00:38:26 · Jonathan Siddarth, Molly O'Shea
Most companies have really amazing human expertise inside their organizations. They just have to transfer that expertise from those human minds into those machine minds and that will give them an edge. It will actually give them an edge over a startup starting from scratch. If you're GEICO, long as you do a good job of fine tuning your custom models on your underwriting data, on your claims processing data, and building systems around it to automate underwriting and claims processing, you can win. But if you don't do that, there could be an upstart AI company that starts today, that does this from scratch, that has a good data collection feedback loop. That company could jump you. But that company will still need time to acquire that data. If you're GEICO or Progressive, you have access to data. So it's your battle to lose. But to do that, you have to act fast to make sure that you own your proprietary intelligence and you don't outsource it away. [00:39:34] S0: With over $300,000,000 in revenue, I'm sure this number has changed because that was a couple months ago, but who are your customers? You can talk categorically. You don't have to tell me their names. I know some of these things are sensitive, but, like, what ways in which are you helping these different industries deploy their own models?
00:39:34 · Molly O'Shea, Jonathan Siddarth
[00:39:34] S0: With over $300,000,000 in revenue, I'm sure this number has changed because that was a couple months ago, but who are your customers? You can talk categorically. You don't have to tell me their names. I know some of these things are sensitive, but, like, what ways in which are you helping these different industries deploy their own models? [00:39:56] S1: So we are working with almost everybody that's building a foundation model. We are working with eight out of the nine frontier foundation labs. And now we've expanded to start to work with enterprises in financial services, in insurance, in pharma, and we are rapidly expanding into more verticals. [00:40:24] S0: So this is like investment banks, this is maybe private equity funds, these are like real financial institutions. That's pretty interesting.
00:40:32 · Jonathan Siddarth
[00:40:32] S1: That's right. And if the job involves analyzing data and making relatively quantifiable decisions like verifiable decisions it's a great use case. And for investment banks, private wealth management firms, these other companies in capital markets, if the job is to figure out good investment opportunities and you're looking to find alpha, AI is sort of an existential risk to ignore. Because now with this technology you can ask a question like, hey how many times did the CEO of this public company talk about this particular topic in the last twelve months and how has that changed? In the pre LLM world, this question would been a very sad piece of work for an intern or some analyst to do for many, many hours. But for LLMs this is like nothing. LLMs are really good at synthesizing gobs and gobs of information to come up with useful signal, especially if they are fine tuned on the right data sets. If you are an investment firm and you have a history of evaluating investment opportunities and making decisions, and maybe you decided to buy a stake in a company at a certain price, or maybe you decided to pass company, and you probably wrote an investment memo outlining your decisions, that's incredibly valuable data to fine tune an LLM. And you own that data. That's your edge. Your data is your moat. And I think the best firms are already doing this to help an investment analyst make better decisions faster and eventually with significantly fewer people.
00:42:35 · Molly O'Shea, Molly O'Shea
[00:42:35] S2: Sourcery is proudly sponsored by Carta. Carta is transforming the private marketplace, connecting founders, investors, and limited partners through software purpose built for private capital. Trusted by more than 65,000 companies in over 160 countries, Carta's platform of software and services lays the groundwork so you can build, invest, and scale with confidence. Carta's fund administration platform supports over 9,000 funds and SPVs, representing nearly $185,000,000,000 in assets under management, with tools designed to enhance the strategic impact of fund CFOs. For more information, visit carta.com/sorcery. That's carta.com/sourcery. [00:43:21] S0: 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?
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?