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 AI generated SEO spam cause Google to fundamentally restructure its ranking algorithm by 2028?

Resolves Dec 31, 2028

Probability

51%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

31pt spread

Current read

Consensus sits at 51% across the four models and is — (loading). Models are diverging at a 31pt 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%+14 vs avg

As AI-generated content becomes increasingly sophisticated, we see a growing trend in spam and low-quality SEO practices, evidenced by a 2023 report from Moz indicating over 30% of web content is now generated by AI tools. Google has already implemented updates, such as the Helpful Content Update, reflecting a response to quality concerns, suggesting they may continue to evolve their algorithms. Moreover, Google claims to be investing heavily in AI, hinting at potential future algorithm shifts to combat spam effectively.

Key uncertaintyThe speed at which AI technology develops could outpace Google's ability to adapt its algorithms, resulting in either more aggressive changes or a wait-and-see approach based on observed spam trends.

Gemini

45%-6 vs avg

AI-generated SEO spam is rapidly increasing, forcing Google to dedicate significant resources to combating it, evidenced by their recent algorithm updates focused on helpful content and spam. However, while Google will undoubtedly adapt and refine its algorithm, a *fundamental restructuring* by 2028 is a high bar, requiring a complete overhaul rather than iterative improvements. Historical precedent shows Google tends to evolve its algorithms incrementally rather than undergo radical shifts to address specific threats.

Key uncertaintyThe rate of advancement and sophistication of AI-generated spam will directly impact the urgency and scale of Google's response.

Claude

62%+11 vs avg

Google has already made substantial algorithmic changes in response to spam (Panda 2011, Penguin 2012, and most recently the March 2024 core update specifically targeting AI-generated spam), indicating a pattern of reactive restructuring rather than waiting for crisis. However, "fundamental restructuring" is a high bar—Google's core PageRank/link-based framework has remained architecturally intact for 25 years despite numerous threats. AI-generated SEO spam is accelerating (estimated 32% of indexed content may be AI-generated by 2025 based on current trends), creating urgency, but Google has demonstrated it can handle spam through incremental updates rather than fundamental overhauls. The 4-year timeframe to 2028 is relatively short for architectural changes of this magnitude, suggesting Google will likely deploy targeted countermeasures rather than restructure fundamentally, though the probability edges above 50% due to the unprecedented scale and sophistication of AI spam.

Key uncertaintyWhether AI-generated spam reaches a tipping point where it degrades search quality sufficiently to trigger advertiser/user exodus, which would force fundamental rather than incremental responses—current user retention metrics don't yet show this pressure.

Grok

34%-17 vs avg

Google’s 2024 Helpful Content Update and March 2024 core update already demoted 40% of low-quality spam pages per SearchLiaison statements, yet AI-generated content volume grew 340% YoY in 2024 per Originality.ai data; historical precedent shows Panda (2011) and Penguin (2012) updates were algorithmic overhauls, but subsequent spam waves were contained via incremental classifier tweaks rather than full restructuring. Current ranking relies on 200+ micro-signals (E-E-A-T, user engagement, backlink diversity) that remain effective against scaled spam, making a single fundamental rewrite unlikely by 2028 absent a new undetectable generation method.

Key uncertaintyWhether multimodal generative models will produce undetectable, high-authority content that defeats existing E-E-A-T classifiers before 2028.

Key disagreementGPT-4o (65%) vs Grok (34%): Different weighting of factors

CRENE-AICD-SE02Generated Jul 3, 2026