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?
00:37:20 · Eric Seufert
[00:37:20] S2: Here, you gotta 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, you know, basically, photorealistic 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 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, like, they invested in, you know, in in, essentially, like, 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. Like, there's 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 it's really fascinating. 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 rates 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 you 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, like, 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 wanna invest in.
00:39:03 · Eric Seufert, Thibault Sottiaux, John Coogan, Jordi Hays
I think if YouTube wants to do that for a general purpose photorealistic video ad tool, we're probably pretty far [00:39:08] S4: off. [00:39:08] S1: Mhmm. [00:39:09] S0: 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 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.
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?