E305: Why 95% of AI Startups Will Never Build a Moat
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What are vertical AI applications and why are they important?
So Nick, you've been investing across your career in AI, fintech, defense, wealth management. Tell me about what you're most excited about today.
I'm most excited about vertical AI applications. I think the first inning of AI innovation, we've seen broad horizontal platforms, the big LLM labs achieve extraordinary things, and they're starting to shift their focus from just providing an LLM to getting into horizontal applications. I think some of the most interesting investments are going to be in vertical AI applications. And the reason I think vertical is so interesting is twofold. One is more defensible. It's very difficult for those horizontal platforms to get access to all the workflow and data in a vertical application. And many of the vertical application incumbents and major customers are very reluctant to give their data to these AI labs.
But the second and maybe the more interesting reason is when you look what drives AI performance, it's context. So general intelligence is interestingly getting commoditized. But when you apply that intelligence to a particular vertical where you've got really rich idiosyncratic data,
How does context drive AI performance in vertical applications?
context the performance can go through the roof and i sometimes get upset with my lms because i ask the l on the question gives me an answer two months later i realized that it was the wrong context or i didn't ask the right question there's a contra there's a counter thesis to what you're saying which is that the horizontal llms will disrupt the vertical application of AI? Does it really come down to the proprietary information? Is there more to it? Why won't the large LM players disrupt the vertical AI players?
It's a few things. I think first, they've got a lot to do. They've got a lot on their plate already in this sort of commoditization struggle. They're all investing a lot to try to get incremental improvements in their core models better. And They can't devote all their attention to trying to win every application category they can. They'll go after a few of them. They'll go after the biggest ones. I think it's very similar to what happened with Google and Microsoft as they extended their search and operating system franchises into horizontal productivity apps. Very profitable for both companies. They're both very good at it. And we use those applications today. They didn't do everything. And they tried to do some things and weren't successful at them.
I think it'll be similar for LLMs. They'll nail some horizontal applications. You can see OpenAI experimenting with planning travel for you or helping you with e-commerce. You can see Anthropic experimenting with business agents. And coding, of course, has been a very attractive area for both companies. But they can't do everything.
And you've invested in terms of the largest players more in the wealth management tech than really any VC that I know. What makes you so bullish on that sector of the market?
When I look at wealth management more broadly, I think there are two kind of structural problems that lead to a lot of opportunities. One structural problem is is the current technology in the industry is terrible. If you were to ask any financial advisor, hey, how much do you like your custodian or these different point solutions, they'd tell you they were very unhappy. So what's happened is financial advisors have to work on legacy custody platforms, primarily Schwab, Fidelity, and Pershing that were developed 20 years ago. They use batch processing, very limited APIs, just technology you would have expected in the 1990s. On top of that, they have to cobble together all these expensive point solutions.
And it means that they spend so much of their time in operations, getting their tech working rather than with customers or growing their businesses. So terrible tech in the industry. The second and interestingly related structural problem is how profitable wealth management is. So I think, as you know, small RIAs have profit margins in the sort of 30 to 35% range. Large REAs have profit margins as high as 45 to 50, maybe even a little bit above 50%.
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Chapters
5 chapters
1
What are vertical AI applications and why are they important?
0:00–1:11
2
How does context drive AI performance in vertical applications?
1:11–7:35
3
Why are large language models unlikely to disrupt vertical AI players?
7:35–13:55
4
What structural problems exist in the wealth management industry?
13:55–19:08
5
How does profitability impact innovation in wealth management?
19:08–40:09
Speakers
2 identifiedMore from How I Invest with David Weisburd
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