Google I/O Afterparty: The Future of Human-AI Collaboration, From Veo to Mariner

Fresh off impressive releases at Google’s I/O event, three Google Labs leaders explain how they’re reimagining creative tools and productivity workflows. Thomas Iljic details how video generation is merging filmmaking with gaming through generative AI cameras and world-building interfaces in Whisk and Veo. Jaclyn Konzelmann demonstrates how Project Mariner evolved from a disruptive browser takeover to an intelligent background assistant that remembers context across multiple tasks. Simon Tokumine reveals NotebookLM’s expansion beyond viral audio overviews into a comprehensive platform for transforming information into personalized formats. The conversation explores the shift from prompting to showing and telling, the economics of AI-powered e-commerce, and why being “too early” has become Google Labs’ biggest challenge and advantage. Hosted by Sonya Huang, Sequoia Capital 00:00 Introduction 02:12 Google's AI models and public perception 04:18 Google's history in image and video generation 06:45 Where Whisk and Flow fit 10:30 How close are we to having the ideal tool for the craft? 13:05 Where do the movie and game worlds start to merge? 16:25 Introduction to Project Mariner 17:15 How Mariner works 22:34 Mariner user behaviors 27:07 Temporary tattoos and URL memory 27:53 Project Mariner's future 29:26 Agent capabilities and use cases 31:09 E-commerce and agent interaction 35:03 Notebook LM evolution 48:26 Predictions and future of AI Mentioned in this episode: Whisk: Image and video generation app for consumers Flow: AI-powered filmmaking with new Veo 3 model Project Mariner: research prototype exploring the future of human-agent interaction, starting with browsers NotebookLM: tool for understanding and engaging with complex information including Audio Overviews and now a mobile app Shop with AI Mode: Shopping app with a virtual try-on tool based on your own photos Stitch: New prompt-based interface to design UI for mobile and web applications. ControlNet paper: Outlined an architecture for adding conditional language to direct the outputs of image generation with diffusion models

• 0:00Speaker 00

  You know, I was talking to a founder, he gave me the analogy of, you know, you want the user to almost be like the way that a director would direct the cast and crew of, you know, change the lighting here, like, can you say this with a little bit more of an accent there? And like, almost like natural language, the way that a director would direct a cast and crew. What do you think is the right way to mold the play?

• 0:19Speaker 01

  I still think it's show and tell everywhere, so I don't think you do everything through text. I think it's kind of actually counterintuitive to have to transcribe everything. So I think there's a lot of like showing and acting and mimicking or giving a reference just as inspiration in addition to the text.

• 0:33Speaker 00

  Yeah.

• 0:34Speaker 01

  But the one thing that's starting to become more clear, at least for me, is kind of video generation, simulation, games, they're kind of like the same thing in this new world. And what that means is basically you're kind of world building, you're saying this is the stage, these are the assets, these are how things are supposed to look. And then you shoot in it and then you can reshoot and refine and pause and correct something and go back in time and regenerate. Like, I think that's where this is heading and UI is going to be fairly novel.

• 1:01Speaker 00

  Yeah.

• 1:19Speaker 00

  Welcome to Training Data. Fresh off of Google I/O, we're exploring some of the exciting AI updates with three leaders from Google Labs, who are the leaders on Google's product experiments around generative video, computer use, and notebook. Thomas Iljic of Wisk and Veo reveals why the future of content isn't just about generation, it's about remixable experiences where the line between movies and games blurs, and where your creations become starting points for others. Jacqueline Consulman of Mariner explains how computer use agents will fundamentally change e-commerce by removing human friction from purchasing. And Simon Takamine of Notebook LM shares why personalized AI content, designed for an audience of one, represents a completely new media category.

• 2:00Speaker 00

  You'll discover why these teams feel like a new chapter is starting for AI at Google. Enjoy the show. It's been exciting to see Google's just cooking in AI. And I O last week was very exciting. And it seems like the court of public opinion has just turned on its head so quickly. And right now, everyone's like, Google's out in front in AI. Why do you think that is? Why did the public opinion change so so quickly?

• 2:23Speaker 02

  I mean, the models, you know, to start with, I think they have a big thing to play with.

• 2:27Speaker 00

  Good. Good answer.

• 2:29Speaker 03

  Definitely the models. And I think just the number of products that we have and seeing all of this breakthrough in technology and AI come out into all of those products, but also all the net new products that we're launching and the net new experiences. It just it was a lot last week and even not just at I O, but like the week leading up to it, I think you had a big moment the day before.

• 2:48Speaker 02

  Yes, yeah, I did, I did. Yeah, it's it's definitely validating to see the, you know, public opinion on the models and Google's position in AI changing. Maybe recently. It does feel to us on the inside at least that it's it's kind of it's it's the it's a result of a lot of work, though. So it feels like we've been improving, to me at least, for at least the last three years in this area of gen AI.

• 3:16Speaker 02

  And maybe what we're what we're seeing externally is is people seeing what we've been up to. It helps that we're number one on many of the leaderboards and it helps that some of the stuff that the models can do is state of the art. And I think is only possible with some of the Google models. But I think entirely, it just feels like the end of chapter one and the start of chapter two. Yeah.

• 3:34Speaker 00

  Wonderful. So here's what I'd love to do today. We have we have three of the leaders from Google Labs in the room with us, for those in the audience. What I'd love to do is spend a little bit of time on each of the topics that you are responsible for, and then we can we can round up with some overall thoughts on AI. Does it sound good?

• 3:52Speaker 02

  Sounds good.

• 3:52Speaker 03

  Sounds great.

• 3:53Speaker 00

  The three topics we'll cover, we'll go into Wisk Flow and VO with with Thomas, which is, you know, Google's video models and kind of creative image generation playground, for lack of other word. We'll go into Mariner, Google's computer use agent with Jaclyn. And then we'll close on Notebook with Simon. And everyone knows Notebook, so that needs no introduction.

• 4:16Speaker 02

  Awesome. I'd like to hear that.

• 4:17Speaker 00

  Okay, Thomas, let's start with you. Tell us about the history of, you know, how you all have been cooking and building and experimenting in the creative image video generation space. And how long have you been experimenting with these products? And what have been the key milestones so far?

• 4:33Speaker 01

  Sure. Yeah, it's been a really exciting space. I think that's a very long question. So, you know, I'll probably rant a little bit. I think the, I mean, we've had for a long time, like good, you know, imagery models. There was like Imagen, there's been Dali obviously externally, et cetera. But something like two, three years ago is when, at least for us in labs, when we're thinking about products, we had the ControlNet paper for people who remember. So it's kind of like, how do you, how do you take the model and start channeling it where you want? So it's not just like a push button thing. You can start saying, I want the pose to be like this or the scene to be like this. That was one. And then the second thing was Laura's, where like you can kind of show the model a range of things. And then suddenly you're able to kind of like, you know, pull from the image and be like, what's the range of possibilities for that particular piece? And so that like iteration, the sense that you can start controlling the outputs,

• 5:16Speaker 01

  that that felt like the right moment for us to start exploring the creative process.

• 5:20Speaker 00

  When was that?

• 5:21Speaker 01

  Probably two and a half to three years ago. And so then a lot of, you know, stumbling and trying things and failing. I think we had things where we, we trained a bunch of our people internally to see what they could do with config UI type workflows. We even had a little, you know, animation thing going on where we create half an episode with artists. And, you know, we published that thing, it's not so supervillain, if you want to check it out on YouTube. And then more recently, we ended up with like a bunch of convictions out of that exercise. So we had things like creation has to be iterative. So we need to build kind of these controls next to the models. Media comes with the blueprint, which is this idea that like, if I generate something, you're able to kind of pick off where I left off. And then the third one was like, it should be show and tell.

• 6:05Speaker 01

  So basically, this driving force was, instead of just telling the model with very long prompts, I can actually like show you images, you know, say like, it should do kind of like this and, you know, we can build off of that. So this is where we started with Wisk on the consumer side for imagery and flow for everything that's, you know, high-end filmmaking.

• 6:25Speaker 01

  Exercise, yeah.

• 6:27Speaker 00

  Really cool. And do you imagine Wisk and Flow will be kind of end consumer products in the kind of, you know, Google portfolio of, you know, billion user scale consumer products eventually? Or how do you, is it your playground for kind of testing model UX and, you know, how best to bring this magic to users?

• 6:44Speaker 01

  Yeah, I think we see it as a spectrum. So I think Wisk is kind of our play in the, you know, really consumer space and thinking about like, everybody now has this visual language at their fingertips. They might not have it necessarily like the most advanced ideas in terms of like storytelling, but they can quickly remix each other's things. And so we're trying to see what those dynamics look like. So I think that's kind of our exploration space with Wisk.

• 7:05Speaker 00

  Yeah.

• 7:06Speaker 01

  We'll see how it picks up. I think a lot of the lessons will probably also graduate in just how we, you know, deal with user inputs and treat those across multiple surfaces. And then Flow is the other side of like, you have a vision, you know what you want. And it's kind of like, how do we give you all the, all the tools to create, you know, the, the best version of this in video?

• 7:25Speaker 00

  Yeah. Okay. Super cool. Who's the ideal user, do you think, for, for Flow and Wisk?

• 7:31Speaker 01

  For, for Flow, I think it's pretty clear for us. We're starting with AI filmmakers. And the reason is we want to build this kind of, we call it the generative AI camera. Like, you know, you're doing world building, then you're shooting inside this world. How do we actually develop the DSLR camera?

• 7:46Speaker 00

  Yeah.

• 7:46Speaker 01

  Of, of, of, you know, generative AI video?

• 7:49Speaker 00

  Yeah.

• 7:49Speaker 01

  And then we'll distill kind of the Android version of the pixel, you know, pixel camera version out of it. Wisk is much more consumer. There's a wide range of audiences. You know, is it you creating something funny with your friends in a chat? Is it kind of more, you know, inside the company, you're trying to create some visuals for slides? This kind of like all this, this whole range that we're exploring, we'll see where it lands.

• 8:10Speaker 00

  Yeah, so cool. Okay, you said AI filmmakers. Is that a thing? Are people calling themselves AI filmmakers now? And it, does it tend to be? Existing filmmakers that are, you know, looking to be more AI savvy? Are you seeing, you know, net new creators come in and try to create feature films?

• 8:26Speaker 01

  I think it's a, it's certainly an ill-defined term. But the reason why I like to say AI filmmakers versus filmmakers is I think if you take the extreme end of the spectrum, these are people who need very bespoke tools. They have like entire workflows and processes and you need to develop very specific ideas. There's one tier under that, which maybe I classify as AI filmmaker or potentially as, you know, pre-visualizations, where you're trying to quickly get like a version out and maybe then you do the full process. Or people who just don't have the budget. So they're like, I don't have a hundred thousand dollars to, you know, put my idea out there, but now I can at least take a shot at it.

• 8:58Speaker 00

  Yeah.

• 8:58Speaker 01

  And so those people are interesting to us because like you can really start from the ground up thinking of like, if you had this generative AI camera, what would the user flow look like? Like, how would you fit those pieces?

• 9:07Speaker 00

  Yeah. Your, um, your answer to my initial question of, you know, the models are the reason that the court of public opinion has flipped so quickly. It's been amazing to see VO's progress and, and, uh, VO three. And, you know, for me, I don't, I don't know what evals you all look at to look at performance, but for me, it's the, the Will Smith spaghetti eating test. And like, we seem to have passed that. So like, are, are we at video AGI or like, how do you think about the, the quality and the performance and what's ahead?

• 9:36Speaker 01

  There's still some room, but it's pretty cool. I mean, the, the GDM team has done really great with VO three. They, it beat, I think the joke last week was that it beat VO two in the rankings. So it's kind of VO, VO, beating VO. So people were very happy about this.

• 9:47Speaker 00

  Good.

• 9:47Speaker 01

  I think it's, you know, adherence is going up. Uh, yes, we don't have the six finger problem. Physics are getting pretty good.

• 9:54Speaker 00

  Yeah.

• 9:54Speaker 01

  There's still things where like, you know, if you want to have, for example, multiple characters and kind of choreographed those characters, have like full consistency across multiple scenes. Like that's where there's still like a lot to come. Um, how do you refine your output? Can you propagate changes across clips?

• 10:09Speaker 00

  Yeah.

• 10:09Speaker 01

  There's gonna be still a lot of like improvements, but in general, yeah, a huge step up. And the biggest reveal this time was audio. So be able to code generate audio with the video that brings kind of like, you know, an image is what like, a video is more than an image and a video based sound is way more than a regular video.

• 10:25Speaker 00

  Yeah.

• 10:25Speaker 01

  Um, that certainly has opened up like a lot of virality.

• 10:29Speaker 00

  Do you think the? You know, the R and D left to do to make the ideal tool for the craft, how much do you think is in the product and in the UI? And how much do you think is gonna need to happen in the model research layer and things like sterability?

• 10:43Speaker 01

  I think it's both, but at least, and I'm sure people will have a wide range of opinions, but it's almost like we're at a state where everything we imagine in terms of controls, I think we have visibility in how they can be built. You know, you wanna have consistency of characters, of scenes, of location. There's like different ideas around this. You wanna reshoot. So that part, I think the part that's hard is still the abstraction of all of it. So how do you put this in the, what are the inputs that you want from users? In the context of audio, for example, where do I define the voice? How do I touch the voice to the character? How do you find the mannerism? How do that propagate? So I think there's gonna be a lot of work in that abstraction layer on top of the models and on top of the controls.

• 11:21Speaker 00

  Oh, so interesting. So you think most of the model kind of R and D is almost a solved problem is maybe too strong of a word.

• 11:28Speaker 01

  Not solved, but I think we.

• 11:29Speaker 00

  We know how to do it.

• 11:30Speaker 01

  It will happen.

• 11:30Speaker 00

  Yeah.

• 11:31Speaker 01

  I think it's pretty clear that it's moving very fast. And then, you know, we see a lot of things just like week after week coming up.

• 11:36Speaker 00

  Yeah.

• 11:37Speaker 01

  But how we do the connective tissue on top, I think is still like pretty much open. Audio is, you know, one of those new frontiers, for example, of like, should I be talking and driving the audio, then changing my voice? Should I be typing the text? How do I do diarization? Like, there's a lot of like, what are the inputs? How do you give, how do you let people mold clay, you know, with all these models?

• 11:55Speaker 00

  What's your guess for how that future is for how people will mold clay? And, you know, I was talking to a founder, he gave me the analogy of, you know, you want the user to almost be like the way that a director would direct the cast and crew of, you know, change the lighting here. Like, can you say this with a little bit more of an accent there? And like, almost like natural language, the way that a director would direct a cast and crew? What do you think is the right way to mold the, mold the clay?

• 12:19Speaker 01

  I, I still think it's show and tell everywhere, so I don't think you do everything through text. I think it's kind of actually counterintuitive to have to transcribe everything. So I think there's a lot of like showing and acting and mimicking or giving a reference just as inspiration in addition to the text.

• 12:33Speaker 00

  Yeah.

• 12:34Speaker 01

  But, but the one thing that's starting to become more clear, at least for me, is kind of, how would I say it? You know, the video generation, video generation, simulation, games, they're kind of like the same thing in this new world. And what that means is basically you're kind of world building. You're saying this is the stage, these are the assets, these are how things are supposed to look. And then you shoot in it and it can reshoot and refine and pause and correct something and go back in time and regenerate.

• 13:01Speaker 00

  Yeah.

• 13:01Speaker 01

  Like, I think that's where this is heading and UI is going to be fairly novel.

• 13:04Speaker 00

  Yeah. You mentioned games. I wanted to ask about this. It feels to me like, you know, the existing way that we consume games versus movies is, you know, is because there's such a tremendous fixed upfront cost of producing a movie. If you imagine that, you know, in a world where every movie frame is generated, not pre-rendered,

• 13:27Speaker 00

  and that, you know, entire story arcs can unfold, it does feel like the movie and the game worlds start to merge. How do you think that plays out?

• 13:36Speaker 01

  I think what, I mean, so for example, we have the genie model that's been really interesting. So you give an image and you can kind of move your character and the world builds in front of your eyes. But what's going to be really interesting is how do you ground it? Like, games are fun because there's like very set constraints. Movies are good because there's like very small details that matter, you know, the expression and the moment and the timing. And so I think it's all about, it's almost about like the constraining of the capabilities towards what we need.

• 14:02Speaker 01

  So I don't know. I think, and the other thing that strikes me and I think a couple of people on the team is like, it's not clear that is, we think in terms of these static formats that we have today, like an image, a video and a game. Is there something in between almost? And what does that mean? And kind of, you know, where is that going to be distributed and interacted with? Like, I can share an image with you, but you can instantly turn it into a scene that you're walking into.

• 14:24Speaker 00

  Yeah.

• 14:24Speaker 01

  So am I sharing an image or am I sharing an experience?

• 14:27Speaker 00

  Yeah.

• 14:28Speaker 01

  Lots of questions, I guess.

• 14:30Speaker 00

  And it does feel like, you know, the story is almost the common thing that makes a game and a movie good. So, and that's different from an image is just a visual, right?

• 14:38Speaker 01

  Yeah, exactly. It's the setting, the constraints, the, you define the rules of the game, basically, and then you let other people enjoy themselves in it.

• 14:46Speaker 00

  Really cool. My understanding is that video is still expensive and somewhat slow to generate.

• 14:54Speaker 00

  Is your sense that that's getting solved quickly? And like, will we have, everybody's gonna be able to generate, you know, two hour films in their, you know, in their pocket in a couple of years' time? Or is your sense that this is a, you know, longer? We got, we got a lot of efficiencies that we need to build in order to make this kind of cost practical?

• 15:14Speaker 01

  I think, I mean, we've seen in imagery and we've seen in video, kind of like the same speed of cost reductions that we've seen in other places. Both, you know, the hardware is getting better. I think the efficiency, to your point, we have like

• 15:26Speaker 00

  Yeah.

• 15:27Speaker 01

  the regular models and then we learn how to distill them to kind of, so that they just take less processing to get to whatever you asked for. So I'm actually pretty optimistic that, you know, the costs are just gonna keep coming down and the speed is gonna increase, kind of aligned with what we're seeing with other models.

• 15:40Speaker 00

  Yeah, got it. Fantastic. What do you think is ahead for AI in the, in the creative space, at least from a Google Labs perspective?

• 15:49Speaker 01

  Well, we just launched Flow, so we have a lot of things to do to just like, you know, deliver on that promise of like, keeping you iterating. I think that's the first thing.

• 15:55Speaker 00

  Yeah.

• 15:56Speaker 01

  Refinement of like, outputs and like, keeping there, going there and like, insertion, editing, reshooting, I think is really interesting to us. But I think the holy grail will be some of these new formats and experiences, like what does it mean as a creator to share something with you that we can interact with? That's something that we wanna explore.

• 16:14Speaker 00

  Really cool. I wanna be able to talk to Will Smith as he's eating the spaghetti.

• 16:18Speaker 01

  Maybe he will.

• 16:20Speaker 00

  Really cool. Thank you so much for sharing what you all are doing over in the creative sphere.

• 16:24Speaker 01

  Of course.

• 16:25Speaker 00

  Okay. Jaclyn?

• 16:27Speaker 03

  Yes.

• 16:27Speaker 00

  I would love to talk about computer use and Mariner. Maybe first off, why is it, why is it called Mariner?

• 16:34Speaker 03

  Great question. So we wanted to give the project a name that really embodied what we were trying to do with this space, which was enable users to just go out and explore, enable agents to go out and explore. And Mariner is sort of this whimsical, open-ended name that just sort of embodies the spirit that we have on the team right now.

• 16:51Speaker 00

  I love that. You all, actually, you guys have really good product names across Google Labs. These are all really whimsical.

• 16:57Speaker 02

  I'm still trying to get rid of the LM bit on Notebook LM. But apart from that.

• 17:02Speaker 00

  You can do it, Simon.

• 17:04Speaker 01

  I'm pretty happy with Wisk and Flow. I think we did decently there.

• 17:07Speaker 02

  We're evolving our approach to naming.

• 17:09Speaker 01

  That's what we evolved at I/O naming.

• 17:12Speaker 02

  There we go.

• 17:14Speaker 00

  Oh, that's funny. Can you say a little bit about how Mariner works? Like, is it computer vision model behind the scenes? Like, it just feels like, you know, pure magic in a box, but give us a peek under the hood.

• 17:26Speaker 03

  I will take pure magic in a box any day. So the way it works is really leveraging the power of Gemini. That's kind of, you know, it's a, it's a action tuned model on a recent version of Gemini. But what that means is that we have all of the multimodal capabilities that Gemini gives us. So it's able to plan and reason when a user enters in a task. We're able to understand that. We're able to come up with a plan on how we should actually fulfill that task. And then the way it actually works is taking that and understanding the screenshots. So this is where the multimodality of the Gemini model really comes in handy. We're able to continue to take screenshots, continue down the trajectory of what it is that we're trying to achieve from the user's task that they gave us and bring it all together that way.

• 18:09Speaker 00

  Yeah, got it. Super interesting. What's the history of the project? And when do you anticipate you'll be rolling it out on this?

• 18:16Speaker 03

  So the project initially started last year, shortly after this time, actually. If we go back at I/O, last year, we kind of graduated the Google AI Studio and Gemini API out of the Labs team onto the developer team now. And that freed us up to start exploring what we thought was coming next. And that happened to be agents that could actually take action on behalf of users, not just answer questions or generate content. So the team started working on it. At that point, we started grouping up with a bunch of different folks across Google to kind of bring together what we launched in December last year, which was Project Mariner as a Chrome extension that took action on your browser.

• 19:00Speaker 03

  And then we continued to iterate on it based off of a lot of the feedback that we got from the trusted testers of that initial launch. So we actually had a large group of trusted testers that we would be talking with regularly and understanding what was working well for them, what wasn't. And we took that feedback and iterated on the most recent launch of Project Mariner, which we announced last week at Google I/O.

• 19:20Speaker 00

  Really cool. What was some of the feedback and like, what are people, what are the magic sparks when people really are like, this is a game changing product for me?

• 19:28Speaker 03

  Yeah, great question. So it's funny, one of the initial kind of magic moments that everybody had was watching Project Mariner take control of the mouse on the browser and being able to click, scroll, typing text into text boxes actually felt net different when you realized it was an agent doing it. But quickly, as you were using the initial version, the feedback became, this is super cool. Can I please use my browser again? Like, I'd also like to be able to do work.

• 19:58Speaker 00

  Yeah.

• 19:59Speaker 03

  Which makes, makes a lot of sense. And so that was one of the, the big motivations behind moving towards this idea of users entering a task in the web app that could then run in the background.

• 20:10Speaker 00

  Like a virtual.

• 20:11Speaker 03

  On virtual machines.

• 20:11Speaker 00

  Okay.

• 20:12Speaker 03

  Exactly. But one of the key things that we did also try to keep true to the initial vision was how can we start to think about bridging the context that a user had on what they were doing in their current environment to the task that they were sending to the VM and Mariner executing in the background. And the way we tried to do that was if you install the companion extension now, it'll actually be able to see all the tabs you have open. So when you're giving Project Mariner a task, let's say you happen to be looking at a recipe on a recipe site and you're like, oh, wouldn't it be great if I could canonical use case, add all these ingredients to my Instacart cart?

• 20:48Speaker 00

  Yeah.

• 20:48Speaker 03

  Now, when you go to Project Mariner, you could say, hey, add all the ingredients from this chicken recipe to Instacart and you can select the tab that you have open with that chicken recipe. And Mariner will understand that context, will be able to revisit that site on the VM and complete the task with the context that you had in your local browser as well.

• 21:07Speaker 00

  And it's almost superhuman in a way because as a human, I only, it's like hard to context switch between browser tabs.

• 21:13Speaker 03

  Yes.

• 21:13Speaker 00

  And you're able to kind of see everything in the tabs all at once.

• 21:17Speaker 03

  Yeah, I think a big, a big net win also was the ability for Project Mariner to do 10 tasks at once, not just one. And that was really a big net unlock.

• 21:27Speaker 00

  Yeah.

• 21:27Speaker 03

  I was using it the other day and, you know, I'd just come back from running an errand and there was a bunch of stuff on my mind that needed to get done. And the first thing I did was open up Project Mariner, enter in three different tasks for it, and then just sent them off to start making progress. And I was able to jump back into the document that I happened to be working on. And it was this like magic moment of just, okay, not only is progress being made on these things, but I just got it off my mind.

• 21:52Speaker 00

  Yeah.

• 21:53Speaker 03

  Like I didn't have to keep thinking about it.

• 21:54Speaker 00

  Do people want to see the computer mouse moving around first for a while before they're like, okay, I trust that thing to go off and do things for me?

• 22:01Speaker 03

  If they do, they have that opportunity in the current Project Mariner experience. You can go into full screen mode, you can see the agent moving around and clicking on things, entering text. You could also pause a task at any point and be able to take over it. So having, or giving the user the ability to take over and/or provide oversight on these tasks is something that we think is still very important when we have an open-ended platform like this, or an open-ended experiment like this, that really lets it up to, or leaves it up to the user to try out different things.

• 22:34Speaker 00

  And what's the user behavior you're seeing? Like, are they like, please, just take the wheel, I don't want to deal with it? Or they actually want to, you know, backseat drive and watch the agent and make sure it's doing what it's supposed to be doing?

• 22:44Speaker 03

  That's a great, great question. I think it, initially, watching it is this fun element, but also it develops a comfort for knowing how the agent is thinking and what it's doing. But one of the pieces of feedback we also got from the initial launch was At the end of a task being complete, we just save the entire conversation history, and it can get quite long. And what users ended up wanting was just a summary of like, what did Project Mariner do to complete this task? So I can make sure it did it correctly. And that really kind of points to the question you're getting at, which is, I want to just hand the task off to this agent. But then I want to be able to just verify what it did at the end of the task, not sit there the entire time and watch it.

• 23:24Speaker 00

  Yeah, yeah, so interesting. What do you think of the solved and the unsolved technical problems so far with computer use? Because computer use still feels like to me, we're maybe in the Will Smith, you know, the spaghetti's still sort of disappearing a little bit phase. And maybe that's an unfair characterization. But I'm curious where you think we are on the evals and the performance so far for computer use? And what are the unsolved problems right now?

• 23:46Speaker 03

  I think that's actually a totally valid comparison. There's a reason we launched this as a research prototype with the experiment label on it right now. I think we've seen really big gains from December to what we launched last week. That said, there's definitely still model quality improvements to go. I think there's also just application level improvements to go. There's more seamlessly being able to have the user, you know, provide context upfront, which will make the agent more capable of understanding what it is it should be doing. And then there's just more planning and reasoning that we could do, like at inference time or at the application layer time, that's sort of in addition to the model improvements, you know, improved system instructions, improved checks and calls to different models.

• 24:31Speaker 03

  And then, of course, right now, Project Mariner entirely completes a task by actuating or taking action on a browser. You want an agent that has more skills than that. You want an agent that knows when to call the right tools, that has memory, that's able to, you know, take advantage of a lot of the other stuff that we already see out there. So I think it's just integrating a lot of that in and starting to innovate and climb on that. And then, of course, right now, Project Mariner, it's in the browser. People use computers. So, you know, we call this computer use. So there's that entire dimension as well that I think we're going to continue to see innovations in.

• 25:10Speaker 00

  Really cool. Were there any contrarian opinions you all took in building Mariner? So, for example, I think some people have said screenshots, it's going to be too slow. It's not, it's not going to be fast enough. You should use the, use the website DOM or whatever. Like, any contrarian bets you guys made?

• 25:26Speaker 03

  So the reason we went with the screenshot is we wanted to make sure that it was a skill that we could develop that could be applied across things that aren't just websites. I think the other aspect of that is like DOM versus accessibility settings or accessibility trees is another leverage. We're kind of betting on this one right now, but I would say everything's evolving. So we're just willing to take pivots when it, if and when it makes sense.

• 25:56Speaker 03

  Yeah.

• 25:57Speaker 00

  Makes sense. What is it capable of doing it today, doing today? And, and what is the speed? Like if I tell it to go, you know, the canonical, go order me a pizza from Domino's. Can it do that? And how long does it take?

• 26:08Speaker 03

  The speed is definitely an area that we want to keep hill climbing on is what I would say. But it's, it's interesting you say that because one of the things that I, so I was recently using Mariner to help me complete a task, which was come up with, let me take a step back. I have a three-year-old at home. She is going to be four soon. Part of that means organizing a birthday party for her and being able to figure out loot bags for kids at a four-year-old's birthday party. This task, as you can imagine, involves understanding what to put in the loot bag and then actually buying all of those things or like finding links somewhere to go buy them. And I gave Project Mariner this task and it was basically a personal research that turned into an action-taking task, which is find me the links and save them.

• 26:51Speaker 03

  And the, the thing that really resonated the most with me on that one is as it was performing this task, first it, you know, did a search for good ideas to go in a loot bag. And then as it like just remembered those five items, that's something any of us could do. Like that itself wasn't impressive, but the first one was I think temporary tattoos. So then it started looking for temporary tattoos. It found a great link for it. Instead of having to copy that link and paste it in a doc somewhere else, it could just remember it. It could remember this like massive URL and then it moved on to the next one. And then at the end of these five items, it just gave me all five URLs that it had been able to inherently store. So when we talk about speed and efficiency, I think there's two dimensions. One is just the model calls and the, you know, taking action and like, how do we improve it with different tool use?

• 27:34Speaker 03

  But then the other one is how can agents just do things in a different way that are inherently faster than the way we would do things? And I think we're going to continue to see improvements on both dimensions.

• 27:45Speaker 00

  Yeah. I wish I could remember five URLs.

• 27:47Speaker 03

  Oh gosh.

• 27:50Speaker 00

  Okay, good point.

• 27:52Speaker 00

  Let's see. What do you think is ahead for Mariner? Where do you see it evolving from here?

• 27:56Speaker 03

  I think there's a couple of things. Number one, we had a bunch of announcements last week around Project Mariner-like capabilities making their way into different Google products. And I think that this is a kind of core capability that you'll start to see emerge everywhere from the Gemini app to AI mode in search. So I definitely see a lot more coming to Google products with the stuff that we're doing right now in Project Mariner and kind of paving that path forward. And then I think for Project Mariner itself, I actually like to think of things in three categories. There's the agent itself. I think that's going to get smarter, that's going to get better, that's a better model, that's tool use, that's memory, that's context. Then there's the environment. We talked about how in December it operated on your local desktop in your Chrome browser,

• 28:41Speaker 03

  so that's in the foreground. Then we moved towards this idea of Project Mariner operating in virtual machines, which meant that it's now operating on VMs. I think there's this middle layer, which is an agent that can still operate on your device but in the background. And there's a bunch of reasons and types of tasks where that becomes a really important kind of way for the agent to operate. And then, of course, there's all the other devices. But really, what you want is a capable agent that's able to operate in a way that is omnipresent across all your devices, locally, on VMs. And then the last one is the ecosystem part, which is where you start to get into the agent-to-agent interaction and like, how does your agent interact with all of the things that exist outside of its own world, essentially?

• 29:24Speaker 00

  Yeah, so cool. I think the canonical examples for computer use are, you know, "book me a flight" or "order me a pizza." Is that your sense of what computer use agents will actually be really good for? Or like, what do you think? I'm sure you spend a lot of time thinking about like, what applications will actually be the bull's eye here? How do you think that shapes out?

• 29:44Speaker 03

  So I think we default to those because they're just easy to understand. The like travel planner, I mean, literally it's a travel agent. It couldn't be more analogous when you think of agents right now. Uh, but no, the way I like to think about it is um on a spectrum uh where you have tasks that are sort of the in what I would consider do it with me. Where you have your agent alongside and you can easily offload certain tasks to it, but it's really working in in unison with you. And then you have these like do it for me tasks, which is, hey, I just want to give my agent a bunch of stuff to go do and it will run it in the background.

• 30:18Speaker 00

  Um.

• 30:18Speaker 03

  Um, I think part of the reason we see these tasks being used is twofold. One, um, they're just incredibly easy to understand and everybody kind of gets what that use case is. And they're usually starting from scratch, like there's no context you need upfront. You can just send on an agent out to go do it and the demo as a result is pretty easy to put together.

• 30:41Speaker 00

  Yeah.

• 30:41Speaker 03

  Um, and then the other one is just where the capabilities are at today. And so as agents get more capable and you start to have more of these realizations on what they are actually um able to do. You'll see much more advanced use cases or much more complex use cases. And that also requires a user having more trust that they can give to the agent. Um, so I think that that will evolve over time and we'll see people come up with even more interesting use cases that they're willing to give an agent go to do on their behalf.

• 31:08Speaker 00

  Yeah, totally. It's also going to require, uh, I guess it's going to inspire, I think, a shift in business model, right? Because like if if you have a bunch of agents going off and browsing, you know, trip planning, for example. They're not necessarily looking at the ads and, you know, the first things that show up. And so it is, I I think it's going to create some business model evolution as well.

• 31:31Speaker 03

  I agree. I think there's a lot of evolution that's going to happen um across business models, across how websites work. Um, across how, you know, users will always want to use the internet going forward. Like, there's a lot of joy, I think, we all get in it from content creation to consumption. Um, but there's also a lot of other tasks that uh there's, it's just ripe for disruption in a lot of ways.

• 31:55Speaker 00

  Yeah, yeah, like, I mean, I I I'm thinking like humans are suboptimal in some ways. We we see the ad, we get excited, distracted, and, you know, I go and buy the dress. Um, and my agent, maybe it's, maybe I can instruct it to ignore the ads. Maybe it actually knows, like, it's going to find the best content regardless of what's what's showing up on the page. So it's. It's kind of interesting to think about how that future plays out for, you know, as agents do more of our browsing.

• 32:19Speaker 03

  It's super interesting. I will say that the dress that, you know, maybe you got distracted. I always get distracted by things too and end up purchasing stuff that gets sent my way, but I'm always happy with it by the time I do end up purchasing it. So I think that there's like new opportunities to think about how do you actually involve agents in this new sort of business model ecosystem? And hence that third bucket of like, there's going to be a lot of evolution happening in that space. And I think that that's where we need to evolve as an entire ecosystem. And it's not just like one player that's gonna say this is how it's done. So it's been interesting just talking to different companies and different people who are also thinking in that space right now.

• 32:56Speaker 00

  Yeah, really cool.

• 32:58Speaker 02

  I mean, I do think also just as a user as well, you know, I often don't buy things on the internet because it's such a pain.

• 33:05Speaker 03

  Oh, I've definitely dropped off on stuff too.

• 33:07Speaker 02

  I cannot, I can't navigate this thing. Either I don't understand it.

• 33:10Speaker 00

  Yeah.

• 33:11Speaker 02

  That happens quite a lot, or it's just like, I've just not got time. That happens as well, or I'm just, I can't be, I can't be bothered, you know?

• 33:18Speaker 00

  Yeah.

• 33:19Speaker 02

  Maybe it's just me, but I'm not a fan of shopping. Let's put it that way in the real world and online. But I'm a fan in what I get, you know, I'm a fan in the outcome. And so, I don't know, I kind of feel like I, I might do more. I would probably do more online shopping, I think. You know, if I, if I didn't have that barrier of actually having to do the shopping bit. I don't know, that would be me though.

• 33:40Speaker 03

  No, I agree. What's, what's really interesting is, I don't know about you, there's certain stores that I'll go on to and I'll just like accumulate stuff in my cart. And I won't want to like pull the trigger until a little bit later on when I've had a chance to think about it.

• 33:54Speaker 02

  Yeah, yeah, yeah.

• 33:55Speaker 03

  But then I end up with a bunch of like half-built carts across a bunch of different websites. And part of me also wonders like, is there a world where my agent is that universal cart essentially? Where I'm like, add all this stuff to it or like create this aggregate area of all the items that I might be interested in buying. And it can be across any site at this point because the agent represents me and it can remember which sites to go on. And then when I'm ready, it's sort of like, okay, one click, like make this entire purchase, basically. And it can go and check out on all of the different sites or all the different stores.

• 34:25Speaker 00

  Yep.

• 34:26Speaker 03

  So that'll be, yeah, that'll be an interesting area to think about.

• 34:29Speaker 00

  Yeah. Okay. What I just heard from you guys is e-commerce conversion is about to go, about to skyrocket then.

• 34:34Speaker 02

  I mean, on my computer it will go up. That's all I'm saying. I don't know about anyone else. Also diversity as well, you know, like I go to the same old sites. Right, but I would love suggestions.

• 34:45Speaker 00

  Yeah.

• 34:45Speaker 02

  You know.

• 34:46Speaker 00

  Yeah, so, yeah, it's like, once you've kind of democratized computer use, then the laziness of humans to get through checkout is no longer the determining factor of which e-commerce companies will do well. It's just like the best product wins. Yeah. So interesting. Okay, cool. Thank you for sharing. You're welcome. Okay, Simon?

• 35:04Speaker 02

  Hi.

• 35:04Speaker 00

  Your last?

• 35:05Speaker 02

  Hi.

• 35:06Speaker 00

  Notebook, Notebook LM or Notebook?

• 35:08Speaker 02

  We'll go with Notebook LM. We're still on Notebook LM. I think it's been so long now that it's definitely Notebook LM. There was a period where we were like, okay, it's now the time.

• 35:16Speaker 00

  Yeah.

• 35:16Speaker 02

  You know, but I, but I think we've gone through that multiple hagiastic moments, which we can talk about. And yeah, it's gonna be hard to remove it. I like it, though. I mean, you know, maybe, um, maybe every product that is kind of like, has an acronym or some weird letters after it, and there are a couple of them in the AI space, regrets that, but at the same time, they've become part of the team and the identity and.

• 35:38Speaker 00

  Totally.

• 35:38Speaker 02

  Yeah, it's nice. I like it.

• 35:39Speaker 00

  I love that. Okay, so Notebook LM was, you know, one of the biggest, one of Google's biggest viral hits.

• 35:45Speaker 02

  Yeah.

• 35:45Speaker 00

  Last year?

• 35:46Speaker 02

  Last year, yeah, yeah. Uh, it went viral last year.

• 35:50Speaker 00

  Yeah.

• 35:50Speaker 02

  Yeah. But it been, you know, the team had been building it for a while before we, it took off, so.

• 35:55Speaker 00

  Totally.

• 35:56Speaker 02

  Yeah.

• 35:56Speaker 00

  Tell me about how it's evolved in the last year.

• 35:59Speaker 02

  Yeah, yeah, well, um, so firstly, the viral moment, you know, so, um, my way into Notebook LM was through audio overviews. Um, so me and the team had a kind of, we were also exploring the future of content, but from a different angle.

• 36:13Speaker 00

  Yeah.

• 36:14Speaker 02

  I think, and, um, well, you know, Notebook was the, the perfect balance of kind of user control, but also, um, the kind of the power of the technology. And, you know, our hypothesis was that there was an opportunity for, um, personal content. So not content that is for everybody, actually a content that's for, um, an audience of one, maybe two, maybe three, small group maximum. Um, and that was kind of, you know, how we, how we shaped the product. We didn't think it was gonna, you know, looking at the Notebook user base back then, we didn't, we thought that it was a great place to. You know, kind of like test PMF, just kind of iterate on the product. But we didn't, we were totally unprepared for the massive success of of audio overviews.

• 36:57Speaker 02

  And then through that notebook LM as well. So it was honestly, the first, the first couple of months was really just kind of hanging on for dear life.

• 37:07Speaker 02

  Firstly, it was making sure that the TPUs don't fully melt as Riser, I think, had a, had a gif out back then. But but there was, there was also just a lot of, a lot of iterations and fixing things and improving things. And, you know, that, that was really the first couple of months. I think since, since the start of this year, maybe we've managed to take stock. At the end of last year, we launched the, the join mode, the ability to join in a podcast and want audio overview, I should say, and talk with the host and ask questions and all this kind of stuff.

• 37:36Speaker 02

  But at the start of the year, we kind of took stock and we've really been thinking about, you know, what, what is a notebook? For the notebook users, how are you know, because it's really leaning into notebooks once they've come in the front door through audio overviews? And we've, we've started to think about, and Jacqueline, you kind of touched on this, I think, you know, the, the criticality of context in, in really enabling these AI systems to be genuinely useful for you.

• 38:01Speaker 02

  And we found that a lot of users, when they're using notebook, they use them for these kind of more longer running, almost like projects that they have. So either their hobbies, or if they're in the world of work, these, these, they, they can be ongoing projects or they can be projects with a goal, you know, like I've got to prepare for a presentation or something like that. And so a lot of what we've been really doing is, is retooling, you know, how we look at notebook. And also, you know, building a strategy as well that leans more into, I think there's more sort of longer running opportunities that, that we see in, in the, in the notebook user data. Of course, we've, we've done a whole bunch of kind of improvements too. So we've just launched the mobile applications finally, so they came out last Monday.

• 38:47Speaker 02

  And we also launched international audio overviews as well, which was kind of the end. It was the end of a long road, honestly, of upgrading the underlying AI infrastructure and models. Away from the very first, almost like research grade model that we used for the initial launch to, you know, native Gemini audio. So what you hear now in the international audio overview at the very least is native Gemini audio. And that was a big push for many teams across labs and also GDM.

• 39:16Speaker 00

  Yeah, super cool. It feels like audio overview was the like, almost the viral hook. And you guys have been building out a lot in almost like the rag UI.

• 39:29Speaker 02

  Yeah.

• 39:29Speaker 00

  And just imagining what that workspace looks like.

• 39:33Speaker 02

  Yeah.

• 39:33Speaker 00

  What do you think the actual just audio overview podcast thing becomes? And actually, I'm curious how you even ended up on the shape of two podcast hosts talking to each other. It's just like, it's such an engaging format. I'm curious how you even landed on that.

• 39:45Speaker 02

  Yeah, yeah.

• 39:47Speaker 00

  You know, I feel like it's only in its infancy still in terms of, I would love, you know, podcasts every morning to type me up for my day and things like that. And so how much of your time is thinking about Notebook? The kind of rag workspace environment, for lack of a better word, versus Notebook? The podcast killer, you know, the training data is going to be built on Notebook in the future.

• 40:06Speaker 02

  Yeah, yeah, yeah. Well, I hope not, but maybe it can help. So the way that we're starting to increasingly look at notebooks is they're comprised of kind of three, they give you sort of like three superpowers. So one of them is they they help you really accumulate information, you know, across over time. And that's, you know, there's a lot of amazing, you know, underlying database technologies that we apply that I think, you know, lean on first party Google technologies in a pretty unique way. The second is they bundle in intelligence. And when we launched last year,

• 40:44Speaker 02

  we used the old Gemini 1.5 Pro model back at that time. But obviously now we've got thinking models and so on. But the third thing is is this ability to, for content and information to be adaptive to your situation. And so, you know, podcasts or audio overviews, a conversation.

• 41:04Speaker 02

  It's one form that information might take, but you can imagine many other forms that that information or knowledge might take as well.

• 41:11Speaker 01

  Hmm.

• 41:12Speaker 02

  So you might imagine it coming at you in the form of a comic book, or maybe a short movie, or maybe a mind map, which we've also launched. But you can imagine many other types of of media that fit the right circumstance and form and function for the moment, for you to understand information, to be able to analyze it, make decisions with it, kind of do something with it. So that's kind of, that's the mindset that I think we have when we're thinking about the different, you might hear us talk about transforming information from one state to another. I think that's a, that's a, that's a, it's a fine word, it's a little bit technical, to be honest, it's more like adapting to you and fitting you, I think, that's really what we're going for.

• 41:54Speaker 02

  But in terms of, just going back to your actual question around audio overviews and, you know, where it's going, there is a huge amount, I think, of, of room left in that, in that technology. So, you know, I enjoy audio overviews and I use them a fair amount, but I also, you know, every now and then, I'll be like, that's weird, you know, why do they say that? Well, they've kind of lost the plot there, I didn't quite get the right narrative, it's, it, it's sometimes it, it like, the uncanny valley or the illusion is broken, you know, when you're, when you're listening to them. And, you know, while it might seem like there's a small amount of work we might need to do to, to kind of, to, to fix that last step, it's actually a ton of work that we've got

• 42:36Speaker 02

  to do, you know, and, and so there's a lot of effort being placed into all of the various components that you'll need to make the, the experience feel like something that, you know, where you suspend your disbelief more completely. And alongside that, you know, there are many other different show types. You know, one, we, we've kind of had one show type for a bit too long, I think, actually, and we're bringing more out, so we're actually working on some really cool things, a lot of them inspired by users, honestly, so one of the things that we saw users do, right back at the start, but you can, you keep on seeing it, is users putting in their LinkedIn. You know, they're putting it on LinkedIn. Well, why? Well, number one, it's kind of fun to like hear people talk about you. But a lot of users are using it to get feedback.

• 43:22Speaker 02

  You know, like to to kind of I to understand from from another person's perspective, who they may not have access to. You know, feedback is truly a gift, like real feedback is hard to find, you know. Um, so you know, how would how would somebody else look at me? How would somebody else talk about my strengths? And how might somebody else talk about areas to improve? You know, this is something that we see users already using audio overviews to to kind of access that sort of content or sort of information. We think we can make that easier for people. You know, so a lot of a lot of what we're thinking about now are different show types that lean into some of the the more viral successes that we've seen our users, you know, explore online.

• 44:02Speaker 00

  Yeah.

• 44:02Speaker 02

  Um, and also think about, you know, brand new formats as well, that I think are going to be fun.

• 44:06Speaker 00

  Okay, so we're gonna have training data, the the comic, the comic strip.

• 44:10Speaker 02

  I'm not saying we're definitely gonna have it, but um, but I mean, it's um, I think I think um, not everything has a story, you know, um, and so applying um, different adaptations um, will almost be sort of context dependent, I think.

• 44:26Speaker 00

  Yeah.

• 44:27Speaker 02

  But oftentimes it does help, you know, so one of the things that we were looking at the other day was um, we were looking at a hundred and fifty page PhD dissertation on um, it was invasive um, invasive worlds, I think, in some part of Europe. Um, and um, yeah, you could have looked at a mind map, you could have looked, you could have maybe listened to an audio overview if you had like ten fifteen minutes to spare. But actually getting a kind of a a comic book rendition of of that PhD was really helpful just to kind of understand the overall narrative within it.

• 45:00Speaker 00

  Yeah.

• 45:00Speaker 02

  Um, so you know, we're still working on things like that. Um, but I think there's a lot of, there's a lot of opportunity there. And of course, you know, comic books are very similar to storyboards and that.

• 45:11Speaker 00

  I was just thinking exactly that, yeah.

• 45:14Speaker 02

  As well. So, yeah, there's a lot of, there's a lot of interesting ways that I think labs projects intersect and we'll continue to explore them. Yeah.

• 45:21Speaker 00

  You can create a hero's journey comic book of somebody's LinkedIn uh, career arc.

• 45:26Speaker 02

  I mean, for an audience of one person and one person only, that's probably going to be the most awesome movie that we've ever ever seen. So maybe, yeah, maybe, yeah, yeah.

• 45:35Speaker 00

  That's awesome. Really cool. Where do you see Notebook going from here?

• 45:38Speaker 02

  Yeah, well, like I said, we're, we're really, I think our focus is, aside from a whole bunch of different adaptations, we're really thinking about how we can be more useful to our users over their more longer running projects. And so both in the world of the knowledge worker, but also in the world of students, these are our kind of like core users, I think.

• 46:00Speaker 02

  The project is really the, the, the, an area where those users both need the most assistance. But it's also where, it's also the point of, of highest value, I think, for them, right? So if you're in the world of work, the project is where value accumulates.

• 46:16Speaker 00

  Yeah, the atomic unit of work.

• 46:17Speaker 02

  Yeah, right. It's a real unit of work. We actually call them units of knowledge, but it's a great way of putting it. And the same for a student as well, you know, the project, if, if it's a project with a goal, passing a test, that's a big deal. You know, or if it's an ongoing lifelong learning thing, that's also really important as well. So I think really focusing on, on, on use cases in those domains is something we're, we're, we're thinking a lot about. I'll say the other thing is, you know, I think, I think one of the things I'm personally very excited about, you know, I've been in the consumer product space for many, many years. And one of the, I guess, one of the things that we did at Google when we went kind of mobile first, in the mobile first era, is we moved a lot of our desktop products to mobile.

• 47:00Speaker 02

  And if you look at those mobile products, many of them are the desktop products shrunk down to a small screen.

• 47:05Speaker 00

  Yep.

• 47:05Speaker 02

  And that's okay, you know, and I think because we were one of the first, because we built Android and a lot of our big products basically got mobilified at that point. We, we find it hard to change at that point forwards. But I've always been really interested in thinking about, if you have a desktop experience, what is a companion mobile experience that doesn't have to just be a carbon copy of the desktop experience, that maybe leverages the, the form factor, the sensors, you know, the, the, the actual, the, the fact that it's with you all at all times, to live, to deliver an additive experience on top of the desktop experience. So, you know, we, we've just launched the, the mobile experience after a fair amount of time in development, it's fair to say.

• 47:49Speaker 02

  But what I'm really most excited about there is the opportunity to actually iterate on that kind of novel mobile experience going forwards. Like, for example, wouldn't it be cool if I was, you know, maybe I'm in a discussion with some amazing, really smart people? And I've popped Notebook down, I've opened a voice, its native voice recorder. And it's just able to to record the conversation for me. And then I can transform that, you know, to later dates and accumulate them and all this kind of stuff. You know, that's the thing that is probably gonna be weird if I open my laptop and push record on my laptop. But for the mobile device, it's the perfect opportunity.

• 48:23Speaker 00

  Totally.

• 48:23Speaker 02

  Yeah.

• 48:24Speaker 00

  Really cool. Thank you for sharing.

• 48:26Speaker 02

  Yeah.

• 48:26Speaker 00

  Okay, we're gonna close it out with some predictions on on AI as a whole. Uh, please jump in. Hot takes, welcome. Um, let's see, let's start with what are your favorite Google Labs projects that we didn't talk about today? Like, what what is under, what is the, what are the gems right now that you're most excited about?

• 48:42Speaker 02

  The unreleased Google products that we're not allowed to talk about?

• 48:44Speaker 00

  Not the unreleased ones, but you guys, you guys just announced like, like fifty fifty things. There, there, there has to be others beyond the three we talked about today.

• 48:51Speaker 02

  Yeah.

• 48:52Speaker 01

  I have one, which is kind of still in this like video and image space. But I think the virtual try on stuff that we presented, like there's a lot of exploration in it. And I think that one to me is really nice because I think it meets a real direct user need. It's the strength of Google. Obviously, we know we have all the inventory and you know how to connect this. And it's just so fun to just see things on this. So I'm very excited about that one. I think this has like a.

• 49:15Speaker 00

  That's my favorite as well. That's so funny. Okay.

• 49:17Speaker 01

  I think that one's a good one.

• 49:18Speaker 03

  Stitch, I think, is really cool to be able to just talk to the product and describe what design you want and have it actually come out with that front end design. Um, I'd been using it a little in dog food before it was launched. And so it's just, I want to spend more time using it now that it's, uh, it's actually live.

• 49:36Speaker 00

  Really cool. What about you?

• 49:38Speaker 02

  Uh, well, mine is gonna be Stitch. So I'm gonna have to think.

• 49:43Speaker 00

  Two votes for Stitch, one vote for shopping.

• 49:45Speaker 02

  Yeah, yeah.

• 49:46Speaker 00

  I'm with you, two votes for shopping.

• 49:47Speaker 02

  Yeah, there we go. There we go.

• 49:49Speaker 00

  Um, what, I guess, what areas do you think will be hottest in the application space for AI broadly in twenty twenty five? Like, I think coding was, you know, maybe the breakout application in the last twelve months. What do you think will be the breakout application in the next twelve months?

• 50:02Speaker 03

  Video.

• 50:03Speaker 01

  I, yeah, I think there's like something around like these remixable content.

• 50:08Speaker 00

  Hmm.

• 50:08Speaker 01

  You know, you generate something, I take your thing, I just riff off of it.

• 50:11Speaker 00

  Yeah.

• 50:12Speaker 01

  There's something around this that I think is gonna pop up somewhere. I hope it's us. Um, but that that part feels really interesting. It's gonna feel like, you know, whiskey's heading a bit that way. You obviously can power a lot of this in video. Um, yeah, I think that's gonna be something this year.

• 50:25Speaker 00

  As you look back at, you know, past predictions of what you thought was gonna be interesting in AI, where have you gotten, been really right and where have you been really wrong?

• 50:32Speaker 01

  We're just laughing.

• 50:33Speaker 02

  Let's say we're really wrong altogether.

• 50:35Speaker 03

  All right.

• 50:36Speaker 02

  Three, two, one. Timing.

• 50:39Speaker 00

  Okay, same, Mark.

• 50:41Speaker 03

  I think there's been several examples where we definitely felt like we were on to something, and we were on to something, we were just too early into the space. And so it's been fun to see like, projects kind of go on pause, or you know, stop for a little bit, and then some of them are starting to even come back around again at this point. And so sometimes we just were a little, a little too early. But it just gives us a jumpstart when the models and the capabilities are ready.

• 51:07Speaker 00

  Good problem to have.

• 51:08Speaker 03

  Yeah.

• 51:09Speaker 00

  Well, you think you've been really right on and like sticking to your convictions on?

• 51:12Speaker 01

  I think this, at least for me in my space, like the show and tell piece. This idea that like, you shouldn't, you shouldn't ask users to kind of write two pages of text to describe, for example, an image. Like the idea is like, you should be able to show and tell like you would do with a friend or an artist that's working with you. I think that has stuck and it's kind of moving people away from prompting and towards kind of instructing and relying on the intelligence that leaves behind. So I think that one. But when I'm sticking with my guns and I think you're there to stay.

• 51:39Speaker 00

  I love it. Okay.

• 51:39Speaker 02

  Yeah. I mean, this is probably obvious at this point, but when we all started in labs, there was no Google LLM API. Google didn't have a functional instruction tuned language model or anything like that. And believe it or not, back then, in fact, I think the, the general consensus was that these were not really things that are easy to build a business around, you know, because of their cost. And you know, I think one of the things that we've all done actually is we've, we've kind of stuck with the technology and now it's obvious, right? But in the early days, it wasn't, it certainly was not obvious.

• 52:17Speaker 00

  Totally.

• 52:17Speaker 02

  So, yeah. And we got that bit of timing right.

• 52:20Speaker 00

  Yeah.

• 52:21Speaker 02

  Yeah.

• 52:21Speaker 00

  Yeah. Yeah, inference costs, just riding that curve and just capabilities up, costs down. And what, what will you build assuming that those curves continue?

• 52:30Speaker 02

  Yeah, exactly. In fact, when we joined, one of the traditions is write to think inside labs that George started, actually. And a lot of the docs that we'd write were around, well, what happens in two years? You know? And of course, yeah, that, that curve is something that I think inspired a lot of us.

• 52:45Speaker 00

  Yeah.

• 52:45Speaker 02

  Yeah.

• 52:45Speaker 00

  Fantastic. Thank you all so much for joining to share what you're doing across the creative sphere, the, you know, computer use sphere and the, what, what do I call the notebook sphere? The podcast killer slash?

• 53:00Speaker 02

  Let's not say podcast killer. But yeah, we can say knowledge.

• 53:04Speaker 00

  Knowledge, creation, transformation space. It's just, it's really, really cool what you all are building. And you guys have such a cool job getting to kind of cook in the little test kitchen of Google. And thank you for giving a preview of some of the stuff that's coming down the pipeline.

• 53:18Speaker 02

  Very welcome.

• 53:18Speaker 00

  Thank you.