Noam Brown – Agent swarms, alignment, & recursive self-improvement
Dwarkesh Podcast

Noam Brown – Agent swarms, alignment, & recursive self-improvement

B2Upper-intermediateSeptember 17, 20261 hr 20 min
  • Interview
  • Technology

New episode with Noam Brown. 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. Watch on YouTube; read the transcript. Sponsors * Jane Street has been interested in AI for a lot longer than you’d think, and not just for trading. In 2011, a full year before AlexNet and over a decade before ChatGPT launched, they hosted th...

Play episode

Opens the player: the full transcript, every word clickable, with translation and flashcards.

Try it: tap any word

Real sentences from the start of this episode. Every word is marked with its level.

Sentence 2 of 12 · 0:04

Today, I'm chatting with Noam Brown, who is a researcher at OpenAI.

,

and now he's working on multi-agent systems.

Hear this sentence

Tap a word to see its meaning in your language.

Do you know these words?

8 harder words from this episode. Tap one to see what it means, then mark it.

  • B20:04
  • C118:18
  • B229:04
  • B230:22
  • B249:34
  • B259:55
  • C166:48
  • B276:01

Mark a few words and we'll tell you how this episode fits your level.

Is this episode right for you?

B2

B2 · Upper-intermediate

Comfortable if you already follow TV news

A1 22%A2 33%B1 24%B2 16%C1 5%
  • 79% of the words are B1 or easier.
  • 189 words a minute: natural, fairly fast speech.
  • 329 harder words (B2 and up) in the whole episode.

Transcript

The opening of the episode. The whole transcript is in the player.

0:00

Today, I'm chatting with Noam Brown, who is a researcher at OpenAI. He was one of the foundational contributors to what became O1 in the reasoning models, and now he's working on multi-agent systems. Speaking of which, you guys announced last week that you solved one of the Millennium

0:15

Price problems with a system of 10,000 different AI agents that spent 130 billion tokens over 88 hours. One of the reasons I was interested in talking to you is I think you were in the first people maybe two or three years ago who was thinking about how the reasoning models would

0:32

allow us to see into the future because if you scale up inference compute, you can see what the base capabilities of the models will be a few years in the future. And I feel like you're in a similar position now to help us understand what future capabilities will look like given the enormous scaling of agent sizes that we can do right now.

That was the opening. The rest of the episode (79 min) is in the player: every word clickable, with translation, saved words and flashcards.

Continue in the player

Automatically generated from the audio. May contain errors. Audio belongs to its publisher and is played from their feed. Rights holders can request removal — copyright & takedown policy