Entrepreneurial Thought Leaders (ETL)
00:00:00 · S0
[00:00:00] S0: This is the Entrepreneurial Thought Leaders series brought to you by Stanford eCorner and the Stanford Technology Ventures Program.
00:00:07 · Ravi Belani
[00:00:07] S1: I am so excited to announce our speaker for this week, which is Carina Hong. Carina is the CEO and founder of Axiom. Axiom is an AI company building a self improving super intelligent reasoner starting with an AI mathematician. Carina, grew up in Guangdong, China. Is anybody here born or raised in China? Woo Woo hoo. Okay. So there's a lot of China love. And was... Is the first generation college student in her family. The first stew... The first generation in her family to go to college, and she broke that barrier with a bang. She not only went to college, she went to MIT. She not only went to MIT, but she graduated in two majors and not easy majors, math and physics. And while an undergrad at MIT, she won the Morgan Prize, which is the highest undergraduate award you can get for research as an undergraduate in the world. From MIT, she then went to Oxford on a Rhodes scholarship as a delegate from China and got her master's in neuroscience at Oxford and also studied machine learning at UCL at the Welcome Center Gatsby... Sainsbury Wellcome Centre unit and then came to Stanford for graduate studies. She came to Stanford to pursue a PhD in math and a JD from law school, but she dropped out after less than a year or after about a year to pursue Axiom. Axiom has gone on to raise over $260,000,000 of venture financing from funds like Menlo Ventures. And Carina is joining us here today to share her story. Carina is under the weather today, so she was sick, But she made an exception to come to class just for all of you. So please give a warm welcome to Carina Hong. So Carina, welcome back to Stanford. It's so good to have you here.
00:01:55 · Carina Hong, Ravi Belani
[00:01:55] S2: Good to see you. [00:01:56] S1: I don't know, did you take ETL or did you know about ETL when you were here as a I [00:02:00] S2: heard about it. Okay. Super famous class. [00:02:02] S1: Yes, yes, yes. So be... I'm curious first before we get into the details of Axiom to talk about that moment when you decided to leave Stanford. I know that when we talked before, you were actually enjoying some of the the graduate work, especially it sounded like at the law school. Can you navigate us through that whole process of when you decided to leave? When was the moment you knew you were going to leave, and how... Was that an easy process or a difficult process? [00:02:26] S2: Yeah. I remember it was something like November when I just started a math PhD. I have been sort of, like, looking at the AI for math papers for two months now, so sort of, like, September, October, November. I was very interested in the in the subject. I think AI for mathematics is gonna change the world. And there's this GitHub repository of about hundreds of papers, and so I've been, like, just reading the abstract of them. And is the abstract interesting enough? Will try to read the whole thing. And then at one point, I realized I'm only doing that. Like, I will be, like, sitting in, a class and, like, read those papers, like, on my laptop. So I was like, well, it doesn't quite make a lot of sense. But then I went to this Night Hennessey luncheon, and I remember there's a speaker. Her name is Julia Zuo, who's the first PM, I think, at Facebook. [00:03:15] S1: Mhmm.
00:03:15 · Ravi Belani, Carina Hong
[00:03:15] S1: Mhmm. [00:03:16] S2: And she was telling us about how she decided to write her book is because she just started writing, then she couldn't stop. So I asked her, I have this problem. Then she said, follow your energy. Where does that take you? So then I shortly started fundraising. Sorry. I'm like a little [00:03:33] S1: Take your time. Under [00:03:33] S2: the weather. But... Yeah. So I started, like, fundraising, and then I think after I have a term sheet I want to take, I decided it's time to drop drop out. [00:03:42] S1: Okay. And take your time. Please drink up, hydrate. And this is for everybody who might be feeling under the weather. Hydrate, rest, life will go on. We appreciate, all the words and thoughts. And I think that's a really helpful heuristic is is that you know when you wanna start something when it grabs you, when it's sort of an obsession, when you can't sleep at night because you're just... You're... All of your attention is gravitating towards that. That's the heuristic. It's not necessarily that you just wanna be a founder. It's because you fell in love with all the math phenomenon happening with AI. So can you... For anybody who doesn't know Axiom, when we hear about a a math company raising not just a little bit of money, but a $250,000,000 for your first raise for over $250,000,000, I think that causes a... People are are are curious. How does a math company raise $250,000,000 straight? Can you share what Axiom is and why it garnered so much capital?
00:04:32 · Carina Hong
[00:04:32] S2: Yeah. I think, like, Axiom is doing something called formal math reasoning, which is the ability to convert mathematics into machine checkable code. And this is quite valuable because if you think about the amount of materials being produced by AI, it lacks like this sort of verification layer. So something that we announced yesterday, which is I think the reason I'm sick, is because we announced the formalization of the classification of finite simple groups. For those of those of you who don't know the theorem, it is the largest human written mass proof. So there literally isn't another proof that's longer than that. And the problem with it being really long is not a single human have read the whole thing. So it's the collection of twentieth century mathematics over generations since, I think, first time the concept of a simple group started from. And then in the sort of nineteenth century, someone decided that, oh, it's like... These, like, five, I think, sporadic groups might be in the family. And then I think nineteen fifties, in the famous ICM, Brauer decided decided to start the hunt for classifying all the finite simple groups. And that is more than 15,000 pages of mathematics, kind of like scattered across many papers and books. And I think the sort of our mission is to basically turn a lot of these mathematics, but also other stuff into machine checkable code, where when you run the computer program, you can be sure about its correctness. That is a 100% verified. I mean, Gödel kind of tells you that you cannot be truly sure of anything. So what it does is it boils down this really large thing into a small trusted kernel. So it's beyond a reasonable doubt, correct. And I mean, it's basically very, very it's only a theoretical question that it will have like a major problem. And then that resulted in more than 50,000,000 lines of code in this programming language, Lean. And the reason it's very interesting, I think, from a commercial perspective is it's also the largest open source software project. So it's larger than Linux. It's larger than like the other like software open source project you have had. And this is also a programming language, just lean. And it reflects this one theorem, one really big proof.
00:06:43 · Carina Hong, Ravi Belani
A lot of mathematicians described their like reaction yesterday, like there's always this cloud sort of on top of all these mathematicians' lifelong work, their legacy, and now that the kind of cloud has fully dissipated. [00:06:56] S1: And so that's profound from an academic perspective on that proof, on the research. Can you share what the vision is of why the venture capitalists are so excited? [00:07:05] S2: I think it's something that hasn't been done before. I think that's... I think one one is emerging cap... Emerging technology. It's always very interesting because the moment you can kind of have the verification layer, it's not really for, oh, I have this code and then like, please tell me if it's correct or not. If I can generate new code and instant time get that verification feedback. So then that is really like the future of AI assisted coding. [00:07:32] S1: So the shift is even though right now people are using ChatGPT co... Codex or Claude Code, that is for creating code that's human created. The vision with what you're building is to actually have agents creating code. Is that is that what you're saying?