@OpenAI and @Cerebras have signed a multi-year agreement to deploy 750 megawatts of Cerebras wafer-scale systems to serve OpenAI customers. This has been a decade in the making. Deployment begins in early 2026, and when fully rolled out, it will be the largest high-speed AI inference deployment in the world. OpenAI and Cerebras were both founded in 2015 with radically ambitious goals. OpenAI set out to build the software that would push AI toward general intelligence. Cerebras set out to rethink computing hardware from first principles. Our teams met as far back as 2017. We shared ideas, early work, and a common belief: there would come a point when model scale and hardware architecture would have to converge. That point has arrived. ChatGPT set the direction for the entire industry. It showed the world what AI could be. Now we’re in the next phase - not proving capability, but delivering it at global scale. The history of technology is clear on one thing: speed drives adoption. The PC industry didn’t operate at kilohertz. The internet didn’t change the world on dial-up. AI is no different. As models grow more capable, speed becomes the bottleneck. Slow systems limit what users can do, how often they engage, and whether AI becomes infrastructure or remains a novelty. Cerebras was built for this moment. By keeping computation and memory on a single wafer-scale processor, we eliminate the data-movement penalties that dominate GPU systems. The result is up to 15× faster inference, without sacrificing model size or accuracy. That speed changes product design, user behavior, and ultimately productivity. For consumers, it means AI that feels instantaneous. For the economy, it means agents that can finally drive serious productivity growth. For Cerebras, 2026 will be a defining year. With this collaboration with OpenAI, Cerebras’ wafer-scale technology will reach hundreds of millions - and eventually billions - of users. We’re proud to work alongside OpenAI to bring fast, frontier AI to people around the world. This is what a decade of long-term thinking looks like.

We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper. It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.

Excited to announce @Furientis @DeptofWar OTA contract for 100mi interceptor and $25M Seed raise led by @benchmark! This couldn't have happened w/o our incredible engineering team testing at industry best pace. Join us- we're hiring across all disciplines! furientis.com/careers

The statue of Prometheus is finally finished. It was cast in Paris, crossed the Atlantic, and was assembled at Starbase. It took us 100 days to go from nothing to this. People of the West, we have lived through an era devoid of dreams, devoid of destiny. But the time has come to reclaim our dream of antiquity. In a few months, we will start building a new city. It will be the most magnificent city in all of history. After all, the great dream of the West has always been to rebuild Rome. Come walk beside us in this endeavor. The world is waiting.

The integrated experience in google slide/sheet/email still feels stuck in q4 2024 chatbot era.. if argon is this good, google has an agent harness problem, not a model problem?

Greptile has reviewed over 395,000 pull requests across @NVIDIA‘s large, complex codebases, where keeping up with code review was taking a growing share of engineers' time. On one team, the average time to merge went from more than 24 hours to 6 hours.

Getting from $1M to $100M in ARR took us 18 months. The second hundred took less than six. Last week @WeAreLegora passed $200M. 130,000 lawyers now use Legora on a monthly basis, across 2,100 firms and legal teams in more than 80 countries. Two things from this year I would not have predicted: The first is how fast the US moved. 18 months ago we had no office in North America. Today over half of the AmLaw 50 work with us, out of our offices in New York, Denver, Chicago, Houston, San Francisco and Toronto. The US is our largest market by revenue. The second is where the demand is coming from. More than 40% of our new customers are now in-house legal teams, including Air Canada, Salesforce and Palo Alto Networks. Two thank-yous, before we get back to work. To our customers, who backed a small company from Stockholm with a lot to prove, and who still tell us plainly when we get something wrong. And to the team, engineers and lawyers sitting next to each other, who turn that into something better, faster than any group I have worked with.

New episode of Uncapped with the founder of Cerebras @andrewdfeldman and my partner @ericvishria. We talked about the company's winding road to success, the market around chips, the current state future of the AI supply chain, the role of investors and Eric's relationship with the company, and more. This was one of my favorites, hope you enjoy. Timestamps: (0:00) Intro (1:07) Why Andrew started Cerebras in 2016 (2:54) Eric on investing without experience in chips (4:10) Attacking Goliath (9:44) Near-death experiences and the valley of death (10:44) 18 months of "still can't make it" (12:16) Solving a 75-year-old compute problem (13:26) What comes after wafer scale (16:19) The chip supply chain explained (22:30) Why the US punted a strategic industry (25:51) How to be a good hardware board member (27:50) Hardware vs. software investing (29:05) The pivot from training to inference (27:00) Specialization vs. flexibility (35:22) Building effective teams for chips (39:14) External relationships and TSMC (42:20) The AI infrastructure buildout (44:12) The data center supply chain (53:14) Speed creates markets (54:10) Disaggregation with AMD and AWS (55:24) What actually makes Nvidia great (57:30) Near-death experiences (59:58) Andrew's childhood next to William Shockley

New episode of Uncapped with the founder of Cerebras @andrewdfeldman and my partner @ericvishria. We talked about the company's winding road to success, the market around chips, the current state future of the AI supply chain, the role of investors and Eric's relationship with the company, and more. This was one of my favorites, hope you enjoy. Timestamps: (0:00) Intro (1:07) Why Andrew started Cerebras in 2016 (2:54) Eric on investing without experience in chips (4:10) Attacking Goliath (9:44) Near-death experiences and the valley of death (10:44) 18 months of "still can't make it" (12:16) Solving a 75-year-old compute problem (13:26) What comes after wafer scale (16:19) The chip supply chain explained (22:30) Why the US punted a strategic industry (25:51) How to be a good hardware board member (27:50) Hardware vs. software investing (29:05) The pivot from training to inference (27:00) Specialization vs. flexibility (35:22) Building effective teams for chips (39:14) External relationships and TSMC (42:20) The AI infrastructure buildout (44:12) The data center supply chain (53:14) Speed creates markets (54:10) Disaggregation with AMD and AWS (55:24) What actually makes Nvidia great (57:30) Near-death experiences (59:58) Andrew's childhood next to William Shockley

A lot of otherwise smart people on Twitter seem 100% convinced AI risks are all fake and stupid and part of some marketing ploy. What is surprising is that some of these people seemingly also believe that AI’s positive uses are on an incredible trajectory of increasing capability with no end in sight. VC Twitter is particularly infected by this pattern. It’s not really coherent. Most positive use cases for AI have a corresponding “dark version”. If you are super human at coding, you are also super human at hacking. If you are superhuman at structural engineering you are likely superhuman at finding structural flaws to knock buildings down. If you are superhuman at designing drugs, you are superhuman at designing novel undetectable poisons. If you can cure viruses, you can create them. Some of these “dark versions” are not so bad, and some are actually pretty scary. Either way these are real societal and technical problems that need to be solved to get the good stuff and avoid the bad stuff. We’re experiencing the first of these with coding and computer security which is the most advanced, but that won’t be the last. I think we’ll be able to solve these problems, but they aren’t solved yet and if you believe in continued AI progress they are surely coming. But “bad people using AI” is not the only problem. Uncontrolled AI autonomously doing bad things, despite sounding kind of nutty, is also something we should be concerned about. AI “killing us all” is not the most likely outcome, but the chance of a major civilization-wide catastrophe doesn’t have to be very high for it to be a concern. Again, this is only a problem if capabilities advance to a point that AI can do really crazy things on their own, which hasn’t happened yet, but I think the Hugging Face incident is a good example of the outline of how things can go wrong when capabilities outpace alignment. We should be glad that the only available bad thing right now is hacking, which isn’t all that bad. It’s clear that as you get to superhuman capabilities you need a level of alignment and control that is correspondingly superhuman. Humans have plenty of misalignment problems themselves (serial killers, mass shooters, tyrants, etc), but it’s a manageable problem because most humans can’t do that much damage and we’ve developed systems to prevent dangerous humans from getting too much power. Talking about these issues is just common sense. It’s not a sign of some kind of neuroticism or pessimism. These are just hard problems that it’s very important to solve for AI to have a positive impact. I’m pretty confident we will solve them. But we haven’t solved them yet, and to my mind we are clearly on a trajectory of rapidly increasing capabilities which means this is important. Mocking people who are worried about this or talking about it, without anything substantive to say about how we can be sure these problems won’t arise, is not really a very helpful contribution.