Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness

The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it. Hosted by Sonya Huang, Sequoia Capital

• 0:00Speaker 00

  So we could edit this set so it looks like

• 0:02Speaker 01

  We should, yes. Yeah, I want this where we were talking off camera. Like, we should do that for the intro. Because I think it just like makes all this stuff more capable. I've seen these examples of like such subtle nuance that like make me appreciate that. It's like the world understanding playing out. I was I was giving a talk and was on stage with with my friend Tulsi, who leads the model team. I had mentioned to someone in the crowd to like edit the video. And they literally like took the picture, edit it with Omni in real time. And this like dog came on the stage in the edited version. The other guests sort of like look down and see the dog. They like chuckle a little bit. This is while I'm like opining about whatever.

• 0:40Speaker 00

  Maybe they're laughing at your jokes.

• 0:41Speaker 01

  Yeah, that it was not my jokes. They they laugh at the dog coming up. It jumps onto my lap. I sort of like acknowledge the dog. I keep talking. I'm like petting it or whatever. And just like, there's like so much subtle subtlety in getting that right. And the model crushed it. And it's just, it's very interesting and like still trying to like absorb and digest like what that means for, you know, the way we make content and all these other things.

• 1:06Speaker 00

  That's so interesting.

• 1:24Speaker 00

  I'm delighted to have Logan on the show. Logan runs Google AI Studio and the Gemini API. You spend a lot of your time thinking and building for the next generation of builders.

• 1:33Speaker 01

  Yes.

• 1:34Speaker 00

  So I'm excited to talk to you about everything from agentic AI to AI coding, world models and more today. And right off the heels of Google I/O. So what better timing?

• 1:43Speaker 01

  Yeah, I'm super excited. Thank you for having me.

• 1:45Speaker 00

  Wonderful. Let's start with agentic AI. So Sundar opened I/O by calling this the agentic Gemini era. What does agentic AI mean for Google?

• 1:55Speaker 01

  Yeah. It's a good question. I think, and we were, we sort of, if you followed closely, we did sort of mention some of these things back with Gemini 2.0, which I think was like a little bit early. And so I think this era, this Gemini 3.5 era feels like it's actually becoming true now. And we're in the era of agentic coding or agentic products and everything agents as far as Gemini goes. I think for us, this agentic layer, and I think we announced this actually at I O, sort of being powered by the anti-gravity agent harness, is this like additional through line for Google that sort of connects all of our products that they're sort of like based on now. And so historically, like prior to Gemini, there actually like wasn't a through line for the, you know, probably

• 2:39Speaker 01

  sub hundred number of Google products that we have, the 50 Google products we have, there wasn't a through line. We had Gemini, it became this through line. Everything is now sort of using Gemini in some way. That's now becoming true for anti-gravity, as sort of all of the products rebase to become sort of like agentic native products and like actually taking action on behalf of users and helping them get things done. You see this like new through line emerging, which I think is actually really, really interesting.

• 3:06Speaker 00

  And sorry, help me with anti-gravity is the IDE, right? Or the non IDE?

• 3:11Speaker 01

  Yeah, anti-gravity is a lot of things, which I think is sort of, again, is an opportunity for us. You have sort of a core IDE, you have sort of like the agent first experience if you want it on the web. You have a CLI, you have an SDK. So I actually think, and I don't know how much we've framed it this way, but like it really is an ecosystem of stuff that we built. And it's designed to sort of like meet developers wherever they are. So you could use it through the Gemini API if you want to, and you want a managed agent that you don't have to do any of the sort of infrastructure work for. And then the most interesting bit is like, it's not just the ecosystem of anti-gravity stuff. It's also powering like literally, it's the same harness is actually powering all the other Google products. So anti-gravity will be powering a bunch of agent stuff in search in the Gemini app.

• 3:54Speaker 01

  Across like cloud and AI Studio, which is really exciting.

• 3:58Speaker 00

  I see. So it used to be the Gemini API. So like the language model was the through line in terms of how AI gets baked into every Google product.

• 4:05Speaker 01

  Yeah.

• 4:05Speaker 00

  And now it's not only the API, it's the coding harness.

• 4:09Speaker 01

  Exactly.

• 4:09Speaker 00

  Um, that's, that's being used in each of these products and therefore it's a coding agent itself that's driving more agentic properties.

• 4:16Speaker 01

  Yeah.

• 4:16Speaker 00

  Other products.

• 4:17Speaker 01

  Yeah.

• 4:18Speaker 00

  Fair description?

• 4:18Speaker 01

  Fair, fair description. I think more generically too, it's just like, it is the agent harness. I think like coding as sort of like a specialized use case of the agent harness, I think is, is obviously powerful, but it is like coding has proved to be the general purpose agent harness in addition to also working really well for coding.

• 4:33Speaker 00

  Are agent harness and coding harness synonymous or not?

• 4:37Speaker 01

  There's definitely nuance. I think there's like optimization that you can squeeze out of like specializing. And actually you see this where like the, you know, technically the agent harness that gets used for the way that AI Studio uses it is like a little bit specialized for, you know, the vibe coding use case and the, the way that the Gemini app is using the agent harness is a little bit specialized for the sort of consumer always on 24 by seven agent. So I think you have that base harness that like probably has like 80% of the same stuff. And then you specialize for, for coding or for whatever the use case is.

• 5:07Speaker 00

  Interesting. How do you think about the cannibalization of the existing business, especially now that you are, you know, going much more aggressively into agentic properties? Because I could see, for example, if all you're doing is search or summarization, um, there's, you know, not as much of a cannibalization fear. Whereas if you're actually going through my emails, replying, replying to them for me, like, am I even going through my email anymore? And so I, I could imagine that there's actually just fewer human eyeball hours, um, uh, on your products as a result of having more agentic capabilities. Is that fair? Or how do you think about the cannibalization?

• 5:34Speaker 01

  Yeah, it's interesting. I think.

• 5:46Speaker 01

  One sort of observation I have is that like at the beginning, and I think Sundar has done a great job of of sort of talking through this is at the beginning of the the sort of current AI era, like everyone assumed that AI being able to answer questions for you was going to be like negative sum for for search. And actually, what's ended up happening is it's been incredibly positive sum for search. Like, people are searching more, people are doing more. And so I think-

• 6:10Speaker 00

  And agents are searching too.

• 6:12Speaker 01

  Yeah, and agent, actually, again, there's like this whole market that spawned at the same time that agents are doing more, at the same time that humans are also searching more. And so I think it will be, obviously, there's a finite amount of like human time in the world. But from from like my early feelings of how a lot of this is playing out, it does feel like it's it's very positive sum from like an ecosystem value creation, like how the human behavior aspect of it turns out. I think is like somewhat clear in the next one to two years, much less clear, you know, three to five years from now, when the technology is improved and the products probably look a little bit different than the way that they do. But ultimately, like, that is the success of product. I think like we we have a bunch of conversations with Demis all the time, and it's like

• 6:53Speaker 01

  the point of building the technology is so that it can go and do stuff for you. Like the point, like, success for Google, like, probably doesn't look like, you know, maximizing eyeball time in front of our products. It's like maximizing outcome for customers to like do the thing that they want to do so that they can go and live their life and do what they want. And so I feel like you'll you'll probably see us go down the route of like maximizing outcomes for customers and like not maximizing eyeballs.

• 7:18Speaker 00

  Yeah. I have this term stuck in my head, agent-led growth. Like, it seems to me, so I'm using using coding agents a lot in my personal time, and you know, I just let the agent make all the infrastructure choices for me. I'm like, I don't care which database, you tell me.

• 7:32Speaker 01

  Yeah.

• 7:32Speaker 00

  And and so and the reason I ask is, you know, it's true in coding today. I would imagine it's maybe going to be generally true for a lot of things, let's say, shopping. Down the line, how do you think that's going to change how advertising works? How value capture works for for the aggregators?

• 7:50Speaker 01

  It feels like it's a very similar trend. This isn't perfectly true, but a lot of these things are just like proxies of each other. Like the way that SEO works, I think, like, is directly correlated with like the way that, like, I forgot what the term now for it's like, G E O is like the generative engine optimization or whatever it's called. And so it does feel like there's a lot of correlation between the between the things. My guess is it looks like much less of a radical shift than than the than I think maybe what we assume right now, just because these things compound on top of each other.

• 8:24Speaker 00

  If you were to, you know, grade the scale of agenticness in terms of crawl, walk, run, where are we in terms of how agentic the Google suite of products is?

• 8:34Speaker 01

  Yeah, that's a that's a great question. It's definitely like crawl right now. And I think some of this is like all of the inherent product tension for Google is like you have what 13 billion plus user products. And so like, I actually think we have some more like labs like experiences where you're probably closer to running or walking. But I think like most of the product experience today is definitely closer to crawling. And I think that's just like the stewardship responsibility we have sort of building a product that's being used by lots of people. Like, I don't think the long tail of customers are like, ready to have A I running and just doing all the things like they probably they want to be in the driver's seat. They're cautiously taking the first step. And I think the the Google team like search is maybe like the most quintessential example

• 9:17Speaker 01

  of this, like, I think they have a lot of responsibility to actually do that in a way that it brings people along and doesn't just like, change everything of how they interact with the Internet and the way they associate with products and stuff like that. So.

• 9:28Speaker 00

  Yeah. Which products do you think are closest to the walk?

• 9:31Speaker 01

  That's a good question. I think Gemini app is definitely closest to walk. And so for for Spark, I think having a 24 seven always on agent, like literally going and potentially doing a bunch of actions on your behalf, is definitely like one of the frontier use cases. And I think you'll see, I think like anti-gravity is another one where it's like you could have autonomous coding agents, you know, rebuilding operating systems and doing, you know, billions of tokens and spending thousands of dollars on your behalf. And I think those are. Again, like more and actually like they're in GDM as well as another angle of this. So I think like GDM is taking like very much like a frontier look at this, where I think like the rest of Google's products, I think are like more incrementally getting there, which again makes makes reasonable sense to me.

• 10:12Speaker 00

  Yeah. Do you think that Google ends up with one, two, three product surfaces for for using AI or thousands?

• 10:21Speaker 01

  It's tough. I think a lot of this is actually big than just like how humans consume products. And my sense is that there's something nice about like having this like compartmentalization and this like specialization of products where like it it becomes if you end up with a product that is like doing everything for you inherently, there's more work involved in using that version of the product. I think I think would be like the default state. I think maybe somebody will spin together like the truly magic experience that doesn't make that true. But I think I think the long tail of folks end up having to spend more mental energy and more time to actually like get the general purpose product to do the thing that they actually want to do versus like there's something nice about I click my calendar app, it just shows me my calendar like I don't need to worry and deal with anything else.

• 11:06Speaker 00

  This is my hot take for why slide decks have existed for so long of just like, you know, the thing, the piece of information, you want it to be exactly in the same place. And I think like we as humans are just actually very used used to that as opposed to the idea of a generative in their face sounds so cool to me, but it's like our do our brains really, isn't that just more cognitive overhead for us?

• 11:25Speaker 01

  It definitely is in certain cases, and I think somebody needs to, again, there's there's a lot of incredibly smart people in the world, and so maybe somebody will find the experience that like makes it feel more natural. But to me right now, I'm I'm maybe not 10,000 is the extreme version. I'm guessing it looks more like more products going after sort of like different. And maybe the other answer is like, I don't know what it looks like for Google for the ecosystem. It looks like a lot more products, I think, like, and that's that's really exciting. I think like how Google will end up strategically deciding like, do our customers want to deal with us having 10,000 products or would it be better to only have three? Will come down to like a strategic decision for us.

• 12:03Speaker 00

  That totally makes sense. Um, when I talk to companies in the enterprise, they say, you know, everyone's talking about agentic AI, but the only place they've seen agents really working is coding agents. Do you agree or disagree with that take?

• 12:17Speaker 01

  Yeah, I think it depends what your bar for working is, which I think is a lot of the nuance of this. Like, I think if you're, if you're truly trying to like offload very complicated tasks for, for domains in which like it's the models haven't actually crossed the threshold of quality, then like, I think that's definitely true. Like the, it's not going to solve the problem, but this is something that I want. I wish we could like measure, a good example is like open router, for example, is like measuring, you know, the, the total toolkit consumption that's happening. And so you can sort of like see these trends play out over time of like how much more intelligence is in the world, you know, now versus a year ago. I- in parallel, the thing that I'm actually really interested to measure is like, how long is the average like thing, the average like agent run or the average task actually

• 13:02Speaker 01

  taking place? And I don't think it's something that they publish, but I feel like they probably have interesting data. I'm sure there's others. Because, because I, I do think you're like seeing these like new model capability lands or new model drop and, and it's like spiking up and, and maybe the, the curve is still like very low right now, but like you're seeing those like early signs of it spiking up or to like long running tasks and all the model labs are talking about like, we've released this new model and it did, you know, three days of autonomous work or whatever it is. Um, that, that's the extreme, but I think in practice, you're seeing that like trickling up like pretty, pretty quickly, which is really interesting. So even if the enterprises haven't felt it outside of coding,

• 13:42Speaker 01

  like they are going to like this year, um, as, as sort of a bunch of those other use cases get, get much better as well.

• 13:49Speaker 00

  From like a, you know, from the deep mind perspective, do you think long horizon agents is like a KPI that matters? Is it they, is it the KPI that matters?

• 13:57Speaker 01

  It definitely, it definitely matters. Um, I think for deep mind, like we're doing lots of things which, which we can talk more about later. Like there's, you know, a, a huge portfolio of, of different bets that are taking place. Long running agents obviously matters a lot. And I think also like specifically coding agents and that matters a lot. Like it clearly is an accelerant of like every other part of your business. If you have a great coding model, um, and so making sure we have that, I think is, is super top of mind.

• 14:22Speaker 00

  Got it. Um, I'd love to shift gears a little bit and talk about coding.

• 14:25Speaker 01

  Yeah.

• 14:26Speaker 00

  Um, okay. I'm gonna ask a hard question. Uh, a lot of my developer friends were using Claude for a long time. OpenAI saw that, declared code red. Codex is now really good. I would say my friends are maybe split 50 50 now in using Claude and using Codex. I don't hear a ton of them using Gemini, which has always kind of puzzled me.

• 14:48Speaker 00

  What's going on with that?

• 14:49Speaker 01

  Yeah, it's a great question. I think there's one, there's one part of the story that I'll add, which is, which, which is, it makes it even more interesting, which is

• 14:58Speaker 01

  December, the narrative was that Google had won. And when we landed Gemini 3, I think it was like such a, such a profound improvement from a model capability perspective. I think a lot of the narrative was like, Google has taken a huge leap forward and, and made that happen. And I think what was interesting to see sort of as a, as an ecosystem participant is like how, not how quickly that narrative shifted, but just like the next wind of the narrative obviously was like all the agentic coding stuff that happened over, over the holidays and then into January and beyond. And that was, that was not that long ago. And so it's a, it is a

• 15:34Speaker 00

  I feel like we've been in warp speed ever since.

• 15:36Speaker 01

  Yeah, for sure. And it, but it is, it's a meta reminder of like just how fast things can, can change. I think the observation is, is not, is not unreasonable. I do think the, what's happening behind the scenes for us is like trying to push the frontier as fast as possible on coding. And so I think anti-gravity actually like is an important part of that. I think one of the takeaways is that it's actually really hard to make a great coding model for this like, for this developer use case of like really long running suite work. If you don't actually have a product that does that. And so I think like Google realized that. It's why the sort of like windsurf deal happened. It's why those folks came over and then ultimately built anti-gravity and sort of we've been using internally, actually.

• 16:18Speaker 01

  And Sundar showed this at I O, just like the graph of growth of token consumption inside of Google. So you sort of like, you need that engine to spin and sort of the meta comment again is like, the engine is spinning. It takes time in order to like actually make model progress. But I'm super confident. I think the, the folks, the group of folks who we have working on code is like, I describe it as like the Avengers of AI internally. And so like, it really is like the, some of the best people inside of Google trying to push the rock up the hill on this stuff. And it, taking it super seriously and trying to push. And I think three flash.

• 16:54Speaker 01

  You know, notwithstanding like some of the conversation about like the price and stuff like that, like is sort of a step towards actually starting to bring a lot of these capabilities. And like the fruits of that labor paying off, like it's a flash model that's better than any pro model we've ever released from a coding standpoint. And the pro models were really good before. So there's another thread of this also, which is like, everyone forgets that there's like pre-training windows. And I wonder like, somebody should like track this online, which would be interesting to see.

• 17:24Speaker 00

  Meaning like the big run, like what clusters have been available?

• 17:27Speaker 01

  Exactly. The big, the big runs are like, are an interesting thread of this. And so it like, it might look from an external perspective that like, oh, you're, you're super behind in some way. And like, actually you, you miss all the context of like where the big runs are and where the large pre-training runs are. So I think that, that also like, obviously there's, pre-training has historically been like a massive strength for DeepMind. Like we have some of the best people in the world. And so excited to see sort of the fruits of that labor and, and everything else that's happened. Like 3.5 flash was like all post-training gains, which is really cool. So a huge, a huge testament to the team, the work that that team did to actually like make the level of gains and like surpass the previous pro model, literally just with post-training, which is awesome.

• 18:12Speaker 00

  How religious are you all about dog fooding internally? Like are, for example, are DeepMind folks still allowed to use other models? Or is it like, you guys are using the Gemini harness now and we have to make this really, really good?

• 18:23Speaker 01

  Yeah, there's, I mean, I think people, it's so healthy to be using other models just because like it's, it's so sometimes hard to like actually grok what's happening in the ecosystem if you're not to like, I use all the models, I use all the products. I think like, you know, folks across the rest of DeepMind are doing the same thing. You definitely have to use the Gemini models though. It's just like great from a, from a feedback flywheel perspective. And it's part of how they get better is like, DeepMind has and Google more broadly has like 100,000 plus incredible engineers who are using the models and giving feedback. And like, it should be a competitive advantage for Google because we have that scale of sort of engineering resources and like the depth of the talent and can run, you know, A B tests and live experiments and all that stuff.

• 19:04Speaker 01

  So I think you have to use all the models, but I think for, for the majority of folks, it's like Gemini as the daily driver, which is great.

• 19:11Speaker 00

  Do you believe in this narrative around like a, like a soft takeoff of like, once you have a good enough agentic coding model? Then it accelerates the pace of research progress and like it's a self reinforcing cycle.

• 19:23Speaker 01

  It seems obvious that that's true, but I maybe I'm too I've drinking too much Kool-Aid that that's that's the case.

• 19:30Speaker 00

  Are you seeing the signs of it yet?

• 19:31Speaker 01

  Yeah, I mean, I you definitely see some signs of this. I think the signs that are like still early is doing this from a model perspective. And I think part of the context of that is like the resource allocation for some of these like larger training runs is just like significant. And so like you you definitely still have like a human in the driver's seat of making those decisions. Because like you're not going to accidentally, you know, take 10,000 TPUs to go kick off some job that like actually doesn't make that much sense. But from a product perspective, you for sure see it like I think we're seeing this on our team. Like we've built mobile apps using anti-gravity and like we'll launch them to the world like faster than I think any team at Google has ever built a mobile app.

• 20:11Speaker 01

  Josh's team did this with the Gemini macOS app and sort of like end to end delivered an app sort of faster than any team had ever delivered a Mac app at Google. And it's because of it's because of agent coding. And so it's great from a product perspective.

• 20:25Speaker 00

  I think you've said in the past that if you could have a system that could build anything with code, humans can't compete on the same level. And that's narrow superintelligence. Do you think we've reached that point?

• 20:37Speaker 01

  It is interesting. I think this like narrow superintelligence example is interesting to see how. Obviously, it kind of feels that way for coding right now where like coding is like just so good. That it does kind of feel like narrow superintelligence. I don't know. It depends how you actually end up the details of quantifying this, but I think the important thing is like to your point earlier, it works incredibly well for code. And so it would be great if it did a bunch of other things, but it's actually just like so impactful that it can be great at code. And so I spent a lot of time just like letting that that fact sort of just like wash over me. Because I think it's like, obviously, building AGI is super important and very interesting.

• 21:20Speaker 01

  But like building AGI, if it's sort of like takes away from the story of like the current present capability of the technology. I think is actually like kind of a bad, a bad sort of like trade off. And so I'm trying to like always hold these two things in my head equal at the same time, which is we need to build general purpose technology. But obviously, it's so impactful to have this thing. And it feels like it hasn't taken away sort of, it's been one of the best positive outcomes is that I feel like it hasn't taken away from like human developers. It really does feel like an accelerant of what human developed, like I as a human developer feel like I have more agency in the world. I feel like I can tackle, this is my personal experience, I feel like I can tackle more ambitious problems.

• 22:03Speaker 01

  I feel like I used to kick around ideas and they were like slightly out of reach and I would just be like, ah, wouldn't it be nice? And now I have the opposite problem, which is I'm kicking around an idea and I'm like, I could probably make this even more ambitious and sort of it does, it adds a different layer of sort of

• 22:24Speaker 01

  responsibility or like some, a different layer of burden, actually, because I'm like, oh, I can't just like do the the sort of MVP of this, like I actually need to like go 10 steps further because the technology enables me. And like resetting my, my level of ambition, I think is something that I, I've also spent a bunch of time thinking about. But I think that will happen in other, these like vertical, super intelligence domains, which will be interesting and it feels like we're going to get a bunch of those before we've like solved, like it's almost like jagged, like jagged super intelligence, I think is what we'll end up with.

• 22:58Speaker 00

  What verticals do you think we'll get super intelligence at next?

• 23:01Speaker 01

  That's a great question. I do spend a lot of my time, too much time probably thinking about coding these days. So I'll think for a second of like the other, the other domains. I think part of this is like things that have like better verifiability, obviously are like the ones where you'll, you'll see the gains happen more quickly. So like things with like math and finance, actually like science could be a really interesting one. Like it would be fascinating to see like some of these domains where there's some level of verifiability, like actually like really start to take off. Which would be cool. And I also think like an important thing in this like broader narrative about just like what a what impact AI is having on the world like you almost like want that to be

• 23:44Speaker 01

  the case in the sequencing of like things that work. You want a lot of these like really, really good impactful, positive things for the world to happen as early on as humanly possible so that like folks understand what the potential positive impact of the technology is. So I think science could be a really interesting one. Yeah, there obviously there's all the stuff happening right now with like math proofs and stuff like that, which I'm not a mathematician. So it's it's somewhat over my head, but.

• 24:08Speaker 00

  I saw a great tweet the other day. Why did others have so many problems?

• 24:12Speaker 01

  Exactly. That's a that's a good one. I like that. That is. It's a good like T shirt.

• 24:17Speaker 00

  So funny. Okay, I but speaking of Twitter, I went through your Twitter before this, so I'm gonna read back another tweet at you. The good thing on Twitter is there's a public record of all your predictions.

• 24:25Speaker 01

  I need to turn on that auto deleting feature or whatever.

• 24:29Speaker 00

  Last October, you tweeted, everyone is going to be able to vibe code video games by the end of 2025.

• 24:36Speaker 01

  Yeah.

• 24:36Speaker 00

  Did that end up being true?

• 24:37Speaker 01

  It feels close. And I think there's, I mean, it obviously not AAA games like you're not building a, you know, the next Call of Duty or GTA yet. But I think it's it feels closer than it's ever been. And I think a lot, actually, a lot of the interesting bit about video games is you actually need to end up building a lot of this like other stuff like models. And we were talking off camera before this, like 3GS is a great example of this, like 3GS makes a lot of things possible that weren't before. But there's still all these like rough edges that like just a coding agent doesn't solve. And so you need like, you know, sprite generation and like the models aren't very good at doing that natively. And so you need like some orchestration

• 25:18Speaker 01

  layer and tooling in order to make that happen. There's a bunch of other things like that that like are core to like the gaming video game experience that need to have a high degree of reliability that I think it feels like it's within reach, but actually like requires a lot of like product scaffolding work in order to create experiences that are like reusable and replayable and sort of like have the level of depth and requires a little bit of taste in there.

• 25:45Speaker 00

  Do you see people making a lot of video games inside AI Studio and the other developer surfaces that you have?

• 25:50Speaker 01

  Yeah. And so this was actually based on like us looking at the early data. And there was something like in AI Studio at the time, it was like 20% of all apps that folks were making were actually games, like people were trying to build games. A lot of it.

• 26:01Speaker 00

  Is that the most popular category?

• 26:02Speaker 01

  It's not the most popular category anymore, just because I think like the ecosystem has shifted and like the user base has shifted, but it's a lot of, a lot of games.

• 26:11Speaker 00

  What is the most popular category?

• 26:13Speaker 01

  I think it was like, it's like 20% like finance related stuff, 20%.

• 26:17Speaker 00

  Wow, people like counting their money that much.

• 26:19Speaker 01

  People like, I think it's, it's something around crypto, actually, I think is what people are doing a lot of stuff with, with finance. A lot of like personal productivity things and a lot of gen media stuff, actually, because obviously the Google suite of gen media stuff.

• 26:33Speaker 00

  It's amazing.

• 26:33Speaker 01

  Yeah, has done a great job.

• 26:36Speaker 01

  But I also think GDM has sort of like, obviously Demis cares a ton about games and sort of like started his career in doing AI stuff because of games. And so I think we'll, we'll have some interesting swings at this and our team actually in, in Kaggle, which is sort of a bunch of the AI benchmarking stuff we do in GDM. Sort of works with GDM to build this game arena, which is sort of our way of sort of like testing. Progress towards AGI, like using games as a proxy, which again is like very deeply rooted in, in GDM's history, so.

• 27:07Speaker 00

  How close do you think we are to, you know, Rando Off the Street, What a Good Idea, Can Vibe Code, a really fun playable game?

• 27:15Speaker 01

  I want to say this year, I actually, I think it's, I think the model capability makes it possible. I think this is where like I've gotten excited on the product side and you know, we were, again, we were also talking off camera about sort of like the startups in this ecosystem because. It feels like it's possible. It doesn't feel like there's a gap in model quality. It feels like there's a gap in like. You, someone who knows what it takes to build a great game, actually like putting the scaffolding together in the right way to make that possible. I think there are folks who are doing this right now. And so some of it is like a discoverability and awareness thing that like people just don't even know that they can do that. And some of it is just like, maybe certain categories of model capabilities are just like slightly off and we're like, you know, weeks or months away from like that. Chasm being crossed and then it just like working for most people.

• 28:01Speaker 00

  And so this is a good segue into, I'll ask about role models next, but do you think vibe code of video games is more likely? Going to be, you know, game engine plus coding agents based? Or do you think it's more likely to be world model based?

• 28:17Speaker 01

  Yeah, I think the, what will end up happening is the definition of world models will blur, which we should, which we should talk about with Omni. And it will still, I think the like coding agent will look like some sort of world model type system. But you actually do need to make world models useful for like real things. You need like scaffolding. And so I think there's, again, there's actually a bunch of interesting startups like doing work, like figuring out what is the scaffolding for world models so that you can take them from these like very open ended, inherent design of world models, very open ended spaces and like do it in a tangible way so that it's like grounded in a use case that like you could use in a reoccurring way.

• 29:02Speaker 01

  That could be, somebody maybe will figure out the scaffolding for world models to make games possible, but like the inherent nature of world models right now, I think, make it so that it's like actually not well suited for like games in the current form. But the progress has been crazy. So who knows, maybe in like two years, the, the versions will be able to, but at least in the short term, it's like coding agent plus some sort of game engine, I think is like where you'll see way more alpha from a, a game's perspective.

• 29:28Speaker 00

  That makes sense. Okay. So you said the definitions of world models are blurry. Can we unpack that?

• 29:33Speaker 01

  Yeah, I mean, I think like Omni is a, is an example of this, you know, we launched this at I O, you can sort of take in any input, create any output. And I think Demis sort of like framed it to the world, rightfully so, as, as a world model because of just like the level of understanding that it has of the world. I think that like technically looks different than, and I'm, I'm not an architecture expert on like the way that we've done world models before, but it is different from an architectural standpoint than what's happened in the past.

• 30:03Speaker 01

  Which I think is positive because it's getting closer to like some of the ways in which it might actually be more scalable. And historically, like it's been like super not scalable. It's like very, very expensive to run.

• 30:15Speaker 00

  Yeah.

• 30:15Speaker 01

  Traditional like online world models.

• 30:17Speaker 00

  Yeah, like genie being.

• 30:18Speaker 01

  Like genie.

• 30:19Speaker 00

  Yeah, okay. So if you think of traditional world models as being like an action conditioned video model.

• 30:24Speaker 01

  Yeah.

• 30:25Speaker 00

  Then like. Right now, when we say world model, what we actually mean is a model that has some understanding of the world as opposed to being strictly technically a action-conditioned video model.

• 30:36Speaker 01

  Yeah, and so the interesting thing, though, is like it has understanding of the world, but then it also has that like really great, and that's where like the line is blurry to me, where it's like it can do a lot of those same use cases. It's not real time right now, but like it can do a lot of those same use cases that you could describe or like visually could create with that same exact world model, which I think is what's most interesting to me. So I do feel like this like world model, video model thing is gonna is gonna change and play out in a different way than was obvious before.

• 31:10Speaker 00

  And how does it work under the hood? Like whatever you're able to share? Like, is it Gemini plus video models? Is it something different entirely?

• 31:17Speaker 01

  It is. It is a single model, which I think is the important part. Like this was actually part of the original desire was like you, we're training like eight different models to do all of those things historically. It's like you have a text model with the baseline Gemini model. You have audio. You have music models with Lyria. You have Nano Banana. You have VO, video models. You have, we have a whole suite of audio models. And like, it would be great for us, our customers, if you just had a single model to do all those things. So it is like a new setup that sort of makes that possible. It's not like routing to a bunch of different models, which like we, you could have imagined, we could have done something like that actually before and done like a Gemini Omni model, but this

• 32:01Speaker 01

  is like a true Omni model. And it's starting with like the, the use case that works the best right now, which is the why it's the one that's available, is this like video editing capability. The, technically, it's like functional with the other things. It's just like the quality isn't, isn't like perfect and is not state of the art. So we haven't rolled that out yet. It's also just like the first. Crank of the model turn on Omni. It's the Omni Flash model, the first iteration. And so we'll have like much, much more capable, powerful versions, which will be, which will be exciting to see.

• 32:35Speaker 00

  So we could edit this set so it looks like we're.

• 32:37Speaker 01

  We should. Yes. Yeah. Yeah. I want this. We're, again, we were talking off camera, like, we should do that for the intro because I think it just like makes all this stuff more capable. And I've seen these examples of like such subtle nuance that like make me appreciate that. It's like the world understanding playing out. I was, I was giving a talk.

• 32:56Speaker 01

  And was on stage with, with my friend Tulsi, who leads the model team, who I don't know if you've ever had on before, but she's amazing. I love Tulsi. And in, I had mentioned to someone in the crowd to like edit the video and they literally like took the picture, edit it with Omni in real time. And this like dog came on the stage and like the other, in the edited version, the other guests sort of like look down and see the dog. They like chuckle a little bit. This is while I'm like opining about whatever AI nonsense.

• 33:23Speaker 00

  Your jokes.

• 33:24Speaker 01

  Yeah, that, it was not my jokes. They, they laugh at the dog coming up. It jumps onto my lap. I sort of like acknowledge the dog. I keep talking. I'm like petting it or whatever. And just like, there's like so much subtle subtlety in getting that right. And the model crushed it. And it's just, it's very interesting and like still trying to like absorb and digest like what that means for, you know, the way we make content and all these other things.

• 33:48Speaker 00

  That's so interesting.

• 33:50Speaker 01

  Yeah.

• 33:50Speaker 00

  I'm, I'm the biggest bull on generative media and what it means. And I mean, one of the things we've thought about for our podcast is the visuals matter as much as the content.

• 34:00Speaker 01

  For sure.

• 34:00Speaker 00

  That's how you catch people's attention in the first place, right?

• 34:02Speaker 01

  Yeah.

• 34:03Speaker 00

  And, and so, okay, I'm excited to, I'm excited to play with Omni.

• 34:06Speaker 01

  I'm excited too. And I think the, and, and I think you probably feel this way as somebody who makes content, but I've historically like been like very for myself personally, like I don't use AI to make any content that I produce. Like it's all my words. It's always my voice. It's always my image and picture showing up. Like I just, I, I feel like there's just like so much alpha and authenticity. And so like, I would much rather it be me than some AI version of me. What I like so much about Omni is that it's like not changing me. It is like changing a bunch of these other bits, which are not me. Like I didn't choose any of the like set around us or the, the coffee table. It's like, so our, our words can stay the same and like you can change these bits that are like not personal.

• 34:49Speaker 01

  And do something more interesting with them, which I think is really, really cool and feels. It feels like the version of what I want sort of like gen media to be, which is like not a bunch of like AI avatars.

• 35:01Speaker 00

  No, no Fruit Island videos?

• 35:03Speaker 01

  Exactly, truly. Like, it really is like, it's the original content, it's the person, it's like, the personhood is there. It's just different and amplified.

• 35:11Speaker 00

  Super interesting. Okay, I'm excited to play with it.

• 35:14Speaker 01

  Yeah, we should, we should send some prompts right after this and try some things.

• 35:17Speaker 00

  I don't, I don't mind the fruit videos, though. I'm, I'm happy for a world of, of both. On the coding side, you launched the ability in AI Studio for people to vibe code Android apps.

• 35:27Speaker 01

  Yeah, yeah.

• 35:28Speaker 00

  I'd love to, you know, hear how that's going so far and, and where you plan to take that.

• 35:33Speaker 01

  Yeah, it's super exciting. I think one of the strategic things for AI Studio, and actually, this is based on like a lot of the feedback from the ecosystem and actually from developers, from others, it's like so many Google products. There's so many different like ways in which you like touch Google through all these different journeys of building a startup or bringing ideas to your life. And so we have this like first-class principle of like, how do we bring things into AI Studio that make it so that you are exposed to other parts of the Google ecosystem without having to like go through nine different UIs across Google? And so Androids are like a great example, not only of that, but also of enabling people who wouldn't have otherwise built an Android app. And so I literally built my first Android app in AI Studio.

• 36:18Speaker 01

  Very cool to see it's.

• 36:20Speaker 00

  What is it?

• 36:21Speaker 01

  Yeah, I, I just did like a plant, not a crypto app, just a, a plant one. I was planting trees in my backyard.

• 36:26Speaker 00

  Oh, like a gardening app?

• 36:27Speaker 01

  Yeah, and so it was just like playing around with the, a gardening app as I, as I was kicking the tires. I haven't had my like breakthrough idea yet of what I want for a mobile app, but I'm gonna, I'm gonna come up with something and see, go compete on the App Store.

• 36:40Speaker 00

  Have you seen anything vibe-coded like really fly in the App Store yet?

• 36:43Speaker 01

  That's a good ques- it'd actually be interesting to like see some analysis. I don't know, I, I'm sure it's like accelerating a lot of things on the App Store, but I don't know how much, like, I don't know anyone like personally who's, who's done that.

• 36:53Speaker 00

  Yeah.

• 36:54Speaker 01

  It is interesting, and I was gonna make the observation too, that I think the last time I checked the numbers, we were viewing it this morning, it was like 350,000 Android apps built in AI Studio since last week, which is crazy. And like, excitingly, it's like 350,000 apps that like probably no one was going to build before. A lot of these are personal too. And so this is where I think this like. Maybe GnuI is like farther out there, but I think like the idea of you building software to solve your personal problem is like very real right now. And like people are doing that. It's like one of the most common use cases of a lot of these products. And being able to like unlock a bunch of the native capabilities of the phone, I think is also really interesting because you just have so much context that's like in different places.

• 37:40Speaker 01

  So I'm getting very excited about sort of that opportunity and Android feels like it's becoming the platform for builders.

• 37:48Speaker 00

  Does it matter that something is an app versus just like the web is so powerful now?

• 37:52Speaker 01

  Yeah, that is, it's also very interesting to see that play out. Web is definitely powerful. There are certain things that the operating systems have that like you just can't unlock, like lots of like native richness that actually like make experiences feel so much richer. I think about this for like text messaging, actually, that like the text messaging experience in all of the, all the main operating systems feel way richer to me than like any AI chat app that I've ever used. Like if I could just talk to AI and whatever texting app I use, like I would be way happier than having to go to some other app. Because I think we're also just like conditioned on like the operating systems, so.

• 38:31Speaker 00

  Yeah, makes sense. Okay, I want to ask about the model eats the harness or the model eats the scaffolding. What are your thoughts?

• 38:38Speaker 01

  Yeah, I think it's true. And I think part of this is like what we have historically thought of as the model is not the model anymore. Like when I think like two years ago when LLMs were popular, it was like the model was like actually just a set of weights. It was a set of weights and it was like really like, how can you like as simple as possible, send tokens in and get tokens out. And I think we've just like progressively step by step by step, we still call it the model, we still call it, you know, Gemini 3.5, you still call it GPT whatever and Claude whatever, but like. It's actually not just the weights anymore. It's like an entire expanding sprawling system that's built around the weights.

• 39:19Speaker 01

  That's sort of like enable a lot of these like next generation experiences from agentic tool calling to tool, you know, like all these hosted tools, search, code execution, et cetera. You know, the models are now being spun up in containers and sort of have an agent harness and all that stuff. So the scaffolding is like oftentimes a couple of steps ahead of like where the act, what is like baked directly into the model. And then what ends up happening is like the model eats that scaffolding and it becomes part of like the native model system. And there's still value in having sort of the external scaffolding in certain cases. And like search maybe is an example of this, like there's lots of folks who use different search providers. And there's different like use cases that you want. And so like, sure, maybe the model can natively use search, but you also want something else.

• 40:03Speaker 01

  Code execution, another example of that. But it does feel like, like maybe the agent harness is like the quintessential example of this right now. Where like everyone's like, ah, we gotta go build a harness and like the harness is where the alpha is. And like, I think that perhaps won't be true, at least in the way that we think of the harness today. In 12 months, I think the models will have sort of just like digested a bunch of that. It'll be upstreamed into the model and the alpha will be somewhere else now. It won't be in sort of trying to spin your own harness because the model just like does it natively.

• 40:35Speaker 00

  But I thought that the part of the reason why people are building their own harnesses is because if you use a harness from any given model provider you locked in, right? So a lot of the application companies want flexibility, which is why they're building their own harnesses.

• 40:45Speaker 01

  Yeah, and I think that's part of the scaffolding story is like that starts out perhaps true. But then as the model capability improves, like it becomes less true over time, actually. I think the model, the like you, you don't have a generalized model if it can't use another harness. And so it is, it is important to, and I, I mentioned this in another conversation with someone a few weeks ago, but we need something like Harness Bench, which is like actually measuring like, how good are all these different models at adapting to all the different harnesses? I feel like that seems like a reasonable thing we should, we should measure as an ecosystem. And I'd be curious to see like what models are actually best, but. I think over time, you expect they'd be able to use every harness unless you're like

• 41:27Speaker 01

  completely out of distribution, which in that case, like, you're still going to be completely out of distribution, even if you're using your own harness. So not sure it matters much.

• 41:36Speaker 00

  Fair enough. What about the application layer? How do you think about where independent companies can, you know, have a hope of surviving? When the model eats the harness and eats, you know, the stuff around it?

• 41:48Speaker 01

  Yeah, it feels like there's, yeah, it's an interesting story that like both of these things feel true. Both on one hand, I everywhere I look, I'm like, there's never been more opportunity to go and build something. At the same time, obviously the models are doing more than they've ever done before. I think there's like, you know, there's that threat of capability overhang, which I think there's a huge amount of alpha in. There's the threat of the model companies are like going after these like very general problems. And there's just like so much value in these like verticalized domains. If you have expertise in that domain, you sort of like know the customers, you know, the ecosystem, like it's just, you can really like run laps around even the best model labs because like focus is the like superpower of startups. Like if you can focus, you can do anything.

• 42:32Speaker 01

  And if you look at all of the companies that are big or doing lots of stuff, like there's just not a lot of focus. And for some, for some reasons, like rightfully so, because you know, maybe I'm overly justifying, you know, Google strategy, but like we just have a lot of products. We have a lot of users. We have a lot of different things going on. And so like we actually can't focus in one domain. We have an obligation to do a bunch of things as a big company. I think that's not true for startups. And so I think like 24 months ago, we were all asking ourselves like, oh, wow, it seems like, it seems like the opportunity space is shifting. And maybe it's possible, one of the outcomes is there's less opportunity for startups in the future. That feels like so far in a way, not what has ended up playing out, which is really positive.

• 43:14Speaker 01

  If anything, it feels like there's just even more opportunity than there was like now. Coding has helped you like close the gap on like larger companies that have like established code bases and all this other stuff, because you can just like run way faster and write software quicker. The agentic like primitive is like a new category that you can sort of build products around that like actually in a lot of cases to the conversation about like the risks involved with building like there's risk involved. And so like, what's your like, the risk appetite of different companies is different. And so if you're willing to take more risk in some domains, like you can win a user cohort who's like interested in also taking risk. There's so much opportunity.

• 43:53Speaker 00

  Awesome. I'd love to talk about Google DeepMind's culture. And I'm curious, what does it feel like to be inside GDM right now? You know, we had Demis at AI Sense, he was so inspiring. I've heard Sergey's back. You guys have Noam Shazer back. Like, walk me through what it's like to be at GDM right now.

• 44:12Speaker 01

  It's incredible. I do try to take it all in because it is like, it's like a moment. I try to reflect as much as possible in the chaos of all the things that are happening, just because there's like so much cool stuff going on. GDM's culture is interesting and like maybe three observations. One, back to this thread of like focus, we're doing a lot of things. And so I think you see sort of, I think about this a lot, like from a portfolio perspective, I think we have like one of the strongest portfolios, which is really exciting. But you do see these moments where like another lab or another company, whatever it is, will like pull ahead in a certain area where like we under invested, just like hadn't been focused

• 44:52Speaker 01

  enough in that domain. And it's cool to see like the, the way we go about trying to like close that gap. I very much, I very much appreciate it. I think I've, I've watched the Demis Thinking Game documentary a few times. And like you see sort of like a lot of like details of that like original culture and just like the way that strikes work and all this stuff, which is actually really similar today is like, you just get a bunch of smart people together and like go solve the problem. And I love that. And it's like very cool to, to be a part of. Another one is this, I think you see the culture permeate from like who the leaders are. And as I maybe.

• 45:35Speaker 01

  This isn't like a perfect characterization of the ecosystem, but like Demis is a Nobel Prize scientist. And like the sort of OG of a lot of this stuff and sort of you feel that in the DeepMind culture. I think like Sam is like the, you know, maybe one of the world's best businessmen ever. And like you sort of see that in the OpenAI culture and the way that they go about the world. I don't have a strong sense of who Dario is, but like I think Anthropic is a very interesting place. And you sort of, at least as an external observer, like he seems like an interesting guy. And so somewhat esoteric, and so that seems like they're sort of like that in the DNA and the culture of the company. You know, the other labs are interesting, but I like this like very scientific approach to the world.

• 46:20Speaker 01

  And the way that like Demis looks at like the reason he's doing this. And the reason they started this mission was like literally to like solve disease and all these things. And it's like so easy to get, and again, I'm always trying to pull myself out of the moment. But like, it's so easy to get lost in this like competitive race of who's pushing a number higher on Sweetbench or whatever it is. It's very easy to lose sight of like, the reason we're doing that is so that like we're can solve problems that humans actually have. And there's a, my favorite quote from all of Silicon Valley is something like, you know, we can't let other people make the world a better place more than we can, which is like what this moment feels like.

• 47:00Speaker 00

  The Gavin Belson quote.

• 47:01Speaker 01

  The Gavin Belson quote, and I think about that all the time. And it's like, we're all fighting over who can make the world better more than the other person. Which is just like, when you frame it like that, it seems really goofy to me. And so it's very much not zero sum. And I think that's like a way of looking at the world. I think the last thing about DeepMind's culture is like, we're very, it's sort of the engine room of Google, which I think is like literally the Twitter bio now of the DeepMind Twitter account, which I love.

• 47:29Speaker 00

  Do you man the DeepMind Twitter account?

• 47:31Speaker 01

  I don't, I don't want any responsibility manning other people's accounts online. Too much, too much responsibility to do that, but it does feel like that too. So it's like, on one hand, you have sort of like the deep rooted lab culture. On the other hand, you have sort of like all of these partners across the Google ecosystem that we're collaborating with, everybody from Android that we talked about earlier to Google Cloud, to, you know, Gmail, to Workspace, et cetera, et cetera. And so it's an interesting blend of like. I think there's lots of research work happening, but like there's tons of applied work that's happening to like, actually like work with some of the like the forefront customers, like deploying Gemini to billion user products is a problem that like only

• 48:12Speaker 01

  two companies in the world have. And we have 13 of those products and like we, the, you know, Google goes through this all the time now. It's such an interesting place to like see that happen and see the innovation that takes place in order to make that actually possible. And I feel like it's, you can only do that inside of, inside of Google, which is really cool.

• 48:30Speaker 00

  Beautifully said. Did they, uh, did it give them a lot of heartburn when you joined and were tweeting a lot?

• 48:36Speaker 01

  That's a good question.

• 48:37Speaker 00

  Did you have to get sign off from, from comms?

• 48:40Speaker 01

  I'm very, uh, one of the, the silver linings to my Google experience has been just like how great that group of like folks across marketing comms are to, to work with. And I think like, uh, you know, their job is protect Google, make sure we tell the right story and make sure a bunch of bad things don't happen. And so I have a, a ton of appreciation and partnership with them, but it's been an incredible experience to like be able to go try to tell the story that resonates with developers in a way that feels authentic and not have a huge amount of, you know, I, you know, I don't have to get my tweets approved all the time and all this stuff. Like it's a very, very positive culture. And I think hopefully I am always trying to walk the line of, uh, not, not burning,

• 49:24Speaker 01

  uh, the, the trust and goodwill that, that I've accumulated with those folks. But it's been super positive because ultimately I think it's like, it's really hard for Google to tell this like authentic story. It's just, there's just like, it's a big company. There's a lot of people. There's a lot of opinions. And so you take the like magic of Google and you water it down through like a lot of people and a lot of process and you actually, you, you miss the beautiful story, which is like Google's doing the most interesting technology in the world and like helping our users with some of the hardest problems in the world. And, um, it feels, it's a privilege to like get to help tell that story. So it's, it's a lot of fun. I enjoy it.

• 50:00Speaker 00

  I love what you're doing. I love what Josh is doing. And you guys have put a really kind of sincere human touch on. As you put it, the most important problem of our time.

• 50:08Speaker 01

  So. Thank you.

• 50:10Speaker 00

  Well, wonderful. Logan, thank you so much for joining me today. This is a very far ranging conversation. Everything from agents and coding to world models and harnesses and GDM culture and lots, lots of nuggets here. Thank you for for joining me today.

• 50:24Speaker 01

  This was a ton of fun. Thank you for having me and I'm excited to see what the folks cook up or where we've been sitting this whole time. Maybe in front of us.

• 50:31Speaker 00

  And maybe there'll be a dog.

• 50:32Speaker 01

  A dog.

• 50:33Speaker 00

  You can make my dog dreams come true.

• 50:35Speaker 01

  I love it.

• 50:36Speaker 00

  Awesome. Thanks, Logan.

• 50:37Speaker 01

  Of course.