Noam Brown
18 statements across 2 episodes · 5 bullish · 4 bearish · 1 people on the record · first statement Apr 25, 2023 by Noam Brown · said 11 times in 5 episodes since 2023 · across every show →
On the record as a speaker too: Noam Brown's record, appearances and statements → this page counts the times other people say the name.
Mentions by year
brought up most by Sarah Guo (9), Eric Steinberger (1), Elad Gil (1)
tap a year for its mentions
2026 3 mentions in 1 episode
2025 3 mentions in 1 episode
2024 3 mentions in 1 episode
2023 2 mentions in 2 episodes 1 per episode
Everything said about Noam Brown, oldest first
Apr 25, 2023 neutral
Apr 25, 2023 bullish
Inference-time search improved Noam Brown's poker AI performance by 100,000x
“If we were to add this search, this planning algorithm that would come up with a better strategy when it's actually in the hand, how much better could it do? And the answer was it improved the performance by about a 100,000 X. It was the equivalent of scaling …”
Apr 25, 2023 neutral
Data availability is not the true bottleneck for AI scaling
“It's not clear that data really is the bottleneck on performance here. And I've talked to AI researchers about this, and I think there isn't as much of a worry about this as people might think. Probably that's because there's a lot more data that's out there t…”
Apr 25, 2023 neutral
Apr 25, 2023 negative
The Turing test is no longer a useful measure for AI
“I think the Turing test is no longer really a useful measure the way it was intended to be. Certainly Just because we have bots that can, I wouldn't say they can pass the Turing test, but I mean, like they're getting close enough that it's no longer that usefu…”
Apr 25, 2023
Apr 25, 2023 neutral
Apr 25, 2023 bullish
Jun 26, 2026 negative
Brown: Scaffolding Easily Inflates AI Benchmark Scores Without Real Gains
“It's really easy to show you can do much better than previous benchmarks or previous, previous models on benchmarks by just, for example, scaffolding a bunch of models together. So if you say, okay, well, we're going to, instead of just running this model once…”
Jun 26, 2026 negative
Current AI safety frameworks fail to account for test-time compute scaling
“The preparedness frameworks and responsible scaling policies, they don't really account for the amount of tests I'm computed. They just say, okay, well, what's the capability of the model? The problem is we're in a world now where the capability of the model i…”
Jun 26, 2026 negative
Noam Brown: AI community stuck in bad equilibrium publishing static benchmark grids
“I would talk to researchers about we, it makes sense to show the benchmarks with an x-axis, whether it's tokens or cost or time, there should be an x-axis, and everybody would say, like, yeah, that makes sense, we should do that, but. Well, really, their respo…”
Jun 26, 2026 positive
Brown: AI benchmarks must control for test-time compute
“And so I think the proper way to, and so my claim is the proper way to evaluate the models now is you either have some kind of budget for the benchmark, whether it's tokens or cost or time or whatever, or you plot the performance as a function of the amount of…”
Jun 26, 2026
Brown: Test-time AI performance scales along a continuous, projectable slope
“You also do see that like the performance is, is it's not just like a discontinuous jump. It's actually like, you can see the slope of improvement over those hundred million tokens. And so you could probably do some kind of evaluation up to a certain budget an…”
Jun 26, 2026 neutral
Brown: Benchmark Gains From Routing May Fail in Real-World Use
“One issue you could run into is that you could optimize for certain benchmarks with the routing and then show like, oh yeah, we see this big improvement on these benchmarks. But in real world use cases, it actually ends up not being a significant improvement.”
Jun 26, 2026 neutral
Brown: GPT-3 Capabilities Could Not Scale With Test-Time Compute Budget
“Like, with GPT-III, you couldn't scale test time compute. Like, if you gave it a budget of ten million dollars and said, okay, well, let's see what GPT-III can do, it really can't do that much, more than what you could do with, like, 10 dollars or one dollar.”
Jun 26, 2026 positive
Jun 26, 2026 positive
Brown says AI models optimized his PhD poker algorithms by 1,000x
“I was really impressed with the model's ability to optimize the algorithms that I had developed in my PhD. It was honestly, it was shocking to see how inefficient I was in retrospect, and they were able to make it like, you know, 1000 x faster.”
Jun 26, 2026 neutral
Brown: Rapid AI Release Cycles Obscure True Model Capability Ceilings
“The model release cycle is, look, we're releasing new models, like, every two or three months at this point, and so a model comes out, it takes two or three months to push it to its limits, and then you have another model come out, and so nobody actually knows…”