From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki
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What is the goal of creating an automated researcher?
The big thing that we are targeting is producing an automated researcher. So automating the discovery of new ideas. The next set of evals and milestones that we're looking at will involve actual movement on things that are economically relevant.
I was talking to some some high schoolers and they're saying, oh, you know, actually the default way to code is vibe coding. I I do think, you know, the future hopefully will be vibe researching.
What does it take to build an automated researcher and can AI discover new ideas on its own? OpenAI's chief scientist, Jakob Pojutsky, and Chief Research Officer Mark Chen joined A16Z general partners Adine Midha and Sarah Wang to unpack GPT-5's reasoning push, why evals must shift to economically meaningful benchmarks and the march towards an automated researcher. We get into Long Horizon Agency, why RL Keeps Working, the new codex for real world coding. Research culture versus product, and why, for now, compute is destiny. Let's get into it.
Thanks for coming, Jakob and Mark. Jakob, you're the chief scientist at OpenAI. Mark, you are the chief research officer at OpenAI. And you guys have the both the the privilege and the stress of running probably one of the most high profile research teams in AI. And so we're just really stoked to talk with you about A whole bunch of things we've been curious about, including GPD five, which was you know one of the most exciting updates to come out of OpenAI in recent times. And then stepping back, how you build a research team that can do not just GPD five, but codecs and chat GPT and an API business and can weave all of the many different bets you guys have across modalities, across product form factors into one coherent research culture and story.
And so to kick things off, why don't we start with GPD-5? Just tell us a little bit about. The GPT-5 launch from your perspective?
How has reasoning in AI evolved leading up to GPT‑5?
How did it go?
So I think GPT-5 was really our attempt to bring reasoning into the mainstream. And prior to GPT-5, right, we have two different series of models. You had the GPT kind of two, three, four series, which were kind of these instant response models. And then we had an O series, which essentially thought for a very long time and then gave you the best answer that it could give. So tactically, we don't want our users to be puzzled by, you know, which mode should I use? And involves a lot of research and kind of identifying what the right amount of thinking for any particular prompt looks like. And Taking that pain away from the user. So We think the future is about Reasy, more and more about Reasoning, more and more about agents.
And we think GPT five is this step towards delivering reasoning and more agenc behavior by default.
There is some. Also a number of improvements across the board in this model relative to O3 and our previous models. But our primary fees for this launch was indeed bringing the reasoning out to more people.
Can you say more about how you guys think about evals. I noticed even in that launch video, there were a number of evals where you're inching up from, you know, 98 to 99%. And that's kind of how you know you've saturated the eval. What approach do you guys take to measuring progress and how do you think about it?
One thing is that indeed for like these evolves that we've been using for the last few years, they're indeed pretty close to saturated. And so yeah, like for a lot of them, like you know, inching from like ninety six to ninety eight percent is not necessarily the most fun thing in the world. I think another thing that's maybe even more important but a little bit subtler. When we were in this like GPT two, GPT three, GT four era, you know, there was kind of one recipe, you just like Pre-train a model on a lot of data and you kind of like use these e-vales as just kind of a a yardstick of how this generalizes to like different tasks. Now we have this different ways of training, in particular reinforcement learning on like serious reasoning, where we can pick a domain and we can really train a model to like become an expert in this domain to reason very hard about it, which lets us target particular kinds of tasks, which will
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Chapters
8 chapters
1
What is the goal of creating an automated researcher?
0:00–1:50
2
How has reasoning in AI evolved leading up to GPT‑5?
1:50–5:07
3
Why are traditional benchmarks no longer sufficient for measuring progress?
5:07–7:20
4
What surprising capabilities does GPT‑5 demonstrate?
7:20–10:40
5
What is the research roadmap for the next 1‑5 years?
10:40–13:14
6
How do long‑horizon agency and model memory affect AI stability?
13:14–17:57
7
What makes a great researcher and how is persistence cultivated?
17:57–27:03
8
How does OpenAI balance compute constraints with product development?
27:03–51:56
Speakers
4 identifiedMore from The a16z Show
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