# Math-training methods may transfer to AI research

Markdown copy of https://realalpha.app/s/math-training-methods-may-transfer-to-ai-research/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Sam Altman](https://realalpha.app/builders/sam-altman/) · Sam Altman is CEO of OpenAI. · Co-founded OpenAI and has led it since 2019, overseeing ChatGPT's launch; formerly president of Y Combinator.
- Host: [John Collison](https://realalpha.app/people/john-collison/) · Co-founder & president, Stripe · Sold his first startup, Auctomatic, at 17, then co-founded Stripe in 2010.
- Show: Cheeky Pint
- Episode: [Why Sam Altman is against a single superintelligence](https://realalpha.app/v/iMsUcCYNKqY/), Oct 29, 2026
- Moment: 31:38 to 34:50 of the episode (3 min), playing on https://realalpha.app/s/math-training-methods-may-transfer-to-ai-research/; the episode from there: https://realalpha.app/v/iMsUcCYNKqY/?t=1898

## Quote

> “I think if you apply **the same pipeline** that we applied to get our models to be so good at math to AI research, it will probably work.”
> — [Sam Altman](https://realalpha.app/builders/sam-altman/) at [32:24](https://realalpha.app/v/iMsUcCYNKqY/?t=1944) · Cheeky Pint

## Takeaways

1. Models can find modest compute-efficiency gains today.
2. Transformer-scale architecture discovery remains unsolved.
3. Altman thinks math-training methods may transfer to AI research.

## About this segment

Sam Altman says models can find modest efficiency gains but cannot yet invent a Transformer-scale architecture. He thinks methods used to improve math ability may transfer to AI research.

From “Why Sam Altman is against a single superintelligence”, Oct 29, 2026. [Watch the full episode](https://realalpha.app/v/iMsUcCYNKqY/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[31:45](https://realalpha.app/v/iMsUcCYNKqY/?t=1905) people think of like very outside the realm of what most people are using AI to do. How does AI driven AI research actually work? Like the math proofs where you say, or the finding the PHP vulnerability where you say, please find me some AI advances. So I think we don't know. know yet how it truly is going to work. We know today how to say, like, find me a 5% computer efficiency win. But it's that high level. Like, I could be an incredibly successful AI researcher now. I don't know about incredibly, but yeah, if you were looking for a new job, you can come do it. What I don't think anyone knows how to do yet is to say, like, go discover a new architecture that is as big of a single leap forward as the Transformer was. And I think that's where things are heading. And what's missing there? Smarter models

[32:28](https://realalpha.app/v/iMsUcCYNKqY/?t=1948) that are smarter in this dimension. The high-level planning and conceptual reasoning. I think if you apply the same pipeline that we applied to get our models to be so good at math to AI research, it will probably work. Now, per an early part of this conversation, I kind of think that at least for a while, maybe for a long while, we are going to have to agree to some limits about how smart we let these single models go.

[32:51](https://realalpha.app/v/iMsUcCYNKqY/?t=1971) But a lack of progress will come from that decision, not from the AI's inability to do good research. Concretely, if I'm using AI to develop improved AI, because we should expect GBD7 to be really good now, because you have GBD6 and whatever, you know, unreleased version of it to develop it. And so my expectations are sky high. But I come into work, you know, with my backpack, and I come into the OpenAI office in San Francisco, and I sit down at my desk.

[33:22](https://realalpha.app/v/iMsUcCYNKqY/?t=2002) Like, am I saying, find me more intelligence in this model? Am I giving a real direction of research? How directed does it get? What does the iteration loop look like? Just what does RSI, because you have a front row seat to RSI. And I'm curious what it looks like. One of the sort of interesting things that people talk a lot about in OpenAI, which sounds very obvious, but it sounds like a good answer, or just like, well, build me, build me RSI or whatever, is what are the questions that you're supposed to ask of GPT-7?

[33:59](https://realalpha.app/v/iMsUcCYNKqY/?t=2039) Probably it's not enough to just say, like, make me RSI. And also, like, it's probably not enough to say, like, buy me a 5% compute efficiency win. I think this is, like, one of the most interesting debates going on right now. And the answer is not just, like, what question to ask it, but it's things like agent swarms. Like, what are we going, how are we going to use these models in a way that, like, actually maximizes what we can get out of them.

[34:23](https://realalpha.app/v/iMsUcCYNKqY/?t=2063) I think we're going to learn a lot about that in the next, like, three months. Okay, but it sounds like you guys do not have a codified OpenAI standard way of doing an AI-enabled AI development. It's still lots of experimentation. It's still lots of experimentation. Yeah. When we're talking about the token budgets, it just strikes me that this is one of the big unresolved questions