The MAD Podcast: How AI Gets Built — with Matt Turck
00:00:00 · George Sivulka, Matt Turck
[00:00:00] S0: You'll actually have hybrid AI and human employees working alongside each other. People that are really good at prompting to be the best managers. Intelligence will become too cheap to meet her. [00:00:10] S1: Welcome back to the MAD podcast. In this episode, I'm thrilled to sit down once again with George Civulka, a Stanford prodigy who walked away from a fully funded PhD when the launch of g p d three convinced him that the real action was outside the lab. Instead, he decided to launch Hebia and became one of the very few founders to get a pre seed investment from Peter Thiel. Fast forward to today, Hebia has become an AI platform that chews through billions of pages for top Wall Street asset managers and tier one law firms and has raised 160,000,000 in venture capital. In this episode, we explore why generalist AI beats vertical AI. [00:00:47] S0: The best investors reading really crazy data sources that have nothing to do with finance. The best lawyers have backgrounds that are way outside of law. You don't want specialization. You want generalization. [00:00:59] S1: The power of inference time super scaling. [00:01:02] S0: And actually, the the whole scaling at inference paradigm was pioneered at Hebia. We're one of the first people to say, Hey, you get way better accuracy from using more large language model calls at runtime. [00:01:13] S1: And how AI is already redesigning org charts.
00:01:13 · Matt Turck, George Sivulka
[00:01:13] S1: And how AI is already redesigning org charts. [00:01:16] S0: And when you think of that from an organizational design perspective, you start to define the agent employee as another node in your org chart. It will probably have to have an email. It'll probably have to have a Slack. It'll probably be doing things the wrong way, and you'll have to manage it in the right direction. [00:01:33] S1: This episode was recorded live at a recent data driven NYC, the in person monthly event we run-in New York in partnership with our friends at Foursquare. Please enjoy this energizing chat with George Civulka. Good to see you. [00:01:48] S0: It was, I think, almost two years ago to this day where we Yes. We started talking about Matrix [00:01:53] S1: in a very similar setting. Actually, Matrix was, if I remember correctly, not even the name, then it was Stealth. [00:02:00] S0: We... It was pre marketing Lindo, but it was it was still in Stealth, but a lot of the early ideas. And and you were my first podcast ever, so I'm very excited to be back. [00:02:09] S1: Wonderful. Well, welcome back. So maybe for anybody that did not attend that event or does not yet know about Hebiah, what is forty five second elevator pitch for what you do and why it matters?
00:02:09 · Matt Turck, George Sivulka
[00:02:09] S1: Wonderful. Well, welcome back. So maybe for anybody that did not attend that event or does not yet know about Hebiah, what is forty five second elevator pitch for what you do and why it matters? [00:02:22] S0: If we're in an elevator, Hebiah is... We actually build AI agents for finance, for investors, bankers, and lawyers. So anyone that does white collar work, whether you're doing or you're discovering as part of what you work on, we build an AI agent platform so that you can leverage some of the latest and the greatest. And, you [00:02:44] S1: know, rewinding back to that chat from a couple of years ago, I remember that you had a very powerful kind of mission statement, which was to keep smart people from doing stupid tasks. And if you watch that back and if you sort of fast forward to today, are you still doing exactly that? In what way has the mission evolved? I think when we when
00:03:07 · George Sivulka
[00:03:07] S0: we started Hebiah, I think that one of the fundamental insights was that you had a lot of the smartest people in the world doing the stupidest tasks. And that it seemed like there was an arbitrage opportunity there to save them some time or some effort or some sweat. And the goal of Hebiah was never to just stop at really highly paid knowledge professionals, the investor or the finance guy or the lawyer. It was actually always to go much more broad. And our vision and mission have solidified really over the last couple of years into building capable AI platform for a billion people. And the idea is that there's lots of consumer AI products, Products where you can go and talk about your sushi restaurant and wine in San Francisco. But there's actually not very horizontal general purpose workplace AI products. And what we mean when we talk about capable AI and AI that can do things is actually something much more horizontal. It's something that's much richer in its interaction. It's actually not as verticalized as you might think, but it still has the depth of what an expert platform would be. So if you think about how that vision and mission have evolved, we started on Wall Street. We started with lawyers. We started with investors and bankers. But now we've built an AI platform for experts that lets anyone, whether you can code or you don't know how to code, or you can only use cursor to code, to build whatever kind of knowledge work application or agent you'd like.
00:04:43 · Matt Turck, George Sivulka
[00:04:43] S1: Could you rewind back to the initial sort of light bulb moment when, I guess, you were a student at Stanford, if I remember correctly. How did [00:04:52] S0: that all come about? I was twenty twenty. And at the time, it was around June or May 2020, no one was talking about AI. No one was talking about large language models. And I remember, I think the topic of what I was studying at the time was meta learning or the idea that you could figure out how to build a machine that could learn how to learn or generalize to any task. And I thought that meta learning would become the most important software of all time. And at the same time, I was working out in my research and then all of a sudden, June 2020 comes around and OpenAI actually released GPT-three. So before ChatGPT, I remember using it. And I think after using it a few times, I sat back in my chair. I was like, this thing is a metal earner. It has beaten me to the research punch that I was working on. And if I couldn't actually play a part in creating that fundamental technology or that technological revolution, I knew I wanted to be a part of applying that in a really interesting and meaningful way. I made the bet that, Hey, this would be worth basically throwing my whole life, my whole research career away on and and and starting a company from scratch. [00:05:58] S1: And not to make you blush, but... So you were doing like a PhD. You were like 21 or 22 when you were starting a PhD. There's some I'm blushing. Stop. Okay. Stop, man. [00:06:10] S0: Not really. Stop telling people.
00:06:10 · George Sivulka, Matt Turck
[00:06:10] S0: Not really. Stop telling people. [00:06:12] S1: Stop forcing me to tell people how smart I am. No. But there was something like that. Right? Like, you you graduated super early from Stanford. You went into your PhD. Just tell that story in two sentences. [00:06:22] S0: I was a young hustler is the reality of the story. But I I was one of the youngest people to graduate Stanford and was the youngest person in my PhD program. I was fully funded. So I was throwing away at the time millions of dollars of research grants and not having to be a TA for the extent of my graduate school, which was actually quite the luxury. Didn't know I could raise. Didn't know we'd be able to build a company that we built and the team that we built. [00:06:46] S1: Great. So speaking of that, so what's happened over the last couple of years? Like any metric, including vanity metric that you can share, fundraising history, number of customer, number of documents, whatever it is that you wanna share to give a sense to people for the reality of the company as of today.