"The Cognitive Revolution"
00:49:31 · Marc Bhargava
[00:49:31] S1: There's a lot of middle ground because there are many point solutions out there in a lot of these industries, legal, accounting, IT, customer support call centers. There are a lot of great technology companies and a lot of solutions. Generally, they're point solutions. Generally, it's not a full platform play. Generally, they're not customized. And generally, they're not trained on your proprietary data. So there's still a lot left and that's why at the core of these transformations is staffing a great... Backing great founders who are great technologists because there's a lot to do in terms of stitching together different point solutions. There's a lot to do in terms of creating custom solutions. There's a lot to do on fine training models and making it unique to your dataset. And so all of those tasks require a really deep understanding of AI and of engineering. And we generally have seen that kind of folks who are coming from Applied AI positions make really good founders in these sort of companies because they absolutely can assess and buy things off the shelf, but then they're also figuring out which model we should use, where, how to stitch things together, do we wanna customize our own model, take an open source one, train it against our unique data. You have to have a lot of really expertise in AI and in building software and actually doing things. It's not just a research question. And we kind of tail off of all the amazing improvements made on the research side by an Anthropic or an OpenAI or a Google or others, whereas they improve their models and can do more, we flow that through and we get the benefit. For example, with legal, at least, has found that, you know, OpenAI is really great at summarization of case text, for example, but the Anthropic model is better at reasoning and asking difficult questions that you might face in a courtroom. And so having kind of both OpenAI and Anthropic every three to six months improving their models, you know, float both of those through at UDIA to deliver value for the end client, which is the in house general counsel at a Fortune 100 firm.
00:51:24 · Marc Bhargava, Nathan Labenz
And so you have to have this technology and AI expertise to understand the different models, where to use which one, and where there are also point solutions that are already built, you know, you have to have the technical expertise to evaluate them and integrate them as well. And so we still think you need a really amazing kind of technical CEO or CTO or founding team to be at the core of these projects. [00:51:47] S0: How much is about customization versus standardization? Like, if you buy 10 businesses, is the idea that you'll have 10 different fine tuned models that, you know, represent the sort of unique flavor of those different local businesses and, you know, a bunch of different custom workflows that kind of recreate the idiosyncratic ways that they did it? Or it seems like maybe more of the economic promise would be in the standardization where you'd kind of say...
00:52:16 · Marc Bhargava
[00:52:16] S1: Yeah. If you buy 10 IT firms, 10 call centers, 10 accounting firms, 10 legal service firms, 10 HOA management firms, The solution base that we're building for each is the same. So there's some slight customization, but it's extremely similar. The workflows and accounting, the workflows and MSP IT, the workflows for HOA management or property management are very similar. So we're backing a team, they're the holding company, then they're going and they're buying maybe 10 HOA management firms, for example, or 10 IT firms or 10 property management firms in the case of dwelling, one of our investments out in London. And the technology you're building is really very similar across those 10. We don't see a huge difference there. But what we do see as unique is as we buy these companies, do get their proprietary data. We can feed it in and we can train models, and we can adjust our software based on having proprietary data and much faster feedback loops on what the client wants. So ultimately, we think our product is, you know, much better on market than someone who maybe isn't doing a roll up strategy that does not have access to to their client's data, does not have as fast a feedback loop. This is a really fast way to scale out Applied AI in our view.
00:53:26 · Nathan Labenz
[00:53:26] S0: Yeah. It makes... I mean, I think the... I will bet on you to do well, and I will also bet on the customers of your portfolio companies to be very happy with the level of service they're getting. I'm a little concerned about how much disruption there might be along the way, but, you know, I... I'm optimistic on some level that we can manage that disruption. How many of... Maybe it varies again by vertical, but it strikes me that, like, you're not gonna acquire that many companies. You know, how many... And I think the whole market goes this way. Right? Like, maybe with very small sort of residual that's just, like, highly... I mean, I, on TikTok the other day, I saw an artisanal pencil sharpener. I don't think there's too many of those out there, but there's apparently at least people are constantly debating in the comments whether it's a bit just for TikTok or whether it's actually, you know, people are sending this guy pencils to be artisanally sharpened. Whatever. There's a long tail. Like, there's all... There will always be an artisanal pencil sharpener, you know, I suppose. But leaving that aside, it does seem to me like all these markets over a not super long timeline are gonna go this way. And, you know, as Tyler Cowen famously said not too long ago, it's the people that are the bottlenecks. You are the bottleneck. You are the bottleneck. So how many of these businesses, these local service businesses that exist today, you know, you're sort of Labenz, Bhargava, and, you know, associates, How many of those make it over the hurdle and, like, get into one of these hybrid things, and how many are just, like, ultimately outcompeting and go out of business?
00:55:06 · Marc Bhargava
[00:55:06] S1: So I think right now, you're already seeing a consolidation, ignoring even There's a consolidation in accounting for example, and the consolidation happening today in many of these industries is being driven by private equity. Because it's saying if we buy two firms that do the exact thing, there's obviously overlap. Can we remove some of the overlap people? This saves cost. With this cost saving, we pay interest payments on our debt and so we could finance these with debt and that's the model. So you're seeing a lot of industry consolidation in many of the areas we're going after already today being driven by private equity. It was especially driven during the period of low interest rates and it's continuing to be driven today as well and just more kind of push on the cost savings. And so we are in some ways offering an alternative to that. If you are running a IT shop, accounting shop, HOA management, you're probably starting to feel the pressure in the industry and you have private equity folks now saying, hey, we can consolidate you. You have the option to sell to them and it's probably really gonna change the profile of your company and maybe even the management. So that's one option. We are trying to come in with an alternative option which is same management, same team, let's AI enable it, let's go for more revenue rather than less cost cutting and that's our path to higher margin. And we're only right if the AI enablement works. Because if we're asking you to go and take twice as many clients with the same group of people, that's just not gonna match up unless we're really giving you the tools so that each person can do twice as much work. Which generally means each person is automating the more basic stuff focusing on the harder piece. So we really need to get this AI automation right in order for our thesis to play out. But the good news is over the last two and a half years of doing this, we are seeing we are getting the AI automation right. And every three to six months, we are seeing we're getting it even more right as more models are released to have more capabilities. Now last fiscal year, 150,000,000,000 was spent on AI, R and D, and CapEx by the MAG seven alone. This year, it'll be closer to 250,000,000,000. So there is a massive investment.
00:57:00 · Marc Bhargava, Nathan Labenz
There are obviously a lot of the smartest minds working on this technology, and it shows in the results. Like, we are seeing an improvement in math and logic and reasoning and automation. And so we think our pitch is the right one, which is you can come in and change these companies to grow rather than come in and change on the cost cut and consolidate. And that's obviously what we're fighting for and why I'm really passionate about what we're doing at Creation and our kind of vision and our mission. And I think it's a different AI story than maybe a lot of people are used to hearing. But anytime there's a new technology, you're gonna have people say, no, this is gonna be really bad. And there are areas we have to be really careful about and we need to think through. But I think overall, this will be a benefit for humanity and to the companies that are AI enabling their workforces. [00:57:46] S0: Yeah. I'm sold on basically all of that with some tail risk that, you know, might imply that it's not a benefit to humanity. But if it's not a benefit to humanity, I don't think it's gonna be because the mundane task automation doesn't pan out. I'm pretty confident we're gonna get that.