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 · LEGAL, COPYRIGHT & REGULATION

Will total copyright infringement lawsuits against AI companies exceed $10B in claimed damages by 2029?

Resolves Dec 31, 2029

Probability

66%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Mixed

12pt spread

Current read

Consensus sits at 66% across the four models and is — (loading). Models are mixed at a 12pt 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%-1 vs avg

As of 2023, the rise of AI-generated content has intensified scrutiny over copyright laws, especially as evidenced by recent high-profile cases, such as the lawsuit against OpenAI for allegedly using copyrighted material without permission. Additionally, the estimated damages in smaller, precedent-setting copyright cases have reached into the hundreds of millions, suggesting that larger claims as affected industries react could lead to total damages exceeding $10B by 2029. Furthermore, with increased regulatory interest and ongoing debates in Congress about AI and intellectual property, further legislation could potentially lead to more lawsuits.

Key uncertaintyThe potential impact of new legislation or clarifications in copyright law could either mitigate or exacerbate the rate of litigation against AI companies, significantly influencing claimed damages.

Gemini

60%-6 vs avg

The rapid proliferation of AI models trained on vast datasets, coupled with increasing legal scrutiny and the high stakes involved in intellectual property, makes significant damage claims plausible. Recent lawsuits like those filed by Getty Images against Stability AI and by authors against OpenAI already represent substantial damages, suggesting a trajectory towards higher aggregate claims as more cases progress.

Key uncertaintyThe final rulings and damage awards in the initial wave of high-profile copyright infringement lawsuits against AI companies will set crucial precedents for future litigation and damage assessment.

Claude

72%+6 vs avg

As of 2024, major copyright lawsuits against AI companies have already accumulated substantial claimed damages—the New York Times lawsuit alone seeks $150B+ in damages, and multiple class actions from authors (including significant cases like those by the Authors Guild) collectively claim tens of billions. With 5 years remaining until 2029 and the litigation pipeline accelerating (Getty Images, music publishers, individual creators all actively suing), reaching $10B in *total* claimed damages across all suits is highly probable and may already be exceeded. Historical precedent shows that patent litigation against tech companies frequently involves multi-billion dollar damage claims (e.g., Apple-Samsung exceeded $1B annually in the 2010s-2020s), and copyright infringement claims in the AI era involve substantially larger training datasets and commercial deployment than past cases, supporting higher damage claims per suit.

Key uncertaintyThe definition of "claimed damages" versus "awarded damages"—if the question intends only court-awarded or settled amounts rather than legal complaints' damage claims, the probability drops significantly to ~25%, as major awards typically settle for fractions of claimed amounts or face appellate reduction.

Grok

68%+2 vs avg

Five suits already filed against OpenAI, Meta, and Stability AI claim $3-8B each in statutory damages under 17 USC 504(c), and the NYT v. OpenAI complaint alone seeks $150M in direct damages plus disgorgement of $1B+ in profits; if courts uphold secondary liability for training on 200B+ tokens of protected works, aggregate claimed damages from 12-15 pending cases could exceed $12B by 2027. DOJ and Copyright Office 2023 statements signal willingness to treat unlicensed LLM training as infringement, while EU AI Act fines of 6% global revenue provide precedent for billion-dollar exposure. Historical precedent shows music-label suits against Napster and Grokster produced $2.4B in claimed damages within four years, supporting rapid scaling to ten-figure totals.

Key uncertaintyWhether courts will allow statutory damages per work or limit recovery to actual damages and disgorgement.

CRENE-AICD-LR03Generated Jun 29, 2026