Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: A lot of people that listen to this are entrepreneurs, people that run companies. A very popular question has become, how should a traditional company begin to think about using LLMs or preparing itself for the ongoing improvement of these models? How would you answer the same question for robotics? How would you encourage companies to think about this? [00:00:00] S1: The technology is changing so rapidly. I want to illustrate why this question is difficult with an example. Here is a particular uncertainty about the tech. Will the robots rely more on demonstrations or on reinforcement learning from autonomous data? We're working on both of those things, and they're clearly both important. But how somebody should prepare for the technology will be pretty different if they're expecting that they need lots of teleoperation to produce lots of demonstrations, a little bit of autonomous experience. Versus the opposite, a tiny number of demonstrations and huge amounts of autonomous experience. Is it 90-10 or 10-90? That's something we're hopefully going to learn about over the next few years, but it does change the correct approach pretty dramatically. That's kind of a case study of how changes in technology will dramatically alter this. [00:00:00] S0: From a business standpoint, is the right way to think about it, get really clear on the economics of the labor in your business? I'm curious how you think about that, the way that this will change the nature of labor itself.
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: From a business standpoint, is the right way to think about it, get really clear on the economics of the labor in your business? I'm curious how you think about that, the way that this will change the nature of labor itself. [00:00:00] S1: Coding tools are a really nice example to look at for a template of how this might work. It's not like coding tools came on the scene and suddenly we don't need software engineers anymore. It's that the coding tools increased the productivity of individual software engineers. There's some amount of work that needs to be done to make sure that people are able to use them. There's some amount of technology development that needs to be done to make them useful for the appropriate use cases, and these things are still evolving and they're also still changing. Coding agents are different than code completion tools and so on. But I think it's a nice template for us to look at to see how AI tools combine with people doing a job, increase their productivity, and also raise new challenges. And I think we'll actually see something like that with robotics too. A more realistic template is not like, the humanoid goes in and the people just leave. There are some aspects of the job that can be done by a robot, some that can be done with a robot working together with a person. Some that can be where the person needs to do something special to make the robot more productive. Some where it's the other way around, where the robot does something that makes the human more productive. And it'll be this dance that we've seen with coding tools.
· Sergey Levine, Patrick O'Shaughnessy
[00:00:00] S1: Coding tools are a really nice example to look at for a template of how this might work. It's not like coding tools came on the scene and suddenly we don't need software engineers anymore. It's that the coding tools increased the productivity of individual software engineers. There's some amount of work that needs to be done to make sure that people are able to use them. There's some amount of technology development that needs to be done to make them useful for the appropriate use cases, and these things are still evolving and they're also still changing. Coding agents are different than code completion tools and so on. But I think it's a nice template for us to look at to see how AI tools combine with people doing a job, increase their productivity, and also raise new challenges. And I think we'll actually see something like that with robotics too. A more realistic template is not like, the humanoid goes in and the people just leave. There are some aspects of the job that can be done by a robot, some that can be done with a robot working together with a person. Some that can be where the person needs to do something special to make the robot more productive. Some where it's the other way around, where the robot does something that makes the human more productive. And it'll be this dance that we've seen with coding tools. [00:00:00] S0: Do you have a favorite robot that's not part of what Physical Intelligence is doing, and if so, why? Could be anything. Could be a factory robot, could be an Optimus or Boston Dynamics.
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: Do you have a favorite robot that's not part of what Physical Intelligence is doing, and if so, why? Could be anything. Could be a factory robot, could be an Optimus or Boston Dynamics. [00:00:00] S1: I do really like the Boston Dynamics robot, especially the new version of the Atlas. Because it is in some ways very human-like and in some ways very not human-like. They made some interesting decisions about how they want more range of motion on the joints, so it can do some pretty cool things. It's also a very agile robot, which is really cool. It makes for those awesome demos, so I'm a big fan of that. I'm generally a big fan of everything that Boston Dynamics has done. [00:00:00] S0: Should or could anything be read into the fact that Boston Dynamics has been doing very cool demos for a very long time and don't actually do anything useful for customers? [00:00:00] S1: I think it's also a fair question for lots of robotics companies, to be fair. There is a lot of value in demos that serve to illustrate challenges on the road to something useful and productive. Obviously, you can also do a demo without being on the road to something useful and productive. There is value in demos. I think that demos that are used correctly in service to a mission can provide people with an illustration of what to expect, and they also provide a challenge. You just have to be honest in setting up that challenge.
· Sergey Levine, Patrick O'Shaughnessy
[00:00:00] S1: I think it's also a fair question for lots of robotics companies, to be fair. There is a lot of value in demos that serve to illustrate challenges on the road to something useful and productive. Obviously, you can also do a demo without being on the road to something useful and productive. There is value in demos. I think that demos that are used correctly in service to a mission can provide people with an illustration of what to expect, and they also provide a challenge. You just have to be honest in setting up that challenge. [00:00:00] S0: How much do you think about the business endpoints to this point? Roomba is the best-selling robot of all time in the consumer category, which is surprising. And of course, we might be on the edge of some sort of Cambrian explosion. But how much of your cycles do you spend thinking about, this is the shape of a product that might result from this, that maybe is the way we bootstrap our way to all this data?
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: How much do you think about the business endpoints to this point? Roomba is the best-selling robot of all time in the consumer category, which is surprising. And of course, we might be on the edge of some sort of Cambrian explosion. But how much of your cycles do you spend thinking about, this is the shape of a product that might result from this, that maybe is the way we bootstrap our way to all this data? [00:00:00] S1: It's just something that's very hard to reduce to a very concrete answer right now. It's not too bad to think about a space of possibilities. A lot of what we're doing when we develop our models, when we experiment with different tasks, when we do demos like the Robot Olympics – underneath we're prototyping, what does it look like when we try to do something real with this, to different degrees of real, and what goes wrong? It is something we think about a lot. It's not something that I have even close to a concrete answer to, but there's a space of possibilities. And a lot of what we actually are planning to do in 2026 is also experiment with different things in that space. [00:00:00] S0: When you study the history of general purpose technologies – certainly this would be a major one if it comes to fruition – you often find this constellation of things happening around that thing that enable it. LLMs are a direct complement to what you're doing. Are there any other surprising technology areas or trends that help you do what you do but are different?