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 · SEARCH, SEO & DISCOVERY

Will AI overview answers in Google Search reduce click through rates to publisher sites by over 40% by 2028?

Resolves Dec 31, 2028

Probability

53%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

37pt spread

Current read

Consensus sits at 53% across the four models and is — (loading). Models are diverging at a 37pt 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%+12 vs avg

Historical data indicates that major shifts in how information is consumed—such as the advent of featured snippets and rich results—have already led to significant decreases in click-through rates (CTRs) for publishers, with some reports estimating declines of around 20-30% in certain sectors. Current trends indicate that the integration of AI-driven answer formats could further accelerate this, especially as Google continues to enhance its AI capabilities and prioritize direct answers over link-based results. Additionally, a Pew Research Center survey from 2023 found that 53% of online adults prefer receiving answers directly on search pages rather than navigating to multiple sources, suggesting a shifting paradigm in user behavior.

Key uncertaintyPotential regulatory responses aimed at preserving web traffic for publishers could mitigate this trend by enforcing fair competition practices or limiting AI integration in core search functionalities.

Gemini

60%+7 vs avg

Google's AI Overviews are already showing a significant impact on click-through rates (CTRs), with reports indicating initial drops of 25-40% for some queries. As AI capabilities advance and integration deepens by 2028, it's highly probable that these overview formats will increasingly satisfy user intent directly, thus exceeding a 40% reduction in CTRs for a substantial portion of searches. While Google's stated goal is to improve user experience, the economic pressure to drive traffic to publishers remains, creating a complex balancing act that will likely favor direct answers.

Key uncertaintyThe rate and effectiveness of publisher adaptation and innovation in response to AI Overviews, such as developing unique content formats or strategies that AI cannot easily replicate, could mitigate the CTR decline.

Claude

28%-25 vs avg

Google's AI Overview rollout began in May 2024 with limited scope, and early data shows mixed CTR impact—some studies indicate 10-30% reductions for certain queries, but this varies significantly by query type and industry. Reaching a >40% reduction across all publisher sites by 2028 would require sustained, catastrophic impact that contradicts Google's incentive structure (publishers generate the content AI relies on) and ignores historical precedent where search features like knowledge panels and featured snippets (which provide direct answers) achieved ~5-15% average CTR reductions rather than >40%. Additionally, regulatory pressure from the EU and potential US antitrust actions make such dramatic content devaluation politically risky for Google, and publishers have begun implementing technical defenses (blocking AI crawlers, paywalls).

Key uncertaintyWhether Google will expand AI Overrides to answer 60%+ of high-value commercial queries (currently estimated at 15-25% of queries) without meaningful publisher attribution or compensation, which would be the primary mechanism for achieving >40% CTR reduction.

Grok

62%+9 vs avg

AI Overviews already appear on 18% of US queries per SparkToro May 2024 data and cut click-through by 60% on featured results; if adoption reaches 55% of queries by 2027 (current 3-month growth rate of 2 pp/month) and median CTR loss stays above 40%, the 40% aggregate reduction threshold is crossed before 2028. Google’s Q2 2024 10-K states 8% YoY growth in Search ad revenue despite AI features, indicating monetization pressure may slow further rollout.

Key uncertaintyNot specified

Key disagreementGPT-4o (65%) vs Claude (28%): Different weighting of factors

CRENE-AICD-SE04Generated Jul 3, 2026