Alexandr Wang: Building Scale AI, Transforming Work with Agents & Competing With China

**Jared Friedman** (0:00)
Since we recorded this Lightcone episode with Scale AI CEO Alexandr Wang, Meta has agreed to invest over $14 billion in Scale, valuing the company at $29 billion. Alex has also announced he will lead Meta's new AI Superintelligence Lab. Our conversation you're about to hear covers the history leading up to this investment, from Scale's early days at YC to its integral role in the training of foundational models. Let's get to it.

**Alexandr Wang** (0:31)
The AI industry really continues to suffer from a lack of very hard evals and very hard tests that show really like the frontier of model capabilities. The biggest thing is you just have to really, really, really care. When you interview people or when you interact with people, you can tell people who just sort of like phone it in versus people who sort of like hang on to their work. It's like so incredibly monumental and forceful and important to them that they do great work. Very exciting time to see how the frontier of human knowledge expands.

**Jared Friedman** (1:15)
Welcome to another episode of the Lightcone. Today we have a real treat, it's Alexandr Wang of Scale AI. Jared, you worked with Alexandr way back in the beginning, actually. What was that like? What year was it? Put us in the spot.

**Diane Hu** (1:31)
Yeah, Alex, I mean, most of what we wanna talk about today is like what Scale is doing now, because like the current stuff is like so awesome and so interesting. Since Scale got started at YC, I thought it just seemed appropriate to start all the way at the start. And it's funny, Diane and I were at MIT last month talking to college students, and like of all the founders, the one that they like most look up to and like want to emulate is actually you. Like everybody wants to be the next Alexandr Wang, because everybody knows the story of how you like dropped out of MIT and ended up starting Scale. But they don't know the real story. And so I thought it'd be cool to go back to the beginning and just talk about the real story of how you ended up dropping out of MIT and starting Scale.

**Alexandr Wang** (2:10)
So before I went to MIT, I worked at Quora for a year. And so this is 2015 to 2016, or no, sorry, 2014 to 2015 was when I worked as a software engineer. And this was already at a point in the market where ML engineers, as they were called, or like machine learning engineers, made more than software engineers. So that was already like the market state at that point. I went to these summer camps that were organized by rationalists, the rationality community in San Francisco. So, and they were for precocious teams, but they were organized by many people who have become pivotal in the AI industry. So one of the organizers is this guy, Paul Cristiano, who is the inventor of RLHF, actually. And now he's a research director at the USAI Safety Institute, who's at OpenAI for a long time. Greg Brockman came and gave a speech at one point. Eliezer Yudkowski came and gave a speech at one point. And actually, I was very, like, when I was, I don't know, I must have been 16, I was exposed to this concept that, like, potentially the most important thing to work on in my lifetime was AI and AI safety. So something I was exposed to very early on. So then when I went to MIT, I was started at MIT when I was 18 I like studied AI quite deeply. That was most of what I did in the sort of day job. And then kind of got ANSI, applied to YC, and then the idea was kind of like, okay, how could, initially it was like, okay, where can you apply sort of like AI to things? And this was in the era of chatbots, which is like crazy to think about actually, that there was like this like mini chatbot bubble. Yeah, yeah, 100%.
In 2016, which was, I guess, spurred by magic, right? Or some of these apps. And Facebook had a big vision around chatbots. And anyway, there was a little mini chatbot boom. So the initial thing that we wanted to work on, and was chatbots for doctors, right?

**Diane Hu** (4:14)
Which is like a funny idea, because do you guys know anything about doctors?

**Alexandr Wang** (4:19)
Yeah, no, not at all. Like basically, no, it was just sort of like, oh, doctors are a thing that sound expensive. And so, and I think it was like, I think it's like indicative of like, I mean, I don't know, you guys see this all the time, but I feel like most of the times young founders, like first 10 ideas are like, first of all, they're very mimetic. So they're probably like, there's a lot of like the same ideas are like, there's like a dating app, there's like some something for like, you know, social life, you know, the same ideas. And then I think that like, I think young people have a very poor sense of Alpha. Like, what are the things that they're actually like, going to be uniquely positioned to do? And I think, you know, most young people don't have a sense of self. So it's, you know, it's not clear. So when we were in YC, we were roommates with another YC company. And we were sort of like, we were sort of observing this like, chatbot boom ahead of, you know, that was happening at the time. But it was very clear that like, chatbots, if you wanted to build them, and this is funny to say in retrospect, required lots of data and required lots of like human elbow grease to be able to get them to work effectively. And so like, just like kind of off the cuff, at one point it was like, oh, like, what if you just did that? What if you just did the data and the like language data and the human data, so to speak, for the chatbot companies? We were also very lost, by the way. I think you probably remember. We were quite lost mid-batch.