Google DeepMind Developers: How Nano Banana Was Made

Summary

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a16z Podcast: Google DeepMind Developers – How Nano Banana Was Made

Episode Date: October 28, 2025
Guests: Oliver Wang (Google DeepMind), Nicole Brytova (Google DeepMind), Product/UX Specialist, Moderator/Interviewer
Podcast Host: Andreessen Horowitz

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Episode Overview

This episode dives deep into the origins, design, and impact of "Nano Banana" (Gemini 2.5 Image) – Google's conversational image generation and editing AI model. The Google DeepMind team discusses the technical and philosophical challenges of developing a model that merges visual quality with multimodal, conversational interaction. They explore questions around creative empowerment, user control, artistic intent, model evaluation, education, safety, and where visual reasoning fits into the future of multimodal AI agents.

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Key Themes and Insights

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1. Origins and Technical Evolution of Nano Banana

[01:11–02:38]

• Background: Nano Banana arose from merging the high-fidelity Imagen models with the interactive, conversational focus of Gemini’s multimodal framework.
• Naming: "Nano Banana" became the informal name, reflecting the model's accessibility and internal popularity.

“It really became kind of the best of both worlds... the Gemini smart and multimodal conversational nature of it, plus the visual quality of Imagen.” — Oliver Wang [02:22]

• Breakout Success: The model’s popularity exceeded expectations with rapid uptake after public release, especially on LM Arena.

“People were going out of their way and using a website that would only give you the model some percentage of the time. But even that was worth it.” — Google DeepMind Researcher [02:49]

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2. Democratizing Creativity & Impact on Artistic Workflows

[00:00–00:27], [05:06–06:55]

• Efficiency for Professionals:
  • The model frees creators from tedious manual tasks, letting them focus on creative work.

“They can spend 90% of their time being creative versus 90% of their time editing things.” — Oliver Wang [05:38]

• Consumer Use Cases: Spans from fun (costume images) to productivity (creating slide decks), and bridges fully automated tasks to collaborative, hands-on creative workflows.

• Empowerment: The tools are likened to new artistic media (e.g., watercolors for Michelangelo).

“It gives you new tools... amazing things come out.” — Nicole Brytova [00:15], [45:10]

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3. Art, Intent, and the Philosophical Question of Creativity

[07:01–08:08]

• What is Art? The team discusses whether art is “out-of-distribution” or a product of intent, concluding that the tool enhances, but doesn’t replace, human creativity.

“The most important thing for art is intent… the most interesting thing to me is the things [artists] create are really amazing and inspiring.” — Google DeepMind Researcher [07:17]

• AI as Artistic Aid: AI offers new techniques and possibilities, but professionals' taste, experience, and intentionality remain irreplaceable.

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4. Personalization, Consistency, and Control

[03:41–04:57], [08:08–10:14], [21:56–23:29]

• Character Consistency:
  • Achieving convincing, personal likeness in images—even zero-shot—is a highlight.
  • Internal “wow moments” often involved seeing oneself, loved ones, or pets transformed through the model.

“This was the first time when it was like zero shot... Just one image of me and it looks like me.” — Oliver Wang [03:41]

• Customization Needs: Users seek more control over character consistency and style transfer than previous AI tools permitted.

• Testing & Tuning: Consistency is evaluated by testing with faces familiar to the team, and then more broadly.

"We started testing it on ourselves and quickly realized... this is what you need to do, because this is a face that I’m familiar with." — Oliver Wang [22:48]

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5. Interfaces: Chatbots, Pro Tools, and the Future of Creativity UIs

[10:14–14:27]

• Spectrum of Complexity:
  • From simple chat-based interfaces for casual users, to power-user node-based UIs (e.g., ComfyUI) for professionals and developers.
  • Each user tier (consumer, prosumer, professional) will likely require a distinct design paradigm.

“For the regular consumer... the chatbot is actually kind of great.” — Oliver Wang [13:09]

• AI-Assisted UIs: The future may see interfaces that anticipate user intentions, reducing the need for explicit “knob-twiddling.”

"Maybe smartly suggest what you could do next based on the context of what you’ve already done." — Oliver Wang [10:47]

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6. Ecosystem, Modularization, and the “One Model vs Ensemble” Debate

[14:27–15:09]

• Model Plurality: No single model will serve every use case: diversity and modularization are essential.
• Workflows: Nano Banana can serve as a node within larger compositional or workflow-based UIs.

“There will always be a diversity of models... so many different use cases and so many types of people.” — Google DeepMind Researcher [14:27]

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7. Education & AI: The Visual Learning Revolution

[15:09–16:46], [17:24–18:27]

• AI as Tutor:
  • Visual learning will benefit enormously from AI that generates personalized, multi-modal learning content.
• Long-term Impact:
  • Speculative future: children might learn art by sketching, and AI assists, critiques, and “autocompletes” images.

“Most of us are visual learners... AI models have a lot of potential as a way to help education by giving people sort of visual cues.” — Google DeepMind Researcher [16:46]

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8. Reasoning, World Models, and Multimodality

[17:24–21:44], [41:20–43:36]

• Multimodal Reasoning:
  • Visual models are evolving beyond rendering into explaining, reasoning, and simulating.
  • Diagrams, figures, instruction sequences: all can leverage both image and language context.

“The future... is where [AI models] are tools for people to accomplish more things... the visual modality is going to be really critical for any of these AI agents going forward.” — Google DeepMind Researcher [17:55]

• 2D vs 3D Representations:
  • 2D is natural and sufficient for most user interfaces, but for robotics and spatial planning, 3D models are key.

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9. Force-Multiplier Features and the Future of Creative AI

[35:17–37:19]

• Unlocking Downstream Tasks:
  • Features like character consistency and low latency enable downstream uses: animation, video, design, accessibility.
• Accessibility: Internationalization and factuality are vital for educational and informational use cases.

“If it's just fast and the quality isn't there, then it also doesn't matter. You have to hit a quality bar, and then speed becomes a force multiplier." — Oliver Wang [35:53]

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10. Model Representation, Editability, and Next-Gen File Formats

[29:29–31:24]

• Beyond Pixels:
  • Discussion around whether pixels remain the right underlying data structure. Hybrid approaches (pixels + SVG + code) may offer future flexibility.
• Interleaved Generation:
  • Ability to generate multiple images in narrative sequence (“bedtime story”) is underused but exciting.

“Interleave generation is what we call the model’s ability to generate more than one image for a specific prompt... People haven’t really found it useful yet or haven’t discovered it. I don’t know.” — Google DeepMind Researcher [49:44]

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11. Model Evaluation, Tradeoffs, and User Diversity

[23:29–27:46], [25:12–26:03], [51:09–52:55]

• Multi-dimensional Evaluation:
  • Tradeoffs (e.g., style vs character consistency vs text rendering) are navigated by prioritizing features with the highest value for the largest or most dependent user groups.
• Shifting from “cherry-picking” best outputs to raising the "worst-case" output quality is the current frontier.

"Now I think the real question is how expressible is this model and what's the worst image you would get... By raising the quality of the worst image, we really open up the amount of use cases for things we can do." — Google DeepMind Researcher [50:21]

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12. The Role of Taste, Expertise, and Artistic Skepticism

[45:09–49:32]

• Artists’ Concerns:
  • Skepticism stems from perceived lack of control and expressivity.
  • As models enable more control, these concerns diminish.

“You want to be able to express yourself… as we make the models more controllable, then a lot of these concerns... may go away.” — Google DeepMind Researcher [46:11]

• Taste & Craft:
  • AI doesn’t have taste—artists’ accumulated expertise still matters deeply.
  • Collaboration with experienced artists helps push model boundaries.

“It doesn’t happen in one prompt and two minutes. It does require a lot of… taste and human creation and craft.” — Oliver Wang [47:17]

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Noteworthy Quotes and Moments

• On Artistic Empowerment:

  “We now have, I don’t know, watercolors for Michelangelo. Let’s see what he does with it.” — Nicole Brytova [00:15]

• On the Power of Personalization:

  “The moment more people realized that it was a really fun feature… is when they tried it on themselves. Because… it makes it so personal.” — Oliver Wang [03:41]

• On the Need for Diverse Interfaces:

  “The chatbot is actually kind of great... For the pros, I agree that you need so much more control.” — Oliver Wang [13:09]

• On Use in Education:

  “Imagine if you could get an explanation where you get the text, but you also get images and figures that help explain how they work. I think everything will be much more useful, much more accessible for students.” — Google DeepMind Researcher [16:46]

• On Raising the Lower Bound:

  “We’re in a lemon picking stage because every model can cherry pick images that look perfect. Now the real question is… what’s the worst image you would get?... By raising [that], we really open up the amount of use cases.” — Google DeepMind Researcher [50:21]

• On Artistic Skepticism:

  “There’s a lot of craft and… taste that you accumulate over decades. And I don’t think these models really have taste. So a lot of the reactions… may also come from that.” — Oliver Wang [47:10]

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Timestamps for Key Segments

• [01:11] – Origin story of Nano Banana / Gemini 2.5 Image
• [03:41] – The “wow moment” with zero-shot likeness and emotional resonance
• [05:06] – Long-term implications for creative arts and professional workflows
• [07:01] – Philosophical discussion: What is art in a world of generative models?
• [08:08] – Need for character consistency and artist-level control
• [10:14] – Interface spectrum: from chatbots to node-based pro UIs
• [14:27] – One big model vs. diverse workflow “nodes”
• [16:46] – Visual AI’s role in education and learning
• [17:55] – Multimodality: reasoning in visual and language domains
• [22:48] – Testing and evaluation for character consistency
• [25:12] – Challenges in model evaluation, taste, and prioritization
• [29:29] – File formats, representation, and future image standards
• [35:53] – Latency as a key force-multiplier for creative uses
• [45:09] – Addressing skepticism from visual artists and creative communities
• [49:44] – Interleaved generation as an underexplored feature
• [50:21] – Raising the lower bound for reliability and new use cases

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Conclusion

The Google DeepMind team presents Nano Banana as both a technical leap and a creative tool that democratizes high-quality, interactive image generation. Their focus is as much on artistic empowerment and user control as on technical sophistication. Nano Banana stands as a bridge between cutting-edge AI and the demands of real-world creators, from casual users to professionals. As AI art evolves, questions of representation, intent, evaluation, and artistic value remain vivid—and open.

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Transcript

[Oliver Wang]

These models are allowing creators to do 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.

[Nicole Brytova]

I'm convinced that this ultimately really empowers the artists. Right. And 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.

[Podcast Host]

One of the hardest challenges in AI isn't language or reasoning. It's vision models to understand, compose and edit images with the same precision that they process text. Today you'll hear a conversation with Oliver Wang and Nicole Brytova from Google DeepMind about Gemini 2.5 image, also known as nanobanana. They discuss the architecture behind the model, how image generation and editing are integrated into Gemini's multimodal framework, and what it takes to achieve character consistency, compositional control and conversational editing at scale. They also touch on open questions in model evaluation, safety and latency optimization, and how visual reasoning connects to broader advances in multimodal systems. Let's get into it.

[Interviewer / Moderator]

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

[Google DeepMind Researcher]

Sure. So our team has worked on image models for some time. We developed the Imagen family of models, which goes back a couple years ago. And actually there was also an image generation model in Gemini before the Gemini 2.0 image generation model. So what happened was the teams kind of started to focus more on the Gemini use cases. So like interactive, conversational and editing. And essentially what happened was we teamed up and we built this model which became what's known as nanobanana. So yeah, that's sort of the origin story.

[Product/UX Specialist]

Yeah.

[Oliver Wang]

And I think maybe just some more background on that. So our imagined models were always kind of top of the charts for visual quality. And 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 being able to generate images and text at the same time so you can maybe tell a story. Just the magic of being able to talk to images and edit them conversationally, but the visual quality was maybe not where we wanted it to be. And so Nanobanana or Gemini 2.5 flash image.

[Interviewer / Moderator]

Nanobanana is way cooler.

[Oliver Wang]

It's easier to say it's the name that Stuck. Yes, it's the name that stuck. But it really became kind of the best of both worlds in that sense, like the Gemini Smart and the multimodal kind of conversational nature of it, plus the visual quality of imagen. And I feel like that's maybe what resonates a lot with people.

[Interviewer / Moderator]

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

[Google DeepMind Researcher]

So I actually didn't feel like it was going to go viral until we had released on Ella Marina. And what we saw was that we budgeted a comparable amount of queries per second as we had for our previous models that were on LM arena. And we had to keep upping that number as people were going to LM arena 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, it surprised even me. I don't know about the whole team, but we were trying to make the best conversational editing model possible. But then it really started taking off when people were going out of their way and using a website that would actually only give you the model some percentage of the time. But even that was worth 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 going to be bigger.

[Interviewer / Moderator]

That's actually the best way to condition people. Only give them a rewards partially, not all the time.

[Oliver Wang]

That's by design. I had a moment earlier, so I've been trying some similar queries on kind of multiple generations of models over time. And a lot of them have to do with things I wanted to be as a kid. So like an astronaut explorer or 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. And you guys play with these models all the time. The only time that I've seen that before is if you fine tune a model using Lora or some other method to 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. 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 kind of fun when you see it on another person. But it doesn't really resonate with people emotionally. It makes it so personal where it's like you, your kids, you know, your spouse, and I think that's dog, your dog. 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 onto something.

[Google DeepMind Researcher]

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

[Nicole Brytova]

No, it's. I mean, we've done it with the whole family and it's a. A crazy amount of fun. So think a bit long term. Where does this lead? Right. I mean, we built these new tools that I think will change visual arts forever, right? We suddenly can transfer style. We suddenly can generate consistent images of a subject. Right. I have what used to be a very complex manual Photoshop process. Suddenly I type one command and magically happens. What's the end state of this? I mean, do we have an idea yet? How will creative arts be taught in a university in five years from now?

[Oliver Wang]

So 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 less tedious parts of the job. Right? They can be more creative and they can spend 90% of their time being creative versus 90% of their time editing things and doing these tedious kind of manual operations. So I'm really excited about that. I think we'll see kind of an explosion of creativity like on that side of the spectrum. And then I think for consumers, there's sort of like two sides of the spectrum for this probably. One is you might just be doing some of these fun things like Halloween costumes for my kid. Right. And the goal there is probably just to share it with somebody, right? Your family or your friends. 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. 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 then it goes out and 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. 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 go do the task and be as minimally involved as possible?

[Nicole Brytova]

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

[Interviewer / Moderator]

If art is out of distribution or in distribution for the model?

[Nicole Brytova]

There we go.

[Google DeepMind Researcher]

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 mean, what is art? I think it's like a very philosophical debate and there's a lot of people that do discuss this. To me, I think that the most important thing for art is intent. And so what is generated from these models 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 I've seen what people can do who are creative people and who have like intent and these ideas. And I think that's the most interesting thing to me is the things they create are really amazing and inspiring for me. So I feel like the high end and 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.

[Product/UX Specialist]

I think one of 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. On one side that was like the 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 the second thing I hear all the time from artists is they love being able to upload multiple images and say, 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, was that something you guys were really optimizing for when you trained this one? Or how did you think about that?

[Google DeepMind Researcher]

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. I think another thing is also the iterative nature of kind of like an interactive conversation. And art tends to be iterative as well, where you make lots of changes, you see where it's going, and you make more. And this is another thing that I think makes the model more useful. And actually that's an area that I also feel like we can improve the model greatly. I know that once you get into really long conversations, it starts to follow your instructions a little bit worse, but it's 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 making something.

[Interviewer / Moderator]

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 launch, the world woke up and they were like, editing model. It's great. Everyone wants it. And then obviously, like, it kind of goes into the customizability, the personalization of it. And then, 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?

[Google DeepMind Researcher]

Yeah, I mean, I think that one thing that Adobe has always done and the professional tools generally require is lots of control, lots of knobs, lots of. So there's always a balance of we want someone to be able to use this on their phone, maybe with just like a voice interface. And we also want someone who can really, like a really professional art creative to be able to do fine scale adjustments. I think we haven't exactly figured out how to enable both of those yet, but there's a lot of people that are building really compelling UIs, and I think there's different ways it can be done. I don't know your thoughts well.

[Oliver Wang]

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. 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 in a way where you probably don't need to learn a hundred things that you had to before, but the tools should be smart enough to suggest to you what it can do based on what you're already doing.

[Interviewer / Moderator]

That's such an insightful take. I definitely had moments when I used nanobanana. I was like, I didn't know I wanted this, but I didn't even ask for this style. I don't even have the words for what that style even is called. So this is very insightful on how image embedding and the language embedding is not one to one. We cannot map to all the editing tasks with language.

[Oliver Wang]

So.

[Interviewer / Moderator]

Oh, go ahead.

[Nicole Brytova]

Let me sort of take a little bit of the counterpoint just to see where this goes at the end. 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 software to some degree. It's 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 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. I mean today if you use a cursor or so for coding, it's not that it has a super easy single text prompt interface. It has a good amount of add context here, different modes and so on. Will we have the ultra sophisticated interface for the power user and how would that look like?

[Google DeepMind Researcher]

So I'm a big fan of comfyUI and node based interfaces in general. And that is complex and that'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 nanobanana 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 generates some of the like. For example, using nanobudana as a way to get storyboards or keyframes for video models, like you can plug these things together and get really amazing outputs. So I think that at the Pro or the developer level, these kinds of interfaces are great in terms of the Prosumer level. I think it's very much unknown what it's going to look like in a couple years.

[Oliver Wang]

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

[Nicole Brytova]

Oh yeah, totally.

[Oliver Wang]

Because you don't have to learn a new ui, you just upload your images and then you talk to them. Right. It's kind of amazing that way. Then for the pros, I agree that you need so much more control 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 that you need more control than the chatbot gives you, but you don't need as much control as what the professional tools give you. And what's that kind of in between state?

[Nicole Brytova]

There's a ton of opportunity there.

[Oliver Wang]

There's a ton of opportunity there.

[Interviewer / Moderator]

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. I don't know what's a model architecture, stuff like that, but I guess is your view that the world is moving to ensemble, a model hosted by one provider doing it all, or do you think the world is moving to more of everyone building a workflow? Nanobanana is one of the nodes in Comfy Work ui.

[Google DeepMind Researcher]

I definitely don't think that 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. I'll give you an example. But we could optimize for instruction following in our models, make sure it does exactly what you want. But it might be a worse model for someone who's looking for ideation or kind of inspiration where they want the model to kind of take over and do other things go crazy. So I just think there's so many different use cases and so many types of people that there's a lot of space, there's a lot of room in this space for multiple models. So 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.

[Nicole Brytova]

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 sketching something on a little tablet? And then you have the AI turn that into a beautiful image. And so that's how they alone get in touch with art.

[Oliver Wang]

I don't know if you always want it to turn into a beautiful image, but I think there's something there about the AI being again a partner and a teacher to you in a way that you didn't have. So I didn't know how to draw. Still don't. I'm don't have any talent for it really. 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. What's the next step that I could take? Or maybe show me a couple of options and how do I actually do this? So I hope it's more that direction. I don't think we all want every five year old's image to suddenly look perfect. We would probably list something in the process.

[Product/UX Specialist]

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.

[Google DeepMind Researcher]

It's funny because we've been trying to get the model to create like childlike crayon drawings, which is actually quite challenging. Ironically, sometimes the things that are hard to make are because the level of abstraction is very large. So it's actually quite difficult to make those types of images.

[Nicole Brytova]

Do dedicated pre K fine tuning.

[Oliver Wang]

Yeah, we do have some in our evals right now to try to see if we're getting better.

[Google DeepMind Researcher]

In general, I'm very optimistic about AI for education. And part of the reason is I think that most of us are visual learners. Right? So that AI right now as a tutor, all I can do 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 on that point.

[Interviewer / Moderator]

One thing that's very interesting to Us is that when nanopanata came out, it almost felt like there's part of a use case is the reasoning model. Like you have a diagram, 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 large models will realize that, oh, to be a good LLM or vlm, we have to have both image and language and audio and so on and so forth?

[Google DeepMind Researcher]

100%? I definitely think so. The future for these AI models that I'm most excited by is where they are tools for people to accomplish more things. 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 this visual mode of communication. But as long as there's people in the loop and as long as the motivation for the task they're solving comes from people, I think it makes total sense that visual modality is going to be really critical for any of these AI agents going forward.

[Nicole Brytova]

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

[Google DeepMind Researcher]

Yeah, absolutely, if it's necessary.

[Product/UX Specialist]

Yeah.

[Oliver Wang]

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

[Nicole Brytova]

Visual deep research.

[Oliver Wang]

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 it presents you with options because maybe you don't want to sit for two hours.

[Nicole Brytova]

Art book.

[Oliver Wang]

10 slide deck.

[Google DeepMind Researcher]

I mean, also, I think if you think about instruction manuals or IKEA directions or something, then breaking down a hard problem into many intermediate steps could be really useful as a way to communicate.

[Interviewer / Moderator]

So when can we generate LEGO sets?

[Google DeepMind Researcher]

Yeah, soon, maybe.

[Nicole Brytova]

Do we at some point need 3D as part of it?

[Interviewer / Moderator]

Right.

[Nicole Brytova]

There's a whole debate around world models and image models and how they fit together. Enlighten us here. What is the short Summary of where.

[Podcast Host]

We'Ll end up there.

[Google DeepMind Researcher]

I mean, I don't know the answer. I think that obviously the real world is in 3D. So if you have a 3D world model or a world model that has explicit 3D representations, there's a lot of advantages. For example, everything stays consistent all the time. Now, the main challenge is that we don't walk around with 3D capture devices in our pocket. So in terms of the available data for training these models, it's largely the projection 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 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 very accurate. And in general, if you look at the history of human art, like, it starts as, like, the projection, right? People drawing on cave walls. All of our interfaces are in 2D. So I think that 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.

[Interviewer / Moderator]

That is very true. 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, we trick ourselves to believing it's 3D war, it's on a piece of paper. But then what human can do that a drawing or a model can do is we can navigate the world. 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?

[Google DeepMind Researcher]

Well, I don't think. Yeah. So if we're trying to solve the robotics problems, I think maybe the 2D representation is useful for planning and visualizing kind of at a high level. I think people navigate 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.

[Oliver Wang]

Yeah.

[Google DeepMind Researcher]

So I think that, like, for that kind of planning, it's reasonable, but for the actual locomotion around the space, like, definitely 3D is important there. So robotics. Yeah, they probably need 3D.

[Nicole Brytova]

That's the saving grace.

[Interviewer / Moderator]

Yeah, yeah. 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. There's a huge, uncanny value to it. 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, I actually felt very turned off by it because I'm like, this is not a real person. So in that case, how do you know where you're generating a scale? Good. And then is it mostly by user feedback or like, I love this, or is this something else?

[Oliver Wang]

You look at faces, you know, but.

[Nicole Brytova]

Face detection, camera, user.

[Oliver Wang]

So not even before you ever released this. Right. So when we were developing this model, we actually started out doing character consistency evals on faces we didn't know, and it doesn't tell you anything.

[Nicole Brytova]

Right.

[Oliver Wang]

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 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. And so we do do a lot of that. And then you ideally tested on different sets of people, different ages, different kind of groups of folks to make sure that it kind of works across the board.

[Google DeepMind Researcher]

Yeah, I think they're right. I mean, that touches a little bit on this bigger issue, which is that evals are really difficult in this because human perception is very uneven in terms of the things that it cares about. So really it's very hard to know how good is the character consistency of a model and is it good enough? Is it not good enough? I think there's still a lot of improvement we can make on character consistency, but I think that for some use cases, we got to a point and we weren't the first edit model by any means. But I think that 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.

[Interviewer / Moderator]

Yeah.

[Product/UX Specialist]

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 I think the arenas and benchmarks and everything are awesome, but especially when you have like multi dimensional things like image and video, 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. 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?

[Google DeepMind Researcher]

Yes, there are. One of the things I like about this space is that there is no right answer. So actually there's quite a lot 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 don't know, I just like this, this look better or you know, this, this feature is more important to us.

[Product/UX Specialist]

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?

[Oliver Wang]

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 rest 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. Two, for advertising use cases, for example, a lot of it is 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 will kind of fall down the wayside. So for this first release, the model is not as good as text rendering 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 X, Y, 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.

[Nicole Brytova]

If you look at the past, right. We had for previous model generations, a lot of things we did with like sidecar models like ControlNet 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 giving a reference image and picking things up from there. Where would this go long term? Do you think this will come back to some degree from the creator's perspective, having, I don't know, open pose information so I can get a pose exactly right for multiple characters? This seems very, very tempting. 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 a little structure we can offer to make this better?

[Google DeepMind Researcher]

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, 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 these 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.

[Podcast Host]

Yeah.

[Google DeepMind Researcher]

So in that sense, I think we can get pretty far with understanding the intent of our users. And maybe some of that is personalization. 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 generally do the type of edit. Is this a very structure preserving edit or is this a freeform? We can learn these kinds of effects, I think, but still, of course, there's one person who's going to really care about every pixel and this thing needs to be slightly to the left. And a little bit more blue. And those people will use existing tools to do that.

[Nicole Brytova]

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 sort of the thing where I think we're still quite a bit away from getting that right. You know, the first try on the other hand with pose information potentially.

[Oliver Wang]

But 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 information, or do you just want to provide some reference image and say like, this is actually what I want, like model, model, go figure this out, right?

[Nicole Brytova]

There are 26 people Alphabet, different style.

[Google DeepMind Researcher]

Fair enough, Yeah. I think in that case I wouldn't spend a ton of time building a custom interface for making this picture of 26 people. Seems like the kind of thing that we can solve, just transfer.

[Interviewer / Moderator]

Do you think the representation of what the AI images are will change? So the reason why I ask 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 procreate or like Fresco, what have you. There's layers that we can also play with. There's the other parameter which is what's a brush? You use like the brush, the texture of it. So every one parameter you can write scriven actually do something very personal about it. Do you think pixel is the right representation, the end game for image generation model, or do you think there's a net new representation that we haven't invented yet?

[Nicole Brytova]

That's an easy question.

[Google DeepMind Researcher]

Wow. I'll say that everything is a subset of pixels.

[Interviewer / Moderator]

That's true.

[Google DeepMind Researcher]

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 if the model is really 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. 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. It could be useful to have kind of mixed generation which consists of pixels and SVGs and other forms. But if we can do it all, if the multi interaction is enough, then I think you can get pretty far with pixels. I will say that one of the things that's exciting about these, 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 some things be rasterized, some things be parametric, stick it all together, train it together. This would be very cool.

[Interviewer / Moderator]

That's such a good point because I did see a tweet of someone asking Cloud Sana to replicate an image on an Excel sheet where every cell is a pixel, which is like a very fun exercise. It was like a coding model. It doesn't really know anything about images yet. It worked.

[Google DeepMind Researcher]

Yeah. There's the classic pelican riding a bicycle, right?

[Oliver Wang]

Yeah.

[Interviewer / Moderator]

Totally.

[Product/UX Specialist]

I have one on model, like on interfaces, if that's okay. Sorry if I'm bringing up too much product stuff, guys. I'm just very curious on the product front. 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 design, like architecture. Each of those things could be. There could be a standalone product built on top of nanobanana 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?

[Oliver Wang]

I think it's a little bit of everything. So I definitely think that the Gemini app is an entry point for people to explore. And the nice thing about nanobadana is I think it shows that fun is kind of a gateway to utility where 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. I think that's a really powerful transition point. There's definitely interfaces that we're interested in building and exploring as a company. And so you may have seen flow from Josh's team in labs that's really trying to rethink what's the tool for AI filmmakers. And for AI filmmakers image is actually a big part of the iteration journey because video creation is expensive. A lot of people kind of think in frames 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. And so there's definitely kind of place that we have in that space of just us trying to think about like, what does this look like. We have the advantage of kind of sitting close to the models and the interfaces, so we can kind of build that in a tight coupling. And then there's definitely the. 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. But I don't think that's something that we will do. But somebody should go and do that. 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 what's the next generation workflow for this specific audience so that I can help them solve a problem. So I think the answer is kind of like yes, all three.

[Product/UX Specialist]

Yeah, I brought that up. I don't know if you guys have been following the reception of Nano Banana in Japan, but I'm sure you've had. It's been insane and it's so funny. Now half of my ex feed is these really heavy Nano Banana users in Japan who have created Chrome extensions. There's one called Easy Banana that's specifically for using Nano Banana for manga generation and specific types of anime and things like that. And they go super deep into basically prompting the model for you and storing the outputs in various places using obviously your underlying model to generate these amazing anime that you would never guess were AI generated. Because the level 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.

[Interviewer / Moderator]

What are some, to Justine'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? 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. Just curious, like, how do you think about what are the force multipliers that you want to unlock? So the next.

[Nicole Brytova]

What's the next big one?

[Interviewer / Moderator]

What's the next. Yeah, big wave of people who can just use nanobanano as the base model for all the downstream tasks.

[Oliver Wang]

So I think one current one actually is also the latency point, right? 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.

[Product/UX Specialist]

Right.

[Oliver Wang]

If you had to sit there and wait for two minutes, 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. You have to hit a quality bar and then speed becomes, of course, 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, it looks nice, but it also needs to be accurate. 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 individuals.

[Interviewer / Moderator]

The diamond age. That was basically. Yeah, yeah, yeah, basically.

[Oliver Wang]

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.

[Product/UX Specialist]

Right.

[Oliver Wang]

And so I think that becomes just like another way to improve and open up accessibility of information to just a lot more people and again, visually, because a lot of people are visual learners.

[Interviewer / Moderator]

Interesting. How do you think about images generated? So the reason why I asked is that there's another very cool example. I've seen someone making a work with nanopanella, 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. 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 the parallel universe. You could have, you know, generated any one of them.

[Nicole Brytova]

It's one big directed graph, right?

[Interviewer / Moderator]

Exactly. And then maybe it's video at the end of the day. So how do you see that? Where does it intersect or not intersect?

[Google DeepMind Researcher]

I think it's very. Yeah, video and images are very closely related. And also I think what we're seeing in these kind of what's coming next or sequence predicting use cases is the, the generalization and world knowledge of the model as well. 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 an obvious next kind of domain. I think that like when, when you have editing, 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 fully interactive and real time is the direction this field is headed.

[Nicole Brytova]

So you are probably in the zero. I don't know how many 0.001% of most experienced people in the world using image models. What are your personal favorite use cases? How do you use it day to day if you're not just testing the existing model?

[Google DeepMind Researcher]

Well, I. So I'm not sure I am in the very top. But I'll tell you, I mean, it's like we were saying earlier, the personalization aspect is the thing that totally drives it home for me. I have two young kids and the best things that I do in the model are the things I do with my kids. And we can make their stuffed animals come to life and these types of applications. And it's just so personal and gratifying to see. We also saw a lot of people taking old pictures of their family, for example, and like 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.

[Oliver Wang]

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.

[Product/UX Specialist]

Right.

[Oliver Wang]

Or one family. And so 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. Now anytime I make a slide deck, I force myself to generate some images that are contextually relevant and then try to get the text right 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, you want the bars and the bar chart to be accurately positioned relative to one another. So 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. We have a team who just works really closely with us on models that we're developing and then they just push the boundary. They'll do crazy things with the model.

[Interviewer / Moderator]

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

[Oliver Wang]

Yeah, this is even just kind of simple things where people have been doing texture transfer.

[Interviewer / Moderator]

Texture?

[Oliver Wang]

Yeah. 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 thought of this being a use case because my brain just doesn't work that way. But people that kind of just push the boundaries of what you can do with these things.

[Interviewer / Moderator]

That is an interesting example of the world knowledge, because texture technically is 3D, because there's the whole 3D aspect of it, there's a light and shadow of it, but this is the 2D transfer. So that's very cool.

[Google DeepMind Researcher]

I think for me, the thing I'm most excited by and maybe most impressed by are the use cases that test the reasoning abilities of the models. So some people on our team figured out you could give geometry problems to the model and ask it to kind of solve for X here, or fill in this missing thing, or present this from a slightly different view. And these types of things that really require world knowledge and the reasoning ability of 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.

[Interviewer / Moderator]

Can it generate compiled code on a blackboard yet? If I take a picture of my, I don't know, code on the laptop, would it know if it compiles on the image model?

[Google DeepMind Researcher]

I've seen examples where people give it an image of HTML code and have the model render the web page.

[Interviewer / Moderator]

Wow, that's very cool.

[Google DeepMind Researcher]

The coolest example I saw. So I came from academia, so I spent a lot of time writing papers and making figures. And one of our colleagues took a picture of one of the result figures from one of their papers with a method that could do a bunch of different things, a bunch of different type of applications in the paper, and asked the model to erase the results. So you have the inputs and ask the model to solve all of these in picture form in a figure of a paper. And it was able to do that. So it could actually 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.

[Interviewer / Moderator]

That's very cool. Has anyone built application on top of that capability yet? What's the application that will come out of that?

[Google DeepMind Researcher]

I think that there are a lot of very interesting, I would say zero shot transfer capability, problem solving type things that we don't even know the boundary of yet. And some of these are probably quite useful. If you want to have a method that does solve some problem X, I don't know, like finds the normals of the scene or something like the service orientations or something, you probably can prompt the model to give you kind of a reasonable estimate. 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 shot prompting that we don't know yet.

[Interviewer / Moderator]

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 it away doesn't mean that the chair should disappear or change color because 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?

[Google DeepMind Researcher]

Yeah, I mean if you think about an image model that has a long context where you can put other things in that context like text, images, audio, video, then I think it's definitely like you're reasoning over the context of things you have to produce a final output image or video. So yeah, I think there's definitely some model capability to do this type of stuff. Already got it.

[Interviewer / Moderator]

I haven't tested it out yet for this. I'll let you know.

[Product/UX Specialist]

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 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 almost entirely new space that's been discovered in terms of 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.

[Oliver Wang]

Yeah.

[Nicole Brytova]

So if you talk to visual artists today, I personally love this stuff. I post about it on the Internet. They get some very skeptical answers. People are like, oh, this is terrible. Do you have any idea what triggers this reaction? I'm convinced that this ultimately really empowers the artist. 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. It's of the similar thing. But what triggers this strong reaction against here?

[Google DeepMind Researcher]

So I think it's 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 one shot. 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 come from the creative community because, you know, most of the decisions that were made were made by the model, by the data that was used to train the model.

[Nicole Brytova]

You can't express yourself anymore physically.

[Google DeepMind Researcher]

Yeah, exactly. 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 the computer is doing everything kind of may go away. And the other thing is, I think that there was a period of time where we were all so amazed by the images that these models could create that, like, we were pretty 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 there was a big rush. And now if you see an image that, you know is 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, which is hard, but it's. 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 people have actually, like, put a lot of control and.

[Nicole Brytova]

Intent to be an artist.

[Oliver Wang]

Maybe you'll get. But. But it is. There's a lot of craft and there's a lot of taste. Right. That you accumulate sometimes over decades.

[Product/UX Specialist]

Right.

[Oliver Wang]

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. So image, video, music. Because we really care about building the technology step by step with them and trying to figure out. They really help us kind of push the boundary of what's possible. A lot of people are really excited, but they really do bring a lot of their knowledge and expertise and kind of like 30 years of design knowledge. We just worked with Russ Lovegrove 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 have a prototype of. And so 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 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. It does require a lot of that kind of taste in human creation and. And craft that goes into building something that actually then becomes art at the end.

[Nicole Brytova]

It's still a tool that requires the human behind it to express the feelings and the emotions and the story.

[Oliver Wang]

Yeah, absolutely. And that's what resonates with you when you probably look at it. Right. 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.

[Google DeepMind Researcher]

I think there's also a bit of this phenomenon that most people who consume creative content, and maybe even ones that care a lot about it, 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. That's right. And then you show it to people and like, oh, wow, that's amazing. But they wouldn't necessarily think of that on their own. So when we're optimizing these models, one thing we could do is we could optimize for the average preference of everybody. But I don't think you end up with interesting things by doing that. And 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 whole perspective of art, because I saw that.

[Nicole Brytova]

There'S the avant garde edition of the model. If I use it at the time, I don't know what's at the other end of the spectrum. The marketing edition or so, where it's very predictable and straightforward.

[Interviewer / Moderator]

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

[Oliver Wang]

Interleave.

[Google DeepMind Researcher]

Yeah, interleaved. I think we've always been amazed that nobody ever posts anything about. So interleave 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, yeah, people haven't really found it useful yet or haven't discovered it. I don't know.

[Interviewer / Moderator]

Oh, interesting. Well, if you're listening to the podcast, go try this out. Yeah. And what's the most exciting technical challenge that you look forward to tackle in the next, I don't know, months, years?

[Google DeepMind Researcher]

So I think that there's really a high ceiling in terms of quality for where we're going. Like, I think people look at these images and say, oh, it's almost perfect. We must be done. And for a while we were in this cherry pick phase where 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 cherry pick images that look perfect. So now I think the real question is how expressible 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. There's all kinds of productivity use cases like beyond this kind of 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 the use cases will be far greater.

[Nicole Brytova]

So that's the moral equivalent of the monkeys on typewriters. Basically. Any model given enough tries will eventually an amazing adventure. But the other way around, it's hard.

[Google DeepMind Researcher]

Yeah, the other way around is hard. One monkey writing a book would be very hard.

[Nicole Brytova]

It'd be a good monkey for that one.

[Interviewer / Moderator]

What are the applications you think that would come out when we raise the lower bound?

[Google DeepMind Researcher]

So the one I'm most interested in, we mentioned this before is education factuality. I have, you know, 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.

[Interviewer / Moderator]

Amazing.

[Oliver Wang]

There's also something about, I think, taking more advantage of the models context window so you can input a really large amount of content into these LLMs. And some companies, you mentioned a few before, they will have 150 page brand guidelines on what you can and cannot do. And they're very precise, like colors, fonts, and the size of a Lego brick maybe. And so being able to actually take that in and follow that to a T when you're doing generation, that's like a whole new level of control that we just can't. We don't have today. Right. To make sure that you're actually kind of like following that to a T. I think that will build a lot of trust with very established brands.

[Nicole Brytova]

So we have a second creative compliance review model that then injects everything that.

[Oliver Wang]

I could use against the model. Should do it on its own. It should kind of have this loop. Yes, it should have this loop as like, okay, I generate this, but then page 52 says that I shouldn't have.

[Product/UX Specialist]

Right.

[Oliver Wang]

Going to go back and try again and then two hours later we'll come back to you with that respect.

[Product/UX Specialist]

Yeah.

[Google DeepMind Researcher]

We saw with the text models how this inference time scaling, how much it can help.

[Interviewer / Moderator]

Right.

[Google DeepMind Researcher]

Being able to critique your own work. So this feels really important.

[Nicole Brytova]

Boy, incredibly, amazingly exciting future for image models.

[Oliver Wang]

Yeah.

[Interviewer / Moderator]

And congrats on all the amazing work.

[Oliver Wang]

Thank you.

[Google DeepMind Researcher]

Thank you for having you.

[Interviewer / Moderator]

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

[Podcast Host]

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on X1 6Z and subscribe to our [e-mail] thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax or investment advice or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund, please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.