How To Build Superintelligence Inside Your Company

**Jared Friedman** (0:00)
How do you build superintelligence inside a company?

**SPEAKER_2** (0:02)
Part of the key thing is not to just use AI as a copilot. This is the thing where you use it as the building layer for everything. And you need to start recording all the artifacts.

**Gary Tan** (0:14)
It's like a shared organizational brain. It's like the closest thing to us being able to like connect our brains.

**Pete Koomen** (0:19)
If you frame this as a way for everyone in an organization to get better at what they do, using the collective skill and instinct of the people they work with, it's incredibly powerful.

**Jared Friedman** (0:39)
Today we have a real treat.
We have a special guest, general partner at YC, our partner, Pete Koomen. He created Optimizely, which was one of the first and one of the best ways to do A-B testing for apps and websites. And since then, he has gone on to create all of our agent infrastructure at YC. So literally all of our harnesses and how we use AI internal to YC. Pete, welcome to The Lightcone.

**Pete Koomen** (1:07)
Thanks, Gary.

**Gary Tan** (1:08)
For the last few years since Chatjbt, YC has been funding mainly AI companies. And we've gone through many different versions of advice for them about how to build AI native companies that build mainly AI products. And we've gone on a crazy journey with them learning all of this. I think a lot of people don't realize that internally, YC is actually building and using a lot of the same stuff that we're helping our startups build and use themselves. And it's been, I think, a very powerful symbiotic relationship for us to actually be adopting these tools and transforming our own organization, which was started way, way pre-AI into a super AI native organization ourselves. And Pete has really been leading the charge for that. And so I'm really excited about this episode because I've actually been wanting to talk publicly about all the stuff that we've built internally. And this is the first time that we're doing it. So Pete, perhaps to start off, can you sort of go back to the beginning and like talk about like there was a particular like moment when we really started adopting these AI tools internally. It was really you who got us started down that path.

**Pete Koomen** (2:15)
Sure.
Happy to tell the story here. And I like framing it that way because it was a project that I and a few engineers got started about a year ago, maybe a little more, but that has since snowballed into just a whole infrastructure layer that's made it possible for us to use AI internally at YC in lots of different ways. And that's actually been one of the neatest parts about this is watching the whole engineering team and many partners also just dive in and contribute to this infrastructure layer. We started building our own harness inside of YC or kind of YC specific agents about a year ago. And the original impetus for the project was some of the work that I and a few of the software engineers at YC were doing with our finance team. Just for a bit of backstory, so YC has for as long as it's existed, as far as I'm aware, run mostly on our own software. In this era, just given us a huge advantage, right? And so with that context back to this moment maybe a year ago, we were sitting down with the finance team talking through a set of tools that we were going to build for them, just to help them run through some of their finance workflows. Booking journal entries, logging priced rounds, like all the sorts of things that make YC run, really. I was seeing kind of two things at once. Like on one hand, we had this sort of loop going internally, right? Where we'd sit down with the finance team, the finance team would describe to our software engineers how this complicated financial workflow worked, and software engineers would go and build some purpose-built software where there was a deterministic workflow encapsulating everything that they had been told, and then hand it back to the finance team and so on. It felt really inefficient. And then at the same time, this was right around the time when agentic tools were really, agentic coding tools were really catching hold, right? And so you had kind of the first generation windsurf and cursor that were well established by this point. I think this is right around when Claude Code was introduced. It felt like this was giving me superpowers, right?
And then kind of watching this sort of old classical way of building software in YC, and then watching how I was doing things on my own machine, this, it just felt like a bigger and bigger divide between those things. And so the original impetus was, why don't we try to build some tools at YC that we could use to run agents that would give the finance team control over their own software, right? Like remove the software engineers from this crazy loop and they have to sort of understand these complicated workflows and give the finance team the tools that they could use to encode their own workflows not as, you know, not as Ruby, but as English with prompts, right?