Dwarkesh Patel: commitment

Transcript context

…In principle, quite a lot. We have a tendency sometimes to think about robots as mechanical people, but that's not the case. People are people and robots are robots. The better analogy for the robot, it's like your car or a bulldozer. It has much lower maintenance requirements. You can put them into all sorts of weird places and they don't have to look like people at all. You can make a robot that's 100 feet tall. You can make a robot that's tiny. If you have the intelligence to power very heterogeneous robotic systems, you can probably do a lot better than just having mechanical people, in effect. It can be a big productivity boost for real people and it can allow you to solve problems that are very difficult to solve. For example, I'm not an expert on data centers by any means, but you could build your data centers in a very remote location because the robots don't have to worry about whether there's a shopping center nearby. There's the question of where the software will be, and then there's the question of how many physical robots we will have. How many of the robots you're training in Physical Intelligence, these tabletop arms, are there physically in the world? How many will there be by 2030? These are tough questions, how many will be needed for the intelligence explosion. These are very tough questions. Also, economies of scale in robotics so far have not functioned the same way that they probably would in the long term. Just to give you an example, when I started working in robotics in 2014, I used a very nice research robot called a PR2 that cost $400,000 to purchase. When I started my research lab at UC Berkeley, I bought robot arms that were $30,000. The robots that we are using now at Physical Intelligence, each arm costs about $3,000. We think they can be made for a small fraction of that.…

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