Brown: Pre-training scaling will hit economic limits before superintelligence without reasoning
Noam Brown · Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI · Jun 19, 2025 · at 23:20
OpenAI research scientist Noam Brown discusses the limitations of relying purely on pre-training scaling to achieve superintelligence.
0:00 / 0:19exact quote · 19.2s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Like, we're gonna scale it, sure, we're gonna scale these things up by a few more orders of magnitude, they're gonna become more capable, but we're not gonna see superintelligence from just that. And like, yes, if we had a quadrillion dollars to train these models, then maybe we would, but like, you're gonna hit the limits of what's economically feasible before you get to superintelligence, unless you have a reasoning paradigm.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Noam Brown
Brown: LLMs implicitly develop world models through scale alone
“I think it's pretty clear that as these models get bigger, they have a world model, and that world model becomes better with scale. So they are implicitly developing a world model, and I don't think it's something that you need to explicitly model.”
Brown: AI models implicitly develop theory of mind through scale
“If these models become smart enough, they develop things like theory of mind. They develop an understanding that there are other agents that like can take actions and have motives and all this stuff. And these models just develop that implicitly with scale and…”
Brown: GPT-4o and o3 are passing the Turing test
“So at this point, like, you know, the truth is, you know, GPT-IV-O and like O-III, these models are like passing the Turing test.”
Brown: Deep Research proves reasoning models work in unverifiable domains
“And that is very clearly a domain where you don't have an easily verifiable metric for success. It's very like, what is the best research report that you could generate? And yet these models are doing extremely well at this domain. So I think that's like an ex…”