We're looking for a new Talent Scout to join our team! You'll help us find great researchers, engineers, and support staff and introduce them to Epoch through outreach and events.

What are the largest software engineering tasks AI can perform? To answer this, we built MirrorCode, our long-horizon SWE benchmark that lets AI code autonomously for days at a time. The best models complete some tasks we estimate would take human engineers several weeks.

What are the strategies of Chinese AI companies? To understand this better, Cheryl Wu, Jean-Stanislas Denain, and Anson Ho scraped >1600 job postings from six major Chinese firms. Here’s what they learned. 🧵

Help shape how the world understands AI. We're hiring two designers at Epoch AI to turn complex research into dashboards and visualizations researchers and policymakers can easily use.

How close is AI to automating AI R&D? Right now, the tools economists use to track automation are too blunt to say. In this week's newsletter, Jean-Stanislas Denain, Joe Kwon, and Anson Ho propose a sharper tool: a thorough taxonomy of 60+ tasks involved in frontier AI research. 🧵

The end of the self-funded AI buildout? Hyperscaler cash capex is growing much faster than cash inflows. On current trends, they will be unable to fully fund the AI infrastructure buildout with cash from operations by the end of this year.

Claude Fable 5 achieves a new high score of 161 on the Epoch Capabilities Index! This beats out GPT-5.5 Pro by 1 point, and is the first time Anthropic has taken the lead on the ECI in over a year.

Claude Fable 5 scores very well on FrontierMath: Tiers 1–4 (v2), reaching 87% on Tiers 1–3 and 88% on Tier 4. This continues a streak of Anthropic models improving rapidly at math.

FrontierMath: Tiers 1–4 (v2) is live. We concluded an audit that addressed errors in 42% of problems. Rankings are similar but scores are higher across the board. The current leaders are GPT-5.5 (xhigh) with 85% on Tiers 1–3 and Google’s AI co-mathematician with 76% on Tier 4.

How big a leap is Mythos in cyber capabilities? Timothée Chauvin, Alexander Barry, Jean-Stanislas Denain, and Anson Ho compiled the public evidence and found that while it’s unclear if Mythos was ahead of trend in discovering vulnerabilities, it represents a big jump in exploiting them. 🧵

The record for computing capacity in a single data center has doubled every 7 months. Colossus 1, Anthropic-Amazon New Carlisle, and Meta Prometheus have each claimed the top spot in turn.

How should we think through various proposals for sharing the gains of AGI? According to Phil Trammell and Anson Ho, the leading proposals for universal redistribution after AGI differ along a primary axis: how much direct control over capital they propose giving citizens. 🧵

AI companies say their models are getting better at finding software vulnerabilities. Is that bearing out in public data? Introducing our Cyber Vulnerabilities explorer, which visualizes Common Vulnerabilities and Exposures (CVE) reported to the CVE Program since 2022.

The AI boom has doubled computing infrastructure's share of US GDP. Investment in AI-related data center construction, compute hardware, and networking equipment accounted for ~0.8% of US GDP in Q1 2026, driving computing infrastructure as a whole to ~1.5% of GDP.

We took another look at the capability gap between open-weight and proprietary models. Since the start of the year, open-weight models have lagged the state of the art by four months.

We've added narrations to our long-form content on the Epoch AI website, including reports, Gradient Updates, and topic overviews. Look for the play button.

Are we nearing a compute crunch? In our latest Gradient Update, @luke__emberson and @Jsevillamol estimate how many tokens all the Blackwell chips on Earth could serve, and compare this to total token demand. Direct comparisons are difficult, but it appears demand is growing much faster than supply.

For frontier AI chips, memory is the largest and fastest-growing component cost. High-bandwidth memory (HBM) has grown from 52% to 63% of total AI chip component spending between Q1 2024 and Q4 2025.

Claude is typically better at software engineering and worse at math than frontier competitors. Aggregating benchmarks to create our domain-specific ECI, we find the Claude family has an average SWE-ECI 2.7 points higher than their general ECI, and a Math-ECI 1.8 points lower.

Servers account for 60% of the total cost of owning a 1 GW AI data center. A typical 1 GW AI data center costs about $38B in up-front capital and $0.9B/year to operate. Annualizing the capital expenses over equipment lifespans, that equates to $8.5B/year, with $5B for servers.

Superstar AI researchers are paid >10× more than their frontier lab colleagues, and >100× more than most postdocs. Why? The naive explanation is that this is just due to differences in researcher quality. But in a new essay, Anson Ho argues that this is very incomplete.

We're looking for a Researcher to join Epoch AI's data center tracking project! You'd help us monitor global frontier AI datacenter buildouts in detail—using satellite imagery, permits, and other sources to inform policymakers and the public.

We are conducting an AI-assisted review of FrontierMath: Tiers 1-4. This has flagged fatal errors in about a third of problems, and we believe most of these flags to be valid. We will release updated scores on a corrected dataset after completing a thorough human review.

Anthropic and OpenAI earn more revenue per employee than the top public tech companies, both now and at their IPOs. Anthropic: ~$9M OpenAI: ~$5.6M Top public co. (Nvidia): ~$5.1M

Every AI chip consists of key components. If the supply of any component stalls, so can chip production. Introducing our AI Chip Components explorer, which tracks supply and consumption of logic, packaging, and memory to identify the bottlenecks shaping frontier AI compute.

We're looking for a new Designer to join our team! The Designer will help design dashboards and data visualizations that make our research more accessible and engaging.

We are launching domain-specific capability scores, tracking the capabilities of models across SWE and Math benchmarks, using the same scale as the general ECI. We also support customization for users who want to create their own variants of the ECI. Link below!

The recipe for “classic” reasoning benchmarks is simple: text-only, several-hour time horizons, easy to grade, with expert human baselines. What next? In this week’s Gradient Update, Greg Burnham argues it’s as easy as dropping one of these four ingredients.

Join us for in-person workshops to develop problems for FrontierMath: Open Problems! We are seeking highly interesting unsolved problems from research mathematics whose solutions can be verified programmatically. These are hard to find. Come take a crack at it! Link below.

How much AI compute has been smuggled to China? We estimate between 290k and 1.6M H100-equivalents by the end of 2025 — representing ~20% to ~60% of China’s total compute.

GPT-5.5 Pro achieves a new high score of 159 on the Epoch Capabilities Index! ECI is our statistical tool that combines multiple benchmarks into a unified scale.

Alongside our report on how fast robot production could scale up, we've also released a series of @metaculus forecasting questions on the future of robotics. If you have views on where these trends are headed, come weigh in!

How fast could production of humanoids, quadrupeds, drones, and other robots scale up, in the event of a large demand shock?

We now estimate that only about 0.3 GW of total facility power is operational for Stargate Abilene, not 0.6 GW. We've moved the 0.6 GW milestone to late May and the 1.2 GW milestone from Q3 to Q4 2026, but both are uncertain. More about this change and our methodology in 🧵 bsky.app/profile/epo...

Yesterday, we shared a chart showing 80% of Claude users live in $100k+ households, more than any other major AI service. But Claude’s user base is smaller than other AI services, so this isn’t the same as being the most popular service among high-income households. bsky.app/profile/epo...

80% of US adults who report using Claude in the previous week live in households earning $100,000 or more a year, compared to 37% of Meta AI users. Other major providers cluster in a relatively narrow band, with 56–64% of users in $100,000+ households.

Opus 4.7 scores 156 on ECI, our tool for combining multiple benchmarks onto a single scale. This puts it a bit ahead of Opus 4.6 and a bit behind only GPT-5.4, Gemini 3.1 Pro, and GPT-5.4 Pro. Thread with individual scores and commentary.

In 2025, OpenAI announced Stargate, a $500 billion data center initiative. We surveyed all 7 US sites and found visible development at each. There's a long road ahead, but the project appears on track to reach 9+ GW by 2029—comparable to New York City's peak power demand. 🧵

Have AI capabilities accelerated? On 3 out of the 4 AI capability metrics we investigated, we found strong evidence of acceleration, around when reasoning models emerged.

According to our latest polls, Claude usage in the US rose by over 40% amid increased attention last month, but remains far behind ChatGPT. Our point estimate would imply several million new weekly users in the United States.

Errata: Yesterday, we discovered that some of our chip owner estimates were stale—Oracle's Nvidia compute wasn’t subtracted from "Other" as intended. This inflated “Other” by ~1M H100e, 5% of the overall total. In our corrected figures, hyperscalers hold 71% of world AI compute.

OpenAI has purchased access to the FrontierMath: Open Problems verifiers. This allows them to check the validity of solutions their models generate. Thread with details.

@NBCNews on our recent AI usage survey: www.nbcnews.com/tech/tech-n...

Five companies — Google, Microsoft, Meta, Amazon, and Oracle — now control about two-thirds of the world's compute, up slightly from ~60% at the start of 2024. Many AI labs (including OpenAI and Anthropic) depend almost entirely on these hyperscalers for access to their compute.

What are the largest software engineering tasks AI can perform? In our new benchmark, MirrorCode, Claude Opus 4.6 reimplemented a 16,000-line bioinformatics toolkit — a task we believe would take a human engineer weeks. Co-developed with @METR_Evals. Details in thread.

The Iran War and Hormuz shutdown have disrupted oil, gas, and helium exports and threatened data centers and investments in the Gulf states. @justjoshinyou.bsky.social explores how a prolonged Iran war could affect AI, and why it probably won’t completely derail the compute buildout.

AI is changing our jobs: among people who use AI regularly at work, 27% say AI has replaced some of their tasks; 21% say it has enabled new tasks. This and other workplace usage findings from our new Epoch AI/Ipsos survey on AI usage in 🧵

We had pre-release access to Meta’s new Muse Spark model and evaluated it on FrontierMath. It scored 39% on Tiers 1-3 and 15% on Tier 4. This is competitive with several recent frontier models, though behind GPT-5.4.

New essay by Anson Ho: Chinese and open model AI labs have ≈10× less compute than the frontier. But they can distill frontier models, replicate innovations fast, and have enormous talent. Is that enough to compete at the frontier? 🧵

Who owns the world's compute? Our new Chip Ownership hub shows that Google leads, holding around 25% of all compute sold since 2022.