These models are allowing creators to do um less tedious parts of the job, right? They can be more creative and they can spend, you know, 90% of their time being creative versus 90% of their time like editing things and doing these tedious kind of manual operations.

>> I'm convinced that this ultimately really empowers the artists, right? It gives you new tools, right? It's like, hey, we now have, I don't know, watercolors for Michelangelo. Let's see what he does with it, right? And amazing things come out.

maybe start by telling us about the backstory behind the nano banano model. How did it come to be? How did you all start working on it?

>> Sure. So um you know our our team has worked on image models for some time. We developed the imagine family of models which is goes back a couple years. Um and and actually there was also an um an image generation model in Gemini before the Gemini 2.0 image generation model.

So what happened was the um the teams kind of started to focus more on the Gemini use cases. So like interactive conversational and and editing

>> um and and essentially what happened is we teamed up and we we built this model which became what's known as nano banana. Um so yeah that's sort of the origin story but

>> yeah and and I think maybe just some more background on that. So our imagine models were always kind of top of the charts for visual quality and you know we really focus on kind of these specialized generation editing use cases and then when 2.0 Flash came out that's when we really started to see some of the magic of like being able to generate images and text at the same time so you can maybe tell a story. Um just the magic of being able to talk to images and edit them conversationally. Uh but the visual quality was maybe not where we wanted it to be. And so Nano Banana or Gemini 2.5 flash image um

Um just the magic of being able to talk to images and edit them conversationally. Uh but the visual quality was maybe not where we wanted it to be. And so Nano Banana or Gemini 2.5 flash image um

>> Nano Banana is way cooler.

>> It's it's easier to say. It's a lot easier.

>> It's the name that stuck.

>> Yes, it's the name that stuck. Uh but it really became kind of the best of both worlds in that sense like the Gemini smartness and the multimodal kind of conversational nature of it plus the visual quality of imagine. And I feel like that's maybe what resonates a lot with people.

>> Wow. Amazing. Um, so I guess when you were testing out a model, as you were developing it, what were some wow moments um that you found, I know this is going to go viral. I know people will love this.

>> I So I actually didn't feel like it was going to go viral until we had released on Ellarina. And what we saw was that we budgeted like, you know, a comparable amount of queries per second as we had for our previous models that were on Elm Marina. And we had to keep upping that number as people were going to Ella Marina to use the model. And I feel like that was the first time when I was really like, "Oh, wow. This is something that's very very useful to a lot of people." Like I it surprised even me. I don't know about the whole team, but like we, you know, we were trying to make the best conversational editing model possible. But um but then it really started taking off when when yeah when people were like going out of their way and using a using a website that would actually only give you the model some percentage of the time. But even that was worth like using going to that website to use the model. So I think that was really the moment at least for me that I was like oh wow this is this is going to be bigger.

>> That's actually the best way to condition people like only give them a rewards partially not all the time by design. Uh I had a moment earlier um and that was when so I've been trying some similar queries on kind of multiple generations of models over time. Um and a lot of them have to do with like things I wanted to be as a kid. So like an astronaut explorer or you know put me on the red carpet and I tried it on a demo that we had internally before we released the model. It was the first time when the output actually looked like me. Um and you know you guys play with these models all the time. The only time that I've seen that before is if you know you fine-tune a model, you know, using Laura or some other method to like do that and you need multiple images and takes a really long time and then you have to like actually serve it somewhere. So this was the first time when it was like zero shot. Oh wow, just one image of me and it looks like me and I was like wow. And then there became these like we have decks that are just like covered in my face as I was trying to convince other people that it was really cool. Um, and really I think the moment more people realized that it was like a really fun feature to use is when they tried it on themselves because it's it's kind of fun when you see it on another person, but it doesn't really resonate with people emotionally. It makes it so personal. It's like you your kids, you know, your spouse and and I think that's your dog,

>> your dog. And and and that's really what started kind of resonating internally. And then people just started making all these like 80s makeover versions of themselves. And that's when we really started to see like a lot of internal activity and we were like, "Okay, we're on to something."

>> It's it's a lot of fun to test these models when we're making them because you just you see all these amazing creative things that people make. Oh, wow. I I never thought that was possible.

>> So, it's it's really fun.

>> No, it's I mean, we've dealt with the whole with the whole family and it's it's it's a it's a crazy amount of fun.

>> So, think a bit about long term. Where does this lead? Right. I mean we we built these new tools that I think will change visual arts forever, right? I mean there we suddenly can transfer style. We suddenly can you generate consistent images of a subject, right? I have I have what used to be a very complex manual Photoshop process. Suddenly I type one command and magically happens. But what's the end state of this? I mean do do we have an idea yet? You know how will how will creative arts be taught in a university in you know five years from now?

want to take that.

>> So I I think it's going to be a spectrum of things, right? I think on the professional side, a lot of what we're hearing is that these models are allowing creators to do um less tedious parts of the job, right? They can be more creative and they can spend, you know, 90% of their time being creative versus 90% of their time like editing things and doing these tedious kind of manual operations. So I'm really excited about that. Like I think we'll see kind of an explosion of creativity like on that side of the spectrum. Um and then I think for consumers there's sort of like two spect two sides of the spectrum for this probably. One is you know you might just be doing some of these fun things like Halloween costumes for my kid, right? And and the out the goal there is probably just to like share it with somebody, right? Your family or your friends. Um on the other side of the spectrum, you might have these tasks like putting together a slide deck, right? I started out as a consultant. We talked about that at the beginning. Um, and you spend a lot of time on like very tedious things like trying to make things look good, trying to make the story make sense. I think for those types of tasks, you probably just have an agent who you give the specs of what you're trying to do and that it goes out and like actually lays it out nicely for you. It creates the right visual for the information that you're trying to convey. And it really is going to be this, I think, spectrum depending on what you're trying to do. Like do you want to be in the creative process and actually tinker with things and collaborate with the model or do you just want the model to like go do the task and be as minimally involved as possible?

>> So So in this new world then what what is art? I mean somebody recently said art is if you can create an an out of distribution sample. Is is that a good definition or or is it is it is it aiming too high? Right.

>> Do you think if art is out of distribution or in distribution for the model?

>> There we go.

I think that out of distribution sample that is a little bit too restrictive. I think a lot of great art is actually in distribution for art that occurred before it. So I I mean what is art? I think it's like a very philosophical debate and there's a lot of people that do discuss this. Like to me I think that the most important thing for art is intent. And so the the what is generated from these models is is a tool to allow people to create art. And I'm actually not worried about the high-end and the creatives and the professionals because I've seen like if you put me in front of one of these models like I can't create anything that anyone wants to see

>> but like and but I've seen what people can do who are creative people and who have like intent and these ideas and I think it's like that's the most interesting thing to me is is the things they create are really amazing and and inspiring for me. So, I feel like the the high-end and the the the professionals and the creatives, like they'll always use state-of-the-art tools, and this is like another tool in the tool belt for people to make cool things. I think one of the the really interesting things that I kept hearing about this model in particular from like creatives and artists was a lot of them felt like they couldn't use a lot of AI tools before because it didn't allow them the level of control that they expected for their art. what on one side that was like the um characters or object consistency like they really used that to have a compelling narrative for a story and so before when you couldn't get the same character over and over it was very difficult and then I think second the like second thing I hear all the time from artists is like um they love being able to upload multiple images and say like use the style of this on this character or add this thing to this image which is something that I think was very hard to do even with previous image edit models. I guess I'm curious like was that something you guys were really optimizing for when when you trained this one or or how did you think about that?

>> I mean yeah definitely sort of customizability and character consistency are things that we closely monitored during the development and we tried to do the best job we could on them. Um, I think another thing is also uh the iterative nature of kind of like an interactive conversation. Um, and you know, art tends to be iterative as well where you you make lots of changes, you see how it where it's going and you make more. Um, and this is another thing I think makes the model more useful and and actually that's an area that I also feel like we can improve the model greatly. Like I know that um once you get into really long conversations like it it starts to follow um your instructions a little bit worse. But like this something that we're planning to improve on and make the model more kind of like a natural conversation partner or like a creative partner in in making something.

>> One thing that's so interesting is after you guys launched Nano Banana, we start to hear about editing models all the time everywhere. Like it's like after you launched the world woke up and they were editing model, it's great, everyone wants it. And then obviously like it it kind of um you know goes into the customizability the personalization of it and then uh Oliver I know you used to be Adobe and then there's also software where we used to manually edit things. How do you see the knobs evolve now on the model layer versus what we used to do? Um, yeah. I mean, I think that, you know, one thing that that Adobe has always done and the professional tools generally require is lots of of control, lots of knobs, lots of of So, there's always a balance of like we want someone to be able to use this on their phone.

>> Um, maybe with just like a a voice interface,

>> and we also want someone who can really like a really professional art creative to be able to do fine skill adjustments. I think we haven't exactly figured out how to enable both of those yet. Um, but there's a lot of people that are building really compelling UIs like um and and and I think it's a you know we're Yeah, I think I think there's different ways it can be done. Um, I don't know. You have thoughts?

>> Well, I also hope that we get to a point where you don't have to learn what all these controls mean and the model can maybe smartly suggest what you could do next based on the context of what you've already done, right? Um, and that feels like it's kind of prime for someone to tackle that on. So like what do the UIs of the future look like um, in a way where you probably don't need to learn a hundred things that you had to before, but like the tools should be smart enough to suggest to you what it can do based on what you're already doing.

>> That's such an insightful take. I definitely had moments when when I used Nano Banana, I was like, I didn't know I wanted this, but

>> but I didn't even ask for this style. I don't even know have the words for that what that style even, you know, is called. So this is like very insightful on how image embedding and the language embedding is not one to one like we cannot map to like all the editing task with language. So oh go ahead.

>> Yeah, let me let me sort of take a little the counter point just to see where this goes.

>> The other the question of how complex the interface be can be limited by sort of what we can express in software, how easy we can make something in software which to some degree is also limited by how much complexity is a user willing to tolerate. And you know if you have a professional they only care about the result they're willing to tolerate a vast amount of complexity they they have the training they have the education they have the experience to use that right then we may end up with lots of knobs and dials it's just very different of dials right I mean today if you use a cursor or so for coding it's not that it has a super easy you know single text prompt interface it has it has a a good amount of you know here add context here different modes and so on right so so will we have Will we have like the the ultra sophisticated interface for the for the power user and how how would that look like? So I'm a big fan of Comfy UI and nodebased interfaces in general

>> and that is complex

>> and it's complex but it's also it's very robust and you can do a lot of things and so you know after we released Nano Banana we saw people building all these really complicated comfy UI workflows where they were combining a bunch of different models together and different tools and that generated some of the like for example using nanobana as um as a way to get storyboards or key frames for video models like you can plug these things together and and get really amazing outputs. So, I I think that like at the the pro or the developer level, like these kinds of interfaces are are great. Um, in terms of like the proumer level, I think it's it's very much unknown what it's going to look like in in a couple years.

>> Yeah. I think it just really depends on your audience, right? Because for the regular consumer, like I use my parents always as an example. The chatbot is actually kind of great.

>> Oh, yeah.

>> Because you don't have to learn a new UI. You you just upload your images and then you talk to them, right? Like it's it's kind of amazing that way. Then for the pros, I agree that like you need so much more control than you know and then there's somewhere in between probably which are people who may want to be doing this but they were too intimidated by the professional tools in the past and for them I do think that there's a space of like that you need more control than the chatbot gives you. Uh but you don't need as much control as what the professional tools give you and like what's that kind of in between state?

>> There's a ton of opportunity there.

>> There's a ton of opportunity there. It is interesting you mentioned comfy UI because it's on the other far spectrum of workflow like a workflow can have hundreds of steps and notes and you need to make sure all of them work whereas on the other side of the spectrum there's nano banana you kind of describe something with words and then you get something out like I don't know what's a model architecture stuff like that but um I guess is your view that the world is moving to an ensemble of a model hosted by one provider doing it all or do you think the world is moving to more of everyone building a workflow. Nano Banana is one of the nodes in comfy work UI.

>> Um I I definitely don't think that that the the broad amount of use cases will be fully satisfied by one model at any point. So I think that there will always be a diversity of models. some like um I'll give you an example, but some you know we could we could optimize for um instruction following in our models. Make sure it does exactly what you want, but it might be um a worse model for someone who's looking for ideiation or kind of inspiration where they want the model to kind of take over and and do other things, go crazy. So like I just think there's so many different use cases and so many types of people that like there's a lot of space there's a lot of room in this space for multiple models. So that's that's where I see us going. I don't think this is going to be like a single to rule it a single model to rule them all.

>> Complete sense. Let's go to the very other end of the spectrum from the professional. Do you think kindergarteners in the future will learn drawing by by sketching something, you know, on a on a little tablet and then you have the AI make turn that into a beautiful image and and so that's how how they they allow get in touch with art. I don't know if you always wanted to turn into a beautiful image, but I but I think there's something there about the AI being again a partner and a teacher to you in a way that you like didn't have. So I

>> didn't know how to draw, still don't um don't have any talent for it really. Uh, but I think it would be great if we could use these tools in a way that actually teaches you kind of the step by steps and helps you critique and maybe again shows you kind of like an autocomplete almost for images like what like like what's the next step that I could take, right? Or maybe show me a couple of options and like how do I actually do this? So, I hope it's more that direction. I I don't think we all want, you know, every 5-year-old's image to suddenly look perfect.

>> We we we would probably lose something um in the process. As someone who struggled the most in high school out of all my classes of the art and the sketching class, I actually would have would have preferred it, but I know a lot of people want their kids to learn to draw, which I understand.

>> It's funny because we've been trying to get the model to create um like childlike crayon drawings, which is actually quite challenging.

>> Um, ironically, you know, sometimes the the things that are hard to make are because the level of abstraction is very large, right?

>> So, it's actually quite difficult to make those types of images. your dedicated prek fin.

>> We we do have seminar evals right now to try to see if we're getting better.

>> I'm in general I'm very optimistic about AI for education. And you know part of the reason is I think that most of us are visual learners, right? So that AI right now as a tutor basically all it can do is is talk to you or give you text to read and that's definitely not how students learn. So I think that these models have a lot of potential as a way to help education by giving people sort of visual cues. You know, imagine if you could get an explanation for something where you get the text explanation, but you also get images and figures that kind of like help explain how they work. I think it just everything will be much more useful, much more accessible for students. So I'm really excited about that. That is a

>> on that point, one thing that's very interesting to us is that when Nano Banana came out, it almost felt like there's part of a use case is the reasoning model. Like you have a diagram. Absolutely. Right. Like you can explain some knowledge visually. So the model not just doing approximation of the visual aspect. There's the reasoning aspect to it too.

>> Do you think that's where we're going to? Do you think all the model large models will realize that oh like to be a good LM or VL like uh VLM we have to have both image and language and audio and so on so forth.

>> 100%. I definitely think so. Um the the future for these AI models that I'm most excited by is where they are tools for people to accomplish more things. Like I think if you imagine a future where you have these agentic models that just talk to each other and do all the work, then it becomes a little bit less necessary that there's like this visual mode of communication. But as long as there's people in the loop and as long as the the kind of the the motivation for the task they're solving comes from people, I think it makes total sense that that visual modality is going to be really critical for any of these AI agents going forward.

Will we get to a point where there's actually so of you know I'm I'm asking you to create an image it sits for two hours reasons with itself has drafts explores different directions and then comes back with a final answer.

>> Yeah. Absolutely. If it's if necessary. Yeah. Like

>> and maybe not just for a single image but to the point of you know maybe you're redesigning your house and maybe you actually really don't want to be involved in the process right but you're like okay this is what it looks like this some this is some inspiration that I like. And then you send it to um a model the same way that you would send it to like a designer.

>> It's the visual deep research.

>> The vis it's like visual deep research basically. I really like that term. And then it goes off and does its thing and searches for maybe the furniture that would go with your environment and then it comes back to you and maybe presents you with options because maybe maybe you don't want to sit for two hours at one thing art book you know 10 10 slide deck. I also I think if you if you think about like um instruction manuals or like IKEA directions or something then like breaking down a hard problem into many intermediate steps could be really useful as as a way to communicate.

>> So when can we generate Lego sets?

>> Yeah, soon maybe

>> Right.

I mean there's a whole debate around world models and image models and how they fit together. thoughts enlighten us here. What is the what is the short summary of where we'll end up there?

>> Um I mean I don't know the answer. I think that um obviously the real world is in 3D. So if you have 3D a 3D world model or world model that has explicit 3D representations. There's a lot of advantages. For example, everything stays consistent all the time.

>> Um now the main challenge is that we don't walk around with 3D capture devices in our pocket. So in terms of like the available data for training these models, it's largely the projection onto onto 2D. So I think that both viewpoints are totally valid for where we're going. I come a bit from the projection side like I think it we can solve almost all the problems if not all the problems working on the projection of the 3D world directly and letting the models learn the latent world representations. I mean we see this already that the video models have very good 3D understanding. You can run reconstruction algorithms over the videos you generate and they're they're very accurate. Um, and in general, if you look at like the history of human art, like it it it starts as like the projection, right? People drawing on on cave walls. Um, all of our interfaces are in 2D. So, I think that like humans are very very well suited for working on this projection of the 3D world into a 2D plane. And it's a really natural environment for interfaces and for viewing. So,

>> that is very true. like um so I'm a cartoonist in my spare time and then drawing in 2D is just light and shadow and then you present yourself with 3D cannot we trick ourselves to believing it's 3D or it's you know on a piece of paper but then what human can do that you know like a drawing or like a model can do is you we can navigate the world like we see a table we can't walk past it I guess the question becomes if everything is 2D how do you solve that problem

>> well I don't think yeah so if we're trying to solve the robot products problems. I think maybe the 2D um representation is useful for planning and visualizing kind of at a high level.

>> Like I think people navigate by um by remembering kind of 2D projections of the world. Like you don't you don't build a 3D map in your head. You're more like oh I know I see this building I turn left.

>> Yeah.

>> So I think that like for that kind of planning it's reasonable but for the actual locomotion around the space like I definitely 3D is important there. So

>> robotics Yeah. They probably need 3D. That's the saving grace.

>> Yeah. Yeah. Um, so character consistency, which you previously mentioned, I really love the example of like when a model feels so personal, like people are so tempted to try it.

>> How did you unlock that moment? The reason why I asked is that character consistency is so hard.

>> Uh, there's a huge uncanny valley to it. like you know like if it's someone I don't know if I see their AI generation I'm like okay it's maybe the same person but if it's someone I know if there's just a little bit of a difference uh I I'm actually felt very turned off by it because I'm like this not a real person. So in that case how do you know what you're generating is good and then is it mostly by user feedback or like I love this or is it something else? You look at faces, you know, and but

face detection camera user and

>> no. So, so, so not even before you ever released this, right? So, when when we're we were developing this model, we actually started out doing character consistency evolves on faces we didn't know and it doesn't tell you anything, right?

>> Um, and then we started testing it on ourselves and quickly realized like, okay, this is what you need to do because this is a face that I'm familiar with. And so there is a lot of sort of eyeballing evaluations that happens and just the team testing it on themselves. Um, and just generally people they know like Oliver probably knows my face at this point enough to be able to tell whether or not it's actually me when it's generated.

>> Um, and so we do do a lot of that. Um, and then you know you you ideally test it on different sets of people, different ages, right? Different different kind of groups of folks to make sure that it kind of works across the board.

>> Yeah, I think they're right. I mean that that touches that touches a little bit on this this um bigger issue which is that like eval are really difficult in this space

>> um because human perception is very uneven in terms of the things that it cares about. So um so really it's hard it's very hard to know like how good is the character consistency of a model and um is it good enough? Is it not good enough? Like you know I think there's there's still a lot of improvement we can make on character consistency but I think that for some use cases like we got to a point and that's you know we weren't the first edit model by any means but I think that like once the quality gets above a certain level for character consistency it can kind of just take off because it becomes useful for so much more.

>> And I think as it gets better it'll be useful for even more things too. Yeah,

>> I think one of the really interesting things we're seeing across a bunch of modalities of which image edit and generation obviously is one is like um I think the arenas and benchmarks and everything are awesome but especially when you have like multi-dimensional things like image and video um it's very hard as all of the models get better and better to condense every quality of a model into like one judgment. So it's like, you know, you're judging, okay, you swap a character into an image and you change the style of the image. Maybe one did the character swap and consistency much better and the other did the style much better? Like how do you say which output is better? And it probably comes down to like what the person cares most about and what they're what they want to use it for. Um, are there like certain, you know, characteristics of the model that you guys value more than other things in like making those trade-offs when deciding which version of the model to deploy or like what to really focus on during training?

>> Um, yes, there are. One of the things I like about this space is that uh there is no right answer. So, actually there's quite a lot of of I don't know if it's taste, but it's like preference that goes into the models. And I think you can kind of see the difference in preferences of the different research labs in the models that they release.

>> So like when we're balancing two things, a lot of it comes down to like, oh well, I I don't know. I just like this this look better or I you know, this this feature is more important to us.

>> I'd imagine it's hard for for you guys, too, because you have you have so many users, right? like Google like being in the Gemini app like everyone in the world can use that versus like many other AI companies just think about like we're only going for the professional creatives or we're only going for the consumer meat makers and like you guys have the unique and exciting but challenging task of like literally anyone in the world can do this. How do we decide what everyone would want?

>> Yeah. And it is sometimes we do make these trade-offs. We do have a set of things that are sort of like super high priority that we don't want to regret restress on. Right? So now because character consistency was so awesome and so many people are using it, we don't want our next models to get worse on that dimension. Right? So we pay a lot of attention to it. We care a lot about images looking photorealistic when you want photos and this is important. One, I think we all prefer that style. too. Um, you know, for advertising use cases, for example, like a lot of it is kind of photorealistic images of products and people. And so, we want to make sure that we can kind of do that. And then sometimes there are just things that like will kind of fall down the wayside. So, for this first release, the model is not as good as text rendering at as we would like it to be, and that's something that we want to fix in the future. But it was kind of one of those things where we looked at, okay, the model's good at XY Z, it's not as good at this, but we still think it's okay to release and it will still be an exciting thing for people to play with.

>> If you look at the past, right, we we we had for previous model generations, a lot of things we did with like sidecar models like control net or something like that where we basically figured out a way to provide structured data to the model to achieve a particular result. It seems like these newer models that has taken a step back just because they're so incredibly good in just prompting or or you know giving a reference image and picking things up from there. Where will this go long term? Do you think this will come back to some degree? Um you know like I mean for from the creators perspective right having I don't know open pose information so I can get get a pose exactly right right for multiple characters. This seems very very tempting, right? Is it like or to rephrase it a little bit, it's like does the bitter lesson hold here that at the end of the day everything's just one big model and you throw things in or is there is a little of structure we can we can offer to make this better?

>> Um, I mean I think that there will be there'll always be users that want control that the model doesn't give you out of the box. But I think we we tried to make it so that um you know because really what really what an artist wants when they want to do something is they want the intent to be understood. And I think that that these um AI models are getting better at understanding the intent of users. So often when you ask text queries now the model gets what you're going for.

>> So you know in that sense I think we can we can get pretty far with understanding the intent of our users.

>> And um and maybe some of that is personalization like we need to know information about what you're trying to do or what you've done in the past. But I think once you can understand the intent then you can you can generally do the the type of edit like is this like a very structure preserving edit or is this like a free form kind of like we can learn these these kinds of effects I think. Um but still of course there's one person who's going to really care about every pixel and like this this thing needs to be slightly to the left and a little bit more blue and like those people will use existing tools to do that.

>> I mean I think it's like you know I want an image with 26 people spelling out every letter of the alphabet or something like that. Right. That's off the thing where I think we're still quite a bit away from getting that right, you know, in the first try. On the other hand, with pose information, it could potentially get

>> But then the then the question I guess is like do you really want to be the one who's like extracting the pose and providing that as an information or do you just want to provide some reference image and say like this is actually what I want like model go figure this out right

>> there are 26 people every now and different style. Fair enough. Yeah, I think in that in that case I wouldn't spend a ton of time building a custom um interface for making this this picture of 46 people seems like the kind of thing that we can we can solve.

>> Just transfer.

>> Do you think the representation of what the AI images are will change? So the reason why I asked the question is that as artists there's different formats we play with. There's the SVGs, we have anchor points and bezier curves.

>> And on the other side, there's, you know, porcy or like fresco, what have you. There's layers that we can also play with. There's the other parameter which is what's the brush you use like the brush, the texture of it. So, every one parameter you can write script and actually uh do something very personal about it. Mhm.

>> Do you think like pixel is the right representation um the endgame for image generation model or do you think there's a net new representation that we haven't invented yet?

>> That's an easy question to

>> wow. Um I I'll say that uh that um everything is a subset of pixels.

>> That's true.

>> So text is a subset of pixels because I could just render all the text as an image. So how far can we get with just pixels is an interesting question. I think you know if the model is really um responsive and handles multi-turn interactions well then I think you can probably get pretty far because the primary reason I think you would want to leave the pixel domain is for editability.

>> Um and so you know in cases where you need to have your font or you want to change the text or you want to move things around just like with control points um it could be useful to have um kind of mix generation which consists of pixels and SVGs and other other forms. Um but if we can do it all, if we can if the multi- interaction is enough, then I think you can get pretty far with pixels. Um I will say that one of the things that's exciting about these um these models that have native capabilities is that you now have a model that can generate code and it can generate images.

>> So there's a lot of interesting things that come in that intersection, right? Like maybe I wanted to write some code and then make make some some things be rasterized, some things be parametric.

>> Yeah.

>> Like stick it all together,

>> train it together. Like this would be very cool. That's such a good point because I did see a tweet of someone asking Cloud Sauna to replicate a image on an Excel sheet where every cell is a pixel

>> which is like a very fun exercise. It was like a coding model like doesn't really know anything about you know images yet it worked.

>> Yeah, there's the classic pelican riding a bicycle test.

>> Yeah, totally. I have one on on model like on interfaces if that's okay. I don't sorry if I'm bringing up too much product stuff guys. I'm just very curious on on the product front like um I guess I'm curious how you think about like owning the interface where people are editing or generating images with Nano Banana versus really just wanting a ton of people to use the model for different things in the API. Like we've talked about so many different use cases like ads, you know, education, um design, uh like architecture. Each of those things could be there could be a standalone product built on top of Nano Banana that prompts the model in the right way or allows certain types of inputs or whatever. Is your guys' vision like that the kind of the product in the Gemini app is like a playground for people to explore and then developers will build the individual products that are used for certain use cases or is that something you're also kind of interested in owning?

>> I think it's a little bit of everything. Um, so I definitely think that the Gemini app is an entry point for people to explore. And the one the nice thing about Nano Banana is I think it shows that fun is kind of a gateway to utility where you know people come to make a figurine image of themselves but then they stay because it helps them with their math homework or it helps them write something, right? And and so I think that's a really powerful kind of transition point. Um there's definitely interfaces that we're interested in building and exploring as a company. And so um you know you may have seen Flo from Josh's team in labs that's that's really trying to rethink like what's what's the tool for AI filmmakers right and for AI filmmakers image is actually a big part of the iteration journey right because video creation is expensive a lot of people kind of think in frames um when they when they initially start creating and a lot of them even start in the LLM space for like brainstorming and thinking about what they want to create in the first place um and so there's definitely kind of place that we have in that space of just us trying to think about like what does look like. Um, we have the advantage of it kind of sitting close to the models and the interfaces so we can kind of build that in in a tight coupling. Um, and then there's definitely the, you know, we're probably not going to go build a software for an architecture firm. My dad is an architect and he would probably love that. Um, but I don't think that's something that we will do, but somebody should go and do that. Um, and that's why it's exciting because we do have the developer business and we have the enterprise business and so people can go use these models and then figure out like what's the next generation workflow for like this specific audience so that I can help them solve a problem. So I I think the answer is kind of like yes all three.

>> Yeah.

>> Yeah. I I brought that up. I don't know if you guys have been following the reception of Nano Banana in Japan, but um I'm sure you've had it's it's been insane. And it's so funny like I now half of my X feed is these really heavy Nano Banana users in Japan who have created like Chrome extensions called there's one called like Easy Banana that's specifically for using Nano Banana for like manga generation and specific types of anime and things like that. And like they go super deep into basically prompting the model for you and storing the outputs in various places. Um using obviously your your underlying model to generate these like amazing anime that you would never guess were AI generated because like the level of of precision and consistency and that sort of thing is just beyond what I've seen any single model be able to do today.

I guess um what are some like to Justin's point what are some force multipliers that you guys have seen in the model? So what I mean by this is for example if you unlock character consistency you can generate different frames and then you can make a video and then you can make a movie right. Um so these are the things that if you get it right and get it really well there's so much more downstream tasks that can derive from it. Um just curious like how do you think about what are the force m multipliers that you want to unlock? So the next

>> what's the next big one

>> what's the next yeah big wave of people who can just use nano nano as the base model for all the downstream tasks.

So I think one one current one actually is also the latency point right because I think because I think it's also just like it makes it really fun to iterate with these models when it just takes 10 seconds to generate the next frame right if you had to sit there and wait for two minutes like you would probably just give up and leave a very different experience so I think that's one just like there has to be some quality bar because if it's just fast and the quality isn't there then it also doesn't matter right like you have to hit a quality bar and then um then speed becomes a force multiplier I think this general idea of just visualizing information to your education point from earlier is sort of another one, right? And that needs

>> good text. It needs factuality, right? Because if you're going to start making kind of visual explainers about something, um it it looks nice, but it also needs to be accurate,

>> right?

>> And so, and so I think that's probably kind of the next level where at some point then you could also just have a personalized textbook to you, right? Where it's not just the text that's different, but it's also the visuals. Yeah, the diamond age that was basically Yeah, basically. Um, and then it should also internationalize really well, right? Because a lot of the times today you might actually be able to find a diagram that explains the thing that you're trying to learn about on the internet, but it's maybe not in the language that you actually speak. Um, right? And so I think that becomes just like another way to improve and open up accessibility um of information to just a lot more people and again visually because a lot of people are visual learners.

>> Interesting. How do you think about like images generated? So the reason why I asked is that there's another very cool example. I've seen someone making it work with nano banana which is he wrote a script and then he kept prompt the model to say generate the frame one second after this and then it became a video. So and then when I saw it I'm like well is every image just one frame in a continuum like you always know about the continuum in a parallel universe. you could have you know generated any one of them.

>> It's one big directive graph that

>> right exactly and then maybe it's video at the end of the day. So how do you see that? Where does it you know intersect or not intersect?

>> I think it's very yeah video and images are very closely related. Um and also I think what we're seeing in these kind of what's coming next or sequence predicting um use cases is the the generalization in world knowledge of the model as well. Um and and this is and so where where do I think it's going? I think that we will have yeah I think video is um an obvious next kind of domain. I think that like when when you have editing um a lot of times what you're asking is like you know what happens if I do this and that's what video has it has the the time sequence of actions. So it's like we have a slow frames per second video that you can interact with,

>> but obviously making something that's like fully interactive and real time and um is the direction this this field is headed.

>> So you are probably in the zero I don't know how many zer 0.001% of most experienced people in the world using image models.

>> What are your your personal favorite use cases? How how do you use it dayto-day if you're not just testing an existing model?

Well, I so I'm not sure I am in the the very top, but but I'll tell you what, um I mean it's it's like we were saying earlier, the personalization aspect is the the thing that totally drives it home for me. I have I have two young kids and like the best things that I do with the model are the things I do with my kids and like we can make, you know, make their their stuffed animals come to life in these types of applications and it's just so personal and gratifying to see. Um, we also a lot of people um taking old pictures of their family for example and like um showing what restoring them and and like so I think that that's that's the the real beauty of the edit models is that you can you can make it about the one thing that matters most to you.

>> So that's what I use it for is is my kids basically.

>> Very nice.

>> Yeah. You're you're basically making content that you probably would have never made before and it's like for the consumption of one person, right? Or or or one family and you're kind of telling these stories that you would have never told before. So, kind of similar like I do a lot of family holiday cards and birthday cards and whatnot. Um, now anytime I make a slide deck, I like force myself to generate some images that are like contextually relevant and then try to get the text right um and all of those things. And then we try to push the boundaries around like can you make a chart in the pixel space? Do you want to that's another question, right? Because you also want the um you want the bars in the bar chart to be accurately positioned relative to one another. Um so I I think we do a lot of these things. I'm actually really impressed with the people we work with on the team who are just like very creative. Um we have a team um who just works really closely with us on models that we're developing and then they just like push the boundary. They'll do like crazy things with the models.

>> What's the most surprising thing you've seen here? Like I didn't know our model can do this. Yeah.

>> This is even just kind of like simple things where people have been doing like texture transfer. Like they will take

>> Yeah. like you take a portrait of a person and then you're like what would it look like but if it had the texture of this piece of wood and I'm like I would have never I would have never thought of this being a use case because my brain just doesn't work that way. Um but people like kind of just push the boundaries of what you're what you can do with these things.

>> That is an interesting uh example of a world knowledge because texture technically is 3D because there's like the whole 3D aspect of it. There's a light and shadow of it but this is a 2D transfer. Yeah. So that's very cool. I think for me the the thing I'm most excited by and maybe most impressed by is um are the the use cases that test the reasoning abilities of the models. So um some people in our team figured out you could like give geometry problems to the model and like ask it to kind of you know solve for X here or fill in this missing thing or like present this this from a slightly different like a different view

>> and like these types of um of things that really require world knowledge and the reasoning ability of like a state-of-the-art language model are the things that make me really go wow that's amazing I didn't think we would be able to do that.

>> Can it uh generate compile code on a blackboard yet? And like if I take a picture of my I don't know like code on the laptop, would it know if it compiles on the image model?

>> Um I've I've seen examples where people give it like an image of HTML code and have the model render the the web page and it can do that.

>> That's very cool. The coolest example I saw, so I came from academia, so I spent a lot of time writing papers and making figures. And um one of our colleagues uh took a picture of one of the result figures uh from one of their papers with a method that could do a bunch of different things. This this one, you know, a bunch of different um type of applications in the paper and asked the model to and like sort of erased the um the results. So you have like the inputs and asked the model to like solve all of these in picture form in a figure of a paper and it was able to do that. So it could actually like figure out what is the problem that this one figure is asking for, find the answer and put it in the image and then do that for a bunch of different applications at the same time which was really amazing. Very cool.

>> That's very cool. Have um has anyone built application on top of that capability yet? Like what's the application that will come out of that?

>> I think that there are a lot of very interesting I would say zero transfer capability like problem solving type things that we don't even know the boundary of yet. And some of these are are probably quite useful like you know if you want to have a method that does solves some problem X I don't know like finds the the the the normals of the scene or something like the service orientations or something um you probably can prompt the model to give you kind of a reasonable estimate. Um so I think there's lots of problems like sort of understanding problems and other types of things that we could maybe solve with zero or few shop prompting that we don't know yet. Yeah, there's one thing you mentioned I found super interesting, which is the world knowledge transfer, but in a lot of world models like or video models, there always is something that keeps the state like just because you look away doesn't mean that the chair should disappear or change color because it's that's not what the state of the world is. How do you see that? Do you think there's relevance there in image model? Is that something you even consider optimizing for? Yeah, I mean if you think about an image model that has a a long context where you can put other things in that context like text, images, audio, video, then I think it's definitely like your reasoning over the context of things you have to produce a final output image

>> or video. Um so yeah, I think there's definitely um some model capability to do this type of stuff already.

>> Got it. I haven't tested it out yet for this big use case, but I'll let you know. That's one of my favorite things about these models is just finding and I'm sure it's really fun for you guys and you guys probably have much more of a hint than we do about what they can do. But sometimes you'll just see some crazy X or Reddit or wherever post about some incredible thing that someone has figured out um how to do that you would never expect that the model might be able to do necessarily and then other people kind of build on that and say like oh and then I tried the next iteration of this thing and suddenly you have this like almost entirely new space that's been discovered in terms of what the what the models are capable of. It must be fun as people much more deeply involved in kind of building these models and building the interfaces to kind of watch that happen.

>> Yeah.

>> So, so if you talk to visual artists today, I I've, you know, I I personally love this stuff. I post about it on the internet. You can get some very skeptical answers. People like, "Oh, this is terrible." Right? Like what do you have any idea what triggers this this reaction, right? I'm convinced that this ultimately really empowers the artists, right? It gives you new tools, right? is like hey we now have I don't know watercolors for Michelangelo let's see what he does with it right and amazing things come out it's of the similar thing but but what triggers this this strong reaction against it

>> so I think it's something something to do with the amount of control over the output so you know in the beginning when we had these kinds of text image models they would be very much like a oneshot you put in some text you get an output and people would be like oh this is art this is this thing I made and I think that maybe rubs people a little bit the wrong way who are come from the creative community because um you know that it's it's most of the decisions that were made were made by the model by the data that was used to train

>> express yourself anymore physically right

>> yeah exactly it's not yeah so as a creative person you want to be able to express yourself so I think as we make the models more controllable then a lot of these concerns of like oh that's just that the computer is doing everything kind of may may go away um and the other thing is I think that that there was a period of time where we were all so amazed by the images these models could create that like we were we were pretty like uh happy to see just like oh this stuff comes out of these models but I think humans get really bored fast of this type of thing. So like there was a big rush and now if you see a if you see an image that you know was just like oh that's just like a single prompt person didn't think about it much you can kind of tell like that's an AI generated image not that interesting. So I think like there's still this boundary of like now you need to be able to make interesting things with the AI tools um which is hard but it this will yeah this will always be you know a requirement. We need someone to be able to do this. And I think

>> we still need artists.

>> We still need artists. And I think artists will be able to also recognize when when people have actually like put a lot of control and intent

>> and still not be an artist.

>> Maybe get but but it it is there's a lot of craft and there's a lot of taste, right, that you accumulate sometimes over decades, right? And I don't think these models really have taste, right? And so I think a lot of like a lot of the reactions that you mentioned maybe also come from that. And so we do work with a lot of artists across all the modalities that we work with. Um so image, video, um music because we really care about like building the technology step by step with them and trying to figure out they really help us kind of like push the boundary of what's possible. A lot of people are really excited, but they they really do bring a lot of their knowledge and expertise and kind of like 30 years of design knowledge. We just work with um Ross Loveg Grove um on fine-tuning a model on his sketches so that he can then create something new

>> out of that and then we design an actual physical chair that we like have a prototype of um and so there there's a lot of people who want to kind of bring the expertise that they've built and kind of like the rich language that they use to describe their work and and have that dialogue with the model so that they can push their work kind of to the frontier. And it is, you know, it doesn't happen in like one prompt and two minutes. Um, it it does require a lot of that kind of taste and human creation and and craft that goes into building something that actually then, you know, becomes art.

>> At the end, it's still a tool that requires the human behind it to to express the feelings and the emotions and the story and everything.

>> Yeah, absolutely. Absolutely.

>> And that's what resonates with you when you probably look at it, right? Um, you you will have a different reaction when you know that there's a human behind it who has spent 30 years thinking about something and then pour that into a piece of art.

I think there's also a bit of this um phenomenon that like most people who consume creative content and maybe even ones that are that care a lot about it like they they don't know what they're going to like next. You need someone who has a vision and can do something that's interesting and different, right? And then you show it to people like, "Oh, wow. That's amazing." But like they wouldn't necessarily like think of that on their own,

>> right?

>> So when we're, you know, when we're optimizing these models, like one thing we could do is we could optimize for like the the average preference of everybody.

>> But I don't think you end up with interesting things by doing that. You end up with something that everyone kind of likes, but you don't end up with things that people are like, "Oh, wow. That's amazing. like I'm going to change my my my whole like perspective of art because I saw that

>> there's the avantguard edition of the model

if I use it with the term there's the I don't know what's what's the other end of the spectrum the marketing edition or so where it's very predictable and

>> very straightforward.

>> Yeah. Well, since we're coming up on time, uh, last couple question. One is, what's one feature that you know the model is capable of that you wish people ask you more?

>> Interle.

>> Yeah, in I think we've always been amazed that nobody ever posts anything about in solely generation is what we call the model's ability to generate more than one image for a specific prompt. So, you can ask for like I want a story like a bedtime story or something like generate the same character over these series of images. And I think that um yeah, people haven't really found it useful yet or haven't discovered it. I don't know.

>> Oh, interesting. Well, if you're listening to the podcast, go try this out.

>> Try

>> Yeah. And what's the most um exciting technical challenge that you look forward to tackling in the next, I don't know, months, years.

>> So, I think that there's really a high ceiling in terms of quality for where we're going. Like, I think you people look at these images and say, "Oh, it's almost perfect. we must be done. And for a while, we were in this like cherrypick phase where we would, you know, everyone would pick their best images. So, you look at those and they're great. But actually, what's more important now is the worst image. We're in a lemon picking stage because every model can cherrypick images that look perfect.

>> So, like now I think the real question is like how expressable is this model and what's the worst image you would get given what you're trying to do.

>> So, I think by raising the quality of the worst image, we really open up the amount of use cases for things we can do. like there's all kinds of productivity use cases like um you know beyond this kind of like immediate creative tasks that we know the model can do and I think that's a direction we're headed we're headed to where if these models can do more things reasonably then they're just the the use cases will be far greater

>> so that's the that's the moral equivalent of the monkeys on typewriters basically any model given enough tries will eventually make an amazing adventure

>> but the the other way around it's hard

>> yeah the other round is hard one monkey writing a book would be very hard

>> it would be a good monkey for that one

>> what are the applications you think that would come out when we reach the lower bound?

>> So, the one I'm most interested in, we mentioned this before, is education factuality. I have um you know, I I have every I don't know how many times I want to use these models for creative purposes a month, but like I have way more use cases for information seeking, factuality, kind of like learning, education type use cases. So, I think like once that starts working, then it'll be opening up all these new areas. Amazing.

>> There's also something about I think taking more advantage of the models context window. Um so you can input a really large amount of content right into these LLMs. And um some companies um you mentioned a few before um they will have like 150 page brand guidelines on like what you can and cannot do, right? And they're like very precise, right? Like colors, fonts, and right um and like the the the size of like a Lego brick maybe. Um and so being able to actually like take that in and follow that to a tea when you're doing generation that's like a whole new level of control um that we just can't we don't have today right um to to make sure that you're actually kind of like following that to a tea. I think that will build a lot of trust with you know very established brands. where we have a second creative compliance review model that then double checks everything that I could do against the the model should do it on its own, right? Like like it should kind of have this yes it should have this loop as like okay I generate this but then page 52 says that I shouldn't have right and I'm going to go back and try again and then two hours later it will come back to you with that respect.

>> Yeah.

>> And we saw with the text models how this inference time scaling how much it can help right being able to to critique your own work. Yep.

>> So this this feels really important.

>> Boy, an incredibly amazingly exciting future for for image models.

>> Yes. And congrats on all the amazing work.

>> Thank you.

>> Thank you.

>> Thanks for having us.

>> Well, thank you so much for coming on the pod.