Building Codex with Tibo Sottiaux

Gergely Orosz (0:00)

Codex is one of the most popular AI coding harnesses today, but how did it all start? Many of you will know today's guest, Tibo, from his generous and pretty frequent Codex usage resets. He was also there when Codex as a product started, and has led the broader Codex team since. Today we cover how Codex started and why it was built in Rust and made open-source, how coderies are changing inside the Codex team and OpenAI, what it means when maintenance and re-architecting are getting ridiculously cheap, what the merge of Codex and ChatGPT looked like, and the many underappreciated engineering challenges of this project. If you want to understand how teams inside of OpenAI plan, review and ship software, this episode is for you.

This episode is presented by Turbobuffer, a ridiculously scalable, fast and cheap hybrid search engine built on top of object storage by an engineering team that I've really grown to like after spending time with them. Turbobuffer is the tool that companies like Entropic, Notion, Cognition and Harvey all use to connect their AI products to massive amounts of unstructured data. When I've talked with the engineers who use Turbobuffer, the theme that always comes up is reliability and performance at scale. The reasons for this have everything to do with Turbobuffer's architecture. Turbobuffer uses only object storage for state and MVME SSDs with memory cache for compute. Data in Turbobuffer is organized into namespaces. You can think of a namespace as a database table or a search index or an S3 prefix depending on the world you come from.

When a namespace is not being queried, it stays on cheap object storage with no associated compute cost. When a namespace is active, Turbobuffer pulls it up into hot-catching tiers so queries are very fast. This design fundamentally makes it effortless to scale to hundreds of millions of namespaces. If you're building a multi-tenant AI product, every user and their agent can have their own dedicated search index without any overhead. And each namespace can hold hundreds of millions of documents without any special configuration. You can scale Turbobuffer virtually without limit, and the performance, reliability and operating model all stay the same. If you need to connect AI to lots of data, Turbobuffer should be your first choice. Check it out at turbobuffer.com/pragmatic.

Tibo, welcome to the podcast. So good to have you here.

Tibo Sottiaux (2:08)

Thank you for having me. It's so good to see you again.

Gergely Orosz (2:11)

It's good to do this last time we did it in person. Now we're doing our video. First, I wanted to ask you, how did you get into tech? When did you first know that you want to work with computers?

Tibo Sottiaux (2:24)

That's a good question. It was a long, long time ago.

My parents actually decided to move out of Brussels where I was born and just thought it was great to just buy a small house and refurbish it. But it was in the middle of a village with not much going on. I think there was roughly 200 people living there. Not many that I felt like I wanted to talk to or could make friends with. And so I got stuck. This is very early, like eight years old. I got stuck in computers. And it's early days of, for me, the Internet. And that was my way to learn about things. And so just the rest came from that. So I owe it to my parents to have moved into the middle of nowhere. And then I had no choice but to get interested in computers.

Gergely Orosz (3:19)

Once you've finished high school, you went on and went to university, right? Actually studying it properly.

Tibo Sottiaux (3:25)

Yes.

I studied mathematics, applied mathematics at university. I went there quite early. And so I graduated early as well. I thought for a long time that I would actually not make it and I would drop out. I had small companies and small consulting business.

While I was studying, I was working for banks. I was very interested in supply chain and applied mathematics problems. And I started selling that and learning a lot through that. Eventually, I ended up in the startup world in Belgium. I did that for a little while and then moved to London. I worked initially at Google and then DeepMind, and then now moved to be here at OpenAI. California, I love the California weather. We can talk about that. It's been very good.

Gergely Orosz (4:18)

Right after university, you founded a startup, right? You had the startup bug in you, or the entrepreneurial bug?

Tibo Sottiaux (4:26)

Yeah, so this startup was all about pharmaceutical supply chain, looking at the supply chain for clinical trials, and try to optimize and decide, hey, should you produce more medicine? Where should you send it? Where should you dispatch it? How do you avoid waste? And through that, making clinical trials more efficient. And this was using traditional non-ML techniques, traditionally more optimization, solving Monte Carlo simulations, these kinds of things, stochastic multi-stage optimization problem, really, and we also applied it on steel industry, and we applied it to electrical grid as well in Europe.