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 review. 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.

67% | Does realized enterprise economic adoption of generative AI materially lag AI in

Reasoning

As of mid-2026, enterprise AI adoption remains concentrated in narrow use cases (customer service, content generation, code assistance) while infrastructure capex continues accelerating—major cloud providers have committed $100B+ annually to AI infrastructure through 2026-2027, yet enterprise spending on AI implementation and integration lags by an estimated 3-5x multiple. Historical technology adoption curves (cloud, mobile) show 18-24 month lags between infrastructure readiness and material enterprise productivity gains; we are approximately 18-20 months into the generative AI cycle (dating from ChatGPT's November 2022 release). The remaining 5.5 months to December 2026 is insufficient for the typical enterprise procurement, integration, and ROI-realization cycle to close this gap materially—most Fortune 500 companies report pilot programs rather than production deployment at scale. The lag will likely persist through end-2026, though early movers in specific verticals (financial services, software development) may show exceptions.

Key uncertainty

Acceleration of enterprise adoption velocity following recent breakthroughs in AI reasoning/planning capabilities (mid-2026) could compress the typical lag timeline if these enable materially higher-ROI deployments, potentially resolving the lag question earlier than historical precedent suggests.

Does realized enterprise economic adoption of generative AI materially lag AI infrastructure investment expansion by December 31, 2026?

The headline probability is not the finding.

Thesis pressure map

Will the enterprise production deployment rate for generative AI in Fortune 500 companies remain below 35% by Q4 2026?

Model disagreement

Does realized enterprise economic adoption of generative AI materially lag AI infrastructure investment expansion by December 31, 2026?

The headline probability is not the finding.

Thesis pressure map

Will the enterprise production deployment rate for generative AI in Fortune 500 companies remain below 35% by Q4 2026?

Model disagreement