No Priors Ep. 65 | With Scale AI CEO Alexandr Wang

(instrumental music plays) Hi, listeners, and welcome to No Priors. Today, I'm excited to welcome Alex Wang, who started Scale AI as a 19-year-old college dropout. Scale has since become a juggernaut in the AI industry. Modern AI is powered by three pillars, compute, data, and algorithms. While research labs are working on algorithms and AI chip companies are working on the compute pillar, Scale is the data foundry, serving almost every major LM effort, including OpenAI, Meta, and Microsoft. This is a really special episode for me, given Alex started Scale in my house in 2016 and the company has come so far. Alex, welcome. I'm so happy to be talking to you today.

Thanks for having me. I've known you of all for, uh, for quite some time, so excited to be on the pod.

Why don't we start at the beginning just for a broader audience? Talk a little bit about the founding story of Scale.

Right before Scale, I was studying AI machine learning at MIT, and this was the year when DeepMind came out with AlphaGo, uh, where Google released TensorFlow, so sort of the, maybe the beginning of the deep learning hype wave or hype cycle. And, uh, I remember I was at college. I was trying to, to use, uh, neural networks. I was trying to train, you know, image recognition, uh, neural networks, and the thing I realized very quickly is that, uh, these models were very much so just a product of their data, and you s- I sort of played this forward and thought through it, and, you know, these models, or AI in general, is the product of, you know, three fundamental pillars. There's the algorithms, uh, the compute and the computation power that goes into them, and the data. Um, and at that time, it was clear, you know, there were, uh, there were companies working on the algorithms, uh, labs like OpenAI or Google's labs or, you know, a number of, of AI research efforts. Uh, there were... NVIDIA was already a very clear leader in building compute for these AI systems. Um, but there was nobody focused on data, and, uh, it was really clear that over the long arc of this technology, data was only gonna become more and more important. And so in 2016, uh, dropped out of MIT, did YC, and really started Scale to solve the data pillar of the AI ecosystem and be the organization that was gonna solve all the hard problems associated with, how do you actually, um, produce and create enough data to fuel this ecosystem? And really, this was the start of Scale as the, the data foundry for AI.

It's incredible, uh, foresight because you describe it as, like, the beginning of the deep learning hype cycle. I don't think most people noticed that a hype cycle was yet going on, um, and so, uh, I just distinctly remember, uh, you know, you working through a number of early use cases, you know, building this company in my house at the time and discovering, I think far before anybody else noticed, that, um, the AV companies were spending all of their money on data. Um, how did you think about... Like talk a little bit about how the business has evolved since then 'cause it's certainly not just that use case today.