那么，我们的第一位演讲者是埃隆·马斯克。作为Tesla和SpaceX的首席执行官，埃隆已经证明，攻克物理工程领域的挑战可能比突破繁文缛节更容易。不过，我认为他致力于同时做到这两件事，继续激励着世界各地的人们。所以，谢谢你加入我们，埃隆。我们刚刚结束介绍环节，在这个环节中，我们明确表示，在本届主席国任期内，我们相信经济增长是对我们所有人来说最重要的驱动力之一。

而且我们相信，技术是其中很重要的一部分。所以你长期以来一直身处这一领域的最前线。我认为，对在座各位而言，一个关键问题，也是我们想和你讨论的问题是：当你在世界多个国家推动创新时，在你看来，是什么将那些创新者能够成功地把突破转化为落地技术的国家，与那些进展实际上陷入停滞的国家区分开来？

因为我认为，关于人们、政府应该做什么，以及或许不应该做什么，从中可以汲取很多经验。

嗯，是的，我想很多人都讨论过这个问题，而且坦率地说，我认为其中一些相当直接，那就是你必须有一个相对不受监管束缚的环境，也就是说，新事物必须默认合法，而不是默认非法。

所以，以欧盟为例，我们发现监管程度异常之高，事情通常默认是非法的。这会抑制新技术的进步，使其放缓。它最终不会阻止进步，但会让进步大幅放缓。

当然，你需要有一个，你需要有风险投资家，以及一个支持新公司的环境。你可以把新公司想成森林里的小树苗。大多数国家往往会做的是，为森林里现有的大树提供过多支持，却没有给小树苗足够的支持。但大树不需要它们的支持。需要支持的是小树苗，也就是初创企业。

所以，总体而言，制度应该偏向支持小树，而不是大树。

但实际情况很少如此，因为大公司能够接触到，通常能够接触到各国领导层，而小型初创企业做不到。所以你确实需要促进年轻公司的成长，并在这方面采取积极措施，而且就像我说的，让事情默认合法，而不是默认非法。

这里很多国家面临的一个问题与采用有关。我认为，人们普遍认识到，采用新兴技术可能对经济增长非常有益。无论任何国家有着怎样的形态和规模，你知道，你如何看待采用？各国应该采取什么措施来鼓励采用？所以，这是否又回到了同样的监管结构问题，或者你如何看待采用作为增长的关键驱动力？

是的，我认为，对于新技术，你应该采取一种积极向前、尝试新技术的态度，否则就会在某种程度上困在过去。

新技术自然需要一些鼓励，而且我认为应该拥抱新技术。

我们将会看到，而且事实上正在看到人工智能带来的显著生产率提升，并且，并且我们将看到机器人技术带来的极为显著的生产率提升。你知道，像Tesla自动驾驶汽车这样的事物，将会带来巨大的福祉，或者说已经给用户带来了巨大、巨大的福祉。

而且我认为，人形机器人将带来令人难以置信的变化。我想，为了让你们对这里的规模有些概念，我认为，我认为人工智能可能会使全球经济增长20%至30%。这是我的粗略估计，也就是每年大约20万亿至30万亿美元。

而且，我大概到明年年底就将能够做任何数字化的事情，任何不需要用手塑造原子的事情。

所以，我相信大家都知道，人工智能在软件方面已经强得不可思议。

而且它正在达到这样一个程度：人工智能不只会擅长软件，还会达到我所说的Stockfish级别的优秀。Stockfish是一款国际象棋程序，可以非常轻松地击败世界上最优秀的国际象棋棋手。事实上，现在你可以在手机上运行Stockfish，并在国际象棋中击败马格努斯·卡尔森。所以，我预测在明年的某个、某个时候，软件将变得如此优秀，人工智能软件将变得如此优秀，以至于达到Stockfish级别的优秀。

这意味着人类不可能在编写软件方面与人工智能竞争，但就像，人工智能作为软件会彻底击败所有人类。而且我认为它会，它会在所有形式的工程和任何数字化事物方面都极其出色，可能达到stockbrush级别，但、但肯定会极其出色。实际上就在1212至18个月内。

其中一个。

关于这一点再补充一件事，抱歉。

所以，全球经济总量增长20 30%。所以，我们这里谈的是仅由数字人工智能带来的相当巨大的繁荣，但就机器人技术而言，就人形机器人而言，基本上可以把它想成一种通用型机器人人工智能。我认为，我们将看到全球经济增长许多倍，也就是说，你可以让经济增长到10倍或更多。这些数字令人难以置信。

是的，我正要说这个。我的意思是，我们在这里提供给大家的一项统计数据是，自ChatGPT算是推出以来的4年里。我想，你知道，全球已经有超过10亿人在使用人工智能。全国各地对它的采用非常迅速。世界各地对它的采用。我的问题是，我认为很多人把下一篇章看作是这种应用型或某种物理人工智能。

你认为，你认为机器人技术大致会朝什么趋势发展？它会多快得到应用？我想我记得，在特朗普第一届政府时期，我们谈论的是某种自动化工厂，而现在我们是一年后，10年后。那么，你认为这些变化在所谓的物理AI世界中发生得有多快？

是的，所以任何实体事物总是比任何数字化事物耗时更长。数字化。

当你以数字方式解决某个问题时，那是软件，你可以轻松地把它复制到其他计算机上。当它是实体的时候，你就得建立一整套庞大的供应链。你得移动大量原子。

而且目前供应链在很大程度上是全球性的。所以这就是它耗时更长的原因。不过，当你考虑人形机器人时，我认为应该这样看，正确的框架是考虑一个人形机器人、一个通用机器人的有用程度，大致将是数字方面，也就是AI软件。AI软件有多好，乘以机器人里的AI芯片有多好，再乘以机电灵巧性有多好，尤其是手部的灵巧性？

现在，这3个方面都在以指数级速度改善。而机器人的有用程度就是这3个方面彼此相乘。然后，当你制造机器人时，机器人将会。

机器人将开始制造机器人。所以你会得到一种递归效应。因此，它起初非常、非常缓慢，但随后会以爆发性的速度增长。所以，如果你说比如从现在起10年后，我会说届时人形机器人将远远超过10亿个。

哇。而每个机器人的生产力可能会是人类的5倍。

意味着。意味着10年后人形机器人的生产力。我认为这是一个保守估计。顺便说一句，这是我愿意押上重金打赌的事情：10年后将至少有10亿个机器人，而且这些机器人的产出将至少是人类的5倍。也就是说，这10亿个人形机器人的生产力将超过全人类的总和。

哇。稍微转换一下话题，谈谈美国当今面临的一个问题。过去6到8个月里，数据中心在美国这里一直是一个重大的政治议题。我认为，人们普遍认识到，为了推动即将到来的AI革命并为其提供动力，我们需要电力和数据中心算力，来完成所有这些AI的训练和推理。

你知道，你如何看待这个具体问题？在已建成的规模与所需规模之间的这条曲线上，我们处于什么位置？在座的政府领导人思考如何为本国经济做好准备，并建设适当的电力和数据基础设施时，他们应该如何看待未来几年大致会有怎样的建设、算力和电力需求？

嗯，实际上确实存在相当严重的电力危机。所以，如果你只是在X平台上大致关注AI话题——顺便说一句，几乎所有关于AI的讨论都在那里进行——我认为你就能非常清楚地感受到事情将走向何方。所以，我就是这样获取新闻的，而且效果好得令人难以置信。AI领域里所有有头有脸的人都会在X上发帖，因此我建议你直接去看X上的AI话题，这样你就会理解所有这些事情，并逐日了解事态。

而目前的共识是，明年将出现严重的电力短缺。所以并不是在遥远的未来。预计，我相信，那些非常密切关注AI领域的分析师的共识估计是，2027年AI芯片将面临至少15吉瓦的电力缺口。所以这或许是人们预料会发生的一件显而易见的事，因为AI芯片的生产速度一直在以难以置信的速度提升。

它们大致正以每年40%，40%到50%的幅度增长。但中国以外可用的电力一直以每年大约10%到20%的幅度增长。所以显然，增长更快的东西最终会压倒增长更慢的东西。因此事实上，我甚至会说，目前在明年之前电力就已经面临挑战了，这就是为什么 Google、Anthropic 和许多其他公司实际上正在向 SpaceX 租用算力，因为到目前为止，我们启用 AI 的能力比其他任何人都强。

但这是通过建造我们自己的发电厂实现的，这是我们能够做到这一点的唯一方式。所以现在中国确实拥有数量为“transmount”的电力，但、但、但由于 GPU 出口禁令，人们无法，你知道，在中国使用最新的芯片建立数据中心。所以，所以，但，所以真正需要考虑的是，中国以外地区有着什么样的电力增长？而目前相对于 AI 芯片产量，电力存在严重短缺。

所以这里存在，这创造了一个机会，我认为，对世界各国来说，只要去，去节省，算是，如果它们对 AI 数据中心感兴趣，就去、去、去建设大量电力设施，并把这些电力提供给 AI 公司。当然，作为交换，这些数据中心会被、会被征税，而且必须支付，你知道，合理的费用之类的。但这确实为许多国家创造了机会。

当然。好的，非常感谢您抽出时间。您能够在这里加入我们，对我们意义重大，真的非常感谢。非常感谢。

不客气。谢谢。

谢谢。很好。现在我们将听取 David Sachs 的发言。作为前白宫 AI 主管，以及我在总统科学技术顾问委员会的联席主席，他是一位成功的。

So 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 end things are generally default illegal. And this inhibits progress with 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 a, 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 large ones.

But that is rarely the case because the large companies have access to, usually they 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, you know, how do you think about adoption? What should countries be doing to encourage adoption? So 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, and we'll see very dramatic gains in productivity from robotics. You know, things like the Tesla self driving car is going to be a tremendous boon or is a tremendous, 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 the, I think AI will probably increase the global economy by 20 to 30%. That's my rough estimate, meaning on the order of 20 to $30 trillion per year.

And I 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, 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 as software. And I think it'll, it will be extremely good, possibly stockbrush level, but, but certainly extremely good at all forms of engineering and anything Digital. Literally in 1212 to 18 months.

One of the.

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 think of it like a general purpose robot AI. I think we'll see many multiples of the global economy, meaning 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. I think there's, you know, over a billion people around the world already using AI. There's been very quick uptake around, around the country on. Around the world on that. I guess the question to you is, I think a lot of people think about sort of the next chapter being in this sort of applied or sort of physical AI.

Where, where do you see sort of robotics trending? How quickly is it going to 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.

When you solve something digitally, it's the 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 to 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.

The robots will start manufacturing the robots. So you get a recursive effect. So it starts off very, very slowly, but then 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.

Wow. And the productivity per robot will be probably five times that of a human.

Meaning. Meaning that the productivity in 10 years of humanoid robots. 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 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, 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.

You know, how do you think about this particular issue? Where are we on the curve of kind of how much we 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 construction, compute and power needs are going to 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, 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, the consensus estimate among analysts that follows the AI space very closely is that there will be at least a, 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, 40 to 50% a year. But the, but the power available outside of China has been rising at like 10 to 20% a year. So obviously the, 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 transmount of electricity, but, but, but due to GPU export bans, one cannot, you know, establish data centers with the latest chips in China. So, so, but, so really the consideration is what, what sort of electricity growth is there outside of China? And that is currently a significant shortfall relative to AI chip production.

So there is this, this creates an opportunity, I think, for countries around the world just to, to save, to sort of, if they're interested in AI data centers, to, to, to construct a lot of power and, and offer that to AI companies. And in exchange, of course, these data centers would be, would be taxed and have to pay, you know, 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.

Thank you. Great. Now we'll be hearing from David Sachs. As a former White House AI czar and my co chair on the President's Council of Advisors for Science and Technology, he's a successful.