Invest Like the Best with Patrick O'Shaughnessy
00:20:50 · Ben Thompson, Patrick O'Shaughnessy
[00:20:50] S2: I go back and forth even just on the question of aggregation theory itself. How much does that apply in the The [00:20:57] S0: same thing. Yeah.
00:20:57 · Patrick O'Shaughnessy, Ben Thompson
[00:20:57] S0: same thing. Yeah. [00:20:57] S2: Yeah. Yeah. Because I like... Like a pushback that people have is one of the key components of valuation theory is zero marginal costs. And zero marginal costs shows in lots of ways. The one that I focused on the beginning was distribution. And people say, oh, I don't have distribution. Have to pay Google friends. Oh no, you have a website. Your problem isn't that you have distribution. Your problem is you don't have demand. And you're paying for demand when you're paying for ads and things on those because the aerators control demand. And they control demand because in a world of abundance, the hard problem is not distribution, it's discovery. How do you actually find what you're interested in? So the companies that solve discovery in their domain come to dominate that market. They get a virtuous feedback loop, that sort of aggregation theory in a nutshell. And the other thing is transaction costs. There's no transaction costs. Google can scale to the whole world and they can scale to the whole world, not just on the user side, but also on the monetization side. The vast, vast, vast majority of advertisers on Google or Meta never talk to someone at Google or Meta. They just go up and they buy ads. It's all done by computers. The perfect business. And those computers from a business perspective cost $0. AI, obviously that changes significantly. Inference costs are real. But then again, how real are they? They're real right now. I don't know, are they?
00:22:07 · Patrick O'Shaughnessy
[00:22:07] S0: Depends on the company, but they're way more real than those prior examples. Well, Like if you look at gross margins.
00:22:12 · Ben Thompson
[00:22:12] S2: For sure. But you have this incredible spread. So you have people, I think the vast majority of people who are using ad today are using it as basically a Google substitute or like a recipe maker or whatever it might be. And my suspicion is that the cost to serve those people is extremely low and low in the basically similar to serving them a web page. I would imagine it's marginally higher, but not that much higher. Then you have on the other extreme people who are actually leveraging test time scaling. It used to be we just scale by making the models bigger and bigger. Now you can scale as far as time. How long do you think about the answer? Well, you could think about the answer for days That or weeks or is directly marginal cost. Every second longer you're thinking is costing more money, which speaks to like we think about AI and inference as this one question. That's why I was pushing back on you. But actually the marginal cost question for the different user, the user using free chat GPT and the user trying to solve a math theorem. They're not even remotely in the same universe. And I think you see this challenge actually in the enterprise in a very interesting way. Microsoft recently, they are shifting their enterprise plan. So they come out with like an E7 plan, $100 per user per month. That includes some amount of usage. But then they also are charging for usage on top of that. I think this is a kind of a fraught position for Microsoft to an extent, because the positive way to think about Microsoft is they do everything you need as a business. Every individual component might not be the best, but you get it all for one price and they all mostly work together. And if you're particularly a small or medium sized business or even a large enterprise, there's real That's value with right. It makes life easy. The moment you start having to think about how much you're paying, it's not just that that's a new decision, number one, that is untethered from headcount. Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee and baked in the cost of that employee is a $100 a month or $50 a month for their license. It was kind of a thoughtless revenue stream from Microsoft. Now, if you think about usage, you have to think every single month, how much do I want to spend?
00:24:28 · Ben Thompson
That introduces two problems. Number one, most companies aren't set up to do this. They make budgets like once a year, this idea we're going to be thinking about through our budgetary allotment on like a monthly basis doesn't compute. There's an aspect where they're used to thinking about CapEx decisions or one time costs. And there's a bit where, when I'm talking about this employee, like the loaded cost of employee, it's CapEx, but it's kind of like CapEx. It's like you make the decision upfront and you don't think about it anymore. The decision is sort of already made. But if you're thinking about usage, you have to it again. The final thing is if you're every month looking at your Microsoft bill and how much did I use, you start thinking about what am I paying for? How good is each of these products? Should I actually just start thinking about and spreading this out? And I think they had to do it because that extreme of user who uses a ton of tokens and who's actually leveraging AI costs way more to Microsoft than a $100 a month. They can't support them, but they want to hold on to the set cost for the vast majority of employees who can fit in that because they need to ask their customers to think a little bit for those extreme employees, but they don't want them to think too much because that breaks the model in very surprising ways.
00:25:46 · Patrick O'Shaughnessy
[00:25:46] S0: Are you surprised at all that the recipe builder user that is very low cost to serve, that there hasn't been a great business model that's emerged around them just yet? Google and Facebook are sort of business perfected in this prior era. They haven't seemed to figure this out at all.