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 organic search traffic to human authored content sites decline by over 30% from 2025 levels by 2029?

Resolves Dec 31, 2029

Probability

37%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Mixed

17pt spread

Current read

Consensus sits at 37% across the four models and is — (loading). Models are mixed at a 17pt 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

40%+3 vs avg

While there is potential for a decline in organic search traffic due to increasing reliance on AI-generated content and algorithm changes by search engines prioritizing alternative content types, historical trends show that human-authored content has maintained significant engagement levels. Data from 2023 indicates a slight dip in organic search referrals; however, sites with high-quality human-generated content still see higher click-through rates (CTR) compared to AI-generated content, which suggests resilience in the face of market changes.

Key uncertaintyThe pace of AI adoption in content creation tools and how search engine algorithms evolve to prioritize these changes could dramatically shift traffic dynamics away from human-authored content.

Gemini

45%+8 vs avg

The increasing sophistication and prevalence of AI-generated content, coupled with search engine algorithms that may favor it due to perceived efficiency and scale, poses a significant threat to human-authored content. However, search engines are also investing in methods to detect and devalue AI content, and users may increasingly seek out genuine, authoritative human perspectives, creating a push-and-pull dynamic. Current trends indicate rapid AI advancement, but also a growing awareness of AI's limitations and potential for misinformation, making a precise decline difficult to forecast.

Key uncertaintyThe effectiveness and evolution of search engine algorithms in distinguishing between and valuing AI-generated versus human-authored content will be the most critical factor.

Claude

28%-9 vs avg

Organic search traffic to human-authored content has faced headwinds from AI-generated content and search algorithm changes (Google's March 2024 core update explicitly targeted low-quality content), but a 30%+ decline by 2029 requires sustained, severe disruption. Historical precedent shows search traffic shifts are typically gradual—the mobile transition took 5+ years to fully manifest. Current data indicates Google maintains strong incentives to preserve quality content (their business model depends on relevant results), and human-authored content still dominates top search rankings. A 30% decline would require either: (1) dominant AI search replacement (currently <10% market share for alternatives like Perplexity), (2) sustained algorithmic suppression of human content (contradicts recent quality focus), or (3) major structural collapse in SEO-dependent traffic. These scenarios combined face significant headwinds, though AI disruption acceleration remains a material risk.

Key uncertaintyThe speed of adoption and quality perception of AI-native search interfaces—if Claude/GPT/Perplexity-style search captures 25%+ market share with demonstrably superior UX by 2027, it could materially accelerate human content traffic decline beyond historical patterns.

Grok

35%-2 vs avg

Search volume to human sites has already fallen 15-20% YoY at several major publishers (NYT, Vox, The Atlantic) per SimilarWeb and internal reports since ChatGPT's Nov 2022 launch, while Google's SGE experiments show 40-60% of queries now receiving AI overviews that cut organic clicks by 20-30% in test cohorts. AI-generated content volume has grown 8-10x since 2023 per Common Crawl snapshots, yet user studies (Pew 2024) still show 65%+ preference for human sources when accuracy matters, suggesting a 25-40% traffic erosion by 2029 rather than >30% decline is the modal outcome.

Key uncertaintyWhether Google fully deploys zero-click AI answers across 70%+ of queries or maintains substantial human-result SERP real estate.

CRENE-AICD-SE03Generated Jul 3, 2026