00:40:40 · Guillaume Moubeche, Matthew Fitzpatrick
[00:40:40] S1: And, you know, like, I think Elon Musk was talking about, like, an era of abundance where people don't have to work, etcetera. Do you feel like... Is this a utopia, or do you feel like there is a world where that exists? [00:40:54] S0: I don't have a strong view on that. I mean, in my mind, we're, as I said, about a decade away from even widespread adoption enterprise. So I think... Ask me again if I may. We... I I I think most real dislocations in job markets occur when the change is so sudden that the labor force does not have time to adapt. And I think the reality is if you think this is all changing next year, then that's a real concern. It's not that. We're a long way away. And so I think you're gonna see kids coming out of college now are gonna have five, ten years to adopt and adapt, and that will be that will be. [00:41:28] S1: My feeling is, like, people are actually working more, you know, with AI and not less. Like, it's... Yeah. [00:41:35] S0: Well, the n b... The NBER study I mentioned earlier would suggest that. I mean, if you basically have a conclusion where everyone's doing 15 to 20% more work and then organizational productivity staying the same, it would suggest exactly that, which is a lot of people are doing more, but it's not really changing the nature of work yet.
00:41:35 · Matthew Fitzpatrick, Guillaume Moubeche
[00:41:35] S0: Well, the n b... The NBER study I mentioned earlier would suggest that. I mean, if you basically have a conclusion where everyone's doing 15 to 20% more work and then organizational productivity staying the same, it would suggest exactly that, which is a lot of people are doing more, but it's not really changing the nature of work yet. [00:41:51] S1: Interesting. And I think on the the on the show with Harry, at some point, you pushed back, you know, on the synthetic data thesis. So basically, that human feedback will become, like, irrelevant. Can you basically, like, walk me through why the math doesn't work?
00:42:12 · Matthew Fitzpatrick
[00:42:12] S0: Sure. I I think there's a couple different dimensions for that. So let's take... First of all, you know, I think there's things like commodity labeling, like cat dog, cat dog, where there's enough data around that you can build synthetic data that exists around it. But if you think about the matrix of language, culture, and expertise topics, so, like, seventeenth century French architecture, but have that person be from... Who speak Lebanese, who speak French with Lebanese accent, French Canadian, or France. Like, those are all very different contexts. And very... So that matrix of topic culture language times all the the kind of potential topics on Earth is just a massive amount of data. And so I think what you've seen is actually most of the publicly available data in the world. And, by the way, part of the reason coding has matured so much is coding is probably the most easily accessible data. There's a ton of available data you can train off of similar to video games. But but if you think of all of the other topics that exist, first of all, there's enormous amounts of kind of future training on that. And, you know, there's variety of different podcasts where you... Like, one in particular I can think of where AI researcher was describing. Like, in the age of agents, then if you take hyper specific tasks, human data becomes more important than ever because you need a way to validate that specific task. So it's not just it's not just topic language culture. It's all of that then for a hyper specific task where there's no really good existing data. So an example with... For that would be if you wanted to make a decision around processing a insurance claim for property and casualty insurance in Florida, whatever it is. You know, you you need enough precedent data, enough comparable to, like, what should that 20 page document look like? Does it look accurate? And that's one of, you know, thousands of examples. So I think, you know, it's the topics. It... And then I think the the final thing I'll say is actually the the concept of chain of thought reasoning. So moving to a world where it's not just a simple answer or an annotation, but has the thought process looked accurate, I think you're gonna want human loops validation for that for a long time. And I think that's where the enterprise, you know, is in a complicated place. If you're...
00:44:25 · Matthew Fitzpatrick, Guillaume Moubeche
Let's do medical coding, for example. You can train a a model to do that, but then that's gonna have to go, you know, have regulatory reviews, etcetera. So you actually do need human loop validation. And so I think the answer for this is that humans are gonna stay in the loop of this for a long, long time. Like, I think the same way if you take the mortgage example I gave earlier, They were 20 into mortgage underwriting. About 15% of the... 10 to 15% of those mortgages still have a human loop. And that's where a lot of these tasks will move is you'll move to a kind of a AI driven workflow with human loop validation testing and and triage as needed. And I think that's gonna be persistent. And I think you're gonna want that... Like, if you had a model that worked in health care and then GLP ones came out the next week, you probably need to change that model, and you're gonna want humans to help you fine tune and evolve it. [00:45:18] S1: Yeah. Makes makes a lot of sense. And I think recently, you made an acquisition. Can you elaborate? We
00:45:18 · Guillaume Moubeche, Matthew Fitzpatrick
[00:45:18] S1: Yeah. Makes makes a lot of sense. And I think recently, you made an acquisition. Can you elaborate? We [00:45:28] S0: we acquired WeCP, which is a talent intelligence platform, has had 2,000,000 interviews, an amazing tea... An amazing team, 2,000,000 interviews, and 18,000 topical assessments. So I think we think what's really interesting about it is it allows us to really rapidly assess with specific detail any expert on any topic as we think about bringing that expertise into AI. And I think that's gonna be a crucial part of the next ten years. I think, like, you know, if I took a task, like I mentioned, architecture, for example, if I wanna source architects and I wanna know how good they are at a task, I can look at a resume, which is somewhat indicative, but actually being able to do topical assessments is much more crucial to that. And, you know, like, I think the WeCP team is an amazing group that is gonna be a huge, huge driver of growth for us in the future. [00:46:17] S1: Nice. I know we're almost running out of time, so I will want to ask you like one final question. Yeah. For any company, you know, who has like a set of proprietary proprietary data and who wants to kind of leverage it to build, like, their their next moats, what would be, like, the best way to to think of it?
00:46:39 · Matthew Fitzpatrick
[00:46:39] S0: Yeah. So proprietary data is a funny thing because proprietary data can mean a lot of different things. There's, like, the monetization of data, which would be, like, you know, selling your data. I think more often, data is useful in actually business initiatives. So, like, cross selling, pricing. It gives you intelligence on how to perform your business. At least, I'd say, in the majority of times, I've seen prior data to be valuable. That's where I see it most. And I think in that world, the key thing is who are the business leaders that are gonna lead the change around it. I think the main advice I always give people when they ask me, like, how do I get AI working with whatever tools I have? My answer is it's not a technology challenge. Who is the business leader that you're putting in in charge of that initiative? Whatever... Like, make sure you have a list of three to four initiatives you wanna go do. Not 20, but, like, three to four, no organization can metabolize 20 initiatives at once. But, like, three to four, against each of those, make sure you have a really clear owner of each one and a business leader that's... And then a a value sizing of how much this is worth so you have a target or a KPI. And then make sure that that business leader has a clear road map to go do it. And I think if you do that, I think if you put a really good operator in charge of each of those things, like if you said to your question, you know, I have a bunch of interesting data on pricing. Let me set a a better dynamic pricing model that you know I have, and you have the right business leader lead... Leading it, you will get there.