The Exchanges

Q Taking a step back, can you explain the evolution of this industry and models? You gave a really good explanation on This Week in Startups with Alex, and I think this was like phenomenal, like it was clipped and everything. Could you explain the evolution of that?

A This industry had a huge shift after the reasoning models came out late last year, O-one being the first. The shift is, before the reasoning models, this industry needed simple data. Gobs and gobs of simple data. You needed a data factory. Or, ah, this industry needed somebody to find people really fast. After the reasoning models came out, the game completely changed. Uh, this is like after O-one and deep seek. Now what the labs need is not a data factory. It's not a talent marketplace. The labs need a strategic research partner, somebody who can collaborate directly with their researchers to understand where the models are weakened today and strategically custom engineer data to improve the model's performance. Uh, they need a research accelerator. In-house researchers collaborate with teams in coding, multimodality, STEM, RL gyms, et cetera, to generate data that'll improve these models. And the data that the models need now needs to be hard. That is model breaking. It has to literally break the model. So you need humans who are smarter than the models. Uh, it has to be, uh, realistic. That reflects how real humans use these models to do real work. Only then AI will actually move the GDP of the world. It has to mirror real world use, not esoteric academic use cases that test whether you've hit the singularity or not. And third, the data needs to be diverse to covers every si…

AI assessment note: “This industry had a huge shift after the reasoning models came out late last year”

Q that, all took the stage. Yeah, one unexpected player stood out as the next breakout AGI leader. Some stats. Three hundred million plus in revenue and profitable. Two hundred and twenty-five million in funding at a last valuation of 2.2 billion. A network of four million engineers and customers that include OpenAI, NVIDIA, Anthropoc, Google, Microsoft, Meta, Salesforce, Amazon, and I'm sure a lot more. So, Jonathan, what is touring?

A So at Turing, we train superintelligence. Uh, we work with seven out of the eight, uh, Frontier Labs. Uh, we work with OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, Amazon, anybody that's building a Frontier Foundation model, we are probably working with them already. Uh, and what's happening, uh, is as these models have become smarter and smarter, the data needed to improve them has become increasingly harder to generate. Uh, the earlier era was, um, almost like, uh, commodity data labeling. That era is over. Now it's all about frontier data. These models need expert human data in every domain imaginable. They need data to train these models in reinforcement learning. And sometimes they need synthetic data. Uh, what we are doing is scaling up our data infrastructure at gigantic scale. So for these models, the cool thing is you, uh, everybody's, uh, aware of all the research breakthroughs that are needed to move these models forward, and all the labs do an amazing job at advancing the research frontier. You need tons and tons of compute, and we, we have, uh, NVIDIA, Cerebrus, uh, Grok, like all these companies to thank for that. But they need a ginormous amount of data. And what's happened, uh, Molly, is these These models ate the internet when they were pre-trained, but the internet data is used up. It was used up like three years ago, right? Where's the data going to co…

AI assessment note: “So at Turing, we train superintelligence. Uh, we work with seven out of the”

Q Because research has become such an important part of this next era of AI, it would be, it would be wrong not to bring up the fact that this is your background. So you were in research and in AI development before this. So how did that lead to founding Turing?

A Turing's founding DNA is AI research. Uh, my co-founder and I met at Stanford. I worked at the Stanford AI lab and the Stanford info lab. My co-founder worked at the Stanford NLP lab. And in an alternate universe, both of us would have gotten our PhD in computer science and AI. And Stanford, at Stanford, there's this radioactive spider that tends to bite people and makes them start companies instead. And that's what happened, happened to me and my co-founder. Um, and, uh, so we saw the power of AI early. Uh, and when Turing was started, it was all about using AI to find the world's smartest software engineers at scale. Use AI to vet them, match them to companies, use AI to manage them, right? So when, um, the AGI wave hit, uh, or I, I remember that fateful meeting with OpenAI when they were training GPT-III and OpenAI wanted to teach GPT-III to code and to do function calling and tool use, which are building blocks for training agents. It, when we saw it, it was obvious what this was going to become. Right? This was the future. And we were perfectly positioned. We had the world's largest platform of software engineers. Where we could build, ah, the ability to generate high quality data from. And it was clear that, ah, the models needed not just coding data, but data in every advanced domain in STEM, in healthcare, legal, finance, et cetera. Um, and that, ah, that really helped …

AI assessment note: “Turing's founding DNA is AI research. Uh, my co-founder and I met at Stanford.”

Q go back in time and decipher these things. So right now there's a lot of competition in the space and it seems like the hottest thing to look at. Ever since the scale AI Acquisition, investment, whatever you want to call it. There's essentially a billion dollars up for grabs, and I want to know between the three leaders, of which you are one of them, How do you differentiate?

A This market is massive and growing. There's unlimited demand for high quality data, unlimited demand, right? I see two axes that you have to be really good at to win in this market. One axis is finding really smart humans really, really fast, right? And there are a few companies that do a good job at that during being one of them. There's another axis, which is you have to be at the forefront of data research. You have to know what type of data is likely to be helpful to the model for it to advance in coding, STEM, advanced reasoning, etc. Turing's great at that. We are the one company in this category that's not just good at finding people and generating data, but being proactive about figuring out what type of data these models are likely to need, and as the models get Getting smarter and smarter every quarter, you have to innovate on the research side too. So on that two by two, I would say Turing's the one company up and to the right. There are plenty of companies that are good at, okay, we have a large, uh, talent network, we work with university students, or we are good at finding people. Uh, we think that's like, um, that's necessary, that's not sufficient. Uh, you have to ensure that the quality of the data is good, and that it can improve the model's performance.

AI assessment note: “I see two axes that you have to be really good at to win”

Q That's C-A-R-T-A dot com slash S-O-U-R-C-E-R-Y. How do you give conviction in your investors, in outside entities, that this is sustainable, durable revenue? The whole image of Mercor, Surge, Scale AI is that this is service-based revenue, and it's not long-term, and we all know the VC world, investors, they love long, sticky revenue, but how do you position this to investors, and how do you think about revenue yourself?

A So, Turing's an interesting company in that, uh, we are a lot like NVIDIA in one sense, in that we are a picks and shovels into the AGI industry, right? Um, these, as AGI continues to advance, they're gonna need lots and lots of compute and lots and lots of data. So that's gonna continue to grow, um, for the foreseeable future. I think we're still a significant distance away from automating everything. If I asked you how much of enterprise workflows have we automated today, uh, you'd probably say next to nothing, right? Um, If I think of a zero to 10 point scale on the consumer side, we are maybe at a three. On the enterprise side, we are maybe at a .25 at best, right? 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, um, and this is one way in which we are different from like the traditional data labeling companies like the ones that you mentioned is Um, we also work with enterprises to help them take advantage of AGI. And for enterprises, they 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. Uh, 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. Uh, but there's the car com…

AI assessment note: “we are a picks and shovels into the AGI industry”

Q On the research side, how are you evaluating researchers, and how are you recruiting them?

A On the research side, our focus is on two things. Number one, ensuring that our data is the highest quality in advancing the model's Along the four pillars of superintelligence, multimodality, reasoning, tool use, and coding. And number two, uh, we do research on what type of data the models might likely need in the future. That's why we were on RL gyms very, very early. We built thousands of, uh, these reinforcement learning gyms to train agents. We were able to do that again because we have an internal R&D team that's constantly Uh, uh, going to AI conferences, understanding where the frontier is, uh, publishing papers, uh, that's been really key. So the focus is on ensuring that the data that we generate is high quality. I think of that as horizon one. That's the here and now. And horizon two is what type of data will these models need three months in the future? That's horizon two. And today, a lot of that work is on embodied AI and robotics and, and coming up with, um, benchmarks That the models, uh, would struggle at today. Like, we are creating data for coding now, for agentic coding, that would stump all of today's models and agents built on top of those models. It gives us a, a little bit of a sadistic pleasure in flunking all of the models today, uh, and a little bit of, like, uh, demonstrating, uh, where the human intelligence frontier is. Uh, but I would give, give …

AI assessment note: “On the research side, our focus is on two things.”