a16z Podcast - Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building

Episode Date: August 16, 2025

Genie 3 can generate fully interactive, persistent worlds from just text, in real time.In this episode, Google DeepMind’s Jack Parker-Holder (Research Scientist) and Shlomi Fruchter (Research Director) join Anjney Midha, Marco Mascorro, and Justine Moore of a16z, with host Erik Torenberg, to discuss how they built it, the breakthrough “special memory” feature, and the future of AI-powered gaming, robotics, and world models.They share:How Genie 3 generates interactive environments in real timeWhy its “special memory” feature is such a breakthroughThe evolution of generative models and emergent behaviorsInstruction following, text adherence, and model comparisonsPotential applications in gaming, robotics, simulation, and moreWhat’s next: Genie 4, Genie 5, and the future of world models This conversation offers a first-hand look at one of the most advanced world models ever created. Timecodes: 0:00 Introduction & The Magic of Genie 30:41 Real-Time World Generation Breakthroughs1:22 The Team’s Journey: From Genie 1 to Genie 35:03 Interactive Applications & Use Cases8:03 Special Memory and World Consistency12:29 Emergent Behaviors and Model Surprises18:37 Instruction Following and Text Adherence19:53 Comparing Genie 3 and Other Models21:25 The Future of World Models & Modality Convergence27:35 Downstream Applications and Open Questions31:42 Robotics, Simulation, and Real-World Impact39:33 Closing Thoughts & Philosophical Reflections Resources:Find Shlomi on X: https://x.com/shlomifruchterFind Jack on X: https://x.com/jparkerholderFind Anjney on X: https://x.com/anjneymidhaFind Justine on X: https://x.com/venturetwinsFind Marco on X: https://x.com/Mascobot Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

All of the applications basically stem from the ability to generate a world that just from a few words, you look at it and there's a world that's generated in front of your eyes and it's amazing that it's happening. I was very excited about how far can we push that. And it's at the point where a human who is not an expert will watch it and think it looks real, right? And I think that's pretty incredible. GD3 from Google DeepMind can create fully interactive, persistent worlds in real time from just a few words.

Today, we're joined by the team behind it. Shlomi Fucter and Jack Parker Holder from Google DeepMind, plus Anjane Midha, Marco Mascoro, and Justine Moore from A16Z. We'll talk about how it works, the special memory that keeps world consistent, the surprising behaviors have learned, and where world models are headed next. Let's get into it. Jack, Shlomi, Jeannie 3 has taken over the internet. We're honored to have you on the podcast today.

as the response surprised you, reflect a little bit about the reaction. We weren't sure how big it's going to be, but today felt definitely that we have something that was for a long time coming, basically being able to generate environments in real time. I think a lot of work that was done in Google DeepMind and outside

pointed to that direction, but we really wanted to make it happen and I hope we have, yeah. Team, why don't we reflect internally a little bit about what we found so game-changing about G-3 and why we're so excited to have this conversation, Mark? Yeah, for sure.

I mean, first of all, it's an amazing model. I think there's a lot of excitement around the special memory, the consistency across all the frames. I think this is the first time I can see, like, you can have some sort of interactive way of doing this stuff with videos. Because it used to be like, you would do one prompt and you would have 15 seconds of a video. But now you can actually have some sort of interactive kind of element to it, which I think is very exciting. So can you elaborate a little bit more like your insights on these? Like, how was like, for example, figure out what data you should collect? like how you make it very interactive and keeping the flow of the whole video,

which I thought was phenomenal. Sure, yeah. So I think you kind of highlighted a few capabilities, sort of the length of the generation, the consistency of the world, maybe diversity as well, of the kind of things you can generate. I think the main thing is that obviously we made progress in quite a few different fronts, right, in separate efforts. So we had this Gini 2 projects that was much more sort of like three-environments

that it could generate. And it wasn't super high quality. It felt like it coming from Genie 1, but it wasn't the same quality as things like V-O-2, which the state-of-the-art video model at the time came out on December, roughly exactly the same time. It came out a week later than Genie 2.

And obviously, internally, there was a lot of discussion between the two projects about the different directions we were pursuing. And then, showing me had also worked on Game & Gen, right, which is the Doom paper, as people know it, which I think you guys also wrote a nice piece on straight after that came out.

So I think that also attracted a lot of attention. And so that we felt that across these different projects, we had quite a lot of interesting things that would naturally kind of combine and we could basically take the most ambitious version of the combined project and see if it was possible. And fortunately it was and quite, I think the timeline is probably the bit that surprised many of us

because obviously we sell ourselves these goals and we tried very hard to achieve them. But you never be totally sure how it's going to actually feel when you've got to that point. I think it ended up being something that resonated with people a lot more than maybe we expected, but we always believe us. Yeah,

I'll just add to this, that I think there is time, so a component is really important, and not many people experience it first hand, but we really tried in the release to at least have a few trusted testers, interact with it, and also get the feel of it by adding these overlays

that show what happens, how people can't use the keyboard to control it. And I think there is something magical about the real-time aspect. I felt it for the first time when our model, like game engine model, started working fast enough. And we were just like, oh, my God, it's actually, I can actually walk around. And it was a bit of a wow moment. And, yeah, I think there is something when it responds immediately that is really magical.

I think that's kind of sparked the imagination of many people when the Doom kind of simulation came out. And here we really wanted to push it to somewhere. We weren't sure it's going to work. So it was definitely at the edge of what's possible, I think. That's how we felt. So we just said, yeah, let's try and see if we can make it happen. I think you guys, I don't know if this was on purpose or not, but you perfectly timed it when everyone on X and Reddit and everywhere was making those videos of characters walking through games.

But they obviously weren't interactive. They weren't real time. And then you guys came out with this release that was like, now this is an actual product and it blew folks away. I'm curious, because you can imagine so many different applications for this, right? Like more controllable video generation or making it much easier to create games, even personal gaming, where someone's just kind of creating their own world, they walk through, like, RL environments for agents, robotics. Are there any particular use cases that you're most excited about?

I think all of the applications basically stem from the ability to generate a world, just from a few words. And I think for me, kind of like this potential, I started looking at video models, I think it was pretty early when I think it was one of the models were like imagined video, which was a model by Google research. But there are a lot of models that they were very basic compared to what we have today, but the ability to simulate something, like you look at it and like there's a world that's generated in front of your eyes, and it's amazing that it's happening.

And I think at this point, I was very excited about how far can we push that, right? So I think Vial was one way to do it, and Jeannie is definitely another way to make it a bit more interactive. So I think all of the applications basically stemmed from this core capability. So it can be entertainment, of course, as you said,

it can be training agents, it can be helping agents to reason about the world, education. So I don't think any particular application is more important or than others. I think it's really up to how developers in the future

will be on top of that. Yeah, I would get basically the same answer in the end with a different journey to get there, right, which is, I personally myself worked in reinforcement learning for a few years before starting the GE project in 2022 and the motivation originally was that in RL at a time

we had this problem where we'd say which environment shall we try and solve right because once you've already done go which people thought was years or decades away and then that was solved in 2016 what's solved but we reached Superhuman 11 2016 and then Starcraft three years later which is not particularly long time

for something incrementally significant So it was 2021 time, it was a big question of what should we try and do with REL. We know that the algorithms can learn superhuman capabilities if they have the right environment, but we don't know what the environment be. And so we were working on designing our own ones with colors. But then instead, it seemed like the more promising path

when you had the first text image models coming out, whereas like, what if we just think long term, what's the way to really unlock unlimited environments? That being said, over the course of the project, and originally we started it, I guess, in 2020, it was very focused on that one application, but it seems quite clear now that this could have a big impact in all those other areas you mentioned, right?

So I think it's like language models in 2021, maybe. You probably wouldn't have guessed like an IMO gold medal a few years later would come that fast as a direct application of that technology, right? It was probably, oh, it can help me with my emails or whatever it was. And I think it's really cool to build these kind of new class of foundation models and then see what people can imagine doing with it. And that's one of the very exciting things about. sharing the research preview, right?

So you've got this kind of feedback. So we're hoping a lot of these things can happen. One of the things in the research preview post, Jack, that blew me away was this, and it wasn't even your first jiff, I think, in the blog post. It was either second or third. You had this visual of somebody painting the wall with the paintbrush,

and then the character moves out of, right? Like out of, to a different part of the wall, paints, and then moves back. And the original paint is still. still there. And I didn't believe it. I was like, there's no way. And then I read, and you're right, it's described as a special memory. So the persistence part for me, I'm not taking away from all the other stuff. The interactivity is amazing. But I think, broadly speaking, folks expected that at some point, video generation, for example, would become real time. When I saw the

Genie 3 posts, it was like, okay, they actually went and did it. But the special memory, the persistence was when I kind of sat up in my chair and I was like, how did that happen? Could you talk a little bit about when did you discover that as an emergent property? Or was that a specific design goal? What's the backstory on that? Because that feels like a big unlock, Jack. Why don't we start with you? Yeah, so that's a great question. I'll say a few things. So the TLDR is it was totally planned for, but still incredibly surprising when it worked that well, right? So that specific sample, when I saw it, it was hard to believe. I actually wasn't sure that the model generating for a second. I was like that it told me to watch it a few times

and like really check and freeze the frames and look back and check that it was the same. But go back a few steps. So obviously, Genie 2 had some memory, right? So this got kind of lost because, I mean, Genie 2 came at a time when there were lots of announcements, very exciting announcements. I mean, VO2, only a few days later. It was a busy time of the year. And the main headline act was that we could generate new worlds at all, right? So that was the thing that we wanted to emphasize. But it did have a few seconds of memory. And we had a couple of examples, like, I created a robot near a pyramid, looked away, looked back, and the pyramids there. but it's like kind of blurry

and it's not perfect but some other models around the same time or more recently didn't have this feature right so people kind of indexed to that because they didn't notice the early signs of it in the Genie 2 work and then for Genie 3 we basically went

much more ambitious on the same sort of approach right and we made it like a headline goal for ourselves is can we make the memory be what it is right we said we want minute plus memory and real time and this higher resolution all in the same model and those are kind of conflicting objectives

right so we sell ourselves this kind of technical challenge and we said if we target this then it's just about feasible and it'll be pretty incredible and then you still don't know obviously it's going to pan out so then when you get to the end of the research

one seven months later to see the samples still is quite mind blowing to be honest so yeah it's kind of planned for but still pretty cool and exciting when you see it because they research projects aren't sure things are they so

one thing that we didn't want to do and we didn't want to build an explicit representation right so there are definitely methods that are able to achieve consistency and they did that through an explicit some 3D you know it's their nerves and Goshen splating and other methods that

basically say okay if we know how the world looks like we use this kind of like prior assumptions on how the word remains static pretty much much than we can build representation and then what you're looking at. So that's great, I think, for some applications, but we didn't want to go down this path because we felt it's somewhat limiting. And I think so we can definitely say that the model doesn't do that.

And it does generate like frame by frame. And we think this is really key for the generalization to actually work. Every time someone interacts with it for the first time and they like test, they look away and then look back, I'm always like holding my breath. And then it looks back and it's the same. I'm like, whoa. it's really cool and how long is this special memory

I don't know if you can talk about it you mentioned a minute plus but is there some sort of like measure that you have is it like can you keep it for half an hour or what is the limit on that there is no like fundamental limitation but currently

the current design we're limited to one minute of this type of memories yeah it's also a real time tradeoff for the guess as well we felt that because of the breadth and the other capabilities that like a minute it was sufficient. So this version,

like, it's quite a significant leap, but obviously, eventually you'd want to make sense of this. One more question related on the, between GNI 1 to,

like, for example, NLMs, like you have Deepseq R1, like they saw in this paper, like the longer they keep it running,

they suddenly will see like these interesting behaviors like the model will start like reasoning or like, would give like a, oh, I'm wrong in this,

I should self-correct. Do you see anything in kind of like this scaling from two to three, do you see any sort of like interesting behavior that you were not expecting that suddenly just appear by increasing the amount of data and the amount of compute? Yeah, I'll just say, I think there is a bit of like overall, definitely like many generative models, we see that improvements happen with scale. I think that's not secret. And I don't think

it's not the same type of intelligence, I would say, like, an LLMAS. I'm not sure if reasoning is the right term. But we do see that some definitely things like it can infer from, you know, If you approach like the door, and it makes sense for the agents to maybe open it. So you might see that it's starting to do that, for example. Or there's some better word understanding that happens over time. And it just, like, things look better and more realistic. So I think these are the trends that we've still observed. Yeah.

And from Gini 2 to 3, I think the real world capability is really increased, right? So on the physics side, some of the water simulations, you can see some of the lighting as well. like a really breathtaking. I think we have this example of the storm on the blog, and that one I think is super cool. And it's at the point where like a human who is not an expert will watch it and think it looks real, right? And I think that's pretty incredible.

Whereas Virginia, too, it was like, it kind of understands roughly what these things should do, but you know it's not real, right? You can look at it and you can clearly see that it's sort of not completely photorealistic. So I think that's quite a big leap on the quality in that side. Yeah, one of the things was really cool in all the examples was the water is sort of a great way to see, like,

does it understand, like, what the world is and how objects interact? And that example, someone posted of the feet going in the puddle was amazing. But then there was also that example of, like, a cartoon character. It was more of an animated style who was, like, running across this kind of green patch of land, and then ran into this blue kind of wavy thing that looks like water, and he started swimming, which I thought was really interesting. Like, were there particular things you had to do around that for the model to be able to understand, like, how characters should interact in different environments and different styles? What you're basically describing is, like, the real breadth of different kind of environment terrains and worlds and things like that, like water or walking on sand versus going downhill and snow and how the agent's sort of interactions should differ given the, like, terrain that they're in? And I think that that really is a property of scale and breadth of training.

So this is very much like an emergent thing. I don't think there's anything like really specific we do for this, right? You again, like you hope the model has learned this because it should have like a general world knowledge. It doesn't always work perfectly. But in general it's pretty good. So for the skiing examples, you do go fast when you go downhill. And then when you try and go back uphill, it's very slow. if not at all possible.

When you go into water, obviously, you hope, as you said, that the agent will start swimming and slashing. And this does typically happen. When you look down near a puddle, hopefully you're wearing Wellington boots. Like, this kind of stuff does just kind of make sense. And I think it feels pretty magical

because it very much aligns with what you were thinking about the world and the models just generated it all. So, yeah, that's also one of the really exciting things for sure. Yeah, and on top of that, one kind of trade-off that typically we have is that we want the model to do two things. We want the model to create the world in a way that looks consistent. So I just said, like, if you walk in drain or in piles, then probably wearing boots. But if we provide it with a different description or, like, the prompt is saying something else, we want it to still follow the prompt.

And there is some tension here because some things are very unlikely, right? you might say I want to wear flip-flops and jump in the rain or whatever, then the model still has to try and create something that is very unlikely. And that's where typically, you know, video models maybe find it more challenging, and that's where, you know, our models might find it more challenging, but still it's still successful to a surprising degree to go into this kind of low-probability area. And I think that's really, in a way, that's what we want, right? Like many people, they don't want to just look at the video that looks like they're on,

no, maybe this room, but something a bit more exciting. And that's where I think this is the magic of the models that they can take you to places that maybe are not so likely to be in reality. The text following is really amazing in this model. And that does feel really magical. I think there's something that the VO does really well as well, right? pretty much what you ask for. It's really well aligned with text.

And we have that with Genie 3. So you could describe very specific worlds and really kind of like arbitrary, silly things. And it pretty much works. Like we actually had this discussion because people were very disappointed to find out that the video I made of my dog actually was not my dog's photograph. I just described her in text.

And, yeah, I don't know if that's a big secret, but it looks exactly like her. And the model just kind of knows, right? I think that's pretty amazing. So I think that that's actually a really important capability that we didn't have with GE2 as well, right? Because we relied on image prompting. And so there was some transfer issue, like,

where you rely on imagine to generate the image. And that often does look really good, but it's not necessarily a good image for starting the world. Whereas, like, going directly from text, you get the controllability to print anything you want. Plus, it just kind of naturally works because it's in the, like, correct space for the model to do its thing. And that's something really powerful.

And why is that, Jack? What do you think led to such a massive instruction following or text adherence gain? Because it's a pretty hard thing to do. Well, I mean, our team had never really worked on this. And so Genie 1 and 2 both worked with image prompting. And so obviously, like, for this next phase, we leverage a lot of the research done internally on other projects

and personnel-wise. I mean, Shlomi's obviously been co-leading the VEO project. And so we were able to kind of build on a lot of other work and ideas internally and that basically may allow us to kind of like turbocharge progress. Right. So if we've done this sort of by incrementally building ourselves on an island, it would have taken, I think, a lot longer than, and being part of Google DeepMind

where we have these teams that have a lot of knowledge in different areas and sort of lean and build on, which I think is super exciting about our being in the company right now is that we have so many experts in different areas

that we can seek out advice and help from. And Shlomi, a question for you on that is having led the V-O-3 work which is kind of mind-blowing. Is there a reason why this is Genie 3 and not like V-O-3 real-time? So I think it's definitely a bit different, right? Like, Genie allows you to navigate an environment and then maybe take actions, right?

And that's not something that Vero at this point can do. But there are other aspects that they're different, that Jenny doesn't have, right? Doesn't it doesn't have audio, for example, right? So we just think it's, while definitely there are potential similarities, it's sufficiently different. also another thing is that at this point generally free is not available as a product and well we do think about it as like a product that is kind of like mainstream and became very

very popular and you know what the future holds I don't know but I mean at this point we just felt it's sufficiently different in terms of what capabilities and how kind of like we think about this so Genie free is pretty much a research preview right it's not something we are releasing at this point you know something we think about a lot is what are the edges of a modality

we're talking about us all the time which is you know the lines start blurring pretty quickly between real-time image and video and then real-time video and interactive whatever world generation world model I don't think we have a good word for what Gini 3 is yet but you guys called that world model which is I think a great term

but in your mind like where does the video generation modalities stop and real-time world's take, you know, start? And do you think in the future, are these converging into basically one modality? Or if you had to predict, over the next few years,

do you guys think, actually, yeah, these will diverge into completely different disciplines? It seems like they share kind of one parent today, which is, you know, video generation. But where is the world going, do you think? Are these two completely different fields? From my perspective, they're different. So I would say modality is one thing, right?

We have text, we have audio. Even within audio, there are different type of sub-motalities. Speech is not the same as music. We have different products for music generation. We have other models for speech generation, speech understanding. So even within one modality, you can have different flavors. And then, of course, you have video and other things. So I think basically I would say the modality is one dimension,

and another is how fast or how quickly we can create, we can create new samples and completely orthogonal maybe the direction is or dimension is how much control we have, right? So I think we kind of picked a specific direction or a specific vector in the space for Gen 3. I think different products, different models can try and go in different direction. I think the space is pretty big and there are a lot of trade-offs to be made. So, yeah, I don't know. I think it really depends. Some people believe there is, you know, one model that's with everything.

Or I think there is still open-end that was the best way. Like, we're in a place where engineering is a big part of our research, right, and actually making those, like, it's not a paper, right, where we want to build something that people can actually use. So I think this really makes it, like, an abstract idea to go to get you to some point, but to actually build things we have to make some concrete. decisions and I think it kind of forces you to decide what you want to do and what you're going to know. Yeah, I think this is a really interesting point might and

ultimately it has to be driven by like technical decisions and also like the goals right so we if you look at the models right now we obviously made a choice that we won V-O-3 and Genie 3 to be separate projects this year right and if you look at look at them both as they are right now, they have very different capabilities that the other model does not have. And technically to combine all of that already into one model, right, would be, I think, very challenging to, I mean, V-O-3 is totally a higher quality threshold than Gene3, right? And it has very different priorities, right? So then the natural things you could say, oh, well, you know, what if we just took these together and combined them? But that may not be the best next step for either of those two

to models, right? So it may not be the case that the thing that the other one has is actually the most compelling thing for a completely different experience. And I think that given the breadth of interest in both models, right, there's actually

quite a small set of people that are like really actively using both and they tend to be more folks like yourselves who are just more broadly interested in AI, right, rather than like really downstream use cases. So like you mentioned agent training for one, which is a very sort of

high action frequency requires more ecocentric sort of I guess more like worlds where tasks can be achieved but doesn't require you know that high quality cinema

style videos you could generate with the B.O. Molobytes quite different. And then on the filmmaking element I mean, I'm also sure that Gini 3 is really there at this point. And that would be necessarily the goal.

I don't know. On filmmaking, Justin can do some pretty incredible things with the filmmaking tools today. You'd be surprised. Give me access, and I will make amazing films with Genie 3. I guess I did kind of get to one of my questions, though, which is the work you guys are doing is incredible, and you clearly probably have so much going on in your brains just to coordinate training these models and managing these teams.

How much do you also have to think about, like, what are the downstream use cases of the model when you're training it? Because you could imagine a world in which you're just like, we don't really know or care what people are going to do with it yet. We're just going to go in the research direction. We think we should go and see what happens. But based on how you guys are talking about it, it sounds like you've also been pretty thoughtful around

what are the different capabilities or features needed for different potential use cases, at least, of different models. Yeah, I'll say that basically we have some applications in mind, but that's not what's driving the research. it's more about how far can we push in this particular direction can we make all of that work

like really great quality really fast generation real time very controllable I think that's kind of like what drives us I think they're two to have to develop journey free and the applications kind of like

follow and I don't think to be honest I don't know what would be the applications for like I think we're very surprised I'd like to mention like very free people find new ways and how it can be useful

and to prompt it with visual stuff people just discovered it right we didn't even think about it initially so I expect kind of the same thing and I think that's why I am excited for more people to be able to accept in the future and in general our approach is to

make sure that over time there is more access to the models we build and I think that's the only way to discover what's the rate potentials. I guess one, somewhat related to that, like, how do you think going forward like Genie 4 or 5

or any other models like, what is like top of mind right now? Like if you wanted, for example, to focus on, I don't know, like it seems like gaming could be one of the applications, having multiplayer type of games, we have two special memories or two different completely views, but at some point they merge. How are you thinking on like going forward? Like, what's next?

Is it like scaling these models just on more data more compute, is it creating this sort of like multi-universe type of things where you have multiple players, multiple people looking at the same model, putting different views. What's like a top of mind for you guys? Top of mind, I think for the next few days might be a vacation. After that, maybe walking my dog in the real world. And then I think you mentioned a bunch of really interesting things, to be honest. And like, I think we are, we're still collecting a lot of feedback on this current model, right? And I think that in general, we are most interested in building the most capable models, right? And so we would hope to have even broader impact in future

and really enable other teams to do cool things with it, right? Both internally and externally. And for me, it's like, I just started this with like a very, very focused vision about AI. And I still think, honestly, what I'm excited about for AGI and which is more embodied agents, I really believe this is the fastest path to getting these agents in the real world. And I think we made a big step towards that. But I'm still like sometimes even more excited about applications I never thought of that come up from other people seeing the model, right? So I think it's kind of this like trade off of, you know, obviously you want to focus on some applications,

but then you want to be open-minded about others. And I think that's the real joy of building models like this, right? Is you get to see all of these people can be way more creative than me with it. So I think that there's always really cool things that we can do. And I honestly can't really tell you in one year what the biggest application will be. But we'll definitely be trying to build better models. Yeah, I'm really excited. But I think we are only as impressive, you know, maybe the model is.

I think they're all very far from actually simulating the world actually. being able to do, kind of put a person in there and then do whatever they want. And, I mean, when I say far, it doesn't mean it's far in terms of, you know, calendar time because we aren't really even accelerated timeline. But it feels like there is more work to do to get there. And I think I just imagine, like once we can actually, you know, whatever the form factor would be, but step into this world and just kind of like maybe tell it how we want to what you want to experience.

There are so many applications. Imagine, for example, someone is afraid of talking to people on a stage or in a podcast, right? They can simulate that, right? Or you can have someone who is afraid of spiders. They can maybe actually see themselves getting over that. So that's like, you know, just one example of something that's actually my wife thought about it. It's not my idea.

So I think it's really, like there's so many things, right? So I think this is just It's all It's all hinges on the ability to simulate the world And we put ourselves in it Maybe seeing ourselves from the side And potentially having agents Interacting with things

And yeah, the realism and really making it work In the way that is similar to our world I think it's really key I am actually personally petrified of skiing And the model is already quite good at that So I might when things quiet and down Spend some time Because I promise my wife

that our children would grow up knowing how to ski and we're getting close to the age where I have to live up to my promise and I'm not sure if I want to do it yet. We have to improve the model for you, Jack, so you can actually... I get that in distribution.

I hope so. We were just talking about before we started that we might see applications like in robotics. I mean, Jack, you were talking about embodied AI and now, like, limitation in robotics is the data, right? Like how much data you can collect

and now probably you can just generate a lot of different scenes that you were not able to do before purely from like just recording videos or so so I think that's another thing that is pretty exciting and I mean congrats on the model it's phenomenal

on the robotics application there was a conversation that I was listening to from Demas yesterday where he was talking about your guys' work on Genie 3 and he mentioned that there's a there's an agent I think you guys call it Sima right which can then

interact with the genie agent and as I was hearing him describe it which was kind of breaking my mind, which is that you had one simulation agent asking the genie agent to essentially create a real-time environment for it to interact in, right? Which was when I realized,

oh, the way you guys have built it, it's composable with other agents. Can you talk a little bit about why that's so important for robotics like Marco was saying? And what are the major limitations today that you think we'd have to overcome as a space to make the robotics, sort of progress, the rate of progress in robotics much faster than it is now.

So we designed it to be an environment rather than an agent, right? So Gene-E-3 is very much like an environment model. Like we don't see it as like an agent itself that can like think and act in the world. It's more just a general purpose sort of simulator in a sense, right? That can actually simulate experiences for agents. And we know that like learning from experience is a really important paradigm for agents, right? That's how we got AlphaGo because the agent, AlphaGo learned by playing Go by itself trying new things, right?

And then learning from feedback with reinforcement learning, learning to improve itself and actually discover new things. Like it discovered new moves that Move 37 that humans didn't think was a worthwhile move, right? But actually AlphaGo learned that it was because it could experience and try things for itself. And in robotics, we have this paradigm right now where there's some data-driven approaches, where you can collect data in a quite a laborious way

but it looks like the downstream tasks and looks real and there's not so much of a mismatch between the two domains or you can learn in simulation right but the robotic simulations are even the best ones and we have some of the best ones at Eid by Mojoko which we work with they're still quite far away from the real world

right and so you have the sim to real gap but even the sim to real gap itself I think is kind of like poorly named because what people consider to be real in robotics is typically still a lab or some very constrained environment where you've got a bunch of spotlights on a robot and then tons of researchers crowding around watching

you know whereas really real for me is mainly references it's the ability to walk my dog when I'm too busy to hold the lead cross the street you know see someone who's scared of dogs

know to go around them see someone with a ball, change directions, like all these challenging situations in the real world, right? And of course, you still have gripping. You still have these other tasks. But you need to really discover your own behaviors from your own experience, right? And that's that doing that in physical embodied worlds

is super challenging because there's so many reasons why firstly that could be expensive to collect data in those settings. You'd have to keep moving the robot back to where it started every time it doesn't do something right. And also it could be unsafe, right? So there's many reasons why we can't really do learning from experience in the physical world, right?

So we do it in simulation. But really what we think with Genie 3 is it's the best of both, right? Because you're taking a real world data-driven approach, right? But then you've got the ability to learn in simulation. So it kind of combines the good parts of each of those. And so that's why I think it could be super powerful. Not just for a robot example, but I really love this idea of having, when it rains in London a lot,

not having to take my dog for the second walk would be great. And as you can see, we built a modern basically for Jack personnel. Vacations, that's what driving the product is the point. There's a lot of dog owners out there. Yeah. I just saying clearly, Jack, it's time to move to California. Yeah. That's solution.

Less rain. Less lag. I mean, I personally love. California, but my wife's not, I'm most not convinced. We're convinced here. Yeah, just to touch on, you know, maybe a final point on the robot's kind of like robotics part, I think there are, like, it's definitely, you know, robotics means it's more than visual, right, like we need to be able to, I think this is an important point.

We want, we can drive the decisions of the robot by looking around, but still it has to, to kind of like, you know, do extraditions, decide where to move, how to respond to the environment. So I think there are definitely some gaps. but still at the core of the problem being able to reason about the environment we think this is something that's the world models

or general purpose world models such as Genie Free can really help with and maybe with future research we can actually bridge those gaps of physical understanding and actually getting responses physical responses from the world

which is a very interesting direction to explore one last question from my side I don't know if you can answer this, but like, is it going to become public? Like, can developers access it at some point? Or is there, like, some sort of idea on this? So, as you can see, we are very excited about having more people accessing it. So we're definitely want to make it happen. There is no kind of like a concrete timeline at the moment.

But, you know, I'm sure once we have more to share, we will do. Awesome. One of the things I've been thinking about a lot is we see sort of with every, like, modality, like, you know, maybe first LLMs and then image and video and audio. There's, like, early kind of glimmers of something really exciting in a project or a research preview. And then there's, like, a ton of data and compute and researchers kind of poured out the problem.

And you hopefully see this sort of, like, exponential progress until you eventually get to the point where, like, you're out of data or the improvements don't come as easily. I'm wondering for your thoughts, like, where we are on sort of that curve for world models. that's a really question. I actually have a super hand-wavy, somewhat swerving answer, right? And I think it's actually both. So I think the current capabilities are actually already quite compelling. And so you could make the case that like if what you wanted was a minutes of

photo realistic any world generation with memory, that could actually be the end goal, right? And two or three years ago, I probably would have said that was a five-year goal. And so at that point, if you just wanted to improve that, I think you probably end up with this maybe like, I think the jump from Genie 2 to Genie 3 was absolutely massive and went from being like kind of a cool bit of research that was like showing signs of life, something that could already be very compelling.

But I think there's a lot more that you can do with this. And Shulami kind of reference this to himself, right? Like it's not the case that you're dropping yourself in the world, right? And like it's like the real being in the real world, for example. It's actually quite different to that. When you do, you know, take a minute to look away from food to screen, it's quite a bit richer out there. And that's just for the real world.

We also want this ability to generate completely new things, right? So I think we've got a huge gap to close, right, with the new capabilities that we want to add. But I think it's made a bit different to language models. Or actually, maybe it is similar to language models, but with language models there's been like lots of new steps that have actually come on top, right? That maybe we didn't think were possible.

We thought things were plateauing. And then a new idea came that made a significant change. And that has happened a couple of times in the past few years. So I think that there's a few more of those left, for sure. My final question for you guys is, are we living in a simulation? Oh, yeah, that's every, every, you know, previous to my thinking about that is, actually, yeah, I thought about it. I think

if we live in a simulation my take is that it doesn't run on our current hardware because it's analog and not like it's continuous all of the observations

that are continuous and there is nothing like but maybe the quantum level is you know some limitation of our you wanted to go philosophical

is some kind of like a hardware limitation of the simulation we run on So, yeah, take it or leave it. That's a great answer. Clearly there's all to work for the TPU team to do. Yeah, maybe quantum computing will be actually running our actual simulation.

So, yeah, yeah. That's a great place to wrap. Shlomi, Jack, thank you so much for coming on the podcast. Thank you, guys. Thanks, guys. Thanks for us. Thanks for listening to the A16Z podcast. If you enjoyed the episode, let us know by leaving a review at rate thispodcast.com.

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