The Secret Supplier Feeding Frontier AI

Yan Liberman (0:00)

What can't agents do today, and specifically, what's getting harder for them to access?

Andrej Radonjic (0:05)

If I'm being honest, most of the internet, and AI kind of flipped that around, where now the internet's blocking everything. And this is what makes Grass actually almost magical. And from the internet's perspective, it just looks like users of the internet accessing the internet as usual. But in reality, it's a massive data infrastructure platform feeding the frontier of AI. At this point, we're at exabyte scale, but Grass has delivered exabytes of data to AI labs. Most people don't know what an exabyte is. Flew into Ashburn and hold an all nighter, kind of slept on the floor, assembled the servers. The vision is for Grass to be the medium through which it accesses that information.

Yan Liberman (0:48)

Full disclosure, Delphi Ventures are investors in Grass and we hold the token. But we'd love to kickstart by kind of learning about you Andrej and what brought you here and kind of a bit about your background.

Andrej Radonjic (1:00)

Sweet. Yeah. I mean, what brought me here specifically was an invitation for you guys.

Well, first of all, I'd actually like to start by thanking you for having me here. This is pretty cool. I haven't been in-person podcast before. I've done a bunch of these things where it's on a Zoom call or some things. This is pretty cool.

But yeah, a bit about me. I'm co-founder and CEO of Wynd Labs. We're best known as a team that builds grass and is actively contributing to it and building on top of it.

Grass is a network of millions of nodes that people opt into. They share their bandwidth and get rewarded for it. Then what happens on the other side of this is all kinds of data infrastructure gets built to use millions of people's bandwidth to access web data at scale. How I fell into this space is, I don't know, maybe it's kind of random. I actually went to school in Canada and I did a program called engineering physics where I specialize in two things, nuclear engineering and photonics engineering. At the time that I graduated, most of the jobs in nuclear engineering, they were in more remote parts of Canada that in my early 20s, I didn't exactly want to move to. I discovered that I could actually build upon a lot of these skills at Barron in physics working in the financial industry. I did that for a few years before going back to academia to pursue computational physics. During that time, the pandemic started. During the pandemic, I was working on this PhD in Applied Math, specifically modeling very complicated physics equations and the math that goes behind that.

Well, when the pandemic starts and you're a PhD student, you find yourself with a decent amount of free time. A lot of that free time got channeled into a bunch of, I don't know what to call it, side quests or hobbies. So I started building a bunch of random things. One of those was an arbitrage bot that would find all kinds of arbitrage opportunities on the internet by looking at different prices across different websites. This required a lot of scraping, and that's how I fell into this web scraping world. And fast forward a few years later, I met my two co-founders and we talked about our experiences, and this was a very, very obvious thing that we wanted to work on, and we've been doing it ever since.

Yan Liberman (3:38)

Yeah, so, yeah, very serendipitous in that sense, where you basically are discovering that access to the public internet was beginning to deteriorate, and kind of, so, you know, in that area, what did you kind of see that others missed, and what led you to really pursuing it?

Andrej Radonjic (3:54)

Yeah, I mean, the biggest signal really was the pandemic. During the pandemic, most businesses went online. So during that time, a lot of businesses, the way that they would conduct intelligence, so things like price intelligence, for example, figuring out what your competitors are charging for a certain product, or even governments trying to calculate CPI. The way you would do that is actually send people physically into stores with a clipboard and like check the shelf price of each item and write it down. Then average that and then run your analytics based on that data. When the pandemic forced most of North America into lockdown, a lot of stores shut down and people are forced to spend more money online. Because of that, web infrastructure became really advanced, but then started the need for web intelligence. So a lot of websites began noticing, hey, we really need our information to be public so people can go and discover us and use our products, buy our services, or pay for our products. However, we want to limit and build a walled garden around this data and make it such that only consumers can visit this website on the public web but block any data centers or anything like that. The need for automated web data infrastructure, I don't want to use the word exploded because it really exploded later on, but this revolution and the need for this infrastructure really kicked off during the pandemic when all these things were forced online. What we were seeing at the time was, well, there's no reason for this trend to slow down. It was very obvious that this is going to happen beyond just quantitative data. So e-commerce websites or any website that displayed prices at scale.