The Exchanges

Q Where are you not investing that you want to be investing?

A I, I think the simplest answer is actual physical world interactions. So what I mean by that is I think a lot of the most interesting data that we don't even really have access to yet is, is things that exist in the physical world that are more complicated to acquire and organize. So I'll give you an example. We, um, we're serving one of the largest, uh, agricultural conglomerates in the US on, um, herd safety. So actually like monitoring risk factors. When should you send a vet for their herd of cows basically? And that whole process relies us on us actually sending forward deployed engineers to farms, dropping Starlink terminals into those farms, and building out custom computer vision models in those contexts. And I think there are so many different physical world contexts that become really, really interesting, but it does take cost and capital to build those out. Like, you know, I think oil and gas, uh, oil rigs are an interesting one as an example. And so I think physical world interaction patterns are the, some of the most interesting growth vectors for this. But they do take time and money to invest in. Robotics being another big part of that.

AI assessment note: “I think the simplest answer is actual physical world interactions.”

Q him. Um, we were chatting before, and I said, listen, where do we have to go? And I just, I always think that the best conversations are led by passion. The first one that you said was there's a gap or a chasm between model performance and adoption. When we break that down, can you explain to me what you meant by that and how we see that in action?

A Yeah, and, and let me set the context, and I'll go into more detail later, but Invisible is an interesting business in that we both train all the large language models with reinforced language and feedback, and we built, we are at the core in a modular software platform where, in enterprise context, we deploy all different enterprise use cases. And I think the cognitive dissonance that occurred, has occurred over the last couple years, is model performance has increased exponentially. I don't think anyone would doubt that. If you look at all the public benchmarks, models have increased 40 to 60% in performance over the last two years. And consumer adoption has been also exponential. So, you know, I think, uh, KPMG just released that 60% of consumers use Gen AI weekly now. But the enterprise is not. You know, I think in the enterprise, uh, MIT just released this report that five percent of Gen AI deployments are working in any form. You know, I think you've seen Gartner saying 40% of, um, enterprise projects will likely be canceled by 27. And I think the reason for that is Deployment of the enterprise is a lot more than just models themselves. It's the data infrastructure to support those models. It's the redesign of workflows. It's the, uh, process figuring out which operational leader takes accountability for that. And most importantly, it's trust. It's observability. It's all…

AI assessment note: “MIT just released this report that five percent of Gen AI deployments are working”

Q him. Um, we were chatting before, and I said, listen, where do we have to go? And I just, I always think that the best conversations are led by passion. The first one that you said was there's a gap or a chasm between model performance and adoption. When we break that down, can you explain to me what you meant by that and how we see that in action?

A Yeah, and, and let me set the context, and I'll go into more detail later, but Invisible is an interesting business in that we both train all the large language models with reinforced language and feedback, and we built, we are at the core in a modular software platform where, in enterprise context, we deploy all different enterprise use cases. And I think the cognitive dissonance that occurred, has occurred over the last couple years, is model performance has increased exponentially. I don't think anyone would doubt that. If you look at all the public benchmarks, models have increased 40 to 60% in performance over the last two years. And consumer adoption has been also exponential. So, you know, I think, uh, KPMG just released that 60% of consumers use Gen AI weekly now. But the enterprise is not. You know, I think in the enterprise, uh, MIT just released this report that five percent of Gen AI deployments are working in any form. You know, I think you've seen Gartner saying 40% of, um, enterprise projects will likely be canceled by 27. And I think the reason for that is Deployment of the enterprise is a lot more than just models themselves. It's the data infrastructure to support those models. It's the redesign of workflows. It's the, uh, process figuring out which operational leader takes accountability for that. And most importantly, it's trust. It's observability. It's all…

AI assessment note: “model performance has increased exponentially... But the enterprise is not.”

Q I did not see the specialization and the unbundling. Is that something that you see too in terms of these very micro niche specialized data requirements?

A Absolutely. I think, you know, Five years ago, this space was what I would call cat dog, cat dog commodity labeling. I don't think anyone, and I think there was a lot of Google Sheets in that era, and you've seen some comments on it. Like this, this, this era, this sector has evolved the same way most technology sectors do, where it started with Google Sheets and cat dog labeling, and it's evolved to real digital assembly lines, huge velocity of expertise, and incredibly specific expertise. So like, you know, we have to give a funny example. We have to be able to validate, um, uh, An architectural expert on 17th century French architecture who speaks French. I mean, that is a, that is a complex thing to do on 24 hours notice, right? And so the ability to source, assess, validate, and I think one of the advantages for us is because we have five years of data on who's been good at what task, there's real institutional data memory in how you do that selection and assessment. I think that's one of the core advantages we have from that.

AI assessment note: “Absolutely. I think, you know, Five years ago, this space was what I would call cat dog”

Q Where are you not investing that you want to be investing?

A I, I think the simplest answer is actual physical world interactions. So what I mean by that is I think a lot of the most interesting data that we don't even really have access to yet is, is things that exist in the physical world that are more complicated to acquire and organize. So I'll give you an example. We, um, we're serving one of the largest, uh, agricultural conglomerates in the US on, um, herd safety. So actually like monitoring risk factors. When should you send a vet for their herd of cows basically? And that whole process relies us on us actually sending forward deployed engineers to farms, dropping Starlink terminals into those farms, and building out custom computer vision models in those contexts. And I think there are so many different physical world contexts that become really, really interesting, but it does take cost and capital to build those out. Like, you know, I think oil and gas, uh, oil rigs are an interesting one as an example. And so I think physical world interaction patterns are the, some of the most interesting growth vectors for this. But they do take time and money to invest in. Robotics being another big part of that.

AI assessment note: “the simplest answer is actual physical world interactions.”

Q If I'm a startup founder thinking, huh, Do we need FDs? How do we do FDs? How do we move into an FDE model? What would you say to them that they should know if they're thinking about starting that model or potentially needing that model, knowing all that you know?

A I think it depends a lot on the nature of the business and what you're trying to build. So, um, you know, if you're trying to build a knowledge management system of public filings for finance, for example, you don't need FDs because what you're building there is, um, a repository of information that people can access. You've seen similar things in healthcare, for example. If you're trying to change workflows, you do need FDs. I think that's the simple paradigm difference in my mind is, If you're building something where the hardest part is getting adoption and workflow embedding, and you need to actually change the way a company works, then yes, forward deployed engineers are the only way to do it. It's interesting, there aren't that many, ah, area, folks that have expertise doing that. So it's, it's a hard thing to train and learn, but I do think it is the only way to get the enterprise working.

AI assessment note: “If you're trying to change workflows, you do need FDs.”

Q I did not see the specialization and the unbundling. Is that something that you see too in terms of these very micro niche specialized data requirements?

A Absolutely. I think, you know, Five years ago, this space was what I would call cat dog, cat dog commodity labeling. I don't think anyone, and I think there was a lot of Google Sheets in that era, and you've seen some comments on it. Like this, this, this era, this sector has evolved the same way most technology sectors do, where it started with Google Sheets and cat dog labeling, and it's evolved to real digital assembly lines, huge velocity of expertise, and incredibly specific expertise. So like, you know, we have to give a funny example. We have to be able to validate, um, uh, An architectural expert on 17th century French architecture who speaks French. I mean, that is a, that is a complex thing to do on 24 hours notice, right? And so the ability to source, assess, validate, and I think one of the advantages for us is because we have five years of data on who's been good at what task, there's real institutional data memory in how you do that selection and assessment. I think that's one of the core advantages we have from that.

AI assessment note: “it's evolved to real digital assembly lines, huge velocity of expertise, and incredibly specific expertise.”

Q What's the best advice That you've been given that you most frequently go back to?

A We kind of talked about this a little bit earlier, but a, a, a, a former, a CEO that I respect a lot when I, when I took the role, I asked him his advice. They're like, what's the best way to think about a team? And he said, look, your job as a CEO is to do three things really well. Recruit great people, create a culture where they love working together and build great things, and try and make them all extremely rich. And I think it's a funny framework, but I think an interesting, it's an interesting Way to think about, like, that is my responsibility to employees. I want them, I want to find great people, help them enjoy each other, and then build something that, that becomes big and helps all of them achieve their dreams.

AI assessment note: “your job as a CEO is to do three things really well. Recruit great people”

Q How important is pay? You know, I think a couple of other providers, you know, kind of, have said that, bluntly, it's about how much you pay. You pay more than the others, you'll get a good talent.

A You know, look, so a weird analogy. I think of our business like Uber. And what I mean by that is, um, we source talent at the price at which people will do the work that is asked of them, right? So the same idea that if you're standing, standing on a street corner, your question is, can I find a ride that will pick you up at this moment within three minutes? And that matter, that's a different price if it's raining. That's a different price if you're in, you know, Rio de Janeiro versus London, right? The price depends on the market context and the Specific place you are. I think expert pay is the same dynamic really. A lot of what we're doing is what I call price discovery. And so the nuance I would add to what you're saying is you can overpay a really bad expert and that is a total waste of everyone's time. And so what I think our customers appreciate is we can tell you between a 150 dollar expert and a 130 dollar expert, the difference in expertise you get.

AI assessment note: “you can overpay a really bad expert and that is a total waste”

Q Okay. I am, you know, we have hundreds of thousands of listeners and many of them are CEOs. If you are a CEO thinking about your CFO being equipped to buy and to manage in this new environment, what should they be thinking about? And do we have the right CFO talent pool to manage this new environment?

A Yeah, so I think one misconception is that that, that leader has to be highly technical to make that decision, and I would actually argue they don't at all. They just need the same muscle memory they've looked at in the past, which would be, to go through it, what do you need to get, to get a GNI initiative working? You need good data that you can work off of for that specific initiative, clear milestones and outputs, clear line ownership of the initiative, and then probably most importantly, you want to actually anchor it in milestones And outcomes where you pay as it works. So I think the other, the other interesting context for a lot of this is what I would call the Accenture paradigm of the last 20 years, right? Which is, a lot of times, the way that if you think about the wrapper that's been around software for the last 20 years, you know, Francis, our founder Francis Peraza has the founding principle of invisible was if there's an app for everything, how come nothing works? And it's an interesting concept, right? Because what that ended up happening is you built, you bought 50 apps, You had Accenture come in and you paid them two hundred million dollars over two years to try and layer them all together, and often you ended up a couple years in with no working data, no linkages between them, and that, that, that kind of layers of sediment has been how the tech paradigm wor…

AI assessment note: “one misconception is that that, that leader has to be highly technical”

Q If I'm a startup founder thinking, huh, Do we need FDs? How do we do FDs? How do we move into an FDE model? What would you say to them that they should know if they're thinking about starting that model or potentially needing that model, knowing all that you know?

A I think it depends a lot on the nature of the business and what you're trying to build. So, um, you know, if you're trying to build a knowledge management system of public filings for finance, for example, you don't need FDs because what you're building there is, um, a repository of information that people can access. You've seen similar things in healthcare, for example. If you're trying to change workflows, you do need FDs. I think that's the simple paradigm difference in my mind is, If you're building something where the hardest part is getting adoption and workflow embedding, and you need to actually change the way a company works, then yes, forward deployed engineers are the only way to do it. It's interesting, there aren't that many, ah, area, folks that have expertise doing that. So it's, it's a hard thing to train and learn, but I do think it is the only way to get the enterprise working.

AI assessment note: “If you're trying to change workflows, you do need FDs.”

Q That's a very clear decision to be profitable. And profitability comes often at the extent of growth naturally. Can you just take me to that decision making for you and how you thought about it?

A Yeah, look, I mean, to me, it was a simple one, which is if you think about the dynamics of return on capital, uh, you can either harvest capital or invest capital, and your decision to invest depends on the growth you see as a result of that investment, and I think we're in the greatest environment for growth that has ever existed. I think Invisible's really uniquely positioned to capitalize on that growth, too, and so I think of our four, five core platforms, I think of the growth vectors across both AI training and enterprise, And there were just way too many different things I thought were interesting to invest in. It was the clear best use of capital. And I look, I'm trying to build this for the next 10 to 20 years. And I think if you want to build enterprise value for 10 to 20 years, now is the time to invest and build. Um, and I hope we never get to the harvest stage, but I definitely not now.

AI assessment note: “you can either harvest capital or invest capital, and your decision to invest depends”

Q Is that not our industry? I'm sorry. I mean, I, I, I don't mean to pick a fight with Marc Andreessen, but like, hello, Marc. Like, our job is to sell and then deliver later. Like, uh, I'm looking at the thinking, well, I'm

A Well, you know, I guess it's all a question of degrees. And I think in my mind, um, like I want to say things where the narratives are the same to the public and to what our team thinks and what our customers experience. And so I think that's part of why I have focused on saying some of the nuances of what's not working and not claiming everything works out of the box. And I think that is, that is a different approach, but it's been a core to how we've thought about building the brand is we are building this around trust where like, I want a company we work with to know that if I say this will work, it will work. And I, I think you only get one chance to do that right.

AI assessment note: “it's all a question of degrees... I have focused on saying some of the nuances”

Q What's the best advice That you've been given that you most frequently go back to?

A We kind of talked about this a little bit earlier, but a, a, a, a former, a CEO that I respect a lot when I, when I took the role, I asked him his advice. They're like, what's the best way to think about a team? And he said, look, your job as a CEO is to do three things really well. Recruit great people, create a culture where they love working together and build great things, and try and make them all extremely rich. And I think it's a funny framework, but I think an interesting, it's an interesting Way to think about, like, that is my responsibility to employees. I want them, I want to find great people, help them enjoy each other, and then build something that, that becomes big and helps all of them achieve their dreams.

AI assessment note: “your job as a CEO is to do three things really well”

Q Can you just talk to me about how does like a 10 year McKinsey, uh, stalwart warrior become CEO of like one of the fastest growing data companies in tech? How does that transition happen?

A Yeah, so, um, I would say my McKinsey journey was non-traditional. Um, I spent 12 years there. I was a senior partner, and I led a group called Quantum Black Labs, which is the firm's global tech development group. So, about 10 years ago, McKinsey actually started hiring engineers, like, and I was a big part of this, and a pretty big quantum, and I think we went from, I, when I started, we had about a hundred engineers total in firm. By the time I left, we had 7000. Uh, I oversaw about a fifth of that group, uh, and all the application development, all of the data warehouse infrastructure, and all of the, uh, Gen AI builds globally. And so, Uh, that journey was, was really interesting, and it, and, you know, over the course of it, spent a variety of my time competing with other large enterprise, um, AI businesses, and I got to know the, the founder, Francis, really well, um, about three years or four years ago now. Uh, we actually met totally not work-related in a, uh, kind of a social context where we were discussing, it was, it was basically a forum called Dialogue. I don't know if you heard it, but you basically talk about different ideas, We bonded over.

AI assessment note: “got to know the, the founder, Francis, really well”

Q How important is pay? You know, I think a couple of other providers, you know, kind of, have said that, bluntly, it's about how much you pay. You pay more than the others, you'll get a good talent.

A You know, look, so a weird analogy. I think of our business like Uber. And what I mean by that is, um, we source talent at the price at which people will do the work that is asked of them, right? So the same idea that if you're standing, standing on a street corner, your question is, can I find a ride that will pick you up at this moment within three minutes? And that matter, that's a different price if it's raining. That's a different price if you're in, you know, Rio de Janeiro versus London, right? The price depends on the market context and the Specific place you are. I think expert pay is the same dynamic really. A lot of what we're doing is what I call price discovery. And so the nuance I would add to what you're saying is you can overpay a really bad expert and that is a total waste of everyone's time. And so what I think our customers appreciate is we can tell you between a 150 dollar expert and a 130 dollar expert, the difference in expertise you get.

AI assessment note: “you can overpay a really bad expert and that is a total waste”

Q When you look forward to the next 10 years, final one, what are you most excited about? Like, you know, for me, my mother's got MS. I look at potential advancements in MS drug discovery, treatment pathways. What are you most excited for? I like to end on a tone of optimism.

A Yeah, you know, I think despite some of my, ah, what I call realism on, on enterprise adoption, I actually am an AI optimist, and I actually think that the current narrative on some of the risks are far outweighed by some of the benefits, and like, just to give a couple examples, right, and I'll go through four, including healthcare. If you take, um, if you take energy as an example, right, there's a lot of question around, like, data center implications for, for energy, but you do the math right now, data centers are about one percent of total global electricity usage. AI data centers are . . . . to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . So if you just, I mean, AI has so many different ways of grid optimization, cooling, where, I mean, the World Economic Forum just came out and said this, it's going to be massively net net positive from an, from a, um, environmental impact standpoint. So I think energy is one where if you think about all the energy needs we're going to have and the investment now going into clean energy because of all this, I think we'll actually be in a much better place 10 years from now. I think healthcare is another interesting one. If you, um, if you look at, uh, US healthcare, we spend 14,000 14,000 per capita per year, uh, on patients in the U.S. So that, that's like a rough spend. That's two to three, two and a half to three, like Ge…

AI assessment note: “I actually am an AI optimist, and I actually think that the current narrative”

Q And how we see the different players. Is this a market where, you said like Uber and Lyft, is this a market where there's one and two players and they take the dominant market share and then there's everyone else? Is it a cloud market where it's much more evenly distributed? How do you project that out in, say, a 10 year horizon?

A Um, You know, I, I think in both AI training and in enterprise, I don't think the answer is one player. You know, I think actually, interestingly in the enterprise, historically, there's probably, it's been Palantir and not many others. So that's kind of, I think why you've seen more, um, people want alternative options to that. I think that, I think that's part of the reason you've seen so much excitement on enterprise AI recently. I think most of these markets end up with three, four or five players. I don't actually think it's even two. And I think the choice in consumers is, is a, um, Markets tend to allow to create that, and that's a good thing, right? Like, I think you'll have some specialization on certain topics, you know, some, you know, maybe some better at coding, some better, better at specialist tasks, some better at PhDs, but I think it'll, it'll stay with a fair amount of choice.

AI assessment note: “I think most of these markets end up with three, four or five players.”

Q When you look forward to the next 10 years, final one, what are you most excited about? Like, you know, for me, my mother's got MS. I look at potential advancements in MS drug discovery, treatment pathways. What are you most excited for? I like to end on a tone of optimism.

A Yeah, you know, I think despite some of my, ah, what I call realism on, on enterprise adoption, I actually am an AI optimist, and I actually think that the current narrative on some of the risks are far outweighed by some of the benefits, and like, just to give a couple examples, right, and I'll go through four, including healthcare. If you take, um, if you take energy as an example, right, there's a lot of question around, like, data center implications for, for energy, but you do the math right now, data centers are about one percent of total global electricity usage. AI data centers are . . . . to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . So if you just, I mean, AI has so many different ways of grid optimization, cooling, where, I mean, the World Economic Forum just came out and said this, it's going to be massively net net positive from an, from a, um, environmental impact standpoint. So I think energy is one where if you think about all the energy needs we're going to have and the investment now going into clean energy because of all this, I think we'll actually be in a much better place 10 years from now. I think healthcare is another interesting one. If you, um, if you look at, uh, US healthcare, we spend 14,000 14,000 per capita per year, uh, on patients in the U.S. So that, that's like a rough spend. That's two to three, two and a half to three, like Ge…

AI assessment note: “I actually am an AI optimist, and I actually think that the current narrative”

Q Do you think you have control of a finite supply of data, uh, providers? If you look at the Seven Powers and Hamilton Ham, one of them is like, acquiring finite supply.

A Um, I don't, I, so I actually don't think finite supply matters. Uh, and what I mean by that is, I think the expertise needed varies so much month to month that if you tried to do a world where you bottled up whatever supply it is, it would change in three months. And we actually relish that concept. I actually think the dynamic, again, why I would use Uber and Lyft, you could use, um, you could use Airbnb and VRBO as the same context is, I don't think people, I don't think experts go on five platforms, right? I think actually what you want to be is, This is a two-way marketplace where you need enough demand for people to be interested, and you need enough expertise that many experts, and I think the reason we get 1.3000000 in bounds is because of that kind of supply-demand balance. So I don't think this moves to a world, and I actually, I would never say it moved to a world where there is one player coming out of this. I think there's benefits to everyone to having numerous players that do AI training, and so it's a question of being one of the players that has that Balance.

AI assessment note: “I actually don't think finite supply matters”

Q You said about kind of the benchmarks. I'm just so interested. Gemini three killed it. It's the best ever. And then yesterday, Opus 4.5 Killed it. It's the best ever. Next week Sam's gonna release one. Does it matter? Like, are we in a world of such transient and flux where really we should detach ourselves from these, bluntly, updates that last for days?

A Look, I, I think the benchmarks are a useful framework for society to gauge progress on this topic. And it's a very, it's a very often discussed topic. So people want a way to say, to answer the question about are the models improving? And I can tell you like unequivocally the answer is yes. I mean, I think by every measure you look at, um, they are, and you know, they're not only including on the bench, improving on the benchmarks, but even on specific tasks like research for investments, for example, you can see the models are much better at doing certain tasks. And I think what you're seeing start to happen Is people, and we're doing this as well, are building very specific work-based benchmarks to calibrate certain things, like how well does the model do on building an LBO model, for example. And you're going to see more and more benchmarks cited. Now, the complexity then becomes if you move from five main benchmarks, like SweetBench and others, to 600 benchmarks, then you kind of lose track of what's doing, who's doing well and which things. Um, but I think my, my, my interesting view on that would be I'm not sure the benchmark progress is what determines enterprise adoption, and what I mean by that is if you take the fact that the models have improved exponentially over the last couple years, and you say consumer adoption has been massive, right, like, um, KBMG had this r…

AI assessment note: “I think the benchmarks are a useful framework for society to gauge progress”

Q You said about kind of the benchmarks. I'm just so interested. Gemini three killed it. It's the best ever. And then yesterday, Opus 4.5 Killed it. It's the best ever. Next week Sam's gonna release one. Does it matter? Like, are we in a world of such transient and flux where really we should detach ourselves from these, bluntly, updates that last for days?

A Look, I, I think the benchmarks are a useful framework for society to gauge progress on this topic. And it's a very, it's a very often discussed topic. So people want a way to say, to answer the question about are the models improving? And I can tell you like unequivocally the answer is yes. I mean, I think by every measure you look at, um, they are, and you know, they're not only including on the bench, improving on the benchmarks, but even on specific tasks like research for investments, for example, you can see the models are much better at doing certain tasks. And I think what you're seeing start to happen Is people, and we're doing this as well, are building very specific work-based benchmarks to calibrate certain things, like how well does the model do on building an LBO model, for example. And you're going to see more and more benchmarks cited. Now, the complexity then becomes if you move from five main benchmarks, like SweetBench and others, to 600 benchmarks, then you kind of lose track of what's doing, who's doing well and which things. Um, but I think my, my, my interesting view on that would be I'm not sure the benchmark progress is what determines enterprise adoption, and what I mean by that is if you take the fact that the models have improved exponentially over the last couple years, and you say consumer adoption has been massive, right, like, um, KBMG had this r…

AI assessment note: “I'm not sure the benchmark progress is what determines enterprise adoption”

Q I just, we do a show every Thursday, which is blown up, which is incredibly, um, nice for us as a business, but Essentially, we have Jason Lemkin and Mario Driscoll, two VCs, and we talk about news, and we talked about Sam Altman and war mode. Um, can you do a war mode, then, in the culture of research and AI where it's maybe more thoughtful? Does that work?

A Yeah, there are definitely parts of our, like, I think if you take our delivery and operations team, they're in warm mode quite a bit of the time. So, so I think, again, I'm more describing general, I think, counter-cultural beliefs I have on how to hire certain sets of great people. I don't think it applies to every single function of the company. I would agree with that. I think there are definitely, um, you have to be able to push really hard to deliver certain outputs, and I think we do a great job of that. Um, but I also think if You know, there, there have been ideas of, like, every great engineer should be able to spend 30% of their time on new projects as well as sprinting on the existing ones. I think it's paradigms like that that are important.

AI assessment note: “I don't think it applies to every single function of the company.”

Q What decision are you scared to make but you think about it often?

A Yeah, I think the simplest answer I'd have to that is that, um, growth in this industry relates to the amount of capital you raise, and, you know, your earlier question on investment. I do think there's a world in which if you pursue hyperscale growth, it is possible, but you have to invest a lot more to do that. Like every new company, every new customer you onboard, you have, it does cost money to do the forward-to-point engineering work. You invest more in your tech. And so there is an interesting, like, do you run a business for, you know, um, consistent, steady growth? For 20 years? Or do you try and build something that gets to 50 to a hundred billion dollars and becomes game changing? And I, you know, I, I think we, we have very much tried to operate in a way where I think we have a path to profitability and everything else, but we are going to invest in the near term, because I think it is, it is a very interesting time to do that.

AI assessment note: “do you run a business for... consistent, steady growth? Or do you try and build something”

Q What decision are you scared to make but you think about it often?

A Yeah, I think the simplest answer I'd have to that is that, um, growth in this industry relates to the amount of capital you raise, and, you know, your earlier question on investment. I do think there's a world in which if you pursue hyperscale growth, it is possible, but you have to invest a lot more to do that. Like every new company, every new customer you onboard, you have, it does cost money to do the forward-to-point engineering work. You invest more in your tech. And so there is an interesting, like, do you run a business for, you know, um, consistent, steady growth? For 20 years? Or do you try and build something that gets to 50 to a hundred billion dollars and becomes game changing? And I, you know, I, I think we, we have very much tried to operate in a way where I think we have a path to profitability and everything else, but we are going to invest in the near term, because I think it is, it is a very interesting time to do that.

AI assessment note: “do you run a business for, you know, um, consistent, steady growth? For 20 years? Or do you try and build something that gets to 50 to a hundred billion dollars”

Q I think, final, final, final one, I promise for a quick fire, you said about always being traveling, and you mentioned a girlfriend earlier. How do you make that work, and what would you advise me as like, hey, tips and tricks to not have a severely pissed off girlfriend most of the time?

A I think the first thing is to find a great girl who understands that you are really passionate about what you're doing and is, is supportive of that. I think my girlfriend Claudia has been, has been great on that front. I am very appreciative of that. But look, I mean, it's tough. I'm on the road, uh, probably 60% of the time. I've, you know, if you look at my last, if you look at my last four, five weeks, um, Riyadh, Geneva, Paris, Berlin, London, um, uh, San Francisco, Boston, Singapore, now London again. So, I mean, that, that's a. Do you enjoy this? I do in some ways. I think that I feel very lucky to be building something at this particular time and with a group of people I love working with, and so, you know, this happens to be what I spent my last decade doing, and it happens to suddenly now be like what a lot of people want to do, which is great, and so I feel very lucky because of that, and so every day I wake up and see what else can I do to be, to kind of push that forward, and so I do kind of live on the road, but look, I think you, I think some of the things I've tried is like, you know, You figure out things like FaceTime. You, you, you make sure you keep the cadence of interaction high, um, because being on the road is tough. Um, but I also don't think it's forever. I also think I'm in that fun stage of trying to take something to, like we kind of went zero to on…

AI assessment note: “You figure out things like FaceTime. You, you, you make sure you keep the cadence”

Q known him for like 10 years. He's a great father, investor, and husband. Three things that I care about. And whenever I have a tough decision, I'm like, what would Pat do? And most of the time, I get to the answer by asking that question in that framework. If I were to ask you, what do you ask yourself How do you find direction when struggling with a decision?

A I'm not a particularly materialistic person. You know, I think when I was coming out of college, for example, everyone was focused on going into large finance jobs, which at that time were pre, pre-financial crisis, obviously where, where a lot of that was. And I don't, I think a lot of what I think about is doing work day to day that I really enjoy with people I really enjoy and then building something. And I, I do think I really enjoyed the decade I spent building at McKinsey. I think that was an incredibly interesting experience to stand up something of that scale within, within a An existing institution. Um, and then I, I do think about, you know, I read a ton. I read a ton about everything from military history to current entrepreneurs to, um, enterprise executives I really admire. And then I have a group of kind of a small group of people whose opinions I ask pretty regularly. And, you know, I think probably the most telling piece of advice, um, my girlfriend and my main mentor, both of them, when I asked within two minutes, were like, absolutely do this. Um, my main mentor is a guy named Samesh Khanna who, um, I've been a senior partner at McKinsey for a long time, is, is on the board of a whole variety of different companies today, and, um, I remember we got lunch, I walked him through the opportunity, I said, listen, it's a big risk, and he goes, the only risk is if yo…

AI assessment note: “I have a group of kind of a small group of people whose opinions I ask”

Q Do you agree with fake it till you make it?

A Uh, that's such an interesting question. Fake it, I think it depends on what faking it means, right? And, and what I, one of the things I think is really complicated about Gen AI is it's non-deterministic, right? So like, if you've never built a machine learning model to do pricing in, uh, industrial manufacturing, you can still understand what data is available, understand how the price is being set today, and get pretty comfortable that what you build, if you say you will build it, will work, and I think that is okay. I think the challenge of non-deterministic systems is there is more risk to, uh, Faking until you make it. Meaning if you, you can kind of go out and say your agent will do anything, and then you actually have to deliver an agent that works, right? And I think that's part of the interesting, you're asking about accounting dynamics. I think it's part of the interesting dynamic of like, a lot of the contracts that the people will sign right now are like, I'll sign up for 50 agents to be delivered. But then the question is, do you deliver the agents? Do they work? And so, I think that is a different thing than SaaS. To go, to go back to your earlier question. If I deliver a SaaS box, I know it will work. If I deliver an agent, in the current world, there was actually a report AWS came out with today, it's interesting, that like, 70% of agents are actually not even,…

AI assessment note: “I think it depends on what faking it means”

Q I think, final, final, final one, I promise for a quick fire, you said about always being traveling, and you mentioned a girlfriend earlier. How do you make that work, and what would you advise me as like, hey, tips and tricks to not have a severely pissed off girlfriend most of the time?

A I think the first thing is to find a great girl who understands that you are really passionate about what you're doing and is, is supportive of that. I think my girlfriend Claudia has been, has been great on that front. I am very appreciative of that. But look, I mean, it's tough. I'm on the road, uh, probably 60% of the time. I've, you know, if you look at my last, if you look at my last four, five weeks, um, Riyadh, Geneva, Paris, Berlin, London, um, uh, San Francisco, Boston, Singapore, now London again. So, I mean, that, that's a. Do you enjoy this? I do in some ways. I think that I feel very lucky to be building something at this particular time and with a group of people I love working with, and so, you know, this happens to be what I spent my last decade doing, and it happens to suddenly now be like what a lot of people want to do, which is great, and so I feel very lucky because of that, and so every day I wake up and see what else can I do to be, to kind of push that forward, and so I do kind of live on the road, but look, I think you, I think some of the things I've tried is like, you know, You figure out things like FaceTime. You, you, you make sure you keep the cadence of interaction high, um, because being on the road is tough. Um, but I also don't think it's forever. I also think I'm in that fun stage of trying to take something to, like we kind of went zero to on…

AI assessment note: “You figure out things like FaceTime. You, you, you make sure you keep the cadence”

Q you come to negotiations with a client, given the revenue concentration, how do you play that staring contest? Cause essentially they go, we know that you, we are one of your core customers and we will squeeze you on price. And you go, I know I'm one of your core data providers. I will stand firm. How do you handle that negotiation? Cause it is a staring contest of sorts.

A I think people are willing to pay for good data. That's my simple framework. If you think about the importance of these models, if you think about the cost of compute, that is actually a huge chunk of the cost base. If you think about one week of bad data burns a lot of compute. Um, I, I think what we've seen, the reason the same players, it's been the same four to five players in this market for a couple of years now, is it's really hard to do well. And so people are willing to pay for good data. And so I think we, we have a very collaborative dynamic with all of our customers on that front. And, um, You know, I, I think that, ah, when you provide a service that's helpful, people are willing to pay for it. And if you provide a service that doesn't work, people don't pay for it. And so, the interesting thing I would say on that front is, a lot of the time in these, they're not, um, the discussion topics anchor around, again, proven value. So we'll get a topic that'll come in, like a multimodal audio model, for example. And we'll go head to head with somebody on that, that week. And at the end of it, we win or we lose. And so, if you win, and your data's way better, people are willing to pay for that.

AI assessment note: “the discussion topics anchor around, again, proven value”