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 · AUTHENTICATION & PROVENANCE

Will blockchain based content verification systems reach over 10M active users by 2029?

Resolves Dec 31, 2029

Probability

40%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

42pt spread

Current read

Consensus sits at 40% across the four models and is — (loading). Models are diverging at a 42pt 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%+25 vs avg

The demand for content verification systems is increasing due to rising concerns over misinformation and digital content authenticity, particularly in the wake of events like the COVID-19 pandemic and political misinformation. According to a Pew Research report, around 64% of Americans believe that misinformation has created confusion about basic facts, signaling a potential user base interested in verification technologies. With the global blockchain market projected to grow at a CAGR of 67.3% from 2023 to 2028, the infrastructure for scalable systems may be in place.

Key uncertaintyThe regulatory environment regarding blockchain technology remains uncertain, especially regarding privacy concerns and potential governmental restrictions on decentralized systems, which could significantly impact user adoption rates.

Gemini

45%+5 vs avg

While blockchain-based verification offers significant potential for trust and transparency in content, widespread adoption to 10 million active users by 2029 faces hurdles in user experience, scalability, and the current nascent stage of most such platforms. Existing blockchain applications, even established ones like certain DeFi protocols, struggle to consistently reach tens of millions of actively engaged users, suggesting a challenging ramp-up for a more niche application like content verification. The current economic environment, with a focus on practical utility and cost-effectiveness, might also slow the adoption of what could be perceived as a complex or expensive solution for general content consumers.

Key uncertaintyThe development of intuitive, user-friendly interfaces and seamless integration into existing content platforms, which could dramatically lower the barrier to entry for everyday users.

Claude

28%-12 vs avg

Blockchain-based content verification systems currently have minimal mainstream adoption, with leading platforms like Proofable and similar solutions serving fewer than 100K active users combined as of 2024. Reaching 10M users by 2029 (5 years) would require 100x growth and represent approximately 0.12% of global internet users—a significant penetration rate for a nascent technology category. Historical precedent shows blockchain applications struggle with adoption barriers: Bitcoin reached ~100M users in 13 years, Ethereum ~50M in 8 years, yet these had major financial incentives. Content verification faces higher friction (no direct monetary reward for most users) and fragmentation across competing standards, though growing concerns about deepfakes and misinformation create tailwinds that didn't exist for earlier blockchain use cases.

Key uncertaintyThe degree to which major platforms (Meta, Google, X) integrate blockchain verification into core products versus maintaining proprietary solutions—if even one major platform mandates it for creators, adoption could accelerate dramatically to 10M+; conversely, development of superior non-blockchain verification methods could eliminate the use case entirely.

Grok

23%-17 vs avg

Current blockchain content verification systems (Lens Protocol: 180k profiles, Verasity: 2.1M monthly actives, Po.et successor projects) remain below 3M combined active users after 5 years of development, while centralized alternatives like YouTube Content ID process 1.2B daily verifications; adoption is constrained by 0.3% wallet penetration among global internet users and regulatory uncertainty following the EU's 2024 AI Act requiring centralized audit trails. Historical precedent shows only 4 of 47 major blockchain consumer apps have exceeded 10M actives (CryptoKitties, Axie, STEPN, OpenSea), with median time-to-10M at 34 months under favorable token incentives now curtailed by 2023-2024 SEC enforcement.

Key uncertaintyWhether Meta or TikTok integrate on-chain verification into their existing 3B+ user bases by 2027.

Key disagreementGPT-4o (65%) vs Grok (23%): Different weighting of factors

CRENE-AICD-AT06Generated Jul 3, 2026