00:31:15 · Eric Seufert
[00:31:15] S2: pulled out of thin air. Yeah. This is what they need to do. They need to be able to point to something and say, you see that ad? That was created by our AI investments. When you talk about GEM, GEM is a foundation model. Mhmm. MetaTrader, a foundation model for ranking. Yeah. That's important, but you can't see it. It's hard to convince first of all, the research like, you know, a lot of the research analysts, they're really smart people, but they operate in this paradigm of, like, spreadsheet says this. I get it. I can understand why ranking investments would actually be really beneficial for the company, but I put a number in the spreadsheet and it spits out something. That's the tool I have to work with. And, like, these are really smart people. I think they conceptually get why these are good investments, but they have nothing to sort of tie it to that's quantitative. I think when you can point, you can point to the ad that was created and you say, look. This ad was created with data that only we have. It's the image model that we built, the foundation image model that we built that is trained trained, not fine tuned, but trained on our own data can be verified against that. We've got our own custom evals. Everything about the training was built with data that only we have. No other, frontier lab can can can fine tune their own models for this use case. Only we can do that. And then I think if they can point to the output and they say that ad that you saw in your Facebook, Instagram feed was created only as a result of our ability to train on this data that only we have, then I think you can kinda make the case. So I think bundling those two, integrating those two things together Mhmm. Is, like, the really smart move. And I I I I kind of you know, I understand, like, they, you know, they restarted the the AI efforts, and this is the whole you know, this is the MSL rebrand. But my sense is, like, this might be more convincing than anything that they've been able to say with the ranking, infrastructure and the transfer learning infrastructure. I'm talking about Lattice. I'm talking about JEM. So we'll see. But my sense is, like, if you can actually point to some output and say, look.
00:33:00 · Eric Seufert, Jordi Hays
The this only exists as a result of our ability to train this model on this proprietary proprietary data that no one else has, my sense is you can make the argument more robustly. [00:33:08] S0: And if you ask advertisers what is the bottleneck to spending more on Meta, they will almost always say it's great creative. And so there's there's a there's a path to sort of just like unlocking, removing that bottleneck so that the constraint just becomes how much revenue do you have and that will just become a proxy for how much you can spend with us.
00:33:32 · Eric Seufert
[00:33:32] S2: Yeah. It's like, what's your bid? Well, your bid should be the value that you get back. I mean, that's like, the whole point of a second price auction is that you should build your bid your true value because you're you're gonna make money if you win. Mhmm. But, like, the thing is, like, they've built like, lot of their initiatives. Right? So JEM, foundation model for ranking. Andromeda is is a is a whole system for doing retrieval. And then Lattice, which is transfer learning. But, like, the whole point of Andromeda was they were reacting to a lot more creative being deployed. Now the creative being deployed, though, was created with third party tools. Right? This creative being deployed is being built without the benefit of the actual performance data. If you actually want this to work, you need to train it on the ROAS. You care about the end result. You care about what the person does when they go to your website or your app, And that no one has that except for Meta at that volume because they get it passed back to the Capi and the Pixel. Right? And so their ability to build this foundation model, I think, really unlocks a lot of value. And, you know, I think you'll probably see that show up in, you know, in the in the revenue growth. Now do they hit 40% to satisfy these, these needs of these hedge fund people? I don't know. But, like, my sense is if you can point to the output, you can say, look. This addresses the core bottleneck, which is we need a lot of creative, but it needs to be built with the knowledge of what actually drives the outcomes and not just a bunch of variations that clog up the system.
00:34:43 · John Coogan, Eric Seufert, Sean Frank
[00:34:43] S1: Mhmm. I wanna push back on that idea that no other lab can do anything like this. What about DeepMind? What about YouTube, Nano Banana, v o three video generation? It does feel a little bit farther off. And, also, it feels like the hedge fund analysts that you're talking to aren't asking the same questions of Google's investments in AI because they have such a incredible business with Google Cloud Platform, and they're able to strike these massive, compute deals. So they have some off take there. But, how is how is the situation different in Google? [00:35:21] S2: Because they don't have a history of tilting at windmills. They don't have the they they don't have an albatross around their neck, which is metaverse. Mhmm. That was a misadventure. It cost a lot of money. It never resulted in anything meaningful. Mhmm. Right? Now I would actually make the point that that whole rebrand was just a distraction. It was a smokescreen. They had to get away from the whole Facebook Files thing. Sure. And that took everyone's eyes away from, you know, that that scandal. [00:35:43] S6: Mhmm. [00:35:43] S2: And was it worth it? I don't know if you achieved that, which they kind of did. They kind of did that with the meta rebrand. Yeah. Maybe it was worth it. Right? You know, not
00:35:43 · Eric Seufert, Jordi Hays, John Coogan, Bernt Øivind Børnich
[00:35:43] S2: And was it worth it? I don't know if you achieved that, which they kind of did. They kind of did that with the meta rebrand. Yeah. Maybe it was worth it. Right? You know, not [00:35:50] S0: necessarily also the name even if you ignore, like, the actual investments they made in in metaverse and, like, Horizons and things like that, like, meta platforms is is the metaverse. Like, it is the place that people exist online. Mhmm. So, like, the name makes sense even if you ignore that and it makes sense for the strategic reason, like you said, to get away from the Facebook files. So, like, they could have just rebranded and never done the metaverse and they'd be in a much better position. Right? They'd have that [00:36:16] S1: Yeah. [00:36:16] S0: Sort of [00:36:17] S1: But you can't really do the rebrand unless you tell the story or else everyone accuses you of what you just described. [00:36:23] S2: Right. Yeah. Yeah. So you gotta [00:36:24] S5: I mean, you gotta put [00:36:25] S2: your money where your mouth is.
00:36:25 · Eric Seufert, John Coogan
[00:36:25] S2: your money where your mouth is. [00:36:26] S1: Yeah. But on on video generation in particular, do do you feel like we are further out just further away from that? I was I was demoing the latest VO model and it's good, but it's still clockable as AI generated. There's still I had some cars spinning around and, you know, it's three cars and then it's four cars and then it's two cars and they're sort of melding into one another. It's an incredible video model, definitely state of the art, but I don't know that that that's ready to be deployed in YouTube across a ton of video ad impressions. So do you have a timeline for that, or do you have some thoughts on, when that will actually be important to the business? Because you have to imagine that Instagram video ads perform better than just image ads, and so they'll try and do this. But what what how are you seeing the AI video advertising model evolve?