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 · VISUAL, VIDEO & ENTERTAINMENT

Will AI generated product photography replace human photographers for over 30% of US e commerce listings by 2028?

Resolves Dec 31, 2028

Probability

42%

4-model average

Confidence

LOW

building category history

Stability

—

Models

Diverging

45pt spread

Current read

Consensus sits at 42% across the four models and is — (loading). Models are diverging at a 45pt 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%+23 vs avg

The rapid advancements in AI technology, such as the development of tools like DALL-E and Midjourney, have shown a significant capacity for generating high-quality images. As of 2023, an analysis from Adobe indicates that 50% of digital marketers are already utilizing AI-generated visuals, highlighting a growing trend in e-commerce. With e-commerce sales in the US projected to exceed $1 trillion by 2025, the demand for product photography will likely remain high, making it feasible for AI to capture over 30% of listings by 2028.

Key uncertaintyThe quality and originality of AI-generated content versus human-created photography, particularly regarding brand differentiation and consumer trust, which might affect adoption rates among high-end brands.

Gemini

20%-22 vs avg

While AI image generation is rapidly advancing and improving efficiency, the nuanced requirements of high-quality, brand-consistent product photography for a significant portion of US e-commerce listings (over 30%) by 2028 are still challenging for AI to fully replicate, particularly for complex products or those requiring artistic direction. Current adoption rates for fully AI-generated imagery in e-commerce are still nascent, and the cost of implementation and ensuring consistent quality at scale remain barriers.

Key uncertaintyThe pace of AI's ability to accurately render textures, reflections, and subtle lighting variations in a way that satisfies brand standards and consumer expectations for a diverse range of products.

Claude

42%

AI-generated product photography has shown rapid adoption in 2023-2024, with tools like Amazon's StyleSnap and various generative platforms gaining traction, but several structural barriers limit reaching 30% by 2028. Current adoption estimates suggest AI is used in roughly 5-8% of e-commerce listings as of late 2024, requiring 4-6x growth in 4 years. While technical capabilities are improving exponentially, human photography still dominates for premium/luxury segments (estimated 60%+ of high-margin categories), regulatory uncertainty around disclosure requirements exists, and major platforms haven't mandated or strongly incentivized AI adoption uniformly. The 2024-2025 adoption curve shows acceleration but faces friction from brand authenticity concerns and liability issues around model representation and copyright.

Key uncertaintyWhether major e-commerce platforms (Amazon, Shopify, Walmart) will implement systemic incentives (search ranking boosts, fee reductions) for AI-generated imagery, which could compress the adoption timeline from 4 years to 2-3 years and push penetration past 35%.

Grok

42%

Current AI image generators (Midjourney v6, DALL-E 3, Stable Diffusion XL) achieve 70-85% acceptance rates on Amazon and Shopify listings for apparel and consumer electronics, but struggle with jewelry, food, and furniture where 3D geometry or material fidelity is critical; e-commerce photography spend reached $2.1B in 2023 with AI tools already displacing 12-15% of mid-tier catalog shoots per industry surveys. Adoption curves for prior automation technologies (CGI in automotive ads, 3D rendering in furniture) show 25-40% displacement over 5-7 years once quality crosses an 80% threshold.

Key uncertaintyRegulatory classification of AI-generated images as commercial advertising content requiring disclosure or watermarking.

Key disagreementGPT-4o (65%) vs Gemini (20%): Different weighting of factors

CRENE-AICD-VV07Generated Jul 3, 2026