About 1 in 4 US workers (26%) used a paid AI service for their job in the past week, according to our October poll. This includes AI paid for by their employer, by themselves, or by both. Another 15% used only free versions, and 55% didn’t use AI for work at all in the past week.

In recent experiments, we found that GLM 5.3 can autonomously compromise recent versions of Mozilla Firefox using security fixes that have been published but not yet shipped to users. We used a deliberately basic setup meant to emulate a relatively unsophisticated attacker with limited resources. After basic initial instructions to inspect those not-yet-shipped fixes, the model reliably built successful exploits for six recent Firefox versions on its own using only information available at the time of release. These exploits, which cost about $1,000 per compromised version, would let an attacker steal passwords and authentication cookies stored in the browser, or read any files on the user's computer, after the user opens a single webpage, with no further interaction. The exploits make use of the "patch gap," the time between when security fixes are made public in beta or "nightly" software updates and when those fixes are generally released to users. Until now, exploiting it required significant human time and skill, or special access to frontier models with advanced cyber capabilities. Our results show that widely available open-weight models can now do this relatively cheaply and quickly. We gave GLM 5.3 up to 48 hours to find an exploit, but its fastest attempt needed only 12 hours. In all of its successful attacks, the patch gap gave GLM 5.3 an initial foothold in the browser. To turn that foothold into reading files off the user's computer, the model also independently discovered and used a previously unpublished vulnerability. We shared our findings with Mozilla, which patched the vulnerability in its October 27 Firefox 159.0 release. We haven't yet confirmed whether the attack still succeeds without it, but expect the fix makes it substantially harder. We're grateful to Mozilla for their responsiveness and collaboration.

Since 2022, 72% of the world’s AI compute has ended up in the US and about 10% in China. And the US share keeps growing. It acquired around 60% of new compute each year from 2022 to 2024, then 72% in 2025, and 78% in the first half of 2026. This data comes from our interactive AI Chip Imports explorer, which estimates and visualizes national compute shares using customs records. It’s the latest piece in our project to map the AI chip supply chain, which also provides data on the components used to manufacture chips, sales volumes, the data centers that house chips, and the companies that own and use them. More on this new data and what we’ve learned below.

We recorded over a thousand runs of Slay the Spire and its sequel for our recent benchmark, SpireClimb. Over the coming weeks, we'll publish them all on our YouTube channel, complete with subtitles showing the models' in-game thinking. Check out the run that produced the clip: And the playlist where we're uploading all videos: Rather than being recorded live, the videos are reconstructed from the models’ play data (inputs and thinking). We don't plan to update the playlist with videos from future models' runs on SpireClimb, but we hope this archive offers interesting insights into how current models play the game.

How far behind the closed frontier are open-weight cyber capabilities? About 6–7 months. Zai’s GLM-5.3 is the highest-scoring open-weight model on our Cyber ECI. Its score of 160 beats OpenAI’s GPT-5.4, released in March.

The New York Times visualizes the staggering amount of math needed to train AI models, using data from Epoch's research teams 📊 Our training compute estimates: The Epoch Capabilities Index (ECI):

Gemini 4 Argon scores 165 on the Epoch Capabilities Index (ECI), on par with Fable 5.1 (165) but behind Opus 5.5 (167) and GPT-6 Astra (166). While behind the ECI frontier, Argon is the closest Google’s come since Gemini 3.1 Pro in Feb, and a big jump from Gemini 3.8 Flash (157)

Six months ago, frontier AI models could barely beat Slay the Spire. Now, according to our new SpireClimb benchmark, they win 90%+ of runs at the easiest difficulty, and up to 20% of runs at the hardest.

Can AI automate Epoch? We're introducing Epoch Automation Reports to evaluate frontier models on realistic, open-ended tasks drawn from our own work. Claude Fable 5.1 and GPT-6 Astra lead, yet they are far from fully automating Epoch’s work.

We’re planning to periodically rerun InnovationEval with new, uncontaminated papers. We hope this will provide early signs if AI approaches automating AI R&D end-to-end, rather than performing individual tasks under human direction. Read more at our website:

AI developers aim to create an automated AI researcher. How close are they? To find out, we built InnovationEval, which tests whether AI can produce post-training innovations comparable in magnitude to a recently published advance. So far, agents’ results are underwhelming.

Who is more exposed to a semiconductor supply shock, China or the US? We split out semiconductors in the ICIO tables economists use to trace supply chains and found that China's exposure is about 2.7× that of the US. Our updated tables, which extend work by the OECD, let us model the impact of a major supply-chain shock. We simulate the global economy 2–3 years after a large fall in Taiwanese productivity plus a full China–West trade decoupling. Our simulation shows China's real GNE falling by ~3% vs ~0.6% for the US. The Chinese electronics industry would pay 81% more for chips overall, and 17× for domestic advanced processors. This work has limitations. ICIO tables run to 2022 and involve heavy imputation and balancing, and our estimates are built on those estimates. Our simulations also rest on some strong assumptions. However, across robustness checks, China's 2022 exposure stays over 2.3× that of the US, and an exploratory extrapolation to 2024 only cuts the gap to 2.2×. The loss to China’s real GNE remains at least 3.7× the US loss, and China-made processors always rise in price more than US-made ones. Read @DanCareyEcon’s write-up and download the full research paper:

Claude Opus 5.5 has taken the top spot on the Epoch Capabilities Index (ECI) with a score of 167, narrowly ahead of GPT-6 Astra. Claude Sonnet 5.5 has roughly matched Claude Fable 5.1 (165).

AI is getting cheaper more quickly than any other transformative tech in history. At a given level of performance, cost has fallen ~47%/quarter since 2023. That’s 4× faster than DNA sequencing, 6× faster than compute, 18× faster than lithium batteries, and (up to 1973) 54× faster than electricity.

The share of math preprints on arXiv that acknowledge AI has risen rapidly, from 4% in April to 25% in August. This increase holds even when filtering to papers with at least one author who published regularly before 2023.

Another problem from FrontierMath: Open Problems has been solved! The solution was elicited by Becker, Greger, and @DominikPeters in an interactive session with GPT-6 Astra. Peters originally suggested the problem for the benchmark. He had this to say.

Introducing Benchmark Reviews: our new initiative to audit AI benchmarks. We are launching with 15 benchmarks: 4 Verified, 9 Flawed, and 2 with not enough information for a review.

Trade data is consistent with more than $3B of chips smuggled into China via Malaysia. Between April 2024 and June 2025, China recorded $3.8 billion in server imports from Malaysia, averaging $106,000 each. Malaysia declared the same shipments at $17,000 each.

Earlier this week, we showed that GPT-5.6 and Claude 5 exhibit very different long-context TTFT scaling, which corresponds to their different pricing structures at long context lengths. OpenAI recently released GPT-6 Astra, which shows the same pattern of increased API pricing beyond 272k input tokens. We collected additional latency measurements, which show a similar curvature to that of GPT-5.6 models.