How I Invest with David Weisburd
00:21:40 · Matthew Harris
[00:21:40] S0: I mean, they're related and inextricable. It is the cash cow. Right? Which means it's got this power base. And it is observably one of the best business models in history. It's 100% margin business. So those two things are like inextricable and together formidable and so far successful in keeping Google out of offering direct consumer financial services, despite having they bought one of our companies so they could get into the business and then they shut it down because of this angst. Now Apple has a credit card. As I said, I think they've done it in inelegant ways because of their own fear and inhibition. But they don't care about the credit card companies. They don't really focus on selling advertising. And so they're more gloves off, I'd say, as it relates to competing in financial services because they're not wed to that entry. Yes, sure, they sell some MacBooks to venture capital firms. But broadly speaking, they their butters, their bread is not buttered by the financial services industry. And Amazon is another fascinating example. If you go back ten years, they had massive plans in consumer payments. They were going to be a proper disruptor in consumer financial services. And you know what killed that? AWS. All of a sudden, their most valuable business had financial services as a core customer. And so it wasn't the cash cow that advertising is at Google. But at the top of the house, you're starting to have really hard conversations about should we launch our own banking product? Should we get consumers to keep money with us? We'll pay them interest and they spend it on our products, basically do what Starbucks did with their app. Amazon's never really done any of that, even though the plans are still on the shelf because JPMorgan Chase is just too valuable a customer if you're running AWS and if the person running AWS then becomes CEO of the company. So these are the conflicts that we look to historically to give us some confidence about where to put our money today based on predictions about what the Foundation Model Labs are going do and not do. And we, having thought about it, resolved that they are unlikely to start law firms, for example.
00:23:57 · David Weisburd
[00:23:57] S1: One thing I've learned from talking to hundreds of investors is that great investment firms aren't built on investment returns alone. The firms under are great at the things most people don't see, their operations, their relationship with LPs, and the quality of information they use to make decisions. And here's what AI has changed. Every firm now has access to the very same models, so the intelligence isn't the edge anymore. The edge is what you could feed it. Firm with its fund operations and data in one connected record can actually put AI to work. A firm running on disconnected disconnected systems simply can't. That's why thousands of GPs run their funds on Juniper Square. Juniper Square puts your fund operations, data, and administration together in one connected record. That means less time managing disconnected systems and more time investing, working with LPs, and building your firm. This episode is brought to you by Juniper Square, the operations partner for private market GPs. Learn more at junipersquare.com/howiinvest. That's Juniper Square dot com slash how I invest. Everyone I talked on the show is chasing the same thing, an edge, and more and more of the edge comes down to your information, not just having it, but being able to trust it when the stakes are highest. AI is doing more of that information gathering for you every day, and most tools are very good at sounding right. The summary reads clean, but can you trace it back to filing the transcript, the specific passage that drove the answer, or are you just trusting the confidence of the output? For investors, that's not a minor concern. A miss filing, a miss weighted source, context that got lost somewhere in the retrieval chain, those aren't edge cases, they're how decisions go wrong. AlphaSense is the AI market intelligence platform built specifically for this. They own the content, over 500,000,000 curated documents from broker research and expert transcripts to filings and earnings calls, and they own the retrieval layer on top of that. So every answer links back to an exact verifiable source because the answer is only as good as what's underneath it. And with AlphaSense, you know exactly what that is. The edge goes to whoever can trust their information and prove it. See it for yourself. Start your free trial at alpha-sense.com/howiinvest.
00:26:07 · David Weisburd
That's alpha-sense.com/howiinvest. Many of the smartest people in the world actually think that industries are not the bottleneck for the large language models. It's actually gonna be compute. Even Elon has said this, which is why he's he's building the TerraFab and doing, data centers in space. Do you not think that compute itself is the bottleneck?
00:26:26 · Matthew Harris
[00:26:26] S0: They're separate questions. I think that you have a focus question within a lab like Anthropic about given a fixed level of compute, where are they gonna spend their time? It could be that the level of compute is too low for them to even entertain starting a law firm. I don't think that will be the reason they don't start a law firm. But I do acknowledge that the differential access to compute is going to be perhaps the prime battlefield in the next five years. I would argue the prime battlefield in the last five was talent and leadership. That it was about having the best researchers, having the best research agenda and the best leadership that separated the wheat from the chaff. But I think it gets a little bit more mechanical here forward if you have the compute to train the most impressive model in the shortest period of time, I think that's going to create compounding advantage. And so the chess move here about one of the three big ones getting ahead and then getting lapped by another. And by the way, here comes Meta and Google and their resources, I think, is among the more fascinating things I've ever seen, particularly when you nuance it by all the external players, the private credit industry, infrastructure investors, insurance company balance sheet and what they're funding, the chip companies and how they're picking favorites or not picking favorites or buying companies to secretly compete. I mean, it it really I wish I could buy the book that'll be written three years from now.
00:28:01 · David Weisburd, Matthew Harris
[00:28:01] S1: There's a lot of layers of complexity there, and I'm tempted to double click on a bunch of them. But let's just keep it to OpenAI in Anthropic today. What's the game theory between them and how they're looking at making these massive investments in compute? [00:28:18] S0: There are a lot of games being played. The original game, I think at both companies, is about advancing the state of artificial intelligence and actually achieving generalized artificial intelligence. And that has become a little muddier as capitalism has kicked in both for the individuals running these companies, more so in the case of Anthropic than OpenAI, but also just their competition itself and the human desire to win and not be second place. And then these massive cap tables who have unambiguous goals, which are just to make as much money as possible. So I think the original game of being first to artificial general intelligence has been subsumed in a revenue and profits game, or at least complemented by it.
00:29:09 · David Weisburd
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