The Exchanges

Q Is that a good business for you? When you think about taking someone else's model, retrofitting it to a business, doing a lot of custom work with your own engineering teams in those businesses, is that a good business?

A I think it, it would be a good business. Like we're still early, but it's growing pretty fast. I think like the, we have this unique vantage point where because we are generating data for all the frontier labs, we get to see a glimpse of the future before it arrives. And the glimpse of the future that I see is that all knowledge work is going to be automated. If a human's job involves looking at a computer, analyzing what's on the screen, using different tools, using a keyboard and a mouse, it's going to be automated. It's only a matter of time. So, I mean, these computer use agents are going to keep improving over the next decade, and that's 30 trillion dollars of digital knowledge work.

AI assessment note: “I think it, it would be a good business. Like we're still early”

Q Of the eight largest providers, do they not spend with all of you?

A They spend with a handful of companies. Uh, they do that for, uh, to have some level of, um, uh, resilience. Um, and I imagine there's some price benefits to having more than one person that they could work with, but I think the resilience piece is important. I mean, we know what happened with the, you know, you know, when the scale investment happened again, like the, it, it was, um, Uh, the labs did benefit from having other partners that they could work with. I would say it's a small handful. It's a small handful that are trusted. And of course, there's a, there's probably a giant pool of smaller startups, but it's a small handful of big companies in the space.

AI assessment note: “They spend with a handful of companies.”

Q It's like traditional revenue numbers. I'm an investor, Jonathan. Essentially, I'm trying to understand and learn from you of how I should weight revenue in today's AI world versus the previous historical world. Like, should I be impressed by these revenue numbers or should I not?

A I think it depends on the type of revenue. I think the, um, obviously these are not SAS ARR numbers, right? These are not those types of revenues. Um, this is a different beast. I think this requires thinking from first principles. Um, there is, um, the revenue here is, um, reoccurring in the sense that oftentimes when you're working, uh, with a lab on, um, Helping, um, helping the models improve in some area. And I'll speak to Turing. I don't want to speak to other companies. Um, when we are helping a lab, let's say, improve their models for coding or multimodality or tool use, or, or working on RL environments for automating all types of professional knowledge work, it's usually a reoccurring project where there is, uh, projects will start, projects will end, um, And as long as you're doing a good job, uh, there is lots and lots of demand. Um, but you have to consistently keep doing a good job. And we also take, it's also important to be a trustworthy partner to the labs. Like we take secrecy very seriously. Like, uh, we have, we make sure that, um, our projects are all firewalled. Uh, between labs. Oftentimes, even with teams within the labs, like, sometimes, like, that's the level of secrecy that you'd need. I'm reminded a little bit about, um, how I've been told Foxconn operates. I mean, I don't know anything about that, but I've been told that, like, they have different f…

AI assessment note: “obviously these are not SAS ARR numbers, right? These are not those types of revenues.”

Q Now, I want to start with a little bit of definitions because everyone thinks they're talent marketplaces, and then everyone pushes back on talent marketplaces. How do you describe it? And why are we not dealing with talent marketplaces anymore?

A So I think of a talent marketplace as something that's basically matching talent to something. Maybe it's an opportunity. So, so Turing is not a talent marketplace. The, um, what we do at Turing is we're training super intelligence. We work with seven out of the eight frontier labs. To get to super intelligence, you need research, compute, and data. Research, the labs do in house with OpenAI, Anthropic, DeepMind, et cetera. For compute, we have Jensen to thank, and maybe Nvidia as well. But on the data side, Turing powers the data pillar. On the data side, there's been a significant shift in the last couple of years. So a few years back, Uh, the models weren't quite smart enough. And as the models have gotten increasingly smarter, the data needed to improve them has become harder to generate.

AI assessment note: “So I think of a talent marketplace as something that's basically matching talent to something.”

Q Is that a good business for you? When you think about taking someone else's model, retrofitting it to a business, doing a lot of custom work with your own engineering teams in those businesses, is that a good business?

A I think it, it would be a good business. Like we're still early, but it's growing pretty fast. I think like the, we have this unique vantage point where because we are generating data for all the frontier labs, we get to see a glimpse of the future before it arrives. And the glimpse of the future that I see is that all knowledge work is going to be automated. If a human's job involves looking at a computer, analyzing what's on the screen, using different tools, using a keyboard and a mouse, it's going to be automated. It's only a matter of time. So, I mean, these computer use agents are going to keep improving over the next decade, and that's 30 trillion dollars of digital knowledge work.

AI assessment note: “I think it, it would be a good business. Like we're still early”

Q Do we lose the phone as the interface to this world? We obviously see Sam and Johnny. I've, you know, there's rumors of pendants and some hardware devices. I'm not asking you to comment on that. I'm just saying, does the phone become, still remain the primary interface and design device?

A We'll have some type of a device, um, that is, that we'll carry that's, um, always on and processing multimodal tokens. For example, as I'm talking to you, If I were to envision my perfect device, it would be something that has, it should have cameras. So maybe it's a wearable as a glass, uh, or something that I'm, uh, having on me that's processing visual input because I want to be able to read your body language. I might have like, um, AirPod-like thing in my ear that's whispering to me that maybe says, Jonathan, as you were talking about multimodality, Harry seemed Less interested. His body cues suggest that he was losing interest. But when we were talking about AR, he perked up. So those types of feedback and cues I think would be good. So I envision a device that, um, I think of it in terms of sensors and effectors. In terms of sensors, like obviously it has to be listening to stuff. It has to be seeing stuff. But in terms of effectors, it'll probably also be speaking in my ear. Um, ideally it should be something that you can talk to and have it do things later. For example, I might say, remind me to follow up with Harry on that idea for, um, uh, using Turing to automate clip generation, right? Like the, so, so it has to like remember that and come back later. So I do think there'll be all sorts of new devices and Glasses, hearing, like these AirPod type devices seem obvio…

AI assessment note: “I do think there'll be all sorts of new devices and Glasses”

Q It's like traditional revenue numbers. I'm an investor, Jonathan. Essentially, I'm trying to understand and learn from you of how I should weight revenue in today's AI world versus the previous historical world. Like, should I be impressed by these revenue numbers or should I not?

A I think it depends on the type of revenue. I think the, um, obviously these are not SAS ARR numbers, right? These are not those types of revenues. Um, this is a different beast. I think this requires thinking from first principles. Um, there is, um, the revenue here is, um, reoccurring in the sense that oftentimes when you're working, uh, with a lab on, um, Helping, um, helping the models improve in some area. And I'll speak to Turing. I don't want to speak to other companies. Um, when we are helping a lab, let's say, improve their models for coding or multimodality or tool use, or, or working on RL environments for automating all types of professional knowledge work, it's usually a reoccurring project where there is, uh, projects will start, projects will end, um, And as long as you're doing a good job, uh, there is lots and lots of demand. Um, but you have to consistently keep doing a good job. And we also take, it's also important to be a trustworthy partner to the labs. Like we take secrecy very seriously. Like, uh, we have, we make sure that, um, our projects are all firewalled. Uh, between labs. Oftentimes, even with teams within the labs, like, sometimes, like, that's the level of secrecy that you'd need. I'm reminded a little bit about, um, how I've been told Foxconn operates. I mean, I don't know anything about that, but I've been told that, like, they have different f…

AI assessment note: “obviously these are not SAS ARR numbers, right? These are not those types of revenues.”