Sam Altman || X Chat @samaltm

David Sacks really understands AI and cares about the US leading in innovation. I am grateful we have him.

David Sacks @DavidSacks

7 months ago

INSIDE NYT’S HOAX FACTORY Five months ago, five New York Times reporters were dispatched to create a story about my supposed conflicts of interest working as the White House AI & Crypto Czar. Through a series of “fact checks” they revealed their accusations, which we debunked

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It has been amazing to watch the progress of the Codex team; they are beasts. The product/model is already so good and will get much better; I believe they will create the best and most important product in the space, and enable so much downstream work.

A first preview of something we expect to see a lot more of soon:

Sebastien Bubeck @SebastienBubeck

7 months ago

3 years ago we could showcase AI's frontier w. a unicorn drawing. Today we do so w. AI outputs touching the scientific frontier: cdn.openai.com/pdf/4a25f921-e… Use the doc to judge for yourself the status of AI-aided science acceleration, and hopefully be inspired by a couple examples!

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New Codex model is a significant improvement!

prinz @deredleritt3r

7 months ago

METR (50% accuracy): GPT-5.1-Codex-Max = 2 hours, 42 minutes This is 25 minutes longer than GPT-5.

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Congrats to Google on Gemini 3! Looks like a great model.

The rate reduction in price per unit of intelligence has been thing I've most consistently underestimated the past couple of years. 300x in a year is nuts!

Chris @ChrissGPT

7 months ago

GPT-5.1 (Thinking High) is about 300 times cheaper per task than o3-preview (Low) while scoring only a few points lower on ARC-AGI-1. 1 year later intelligence has gotten 300 times cheaper. This is why I can’t stand people who say “wahh the models too expensive” it will become

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a new type of research company!

Louis Andre @louisnandre

8 months ago

Today, we're announcing @episteme, a new type of R&D company that recruits exceptional scientists to pursue high-impact ideas. Science isn’t bottlenecked by the availability of talent, but by places where they can do their best work. Scientific progress has driven human

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This is exciting; I expect we are going to see a lot more things like this and it will be one of the most important impacts of AI. Congrats to the Future House team. edisonscientific.com/articles/annou…

Small-but-happy win: If you tell ChatGPT not to use em-dashes in your custom instructions, it finally does what it's supposed to do!

GPT-5.1 is now available in the API. Pricing is the same as GPT-5. We are also releasing gpt-5.1-codex and gpt-5.1-codex-mini in the API, specialized for long-running coding tasks. Prompt caching now lasts up to 24 hours! Updated evals in our blog post.

Understanding neural networks through sparse circuits:

OpenAI @OpenAI

7 months ago

We’ve developed a new way to train small AI models with internal mechanisms that are easier for humans to understand. Language models like the ones behind ChatGPT have complex, sometimes surprising structures, and we don’t yet fully understand how they work. This approach

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We’re fighting this overreach on user privacy. As @sama has mentioned before, we need a new form of privilege - AI privilege - given some of the kinds of conversations people are having with these tools today. Fittingly, Nils Gilman published an op-ed in the NYT discussing AI privilege just the other day. The conversation by conversation analysis necessary to provide that kind of respect highlights just how wild it is to ask for 20m conversations indiscriminately.

OpenAI @OpenAI

7 months ago

A letter from our CISO: "Fighting the New York Times’ invasion of user privacy" openai.com/index/fighting…

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GPT-5.1 is out! It's a nice upgrade. I particularly like the improvements in instruction following, and the adaptive thinking. The intelligence and style improvements are good too.

Also, we've made it easier to customize ChatGPT. You can pick from presets (Default, Friendly, Efficient, Professional, Candid, or Quirky) or tune it yourself.

Welcome @sk7037 to OpenAI! Incredibly excited to work with him on designing and building our compute infrastructure, which will power our AGI research and scale its applications to benefit everyone.

This is an important one, I think. AI progress and recommendations: openai.com/index/ai-progr…

The government has played a role in critical infrastructure builds. Our public submission (posted on our blog) shares our thinking and suggests ideas for how the US government can support domestic supply chain/manufacturing. This is very in line with everything we have heard from the government about their priorities. We think US reindustrialization across the entire stack--fabs, turbines, transformers, steel, and much more--will help everyone in our industry, and other industries (including us). To the degree the government wants to do something to help ensure a domestic supply chain, great. This is part of a national policy that makes sense to me. But that's super different than loan guarantees to OpenAI, and we hope that's clear. It would be good for the whole country, many industries, and all players in those industries.

Dean W. Ball @deanwball

8 months ago

Some thoughts on the whole 'OpenAI loan guarantee" situation. 1. First, for context: this issue began a few days ago when openai CFO Sarah Friar publicly floated the idea of the federal government providing a loan guarantee for the development of ai data centers. 2. I, and

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I would like to clarify a few things. First, the obvious one: we do not have or want government guarantees for OpenAI datacenters. We believe that governments should not pick winners or losers, and that taxpayers should not bail out companies that make bad business decisions or otherwise lose in the market. If one company fails, other companies will do good work. What we do think might make sense is governments building (and owning) their own AI infrastructure, but then the upside of that should flow to the government as well. We can imagine a world where governments decide to offtake a lot of computing power and get to decide how to use it, and it may make sense to provide lower cost of capital to do so. Building a strategic national reserve of computing power makes a lot of sense. But this should be for the government’s benefit, not the benefit of private companies. The one area where we have discussed loan guarantees is as part of supporting the buildout of semiconductor fabs in the US, where we and other companies have responded to the government’s call and where we would be happy to help (though we did not formally apply). The basic idea there has been ensuring that the sourcing of the chip supply chain is as American as possible in order to bring jobs and industrialization back to the US, and to enhance the strategic position of the US with an independent supply chain, for the benefit of all American companies. This is of course different from governments guaranteeing private-benefit datacenter buildouts. There are at least 3 “questions behind the question” here that are understandably causing concern. First, “How is OpenAI going to pay for all this infrastructure it is signing up for?” We expect to end this year above $20 billion in annualized revenue run rate and grow to hundreds of billion by 2030. We are looking at commitments of about $1.4 trillion over the next 8 years. Obviously this requires continued revenue growth, and each doubling is a lot of work! But we are feeling good about our prospects there; we are quite excited about our upcoming enterprise offering for example, and there are categories like new consumer devices and robotics that we also expect to be very significant. But there are also new categories we have a hard time putting specifics on like AI that can do scientific discovery, which we will touch on later. We are also looking at ways to more directly sell compute capacity to other companies (and people); we are pretty sure the world is going to need a lot of “AI cloud”, and we are excited to offer this. We may also raise more equity or debt capital in the future. But everything we currently see suggests that the world is going to need a great deal more computing power than what we are already planning for. Second, “Is OpenAI trying to become too big to fail, and should the government pick winners and losers?” Our answer on this is an unequivocal no. If we screw up and can’t fix it, we should fail, and other companies will continue on doing good work and servicing customers. That’s how capitalism works and the ecosystem and economy would be fine. We plan to be a wildly successful company, but if we get it wrong, that’s on us. Our CFO talked about government financing yesterday, and then later clarified her point underscoring that she could have phrased things more clearly. As mentioned above, we think that the US government should have a national strategy for its own AI infrastructure. Tyler Cowen asked me a few weeks ago about the federal government becoming the insurer of last resort for AI, in the sense of risks (like nuclear power) not about overbuild. I said “I do think the government ends up as the insurer of last resort, but I think I mean that in a different way than you mean that, and I don’t expect them to actually be writing the policies in the way that maybe they do for nuclear”. Again, this was in a totally different context than datacenter buildout, and not about bailing out a company. What we were talking about is something going catastrophically wrong—say, a rogue actor using an AI to coordinate a large-scale cyberattack that disrupts critical infrastructure—and how intentional misuse of AI could cause harm at a scale that only the government could deal with. I do not think the government should be writing insurance policies for AI companies. Third, “Why do you need to spend so much now, instead of growing more slowly?”. We are trying to build the infrastructure for a future economy powered by AI, and given everything we see on the horizon in our research program, this is the time to invest to be really scaling up our technology. Massive infrastructure projects take quite awhile to build, so we have to start now. Based on the trends we are seeing of how people are using AI and how much of it they would like to use, we believe the risk to OpenAI of not having enough computing power is more significant and more likely than the risk of having too much. Even today, we and others have to rate limit our products and not offer new features and models because we face such a severe compute constraint. In a world where AI can make important scientific breakthroughs but at the cost of tremendous amounts of computing power, we want to be ready to meet that moment. And we no longer think it’s in the distant future. Our mission requires us to do what we can to not wait many more years to apply AI to hard problems, like contributing to curing deadly diseases, and to bring the benefits of AGI to people as soon as possible. Also, we want a world of abundant and cheap AI. We expect massive demand for this technology, and for it to improve people’s lives in many ways. It is a great privilege to get to be in the arena, and to have the conviction to take a run at building infrastructure at such scale for something so important. This is the bet we are making, and given our vantage point, we feel good about it. But we of course could be wrong, and the market—not the government—will deal with it if we are.

A thing often in common among great startup investors, founders, and researchers: Trading making a lot of small mistakes in exchange for getting a few giant wins. (Surprisingly many people seem to prefer a few big mistakes in exchange for a lot of small wins.)

interesting post from @boazbaraktcs: windowsontheory.org/2025/11/04/tho…