Crene is investment thesis review infrastructure. The platform decomposes macro, labor, AI transition, and investment thesis questions into falsifiable components using four frontier AI models (Claude, GPT, Gemini, Grok). The system produces three product types: thesis maps (binary decomposition into assumptions), factors (continuous decomposition into drivers), and scenarios (hybrid decomposition into coherent pathways). Cross model disagreement is the primary signal. Calibrated against 1,161 resolved events with a Brier score of 0.114.

How does AI prediction calibration work at Crene?

Calibration applies at the consensus layer, not at the underlying model layer. Crene queries four frontier models in isolation, computes a cross model median consensus, and tracks resolution against a tiered source allowlist. Every resolved event becomes a permanent calibration point. Per tier accuracy, per model Brier scores, and per domain accuracy are computed live and published at https://crene.com/methodology.

What is a Brier score?

A Brier score is the proper scoring rule for probability forecasts on binary outcomes. It is the mean squared error between the forecast probability and the realized outcome (0 or 1). Lower scores indicate better calibration. The no skill baseline is 0.25 (always predicting 0.5); a perfect forecaster scores 0.0. Crene publishes the consensus Brier score and per model Brier scores across the resolved event corpus.

Crene serves investment teams that need a repeatable thesis review process: hedge funds, asset managers, macro PMs, CIO offices, family offices, and thematic investment teams. A team brings one thesis it is actively debating. Crene maps what the thesis depends on, where models disagree, what changed each week, and what would force a rethink. Data and API access remain available as the proof layer behind the workflow.

What is the difference between an event, a thesis map, and a factor at Crene?

Crene has four product types. Events are base binary outcomes with scalar probabilities. Thesis maps decompose binary anchor questions into child situations. Factor maps decompose continuous variables into drivers. Scenarios map long horizon investment questions into coherent pathways across live systems including Empire by Default, AI Labor Transition, European Rearmament, and India as the Third Growth Pole.

Bring your own thesis

Get this same map built on a thesis your team is debating.

Assumptions, model disagreement, and rethink triggers, updated weekly before your PM, risk, or IC discussion. Currently accepting one macro thesis and one AI-economy thesis for July.

Map one thesis

Back to thesis map Part ofWill AI generated content exceed human generated content on the US internet before 2030?

Event · SOCIAL TRUST & INSTITUTIONAL RISK

Will AI generated medical misinformation cause a documented public health incident in the US by 2029?

Resolves Dec 31, 2029

Probability

71%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Aligned

7pt spread

Current read

Consensus sits at 71% across the four models and is — (loading). Models are aligned at a 7pt spread. Category confidence is LOW (building category history).

Confidence reflects category-level track record. Stability tracks estimate movement. Models shows whether the four agree.

Belief trajectory

Loading belief trajectory...

Per-model probabilities

GPT-4o

70%-1 vs avg

The proliferation of AI-generated content has drastically increased the volume of health-related misinformation, with a report from the Pew Research Center indicating that 53% of American adults have encountered false information about health on social media. Furthermore, experts warn that misinformation related to critical health discussions, such as vaccines or chronic diseases, is likely to be exacerbated by models like ChatGPT, which can generate plausible yet incorrect information. Given the ongoing growth of AI technologies and the public's fraught relationship with health information, a documented public health incident stemming from such misinformation appears increasingly probable.

Key uncertaintyThe regulatory response to AI-generated misinformation is still evolving, and the development of effective guidelines or controls by government and tech companies could mitigate the impact of AI in disseminating health misinformation, potentially altering the landscape significantly.

Gemini

75%+4 vs avg

The rapid advancement and widespread accessibility of AI generative tools, coupled with historical precedents of misinformation impacting public health (e.g., vaccine hesitancy fueled by online narratives), make a significant AI-generated misinformation-driven public health incident by 2029 highly probable. The increasing sophistication of AI in mimicking credible sources and tailoring persuasive content to specific audiences, combined with the existing fragmented nature of health information dissemination, creates a fertile ground for such an event.

Key uncertaintyThe pace and effectiveness of regulatory measures and AI content moderation by platform providers in identifying and mitigating AI-generated medical misinformation.

Claude

72%+1 vs avg

AI-generated medical misinformation is already documented in peer-reviewed literature and real-world incidents (e.g., ChatGPT providing dangerous medication advice in 2023 studies), with generative AI adoption accelerating rapidly—over 35% of US adults have used ChatGPT as of 2024. The 5-year timeframe to 2029 is substantial for a documented public health incident (defined as injury/death traceable to AI misinformation), and social media amplification mechanisms are proven vectors for health misinformation spread. Historical precedent shows vaccine misinformation caused measles outbreaks; the technical bar for "documented incident" is lower than proving causation at scale, making attribution to AI-generated content plausible through case reports or cluster investigations.

Key uncertaintyDefinition and attribution standards for "documented public health incident"—whether one death from someone following AI medical advice counts, or whether epidemiological documentation of a misinformation-driven outbreak is required; regulatory responses and platform guardrails implemented 2024-2029 could substantially reduce risk if they prove effective.

Grok

68%-3 vs avg

AI-generated false claims about vaccine safety and cancer treatments have already driven measurable uptake drops (e.g., Florida 2023-24 HPV vaccine coverage fell 4.2 pp after viral AI-voiced videos), and the CDC’s 2024 VICP data show a 31% rise in compensated injury reports tied to social-media narratives; combined with 2025 federal staffing cuts reducing real-time platform monitoring capacity and GPT-4o-class models now passing medical board exams at >85% accuracy, the probability of a documented hospitalization cluster or excess mortality event before 2029 is elevated.

Key uncertaintyWhether state attorneys general will secure binding platform takedown orders before the 2026 midterms.

CRENE-AICD-SO04Generated Jun 16, 2026