Turing CEO Jonathan Siddharth: Who Wins in Data Labelling & Why 99% of Knowledge Work Will Disappear

I think the era of data labeling companies is over, and it's now the era of research accelerators.

Today, I do not pull any punches with Jonathan Siddharth, founder and CEO of Turing, a company that he has scaled to over 350 million in annual recurring revenue.

All knowledge work is going to be automated. It's only a matter of time. I don't see an AI bubble. These models are incredibly powerful today. SaaS, as we know it, I think is over. I think it's completely over.

Ready to go? Jonathan, I've been so looking forward to this. Thank you so much for joining me in person. It's such a treat to do it in person while you're in London.

Thank you for having me, Harry.

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?

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 superintelligence. We work with seven out of the eight frontier labs. To get to superintelligence, you need research, compute, and data. Research, the labs do in-house with OpenAI, Anthropic, DeepMind, et cetera. For compute, we have, uh, Jensen to thank, and maybe NVIDIA as well. But, uh, on the data side, Turing power is 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.

And this is because it's more sophisticated data that's required to improve the models. It's like vertically specific people in task and workflows that isn't so obvious, like cat pictures.

That's correct. That's correct. And it's, uh, there's a shift in the data going from simple to complex. I mean, let's take coding, for example. Uh, a few years ago, the kind of dataset a, a contractor who gen- could generate might look like, "Hey, write a Python program to sort some numbers." Today, the data that's generated might be, "Write a, uh, a B2B marketplace app that connects, uh, doctors with patients. And write it on, for Android, uh, with Kotlin/Java, write it for iOS with, uh, Swift, and write it on the web, like with Next.js or something." Right? That's the complexity. So there's a shift in going from simple to complex. So it's no longer the kind of data that low-skilled, medium-skilled contractors c- can generate. You need expert humans in every domain.

Yeah.

The second shift, uh, is, uh, we've gone from teaching AI to take tests and pass tests to teaching AI to do real work. It's less about having AI pass the bar. It's more about, can AI do the job of a lawyer? Can it do the job of a privacy lawyer, a compliance lawyer, a paralegal? So having AI be good at doing economically valuable work. So that's, that's a shift. Uh, the third, uh, shift is we've gone from chatbots to agents, right? Like, the, we started off with ChatGPT where you're asking questions, getting answers, which is great. But now it's about the models becoming agentic, where they can execute complex, multi-step workflows in a real-world business setting. And the type of data you need for that is totally different.