00:28:00 · Jordi Hays, John Coogan, Eric Seufert
[00:28:00] S0: Can you hear us? [00:28:01] S1: Can you hear us? Okay. Let's bring him back in in a second because I want to hear his take about Meta's vertical integration, specifically with regard to their image model because their image model has the potential to feedback in the ads product where they are getting a signal from what ads are performing, generating new images, and then using that as fine tuning data and reinforcement learning data for the image model. And it's a very different cycle than what you see in ChatGPT images where, you know, they're trying to be useful, educational, sometimes funny. Same thing with Nano Banana. But Meta has a different job to be done, a different process, and a different different goal because that model although it might wind up being something that people use just to post on Instagram and people might just use it like any other image model, the real killer application of that is in the ads manager. And we also have Sean Frank coming on later in person to tell the more business side of that story. He's built Ridge into a 9 figure d two c brand, obviously, has been deeply ingrained in the meta ecosystem for probably over a decade now and and can comment on everything that's happening in AI generated advertising. But I believe we have Eric Souffert lined up now with audio. We'll bring him in to the TV panel with him. Eric, how are you doing? [00:29:27] S2: Hey, guys. Thanks for having me. It's good to be back. Sorry about the the It technical mess [00:29:32] S1: happens. It happens.
00:29:32 · John Coogan, Jordi Hays, Eric Seufert
[00:29:32] S1: happens. It happens. [00:29:33] S0: It's great to have you here. Question. Did you did you rebrand MobileDevMemo? [00:29:39] S2: What no. I didn't work out. [00:29:41] S0: To Heracles or is this is this an error on our side? [00:29:43] S1: This is an error on our side. It's mobile dev memo. Hercules is the fund. Is that right? [00:29:49] S2: Yeah. The fund. Yeah. [00:29:50] S1: Yes. Got it. Anyway Great. We were just talking about Meta. They just launched Muse Spark 1.1, their LLM. They're selling that over API. They're also selling some compute. But I wanted to I want your take on the image model specifically and why that is an important, technology for them, why vertical integration makes sense there, why specifically is is their image model like, it it feels like it has a different business case around it than a Nano Banana or a ChatGPT images.
00:29:50 · John Coogan, Eric Seufert, Bernt Øivind Børnich
[00:29:50] S1: Yes. Got it. Anyway Great. We were just talking about Meta. They just launched Muse Spark 1.1, their LLM. They're selling that over API. They're also selling some compute. But I wanted to I want your take on the image model specifically and why that is an important, technology for them, why vertical integration makes sense there, why specifically is is their image model like, it it feels like it has a different business case around it than a Nano Banana or a ChatGPT images. [00:30:21] S2: Yeah. So I think this is brilliant. Right? Like, this actually gives them the narrative firepower that they need to to sort of undermine the skepticism that investors feel about the AI investment. Right? So, like, I was at this dinner. I did these dinners a lot, like, these ideas dinners. Mhmm. Like, a a a research company will bring me in and talk to, like, much of their hedge fund clients. And, like, I was talking about, you know, why these investments that Meta's making right now are bearing fruit right now. Like, 33% advertising revenue growth last quarter on $55,000,000,000 in revenue. That's incredible. Like, you look at Google Search was 19%. Amazon, which is much smaller, was 24%. They're outgrowing everyone except for AppLovin and Reddit. And people don't believe it, and I asked somebody, okay. What would it take to convince you that these investments are actually productive in this moment in time? And they said 40%. 40% growth on 55 to $60,000,000,000 in revenue. Where did that number come from? [00:31:14] S5: This is
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.