Alexandr Wang on why Paul Graham’s “Schlep Blindness” essay was seminal for Scale AI

One of the secrets to scale, and I think this applies across almost every industry, but the problem that we originally were solving, which is around building really high quality data sets, was something that most machine learning teams knew was very important. And so most people agree that was very important. But it wasn't necessarily the sexiest problem that every AI scientist wanted to spend their days and nights working on. And this is like collecting images. Labeling them. Yeah, collecting images and labeling them and making sure that was done very well, very high quality. It was a very, you know, frankly, a very operational problem. And we've built a lot of technology to support it, but again, at its core, it's a very operational problem. And, you know, there's one article that was pretty seminal for me early on, which was, it was an article by Paul Graham, or an essay by Paul Graham, I should say, called Schlep Blindness. And, you know, I encourage everyone to read it if you get a chance. But basically the idea was, was that most people avoid thinking about the really just hairy, ugly, difficult, annoying problems that exist in the world, but they're really, really important. And he actually uses Stripe as one of the examples in his essay. But these problems are everywhere. The sort of ugly, hairy problems that everyone knows are important but aren't sexy to work on. And, and if you can identify what those problems are, then those generally make really exciting startup ideas. So that was a lot of the original pitch was like, you know, this is important, but you probably aren't the most excited to work on it. And then we were just, you know, I think we even very early on, our whole team was super duper scrappy. And so I think a lot of teams and a lot of customers that we work with, like saw our velocity and our product velocity and how fast we were moving and thought to themselves, like, hey, yeah, these guys, even if they don't have the perfect part, today they're going to get to a product that we're going to be able to rely on really, really quickly.