Epoch AI

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.

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.

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.

Can AI tell if you've built your IKEA furniture wrong? Our new benchmark, the Furniture Assembly Benchmark (FAB), gives models the manual and a photo of a half-completed piece of furniture and asks them to spot the mistake. The top score has gone from 28% to 80% in just 10 months.

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.

GPT-6 Astra leads the Epoch Capabilities Index (ECI), ahead of competing models like Claude Fable 5.1, and its Math-ECI also sets a new record. However, on software engineering benchmarks, Fable 5.1 remains state-of-the-art.

We're scaling our AI Data Centers research to provide a more comprehensive map of the world's AI infrastructure. Our data now captures ~44% of global AI compute, with coverage growing fast.

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.

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.

The share of US adults experiencing a cyber incident hasn't grown since Claude Fable 5's release. About 45% reported at least one incident in the past 12 months when we asked in September, compared with 46% in June. Other indicators have increased, such as vulnerability disclosures from notable organizations (around 5× per our previous research). Our poll with @Ipsos measures something different: whether more people experience incidents themselves.

OpenAI recently published data on its researchers’ coding-agent usage. We took their weekly figures from January to mid-August 2026 and fitted trends to summarize how quickly spending grew. We found that spending has been doubling about once a month.

We estimate AI infrastructure could soon support hundreds of millions or even billions of AI agents. At the high end, that’s enough to rival the working hours of the global human workforce. The exact number of agents depends on the efficiency of the models they run on and whether soaring demand for AI continues. We analyzed different scenarios 🧵