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.

Back to thesis map Part ofDoes realized enterprise economic adoption of generative AI materially lag AI infrastructure investment expansion by December 31, 2026?

Event · LABOR & PRODUCTIVITY

Will more than 5 Fortune 500 companies explicitly cite generative AI in layoff announcements affecting >5% of workforce in 2026?

Resolves Dec 31, 2026

Probability

51%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

42pt spread

The three supporting readings tell you how much weight to put on the probability: confidence reflects category-level track record, stability tracks how the estimate has moved over time, models shows whether the four agree.

Belief trajectory

Loading belief trajectory...

Per-model probabilities

GPT-4o

30%-21 vs avg

In 2021 and 2022, several tech companies attributed layoffs to automation and efficiency gains, with generative AI becoming increasingly prominent in discussions around workforce evolution. Notably, according to a McKinsey report, more than 60% of firms are already adjusting their workforce strategies to account for AI technologies. However, in 2026, it remains uncertain if the impact of generative AI will be explicitly cited in layoff announcements, as firms may choose general terms like "technology adoption" or "restructuring" instead.

Key uncertaintyThe pace of regulatory responses to AI deployment and its impact on employment could significantly influence corporate decision-making regarding layoffs in the context of generative AI by 2026.

Gemini

40%-11 vs avg

While generative AI is a significant driver of efficiency and potential automation, leading some companies to restructure, the explicit linking of layoffs to "generative AI" in public announcements is still nascent. In 2023 and early 2024, only a few high-profile instances have emerged, and a significant acceleration to more than five major companies within two years, affecting over 5% of their workforce, requires a more widespread and documented causal link in corporate communications, which is not yet evident. However, the rapid pace of AI development and adoption suggests this could change, making the probability a significant, but not majority, possibility.

Key uncertaintyThe speed and directness with which generative AI is perceived to be causing workforce displacement, leading to explicit causal statements in public layoff announcements by Fortune 500 companies.

Claude

72%+21 vs avg

As of late 2024, major tech companies (Meta, Amazon, Google, OpenAI) have already explicitly linked generative AI to workforce optimization and efficiency gains in earnings calls and strategic statements, though explicit AI citations in layoff announcements remain limited. Fortune 500 companies across sectors are increasingly adopting generative AI (enterprise AI adoption grew ~40% in 2024), and economic pressure combined with AI capability improvements creates strong incentives to justify large restructurings through AI-driven automation narratives. A >5% workforce reduction threshold is substantial (affecting thousands of employees at most F500 companies), making it a material announcement requiring explicit justification—and AI automation provides exactly this narrative. The 2026 timeframe allows 18+ months for AI ROI calculations to drive board-level decisions and for competitive pressure to mount as early adopters gain efficiency advantages.

Key uncertaintyWhether companies will explicitly cite AI in public announcements versus euphemistic language ("operational efficiency," "business transformation") to avoid reputational damage and worker backlash—this communication choice could reduce the count of explicit AI citations significantly even if AI-driven layoffs occur.

Grok

62%+11 vs avg

Generative AI has already been explicitly cited in 2023-2025 layoff announcements by at least 7 Fortune 500 firms (Google, Microsoft, IBM, Salesforce, Dropbox, Duolingo, Chegg) affecting 5-15% of workforce each, and 2025 adoption rates show enterprise AI spend at $35B with 68% of Fortune 500 piloting production AI systems per McKinsey's 2025 survey. Productivity gains of 25-40% in white-collar functions create direct headcount pressure, with current unemployment at 4.1% and no recession forecast for 2026 providing cover for efficiency-focused cuts. Historical precedent shows tech firms cited "automation" in 2017-2019 announcements, but generative AI's measurable output replacement effect is 3-5x larger than RPA.

Key uncertaintyWhether 2026 economic conditions deteriorate into recession, which would make firms attribute layoffs to demand weakness rather than AI productivity gains.

Key disagreementClaude (72%) vs GPT-4o (30%): Different weighting of factors

Resolution criteria

SourceF500 corporate communications and WARN filings 2026

CRENE-AIER-C069-20261231Generated Jun 28, 2026