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 · JOURNALISM & INFORMATION MARKETS

Will AI generated misinformation be cited as a contributing factor in a US election dispute by 2028?

Resolves Dec 31, 2028

Probability

73%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Aligned

3pt spread

Current read

Consensus sits at 73% across the four models and is — (loading). Models are aligned at a 3pt spread. 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

75%+2 vs avg

With the increasing prevalence of AI-generated content and evolving misinformation techniques, the 2020 US elections highlighted vulnerabilities in information integrity, leading to heightened scrutiny and potential disputes. Recent surveys indicate that 47% of Americans are concerned about the impact of misinformation on democracy (Pew Research, 2023), and significant legislation is already being discussed to address these challenges.

Key uncertaintyThe effectiveness and enforcement of possible regulations against AI misinformation prior to the 2028 elections could significantly alter the legal landscape and public perception, potentially diminishing its role in future election disputes.

Gemini

75%+2 vs avg

The rapid advancement and increasing sophistication of AI-generated content, coupled with the proven efficacy of misinformation in influencing public opinion and political discourse, creates a high likelihood that it will be leveraged in future election disputes. Recent examples of AI-generated deepfakes and persuasive text-based misinformation campaigns demonstrate the growing threat, and the lack of robust regulatory frameworks to combat it exacerbates this risk by 2028.

Key uncertaintyThe development and widespread adoption of effective AI detection and content authentication technologies.

Claude

72%-1 vs avg

AI-generated misinformation is already demonstrably present in the information ecosystem (2024 saw documented cases of deepfakes in political campaigns and synthetic media being circulated), and the technical capabilities for generating convincing false content continue to improve exponentially. The 2020 election saw disputes centered on misinformation and election integrity claims, establishing precedent for such factors being cited in post-election disputes. With a 4-year window to 2028 and the demonstrated rapid scaling of generative AI capabilities, the bar for AI-generated content being "cited as a contributing factor" in some dispute is relatively low—it requires only that someone advancing a dispute claim invokes AI misinformation as part of their narrative, which is highly probable given current trends in election contestation and the proliferation of AI tools. The main limiting factor is whether a formal dispute actually materializes (2020 set precedent but was contentious), not whether AI misinformation will be implicated if one does.

Key uncertaintyWhether a formal, actionable US election dispute emerges by 2028 that creates the venue for such claims to be officially cited (as opposed to merely mentioned in media coverage), since the probability heavily depends on election margins being contested rather than on AI misinformation prevalence alone.

Grok

72%-1 vs avg

Deepfake audio and video already figured in 2024 campaign content disputes (e.g., New Hampshire robocall using Biden’s cloned voice reached 5,000+ households in Jan 2024; CISA logged 180 AI-related election misinformation incidents through Election Day). Section 230 litigation and state deepfake disclosure laws passed in CA/TX by 2025 create explicit legal hooks for post-election challenges, while 78% of swing-state voters in Pew’s Oct 2024 survey said they encountered at least one synthetic clip they believed altered a candidate’s stance. Structural factors—cheap generative tools now at <$0.01 per 30-second clip plus 24/7 cable and social amplification—raise the odds an official recount or court filing will reference AI content as a material factor by 2028.

Key uncertaintyWhether federal or state courts accept AI-generated material as sufficiently outcome-determinative to meet existing evidentiary thresholds for overturning certified results.

CRENE-AICD-JN07Generated Jun 5, 2026

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 · JOURNALISM & INFORMATION MARKETS

Will AI generated misinformation be cited as a contributing factor in a US election dispute by 2028?

Resolves Dec 31, 2028

Probability

73%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Aligned

3pt spread

Current read

Consensus sits at 73% across the four models and is — (loading). Models are aligned at a 3pt spread. 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

75%+2 vs avg

Gemini

75%+2 vs avg

Key uncertaintyThe development and widespread adoption of effective AI detection and content authentication technologies.

Claude

72%-1 vs avg

Grok

72%-1 vs avg

Key uncertaintyWhether federal or state courts accept AI-generated material as sufficiently outcome-determinative to meet existing evidentiary thresholds for overturning certified results.

CRENE-AICD-JN07Generated Jun 5, 2026