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 · CONTENT ECONOMICS & BUSINESS MODELS

Will content farm revenue from AI generated articles exceed $10B annually in the US by 2028?

Resolves Dec 31, 2028

Probability

26%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Mixed

15pt spread

Current read

Consensus sits at 26% across the four models and is — (loading). Models are mixed at a 15pt 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

35%+9 vs avg

The market for content farm revenue, particularly from AI-generated articles, shows potential for significant growth given the rising advancements in AI technologies and increasing demand for content. However, while there was an estimation of around $4 billion in revenue from AI-generated content in 2023, reaching over $10 billion by 2028 would require nearly a 150% increase within five years, which is ambitious. Additionally, regulatory scrutiny around AI-generated content and its implications for copyright could hinder growth if policies restrict its use.

Key uncertaintyThe evolving regulatory environment surrounding AI and digital content could drastically affect the viability and scalability of AI-generated content in the marketplace.

Gemini

20%-6 vs avg

While AI-generated content is rapidly proliferating, content farms typically rely on low-cost, high-volume production for advertising revenue. For this to reach $10 billion annually in the US by 2028, it would require an exponential and sustained growth in AI content farm revenue, far exceeding current projections for the broader digital advertising market's growth and the demonstrable value proposition of solely AI-generated content for premium advertisers. The current market for AI-generated content primarily targets low-value, SEO-driven traffic, making the $10 billion threshold highly improbable within this timeframe.

Key uncertaintyThe regulatory landscape surrounding AI-generated content and its monetization, including potential copyright and disclosure laws, could significantly impact the viability and revenue potential of content farms.

Claude

22%-4 vs avg

Content farm revenue models have historically struggled to achieve scale profitably (e-content farms peaked at ~$2-3B annually in mid-2010s before decline), and AI-generated content faces structural headwinds: major platforms (Google, Meta) are actively deprioritizing AI-generated content in feeds and search rankings as of 2024, advertising networks are tightening policies against pure AI content farms, and SEO effectiveness of bulk AI content continues degrading. For revenue to reach $10B by 2028 would require a ~4-5x expansion of a shrinking market segment in just 4 years—possible only if regulatory/platform policies dramatically reverse or if a new distribution channel (e.g., enterprise/B2B applications) emerges unexpectedly. Current trajectory suggests continued fragmentation into niche applications rather than mainstream content farm consolidation.

Key uncertaintyWhether large tech platforms reverse course on AI content penalties due to competitive pressure (e.g., if ChatGPT-generated content becomes dominant consumer preference) or if advertisers overcome brand safety concerns and actively fund AI content farms as a high-margin inventory source.

Grok

28%+2 vs avg

Current AI content farms generate ~$400-600M US revenue in 2024 (AdSense + programmatic), with 35-40% YoY growth from 2022-2024; scaling to $10B by 2028 requires 70%+ CAGR sustained for 4 years, exceeding historical digital ad growth rates (12-18% CAGR) and the 25-30% observed in high-growth verticals like short-form video. US digital ad spend is projected at $340B by 2028 (eMarketer), but AI articles face 60-70% lower CPMs due to quality filters from Google, Meta, and programmatic platforms.

Key uncertaintyWhether major platforms implement stricter AI-content detection algorithms that could reduce monetizable inventory by 50%+ within 2 years.

CRENE-AICD-EB01Generated Jun 27, 2026