This AI Makes a Video Game World in 40 Milliseconds

We had ⁠Dean Leitersdorf⁠ on the pod and he did something no guest had ever done. Mid-sentence, he transformed from a startup founder in a black t-shirt to a wizard with light shooting from his hands. Then, he was in a white-walled game universe, and when he picked up the tissue box on his table, it morphed into a gun which he could shoot by moving his arm. He did it with one of his products, ⁠Mirage⁠: It takes any live video feed (like Dean on the pod) and instantly renders each frame into a new style of your choosing—40 milliseconds from input to output. Dean is the co-founder and CEO of the creators of ⁠Decart⁠ which makes Mirage. They recently raised $100 million at a $3.1 billion valuation to build a new era of real-time generative AI experiences like this. Realtime generative video models are going to change video games forever, and Dean is on the forefront: imagine creating endless variations on existing titles, like GTA-V with a frigid winter filter, or taking a bare-bones vibe-coded prototype and using Mirage to texture it. But games are just the beginning, Dean sees Mirage as opening the door to a new medium, a new experience created by AI. In this episode, we take a look at how Mirage works under the hood, and what the Decart team learned about the future of software while wrestling with its toughest research problems. We also debate AGI—how close it really is, what counts as progress, and what kind of society it might create. This episode is a must watch for anyone interested in the future of gaming, creativity, or if you just want your mind blown by what’s already possible. If you found this episode interesting, please like, subscribe, comment, and share! Want even more? Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: ⁠https://every.ck.page/ultimate-guide-to-prompting-chatgpt⁠. It’s usually only for paying subscribers, but you can get it here for free. To hear more from Dan Shipper: Subscribe to Every: ⁠https://every.to/subscribe⁠ Follow him on X: ⁠https://twitter.com/danshipper⁠ Timestamps: Introduction: 00:00:47 A demo of Mirage, the first real-time video-to-video model in the world: 00:02:38 How Mirage can take your vibe-coded game to the next level: 00:06:22 The new architecture of modern software: 00:08:45 How Mirage works so blazingly fast: 00:16:34 Inside Decart’s invention of a new “live stream diffusion” model: 00:20:33 Solving the error accumulation problem for real-time video: 00:21:17 How Dean thinks about inventing a new creative medium: 00:29:55 Dean’s take on the post-AGI world: 00:39:43 Why AI brings back the age of the generalist: 00:51:15 Links to resources mentioned in the episode: Dean Leitersdorf: @DLeitersdorf Decart: ⁠https://about.decart.ai/⁠ Try Mirage and Delulu: ⁠https://mirage.decart.ai/⁠, ⁠https://play.google.com/store/apps/details?id=ai.decart.delulu⁠, ⁠https://apps.apple.com/il/app/delulu-by-decart/id6749955738⁠ More about Yan LeCun’s error accumulation problem: ⁠https://x.com/ylecun/status/1640123182983045120⁠

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

• 0:00Speaker 00

  Let me let me show you one cool thing that we can dive in right into how this works. And then you get like tissue holder, then you can turn it into a gun.

• 0:07Dan Shipper?

  Whoa.

• 0:07Speaker 00

  Every once in a while, if you do this, then it like shoots out something.

• 0:11Dan Shipper?

  How did you make this?

• 0:12Speaker 00

  That's a really fun question. So so what you're doing with what you see with mirages? It gets a live input stream and creates a live output stream. It just generates them frame by frame and not the entire video at once. So it's kind of like training a video model just on next frame prediction and not next token prediction. You just have to predict the next frame each time.

• 0:47Dan Shipper?

  Dean, welcome to the show.

• 0:48Speaker 00

  Thanks so much for having me. It's been been a while, been a few months.

• 0:51Dan Shipper?

  Thanks for coming. It has been a while. You're one of my favorite people to talk to in AI. You're in this like really interesting intersection of like doing incredible new stuff at the frontier, but you're also a big philosophy nerd.

• 1:04Dan Shipper?

  And so there's there's, we're just going to talk about a lot of good stuff. So for people who don't know, you are the co-founder and CEO of Descartes. You can describe for us what Descartes is in a second, but you do awesome real time generative video models. You just raised a hundred million dollars at a three billion dollar valuation. Congratulations.

• 1:24Dan Shipper?

  Yeah, welcome to the show.

• 1:26Speaker 00

  So so excited to be here. You know, I read the the newspaper every when it gets to my to my inbox. And always fun to have these conversations. Yes, we have so many exciting things going on right now. Like, these have been incredible past few weeks, and we have tons of launches for the weeks for the upcoming weeks, which I can get into to a few of them here, but it's it's exciting times. We're creating a completely new way for just people to interact with AI, just to have fun with AI.

• 1:57Dan Shipper?

  So you just, just to give people a sense of what you do, you just launched this thing called Mirage, which is kinda crazy. Can you just show us Mirage?

• 2:06Speaker 00

  Yes, let me, let me pull this up. But we just launched Mirage, it was a few weeks ago, like three weeks ago. By the time this will probably, it'll probably be four or five weeks since it was launched. Mirage is the only real-time video-to-video model in the world right now. And what that means is that you can take any, any video stream that you have, whether it's this conversation, or your camera, or a game even, and just put it in through the model and change it just with a prompt. So, well, we can, I'll, I'll bring this up right now so I can show it. Let me share my screen. And as you can see, see, this is, this is real me in the, you know, in the interview screen. And then you have the Mirage version of me. So the current Mirage version of me is in the Versailles Palace.

• 2:50Speaker 00

  Or you can do some blocky kinda thing, okay? Or we can do an anime version.

• 2:58Dan Shipper?

  This is, this is so amazing. I literally just saw this for the first time, like right before we started recording, and it was like a, oh my god moment for me. For people who are listening, basically what's happening is you're on a webpage and you have your camera going. So I see an image of Dean on my screen, and then when you started Mirage, it just, in real time, transforms you into effectively a Pixar character, something that looks a little bit like a Pixar character, but different themes and in different settings. So right now you're on Jingle Bells, now you're a wizard, you've got like lights coming out of your hands. It's, it's crazy and

• 3:37Dan Shipper?

  I, like, I feel like-

• 3:38Speaker 00

  You can be Lego.

• 3:41Dan Shipper?

  So, how did you make this? How does this work?

• 3:43Speaker 00

  I'm made of Lego. Look at this. Okay, let me, let me, okay. Let me, let me show you one cool thing that I can dive in right into how this works. But I love, this is the one, this is one of the prompts I love. If you put in the portal world, and then you get like, like physical objects, like a blocky thing, okay, like this is like, you know, tissue holder, then you can turn it into a gun. And every once in a while, if you do this, then it like shoots out something.

• 4:10Dan Shipper?

  Whoa.

• 4:11Speaker 00

  It's uh, yeah, like, like one of the things I'm dying for the next version to have is so I can be like this and be like, okay, like, and then like a huge portal pops up or something like imagine a real Rick and Morty gun.

• 4:23Dan Shipper?

  Dude, this is so cool.

• 4:25Speaker 00

  That's actually cool.

• 4:29Dan Shipper?

  Um, so yeah, just for people listening again, basically you're in your portal world, portal world. You picked up a tissue box and the tissue box became a gun. And if you move the gun in the right way, or you move the tissue box in the right way, it kind of shoots a little bit. Um, so it sounds like, I mean, right now, the possibilities are like, oh, real time, real time video games, uh, that you can just, you can just play. Uh, is that, is that what we're like where you're going with this?

• 4:54Speaker 00

  So I think the possibilities with this, wha- what this kind of tech does

• 5:00Speaker 00

  is it actually enables a different way to interact with AI video. What did AI video do so far? It was a great piece of tech that gave us cat videos on TikTok. Okay, that's, that's we got out of AI video. Now, for the first time, computers can actually respond to us as we're, you know, moving things, changing things, they can in real time change it. So it can be used anything from for gaming. We're going to have a mod coming out for Minecraft. Uh, we have an alpha testing right now. It'll be coming out in the next few weeks. We can, you, you're playing Minecraft and you just through, through the game, you, you tell the mod, hey, change, change Minecraft to be Barbie land and everything becomes Barbie. So you can take it, you can take it to gaming. We're also integrating with lots of the game engines.

• 5:45Speaker 00

  So that as you're developing a game, you can actually see this, or that you can vibe code a game end to end. So imagine vibe coding with, with an L M, a game and doing all the texturing with Mirage. So, so that's, that's one category, all the real time, interactive gaming category is huge.

• 6:02Dan Shipper?

  Wait, let me stop you there for a second. So are you saying that, you said it's video to video. So when you're vibe coding a game, how does that work? Is it, it's generating video that you're playing? Or is it also able to output like the 3D mesh that you might import into Minecraft?

• 6:17Speaker 00

  So one of the one of the things that people have been going crazy over, you can just vibe code a game. And like, you know, low poly, just a bunch of blocks moving around. And and turn that into into just Mirage do the the coloring of that.

• 6:34Dan Shipper?

  Oh.

• 6:35Speaker 00

  Let's see if I can find an example for the Spiker again.

• 6:39Dan Shipper?

  Okay, let me see if I understand. So basically what you're saying is. Um, it's really hard to make a triple A blockbuster shooter game, let's say. Um, but it's not that hard to make a 3D game, especially if you're vibe coding a 3D game. That's uh, just a first person shooter where the uh, the gun is a like a rectangle or like a a cube.

• 7:02Speaker 00

  Exactly.

• 7:03Dan Shipper?

  And so basically what you're saying is you make that game. And then. When when your game engine renders that really basic game Mirage takes it and turns it into a video that has all the textures and all that kind of stuff.

• 7:16Speaker 00

  Exactly. Spot on. And that's uh, people have been playing with this online. And it is just mind blowing to see what people came up with. That you can actually, you can just uh, some some people just use, you know, outputs from Unreal Engine and pass them through Mirage. Uh, others. Actually, just, you know, vibe coded games and added Mirage on top. I can I can send a few examples and uh. You you're editing this video, we can put it in. Um, but it actually just lets you. Build a game very quickly with an L L M, just half an hour, like, oh, add this tree here, add that there. I don't even have to build a tree, it just builds a blob that kind of looks like a tree. And Mirage just comes and completely changes it. So.

• 8:01Speaker 00

  It's going to do something. I don't know exactly what that's going to do something big to gaming.

• 8:07Speaker 00

  Uh, either taking existing games, you know, where we were here at the office, we were playing GTA a few days ago. And pass GTA through a winter filter. And then boom, all of GTA V just turned into a winter version. And there's no winter in regular GTA V. So sure, you're going to be able to get a bunch of varieties of existing games, but you're also going to be able to create a game end to end, just fully AI based. Just vibe code it and texture it.

• 8:33Dan Shipper?

  One of the things this is making me think of, and I'm sorry, we're already going to the philosophy stuff. But I'm not actually sorry. One of the things that it's making me think of is there's this really interesting thing happening where the underlying game is still works with classical code. And then the, what Mirage is doing is sort of doing this emergent layer of texture in the same way that, you know, maybe you can break down the world into, you know, atoms and molecules that that work according to like specific equations that we can, we can figure out. But then our conscious experience of the world is this emergent,

• 9:19Dan Shipper?

  textured, full of qualia type thing. And you kind of need like, or at least the way that we understand the world is, is it has both, or at least the way I understand it. And it seems like previously we had to make that,

• 9:34Dan Shipper?

  all games had to be able to reduce down to that deterministic code. And it was very, very difficult and time consuming to, to set that world up. And now you have this sort of emergent layer that you can just vibe on top of the basic, I don't know, Newtonian or classical world that you can build with programming. Is that kind of what it is?

• 9:56Speaker 00

  I love, I love that you're going into this.

• 10:00Speaker 00

  You got me thinking about this. There's some stuff that regular coding is great for and other stuff that AI is great for. What's AI great for? AI is great for doing stuff that's differentiable, that's continuous. Okay, fine, drawing a picture that's kind of continuous. It doesn't matter if I get exactly your face or something closed, it's fine. What's AI definitely not good at? Um reversing a hash function or something. Okay, or even just implementing a hash function. Anything that that is that is not differentiable. AI would suck at.

• 10:32Dan Shipper?

  Can you define differentiable for for people? I know you're a, you know, you're you're like a math genius. And so I think I might have an idea of what what you're saying here. But can you define? Can you define it for us?

• 10:45Speaker 00

  Definitely. So so think of a differentiable or a continuous function. Something that it's it's okay if you're close by. Okay, like it's fine if I if I'm creating your image. It's fine if I get the exact image, but it's also fine. You know, if I'm missing a few things, if your shirt's a bit off, if the if the wall behind you is slightly different. If your glasses are slightly bigger, I don't have to exactly get what you're talking about. And and that's that's a quantities of the of the differentiable or a continuous function. If if you're not talking about a if you have a function that's not differentiable, not continuous. It can be it's very critical that you get it exactly right. Now, regular CPUs are just great at doing stuff that needs to be exact.

• 11:29Speaker 00

  Regular CPUs in our computers, if we tell them add five and five, they will always give us 10. And they know exactly how to do these these deterministic operations. AI can do the non deterministic operations where we don't need stuff to be exact.

• 11:42Dan Shipper?

  I told that this this is something I've been playing with. A way that I've been formulating it is.

• 11:49Dan Shipper?

  And I'm I'm going to probably not get it exactly right, but we can find it together. There's two ways of seeing the world. One is as this sort of countably infinite set where there's one right answer. And you can guarantee yourself that you're going to get to the right answer. If you just keep going through like counting through the set. And that's the sort of classical way of of seeing the world. And that's the sort of classical search function for the right answer for a a program. Another way of seeing the world is there's this it's an uncountable infinity of different solutions. And so you're not ever guaranteed to get to the right solution. But every solution is like kind of meaningful, is kind of right, or it's close. You can get further or closer to it. And both of those are really valuable, but you need to. At least my view of reality is you need to start with that kind of

• 12:35Dan Shipper?

  uncountable infinity where everything is full of meaning. Until you get to kind of like the right zone and that's when you can start to solve with the countable infinities and like count out the right solutions. I know I'm probably sounding very galaxy brain to people listening, but I will we'll unpack this in a way, but I'm curious what that makes you think of.

• 12:51Speaker 00

  Oh, but I definitely agree with everything that you're saying. And I think that we need both. So we both need some problems are just a big haystack with a single needle and you need to find that exact needle to solve it. And other haystacks have an entire area that's full of needles and and they're not all the same needle. And maybe one needle is better than the rest, but they're all an okayish needle. And then you just need to find the rough area where you have that that that bunch of needles concentrated. Now.

• 13:19Speaker 00

  Let's take an example. So you know, Google released a genie three recently, or you know, they showed a demo of it. They they didn't productize it. Where they claimed, okay, well, it's it's a world model can generate as you go. You just you put in your keys and your mouse, you move them around and it generates frames for you. Okay.

• 13:37Speaker 00

  That Jean Jean was trying to claim, well, let's replace everything. Let's just have an AI create a completely new world for us. And we don't need any of the of the old software like game engines that existed beforehand. AI can just do everything. But we're talking about with Mirage is a combination. You'll have you have the game engine doing the deterministic stuff. Oh, I need to remember that you have exactly seventy one gold coins in your pouch. And I need to remember that, you know, you took this pickaxe and you put it in that chest five years ago. And if you go back to that chest, it'll still have the same pickaxe you put it there five years ago. How can the game engine do that? But the the more creative parts where it doesn't matter if you specifically hit the exact needle, but you get something right.

• 14:19Speaker 00

  How do I do that? For example, texturing it or making it look different.

• 14:24Dan Shipper?

  I think a good, another good analogy that might unpack this for people is you need a skeleton to have your body work and your bones define like, those are the hard limits on what you can do. But you have joints and you have muscles and you have tendons that your, your bones can pivot around and that allow you this infinite flexibility within this rigid system that guarantees certain things. And if you have bones, you can stand up. And if you have muscles and sinews and tendons, you can move around in all of these different ways that just bones wouldn't let you do. And for a long time, we've only been able to build computer systems that, that use bones.

• 15:06Dan Shipper?

  And now we have muscles and tendons that like, that, that allow us to, to move around in all these different new ways, which is really cool. But you need the bones because if you're just tendons and muscles without bones, you're kind of like a jellyfish. Like it's just very hard to pin you down. You can do certain things, but it's, it's hard.

• 15:22Speaker 00

  I love the jellyfish analogy. By, by the way, if I'm understanding, you're right. You're saying that when we build Terminator, it's not just going to be metal robots, but they'll also have, you know, they, they might have skin and boats around, but skin surrounding them. But yeah.

• 15:35Dan Shipper?

  Maybe.

• 15:38Speaker 00

  Uh, but no, I, I completely agree. And, and this is really what we're getting at, that we're enabling this, from a technical perspective, we're creating video in real time that can touch everything from gaming, but also to, you know, the zoom call, or this is, this is a riverside call, riverside, instead of having, you know, just you seeing, we seeing each other, we could be seeing something completely different. Um, but it's, it's touching the ability for people to take stuff that's in their imagination and add it to an existing real world. So we'll have this, this core that's the same, the world's the same, physics is the same. You can take your imagination and apply it to, to what you're seeing.

• 16:21Dan Shipper?

  How did you make this?

• 16:23Speaker 00

  That's a really fun question. So to crack real time video, we had to crack two things.

• 16:31Speaker 00

  On the one hand, we have to write lots of very optimized GPU code. We sat and wrote lots of assembly for GPUs, for Nvidia GPUs. It's called PTX. And we had the things that's below CUDA. So like lots of people know CUDA, and that's like the Nvidia software stack that lets you really write good stuff for GPUs. We have to write in PTX, which is like the layer below CUDA. It's the actual assembly that gets written on the GPUs. So you had to do that to actually make it very, very, very efficient.

• 16:58Dan Shipper?

  And did you vibe code this with Claude, or how did you do it?

• 17:00Speaker 00

  Oh, no, no, no, no.

• 17:04Speaker 00

  Oh, yeah, Claude's amazing. I love Claude code. By the way, I use Claude code a lot. Good job, Anthropic. But no, it like it has no idea what it's doing on those levels of the stack. It's the dark abyss of AI that no one wants to touch, including the AI themselves. Now, so, yeah, on one hand, we have to write really lots of assembly code for GPUs to get this to be efficient. You know, the Mirage, the current version that you saw is 40 millisecond delay. That's 0.04 seconds between when a frame comes in and when the model spits it out. The next version of Mirage is going to be 16 milliseconds delay, 1.6.

• 17:44Dan Shipper?

  Wow.

• 17:44Speaker 00

  Okay. And to do that, you really have to write very, very, very optimized assembly code for GPUs. Now, on the other hand, you have to completely build a different kind of model. So all the video models that we know today are what's called bidirectional. We had to build an autoregressive one. What does it mean? All the video models today, if you go to, you know, Google Vio or a bunch of these, you put in a prompt and then generate, spits out like a five second clip. And it does a lot of processing. So it's things for like a minute and then you get your five second clip. What we have to do here is if we're generating a five second clip or an hour long clip, doesn't matter. We're not generating the entire clip at once, but we're generating frame by frame.

• 18:25Speaker 00

  So what you're doing with, what you see with Mirage is it gets a live input stream and creates a live output stream. It just generates them frame by frame and not the entire video at once. That to do that, it's kind of a combination between a video model and an LLM. So you know how LLMs just generate the next token?

• 18:45Dan Shipper?

  Yeah.

• 18:46Speaker 00

  So it's kind of like training a video model just on next frame prediction and not next token prediction. You just have to predict the next frame each time.

• 18:53Dan Shipper?

  Okay, so what you're saying with the auto aggressive stuff is,

• 18:59Dan Shipper?

  what you're doing is feeding in, let's say you're you're halfway through the video. You're feeding in the the the the frames of video that you've already generated to the model to produce the next one. I assume you're also feeding in like a source frame of the video that you want to generate. Yeah, and that's how it works.

• 19:17Speaker 00

  Exactly. So you feed it in two two video streams. One is just the video stream that needs to edit. And two, what it already generated, and it needs to predict the next frame.

• 19:28Dan Shipper?

  And what is the advantage of doing it that way versus just putting in the the two frames, the last two frames? You know, last video generated frame and the the last video input and doing that next frame versus like a bunch of frames? What is the advantage of that?

• 19:45Speaker 00

  Nice. So you you really need the the longer context you have, the more you know of what happened. Because if if, for example, you know, I was wearing a black shirt in in in this stream. And now I leave the camera and I come back. How do you know like to to put on the black shirt again, not to change it? So it's it's kind of like the model's memory to be able to see what was in the past. And also, it's very critical for the model to to see motion. Because it's not just images. It's if if, for example, you use the model to add a portal done, it needs to be able to see me doing this action in order to be like, okay, now let's create a portal. So it's very valuable for the model to see motion and not just static images.

• 20:28Dan Shipper?

  I see. And is it like a diffusion model or how to like, what kind of model is it?

• 20:31Speaker 00

  Yes, it's a diffusion. We call it an LSD model, a live stream diffusion model. I have no idea why you're laughing. It's it's a very technical term.

• 20:40Dan Shipper?

  Yeah, I see that.

• 20:43Speaker 00

  These LSD models, it's a live stream diffusion model. So it's a combination of a diffusion model with an autoregressive transformer, which which really just lets you generate predict the next frame every time that you're generating it. Now, the hard part about this, do you remember like early L L M's like even like G P T two, two point five days? Even when G P three point five came out, they get stuck in loops. Do you remember those days?

• 21:12Dan Shipper?

  Yes.

• 21:13Speaker 00

  Ever happen to you? So so so you know, for listeners, sometimes like in the early days of L L M's, you talk to it and it'd be great for like, you know, a few, a few back and forths. And then we'll just start repeating the exact same thing. You'll just say, you know, repeat, get stuck on a word or in a sentence is keep on saying that exact same thing over and over and over again.

• 21:32Speaker 00

  That same problem that L L M's dealt with a few years ago comes back when you try to do autoregressive video models. What happens is the autoregressive, like the model's great for the first few seconds. Mirage, well, their biggest challenge was we could easily get Mirage to be great for two or three seconds. But then slowly starts to degrade and it gets stuck in this loop until it just gets stuck on a single color. And your entire screen just becomes reds or blue or green. And and solving that repetition problem was the hardest thing about Mirage. Now, if you use Mirage, it can do an infinite stream. You can keep on going with it for hours on end.

• 22:11Dan Shipper?

  Is that the um, is that from the Jan LeCoon problem with L L M's, which is that errors start to compound? And so the

• 22:19Speaker 00

  Exactly.

• 22:19Dan Shipper?

  The more, yeah. So how did you solve that?

• 22:22Speaker 00

  So it's the exact same problem. It's the air accumulation problem. But every frame you're generating, you're going further and further from the distribution that you actually saw. You get slightly more error as as you keep on going until after like the hundred, two hundred frames, you just lose everything and you're a static color. So the there's solving that took, you know, six months of research. We we we had an easy version of Mirage that worked six months ago, just could hold for like two seconds. And So going back to said, your question was how to build this. So on the one hand, you have to do lots of GPU code to actually get this to run fast. On the other hand, we had to solve lots of very tough

• 23:04Speaker 00

  research engineering problems on the model layer. It required, I think at this point, we ran several thousands of experiments until we really got it right.

• 23:13Dan Shipper?

  And is it, is it one thing that you unlocked where it just, it fixed everything? Or is it a bunch of little incremental things that you fixed here and there?

• 23:21Speaker 00

  You know, what you're talking about is a great, it's a great point about AI. It was roughly seven or eight things that we had to unlock. And the annoying part is it's binary. If you, until you unlock it, nothing works. And then when you unlock it, suddenly it's all better. And that's, that's an annoying part about AI research. That you never know how close you are. You have no idea if you're going to solve this problem next week. Or two weeks from now, or two months from now, or two years from now.

• 23:51Dan Shipper?

  But how did you know, for example, on the second thing that you added? It's still not working, but how did you know that it was better and you should keep that improvement?

• 24:01Speaker 00

  So we broke this down to lots of different smaller problems. First, be able to do this just with images. Or be able to do this just with, with certain types of conditions. As you know, for example, with Oasis was, Oasis was the last thing that we launched that went insanely viral back in November. It could generate Minecraft.

• 24:19Dan Shipper?

  Remind people what Oasis is?

• 24:21Speaker 00

  Yes, it's, it's a, it's a model that could generate Minecraft in real time. Went insanely viral, everyone played with it, was very fun for us. We just came out of stealth. And that, that was a real time video model that worked just for Minecraft. The reason that was actually an interesting step in the way towards Mirage. If you tried to create a video model that is great just for one single distribution. So it just doesn't Minecraft, doesn't know how to do everything in the world. It is much easier for the model to learn that. So for example, that that's one of the the problems that we use as a stepping stone. We would take larger and larger distributions. We begin with Minecraft, which is a very small distribution. It's all roughly the same. And then you do it, OK, let's do um

• 25:05Speaker 00

  just like street walk, take like a bunch of just video of like streets across the world and let's be able to simulate that. Until you grow and you add more and more complex distributions. And then each time you see like, OK, can I crack the next distribution? Can I actually get it to work with with this additional challenges?

• 25:21Dan Shipper?

  It's sort of like, I don't know, the way a child learns to run. It's like first you you sit up and then you pull yourself up and then you like, there's

• 25:33Speaker 00

  Exactly.

• 25:33Dan Shipper?

  There's a bunch of steps, right?

• 25:36Speaker 00

  They have to use more and more muscles as as they the challenges get to get harder and harder.

• 25:41Dan Shipper?

  Yeah.

• 25:43Speaker 00

  But you know, all this takes me back to why we're even doing this? So I'm a huge believer. There's some some entertainment, fun, creativity experience that we're going to unlock in the next year that will completely change everyone's lives. And that that's what that's um that's what we're here for. And the way the way I like think about is this.

• 26:09Speaker 00

  What's the Internet? What was the what is the old Internet before before ChatGPT? The old Internet before ChatGPT three years ago? It used to be four things. It was knowledge. It was creativity. Like knowledge is everything Google oriented. Creativity is Facebook and TikTok and Instagram, but also Netflix and Roblox, everything you do when you're not doing your homework. Shopping and messaging. That's the Internet. That's that's the entire Internet. There's nothing else.

• 26:38Dan Shipper?

  You're forgetting porn.

• 26:38Speaker 00

  Just these four categories.

• 26:42Speaker 00

  That's the four categories of the internet.

• 26:47Dan Shipper?

  But continue.

• 26:50Speaker 00

  That is an interesting point. Anyway, the internet used to be four things. Uh, not five, for anyone wondering. Now, the first thing, knowledge. Chatbots have just obliterated knowledge. In the new internet, anything we used to do knowledge oriented, whether it was search, or docs, or anything knowledge sharing, or knowledge creating, or maps, it all is going to the chatbots. Doesn't matter if OpenAI wins, or Gemini wins, or XAI wins. Someone's gonna win that, but chatbots are replacing knowledge.

• 27:24Speaker 00

  What happens to creativity?

• 27:28Speaker 00

  Is there gonna be a completely new experience that lets us be creative, lets us have fun? That's different than what we already know so far? And the way I like for you to get the easiest way possible is, if you go up to a 12-year-old today, a random 12-year-old, either in Kansas, or in Germany, or in Japan, and you ask them, "What's ChatGPT?", what are they gonna tell you?

• 27:52Dan Shipper?

  A chatbot?

• 27:53Speaker 00

  Yeah, what do they do with it?

• 27:55Dan Shipper?

  I do my homework with it.

• 27:57Speaker 00

  Exactly. Every 12-year-old on the planet will tell you the exact same thing. "Oh, ChatGPT, oh yeah, that's the AI that does my homework for me." Okay? It helps me with my homework. And then you ask them the follow-up question. "What AI do you use when you're not doing your homework?" And they have no idea. Okay, they stare at you like, "I don't use an AI when I'm not doing my homework. I'm on Fortnite, or Minecraft, or TikTok. What do you mean an AI when I'm not doing my homework?" And my entire belief is AI is changing everything. It really, it really is. And it can't be that AI doesn't create completely new experiences that we just get to have when we're not doing our homework.

• 28:39Dan Shipper?

  Yeah, I mean, I definitely resonate with this. I think...

• 28:45Dan Shipper?

  It's really clear that every new technology creates new content formats, creates new art forms. And we maybe have cracked some of that, you know, you can see like Suno, for example, is like gesturing in that direction. But there's probably or definitely a bunch of things that are on the horizon that we haven't quite figured out yet. Because, for example, video games used to be really hard to make. Now everyone can make a video game. That's a really interesting thing. What does that look like? And it's probably not going to look like traditional triple A games will evolve it into something else that has for the for the medium that we've just invented.

• 29:24Speaker 00

  Exactly.

• 29:25Dan Shipper?

  I think my question for you is, how do you think about the strategy of inventing a new medium like that? So you're obviously starting with the foundation model. An interesting question for me is, for example, OpenAI started with the foundation model, but it didn't really work until they created ChatGPT, didn't really work until there was a form factor that was consumer friendly. Are you all working on that? How's, what is your strategy for taking this foundation model and turning it into something that people actually use to make stuff?

• 29:55Speaker 00

  So right now, there's no app outside, but by the time this gets, uh, this gets published, there will be an app.

• 30:02Speaker 00

  This is coming out in the next few days and I completely agree that this, this great tech has been, you know, there's this term called AI experts that it's people who know how to use the AI, know how to prompt these systems and everything. You have to turn everyone into what's called an AI expert. The a random kid on the street or, you know, a grandma living somewhere should be able to be an AI expert and just and what we need to do is really find ways to put this in hands of people, put it in an app, put it in something that's comfortable for them to use and let them access this great tech that's been built over the past few years and

• 30:43Speaker 00

  in audio and video, I think it's a combination of what's going to create these new experiences and really put this in the hands of people. I think we'll do a lot of things. We're gonna publish lots of different experiences through this app. And it remains a mystery to see which ones will take off and which ones won't. And it's kind of an experiment. We're gonna be running together. Like, we're all gonna, we're all as humanity going to understand. Okay, what what is fun about these experiences that we can do?

• 31:10Dan Shipper?

  Can you show us the app?

• 31:12Speaker 00

  I actually can't on this computer. It's a new computer. So let's do this. I can explain it here. So the the app's called the Lulu. Okay, it's a

• 31:25Dan Shipper?

  Very Gen Z of you.

• 31:26Speaker 00

  Just come on, we all have to be, we all have to be a bit delusional, okay? It's it's it's what we're building. We're building the one place you go to when you're not doing your homework, which is the Lulu. Okay, now what you're gonna get in the Lulu right now, if you go and then download it. You're gonna be able to just upload a picture of yourself. I have my camera roll, you're from, you know, take a take a photo. And and the app itself will just create hundreds of variations of it instantly. So you can see yourself eating pizza on the Eiffel Tower on top of Mars. Or you can see yourself, um, it's it's actually all the the gender stuff is really fun when turns a guy into a girl and then vice versa. So we can see you in a dress. Or

• 32:09Speaker 00

  you can use it to express your emotions. You can be, you know, one of the things you can do is you can be angry at, you know, friend or something. You take a selfie and you tell the eye, okay, turn me, make me angry. And you know, you'll suddenly get like devil horns or something and like smoke coming out of your ears. So what you've been able to do is there is you can take any any photo that you have and just express yourself in a way that you just couldn't before. Right now, if you download it, you're gonna get all these uh these imaging uh tools. You're also gonna get over the next few days, the same thing with video. And it's gonna be added as a camera. So you open up your camera and you just point it at something. And you you point at your friend, you're like, okay, turn my friend into Elsa. And suddenly your your friend becomes all blonde and with a blue with a blue dress.

• 32:54Speaker 00

  Or I can point at your friend and say, hey. Turn like add a little monkey on their shoulder and you'll have a little monkey sitting right here and they can high five the monkey and the high five, the monkey high fives in the back. So all that's in the Lulu and there are going to be so many releases coming out over the next few weeks. Every week we're going to have a release for the Lulu with, with new capabilities that we're adding in constantly on the video and the audio side.

• 33:19Dan Shipper?

  And is it like a social network or are you posting this to Instagram or Facebook or whatever?

• 33:24Speaker 00

  First stage, you're going to be posting this to Instagram and whatever. But what we really found interesting is, is trends. When someone finds, someone here at the office playing around with the Lulu and it turned their hair into grapes. And that was actually super cool. And they wanted to share that with someone else so that you could also turn your hair into grapes. Because, because other people in the office were like asking me, hey, how'd you do this? Can I, can I do this as well? And so I think one of the key parts of that we already find that is very interesting and in this category is, how do you have this place you can discover new ideas for what the AI can do with, together with you? And, and try these out yourself instantly and see if you like them, see what,

• 34:05Speaker 00

  see what they make you feel. So I'm not, I don't think it's going to be a social network in the sense that we got used to, but it's going to be something completely new. Like adding in a feed there. I'm not sure it's, it's, it's the thing people will want or that will want, but we will want something else. We'll want the ability to find new trends, to find new cool stuff that's happening. Another thing that we found was really interesting.

• 34:32Speaker 00

  You know how social media today is not really social? Like it used to be all social graph and friend based. You used to be able to do things on Facebook, Instagram with your friends. Now it's all influencer based. You, you see something cool as you see trends that are happening, but you don't, it's, they're not your friends.

• 34:47Dan Shipper?

  Thank God for my, for, for me, because that's my business model.

• 34:51Speaker 00

  Exactly.

• 34:55Speaker 00

  But there used to be days when we had, you know, like our actual friends on social media. And we found that this was actually really cool with Delulu. People actually cared what their friends thought about the edits they were making. So I can make like a hundred different variations of myself and be really cool to see if my friends or my family, I'd send it over to them. And, and they usually would like the ones that I hated and that would be like, that would trigger like a huge spark inside of you like, holy shit. This is, they're, they're, they're, they're seeing the world in a way that's different than I am. And it's, it's fun to do that.

• 35:32Dan Shipper?

  So I think this is very cool. It reminds me though of some things that have already come out. So easy one is emojis from Apple. Um, there's also like a whole trend, maybe like two years ago of, uh, you know, I can't remember what it was called. It was like Dreambooth or something like that. Basically, um, stable diffusion generations of people, you know, headshots, basically, you know, it's you in a forest or whatever. So there's, there have been some waves of this before. Um, and those have all kind of, uh, burned, like gone viral and then burned out. So what is your, how do you think about that? And how do you think about this as being different?

• 36:11Speaker 00

  So by the way, it's, um, you know, you bring up Dreambooth. We have one of the experts on the subject, the, the person who was, um, last author on the, on the Dreambooth paper, Kfier, he just joined us a month and a half ago. Uh, in our, in our SF office, he used to be at Snap at Google before that. Um, and you know, I was having this conversation with him as well. They're like, okay, what's, uh, what, what's, what's happening here? Now, the tech has come so far over the past two years since Dreambooth came out. But back then it used to be a great POC. Now it actually works. Like you look at the picture and like, oh wow, that really is me and doing something insane. And it doesn't take any time to do that.

• 36:55Speaker 00

  The AI is creative enough also to, to come up with new scenes. So I think there's A, there's been a huge, huge jump on, on the quality of these things. That actually really gives you this experience. But B, I agree that images are just a gateway to, to what's enabled with video and audio. Per version to Lulu, I think we've all seen these image editing apps. And here, when we're making it much easier for for active people to access completely for free, that that creates a different dynamic, but it's still just a precursor to to the things that are being added right now, which is video and audio. When you do this with with video and audio, when you do it in real time, when I just take out my camera and point it at something and and, you know,

• 37:36Speaker 00

  ask the the trash can that's here next to me like to turn into an elephant and it becomes a little elephanty trash can just walks around the room. That is something completely new that we just never had before. If I ask if I ask the AI to to add a little turtle here on my on my shoulder and the turtle starts crawling around my shoulder and like rests down here and goes to sleep. These are things we never could really do. And I think it's very interesting to see what people will do with it.

• 38:07Dan Shipper?

  And how are you? How are you testing this internally? It seems like um. The idea is. Probably kids are going to be using this a lot. And so do you have alpha or beta testers who are in that age category that you're you're doing this with? Or how do you how do you make something that you think kids are going to love if you're not kids yourself?

• 38:30Speaker 00

  So first of all, we have a bunch of alpha and beta testers. And that's the thing that, you know, is exciting me the most, talking to to all these people. By the way, if you're listening in and you want to be an alpha beta tester, shoot me an email directly, dean at the cart, D E A N at the cart dot AI. Um, would love to be able to show your tech before it gets released because that's the way that we're exploring this together. I mean, we're creating great tech, we're using it for ourselves and it's it's fun for ourselves, but but like having people experiment with it and understand together. What is what this is going to enable humanity to do? We're all just building this together and and we'd love we'd love to have everyone's input on this.

• 39:14Speaker 00

  I think one of the critical things from our perspective. In two, three years, sure, the world is going to have almost all its pixels and audio waves be AI-generated or AI-modified. And we have to put this tech into people's hands right now so that people also build the tolerance and understand how to use this and what this means for them in their lives.

• 39:39Dan Shipper?

  What are your AGI timelines?

• 39:41Speaker 00

  Oh, I think there are only two interesting problems in the world right now. Only two. Nothing else matters. Really, nothing else matters. One is the race to AGI. And two is the race for consumer dominance. That's it for building the biggest consumer company in the world. Those are the only two races that matter. Nothing else really matters. Everything else is fun. Everything else, you know, is cute and maybe worthwhile, but not, not, not actually interesting. I think with AGI, we have to split into two. There's the Terminator stage where, you know, we get to singularity and it's smarter than all of us. And, and we have, you know, it's a good question, what happens to humanity then?

• 40:26Speaker 00

  And the second question is, when do you get economic AGI? When just machines are able to do any economic job better than all of us, or better than the vast majority of humanity. That just makes no more sense to do any kind of virtual job, virtual work anymore. The second one, the latter, is probably a few years away. The former, the, the, the former is a few years away. The latter, though, the economic AGI could easily start creeping up on us in 12 to 18 months.

• 40:55Dan Shipper?

  Wow.

• 40:56Speaker 00

  That

• 40:58Speaker 00

  will, will slowly start seeing AI just be economically better than a lot of us. And, and creating economic value. And that will, will have dramatic impact on society.

• 41:14Speaker 00

  I'm a huge believer that this is going to be humanity's best, best golden age, best time possible. We're going into a new world. Sure, AI is getting better, it's getting stronger, it's getting smarter. But humanity's future is very, very vivid and we have to, we have to understand how we, how we build this and at the same time, just build the best world possible for humans.

• 41:40Dan Shipper?

  Okay, let's, uh, let's take that one at a time. I think the, the, the big important thing I want to talk about is, uh, if I understand you correctly, you think that, uh, most economically valuable work will be able to be done by AI in the next 12 to 18 months.

• 41:58Speaker 00

  Yes.

• 41:58Dan Shipper?

  Another way to say that is, um, it will be less and less appealing to hire humans to do work and we'll be hiring AI to do work. Or what is, what do you think are the implications of that? What are you really saying when you say that?

• 42:14Speaker 00

  I think that it's very clear at this point that much of the work, whether it's lawyers or, or, you know, accountants or lots of jobs that are very, very virtual oriented.

• 42:27Speaker 00

  A lot of that work can be done really well by AI and it's getting it better and better. And, and there's no reason to assume that it won't continue to get better. Now, it does mean that a lot of us will have to adapt. Just like we all adapted when the internet came out or when previous generations of technology came out.

• 42:49Speaker 00

  And it also means that humanity will get a lot more time to, to, to think, to be creative and to explore what's, what's in our hearts and not just what's in our minds. What I like going back to a lot,

• 43:06Speaker 00

  why did democracy first start in, in Greece? I, I was actually very intrigued by this. Democracy first started in Greece because people had the time to think. You didn't have to work the fields 24/7. Before that, they had to be, agriculture was very, very hard. You would be in the field every day for 14, 15 hours and you would just go to sleep and go back to work the next day.

• 43:33Speaker 00

  As technology got better and people started getting some time to be able to to take off and not work all the time. The first thing that we got was that the ancient Greeks became philosophers and they started thinking, okay, why are we even on earth? Why? What are we doing here? Uh, what's the right way to govern people? And they created all these systems that we still use today. And we were able to get that only because humanity had more free time on its hands.

• 43:59Speaker 00

  With more and more economic value coming out of AI. We will have more time on our hands to be creative and to use our imagination. And that's something I'm very excited for. That that's that's something that we have to give an avenue for people to be able to use their added their their more added time to be more creative. To to actually do stuff that makes them feel something.

• 44:23Dan Shipper?

  So there's a lot here and I wanna go into the the ancient Greece stuff because I have a whole. I have a whole take on that too. And I think there there's a lot of overlaps, actually.

• 44:32Speaker 00

  I would love to hear what you think on that. But yes, there's so many parts here.

• 44:36Dan Shipper?

  But I want to start with the just going back to knowledge work, you pretty much be done by AI. Because I just, uh, I will, I'll take the opposite on that. I don't think in 12 to 18 months, if if you come back on the on the podcast, that we'll we'll have seen that happen. And there's a couple, there's a couple of reasons I have, but maybe I'm wrong. The first thing is, I think you're right. Like, let's take the lawyer example. I think that you're right that, um.

• 45:02Dan Shipper?

  If I think about the things that I I asked my lawyer to do. Given the right prompt, the AI is going to be better and faster and cheaper than my lawyer. However, given the right prompt is like an incredibly difficult question. And, um, I don't think we're 12 to 18 months away from a self sufficient AI that's able to know what prompt to give it at itself at the right time. And I don't think that that is a, uh, that's just a point that we pass. I think that's very spiky depending on the domain and the context and all that kind of stuff. So the way that I think about AGI, for example. Is, um, I actually think about it in terms of child development.

• 45:47Dan Shipper?

  When a child is first born, they are not independent at all from their mother. As they, as they get a little bit older, you can leave them alone for a minute, five minutes, 10 minutes at a time. Until when they're 20 or 20 years old or whatever, 18. Theoretically, they're adults, they can do whatever they want. Um, and I think you can see AI following that same trajectory where you take coding, for example.

• 46:16Dan Shipper?

  It started with tab completes. It's like one step.

• 46:20Speaker 00

  Right? It was just tab completes. It was like, and this was two years ago.

• 46:24Dan Shipper?

  Exactly. Um, uh, but if you, uh, but now it's like Claude code is like 10 or 15 minutes, but only sometimes. So like, you can't use Claude code on your deep down assembly stuff, right? But I think, I, I, I think a better definition of AGI is when it is economically profitable to leave your AI on all the time. It's always working. It's always doing something.

• 46:53Dan Shipper?

  However, even in, in that scenario, which I think is pretty far away, like several years away at least. Um, because it, well, let me just say like, I think that's a good definition because it will require a lot of things that we don't currently have. So for example, continuous learning. I think the only way to get AI that knows how to prompt itself in the right situation at the right time is if it's good at continuous learning. And I don't think that, um, better context engineering is actually, is actually going to fix that. I think that's going to be maybe part of the solution, but you have to be able to update your weights, um, is my, my belief.

• 47:29Dan Shipper?

  So, so that's why I think, I don't think it's 12 to eight, I don't think it's 12 to 18 months away. I think it's, I think it's much farther and, um, there, it will be very, very domain specific when it is totally AGI, totally independent. So maybe coding is first, but that'll still be only some kinds of coding. Like it's still not going to be for your, your CUDA improvements necessarily, or on our PTX improvements.

• 47:53Speaker 00

  Right, right.

• 47:54Dan Shipper?

  So, so that, that's kind of, that's my AGI take. Um, what do you think?

• 47:59Speaker 00

  I love this because it, you said, when does it become economically viable, profitable to just keep your AI running all the time? Okay. And clearly by the, we're at that point with computers. Keeping my, just not my laptop on all the time, okay? Is, is something that generate like the, the, I didn't even think about how much electricity it costs me to keep my laptop running. Because I just know the value I'm getting out of it is insanely higher than the, I don't know. How much is my electrical bill for my Mac cost a month? Twenty bucks maybe? Um, so it's, it's definitely economically viable to keep your, uh, your Mac on, uh, twenty four seven.

• 48:42Speaker 00

  And we're probably gonna get there with AI as well, that like, all, every, uh, uh, any, any price I'll, I'll pay for actually running this AI will just make itself back instantly. And you gave like the Claude code example, I completely agree that's not gonna replace coders. Because I've been using, I've been using Claude code over the past, uh, two weeks to, to code some stuff for our, for our app. So I've been using Flutter, I have no idea how UI works. I literally never wrote any CSS or HTML or JavaScript or any of these things, okay? Uh, I'm in the, through, through, me, like, write assembly for GPU codes, I love that, no idea how UI works. But I was with the team working on the, on the UI of the app.

• 49:24Speaker 00

  And I was able to actually contribute a lot to that project via Claude code without knowing anything about the syntax of these languages.

• 49:33Speaker 00

  And what the contributions were, were that I had intuition of like, okay, okay, okay, okay. You gotta be doing something wrong because there's no way you're storing the image here, but also somewhere else. And, and so you, you must have sent it through something else in between. And having the intuition of, okay, what's the actual challenging part you had to do? Of, of moving the data around or whatever, and just letting Claude code do the actual typing. That was something that personally I've been experiencing over the past two weeks and it's been really great. Um, and it's been very valuable. So I think that maybe a different way to phrase is it, there's a chance in twelve to eighteen months. AI lets us be so productive.

• 50:14Speaker 00

  That we're able to create companies that are just way bigger than anything we saw so far.

• 50:21Speaker 00

  And I'll give a concrete example of this. Today we have ten trillion dollar companies in the world. Give or take depending on exactly when you check and how Tesla's stock does. But we have, we have ten companies in the world that are past a trillion dollars in market cap.

• 50:38Speaker 00

  Back in 2017, we had zero. We got our first one in 2018 when Apple crossed the line. And that's, that's what 2018, that's seven years ago. Okay, seven years ago, we crossed the line of first having a first trillion dollar company. And now we have 10 of these.

• 50:55Speaker 00

  It's a lot, a lot of it is due to just technology making this that more efficient so that we could actually make a lot more money and a lot more, bring a lot more value to the world. And there's, I think there's a very good chance the next 12 to 18 months, AI just creates so much value in humanity that the entire stock market doubles in value.

• 51:14Dan Shipper?

  That's an interesting one. So now I want to go back to Athens, because I think this, this actually, this dovetails with, with the point you're making. So I'd probably say yes. I don't think 12 to 18 months. I think that's way too quick. I think it will take many, many years for it to like, uh, even, let's say you, you invented this, this AGI, let's, whatever our definition of AGI is, but you never want to turn the AI off. We invented it tomorrow. I think in 12 to 18 months, maybe there'd be a bubble in the stock market, but it wouldn't be like a thing that would

• 51:43Speaker 00

  True value.

• 51:44Dan Shipper?

  Maintain. Yeah.

• 51:45Speaker 00

  Yeah.

• 51:45Dan Shipper?

  Okay. I think it'll take a lot longer, but I also, but I do grant your point, like, uh, it will allow us to build much bigger companies than we could, we could before. I also think it will do something else, um, which is allow us to build many more smaller companies that accomplish much more than, uh, than you could as a small, as a small company. Uh, and, and, and, and I want to talk about why. So back to your Athens point. I actually don't know why democracy arose in Athens. I like, I remember like Solon's laws or whatever from my history of ancient Greek class, but I can't remember why he, why he did it.

• 52:26Dan Shipper?

  But, but the thing that I love about Athens is, uh, is, is it's a society of general, generalists, right? Democracy sort of requires that, uh, direct democracy requires that where you can be a statesman, you have to be a, a lawyer, you are a prosecutor, you are a juror, you are a warrior.

• 52:47Speaker 00

  You're everything. You need to be good at everything.

• 52:50Dan Shipper?

  You have to be good at everything. And so the question is, why did that stop working? And the reason it stopped working is because Athens became an empire. It became the equivalent of a trillion dollar corporation. And what's interesting about

• 53:05Speaker 00

  I love that.

• 53:07Dan Shipper?

  What's interesting about being an empire is you actually need specialists because you can't send some farmer to be the general on your Sicilian expedition. Like you want someone who's got a lot of experience. And so specialization requires, sorry, specialization allows for collaboration over across larger and larger organizations of people. But then you lose the sort of, this sort of generalist thing. You incentivize being a specialist, which I think has basically just, just continued in Western society over the last like couple thousand years.

• 53:44Dan Shipper?

  And there's a lot of good things from it, but also if you're like me and you, we love being generalists. It's fun.

• 53:50Speaker 00

  Exactly.

• 53:52Dan Shipper?

  And I think what's interesting about AI is because you have this, this thing in your pocket, that's like a thousand specialists in your pocket. It allows you to do a lot more for longer as a generalist. You don't have to specialize as much. And so, for example, if we take every, we've got 15 people, almost everybody inside of every is a generalist and is doing multiple jobs. The like lines between jobs starts to blur.

• 54:18Speaker 00

  Incredible.

• 54:19Dan Shipper?

  And they can do that because they have AI. And so my hope is, maybe in a similar way to yours, we get back to, we, you start moving back toward more generalists. And this sort of like golden age of Athens type vibe, because people can get more done with, with AI. They have all the specialists and they can coordinate across more people without having to specialize.

• 54:47Speaker 00

  I love this theory that we're going towards a world which is going to be more, more, we'll actually have space for generalists. Nothing's gonna happen, just two reasons. One is exactly what you're saying. If we, if AI gives us tools to jump into fields we're not experts in. But, but the AI has, you know, it has seen that experience and we can just, through the AI, actually manifest our general abilities. Yes, exactly like, you know, so like what I was doing this, these past two weeks with Claude Code and Flutter. I have no idea how to write iOS or Android apps, but I do have common sense. What I have is common sense. And, and I can use that common sense combined with what Claude knows about how to write code to, to create this thing.

• 55:32Speaker 00

  Uh, you know, my, my dad, he's retired. He used to be dean of medicine at, at one of those really universities. He's now, now he's,

• 55:45Speaker 00

  he's not officially at the cart, but he does, he does come with us to, he, he, he hops by the office like twice a week. And, and he likes being called the senior advisor for common sense. Okay. And then look, I'm not a lawyer. I'm not a coder. I'm not a business person. Okay. But I have common sense. That's what I have. That's what I can contribute to this, to the situation. And so I think that's, that's really a huge point that they like, what you're saying is, yeah, we'll actually be able to have journalists that can act as if they were specialists in each field. So that's, that's really valuable. Something else that I think about a lot in terms of AI.

• 56:28Speaker 00

  So every is 15 people. The cart is roughly 70 people now. And I think you've done a tremendous job at having 15 people that almost all of them are generalists. That is typically incredibly hard to do. And, and you know, it, it speaks for itself because you're actually able to create insane stuff that no one else can. Also here at the cart, almost everyone at the company is, is a generalist and an independent thinker. And they just go ahead and they do stuff without being told. Like we're, we're still, we're still, we're 70 people. We're still completely flat. For many years, I spent around two years of my life doing this. I was thinking a lot about what organizations work and what don't. Now, here are lots of organizations, like you said, like the most trillion-dollar companies today just don't work.

• 57:12Speaker 00

  Okay, Google has 180,000 people. They don't really work. It doesn't, we can't get the best out of them. They, they, they're smart people, they're talented people, but Google has a hard time getting the best out of them. But it's not just Google, it's every big tech. And, and this got me thinking, what's the biggest bottleneck to actually running? A company or a group of people and telling them, hey, you all have to do the same, like, go build that one huge thing together? What is the bottleneck? And? What do you think?

• 57:46Dan Shipper?

  Um, so I would say, there's, one thing is just, uh, there's a real information flow problem. So flowing information from the bottom to the top and from the top to the bottom, which AI solves. And then the other thing that I think is, is really important is humans, uh, have different motivations depending on the circumstances that they're in. And inside of a big company, they tend to be more, most interested in the thing that will get them promoted rather than like just doing the right thing. Um, and that's for a lot of different reasons. Uh, it's partly due to information flow, but that's, that's the, that's, I think, a big thing.

• 58:28Speaker 00

  Right, so I know you how to solve the second one.

• 58:30Dan Shipper?

  Yeah.

• 58:30Speaker 00

  Uh, and I agree that's, you know, fine, okay. But even if we did have tons of people who really wanted to actually build insane stuff, I'm not sure you can fit a thousand of them in the same company. And, and still actually get things done because of the first constraint that you have, the, the information flow constraint. And when you see in the organizations that function incredibly well and those that don't. That's really the, the key difference. So, so with an organization that's up to a hundred people, sure, information, information will flow and it'll be fine because you can get everyone in the same room. And they can all talk to each other and just move information that way. There's also really big organizations that did work really well. The ancient Roman legions.

• 59:11Speaker 00

  Okay, you can have a hundred thousand people with swords and shields and you'll tell them, okay, go conquer that hill. And then go conquer that hill. Now, the reason that those organizations worked is because not a lot of information really needed to flow. You could just tell them, look, we're on this hill, they're on that hill, march and stab. Okay, that's the information that you needed to get to your 100,000 people on the field. And

• 59:42Speaker 00

  what I tried to do over a few years and then the conclusion I got to was organizations that work versus ones that don't. It's how many generalists do you have inside the org or how many slots do you have for generalists? If you have only 20, 30 generalists in the organization, that's great. They'll think of crazy stuff and they'll go ahead and they'll uh they'll they'll pull it off. But if you have a thousand generalists, they will get stuck on communicating with each other and understanding, wait, can I do this? Can I not do this? Is it okay that I do this? And and that's where Manny has never been able to construct an organization that gets a thousand creative people

• 1:00:25Speaker 00

  and lets them all be creative at once. So any organization you're in can only enable maybe 20, 30 people to actually be creative. Whether it's a 20, 30 person startup or a Roman legion, fine, you had 20 creative generals and 100,000 foot soldiers. Who didn't have to be creative at all.

• 1:00:43Dan Shipper?

  Really interesting. And why is that?

• 1:00:47Speaker 00

  It's a good question why that is, but I think that when your job doesn't require creativity, it's rather easy to define. Okay, your job is to go ahead and you're you're working a Ford factory. Your job is to pick up this tire and put it on the car. Okay. Um, and it doesn't matter if you do it exceptionally well or you do it average. If you do it average or exceptionally well, maybe it's 50% difference. Uh, but if you have to be creative, like, okay, go build a completely new experience for people or build some breakthrough technology.

• 1:01:19Speaker 00

  That is a role that you need to be able to to take risks. You need to be able to do stuff that that's much more complicated. It's hard to define. And for people, the way that's we have information bottomless in that we can't communicate too much. We can't, I can't take everything that's in my mind just, you know. telepathically give it over, uh, send it over to you. And so we have to be able to dumb things down and explain stuff in very, very simple terms. Like, okay, your just job is just to do this, and I can explain in three bullet points. And so when you need, when something's creative, it's, it's early stages, not that easy to define.

• 1:01:56Dan Shipper?

  I think, um, I, I agree with this. I think the, the way that I would describe it is in any kind of work, there's a, there's at least, there's two phases. And they're kind of fractal within each other, but like, we can just for now say there's two phases. There's the explorer and then there's the exploit phase. And the exploit phase, you're trying to figure out like, what do we even want to do? And the exploit phase, it's like, it's just an execution problem. You're just solving puzzles that you, like, the, the frame of the puzzle is set. And, um,

• 1:02:26Dan Shipper?

  we currently use a lot of humans for the exploit thing. And we may be able to use a higher, a higher ratio of humans for explorer now that a lot of the AI is going to be able to do exploit. Cause AI is currently very good at problems you can define, but there are surprisingly few problems you can define that are valuable. Um, compared to all the, all the problems in the world. Um, and actually I think this goes right back to, this is a really good bookend to the podcast. Because this goes right back to the, the first thing we talked about, which is, um, one needle in a, in a, in a, in a haystack. Uh, or this sort of like infinite, uh, stack of,

• 1:03:04Dan Shipper?

  this infinite forest that you can go down and every, every path you can go down is meaningful. And you kind of have to, uh, you kind of have to explore that infinity and there's no right answer. Um, and so I think we can, we can, in a world where AI is quite good at finding the needle in the, in the, in the haystack or something like that, uh, we, we can spend a lot more time exploring. And AI is also a good tool for exploring, but it, it ultimately comes down to, uh, those, I guess those two phases of creative work. Maybe as a, as a way to say what you're saying.

• 1:03:34Speaker 00

  Exactly. And AI, AI is gonna.

• 1:03:38Speaker 00

  Leave the creative stuff to us. It's gonna do the stuff that is well-defined. And that's great. And not only that, because it overcomes lots of communication bottlenecks, it can actually see everything we're doing, you know, in a thousand-person organization beforehand, before AI, no one, not a single person knew everything that was going on in a thousand-person organization. No one read every email and heard every phone call. Now AI actually can, and so it can help us communicate better and help us each be creative and let us explore until we find something that's valuable to go ahead and exploit and then it can just do the exploitation on its own. I think that's a great way to sum up what I think will happen

• 1:04:17Speaker 00

  over the next few years. That AI will do the stuff that's more well-defined and will leave, it will leave it to humans to be creative.

• 1:04:27Dan Shipper?

  Dean, it's a great place to leave it. This is an amazing conversation. Thank you so much.

• 1:04:31Speaker 00

  Thanks so much, Dan. This was, this was amazing and we should do it more often.

• 1:04:36Dan Shipper?

  I would love that.

• 1:04:44Speaker 02

  Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat craving for more. It's not just a show, it's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.