Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI

**swyx** (0:03)
Okay, we're here in the studio with Akshay from OpenAI. Welcome.

**Akshay Nathan** (0:07)
Thank you.

**swyx** (0:08)
And with our trusty co-host Vibhu. We, so you recently launched ChatGPT Work. You lead Core Product Engineering. You know, it's been a long journey into all this. I find it very interesting that you started with NoCode or LowCode with Walrus and Airtable.
And to some extent, ChatGPT Work is kind of like the superapp of superapps of, well, here is the ultimate NoCode. You just write a prompt.

**Akshay Nathan** (0:33)
Yeah, it's funny how things come full circle. I mean, I think for a long time in my career, I mean, I started my career working consumer fintech. But then after that, there's this hypothesis that the things that we were able to do with code as engineers, if we could bring that to many more people in a more accessible way, then that would be truly magical. We were working on a startup. It's actually funny, before LLMs, before vision LLMs on how to do automated testing with AI.
And it was just kind of jank back then, but doing what we can and then worked at Airtable for a while on the same thesis, so if we can bring a database or the parameters behind a database to people, that would be really useful to them. But once I think LLMs came onto the scene, it became clear that this was the missing piece, the missing technology required to bring the magic of code to everyone without them having to know what's going on underneath the hood. And so I think this launch and a lot of the stuff that we've been up to is the manifestation of that.

**Vibhu** (1:33)
How was stuff when you joined? So you joined OpenAI 2023 Now we've got so much more stuff. So ChatGPT, Codex app, ChatGPT for Work, have things changed?

**Akshay Nathan** (1:44)
I think the more interesting thing is how things haven't changed. I guess one, I joined, I remember when I joined, it was 500 people. One thing I was worried about was I was looking for something more early stage and was going to feel start up enough. I joined and I was like, this feels even more start upy than I could ever imagine. That really hasn't changed even till now. I think the level of bottoms up ambition and the ability of anyone to do anything or have an idea and ship it is really cool. But on the mission side, I think what was really compelling to me is this mission of bringing Frontier Intelligence to everyone, building AGI and then bringing it to everyone.
I think acknowledging back then that that vision is going to not be a linear progression. We're probably going to try different products and have different things that succeed and don't.
But the vision has stayed the same and the mission has stayed the same. We're starting to see the pieces fall together and that's really cool.

**swyx** (2:40)
You worked on Enterprise. A lot of people never touched ChatGPT for Enterprise God.
What is something that you learned from there that you're bringing into your work now?

**Akshay Nathan** (2:52)
I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, we would talk to customers and that was when I think it was a year after ChatGPT was released and everyone was so excited to bring AI into our enterprise and all these teams that were being stood up, the AI deployment team, these enormous budgets.
If you ask anyone, what were they excited about? What were they excited about solving? First, you'd get the baseline answers of, we have all these contacts and data and all this stuff. But if you ask them, what was a discrete use case? They want AI to enable in their workplace. You get such a different variance, explosion of different types of answers. It's interesting, using all these models and these products, you have this box and you can say anything to it, which is the magic. But on the flip side, it also means that you don't know what to do with it. In the enterprise, I think a big part of that is actually meeting the users where they are, what use case were they trying to solve, and then actually teaching them how they can use AI to gain leverage there.

**swyx** (3:56)
Do you meaningfully differentiate that from forward-deployed engineering or?

**Akshay Nathan** (4:01)
I think there's the go-to-market side of it, and then there's the product side of it, I think. You need to see more in the product side.

**swyx** (4:07)
Yeah.

**Akshay Nathan** (4:08)