The MAD Podcast: How AI Gets Built — with Matt Turck
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.
00:07:03 · George Sivulka
[00:07:03] S0: I can't forget that you're a VC, so I'm not allowed to share any any metrics over here. But there there's some public ones. I think things that I'm really proud of are really lasting, first and foremost our team. So we built a team of a 100 amazing, incredibly smart folks all five days a week, sometimes six in office in New York City. And we are now just starting to become a multinational corporation. And so we're opening SF and we've already opened a London office, which is incredibly exciting with goals to end the year at 300 to 400 employees. Lots of exciting growth. A lot of it here in Silicon Alley, and unfortunately, Silicon Valley. We have to move out there a little bit. But I think on like AI metrics or a little bit more about the product and how it's used, one of our favorite things to track is the amount of unstructured data, the amount of pages that are processed by the platform. And a really interesting thing is, hey, last year, Hebia and probably all of the other major consumer model providers processed around a 100,000,000 pages, probably around whatever hundreds of years, maybe thousands of years of reading. This year, we're already on track to process around four to 5,000,000,000 pages. Somewhere around fifty thousand years of reading for a human that's taking the right amount of breaks. And that exponential just one of the most phenomenal curves that you've ever seen. It's like in a big board in our office. And I think the reason we're so proud of it is, whereas other AI platforms are having a bit of a transactional relationship with the AI. You ask a question, you get a response. With Hebia, you can give it these complex tasks and it really churns through vast quantities of data and does work the way you work. It's much more of an agent than a chatbot. And so when you're actually looking at the work that it's doing or that it would take in people hours, a team of really highly paid professionals to do, it's actually having a massive impact at the organizations we've rolled out. And now we're deployed at, I think, forty and fifty percent of the world's largest asset managers, some of the tier one investment banks in the world. We have an incredibly fast growing legal segment. So I think even just sort of the share of our revenue that's in law is increasing at an exponential clip.
00:09:32 · George Sivulka, Matt Turck
And so, I think it's just an incredibly exciting time to be at Hebia and in AI. [00:09:37] S1: So, we're going to go into all things Hebia, the product, the tech, and all those things. But before we do that, just maybe six or seven days ago, you had a really interesting blog post that you published where you talked about the concept of agent employee. [00:09:52] S0: Mhmm. [00:09:52] S1: Yes. So going to that, what is an agent employee in your world? I think that
00:09:58 · George Sivulka
[00:09:58] S0: the idea behind the blog post and talking about an agent employee actually stemmed from a way that I think organizational design is starting to change our customers. And I actually likened it to an old organizational trichotomy of remote work, where the internet actually came out and it basically decoupled the output of labor from where you were. So you could be all in the same room in New York, or you could be across the world. And the internet basically democratized access to talent. With AI and these agent employees, you're actually also starting to see, Hey, I'm going to have full time things, whether they're employees or not employees or whatever you want to call them, where instead of actually having output or work or labor coupled with personhood, it will now be completely decoupled, not only from location, but from whether or not you can pay salaries or have humans doing the jobs. And so the entire essence of the piece was, hey, just as we have remote employees, we have hybrid organizations, fully remote organizations and in person organizations. You're going to start to have fully human organizations, actually fully AI organizations, like the one person billion dollar startup or these things that are effectively just APIs. And you'll actually have, and this will be the most common thing, hybrid AI and human employees, agent and human employees working alongside each other. And when you think of that from an organizational design perspective, you can start to define the agent employee as another node in your org chart. And it becomes very clear that if that's a 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. And the makeup, whether you're 50% agents or 10 agents or 90% agents or 0% agents, will actually define the output of the organization. Amazon is a perfect example of how this happened, how org design shaped what they built. If you look at AWS's offerings, every single offering in that big menu is a different startup all together with its own GM. And that intentional organizational decision impacts their product and impacts how it works and impacts whether or not those products work together really elegantly.