Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”

**Garry Tan** (0:07)
All right, full rock star treatment for Alexandr Wang, everyone. All right.
So why don't we start out? Backstage, we were saying, you know, one of the cool ways to think about this event is like, you know, this room is actually full of people who are just like us, but when we were 18 or 20, you know, there's some six of us, you know, there's some 16-year-olds in this audience, you know. Let's jump to your story. I mean, you got, it came up always really smart, like Math Olympiad, like jump us to, you know, the Alex of that time. Like what were you feeling? What were you thinking? And what drove you down this road?

**Alexandr Wang** (0:47)
Yeah.
Well, I grew up in New Mexico, Los Alamos, New Mexico, which now Oppenheimer famous, but it really was the middle of nowhere. And I remember I did all these math competitions, all these computer science competitions, but then I knew I wanted to do really big things. And it was like not exactly clear how or what the exact paths to do that would be. And I had a friend who was really into programming. And, you know, after high school, he got an internship in the Valley. I think his first internship was at Palantir.
And he, you know, he was kind of this influence for me. And so after I finished high school, I ended up working at Quora here in Silicon Valley. And then I worked there for a year. I took a gap year to work there. And then I went to MIT. And this was, I was 19 when I worked at Quora, I was 18 when I went to MIT, and there was 19 when I started Scale. And I remember this period from 17 to 19 It was, I felt like I was constantly changing, like exactly what I wanted to do was constantly changing. I was learning so much just from the people around me. And it was just like, I felt like I was drinking from the fire hose pretty constantly during that time. And I would definitely recommend, the two things that were really important. One is, I think working at a company was really valuable, because I think from the outside in, you have no idea how companies work. You have no idea what it looks like to actually build something. You have no idea what it looks like to iterate on something. You have no idea what it looks like for groups of people to make decisions. And so I thought that was really important. And then going to school at MIT was actually really important because it just gave me a lot of opportunity to explore what was interesting. And so it was at MIT that I started training my first models and that I played around with TensorFlow, which had just come out that year at MIT, and where I ultimately came up with the idea of scale. And then after one year of MIT, I applied to YC, it felt like a miracle to get in at that time.
And YC was really critical to my entrepreneurial journey. Like I don't think, like YC is this amazing blend of, they're very supportive and they obviously want you to succeed, but they also give it to you very real and they tell you when you're being a dumbass, which I think is what we all need in life. So yeah, that was, I think, the story till then I was 19, started Scale and the rest is history.

**Garry Tan** (3:25)
I guess you worked with Jared Friedman at the time, and you came in with actually a very different idea than what ended up becoming Scale.

**Alexandr Wang** (3:35)
Yeah, so we wanted to build an AI agent, probably not, to help people get medical care. It was a great example of an idea that I think will ultimately exist. I think we're even seeing it now, like AI agents to help people get medical care are very real, but it was the wrong timing. We worked on it for about a month or two before Jared pulled us aside and we're like, guys, this is, I don't know if this is going to go anywhere.
That's exactly what we needed to hear. It was at that time when I had studied AI at MIT, I had trained models and we thought, we went back to the drawing board, thought deeply about where the opportunity was and came up with Scale.

**Garry Tan** (4:21)
I guess selling data at the time, large language models had not really come to the fore yet, but self-driving cars were coming up and computer vision suddenly became. So that was the first market, is that right?