Eric Seufert: Meta's image model is its most compelling AI investment thesis — but the API business may be a mistake

Speaker 1: Eric, how are you doing?

Speaker 4: Hey, guys. Thanks for having me. It's good to be back. Sorry about the the technical mess up.

Speaker 1: It happens. It happens.

Speaker 2: It's great to have you here. Question. Did you did you rebrand MobileDevMemo? What no. I didn't To Heracles? Or is this an error on our side?

Speaker 3: This is an error

Speaker 1: on our side. It's mobile dev memo. Hercules is the fund. Is that right?

Speaker 4: Yeah. The fund. Yeah.

Speaker 1: Yes. Got it. Anyway Great. We were just talking about Meta. They had just launched Muse Spark 1.1, their LLM. They're selling that over API. They're also selling some compute. But 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.

Speaker 4: 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 Yeah. 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? This is pulled out of thin air.

Speaker 1: Yeah. This is

Speaker 4: 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.

Speaker 1: Mhmm.

Speaker 4: Meta trained, 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. It 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 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, the this only exists as a result of our ability to train this model on this proprietary data that no one else has, my sense is you can make the argument more robustly.

Speaker 2: 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 Yeah. There's a there's a path to sort of just like unlocking and 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.

Speaker 4: 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 a 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, 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 and 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. Mhmm.

Speaker 1: I want to push back on that idea that no other lab can do anything like this. What about DeepMind? What about YouTube, Nano Banana, VO3 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 an 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 the situation different in Google?

Speaker 4: 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.

Speaker 1: Mhmm.

Speaker 3: And was it worth it?

Speaker 4: I don't know if you achieved that, which they kind of did. They kind of did that with Meta rebrand. Yeah. Maybe it was worth it. Right? You know, not

Speaker 2: 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 Right. 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 Yeah. Sort of

Speaker 1: But you can't really do the rebrand unless you tell the story or else everyone accuses you of what you just described.

Speaker 3: Right. Yeah. Yeah. So you gotta

Speaker 4: I mean, you gotta put your

Speaker 3: money where your mouth is.

Speaker 1: 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'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?

Speaker 4: Here you got to look at ByteDance. What ByteDance is doing is incredible on this front. Right? So they put out this paper a month ago. Mhmm. I did a summary on Twitter and and LinkedIn, but, what they're doing with TikTok shop is these real like, know, basically photo realistic three d avatars that are selling stuff. Right? Like, so infomercials. And they've, you know, they've built custom models to build those ads. And those are all ads. A lot of like, I mean, not all of it, but like, if you go on TikTok and you're looking at the TikTok shop stuff, a lot of it is AI generated. Mhmm. And so what they they in this paper that I summarized, they invested in, you know, in in essentially fine tuning this model to make sure that there was no collision with the hands. What they were finding is that, like, so when you get in that uncanny valley Mhmm. Situation where people can tell it's AI, then they turn off. There's Like, a lot of research that's been done in this. If people know that it's AI, they penalize the ad. But when they don't know it's AI, the AI ads outperform the human creative ads. It's really fascinating. Mhmm. But so what they found was like when you saw the collision between someone holding something in their hand and the object, then people the the the click to raise dropped, the conversion rates dropped. But so what they did was they fixed that. So they built this whole like visual interpretability model that just focused on that with it with with an expert like in the model. And so it just addressed the hands. Mhmm. And so, like, if but that's use case specific. You needed a general purpose model that's gonna build photorealistic video. We're probably pretty far off of that for, all ads of all types. But I think with something like, you know, okay, well, we need human photorealistic kind of like infomercial style. I think you could get to that point now. And maybe we were there now, and maybe ByteDance is there now. But I think it it takes a lot of investment. Right? I mean, they had something that if I remember correctly from the paper, it's been a while since I saw, like, twelve thousand hours of live human product interactions. I mean, it takes a lot of data to do that. Right? And so, you know, it's just it's whatever you want to invest in. I think if YouTube wants to do that for a general purpose photorealistic video ad tool, we're probably pretty far off.

Speaker 2: Mhmm. How do you think that the market will react if Muse 1.1 is like a very much like a base hit on the API? Where they they get some comp you know, big companies move over some workloads, but it's not, you know, this runaway hit. And and I say that because so many models that have been good, not great, have a little demand just because there's a lot of demand for AI, but but they don't sort of like have these sort of breakout, revenue charts.

Speaker 4: 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 is you it priced like a commodity? Yeah. Like like, if if you think about, like 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, 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. I'm gonna do AB test. 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?

Speaker 2: 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.

Speaker 1: It's not a lot of work to switch over. Not It's it's it's risk but it's not a of work.

Speaker 2: 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 on the business overall. So it like doesn't solve at least what those analysts in particular were talking about. Mhmm. And like 0 to 3,000,000,000 on like a new business line

Speaker 1: Crazy.

Speaker 2: 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 a 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, 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.

Speaker 4: 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 cited Ben's brilliant essay from yesterday or the day before about like what Sott 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

Speaker 2: 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 would I would expect them to spend, like, maybe, like, $10,000,000,000.

Speaker 1: 10,000,000,000.

Speaker 2: Right? Yeah. 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

Speaker 1: Mhmm.

Speaker 2: 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?

Speaker 4: Well, that's where you actually do need the frontier. Right? Like coding tasks, you actually benefit from having the frontier. But if you're having like customer support stuff, right, like that doesn't need a frontier model that could use like a three or four, you know, sort of like, release back model. And it'll be you know, it'd be producing reliable results that you know work. Like, you've measured, you tested, and you don't need to upgrade, 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.

Speaker 2: 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 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 Probably not the case. But then when you look at Again, you look at the market, like, 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.

Speaker 4: It's a just totally misdirected move. Is that really the space you want to go into right now? That is so politically fraught. That is such a political hot potato. Why do you even want to touch that? I would steer clear that I would say, look, Facebook apps, it's time well spent. You connect with friends. All this gambling, you're gonna get addicted to that. Don't spend time there. Spend time in Instagram. It's more wholesome. Why are you gonna touch that at all? Are you just gonna open the door to more scrutiny? That's insane. I really have no idea why they're even talking about that. Yeah. It doesn't make any sense. But they've got like, know, look, they said we're gonna we're gonna publish a lot more apps. They've got the new pocket app. I mean, it seems kind of interesting. Maybe some of this stuff sticks. I think they could just try a bunch of stuff. But why would you touch the most politically toxic area right now, like, when you could just be touching anything else?

Speaker 1: Yeah. Take us through the Prosperous Society. I wanted that's why originally why I wanted to bring you on. I want the thesis. I want to dig into it because I I I found the it's a three hour four part podcast series. You've written a lot about it, but introduce it for those who haven't been following along.

Speaker 4: Yeah. Prosper Society has started out I wanted to write an economic bull case for AI. We've heard all of the, you know, bear cases.

Speaker 5: We've heard

Speaker 4: all of the, you know, doom narratives around the around the economy. It's gonna just basically displace all white collar work. You know, you're gonna get DoorDash created with the vibe coding session and so all these companies are gonna go out of business. And I wanna make the case that it's probably not gonna that's probably not gonna happen. And actually, there's a lot of reasons to be optimistic. Right? And I think, you know, in writing that, it ended up becoming you know, we live in a in a sort of like very pivotal moment, I think. You know, if you look at the elections, you know, in the sort of the the house primaries in New York, you look at what's happening with the the, you know, the New York City mayoral election, you look at what's happening in the LA mayoral election, like, there's there's this this this sort of moment where these sort of, like, impulses against capitalism have become a lot more popular. Right? Like there's reasons for that and, you know, I know, I'm I'm not an expert on those reasons, so I won't delve into them. But I think, you know, ultimate's a mistake to go down that path. And the thing is, like, my sense is a lot of the AI or the anti AI narratives are actually have nothing to do with Right? That's just seen as an avatar or like a bogeyman for capitalism. And so what I wanted to do is sort of, like, anchor this economic defense of AI, this economic bull case of AI in, you know, the sort of, like, liberal tradition of of of the Western world. And and and in in doing so, like, you could say, like, you know, you could anchor it to these these sort of, like, these these great thinkers, you know, this sort of, like, enlightenment thinkers and and these sort of the economic giants that have built, you know, built this sort of intellectual framework that our that our western civilization is is based upon. And people could say, look. Well, look. You've you've misinterpreted them and said, okay. Well, maybe. But but if if that's not the case, then they you're gonna make them drop the mask.

Speaker 1: Mhmm.

Speaker 4: You're gonna make them drop the mask and say, That's not my problem with AI. I just don't wanna live in a liberal economic society based on these Western thinkers. And I think if you actually kind of force that to be articulated out loud, you make a lot of progress against the a anti AI narratives. But the whole point of the prosperous society is my sense is, you know, a lot of these AI investments, they're going to push the economic constraints away from production and towards just distribution. Right? They're gonna mean distribution the binding constraint. Mhmm. And so because you just get this this this flourishing of of content creation. And and and and so when that becomes the actual problem with distribution and these AI investments go into things like ads platforms, digital advertising, you know, REXIS, recommend recommendation systems, then actually commerce, the economy, becomes much more efficient. Mhmm. And it's a really good thing, and you just generate a lot of value by pushing that binding constraint to the distribution layer. Mhmm. And you get then as a result, you get a lot more heterogeneous product development because you can actually reach those people economically. Right? So like what is the constraint now? It's like, well, can I reach a big audience? Right. Right? Can I reach a big enough audience to support a business because what? I've just got this kind of like blunt tool. But if these AI investments go to making recommendation systems better, digital ad systems better, reaching these these these these pockets of people that have these very specific interests that were totally un unserved before, then you enable a lot more commerce. Right? Yeah. And and then and then, you know, you support this, like, flourishing of people making this wide diverse variety of goods. You get rid of this idea of, the Pareto principle. We have to serve, you know, the the 20% that supply 80% of the commerce. Well, no. Now you can serve everybody, and they can pay what they're willing to pay for these things. You reduce consumer surplus sorry. You reduce consumer surplus, you just you create this flourishing of of of everyone getting exactly what they want. Yep. Right? And that's the prosperous society. And so the way I frame it is kind of a reaction or, like, call call it a conversation with John Kenneth Galbraith. He wrote The Affluent Society. Right? But this was written in the post war economy. It serves kind of as the degrowth or handbook. And in my sense, it's like, you know, John Kenneth Galbraith is a brilliant man. I'm not saying he's wrong or he was wrong when he wrote the book, but I'm saying it just doesn't apply anymore. Mhmm. Right? He was talking he had this idea of the dependence effect. Advertising actually is a way to whip up demand so we can maximize production because that was what he called this conventional wisdom at the time. You should be maximizing production. Mhmm.

Speaker 3: But the

Speaker 4: the reality is in the time he wrote this book, 1958, you had people moving to the suburbs, getting houses. They you know, the GI bill helped them buy these homes. You had the suburb the the idea of the suburbs was was being deployed. And so, you know, people needed washing machines. They needed cars. They needed refrigerators for the first time. And so you had these big companies that made these mass market goods. They advertised in mass market media. And they and and and John Kenneth Goldberg's idea was, like, well, that's just creating demand. The the there there is no actual inherent demand for these things. It's creating it through advertising. And my point is the opposite. Yep. You know, we don't have this homogenized society anymore and we don't have people that need these homogenized goods anymore and we have a lot more particular specific media now. And we can reach people and advertise to them the things that they have demand for

Speaker 1: Mhmm.

Speaker 4: For products that that weren't economically viable prior to these systems, these distribution systems. And that is the prosper society. It's being able to reach people to meet the demands that they have with the products that they couldn't access before with ad ads that they wouldn't otherwise see absent these systems. And so I think it's it's, you know, I think it's a very it's my sense is, like, you can make a very credible bull case if that's what AI delivers to us. And it's not about wiping out white collar labor because the reality is, like, that's gonna create more jobs. And we're seeing that now. Like, there's no there's no justification for that skepticism. It just doesn't exist. We're seeing an increase in hiring. Maybe not at the entry level, and you could Yeah. Discuss if there should be some intervention there. But my sense is, like, AI actually, if you look at the data and there's a Financial Times article about this other day. If you look at the data, it doesn't support that bear case. And so Yeah. That bear case should be absolutely eliminated as something that even enters the conversation.

Speaker 1: Yeah. No. I completely agree. That was an amazing speech. I don't know if I have anything else. Yeah. No. I I I love this idea. We've been talking about it a bunch, just more customization, the long tail of commerce getting even longer. And it feels unfathomable.

Speaker 2: Yeah. We've

Speaker 1: because seen this it's already, like, you know, specific shirts that are just for you designed and targeted to you on Facebook. We've seen that. But, like, it can in fact get more personalized.

Speaker 2: Well, yeah. I mean, we've we've seen this with like content platforms. Right? Totally. They're very good at they're very good at like find they're they they they are very good at serving you. They can serve you a video that has 50 views from a new channel on YouTube and you'll be like, that's interesting. I will watch this. Right? And it's a niche that is like so small, it never could have existed in the era of, you know, radio and television and, you know, seeing that seeing that trend accelerate. It reminds me, I was, I I was wanting, you know, the you know, these, like, kids RC, like, ride on cars? I got, like, incredibly frustrated with these because I've tried a bunch of the different brands. I've tried spending, like, $800 on them and, you know, $400 and all of them just suck like the kids even when you have the you're driving the kid on the controller, the kid can still hit the gas and just like run into stuff and, you know, it's just absolute chaos. I'm like, what is the version of this that is like, you know, know, the the top of the line version of this? Because I wanna get it. I I use these things a lot. And I searched around, couldn't find it anywhere. And then John just like

Speaker 3: Within twenty four hours, I got served

Speaker 1: exactly what he was looking for. I don't even know how

Speaker 2: how Yeah.

Speaker 1: It got served to me, but it was fantastic. Thank you so much for taking the time to come chat with us.

Speaker 2: I like to drink.

Speaker 1: Always always a great time.

Speaker 2: My favorite my favorite conversations. When you're on, it makes me feel like we're on we're on we're actually, you know, on SportsCenter. Yeah. Because most people that have talked about this stuff are not high energy. It's just like full on SportsCenter. It's amazing. Amazing. I love

Speaker 3: it.

Speaker 1: Well congratulations on all the progress and and the Prosperous Society. Go listen to it. It's a three hour four part series and sign up for Mobile Dev Memo if you haven't already. Of course, you should. But thank you so much for coming on the show, Eric.