Noam Brown
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27 October
Every winter, @MIT has run a 1-month class (6.9630) in which students make poker AIs and compete against each other for cash prizes. Coding agents have accelerated that process so much that it's now possible to build a poker bot in an *hour*. Also, I'll be at the finals! Quoting @sequoia Announcing No Limit: a bot building tournament hosted by @Sequoia, @citsecurities and @OpenAI. Teams of 1 or 2 get 60 minutes to build a pokerbot from scratch. Agents play thousands of games across six scored rounds. Open to university students and recent grads (‘25 or ‘26). Regionals in Boston, Pi… coding agents
Only 2.5 years ago, AI labs would highlight their frontier LLM performance on grade school math benchmarks (GSM8K). I can't wait to see where we'll be in another 2.5 years. Quoting @_AashishReddy It just never gets old LLMs benchmarks
23 October
Last month a large team of agents used over 100 billion tokens to resolve the Navier-Stokes Millennium Prize problem. Now we're using a similar approach for red-teaming to find and fix infra vulnerabilities. Quoting @Eric_Wallace_ To make sure our frontier models don't cause another HuggingFace incident, we built an offline world that they use during training. It provides everything that the models need in a fully cached, indexed, or synthetic manner. We then built a huge team of our most cyber-capable models and told them t…
11 October
This phishing email made it past Gmail’s filters, but my dot caught it and warned me.
7 October
LLMs have crossed an important threshold: surpassing top human experts on some research problems. Capabilities have been steadily improving for years, but in scientific discovery there is a big difference between a model just below top human performance and one just above it. That helps explain why the surge in math results feels so sudden. LLM capabilities remain jagged, and there are many ways they are still worse than humans. But as models continue to improve, I expect breakthroughs in other domains to follow, leading to a new era of scientific discovery. Quoting @markchen90 The Navier-Stokes moment was always much more about the figure below than about the Navier-Stokes problem itself. The run represents a decade of mathematical progress in a week. Can't wait to point these tools at life sciences, building our next models, and alignment! LLMs
30 September
I met @ssokota 5 years ago at a conference poster session. I was impressed with his explanation, so I offered him an internship. We continued collaborating and he now works with me at @OpenAI. Later, he told me I was the only person to stop by his poster for the entire session. Quoting @ssokota In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. 1/N OpenAI
29 September
I’m happy that @OpenAI presents model evals this way. We should measure intelligence as a function of cost. Quoting @OpenAI GPT-6.1 Sol: near-Astra intelligence for a fifth of the price. It’s the most cost-efficient model for its performance available today. OpenAI
Was testing out dots over the weekend and it figured out how to save me ~$500/yr in recurring charges. Most impressive part was when it connected to customer service and handled a whole texting convo on my behalf. Quoting @OpenAI Introducing dots, powered by GPT-6 Astra. Remarkably capable, always-on agents built to handle everything.
22 September
GPT-6 Sol and Luna are out, and they are better AND 50% cheaper than 5.6. Luna is now $0.10 input / $0.50 output per 1M tokens. This is on top of the 80% price cut to Luna we made at the end of July. Output went from $6 -> $0.50 within two months. Quoting @OpenAI Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the sav…
18 September
A few thoughts on this: 1) If you’ve only seen clips of this interview, I’d encourage you to watch the full podcast. I push back on plenty of AI hype in it. 2) As I said in the podcast, this example is academic. My intention was to illustrate how hard it is to make absolute guarantees about isolation, which is why it's important to have layers of defense. The part before the clip starts is me talking about other layers of defense. 3) The example I'm bringing up isn't about weight exfiltration via temperature sensors, it's about coordination between agents that are supposed to be fully isolate… Quoting @firesidealpha OpenAI's Noam Brown says air-gapping the computers may not stop a misaligned AI, because two air-gapped machines can still talk by running a CPU hot and reading the temperature change "But I think the major takeaway from the incident is that people underestimated the AI. And we never want to be in…
17 September
Happy to finally do a deep dive on multi-agent with @dwarkesh_sp! None of it would have happened without the great work on multi-agent from my @OpenAI teammates @kevinleestone, @mikegmalek, @__eknight__, @amuellerml, @zhangir_azerbay, @CheukHeiChu, and many others. Quoting @dwarkesh_sp New episode with @polynoamial We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. 0:00:00 – Multi-agent a… OpenAI
8 September
Very sad to see Levent double down on the plagiarism accusation. I hope my friends at @AnthropicAI stand up to this internally. It should be clear by now what the truth is.
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