World Models, Robotics, and the Future of 3D AI

World Labs co-founder Justin Johnson joins MTS hosts Theo Jaffee and Sophia Puccini to discuss Atlas, World Labs’ latest world model, and the broader case for AI systems that understand and interact with the physical world. Justin explains how Atlas approaches three core tasks: generating new worlds, reconstructing real environments from images, and simulating how objects or robots might behave within them. Underlying it is a bigger thesis: just as language models became general-purpose engines for working with text, world models could become a horizontal layer for visual and physical intelligence across industries from entertainment and gaming to construction and robotics. They also explore how world models could change video games and creative tools, why precise spatial control matters, and the potential for “real-to-sim-to-real” robotics, where a few photos of a physical environment could eventually be enough to build a simulation and adapt a robot to that specific space.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Language models are these general horizontal engines for processing streams of discrete text, discrete tokens. Those have had tons of applications from everything that we know and love today. And our thesis is that there exists another category of model called world models that should be based in visual understanding, should be based in physical understanding, that can be used to generate, simulate, reconstruct worlds. And if we can build these with the right generality, they should be applicable to tons of different industries. From entertainment to VR to construction to robotics. We live our lives in this physical built space all around us and we need models to help us with these things as well.

• 0:31Erik Torenberg?

  Language models gave AI a way to work with words. What happens when models can understand and simulate the physical world? World Labs co-founder Justin Johnson joins Theo Jaffe and Sophia Puccini on MTS to discuss Atlas and the broader idea behind world models. They explore how models could generate and reconstruct environments. What separates them from video generation? And applications across gaming, VFX and robotics, including turning a few photos of a real space into a simulation for training a robot.

• 1:05Speaker 02

  We are live with Justin Johnson, who is a co-founder of World Labs, which is a startup that builds spatial intelligence products. And just today they announced Atlas, the world's first multimodal world model that generates image and video frames with pixel-perfect camera control and reconstructs them in 3D, model the world, move the camera and simulate space and time. So, congrats on the launch. The launch video looks really cool. Welcome to MTS.

• 1:29Speaker 00

  Yeah, thanks for having me.

• 1:30Speaker 02

  Absolutely. So tell us a little bit more about what this product is specifically, what you can do with it, what people will use it for.

• 1:37Speaker 00

  Yeah, so this is not a product launch. This is a model announcement. So the products are coming later. This is the base model. It's going to be used to power our future products from World Labs. But at its heart, it's a world model. It does basically three different kinds of things. Generation, reconstruction and simulation. Generation is you want to generate new worlds that don't exist. Like I can start from a text prompt, I can start from an image prompt, then I can direct the camera and sort of fly through that world and whatever, have the model generate a world that never existed before.

• 2:05Speaker 03

  Yes.

• 2:05Speaker 00

  So that obviously has tons of applications across many things in creativity. From VFX to gaming to all the kinds of use cases that people are using there. The second major capability of Atlas is reconstruction. There, sometimes I don't want to generate a new world. Sometimes I have an existing space in the real world that I like to model and bring into virtual reality or the virtual world in some way. So there, what Atlas can do is sparse reconstruction. I can take just a few photos of a space, as few as one, up to 100 or more. And the more photos you give it, the more you can reconstruct that existing real world space accurately. And the third thing we can do is simulation. And this is going after more robotics. Well, both VFX and robotics. But there, we want to be able to take an existing space, recreate that space in 3D,

• 2:50Speaker 00

  then simulate how a robot might behave in that space. And we show some examples of all these different use cases on the blog.

• 2:56Speaker 03

  Yeah.

• 2:57Speaker 02

  Is the ultimate vision for World Labs something more like environment for teaching robotics how to work? Or is it more like a metaverse that people will like spend time in?

• 3:07Speaker 03

  All of the above, probably?

• 3:07Speaker 02

  Or is it just kind of all of the above?

• 3:08Speaker 00

  Yeah, I think it's all of the above. The central thesis here is that there's a new, there's a different kind of horizontal model that we can build called a world model. Like language models are these general horizontal engines for processing streams of discrete text, discrete tokens. Those have had tons of applications from everything that we know and love today. And our thesis is that there exists another category of model called world models that should be based in visual understanding, should be based in physical understanding, that can be used to generate, simulate, reconstruct worlds. And if we can build these with the right generality, they should be applicable to tons of different industries. From entertainment to VR to construction to robotics. We live our lives in this physical built space all around us and we need models to help us with these things as well.

• 3:47Speaker 03

  Right. So the model is the input that would go to all of those industries, like the input intelligence. I'm curious like how this has evolved and why Atlas is a big jump here. So I would imagine like one of the difficulties with procedurally generating worlds is that like after a certain point, like I feel like, you know, with video models, like 10 seconds in, I feel like the output could start to get jumbled, especially if you have like a reference image. And then it starts to take its own direction. It starts to like hallucinate in its own way. So how do you fix this thing of like at a certain horizon, things would start to get jumbled or distorted?

• 4:19Speaker 00

  Yeah, that's a great question. So here, we built Atlas specifically around spatial control and the notion of a spatial context. So when you give Atlas reference images, they aren't just images. They're actually grounded in 3D space. So you can stage those images in 3D and say, this reference image should be here. This reference image should be here. This reference image should be here. And when you generate a video from Atlas, you're not just giving it a text prompt. You also go in and steer the camera and say, I want the camera to fly this way, this way, this way, this way, with exact precise pixel control. So then we show some examples where even up to a minute of video, we give it more reference images and sort of leave breadcrumbs of those images along the trail in 3D. And then you get that directorial control over the video that you're generating.

• 5:01Speaker 00

  So the idea here is that even as you go to really long generations, it's not a slot machine. You're generating and directing and really controlling with the model outputs.

• 5:11Speaker 03

  Yeah.

• 5:12Speaker 02

  So, yeah, we were just talking right before we went on about, our friend Tyce from OpenAI did this project that went kind of viral that was like GTA, but at San Francisco. So it like, I think it was like a giant Gaussian splat of the whole city or something. And you can like walk around in it, you can steal cars and drive around, you can fly around. It's not very high resolution, definitely not as much as Atlas. Do you think ultimately this is something you'll be able to do with Atlas?

• 5:39Speaker 00

  Yeah, I think so. And I think there's actually two different futures here of like the tech path that gets us there. One is sort of the explicit 3D route that you mentioned, right? Like the idea of generating a Gaussian splat reconstruction, then importing that explicit asset into a game engine or something else. This is something Atlas can do, right? It can be used to generate 3D Gaussian splat scenes. And then you could import them and generate and build games around them. Another direction where this could go is, you know, you skip the engine entirely and go directly for frame generation, right? And I have the model directly outputting the frames without any explicit 3D in the mix anywhere. And Atlas is built to do this as well. So it's one model that both handles, you know, 2D when you need it, 3D when you need it. And can handle both modalities in a native way.

• 6:19Speaker 03

  If you're somebody using this world model and you have all these inputs, like how would you steer Atlas to do what you intended? Like if you're like a film producer or video game developer, how would you work with it?

• 6:32Speaker 00

  Yeah, we have some internal tooling for this, but it's going to take a little bit more, a little bit more iteration on the product side to figure out the right way to expose that. But the internal tooling is actually pretty awesome, right? You can basically take images, drag them into this interface, have this sort of spatial 3D canvas for all your input images. Then they end up with this like proxy geometry in 3D space and you can steer your camera kind of with an FPS type control to like decide exactly where you want this thing to go. And that's a pretty complicated user experience or user interface. So I think we'll probably iterate on different ways to expose these capabilities in nice ways.

• 7:04Speaker 02

  How does it differ from Marble, which was World Labs' first product?

• 7:07Speaker 00

  Yeah, so it's a totally rebuilt stack of models, right? I think the central thesis is the same. We want to build multimodal models that can input many different modalities, output many modalities. This is something that Marble did as well. But one way in which they differ is that Marble really put Gaussian splats really at the center of many things that it did. So Marble could output videos, Marble could output 3D scenes, Marble could output meshes. But everything was kind of bottlenecking through a 3D Gaussian splat representation. So whenever we output videos or images from Marble, you were always generating a Gaussian scene, then rendering those Gaussians back to images or videos. And this is really nice, but we wanted to break that dependency. So Atlas, you know, was built fully from the ground up to both rethink the architecture, to make it more unified, more easy to scale,

• 7:49Speaker 00

  and to have that sort of that branch between 2D and 3D happen earlier. So that when we're generating 2D outputs or video outputs from Atlas, you're not bottlenecking through a Gaussian splat scene anymore. The model is directly giving you those beautiful 2D pixels. And then only you lift them up to 3D only for the applications that you actually know that you need 3D.

• 8:08Speaker 03

  So I guess at this stage, like are we outgrowing Gaussians as a technology? Or like what is their role within all of this?

• 8:17Speaker 00

  I think Gaussians are a tool, just like anything else. And you've got to use the right tool for the job, right? Like if you're working on something like VFX, then the ultimate output is a 2D image or a 2D image or 2D video, and maybe that's all you need. But for other applications, especially real-time applications on embedded devices, like I think Gaussians and other explicit 3D representations still have their place. I think there might be a world in the future where we're all streaming pixels directly off these models in real time from powerful server farms. But you know, we're not quite there yet. And even if we are, it's going to be pretty expensive. So something like a Gaussian or a mesh is really useful for client-side rendering, especially on mobile or VR devices. And also really useful for integrating with existing workflows and pipelines. Like it's, you know, I think we all believe in the future of technology

• 9:00Speaker 00

  and we're excited about building these solutions that, you know, give us whole new experiences. But the reality is that it takes a lot longer than these, it takes a lot longer for these things to permeate than we expect, right? So people in gaming and VFX and design and architecture, they have their existing workflows that work on 3D representations. And you know, instead of trying to switch them over all at once to a fully AI-native thing, if you can get with AI into their workflows and meet them where they are, then that's another great use of splats and other explicit 3D representations.

• 9:29Speaker 02

  Got it. Yeah, one of my favorite video games ever is Microsoft Flight Simulator. Which is, you get in a plane and you fly around the world and it uses like a lot of, not quite real time, but it uses satellite data in order to like reconstruct a 3D model of the world, basically, in reasonably high fidelity. Not incredibly high, reasonably high.

• 9:52Speaker 02

  Do you think Atlas will eventually be able to, like what will it take to get Atlas from where it is now to able to dynamically generate content quickly enough to

• 10:03Speaker 02

  do what Flight Simulator does? And like, is this even like the right tech tree for that?

• 10:07Speaker 03

  Imagine like Google Maps in this world, like you could just traverse like any space, basically.

• 10:12Speaker 02

  Is it kind of overkill though?

• 10:13Speaker 00

  I mean, I think it'll happen sooner than you expect, right? And that'll happen in one of two ways. Like there's one sort of, and actually, you know, Atlas was built to handle both of those tech trees. One is the explicit 3D route, right? Like maybe we're generating a Gaussian scene or a 3D point cloud scene and then interpolating that on the client. That's something Atlas can do because of its native 3D outputs. The other version, like I said, is just directly streaming the frames, right? And that's something that I think we'll be able to do pretty soon.

• 10:38Speaker 03

  Okay. I'm also really curious about real-to-sim for robotics. So, you know, taking like a real video and then building a simulation. So how is this useful and how do you see this possibly being, like helping evolve the field?

• 10:53Speaker 00

  Yeah, so I think there's a couple of different ways that this is useful. One is for evaluation, right? Like, you definitely want to evaluate robotics in the final situation, in the final environment where they're actually going to end up.

• 11:03Speaker 03

  Yeah.

• 11:04Speaker 00

  If you're going to have a robot working in a particular factory environment with a particular type of machine in a particular environment, like, you want to make sure the robot works exactly in that intended environment. So there's a really useful role in simulation at that last mile, that last mile part of robotics training and evaluation, where we build a simulation that matches the final use case as accurately as possible. Then even if you've got your big pre-trained robotics foundation model that knows about a lot of robotics actions in general, then you could either fine-tune that thing directly in that final simulation that matches the end application, or even do evaluations to make sure that it's going to work.

• 11:38Speaker 03

  Yeah.

• 11:39Speaker 00

  So that gives you a different avenue than, you could just collect a ton of demonstrations of a robot either working in that space, or a person teleoperating the robot in that space. But simulation gives you a different angle to simulate those environments.

• 11:52Speaker 03

  Yeah, interesting. So is there a world where, like, you have your general pre-trained robot, but then you always need a real to sim of a specific environment? And could that get us basically to working like general robot faster by scaling this?

• 12:08Speaker 00

  Maybe. I think there's a couple of different tech trees that people are working on. One is this notion of fully in-context learning, right? Like, maybe I've got a robotics foundation model, then I can kind of demonstrate the robot once how a task should be performed, and that's enough for the robot to figure out, like, stick that demonstration in the context, and now the robot can figure out that task. Another version is more this, you know, what we're calling real to sim to real. There, maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment, right? Like, this studio, like, this table, like, moving this microphone around.

• 12:38Speaker 03

  Yes.

• 12:39Speaker 00

  Then there's a version of that where you could come in here and take just a couple casual videos with your phone, or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space in particular. And if you could lower the barrier to doing that, like, literally, you could do it in five minutes, to like, take five photos, upload it to Atlas, have it generate a simulation, maybe describe in natural language what you think, what kind of task you want the robot to do, have an agent go build that simulation for you, fine-tune your thing, like, RL, fine-tune your general-purpose robotics foundation model, and now you've got a robot, maybe in the span of a couple minutes even, that could come in and is perfectly adapted to this space.

• 13:18Speaker 03

  Yeah, like a very fast onboarding.

• 13:19Speaker 00

  Exactly.

• 13:20Speaker 03

  Yeah, that'd be great.

• 13:21Speaker 02

  I'm kind of curious about, all the way back in 2023, 2024, when the original version of Sora came out, OpenAI's paper on it was called, like, "Video Generation Models as World Simulators," which seems like very similar to the vision of World Labs. And then, you know, they did Sora as that, they did Sora 2 basically as like a TikTok clone that didn't seem that interested in the fundamental research direction of video models as world simulators, it seemed like a consumer product instead. And then they dropped Sora, and now they're not working on world models at all? So why do you think they decided to drop it?

• 14:01Speaker 00

  Well, I don't want to comment on OpenAI in particular, I don't really know much about their internal strategy or discussions. But our thesis is that world models are this general horizontal platform that can be used to go after a lot of different applications, and there's going to be a ton of value unlocked there. I think there is maybe a sense of, a bit of innovator's dilemma, right? Like, you're sitting on LLMs and you've got this as a really powerful thing, and you're leading the pack there, it doesn't necessarily make sense to try to invest in that next thing. But, you know, for us, we think that there is another category of models out here, there's a ton of opportunities out here, and we want to be the ones to go and grab them.

• 14:31Speaker 03

  Yeah. Do you think that there's like, I'm curious about the mass, like, consumer applications of world models or the first ones? I feel like VR or AR feels natural, and then video games feels very natural. I feel like there could be, like, we could start shifting in a direction where everyone's like vibe-coding video games, and world models could be a part of that. Whereas right now, we're just like vibe-coding websites.

• 14:55Speaker 02

  We're already kind of doing that, right?

• 14:56Speaker 03

  Yeah, I think we're moving towards that. Do you see, like, world models being that rapidly deployed, so that everyone can kind of play around with them?

• 15:04Speaker 00

  Yeah, definitely, and that's what we want to do. So we want to get this, that's kind of like, fits into our thesis about these models as horizontal, general things, right? So if we want to expose these models to a lot of people, let them, you know, be consumable by humans and agents alike, and let them interoperate with other tools, right? So if we can provide a model that has all these general capabilities in simulation, in reconstruction, in generation, in explicit 3D, in 2D, and then we can have a coding agent go and stitch all these capabilities together, then we can let people really quickly iterate on really cool experiences. So I'm really excited to see, once we get this into people's hands, what kinds of cool experiences people will build with it.

• 15:39Speaker 03

  Right. Yeah, I'm also curious, like, how you think they'll naturally work with LLMs? So are we all going to basically be, like, adding another subscription to our AI stack? And like, do you think, like, agents are very easily able to, like, work with world models in theory?

• 15:56Speaker 00

  Yeah, definitely. I don't see why not, right? Like, if you, once you offer something on an API, like, you know, you get certain things in, certain things out, like, you can, they interoperate with other pieces of software, like, I think we've always wanted to build these things for both humans and agents to consume.

• 16:09Speaker 02

  Yeah, I do wonder if, like, GTA 6 is just the last great piece of, like, human-written software before, like, all future video games are kind of generated on the fly with stuff like Alice?

• 16:23Speaker 03

  I don't know. I disagree with that. I think there will always be, like, human in the loop with, because video games are an art.

• 16:30Speaker 02

  I'm not saying there won't be humans in the loop, but I'm saying like-

• 16:31Speaker 03

  But like, fully hand-stitched to, like, video game?

• 16:33Speaker 02

  Yeah, like, you know, you won't need to, like, you know, physically place, like, millions of different assets in a game engine or anything.

• 16:40Speaker 00

  Yeah, but this is just the evolution of technology in general, right? As we see, like, you know, from the early, like, some of the early Disney animation films where they go and they hand-paint every cell, like, that was already a new type of technology that enabled a new way to make movies and creative experiences at that time, right? Or some of the early Disney films figured out how to sync audio and video for the first time, and that was a really transformative experience. But, you know, new technologies come out, and creative people always find really interesting and innovative ways to enable new experiences. So moving from, you know, like, live-action silent films to, like, animated early Disney films, to, you know, the kind of computer-generated films, like, you know, the Pixar era of CG, you know, into AI, I view these as just new creative tools, and there'll always be people,

• 17:22Speaker 00

  like, creative people behind the wheel, that will be interested in creating new experiences.

• 17:26Speaker 03

  Yeah, I can imagine, like, a Pixar but in 3D, like, in like a 3D navigable space sort of thing emerging, like, I think this is going to be really good for creative specifically.

• 17:37Speaker 00

  Yeah, I think so. Right? And what we've wanted to build on all these things is this aspect of creative control, right? We never wanted to be building these generative slot machines, where you just, like, pull the thing and hope you get out a good generation, or you don't feel like that generation was yours, or that you controlled it, or that you knew what you were getting out. Like, we want to build these tools that have really deep control, right? Like, whether it's 3D control, whether it's text control, that you feel like you're a director in the thing, guiding this model and deciding exactly what it outputs, rather than just, like, the model spits something out and maybe I can stitch it together after the fact.

• 18:07Speaker 03

  Yeah, totally.

• 18:08Speaker 02

  How does Alice perform with, like, difficult physics stuff? Like, for example, if I wanted to do a simulation of a black hole or something, like, would it be able to say, like, "Oh, this is like a scenario where, like, normal object collisions don't really apply, you'll need to do, like, spaghettification or whatever?"

• 18:23Speaker 00

  Yeah, black hole's a good one, I haven't tried that, maybe I'll try it this afternoon and tweet it, but I-

• 18:29Speaker 02

  You should do that.

• 18:30Speaker 03

  You should, you should. I'm actually really curious to hear.

• 18:32Speaker 00

  Yeah, my guess is it probably won't do something super exciting for black holes. We'll see, we'll see.

• 18:37Speaker 03

  Yeah, is the value-

• 18:38Speaker 02

  Or what about just like hair or something?

• 18:40Speaker 00

  Hair is pretty good. Like, I think one of the most amazing applications we got of these, out of this thing, that was actually a bit of a surprise to us, how well it worked, were these bullet time shots. So we can do this thing, or you can take, like, a couple regular iPhones and stick them on tripods, just like, three iPhones, take synchronized capture of some event happening, and then you can use Atlas to do these bullet time reframing shots, like, freeze frame fly around. And this worked better than any of us were expecting, it's completely insane. So one of our headline or examples there is a strawberry dropping into a bowl of oat milk, and then we can, like, as soon as the strawberry hits the milk, the milk splashes up, we freeze time and, like, go in there and fly all around it, like, it's just incredible that this works from just three input views.

• 19:20Speaker 03

  Yeah, I am also curious about that, like, added capability of being able to, like, adjust where the camera is. Does that, like, what part does that help with? I can imagine, like, that helps if you're trying to make, like, a movie or something, but, yeah, how is that a useful capability?

• 19:37Speaker 00

  Yeah, I think it's very useful as a, so I think there's a couple notions, places where 3D is useful.

• 19:42Speaker 03

  Yeah.

• 19:43Speaker 00

  One, like, we already talked about the output side, like, sometimes you want to have that explicit 3D output to plug into a workflow or a game engine or something like that. But I think there's an underappreciated value of 3D on the input side as well, right? Like, even in a situation like a VFX shot or something like that, where the ultimate output is 2D, it's useful for the model to offer these 3D control surfaces to let the human director figure out, like, what kind of model, what kind of scene the model should output.

• 20:10Speaker 03

  Right.

• 20:11Speaker 00

  So in this case, you know, we can do this, you know, there you can control, like, precisely where does the camera move? Because I don't just want to write in text, like, "Pan in, pan out, truck left."

• 20:19Speaker 03

  Yeah.

• 20:20Speaker 00

  I want to be able to grab the camera and make it, like, zoom exactly as I like. And there's really no way to describe that explicitly in text, you really want to be able to just, like, grab the camera and make it do exactly what you wanted. So Atlas has camera control as a native 3D, as a native input type, right? It's multimodal, and one of those modalities is cameras.

• 20:38Speaker 03

  Yeah.

• 20:39Speaker 02

  So if you think it wouldn't do a black hole correctly now, like, what more would be required in order to get it to handle all kinds of edge cases in physics?

• 20:46Speaker 00

  Oh, I mean, so, I think there's a couple of different regimes of physics. So, like, anything that's, like, roughly Newtonian, like, that was in the pre-training data. So anything that's, like, roughly Newtonian that doesn't work at, is probably just a quick SFT away.

• 20:58Speaker 03

  Okay.

• 20:59Speaker 00

  If you want something that's sort of non-Newtonian, right? Like, black hole physics, or like, you know, microscopic stuff, and once you're down in the quantum mechanics realm, I think then you'd have to rethink a bit the architecture and the data side. But the black hole example is really interesting, I'll have to give this a try.

• 21:13Speaker 03

  Yeah, yeah. I would definitely be very curious to see, like, at a nanoscale.

• 21:18Speaker 00

  Yeah, that's probably not going to work.

• 21:20Speaker 03

  Yeah.

• 21:21Speaker 00

  Not yet, not yet. But one of our dreams, we joked when starting the company, is you want to build these 3D world models, and how cool would it be to use a 3D world model to design a chip that we then use to train the next generation world model?

• 21:32Speaker 02

  I was literally just thinking that, actually.

• 21:34Speaker 03

  Yeah, yeah, yeah.

• 21:35Speaker 02

  Because we're talking about quantum stuff.

• 21:36Speaker 03

  You have your, like, semiconductor, also, to me, what feels very natural is that, like, the natural modality for navigating these, and even prompting the world models, is just going to be VR, basically.

• 21:48Speaker 00

  Yeah.

• 21:49Speaker 03

  Because you can, like, I guess, do WASD, and then, like, move your mouse around to traverse a world, but I think eventually you will have to, basically, like-

• 21:57Speaker 00

  Well, not just eventually, we've had Marble in VR for, like, almost a year at this point. It is, that is a really transformative experience, if you guys haven't tried it, right?

• 22:04Speaker 03

  I haven't tried it.

• 22:05Speaker 00

  So then you were, like, really, like, stick on the headset, and like, "I'm in this complete AI-generated world that I created," like, it's quite amazing.

• 22:11Speaker 03

  Yes, yeah, yeah.

• 22:12Speaker 02

  Yeah, absolutely. Yeah, I can't wait to, like, see all the amazing stuff that people are going to do with Alice once it's fully out into the world.

• 22:20Speaker 00

  Yeah.

• 22:21Speaker 02

  Yeah. Justin, thanks so much for coming on A16Z.

• 22:23Speaker 00

  Yeah, thanks for having me.

• 22:24Speaker 02

  Absolutely.

• 22:25Speaker 03

  This was awesome.

• 22:26Speaker 02

  This was really awesome, yeah.

• 22:27Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z, and subscribe to our Substack at A16Z.substack.com. Thanks again for listening, and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z.

• 23:09Erik Torenberg?

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