#2281 Garry Tan: Y Combinator Startups Growing 5X Faster – Here’s What Changed

**Andrew Warner** (0:00)
Hey there, Freedom Fighters, my name is Andrew Warner, and this is a new series for me. It's called The Next New Thing. Here's what's up in this interview, then an intro, then we'll get right to it. How do you compete with these bigger players?

**Garry Tan** (0:12)
We're seeing routinely YC companies with 10 or 20 people get to 10 or 20 million dollars a year in revenue in 10 or 20 months. That's like literally never happened before in software.

**Andrew Warner** (0:24)
Talk to me about how you use AI in your video creation.

**Garry Tan** (0:27)
I took the scripts of all of the top videos that I ever made from my YouTube channel. I throw it at this prompt and then it would generate these beautiful three act narratives. I could have a new 10 minute script ready, whereas it normally would take me like several hours.

**Andrew Warner** (0:44)
How are you changing Y Combinator?

**Garry Tan** (0:46)
Let's stop competing with all the other VCs. Let's be their partners.

**Andrew Warner** (0:49)
I'm going to ask you to do something you're uncomfortable with.

**Garry Tan** (0:51)
Oh yeah, what's up?

**Andrew Warner** (0:52)
Garry Tan is the president and CEO of Y Combinator. Why don't we start with the case text story? Because I feel like there's a before AI for that story, an AI experimentation, and then once it took AI, everything changed.

**Garry Tan** (1:10)
I worked with Jay Keller, the founder of Case Text, back in 2012-2013 when he first went through Y Combinator. And that was also my first stint at YC as a partner.
And they were sort of doing basically Web 2 for law. So literally, what's happening with case law and new legislative, I mean, whether it's legislation or literally judgments, like all of the documents that the legal profession throws off, they would index, which would help you understand the law. And that was really what they built for something like going on 10 years. It grew by SEO, and Jake's both a great technologist and a great lawyer. And so he was really able to go into that market and make something based on what was happening in society and in tech at that time.

**Andrew Warner** (2:03)
I think there was also like a Q&A component of this, right? So they could go and talk to other lawyers. We're going to get into how things get better. Why wasn't that enough?

**Garry Tan** (2:11)
Some things can become huge and drive billions or tens of billions of dollars in revenue every year.
And some things really could only get to, they only provide value that, and then you multiply it out by all the people who need it, and that might only total up to 10 or 20 or 50 million. Like that's weirdly quite common. I think a lot of founders are worried about that early, but my sense is maybe it's premature worry, because embedded in that is also the case text pivot, that they got users and an understanding and a useful corpus of data, all of which turned into a tremendous moat for them, literally right at the correct moment. As technology itself shifted, something that could only make tens of millions a year could suddenly become something that could make hundreds to billions of dollars per year. And that was the dawn of the large language model in 2023

**Andrew Warner** (3:07)
Tell me that story of like how they came up with that.

**Garry Tan** (3:10)
The cool thing about YC was that Jake basically had access to early versions of ChatGPT, GPT-3. These were sort of toy earlier versions of it. And they were certainly astonishing and interesting, but they were not useful yet because the LLMs actually would just hallucinate. They were early in the journey, so there wasn't enough data.
The number of parameters, the sort of size of the models was too small. And that's what Jake found as he tried to use large language models to do a lot of the things that you and I take for granted today. Right at the dawn of this stuff, it was not that useful. It was sort of a horseless carriage, if you will. It was an oddity. You could look at it and say, well, maybe this will work, but it's mostly a toy and nobody will actually use it yet. Possibly ever, right? And certainly, when I first saw it, I'm embarrassed to say, like, even as an investor and technologist myself, it's like, that was the consensus at the time. And at that moment, at the dawn of large language models, that was correct. Like, you couldn't use it for useful things yet. Jake, being a great designer, engineer, and lawyer, he tried really hard to make it work. And it would hallucinate. And, you know, he also was operating in an area that, in particular, really had high sensitivity to hallucination. You get one thing wrong and you're fired as a lawyer. So, you know, his particular space was fascinating to me because it particularly could not withstand any hallucination. And as technology curves and cost curves go, this was something that I think surprised everyone. If you were Greg Brockman or Dario Almoday at that moment, you started, internally, you were talking about the scaling laws and that the loss function was going down as log-linear to the amount of data and compute you were putting in. And that was an astonishing realization that, like, there was a path to potentially AGI or ASI. The rest of us on the outside had no idea. And I think Jake also didn't have any idea. But because he was in the YC community, the OpenAI itself was a spin-out from YC research by Sam Altman.