00:39:41 · Eric Seufert
[00:39:41] S2: Well, you know, they clearly are gonna be very aggressive on the pricing. I mean, they talked about that today. Right? And so my sense is, like, what you're gonna start seeing is that people don't need to operate at the frontier, and you have a lot of use cases that work just fine with and and and, basically, are just good enough, right, with some legacy model. And so it just comes down to then, okay. Well, that's commodity, and so are you priced like a commodity? Yeah. Like like, if if think about, like and and also, like, I think we're gonna see a lot more, like, people relenting from needing to be on the on the frontier when they've built stuff using a model that then gets upgraded. And the whole idea there is, like, well, am I gonna upgrade this tool because I have to adapt it to the new model? Right? Like, if I'm if I if I you know, because essentially, like, the model name like, if you if you use, like, Vertex AI. Right? You you just got a model name as a variable. Like, you're sending this, you know, system prompt to to, you know, Google, but, like, it's just a variable. You could swap that out in thirty seconds. But the the fact of the matter is you're sampling from a new distribution if you do that, and it's gonna change the output. It's gonna qualitatively change the output, and it might change it in quantitative ways that, like, with retention and engagement. And so the thing is, like, if okay. Well, now we're talking about this big cycle. It's a new product development cycle because I have to adapt this product that I built to this new model and the output that it provides. Right? And maybe it was working perfectly. It was working exactly as I expected it to before. Mhmm. Now I've got to invest a bunch of hours, much of engineering time in adapting it to this new model. So even if it's just a swap of a variable name, it's still a whole lot of testing, QA, determining, like, how that impacts long term retention. They're gonna do AB tests. So my sense is, like, you're gonna see a lot more people just saying, no. This works fine. This is perfect. And and getting more getting, like, more robust output let's say the token price is exactly the same. Getting more robust output wouldn't benefit me. Why am I gonna invest the resources into adapting to the new model?
00:41:18 · Jordi Hays, John Coogan, Eric Seufert
[00:41:18] S0: Mhmm. Yeah. But isn't that so so if they're going after workloads that are running on old models that are working fine And it's a lot of work to switch over. [00:41:30] S1: It's not a lot of work to switch over. Not It's it's it's some risks, but it's not a work. [00:41:34] S0: To switch, but then, again, you have to go through this process of, like, QA ing and running it through your own benchmarks and all this stuff. Like, the question is, like, how I I I just don't know. Like, I I'm I'm thinking about a scenario where, like, a year from now, we're sitting here and, like, Meta has a $3,000,000,000 AI API business. Mhmm. And the analysts that you're talking to are, like, that like, to me, they're like, that doesn't get you to 40% year over year revenue growth on the business So [00:42:05] S2: it, [00:42:05] S0: like, doesn't solve at least what those analysts in particular were talking about. And, like, 0 to 3,000,000,000 on, like, a new business line Crazy. Would be crazy and is like, you know, only been done a handful of times throughout history over the last few years. Right? Yeah. So Yeah. I'm just saying like, we're we're there's such big numbers now that there's possibility where you have like this incredible breakout revenue growth, but it doesn't actually move the needle enough that that the market still says, like, hey, we're we're not super confident about about about, like, the next CapEx cycle. Right? This twenty twenty seven numbers that are coming up.
00:42:42 · Eric Seufert, Jordi Hays, John Coogan
[00:42:42] S2: Well, that's and that's the problem with this whole business line in the first place. Like, I think it's a mistake. I think it's a capitulation. I think you're gonna get much more value out of that compute if you apply it to your own core business, which is advertising. My sense is you get better growth. But, like, the problem is the investors don't buy that right now. Like, they've got a narrative issue. It's not a it's not a productivity or a competency issue. It's a narrative issue. And, like, the problem is, like, you know, and, you know, you you you cited Ben's brilliant essay from yesterday or the day before about, like, what stocks should say. He should say that. Like, he Ben is totally right. He should say that. He should come out and say, look. We like, senator, we run ads. Yeah. Senator, we run ads. When he said that, I was at f eight. It was, like, the next month. Every Facebook employee is wearing a shirt that said, senator, we run ads. They know what business they are in. Zuck seems to be confused about it. Like, I don't understand why they should come [00:43:29] S0: Here's here's a big question that'll be interesting. This year, they're gonna spend I don't know how much they're gonna spend on external models. Like, I would say I would I would expect them to spend, like, maybe, like, $10,000,000,000. [00:43:38] S1: 10,000,000,000. Right? Yeah.
00:43:38 · John Coogan, Jordi Hays
[00:43:38] S1: 10,000,000,000. Right? Yeah. [00:43:39] S0: Like, something in the range of $10,000,000,000 from from Google, Anthropic, and OpenAI. If next year they can say, we're not spending money on any external models, then then that could help them with the narrative issue of saying, like, hey, like, we're we're invest we're basically getting instead of having to give this money to other businesses, we're just using our own infrastructure. It's a lot more efficient. We can, like, token max. We can use way more tokens. We can do way more workloads. And so that's potentially it's potentially setting but but the question is, can they actually move all the workloads that are on these other models to their own models?
00:44:17 · Eric Seufert
[00:44:17] S2: Well, so that's where you actually do need the frontier. Right? Like coding tasks, like actually benefit from having the frontier. But if you're having customer support stuff, that doesn't need a frontier model. That could use a three or four release back model. And it'll be producing reliable results that you know work. You've measured, you tested, and you don't need to upgrade, like, the customer support or, like, the chatbot, you know, for customer support integration to the to to the the bleeding edge model every single time. But, like, coding, yeah, if you're actually using these models to build models, you probably want the best of the best. Right? And, like, what Meta is doing is really actually at the frontier with, like, integrating agents into the coding workflow. If you look at, like, their system they built called Confucius, it's like a self learning agent, like, that actually helps them deploy better. They've got a whole pipeline for data science and machine learning tasks that helps them decide, like, okay, which which which where should we even apply this? Where should we even do testing? Right? Because that's actually takes a lot of time. Like, you're just figuring out what kind of experiments, what kind of tests you wanna run. They built a whole pipeline around that that's all driven by agents. Right? So my sense is, like, there's where you want the the the top of the line, and maybe their own models don't perform best there. But, like, also, don't know how how excited investors are gonna get when you say that we cut expenses. I think they really need to see the revenue growth at the top line. And so my sense is, like, you get that you get more of that by just making the ads platform better, and they've done that. All they need to do is say, look. We can if they could forecast out that growth and say, look. We're really dedicated to this. This is what we're pointing everything at. My sense is you could get investors excited over time. You keep printing 33% or whatever every quarter, like, you're gonna get investors excited after some time. If you start saying we're gonna compete with Kalsi, we're gonna build an AI pendant hardware, like, they're not gonna get excited. They're gonna think you don't know what to do, and they're gonna think you've got all this compute capacity. You don't know how to use it.
00:45:57 · Jordi Hays
[00:45:57] S0: Help help us understand the the prediction markets play. Is that I think the the answer that we landed on was it is just in Meta's nature to copy the new hot thing. So regardless of what it is, we're just gonna do it. Right? And you can see the history of, like, these shots on goal with, like, every new hot thing in consumer. They just build a version of it or they try to buy it. So I think that's the most simple explanation. John was trying to explain it as like maybe there's some way to do it with like, you're there there there is no dollars and it's just for like social status, you know, who who can be, you know, an oracle. I think that that was we got more news that maybe showed that that wasn't But the then when you look at again, look at the market, like, you look at the total market for, like, gambling and again, even if they got a meaningful amount of that market, they would not really move the needle in the way they need to on the core business. And they would invite all these new regulators that are now saying like, you're not only trying to harvest, you know, my teenager's attention and making them, you know, sad about their life, you're also getting them Like it just feels like it opens up this huge can of worms for no reason.