00:43:54 · Jonathan Siddarth
[00:43:54] S1: So Turing's an interesting company in that we are a lot like NVIDIA in one sense, in that we are a picks and shovels into the AGI industry. AGI continues to advance, they're going to need lots and lots of compute and lots and lots of data. So that's going to continue to grow for the foreseeable future. I think we are still a significant distance away from automating everything. If I asked you how much of enterprise workflows have we automated today, you'd probably say next to nothing. If I think of a zero to 10 scale, on the consumer side we are maybe at a three. On the enterprise side, we are maybe at a 0.25 at best. So there's a huge market ahead that's massive and growing on the data side. So we're very excited about that. The thing that we've discovered, and this is one way in which we are different from the traditional data labeling companies like the ones that you mentioned, is we also work with enterprises to help them take advantage of AGI. And enterprises don't need just data. They need somebody to help them build end to end AI systems in the first mile and the last mile. I think that's going to be an even bigger market potentially that we are perfectly positioned to. I think of it a little bit like we are working with the Formula one teams in the Frontier AI Labs but there's the car companies where there's a trickle down effect of some of this technology. We are now building RL gyms for giant financial institutions as well. And that's a massive market. If you look at the services market, it's a trillion dollar market. And we are talking about $30,000,000,000,000 of knowledge work that's about to be automated. And enterprises need all the help they can get to not just build the right models, but to build the right systems around these models to automate workflows. So I think like Turing will probably replace companies like McKinsey, Bain, BCG, and every services company like Accenture, TCS, Wipro, Infosys, like all of these companies doing vanilla services that's going to be automated with these agentic systems. And we are perfectly positioned as the company that's helping the Frontier Labs build smarter and smarter superintelligence. We are simultaneously helping enterprises take advantage of these superintelligent systems and one helps the other. If you're just a pure data vendor, you're not touching reality.
00:46:37 · Jonathan Siddarth, Molly O'Shea
You're just selling data to labs. But because we also try to build stuff on top of these models, we touch reality. We know where the models break when you try to solve underwriting. When you, one of the use cases that you told me was, after you record an interview, you try to, pick the right clips from those interviews that are likely to go viral. In the future, you'll probably use a Turing model that is fine tuned for your own use case to do that. That's an example of something that actually touches a real world use case. So the fact that we are also helping enterprises helps us do a better job serving the Frontier AI Labs. [00:47:21] S0: So talking about revenue, revenue has become really public because all of a sudden there's a billion dollars up for grabs with the Scale AI acquisition, acquihire, whatever you want to call it, investment round. How do you think about revenue and your competitors?
00:47:39 · Jonathan Siddarth
[00:47:39] S1: So this is a massive market that's growing super fast. And interesting thing is and there are a few companies in the space. There's companies like Surge, Mercor, Scale AI, of course, being the company that started in the space early. There's unlimited demand for really high quality data. If you look at the competitive landscape, I see three categories of companies. There is the pure play data labeling companies. There are recruiting talent marketplaces. We are none of them. We are a research first data accelerator. That sets us apart. Of course, we are really good at finding really smart people. And we built a platform that helps us generate data really efficiently. But what sets us apart is our research first posture. Old school data labeling is over. And the era of recruiting or a talent marketplace is also over. These labs need more than just data thrown over a wall. They need more than just higher 50 PhDs in physics, chemistry, math, biology. They don't need just that. What they need is a true partner that can understand their research objectives in improving their models for coding, for math, for STEM, for reasoning, for agentic workflows. And they need a partner that can help them figure out what type of data is likely to be helpful and do things before being asked. That sets Turing apart. I would say most of the other companies in this space fall into either being a data labeler or a people finder. And Turing is a research first accelerator. We are a research accelerator for the frontier labs.
00:49:33 · Molly O'Shea
[00:49:33] S2: Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sourcery.vc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sourcery today, and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.