Invest Like the Best with Patrick O'Shaughnessy
· William Hockey
I'll go on the record talking about this. The Fed is a pretty good tech team. Their systems are actually pretty good. You think about the US right now, through the Fed, has a capability to move money and to clear money through all these institutions, 24/7. Faster than stablecoins, faster than crypto. Right now, as we speak. We've had that for decades. Systems are very good, extremely reliant. They're fantastic. I think it's very challenging for Silicon Valley to build something better. The problem isn't in the fundamental infrastructure, it's in our implementation of it. The reason why community banks can't send money 24/7 isn't because the technology doesn't exist at the Fed. It's because there are constraints in those business models that make it very challenging and something to do. Give you an example. If you can send money out of your community bank 24/7 – well, that bank could run on a weekend. I don't know if you guys have ever like been to a rural community, the community bank with 50 people – you can't get people to work on the weekends. If you're JP Morgan, if you're Stripe, yeah, you can manage 24/7 liquidity. But if you're a small community bank, you can't have that. It's why I think people conflate "We don't have something, we don't have access to something," with "We don't have the fundamental ability to do that." But actually the reason is implementation, not the underlying infrastructure.
· Patrick O'Shaughnessy
[00:00:00] S0: I feel like because of your unique setup as a business and your unique focus on the boring problem as you've described, you have such interesting perspective on so many things. Is there anything that we haven't covered, either about company building or the way that the world works, that you think is interesting that we've missed?
· William Hockey
[00:00:00] S1: One of the things that I think about – AI is here. We're probably much closer to ASI than people think. And it's gonna be like a tidal wave through the economy. You have to think about what are the implications for your business. And I think in my perspective – if you're not a researcher, if you're not a lab – how do you play? I actually don't think "AI companies" are very set up for success. That's actually probably not where value is going to accrue. The value is going to accrue in two areas. Most important area I think, do you have massive distribution? Because if you have massive distribution, if you have massive costs, AI is going to be a massive boon for you. And so the thing I think a lot about is, how do you think about – not just from a software perspective, but from a distribution perspective and a brand perspective, what you would best capture to utilize AI? Because I think people talk a lot about the railroads. Yes, the value accrued to the railroads. Yes, the value accrued to the ISPs. Yes, the value accrued to the people like building mobile phones, but the value actually accrued to the people that could harness that the best. The value in railroads accrued to the oil companies. That's a little bit hard to fundamentally understand, that Standard Oil was the biggest beneficiary, biggest boon for railroads by an order of magnitude. What is the equivalent area for AI? And I sometimes don't think we're focused on that enough. Hot take here, I think like the biggest, fattest, most inefficient brands are going to be the best beneficiary of AI, because brands have a massive moat and man, there's a lot of costs to cut there.
· Patrick O'Shaughnessy
[00:00:00] S0: How do you think AI will most affect financial services specifically? Maybe even just like your own products and services?
· William Hockey
[00:00:00] S1: Financial services, especially large banks – they suffer from the fact that the business model's too good. Banks are pretty profitable. Financial services, it's like, if you own a bank and you aren't profitable, that's a problem. You've clearly done something wrong. The business model fundamentally is really good. That's made them laggards in a lot of ways. But it also, I think, makes them a massive opportunity, where I think financial services and legacy banks tend to be the largest inefficiencies out there. They also are very hard to take over. There's not a lot of private equity activity in financial services because it's highly regulated. And so I think the large banks that can actually effectively harness that are going to be some of the largest beneficiaries, because banks don't have a lot of physical assets. Places that have massive capex and physical assets, it's hard to tell a good AI story there. How's AI going to make railroads more efficient? If 99% of your money is spent on fuel, track maintenance, then people – the human cost in railroads is de minimis. You take conductors down from 1000 to 500, it doesn't like change the equation at all. But if you think about a traditional bank, it's pretty headcount focused and it's pretty technology focused. That's where a majority of the money goes to. The actual physical branch infrastructure is such a minority of your balance sheet. I do think they will be either the most disrupted or probably actually like the largest beneficiaries. The banks that are like most effectively run, have the largest distribution of largest cost structure are going to be the largest beneficiaries. I think the UX of financial services will change a lot because people think, "Oh my gosh, my bank is hard to use. My bank is hard to move money. It's like slow to move money." That's actually a feature, not a bug. I think what fintech sometimes starts with is they start with, "Hey, we're going to make it like super easy and super free to move money all over the place." Then it actually starts to slow down. The reason it slows down is because fraud is super expensive. If you make it really easy for your grandma to send money to somebody in Nigeria – yes, that's great for remittances, but that's really bad for romance scams. That's really bad for fraud.
· William Hockey, Patrick O'Shaughnessy
But I do think with AI, we can actually build those detection models a lot better and so it's probably less likely your grandma is going to fall prey to elder abuse. And if the banks don't have to optimize so much for that, and it comes a little bit for free, we can actually make the UX better for everybody else. Because we have the technology right now to make financial services almost entirely instant and entirely friction free. All the friction is actually built to protect the 5 to 10% of consumers that can get hurt. People forget that. But I think if we can build models that are just as good as humans at detecting that stuff and that can happen instantly, we can actually take that tail away and it actually is massively beneficial for everybody else. [00:00:00] S0: Do you think it's a good time to be a new entrepreneur in financial services?