xAI's Colossus 2 data center is running, but likely won't reach 1 GW of power until May, despite prior claims by Elon Musk. Our updated analysis shows the facility lacks the cooling capacity to run 550,000 Blackwell GPUs at full power, even in winter conditions.

AI data centers can now use as much power as New York State uses on the hottest days of the years. We find that data centers currently have a total capacity of around 30 GW.

How well did forecasters predict 2025 AI progress? According to the AI Digest's survey, forecasters: - Mostly nailed benchmark scores - Underestimated risks from AI-enabled bioweapons - Underestimated revenue by almost 2× - Overestimated public concern about AI Details in 🧵

Our 2025 Impact Report is out. The AI industry is scaling exponentially - investment, compute, data center buildouts. So, it turns out, is demand for making sense of it all. See how we’ve kept up!

We loved this quick visual rundown of the Frontier Datacenters hub. Thanks to Rowan Cheung for featuring our project! www.youtube.com/shorts/szAW...

Frontier labs are investing massively in RL environments, yet most of what happens in this space stays behind closed doors. @chrisbarber and @js_denain interviewed 18 people from RL environment startups, neolabs, and frontier labs. Here's what they found:

Anthropic's data center in Indiana is likely the largest in the world today: 750 megawatts by our calculations. Soon, it will pass the gigawatt milestone. How did they do it, and why do we think it's this big? 🧵

Total AI compute is doubling every 7 months. We tracked quarterly production of AI accelerators across all major chip designers. Since 2022, total compute has grown ~3.3x per year, enabling increasingly larger-scale model development and adoption. 🧵

Global AI compute capacity now totals over 15 million H100-equivalents. Our new AI Chip Sales data explorer tracks where this compute comes from across Nvidia, Google, Amazon, AMD, and Huawei, making it the most comprehensive public dataset available.

Since 2023, every model at the frontier of AI capabilities has come from the United States. Chinese models have trailed by 7 months on average.

It's been a big year for Epoch – we published more plots in 2025 than in all previous years combined!

The scale of decentralized AI training runs over the internet has grown exceptionally fast in recent years. Could they catch up to the frontier of compute? 🧵

Benchmarking is crucial to keep track of AI progress. However, benchmarking is hard: each step of the pipeline involves moving parts that can affect the final headline result. Let's dive deeper:

AI capabilities accelerated in 2024! According to our Epoch Capabilities Index, frontier model improvement nearly doubled, from ~8 points/year to ~15 points/year.

Looking back on 2025: what were our most popular short-form research posts? We published 36 Data Insights and 37 Gradient Updates newsletters. Here's what our website readers found most interesting: 🧵

We benchmarked several open-weight Chinese models on FrontierMath. Their top scores on Tiers 1-3 lag the overall frontier by about seven months.

We took an updated look at the evidence on AI adoption. Key finding: AI adoption has continued to grow faster than almost any other technology in history — but the drivers of this have started to change. 🧵

Over the past month we've partnered with @blueroseorg to survey 5,660 Americans about their AI usage habits. Today we're releasing the results. A key takeaway: A majority of Americans use AI on a weekly basis, with 35% using ChatGPT, 24% Gemini, and 13% Meta AI.

Gemini 3 Flash scored 36% on FrontierMath Tiers 1–3, comparable to top models. It scored comparatively less well on the harder Tier 4.

GPUs only account for about 40% of total power in a frontier AI data center at peak operation. Where does the rest go? 🧵

Will AI drive Europe towards a “high interest rates, no growth” future? How will automation impact entry-level workers and the structure of firms? What role does Europe play as the US and China continue to push AI progress?

Conventional wisdom says that the US can’t build power but China can, so China’s going to “win the AGI race by default”. We think this is wrong. The US likely can build enough power to support AI scaling through 2030 — as long as they’re willing to spend a lot. A thread:

What if AI capabilities suddenly accelerated in 2027? How would the world know? We developed a statistical framework that lets us detect capability accelerations from benchmark scores. In simulations with synthetic benchmark data, we spot a 2× acceleration within 2-3 months.

We’ve added ECI scores for Gemini 3 Pro, Opus 4.5, and GPT-5.2. ECI is computed from many benchmarks, but it correlates with other benchmarks too. So, we can use it to make predictions! Here’s what we get for METR’s Time Horizons. Gemini 3 Pro: 4.9h GPT-5.2: 3.5h Opus 4.5: 2.6h

We just opened applications for two new roles—a part time design contractor, and full-time software engineer for our benchmarking team! Designer applications are rolling, but apply by Jan. 11 to be considered for our benchmarking team. As always, both roles are fully remote! 📈

GPT-5.2 scores 152 on the Epoch Capabilities Index (ECI), our tool for aggregating benchmark scores. This puts it second only to Gemini 3 Pro. 🧵 with individual scores.

We upgraded our ECI methodology! Our initial release treated different model settings separately—e.g. Claude Sonnet 4.5 16k and 32k thinking each got separate scores. Starting today we will use the best score for each benchmark across a model's evaluation settings.

We’ve made two additions to the suite of benchmarks we run ourselves: SimpleQA Verified and a new benchmark of chess puzzles! We will use these easy-to-run benchmarks to get quick estimates of our Epoch Capabilities Index (ECI) for newly-released models. Details and examples in 🧵

Individual AI benchmarks saturate too quickly to give us a long-run trend of AI progress. We can solve this by "stitching" them together. This lets us forecast AI capabilities, quantify algorithmic improvements, and detect accelerations in AI progress. www.youtube.com/watch?v=vwiD...

How much does it cost NVIDIA to make a B200 GPU? By our estimate, around $6,400, implying chip-level gross margins of ~80%. The counterintuitive part: the logic die, the core component of the GPU, is one of the cheaper parts, accounting for less than 15% of the total cost. 🧵

Microsoft's Fairwater Atlanta (today's largest data center) could likely train over 20 models the size of GPT-4 in the course of a month. This computational power will enable AI companies to increase the number and scale of both experiments and training runs.

We’ve identified an Alibaba data center in Zhangbei, China with an estimated 200–500 MW capacity. Roughly half of that predates the AI boom and likely hosts little modern compute. But several newer buildings show a high power density consistent with advanced AI chips. 🧵

Most AI benchmarks share a common flaw: they saturate too quickly to study long-run trends. Our solution: “stitch” many benchmarks together. This lets us compare models across a wide range of capabilities on a single unified scale. Here’s how this works.🧵

As 2025 closes, we're gathering stories about how our research has informed decisions, shaped strategies, or sparked new thinking this year. Your feedback helps us double down on what's working, prune what’s not, and sharpen our 2026 priorities. 10-min survey follows. 👇

In May, OpenAI announced a 1 GW Stargate cluster in Abu Dhabi, with an initial 200 MW expected in 2026. We located this cluster from space and found it will barely make that 200 MW milestone, by end of 2026. Optimistically, the cluster can scale to 1 GW by Q3 2027. See how in 🧵

Within two years, the largest data centers may use more power than major cities. We think Microsoft's Fairwater Wisconsin campus will have a peak electrical load of 3.3 GW when its fourth building comes online in late 2027. (Los Angeles used 2.4 GW on average in 2023)

How Claudey is Opus 4.5? We previously described Claudiness as "good at agentic tasks while being weaker at multimodal and math". This pattern remains when comparing Opus 4.5 to other newly-released models, though the gap on agentic coding and tool-calling benchmarks is small.

We benchmarked Opus 4.5 on FrontierMath. It scored 21% on FrontierMath Tiers 1–3, continuing a trend of improvement for Anthropic models. This score is behind Gemini 3 Pro and GPT-5.1 (high) while being on par with earlier frontier models like o3 (high) and Grok 4.

Gemini 3 Pro set a new record on GPQA Diamond: 93% vs. the previous record of 88%. What you can’t tell from the headline: almost all of this gain came in organic chemistry. 🧬🧵

We’ve optimized our Frontier Data Centers hub for mobile. You can now examine annotated, recent, high-resolution satellite imagery of the world's largest compute clusters directly from your phone at epoch.ai/data/data-c.... Here’s a look at the updated Satellite Viewer:

Gemini 3 Pro set a new record on FrontierMath: 38% on Tiers 1–3 and 19% on Tier 4. On the Epoch Capabilities Index (ECI), which combines multiple benchmarks, Gemini 3 Pro scored 154, up from GPT-5.1’s previous high score of 151.

Benchmarking data is dominated by a single “General Capability” dimension. Is this due to good generalization across tasks, or to developers pushing on all benchmarks at once? 🧵 with some analysis, including the discovery of a “Claudiness” dimension.

It’s easy to talk about ‘large AI data centers’ and still underestimate the scale. Our Frontier Data Centers database shows that some upcoming campuses will cover a substantial portion of Manhattan. Meta's Hyperion data center will be nearly four times the size of Central Park.

GPT-5.1 is about as capable as GPT-5. That’s according to the Epoch Capabilities Index, our tool for combining results across multiple benchmarks. With “high” reasoning, both GPT-5.1 and GPT-5 score 151 on ECI. See 🧵 for individual benchmark scores!

Data centers supporting AI training runs could require 1-5 GW by 2030, enough to power entire cities. Join us for a live webinar/Q&A on our new Frontier Data Centers Hub, exploring what this infrastructure buildout means for AI. Nov 20, 1-2 PM PT luma.com/oste01d0

Sam Altman: “Our goal is, by March of 2028, to have a true automated AI researcher” Some say this kicks off a software singularity, where AIs recursively improve themselves and rapidly get smarter. Others think there’ll be a bottleneck. So how can we tell who’s right? 🧵

AI data center buildouts already rival the Manhattan Project in scale, but there’s little public info about them. So we spent the last few months reading legal permits, staring at satellite images, and scouring news sources. Here’s what you need to know. 🧵

How fast can you build a gigawatt-scale data center? Some hyperscalers plan to do it in just 1-2 years from the start of construction. If they succeed, we’ll see the first GW-scale data centers online in 2026, marking one of the fastest infrastructure build-outs in history. 🧵

The Epoch Capabilities Index is a useful way to measure model capabilities, but what does a score of 150 actually mean? One way to read our new capability index is by plotting the benchmark performance you expect to see, for a range of ECI scores 🧵

Anthropic's recently-reported projection of $70B revenue in 2028 may be less than OpenAI's projection for the same year, but it would still represent historically fast growth. bsky.app/profile/epo...