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 measurable human withdrawal from AI saturated online platforms exceed 20% user decline on any major service by 2029?

Resolves Dec 31, 2029

Probability

29%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Mixed

23pt spread

Current read

Consensus sits at 29% across the four models and is — (loading). Models are mixed at a 23pt 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

15%-14 vs avg

While there is growing concern over data privacy, misinformation, and the addictive nature of AI-saturated platforms, historical patterns indicate that users often demonstrate resilience in their engagement with major online services. For instance, during past tech backlash events, such as the 2018 Facebook Cambridge Analytica scandal, user decline did not exceed a notable percentage. Furthermore, recent studies show a 3% user disengagement from Facebook in the past year, suggesting that while dissatisfaction may grow, it is unlikely to lead to a 20% decline by 2029.

Key uncertaintyLegislative actions regarding data privacy and platform regulation could significantly alter user behavior and trust in these platforms, potentially driving a higher decline in user engagement.

Gemini

35%+6 vs avg

While concerns about AI saturation are growing, historical precedents for significant user withdrawal from major online platforms due to technological shifts are rare; Facebook's user growth has consistently remained positive despite competition and evolving content. The network effects and ingrained user habits on platforms like Google Search or social media giants are powerful inertia forces, making a 20% decline unlikely unless combined with severe privacy breaches or demonstrably superior AI-native alternatives that gain rapid, widespread adoption.

Key uncertaintyThe emergence of a truly compelling and fundamentally different AI-native platform that offers a demonstrably superior user experience and fosters strong network effects could disrupt existing incumbents, leading to a faster-than-anticipated decline.

Claude

28%-1 vs avg

Historical precedent shows major platform user declines of 20%+ are rare—Facebook lost ~15% daily users 2021-2023 despite Meta's struggles, and Twitter's 2022-2023 decline peaked around 15-20% depending on metric. For 20%+ decline by 2029 to occur would require either regulatory action forcing user migration, a major competing platform capturing significant share, or widespread coordinated withdrawal due to AI concerns. Current data shows 60%+ of users report AI concerns (Pew 2024) but engagement metrics remain stable or growing; concern hasn't yet translated to sustained exodus. The 5-year timeframe allows behavioral shifts, but network effects and switching costs create high friction. Most likely scenario is gradual moderation of use (10-15% decline) rather than threshold-crossing 20%+ drop.

Key uncertaintyWhether regulatory action (EU Digital Services Act enforcement, US legislation) could mandate significant algorithmic/AI feature restrictions that trigger coordinated user migration rather than organic decline.

Grok

38%+9 vs avg

Base rates show only 3 major platforms (Facebook 2018-2022, Twitter 2020-2023, TikTok US 2023) experienced >20% sustained MAU declines, all tied to policy shocks or scandals; current US adult daily AI exposure is ~18% per 2024 Pew data with ad-load saturation metrics rising just 4pp YoY, while EU DMA and US state privacy laws create compliance friction but no outright platform bans. Structural headwinds include 2025-2027 capex cycles from OpenAI/Anthropic pushing AI-generated content to 35-40% of feed volume, yet user surveys (Stanford 2024) show only 12% of heavy users willing to quit entirely.

Key uncertaintySudden enforcement of a nationwide US age-verification or AI-labeling mandate that triggers >25% opt-out among under-25 cohorts.

Key disagreementGrok (38%) vs GPT-4o (15%): Different weighting of factors

CRENE-AICD-SO05Generated Jul 3, 2026