Retour sur Google I/O 2026 avec Logan Kilpatrick, Josh Woodward et Tulsee Doshi

Welcome back to release notes. My name's Logan Kilpatrick. I'm on the Google DeepMind team. Today, I'm joined by Josh and Tulsi, the dynamic duo. We are back. Maybe we're the dynamic trio. I like it. Back at Google I/O 2026. So many announcements yesterday, so many fun launches, models, products, everything in between. Maybe we can start talking about models, and then we can talk about products. Sounds good. Sounds fun. Yeah. So you want to kick us off? >> Um yeah, and actually I think the cool thing is this year really you can't really talk about the models without the products. So I think they're going to be a really nice tie-in. Um and maybe that's the first thing is that like I feel like this year the phrase we're using to talk about Gemini 3.5, which is one of our big releases, is intelligence with action. Um and I think that really speaks to what we're trying to bring through all of our launches this year with the models is that we're really trying to focus on how do we enter the agentic era, you know, even more seriously than we have in the past with uh models that have amazing tool use capabilities, that are really strong for coding, that can handle really complex like long workflows, and then how do we bring that magic to life in the product? Um so Gemini 3.5 Flash is our big model release. Um really excited about that model because I actually think it's a great combination of performance and speed, um and it does this like really good job, I think, of bringing a lot of that magic to life in a way that you can actually quickly iterate with. We also released Omni, our multimodal model. It really actually brings out like the nano banana for video vibes. So really actually allows you to edit videos, build kind of amazing experiences, and the world knowledge from Gemini that you're bringing into the model makes it even stronger. >> It's super fun. We did that episode with the Omni team, and they edited the circle freeze at the beginning. So let's take a look at it. There's so many different kinds of edits that As you know, okay, there's the parrot. No, one of the best parts there are all the yellow birds, right? >> Yeah, yeah. But there's one where there's the sloth. [laughter] I don't I still don't know why why am I holding a plant? Well, this is this is uh The cat. >> I know. There's nothing there. It's just me. Yeah, yeah, it's just Logan straight up. >> [laughter] >> A few hyenas wandering across the set. I think it's you could just have an endless possibilities here, right? You can do anything. That video I showed that to my girlfriend and she was like, I get it now. Like I don't I think I think there's something interesting about like how we showcase like what it's capable of and it's like um yeah, there's some I so I really like that format of like taking something existing that you know and people doing something normal and then sort of changing it in a way that like doesn't change the coherence of what they're saying, but then like >> Just makes it feel magical a little bit, yeah. >> magic. >> I was trying this yesterday with some dance videos of mine and like was having it like every time my arms move in a certain direction, like sparkles would come out or like petals would fly or things like that. And again, like the coherence of the video doesn't change. The actual dance has not changed, but you're just adding something to it that makes it like memorable or remarkable in a way that you didn't think about, right? >> Yeah. The other cool thing with Omni I would say, it's incredible at camera angles. Mhm. So, I showed on stage 360° view, but you can do drone footage, you can take shots. One of the most amazing things I think we showed was Google Flow took one shot, 16 different frames from 16 different angles. And it it contains the same sort of scene consistency, character consistency. That's really cool. Yeah. People are going to go wild with this. Um in the Gemini app, in YouTube, and in Flow, they can they can make all those uh all those dynamic videos, which will be really exciting. One more thing is like 3.5 Flash is our most powerful model to date. It is the sort of like most capable model that we've shipped. Um and I feel like it's uh you know, Flash I think people see as this workhorse model and sort of it's like a little bit less intelligent and I feel like we keep doing this thing where like flash ends up actually being really really capable in a conversation yesterday with Oriel and Jeff sort of just like continuing to hit home on like the distillation capabilities we have are just so good. And Jeff was like, yeah actually it's pretty we're pretty much doing the same thing that we had in the original distillation paper and like it just keeps working. So maybe there's like something magic about like how we keep landing the pro intelligence into a flash model. Yeah, I think one of these two things, right? So I mean maybe to like hit your point home harder like this model literally exceeds 3.1 pro on most benchmarks. And I actually think this is a a cycle we've been seeing, right? So the previous flash exceeds it. I think there's two things there. One is I do think flash is still a great workhorse model but the kinds of work that you can do with it is now changing. >> Yeah, yeah. Right? So I think when we thought about workhorse model, you know, like two years ago >> This is a this is a great tweet for you. I love this. [laughter] I'm just I'm just like Because I I don't cuz I think I think you say that and I'm like I get it. That makes complete sense. >> Yeah, like I think it's just like the the kinds of things you want to do in the Gemini app today versus what you could maybe do a year ago or two years ago are meaningfully different. And flash is still the model powering it but you need a more powerful flash to be able to actually like do the kinds of things you can do in Gemini spark for example which you know you should talk about but like I think that's pretty awesome. Um and then to your point like this model is really a an amazing showcase of post training excellence. Right? >> [clears throat] >> Um because it takes uh Gemini 3 and actually builds on it. Um and so what we've done from a perspective of RL, what we've done from a perspective of taking uh the feedback actually that we're seeing in jet ski and actually bringing the model and the harness closer together to actually like train the model more effectively. I think that's a really great example of what that can do for model performance especially when you think about just like real world usage. Yeah. Yeah. >> Which is which is really awesome. Well, and I would say we're using it all over the place internally to build. I mean, Sissie and I talked a little bit about that on stage. Dude, Sissie and I were talking count internally. I was like, this is cool. Yeah, yeah, token [laughter] counts up in the tees in the Is that Harrison using all the tokens? That's what That's what That's all it is. I'm like, half of the tokens is just Harrison on whatever side quest he's working on. >> That's my side quest encouraged. No, but I do think it's unlocking all kinds of things we didn't think were possible. And so that does come through in features like Gemini Spark. It comes through even in things you wouldn't expect. Just the depth, the quality of the answer, so much better. Yeah. Yeah. Gemini 3 feels like the era of product model harness symbiosis. And then this sort of like flywheel. Like you actually can't have one without the other to your point from earlier. And maybe Spark is like the maybe the perfect example of that, I don't know. And so for folks who missed the news, maybe what is what is Spark and Yeah. Yeah, yeah. Well, Sparks are 24/7 always-on agent. Works in the background. I think the way to think about it is when you use it, it almost feels like you're just throwing tasks over your shoulder and Spark catches them and just runs with them. So you can say just brain dump a bunch of stuff into it. It'll break it down into individual tasks. It'll give you this great dashboard. You can see what it's doing. And then you come back to it and a lot of stuff's just done. So it's kind of this amazing step of how do we move Gemini from something you're just chatting with to like Tulsie said, something that's acting on your behalf. And so it's a big shift. Very excited about it. It'll be rolling out next week starting in the US for Google AI Ultra subscribers. And the plan is to kind of basically see how people use it. We have no idea also how many tokens it's going to consume. [laughter] So we want to start kind of small and then scale it up very quickly. We're very excited about it. A lot of us have been using it for yeah, it's a it's a daily active thing. Once you get into the swing of it, you realize there's so many tasks you can just hand off and come back to. And so I think that's one of the things we're really excited about. >> Yeah. Yeah, Josh, one of the things you mentioned is sort of you throw things over the wall to it and then some of the tasks are done. For the ones that aren't, I think something you mentioned if I remember from yesterday is just like there it actually does like come back to you when it needs sort of like a human in the loop in order to like take some action. If there's something like you know, you're about to spend a thousand dollars and you're spending money, maybe you should check in with me before you go and order me a bunch of crazy stuff. Which I think is a really interesting paradigm and I'm actually curious like is it if that's also one of the things that we're like it's like still a research frontier like how how yeah, how good it is at knowing when it should come to you versus not. Yeah, absolutely. It is very deliberate sort of product design choices right now. We're airing on the side of being a bit more conservative cuz one of the main pain points we've seen when people set up their claws or anything else is they'll like sure, I'll yolo this and then they realize like no, no, I don't want that to happen. So we're going to start a little bit more conservative. We'll check in. It'll say it needs input. Usually you go in, you say yes or no and it keeps going. And then we're going to gradually allow you to kind of set like always remember this choice. So we want to make sure we build up that trust and that comfort and get it right. So today it might ask you if you want to book a calendar meeting and then you can be like yes or no and then you can kind of remember that. Anything with payments, anything with accepting consents, these are all areas where we want to stop it, have it come back to you. But I'm very excited. One of the other announcements Vidia did actually yesterday with some of the stuff we're doing with some of our agent payments protocol. This is very interesting. It's very Google in the sense that it hooks up to Google Wallet and you'll be able to set budgets for your agents and even per merchant sort of constraints. So I'm okay to you this much money with this merchant. And it's really interesting as we're testing this, it feels a lot like you're giving almost like a teenager their first debit card. It's an allowance. It's an allowance, exactly. So, it's kind of constrained, but I think that's a whole other area by people using all the the cards they already have in Google Wallet to be able to sort of spend that way with their agents later this summer and into the fall. Yeah, and I think actually this is where we're going to start seeing how different users have different boundaries of comfort. >> Yeah. Right? And I think we want to be able to provide everyone the flexibility. Right? I think there's going to be users who um are going to be a lot more comfortable saying, "Yeah, I scheduled this meeting. Remember it. I'm good." And also based on the constraints of maybe their job or their personal life or who they're even setting up meetings with. And then there's going to be others who maybe want to keep that level of of feedback loop. And you should be able to have the flexibility to decide how you want to engage and what level of of kind of control you you feel comfortable with, I think. Yeah, and I I think actually for both of you on this, one of the things that we announced yesterday was that the Gemini app sort of passed, I think, 900 million monthly active users, which is crazy. Josh, you're on the you're on the warpath to get you know, what what's the slide where we show the billion user products? And I'm like, I showed I saw yesterday we put it up. It was like, what it was like 12 products or something crazy like that. So, hopefully hopefully Gemini app soon. >> Josh, was it like a big party? Yeah, that would that would be you know, No, no. Don't wait till IO next year, but we should definitely have a party when that happens. I feel this tension for both of you sort of as we try to make a model that works for everyone as we try to make a product that works for everyone. Like, obviously the like I'm still showing up like the next 100 million users to come to the Gemini app. Like, maybe it is the first time they've ever like used these product experiences. And then the other end of the spectrum, there's a person who's like, "Give me a 24/7 always-on agent that like I'm just throwing things over the wall to and I've got my payment set up and I can sort of give it a different budget." And I'm curious like it feels like the the frontier continues to push forward and yet we still have that capability overhang. We still have people who are sort of just coming into these new products and and for both of you like the tension of uh, trying to build in that world. Uh, I'm curious like thoughts or reactions. Yeah. When I first joined Google, I worked on search and I worked on YouTube. And when I joined, those were already massive products that had really started to already build the muscle of how do you build for a very diverse set of users, right? How do you think about running live experiments in ways that you can get uh, insight from how various different users use the product. How do you really lean into user research with different segments of users. And I think it's awesome actually that now we're at the point where we're having this conversation about how do we do this with our Gemini models and our product? Yeah. Um, and so I think for us actually what's started becoming even more important on the model side is actually this idea of like feedback loops with every single product surface and not just with the surface, but actually going deep like a level deeper to say, okay, what are the feedback loops with users who are using maybe the flashlight um, version of the app versus flash versus pro. How is that usage differing, right? How does that usage differ in different regions? How does that usage differ for like power users versus users who are coming in like once a month? And actually like being able to run those live experiments, get that feedback and actually like take that and say, okay, what are our priorities then for the model? We're seeing that even just actually as we are starting to use the models more and more for these kinds of always-on tasks internally, it's been really helpful to get that feedback. But that does look different than what external feedback looks like. Like even running in a live experiment on our internal version of anti-gravity versus our external version of anti-gravity give us different results. Yeah. And so like now it's really about how do we scale that feedback loop, which I think has been really cool and it's kind of awesome actually where like now pulling in some of the best tools that we've been using across Google for years to actually like even evaluate our results because we have that infrastructure in place and we have so many people who have done this for so long, how do we like bring that into our our model development process? >> Yeah. Well, and I think that on the modeling side, it's all of that. And on the product side, it's it's an incredible design challenge. Is how do you design an interface that can scale to someone's like first time they've ever touched AI, which there's still billions of people in the world who haven't actually fully adopted or tried Yeah. AI. Um, to people doing the 24/7 agent setting that payment, you know, getting very sophisticated. So, I think we're trying on the Gemini side at least to think about how do we design it in a way where it kind of scales with your level of comfort and skill. And so, you'll see things like even how we launched Gemini Spark. First, it's for the paid users to start. They tend to be a bit more sophisticated. They want to open up the hood, upload their own skills, set up heartbeat schedules, triggers. So, it allows for all of that. But then you also saw us do something like the daily brief, which is a very accessible daily morning digest that's personalized for you. It's all running the same 3.5 model under the hood, but it kind of how we package it and present it is different. Um, >> And I think maybe that's actually a really important point, which is like as the models get better and as we tie to your point about this whole symbiosis of like model harness product, I think actually a big need for the model is to be steerable. Mhm. And then a big need for us between model and product is to actually start designing these things more hand in hand. Yeah. Um, so that we actually build in that notion of like what is good for different types of users in how we're actually like building this out. So, like, okay, if we want to be able to level support that level of flexibility, what needs to be true about the model? And then how do we actually design that end to end? What what I hear Tulsi is that your job just becomes more difficult over time. [laughter] It's like, no, don't just make a model, actually also work with every product. I mean, you're you're already doing this today, but work with more products across Google. >> the amount of time we spend actually like Josh and I were talking about this earlier. Like, one thing that I think has gotten a lot better over time with Gemini is like how our teams work together um, to actually land the plane. It's not like we take we build the model and then throw it over the fence and we're like good luck. Enjoy, [laughter] right? It's actually like PMs from my team are sitting with folks from Josh's team. We're literally iterating on system instructions together for different features at the same time, then running a live experiment and being like okay, system instruction maybe went a little too far. How do we actually kind of recalibrate? And like that process I think has been working >> Yeah. even better and better every launch, which has kind of been kind of fun. It's beautiful to see. I'm curious like from last year to this year, anything that's like surprising as you look at the set of announcements and things that we've landed over the last the last couple of days. >> Okay, I have one. Yeah. Last night we had together we got together with some of our power users from across a lot of our projects, Gemini and Google Labs, maybe I studio people too. And one of the questions in the audience was do of all the things you announced at IO this year, how many of them were on your one year road map? And I thought for about I don't know, a second, zero. Yeah, I don't [laughter] know like the what. So, I actually think that is probably the biggest shift is the things we were doing on stage at IO this year, a lot of them weren't even in our minds yet. And I think it's a testament to sort of how fast the capabilities are coming and how much we're trying to invent new things. And I eventually settled, I thought about it to try to give a little a little bit more satisfying answer to the guy. [laughter] I was like, actually maybe we have like a 90 to 120 day road map. That may be all we have. And then I thought about it, I was like, maybe for the last five years that's kind of been how we've been operating ever since it all got started. I think to give you guys more credit though, I do think it's like you I feel like you sort of know directionally you want to do. Like you know it always on a system. It's like sort of roughly like we want to build a direction. But it's like you don't know how to make it happen and then sort of the capability lands and then it's like all of a sudden you have the how, maybe. >> yeah. But I I still think I think you're right, which is like the actual product implementation, it's not like we were working on Spark. Really, we were loosely working on Spark maybe for the last year, but not in the way that like people would think about >> Yeah, I mean, we had something in Gemini agent mode last year, and there were kind of early glimpses of it. But then one day, you wake up wake up, and it's like, "It's here. It works." So, >> [laughter] >> I think it's like a really important thing for people building out there. And what how we try to think about is like, you want to be right in that like almost possible zone. >> Because then it'll tip one fine day, and then it's like, "Here we go." >> Hopefully hopefully with 3.5 flash. Hopefully That's right. That's right. Well, I will say a lot of things tip for us. And I mean, we haven't talked a lot yet, but there's a lot of Labs projects that suddenly with 3.5 flash have just become possible. Well, what are some examples? We can talk about that. Well, I mean, what we showed on stage yesterday, we talked about Google Flow a little bit. A whole big part of Flow is the Omni model, but another part is this assistant director. You can now talk to Flow almost like you're talking on a set like this, and you'll be able to be like, "Change this, change that, change that." It's like, and it just does it. Um so, that's incredible. Everything with Flow music is also real leveraging the same 3.5 sort of goodness that showed up. Um there's some very exciting notebook LM stuff we have coming soon, so watch out for that. But I think it kind of completely rewrites kind of things we thought were impossible are now possible. And so, that's like all you always want to be on the lookout. So, now we're like having to reset our expectations of like, "What's going to be almost possible for the next rev?" Um it's very fun. Yeah, this is my advice to everyone about just like you have to be I I framed it before as like you have to be reset your ambition. Because I feel like the models humble you so much to be like >> [laughter] >> They're like, "Nope, that will literally just not work." And so, you're like, "Okay, I'm going to try something else, try a different version of this." And then you sort of sort of set your bar for ambition, and then every time the model crank turns, you have to like reset it and go in with a fresh mind, which is so as a as a human building with the technology, it's so hard to do, but like it is I think it is where the alpha is when you're building this stuff. >> is like there's something really amazing about people who can play with the models and actually find the magic in them, too. Yeah. Because I think some of it also is like yes, we are we are intentional about the capabilities we want to build into the models, but they also continue to surprise us. >> Yeah. Right? And it's not really until you start playing with the model and trying something like trying to build an assistant director that do you realize like A, the model is falling over in places we didn't even realize it was falling over. Yes. >> Or B, there's like something magical that you can eke out of the model that you didn't realize. And then once you find that then it goes like really quickly from there. And then you also see that kind of industry-wide because then you're able to like someone builds on your idea, you build on someone else's idea, and there's actually this kind of like cross-industry um development magic that's happening right now, too, that I think is kind of cool and very unique. >> Mhm. Um because everybody is sort of learning from each other whether you're in the same organization and building the same product or actually building kind of cross products. >> Yeah. Yeah. Oh, can we talk about one other? >> Please, please. >> Voice. Yes. >> I think voice Yes. having this is like right on the cusp. It's a moment. It's a moment. I mean, well, we can talk a little bit. I know you all teased like AI studio, the mobile pre-registration. I showed some stuff with the Gemini Mac app using voice. There's all kinds of stuff in Gemini live. I think anti-gravity's going to have voice uh there's just so much that's going to be possible there. I know. really proud of the the where the audio models are getting to. I think like um the the you know, there's two parts to like the voice part that we're talking about here. One is this whole idea of like just being able to throw out your ideas, like being able to almost like ramble your your way >> Yeah. out of doc or Yeah. out of Uh actually you can already do this in anti-gravity. Like you can just talk and and see what comes out of the other side. And it's actually so good. That's actually been my number one workflow change. Mhm. Right now is is I'm just I'm just talking at my laptop. >> you do best, so >> I can do what I do best. I just ramble away. I ramble away. It's great. It's awesome. No, it's actually though, it's kind of works. And then also, the dialogue models are getting really good. And so actually like the for for those who haven't been building with them yet, even you can already build with them in AI Studio, you can already build with them in the API. The naturalness of the of the voice really does make a huge difference in the experience. >> It's good. The expressiveness, like the dialects we showed, pretty fun. So it's going to be I think voice is going to be a really interesting next few months. It also it's selfishly for all of us, it makes doing a lot of these demos way easier because if you like you historically have to like I this is my least favorite thing is like trying to like talk while I type things and I know Tulsi you you cannot multitask, so this is perfect for you because [laughter] you just talk you just talk right into it. Um actually Tulsi, what you you need to answer the question. Something surprising from this IO. Oh, um So I I think to the point about like what we didn't see predict, I think one thing that's interesting is last year we talked about how the through line of IO is really Gemini and Gemini coming into all of our products. I think this year one through line is actually anti-gravity and the harness. And we like I think it was interesting as we started putting the package for IO together, really realizing how much that actual like the harness consistency across our products was starting to come through. Right, so like behind Gemini Spark, behind things we were showing in AI mode, obviously behind anti-gravity the product itself, AI Studio. We're really starting to see that actually not just having a consistent model, but actually building on a consistent harness speeds up our productivity, it makes the products more consistent, it allows us to push the model capabilities as well. I think that also was something that I like didn't expect to to see be as like consistently through. Yeah, that's a great one. Predictions, IO 2027, which I was saying to someone yesterday sounds like a made-up year. Sounds like I'm like 2027, I'm like it's >> I mean, and Josh just said that we didn't know what was coming this year from last year. I don't know how we predict >> did this last year though. We tried to make predictions, right? And actually, maybe I'll get you both on record with my new favorite question, which is which is, you know, in 5 years, will Google have three products or 10,000 products? And Josh, you I I sort of I preceded this to you, so you hopefully had time to sort of Well, I mean, we've been thinking about this a little bit in the context of Google Labs, actually. That's what we were talking in the hallway, I think, about this one. I don't know. I could see it being a lot more. Yeah. You know, right now we have something like 20 or so products that we're working on at the same time. I'm sure I'm sure it's 100. I'm It's a few dozen. >> [laughter] >> But, I mean, we are very interested in like what would it take to really just like add some zeros to that. >> Yeah, yeah. So, we'll see. I don't know. Some people talk about it like software factory, you know, other things like that. I'm more interested in like are there I think where a lot of this is going to shift is like a real good eye for problems worth solving. Yeah. And could could Google Labs be a place where there's just like, I don't know, maybe a few hundred. I don't know if it's thousands. We'll see. But like like there's a lot of problems in the world. And it's interesting to think about like how you could kind of go after lots of them. But do you think maybe like uh I guess one of my I'm curious if I appreciate your take on this. I do think we're going to like scale up the number of problems we're solving and the number of product experiences we build for those. >> Yeah. But I also think especially to your point about voice and also to our point about like the kind of agents being able to do things for you, that the number of interfaces by which you try to access these products might go down. In the sense that like you might as a user be able to do a lot more by just saying like I'm going to talk to the Gemini app. And I'm going to say, "Hey, like this is what I'm interested in. This is what I need." And then the Gemini app also has the ability to like connect you to the the right product experience for the thing that you're trying to do. So maybe the cognitive overload overhead overhead of like, "Oh, I need to go use product A for X and then product B for Y and then product C for like that we might be able to streamline if that makes sense. I'm curious actually this came up in a conversation and Oriol had the comment about sort of this like mental compartmentalization that humans do so well and like the it's sort of it's like really hard to have people get used to this like very flexible product experience that they can do anything with and actually like to the point of maybe there will be 10,000 different products. It's like humans are so tuned to be like this thing does this thing and I'm going to go and use it for that and like you can really sort of go the nth degree of customization and detail. So I love this question now because I'm like it's so so interesting to see how it's going to end up shaking and I don't I don't think it's clear what the right answer is. Yeah, well it's kind of the whole like Clayton Christensen like jobs to be done, right? You hire a product to do a certain job and I do think maybe that mental model will blur over time and people will hire one product and it'll do many job I don't know, maybe. I also think they're not necessarily contradictory. So we may still need to build 10,000 products but they may be behind the scenes and sort of in certain ways. 10,000 agents, that's what it'll be. Maybe, yeah. Or is it 10,000 skills or I don't know actually what form it's going to take but to me that's also why when people are always like, "It's all in the model." I think that's kind of half the story cuz there's also going to be a product design sort of how do you educate and bring people along into this whatever this new interface or interfaces may look like. >> also that's why I love a lot of the Labs products because I think they push the they always push us on the model side to remember that yes the model is a product and yes we should have it be able to do a bunch of things but how you package that model for users like like I think notebook LM was really that moment for me in particular where it was like you can package the model in a way that truly creates a magical experience for users and I think it's really easy when the model is so good to just be like well the model is the product here right but that doesn't mean someone actually knows how to use it in a way that you can get real value out of and that should be our jobs. >> it's true the next I mean I'm trying to remind myself of this the model is the products quote but then the quote that goes right after that is something exactly to what you're saying which is like the model is the product and yet you need you need more to bring it to life than just the model itself yeah you need you need the experience to scaffold it my my really quick prediction which will we'll see whether this ends up being right is that I think the model has so consistently like continue to eat the scaffolding layer. And so I think as we talk about sort of like this this like agent harness as a through line of the conversation I think it'll be very interesting to see like whether or not 12 months from now we're talking about that or whether that's just like it's just the model it goes back like today it's something different because it's sort of new but then like it goes back to just being the model in 12 months and so it'll be something else I'm sure that we're talking about in 12 months but that's my guess is that it does go back to just like people see it as the model we talk about it internally as just the model and the model becomes this like much more complex system that we we sort of think about and and associate with so. >> That's fun for my job. >> Yeah fun for your job [laughter] more more stuff but save this podcast. We had a lot of predictions last year we should do some sort of. I [snorts] don't remember maybe we'll pull it up we'll pull it up pull it up yeah. Yeah do you want to go first? Sure. Yeah. I mean I think it's actually interesting because when you say >> [laughter] >> 2030 I feel like there's like three things that come to mind. This is good I'm wearing my IO outfit here. It looks like the same shirt. >> [laughter] >> gets to 2030, right? Um I mean, we're already seeing like just from 2024 to 2025, like what Gemini can do has been amazing. And so, we're going to see this world where like so much of what Demis presented yesterday on this vision of like the universal virtual assistant and this idea of like Gemini as this like proactive part of your life. >> Hey, we're doing that in 2024. actually realize materially. So, I actually think the like way Gemini works a lot of it this year. We're talking about Google products. I wasn't thinking big enough 4 years out of the curve. technology and interact with the world is going to be different. And so, I think that's going to be a big part of IO. I think the second part of IO, to your point, is going to be about I think we're we're really bringing these pieces together into a more unified >> This is why Raspberry works so much. >> [laughter] >> I'm going over here and I'm going over here and I'm going over here and I'm getting three different things versus I actually like Gemini is working for me across my surfaces, across my devices. >> I'm predicting the same thing. There we go. There we go. And then the third, I think is just I I expect IO will also look different in in 2030. It'll be so hot. >> [laughter] >> 2030 and the way we like consume and engage with content is going to look different. >> send your agent to IO, and then it it consumes all this stuff. It's [laughter] all awesome. Done. No, but it's it's just going to look different, I think. I I think what's also funny is like 2030 feels like so far so far away. The first three actually said 2030. I originally was thinking like >> 2027. I don't know. >> Yeah, maybe we can do Thursday first. >> Preempted the question. I I mean, it's interesting. We have this exercise in Google Labs sometimes we do where we think about like jump in this kind of like magical flying saucer that shoots you out into the future. You get out. You have a few minutes to look around, write down everything about the future, and then you come back. Do you still do it? We've only done that exercise through 2028 right now. So, I don't know the actual after 2 years 2030 But it does feel like I mean, one of the things even we're seeing a lot with some of the stuff we launched yesterday at IO is this kind of blurring the lines of kind of what's becoming possible Yeah. and sort of the lowering of the bar when it comes to like how much time it takes or how many skills or even the cost of things. Or who can use something like and who and all that relates to who. So I think when you think about the future of software development or creativity or knowledge or any of these things yeah, 4 5 years out it's going to be really interesting. Um but I do think that's almost like the arc is towards democratizing a lot of this stuff and that's probably when you look at products like flow like we launched yesterday or any others that sort of feels like that'll be a principle and then yeah in terms of the show format and everything who knows. We're spot on. >> [laughter] >> I don't know about that. No, I feel like I feel like directionally those are the things that are happening. Yeah. We that's why we need more concrete predictions. Concrete [laughter] predictions. So that way we can hold you all accountable. Thank you both for taking the time to sit down. It was a it was a crazy I/O lots of stuff shipped but it was a ton of fun and hopefully hopefully we'll be sitting back here I/O 2027 which sounds so far away. Hopefully talking about a lot of the progress that we've been made among the along the dimensions that we that we talked about today. So Josh Tulsi thank you for thank you for doing this. >> Yeah. See you all. And thank you for tuning in to this episode of release notes. We'll see you in the next one.