NVIDIA + Figure CEO Reveal AI Humanoid Robot Future For 2026

The figure has probably made about five years of progress in the last 12 months. We have labor shortages all over the world. If we had more workers, companies would make more money. If we were productive, more productive, companies would make more money. When we make more money, we invest more money so that we hire more people. And so it’s very likely that the companies that use AI first, that use robotics technology first, will be the most successful first and they will end up hiring more people. We have robots out now commercially running every single day.

Like we have a robot right now running commercially in a manufacturing commercial partner of ours. And we have robots that can do end-to-end activities, like say like full laundry, do dishes, only with neural nets. But it comes down to one thing and one thing only. Profitable token generation. The moment we have intelligence that’s worthy of being paid for, and as soon as that flywheel goes, then everything else will take care of itself. And this is no different than the moment that human robotics starts to do productive things and people are willing to pay for it and it’s a profitable endeavor, then you could scale up as long as you want to scale up.

And so that’s really the moment we’re looking for. We’re looking for that virtual cycle where AI starts to generate productive tokens, profitable tokens. After that, the flywheel takes off. So the hill we need to climb, like the hill we’re trying to hill climb now, is how do we build a horizontal AI stack that can do everything a human can. And quite frankly, we have robots that are working in the commercial market as of right now, like to this very moment. And it’s been really good for us to get those lessons learned, but it also gave us insight to build like what is the right technology stack to go build.

And that technology stack is like end-to-end deep learning. I think to be candid, the problem that the entire space feels for humanoids is we have to solve like a general purpose robot. You have to get to be able to like just talk to it and have it do anything you wanted to do in unseen locations. That problem is not solved. That problem is 10 times, 50 times, 100 times harder than making a humanoid robot. Computers speak numbers. Tokens are numbers. Tokenization, we tokenize almost everything. We tokenize images, words. Brett tokenizes motion. You know, a sequence of plans that a robot performs.

And so everything that we can tokenize, AI can understand. So if we can tokenize proteins, if we can tokenize chemicals, we can tokenize motions of what action and behavior, all these things that Brett is trying to tokenize. We tokenize video. We tokenize 3D. Everything, we tokenize fluid dynamics. We tokenize anything we can tokenize, AI can understand. And everything that AI can understand, it can translate. It can generate. And so that is the revolution that we’re in. And that’s why token is at the core of everything that we do. And ultimately, what we fundamentally do is to transform electricity into tokens.

And that’s what an AI factory does. So I think for us, we’re trying to help climb that problem. In parallel, we’ve now announced BotQ this year, which is our high-scale manufacturing facility out in California. We do all the final assembly testing and robot shipping from there. And that problem is hard. It’s more like a consumer electronics manufacturing problem. But it dwarfs in comparison to solving a general purpose humanoid robot, which is just like on the surface, like an incredibly hard problem. It’s tractable. It’s an approachable problem. But that’s the problem we need to solve first.

And then from there, we need to figure out how to manufacture at scale. That’s like heavy automation. We need robots in the loop for end-to-end manufacturing. And we’re doing it now in-house. So we figure out how to build MBS systems, how we build lines, how we do end-to-line testing, how we deliver out the customer and make that better and validate it. So we’re basically trying to get good at manufacturing while we’re trying to solve the problems of how do we solve general robots. The figure is trying to basically build humanoid robots so it can do everything a human can.

You can’t code your way out of that problem. Mathematically, we have 40 different joints in the robot that can ultimately be in… I mean, every joint, it’s a motor, can move like 360 degrees. So you basically have 360 to the power of 40 equals the number of states the robot could possibly be. Like the basic body positions. More than atoms in the universe. So you have to solve this with neural nets. And so for us, we have this kind of phrase we use where we’re trying to give AI a body to do everything a human can.

And so if you look at NVIDIA today, 100% of our software engineers use cursor. 100%. And we’re hiring faster than ever because we’re creating more chips than ever. We’re developing more software than ever. We’re more productive than ever. And it looks like the company’s growing faster than ever. So there’s a lot of great things that goes along with being productive. I think the story here is that if we can create mechanical or synthetic humans, it’ll create the largest economy in the world by a long shot. Like a little under half a GDP is human labor.

Figures probably made about five years of progress in the last 12 months. We have robots out now commercially running every single day. Like we have a robot right now running commercially in a manufacturing commercial partner of ours. And we have robots that can do end-to-end activities like say like fold laundry, do dishes, only with neural nets. I think what’s happening is we’re approaching this kind of singularity point for humanoid robots where it’s in the coming years, we’re going to see humanoids out in the real world. I think at scale that are doing like end-to-end useful work for humans.

Cash flow matters. Power matters. Land power and shell matters. Power generator matters. These are all things. Factories matter. These are all things that Brett and I think about all the time. And his business, my business, is really very similar. But today, technology is at the forefront of almost every single geopolitical conversation. Technology is obviously one of the most important industries in the world. And what I can say is that on behalf of, and both Brett and I, you know, have the benefit of serving and being part of the American technology industry that I would say is our national treasure.

This is our single most valuable industry, unquestionably one of the most important industries in the world. And we’re really proud to be part of it. So that means everything from pre-training, like we have to basically build large-scale data collection efforts of human-like data to do training. We use NVIDIA there. And then that means at test time, when we’re running policies on robot, we’re doing inference on NVIDIA GPUs on robot without any network connection. So we can basically run robots in full-end-in situations like doing work without any network, outside network.

So for us, we think of ourselves like an AI business. We happen to be building like these physical agents around the world, like similar to web agents, basically just like in the physical world touching things. And NVIDIA has been a very significant partner and investor of ours, and I think they will be significant in the future as well. Thank you. [tr:trw].