Elon Musk UNLEASHES his AI VISION at the G20: the FUTURE is CLOSER than YOU THINK

First, our first speaker is Elon Musk. As the CEO of Tesla and SpaceX, Elon has shown that conquering challenges of physics engineering can be easier than cutting through red tape. And I think his dedication to doing both, however, continues to inspire people around the world. So thank you, Elon, for joining us. We were just wrapping up our introductory session where we made clear that under this presidency, we believe that economic growth is one of the most important drivers for all of us. And we believe that technology is a big piece of that. So you've been on the front lines of this for quite a while. And I think one of the key questions, I think, for the group and something that we wanted to talk to you about was, as you have driven innovation across multiple countries around the world, what, in your opinion, separates countries where innovators can successfully turn breakthroughs into deployed technologies from those where progress actually stalls? Because I think there's a lot of lessons learned about what people, governments, should be doing and what maybe they shouldn't be. Well, yeah, I think a lot of people have talked about this. And I think some of it, frankly, is pretty straightforward, is that you have to have an environment that's relatively free of regulation, meaning that new things must be default legal as opposed to default illegal. So in the EU, for example, we find that the regulation level is extraordinarily high, and things are generally default illegal. And this inhibits progress of new technologies. It slows it down. It doesn't ultimately stop it, but it slows it down quite considerably. Of course, you need to have venture capitalists and an environment that is supportive of new companies. You can think of new companies like they're like small saplings in a forest. So what most countries tend to do is they tend to provide too much support to the large existing trees in the forest and not enough to the small saplings. But the large trees don't need their support. It's the small saplings that do, the startups. And so the system should be generally biased towards supporting the small trees as opposed to the larger ones. But that is rarely the case, because the large companies have access to the leadership of the countries, and the small startups do not. So you really need to foster the growth of young companies and take active steps in that regard. And like I said, make things default legal, not default illegal. One question that a lot of the countries here face is a question around adoption. I think there's a general recognition that adopting emerging technologies can be very beneficial to economic growth, no matter what sort of shape size of any country is. How do you think about adoption? What should countries be doing to encourage adoption? Does it go back to the same sort of regulatory structures, or how do you think about adoption as a key driver for growth? Yeah, I think you want to have a lean forward, try new technologies approach to new technologies, otherwise, as opposed to be somewhat stuck in the past. Naturally, new technologies need some encouragement, and I think should be embraced. We are going to see, and are seeing, in fact, significant productivity gains from artificial intelligence, and we'll see very dramatic gains in productivity from robotics. Things like the Tesla self-driving car is going to be a tremendous boon, or is a tremendous boon already to users. And I think humanoid robotics will be just an incredible change. I think, just to give you some sense of scale here, I think AI will probably increase the global economy by 20% to 30%. That's my rough estimate, meaning on the order of $20 trillion to $30 trillion per year. And AI will be able to do anything digital, anything that does not require shaping of atoms by hand, probably by the end of next year. So, as I'm sure people know, AI is already incredibly good at software. And it's getting to the point where, AI won't just be good at software, it'll be what I call stockfish-level good. So, stockfish is a chess program that can beat the world's best chess players very easily. In fact, at this point, you could run stockfish on your phone and beat Magnus Carlsen at chess. So, at some point next year is my prediction, software will be so good, AI software will be so good that it will be stockfish-level good, meaning that it is impossible for a human to compete in writing software with AI. But, like, AI will just crush all humans at software. And I think it will be extremely good, possibly stockfish-level, but certainly extremely good at all forms of engineering and anything digital, literally in 12 to 18 months. One of the stuff- So, let me add just one more thing to that, my apologies. So, 20, 30% increase of total global economy. So, this is quite a lot of prosperity we're talking about here, just from digital AI. But from robotics, from humanoid, basically thinking of it like a general-purpose robot AI, I think we'll see many multiples of the global economy, meaning, like, you can increase the economy by a factor of 10 or more. These are mind-boggling numbers. Yeah, I was just about to say that. I mean, one of the stats we gave to the group here was that in the four years since sort of ChatGPT was launched, there's over a billion people around the world already using AI. There's been very quick uptake around the world on that. I guess the question to you is, a lot of people think about sort of the next chapter being in this sort of applied or sort of physical AI. Where do you see sort of robotics trending? How quickly is it gonna be implemented? I think I remember in the first Trump administration, we were talking about sort of automated factories, and now we're a year later, 10 years later. So, how quickly do you think these changes are happening in the sort of physical AI world? Yeah, so anything physical always takes longer than anything which is digital. Digital, you know, when you solve something digitally, it's a software that you can easily copy across other computers. When it's physical, you've got to build up an entire massive supply chain. You've got to move a lot of atoms. And supply chains are very much global at this point. So, that's why it takes longer. Nonetheless, when you think of humanoid robotics, the way you think about it, I think the right framework is to consider that the usefulness of a humanoid robot, a general purpose robot, is going to be roughly the digital, the AI software, how good is the AI software, times how good is the AI chip in the robot, times how good is the electromechanical dexterity, especially of the hands. Now, all three of those things are improving exponentially. And the usefulness of the robot is those three things multiplied by each other. Then, when you make the robots, the robots will start manufacturing the robots. So, you get a recursive effect. So, it starts off very slowly, but then it grows at an explosive rate. So, if you say like 10 years from now, I would say there are well over a billion humanoid robots. And the productivity per robot will be probably five times that of a human, meaning that the productivity in 10 years of humanoid robots, and I think this is a conservative estimate, by the way, this is what I would be willing to put serious money betting on, that there will be at least a billion robots in 10 years, and that those robots will be at least five times the output of a human, meaning that the billion humanoid robots will be more productive than all humans combined. Wow. Shifting gears just a little bit to a question that's kind of facing the US today. Data centers have been a big political issue over the last six to eight months here in the United States. And I think there's a general understanding that in order to drive and power the AI revolution that's coming, we need to have the electricity, and the data center compute capacity to do the training and the inference of all this AI. How do you think about this particular issue? Where are we on the curve of kind of how much we've built versus how much we need? And as government leaders here think about how to prepare their economies and build the right power and data infrastructure, how should they be thinking about where sort of compute and power needs are gonna be in the next few years? Well, there actually is quite a crisis of power. So this is something if you just sort of follow the AI topic on the X platform, which by the way is where almost all of the AI discourse takes place. I think you get a very good sense for where things are headed. So that's how I get my news and it's incredibly good. Everyone who's anyone in AI posts on X. So that's why I'd recommend just go on the AI topic on X and you'll understand all these things and get a day-by-day account of things. And what the consensus is at this point is that there will be a significant power shortfall next year, so not like distant future. There's expected to be, I believe the consensus estimate among analysts that follows the AI space very closely is that there'll be at least a 15 gigawatt shortfall of power in 2027 for AI chips. So this is perhaps an obvious thing that one would expect to occur because the rate at which AI chips is being produced has been rising incredibly rapidly. They're sort of rising on the order of 40 to 50% a year, but the power available outside of China has been rising at like 10 to 20% a year. So obviously the faster rising thing will eventually overwhelm the slower rising thing. And so in fact, even I'd say at this point, there are challenges with power even before next year, which is why Google and Anthropic and many other companies are actually leasing compute from SpaceX because we've been able to turn on AI better than anyone else so far. But this is by constructing our own power plants is the only way we were able to do it. So now China does have a tremendous amount of electricity, but due to GPU export bans, one cannot establish data centers with the latest chips in China. So really the consideration is what sort of electricity growth is there outside of China? And that is currently a significant shortfall relative to AI chip production. So this creates an opportunity, I think, for countries around the world just to save, if they're interested in AI data centers, to construct a lot of power and offer that to AI companies. And in exchange, of course, these data centers would be taxed and have to pay reasonable fees and stuff, but it does create an opportunity for a lot of countries. Absolutely. Well, thank you so much for your time. It meant a lot that you could join us here and really appreciate it. Thank you so much. You're most welcome. Thank you.