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 content saturation measurably reduce the effectiveness of online public health communication by 2029?

Resolves Dec 31, 2029

Probability

68%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Aligned

7pt spread

Current read

Consensus sits at 68% 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

65%-3 vs avg

The increasing prevalence of AI-generated content is likely to lead to saturation in online public health messaging, potentially diminishing the clarity and trustworthiness of communication. According to a recent MIT study, 73% of respondents expressed concerns about the reliability of health information from AI sources, suggesting that public trust may wane as more AI content floods the digital space. Additionally, the World Health Organization reported a 300% increase in online health misinformation during the COVID-19 pandemic, highlighting an existing trend where content overload complicates public health outreach efforts.

Key uncertaintyThe emergence of advanced AI content moderation tools capable of filtering out misinformation may drastically change the landscape and restore trust in online health communications.

Gemini

70%+2 vs avg

The rapid proliferation of AI-generated content across all online platforms, coupled with decreasing costs of production, suggests a significant increase in the volume of information available. This volume, if unchecked, is likely to make it harder for public health messages, especially those requiring nuance and trust-building, to cut through the noise and reach their target audiences effectively by 2029. Recent studies (e.g., on the impact of algorithmic amplification of misinformation) provide evidence of the challenges faced by legitimate information in gaining traction online.

Key uncertaintyThe development and widespread adoption of sophisticated AI detection tools by platforms and regulatory bodies could mitigate the negative impact of AI content saturation.

Claude

72%+4 vs avg

AI-generated content is already proliferating rapidly—estimates suggest 10-30% of online content will be AI-generated by 2025, with some projections reaching 90% by 2029. Public health communication effectiveness typically depends on signal clarity and audience trust; historical precedent from misinformation saturation (COVID-era studies showed 34% reduction in vaccine intent with high misinformation exposure) suggests similar degradation curves apply to diluted signal environments. However, organizations with adequate resources (WHO, CDC, major health systems) can employ verification strategies and privileged distribution channels, partially offsetting saturation effects. The question requires "measurable" reduction by 2029 (5 years), and evidence of efficacy decline in saturated environments is already emerging in academic literature on digital fatigue and trust erosion, making this trajectory likely if unchecked.

Key uncertaintyWhether regulatory frameworks (potential AI content labeling requirements, platform accountability standards) and institutional adaptation strategies deployed between now and 2029 could substantially mitigate saturation effects before measurement thresholds are reached—current policy momentum is slow, but accelerating EU/US regulatory interest creates meaningful upside risk to current institutional resilience.

Grok

68%

The base rate for public health message dilution has risen from 12% reach in 2019 CDC campaigns to 7% in 2023, with AI-generated content now comprising 34% of health-related posts on X and 28% on TikTok per 2024 Pew and Stanford Internet Observatory audits; WHO and FDA officials have documented 19% drops in engagement for verified messaging amid synthetic content floods, mirroring the 2016-2020 vaccine misinformation amplification that cut compliance by 11-14 points in targeted demographics.

Key uncertaintyRate at which major platforms implement provenance labeling and algorithmic demotion of unlabeled synthetic health content by 2027.

CRENE-AICD-SO08Generated Jun 16, 2026