The a16z Show - Fei Fei Li: The Race to Build World Models For AI

Episode Date: September 4, 2026

World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence.At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world.They discuss the technical bets behind the model, what it can and can’t yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today’s biggest constraints is access to real-world training data. Resources:Follow Fei-Fei Li on X: https://x.com/drfeifeiFollow Justin Johnson on X: https://x.com/jcjohnssFollow Ben Mildenhall on X: https://x.com/BenMildenhallFollow Martin Casado on X: https://x.com/martin_casadoLearn more about Atlas: https://www.worldlabs.ai/blog/atlas  Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On the path to spatial intelligence, generating pixels that are truly spatially contextualized and grounded, that is the very hard step that Atlas has taken. We know LLMs are built on Next Token prediction. We've seen video models as being built on Next Frame prediction. Atlas is really new view prediction. This is the real place where AI can actually unlock a ton of value for people in their process. We're saying like 50, 100 X reduction. There was a famous shot in the first Matrix movie where Neo is falling down. Exactly. They had hundreds of cameras viewing. that angle on a green screen.

On Atlas, we can do this with just three cameras. No studio capture, no green screen, no expensive calibration. No one has ever seen this result. When you set out to do this, did you know it was going to work? I was pretty sure. Each time we made the model bigger, and each time we trained it for longer, it got significantly better. Does that mean we're going to get 40 video? They're like, go walk around.

Language models are built around predicting the next token. What happens when a model instead learns to predict the next view of the world? In this episode, Martine Casado sits down, with World Labs co-founders Faye-Fei Lee, Justin Johnson, and Ben Mildenhall to discuss Atlas and the broader challenge of building AI that can reason about physical space. They explain how Atlas combines generation and 3D reconstruction, why the team chose Newview Prediction as its underlying primitive, and what they learned trying to scale an approach that hadn't been tested before.

They also get into what's still missing, including richer dynamics and interaction, and how world models could eventually connect simulation. with robotics and planning. Underlying it all is a bigger hypothesis. Could New View prediction play a similar role for spatial intelligence that next token prediction has played for language? So Big Day yesterday, you launched a new frontier model, which has got an amazing reception, which is still coming in.

I think maybe a good way to structure this conversation. Let's just talk about exactly what that was, and then we'll go back to history and work our way back up. So maybe Justin do want to talk about what was launched yesterday, why it's significant. Yeah, so Atlas is our new next generation world model, but has three basic things. It can generate, reconstruct, and simulate the world.

So within that, there's a couple different major capabilities. It has really good camera condition generation. So you can input an image together with the camera trajectory and steer the model and have it generate video frames as long as any perspective you want. It's really good at sparse 3D reconstruction. You can input one or multiple up to 100 frames that are views of the real world

and use those to reconstruct the real world. And that reconstruction can take the case either of novel of video flying through the space or an explicit 3D reconstruction of the space. then finally it can be used for simulation. And for this, we show off these awesome bullet time videos, which got a lot of attention online. And then also robotic simulation.

What's a bullet time video? A bullet time video. This comes from the Matrix. There was a famous shot in the first Matrix movie where Neo was like falling down. Oh, yeah. So then remember in that famous shot, he's falling down. It's in slow motion and the camera flies all the way around. The way that they did that shot is they had a ring of hundreds of cameras.

So then he fell over in the studio. They had hundreds of cameras viewing that angle on a green screen. And then they use those hundreds and hundreds of cameras to make that famous shot in Matrix. But now with Atlas, we can do this with as few as three cameras. So no studio capture, no green screen, no expensive calibration. We can literally stick three iPhones on tripods. Use these to take sort of a video of something happening, like someone shooting a basket, someone dropping a strawberry into a bowl of milk. And then from those three iPhone videos, we can then reframe the shot.

And imagine like freeze time, have the camera fly in as the milk is splashing up and get these amazing frozen time views. And we can do this with just a couple cameras. What is the simplest description of what Atlas does, what goes in and what comes out? Yeah, so one of the really core principles of Atlas, the most fundamental thing is it does new view prediction. And this is a really fundamental primitive that we think is super exciting,

a super new primitive for base models that no ones that have ever done before. Right. So we know LLMs are built on Next Token prediction. We've seen video models as being built on Next Frame prediction. Atlas is really new view prediction, right? That given some number of views of a scene or a description of a scene,

those go into what we call a spatial context that describes implicitly what is the world that we want to talk about. Then you can point a virtual camera at an arbitrary point in space and time, an Atlas will understand what that world is supposed to look like from that position in space and time.

Ben, the video models out there all claiming to be world models and all claiming to have novel views. And can you maybe tease apart kind of more concretely how this is different from the myriad models that have come before?

Yeah, I think what Justin was saying about the spatial context aspect is super important here. So there's many video models. A lot of video models actually got their claim to fame from their single image input or their start to last frame interpolation. Now we're starting to see models that can do this kind of Omni reference thing

with 20, 30, 50 images. But what's key with Atlas is that it actually has a kind of like spatially grounded meaning to every frame you put into it. So it's not just an image that the model is going to interpret whatever way it wants or you can kind of try to argue with it and the text prompting

and get it to do something specific. With Atlas, every image, actually has an associated three-dimensional camera pose, and that means that you can perform this task of reconstruction with an extremely high degree of precision, right? So if we had four views of this room, one at each corner, you can put those into the model and then get an exact replication of everything you see in this room,

and it's not going to guess what's in the other corner, like the relationship between things. It's just going to reproduce exactly what you give it. And you can also do that in a kind of creative or imaginative sense, too. If you take two photos from different AI generations or real-world locations, you can actually position and stage those to build these kind of intentionally directed fly-throughs that are really governed by exactly the precise place that you put the content you want and where the camera's going to look and travel, which is very different, I think,

than the kind of like more slot machine effect you get of having to retry generations over and over with just that kind of higher level of text control you get with video models. Is this just kind of an obvious scaled-up version of a traditional video model, or is it a new architecture? I think it's a pretty new thing for a couple different reasons. One that we talk about is it does both generation and reconstruction jointly in the same model. Like Ben was saying, this thing can take a couple views of this room and then reconstruct everything in this room exactly as you see it. And historically, reconstruction has been its own subfield and computer vision with its own specialized task, its own specialized models.

And generation is what all the text of video models are really good at, like all the big diffusion models we've seen the last couple of years. And those are great for creative applications. I want to imagine something that's never been there before. But now with Atlas for the first time, we're putting these two different parts of visual intelligence together in one model. So we can do both 3D reconstruction and generation together in one architecture.

So to do that, we had to make a couple changes. One is we had to make it multimodal from the start. So this thing natively works on text. It works on images. It works on videos. It also works on camera poses as a native input to the model, which I don't think anyone's ever done at the pre-training phase before. And it uses 3D as a native modality

that it works on. So this thing from the beginning was designed to be natively multimodal in a way that no one else. Sorry, I don't know, the space super well. By 3D is this like depth or models or like, what does that mean? Yeah, so the formulation we used so far

is depth maps. Okay. Right? So right now, when you have a frame that has a virtual camera telling its position in 3D space, that camera position and camera parameters are a native input to the model.

And then what attached to that camera position, you can have both like RGB telling you what does that position in space look like? And you can have a depth math that tells you what is the spatial structure of that position in 3D space. So then text, image, video, 3D cameras are these modalities that this thing all it does jointly in a multimodal way. I want to add something because I think what Justin just said is actually so important

and also what Ben said that it's underappreciated. It's the first time we have a unification of pixel generation and pixel reconstruction. In the world of computer vision, this field has been around for more than. a half a century. City here, having been in this field for decades, I cannot tell you how many PhD thesis have been written on the problem of reconstruction or novel-view synthesis. And also, our field traditionally have multiple tracks. You go to a computer vision conference. You have the pixel generation track. You have some recognition track and you have 3D reconstruction track. this is an elegant model that combines or unifies the problem of reconstruction and generation

by anchoring on viewpoints and the viewpoint estimation. And that's just incredibly powerful. Can we take a step back and then maybe you just fill something out? So when you started the company, I remember you saying you want to tackle spatial intelligence, right? And now we have this new model. And so as a lay person, it feels very gentle to me. You've got next particular prediction and this is next new view prediction. New view prediction, right?

So you can get one view or a set of views and you get a new view. Maybe you pencil out how this is a significant step to this general problem of spatial intelligence, maybe by starting to describe what spatial intelligence is. Well, spatial intelligence eventually must enable us to both generate what the space is, reason within it, and being able to edit and interact within it.

Now, we can argue is it 3D or 4D, ultimately it's 4D with the time dimension. But even just 3D, these are the fundamental tasks that one has to do, or spatial intelligence has to enable. And then we talk about with that you can render, you can simulate, and you can plan actions. But to do that, a fundamental problem to solve is to understand the geometry and structure and the physics of the space. And I do believe Atlas is a significant step forward because now with every single frame, you can generate and estimate an important piece of information, which is the viewpoint, the camera pose. And that is the most critical information

one needs about the geometry of the space. And that can lead to all the emergent behaviors we see in the downstream of the model, which we showed in the blog. So on the path to spatial intelligence, Because generating pixels is definitely a early step, which we have seen with what you call it, gazillions of models. But generating pixels that are truly spatially contextualized and grounded is absolutely another major step. And that is the very hard step that Atlas has taken. We definitely have, you know, we can just keep going here, right? Like there is the fourth dimension of time, which will bring in dynamics. and there is more higher fidelity, simulation, and delineation of the space.

So this is part of the roadmap of spatial intelligence. Great, yeah. I mean, I definitely want to dig into, like, where this is going. But first, maybe let's talk about getting here. How long has World Life's been in existence? Two and a half, right? Two and a half, yeah. And so you've actually released models before.

So why didn't you just jump right to Atlas? Great question. It's so magic, right? Justin's team needs a lot of chips. Yeah, you need a lot of GPUs to actually scale this thing up. So last year we released our Marble World Model, and that was the first kind of big major world model that we put out. That powers our current Marvel product.

And Marble is really cool. Marble can take images, it can take videos, it can take text prompts, and use these to generate 3D worlds. But one of the biggest differences between Marble and Atlas is exactly what is that output modality. So Marble was really focused on Gaussian Splats is an output representation. So whatever you're inputting,

it's going to output a 3D world represented as a Gaussian splat. And Gaussian splats are really useful, right? They're really nice. They're easy to render. They can render efficiently on mobile devices, on VR devices. They can interoperate with other,

with game engines, with simulation engines. So there's a lot of nice things about Gaussian splats. But, you know, I think that was kind of a bottleneck in the previous marble model. So what we did with Atlas

is redesign the thing a little bit. And we realized that we need to bifurcate these modalities earlier and actually have these things, all these modalities working in a more unified way in the model. So now with Atlas, the fundamental primitive is not like a gauch and splat world. The fundamental primitive is, as we said, new view prediction. And that can generate RGB frames that can generate 3D. And we can use those to generate a beautiful Gaussian splat worlds when you need them. But we don't need to

bottleneck our outputs through the Gaussian splats when we don't need to. And that was a, that actually took a lot of, you know, blood, sweat and tears to understand like what are all the pros and cons of these different representations. That's one part of it. The other part is you've got to like climb the scaling ladder, right? You got to like work your way up and like do smaller experiments, do smaller models like to build your conviction on what's going to work and what's going to scale. And there's, there's, you know, if you could instantly know the right thing that's going to

scale, you know, you should just do that. But when we started the company left, the world was a very different place. There is no scaling law of spatial intelligence. Right. So like when we started the company, like the world was in a very different place. The tech was in a very different place. We had a lot of ambitions about where we wanted it to go. But it took a couple iterations for us to hit upon this formulation that we thought is actually like this is the one.

This is the one that can scale out. You know, Ben, you know, being the creator of Nerf and doing a lot of 3D and reconstruction, so it's not so obvious to me that like if you have multiple views that you actually end up with a 3D thing. But like you've kind of like made a career of ending up with a 3D thing. So maybe talk a little bit about like kind of that step. Yeah, yeah. Yeah. I mean, as you said, I've spent many, many years of my career as a vast majority of my career actually working on producing 3D things from images.

And it's actually something we talked about early on in the company, even of like, is this going to be the approach that produces 3D, right? Are we going to synthesize multiple views and then build 3 out of that? Or are we going to try to go direct to 3D? Like, there's been a lot of uncertainty in the field around, like, which of those approaches kind of will win out or will kind of like reap the best advantages earlier on. But I did have a lot of conviction just from seeing the kind of

of power of what I would almost call the brute force scaling. Scaling in a very, very, very small baby scale, not like a real model scaling, but the scaling of dense reconstruction that we had seen happening over the past three years before. So basically we put out... Why is dense reconstruction dense? Yeah, dense. Because I know we're going to tell you was sparse, and I want to make sure that people

understand what is dense and what is sparse. Yeah, so I think this is actually, even on the kind of like, business and commercial side, I think, been one of the challenges of productizing. 3D reconstruction technology, like at a fundamental level, right? People kind of don't, in a casual sense, like you think I took three photos of this object or I took six photos of this room. Like I look at the photos.

I can understand in my mind how those pieces together, I can kind of fill in the gaps and get it. But there's just never been really any kind of reconciliation between those like really data-driven priors and then the kind of brute force dense reconstruction, which it actually is much more. akin to almost like scientific or medical imaging what we did in dense reconstruction right you basically have to say every single thing i want to appear in this reconstruction i need at least

three or four views of it and if you think about that like even just in this room right there's like under the microphone under the table between every different crack and crevice and the plant leaves right to actually truly get a picture that covers every one of those spots it's this like very tedious and exhaustive effort to walk around the room i think you've all seen me running around various places like capturing them it takes you know, for someone who's well trained per se, like it can take minutes, but if you hand a casual consumer

or even some like kind of professional trying to do this for the first time, an average cell phone camera or capture device, like it's going to take them probably an hour. I've seen someone for their first time trying to scan a multi-room environment, spend like two hours walking through it and get enough coverage.

And that's just this like very, very exhaustive and tedious loop. And so yeah, when we say dense, we really mean dense. It's like this room. I want like a hundred photos. Lots of videos. So many photos, right?

I want like 100, 200, 300 photos of this room to capture it. And what we're trying to do is bring that down to like three, right? Wow. We're saying like 50, 100 X reduction. And then that's at that scale where it just completely like flips that calculus on its head of like what type of captures you reconstruct. You can go back to existing imagery you have. You can go to stuff you find on the internet and even build scenes out of that. You can go to casual videos and like kind of unearth a lot of footage in the past.

We would never have treated as reconstructable and go back and like bring it to life as 3D potentially. something we've been playing around with a lot with Atlas, right? Like, taking old clips. Like, I've taken a bunch of my own old captures that never worked before and then put them through the system and then kind of seen a reconstruction of the first time or taking my old captures and thrown away 95% of the photos I took. And, you know, imagine angles that I never would have gotten

from a traditional kind of like Nerf or Splat type reconstruction. One thing that's underappreciated on the website of the demos is the Stanford demo where Ben showed anywhere between three to 25 images, you can reconstruct that entire Stanford quad. But the thing is, we had to show it from an aerial view, but every single input image is been standing on the ground, taking a picture from the ground.

So everything you see are generated, but according to the laws of reconstruction, and this is really magical. And this is where, like, generation and reconstruction need to interplay, in a really fundamental way to solve this problem. Because under the classic kind of reconstruction stuff that Ben was talking about,

like the reason you need so many views is because I need like multiple images and I need to triangulate this point in 3D space and see it from multiple viewpoints. So that means like that's required in the traditional version. And on the flip side, anything that wasn't captured in these views, like any pixel that was not visible

in one of the input views will be a hole in a 3D reconstruction. Because fundamentally, like if a thing wasn't visible in the input views, you know, you need to imagine it to fill in the gaps. And that's fundamentally a generative process. So even in this room, even if we set Ben loose with a DSLR and like let him like capture like hundreds of views of this room, even the world expert on doing these dense captures is still going to miss some spots. Like he's not going to get like underneath all of the microphones or underneath all the tables or like in between all the chairlikes. You're always going to miss something, no matter how many views you get. So that that's where you need generation as another mechanism in the

model because you're never going to get everything. So you need to have some generative capacity for the model to imagine, oh, based on what I'm seeing, then, like, first triangulate what I can see, but then fill in the gaps of the stuff that inevitably was not captured. Yeah, and there's something like super cool about this that L-LMps have really understood this for a long time, right? There was kind of almost these like context wars, like the first double years. It was like, oh, we got to 128 to 2B2 to 6 to like 5-12, we got a million, right? And everyone kind of understands now at a pretty tangible level the value of, you know, you crank your context length to high when you're using your coding model. It's a hard problem. Like, everyone has a feel for that. But, like, no one has

pushed that at all on the image and video model side in the same kind of, like, principled way. Like, no one's out there trying to, like, put an hour-long video through and do a needle and a haystack retrieval of, like, a frame at the 37-minute mark. Whereas with Reconstruction and generation, you actually have the same exact thing. It's just, like, generation with a really long context and you put a lot of stuff in it. Right? Like, that's the way to actually build this continuum where you kind of bridge between those two things. And like Atlas, like being able to, like, this is the thing we can never do with Marvel. Marvel had this kind of fundamental blocker of like you couldn't really jam more than honestly like a couple images in.

But Atlas, I can go and I can actually take like a 64 image capture and do like a fly through of an entire house. And everything is grounded by being, you know, seen or like almost seen or like slightly extrapolated from what's not there. But you're just getting these, you know, I'm taking captures I did with 2,000 images of a multi-room house and taking it down to like 30, 40 inputs. and the fly through looks like basically the same. And this is just like totally inconceivable before. And it's all enabled by building this gracefully scaling kind of context window that you can dump stuff into. And so the way to think about it is like the sparseness

are the pictures that you physically took. And then Atlas as a model creates the rest of the views. And then you use classic reconstruction techniques. Is that roughly the way to think about it? In some sense, yeah, yeah. I mean, that's the beauty of Atlas. It's like you can take however many inputs you have down to like a single view and then you can almost use Atlas as this rendering engine to produce anything else you

wrong right you can you can navigate it in virtual camera yeah exactly you can just you can say like okay i have a picture here i want a picture there there there you can make a couple of those then you can stage a dense fly through you can do this in sequence because it's an auto-regressive model it's up to you right to kind of pick and choose what you add interactively into the context as you generate i mean the thing that i just blows my mind is listen i just have a very simple mental mental model i i i four pictures and then I've got to like have the model extrapolate between them and then it has to fit when you're in control. Like it's got to be 3D. Like, and I always think of these diffusion models as like being visually great but not accurate. And so like, and I don't even know if

there's a question here, but like how is it like the room fits? Like how is it that it's 3D consistent? Is it just lots of data or? Yeah, I mean, it's a part, partially it's a belief in the scaling hypothesis, right? Like, you know, did you, by the way, but I have to ask, when you set out to do this, did you know how it was going to work? I was pretty sure. That's not. So, so, okay.

Were you sure? I think three of us have total conviction about the scaling law. That, that I think we do. I do think the exact architecture choices and data mixtures is where the devils are in the details. I, you know, have watched Justin and his team going from, we really don't know how long this is going to take to, oh, maybe sign of life, to, wow, this is going to work. So no one has done it.

But I think the hypothesis, two hypotheses, one is scaling law hypothesis. The other one is next viewpoint prediction. We had a conviction of these two things from early on. So I think I was very convicted that it was going to work. I was not sure it was going to work this well at this fast, right? Like, I thought there's a chance that we do this. Maybe it's not clear that like the first cycle of pre-training a new model with a new architecture and a new paradigm. Like the first cycle of that working is insane.

So I thought there was a chance in which we had to, we might have had to do a couple more turns of that pre-training cycle before we got to the level of quality we were. So we wanted. Is there, is it, are we kind of like at the end of like the scaling for this architectural approach when we need another breakthrough or is there? No, no, we're at the beginning. Really? Without changing the architecture. I think we're basically at the beginning. I think we're basically at the beginning and we're basically limited by compute at this point. Right.

Like data is very important, as Fei-Fei likes to point out. But everything has a bottleneck. And I think the main bottleneck on continuing to scale this thing is actually training compute. Right. Like during development, we trained a sequence of models. We read up with this in the blog post a little bit, but we trained a couple models that like the first couple of rungs of the scaling ladder. And each time we made the model bigger and each time we trained it for longer, each time we put it on more chips. like it got significantly better.

And the model size that we, like the model that we showed in the blog post is obviously the biggest and best one that we trained, but the thing that was limiting it was not the scale or the data or anything like that. It was literally like we had a deadline of when we wanted to release this thing and therefore we backed up what we could afford

to train in after that deadline. But here's a little bit of an insider story, right? Like Justin and team are training from the smaller and slightly bigger, you know, are having these roadmaps. And then there was one day in summer, early summer, that it's not even the current Atlas model size, it's a smaller model. And then Ben, Justin, Ben, feed it into, you know, the viewpoint generation.

And remember that famous table, the garden table for nerve paper and many papers, that overnight I got a slack. I mean, we all saw the slack from Ben that our camera flew through under the table. With the soccer ball. Yes, with a soccer ball. Is the soccer ball emergent or is that in the original picture? It was real, right? Okay.

That morning, the three of us looked at each other in the eyes and said, that's it, this is, we're going to build this. Like we made a decision within five seconds. This is just a no one has ever seen this result. Ben, can you talk through maybe more specifically the use cases? So World Labs has historically had a lot of users. that were creatives and they use it for like consistency and whatever 2D images and for

movies and for 3D and for games et cetera and so maybe can you talk about how this extends to use cases that are catered to the existing ones and then we'll like to talk about robotics actually. Yeah sure yeah I mean it's kind of funny actually one of the kind of main ways we even saw people using Marvel plays

exactly into this new view prediction case like a lot of our... Marvel being the previous... Sorry, Marvel our previous product like people would take that product put an image in, get a full 3D scene as a Gaussian splat, take a couple screenshots of it from different points of view at leap. And we're like, we can just make those images and that's data role, right? So I think like, and you know, there's a lot of degradation there.

They're like, oh, this splat could look better. And it's like, okay, what if we just generatively model those viewpoints without exact modality of control? So I think, like, even that core capability of just like view synthesis, it's sort of been this academic problem for a long time. But in the sense of, oh, you're going to do this really dense capture. like generative view synthesis is relatively quite a new problem. And we just see so many people who, in this creative pipeline, right,

people have a multi-stage workflow. I don't think there's a single person out there using one monolithic model, not even C-Dance or whatever, for their entire task. People will have this, like, you know, kind of bunch of story boards and mood boards of images they pull out from like their favorite collection of image models. And then they'll go to different video tools and, like, build those together as keyframes.

Then they'll go and, like, clip and edit those later, right? So we were seeing this like sort of, you know, niche, but very specific use case for marble as just providing that, like, sanity that you can ground your generations in some kind of 3D consistent world, right? People, you know, I thought with various image models to ask them to, like, give me different viewpoints of a room. And every time you can just look and see, oh, things kind of moved around, like it's not stable. And like, even that one seed of a use case, I think kind of signals that there's this value. And there's hiding under the surface there, like there's just decades of people being used to. persistent 3D state, like virtually modeling what they would be doing in the real world and having, you know, a stage and props and like elements there, whether it is for a movie or a show or

marketing shot or like building out game environments. Like this, this statefulness and persistence is so key in how people think about spatial reasoning and like developing an environment over time. Like people don't think in this ephemeral like generated thing, generate a thing, like, just throw it away, keep my text prompts. Like people want to build this like collection of assets and like model a world in that way. So we're trying to provide like, again, with this facial context mechanism and other things, like we're trying to provide that level of control and precision

and the ability to adjust different modalities of input, starting with the post images. But, you know, we want to give people more control over the elements of the things in the scenes you're looking at and editing and interaction and all that as we go forward. And I think that that, it unlocks like further use cases in those areas we're already seeing, but also expanding out into kind of any place people want to

create a virtual replication or like, you know, a pre-imagination of a real world space they need to build, right, for architecture and construction. Like I talked to a guy at some point building booths for conferences, right? There's just so many things in the world you don't think about need to be fabricated. And every single one of those basically goes through this like pretty painstaking virtual design phase. And of that process, like the part where you go into 3D software is kind of one of the most like arduous and like labor intensive parts right now. Like, like taking feedback on a 3D design from kind of like verbal commentary or sketches or really, really quick stuff you got from like a creative director or like a design director or an architect or whatever.

Like mapping that back into the 3D representation is like 95% of the work, right? You can have a meeting get feedback and then you go back and do a week of provisions. And that's just because like our software is kind of decades old at this point. And it just never became as intuitive as, you know, playing with Legos or like pottery or doing this stuff with your hands or sketching with a. pencil. And this is the real place where AI can actually unlock a ton of value for people in their process, whether it's a creative application or something more industrial or design or whatever. And that, like, really motivates me to kind of build different flavors of our model to cater to those

kind of people. Yeah, I can understand how it helps with the creatives because, like, Marvel did that. And also how that extends to things like design or architecture. If you acquired a robotics company. It's just a less... We just talked about it too much. I know, I know. But it's less obvious to me, especially in the context of Atlas, like how that maps to robotics. So if you wouldn't mind just penciling that out. Yeah, actually, Atlas is a key part of the puzzle.

So we acquired this company that was formerly known as Cynx. And what is their key technology? Right now, their key technology is a system that goes from real to sim and then sim to real. And what does that mean in robotics situation? You want to train. a robotic arm to, you know, figure out how to do cabling, let's say, in an industrial setting. Well, you need a whole bunch of data to first train a robotic policy to do these cables, cabling activity, and then you want to evaluate if the robotic policy is doing a good job,

and then you deploy the robot into the cabling environment. In order to train what you, what this, company, cynics, and now our robotics team used to be doing this, doing exactly what Ben was saying, dense reconstruction. You take, you know, pictures of a situation, and then try to reconstruct that environment is exquisitely painful, takes a long time, laborious, and it really blocks the velocity of robotic simulation, real to send, right?

So Atlas really is the next generation technology for that. And this is not just for robotics cabling or anything. We should zoom out and recognize the biggest problem right now in robotics is actually data. One day it will be chips, but for now it's data. Because it's so hard to collect real-world data where robots, you know, are operating on. And in order to not only you need to collect the data of, let's say, the cabling situation or dishwashing situation or whatever, there is also a very important step called randomization is that you have to take the same environment and then randomize the conditions.

So the cable doesn't literally only bend this way. It can bend a different way or the box can have different sizes, colors, different lids and all that. in different parts of the scene. So you have to go through a real-to-sim situation in order to get enough of that data in addition to other data you can get from Internet. So this real-to-sim step will be, you know, really helped by Atlas.

That's just the first part of this is meeting the robotics needs in the current technology because we don't yet have a Frontier Foundation model that's robust enough. for robotics. But Atlas is an omni model, is a multimodal model. It takes different kinds of input

and generates different kind of output. You can totally imagine the next step is Adelis taking in data that's dynamical. And that can really start to bridge the gap between, you know, action planning. and robotics and the Atlas output. So that's on that roadmap.

Were you going to say? Yeah, I was going to say there's something fundamentally different about training a robotics policy compared to really any other application in AI we've seen before. And that's like if you're generating a piece of code, like you're generating an image,

you're generating a video. The model is fundamentally creating this artifact. And that artifact, like, there's a lot of examples of artifacts that you can go out on the web or somewhere and collect. Right? You want to generate images. There's a lot of images out there.

You want to generate videos. There's a lot of videos out there. You want to generate a code base. There's a lot of code bases out there you can learn from. A robotics policy is something fundamentally different. It's not producing a static thing. It's instead a policy that's going to go out into the world, make actions and, like, try to achieve a goal. And the world's not always going to respond the way you expect, right?

Unexpected stuff is going to happen. So a robotics policy is like fundamentally an agent that is out in the real world interacting with the real world. And stuff happens. So you need, like, a critical part of that is those policies during training need to be exposed to every possible thing that could go wrong during their deployment. And that's where simulation is really key for robotics. So there's the, and then there's two angles on that. Like one is the kind of classical simulation. You can go out, you can go and like go to your favorite physics engine and like try to imagine creatively as a human designer.

What are all the scenarios that might happen in this, in this, when achieving this task? and then try to write explicit code that models them all. That's one angle, and that's an interesting angle with coding agents, like that actually gets supercharged too. But there's another angle, which is try to more data-driven simulation, right? Like, maybe we can't, can we have a learned model that can understand how the environment, how the world is going to respond to actions, and maybe it might respond in unexpected ways sometimes?

Then could we build these neural simulators that are trained on as much data as we can, then use these neural simulators, these learned neural simulators, you know, as a simulation bed to train robotic policies. And that's a really interesting future direction of Atlas. But then it doesn't stop there, right? But once you have, you know, this Learn Simulator, like this Learned Simulator kind of already has in its like mental brain, like it understands the world, it understands how the world is going to respond to actions.

And why doesn't the simulator itself become the planner? Right? Right. And that's kind of the core thesis that we've had around world models and their generality, that there are some core stuff that a model should understand around generating worlds, simulating them, understanding how they appear in different situations, and understanding how the world's going to respond to an action

is highly related to imagining what kind of action I need to take to make the world respond in a particular way. What piece of feedback that I got, by the way, congrats on the launch. It was overwhelmingly positive. I think it was probably the most significant model launch this year. And yeah, I've said glowing things. One person who's an expert in the space who I texted was like, what did you think?

And it's a great person. It's fantastic. It's amazing, but there needs to be more dynamics. And so it seemed to be at least the robotics case, but generally it was kind of ideal to actually have a world that moves. And so maybe talk a little bit about that and then any other future directions that, A, you're comfortable sharing what you think are worth talking through. Yeah, I mean, like, dynamics is clearly going to happen. Like, actually... We have baby dynamics.

We actually do have baby dynamics already. And this is something I think people didn't quite appreciate it. We didn't really highlight in the blog post. But like the previous Marble World model, it was like fundamentally static. Yeah. Like the model just like could not handle any dynamics at all. And that was just like baked into the model architecture, baked into the training. Like the whole thing was fundamentally static.

We already knew that that was a big problem post marble. And we already fixed it in Atlas, right? Like the Atlas architecture is already fundamentally supports dynamics. The Atlas training data fundamentally has dynamics. And if you look carefully in some of the videos that we even posted, There actually is... I saw you see it by being a little bit. Yeah, the waves, the water waves.

Yeah, so, like, some of the examples, there's, like, waves in the water, like, in some of the, like, generated aerial views. There's, like, little cars moving around. So, like, dynamics is actually already in this model. But I said, by the way, the dynamics seems very problematic to me if you're trying to reconstruct 3D for multiple views, right? So, like, are these things, like, at odds or... No, so, actually, one of our thesis here is that, like, you know, if you're going to do

fundamental 3D reconstruction, you actually want to have no dynamics. Like, you want to be able to model, like, exactly. views of the scene with exact frozen time. But then, like, this is actually kind of a problem with our previous marble approach, right? Like, they're, like, you can try to find data that's fully static, but that's really hard to scale and really hard to get more of. And the thing we realized is that even in the case where I want static output in the end, the best way to get it is actually expose the model to dynamics. Right? Like, expose the model to as much dynamic stuff as you got, as much static stuff as you got, and let the model figure out how to factor out the dynamic stuff.

So especially in like the Oh interesting So in the Atlas pre-training already like it saw a ton of dynamics in the pre-training already Then the post-training that we did specific to this checkpoint and this release was focused a lot more on static stuff focused a lot more on spatial movement

and less on temporal But like we already have we already like I'm pretty sure this the pre-trained checkpoint already has a lot of latent dynamics in it And this is something we're going to improve quite a lot going forward So Ben does that mean we're going to get 40 video you just can like go walk around you can see the smile on their face

so I actually can see if like you just stops now and you only did kind of bigger you know faster better you can build almost an entire industry it feels like a very horizontal primitive and if you did nothing else but are there other things that are not just kind of bigger faster

that you're excited about the applications you're focused on which tend to be kind of more on the kind of content created 3D side yeah I'm really excited about pushing that kind of multimodal aspect I think different modes of control is so critical here. I think it's super underappreciated, especially in the academic community, how critical it is to add control conditioning to these models

to kind of get out what's inside. I mean, honestly, this dynamic versus static is... I'm not trying to even understand what those. To translate in layman's language is just like editability. Yeah. I think editability is the key here. Yeah. So, I mean, this is something we've seen in like sort of single image models.

And starting this year in video models is starting to be unlocked in terms of, oh, like, getting that flavor of like multi-turn or really like intuitively interpreting like I want like this person and this object and this thing to happen and kind of combining those all together and like one pastiche without having to do a lot of like manual work with the system like it just interprets it like kind of frontier image models are kind of there right for in terms of editing but we haven't seen that propagate out as strongly into video yet and then into world models right we've seen some really kind of toy examples of oh i can like put in a sentence and like you know a dinosaur appears or

something with these like sort of real-time models. But I want to like turn that up to really industrial strength and make that because like the trick here is you got to add control but not compromise the quality of the model or just becomes a party trick basically. Like it's like no one is going to seriously think about swapping their like cutting edge frontier video model usage for your model if you give them extra knobs but the quality degrades. So I think it's really that game of like how can we maintain like the high bar we've set was the outputs we're able to get in the current model and then add all kinds of interesting stuff that people will ask us for in terms of like, I want to interact with the scene or control the layout or control like the identity of

the objects and the things that we're seeing within there or control time, right? And I think that's like an access where it opens up like a ton of really interesting product and interface work. The more complexity you add their richness in terms of kind of like enabling you to really think about like redesigning almost from scratch the way people interact with sort of like stateful, you know, 3D worlds in the computer. Like, that's really the end goal here is getting like all the capabilities you need to build that kind of system.

Awesome. And you can add to that as far as new functionality that you'd be excited about that's not just bigger or better? I think for me, let's go back to the first principle of intelligence. Intelligence is not sitting there stack and just seeing something or interpreting something when it comes to space and physical space, right? It's really this closing the loop between seeing and experiencing an interaction. So just thinking about going up that ladder is exactly what Ben said.

I think one interesting motion there is this notion of AI completeness. You read this before? Yeah, yeah. So like everyone. I hear about AI complete, by the way, in terms of LLMs, which is like you have to be basically, you know, like the smartest LLM to answer the question, what the smartest LLM will need to answer or you have to solve general intelligence. No, no, it's basically, it's a connection to Turing complete. right? Like the idea being that like a task is turn complete like in classical complexity theory

if like I can take any class in any problem in this category reduced to that one problem. Yeah, yeah, yeah, yeah. So you can take any NP hard problem and reduce it to three set. Therefore you can use three set to solve any problem. Yeah, yeah. So then like the kind of like the soft definition of AI completeness is like there's this fundamental primitive that's an AI task. But if I could solve this AI task in its full broadest generality, it would solve any intelligence problem. And like, Like the classic example at LLMs is like Next Token prediction is A-I-Complete because I could like, you know, there's the classic example I think from Ilya

where like there's a mystery novel and like the thing has to read the whole mystery novel and the final sentence of the mystery novel is like, and the killer was predict the next token. So like you could basically like frame any kind of intelligence task in terms of that. So clearly Next Token prediction is something that people believe is AI complete. But I think there's something we're kind of realizing and Ben was talking about this earlier today is like new view prediction, this primitive that we have an Atlas, especially generative new view prediction. This is also AI complete, right?

And because I could take something like... You can have the movie and you do all of the frames of the movie and then like the killer walks out and then you predict to Shaq me who walks out. Exactly. Not just that one. I want to have a world where like Martín is like writing a proof of the remod hypothesis. I don't know. And the camera buttons over to the next whiteboard.

And he solves the... So, okay, so to take an evolutionary view, right, that new viewpoint prediction is exactly evolution had to solve by making animals move. Nature give animals eyes, but nature didn't give trees eyes. Why? Because when you move, you see a new viewpoint. And that is whether you call it AI complete or intelligence complete. So we do believe very strongly that next viewpoint prediction is, is the equivalent of next token prediction.

Amazing. Well, with that, congratulations to all of you on a phenomenal model launch. We're very excited for future model launches, and thanks for coming. Thank you. Thanks for watching 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 Podcast, and Spotify.

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