This Week in Startups
00:38:39 · Jason Calacanis, Oliver Cameron
[00:38:39] S0: Explain. [00:38:40] S2: Yeah. So the double slit experiment is this famous experiment that basically says when you pass light through two slits and you observe it going through those two slits, do you get a pattern that is, conducive to light being a wave or to a particle? And, that is something that we very much believe is indicative of us understanding, how the universe works. When you see something, it observes this pattern. When you don't observe something with an instrument, it has this different pattern. And that might be indicative of some sort of, I don't know, performance characteristic of the universe. [00:39:30] S0: In the double slit experiment, you you you have, like, a light going through two slits in a piece of wood, a piece of paper, whatever it is, and then you have it projected. And so here's your light source. It goes through a metal sheet, and then it creates light, and then the light hits a screen. Now, when you're observing it as a human, you see a different pattern than when you're not observing. In other words, the observer changes reality. Am I correct in describing that as a layperson there, Oliver? [00:40:03] S2: To an extent. I don't think we really understand yet what observation really means. To us, yes, we're observing it vis a vis. Is it that simply articles are interacting? Is that observation? And there's really limited understanding of this.
00:40:03 · Oliver Cameron, Jason Calacanis, Lon Harris
[00:40:03] S2: To an extent. I don't think we really understand yet what observation really means. To us, yes, we're observing it vis a vis. Is it that simply articles are interacting? Is that observation? And there's really limited understanding of this. [00:40:18] S0: And then popular science fiction, Lon, you have this moment in time when Kryptonians come to Earth because they have supernatural perception. When they take their mask off and they get hit by the sun, they hear everything on the planet. They see everything on the planet, and general Zod drops to his knees because he's disoriented, and Superman's been on the planet for thirty years. [00:40:40] S1: Earth's yellow sun. Yeah. [00:40:42] S0: Yeah. But Superman's learned how to tune things out and fine tune it. So what is the business here, Oliver, now that we've gone down a science fiction rabbit hole for fifteen, twenty minutes, which makes it super interesting. You know, obviously, you're not building a simulation to, have, you know, philosophical discussions like this and pop culture discussions. You're doing it for a business. What is the business here, and how close are you to that business?
00:41:05 · Oliver Cameron
[00:41:05] S2: So the business is world models for robots and world models for agents. And before starting Odyssey, I spent eight years building driverless cars, and it turns out we were building that technology all wrong, I believe. And the reason we were doing it all wrong is because what intelligence should learn from is a very general set of concepts before it tries to accomplish a task. Now what we did in Driverless Cause is just show the same task over and over again. We showed those models, here's how you drive a car, here's how you drive a car, here's how you drive a car. And so then we're a little bit surprised when it can't handle crazy edge cases or it gets stuck and does something dumb. Well, the reason it does that is because all it's seen is the world through the lens of a a car. And so, really, what a world model proposes to be is a much more general foundation for robots, big and small, like cars to build upon. And so the paradigm now with world models is that you take this very general foundation model, a world model, and then you show it just a few hours of experience. So for example, we as humans, we learn for seventeen years, then we go learn how to drive, and we're exposed to about twenty, thirty hours of experience of driving a car. And so the equivalent of that for world models is that you show it twenty to thirty hours of humans driving cars. And then what we see is that the model's able to take those very general concepts and the twenty to thirty hours of experience and then become a really good driver. And this same concept applies to robots that you can show at twenty, thirty hours of manipulating, some task that you want the robot to do, and then it can perform that task with lots of robustness to the world. And so this this whole... That's our business, to build that foundation model to then work
00:43:01 · Jason Calacanis, Oliver Cameron
[00:43:01] S0: on customers. Are robotics companies, factory... Companies that own factories, or own depots? [00:43:08] S2: We we sell to physical AI companies. And so that's robot labs, humanoid labs, self driving car companies, drone companies. They take our base model, build upon it, and then they have more reliable [00:43:18] S0: Have have they started, actually paying for it, and they don't see that as their core business? They think it's better to just buy yours because it's, you're working on that problem, and it's like they got enough problems on their own to get the hands working on these things. [00:43:30] S2: Yeah. Yeah. We generate revenue today. And I I think something changed when language models, really landed, which was... I think everyone went from in deep tech, we have to build everything ourselves to be more open to doing post training as the value add. And so the robot labs we work with, they primarily post train Odyssey-three, our latest foundation world model, and then they deploy that model into their robot, then they deploy that in the field. And so that's definitely been a shift, in the in the space.
00:43:30 · Oliver Cameron, Lon Harris
[00:43:30] S2: Yeah. Yeah. We generate revenue today. And I I think something changed when language models, really landed, which was... I think everyone went from in deep tech, we have to build everything ourselves to be more open to doing post training as the value add. And so the robot labs we work with, they primarily post train Odyssey-three, our latest foundation world model, and then they deploy that model into their robot, then they deploy that in the field. And so that's definitely been a shift, in the in the space. [00:44:02] S1: I mean, I'm curious because... So it's it's all visual input is what I'm hearing. That's how you're training. That's how you're building the world model. You're showing footage. But so much of how we engage and learn about the world is is is more tactile. Like, I I know which of these objects is gonna be heavier before I pick it up just because I have years of experience gauging object size and weight. And you know, like if I'm driving down the street and there's like a paper bag blowing, I'm like, I can hit that and it won't matter. It's not worth veering out of the way. If it's a rock, I go like, I gotta get out of the way. How purely from visuals can robots and cars learn everything they need to know about our world and how matter moves through it?
00:44:43 · Oliver Cameron, Jason Calacanis
[00:44:43] S2: So, really, if you think of a world model like this, you've got this really attentive learner. You place it on a couch in front of a TV screen, and you show that really attentive learner many, many thousands of years of video. And it's just sat there. It never gets distracted. It's got this notebook, and it's writing notes on every single thing it sees. It's keeping notes on dynamics, on motion, on humans, and everything you could possibly think about. And then at the very end of that, this notebook is all filled out with all of the knowledge that it's learned through that process, and that that notebook is now massive. Right? It it describes so much about the world. And that really is how these models learn, that just simply taking copious notes of how physics evolves over time, how motion evolves, how humans work together, how we don't work together, and it just learns lots of concepts from from simply doing that. And so when it comes to world models, one of the hills that we believe is most important to climb is physics accuracy. Yeah. If we climb physics accuracy, that means the model is learning fluid dynamics. It's learning gravity. It's learning all these concepts. And so what we see now is they they are just from a very simple learning objective, which is predict during training time how the world's gonna evolve next. It learns very general, concepts. [00:46:03] S0: Amazing. Alright. Listen. Continued success on it. Where are you based, and, are you hiring, at the moment? I know you raised some money.