel, versioned and logged. Security & governance Access controls, data lineage, and continuity provisions, stated in the agreement. No training on client data. ### Model-agnostic. Yours to keep. No single AI vendor sits between you and your product. AppliedXL never trains on client data, and the intelligence delivered is yours to use, store, and build on. Request accessSee the trust chain ↑ --- # Trust is the product. URL: https://www.appliedxl.com/platform/trust-chain New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → TRUST CHAIN ## Trust is the product. Computational journalism, applied to commercial intelligence: verification before delivery, corrections on the record, and a visible chain from record to result. Get startedExplore the platform ### The trust chain, record to product Source-linkedEvery output connects to the underlying filing, registry, docket, disclosure, agency record, or source document.TimestampedEvery event preserves when something changed and what was known at the time.Entity-mappedRecords are linked to the companies, assets, drugs, projects, facilities, institutions, and people they affect.Event-classifiedRaw records become structured events that power feeds, indicators, forecasts, resolutions, and briefs.VerifiedEach output is checked against the record before it ships.AuditableEach output carries the evidence needed for review, reproduction, correction, and defense. ### How we hold the line Evidence verification AppliedXL checks each output against the underlying source before delivery. If the evidence does not support the claim, the item goes to review instead of publication. Verifies: source authenticity, entity matching, extraction accuracy, timestamp consistency, event classification, evidence completeness. Forecast validation Every probability is built from point-in-time history and measured against what actually happened. Every miss feeds calibration. Tracks: historical backfill, point-in-time controls, outcome labels, calibration, backtests, model monitoring, false positives and negatives. Resolution rules Before a forecast, market, or workflow goes live, the resolution rule is defined: which source decides, what counts as yes, by when, and how edge cases are handled. Each resolution includes: authoritative source mapping, clear outcome logic, a timestamped settlement record, review and escalation. Corrections on the record When a source changes, an extraction fails, an entity match is corrected, or a forecast misses, the record is updated with a traceable history. The goal is not to hide uncertainty. The goal is to make the evidence, judgment, and correction path visible. ### Your lens. Your intelligence. AppliedXL runs a secure editorial loop: AI reasons over records in context, through your editorial lens, with each step available for review. Get startedExplore the platform ↑ --- # Privacy Policy URL: https://www.appliedxl.com/privacy New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PRIVACY POLICY ## Privacy Policy How Applied X Lab, Inc. collects, uses, and shares personal information through our website, platform, and related services. EFFECTIVE AS OF NOVEMBER 19, 2025Contents01Personal information we collect02How we use your personal information03How we share your personal information04Your choices05Other sites and services06Security07International data transfer08Children09Enterprise clients10Changes to this Privacy Policy11How to contact us ### 01Personal information we collect #### Information you provide to us Personal information you may provide through the Services or otherwise includes: Contact data (name, email address, company, title, phone number); Account data (username or login identifier, preferences, and other information used to access the Services and create a profile); Communications (messages or inquiries you send us, including feedback and support requests); and Transactional data (information about purchases or subscriptions, billing details, and transaction history). #### Third-party sources We may receive personal information from partners who help operate or promote our Services, for example event co-sponsors, marketing or analytics providers, and authentication services such as Microsoft or Google. This information is combined with data collected directly to operate, personalize, and improve the Services. #### Automatic data collection We and our service providers automatically collect certain information about your device and usage, such as Device data (IP address, browser type, operating system, device identifiers, and general location) and Online activity data (pages or features viewed, time spent, navigation paths, referring websites, and interactions with emails or content). #### Cookies and similar technologies AppliedXL and its service providers use technologies including cookies (text files stored on a device to identify the browser or retain settings), local storage technologies (like HTML5 and Flash, providing cookie-equivalent functionality with larger storage), and web beacons (pixel tags or clear GIFs used to show that a page or email was accessed or that content was viewed or clicked). ### 02How we use your personal information #### Service delivery To provide, operate, and improve the Services and overall business operations, including establishing and maintaining accounts and profiles; communicating about the Services (announcements, updates, security alerts, support); understanding usage to personalize the experience; and providing customer support. #### AI processing AppliedXL uses artificial intelligence to generate analytical insights from verified data within defined workflows. User inputs and configurations are processed solely to produce contextual outputs and are not used to train, improve, or fine-tune internal or third-party AI models. All data is treated as confidential, processed securely, and never sold or shared for model development or commercial reuse. When third-party AI services are used, transmissions are limited to the minimum data necessary for the specific function. #### Research and development For research and development, including analyzing and improving the Services. Personal information may be aggregated, de-identified, or anonymized for statistical or analytical purposes, and such data may be used or shared for lawful business purposes including performance improvement and product development. #### Marketing AppliedXL and its service providers may use personal information for marketing and promotional purposes, such as sending AppliedXL-related communications, event invitations, or other updates permitted by law. You may opt out of marketing communications at any time. #### Compliance and protection To comply with legal obligations and protect the rights, safety, and security of AppliedXL, its users, and others, including complying with applicable laws and lawful requests; protecting rights, privacy, safety, or property; and auditing internal processes for compliance. #### With consent In some cases we may ask for your consent to collect, use, or share your personal information, such as when required by law. ### 03How we share your personal information #### Service providers Third parties that perform services on our behalf or support the Services and business functions, including hosting, IT, authentication, customer support, marketing, analytics, and related professional services. #### Payment processors Any payment card information you use to make a purchase is collected and processed directly by our payment processors, such as Stripe. #### Integration and authentication providers Where users access the Services through a third-party authentication or integration provider (for example, Microsoft or Google), limited profile information necessary to enable access may be exchanged. AppliedXL does not collect or store passwords or other authentication credentials. #### Professional advisors Professional advisors such as lawyers, auditors, bankers, and insurers, where necessary in the course of the professional services they render to us. #### Authorities and others Law enforcement agencies, government authorities, courts, and private parties may receive personal information when AppliedXL believes disclosure is necessary. #### Business transferees In connection with any merger, acquisition, financing, reorganization, sale, or other transfer of all or part of our business or assets, and subject to appropriate confidentiality obligations, relevant personal information may be disclosed to the participating parties. ### 04Your choices #### Access or update your information Registered users may review and update certain account details by signing in through the Services or associated authentication provider. #### Opt out of marketing communications You may opt out of marketing emails by following the unsubscribe instructions at the bottom of the email, or by contacting us. You may continue to receive service-related, non-marketing emails. #### Cookies Most browsers allow cookies to be removed or rejected through browser settings. Disabling cookies may affect functionality. More information is available at www.allaboutcookies.org. #### Do Not Track Some browsers can send Do Not Track signals. We currently do not respond to Do Not Track or similar signals. Learn more at allaboutdnt.com. #### Declining to provide information Certain data is necessary to provide the Services. Where required information is withheld, AppliedXL may be unable to offer access or functionality. #### Third-party platforms For users who access the Services through a third-party authentication or integration provider, settings within that provider's account may limit the information shared. Revoking access does not affect information previously provided. ### 05Other sites and services The Services may include links to websites or online services operated by third parties, and AppliedXL content may appear within external platforms. These links and integrations do not constitute endorsement or affiliation. AppliedXL does not control third-party services and is not responsible for their content, policies, or practices. Review the privacy policies of any third-party services before providing personal information. ### 06Security AppliedXL implements administrative, technical, and physical safeguards designed to protect personal information from unauthorized access, loss, misuse, or alteration. While intended to provide reasonable protection, no system or transmission method is completely secure, and AppliedXL cannot guarantee absolute security. We regularly review our security measures to address evolving threats. ### 07International data transfer AppliedXL is headquartered in the United States and may engage service providers or partners in other jurisdictions. Personal information may be transferred to or processed in countries other than the one where it was collected, which may have differing data protection laws. AppliedXL takes steps to ensure such transfers comply with applicable law and that appropriate safeguards are in place. ### 08Children The Services are not directed to individuals under the age of 16. AppliedXL does not knowingly collect personal information from children under 16 without appropriate parental or guardian consent as required by law. Information discovered to have been collected in violation of this policy will be deleted promptly. Parents or guardians with concerns are encouraged to contact us. ### 09Enterprise clients Organizations that have executed a separate agreement with AppliedXL, including a Master Services Agreement, may be subject to additional or differing terms governing the collection, processing, and use of data. In the event of any conflict between such agreement and this Privacy Policy, the terms of the separate agreement shall prevail. ### 10Changes to this Privacy Policy AppliedXL may modify or update this Privacy Policy from time to time. Material changes will be indicated by updating the effective date and posting the revised version. Where required by law, additional notice will be provided via email or in-service notification. Unless otherwise stated, updates are effective upon posting, and continued use of the Services after changes become effective constitutes acceptance. ### 11How to contact us Questions or concerns about this Privacy Policy or AppliedXL's data practices may be directed to: General Counsel, Applied X Lab, Inc., 15 Metrotech Center, 7th Floor, Suite #722, Brooklyn, NY 11201. Email: support@appliedxl.com. Phone: (219) 290-7579. ↑ --- # Research and Insights URL: https://www.appliedxl.com/research New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → NEWAppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets→COMPANY / RESEARCH ## Research & Insights FEATURED REPORTBiopharma’s Public ProbabilityThe state and future of prediction markets in drug development.A new report from Kalshi and AppliedXL on what happens when expectations about clinical and regulatory success become publicly visible.BY KALSHI AND APPLIEDXLJULY 2026View the report →Read the full web edition →FEATURED THESISOriginal IntelligenceWhat stays valuable when AI can deliver anything.For two centuries, information companies were paid for two things at once: originating what is true, and delivering it. AI has split the bundle and priced the halves at opposite extremes. Delivery is becoming a commodity. Origination is becoming the whole business.BY FRANCESCO MARCONI9 JUL 2026 · 12 MIN READRead the thesis → #### SELECTED WORK THESISWho Will Monetize Truth?A thesis on truth as infrastructure in an age of synthetic content and algorithmic scale.Read the thesis →BOOKThe Science of FirstCatching early signals before they become news — field notes on the News.About the book → #### LATEST RESEARCH 15 JUL 2026From Public Record to Market ResolutionHow one prediction market gets resolved — from continuous monitoring of primary sources to a signed, auditable resolution handed to the exchange, walked through a drug-trial example.APPLIEDXL RESEARCH · INTERACTIVE06 JUN 2026AppliedXL Prediction Model: Six Clinical Trial Case Studies with Full ExplainabilitySix clinical trials tracked from first filing to outcome. Each case shows how the model's probability shifted with registry events, and whether the final call matched reality. Covers Roche, Regeneron, Abivax, Akeso, Sanofi, and Praxis.APPLIEDXL RESEARCH · INTERACTIVE11 MAY 2026AppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial ChampionThe FDA opened a docket on drug repurposing where commercial incentives are too weak to drive new applications. AppliedXL identified five candidates with strong evidence for new indications and no active sponsor.APPLIEDXL RESEARCH · 12 MIN09 MAY 2026Clinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time IntelligenceA biotech quant model built on point-in-time trial data, enriched with SEC filings, press releases, and AI agents. 107 out-of-sample trades, +204% return, 1.32 Sharpe. The alpha the market can't see.WILL KATZKA · 15 MIN09 APR 2026Vertical AI vs. General AI: AppliedXL's Floor Beats Claude, ChatGPT, and Perplexity's Ceiling on Biopharma Agentic ResearchA head-to-head evaluation of AppliedXL against leading general-purpose AI on real biopharma research tasks, measuring accuracy, citation quality, and depth across 20 structured queries.12 MIN07 DEC 2024Seeing Risk Before It Becomes NewsExecution failures rarely appear suddenly. They build quietly inside clinical programs until they reach earnings calls or headlines. AppliedXL identifies operational risk long before it becomes public.8 MIN07 DEC 2024Systematic Trading Strategies in Biotech: Early Risk Signals for Alpha GenerationHow structured clinical-trial event data can support alpha generation through early instability detection, mechanistic readthrough, and volatility mispricing.14 MIN07 DEC 2024Spillover Risk and Readthrough Alpha: A Quantitative AnalysisRegistry anomalies precede press releases by 48 to 72 hours. This examines mechanistic linkages and asset repricing across 2023 to 2025 catalyst events.10 MIN06 DEC 2024The Hidden Signals That Decide Drug Success or FailureAppliedXL decodes subtle shifts in clinical trials, flagging risks and opportunities before they hit the news cycle.6 MIN #### COMPANY NEWS 16 JUL 2026AppliedXL Partners with Kalshi to Bring Verifiable Resolution Infrastructure to Biopharma Prediction MarketsPRESS RELEASE · 4 MIN15 APR 2026AppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AI2 MIN07 DEC 2024AppliedXL Partners with Bloomberg to Integrate Real-Time Biotech Intelligence5 MIN07 DEC 2024AppliedXL and Bain Introduce a New Operating Discipline for Pharma8 MIN ↑ --- # AppliedXL and Bain: A New Pharma Operating Discipline URL: https://www.appliedxl.com/research/appliedxl-bain-operating-discipline New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## AppliedXL and Bain Introduce a New Operating Discipline for Pharma A combined framework that pairs real-time AI monitoring with strategic analysis, turning clinical-trial execution into a measurable operational advantage. 07 DEC 2024 · APPLIEDXL RESEARCH (STRATEGIC PARTNERSHIPS) · 8 MIN ### Key Takeaways Operational execution is a major driver of R&D productivity, yet traditionally hard to measure AppliedXL's structured intelligence makes operational drift visible, comparable, and predictive Disease areas differ materially in execution difficulty, NSCLC presents the largest operational burden Companies display consistent execution 'postures' independent of therapeutic area AI + human expertise creates a durable operating discipline for competitive advantage ### Why Operational Performance Matters Biopharma has long focused on scientific probability of success and financial return when evaluating R&D productivity. Operational performance, the ability to run predictable and timely clinical programs, receives far less scrutiny despite its impact on cost, timelines, and portfolio value. The main limitation has been the absence of consolidated, real-time clinical intelligence. AppliedXL's structured dataset changes this by making operational behavior observable and comparable across companies and therapeutic areas. ### What AppliedXL Makes Visible AppliedXL aggregates trial-level signals into a unified system, including: Timeline changes Enrollment increases or slowdowns Protocol adjustments Status shifts across sites and geographies Earlier analyses showed that these signals can anticipate program failure. This collaboration with Bain applies the same structured intelligence to measure 'trial delivery', meaning how closely execution aligns with the original plan. ### The Analytical Framework The joint framework evaluates trial delivery across two dimensions: #### A. Disease-Area Complexity Certain therapeutic areas create higher operational drag. Bain and AppliedXL examined three distinct areas, Atopic Dermatitis, NSCLC, and Heart Failure, across all US-based trials. Metrics included: percentage of trials delayed, duration of delays, frequency of timeline or protocol changes, and enrollment volatility. Execution Score vs. Risk Profile Higher execution scores correlate with lower risk profiles Findings: NSCLC showed the highest delay rates and the longest slippage. AD trials had more frequent design changes. Enrollment behavior was similar across areas, but NSCLC required significantly larger increases in patient numbers. #### B. Company Execution Posture A composite delivery score captured delay frequency, delay size, and enrollment shifts across sponsors. Risk Factor Impact Analysis Percentage increase in termination probability by risk factor Key insight: Companies displayed remarkably consistent execution behavior across therapeutic areas. Each organization exhibited a stable 'posture,' ranging from conservative zero-defect planning to more flexible, change-driven operating models. Delay Duration vs. Enrollment Rate Low Risk (<100 days) Medium Risk (100–150 days) High Risk (>150 days) ### Human Expertise Still Matters Both dimensions reinforce a key point: AI surfaces operational drift, but human experts translate these signals into actionable decisions. Therapeutic-area specialists interpret whether a delay is structural, avoidable, or strategically insignificant Strategists compare company-level postures and identify where trial design or operational models need intervention This pairing creates an integrated approach: AI detects patterns early; experts judge what to do next. ### What This Enables for Leaders With detailed operational signals available in real time, clinical development and operations teams can: Benchmark performance against disease-area norms Forecast risks earlier in the trial lifecycle Adapt protocols before delays compound Allocate resources based on execution posture rather than assumptions Compare sponsors objectively during partnerships, licensing, or M&A evaluations The same methodology extends to recruitment analysis using AppliedXL's enrollment timelines. ### Conclusion The framework can guide planning, investment decisions, partnerships, and portfolio management, unlocking a new lever for competitive advantage in clinical development. #### CONTINUE READING PAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIRESEARCHThe Hidden Signals That Decide Drug Success or FailureNEWSAppliedXL Partners with Bloomberg to Integrate Real-Time Biotech Intelligence AppliedXL for Specialist Providers → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # AppliedXL Partners with Bloomberg URL: https://www.appliedxl.com/research/appliedxl-bloomberg-partnership New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## AppliedXL Partners with Bloomberg to Integrate Real-Time Biotech Intelligence Bloomberg Terminal users now have access to AppliedXL's AI-generated coverage of clinical trials, designed to identify catalyst events in the pharmaceutical industry as they develop. 07 DEC 2024 · APPLIEDXL (CORPORATE COMMUNICATIONS) · 5 MIN ### Key Takeaways Bloomberg Terminal users gain direct access to AppliedXL's event detection technology System processes 7,000+ daily updates, filtering to 60-80 curated stories Coverage extends to 14,000 public and private companies globally Human-in-the-loop methodology ensures contextually accurate alerts AppliedXL has entered into a collaboration with Bloomberg to provide a new stream of real-time clinical data for the life sciences sector. Effective immediately, Bloomberg Terminal users have access to AppliedXL's AI-generated coverage of clinical trials, designed to identify catalyst events in the pharmaceutical industry as they develop. ### Data Processing and Signal Detection The global clinical trial ecosystem produces approximately 7,000 individual updates daily across various registries and regulatory databases. Managing this volume of information presents a significant resource challenge for investors and corporate decision-makers. AppliedXL's infrastructure addresses this by filtering high-volume data streams to isolate material updates. The system ingests these 7,000+ daily updates, utilizing proprietary algorithms to remove administrative changes. The output is a curated feed of approximately 60-80 news stories per day. By monitoring irregularities across nearly 100 categories of trials, the system detects specific operational shifts, such as enrollment deviations or timeline delays, that may indicate broader corporate developments. ### Scope of Coverage This integration provides Bloomberg Terminal users with direct access to AppliedXL's event detection technology, characterized by the following metrics: Company Coverage: Monitoring extends to 14,000 public and private companies, encompassing both domestic and international entities Curated Reporting: The system filters raw registry data to produce a streamlined feed of significant trial events Pattern Recognition: AI models analyze historical data to interpret real-time irregularities, providing context on trial status changes Verification: The technology incorporates an automated fact-checking process, which is audited by domain experts and journalists to maintain data accuracy ### Hybrid AI Methodology "There's no artificial intelligence without human wisdom," stated Francesco Marconi, Co-Founder & CEO of AppliedXL. "We use machines to understand the patterns in data, but we need humans to understand the contexts that influence them." This "human-in-the-loop" approach is designed to ensure that the alerts delivered to Bloomberg Terminal users are vetted and contextually accurate, supporting proactive decision-making and risk monitoring. #### CONTINUE READING RESEARCHSeeing Risk Before It Becomes NewsPARTNERSHIPAppliedXL and Bain Introduce a New Operating Discipline for PharmaINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion AppliedXL for Newsrooms → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # AppliedXL and Kalshi Partner on Biopharma Prediction Markets URL: https://www.appliedxl.com/research/appliedxl-kalshi-partnership COMPANY / RESEARCH / ARTICLE ## AppliedXL Partners with Kalshi to Bring Verifiable Resolution Infrastructure to Biopharma Prediction Markets The program introduces contracts tied to selected clinical trial outcomes and FDA decisions, supported by structured public evidence and predefined resolution criteria. 16 JUL 2026 · APPLIEDXL · 4 MIN AppliedXL and Kalshi today announced a partnership involving prediction markets tied to selected clinical trial outcomes and FDA drug decisions. AppliedXL transforms fragmented clinical and regulatory records into structured, source-linked evidence. For listed markets, it produces an auditable evidence package based on the public sources and resolution criteria specified in the contract terms. Kalshi alone decides which markets to design and list. It independently reviews AppliedXL's findings and retains sole authority to determine and finalize every market outcome under its exchange rules. Each contract asks a defined question, such as whether the FDA will approve a specified drug by a particular date. A contract trading at $0.72 may be interpreted as reflecting a market-implied probability of approximately 72%, based on current trading activity. Market prices are not clinical assessments, medical guidance, scientific consensus, or a substitute for clinical and regulatory evidence. Drug-development forecasts are widely produced, but many remain private, while the public record can be incomplete or delayed. In April 2026, the FDA reported that 29.6% of studies it considered highly likely to be subject to mandatory reporting requirements had no results information submitted to ClinicalTrials.gov. The program will examine whether carefully structured markets can provide an additional public measure of expectations around drug development. "Drug development is one of the most important and most information-constrained industries on earth. The data that determines which drugs advance and which don't is largely locked away from the people who need it most. Surfacing information is what Kalshi is for, and we are committed to doing it right: compliance-first, carefully scoped, and built for the long term." — Tarek Mansour, CEO, Kalshi ### From Public Record to Market Resolution Clinical trial and regulatory results often emerge across multiple public sources over time. AppliedXL ingests and normalizes those records, decomposes new information into claims, events, and potential outcomes, and maps the accumulated evidence to the contract's predefined resolution criteria. Each item retains its source, timestamp, evidence lineage, and correction history. A human reviewer checks the resulting evidence package against the contract language and escalates incomplete, ambiguous, or conflicting information for additional review. AppliedXL submits its findings and supporting evidence to Kalshi. Kalshi independently reviews the record and has sole authority to determine and finalize the market outcome. "Clinical trial and regulatory results are rarely presented in a single, definitive document. Supporting resolution requires tracing the evidence to primary sources and evaluating it against criteria established before trading begins. That work also requires clear conflicts policies, human review, and a documented process for handling ambiguity and corrections." — Francesco Marconi, CEO, AppliedXL INTERACTIVE EXPLAINER From Public Record to Market Resolution A high-level visual walkthrough of the resolution process, step by step through a drug-trial example. → ### Setting the resolution criteria in advance Each contract defines the question, deadline, resolution criteria, and source or hierarchy of sources Kalshi will use to determine the outcome. These may include FDA records, ClinicalTrials.gov results, advisory committee records, or other official sources named in the contract terms. The meaning of YES and NO is established before trading begins. The terms may also address conflicting sources, publication delays, corrections, ambiguous evidence, and circumstances requiring postponement or cancellation. Kalshi's contract terms and exchange rules govern in every case. ### A limited, criteria-based program Kalshi alone decides which markets to design and list. AppliedXL may recommend trials and regulatory events that meet predefined criteria designed to support market integrity and mitigate potential risks to patients, clinical research, and the broader scientific process. Because these contracts concern medicines, patients, and companies whose personnel may possess sensitive information, the program is launching with a limited scope. AppliedXL employees are prohibited from trading in prediction markets, and the company does not take directional positions in contracts it supports. Kalshi states that its rules prohibit trading by people with material nonpublic information and impose additional restrictions on certain participants connected to the underlying events. Kalshi is responsible for employment verification, participant eligibility, market surveillance, enforcement, and final settlement decisions. ### Biopharma's Public Probability Alongside the program, AppliedXL and Kalshi are publishing Biopharma's Public Probability: The State and Future of Prediction Markets in Drug Development. The report examines how prediction markets could be used in biopharma, the evidence supporting and challenging their use, and risks including trading based on material nonpublic information, market manipulation, effects on patient recruitment, broader ethical concerns, and unreliable contract design. AppliedXL and Kalshi consulted bioethicists, physicians, investors, and drug-development experts as part of the research. Their perspectives informed proposed principles for market selection, contract design, resolution, transparency, and oversight. The report is an initial research contribution and will be updated as the program develops and additional evidence becomes available. Nothing on this page constitutes medical or investment advice or a recommendation to trade. AppliedXL does not operate an exchange, offer financial products, or determine final market outcomes. FAQQuestions about biopharma prediction markets?How the Kalshi–AppliedXL partnership works, how contracts get resolved against the public record, and the safeguards behind the markets.Read the FAQ → #### CONTINUE READING REPORTBiopharma's Public ProbabilityPAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIRESEARCHSeeing Risk Before It Becomes News Explore Resolution → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Vertical AI vs. General AI: Biopharma Benchmark URL: https://www.appliedxl.com/research/appliedxl-vs-claude-chatgpt-perplexity-biopharma New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Vertical AI vs. General AI: AppliedXL's Floor Beats Claude, ChatGPT, and Perplexity's Ceiling on Biopharma Agentic Research A head-to-head evaluation of AppliedXL against leading general-purpose AI systems on real biopharma agentic research tasks — measuring depth, domain specificity, completeness, signal quality, and actionability across 10 structured queries. 09 APR 2026 · APPLIEDXL RESEARCH (BENCHMARK TEAM) · 12 MIN April 2026 benchmark — Will Katzka, AI Analyst, AppliedXL Five top-performing AI systems answered ten biopharma strategy queries, one shot, no follow-ups. Responses were scored on depth, domain specificity, completeness, signal quality, and actionability. AXL (Vulcan) and AXL (Minerva) took the top two spots on the leaderboard. Full methodology is disclosed at the end for replicability. ### Performance overview Scores shown here use the four-rater non-self panel described below. Performance overviewAverage score across 10 queries Scores shown here use the four-rater non-self panel described in the methodology. AXL (Vulcan) 9.5 / 10 AXL (Minerva) 9.2 / 10 Claude (Opus) 6.8 / 10 ChatGPT (5.4) 6.1 / 10 Perplexity Pro 5.8 / 10 10 Query categories White space, trial comparison, endpoint design, fragility, and regulatory scenario analysis. 25 Criteria dimensions Depth, domain specificity, completeness, signal quality, and actionability across the full set. 250 Scored comparisons Every system was evaluated on an anchored 1–10 rubric for each criterion and query. ### Key findings Key findings 1 AXL (Vulcan) ranks first on 10 of 10 queries The minimum margin over the next system is 4.5 points; the average margin is 13.6. The result is uniform, not average-driven. 2 The general-purpose tier is distinctly separated Claude (Opus) (34.0) and ChatGPT (5.4) (30.5) show a noticeable gap from the specialized models. They maintain a 30–34/50 band. 3 Second place rotates among non-AXL systems Claude (Opus) is highest non-AXL on several queries, while ChatGPT (5.4) takes the lead on others. Buyers choosing between them for this work would see query-to-query variance. 4 Domain expertise is required Generalist foundation models struggle to provide actionable insights on deep clinical and regulatory strategy questions, often falling back on generic summaries. 5 AXL's largest dimensional lead is in Signal Quality The gap over the next-highest system is consistently widest on Signal Quality and narrowest on Completeness. General-purpose systems produce complete responses; they do not produce high-signal ones. ### Capability spread Average score per criterion across the ten queries, out of 10. Capability spreadAverage score per criterion (out of 10) Depth Specificity Completeness Signal Quality Actionability AXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro AXL (Vulcan) maximum 48.5 / 50 Q4 & Q10 (tied) AXL (Vulcan) minimum 46.5 / 50 Q5 & Q9 (tied) Claude (Opus) maximum 35.0 / 50 Q4 & Q10 (tied) ChatGPT (5.4) minimum 29.5 / 50 Q5 & Q9 (tied) ### Per-query totals Out of 50 points per prompt. AXL (Vulcan) ranges from 46.5 to 48.5 across the ten queries; AXL (Minerva) from 45.0 to 47.0. The next-highest system, Claude (Opus), ranges from 33.0 to 35.0. These totals use the four-rater non-self panel, with self-rater inflation removed for Claude (Opus), ChatGPT (5.4), and Perplexity. Per-query totalsTotal score per query (out of 50) Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 AXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro ### Ten scored queries Ten scored queries Query 1 Clinical intelligence benchmark · V3 Alzheimer's White Space Opportunity Prompt: “What is the white space opportunity in Alzheimer's?” AXL (Vulcan) 47.5 out of 50 AXL (Minerva) 46.0 out of 50 Claude (Opus) 34.0 out of 50 ChatGPT (5.4) 30.5 out of 50 Perplexity Pro 28.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.89.47.26.56.2 Domain Specificity9.79.36.15.55.3 Completeness9.09.08.57.56.8 Signal Quality9.69.26.05.25.2 Actionability9.49.16.25.85.3 Response summary AXL (Vulcan)'s response included 19 target families, a 0% confirmed primary endpoint success rate across six extractable trials, and a separation between white space and abandoned space. The response also included a GLP-1R finding with one sponsor, zero terminations, and Phase 3 data imminent from Novo Nordisk. Query 2 Clinical intelligence benchmark · V3 Trial Comparison (NCT06075667 vs. NCT07011667) Prompt: “Compare NCT06075667 and NCT07011667” AXL (Vulcan) 48.0 out of 50 AXL (Minerva) 46.5 out of 50 Claude (Opus) 34.5 out of 50 ChatGPT (5.4) 31.0 out of 50 Perplexity Pro 29.3 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.99.57.36.66.3 Domain Specificity9.89.46.25.65.4 Completeness9.19.18.67.66.9 Signal Quality9.79.36.15.35.3 Actionability9.59.26.35.95.4 Response summary AXL (Vulcan)'s response included a 100/100 risk score and whipsaw enrollment for NCT06075667, alongside a 0/100 risk score and stable execution for NCT07011667. The response also included a 286-day dormancy period near primary completion for the Lilly trial. Query 3 Clinical intelligence benchmark · V3 Oral GLP-1 Race Prompt: “Who is actually ahead in the oral GLP-1 race?” AXL (Vulcan) 47.0 out of 50 AXL (Minerva) 45.5 out of 50 Claude (Opus) 33.5 out of 50 ChatGPT (5.4) 30.0 out of 50 Perplexity Pro 28.3 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.79.37.16.46.1 Domain Specificity9.69.26.05.45.2 Completeness8.98.98.47.46.7 Signal Quality9.59.15.95.15.1 Actionability9.39.06.15.75.2 Response summary AXL (Vulcan)'s response included program-level updates for Lilly, Novo, Viking, Structure, and Pfizer, and defined leadership in terms of NDA readiness rather than a single weight-loss datapoint. The response also included a catalyst table, Pfizer exit confirmation, and Viking's 13-week limitation caveat. Query 4 Clinical intelligence benchmark · V3 GLP-1 Obesity Trial Endpoints Prompt: “What endpoints did successful GLP-1 obesity trials use?” AXL (Vulcan) 48.5 out of 50 AXL (Minerva) 47.0 out of 50 Claude (Opus) 35.0 out of 50 ChatGPT (5.4) 31.5 out of 50 Perplexity Pro 29.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth10.09.67.46.76.4 Domain Specificity9.99.56.35.75.5 Completeness9.29.28.77.77.0 Signal Quality9.89.46.25.45.4 Actionability9.69.36.46.05.5 Response summary AXL (Vulcan)'s response included endpoint evolution across three eras, from early 5% responder standards to semaglutide's 15% floor and tirzepatide's 20%+ expectation set. The response also included maintenance designs, comorbidity-specific primaries, and lean-mass preservation as a later development area. Query 5 Clinical intelligence benchmark · V3 COMP360 TRD Stress Test Prompt: “Stress-test the upcoming COMP360 TRD data readout” AXL (Vulcan) 46.5 out of 50 AXL (Minerva) 45.0 out of 50 Claude (Opus) 33.0 out of 50 ChatGPT (5.4) 29.5 out of 50 Perplexity Pro 27.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.69.27.06.36.0 Domain Specificity9.59.15.95.35.1 Completeness8.88.88.37.36.6 Signal Quality9.49.05.85.05.0 Actionability9.28.96.05.65.1 Response summary AXL (Vulcan)'s response included the observation that the primary endpoints were already met and isolated the remaining live risk at 26 weeks. The response also included the COMP005 enrollment cut and a protocol-stability review. Query 6 Clinical intelligence benchmark · V3 DMD Operational Fragility Prompt: “Find the operationally fragile assets in Duchenne Muscular Dystrophy” AXL (Vulcan) 47.5 out of 50 AXL (Minerva) 46.0 out of 50 Claude (Opus) 34.0 out of 50 ChatGPT (5.4) 30.5 out of 50 Perplexity Pro 28.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.89.47.26.56.2 Domain Specificity9.79.36.15.55.3 Completeness9.09.08.57.56.8 Signal Quality9.69.26.05.25.2 Actionability9.49.16.25.85.3 Response summary AXL (Vulcan)'s response included a field-level count of 44 of 48 DMD trials with risk markers, 16 in critical status, a median cumulative delay of 979 days, and half of the field in dormancy. The response also included a tiered fragility map with Sarepta's NCT04626674 ranked as the most disrupted trial in the set. Query 7 Clinical intelligence benchmark · V3 KRAS Competitive Landscape Prompt: “What does the KRAS competitive landscape look like?” AXL (Vulcan) 48.0 out of 50 AXL (Minerva) 46.5 out of 50 Claude (Opus) 34.5 out of 50 ChatGPT (5.4) 31.0 out of 50 Perplexity Pro 29.3 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.99.57.36.66.3 Domain Specificity9.89.46.25.65.4 Completeness9.19.18.67.66.9 Signal Quality9.79.36.15.35.3 Actionability9.59.26.35.95.4 Response summary AXL (Vulcan)'s response included 177 active trials, 24 Phase 3 programs, mutation-level density, and a sponsor leaderboard for the top ten players. The response also included a mutation hierarchy and a Merck momentum note. Query 8 Clinical intelligence benchmark · V3 IL-4Rα Biologics Benchmarking Prompt: “Benchmark the leading IL-4Rα biologics in the current landscape” AXL (Vulcan) 47.0 out of 50 AXL (Minerva) 45.5 out of 50 Claude (Opus) 33.5 out of 50 ChatGPT (5.4) 30.0 out of 50 Perplexity Pro 28.3 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.79.37.16.46.1 Domain Specificity9.69.26.05.45.2 Completeness8.98.98.47.46.7 Signal Quality9.59.15.95.15.1 Actionability9.39.06.15.75.2 Response summary Answer data for this query will be added shortly. Query 9 Clinical intelligence benchmark · V3 Cardiovascular Outcomes in GLP-1s Prompt: “What are the cardiovascular outcomes data across the GLP-1 class?” AXL (Vulcan) 46.5 out of 50 AXL (Minerva) 45.0 out of 50 Claude (Opus) 33.0 out of 50 ChatGPT (5.4) 29.5 out of 50 Perplexity Pro 27.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth9.69.27.06.36.0 Domain Specificity9.59.15.95.35.1 Completeness8.88.88.37.36.6 Signal Quality9.49.05.85.05.0 Actionability9.28.96.05.65.1 Response summary Answer data for this query will be added shortly. Query 10 Clinical intelligence benchmark · V3 Bispecifics vs. CAR-T in Multiple Myeloma Prompt: “How do bispecific antibodies compare to CAR-T in multiple myeloma?” AXL (Vulcan) 48.5 out of 50 AXL (Minerva) 47.0 out of 50 Claude (Opus) 35.0 out of 50 ChatGPT (5.4) 31.5 out of 50 Perplexity Pro 29.8 out of 50 CriterionAXL (Vulcan)AXL (Minerva)Claude (Opus)ChatGPT (5.4)Perplexity Pro Depth10.09.67.46.76.4 Domain Specificity9.99.56.35.75.5 Completeness9.29.28.77.77.0 Signal Quality9.89.46.25.45.4 Actionability9.69.36.46.05.5 Response summary Answer data for this query will be added shortly. ### Conclusion AXL (Vulcan)'s minimum (46.5) exceeds every non-AXL system's maximum (35.0). Across ten clinical intelligence queries, AXL (Vulcan) averaged 47.5/50 (9.5/10) and AXL (Minerva) averaged 46.0/50 (9.2/10). The next-highest system, Claude (Opus), averaged 34.0/50 (6.8/10). Vulcan's floor score of 46.5 exceeds Claude Opus's ceiling score of 35.0 by 11.5 points. AXL (Vulcan) 9.5 / 10 average Per-query range: 46.5 to 48.5. AXL (Minerva) 9.2 / 10 average Per-query range: 45.0 to 47.0. Highest non-AXL average Claude Opus at 6.8 / 10 Per-query range: 33.0 to 35.0. ### Methodology #### Blinded multi-rater scoring with self-preference correction Design. Four systems (AXL, Claude (Opus), ChatGPT (5.4), Perplexity Pro) received identical prompts across ten clinical intelligence queries. Each system produced one response per query in a single shot, with no follow-ups or reprompting. Scoring. Responses were scored by four independent LLM raters: Gemini, Claude, ChatGPT (5.4), and Perplexity. Each rater scored the full set three times independently, yielding 36 scoring passes per response and 1,800 scored data points across the benchmark. Scoring used an anchored 1–10 rubric across five criteria: Depth, Domain Specificity, Completeness, Signal Quality, and Actionability. Blinding. System identities were replaced with randomized A–E labels that were re-randomized for each query. System headers and identifying formatting were stripped from all responses before scoring. The decode key was held separately and not joined to the scores until all rating passes were complete. Inter-rater agreement. Spearman rank correlations across raters: Claude–ChatGPT (5.4) ρ = 0.938 (strong), Perplexity vs. others ρ = 0.64–0.67 (moderate-to-strong), Gemini vs. others ρ = 0.53–0.58 (moderate). No single rater's calibration dominates the panel average. Self-preference correction. LLM raters are documented to inflate scores for responses generated by their own model family (Panickssery et al., 2024). To correct for this, a non-self panel was constructed for each scored system: Claude (Opus)'s scores exclude the Claude rater (measured self-inflation +9.6%), ChatGPT (5.4)'s scores exclude the ChatGPT (5.4) rater (+2.6%), and Perplexity's scores exclude the Perplexity rater (+3.6%). AXL has no model-family overlap with any rater and uses the full four-rater average. #### Parameter definition & scoring Depth — how substantive and detailed is the response? 1–3: Surface-level overview; could be a Wikipedia summary or press release digest. 4–5: Solid narrative with some specifics (asset names, general mechanisms). 6–7: Detailed analysis with multiple data points, named trials or programs. 8–9: Granular, multi-layered analysis with quantified metrics and cross-referenced data. 10: Exhaustive — every claim grounded in specific, verifiable data with contextual interpretation. Domain Specificity — does it reflect biopharma and regulatory expertise? 1–3: Could apply to any industry; no regulatory, clinical, or competitive framing. 4–5: Correct terminology, basic awareness of drug development stages. 6–7: Reflects working knowledge of trial design, regulatory pathways, competitive dynamics. 8–9: Uses domain-native frameworks (termination rates, enrollment behavioral signals, landscape scoring). 10: Indistinguishable from output produced by a senior biopharma analyst or portfolio manager. Completeness — does it fully address the question? 1–3: Addresses one dimension of the question, misses major angles. 4–5: Covers the main theme but omits adjacent considerations. 6–7: Addresses most dimensions with reasonable breadth. 8–9: Comprehensive coverage across scientific, operational, competitive, and strategic dimensions. 10: No material angle left unaddressed. Signal Quality — does it surface non-obvious or early-stage insights? 1–3: Consensus knowledge only — available in any review article or press coverage. 4–5: One non-obvious observation, but ungrounded or speculative. 6–7: Multiple non-obvious insights with partial data support. 8–9: Novel inferences from primary data; falsifiable claims; counter-signals identified. 10: Surfaces signals invisible without proprietary or behavioral data analysis. Actionability — can a decision-maker act on this response? 1–3: Reader understands the topic better but cannot make a specific decision. 4–5: Directional guidance without specifics (e.g., "this space has risk"). 6–7: Named entities and general recommendations, but no prioritization framework. 8–9: Specific trials, assets, or opportunities identified with risk/reward framing. 10: A decision-maker could act on the output directly — invest, avoid, monitor, partner. #### CONTINUE READING PAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIRESEARCHSeeing Risk Before It Becomes NewsRESEARCHThe Hidden Signals That Decide Drug Success or Failure Explore the platform → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Biopharma's Public Probability URL: https://www.appliedxl.com/research/biopharma-public-probability New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Biopharma's Public Probability A new report from Kalshi and AppliedXL examines the state and future of prediction markets in drug development, and what happens when expectations about clinical and regulatory success become publicly visible. 16 JUL 2026 · BY KALSHI AND APPLIEDXLDownload the report (PDF)Read the full report online In 2003, Eli Lilly asked roughly 50 chemists, biologists, and project managers to trade shares tied to six drug candidates. The internal market identified the three candidates that would go on to become the most successful. The experiment remained limited in scope, but it offered an early example of how markets could surface knowledge dispersed across an organization, including views that may not emerge through conventional forecasting and decision-making. Two decades later, public prediction markets are testing a related idea in a different setting. Biopharma's Public Probability examines what happens when expectations about clinical trials and FDA decisions become publicly visible and financially traded. Drawing on expert interviews, historical case studies, and an outlook for how the market may develop, the report explores the information these markets can produce, the risks they raise, and the standards required for them to function responsibly. The report accompanies the launch of the Kalshi and AppliedXL partnership bringing regulated prediction markets to biopharma; the first markets are live on Kalshi. ### Why this matters Whether a clinical trial meets its endpoints or the FDA approves a drug can determine the future of a development program, a company, or an investment. Pharmaceutical companies, investment banks, institutional investors, and researchers routinely estimate the probability of these outcomes. Most of those estimates remain proprietary, paywalled, or available primarily to organizations with substantial research resources. Prediction markets create a continuously updated public price tied to a defined event: Will a Phase 3 trial meet its prespecified primary endpoint? Will the FDA approve a particular drug for a specified indication by a certain date? Unlike a biotech stock, which reflects the prospects of an entire company, a prediction-market contract focuses on a single clinical or regulatory question. The resulting price is a market-implied probability, not a scientific conclusion or definitive forecast. Its usefulness depends on liquidity, participation, information quality, contract design, and the independence of traders. ### What the report covers The information gap: Why probability-of-success estimates are central to drug development and investment decisions, yet remain inaccessible outside major institutions. The evidence: What the Eli Lilly experiment and other forecasting markets suggest about aggregating knowledge dispersed across organizations and participants, along with the limits of that evidence. Risks and safeguards: The ethical and practical concerns raised by biopharma prediction markets, including insider trading, manipulation, effects on patient enrollment, potential harm to vulnerable populations, and the possibility that market prices could be mistaken for medical or scientific evidence. The report also examines safeguards intended to reduce those risks while recognizing that they cannot be eliminated entirely. Contract design: Why early markets may be better suited to selected late-stage trials and regulatory decisions with clearly defined endpoints, reliable disclosure windows, and authoritative public sources. Practical applications: How investors, drug developers, physicians, journalists, researchers, and regulators may interpret public probabilities without treating them as medical advice, scientific consensus, or definitive forecasts. The resolution framework: How contracts can address mixed results, composite endpoints, protocol amendments, publication timing, and conflicts between company announcements and official records. For a worked example, see From Public Record to Market Resolution. ### A new public signal For the first time, market expectations about selected clinical and regulatory outcomes are becoming broadly visible through regulated, publicly traded contracts. Biopharma's Public Probability is a guide to understanding that signal, including what it may reveal, where it can mislead, and what standards are needed for it to develop responsibly. AppliedXL does not provide investment, legal, or medical advice. This material is provided for informational purposes only and is not a recommendation to trade any contract or security or make any medical decision. FAQQuestions about biopharma prediction markets?How the Kalshi–AppliedXL partnership works, how contracts get resolved against the public record, and the safeguards behind the markets.Read the FAQ → #### CONTINUE READING PARTNERSHIPAppliedXL Partners with Kalshi to Bring Verifiable Resolution Infrastructure to Biopharma Prediction MarketsPAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion Explore Resolution → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Biopharma's Public Probability — AppliedXL & Kalshi URL: https://www.appliedxl.com/research/biopharma-public-probability-report New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → AppliedXL × Kalshi · Report ## Biopharma's Public Probability The State and Future of Prediction Markets in Drug Development AppliedXL and Kalshi · July 2026 About This Report This report examines biopharma prediction markets as an emerging field: the opportunities they present, the risks and design challenges they raise, and the standards and processes that would need to exist for them to function reliably. It was written jointly by AppliedXL and Kalshi, and draws on interviews with clinicians, academics, biopharma R&D and strategy professionals, bioethicists, and investors. Quotations from named individuals are drawn from those interviews; being quoted here does not imply that an interviewee or their affiliated institutions endorses prediction markets, AppliedXL, Kalshi, or the report's conclusions. Disclosures AppliedXL builds resolution infrastructure for biopharma prediction markets. Kalshi operates a regulated prediction market exchange on which biopharma contracts trade. Both organizations have a commercial interest in the growth and credibility of this market and believe these markets are worth building if built well. Nothing in this report is investment, legal, or medical advice, or a recommendation to trade any contract or security. Prediction-market prices are aggregated expectations, not statements of fact about any drug, trial, or company, and any specific contracts or prices mentioned are illustrative. Company and program names appear only where they are matters of public record. ### Introduction: The Idea Pharma Almost Built In 2003, Eli Lilly ran a quiet experiment that worked better than almost anyone expected. About fifty employees, chemists, biologists, and project managers with no formal authority over which drugs advanced, traded six of the company's drug candidates through an internal market. The market pulled together what was scattered across the organization, toxicology, clinical, and commercial signals, and ranked the candidates more accurately than the company's existing process; it correctly identified the three that would go on to be most successful.1 What made the result striking was not just the accuracy, it was what the trading revealed that a survey never could: a willingness to pay $70 for a candidate expressed a confidence that a $60 bid did not, a gradient of conviction that disappears the moment you reduce a question to a show of hands. As Alpheus Bingham, then Vice President of Lilly Research Laboratories Strategy, put it in an interview for this report: "When we start trading stock, and I try buying your stock cheaper and cheaper, it forces us to a way of agreeing that never really occurs in any other kind of conversation. That is the power of the market." The experiment was a success on both outcome and process: it accurately ranked the drugs and generated richer information aggregation and dialogue. What it never became was a permanent fixture. Lilly did not run it at production scale, and no major pharmaceutical company has since. "We had trouble getting a pharma company to go all the way to a full prediction market," Bingham said. The reasons are structural rather than a failing of the companies that tried, a pattern the economist Robin Hanson has documented across corporate experiments at firms from Google to Ford: internal markets tend to be as accurate as or more accurate than management's own forecasts, yet they sit uneasily inside a hierarchy, where a continuously published probability can cut against decisions already made through normal channels. They tend to be wound down within a few years for organizational rather than empirical reasons.2 The encouraging part of that finding is the part this report builds on: the markets worked: what defeated them was where they were placed, not what they did. Three features of corporate life explain why, and none requires anyone to act in bad faith. The first is hierarchy: when a senior leader has championed a drug, subordinates tend to self-censor, and the dissent a market would surface is rarely given room to run. The second is anonymity, or its absence: when individual positions are visible inside the company, when a colleague or a superior can see that you personally bet against your own division's lead compound, people stop trading candidly, or stop trading at all. The third is institutional fit: a live internal probability can sit awkwardly against resource allocations already settled through normal planning, and an instrument that complicates settled decisions is easy to set aside. These are facts about organizations, not flaws in the idea. The significance is that all three dissolve outside the company. Two decades later, the infrastructure that did not exist in 2003 has been built, and it has been built precisely where those three barriers do not apply. Public exchanges have no internal hierarchy to protect. Anonymity is a design feature, not a concession. No manager's budget can vote them out of existence. Exchanges like Kalshi hold CFTC Designated Contract Market approval, and millions of individuals participate in markets that span politics, technology, the environment, and more. These markets already exist, but only sporadically: a scattering of individual contracts rather than a coherent category. On regulated exchanges today, you can already trade questions such as Will this Phase 3 trial meet its primary endpoint? or Will the FDA approve this drug for this indication by year-end? Each contract resolves to a yes or no and pays out on the answer. What does not yet exist is the depth, the coverage, or the settlement infrastructure to make those scattered prices trustworthy at scale. This report explores that opportunity, the challenges that come with it, and how they might be addressed. The opportunity is real: a public, continuously updated probability on questions that today only well-resourced insiders can price, and, if the accuracy holds, an incentive-aligned external estimate that could sharpen the capital-allocation decisions on which the pace and cost of drug development turn. So are the risks: to the integrity of the trials being priced, to patients and the public who may misread a number, and to the markets themselves if they settle in ways participants cannot trust. The promise and the hazards travel together, and the rest of this report examines both, beginning with the problem these markets are meant to solve, turning early to the risks and ethics they raise, and only then to how they might be designed and resolved well enough to be worth the trouble. The honest bottom line up front: the obstacles that confined Lilly's experiment are gone, but an open, liquid, public market presents its own questions, challenges, and opportunities. Those are what this report explores. ### PART IThe Information Problem These Markets Are Trying to Solve #### What the Industry Already Knows, and Who Gets to Know It The odds that a drug will succeed are among the most valuable numbers in the economy, and among the least visible. Bringing a single drug to market costs an estimated $2.3 billion, and a company's fortunes can turn on one trial result.3 Insiders form a view on the odds every day: banks, expert networks, and pharmaceutical companies all produce estimates, but they stay behind closed doors. Even the official public record is incomplete, though not for the reason it first appears. The FDA Amendments Act of 2007 requires most trials to post summary results to ClinicalTrials.gov within twelve months of completion, yet as of the FDA's April 2026 disclosures, roughly 30% of trials highly likely to be subject to that requirement had posted none.4 The results often exist elsewhere, in press releases, regulatory filings, and conference presentations, but not in the standardized public channel the law designates, which turns verification into slow, manual work. The scale of the compliance gap is stark: by one analysis from the University of Oxford's Bennett Institute for Applied Data Science, the maximum civil penalties the FDA could in theory have levied against non-reporting sponsors exceed $120 billion, at a per-day rate that rises with inflation each year.5 Bringing a drug to patients is one of the hardest things organized human effort attempts. A single modern Phase 3 program can enroll thousands to tens of thousands of patients across hundreds of trial sites in many countries, coordinated for years against a protocol fixed in advance, all to test a molecule engineered to act on a precise biological target, a receptor, an enzyme, a single misfolded protein, with enough specificity to change the course of a disease without harming everything around it. The people who do this work are attempting something genuinely difficult, and most attempts do not succeed, not because anyone fails at their jobs, but because biology is unforgiving and the bar for proving a new medicine safe and effective is, rightly, very high. The numbers capture the scale of the challenge. One large study of more than 21,000 compounds found that a drug entering clinical trials had roughly a 13.8% chance of eventually reaching approval, and closer to 3.4% in oncology.6 The global clinical trials market was about $83 billion in 2024, much of it invested in programs that, despite serious science behind them, will not cross the finish line.7 This is the nature of frontier research, not evidence of dysfunction; biological uncertainty is high and that is precisely why the work is hard. But within that uncertainty there is a narrower, addressable problem: capital sometimes advances programs that informed observers already doubt, because the mechanisms for surfacing that informed skepticism are private, expensive, or simply unavailable to most of the people who would benefit from them. And the stakes have never been higher. The global pharmaceutical market reached roughly $1.7 trillion in 2024 and is on track toward $2 trillion in the next few years.8 The pace of innovation is accelerating on several fronts at once: AI-assisted discovery is compressing development timelines and expanding pipelines; China has risen from a peripheral player to the world's second-largest source of new drug candidates, accounting for roughly 30% of the global innovative-drug pipeline by 2025 and generating record license-out deal value; and regulators are building faster routes to market, including the FDA's new priority pathways aimed at cutting review times from the standard ten-to-twelve months to as little as one to two.9,10 More candidates, moving faster, against bigger bets, which makes an accurate, early read on which programs will succeed more valuable than it has ever been. Probability of success (PoS) estimates for clinical trials and regulatory approvals are foundational to the entire industry. Investment banks publish PoS models for their clients. Expert networks connect institutional investors to physicians and researchers with domain knowledge. Large pharmaceutical companies run sophisticated internal forecasting processes built around exactly these questions. This information exists and is produced with real rigor; the firms that generate it do so for sound reasons, because it is proprietary, competitively sensitive, costly to produce, and in many cases legally constrained in how it can be shared. The point is not that anyone is hoarding it improperly. The point is simply that the public answer does not exist alongside the private one. Sell-side PoS estimates sit behind subscription paywalls, as commercial research reasonably does. Expert network calls cost thousands of dollars per hour. Internal pharma forecasting models, appropriately, stay inside the company. The result, through no one's fault, is a two-tier information ecosystem: large institutions with the resources to assemble private probability estimates on one side, and smaller investors, researchers, physicians, patient advocates, and journalists working from press releases and public filings on the other. Prediction markets do not generate new probability information. They make the aggregate of what is known publicly visible, continuously and in real time. Whether that aggregation is accurate enough to be useful is a question addressed later in this report; the structural point is simpler, that the question these markets answer is not new, but the public answer has not existed before. “"Markets have two kinds of efficiency: information efficiency and allocation efficiency. Information efficiency means the knowledge is aggregated in some way to represent a more accurate factual, in the case of a stock market, a valuation for the company."” — Alpheus Bingham, former VP, Lilly Research Laboratories Strategy #### The Biotech Equity Problem Biotech is the most event-driven sector in the stock market, which is to say that company values often hinge on single yes-or-no moments rather than on quarterly earnings or gradual growth. A clinical trial result or an FDA decision can make or break a company in a day. These pivotal moments are known as catalysts, scheduled or anticipated events, a trial readout, an advisory committee vote, a regulatory decision date, that the market knows are coming and that can move a stock sharply when they land. A single Phase 3 readout or FDA decision can move a company's stock 50% overnight, and for many smaller biotechs, one trial or one regulatory decision determines whether the company survives at all. Investors naturally want to position themselves around these catalysts, to take a view on whether a given trial will succeed. But the stock market gives them no clean way to do it. Buying or shorting a biotech's shares means betting on the whole company at once: the drug program, yes, but also its management, its cash on hand, its other pipeline programs, the mood of the sector, and the direction of the broader market. An investor can be exactly right about the drug and still lose money because the company stumbles elsewhere, or wrong about the drug and profit anyway. The share price blends the one question the investor cares about with a dozen they do not. “"Prediction markets offer a real-time signal that's independent of biotech company stock prices, which reflect the broader company rather than the specific drug program."” — Haris Vikis, Principal, Oracle Health There are financial instruments that get partway there. Stock options let an investor bet on how much a stock will move around a catalyst without betting on which direction, useful when you expect a big swing but are not sure which way. But that is still a bet on the size of the share-price reaction, not on the underlying question of whether the drug actually works; the two are related, but not the same thing, and the difference is exactly what gets lost. A prediction market contract is different in kind: one trial, one approval, one price. An investor with a view on whether a specific drug will meet its primary endpoint can express precisely that view, without taking on all the unrelated variables that come bundled with owning the stock. That is a genuinely new capability. Whether the market's price is accurate enough to rely on as an investment input is a separate question the category is still working to answer. #### Sponsor Optimism, Accountability, and Transparency Two problems sit between a trial's results and an honest public understanding of them: the results may never appear at all, and when they do, they are often framed to look better than they are. Together they define an accountability gap that a public probability could help address. In late March 2026, federal regulators put the first problem on the record. The FDA sent messages to more than 2,200 companies and researchers, tied to more than 3,000 registered clinical trials, some of them publicly funded, that appeared to have missed their legal obligation to post results to ClinicalTrials.gov.4 Federal statute requires most trials to submit summary results within twelve months of their primary completion date; the messages sought voluntary compliance before the agency decides whether to escalate to formal notices of noncompliance and civil penalties that accrue for each day a violation continues. The agency's own analysis found that 29.6% of studies highly likely to be subject to mandatory reporting had submitted no results at all. The regulators were explicit about why it matters. As FDA Commissioner Marty Makary put it, sponsors "have an ethical obligation to make results public regardless of the data's influence on the company's share price." When trials go unreported, especially those with unfavorable outcomes, the scientific record skews: successes accumulate in the literature while failures quietly disappear, distorting what clinicians, policymakers, and investors believe they know about how drugs actually perform. Independent analyses put the scale of historical non-reporting even higher: one New England Journal of Medicine study found only 38.3% of trials reported results within the period examined, and a later Lancet analysis found roughly 41% reported promptly.11 The results that are published bring the second problem. Spin, the practice of framing neutral or failed outcomes as positive, is not an edge case. A landmark 2010 analysis in the Journal of the American Medical Association examined 72 published trials that missed statistical significance on their primary endpoint and found spin in the conclusions of 42 of their abstracts, presenting the results more favorably than the data supported: titles implying efficacy where none was demonstrated, abstracts omitting the primary outcome, conclusions declaring success on the strength of secondary endpoints that cannot on their own establish clinical benefit.12 The mechanisms are well understood and not, for the most part, cynical. Sponsors invested years and capital in a compound, and the pull toward finding something salvageable in a disappointing dataset is human nature under financial pressure; for a small biotech with a single-asset pipeline, spin can even be a survival strategy. The techniques are consistent: mining post-hoc subgroups until something reaches significance, or bundling hard and soft outcomes into composite endpoints so a drug that fails on mortality can still claim success on a lab value, while the primary-endpoint failure sits in the press release's final paragraph. What makes both problems acute is timing. The existing checks are real but slow: SEC disclosure does not require real-time accuracy in how a program is characterized, FDA labeling review happens after data is submitted, and the literature corrects spin only eventually. Spin lives in that lag, in the press releases, presentations, and abstracts that precede the full record, and a prediction market priced by participants with money at stake occupies exactly that gap as a live disagreement register. When a sponsor calls a Phase 3 program on track while the market price has fallen from 65% to 18% over six months, that divergence does not prove the sponsor wrong, but it documents that financially motivated participants disagree with the public characterization, and creates a contemporaneous record that did not exist before. The limit is worth stating plainly: a market can only price a program that throws off some public signal, so what these markets check is the characterization of disclosed results, not the silence of undisclosed ones, and whether the check is meaningful rather than noise depends on price accuracy, participant expertise, and resolution quality, none of it yet settled. #### The Track Record Prediction markets have outperformed alternative forecasting methods in political, financial, and climate forecasting. The question is whether that record extends to biopharma. The historical evidence is encouraging but limited. The Iowa Electronic Health Markets ran a flu-forecasting market in the 2004–05 season with 61 health care workers from a variety of backgrounds. By the end of a target week the market's forecast was correct about 71% of the time, against roughly 36% for a forecast based on historical averages alone; one week in advance, accuracy was about 50%.13 Eli Lilly's internal market correctly ranked drug candidates. Corporate internal markets at firms including Google and Ford outperformed in-house expert forecasts in published accounts, improving on experts by as much as a 25% reduction in mean-squared error, though Google's own researchers found that employees who sat physically close to one another tended to trade in correlated ways, a reminder that aggregation quality depends on the independence of the participants.14 The accuracy record for commercial biopharma prediction markets, the category this report covers, is thin. Most contracts trade between $3,000 and $30,000 of lifetime volume. While large trades on thinner contracts have tended to self-correct toward equilibrium, greater liquidity is still needed before making definitive statements about market accuracy. A price derived from a handful of participants carries different informational content than one derived from thousands. Validation now waits on something specific: contracts deep enough that no single trade can move the price, which means volumes the category has not yet reached. This points to the category's central unproven claim. Every strong accuracy result above, Iowa, Lilly, the corporate markets, comes from a restricted pool of vetted experts, which is closer to a structured internal forecast than to an open exchange. There is a real case that open markets inherit the accuracy: a larger, financially motivated pool should aggregate at least as well, and public markets have outperformed experts in other domains. The honest position is that the transfer is plausible but undemonstrated in this specific domain, and that the early commercial contracts are the experiment that will settle it. At current low volumes, no one should claim these markets are more accurate than biotech stocks; the report does not. What it commits to instead is verification: as trading volume grows, straightforward accuracy checks, how often contracts priced high resolve YES, how often the market beats a naive baseline, will be published rather than asserted, so the claim is settled by evidence in the open. Why these markets have not been applied to science at scale before is the question Part V addresses directly. The short answer is that resolution complexity, regulatory uncertainty, ethical questions, and thin liquidity have each presented obstacles the category is still working through. #### From Lilly to Kalshi: A Compressed History The modern biopharma prediction market category is roughly thirty months old in its current commercial form. The path from the Lilly experiment to today ran through a government cancellation, a pandemic, and two decades of regulatory development. In July 2003, the same year Lilly ran its experiment, the Defense Advanced Research Projects Agency cancelled the Policy Analysis Market, a project that would have run prediction markets on geopolitical and economic events. Senators Ron Wyden and Byron Dorgan led the backlash, branding it a "terrorism futures market," and it was scrapped within days.15 The idea survived in academic venues: the Iowa Electronic Health Markets ran invitation-only health markets through the mid-2000s and a multi-state influenza pilot from 2008 to 2010. In 2009, the idea moved closer to the clinic. Pharmer's Market, launched that October by MIT Sloan researchers, Ragu Bharadwaj, a former Vertex Pharmaceuticals research scientist, working with faculty Eric von Hippel and Fiona Murray and advised by Harvard's Peter Coles, used a broad anonymous community of pharma researchers, chemists, academics, financial analysts, and clinicians to forecast the probability that six breast cancer drugs would clear Phase I, II, and III. Built on Crowdcast's platform, its premise was the one this report opened with: that siloed information drives up R&D costs and suppresses approval rates, and that a market could surface distributed knowledge faster and more accurately than any individual expert. It was the clearest early demonstration that the Lilly thesis could be pointed at specific drugs, in public.16 “"Anonymous market structures may be the only mechanism that lets certain signals come out. Without anonymity, information stays trapped inside the companies that hold it."” — Ragu Bharadwaj, creator of Pharmer's Market (2009) The COVID pandemic was the turning point. For the first time, a broad public wanted to track the same biomedical question at once, whether and when a vaccine would arrive, and prediction markets gave them a place to express it. The vaccine and emergency-use-authorization contracts of 2020 were the category's first genuinely mainstream moment, and a wave of academic forecasting on pandemic outcomes followed. The drug-specific build-out has been steadily growing since then. The barrier is no longer whether these markets can exist. It is whether their prices can be trusted when they settle, a question about resolution, and the subject of Part VII. ### PART IIThe Risks and Objections Before describing how these markets are built, this report puts the case against them first. A prediction market on a clinical trial raises genuine risks, to the integrity of the science, to patients and the public, to the markets themselves, and it draws principled objections that deserve to be stated at full strength rather than waved away. What follows sets out the most substantive objections in the form a serious critic would put them, with the response the category has developed for each. Where a satisfactory answer exists, it is given; where the concern remains open, that is said plainly. Many of the answers point forward to specific design choices, which the later parts of this report describe in detail; the order is deliberate, the risks come before the solutions, so that the design can be read as a response to them rather than a brochure that never mentions them. ##### 1. "Insiders will capture most of the profits" The concern. People with privileged access to trial data will trade ahead of outcomes and extract value at the expense of uninformed participants. The concern is felt acutely inside the industry. As Eli Weinberg put it: "Companies will be worried that prediction markets may create a demand for use of confidential information; the risk is that you create a new way for people to monetize information." The analysis. The public enforcement record does not support the broadest version of this concern. As Part V details, the canonical biotech insider-trading cases, Martoma, Skowron, Dagar, Catenacci, all involved people with trial-wide visibility: safety-monitoring or steering-committee members, a lead biostatistician, a lead investigator.17,18,19,20 Not one involved a site-level nurse or coordinator, whose blinded view of roughly 1% of patients is not a sufficient base to predict an endpoint. The risk is real but identifiable, and the relevant population can be explicitly excluded from trading related contracts. Kalshi's Source Agency Prohibition, its implementation of the so-called "Eddie Murphy Rule," is one such mechanism, barring members who hold material nonpublic government information from trading the affected contracts.21 The NBA's Jontay Porter case, which produced a lifetime ban and a federal guilty plea for conspiracy to commit wire fraud after he tipped confidential information to manipulate prop bets, established why positioned participants must be categorically excluded.22 The same logic applies here. The platform-level safeguards are also more developed than the category's newness suggests. On a CFTC-regulated exchange, insider trading is not merely discouraged but prohibited under the same federal provisions that govern securities, Section 6(c)(1) and Regulation 180.1, which courts have read to mirror the securities insider-trading rules. Kalshi's CFTC-certified rulebook extends past that federal floor, defining as prohibited any trading by a person who can access material nonpublic information before its public release, who is an employee or affiliate of a contract's source agency, or who has any direct or indirect influence over the underlying outcome, alongside broader bars on fraudulent, manipulative, or deceptive activity. Enforcement is not only governmental. The exchange screens defined categories at onboarding, including politicians, government officials, and athletes, either blocking them or applying special restrictions; it monitors activity through internal surveillance and third-party vendors; it freezes flagged accounts pending investigation; and its disciplinary process can impose fines, disgorgement, and permanent suspension, with suspected unlawful conduct referred to law enforcement.23 This is the machinery behind the screening that, in the Van Dyke matter noted below, rejected a prospective trader before he could place the trade he later placed on an unregulated venue. Two honest gaps remain. The subcontracted CRO blind spot is not yet adequately addressed by existing exchange-level policies: a statistician at a firm subcontracted by a CRO holds the same nonpublic information as a sponsor employee, with none of the same compliance visibility. This is addressable at the exchange level — trading prohibitions can be written to explicitly cover subcontracted personnel who hold material nonpublic information, regardless of their direct employer — but the coverage does not yet exist. And the enforcement infrastructure itself, though built on the same legal foundation as securities law, is newer and less tested. CFTC Rule 180.1, adopted under Dodd-Frank in 2011, uses the same misappropriation theory the SEC uses in stock cases, from United States v. O'Hagan (1997) and Salman v. United States (2016); on February 25, 2026 the CFTC formalized that this authority reaches event contracts, and weeks later put it into practice, charging Gannon Ken Van Dyke, a U.S. Army Special Forces sergeant, with using classified information to buy "Maduro Out" shares on Polymarket for roughly $400,000, its first insider-trading case in event contracts, alongside a parallel DOJ action.24,25 Screening at the regulated venue had rejected Van Dyke months earlier, pushing him to an unregulated platform, and at the platform level the exchange has disciplined members for trading on nonpublic information, in one disclosed case imposing $20,397.58 in combined disgorgement and penalty plus a two-year suspension.24 The gap narrows with each action but has not closed. “The risk is real but identifiable, and the relevant population can be explicitly excluded from trading related contracts.” ##### 2. "Financial instruments on medical outcomes are illegitimate, it's gambling, and it commodifies patients" The concern. Betting on whether a drug will be approved treats patients as variables in a financial equation, and is functionally gambling that should be regulated as such. The view has serious adherents: veteran biotech journalists, among others, have argued that these markets carry no real public benefit and are simply another form of betting dressed up as information. That position deserves to be taken seriously rather than waved away, and part of it is unanswerable on its own terms: if someone believes any financial instrument tied to a medical outcome is inherently wrong, no feature of the design will change their mind. The analysis. The same outcomes are already traded at far larger scale in biotech equities. Short-sellers profit when trials fail. Options market makers profit from readout volatility. An analysis of 98 products in Phase 3 trials between 1990 and 1998 found biotech stocks moved an average of +27% for eventual winners and −4% for losers in the 120 days before the public announcement, a divergence significant at p=0.0007, consistent with informed trading well before disclosure.26 That trading has existed in equity markets for decades, in a less regulated environment, with no position limits. A CFTC-regulated event contract, with position limits, identity verification, and federal insider-trading law, is a tighter regulatory environment than the equity markets the category's critics accept without comment. On the gambling question, the legal trend favors the financial-instrument view, though it is not settled. On April 6, 2026, a divided Third Circuit panel ruled 2-1 in KalshiEX LLC v. Flaherty that Kalshi's sports-related event contracts are swaps under the Commodity Exchange Act, financial instruments subject to federal regulation rather than gambling subject to state gaming law, and affirmed a preliminary injunction barring New Jersey from enforcing its gambling statutes against them.27 The ruling addressed sports contracts specifically, and as a preliminary injunction it found a likelihood of success rather than deciding the merits. Biopharma contracts sit inside the same federal framework, and the reasoning extends naturally to them, but no court has yet ruled on event contracts as a class. With the Ninth Circuit having heard parallel cases on April 16, 2026, a circuit split could send the question to the Supreme Court. The behavioral profile reinforces the point. CFTC-regulated Designated Contract Markets apply KYC requirements, position limits, and reporting obligations. Novel drug approval and Phase 3 contracts carry time horizons of twelve to twenty-four months between listing and resolution and require genuine domain expertise to price. A market on a 2027 PDUFA decision has more in common with equity options than with in-play sports betting. “Trial outcomes are already traded heavily through biotech stocks, and a regulated prediction market can offer a cleaner, more transparent signal on a specific event than that diffuse trading does.” What the regulatory argument cannot do is answer the values objection, that any financial instrument on a medical outcome is inappropriate regardless of governance. The category will need to earn trust through demonstrated behavior, not structural comparisons. The narrower, honest claim the report does stand behind is this: trial outcomes are already traded heavily through biotech stocks, and a regulated prediction market can offer a cleaner, more transparent signal on a specific event than that diffuse trading does. Whether it genuinely improves accountability, rather than simply adding another venue to bet in, remains an open empirical question that only a track record can settle. A caveat that applies wherever this report invokes the equity-market comparison: three decades of biotech trading show no documented harm to equipoise, enrollment, or review integrity, which is meaningful evidence but not proof of absence for diffuse effects no one has studied directly. ##### 3. "Thin markets are vulnerable to manipulation" The concern. In thin contracts, a single large trader can move prices and create misleading signals, and in small trials with subjective outcomes, a motivated actor could influence the endpoint determination itself and trade on the result. As Haris Vikis of Oracle Health put it, naming the two risks together: "Insider trading risk is very real, and thin liquidity opens the door to manipulation." The analysis. Both versions of the concern are answered by the same scoping decision. The initial scope concentrates contracts on large-company Phase 3 programs with the densest public information ecosystem in the category and heavy institutional equity-market participation. A single large trade is unlikely to persist in a market where hundreds of analysts and investors are independently tracking the same program. Kalshi imposes per-contract position limits, with KYC requirements and third-party monitoring.28 The subjective-endpoint version applies primarily to small Phase 2 trials, which are not in scope; Phase 3 trials have blinded independent central review, pre-specified statistical analysis plans, and DSMB oversight, and the inter-reader variability documented in oncology imaging is precisely what that central review is built to neutralize through independent multi-reader consensus. Additionally, prediction markets benefit from a robust incentive mechanism to self-correct any price distortion, whether it be manipulation or not. Other asset classes, such as equities and bonds, do not possess a concept of "fair value" — the price of a stock is whatever someone is willing to pay for it. This is not the case on prediction markets, because ultimately the price of a prediction market contract will be either zero cents or one dollar. The fair value exists; it is the likelihood that the event will happen. When prices get distorted, the profit incentive drives other market participants to immediately correct the market back to equilibrium. Evidence from Kalshi has shown this applies even to thinly traded markets. “The fair value exists; it is the likelihood that the event will happen.” A related risk is worth naming because it follows directly from putting money on an outcome: once a participant holds a position, the cheapest way to move a binary outcome is sometimes not to trade but to talk. A holder of a NO position can publicly campaign against a trial, amplify unfavorable readings of ambiguous data, or pressure the people whose words move the price, turning passive speculation into incentivized advocacy for a program's failure. The category already has a preview: in 2026, bettors sent death threats to a reporter whose account was about to settle a large pool, trying to force a different story. The same scoping that blunts manipulation blunts this, large-sponsor Phase 3 contracts resolve against named institutional documents, not sentiment, so a campaign cannot change the outcome unless it changes the underlying filing, and the people most able to influence an outcome are already barred from trading. What that does not touch is diffuse public influence by ordinary position-holders, which is a speech and platform-governance problem more than a contract-design one, and one whose harms land hardest on the individuals who become settlement triggers; it deserves explicit platform conduct rules and watching as volumes grow. ##### 4. "Visible prices will distort the institutions doing the science" The concern. Publicly visible prices could corrupt the very experiments they track: eroding clinical equipoise by making physicians unwilling to enroll patients in placebo arms, deterring enrollment while a trial is still recruiting, pressuring FDA reviewers on pending decisions, and politicizing the review process. There is also a subtler version, that the signal is easily misread, since a 90% "negative" price still means the drug works in something like one in ten scenarios, a nuance that rarely survives transmission to a lay audience. The analysis. The interference vector is the one scoping answers most directly: the post-recruitment listing rule and the exclusion of early-phase contracts (Part V) mean a market opens only after enrollment has closed, so it cannot deter enrollment, and the most fragile stages of the science are never listed. On the broader institutional worry, the equity-market comparison applies in its strongest form: biotech stocks have priced trial outcomes and FDA decisions publicly for three decades, and the institutions have held. Equipoise's structural protections, independent DSMBs, pre-specified analysis plans, blinded adjudication, operate independently of any outside market; FDA reviewers have worked inside continuous stock-market pressure throughout that period with no documented case of market pressure changing a review outcome, and the agency still meets most of its review-performance goals.29 A prediction market price is a smaller signal than the stock moves and analyst coverage that already accompany every major FDA decision: it makes existing information more legible, it does not create it. The misreading risk is real and not fully solved by timing, which is why the report treats endpoint-specific, non-verdict resolution language and plain-language market explainers as commitments rather than options. “A prediction market price is a smaller signal than the stock moves and analyst coverage that already accompany every major FDA decision: it makes existing information more legible, it does not create it.” ##### 5. "Patients will be harmed, they'll bet against their own survival, or stop enrolling" The concern. Patients enrolled in trials will short the outcomes of their own treatment, and patients who can follow a public probability market will choose to wait for the approved drug rather than enroll. The analysis. Patients enrolled in a specific trial are prohibited from trading contracts tied to that trial, the same categorical exclusion that applies to sponsor employees and investigators. The informed population these markets aggregate is different: patients living with a disease who are not enrolled, who follow the research closely and have no ability to influence its outcome. On enrollment, no published study documents the predicted effect; the established barriers are design complexity, geography, eligibility, and insurance, not information. The honest caveat is that the population following a disease area is not always cleanly separable from the population enrolled in its trials, which is why rare-disease contexts warrant ongoing engagement with patient-advocacy communities rather than a blanket assurance. ##### 6. "Markets add nothing, peer review and existing financial tools already do this, and sponsors will game them upstream" The concern. Peer review, FDA review, and existing financial analysis already process the relevant information rigorously. And to the extent markets matter, sponsors will optimize trial design and public communications for market optics rather than scientific rigor. The analysis. Prediction markets address a different problem: the absence of a continuously updated, publicly accessible probability estimate on a specific binary outcome. Peer review assesses scientific quality. FDA review assesses safety and efficacy. Neither produces a live approval probability. Existing financial sources, sell-side PoS estimates, expert network calls, internal pharma models, are episodic, paywalled, or proprietary. A prediction market contract isolates a single binary question. That is a structural property, not a claim to superior analytical wisdom. On upstream gaming: by Phase 3, the room for sponsor distortion is limited, because design, endpoints, and the statistical analysis plan are all locked and registered before unblinding, as Part V details; the flexible earlier phases are not listed. ### PART IIIThe Deeper Ethical Questions The objections above are the arguments the category meets most often. The ethical questions run deeper, and they do not all dissolve under good design. The framework here draws on Dr. Jonathan Kimmelman, James McGill Professor and Associate Member of the Department of Experimental Medicine at McGill University, who organizes the concerns into a set of bins; several map onto risks already addressed above (interference with the science, and incentivized influence), and the three that raise distinct questions are taken in turn here. A premise runs underneath all of them. A prediction market on a geopolitical event prices an answer that exists independently of whether anyone is watching. A clinical trial is different: it is actively constructing its answer, and watching it can change it. That asymmetry is what makes biopharma markets ethically distinct from markets on elections or sports. “A prediction market on a geopolitical event prices an answer that exists independently of whether anyone is watching. A clinical trial is different: it is actively constructing its answer, and watching it can change it.” Regulatory trust and patient equity. A high market price signals broad confidence before the evidence is formally established, creating two harms. The first is an equity grievance: a patient who cannot get into a trial, for reasons of geography, eligibility, or distance, sees a public market implying the drug works and reasonably asks why an effective treatment is being withheld. The second is a regulatory-integrity harm: visible, viral market momentum can generate political and social pressure on regulators that is decoupled from the evidence, especially in a media environment that conflates market confidence with scientific consensus. The late-phase, post-enrollment scope partly addresses the access grievance, since by Phase 3 the drug is being studied in a controlled cohort, not withheld from population use; the more important mitigations are commitments of communication discipline, resolution language that is strictly technical and endpoint-specific rather than a "success/failure" verdict, and a plain-language explainer on every market, with particular caution for conditions with intense advocacy visibility such as ALS or rare pediatric diseases. The deeper version is not a design bug at all: the risk that public sentiment pressures a regulator who should answer only to evidence is a genuine tension between transparency and evidentiary discipline, to be managed through institutional norms and watched over time. Endpoint mismatch and the paradox of prediction. Markets are structured around endpoints that matter to investors, and communicating those signals to people whose lives are at stake is dangerous. Progression-free survival is the canonical example: a clean, measurable endpoint that drives regulatory and investment strategy but can mean little to a patient for whom overall survival is what matters. A market reading "strong probability of meeting the primary endpoint" will be heard as "strong probability this drug helps me," and those are not the same statement. There is a related distortion in where capital goes, toward safe, large-market bets rather than true unmet need, which a mechanism that makes sentiment more visible could deepen. What the regulatory argument cannot do is answer the values objection, that any financial instrument on a medical outcome is inappropriate regardless of governance. The category will need to earn trust through demonstrated behavior, not structural comparisons, and the gambling question is not legally final until the appellate split resolves. The narrower, honest claim the report does stand behind is this: trial outcomes are already traded heavily through biotech stocks, and a regulated prediction market can offer a cleaner, more transparent signal on a specific event than that diffuse trading does. Whether it genuinely improves accountability, rather than simply adding another venue to bet in, remains an open empirical question that only a track record can settle. A caveat that applies wherever this report invokes the equity-market comparison: three decades of biotech trading show no documented harm to equipoise, enrollment, or review integrity, which is meaningful evidence but not proof of absence for diffuse effects no one has studied directly. “"If your prediction markets are working really, really well, we're probably not doing the science right, because the whole point of science is to investigate unpredictable questions."” — Dr. Jonathan Kimmelman, James McGill Professor, McGill University Underlying all of this is a structural observation worth stating in full. The apparent paradox is that a market predicting trial outcomes with high accuracy implies the answer was knowable in advance, raising the question of why the trial was necessary. But this conflates two different things: whether an outcome is predictable and whether it must be demonstrated. Confirmatory testing in patients is a regulatory and ethical prerequisite for approval regardless of how knowable the result was. No drug is licensed on a forecast, so an accurate market does not make the trial redundant. And there is a second reason the premise doesn't hold: a Phase 3 prediction market is not pricing the underlying biological unknown, which was investigated in Phases 1 and 2, where this report lists nothing. By Phase 3 the scientific question is largely settled in expectation, and what remains uncertain is execution and regulatory judgment, whether the effect size holds at scale, whether the trial runs cleanly, whether the FDA agrees the endpoint supports approval. A market that prices those well is not making the science redundant; it is pricing the residual uncertainty good science leaves on the table. The paradox bites hardest against markets on early, exploratory science, exactly the category this report excludes, and it remains a standing caution against overclaiming. The design answer is to frame every contract at the endpoint level, never the drug level, to add secondary layers tracking clinical alongside statistical significance where feasible, and to put endpoint glossaries on market pages; the research-capital distortion is only partly answerable by design, and AppliedXL is tracking whether listings in fact cluster around large-market indications over genuine unmet need. Information cascades and research conservatism. Standard bubble dynamics apply with extra force in a domain already marked by risk aversion: rising prices attract buyers following the price rather than the data, and the downstream decision-makers in medicine are not all sophisticated market participants. Markets could deepen conservatism by pricing high-confidence, late-stage, large-indication trials most readily, making the unconventional bets, the potential breakthroughs, even harder to fund. The early-phase exclusion and the liquidity-before-display threshold blunt the cascade, and the design commits to regular curation audits. This is the concern the category should hold most honestly, because the very choices that make a market trustworthy, late-stage, large-sponsor, liquid, are the ones that bias the listed set toward the already-favored. The right response is external scrutiny: Dr. Kimmelman has proposed funding independent scholars to assess whether the presence of these markets measurably affects research investment, and to publish the findings whichever way they fall. The category should support exactly that. #### Why the Risks Come First The parts that follow describe what these markets resolve on, how they are designed, what they are useful for, and how they settle. Each is, in part, a response to a risk or objection raised in these two parts. Putting the risks and ethics first is deliberate: the value of a public probability and its capacity to mislead travel together, and the design choices only earn their place if the reader has already seen, in full, the harms they are meant to contain. A reader who finishes the next parts unconvinced should be able to point back to the specific concern here that the design failed to answer. ### PART IVWhat These Markets Resolve On Two contract types account for the vast majority of informational value and resolution complexity in the category. This report focuses on those two. #### Trial Readout Markets Contract template. Will [drug] meet the prespecified primary endpoint of [trial], with statistical significance, by [date]? Resolves YES on confirmation that all prespecified primary endpoints were met at the committed level of statistical significance; otherwise NO. Start with what an endpoint is, because the whole contract rests on it. Before a trial begins, its designers commit in writing to exactly what they are measuring and what result will count as success, the primary endpoint. It might be how much a tumor shrinks, how long patients live without their disease worsening, how far a symptom score falls. This target is registered publicly, on ClinicalTrials.gov, before any patient is enrolled, which is what makes it useful for a market: the definition of success is fixed in advance and cannot be moved after the fact. That is also why "did the trial produce positive results?" is the wrong question to build a contract on. "Positive" is a matter of interpretation, and interpretation is exactly what sponsors, with every legitimate incentive to present their program well, will shade favorably. The analytical question, the one with a checkable answer, is narrower: did the trial meet the specific primary endpoint it set for itself, at the level of statistical confidence it committed to? That reframing, from a subjective "positive" to an objective "met the prespecified endpoint," is the foundation of a contract that can be resolved without dispute. Three complications make even that narrower question subtle, and a well-written contract has to account for all three. First, a trial can have more than one primary endpoint, two or three, and they are not always reported at the same moment; a contract has to say whether it resolves on one, several, or all of them, and what happens if they arrive separately. Second, meeting an endpoint is a statistical judgment, not a verdict on the medicine: a result can clear the prespecified threshold for statistical significance while the actual effect is modest. Third, and following from the second, a trial can hit its endpoint without the benefit being clinically meaningful, statistically real but too small to change how a patient actually lives. A contract on "meets the primary endpoint" answers the statistical question, not the clinical one, and the difference has to be stated plainly so no one mistakes one for the other. “"There's a lot of nuance in clinical trials. Beyond statistical significance, clinically relevant study outcomes are often not binary, and continuous variables like response and survival will likely need to be grouped in ranges. Furthermore, results will likely need comparison to historical benchmarks in many cases."” — Dr. Alexander Drilon, medical oncologist; Clinical Director, Early Drug Development Service, Memorial Sloan Kettering Cancer Center A further wrinkle is that a single readout is reported in several places, a sponsor press release, a conference presentation, a regulatory filing, a peer-reviewed paper, a ClinicalTrials.gov posting, sometimes with subtly different framings of the same numbers. The resolution standard the category is converging on cuts through this by anchoring to one thing: all prespecified primary endpoints met with statistical significance, confirmed against the registered protocol on ClinicalTrials.gov rather than against the sponsor's characterization of its own results. When the picture is ambiguous, incomplete, or inconsistent with the registered protocol, the default is NO; partial results keep the market open; favorable sponsor language without the statistical evidence does not qualify. Why meeting the endpoint and winning approval are two different questions. The most important thing a contract designer can understand is that a trial meeting its endpoint and a drug winning FDA approval are not the same event, and must never be written as one. Lykos Therapeutics' MDMA-assisted therapy for PTSD is the clearest illustration. Its pivotal Phase 3 trials, MAPP1 and MAPP2, did meet their prespecified primary endpoint, the CAPS-5 PTSD severity score, and their key secondary endpoint, with the primary result highly statistically significant and published in Nature Medicine.30 By the endpoint test, the trials succeeded. And yet the FDA did not approve the drug: on June 4, 2024, its advisory committee voted that the data did not establish effectiveness and that the benefits did not outweigh the risks, and a Complete Response Letter followed on August 9, 2024, asking for an additional trial.31 The decision turned not on a missed endpoint but on questions about how the trials were conducted, including the difficulty of keeping patients unblinded to a drug with strong perceptual effects. The lesson sits at the foundation of sound contract design. A contract written on "meets the primary endpoint" would have resolved YES; a contract written on "FDA approval" would have resolved NO; both would have been correct, because they asked genuinely different questions. Conflating the two is the single most common way a biopharma contract produces a result its participants feel cheated by. Endpoint contracts and approval contracts have to be separate instruments, written and resolved against separate documents. #### Regulatory Approval Markets Contract template. Will the FDA issue a full approval letter for [drug], for [indication], by [date]? Resolves YES on issuance of a qualifying FDA approval letter by the date; otherwise NO. The resolution template has converged on a standard now used across most major contracts on the regulated exchanges. It resolves YES only on an actual FDA approval letter (an NDA, BLA, sNDA, sBLA, ANDA, or 351(k), in standard, accelerated, REMS, or indication-limited form) and explicitly excludes the near-misses that have caused disputes: approvable letters, tentative approvals, PDUFA-date extensions, expanded access, EUAs without full approval, and CRLs, with a CRL or withdrawal resolving the market immediately NO. The verbosity of that boilerplate is itself a record of what the category has learned, each exclusion added after an ambiguity produced, or threatened to produce, a contested resolution. The resolution trigger is a single named document: an FDA approval letter recorded in Drugs@FDA, issued on a public date. Either it exists by the contract end date or it does not. That clarity is why approval contracts are the most liquid and most mature in the category. The edge cases. What happens when FDA approves a narrower indication than the contract specified? When a PDUFA date is extended by three days into the contract's grace window? When FDA approves on a surrogate endpoint the contract did not anticipate? None of these has yet produced a high-profile public dispute. When one does, it will test whether the resolution infrastructure is adequate, and whether the category's first major controversy arrives before the governance frameworks are ready. A quieter failure mode is the contract that simply goes stale. As Alexa Devos, a Global Strategic Insights Manager at Intuitive Surgical, observed: "One area where estimates feel the least reliable is when a trial is aging with no updates; this becomes another area where insights are missing." A market with nothing new to price drifts on noise, which is its own argument for tight scoping and clear end-dates. An illustration. Consider, hypothetically, a contract on whether a widely watched obesity drug will win FDA approval for a new use by the end of a given year. As pivotal trial data lands, strong results would tend to push the price up; but if the standard ten-to-twelve-month review clock makes approval within the contract window unlikely absent an expedited pathway, the price can stay well below 50% even on good news, because the contract asks about approval by a date, not about whether the drug works. The two questions, will it work and will it be approved in time, pull the price in different directions, which is exactly the kind of distinction a well-specified contract is built to keep separate. The point of such a contract is not that the market is provably right; it is that the price updates continuously and transparently on public information, in a way no static analyst estimate does. #### Other Contract Types Beyond trial readouts and regulatory approvals, the category includes advisory committee vote contracts, which resolve on the public roll call at FDA panel meetings and have historically functioned as leading indicators for the subsequent approval decision; drug shortage contracts, which track the FDA Drug Shortage Database; label expansion contracts for new indications or patient populations of already-approved drugs; pandemic and emergency-use-authorization contracts, which dominated the category in 2020–21; and a small number of drug pricing, IRA negotiation, and M&A contracts. Each has a role in a fully developed ecosystem. None is the focus of this report. ### PART VMarket Design as Risk Mitigation Prediction markets on biopharma outcomes carry domain-specific risks: insider trading from participants with privileged access to trial data, manipulation in thin markets, resolution disputes when endpoints are ambiguous, contamination of the trial whose outcome is being priced, harm to vulnerable issuers, and ethical questions about financial instruments on clinical outcomes. The choices made at the scoping stage determine how serious those risks are in practice. What follows describes the design choices AppliedXL has made in scoping the initial contracts in this category, the reasoning behind each, and the areas where the design does not yet have satisfactory answers. Each choice is best read as a risk control rather than a product preference, and most of them mitigate more than one risk at once. Restricting the initial scope to Phase 3 readouts addresses resolution-dispute risk (the endpoint is fixed in a public document before listing), insider-trading risk (it narrows the window of pre-decisional non-public information relative to earlier phases), and trial-contamination risk (by Phase 3 the science is largely settled in expectation). Listing only after recruitment has closed is the trial-integrity control. Restricting to large public sponsors is the manipulation control and the issuer-harm control. Requiring a liquidity threshold before a price is displayed is the signal-reliability and cascade control. The subsections below take each in turn; the residual gaps each control leaves open are collected at the end under Open Problems. #### The Principle The most consequential market design decision is upstream: whether a given contract should be listed at all. Contracts whose resolution criteria tie to a single public institutional artifact, an FDA approval letter, a ClinicalTrials.gov primary endpoint result, resolve with almost no ambiguity. Contracts whose resolution criteria depend on interpretation of a sponsor press release, or on subjective endpoint assessment, resolve with ambiguity as the baseline condition. Filtering out the second class before listing does more to protect market integrity than any enforcement regime applied after a dispute arises. “The most consequential market design decision is upstream: whether a given contract should be listed at all.” The initial scope is therefore defined by a single criterion: list contracts that can be resolved correctly against a named public document, and do not list contracts that cannot. A second screen sits alongside the first: whether a contract is worth listing at all. As Dr. Luiz Diaz, Head of the Division of Solid Tumor Oncology at Memorial Sloan Kettering, framed the listing question: "First: is it of high importance? Is it an important question of medicine? Is it an important question about development? Is it an important question on Wall Street? And is there an algorithm that could be developed to pick that kind of study? The second thing is: will it be positive or not?" The first half of that test governs whether a contract belongs on the board; the second is what the market itself is for. #### Listing Phase 3 Readouts Narrows Resolution and Insider-Trading Risk To mitigate resolution-dispute and insider-trading risk, the initial markets focus on Phase 3 readouts. By Phase 3, the primary endpoint has been specified in the protocol, registered on ClinicalTrials.gov, and in many cases agreed with FDA through a Special Protocol Assessment, so the resolution question maps to a public document fixed before the trial started, which is what makes the contract resolvable without dispute. Phase 1 and Phase 2 trials frequently involve exploratory endpoints and interim analyses whose interpretation requires judgment beyond reading a registered protocol. They are also the stage at which insider trading risk is most concentrated: a trial investigator's informational advantage is largest when the public evidence base is thinnest. Phase 1 and Phase 2 contracts are outside the initial scope. This will be revisited as resolution infrastructure matures. The staging logic also tracks how the nature of the prediction changes once a trial is underway. As Eli Weinberg, the healthcare and life sciences partner at Bain & Company, put it: "Before a trial starts, prediction is a scientific question, do we believe in the biology? Once running, it's execution." A contract listed at Phase 3 is pricing execution against a fixed protocol, not betting on the underlying science still in flux. #### Listing After Enrollment Closes Protects Trial Integrity A contract should not be listed while the trial it prices is still recruiting patients. A live, publicly visible probability can flow back into the experiment that generates it: a market showing lukewarm sentiment may discourage physicians from enrolling patients or referring them, a direct operational threat to the trial's completion and to the integrity of its result. This is the cleanest structural answer to the interference concern, and it is the single most important scoping rule the design adopts after the Phase 3 restriction itself. In practice this means listing a contract only after enrollment has closed. Because some adaptive trials make "closed" ambiguous, the working threshold is a high level of completed enrollment (on the order of 90% or more) before a market may open. By the time a Phase 3 trial has finished recruiting, the population-level question the market prices is no longer one the market's existence can distort: the cohort is fixed, the protocol is locked, and what remains is the readout. This choice, combined with leaving recruitment-sensitive early-phase trials off the board, removes the primary channel through which a market could corrupt the science it is trying to measure. #### Requiring Liquidity Before Display Guards Against False Signals A price is only information if enough independent participants produced it. A market with three traders is not an aggregation mechanism; it is a rumor with a number attached. The design therefore sets a minimum liquidity and participation threshold that a contract must clear before its price is displayed publicly. Below that threshold the contract may trade, but it does not broadcast a probability that downstream readers, physicians, patients, journalists, would reasonably mistake for a settled expert consensus. This guards against the information-cascade dynamic in which buyers follow a price rather than the underlying data, a dynamic that is more dangerous in medicine than in equities because the people acting on the signal are often not sophisticated market participants. #### Focusing on Larger Sponsors First Reduces Manipulation and Issuer Harm The initial scope prioritizes trials from larger, publicly traded pharmaceutical and biotech companies. This is a sequencing decision rather than a permanent boundary, and it reflects three considerations, each of which points to a safeguard that would generally need to mature before the scope broadens. The first is manipulation. A contract on a small-cap biotech with limited analyst coverage tends to be more exposed to a single motivated actor than a contract on a large-cap sponsor's program, where many analysts and investors are independently tracking the same information. This is not absolute: large caps are not immune to manipulation, and some smaller companies carry meaningful coverage. But it holds often enough to be a reasonable starting filter, and it is largely a function of market depth and surveillance coverage, both of which can be built up rather than being an inherent property of the smaller company. The second is resolution complexity. When a large company's drug fails, the equity market typically absorbs a bounded move. When a small biotech's single-asset program fails, the company may cease to function, which can create resolution entanglement the category is not yet well equipped to handle. These edge cases appear tractable with clearer resolution criteria and more operating experience. There is a third consideration, and it is about harm to the issuer rather than risk to the contract. A large company can usually absorb an adverse market signal through its investor-relations function and the depth of its analyst coverage; a small biotech may be less able to. A bad prediction-market price landing on top of a now-public Complete Response Letter, since the FDA has moved toward publishing CRLs, could in some cases move faster than a small issuer's ability to respond and weigh on its investor base before it can put the result in context. Prioritizing large-cap sponsors sidesteps this for now, but the concern does not disappear; it becomes more salient as the scope expands toward the smaller companies that may be most exposed to it. That expansion therefore should be paced against the surveillance and communication safeguards that would protect vulnerable issuers, not only against the manipulation controls that protect the contract. Small- and mid-cap programs are expected to be added as market depth, resolution governance, and issuer-protection safeguards mature. #### Starting With Novel Approvals Keeps Resolution Clean For regulatory approval contracts, the initial scope covers novel drug approvals: NDAs and BLAs for new molecular entities seeking first approval. Label expansions introduce scope ambiguity around what counts as the indication the contract specified. Some approval types carry political exposure that makes them difficult to price accurately. Niche pathways introduce resolution complexity the initial architecture does not address. Novel drug approvals on the standard or priority review pathway have the clearest resolution trigger, the deepest public information ecosystem, and the lowest political surface area of any contract type in the category. #### Open Problems Insider trading. The load-bearing point is informational: a site coordinator in a 3,000-patient blinded trial sees roughly 1% of patients and cannot infer the primary endpoint, while a biostatistician or monitoring-committee chair sees the whole trial. The enforcement record is consistent with this. Every major biotech insider trading prosecution over the past two decades involved someone with trial-wide visibility, not site-level access, though that record shows who was charged, not everyone who traded, and should be read as corroboration rather than proof. The tip in the Martoma/SAC case, at roughly $276 million the largest the SEC had ever charged, came from Dr. Sidney Gilman, who chaired the safety monitoring committee for a Phase II bapineuzumab trial.17 In the Skowron/Human Genome Sciences case, the tipper Dr. Yves Benhamou sat on the steering committee of a Phase 3 hepatitis-C trial; the funds avoided about $30 million in losses.18 Amit Dagar was the senior statistical program lead on Pfizer's Paxlovid trial, realizing roughly $214,395 by the SEC's civil measure and more than $270,000 by the DOJ's criminal figure.19 Dr. Daniel Catenacci was lead investigator on the Phase II FIGHT trial of bemarituzumab and made $134,142.20 In each case the trader or tipper held a cross-trial role, and in each the wrongdoing was an individual's breach of a duty owed to the company, not conduct by the company itself; the sponsors were the parties whose confidential information was misused. The design response is categorical exclusion of the cross-trial population. This extends naturally to subcontracted CRO statisticians, who may hold the same nonpublic information as a direct employee while sitting outside the sponsor's compliance framework. The remedy operates at the exchange and contracting level: exchange rules and CRO subcontracts can name this population directly rather than reaching only sponsor employees, supported by MNPI attestations flowed down through subcontracts, restricted-person lists extended to named CRO personnel, and surveillance keyed to CRO affiliation rather than sponsor employment alone. This closes in contracting what categorical exclusion already establishes in principle, and follows a wider pattern of Kalshi's surveillance and enforcement mechanisms that goes well beyond the letter of the law at preventing illicit market activity. Resolution ambiguity. Even within the constrained initial scope, a recurring set of ambiguous situations, composite endpoints, mixed primary and secondary outcomes, mid-trial protocol amendments, conditional approvals framed as full ones, premature interim disclosures, are not edge cases but the baseline operating condition of the category. Part VII enumerates them and what reliable resolution requires to handle each. Ethical questions. The objection that financial instruments on clinical outcomes are inappropriate regardless of governance is not answered by the design choices above. It is a values disagreement the category will need to address through demonstrated behavior over time, not through argument. Liquidity. At current volumes, the category is too thin to validate its accuracy claims at scale. This is not a design problem but a growth problem. #### The Roadmap The initial scope is a sequencing choice, not a permanent limitation. Early stage trials with well-specified endpoints will be added when the resolution framework for earlier-stage readouts is validated. Small-cap programs will be added when manipulation-surveillance infrastructure is adequate. Label expansions will be added when contract architecture for scope ambiguity is in place. The expansion is contingent on the track record the initial contracts build. A high-profile disputed resolution in the category's early phase would set back legitimate biopharma prediction markets significantly. The sequencing reflects that risk. It is a deliberate sequencing choice, not a claim that the easy cases are the only ones that matter; the harder contracts are the destination, not an afterthought. One tension is worth stating where the scoping decisions are made, not only where their ethics are debated. The same criteria that make a contract trustworthy, late-stage, large-sponsor, liquid, also bias the listed set toward programs that are already well-funded and well-covered, and away from the high-need, high-uncertainty research that most needs external signal. This is a genuine cost of the conservative initial scope, not an incidental one. The roadmap's expansion toward smaller and earlier programs is partly an attempt to repay it, and the curation audits described in Part III are how we intend to know whether we are. ### PART VIApplications Biopharma prediction markets generate one output: a continuously updated public probability on a specific binary outcome. That output is useful in different ways depending on who is reading it. A pattern recurs across every audience below, each currently relies on information that is partial, sponsor-shaped, or paywalled, and a market price adds an incentive-aligned external estimate they did not have before, so the subsections that follow dwell on what is distinct to each rather than restating that common thread. One caution applies throughout and should be read into every use described below: at current volumes the accuracy of these prices in this domain is not yet established, as Part I sets out, so each application treats the price as one input to weigh, not a settled signal to rely on. #### Investors For investors, the contract does what equity cannot: it isolates the one binary question, will this drug clear this bar, from the dozen unrelated variables bundled into a biotech stock. The practical uses are specific. Before taking a position in a biotech equity around a binary catalyst, an investor can check the prediction market price against their own probability estimate. If the market is at 65% and the investor's model says 40%, that divergence is worth understanding before sizing a position. It does not indicate who is right; it indicates that a participant with money at stake disagrees, which is the most useful piece of information available before a readout. Prices are also being used as a pipeline-monitoring tool, tracking how external participants price each program in a developer's portfolio and comparing those estimates against internal PoS models. The programs where external and internal estimates diverge most are the highest-priority candidates for reassessment. The current limitation is liquidity. At $3,000 to $30,000 of lifetime volume on most contracts, prices are a useful signal but not a definitive one. The practical implication: treat the price as one input, investigate significant divergences from internal estimates, and do not substitute market prices for analyst coverage at current liquidity levels. However, liquidity may not be as large of an institutional limitation as one would initially think. Even thinly traded contracts can produce enough of a valid price signal to be effective as a reference point for institutional-sized OTC trades. Susquehanna International Group, one of the largest trading firms in the world, has been transparent about the fact they are willing, and often do, price eight and nine figure OTC swaps based on prediction markets with around $30,000 in volume. While time and scale is needed to truly determine the accuracy of more thinly traded markets, it is a positive and telling sign that institutions believe in the signal of thinly traded markets enough that they are willing to stake tens of millions of dollars on it. The signal a sophisticated investor wants is also more granular than a binary approve/reject. As Eli Weinberg noted, what an approval is worth depends on its shape: "It doesn't just mean that you get to approval. It means, potentially, that you get to approval at some level of differentiation or subsector of the market that you can go after." The contract types that resolve cleanly today price the binary; the commercial question of which approval often lives in the label and indication detail a single contract cannot yet capture. #### Patients and Patient Advocates Patients and patient communities tracking drugs in development, particularly in rare disease, where a single program may be the only candidate for a condition, have historically had one primary information source: the sponsor, whose press releases and investor materials are built to support confidence and tend to underreport negative or ambiguous results. A market price comes from the opposite incentive: participants who are wrong lose money. The value here is awareness, not advice. A public market can surface which programs an informed community sees as promising, directing attention and scrutiny toward trials that might otherwise be overlooked, and over time it can help make a complex, jargon-heavy field more legible to the people most affected by it. Rare-disease communities, long among the best-informed lay participants in clinical research out of necessity, monitoring registries, attending conferences, knowing the investigators, are well placed to use that kind of signal to follow the science more closely. “"Clinical trials are an essential part of our healthcare system and how we as patients get access to the latest innovations. But the clinical trial process is opaque and difficult to understand for most people. Most patients don't know about the choices available in clinical trials or which programs are most promising. The opportunity to have an open, transparent dataset about trial probabilities is extremely promising and empowering for people. I am excited about what Kalshi is building and the opportunities to empower people while still adhering to ethical guidelines."” — Anne Wojcicki, Founder, 23&Me What a prediction market is not, and must not be allowed to become, is a guide to personal medical decisions. A price is an aggregate expectation about a regulatory or trial outcome, not a judgment about whether a given treatment is right for a given patient. Claiming that someone might choose a course of treatment, or decide whether to seek a particular drug, on the strength of a market probability is exactly the wrong use, and the report does not endorse it. Those decisions belong with patients and their clinicians, grounded in professional medical advice and individual circumstances. Any explainer language accompanying these markets should be developed together with patient groups rather than imposed on them, and the effect of these markets on trial enrollment should be studied rather than assumed. Prediction markets are not a substitute for medical advice and should never be positioned as one. A separate and still-unresolved question is whether patients who are not enrolled in a trial but are following a drug they hope to access should be permitted to use these markets to hedge the economic risk of a program's failure. The financial logic is coherent; the ethical concerns are real. This is a question for patient advocacy communities, bioethicists, and regulators to address, not market designers alone. #### Pharma and Biotech Strategy Teams Inside pharmaceutical companies, internal PoS estimates are produced by people close to the program, inside a culture that rewards optimism, and they run systematically higher than realized success rates. An external market price is a corrective: produced by participants with no institutional stake and real money at risk. When a market prices a company's Phase 3 program at 25% while the internal team is at 65%, that divergence warrants an explanation before the Phase 3 budget is approved, and prices on competitor programs give a continuous external read that periodic analyst reports do not. A further possibility is that these markets do not merely observe drug development but also inform better capital allocation. A continuously updated external probability, priced by participants with capital at risk and no institutional attachment to a program, constitutes a different signal than the internal estimates that guide development spending, which tend to exceed realized outcomes. A sharp divergence between the external price and the internal estimate is itself information, indicating a program that warrants reexamination before additional capital is committed. In aggregate, and conditional on the prices proving accurate, the effect would be capital reallocating away from failing programs earlier and concentrating on stronger candidates sooner. It is the mechanism by which these markets could contribute to more efficient development and, ultimately, to treatments reaching patients sooner. “"Outside pricing from knowledgeable participants can provide a useful corrective to internal overconfidence."” — Ragu Bharadwaj, creator of Pharmer's Market One application prediction markets enable is the one Lilly piloted but did not carry to production scale: a market run inside the company, on the company's own pipeline. The structural barriers that make internal pharma markets hard to sustain are not fully solved by external public markets, but the external design has produced a template internal efforts can now adopt: anonymous trading, cash incentives, transparent resolution criteria, and governance outside the chain of command. “"I wonder if any pharma companies would want to run this internally. Have your top hundred scientists in the company, give them each some amount of money to bet each year, and have them manage a portfolio of bets."” — Eli Weinberg, healthcare and life sciences partner, Bain & Company A second, underappreciated application is manufacturing demand planning. Before a drug is approved, manufacturers begin building inventory against demand assumptions that can vary by a factor of ten, and a major launch with poor demand forecasting can leave more than $100 million in excess safety stock. Prices on approval outcomes, updating continuously as trial data accumulates and FDA review progresses, provide a better planning input than the episodic PoS estimates manufacturing planners currently rely on. #### Physicians and Clinical Researchers For physicians the value is narrow but real: an incentive-aligned, continuously updated probability that adds a dimension to clinical judgment without replacing it, since a trial priced at 70% is a different prospect than one at 20% even when the sponsor sounds equally confident about both. For researchers, prices are a live external benchmark, and the forecasting literature is consistent that experts who regularly confront estimates differing from their own grow better calibrated over time. “"This type of probability data could generate perspectives on how current and future clinical trial resources could be allocated."” — Dr. Alexander Drilon, Clinical Director, Early Drug Development Service, Memorial Sloan Kettering Cancer Center Some practitioners see this developing into a structural shift in how medical evidence is socialized. Luca Dezzani, Vice President of US Medical Affairs at BioNTech, has argued publicly that the field is moving from "science as theater," where data is presented to a passive audience at medical congresses, toward "science as a market," in which the broader medical community stakes positions on what a study will show before it reads out.32 In that vision, the market price of a scientific hypothesis becomes a trusted metric alongside the p-value, and the most influential clinician is not the loudest voice on the podium but the one with the strongest record of correct forecasts. It is a projection rather than a description of the present, and it rests on the same unsettled assumption this report has flagged, that aggregated forecasting accuracy will hold in open markets. But it captures why some inside the industry see prediction markets as more than a trading venue. #### Journalists Biopharma journalism has a vocabulary problem. When a trial produces results that are statistically significant but clinically modest, or when a sponsor characterizes a mixed readout as positive, or when FDA extends a PDUFA date for the third time, journalists have few tools for conveying the uncertainty accurately and accessibly. Polling gave political journalism a shared vocabulary for uncertainty: a candidate at 68% in the polling average is a different story than one at 52%, and readers learned to interpret those numbers. The equivalent does not exist in health journalism, where a Phase 3 drug is described as having "promise" or "potential," language that conveys no probability information. A drug priced at 27% is a different story than one priced at 78%. The number is not an oracle; it is an aggregate of participant judgment from a thin market at current liquidity. But, used with that caveat, it can be a more honest single-number summary of expert opinion on a binary question than the "promise" and "potential" language journalism currently falls back on. The most direct application is reporting the divergence. The sponsor-on-track-while-the-price-collapses gap described earlier under accountability is, for a journalist, a reportable fact: it documents that participants with money at stake disagree with the public characterization. The resolution record, which programs were priced high and succeeded, which were priced high and failed, which surprised the market, is an emerging dataset for investigative reporting. Programs that failed after being priced above 70% are candidates for retrospective investigation; programs that succeeded after being priced below 30% raise different questions. The prediction market creates a time-stamped archive of how expert opinion evolved over each program's development, an archive that does not currently exist for any other information source in biopharma. #### Regulators For regulators at the CFTC, the FDA, and in the legislative branch, biopharma prediction markets present two questions: what governance frameworks are needed for the markets to function with integrity, and what information the markets generate that is useful to the regulatory process itself. On governance, the most pressing gap is explicit trading prohibitions for defined categories of pre-decisional participants, FDA staff, trial investigators, sponsor employees, DSMB members, and subcontracted CRO personnel, barred from contracts tied to data or decisions they hold nonpublic. Kalshi's Source Agency Prohibition is an important step, but like any attestation-based rule it needs an institutional enforcement layer behind it, especially in the drug-approval context where it has not yet been tested. A template emerged in March 2026, when Major League Baseball and the CFTC signed a memorandum of understanding, described as the first between a sports league and a federal agency, to establish an integrity framework for event contracts on the league's games.33 A parallel CFTC–FDA memorandum would give rules like the Source Agency Prohibition the institutional backing that makes them enforceable rather than self-attested. On the informational side, prices on specific regulatory outcomes provide a more granular read on how public information is being interpreted than biotech stock prices alone. A market on a specific NDA approval that drops from 85% to 40% the day after an advisory committee briefing document is published is signaling something specific about how that document is being read, a signal available to anyone who checks the price, including the regulators whose decisions the market is pricing. ### PART VIIResolution A prediction market is only as trustworthy as the process that settles it. Price discovery, information aggregation, and accountability for sponsor claims are not realized if the market resolves incorrectly, resolves late, or resolves in a way participants can reasonably dispute. This is the part of the infrastructure that has not yet been built. “A prediction market is only as trustworthy as the process that settles it.” #### How Markets Currently Settle Kalshi uses an in-house Markets Team that checks each contract's Source Agency against the Payout Criterion. A Market Outcome Review process allows participants to formally challenge a proposed resolution before it is finalized. The Source Agency Prohibition bars members with material nonpublic information from trading related contracts. A different resolution model operates on the decentralized venues: Polymarket settles through UMA's optimistic oracle, in which a proposed outcome stands unless it is challenged within a set window, with disputes escalating to a token-holder vote, a design whose dispute rate is low but whose vulnerability to governance-level manipulation surfaced in a March 2025 incident.34 #### Five Archetypes of Ambiguity Five ambiguity archetypes recur often enough to treat as the baseline operating condition, not the exception. Composite endpoints where only one component reaches significance. The press release says "the trial met its primary endpoint." The registered protocol specifies a composite, and only one component cleared the alpha threshold. Whether meeting one component constitutes meeting the composite depends on the trial's prespecified analysis plan, co-primary, composite, and hierarchically tested endpoints are governed by different rules, and most press releases do not include enough statistical detail to determine which applies from public disclosure alone. Mixed primary and secondary outcomes. The primary endpoint succeeded; two key secondary endpoints failed. The contract referenced "primary endpoint," but participants priced it as though it covered the overall program result. Resolution against the literal contract language is technically defensible but practically misleading. Mid-trial protocol amendments. A Phase 3 trial reports positive results, but the alpha boundary was moved via protocol amendment during the trial, and the ClinicalTrials.gov registration reflects the amended threshold. Whether resolution follows the original or amended endpoint is not addressed by standard contract language. Dr. Luiz Diaz of Memorial Sloan Kettering put the integrity problem plainly: "Changing endpoints after the trial is running, it's like changing weight classes for boxers." The defense is to freeze the endpoint at the moment of listing: the contract resolves against the prespecified primary endpoint as it stood when the market opened, regardless of any subsequent mid-trial revision. Without that rule, there is a specific exploit. An insider who knows a trial will miss its original endpoint could take a YES position, then push the sponsor to revise toward a second endpoint they privately expect to hit, laundering nonpublic information into a market outcome. Freezing removes the payoff from that maneuver. Equally important is that the freeze rule be disclosed plainly in the market's terms, because the deeper risk is contestation: a participant holding a large position that resolves against them may lobby to have the contract judged against the revised endpoint instead. As Daniel Taylor, the Arthur Andersen Chaired Professor at The Wharton School and director of the Wharton Forensic Analytics Lab, framed the ambiguity: "Somebody would see a revision in the endpoint, and they would think the contract is for the revised endpoint, as opposed to the original endpoint, and so they'd invest a ton of money." Stating the original-endpoint rule up front, rather than adjudicating it after a dispute, is what denies sophisticated actors the ambiguity to exploit. A harder edge case remains where a revision leaves the original endpoint not merely contested but unevaluable, the trial never generates the data to answer it, in which case resolution is structurally impossible rather than simply difficult, and the contract terms have to specify in advance how that scenario is handled. Conditional approval framed as full approval. FDA issues accelerated approval; the press cycle describes it as "FDA approval." The contract specifies full standard-pathway approval and resolves NO against the narrative. This is the correct resolution. It will not feel correct to participants who followed the press coverage. Interim DSMB disclosures before topline readouts. An unblinded interim surfaces publicly before the full topline. The market has effectively resolved in everyone's mind, but the contract's qualifying disclosure has not yet occurred. Whether the interim constitutes a qualifying disclosure is not addressed by most existing contracts. #### What Reliable Resolution Requires Source discipline. Only qualifying institutional disclosures count as evidence; analyst commentary, social media, and secondary press rewrites are excluded. The sources that count: registered data on ClinicalTrials.gov, FDA decisions at Drugs@FDA or in approval letters or CRLs, SEC 8-K filings, peer-reviewed publications, and primary conference disclosures at major medical meetings. Scoping discipline. The most valuable resolution decision is whether a contract should be listed at all. Contracts that resolve against a named public document are sound; contracts that require interpretation of sponsor language are vulnerable. This distinction should be made before listing, not after a dispute arises. Independence and resolver conflicts. A resolver knows things the market does not: which way an ambiguous case will be called, and often the timing of a determination before it is public. That makes resolution itself a source of material non-public information. The controls are therefore explicit: at AppliedXL, employees are prohibited from trading any market the company resolves, and the timing and internal processes of a pending resolution are kept confidential. Reviewers cannot hold positions in contracts they adjudicate, and any affiliation with an entity whose outcome is being resolved must be disclosed. A resolver that resolved ambiguous cases to favor an exchange would trade its only durable asset, credibility, for a one-time gain; independence from the venue's commercial interest is the core of the function, not a nicety. Methodology and human adjudication. Reliable resolution borrows its discipline from journalism. AppliedXL's standard replicates a newsroom's fact-checking process, accelerated with technology but with a human in the loop for adjudication on both the AppliedXL and Kalshi sides, so no contract settles on a machine reading alone. The people overseeing that adjudication have backgrounds in biology and biopharma, so the judgment calls, what a disclosure qualifies as, whether an endpoint was genuinely met, are made by people equipped to read the science. Resolution relies only on publicly disclosed information, FDA actions, trial registries, scientific publications, and company statements; no patient-level or otherwise non-public clinical data is used or disclosed. Source-correction handling. Public sources are not infallible: a ClinicalTrials.gov posting can be corrected, an 8-K amended, a press release superseded by the authoritative filing. The resolution standard must specify which source governs when they conflict (the authoritative regulatory document over the sponsor's characterization) and how a resolution is treated if the governing source is itself later corrected, so that the rule is fixed in advance rather than improvised under dispute. Auditability. Every resolution must produce a complete, time-stamped audit trail: which sources were consulted, what the assessment recommended, what the reviewer decided, and why any deviation from the recommendation was made. A default-to-NO rule. When a qualifying disclosure is ambiguous, incomplete, or inconsistent with the registered protocol, the default resolution is NO. This is a procedural commitment to the principle that a market resolves YES only when the qualifying event has unambiguously occurred. #### The Ecosystem Gap The category does not yet have a credible, independent resolution-data provider, one that checks the authoritative public sources against the registered protocol using a transparent, inspectable method. Existing services track when decisions are expected; they do not verify what happened against a registered protocol. Kalshi's in-house Markets Team resolves Kalshi's contracts capably, but an exchange resolving its own markets is structurally different from an independent provider whose only product is credible resolution. That limitation isn't a criticism of the platform; it simply reflects that the independent layer is a different function from the one it was built to perform. This gap is the most consequential unmet need in the category's current infrastructure. The exchanges have been built. The oracle infrastructure has been built. The independent resolution layer that makes prices trustworthy enough to serve institutional, clinical, and journalistic audiences has not. The deeper point is that the market structure itself is a trust mechanism, but only if it settles credibly. As Ragu Bharadwaj, who built one of the first pharma prediction markets, put it: “"The anonymous market structure, with money around it, is a good substitute for the trust."” — Ragu Bharadwaj, creator of Pharmer's Market The structure substitutes for trust on the trading side. Resolution is where that substitution either holds or fails. ### Conclusion Eli Lilly's 2003 experiment worked. The market correctly identified the most promising drug candidates and revealed shades of opinion that formal processes could not surface. It was never run at production scale, not because it failed, but because a continuously published probability sits uneasily inside a hierarchy, an incompatibility of structure, not a failing of the company that had the foresight to try it. Twenty years later, the infrastructure that did not exist in 2003 has been built. The exchanges are regulated. The legal framework for event contracts is taking shape, even as the appellate courts work through its boundaries. The anonymity that internal markets could not provide is a design feature of every major platform. The barriers that killed corporate prediction markets do not apply to external public exchanges. What has not been built is the resolution infrastructure that decides whether those prices can be trusted. The harder problems this report has named, the ambiguity archetypes, the subcontracted-CRO blind spot, the unproven transfer of accuracy from vetted pools to open ones, the values objection the clinical community has not conceded, are not rhetorical hedges. They are the conditions the category has to satisfy before its prices deserve the weight this report argues they could carry. The evidence does support three things. Drug development has a structural information problem. Internal mechanisms to surface dispersed expert knowledge have proven hard to sustain for twenty years. And external public prediction markets sidestep the specific structural barriers that constrained every internal attempt: the hierarchy that discourages dissent, the absence of anonymity, and the awkward fit between a live probability and decisions already set through normal channels. Whether they clear those barriers accurately enough, at sufficient scale, with adequate governance, is what the next several years will decide. The Lilly experiment demonstrated the concept. The current commercial category is the first serious attempt to operationalize it at scale. The gap between demonstration and operationalization is where the work is, and the resolution layer is where that gap is widest. ### ReferenceGlossary Accelerated approvalAn FDA pathway that allows a drug to be approved on the basis of a surrogate endpoint reasonably likely to predict clinical benefit, subject to confirmatory post-approval trials. Distinct from full, standard-pathway approval. Advisory committee (AdCom)An external panel of experts the FDA convenes to review a drug's data and vote on questions such as efficacy and benefit–risk. The vote is non-binding but historically a strong leading indicator of the agency's decision. BLA / NDABiologics License Application / New Drug Application, the formal submissions through which a sponsor seeks FDA approval to market a biologic or a small-molecule drug, respectively. CAPS-5The Clinician-Administered PTSD Scale, a standardized severity measure used as the primary endpoint in the MDMA PTSD trials discussed in the report. ClinicalTrials.govThe U.S. public registry where trial protocols and prespecified endpoints are recorded before a trial begins and where results are later posted; a primary source for resolving trial-readout contracts. Complete Response Letter (CRL)The FDA's formal notice that it will not approve an application in its current form. A CRL resolves an approval contract NO. Composite endpointA single trial endpoint combining several outcomes (for example, a count of any of several adverse events). Meeting one component does not necessarily mean the composite was met. Designated Contract Market (DCM)A CFTC-regulated exchange authorized to list event contracts (Kalshi is one). DCM status brings KYC, position-limit, and reporting obligations. DSMB (Data and Safety Monitoring Board)An independent committee that reviews unblinded interim trial data for safety and efficacy and can recommend stopping or continuing a trial. Endpoint (primary / secondary)The prespecified outcome a trial is designed to measure. The primary endpoint is the basis on which the trial is powered and judged; secondary endpoints are supporting measures. Equipoise (clinical)Genuine uncertainty in the expert community about whether a treatment is better than the alternative, the ethical precondition for randomizing patients, including to a placebo arm. Event contractA financial contract that pays out based on whether a specified real-world event occurs; the instrument traded on prediction-market exchanges. Held by the Third Circuit (for sports contracts) to be a "swap" under the Commodity Exchange Act. Misappropriation theoryThe legal doctrine, established in securities law (O'Hagan) and applied by the CFTC under Rule 180.1, that trading on confidential information in breach of a duty owed to the source of that information is illegal insider trading. Optimistic oracleThe dispute-based resolution mechanism (used by UMA, which settles Polymarket contracts) in which a proposed outcome stands unless challenged within a window; challenges escalate to a token-holder vote. PDUFA dateThe target date by which the FDA, under the Prescription Drug User Fee Act, aims to complete its review of an application, the most-watched single catalyst in the category. Phase 1 / 2 / 3The sequential stages of human clinical testing: Phase 1 (safety, small), Phase 2 (efficacy signal and dosing), Phase 3 (large, confirmatory, the basis for approval). The report's initial scope covers Phase 3 readouts. Position limitA cap on how large a position a single participant may hold in a given contract, used to limit manipulation in thin markets. Price discoveryWhat a market does while it is open: continuously aggregating participant judgment into a live probability. Probability of success (PoS)An estimate of the likelihood a program reaches a defined milestone (endpoint success, approval). Produced privately today by banks, expert networks, and internal pharma models. ResolutionHow a market settles when it closes: determining the binary outcome and paying out. A public probability is only as trustworthy as the resolution that settles it, the distinction that runs through this report to its closing argument. Source Agency ProhibitionKalshi's rule (an implementation of the "Eddie Murphy Rule") barring members who hold material nonpublic information from a relevant source agency from trading the affected contracts. Special Protocol Assessment (SPA)A binding agreement between a sponsor and the FDA on a Phase 3 trial's design and endpoints, reached before the trial begins. Surrogate endpointA laboratory or imaging measure used as a stand-in for a direct clinical outcome (for example, tumor shrinkage in place of survival), often the basis for accelerated approval. ### ReferenceSources and Notes 1Hamel, G. (2009, September 24). The end of management. Time. The figures — roughly 50 employees, six drug candidates, and the market correctly forecasting the three most successful — are documented in Prediction markets for corporate governance (Working paper), University of Chicago Law & Economics, chicagounbound.uchicago.edu, which cites Pethokoukis, J. M. (2004, August 30). All seeing, all knowing. U.S. News & World Report. The experiment was also reported in Nature (2005). Quotations from Alpheus Bingham are from an interview conducted for this report. 2Hanson, R. Prediction markets need trial & error. Overcoming Bias, overcomingbias.com. See also Cowgill, B., & Zitzewitz, E. (2015). Corporate prediction markets: Evidence from Google, Ford, and Firm X. The Review of Economic Studies, 82(4), 1309–1341. 3McKinsey & Company. (2025, February 11). Pharma's Rx for R&D [The Week in Charts]. mckinsey.com. Average cost to bring a single drug to market, approximately $2.3 billion. 4U.S. Food and Drug Administration. (2026, April 13). FDA reminds more than 2,200 sponsors and researchers to disclose trial results [News release]. fda.gov. The agency's analysis found that 29.6% of studies highly likely to be subject to mandatory reporting had submitted no results; Commissioner Marty Makary is quoted therein. 5FDAAA TrialsTracker [Live informatics tool]. (2018). University of Oxford, Bennett Institute for Applied Data Science (EBM DataLab), fdaaa.trialstracker.net. On methods, see DeVito, N. J., Bacon, S., & Goldacre, B. (2018). FDAAA TrialsTracker: A live informatics tool to monitor compliance with FDA requirements to report clinical trial results. bioRxiv. The penalty figure is a theoretical ceiling: the per-day maximum civil penalty under 21 U.S.C. § 333(f)(3)(B) ($11,569 in the current schedule, adjusted annually for inflation) applied across all delinquent trials for each day past the reporting deadline, assuming notice issued the day after the missed deadline. Actual penalties levied are far lower; the figure illustrates the scale of non-compliance, not fines owed. The FDA Amendments Act of 2007 (Pub. L. No. 110-85), Title VIII, establishes the ClinicalTrials.gov results-reporting requirement; the twelve-month deadline is set out in the 2016 Final Rule (81 Fed. Reg. 64982). 6Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273–286. 7Precedence Research. Clinical trials market size, 2024, approximately $83.75 billion. This is the size of the clinical-trials services market, distinct from total industry R&D spend. 8Statista. Global pharmaceutical industry — Statistics & facts. Global pharmaceutical market approximately $1.7 trillion in 2024; industry forecasts from multiple research firms (e.g., Precedence Research, Fortune Business Insights) place the market between roughly $1.8 trillion and $2.1 trillion in 2026. 9Clifford Chance. The rise of the Chinese biotech sector; and Center for International Relations and Sustainable Development. A biopharmaceutical superpower: China's rise. China accounted for roughly 30.5% of the global innovative-drug pipeline in 2025 (versus 33% for the U.S.), up from 23% in 2023, and generated record license-out transaction value (figures ranging from approximately $50 billion to $135 billion depending on methodology and period). 10U.S. Food and Drug Administration. Novel drug approvals for 2025, fda.gov; and GoodRx. FDA approval stats, goodrx.com. FDA novel-drug approvals totaled 46 in 2025 (CDER), alongside new expedited-review mechanisms such as the Commissioner's National Priority Voucher program, which the agency describes as compressing review timelines from the standard 10–12 months toward 1–2 months. On AI's effect on discovery timelines and pipeline expansion, see Precedence Research / BioSpace. Pharmaceutical market transformation: AI driving innovation. Note that annual novel approvals have not risen monotonically (55 in 2023, 50 in 2024, 46 in 2025); the acceleration is in pipeline size, discovery tooling, and review pathways rather than in raw approval counts. 11Anderson, M. L., Chiswell, K., Peterson, E. D., Tasneem, A., Topping, J., & Califf, R. M. (2015). Compliance with results reporting at ClinicalTrials.gov. New England Journal of Medicine, 372, 1031–1039; and DeVito, N. J., Bacon, S., & Goldacre, B. (2020). Compliance with legal requirement to report clinical trial results on ClinicalTrials.gov: A cohort study. The Lancet, 395, 361–369. 12Boutron, I., Dutton, S., Ravaud, P., & Altman, D. G. (2010). Reporting and interpretation of randomized controlled trials with statistically nonsignificant results for primary outcomes. Journal of the American Medical Association, 303(20), 2058–2064. Of 72 eligible trials with nonsignificant primary outcomes, spin appeared in the abstract conclusions of 42 (58.3%), with more than 40% of reports showing spin in at least two main-text sections. 13Polgreen, P. M., Nelson, F. D., & Neumann, G. R. (2007). Use of prediction markets to forecast infectious disease activity. Clinical Infectious Diseases, 44(2), 272–279. Accuracy figures (71% by end of target week, approximately 50% one week ahead, approximately 36% historical-average baseline) as reported by CIDRAP. Iowa group bets on market to predict pandemics, cidrap.umn.edu. 14Cowgill, B., & Zitzewitz, E. (2015). Corporate prediction markets: Evidence from Google, Ford, and Firm X. The Review of Economic Studies, 82(4), 1309–1341; and Chen, K.-Y., & Plott, C. R. (2002). Information aggregation mechanisms: Concept, design and implementation for a sales forecasting problem (Social Science Working Paper No. 1131). California Institute of Technology. On the geographic-proximity effect, see the Google internal-markets findings reported in Cowgill & Zitzewitz. 15The DARPA Policy Analysis Market (part of the FutureMAP program) was announced and cancelled in late July 2003 following criticism led by Senators Ron Wyden and Byron Dorgan. See Congressional Research Service / Federation of American Scientists records, sgp.fas.org/congress. 16Details of Pharmer's Market — the participants, the platform, and the slate of drug contracts — are drawn from an interview with its creator, Ragu Bharadwaj, conducted for this report, and from contemporaneous accounts of the project's 2009 launch on the Crowdcast platform. 17U.S. Securities and Exchange Commission. (2012). SEC charges hedge fund firm CR Intrinsic and two others in $276 million insider trading scheme involving Alzheimer's drug (Press Release No. 2012-237). The tip to Mathew Martoma came from Dr. Sidney Gilman, chair of the safety monitoring committee for the Phase II bapineuzumab trial. 18U.S. Securities and Exchange Commission. (2011). Litigation Release No. LR-21928 and Press Release No. 2011-91. See also Hedge fund manager charged with insider trading in Human Genome Sciences case. (2011, April 13). The Washington Post. Dr. Yves Benhamou served on the steering committee for the Phase 3 Albuferon (albinterferon alfa-2b) hepatitis-C trial; Joseph "Chip" Skowron's funds avoided roughly $30 million in losses. 19U.S. Securities and Exchange Commission. (2023). Press Release No. 2023-123. The release states that Amit Dagar's trading generated approximately $214,395 in illicit profits; the parallel DOJ/SDNY criminal case cited a figure of more than $270,000. Dagar was senior statistical program lead on Pfizer's Paxlovid trial. 20U.S. Securities and Exchange Commission. (2021). Litigation Release No. LR-25813 and Press Release No. 2021-264. Dr. Daniel V. T. Catenacci, lead investigator on the Phase II FIGHT trial of bemarituzumab (Five Prime Therapeutics), realized $134,142. A related case, SEC v. Holly Hand (Neuralstem), involved a clinical-trial project manager and is documented in SEC Press Release No. 2021-94. 21Kalshi's "Source Agency Prohibition" (an implementation of the so-called "Eddie Murphy Rule") is defined in the KalshiEX LLC rulebook (CFTC org-rules filings), cftc.gov. 22Jontay Porter received an NBA lifetime ban (April 2024) and pleaded guilty to conspiracy to commit wire fraud. See ESPN and CBS Sports. 23Kalshi's prohibited-trader definitions, surveillance and onboarding-screening practices, and disciplinary process are described in Insider trading prohibitions, Kalshi Market Integrity Hub. The federal provisions referenced (7 U.S.C. § 6(c)(1) and CFTC Regulation 180.1) and the Section 4c(a)(4) "Eddie Murphy" rule are summarized there and in the CFTC enforcement advisory cited at note 24. 24U.S. Commodity Futures Trading Commission, Division of Enforcement. (2026, February 25). Advisory on enforcement authority over event contracts (CFTC Release No. 9185-26). The advisory documents a Kalshi platform disciplinary action: $5,397.58 disgorgement plus a $15,000 penalty ($20,397.58 total) and a two-year suspension of a member who traded on advance knowledge of unreleased content. 25U.S. Commodity Futures Trading Commission. (2026). CFTC charges U.S. service member with insider trading in Nicolás Maduro-related event contracts (Release No. 9217-26); and U.S. Department of Justice. (2026, April 23). U.S. soldier charged with using classified information to profit from prediction market bets. Defendant Gannon Ken Van Dyke; profits of roughly $404,000 (CFTC) to approximately $409,881 (DOJ). The CFTC characterized this as its first insider-trading case involving event contracts. 26Biotechnology stock prices before public announcements: Evidence of insider trading? (2000). PubMed PMID 10736971. Across 98 products in Phase 3 trials, 1990–1998, the divergence between winners (+27%) and losers (−4%) over the 120 days before announcement was significant at p = 0.0007. 27KalshiEX LLC v. Flaherty, No. 25-1922 (3d Cir. Apr. 6, 2026), 2-1 (Porter, J., joined by Chagares, C.J.; Roth, J., dissenting). See Holland & Knight, Federal appeals court: CFTC jurisdiction over sports event contracts likely exclusive. The Ninth Circuit heard consolidated argument in parallel cases on April 16, 2026. 28Kalshi imposes position limits and accountability rules under its rulebook (provisions historically numbered Rule 5.14; numbering varies by rulebook version), per CFTC org-rules filings. 29U.S. Food and Drug Administration. FY 2025 PDUFA performance report. The agency met or exceeded the large majority of its review-performance goals. 30Mitchell, J. M., Bogenschutz, M., Lilienstein, A., Harrison, C., Kleiman, S., Parker-Guilbert, K., et al., Doblin, R. (2021). MDMA-assisted therapy for severe PTSD: A randomized, double-blind, placebo-controlled Phase 3 study [MAPP1]. Nature Medicine, 27, 1025–1033; and the confirmatory MAPP2 trial, Nature Medicine (2023). See MAPS PBC announcements. Both trials met the primary (CAPS-5) and key secondary (Sheehan Disability Scale) endpoints. 31FDA Psychopharmacologic Drugs Advisory Committee vote, June 4, 2024 (2-for/9-against on effectiveness; 1-for/10-against on benefit–risk), per Pharmaceutical Executive. Complete Response Letter issued August 9, 2024, per Fierce Biotech and BioSpace. 32Dezzani, L. [Public post on the future of medical affairs and scientific prediction markets]. LinkedIn. Luca Dezzani is Vice President of US Medical Affairs, BioNTech. The remarks are his own forward-looking views, expressed publicly rather than in an interview for this report, and are reproduced here in paraphrase with short quoted phrases. 33MLB–CFTC memorandum of understanding announced March 19, 2026. See MLB.com; Sports Video Group, MLB names Polymarket exclusive prediction market exchange partner and signs agreement with CFTC to establish integrity framework; and DeFi Rate. Sportradar serves as MLB's data distributor. 34UMA. Improving oracle efficiency with managed proposers. The post states an optimistic-oracle dispute rate of approximately 1.3%; the $750 USDC proposer bond is documented in Polymarket's developer and help-center documentation. On the March 2025 governance incident, see Orochi Network, Oracle manipulation in Polymarket 2025. AppliedXL does not provide investment advice; all information is provided for informational purposes only. ■  AppliedXL & Kalshi, July 2026 ↑ --- # Clinical Trial Data as Alpha: Biotech Quant Model URL: https://www.appliedxl.com/research/clinical-trial-alpha-quant-model New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Clinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time Intelligence AppliedXL built a biotech quant investing model on point-in-time clinical trial data enriched with SEC filings, press releases, and AI agents. 107 out-of-sample trades. +204% cumulative return. 1.32 Sharpe. Here's the alpha the market can't see. 09 MAY 2026 · WILL KATZKA (APPLIEDXL RESEARCH) · 15 MIN ### Key Takeaways +204.7% cumulative return across 107 out-of-sample trades, 2021–2025 1.32 Sharpe, 1.83 Sortino, –7.1% max drawdown 72% overall hit rate — 71.3% longs, 83.3% shorts Execution risk varies 120–179% between sponsors; disease area varies less than 10% Alpha source: divergence between quant scores and independent AI agent verdicts No backfilled history — all signals generated prospectively from 2020 Every clinical trial dataset used for quantitative research carries the same flaw: it reflects the registry as it exists today, not as it existed when a trade would have been placed. Endpoints get amended after interim analyses. Enrollment targets get cut after site failures. Completion dates slip. A model built on today's record is reading an edited document. AppliedXL built detection systems to track those changes in real time across 500,000+ trials. This is what a simulated trading model built on that signal produced across 107 out-of-sample trades. +204%Cumulative Return107 out-of-sample trades 1.32Sharpe Ratio–0.01 baseline (IBB ETF) 72%Hit Rate55%+ is strong event-driven 107Trades2021–2025, walk-forward $0.00Net Market Betalong/short balanced ### The Data Problem AppliedXL built detection systems that track changes across 500,000+ clinical trials — field by field, amendment by amendment, across clinical registries (ClinicalTrials.gov), scientific literature (PubMed, conference papers, biomedical ontologies), federal regulatory filings (Federal Register, 430+ agencies), and corporate disclosures (8-Ks, press releases). Custom models detect, normalize, and link events via deterministic and language-tuned pipelines. This analysis draws on 37,391 interventional trials — over one million event-level records — structured against a domain ontology built for temporal integrity. Using the registry as it exists today is survivorship bias by another name. The document has already been revised by the outcome. Every trade in the backtest used only information available at the moment of hypothetical entry. No future data touches any stage of the pipeline. ### Two Layers of Signal Scientific signal in clinical trials — mechanism of action, endpoint design, therapeutic indication — is actively monitored and efficiently priced. The operational layer is not. Timeline integrity, enrollment behavior, protocol amendments, and status transitions accumulate in the registry unread by any commercial provider. Scientific Signal Well-modeled by the market What analysts track Trial design & endpoints Therapeutic indication Mechanism of action Scientific publications KOL sentiment Operational Signal Systematically underweighted What no one monitors holistically Timeline integrity & date slippage Enrollment behavior vs. targets Status transitions and regressions Protocol amendments Dormancy and silence patterns 0% of commercial providers monitor this layer AppliedXL's analysis across 377 trials in three indications quantified the gap. Execution risk varies 120–179% between sponsors. Disease area risk varies less than 10% across those same companies. The same companies — GSK at 0.00, Xencor at 0.45 — maintain consistent execution scores across NSCLC, Alzheimer's disease, and heart failure. 12–20× Execution effect vs. disease effect on trial risk Company execution capability varies 120–179% between sponsors. Disease area varies less than 10% across the same companies. The operator predicts the outcome far better than the indication. Disease area<10% Execution120–179% GSK in NSCLC: risk score 0.00, zero delays.Xencor in NSCLC: risk score 0.45, 50% of trials delayed.Same indication. Opposite execution. Execution capability is institutional. It persists across disease areas, pipeline stages, and market cycles. Poor operators fail everywhere. Strong operators succeed everywhere. The market prices scientific risk with reasonable efficiency. Operational risk is priced as if it doesn't exist. ### Why the Signal Exists Registry updates are made by clinical operations teams, not investor relations. FDAAA 801 mandates reporting within 30 days of any change, with penalties up to $15,000 per day for non-compliance. Companies cannot opt out. The parallel to SEC mandated disclosures is direct: Form 4 insider sales and 13F filings carry signal because they are mandatory and behavioral. CT.gov is the clinical equivalent — and receives a fraction of the analytical attention. Mandated FDAAA 801 requires updates within 30 days of changes. Penalties up to $15,000/day for non-compliance. Companies must report — they cannot opt out. Timestamped Every modification is versioned with date and time. The full behavioral history from first registration to termination or completion is preserved exactly as it occurred. Behavioral Registry changes reflect decisions by clinical operations teams — not investor relations. The principal-agent gap between clinical ops and IR is the signal source. The principal-agent gap between clinical operations and investor relations is the signal source. Clinical ops updates the registry. IR manages the narrative. These are different people with different information and different incentives. Registry behavior diverges from what executives say on earnings calls, and the registry update arrives first. ### What the Registry Reveals Occurrence alone understates the signal. The same event carries different information depending on when it arrives in the trial lifecycle, how large it is relative to the original plan, whether it repeats, and what has not been reported. 1.2 million classified operational events across five years establish the empirical baseline for each pattern type. Absence No CT.gov updates for 6+ months Silence is the signal.Six months without an update makes a trial 1.8× more likely to terminate. Organizational abandonment precedes formal disclosure. Magnitude Enrollment cut 75% vs. 20–50% vs. <20% Size changes meaning.A 75% enrollment cut doubles failure probability vs. a minor adjustment. Same signal type, different severity. Sequence Delay then pull-in (whipsaw pattern) Order reveals information flow.Delay-then-acceleration is irrational unless negative information was received between updates. 1.6× termination risk. Distinguishing a 75% enrollment cut from a 20% one, or a delay-then-acceleration from an acceleration-then-delay, required five years of domain annotation and empirical validation against known outcomes. The classification framework is the barrier. The registry data is public. The baselines that give it meaning are not. ### The Architecture Three layers. Each feeds the next. None shares information upward until the final stage. 01 Multidimensional Prediction Engine Five independent scoring dimensions — trial completion, timeline, primary endpoint, regulatory clearance, and clinical significance — each built from enriched registry data, SEC filings, and press releases, each answering a fundamentally different question. Not a single probability score. Five distinct failure modes, each assessed separately through domain-specific signal rules and behavioral baselines. Trial design quality carries 4× the signal weight of the next strongest feature group. The clinical significance dimension — assessing whether results will actually move markets, not just cross a p-value threshold — is, to our knowledge, original. 02 Adversarial AI Research Layer Six proprietary AI agents run sequentially on every trial before a position is considered. A context researcher builds the factual dossier. A model narrative agent explains the prediction scores. Two adversarial researchers take opposing sides — bull and bear — searching peer-reviewed literature, FDA filings, and current press. A blind verdict agent weighs the evidence without ever seeing the model's probabilities. Only at the final stage does a reconciliation agent see everything — model scores, evidence, and blind verdicts — for the first time, adjudicating where they agree and where they diverge. 03 Readout Timing Model Direction without timing is noise. A separate model predicts when a trial will publicly announce results — before anyone else knows — using behavioral signals embedded in registry update patterns. Timing accuracy determines instrument selection: short-dated options, intermediate options, equity, or LEAPS. Get timing wrong and even a correct directional call bleeds to theta. Agent independence is architectural. Allowing agents to observe quant scores before forming conclusions would introduce the anchoring bias that keeps sell-side analysts clustered around consensus. The structural separation prevents it. ### Feature Signal Feature group ablation removes each cluster entirely and measures the degradation in signal accuracy. Trial design dominates across all five scoring dimensions: endpoints, controls, statistical power, and patient stratification account for roughly four times the signal weight of the next strongest group. How a trial is designed predicts outcome more reliably than what it is testing. Feature Group Ablation Δ AUC when group removed Larger bar = more signal lost = feature group matters more. Negative values = removal hurts performance. Trial design –0.028 Translational biology –0.013 Temporal trajectory –0.006 Target biology –0.004 Sponsor track record –0.002 Chemical properties –0.001 ### The Divergence Signal Agreement between the quant scoring and the research agents generates clean signal with limited edge — the market frequently agrees too. The alpha is in divergence. Among the out-of-sample trades where the quant score was bearish and agents independently bullish, the success rate was 67 to 75 percent. 67–75% Success rate on divergence trades — out-of-sample backtest, 2021–2025 The quant model reads patterns in structured historical data. The agents assess what is happening now — literature, filings, current registry behavior. When these perspectives conflict, the agents are reading information the historical baseline has not yet absorbed. That gap is the alpha source. The divergence signal exists only because the agents cannot anchor to the quant score. Remove the structural separation and the signal collapses. ### The Signal in Practice Four out-of-sample calls from the prediction model — spanning endpoint, clinical significance, and regulatory dimensions. Every score was generated before the catalyst date using only data available at signal time. Returns reflect simulated trading in AppliedXL's backtest — not live positions. NKTR · PIVOT IO-001Nektar Therapeutics — BempegaldesleukinNCT04969861 P(Endpoint Met) — Out of Sample1.7% Simulated Short Return+61.9% Drug Score23.6% Ops Score1.1% QuadrantBad Drug + Bad Ops Why Wall Street Got It Wrong One of the most hyped immuno-oncology partnerships in history. Bristol-Myers Squibb invested $3.6B in the NKTR-214 collaboration in 2018. Multiple analysts had price targets 3–5× the eventual post-failure price. Why the Model Got It Right Drug score of 23.6% flagged the molecule as weak. Operations score of 1.1% was near-zero — severe trial execution red flags the hype cycle obscured. Both pillars failed independently. NVO · SUSTAIN FORTE-2Novo Nordisk — Semaglutide + EmpagliflozinNCT05444153 P(Endpoint Met) — Out of Sample22% Simulated Short Return+22.0% Drug Score89% Ops Score94% QuadrantGood Drug + Good Ops Why Wall Street Got It Wrong Novo Nordisk's combination of semaglutide with empagliflozin was expected to demonstrate additive metabolic benefit. With both drugs individually proven blockbusters, analysts viewed the combination trial as low-risk. Why the Model Got It Right Both pillar scores were high — drug 89%, ops 94%. The 22% prediction came from cross-validation detecting that trials adding a second agent to an already-effective therapy frequently fail to show incremental benefit. A structural pattern, not a quality failure. ACAD · COMPASS PWSACADIA Pharmaceuticals — CarbetocinNCT06173531 P(Clinical Significance) — Out of Sample13.3% Simulated Short Return+11.0% Drug Score61% Ops Score24% QuadrantGood Drug + Bad Ops Why Wall Street Got It Wrong Carbetocin targets the oxytocin receptor — a biologically plausible mechanism for hyperphagia in Prader-Willi syndrome. Analysts pointed to the unmet need and orphan economics. ACADIA's commercial track record with Nuplazid added credibility. Why the Model Got It Right The model scored clinical significance at just 13.3%. The same drug had already failed the identical endpoint in the prior CARE-PWS trial — a rescue attempt on a failed asset with no mechanistic differentiation. COMPASS PWS showed no separation from placebo on primary or any secondary endpoint. UTHR · TETON-2United Therapeutics — Treprostinil (Tyvaso)NCT05255991 P(Regulatory) — Out of Sample96.2% Simulated Long Return+33.0% Drug Score57% Ops Score62% QuadrantGood Drug + Good Ops Why Wall Street Got It Wrong IPF has been a graveyard for drug developers. The consensus view was that a prostacyclin analogue repurposed from PAH was unlikely to show meaningful FVC improvement in fibrotic lung disease. Analysts modeled low probability of success. Why the Model Got It Right The model scored regulatory probability at 96.2% — Tyvaso was already FDA-approved for PAH, and the regulatory pathway for a label expansion with strong Phase 3 data was well-precedented. TETON-2 showed 95.6 mL FVC improvement over placebo. UTHR surged 33% in a single session. ### The Results 107 out-of-sample trades, 2021 through 2025. Walk-forward re-training with strict temporal holdout at each annual cutoff. Equal position sizing, no compounding, no transaction costs. All positions — long and short — are simulated within AppliedXL's backtesting framework. Every signal generated on data the system had not previously seen. +204.7%Cumulative Return–13% baseline 1.32Sharpe Ratio–0.01 baseline 72.0%Hit Rate55%+ is strong for event-driven 1.83Sortino Ratioabove 1.5 = strong downside adj. –7.1%Max Drawdownworst peak-to-trough 2.92Profit Factorwinners outweigh losers +199.5%Long P&L101 trades / 71.3% hit +5.2%Short P&L6 trades / 83.3% hit –7.1% maximum drawdown against +204.7% cumulative return. The equity curve ascends consistently across a period that included interest rate shocks, FDA policy shifts, and multiple sector-wide biotech corrections. Simulated long positions produced +199.5% at a 71.3% hit rate across 101 trades. Six simulated short positions produced +5.2% at 83.3%. ### Methodology Note On methodology: Every feature passes a seven-pattern temporal leakage audit. Walk-forward validation means the system never scores a trial on data used to build its signals. No survivorship bias — failed, withdrawn, and terminated trials are in the signal development set. No parameter snooping — validation splits were established before signal construction began. Temporal integrity is enforced structurally, not by convention. The backtester uses only the snapshot of data available at each historical entry point — registry fields, SEC disclosures, press releases — and applies no forward-looking information. No backfilled history. AppliedXL began proprietary real-time data collection in 2020. Every signal in the dataset was generated prospectively at the moment the underlying source updated — not retroactively applied to historical records. The depth of the dataset is a function of collection tenure. There is no synthetic history. All results are from simulated trading within AppliedXL's backtesting framework. No actual short positions were taken. Equal position sizing, no compounding, no transaction costs or slippage. Past simulation performance does not guarantee future results. This is not investment advice. #### CONTINUE READING QUANT RESEARCHSystematic Trading Strategies in Biotech: Early Risk Signals for Alpha GenerationQUANT RESEARCHSpillover Risk and Readthrough Alpha: A Quantitative AnalysisPAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AI Explore Forecasts → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # State-of-the-Art Clinical Trial Prediction URL: https://www.appliedxl.com/research/clinical-trial-prediction New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## AppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AI Clinical trials don't just succeed or fail. They pass through five sequential checkpoints, and failure can concentrate at any one of them. Our new paper presents a framework that predicts where in that sequence a trial is most likely to fail, achieving 0.873 AUC on endpoint prediction — 17 points above the best published baseline. 15 APR 2026 · WILL KATZKA (APPLIEDXL RESEARCH) · 2 MINDownload the paper (PDF) ### Key Takeaways 0.873 AUC on endpoint prediction — 17 points above the best published baseline Five-stage conditional decomposition: completion, schedule, endpoint, regulatory, clinical significance Only 12% of trials clear all five checkpoints 280 biological and operational signals reconstructible at any historical date +11 AUC points attributable to the temporal LSTM architecture alone Every published model in clinical trial prediction asks the same question: will this trial succeed or fail? The framing is intuitive, but it discards information that matters. A trial terminated for enrollment failure is a different event than one that meets its endpoint with an underwhelming effect size. Investors price these differently. Models that don't distinguish them have plateaued in the 0.65 to 0.70 AUC range across the literature. Our framework decomposes the trial lifecycle into five conditional stages: completion, schedule adherence, endpoint attainment, regulatory approval, and clinical significance. Each stage carries a distinct dominant failure mechanism and requires a different feature set to predict. Only 12 percent of trials clear all five. ### Architecture The architecture has three components. A domain-specific LLM extraction pipeline reads a five-year corpus of biotech press releases to construct outcome labels with confidence scoring and human validation. A point-in-time feature layer assembles 275 biological and operational signals reconstructible at any historical date. A five-head temporal LSTM rescores predictions at every public trial announcement. ### Benchmarks All comparators evaluated on the endpoint prediction task under their original stratified cross-validation protocol. MethodAUC Logistic Regression — Fu et al. (2022)0.650 Random Forest — Fu et al. (2022)0.663 XGBoost — Fu et al. (2022)0.667 Neural Network — Fu et al. (2022)0.681 COMPOSE — Lo et al. (2019)0.700 AppliedXL0.873 The 17-point gap exceeds the entire spread between published baselines. Controlled ablation attributes +4 AUC points to richer features (algorithm held fixed) and +11 AUC points to the temporal LSTM. Each contribution alone would beat the prior state of the art. ### Temporal Validation Performance under walk-forward retraining across quarterly cutoffs: 0.91 on regulatory approval (cohort-weighted across phases), exceeding every published benchmark including Novartis DSAI winner (0.88) and Lo et al. 2019 (0.78 P2, 0.81 P3); 0.865 on completion; 0.816 on endpoint; and 0.796 on clinical significance — a task with no published comparator. Trust-tiered predictions reach expected calibration error of 0.045 in the high-confidence band. FULL PAPER Read the full paper DOWNLOAD PDF #### CONTINUE READING RESEARCHAppliedXL Prediction Model: Six Clinical Trial Case Studies with Full ExplainabilityANALYSISClinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time IntelligenceINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion Explore Forecasts → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # FDA Drug Repurposing: 5 Drugs Without a Champion URL: https://www.appliedxl.com/research/fda-drug-repurposing-intelligence New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## AppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion The FDA opened a public docket requesting input on drug repurposing opportunities where commercial incentives are insufficient to drive new applications. AppliedXL identified five candidates with compelling evidence for new indications and no active sponsor pursuing approval. 11 MAY 2026 · APPLIEDXL RESEARCH (APPLIEDXL) · 12 MIN ### Key Takeaways Gabapentin has two multicenter RCTs meeting primary endpoints for vasomotor symptoms — no NDA has ever been filed Topiramate met primary endpoints in two JAMA-published AUD trials with 521 total patients — no commercial champion exists to file Pioglitazone showed HR ~0.73 for dementia in the pre-specified IRIS trial secondary — TOMORROW's failure reflects population heterogeneity, not mechanism failure Clomiphene fills the gap left by enclomiphene's CRL — same mechanism, genericized, no approved non-TRT option for men desiring fertility Metformin cancer prevention has 2,000+ citation meta-analytic evidence base and an active Phase 3 TAME trial with FDA IND clearance All five meet the FDA's three drug repurposing identification criteria: commercial gap, dosage form conformity, safety comparability Editor's Note: In May 2026, the FDA opened a public docket soliciting input on drug repurposing opportunities where commercial incentives are insufficient to drive new indications to approval. AppliedXL submitted the memo below. It draws on the company's clinical trial intelligence infrastructure to identify five candidates with documented evidence for new uses and no active sponsor pursuing a supplemental application. ### Approach AppliedXL continuously monitors ClinicalTrials.gov results postings, FDA filings, and published literature to detect signals in public clinical data before they reach conventional channels. For this submission, that infrastructure was directed at a specific question: which FDA-approved drugs carry meaningful clinical evidence for a new indication, and have no commercial sponsor positioned to act on it? The screen targeted two structural patterns. The first: trials that failed their primary endpoint in one indication but achieved statistical significance on secondary endpoints that would qualify as primary endpoints in a different disease area. The second: drugs that met primary endpoints in investigator-sponsored trials never followed by an NDA submission, not because the evidence was insufficient, but because no commercial actor had reason to carry it forward. Both patterns point to the same underlying condition. The evidence exists. The regulatory pathway is defined. What is absent is the organizational and economic mechanism to move one toward the other. The five cases documented here share that structure: an evidentiary record that meets a reasonable threshold for regulatory consideration, and no private actor with the incentive to present it. Table 1. Candidate drug repurposing opportunities identified from public clinical trial registry data, submitted in response to FDA Docket No. FDA-2026-N-4492 (May 2026). DrugProposed New IndicationFDA Priority AreaEvidence GabapentinVasomotor symptoms of menopause (non-hormonal)Women's health conditionsStrong Clinical TopiramateAlcohol Use DisorderSubstance use disordersStrong Clinical PioglitazoneDementia prevention in post-stroke, insulin-resistant patientsNeurodegenerative conditionsEarly Clinical Clomiphene citrateMale hypogonadotropic hypogonadism secondary to obesityMen's health conditionsObservational MetforminCancer prevention in patients with Type 2 diabetes mellitusMetabolic diseasesStrong Clinical All candidates satisfy the three drug repurposing identification criteria specified in the docket: commercial gap, dosage-form conformity, and safety comparability. ### Drug Repurposing Identification Criteria Every candidate was required to meet three drug repurposing identification criteria before evaluation. 01 — Commercial Gap The drug must have no active commercial sponsor pursuing the proposed new indication through a supplemental application. In practice: the brand has been discontinued and only generics remain, or the market is genericized to the point where no sponsor has economic incentive to file. 02 — Dosage Form Conformity The proposed new use must use the identical dosage form and route of administration as the approved indication — the threshold the FDA has specified as a condition for this initiative. 03 — Safety Comparability The patient population for the new use must have a comparable safety profile to the population for the approved indication, using the existing safety database as the foundation. ### Repurposing Candidates Drug Repurposing Opportunity 01 Women's Health Strong Clinical Scenario 1¹ #### 1. Gabapentin for Vasomotor Symptoms of Menopause Gabapentin is currently approved for neuropathic pain and partial-onset seizures. This submission proposes its use as a non-hormonal treatment for vasomotor symptoms (hot flashes) in postmenopausal women — where two multicenter RCTs have met pre-specified primary endpoints and no commercial NDA has ever been filed. Drug Profile ApprovedPostherpetic neuralgia and adjunctive therapy for partial-onset seizures (oral capsule/tablet; Neurontin brand genericized; multiple ANDAs active) SafetySomnolence, dizziness at higher doses; doses used for VMS (300–900 mg/day) are at the lower end of the approved range and well-tolerated in postmenopausal women. ID CriteriaGeneric-only; no active commercial development for vasomotor NDA. Oral tablet same route. Postmenopausal population safety acceptable at VMS doses. All criteria met. Assessment Unmet NeedHigh — ~75% of menopausal women experience VMS; substantial proportion cannot use estrogen; fezolinetant (Veozah) newly approved but expensive; no genericized labeled option Commercial IncentiveUnlikely — no patent protection; women's health specialty pharma or non-profit 505(b)(2) pathway operative Recommended Next Step505(b)(2) NDA using Pandya 2005 multicenter RCT as the anchor study; key regulatory question is whether the epilepsy/PHN safety database bridges to the VMS population without a new safety study Mechanistic Basis Binds the alpha-2-delta subunit of voltage-gated calcium channels in thermoregulatory neurons of the hypothalamus, dampening the aberrant calcium channel activity that drives the neurogenic vasodilation and perspiration cascade underlying hot flashes. Distinct from the NK3/neurokinin mechanism (fezolinetant) and the serotonergic mechanism (paroxetine) — mechanistically complementary in patients who fail those approaches. The mechanistic bridge from neuropathic pain to vasomotor symptoms runs through a shared neuropeptide: Substance P. Gabapentin binds CACNA2D1 with nanomolar affinity (Kd ~100–200 nM; Gee et al., 1996), reducing calcium-dependent neurotransmitter release from presynaptic terminals. Among the neurotransmitters suppressed is Substance P, which plays a well-characterized role in nociception — but also participates directly in hypothalamic thermoregulation. Substance P-expressing neurons in the arcuate nucleus are activated by estrogen withdrawal; their projections to the median preoptic nucleus directly trigger vasodilation and sweating. Gabapentin's calcium channel inhibition reduces Substance P release at both nociceptive and thermoregulatory synapses — a single mechanism serving two indications through the same intermediate node. The pathway is traceable: gabapentin inhibits CACNA2D1, which reduces calcium ion import, which modulates vasodilation — the same vascular instability that drives vasomotor flushing. CACNA2D1 is expressed in arterial tissue (GTEx: aorta 3.88, coronary 3.79, tibial 4.21 TPM), though at lower levels than skeletal muscle (5.26 TPM), suggesting the central hypothalamic mechanism likely dominates over direct peripheral vascular effects. Key Evidence 01Guttuso et al. 2003 (Obstetrics & Gynecology, N=59, RCT): gabapentin 900 mg/day significantly reduced hot flash severity and frequency vs placebo — primary endpoint met (p<0.015) 02Pandya et al. 2005 (JNCI, N=420, multicenter RCT): gabapentin 900 mg/day reduced hot flash frequency significantly in cancer survivors with VMS — pre-specified primary endpoint met 03North American Menopause Society (NAMS) and ACOG both reference gabapentin as an evidence-based non-hormonal option in clinical practice guidelines 04No NDA has ever been submitted — fully genericized with no commercial champion 05Gee et al. 1996 (J Biol Chem): gabapentin binds the α2δ-1 calcium channel subunit with nanomolar affinity (Kd ~100–200 nM), establishing the molecular target 06AppliedXL mechanistic analysis (2026): independent pathway analysis identifies Substance P as a shared biological node between gabapentin's analgesic mechanism and thermoregulatory dysfunction. The drug's known calcium channel inhibition traces through calcium ion import to vasodilation — the same vascular instability underlying menopausal flushing. 96 overlapping biological nodes between gabapentin's mechanism and the vasomotor disease neighborhood. Drug Repurposing Opportunity 02 Substance Use Disorders Strong Clinical Scenario 1¹ #### 2. Topiramate for Alcohol Use Disorder Topiramate is currently approved for epilepsy and migraine prophylaxis. This submission proposes its use in treating alcohol use disorder — where two JAMA-published multicenter RCTs have met primary endpoints with 521 combined patients and no commercial sponsor has ever pursued an NDA submission. Drug Profile ApprovedEpilepsy and migraine prophylaxis (oral tablet/capsule; fully genericized — 35+ ANDA approvals; Topamax brand discontinued) SafetyCognitive effects, paresthesias, weight loss — weight loss is neutral-to-favorable in alcohol-dependent populations with metabolic comorbidity. ID CriteriaGeneric-only market; oral tablet identical route; adult AUD population safety comparable to epilepsy/migraine populations. All criteria met. Assessment Unmet NeedHigh — 29.5 million Americans meet AUD criteria; fewer than 10% receive any pharmacotherapy Commercial IncentiveUnlikely — fully genericized; academic NDA or GRIK1-enriched precision label pathway operative Recommended Next StepFDA Type B pre-submission meeting to establish whether Johnson 2003 and 2007 JAMA trials constitute adequate and well-controlled studies under 21 CFR 314.126 for 505(b)(2) purposes Mechanistic Basis Potentiates GABA-A receptor activity and antagonizes AMPA/kainate (glutamate) receptors, directly suppressing the mesolimbic dopamine reward pathway that mediates alcohol craving and reinforcement. This dual mechanism — enhancing inhibitory tone while dampening excitatory glutamatergic drive — targets the neurobiological substrate of compulsive alcohol seeking more directly than the approved AUD agents (naltrexone, acamprosate, disulfiram). These two mechanisms converge on the mesolimbic reward circuit through parallel but independent pathways. The GABAergic arm enhances inhibitory tone, reducing the anxiety that drives stress-induced drinking. The glutamatergic arm, through kainate receptor antagonism, dampens excitatory drive to dopaminergic neurons, directly suppressing the reward response to alcohol. Both pathways terminate in altered dopamine and glutamate secretion, the core neurotransmitter systems of the mesolimbic circuit — and both are constitutively active, meaning topiramate's effects accumulate across drinking episodes rather than requiring acute dosing coincident with craving. The GRIK1 rs2832407 variant adds a molecular basis for treatment response: GRIK1 encodes the GluK1 kainate receptor subunit, which is a direct target of topiramate's glutamate antagonism. Patients carrying specific GRIK1 variants may have altered GluK1 receptor function that increases sensitivity to topiramate's antagonism — a pharmacogenomically enriched population that could define a precision label. One mechanistic nuance warrants attention. GABRA1 expression is highest in the cerebellum (5.82 TPM) and cortex (5.01 TPM), but lowest in the caudate (2.51 TPM) — a key mesolimbic structure. This expression pattern suggests topiramate's therapeutic effects in alcohol use disorder may operate more through cortical cognitive control and anxiety reduction than through direct suppression of striatal reward signaling. The clinical implication: topiramate may be most effective in patients where cortical dysregulation — impulsivity, anxiety-driven drinking — dominates over purely reward-driven compulsion. Key Evidence 01Johnson et al. 2003 (JAMA, N=150): topiramate 25–300 mg/day significantly reduced percentage of heavy drinking days vs placebo — primary endpoint met (p<0.001) 02Johnson et al. 2007 (JAMA, N=371, multicenter): topiramate 300 mg/day significantly reduced heavy drinking days, drinks per day, and drinks per drinking day — primary and multiple secondaries met 03Kranzler et al. 2014 (Am J Psychiatry) and 2021: GRIK1 rs2832407 identified as pharmacogenomic predictor of topiramate response in AUD — the variant encodes the GluK1 kainate receptor subunit, a direct molecular target of topiramate's glutamate antagonism 04NIAAA treatment guidelines reference topiramate as an evidence-based off-label option 05AppliedXL mechanistic analysis (2026): two independent mechanistic pathways identified connecting topiramate to alcohol use disorder — one through GABAergic anxiety modulation, one through glutamatergic withdrawal suppression. Both converge on dopamine and glutamate secretion, the core neurotransmitter systems of the mesolimbic reward circuit. GTEx expression analysis reveals GABRA1 is highest in cerebellum and cortex, suggesting cortical cognitive control may dominate over direct striatal reward suppression. Drug Repurposing Opportunity 03 Neurodegenerative Early Clinical Scenario 1¹ #### 3. Pioglitazone for Dementia Prevention Pioglitazone is currently approved for type 2 diabetes. This submission proposes its use for dementia prevention in insulin-resistant patients following ischemic stroke — where the IRIS trial (NEJM 2016, cited 1,095 times) demonstrated a pre-specified secondary endpoint of HR ~0.73 in a biologically enriched subpopulation, with no commercial sponsor pursuing a supplemental NDA. Drug Profile ApprovedType 2 diabetes mellitus (oral tablet, 15–45 mg/day; fully genericized; Actos brand discontinued) SafetyWell-characterized over 25+ years; fluid retention, mild weight gain, rare bladder cancer signal at cumulative high doses; no CNS-specific safety concerns. ID CriteriaGeneric-only market; oral tablet identical route; T2DM/insulin-resistant population safety profile comparable. All criteria met. Assessment Unmet NeedCritical — no approved disease-modifying agent for dementia prevention; estimated 800,000+ annual ischemic stroke survivors in the US Commercial IncentiveUnlikely — fully genericized; white space for academic NDA, non-profit, or specialty pharma Recommended Next StepFDA Type B pre-submission meeting to align on enriched trial design (post-stroke insulin-resistant population) and endpoint acceptability Mechanistic Basis PPARγ agonist; enhances insulin sensitivity, reduces neuroinflammation via microglial modulation, decreases tau phosphorylation, improves mitochondrial function. PPARγ activation corrects cerebral insulin signaling and suppresses microglial-driven neuroinflammation through NF-κB pathway inhibition, directly addressing the insulin-resistance/cerebrovascular axis biologically enriched in the post-stroke population. Molecular docking confirms that pioglitazone binds PPARγ with the strongest affinity of any drug-target pair in this analysis (8.86 kcal/mol). PPARγ is not a narrow single-pathway target — it functions as a master transcriptional regulator with unusually high network connectivity (degree 6.7, the highest of any target examined here), meaning its activation cascades through multiple downstream pathways simultaneously. The metabolic-neurological bridge operates through at least three convergent mechanisms. First, PPARγ activation shifts microglial phenotypes from pro-inflammatory M1 to anti-inflammatory M2 states (Heneka et al., Nature 2015), reducing production of neurotoxic IL-1β and TNF-α. Second, improved insulin signaling inhibits GSK-3β, directly reducing tau phosphorylation at pathogenic Ser396/404 epitopes — with 40–60% reductions demonstrated in 3xTg-AD mice (Sato et al., J Neurosci 2011). Third, PPARγ activation upregulates PGC-1α, restoring mitochondrial biogenesis in neurons — a mechanism independent of, and additive to, the vascular and neuroinflammatory pathways. PPARγ is already linked to cerebrovascular disease in established mechanistic databases — the bridge to the post-stroke population is not a theoretical leap but a documented connection. The further link from cerebrovascular pathology to dementia, through shared nodes including memory impairment and acetylcholine neurotransmission, is where the IRIS trial's clinical signal provides the human evidence for what the biology predicts. Key Evidence 01Takeda TOMORROW trial (NCT01931566, Phase 3, N=3,494, terminated 2018) failed primary endpoint in a broad high-risk population — reflects biological heterogeneity, not absence of effect 02IRIS trial (NCT00091169, NEJM 2016, cited 1,095 times): pioglitazone after ischemic stroke/TIA significantly reduced the rate of dementia as a pre-specified secondary endpoint (HR ~0.73 in insulin-resistant patients) 03Ha et al. 2023 (Neurology): replicated the IRIS dementia signal in real-world Korean claims data 04TOMORROW failure reflects biologically heterogeneous population; IRIS signal persists in the insulin-resistant/cerebrovascular-enriched subpopulation — a narrower, better-defined target 05Heneka et al. 2015 (Nature): PPARγ activation shifts microglial phenotypes from pro-inflammatory M1 to anti-inflammatory M2 states, establishing the neuroinflammation mechanism 06Sato et al. 2011 (J Neurosci): pioglitazone reduces tau phosphorylation at pathogenic Ser396/404 epitopes by 40–60% in 3xTg-AD mice 07Femminella et al. 2016 (Diabetes Care): pioglitazone reduces CSF tau/Aβ42 ratios in T2DM patients, demonstrating the metabolic-neurological bridge in humans 08AppliedXL mechanistic analysis (2026): molecular docking confirms high-affinity PPARγ engagement (8.86 kcal/mol — strongest of all five candidates). PPARγ is directly linked to cerebrovascular disease in established mechanistic databases, bridging to the post-stroke population. Shared biological nodes with dementia pathology include memory impairment and acetylcholine neurotransmission — the cholinergic pathway targeted by approved Alzheimer's therapies. Drug Repurposing Opportunity 04 Men's Health Observational / Early Clinical Scenario 2² #### 4. Clomiphene Citrate for Male Hypogonadotropic Hypogonadism Clomiphene citrate is currently approved for ovulation induction in anovulatory women. This submission proposes its use for hypogonadotropic hypogonadism in obese men — where the underlying mechanism is biologically identical, the drug is fully genericized, and no approved non-testosterone-replacement option exists for men who wish to preserve fertility. Drug Profile ApprovedInduction of ovulation in anovulatory women (oral tablet, 50 mg; Clomid and Serophene brands discontinued; fully genericized) SafetyVisual disturbances at higher doses, mood effects — well-characterized from decades of off-label use; comparable to the anovulatory women population. ID CriteriaGeneric-only market; enclomiphene (active isomer) failed FDA approval (CRL 2016, 2018; Repros Therapeutics dissolved); no active NDA holder; oral tablet identical route. All criteria met. Assessment Unmet NeedHigh — estimated 20–40% of obese men have low testosterone; TRT contraindicated in men desiring fertility; enclomiphene's FDA failure left no approved non-TRT option Commercial IncentiveUnlikely for generic manufacturers; enclomiphene precedent confirms interest but requires redesigned trial Recommended Next Step150–200 patient Phase 2b RCT comparing clomiphene 25 mg/day to placebo on serum testosterone normalization as primary endpoint Mechanistic Basis Selective estrogen receptor modulator (SERM) blocking hypothalamic estrogen negative feedback, increasing GnRH pulsatility and downstream LH/FSH secretion, stimulating Leydig cell testosterone production. In obese men, peripheral aromatization of testosterone to estradiol creates excessive estrogen negative feedback — a functional hypogonadotropic hypogonadism mechanistically identical to the pituitary axis dysregulation clomiphene corrects in anovulatory women. The biological plausibility of this cross-sex application is stronger than it might appear. ESR1, the estrogen receptor alpha that clomiphene modulates, has the highest protein interaction network connectivity of any target examined here (degree 7.0) and a probability of loss-of-function intolerance (pLI) of 1.00 — the maximum possible score, indicating extreme evolutionary conservation. This is a gene the body cannot afford to lose, consistent with its master regulatory role in reproductive biology across vertebrates. Tissue expression data confirms the anatomical basis: ESR1 is expressed in the pituitary at 2.77 TPM, comparable to ovarian tissue at 3.12 TPM. The receptor is present in the same hypothalamic-pituitary structures in both sexes — the difference is not the receptor, but the hormonal milieu acting on it. Clomiphene's high lipophilicity (LogP 6.56) ensures blood-brain barrier penetration for hypothalamic access. The literature evidence linking ESR1 modulation to hypogonadism is the strongest of any candidate-indication pair in this analysis (score 0.874) — a formal assessment that the biological connection between the drug's target and the proposed disease is supported by published literature. The absence of this mechanism from curated pharmacological databases — despite decades of clinical use and AUA guideline endorsement — reflects a systematic blind spot: cross-sex-indication mechanisms are underrepresented in databases built primarily from indication-specific drug development programs. Key Evidence 01Shabsigh et al. 2005 (BJU International, N=36): clomiphene 25–50 mg/day significantly increased serum testosterone in hypogonadal men — primary endpoint met 02Moskovic et al. 2012 (BJU International, N=86): sustained testosterone normalization with long-term clomiphene in hypogonadal men with preserved fertility 03American Urological Association (AUA) 2018 guidelines acknowledge clomiphene as an off-label evidence-based option for men desiring preserved fertility 04Enclomiphene CRL precedent confirms commercial interest exists but requires a better-designed trial 05AppliedXL mechanistic analysis (2026): ESR1 protein network centrality (degree 7.0 — highest of all five targets) and pLI score of 1.00 independently confirm its role as a master regulatory node under extreme evolutionary conservation. Pituitary expression (2.77 TPM) comparable to ovarian tissue (3.12 TPM) supports equivalent receptor engagement across male and female hypothalamic-pituitary axes. Literature evidence score of 0.874 is the strongest of any candidate-indication pair in this analysis. Drug Repurposing Opportunity 05 Metabolic Diseases Strong Clinical Scenario 1¹ #### 5. Metformin for Cancer Prevention Metformin is currently approved for type 2 diabetes. This submission proposes its use for cancer prevention in the T2DM population — supported by a 2,000+ citation meta-analytic evidence base, an active Phase 3 trial (TAME) with FDA IND clearance, and 88 generic ANDAs with no commercial NDA holder pursuing this indication. Drug Profile ApprovedType 2 diabetes mellitus (oral tablet, IR and ER; 88 generic ANDAs approved) Safety70+ year safety record; primary GI side effects well-managed with ER formulation. ID Criteria88 generic ANDAs, no commercial NDA holder pursuing cancer prevention; oral tablet same route; T2DM population safety well-established. All criteria met. Assessment Unmet NeedHigh — no approved pharmacological intervention for cancer prevention in the high-risk T2DM population Commercial IncentiveUnlikely — 88 generic ANDAs; non-profit, academic, or government-funded NDA pathway operative Recommended Next StepFDA Type B pre-submission meeting using existing meta-analytic evidence and TAME biomarker validation data to establish 505(b)(2) pathway for the T2DM cancer prevention subpopulation Mechanistic Basis AMPK activator; mTORC1 inhibition suppresses anabolic tumor-growth signaling; reduction of IGF-1 signaling and circulating insulin levels (a cancer growth factor). The T2DM population was the index studied population — cancer incidence was observed to be significantly lower than expected across multiple independent cohorts. Docking metformin against AMPK yields a binding energy of 4.08 kcal/mol — the weakest target engagement of any drug-target pair in this analysis. For context, pioglitazone scores 8.86 against PPARγ. Binding energies below 6 kcal/mol typically indicate weak or non-specific interactions. This weak binding is itself the finding. The emerging consensus is that metformin does not directly bind and activate AMPK. Instead, the mechanism operates through an indirect cascade: metformin enters the cell via organic cation transporters and inhibits mitochondrial complex I of the electron transport chain. Complex I inhibition disrupts ATP synthesis, raising the cellular AMP:ATP ratio. The elevated AMP:ATP ratio activates AMPK allosterically — as a metabolic stress signal, not as a direct ligand interaction. Wheaton et al. (eLife 2014) confirmed this by showing that metformin's anti-tumor effects are abolished in cancer cells expressing a metformin-resistant form of complex I (NDI1), establishing mitochondrial complex I as the necessary upstream target. This distinction matters. If metformin's anti-cancer effect operated through direct AMPK binding — a single receptor interaction — one would expect organ-specific activity dependent on local AMPK density. Instead, the indirect mechanism through mitochondrial energetics explains why metformin's cancer risk reduction spans multiple organ sites (colorectal −25–40%, breast −15–25%, endometrial −30–40%): mitochondrial complex I is ubiquitous, and the downstream metabolic signaling it disrupts is constitutively active in proliferating cancer cells regardless of tissue type. Key Evidence 01Multiple large observational cohort meta-analyses: colorectal cancer risk reduction ~25–40%; breast cancer ~15–25%; endometrial cancer ~30–40% in T2DM cohorts 02ADA/ACS consensus 2010 (Giovannucci et al., Diabetes Care, cited 2,232 times): metformin and cancer risk in the T2DM population 03TAME (Targeting Aging with Metformin) is a planned Phase 3 trial led by Barzilai et al. at Albert Einstein College of Medicine, designed to enroll 3,000 participants aged 65–80 across 14 U.S. sites — the first trial for which the FDA accepted aging itself as a clinical endpoint framework, establishing regulatory precedent for metformin's mechanism in age-related disease prevention. TAME builds on the MILES pilot study (Metformin in Longevity Study, NCT02432287), which established the biomarker and feasibility foundation at the same institution. The trial is funded through AFAR and philanthropic sources; no commercial sponsor is involved. 04Nature Reviews Endocrinology 2023 metformin repurposing review (cited 616 times) 05Wheaton et al. 2014 (eLife): metformin's anti-tumor effects abolished in cancer cells expressing metformin-resistant complex I (NDI1), establishing mitochondrial complex I — not AMPK — as the necessary upstream target 06AppliedXL mechanistic analysis (2026): molecular docking yields weak AMPK binding (4.08 kcal/mol — lowest of all five candidates), consistent with indirect activation via the complex I → AMP:ATP ratio → AMPK cascade rather than direct drug-protein engagement. Five shared biological nodes with cancer pathology — oxidative stress, cell death, translation, receptor tyrosine kinase activity, and cAMP — span the three proposed anti-cancer axes: mTORC1 suppression, metabolic starvation, and cancer stem cell inhibition. ### Analytical Methodology 01Established Drug-Disease Mechanistic Pathways For each candidate, we identified the intermediate biological nodes — proteins, enzymes, biological processes, molecular activities, and phenotypic features — through which the drug exerts its approved therapeutic effects. We then asked whether any of those same intermediate nodes appear in the known pathology of the proposed new indication. When they do, the shared node constitutes a mechanistic bridge: the drug acts on a biological pathway that is simultaneously dysregulated in the target disease, even if the connection has never been recognized in the clinical literature. 02Physics-Based Molecular Docking Simulations Three-dimensional drug conformers were docked against AlphaFold-predicted target protein structures using AutoDock Vina, producing binding energies in kcal/mol that reflect the strength of atomic-level interactions — hydrogen bonds, hydrophobic contacts, van der Waals forces — between the drug and its target's binding pocket. These scores are not in themselves diagnostic; they quantify how tightly the drug engages its known molecular target, providing an independent measure of mechanistic confidence that complements the pathway and clinical evidence layers. 03Target Protein Characterization Each drug's target protein was characterized through tissue-specific gene expression (GTEx), protein interaction network centrality, evolutionary constraint (pLI scores), and bioactivity profiles (ChEMBL). These features assess whether the target is expressed in tissues relevant to the proposed indication, whether it occupies a central or peripheral position in biological signaling networks, and whether the target-disease association has independent support in the published literature. These methods are not specific to the five candidates presented here. The underlying analytical infrastructure encompasses 4,846 curated mechanistic pathways across 1,585 drugs and 1,015 diseases, decomposed into 2,547 intermediate biological nodes. Applied systematically, it identifies mechanistic bridges between drugs and diseases that have not been previously connected — including cases where established databases contain the relevant intermediate biology but no one has traced the connection from the approved indication to the proposed one. The FDA docket represents one application of this infrastructure. It is the same infrastructure that powers AppliedXL's clinical trial intelligence platform. Disclaimer. This analysis is provided for informational purposes only and does not constitute medical advice, investment advice, or a solicitation to buy or sell any security. Clinical trial data referenced herein is drawn from public registries. Consult a qualified healthcare professional before making any medical decisions. #### CONTINUE READING PAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIRESEARCHThe Hidden Signals That Decide Drug Success or FailureRESEARCHSeeing Risk Before It Becomes News Explore Signals → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # The Hidden Signals That Decide Drug Success or Failure URL: https://www.appliedxl.com/research/hidden-signals-drug-success-failure New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## The Hidden Signals That Decide Drug Success or Failure AppliedXL decodes subtle shifts in clinical trials flagging risks and opportunities before they hit the news cycle. 06 DEC 2024 · APPLIEDXL RESEARCH (SIGNAL INTELLIGENCE TEAM) · 6 MIN ### Key Takeaways Delays beyond 150 days increase termination risk by 41% Enrollment drops of 75% double the chance of early failure System maps relationships across 100+ event categories covering 22,000 organizations AI agents replicate the rigorous research process of biotech analysts Thousands of clinical trial updates are posted daily in the U.S. Clinical Trials Registry. While most are routine, status changes, enrollment updates, small deviations can signal major shifts in trial outcomes, regulatory risks, or commercial potential. Traditional monitoring often misses these subtle but critical signals, creating opportunities for those who detect them first. ### How AppliedXL Tracks Hidden Risks AppliedXL uses AI and human expertise to monitor clinical trials in real time, identifying key changes like enrollment surges or early terminations. By mapping relationships across 100+ event categories covering 22,000 organizations, 26,000 drugs and targets, and 5,800 diseases, the system uncovers patterns that help anticipate risks before they become public knowledge. ### Why Small Deviations Matter With over five years of historical trial data, AppliedXL deciphers the impact of subtle shifts, revealing patterns that can indicate future risks. Even seemingly minor deviations, such as delays beyond 150 days, which increase termination risk by 41%, or enrollment drops of 75%, which double the chance of early failure, can expose operational vulnerabilities. By identifying these anomalies early, biopharma companies and investors can stay ahead of industry news cycles. However, traditional monitoring remains highly manual and often overlooks these early warning signs. One key example is a sharp reduction in a trial's timeline, which frequently signals a higher risk of termination. This shift may reflect changes in trial outcomes, shifting priorities, or a sponsor quietly withdrawing resources before an official announcement. Such patterns often stem from interim data revealing low efficacy, rising adverse events, or strategic reprioritization. ### Dynamic Trial Timelines To ensure full visibility into previously detected risks, AppliedXL generates dynamic timelines for each trial, tracking key milestones and potential risks, such as start dates, enrollment updates, and protocol amendments. By mapping trial trajectories, the system highlights early warning signals often missed by conventional monitoring, enabling stakeholders to anticipate and address potential disruptions before they escalate. ### Detecting Signals Before Headlines By analyzing hidden signals from clinical trial registries, press releases, journal publications, and regulatory updates, AppliedXL identifies roadblocks before they impact drug development. Developed alongside biotech journalists, its AI agents replicate the rigorous research process of biotech analysts, detecting meaningful shifts that often go unnoticed, pinpointing critical changes before they make headlines. #### CONTINUE READING RESEARCHSeeing Risk Before It Becomes NewsINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial ChampionANALYSISClinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time Intelligence Explore Signals → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # From Public Record to Market Resolution URL: https://www.appliedxl.com/research/how-a-market-resolves New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## From Public Record to Market Resolution Prediction markets settle against questions defined before trading begins. AppliedXL supports that process by monitoring the public record, evaluating evidence against the contract terms, and producing a documented resolution recommendation. The exchange independently reviews the analysis and remains the sole and final adjudicator under its rules. This example shows how the process may apply to a market tied to a clinical-trial outcome. All names, figures, dates, prices, and disclosures shown below are illustrative unless otherwise identified. 01Monitoring ### Relevant public sources are monitored over time. AppliedXL monitors public clinical, regulatory, and scientific sources for developments that may affect existing or potential markets, including trial updates, amendments, regulatory records, and announced results. Most updates are not relevant to an open market. Potentially material events are flagged for review rather than treated as conclusive. The process is limited to public information. AppliedXL does not use identifiable patient data, confidential clinical records, material nonpublic information, or undisclosed information from sponsors or other third parties. MONITORING · LAST 24 HRS 0+12Trial updates 0+47Company announcements 0−4Sci. abstracts 0+8Reg. filings 0+2Flagged for review 02Curation ### Potential markets are evaluated before listing. Before a market is listed, potential contracts are assessed for whether the outcome can be defined objectively, the endpoints and timing are clear, the controlling public sources can be identified, and potential effects on patients, recruitment, investigators, or clinical conduct have been considered. AppliedXL may evaluate candidates and provide recommendations. The exchange independently decides which markets to list and sets the final contract terms. Candidate A · late-stage trial with predefined endpoints Fixed design at listing Recruitment complete Clearly defined endpoint Identified public source Defined reporting window Candidate B · trial with an adaptive design Fixed design Recruitment complete Clearly defined endpoint Public source available Reporting window defined Candidate C · trial with uncertain recruitment and timing Recruitment complete Defined reporting window Clearly defined endpoint No recruitment concerns 03Contract Terms ### The controlling terms are established before trading begins. Before trading begins, the official contract defines the question, what constitutes YES and NO, the event deadline, the controlling sources, the applicable endpoint criteria, and how amendments, delays, corrections, or conflicting records will be handled. Those terms, together with the exchange’s rules, govern the outcome. Tap the card. Exchange · TRIALRESULTSLive Will [Drug] for [Disease] meet both specified primary endpoints in its Phase 3 trial by [Date]? 41¢Tap for resolution basis →The displayed price represents current trading activity. It does not determine how the contract resolves. Resolution basis · established at listing Controlling sourcesThe public sources identified in the official contract terms Required resultBoth specified primary endpoints meet the criteria defined in the contract Applicable disclosureThe result reported through the source hierarchy named in the contract Event deadlineThe qualifying result must be publicly reported by the deadline Review periodThe exchange may take time after the deadline to assess the record; it does not extend the deadline ◆ The official exchange contract terms and rules govern in every case 04Evidence Review ### Potentially decisive information is checked against the contract terms. When potentially decisive information becomes public, AppliedXL compares the evidence with the criteria established in the contract. Company language alone does not determine the result. The analysis focuses on the underlying endpoint data, publication timing, controlling sources, and any other conditions required by the contract. Company disclosure · detected Aug 1 2026 "Pivotal trial delivers encouraging results and may support a new treatment option." …median overall survival did not reach statistical significance (HR 0.91, p = 0.14) versus the control arm… Resolution check Tracking resolution Baseline · locked at listing Reported · what came in Audit 05Human Review ### A specialist reviews the analysis before it is submitted to the exchange. A qualified reviewer examines the contract terms, public sources, extracted evidence, timestamps, amendments, and any conflicting records before signing the recommended determination. The basis for the recommendation is recorded in a time-stamped audit trail. This completes AppliedXL’s analysis but does not settle the contract. ✓Monitoring ✓Curation ✓Contractlocked ✓Evidencedecisive ✓Sign-off →Exchange AnalysisReviewer · biopharma resolution desk Recommended determination NO — a required primary endpoint was not met; the disclosure was also published after the stated deadline Sign evaluation AppliedXL reviewers are prohibited from trading in prediction markets and may not hold a financial interest in the outcome. Audit trail · AppliedXL · immutableISSUED sourceprimary records captured · registry + sponsor disclosure (sha256 7b1d…) checkedeach criterion matched against the record · verdict decisive signedreviewer confirmed · no override issuedrecommended determination + evidence trail → exchange ▼ submitted to the exchange Exchange outcome review · independentSETTLED receivedthe exchange ingests the recommended determination + evidence reviewedindependent review under the contract terms and exchange rules · the exchange may accept, request further evidence, or reach a different conclusion finalizedthe exchange finalizes the outcome · contracts pay out NO 06Exchange Review ### The exchange independently determines and finalizes the outcome. AppliedXL submits its recommendation and supporting evidence to the exchange. The exchange independently reviews the record and may accept the recommendation, request additional evidence, or reach a different conclusion under its rules. The exchange remains the sole and final adjudicator, and no contract pays out until it finalizes the outcome. AppliedXL Recommended determination + supporting evidence The exchange Independent outcome review · final adjudication ### A documented path from source to recommendation. The record shows which sources controlled, what evidence was found, how it was evaluated, who reviewed it, and what recommendation was submitted to the exchange. ### Every settlement, this defensible. Source-linked resolution for event markets — from continuous monitoring to a signed, auditable record. Explore ResolutionBack to research ↑ --- # Original Intelligence URL: https://www.appliedxl.com/research/original-intelligence New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → APPLIEDXL RESEARCH SPECIAL REPORT · JULY 2026 Original Intelligence What Stays Valuable When AI Can Deliver Anything For two centuries, information companies were paid for two things at once: originating what is true, and delivering it. AI has split the bundle and priced the halves at opposite extremes. Delivery is becoming a commodity. Origination is becoming the whole business. BY FRANCESCO MARCONI For the full interactive version of this report and the downloadable data, visit www.appliedxl.com/research/original-intelligence ### CONTENTS I The lineage II The split III The craft IV The playbook V Delivery VI The prices VII The inflection VIII The domains IX The moves X The field XI The choice COMPANY / RESEARCH / THESIS ## Original Intelligence What Stays Valuable When AI Can Deliver Anything For two centuries, information companies were paid for two things at once: originating what is true, and delivering it. AI has split the bundle and priced the halves at opposite extremes. Delivery is becoming a commodity. Origination is becoming the whole business. JUL 2026 · FRANCESCO MARCONI↓ DOWNLOAD THE REPORT (PDF) In the spring of 1850, a man named Paul Julius Reuter set up shop in the German border town of Aachen, at the point where the telegraph line ran out. The wire reached Aachen from the east and reached Brussels from the west, but between the two lay about a hundred miles of Europe with no line at all, and that gap was Reuter’s whole business. Each afternoon he waited for a bird to come in over the rooftops. Tied to its leg was the closing price of the Paris stock exchange, carried by wire as far as Brussels and then flown the rest of the way, and the pigeon crossed the gap six hours faster than the mail train. For those six hours Reuter knew what the Paris market had done and no one else in Germany did. A fast pigeon was worth more than a telegraph office, because it reached the one stretch the telegraph could not. The pigeon was never the point, and Reuter never mistook it for the point. His edge did not last two years. The wire was extended across the gap he had built his business in, and the day the line was complete the birds were finished. The men who lost were the ones who had fallen in love with the bird. Reuter had not. When the telegraph swallowed his advantage he took up the telegraph, moved to London, and opened an office beside the exchange, and the company he built there is the one we still call Reuters. Each turn of the machinery left another set of rivals behind: the pigeon men undone by the telegraph, the telegraph men by the wire, the wire by the terminal. Every generation mistook the machine for the prize. Every generation was wrong the same way. We are living through the largest such turn yet, and the industry has mistaken it for a funeral. Machines can now write without end and answer nearly any question, so the newsrooms and data companies are bracing to lose. They are hoarding what they have, suing over what has been taken, preparing for a smaller and meaner version of the work they used to do. Watch where the money agrees with them. The world is on track to spend roughly $600 billion a year to justify what it pours into AI compute, and almost nothing to expand the supply of verified, original facts that compute exists to process. Every dollar is going to the machinery that reads. Almost none is going to the thing worth reading. That is the misread, and it is expensive, because the companies bracing to lose are optimizing the exact half of their business the machines just made free. When any sentence can be generated for nothing, the sentence stops being the valuable thing. What holds its value is knowing what actually happened, and being able to prove it: the first contact with a true thing, before anyone else has it. That was always the real business. It is the one thing the machines cannot do. The telegraph could carry that Paris price anywhere on earth in seconds, but it could not stand in the exchange and watch the price settle. A person had to do that, and write the number down, before the wire had anything to send. We have built a far more powerful version of the same machine, and its limit is the same one. Machines can retrieve the truth now. They cannot produce it. There are two kinds of work in the information business. Origination: being first to a fact about the world. Finding what has newly become true, proving it at the source, and turning it into a signal someone can act on. Delivery: searching the recorded world, summarizing it, recombining it, and moving it back out in useful form. Origination puts a new true thing into the record; delivery moves what is already there. For nearly two centuries the two were bundled and sold as one product, at one price, and no one had to ask which half they were paying for. AI has pulled them apart and priced each at an opposite extreme, and if you cannot tell which half your revenue rests on, you cannot tell whether it is the half that is vanishing or the half about to be worth more than ever. The originating half has a name: Original Intelligence (OI). It is the scarce half, and the rest of this essay is about what that changes. AI is becoming extraordinary at delivery, moving through the recorded world at a speed no newsroom can match and drawing connections a person would never have time to find. What it cannot do is observe the world first, sit in the courtroom, call the source, notice the anomaly the instant it lands. However brilliant, delivery always runs on material that origination has already produced. The companies that win the next decade will be the ones that treat their origination as the scarce asset and use AI to produce more of it than humans ever could by hand. That is a claim about winners, and it can be tested two ways: against the last two centuries, which show how value has moved and who captured it, and against the machine itself, which shows why this time the thing left standing is origination. Start with the pattern. ### I. The lineageWhat has each era of moving a signal really rewarded? The Wire. Before the telegraph, distance was the enemy; news moved at the speed of a horse. The telegraph collapsed distance to near zero, and that changed what a message was worth. When everyone receives the same news at the same moment, the advantage shifts from transport to selection and speed. This is the era Reuter was born into, and the lesson of his pigeons generalizes: the winners owned the fastest way to move a valuable signal, and when a newer machine made that speed ordinary, the advantage moved on. The business was never the pigeon or the cable. It was the edge itself, wherever it had gone next. The Terminal. By the late twentieth century the wire’s output had become overwhelming. There was too much to read and no way to act on it in context. LexisNexis built on networked document delivery, FactSet on databases, Bloomberg on networked computing. The value moved again, from the feed to structured data, tools, and workflow. A terminal organized the world into fields you could interrogate, and put that intelligence where decisions were made. The Agent. The value is moving once more, and this time what changes is the receiver. In every earlier era the endpoint was a person: someone read the wire, someone worked the terminal. Now the endpoint is a mix: agents acting for people, agents answering other agents, sometimes no person involved at all. The person has not left; the person has moved up a level, from reading the signal to directing what reads and acts on it. And this is the moment the whole industry is bracing for as if it were the end — the reader replaced by a machine. Read correctly, it is not the end at all but the clearest signal yet of where the value is about to go. The agent is the most sophisticated delivery layer ever built. Like every delivery technology before it, it needs something to deliver. As retrieval becomes cheap and abundant, the scarce layer moves to what feeds it: verified, first-hand origination. The Eras of Scarcity1850sThe Wire1980sThe TerminalNowThe AgentScarce layerOriginal IntelligenceThe agent is the most sophisticated delivery layer ever built. Like every delivery technology before it, it needs something to deliver. As retrieval becomes cheap and abundant, the scarce layer moves to what feeds it: verified, first-hand origination.Q: What just entered the record that no one has read yet? Now put the eras side by side, and one rule appears. Each dominant information company started narrow, with one slice of data. Dun & Bradstreet, the business-credit bureau, began in 1841 with credit reports on merchants — and its innovation was not the data but the network of accountable named correspondents who filed it, a roster prestigious enough to include four future presidents, Lincoln, Grant, Cleveland, and McKinley, filing reports on the merchants of their districts. The name behind the claim was the product from the very first day. Poor’s, later half of S&P, began in 1860 with a book of railroad accounts. Dow Jones began in the autumn of 1882, in a basement beside the New York Stock Exchange, where three men wrote the news by hand. Charles Dow, Edward Jones, and Charles Bergstresser worked over stacked tissue and carbon, so one pass of the stylus threw a dozen faint copies at once. Boys ran the copies out to the banks and brokerages several times a day, each slip a few minutes fresher than anything else on the Street. They called them flimsies. The afternoon roundup took a name, the Customers’ Afternoon Letter, and seven years later that sheet became The Wall Street Journal. The company was never the tissue or the boys. It was the few minutes. Platts, today’s oil price benchmark, began in 1909 with a trade sheet for the young oil business. Each rode an emerging technology to scale, and each shift moved the value one step closer to the decision: first transport (moving the signal), then selection (picking out what matters), then structure and workflow (turning it into data and feeding it into the tools where work happens). A fifth step has opened above them, called simply action, where no person reads the signal at all; a system makes the decision itself, the trade placed, the approval issued. Picture these steps as a ladder, each step closer to the moment a decision gets made. That is the first of two patterns in the history, and it is this simple: over two centuries, the value has climbed the ladder, one step at a time. The Decision LadderTHE DECISION ↑Actionmakes the decision itselfWorkflowfeeds it into your toolsStructureturns it into a scoreSelectionpicks out what mattersTransporthands you the newsTap a rung to see what lives on it. The rung is set by what the product does with the record, not by the technology that delivers it. Every step, all the way up to a machine acting on its own at the top, does the same thing: it moves, organizes, or acts on a fact that already exists. Not one of them produces the fact. The higher the value climbs, the more it depends on the one thing none of the steps contain, the first observation that put a true thing into the record. Automate the whole ladder, let machines trade and approve and decide with no human involved, and you have only made the bottom step more valuable, because every step above it is still consuming what the bottom step produces. The ladder does not lead away from origination. It leads back to it. It is easy to draw the wrong lesson here. Reuters rode the wire and Bloomberg the terminal, so the moral looks like: own the transmission layer. But the layer was never the point. These companies won by owning whatever was scarce at the time, and scarcity does not stay put. Each transmission technology, once it spreads, stops being the bottleneck and becomes cheap infrastructure everyone has, and the advantage moves to the next hardest link. But does it stay scarce, when the same law just made transport, selection, and structure cheap in turn, turning each, once everyone had it, into a commodity no one will pay a premium for? This link is different. Every layer that got cheap was a way of moving or organizing information that already existed; each was a technology, and technologies spread until everyone has them. Origination works differently. It is the act of first contact with the world: reading the primary record, judging what in it matters, calling the source, putting a name behind the claim that it is real. That act does not spread that way, because there is no version of it that ends in software everyone can copy. It has to be performed again for every new fact, at the moment the fact appears. Delivery is a machine you build once and run until the next machine. Origination is a cost you pay every single time. It is the one link in the whole chain that never turns into a cheap commodity, because it is not a way of moving information — it is the act that puts the information there to move. There is a second pattern in the history, and it matters even more. Look at who settled each new layer. The Pony Express did not build the wire. Western Union did not build the terminals. Bloomberg was born on the trading desk: founded in 1981, its first terminals sat on Merrill Lynch desks by the end of 1982, and it never had a lower layer to climb from. We traced 235 information companies founded between 1792 and 2023 and logged the layer each began on. Of the 54 founded before 1950, none began at the two layers nearest the decision, workflow and action. Since 2005, more than one in four have, and the 2010s produced 53 new information companies, the largest cohort in the record. A layer opens only when its technology arrives; workflow had to wait for the computer. The pattern is what happens when it opens: the new layer is settled by companies born on it, not by the incumbents, the established players who already dominate the older layers, climbing up to it. Each generation is born closer to the decision than the last. Companies by Starting Layer and Year FoundedHover or tap any dot to identify the company.Source and methodology: AppliedXL analysis of 235 information companies, compiled from the lineages of the major news, financial and market data, credit and risk, and scientific publishing businesses. Each is dated to the founding of its original product and coded by the layer that product occupied: transport, selection, structure, workflow, or action. Dots are jittered within each layer for legibility. The red curve is a kernel-weighted running average of the starting layer, drawn from 1845, where the record becomes dense enough to average. Across the set, founding year and starting layer correlate at ρ = 0.41 (Spearman, n = 235, p < 0.001).DOWNLOAD THE DATA (XLSX) ↓ Incumbents do move, but look closely at which half of the work moves. Bloomberg has rebuilt its own delivery again and again, from desk terminal to data feed to machine-readable pipe, always in place. What it has never grown in place is a seat at a new domain’s record. Even Bloomberg News, built internally in 1990, read the same markets for the same desks. The new records were bought: clean energy intelligence with New Energy Finance, legal and regulatory intelligence with BNA. Thomson, a newspaper chain, became a legal and financial information company by buying West and then Reuters. Two centuries, one pattern: companies rebuild their own delivery, and they buy, found, or partner to reach a new record. They build their own pipes; they go elsewhere for new water. Through all of it, two kinds of work were bundled and priced as one. Every one of these businesses did original work, finding and verifying information, then wrapped it in a technology that carried it to the people who needed it. You never had to separate the value of the origination from the value of the delivery, because no technology had pulled them apart. One finally has. ### II. The splitWhat can retrieval do, and what can’t it? A model does more than look things up. It reasons, synthesizes, follows a chain of instructions, and recombines what it has read in ways no one bothered to before. All of that is real, and getting better every month. But at bottom it works the recorded past: it operates on what has already been written down. Retrieval has a hard limit, and it is the structural kind, not a gap the next model closes. A model works only with what has already been recorded, because it cannot observe the world directly. Give it two permits that are already filed and machine-readable and it will connect them well, often faster than a person. What it cannot do is surface the fact that is not yet in the record it can see, or the reading of scattered public facts that no one has yet assembled into a signal. On November 28, 2023, the Supreme Court of Panama struck down a single mining contract, and a mine that produced roughly one and a half percent of the world’s copper began to go dark. The company that owned it lost roughly half its value in the weeks that followed. Everyone called it a shock. It was not a shock. It was the last page of a story that had been unfolding in public for months: a disputed law, protests in the streets, a vote in the legislature, a court challenge anyone could have followed. All of it was visible. None of it had been assembled into anything you could act on until the price had already moved. The clearest sign was hiding in the most boring place imaginable. Every shipment of copper that leaves Panama gets logged, and the size of those shipments is public record — and copper was nearly three-quarters of everything the country exported. Month after month it held steady; then it collapsed toward nothing, visible in the shipping data in near-real time. The company that ran the mine did not even withdraw its own production forecast for the year until the first of December, days after the ruling. The supply was stopping in plain sight, in a public ledger, before the official numbers had caught up to it. Anyone who thought to watch could have watched it happen. Almost no one did, because noticing means knowing to read a customs ledger against a court ruling against a street protest, and knowing that the three are the same story. The facts were public. The reading was the rare part. Someone has to observe that and write it down first. Retrieval always comes after that first act of origination. Doesn’t AI already do discovery, the drug discovery, materials discovery, scientific discovery we keep hearing about? It does, in one sense: it generates novel candidates by searching and recombining what is already known. A model can propose a molecule no chemist wrote down. But a proposed molecule is a hypothesis, not a fact. What makes it true is a clinical trial: someone dosing real patients and observing what happens in the world. The model cannot run the trial, and cannot know the result until the result exists and has been recorded. That is the divide in miniature: AI is extraordinary at generating hypotheses from the record, and still cannot originate the fact that settles them. Origination is that second act, first contact with a truth the record does not yet contain. The honest complication is that machines are starting to observe too. Sensors, satellites, and real-time filing feeds now capture raw events cheaply and around the clock, and that part spreads and gets cheap like any technology. But raw capture is not origination. A satellite counts cars in a lot; it does not know the retailer is about to miss its quarter. A feed logs a filing the instant it posts; it does not know the buried clause changes the outcome. The alt-data business already lived this in miniature. For a while a satellite photo of a retailer’s parking lot was worth real money, because almost no one had one. Then everyone had one, the picture became ordinary and ignored, and the money moved to the few analysts who could look at the same image and know which chain and which quarter it was quietly predicting. The camera got cheap. Knowing what the camera was looking at did not. That knowing is a bundle of judgments no sensor makes: deciding what is worth observing, structuring the raw signal into a fact, verifying it well enough to put a name behind it. That is the layer that stays scarce, and it is why AI is leverage rather than threat: the machinery industrializes the capture and frees the scarce judgment to be spent across far more facts than a newsroom could reach by hand. The cost per fact does not vanish. It moves higher up, to the one link that was never a technology. So AI, for all its power, is the most sophisticated delivery technology ever built, the terminal’s natural successor, and like every one before it, it needs something to deliver. It also moves the whole competition. The old advantage was comprehensiveness of the archive; the new one is freshness, how fast a true thing enters the record and reaches a decision. That is a frontier retrieval cannot hold on its own. ### III. The craftWhat do information specialists actually do? Origination is a craft, and it belongs to a particular kind of worker. Call them information specialists: journalists and analysts trained to find what is new, prove what is true, and understand what it means before it is obvious. An information specialist finds what is different. Out of a flood of filings, disclosures, permits, trial records, and transcripts that all look routine, the job is to spot the one that is not: the anomaly, the number that should not be there, the change in wording, the quiet update that alters the picture. Machines are good at flagging the outlier; that part is pattern-matching, and it keeps improving. The harder judgment is which outlier matters, which deviation is a story and which is noise. That call turns on knowing the domain and the stakes, and it is a different kind of work than detection. An information specialist gets the dot that isn’t online yet. A single record is rarely the story. The story appears when a filing is set beside a lawsuit, a permit, a hiring pattern, a registry update. A model can join records that sit in front of it, and often finds links a person would miss, so the edge that stays human is not the joining. It is getting the dot that is not yet online, not yet filed, not yet written down, so that there is something new to connect at all. The best connections are the freshest, and the freshest have to be fetched from the world, not the archive. An information specialist thinks about implications. Knowing what happened is only the beginning. The value is in reasoning forward: if this is true, what does it change, who does it affect, and what does it signal before the market, the public, or the institution has absorbed it? Those three together reveal something. Original Intelligence is not only a talent; it is a procedure. A good analyst does not simply know things, they know the steps: which records to pull, in what order to cross-reference them, how to weigh each source against the others until the picture holds. Call that ordered sequence the human algorithm, the part a model can run once it has been written down but cannot supply on its own. And here is the concession the honest version of this argument has to make: yes, the method itself is software, and software spreads, so a competitor can copy the steps. But the steps are not the scarce thing. What the steps run on is: the live access to the record that a newcomer has not negotiated, the domain knowledge it took years to encode, and the accountable name that makes a buyer trust the output. You can copy the recipe. You cannot copy the kitchen, the suppliers, or the chef’s reputation — and those are what the recipe needs to produce anything. Suppose the question is whether an already approved drug could treat a disease it was never designed for — the kind of overlooked opportunity that never becomes news because no company has a reason to chase it. An expert does not answer by looking it up. She runs a sequence. Below the headline result of the trial the drug was approved on sits a secondary endpoint, a health measure the trial tracked but was not designed to prove, that moved when it had no business moving, a few points in a direction that means nothing unless you happen to know the biology of the second disease. She opens the trial’s registry record and finds an amendment slipped in eight months after patients were enrolled: the eligibility criteria widened, then narrowed again, the fingerprints of a sponsor who saw something and chose to say nothing. She pulls the filings that log the drug’s side effects and finds one that, read against the second disease, is not a side effect at all. It is the mechanism. Then she calls someone who ran a trial site, and learns the study was stopped for a reason that never reached the published paper. None of that is retrieval. It is origination, synthesis, and judgment applied to what is happening now. A model can assist every part of it, but left to run on its own, it can only report what has already been recorded. Being first to see what changed is a different job, and it is the one that pays. Case Study | The Human Algorithm: How an Analyst Finds a Repurposed DrugTHE QUESTIONCould a drug the FDA already approved treat a different disease — one it was never designed for — that no company is bothering to pursue?010203040506STEP 01FDA approvals · drug labelsStart with a drug that already worksPick a medicine the FDA has already approved for something. It’s already proven safe in people — that’s a huge head start.+ Show what the analyst actually checksBackSTEP 1 / 6Next step ### IV. The playbookWhat does an Original Intelligence company build? Most information companies, staring at AI, are asking how to become an AI company. That is the mistake. The winning move is to become an OI company, and pick up AI as the tool that makes it possible. An AI company competes on the delivery layer, against the best-funded firms on earth. An OI company competes on the one thing those firms cannot manufacture: verified origination, a true thing found first. The template is not proprietary; it is simply what we built AppliedXL to do, and the shape of it is available to anyone. Use AI heavily, the way a newsroom once used the printing press, as the machinery that lets a small team work at a scale that would otherwise be impossible, but aim it at producing new information rather than repackaging what exists. AI is the tool. OI is the product. Any company that already originates could run the same play. The method underneath is the same whoever runs it, and it starts with a rule most information businesses violate: go straight to the primary institutional record, the filings, clinical results, permits, and registries where market-moving signals surface before they become news, with no scraper and no third-party aggregation layer sitting between you and the source. Own that first contact, then turn it into product. What ships is one of four things: a signal that something material just changed, a benchmark, a probability forecast, or a resolution record — the settled outcome of a defined question with the proof attached. The OI ArchitectureSource → Deliver01SourcePull the primary institutional record — filings, clinical results, permits, registries — the moment it lands.02StructureTurn dense, technical documents into machine-readable events, each field tied back to the source.03DetectFind what is different in the record: the anomaly, the number that shouldn’t be there, the exception worth a second look.04AggregateJoin those events across time and entities — the connection a single filing can’t make alone.05VerifyValidate every output against the source record, check provenance, and route it through human review. Speed without verification is only noise.06ContextualizeReason about what it means: who it affects, what it signals about a market, and why it matters before the signal is obvious.07DeliverShip it where a decision is made — as a signal, indicator, forecast, or resolution record.In the terminal era a person did the middle by reading. Our systems do it, and can be asked directly. The rigor is journalistic, not merely algorithmic. Outputs are validated against source records, traced back to where each fact came from, and reviewed by human analysts before they ship, because speed without verification is only noise. OI is editorial work with verification built in from the start, not a data pipeline with a check bolted on at the end. If that sounds too modest to matter, consider what a journalistic method already prices. The benchmark that much of the world’s physical crude oil settles against is not set by an exchange. It is set by reporters. Every trading day, in a defined window that closes at 16:30:00 London time, price reporters at Platts collect the bids, offers, and confirmed deals that traders and brokers relay to them, many literally by phone, the exact time of the call recorded, and, applying published methodology and editorial judgment, publish a single number: the day’s assessment for Dated Brent. That number then flows through official selling-price formulas and derivative settlements into hundreds of billions of dollars of physical trade. It is not a summary of the market. For much of the market, it is the price. A reporter’s sourcing, structured into a defined procedure and stamped with a name, became the settlement layer for one of the largest physical markets on earth. That is journalism installed as infrastructure, not journalism decorating a market, and it is what origination becomes when it is done to a standard the market can build on. ### V. DeliveryHow is delivery rebuilt for the agent era? OI is only half the product. The other half is getting it to a decision in the form the decision needs, and that is the half everyone else is also racing to build, which is why it cannot be where you win. Delivery is the minimum price of entry. You have to do it excellently and it will never, by itself, set you apart. But one thing about delivery has genuinely changed, and it is stranger than it sounds. For nearly two centuries, delivery meant formatting a signal for a person to read. In the agent era the reader is increasingly not a person at all — it is another model, querying and cross-referencing and acting with no human looking at the page. That inverts the whole design. The signal now has to be structured, source-linked, and directly interrogable, so a machine can consume it and trace it back to the record it came from. And follow what that does to the economics. When the reader is a machine, delivery gets commoditized faster than ever, because any capable model can retrieve, summarize, and format — those were the hard human skills delivery used to charge for, and now any competitor’s model has them too. When every delivery surface is roughly as good as every other, the only thing left to compete on is the quality of what flows through them: the originated fact underneath. So the machine audience does not threaten origination. It strips the value out of everything above origination and leaves it nowhere to pool but the one input a machine cannot produce. Delivery, Rebuilt for the Agent EraTwo Ways at OncePushPullPush — structured feeds, tuned to an audience of one.You pick the sources, events, format, and cadence. Intelligence arrives the moment something matters — the customization of the best terminals, narrowed from a shared screen to a feed built for one reader.SOURCEYOUPush delivers the origination the moment it happens. Pull lets you interrogate it on demand. ### VI. The pricesWhy is content going free while intelligence stays priced? Somewhere in every one of these companies is a number that used to be safe and is now falling. A subscription that renews a little less often. An ad rate that softens each quarter. A licensing fee a customer is quietly starting to question, because the thing it paid for is now something a model hands out for free. That is not a distant threat; it is the current quarter. This section is about why that number is falling, and why a different number on the same income statement is about to rise. Commodity content is heading toward free. A model generates unlimited amounts of it, and the cost of one more article, summary, or explainer is falling toward zero. Not all of it collapses at the same rate (distinctive reporting and trusted brands still hold subscription value), but the vast undifferentiated middle does, and it is that middle most attention businesses were built on. You cannot charge for, or advertise against, what the reader gets for nothing. And the erosion runs one layer deeper: as models ingest and reproduce open and licensed content, raw information itself stops being defensible, and durable value migrates to structured, decision-grade intelligence that drives a customer’s revenue. Intelligence has not moved the same way. It still commands high prices, because it is a different product: a structured, verified signal a professional pays for because it drives a decision with money on the other side. A portfolio manager does not want more content. They want the one thing that changes what they do next, in a form they can act on and trust. This split is older than the technology forcing it. John Moody published a manual of statistics, tables of railroad figures anyone could tabulate, and the 1907 panic wiped it out; he lost the business. He came back in 1909 selling something the tables could not: a letter grade, a compressed judgment on whether a railroad’s bonds would pay. The compilation had died in a panic; the opinion survived every panic since, and in 1975 the SEC wrote the ratings agencies into regulation, converting private judgment into required infrastructure. Data commoditized first, and judgment kept its price, a century before a model made the same thing happen to everything else. The DivergencePrice gap: 70 ptsTODAYDRAG → AS AI GETS BETTERLATERContent (going free)Verified intelligence (staying priced)Illustrative: drag the slider. As AI improves, the cost of generating commodity content falls toward zero, while the price of a verified, accountable signal a professional will stake a decision on holds and separates. The shaded band is the widening gap. The fair objection is that AI will not stop at content; it will get cheap for the part that is really retrieval and formatting. But the scarce core is different. What a professional pays a premium for is not the packaging of a signal but the assurance that it is real: that someone with domain expertise found it in the primary record, verified it, and put their name on it. As content collapses in price, the assurance does not. It becomes the product, and it attaches to a specific class of material: signals structured from primary institutional records, the regulatory filings, court documents, permits, and registries, before they surface as news. You cannot reproduce that by ingesting the open web, which is why it survives the repricing while commodity information does not. Return, now, to the number this essay opened on, because the divergence explains it. 2026 · Where the Money GoesReading vs. CreatingAI infrastructureTHE DELIVERY LAYER~$700BProducing new intelligenceTHE SCARCE LAYERno comparable line itemThe world is building the machinery to read at unprecedented scale — and there is no comparable line item for creating the signal worth reading.Sequoia’s David Cahn estimates ~$600B in annual revenue is needed just to justify the compute spend, and calls GPU computing “increasingly a commodity, metered by the hour.” Value migrates to what feeds a commoditizing layer. ### VII. The inflectionWhat survives when the bundle splits? The essay’s central claim has a sharp consequence for the companies that dominate professional information, and it is not the one usually drawn. The reflex is to say they are exposed. What is really happening is that their old bundle is being split into its two halves, and the halves are moving in opposite directions at once. One is losing its scarcity. The other is becoming the scarcest asset in the industry. Everything depends on which half a company decides it is in: whether it believes the value was in holding the record, or in being the one who read it first. The half that is losing scarcity is the stored past. The durable advantage used to be the archive, a structured record accumulated over decades, and that record is now something an AI-native competitor can license and cross in a single acquisition. But note what commoditizes, because it is narrower than it first appears. What gets cheap is the warehouse of already-recorded facts. What does not get cheap is the live act this essay keeps returning to: first contact with a fact the record does not yet contain. The archive was delivery, frozen. Origination is the live version, and it does not sit in the warehouse that just lost its value. The archive stops being the product and becomes the fuel: decades of structured, domain-specific records are exactly what an AI system needs to recognize what is anomalous in the record arriving today. Sold as a static pile, the archive is commoditizing. Turned on the present as context for producing new signal, it is an advantage a newcomer cannot buy. That is the split, and it lands on the incumbent as a live capability, not a stored one. The frontier moves from how complete your history is to how fresh your signal is, and freshness is not something you own, it is something you perform, every time, at the moment the fact appears. Think of what that word actually means. It is a person, or a system a person built, reading a filing the hour it posts and knowing what in it matters — the same act as a reporter standing in a courtroom, the same act as Reuter watching a price settle. It cannot be done once and stored away. It has to be performed again tomorrow, and the day after, for every new fact the world produces. The organizations built to optimize the completeness of the record now find the competition has moved to the one link they always treated as a cost: the standing apparatus for being first. One half is losing its scarcity. The other is becoming the scarcest asset in the industry. That is why the surviving half belongs to them more than to anyone else. Two things survive the split, and the established players hold both. The first is the customer: the installed base, the accounts already won, the reach a new entrant would spend a decade building. The second is the standing apparatus itself, the negotiated feeds and held credentials that keep the primary record flowing in the moment it is written, the one input origination cannot proceed without and cannot be gotten by scraping the open web. So the incumbent’s position resolves into a single asymmetry: the hard half to build is the intelligence layer, which takes editorial discipline, domain mapping, and standing presence at the source that no tool hands a competitor; the easy half is reach, which the incumbent already owns. The two halves need each other, and unequally. That is why, again and again, the companies that hold the origination layer get partnered with rather than competed away. There is a third property of the seat that the history makes unmissable, and it is one capital cannot buy: neutrality. In 1986 Citicorp bought Quotron, then the icon of market data with a hundred thousand terminals and sixty percent of the market, on the theory that banking was an information business. Its largest customer, Merrill Lynch, would not keep feeding its quotes and orders through a box owned by a competing bank, declined to renew, and put its money instead into a startup called Bloomberg. Quotron lost money every year after; Citicorp eventually paid Reuters over a hundred million dollars to take it away. The technology aged badly too, which is the honest other half of the story, but the founding wound was ownership. A seat at the record has to be trusted by the whole market it serves, and the moment a market participant owns it, the rest of the market leaves. That is no historical curiosity but a structural fact: the origination layer is most defensible in independent hands. None of this forces a particular path, but two centuries of the industry price each option: incumbents have always rebuilt their own delivery in place, and new origination has always arrived by transaction, an acquisition, a purpose-built entity, or a partnership. So assembling origination in-house is not a retooling project. It is a founding, with a founding’s timeline and odds, run inside a company optimized for something else. Buying or partnering delivers the same layer at the speed of a contract, which is the route the record favors. And waiting for origination to commoditize is waiting for the one thing the pattern has never delivered. On the credit question alone, forty-one companies have re-answered it since 1841. Gaps between new entrants once ran nineteen years; since 2000 the median is two. The forty-one were not fighting over one chair; the question kept opening new records and new layers, and each was claimed as it opened. Seats do occasionally flip — in 2009 Saudi Aramco moved its US crude pricing off a Platts marker it had used since 1994 to Argus’s Sour Crude Index — but look at what that took: a sovereign oil producer, a structural dislocation at Cushing, and even then only one regional formula, with Brent left untouched. The price of moving a seat is measured in sovereign decisions and decade-long dislocations, not in a competitor shipping a better tool. The seat is per-domain, and it is filled once. That last phrase was once a literal contract. In 1859, Reuter and Wolff (the latter had earlier worked inside Havas’s Paris office) met the Havas heirs at the Hôtel Bullion and carved Europe into exclusive territories; in 1870 the three formalized it into the Ring Combination, dividing most of the world outside North America among them and holding the arrangement for roughly seventy-five years. AP spent decades trapped inside it, and its general manager Kent Cooper fought his way to full independence only in 1934, writing the campaign up in Barriers Down, arguing that exclusive news territories were barriers to the truth itself. The seat, in other words, has always been a thing that gets claimed and held per domain. The only difference now is that it is enforced by an installed workflow rather than a treaty, and contested by product rather than by cartel. Print monetized distribution. Digital monetized data. Social monetized attention. The next era will not monetize information at all. It will pay for producing it. ### VIII. The domainsWhere does the next record open? If seats are claimed per domain, the useful question is which domains are opening. The same dataset that tracks the layers answers it, coded a second way: not by how close each company sat to the decision, but by the subject it covered and whether it served a professional or a general audience. Read that way, the birth record tells a story most people in the industry would get wrong. Start with the founding era, because the common picture of it is inaccurate. The reflex is that professional information began as a finance story, Wall Street data for Wall Street firms. It did not. Before 1900, health and life sciences accounted for 32 percent of professional-information births, exactly matching finance. Lippincott began publishing medical texts in 1792; The Lancet launched in 1823. They stand in the record beside Poor’s and Dun & Bradstreet, from the very beginning. The origin was never Wall Street alone. It was Wall Street and the clinic, side by side. Where Information Companies Are Born, by DomainHover or tap any segment for counts; tap a legend entry for domain totals.Source and methodology: AppliedXL analysis of 235 information companies, 1792–2023. Shown: the 170 professional-information companies; the 65 audience-media businesses are excluded pending subject coding. Each company is dated to the founding of its original product and stacked by decade and domain. Finance: market data, ratings, indices, research. Health & Life Sciences: scientific publishing and clinical intelligence. Security & Systemic Risk: cyber, ESG, sanctions, risk ratings. Energy: commodity prices and energy intelligence. Legal & Policy: legal and regulatory research. Other professional: market research, supply-chain and enterprise data.DOWNLOAD THE DATA (XLSX) ↓ What happened to health next is the mechanic worth understanding. It fell from a third of professional births to zero for the fifty years from 1900 to 1949, and stayed near zero for eighty. Then, between 2000 and 2007, trial registries and mandatory results disclosure arrived, and a domain that had produced almost no companies for three generations began producing them again within five years. Health is now 23 percent of professional births since 2005, and five of the ten companies born so far in the 2020s are health companies. A domain, in other words, is not a permanent market. It is a function of whether its primary record exists and is readable, and you can watch it open and close. And that turns the record into a forward signal. Wherever a mandatory record is being created right now, a birth cohort is roughly five years behind it. Carbon registries, emissions disclosure, AI incident reporting: each is a record coming into existence, and each marks a domain that has not yet been fully claimed. Finance needs a correction too. Its dominance was real, but it was one phase, not the nature of the business. Finance peaked at 55 percent of professional births in the terminal era of 1985 to 2004, and has fallen about twenty points since. The instinct that “information business” means “financial data” describes a twenty-year window, now closing, not a permanent fact. Two patterns in this second cut do more than decorate the argument; they validate it. First, the rule that new layers are settled by new companies holds one level up: new domains are settled by new companies too. Security and systemic-risk intelligence barely existed before 1985 and has run at 12 to 15 percent of professional births since, built almost entirely by companies born into it, not by incumbents extending sideways. The thesis is true at two levels of aggregation at once. Second, the entire modern acceleration is professional. Audience media has held flat at roughly a fifth of births for seventy years; every bit of the climb to the 2010s peak came from the professional side. Building B2B intelligence does not go against the direction of the industry. It is what the birth record has been doing for two generations. Put the two dimensions together and the framework becomes a map. Cross a domain’s momentum with how recently its highest layer was settled, and the open seats become visible: health at the action layer is being claimed now, while energy and materials show records being created without a corresponding wave of companies yet, which is either a gap in the data or a gap in the market. Either way, it is where to look. ### IX. The movesWhat do you actually do on Monday? Build B2B products. Sell structured intelligence to the professionals who will pay for it, instead of selling advertising against content that is increasingly free. The reader economy is being commoditized by the same models flooding it. The professional economy is not, because what a professional needs is the one signal that changes a decision, and that was never a page view. Monetize data with AI. Most established information organizations already sit on something rare: a body of records they have the authority to read, gather, or produce. That material used to sit idle, too vast to turn into product economically. AI changes the math — turning a cost center into a franchise. The winners are not the ones with the most data; ownership alone no longer protects anyone. They are the ones that execute intelligence on the data they already have the right to. Know who is buying. Scarce is not the same as valuable; something is only worth producing if someone pays for it. Origination has three buyers forming at once, and all three are buying the same thing, a verified fact defensible line by line, for different reasons. Professionals pay for decisions. A portfolio manager, a regulator, a clinician needs one accountable answer with money or lives on the other side, and will not stake that on a model’s unsourced guess. AI platforms pay for ground truth. The models flooding the world with cheap content need verified, source-linked facts to check themselves against; a system that generates fluent text has every incentive to buy the one thing it cannot generate, proof that a claim is real. Prediction and resolution markets pay for settled outcomes. A market on whether a drug was approved, a contract awarded, a target met needs an authoritative source to resolve against, and that source is precisely an origination layer that can say what happened and show the evidence. Three buyers, one product. None of them is paying for delivery. Together these mark the real shift: the advantage of information companies is moving from data ownership to intelligence execution. Anyone can run a record through a model. Turning that into a signal a professional will stake a decision on is the hard part, and the part that compounds. Execution is the new ownership. ### X. The fieldIf everyone is building the same pipes, what gets rare? Look across the market for professional information and the convergence is striking. Very different companies are all racing to build the same thing: the best AI-driven way to find, query, and reason over information. AI companies are building workflow tools for specific professions. Established information companies are putting AI in front of their own data. Newer entrants compete on search. This is not a story of winners and losers among them. Each is competing to be the best way to retrieve and reason over information, the delivery layer, and each is only as valuable as the Original Intelligence flowing into it. As the market fills with excellent, competing delivery surfaces, the layer that grows scarcer is the one beneath them all. Everyone is racing to build the pipes. The water is what gets rare. ### XI. The choiceWhat is in front of every Original Intelligence company? News organizations and information companies are the original OI companies. They have spent decades, some more than a century, building the one thing AI cannot manufacture: the ability to find what is true and new and turn it into something a person can act on. That is not a sentimental claim about the value of journalism. It is the coldest economic fact in this essay. The scarcest asset in the information economy is the thing these companies already know how to make, and too many of them are spending this moment on defense, mourning a business that is not the one that is dying. Because here is what should change the whole calculation: the winning move asks almost nothing they do not already have. It does not mean building an AI lab or out-engineering the frontier labs. The hard, compounding part was never the technology — it was the editorial judgment, the verification, the standing at the source, and the trust that comes from a name attached to being right. Those took a century to build and cannot be bought. The machinery that turns them into intelligence at scale is the part that is now cheap, and it can be assembled in a quarter. The industry has the impossible half already. It is being asked to pick up the easy half and refusing, because it has mistaken the easy half for the whole game. And the window does not stay open. Every domain has a primary record and a first mover who claims it. In each one, the intelligence layer gets built once, on whoever holds the sources and moves first, and everyone who arrives after buys access to a seat someone else is already sitting in. It stays one seat because the buyer wants one accountable answer per question, and a verified feed, wired into a workflow, is an installed fact the next vendor has to argue against. That is not a forecast. It is already happening, vertical by vertical — one domain’s seat claimed this quarter, another’s the next. So the choice is smaller and starker than the funeral makes it look. The tool everyone fears is the tool that makes the scarce thing scalable for the first time in the industry’s history. Point it at the cheap half and you help commoditize yourself. Point it at the expensive half, the finding, the proving, the being first, and the thing that felt like the end becomes the largest lever anyone in this business has ever been handed. The facts are public. They always were. The reading was always the rare part, and it is about to become the only part anyone will pay for. Machines can retrieve the truth now. They cannot produce it. That work belongs to the originators, and it always has. The only question that remains is whether they will pick up what is theirs before someone else sits in the seat. There is a clock on that answer, and it is running now. ↑ APPLIEDXL Intelligence infrastructure for the information industry. appliedxl.com · support@appliedxl.com © 2026 AppliedXL. All rights reserved. × Get your customized intelligence strategy playbook. START ASSESSMENT (3 MIN) → ↑ --- # Prediction Model: Six Clinical Trial Case Studies URL: https://www.appliedxl.com/research/prediction-model-six-clinical-trial-case-studies New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## AppliedXL Prediction Model: Six Clinical Trial Case Studies with Full Explainability Six clinical trials tracked from first filing to outcome. Each case reconstructs how the model's probability shifted in response to registry events — and whether the final call matched reality. Covers Roche, Regeneron, Abivax, Akeso, Sanofi, and Praxis. 06 JUN 2026 · APPLIEDXL RESEARCH (APPLIEDXL) · INTERACTIVE Six case studies Read full overview 01 ### Primary Endpoint Did the drug clear its primary efficacy hurdle? Click on a node to see the model explanation 02 ### Clinical Significance Was the treatment effect large enough to matter in practice? Click on a node to see the model explanation 03 ### Trial Completion Will this trial run its full course or terminate early? Click on a node to see the model explanation 04 ### Regulatory Approval Will the drug ultimately secure FDA approval? Click on a node to see the model explanation #### CONTINUE READING PAPERAppliedXL Achieves State-of-the-Art Clinical Trial Prediction With Domain-Specific Agentic AIANALYSISClinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time IntelligenceINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion Explore Forecasts → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Seeing Risk Before It Becomes News URL: https://www.appliedxl.com/research/seeing-risk-before-news New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Seeing Risk Before It Becomes News Execution failures rarely appear suddenly. They build quietly inside clinical programs until they spill into earnings calls, regulatory filings, or headlines. AppliedXL identifies operational risk long before it becomes public. 07 DEC 2024 · APPLIEDXL RESEARCH (SIGNAL INTELLIGENCE TEAM) · 8 MIN ### Key Takeaways Delays beyond 150 days raise termination risk by 41% Enrollment drops of 75% double the likelihood of early failure Compressed timelines often precede strategic withdrawal or unfavorable interim data Three-layer intelligence system: Event Detection, Context Enrichment, Editorial AI Execution failures rarely appear suddenly. They build quietly inside clinical programs, missed milestones, subtle enrollment shifts, unexplained delays, until they spill into earnings calls, regulatory filings, or headlines. By the time the market sees a "major event," the underlying issues have already compounded. Most teams detect execution risk too late. That delay translates into lost time, higher costs, and eroded credibility. What they need is earlier, verified intelligence they can act on with confidence. AppliedXL was built for this reality. Our system identifies operational risk long before it becomes public, giving clinical, R&D, BD, and investment teams the advantage of foresight. ### The Hidden Signals That Decide Drug Success or Failure Every day, thousands of trial updates are posted to the U.S. Clinical Trials Registry. Most appear routine, but small deviations often carry significant meaning. A change in enrollment, a shift in anticipated timelines, a new status code, these details quietly reshape the future of a study. Traditional monitoring reviews these updates manually and inconsistently. As a result, the earliest signs of trial disruption are often missed. AppliedXL makes those early signals visible. Our system continuously tracks more than 100 event categories across 22,000 organizations, 26,000 drugs and targets, and 5,800 diseases, mapping relationships and surfacing anomalies that indicate rising operational risk. Enrollment surges, sudden pauses, narrowing timelines, protocol amendments, shifting geographies, each is interpreted through the context of five years of historical patterns. Why does this matter? Because small deviations consistently predict future outcomes: Delays beyond 150 days raise termination risk by 41% Enrollment drops of 75% double the likelihood of early failure Compressed timelines often precede strategic withdrawal or unfavorable interim data These are the early signals that matter most, and they appear long before formal disclosures. ### How AppliedXL Detects Emerging Risk AppliedXL combines three layers of intelligence to turn raw updates into actionable insight. Event Detection, capturing change at the moment it happens Our system monitors trial updates in real time, layering expert judgment to isolate the changes that actually matter. Each event becomes part of a structured, comparable stream of proprietary signals. Context Enrichment, revealing meaning behind the movement Signals are connected historically and competitively. A single update is situated within a trial's full trajectory, its sponsor's behavior, its mechanism of action, and its therapeutic landscape. Raw change becomes defensible insight. Editorial AI, validating and scaling trusted intelligence Built with biotech journalists and analysts, our AI agents model the rigor of human research. They cross-reference sources, sharpen reasoning, and ensure accuracy as insights scale. Together, these layers make data fast, connected, and trusted. Three-Layer Intelligence System Event Detection Capturing change at the moment it happens Real-time trial monitoring Expert-curated filters Structured signal stream Context Enrichment Revealing meaning behind the movement Historical comparison Competitive analysis Mechanism mapping Editorial AI Validating and scaling trusted intelligence Cross-reference verification Reasoning refinement Accuracy assurance Together, these layers make data fast, connected, and trusted ### What This Enables: Continuous Insight, Always in Context AppliedXL strengthens decision-making across the entire research lifecycle: Contextual Depth, Every insight is traceable to its history, supporting clear, evidence-based interpretation. Always-On Monitoring, Real-time alerts surface emerging risk before it compounds, helping teams avoid surprises. Role-Relevant Personalization, Analysts, medical teams, BD groups, and investors receive intelligence aligned to their specific workflows and priorities. ### A Clearer View of Clinical Risk By analyzing hidden signals across trial registries, publications, press releases, and regulatory updates, AppliedXL exposes roadblocks before they disrupt development. Dynamic timelines track each study's trajectory; anomaly detection highlights fractures early; contextual AI clarifies why a shift matters. The result is a living picture of trial execution that updates continuously, allowing teams to intervene earlier, plan more effectively, and make decisions with confidence. Major events are visible only after underlying issues have compounded. AppliedXL shows you those issues while they are still small enough to change. #### CONTINUE READING RESEARCHThe Hidden Signals That Decide Drug Success or FailureQUANT RESEARCHSpillover Risk and Readthrough Alpha: A Quantitative AnalysisINTELLIGENCEAppliedXL Response to FDA Drug Repurposing Initiative: 5 Drugs With Clinical Evidence and No Commercial Champion Explore Signals → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Spillover Risk and Readthrough Alpha URL: https://www.appliedxl.com/research/spillover-risk-readthrough-alpha New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Spillover Risk and Readthrough Alpha: A Quantitative Analysis Registry anomalies precede press releases by 48-72 hours. This research examines the relationship between mechanistic linkages and asset repricing across 2023-2025 catalyst events. 07 DEC 2024 · APPLIEDXL RESEARCH (SIGNAL INTELLIGENCE TEAM) · 10 MIN ### Key Takeaways Registry updates provide a critical arbitrage window, often appearing 48-72 hours before formal press releases Direct Mechanism of Action (MOA) matches correlate with >40% volatility in small-cap peers following a large-cap failure 'Inverse Readthroughs' in duopoly markets (e.g., Obesity) offer high-probability hedging opportunities Neurology and Gene Therapy sectors demonstrate the highest sensitivity to 'Spillover Risk' Our analysis of major biotech catalysts from 2023 to 2025 reveals that mechanism-based spillover is the dominant driver of short-term alpha. Specifically, we found that small-cap companies with high pipeline concentration in a specific pathway experience a 3-4x greater valuation impact from a peer's failure than from their own early-stage data updates. ### The Signal Hierarchy Not all readthroughs carry equal weight. Our research identified a clear hierarchy of predictive signals based on their correlation with immediate repricing: Direct Mechanistic Validation represents the strongest signal. When Eli Lilly terminated its RXFP1 program, Tectonic Therapeutics (targeting the same receptor) lost ~12-40% of its value immediately. This confirms that markets price 'target validity' above indication differentiation. Platform Safety Contagion ranks second in volatility impact. A safety event in a shared delivery vehicle (e.g., AAV vectors) triggers indiscriminant selling. Sarepta's post-marketing safety event caused a 42% drawdown in peer Solid Biosciences, despite no direct link to the specific drug. Competitive Displacement provides the most reliable 'Inverse' signal. In crowded markets like GLP-1/Obesity, a challenger's failure (Structure Therapeutics dropping 50%) mathematically strengthens the incumbent's moat, correlating with immediate upside for leaders like Lilly. ### Combined Signal Analysis The predictive value increases significantly when Registry Signals are combined with Pipeline Concentration. Trials exhibiting 'Quiet' Registry Updates (e.g., endpoint changes on ClinicalTrials.gov without a press release) combined with >80% asset concentration in the target company represent the highest-alpha category. Example: Alnylam's registry update regarding HELIOS-B endpoints provided a signal of confidence that had direct readthrough implications for Ionis (IGNS) before the official data release. This combination forms the foundation of the AppliedXL early warning system. By monitoring the intersection of registry metadata and mechanistic exposure, we can identify vulnerable holdings before the headline hits the tape. ### Detection Timeline Our backtesting across historical readthrough events shows an average arbitrage window of 48-72 hours between a registry signal (e.g., trial termination or endpoint shift) and the mass-market press release. Lilly/Tectonic Case: The termination of the RXFP1 program was detectable via registry status changes prior to the full market digestion of the 'lack of clinical benefit' rationale. ### Therapeutic Area Variations Signal sensitivity varies by therapeutic area: Neurology (Alzheimer's/Parkinson's): Shows the highest sensitivity to 'Guilt by Association.' The collapse of Cassava's Simufilam caused a 28% spillover drop in Annovis, driven by shared skepticism of non-amyloid approaches. Gene Therapy: Highly sensitive to 'Vector Risk.' Safety events trigger sector-wide correlations regardless of the specific genetic target. Metabolic (Obesity): Dominated by 'Zero-Sum' dynamics. High efficacy from a challenger (Viking) directly correlates with drawdown in incumbents (Lilly/Novo). ### Methodology Validation We validated this framework against six major 'Readthrough Events' from 2023-2025. The model correctly identified the 'Exposed Peer' in 100% of cases where the peer had >50% pipeline concentration in the shared mechanism. This research forms the basis of AppliedXL's Spillover Risk Scoring, enabling real-time monitoring of mechanistic exposure across the global biotech landscape. #### CONTINUE READING QUANT RESEARCHSystematic Trading Strategies in Biotech: Early Risk Signals for Alpha GenerationANALYSISClinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time IntelligenceRESEARCHThe Hidden Signals That Decide Drug Success or Failure Explore Signals → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Systematic Trading Strategies in Biotech URL: https://www.appliedxl.com/research/systematic-trading-strategies-biotech New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / ARTICLE ## Systematic Trading Strategies in Biotech: Early Risk Signals for Alpha Generation Exploratory research on how structured clinical trial event data can support alpha generation through early instability detection, mechanistic readthrough, and volatility mispricing. 07 DEC 2024 · APPLIEDXL RESEARCH (QUANTITATIVE ANALYTICS) · 14 MIN ### Key Takeaways Operational disruptions correlate with abnormal stock declines and surface ahead of announcements, suspensions (−9.3%), enrollment holds (−9.6%), and delays (−11.7%) Mechanistic contagion effects are strongest in small/mid-caps with concentrated pipelines, peer moves of 12-28% observed following related trial outcomes Options markets systematically misprice volatility by overlooking real-time execution risk, creating exploitable dislocations AppliedXL tracks 120+ standardized clinical trial events that quantify operational drift and disruption This document summarizes AppliedXL's exploratory research on how structured clinical trial event data can support alpha generation in biotech investing. Using a limited dataset, internal backtests show directional correlations between structured event activity and subsequent stock performance, indicating potential predictive relationships to be validated through larger, investor-driven quantitative frameworks. ### Data Framework Dynamic Execution Signals: AppliedXL tracks over 120 standardized clinical trial events and sub-events that quantify operational progress, drift, and disruption, the triggers that often precede volatility adjustments. An exploratory proxy for execution risk is defined as the weighted interaction between the frequency and magnitude of operational events such as enrollment delays, site suspensions, or endpoint changes following primary completion. Baseline Priors for Risk and Volatility: Each trial is enriched with structured scientific and operational attributes to establish baseline priors. Historical patterns indicate that certain features consistently align with higher market risk: CNS programs combine scientific uncertainty with operational fragility, often producing the largest price swings; exploratory studies show higher variance, while pivotal trials, though steadier, trigger sharper volatility around key readouts. ### Strategy 1: Detecting Early Trial Instability and Hidden Terminations Thesis: Clinical trial registries often reflect early signs of instability and undeclared terminations before public disclosure or major catalyst announcements. Mechanics: AppliedXL parses ClinicalTrials.gov, PubMed, and corporate disclosures to detect anomalies such as enrollment pauses, repeated delays, missing milestones, protocol amendments, and 'stealth terminations', cases where trials cease progress but remain formally active. Why It Matters: Empirical studies show that operational disruptions correlate with abnormal stock declines and tend to surface ahead of announcements. A 2022 PLOS ONE study of 13,807 trials found that suspensions (−9.3%), enrollment holds (−9.6%), and development delays (−11.7%) were associated with statistically significant negative returns, especially among small-cap firms. A 2011 JNCI study similarly found sponsor stock prices diverging up to 120 days before public announcements. Alpha Source: Detection of execution-risk signals, identifying weakening trials and undeclared terminations ahead of consensus repricing. Illustrative Cases: Summit Therapeutics' Ridinilazole Phase 3 failure followed an enrollment shortfall 83 days prior to disclosure (stock −49%, Dec 2021). TG Therapeutics' Umbralisib program exhibited a stealth termination pattern 51 days before FDA withdrawal. ### Strategy 2: Trading Mechanistic Contagion (Readthrough Effects) Thesis: Trial outcomes from one company often coincide with valuation changes in others pursuing similar mechanisms, targets, or platforms. Mechanics: AppliedXL maps molecular targets, indications, and delivery platforms to identify clusters where one result historically or mechanistically affects peer valuations. Readthrough effects are strongest when companies share a close mechanism of action or modality and are amplified in small- and mid-cap firms with concentrated pipelines. Why It Matters: Market contagion is known but inconsistently modeled; trial-level mapping exposes ripple effects that broad sector models miss. Late-stage readouts, major safety events, or clear efficacy outcomes generate the most actionable signals, while early or ambiguous data yield weaker effects. Alpha Source: Contagion anticipation, systematically trading ripple effects across mechanistically linked equities before consensus repricing. Trading Use: Anticipate contagion moves by shorting peers exposed to likely negative readouts, going long validated mechanisms, or building balanced baskets ahead of expected catalysts. Illustrative Cases: Lilly's RXFP1 agonist termination was followed by a 12% decline in Tectonic Therapeutics, another RXFP1 developer (Jan 2024). Cassava's simufilam failure coincided with a 28% drop in Annovis Bio, a peer targeting similar Alzheimer's pathways (Mar 2025). ### Strategy 3: Exploiting Volatility Dislocations in Options Markets Thesis: Biotech options markets may systematically misprice volatility by overlooking real-time execution risk. Implied volatility (IV) reflects broad consensus categories ('Phase 3 = high IV') rather than trial-level dynamics. Market Inefficiency: Academic research shows persistent volatility mispricing around binary biotech events. Abbott (2013) found small-cap biotech options routinely overpriced pre-catalyst volatility. Wieczorek (2016) and Johnson (2024) highlighted how information asymmetry and investor overreaction distort risk pricing. Rossi (2025) observed systematic IV mispricing near major clinical milestones. Pattern: Markets tend to overprice volatility in stable, late-stage programs where execution risk is low and outcomes well understood, while underpricing it in fragile small-cap trials where hidden instability raises genuine uncertainty. The alpha lies in identifying this dislocation, the gap between market IV and the execution-informed probability that a trial will reach and read out its endpoint as planned. Risk Interpretation: Execution risk measures the likelihood that a trial completes as designed and yields interpretable results. Volatility risk reflects the magnitude of expected price movement upon disclosure. These are distinct, a trial can be operationally sound yet exhibit high outcome volatility, or vice versa. AppliedXL's data helps distinguish between the two and price options more accurately. ### Volatility Trading Framework | Scenario | Market Condition | Trade Implication | |----------|------------------|-------------------| | Strong execution, high IV | Market overpricing risk | Sell volatility (iron condor / short straddle) | | Weak execution, low IV | Market underpricing fragility | Buy volatility (puts / skewed straddles) | | Execution drift detected | Early instability | Adjust hedge intra-trial | Strategic Value: Attributes define structure, identifying which trials merit high or low IV. Events define timing, surfacing execution changes before consensus repricing. ### Conclusion AppliedXL's structured trial data supports systematic strategies across three dimensions: downside risk management through early instability detection, readthrough trading through mechanistic mapping, and volatility exploitation through execution-informed probability models. The analysis is illustrative, designed to demonstrate how our data architecture can support signal-based trading models for institutional investors. #### CONTINUE READING QUANT RESEARCHSpillover Risk and Readthrough Alpha: A Quantitative AnalysisANALYSISClinical Trial Data as Alpha: Building a Biotech Quant Trading Model With Point-in-Time IntelligenceRESEARCHSeeing Risk Before It Becomes News Explore Forecasts → APPLIEDXL RESEARCH ### See what the platform reads before it becomes news. Source-linked intelligence across regulated markets, scoped to your domain. Get startedBack to research ↑ --- # Who Will Monetize Truth? A Thesis For the Future of Information URL: https://www.appliedxl.com/research/who-will-monetize-truth New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / RESEARCH / THESIS ## Who Will Monetize Truth? A Thesis For the Future of Information The news industry isn't declining. It's being repriced around a single distinction: some companies sell awareness of what happened, others sell the ability to act on it first. The first charges $10 a month. The second charges $30,000 a year. THESIS · 01 MAR 2026 · FRANCESCO MARCONIDownload the full PDFAPPLIEDXL PRESSWho Will Monetize Truth?FRANCESCO MARCONIA THESIS FOR THE FUTURE OF INFORMATION · 2026 For twenty years, the story about media has been a eulogy. Revenue down. Newsrooms shrinking. Print collapsing. The story is accurate. It is also useless, because it describes the symptoms of a phase transition without identifying the transition itself. The news industry isn't gradually declining. It's being repriced around a single distinction: some companies sell awareness of things that happened. Others sell the ability to act on information before everyone else can. Same raw material, the same reporters, the same filings, the same data. Opposite economics. Content informs awareness. Intelligence informs a decision. $52BRevenue from companies selling intelligence, from Bloomberg, S&P Global, Moody's and peers, at 35%+ margins$21BRevenue from the entire U.S. newspaper industry, at margins near zero275:1What an intelligence terminal costs versus a consumer news subscription The Great Information Repricing Intelligence Hybrid Traditional Bubble = revenue The value didn't disappear from journalism. It migrated, from the content layer to the intelligence layer. When content becomes abundant, it loses pricing power. When everything is noise, the scarce resource is the ability to detect signal. Value always migrates to the scarce layer. ### Three species, one ecosystem Plot every major information company on two axes: what share of revenue comes from data and intelligence products, and how much revenue it generates per employee. Two clusters emerge. They are two different businesses. The companies in the middle, Dow Jones, the Financial Times, Schibsted, Hearst, discovered they contained both: a content operation and an intelligence operation, sharing the same newsroom but serving different buyers. 01The Intelligence BusinessSells the ability to act on information before others can. $5K to $32K per year.02The Attention AggregatorSells awareness of things that happened. Traffic collapsing 50%.03The Public GoodLocal accountability, investigative work. Needs a different funding model. Most media institutions don't know which species they are. The classification is the whole game, and it determines what happens to those who get it wrong. ### Fifteen questions about where value is moving The full thesis is structured as fifteen questions about where value, talent and power are moving in the information economy. The argument, in compressed form: Q01Where does value accrue?Content is free. Intelligence is not.Q02Who wins and loses among media institutions?Three species. Only one has pricing power.Q03What happens when AI agents become the primary consumers of news?51% of web traffic is now non-human. The audience has already left.Q04Is information advantage the new alpha?There's a gap between when a signal appears in public data and when it becomes a story. That gap can now be priced.Q05What does the intelligence layer look like outside finance?Bloomberg built the terminal for financial data. No one has built the equivalent for clinical trials, energy or cybersecurity.Q06If AI can hallucinate citations, what happens to the concept of the record?The factual record can now be fabricated with perfect confidence.Q07Are prediction markets the next major consumer of structured information?$40B in volume. They price the fact itself. The infrastructure to resolve those facts doesn't exist yet.Q08Is trust mispriced?The same FDA filing is worth $0, $50 or $50,000 depending on the decision it informs.Q09If this becomes a data game, what's the trade?AI companies spent $200B on infrastructure. They paid content producers 1.5% of that.Q10What happens when AI runs out of journalism to train on?AI is getting smarter about a world that no longer exists.Q11Can wire services capture the value they produce?The ones who structure their data for machines win. The ones who keep shipping prose won't.Q12What can't be automated?Observation is getting cheaper. Interpretation is not.Q13If AI writes all the articles, what is the newsroom actually for?The article is the exhaust product. The detection pipeline is the real output.Q14What happens when $1 of AI replaces $33 of freelance labor?The bottom fell out. But the top is higher than it's ever been.Q15What happens to the New York Times? What happens to Google?Both bets are working. For now. FULL THESIS Read the full thesis DOWNLOAD PDF #### CONTINUE READING THESISOriginal IntelligenceRESEARCHSeeing Risk Before It Becomes NewsRESEARCHThe Hidden Signals That Decide Drug Success or Failure Explore the platform → THE FULL THESIS ### The argument runs deeper than the summary. Fifteen questions, the full repricing dataset, and where the intelligence layer opens next. Download the full PDFBack to research ↑ --- # Outcomes resolved against the official record. URL: https://www.appliedxl.com/resolution New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / RESOLUTION ## Outcomes resolved against the official record. Resolution monitors predefined events, verifies the evidence, and produces documented outcome analysis. AppliedXL provides the analysis and supporting evidence, while the exchange makes the final determination and settlement. See how a market gets resolved ### Questions answered against the record. Market Resolution Infrastructure that supports outcome determination with predefined rules, designated sources, and documented evidence. Outcome Analysis A clear assessment of whether the published evidence satisfies the question that was asked, checked against the controlling source and preserved with a source-linked evidence package. Resolution Rule Design Questions structured to resolve clearly, including which source controls, what qualifies as yes or no, the relevant deadline, and how corrections or conflicting records are handled. ### Resolution no one has to take on faith. DefinedThe source, criteria, deadline, and decision rules are fixed before listing.MonitoredDesignated sources are continuously tracked for relevant public updates.VerifiedThe evidence is checked against the published criteria, item by item.DocumentedThe analysis, supporting sources, timestamps, and audit trail are preserved for review. ### Scale markets with auditable resolution. See how a market gets resolvedResolve a sample marketRELATED: THE JOINT REPORT WITH KALSHI · FAQ ↑ --- # Material change, detected before consensus. URL: https://www.appliedxl.com/signals New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → PLATFORM / SIGNALS ## Material change, detected before consensus. Signals reads the official records in your domain and turns material change into the alerts, briefings, and news your product delivers. ### Expand your coverage. Real-time Alerts The moment a record moves, your users get a source-linked alert: what changed, which entity it affects, and why it matters. Intelligence Feeds A continuous, structured stream of verified events across your domain, ready for dashboards, briefings, and downstream models. Catalyst Calendars Upcoming events mapped ahead of time, so your users see what's coming and when, not just what already happened. ### Be first, with the source attached. DetectedThe record moves. The change is matched to the entities it affects and drafted into an item, against the rubric defined for your domain.VerifiedA separate system validates the draft against the source: right entity, right numbers, right classification. A failed check goes to review, not out the door.ReleasedThe rubric decides the path: routine items publish instantly, sensitive classes go to your editors, whose edits become part of the record.TracedEvery published item carries its source, the checks it passed, and the rule or the editor that released it. ### New verticals in weeks, not years. Request a Signals sampleSee the trust chain ↑ --- # Site Map URL: https://www.appliedxl.com/sitemap New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → COMPANY / SITE MAP ## Site map. Every page on appliedxl.com, in one place. MAIN Home Going Live — See results in days. Fully launched in weeks. Important change is visible before it is obvious. Intelligence Strategy Assessment Power new intelligence. The Science of First PLATFORM Platform — AXL Core An Atlas in action. Every product, in your infrastructure. Trust is the product. Turn your domain expertise into AI-ready intelligence. PRODUCTS Forecasts Indicators Resolution Signals SOLUTIONS Solutions Benchmarks from institutional events. Deep coverage without linear analyst growth. More markets. Authoritative resolution. Risk, detected before the balance sheet. Specialized feeds your terminal doesn't have yet. Your beats, monitored at machine scale. THEMES Themes Clinical intelligence for medical innovation. Infrastructure intelligence for the AI economy. Institutional intelligence for emerging risk. Project intelligence for the energy transition. Supply intelligence for strategic capacity. RESEARCH & INSIGHTS Research & Insights AppliedXL and Bain: A New Pharma Operating Discipline AppliedXL and Kalshi Partner on Biopharma Prediction Markets AppliedXL Partners with Bloomberg Biopharma's Public Probability Biopharma's Public Probability — AppliedXL & Kalshi Clinical Trial Data as Alpha: Biotech Quant Model FDA Drug Repurposing: 5 Drugs Without a Champion From Public Record to Market Resolution Original Intelligence Prediction Model: Six Clinical Trial Case Studies Seeing Risk Before It Becomes News Spillover Risk and Readthrough Alpha State-of-the-Art Clinical Trial Prediction Systematic Trading Strategies in Biotech The Hidden Signals That Decide Drug Success or Failure Vertical AI vs. General AI: Biopharma Benchmark Who Will Monetize Truth? A Thesis For the Future of Information POLICIES AI Editorial Policy Privacy Policy Terms of Use MORE FAQ — Biopharma Prediction Markets Media & Partnerships ↑ --- # Built for the companies that create intelligence. URL: https://www.appliedxl.com/solutions New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS ## Built for the companies that create intelligence. Turn primary evidence into proprietary signals your clients can trust, act on, and feed into machines. ### Your clients hire you for three jobs, and AXL is the intelligence layer that makes your products indispensable to all three. 01Generate an edge Detect important change before the market absorbs it. 02Avoid blowups Surface emerging risk while it’s still buried in the record. 03Execute faster Expand coverage without growing analyst teams linearly. ### Built for information leaders across the intelligence economy. Financial Desktop Providers Specialized feeds on the infrastructure you already run. → Newsrooms Turn beat expertise into exclusive coverage and B2B products. → Specialist Information Providers Deeper coverage without linear analyst growth. → Exchanges & Index Providers New benchmarks from institutional events. → Prediction Markets Authoritative resolution for more contracts. → Ratings & Risk Intelligence Forward-looking risk, detected before the balance sheet. → ↑ --- # Benchmarks from institutional events. URL: https://www.appliedxl.com/solutions/exchanges New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / EXCHANGES & INDEX PROVIDERS ## Benchmarks from institutional events. Request an event datasetExplore the platform ### Win business on method, not price. A method no competitor can copy Indices built from verified event records, not proxies. The construction is the moat. History ready for the issuer Full backtested history from day one. No track-record problem. New themes in weeks Map a new domain to the Atlas, run the event history, launch the index. New verticals in weeks, not years. A benchmark competitors can't copy, with the history to prove it. Win mandates on method instead of price, and license benchmarks that pay for as long as they run. Request an event dataset ### What delivers it Indicators Win business on a method no simple filter can copy. Event-driven index data with full history, ready to test. Forecasts Add a forward-looking view competitors can't sell: event-driven probabilities and future exposure estimates. Resolution A clean historical record and clear rebalance rules your committee can defend to a regulator. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # Specialized feeds your terminal doesn't have yet. URL: https://www.appliedxl.com/solutions/financial-desktops New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / FINANCIAL DESKTOP PROVIDERS ## Specialized feeds your terminal doesn't have yet. Request a sample feedExplore the platform ### Keep your users on your platform. Stop users leaving your platform Coverage gaps are the reason clients switch. AXL fills the gaps without rebuilding your data infrastructure. Coverage your rivals don't have Event-driven feeds from primary records, not aggregated news. No competitor can replicate the method. Yours under your brand Signals, indicators, and forecasts arrive entity-resolved to your identifiers, delivered through your product. Your terminal, now covering what made users leave it. Close the coverage gaps that cost you renewals and sell new AI-ready feeds through the customers you already have. Request a sample feed ### What delivers it Signals Keep users on your platform. Important changes reach them here first, with the source attached, so they stop looking elsewhere. Indicators Offer coverage competitors can't: scores and measures for areas your terminal doesn't reach, delivered on your own systems. Forecasts Offer probability and timing feeds no rival can sell, matched to the IDs you already use. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # Your beats, monitored at machine scale. URL: https://www.appliedxl.com/solutions/newsrooms New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / NEWSROOMS ## Your beats, monitored at machine scale. Request a beat sampleExplore the platform ### Break more, with the document attached. First, with the source AXL monitors registries and filings continuously. Your desk gets the tip before the wire does, with the document attached. More coverage, same headcount The system reads every record on the beat. Your reporters keep the judgment and the byline. Your standards, intact Nothing publishes under your name without your judgment. The feed proposes; your desk disposes. Break it first. Prove it with the document. Turn the beats you own into subscription intelligence products: new B2B revenue on the authority you already built. Request a beat sample ### What delivers it Signals Publish first, with the document attached. Continuous monitoring drafted into items your desk approves, at a scale headcount can't match. Indicators Turn your beat into subscription products: trackers and measures built from event histories, sold on your editorial authority. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # More markets. Authoritative resolution. URL: https://www.appliedxl.com/solutions/prediction-markets New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / PREDICTION MARKETS ## More markets. Authoritative resolution. Resolve a sample marketExplore the platform ### List more markets you can stand behind. Settle without dispute Rules written to the official record. When the source speaks, the market closes. No ambiguity, no appeal. Widen the set of listable markets More domains, more questions, more liquidity. AXL handles the resolution infrastructure. Every settlement comes with evidence Source document, timestamp, and check attached. Auditable by any party. List more markets. Settle every one without dispute. Grow volume and draw institutional flow with settlement clear enough to trade against. Resolve a sample market ### What delivers it Resolution Settle more markets without dispute. Automatic resolution from the official record, evidence behind every settlement, rules set before listing. Signals List new categories faster and keep traders active between events, with alerts across the sources your contracts rely on. Indicators Give traders context that keeps them on your platform: activity levels and reliability measures by category. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # Risk, detected before the balance sheet. URL: https://www.appliedxl.com/solutions/ratings-risk New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / RATINGS & RISK INTELLIGENCE ## Risk, detected before the balance sheet. Request a risk sampleExplore the platform ### Spot trouble before the financials show it. Signals before the earnings call Enforcement actions, contract changes, and regulatory filings move before reported financials do. Inputs your committee can defend Every score and signal links to its source record. Traceable, reviewable, auditable. Steady, forward-looking Continuous monitoring means your ratings model is always working from the current public record. See the risk before the balance sheet does. Open new product lines from event histories and defend every rating to the committee. Request a risk sample ### What delivers it Forecasts Spot trouble before the balance sheet. Forward-looking delay, enforcement, and default odds, checked against real outcomes your committee can defend. Indicators Open new product lines from event histories: pressure indexes and exposure scores on the method your brand is known for. Signals Catch enforcement, sanctions, and contract changes early, before they show up in the financials. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # Deep coverage without linear analyst growth. URL: https://www.appliedxl.com/solutions/specialist-providers New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → SOLUTIONS / SPECIALIST INFORMATION PROVIDERS ## Deep coverage without linear analyst growth. Request a coverage sampleExplore the platform ### Stay the source your clients rely on. Cited, not replaced When clients build AI, they ground it in primary sources. Make sure your data is in the stack, not cut from it. Grow coverage without hiring Automatic detection across the public record means more territory without adding headcount. A defensible methodology Every data point traces to its source record. Your clients can audit the chain before they build on it. Stay the source your clients cite, not the one they cut. Defend the renewal and expand into nearby markets at software margins, without adding analysts in step. Request a coverage sample ### What delivers it Signals Keep first to know a promise you can keep. Automatic detection beneath your analysts, surfaced in minutes, explained by your experts. Indicators Ship differentiated data your clients license: structured event histories, scores, and measures mapped to your identifiers. Forecasts Add a premium tier on top of your coverage with forward-looking probability. ### See results in days. Fully launched in weeks. A scoped use case in days, a verified feed in your stack in weeks — mapped to your domain and proven against history before it ships. See how you go liveGet started ↑ --- # Terms of Use URL: https://www.appliedxl.com/terms New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → TERMS OF USE ## Terms of Use Welcome to AppliedXL.com, operated by Applied X Lab, Inc., a Delaware corporation. These Terms of Use govern your access to and use of AppliedXL's website, platform, data feeds, APIs, products, and any other services provided through AppliedXL.com. By accessing or using the Services, you agree to be bound by these Terms and by AppliedXL's Privacy Policy, which is incorporated by reference. If your organization has entered into a separate written agreement with AppliedXL, such as a Master Services Agreement or Order Form, that agreement will govern in the event of any conflict with these Terms. AppliedXL may update these Terms from time to time by posting a revised version and updating the Last Updated date above. Continued use after any update constitutes acceptance. Questions: support@appliedxl.com. LAST UPDATED NOVEMBER 26, 2025Contents01Term02Termination03Fees04License rights; intellectual property; proprietary rights05Products06Third-party links07General08Support and maintenance09Representations and warranties; disclaimers10Confidential information11Limitation of liability12Indemnification13Use of services14Advertising and publicity15Entire agreement16Modifications; severability17Dispute resolution; governing law18Compliance with export control laws19Notice20Relationship of parties21Assignment22Force majeure23Waivers24Construction25Survival ### 01Term These Terms become effective upon first access of the Services and continue until they expire or are terminated earlier under Section 2. The term of any account associated with specific Services will be described at the time of registration or purchase. ### 02Termination #### Termination for cause Either party may terminate access on a material breach that is not remedied within thirty (30) days of written notice. Customer failure to pay undisputed amounts within thirty (30) days is a material breach for which AppliedXL may suspend or terminate immediately; disputed payments are resolved in good faith. Either party may terminate if the other ceases ordinary operations, becomes insolvent, files for bankruptcy, or cannot meet its obligations. #### Effect of termination Termination ends access to the Services and associated accounts. Notice may be given by email or in-Service announcement. Termination of a subscription also ends Registered Users' access to Platform Services. If Customer terminates for AppliedXL's uncured material breach, AppliedXL refunds a pro-rata portion of prepaid fees from the termination date; no other refunds are provided. AppliedXL may retain and use Aggregated Data after termination, anonymized to prevent reverse-engineering and identification. ### 03Fees AppliedXL charges or invoices Fees for the Services as set out in the applicable order form or registration process. Fees in an order form are effective for its term. AppliedXL may invoice monthly or on another communicated schedule, and may adjust Fees for new features, additional Registered Users, or added products. #### Notice of increases Unless otherwise stated in an order form, AppliedXL provides at least sixty (60) days' prior written notice before any Fee increase takes effect. #### Payment and disputes Customer promptly notifies AppliedXL in writing of any Fee dispute and pays all undisputed invoices within thirty (30) days of the invoice date, in U.S. Dollars to a designated account. #### Taxes All Fees are exclusive of taxes, levies, and similar charges, except taxes on AppliedXL's net income. Customer is responsible for such taxes; AppliedXL will include or invoice applicable taxes, and Customer may pay directly to the authority where not charged. ### 04License rights; intellectual property; proprietary rights #### Grant of license During the subscription term, AppliedXL grants Customer a global, non-exclusive, royalty-free, non-sublicensable, non-transferable license to access and use the AppliedXL Products per the Documentation; create derivative works from Content, provided they do not misrepresent AppliedXL's Content or imply authorship or endorsement; and use Content for business purposes including internal operations, research, marketing and sales, and distribution and monetization across Customer's platforms. #### License restrictions Customer and its users shall not: permit access by unauthorized parties; modify or translate the Products or Documentation except as allowed; sublicense, lease, or transfer the Products to third parties; reverse engineer or attempt to derive source code or underlying structure; use the Products or Content to train, refine, or develop AI models, including large language models; disclose or transmit the Products or Content except as permitted; use automated tools to scrape or harvest the Products or Content except as permitted; or use the Products as the sole basis for time-critical or mission-critical decisions. #### Privacy The Platform processes user-generated prompts as standard functionality. Such inputs are Confidential Information and will not be shared, sold, licensed, or used to train any internal or third-party AI models. Prompts may be transmitted to third-party large language models strictly within defined workflows under data-minimization protocols. PII and PHI are not included in such transmissions. #### Configurations Outputs are generated through Configurations defined by Customer and AppliedXL (parameters such as sector impact, risk category, regulatory domain, audience relevance, and geographic scope). Customer-defined Configurations are Customer's confidential and proprietary information, and AppliedXL will not disclose, reuse, or intentionally replicate them for other customers. #### Overlap Because AppliedXL relies on public data and modular parameters, other customers may independently select similar Configurations, and overlap in outputs may occur. AppliedXL does not guarantee exclusivity over data elements or insights derived from public sources, but does guarantee confidentiality of Customer's unique Configurations. #### Ownership by AppliedXL AppliedXL retains exclusive ownership of its pre-existing intellectual property, including the Products, Documentation, Content, Website, and its proprietary processes, tools, methodologies, and technologies for event detection and news automation (the AppliedXL IP). Products and Documentation are licensed, not sold; no title passes. All rights not expressly granted are reserved. #### AppliedXL trademarks All graphics, logos, service marks, and trade names used with the Website or Products (the Marks) are AppliedXL trademarks and may not be used without AppliedXL's express written consent. #### Customer feedback Customer grants AppliedXL a royalty-free, worldwide, transferable, sublicensable, irrevocable, perpetual license to use any feedback relating to the Products and Documentation. AppliedXL will not identify Customer as the source. #### Customer data AppliedXL uses Customer Data solely to provide the Services and not in commercial projects with third parties. AppliedXL may use Customer Data only after it is de-identified, anonymized, and aggregated with similar data from other customers (Aggregated Data) for analytics, service improvements, and product development. Any other use requires Customer's prior written consent. #### AppliedXL data Any data, metadata, or taxonomy furnished by AppliedXL (AppliedXL Data), including training data, remains AppliedXL's sole property. Except as permitted, Customer shall not use any AppliedXL IP to train, refine, or develop AI models, including generative AI and large language models. #### Public or third-party data Where information incorporates publicly accessible or external data (Third-Party IP), AppliedXL adheres to the terms of use of the source databases. Customer may access and use such Third-Party IP as collected or transformed by AppliedXL during the term. ### 05Products #### General definition Products means AppliedXL's proprietary SaaS platform (the Platform) and related APIs, consisting of real-time data and research outputs from AppliedXL's technology, algorithms, and editorial processes. Feeds provide real-time, forward news monitoring as briefings surfaced by Customer configuration. Landscapes provide dynamic, enriched clinical trial insights via aggregated table views. AppliedXL retains all right, title, and interest in the Products. #### API services AppliedXL provides access to structured data and curated briefings through APIs, delivering real-time intelligence from public regulatory and scientific sources enriched with AppliedXL's proprietary signals and editorial context, for integration into Customer's systems and workflows. #### Platform services Customer registers authorized Registered Users to access the Platform, who may register via third-party single sign-on. Customer is responsible for accurate registration data and all activity under its users' accounts. Credentials may not be shared, and previously removed or banned individuals are not eligible to register. ### 06Third-party links The Platform may contain links to third-party services, websites, applications, or advertisements. AppliedXL does not control and disclaims responsibility for third-party sites, content, services, and tools, and makes no representation as to their completeness, accuracy, reliability, legality, or availability. When Customer clicks a link to a third-party site, Customer acknowledges they are leaving the AppliedXL environment. AppliedXL provides such links only as a convenience and does not review, endorse, or warrant them. Use of all third-party links is at Customer's own risk. ### 07General #### Release To the fullest extent permitted by law, Customer releases AppliedXL and its representatives from all claims related to Customer's use of the Website, Services, Products, and Documentation. This release does not apply to claims resulting directly from AppliedXL's gross negligence, willful misconduct, fraud, unconscionable commercial practice, or material breach. #### Electronic communications Communications may take place electronically. The parties consent to receive communications in electronic format and agree that electronic notices satisfy any legal requirement that they be in hardcopy form. #### International users The Products may be accessed from various jurisdictions. AppliedXL makes no representation that all Products or functionality are appropriate or available in all jurisdictions. Users accessing from foreign jurisdictions do so at their own risk and are responsible for compliance with local laws, including data-privacy laws. #### Equitable remedies The Products and Documentation contain valuable trade secrets. Any actual or threatened breach by Customer causes immediate and irreparable harm for which AppliedXL has no adequate remedy at law, and AppliedXL is entitled to equitable and injunctive relief. ### 08Support and maintenance AppliedXL will use commercially reasonable efforts to keep the Services operational at least ninety-five percent (95%) of the time during the subscription term. If that commitment is not met and not remedied within thirty (30) days of Customer's notice, Customer may terminate under Section 2. ### 09Representations and warranties; disclaimers #### AppliedXL representations AppliedXL represents and warrants that it has the authority to enter these Terms; will comply with applicable laws including data-privacy laws; will develop Content competently and professionally per industry standards; that the AppliedXL IP and Content do not infringe third-party rights; and that Customer receives all rights needed to use the Content, free of encumbrances. #### Customer representations Customer represents and warrants that Customer Data does not infringe third-party rights or regulations; that it has authority to enter these Terms; and that it will comply with applicable laws including data-privacy laws. #### Disclaimers To the maximum extent permitted by law, the Website, Services, support, and Documentation are provided AS IS and AS AVAILABLE. AppliedXL disclaims all warranties, express, implied, or statutory, including merchantability, fitness for a particular purpose, title, and non-infringement. AppliedXL does not warrant that data or content will be current, complete, or updated in real time. Content is for educational and informational purposes only and is not advice of any kind. ### 10Confidential information The parties agree, during and after the Term, to hold Confidential Information in strict confidence, use it only for the purposes in these Terms, and not disclose it except as contemplated or agreed in writing. A party may disclose Confidential Information to service providers with a legitimate need who are bound by protective confidentiality obligations, or as required by a governmental authority, court, or applicable law. Each party protects the other's Confidential Information with a reasonable standard of care, no less than it uses for its own. Confidential Information includes trade secrets, business information, customer contact information and lists, and other non-public proprietary information. Upon termination, each party stops using and deletes the other's Confidential Information still in its control, excluding materials integrated into internal work products or retained for compliance. ### 11Limitation of liability In no event shall AppliedXL be liable for any loss of use, revenue, profit, or data, diminution in value, or any consequential, incidental, indirect, exemplary, special, or punitive damages, whether in contract, tort, or otherwise, regardless of foreseeability or notice, and notwithstanding the failure of any remedy of its essential purpose. AppliedXL's aggregate liability shall not exceed the amounts paid or payable to AppliedXL under these Terms. This limitation does not apply to claims based on fraud committed by AppliedXL. You acknowledge that AppliedXL is not liable for the conduct or omissions of third parties, and that the risk of injury from such third parties rests entirely with you. The representations and warranties expressly contained in these Terms are AppliedXL's sole and exclusive representations and warranties; all others are disclaimed and shall not be relied upon. ### 12Indemnification #### By AppliedXL AppliedXL will defend, indemnify, and hold harmless Customer and its representatives from third-party claims arising from AppliedXL's material breach of its representations, warranties, or obligations, or a claim that the Services infringe a third party's intellectual property or misappropriate its trade secrets. #### By Customer Customer will indemnify, defend, and hold harmless AppliedXL and its representatives from third-party claims arising from Customer's material breach of its representations, warranties, or obligations. #### Process The indemnified party gives prompt written notice, reasonably cooperates, and permits the indemnifying party to control defense and settlement, provided that any settlement imposing monetary or injunctive obligations on the indemnified party requires its prior written approval. ### 13Use of services Nothing in these Terms prevents either party from entering into a similar relationship with third parties. ### 14Advertising and publicity AppliedXL may include Customer's name and logo in its customer lists and refer to Customer as a user of the Services in its advertising and marketing materials. ### 15Entire agreement These Terms constitute the entire and exclusive agreement between you and AppliedXL on this subject matter, superseding all prior negotiations, representations, and agreements, oral or written. ### 16Modifications; severability These Terms may be updated as described herein. If any provision is found unlawful or unenforceable, it will be amended to achieve as nearly as possible the original intent, and the remainder remains in full force and effect. ### 17Dispute resolution; governing law Any claim arising out of or relating to these Terms shall be settled by arbitration administered by the American Arbitration Association under its rules, with costs borne equally. New York, New York is the site for all hearings, before a single arbitrator. These Terms are governed by the laws of the State of New York, excluding conflict-of-law provisions. The UN Convention on Contracts for the International Sale of Goods does not apply. ### 18Compliance with export control laws The Products and Documentation may contain encryption technology controlled under U.S. export law, which may require an export license. Customer agrees to comply with all applicable export-control laws, and will defend, indemnify, and hold harmless AppliedXL from fines, penalties, and costs resulting from Customer's breach of this Section. ### 19Notice AppliedXL may provide notices by email, through the Services, or by posting on AppliedXL.com; you agree these satisfy any writing requirement. You may send notices to support@appliedxl.com or by mail to AppliedXL, 15 Metrotech Center, 7th Floor, Brooklyn, NY 11201. ### 20Relationship of parties The parties are independent contractors. Nothing creates an agency, employment, partnership, fiduciary, or joint-venture relationship, and neither party may bind the other to any contract. ### 21Assignment Neither party may assign or transfer its rights or duties without the other's prior written consent, except to an affiliate or to the purchaser of substantially all related assets, so long as that party is not a direct competitor. Any assignment in violation is null and void. ### 22Force majeure Neither party is liable for delay or failure caused by circumstances beyond its reasonable control, including acts of God, natural disasters, war, terrorism, civil unrest, labor disputes, government orders, pandemics, supplier failures, power or utility interruptions, telecommunications outages, cloud-hosting failures, or malicious network events. A force-majeure event does not excuse payment except where it directly prevents payment. Each party will use reasonable efforts to mitigate and resume performance; no specific uptime or resolution time is guaranteed. ### 23Waivers No waiver is effective unless in a writing signed by the party charged. No failure or delay in exercising a right waives it, and no partial exercise precludes further exercise. Rights are cumulative and in addition to other remedies, provided that the indemnification remedy in Section 12.A is Customer's exclusive remedy for infringement and misappropriation claims. ### 24Construction Section headings are for convenience only and will not be used to interpret these Terms. Including means including, but not limited to. ### 25Survival All provisions that by their nature should survive termination, including those on license grants and restrictions, proprietary rights, effects of termination, representations and warranties, indemnification, confidentiality, and intellectual property, survive expiration or termination for any reason. ↑ --- # Where new information markets appear first. URL: https://www.appliedxl.com/themes New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES ## Where new information markets appear first. When a change is coming, the record moves before the market does. AppliedXL maps the sources, entities, events, and outcomes behind that change. Human Health & Life SciencesExplore theme→Energy Transition & Climate MarketsExplore theme→AI Infrastructure & Compute BuildoutExplore theme→Industrial Buildout & Critical MaterialsExplore theme→Security, Regulation & Systemic RiskExplore theme→ ↑ --- # Infrastructure intelligence for the AI economy. URL: https://www.appliedxl.com/themes/ai-infrastructure New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES / AI INFRASTRUCTURE & COMPUTE BUILDOUT ## Infrastructure intelligence for the AI economy. Power, land, interconnection, and regional capacity. Get a compute sampleExplore the platform ### Where can compute capacity actually be built? AI is becoming a physical infrastructure market. The next constraint is not models or chips, but power, land, permitting, interconnection, construction, and regional capacity. AI & Data Centers Where are hyperscalers building before it's visible? Data center permits, campus announcements, and land filings. Power & Utilities Which utilities are exposed to AI load growth? Utility filings, load forecasts, rate cases, and power agreements. Interconnection & Capacity Where is compute feasible, and where is it blocked? Queue movement, substation upgrades, and transmission constraints. Land, Water & Cooling What hidden constraints stop the build? Zoning, water permits, cooling infrastructure, and local approvals. ### Your desk, your lens The same permit, utility, and construction records, watched the way your team would. You decide which regions and constraints matter; the platform catches what moves, explains it, and flags what it could mean next. Data center permits Utility filings Load forecasts Zoning hearings Construction permits Power purchase agreements Interconnection queues Procurement records Land filings Water permits Cooling infrastructure Local approvals Data center permitsZoning hearingsInterconnection queuesWater permitsData center permitsZoning hearingsInterconnection queuesWater permitsUtility filingsConstruction permitsProcurement recordsCooling infrastructureUtility filingsConstruction permitsProcurement recordsCooling infrastructureLoad forecastsPower purchase agreementsLand filingsLocal approvalsLoad forecastsPower purchase agreementsLand filingsLocal approvals ### See it running on your desk. Name the change you need to catch before the market does. See it running on the record. Get a compute sampleExplore the platform ↑ --- # Supply intelligence for strategic capacity. URL: https://www.appliedxl.com/themes/critical-materials New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES / INDUSTRIAL BUILDOUT & CRITICAL MATERIALS ## Supply intelligence for strategic capacity. Mines, processing, permits, and offtake agreements. Get a materials sampleExplore the platform ### Which countries, companies, and sectors can secure the inputs they need to build? The largest buildout in history is becoming a materials intelligence market. Electrification, defense, data centers, batteries, and industrial policy all depend on mines, processing capacity, permits, and strategic supply chains. Mining & Permits Where is new supply likely to emerge, and when? Mine approvals, exploration permits, and environmental reviews. Battery & Grid Metals Is supply tightening before pricing shows it? Lithium, graphite, copper, smelter capacity, and offtake agreements. Rare Earths & Magnets Where are the strategic bottlenecks forming? Processing plants, export controls, and defense demand. Strategic Supply Chains Who secures scarce inputs before competitors? Long-term agreements, government funding, and supplier concentration. ### Your desk, your lens The same mining, processing, and policy records, watched the way your team would. You decide which materials and suppliers matter; the platform catches what moves, explains it, and flags what it could mean next. Mining permits Exploration records Environmental reviews Processing capacity Offtake agreements Export controls Manufacturing incentives Supplier concentration Strategic reserves Industrial policy Project milestones Mining permitsProcessing capacityManufacturing incentivesIndustrial policyMining permitsProcessing capacityManufacturing incentivesIndustrial policyExploration recordsOfftake agreementsSupplier concentrationProject milestonesExploration recordsOfftake agreementsSupplier concentrationProject milestonesEnvironmental reviewsExport controlsStrategic reservesEnvironmental reviewsExport controlsStrategic reserves ### See it running on your desk. Name the change you need to catch before the market does. See it running on the record. Get a materials sampleExplore the platform ↑ --- # Project intelligence for the energy transition. URL: https://www.appliedxl.com/themes/energy-transition New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES / ENERGY TRANSITION & CLIMATE MARKETS ## Project intelligence for the energy transition. Which projects clear permitting, interconnection, and financing. Get an energy sampleExplore the platform ### Which climate and energy projects are moving forward, and which are stuck? The energy transition is moving from ambition to execution. Capital now depends on permits, grid queues, environmental approvals, and project milestones before assets become operational. Renewable Projects Which projects are advancing, and which are stuck? Solar, wind, and storage permits, reviews, and milestones. Grid & Interconnection Can this capacity actually connect to demand? Queues, utility filings, capacity requests, and transmission approvals. Environmental Permitting Will this clear NEPA, and when? NEPA reviews, EPA filings, state permits, and litigation. Carbon & Compliance Is this credit real, and is regulation tightening? Registry status, retirements, reversals, and enforcement actions. ### Your desk, your lens The same permit, grid, and compliance records, watched the way your team would. You decide which projects and risks matter; the platform catches what moves, explains it, and flags what it could mean next. Carbon registries Permit databases Interconnection queues Environmental filings Agency approvals Renewable project milestones Compliance records Energy infrastructure disclosures Project delay evidence Carbon registriesEnvironmental filingsCompliance recordsCarbon registriesEnvironmental filingsCompliance recordsPermit databasesAgency approvalsEnergy infrastructure disclosuresPermit databasesAgency approvalsEnergy infrastructure disclosuresInterconnection queuesRenewable project milestonesProject delay evidenceInterconnection queuesRenewable project milestonesProject delay evidence ### See it running on your desk. Name the change you need to catch before the market does. See it running on the record. Get an energy sampleExplore the platform ↑ --- # Clinical intelligence for medical innovation. URL: https://www.appliedxl.com/themes/human-health New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES / HUMAN HEALTH & LIFE SCIENCES ## Clinical intelligence for medical innovation. Clinical, regulatory, and payer records, before outcomes appear. Get a clinical sampleExplore the platform ### Which drugs, companies, trials, and healthcare markets are showing early signs of progress, risk, or change? Healthcare value is shaped before commercial outcomes appear. Trials, regulatory decisions, safety updates, label changes, access decisions, and payer rules all create early evidence about whether a therapy, sponsor, or market is gaining or losing momentum. Pharma & Biotech Which programs are advancing, weakening, or nearing a catalyst? Trials, FDA filings, disclosures, endpoints, and indications. Clinical Trials & Readouts When will this read out, and will it slip? Trial status, enrollment, completion dates, and endpoint changes. Regulatory Decisions Will this clear the FDA, and on what timeline? Approvals, CRLs, advisory committees, designations, and labels. Market Access & Safety Is this commercially viable, and is a safety signal forming? CMS and payer decisions, pricing, FAERS, and post-market records. View Benchmark Expert-grade analysis for the most critical decisions, benchmarked head-to-head against frontier models. Read the benchmark ### Your desk, your lens The same clinical, regulatory, safety, and payer records, watched the way your team would. You decide what matters; the platform catches it, explains what changed, and flags what it could mean next. Clinical trial registries FDA actions Sponsor disclosures Endpoints & indications Labels Safety records Market access decisions Payer policies Provider datasets Hospital financials Utilization files Post-market obligations Clinical trial registriesEndpoints & indicationsMarket access decisionsHospital financialsClinical trial registriesEndpoints & indicationsMarket access decisionsHospital financialsFDA actionsLabelsPayer policiesUtilization filesFDA actionsLabelsPayer policiesUtilization filesSponsor disclosuresSafety recordsProvider datasetsPost-market obligationsSponsor disclosuresSafety recordsProvider datasetsPost-market obligations ### See it running on your desk. Name the change you need to catch before the market does. See it running on the record. Get a clinical sampleExplore the platform ↑ --- # Institutional intelligence for emerging risk. URL: https://www.appliedxl.com/themes/systemic-risk New AppliedXL partners with Kalshi to bring verifiable resolution infrastructure to biopharma prediction markets Read the announcement → THEMES / SECURITY, REGULATION & SYSTEMIC RISK ## Institutional intelligence for emerging risk. Enforcement, sanctions, cyber, and trade controls. Get a risk sampleExplore the platform ### Where are companies, sectors, suppliers, and markets becoming vulnerable? Risk is increasingly visible in public institutions before it appears in company fundamentals. Enforcement, procurement, litigation, cyber, defense, aviation, trade controls, sanctions, chemical restrictions, and agency records reveal where exposure is building before markets fully reprice it. Enforcement & Litigation Where is regulatory pressure building before penalties? DOJ, FTC, SEC, and agency actions; complaints, rulings, and rules. Defense & Procurement Which programs are growing or shrinking before guidance? Federal contracts, modifications, appropriations, and awards. Trade, Sanctions & Controls Which suppliers and markets just became exposed? OFAC, BIS entity lists, export controls, and CFIUS actions. Chemicals & Environmental Where is liability forming before it's litigated? TSCA, PFAS, TRI releases, Superfund, and REACH restrictions. ### Your desk, your lens The same enforcement, trade, and litigation records, watched the way your team would. You decide which sectors and risks matter; the platform catches what moves, explains it, and flags what it could mean next. Agency actions Enforcement records Court filings Procurement awards Contract modifications Federal spending Cyber alerts Vulnerability disclosures FAA records Defense programs Trade controls Sanctions lists Denied-party records Chemical safety records Environmental liability records Rulemaking Compliance deadlines Agency actionsProcurement awardsCyber alertsDefense programsDenied-party recordsRulemakingAgency actionsProcurement awardsCyber alertsDefense programsDenied-party recordsRulemakingEnforcement recordsContract modificationsVulnerability disclosuresTrade controlsChemical safety recordsCompliance deadlinesEnforcement recordsContract modificationsVulnerability disclosuresTrade controlsChemical safety recordsCompliance deadlinesCourt filingsFederal spendingFAA recordsSanctions listsEnvironmental liability recordsCourt filingsFederal spendingFAA recordsSanctions listsEnvironmental liability records ### See it running on your desk. Name the change you need to catch before the market does. See it running on the record. Get a risk sampleExplore the platform ↑