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 · PLATFORMS & SOCIAL MEDIA

Will Meta, X, or TikTok require AI content labeling for all posts on their US platforms by 2028?

Resolves Dec 31, 2028

Probability

57%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

47pt spread

Current read

Consensus sits at 57% across the four models and is — (loading). Models are diverging at a 47pt spread, so the average conceals live disagreement worth inspecting below. 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%+8 vs avg

Given the increasing regulatory scrutiny around misinformation and online content, recent proposals from US lawmakers in 2023 indicate a growing push for social media companies to implement AI content labeling for greater transparency. The White House’s National Strategy on Artificial Intelligence, released in 2023, emphasizes transparency in AI use, putting pressure on platforms like Meta, X, and TikTok to adopt labeling measures by 2028. Additionally, several high-profile incidents involving harmful misinformation have led to heightened public demand for accountability within digital platforms.

Key uncertaintyThe evolving political landscape and potential shifts in legislative priorities depending on election outcomes in 2024 could either accelerate or delay the implementation of content labeling requirements.

Gemini

75%+18 vs avg

Regulatory pressure from the US government regarding AI transparency is mounting, with discussions around potential legislation and voluntary commitments from tech companies increasing. The EU's AI Act, for instance, already mandates labeling of synthetic media, setting a precedent that US regulators are likely to follow or adapt. The sheer scale of misinformation risks associated with unlabelled AI-generated content, particularly in election years, will further incentivize platforms to adopt such measures by 2028.

Key uncertaintyThe specific enforcement mechanisms and the breadth of what constitutes "AI content" will determine the final implementation and compliance challenges for the platforms.

Claude

28%-29 vs avg

AI content labeling mandates face significant regulatory momentum (EU AI Act implemented 2024, Biden executive order on AI, FTC scrutiny of synthetic content) but the US lacks comprehensive federal AI regulation, making platform-level requirements unlikely without legislative action by 2028. Meta, X, and TikTok have voluntarily implemented limited AI disclosure features (Meta's "Made with AI" labels since 2023, X's synthetic media labels), suggesting they prefer gradual voluntary compliance over blanket mandates. The 4-year timeframe (2024-2028) is relatively short for new federal legislation requiring all three major platforms to implement identical systems, though state-level regulation (California, New York) could create pressure. Precedent from cookie labeling and content warnings shows adoption takes 5-7 years post-regulation.

Key uncertaintyWhether Congress passes comprehensive AI regulation with specific content labeling requirements before 2028, or if a major synthetic media incident (election interference, deepfakes) triggers emergency executive action that accelerates implementation timelines.

Grok

62%+5 vs avg

The EU Digital Services Act already mandates AI labeling for generative content by August 2025, and US state laws like California's AB 2019 and New York's 2024 AI disclosure bills create precedent for platform-level requirements; Meta's 2024 Community Standards updates and TikTok's 2023-2024 labeling pilots show voluntary compliance trends that historically precede mandatory rules within 3-4 years. X's current resistance under Musk contrasts with advertiser pressure and FTC enforcement signals from 2023-2024 AI deception cases. Platform-wide US mandates typically follow state-level adoption by 2-3 years.

Key uncertaintyWhether federal legislation like the proposed NO FAKES Act passes before 2027, which would preempt state rules and accelerate platform requirements.

Key disagreementGemini (75%) vs Claude (28%): Different weighting of factors

CRENE-AICD-PM02Generated Jul 3, 2026