# OpenAI wants AI to conduct research end to end

Markdown copy of https://realalpha.app/s/openai-wants-ai-to-conduct-research-end-to-end/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Mark Chen](https://realalpha.app/builders/mark-chen/) · Mark Chen is chief research officer at OpenAI. · Led the teams behind DALL-E and Codex and visual perception in GPT-4, now oversees OpenAI's research.
- Host: [Ashlee Vance](https://realalpha.app/people/ashlee-vance/) · CEO, Core Memory · Wrote the best-selling biography Elon Musk; covered tech for Bloomberg Businessweek, The Economist and The New York Times.
- Show: Core Memory
- Episode: [How OpenAI Shapes Its Research And What's Next - EP 46 Mark Chen](https://realalpha.app/v/ZeyHBM2Y5_4/), Dec 1, 2025
- Moment: 1:09:16 to 1:14:57 of the episode (6 min), playing on https://realalpha.app/s/openai-wants-ai-to-conduct-research-end-to-end/; the episode from there: https://realalpha.app/v/ZeyHBM2Y5_4/?t=4156

## Quote

> “within two and a half years, we want AI to be doing **end-to-end research**.”
> — [Mark Chen](https://realalpha.app/builders/mark-chen/) at [1:14:04](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4444) · Core Memory

## Takeaways

1. OpenAI wants scientists outside the company to benefit from its tools.
2. Chen says some mathematicians still doubt AI can discover new theorems.
3. The near-term goal is AI handling implementation and debugging.

## About this segment

Mark Chen describes building tools to help scientists use AI, citing early mathematical and scientific discoveries. He sets a goal for AI interns within a year and end-to-end research within two and a half years.

From “How OpenAI Shapes Its Research And What's Next - EP 46 Mark Chen”, Dec 1, 2025. [Watch the full episode](https://realalpha.app/v/ZeyHBM2Y5_4/)

## Transcript

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

[1:09:29](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4169) So, I mean, I was so inspired by that encounter with the physicists that, you know, went back and thought, hey, well, we should just create OpenAI for science. And the goal being, I think for the small set of scientists today who realize the potential of these models and feel like they want to lean in and accelerate, we should do the best that we can to accelerate them. And I know there are similar efforts that other companies aim towards pushing the scientific frontier. But I think what we want to do, and I would say a little bit of a framing in terms of how we differ from, let's say, Google's efforts to work on science is we want to allow everyone the ability to win the Nobel Prize for themselves. It's less so about us winning that at OpenAI, which would be nice, but we want to build the tooling and the framework so

[1:10:22](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4222) that all scientists out there feel that accelerative impact. And we think we can push the field collectively. And when the discoveries that you're saying you're excited about, I mean, are there any others like specifically that you've... Yeah, yeah, yeah. So I think there's, you know, if you want a huge list of these, you can go on Seb's Twitter account. So recently, you know, there's a GPT-5 paper on an open convex optimization problem that, you know, is actually- Wait, whose Twitter account?

[1:10:49](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4249) Sebastian Mubek. Yeah. Yeah. And, you know, it's like very related to some of the core ML problems that we're solving. I know there was, I think people kind of dismiss these things as, oh, is it just fancy literature search or something like that um it's quite a bit more complicated than that and you know there's some examples i could go into right i might i'm honestly overwhelmed at the moment because yeah you know i'm i'm sort of a generalist but i cover biotech a lot and it's like every two days man i'm walking in and it's well we we're making an ai scientist we we one shotted an antibody and and then so like part of me gets excited and you know at least a handful of these companies i know the people and the real scientists and yeah yeah and there's so much of it that i'm like either something amazing is happening or everywhere it's it's like it's it's kind of too

[1:11:41](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4301) much for me to be able to discern where reality is yeah i mean i wouldn't be surprised if it's happening in biology um personally i have the most expertise in you know computer science and mathematics and you know we we do have the experts there that can confirm that these are discoveries is being made. So that's the thing that gives me the most confidence, but I'm not surprised at all it's happening in biology.

[1:12:03](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4323) But like what you're saying is, it's kind of different than the, I agree, the narrative changes every like three weeks, it seems like, but like what you're saying is sort of different because the biggest knock, even before Andre said that, it seemed to me from the, you know what, I was listening to, I was listening to like a politics podcast, Sagar, I think it's breaking points is their podcast you know he's he's pretty smart guy who's knowledgeable but i mean he's just been shitting on ai and the lack of progress and this is all like make believe and and all in and so you know if these discoveries are happening i mean i feel like the yeah i mean aware of this just to be clear you know um while setting up open air for science we've talked to a lot of physicists a lot of mathematicians and actually most of the people

[1:12:56](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4376) we've talked to aren't that bullish on AI. I think they still believe, hey, this thing isn't something that can solve new theorems. There's no way it could do that. There must be something else going on. And that's why I feel like empowering the set of people who really do believe and lean into it, those people are going to just outrun everyone else. And we want to build the tools and convince people this is the right way to do scientific research.

[1:13:22](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4402) Okay. And so, I mean, so like on that point, I mean, I grant you that everybody's definition of AGI is different, but you're like, well, at least what I'm hearing is, I mean, you, whatever you want to call it, um, you feel like in the next year or two, we're just seeing dramatic things. Yeah, I mean, it is a bit of a meme, right? It's like, you ask someone, when is AGI? It's two years away, right? And I don't think we're in that world anymore. And it's like these results in math and science that are giving me this conviction. But at OpenAI, within the research org, we set two very concrete goals, right?

[1:14:01](https://realalpha.app/v/ZeyHBM2Y5_4/?t=4441) within a year, we want to change the nature of the way that we're doing research. And we want to be productively relying on AI interns in the research development process. And within two and a half years, we want AI to be doing end-to-end research. And I think it's very different. Today, you come up with an idea, you execute on it, you implement it, you debug it. it means within a year we're quite confident we can get to a world where we control the outer loop we come up with the ideas but the model is in charge of the implementation the debugging yeah okay are there i beyond pre-training when i talk to you guys um