Read the full transcript of a fireside with Elon Musk at AI Startup School in San Francisco on “Digital Superintelligence, Multiplanetary Life, How to Be Useful”, June 19, 2025.
Digital Superintelligence and the Intelligence Big Bang
ELON MUSK: We’re at the very, very early stage of the intelligence big bang. Being a multi planet species greatly increases the probable lifespan of civilization or consciousness and intelligence, both biological and digital. I think we’re quite close to digital superintelligence. If it doesn’t happen this year, next year for sure.
埃隆·马斯克：我们正处于智能大爆炸的非常非常早期阶段。成为多星球物种会大大提高文明、意识以及智能（包括生物智能和数字智能）的预期寿命。我认为我们已经非常接近数字超级智能了。如果不是今年实现，那明年肯定会实现。
GARRY TAN: Please give it up for Elon Musk. Elon, welcome to AI Startup School. We’re just really, really blessed to have your presence here today.
加里·谭：让我们热烈欢迎埃隆·马斯克。埃隆，欢迎来到AI初创学校。我们今天能有你在场真是太幸运了。
ELON MUSK: Thanks for having me.
埃隆·马斯克：谢谢你邀请我来。
Building Something Useful vs. Building Something Great
打造有用的东西 vs. 打造伟大的东西
GARRY TAN: So, from SpaceX, Tesla, Neuralink, xAI and more. Was there ever a moment in your life before all this where you felt I have to build something great? And what flipped that switch for you?
加里·谭：你做了SpaceX、特斯拉、Neuralink、xAI等等项目。在这一切发生之前，你人生中有没有某个时刻让你觉得，“我必须要打造某个伟大的东西”？那个转变是如何发生的？
ELON MUSK: Well, I didn’t originally think I would build something great. I wanted to try to build something useful. But I didn’t think I would build anything particularly great if you said probabilistically. Seemed unlikely, but I wanted to at least try.
埃隆·马斯克：其实最初我并不认为自己会做出什么伟大的事情。我只是想尝试打造一些有用的东西。但如果从概率上讲，我并不觉得自己会做出特别伟大的东西，这似乎不太可能，但我至少想要尝试一下。
GARRY TAN: So you’re talking to a room full of people who are all technical engineers, often some of the most eminent AI researchers coming up in the game.
加里·谭：你现在是在对一群技术工程师说话，其中很多人都是AI领域正在崛起的一流研究人员。
ELON MUSK: Okay, I think we should, I think that, I think I like the term engineer better than researcher. I mean, I suppose if there’s some fundamental algorithmic breakthrough, it’s a research, otherwise it’s engineering.
埃隆·马斯克：嗯，我觉得我们应该……我觉得我更喜欢“工程师”这个称呼而不是“研究人员”。我的意思是，如果你实现了某种基础性的算法突破，那可以叫做研究，否则它就是工程。
The Early Days: From Stanford to Zip2
早期岁月：从斯坦福到Zip2
GARRY TAN: Maybe. Let’s start way back. I mean, when you were, this is a room full of 18 to 25 year olds. It skews younger because the founder set is younger and younger.
加里·谭：也许我们可以从很早以前说起。这屋子里坐着的，几乎都是18到25岁之间的年轻人。创业者的年龄越来越小了，所以整体偏年轻。
Can you put yourself back into their shoes when you know, you were 18, 19, you know, learning to code, even coming up with a first idea for Zip2. What was that like for you?
你能设身处地地回想一下你18、19岁的时候吗？那时你还在学编程，甚至已经有了Zip2的初步想法。那对你来说是怎样的经历？
ELON MUSK: Yeah, back in 95, I was faced with a choice of either, do you know, grad studies, PhD at Stanford in material science, actually working on ultracapacitors for potential use in electric vehicles, essentially trying to solve the range problem for electric vehicles, or try to do something in this thing that most people had never heard of called the Internet.
埃隆·马斯克：是的，1995年时我面临一个选择：要么去斯坦福读材料科学的研究生，攻读博士，研究用于电动车的超级电容器，基本上是想解决电动车的续航问题；要么去做一些跟一个当时大多数人都没听说过的东西有关的事情——互联网。
And I talked to my professor, who was Bill Nix in the material science department and said like, can I defer for a quarter because this will probably fail and then I’ll need to come back to college. And then he said, this is probably the last conversation we’ll have. And he was right. But I thought things would most likely fail, not that they would most likely succeed.
我和我的导师比尔·尼克斯谈了这件事，他是材料科学系的。我问他能不能休学一个学期，因为我觉得这个（互联网创业）可能会失败，然后我就可以回学校继续念书。他说，“这大概是我们最后一次对话了。” 他说得没错。我当时真觉得这事大概率会失败，而不是成功。
And then in 95 I wrote basically, I think the first or close to the first maps, directions, Internet white pages and yellow pages on the Internet. I just wrote, I just wrote that personally, I didn’t even use a web server. I just read the Port directly because I couldn’t afford and I couldn’t afford a T. One original office was on Sherman Avenue in Palo Alto. There was an ISP on the floor below. So I drilled a hole through the floor and just ran a LAN cable directly to the ISP.
然后在95年，我写了基本上是互联网上最早的地图、导航、电子黄页和白页。我是自己一个人写的，连Web服务器都没用，而是直接操作端口。因为我买不起服务器，也买不起T1线路。我们最初的办公室在帕洛阿尔托的舍曼大街。楼下刚好有一家互联网服务提供商，我就在地板上打了个洞，把局域网网线直接插进了他们的网络里。
And you know, my brother joined me and another co founder, Greg Curry, who passed away. And we at the time we couldn’t even afford a place to stay. So we just. The office was 500 bucks a month. So we just slept in the office and then showered at the YMCA on Page Mill and Camino.
我哥哥后来加入了，还有另一位联合创始人格雷格·库里（他后来去世了）。当时我们连住的地方都租不起。办公室租金是每月500美元，所以我们就睡在办公室里，然后去Page Mill和Camino路口的YMCA洗澡。
And yeah, and I guess we ended up doing a little bit of a useful company, Zip2 in the beginning. And we did build a lot of really good software technology. But we were somewhat captured by the legacy media companies and that Nitrotter, New York Times, the Hearst whatnot were investors and customers and also on the board. So they kept wanting to use our software in ways that made no sense. So I wanted to go direct to consumers.
是的，最后我们创建了一家多少还算有点用的公司，Zip2。在初期我们确实开发了很多非常不错的软件技术。但我们当时有点被传统媒体公司“绑架”了，像《纽约时报》、赫斯特集团等既是投资人也是客户，甚至还在董事会里。他们总想用我们的软件做一些完全不合逻辑的事情。而我想直接面向消费者。
Anyways, long story, dwelling too much on Zip2, but I really just wanted to do something useful on the Internet because I had two choices. Do a PhD and watch people build the Internet or help build the Internet in some small way. And I was like, well, I guess I can always try and fail and then go back to grad studies. And anyway, that ended up being like reasonably successful. It sold for like \$300 million, which was a lot at the time. These days that’s like, I think minimum impulse but for an AI startup is like a billion dollars. It’s like there’s so many frigging unicorns. It’s like a herd of unicorns at this point. You know, unicorns, a billion dollar situation.
总之，话说太多了，不该一直讲Zip2。但当时我真的只是想在互联网上做点有用的事情。那时我有两个选择：要么读博，眼睁睁看别人建设互联网；要么自己参与进去，哪怕只是出一份小力。我想，就算失败了也还能回去读研究生。结果这事还算挺成功，最后卖了大约3亿美元，那在当时算很多了。现在嘛，我觉得AI初创公司的最低启动值差不多就是10亿美元。独角兽实在太多了，现在简直像独角兽牧场一样。你知道的，“独角兽”指的是估值十亿美元的公司。
AI Valuations and Market Dynamics
人工智能的估值与市场动态
GARRY TAN: There’s been inflation since, so quite a bit more money actually.
加里·谭： 从那时起已经发生了通货膨胀，所以实际上现在的钱要多得多。
ELON MUSK: Yeah, I mean like 1995, you could probably buy a burger for a nickel. Well, not quite, but I mean, yeah, there has been a lot of inflation. But I mean the hype level in AI is pretty intense. As you’ve seen. You see companies that are, I don’t know, less than a year old getting sometimes billion dollar or multibillion dollar valuations, which I guess could, could pan out and probably will pan out in some cases, but it is eye watering to see some of these valuations. Yeah. What do you think?
埃隆·马斯克： 是啊，我的意思是，比如1995年，你可能只需五美分就能买个汉堡。嗯，虽然没那么夸张，但确实经历了严重的通胀。现在人工智能的炒作程度非常激烈，你也看到了。有些公司可能还不到一年大，就已经获得了十亿美元甚至数十亿美元的估值。我觉得这些估值有些可能会实现，甚至大多数会实现，但看到这些数字确实让人瞠目结舌。你怎么看？
GARRY TAN: I mean. Well, I’m pretty bullish. I’m pretty bullish, honestly. So I think the people in this room are going to create a lot of the value that, you know, a billion people in the world should be using this stuff and we’re not even scratching the surface of it. I love the Internet story in that even back then, you know, you are a lot like the people in this room back then in that, you know, the heads of all the CEOs of all the legacy media companies look to you as the person who understood the Internet. And a lot of the world, you know, the corporate world, like the world at large, that does not understand what’s happening with AI, they’re going to look to the people in this room for exactly that. It sounds like, you know, what are some of the tangible lessons? It sounds like one of them is don’t give up board control or be careful about having a really good lawyer.
加里·谭： 嗯，说实话，我非常看好。我认为在座的这些人将创造巨大的价值——全世界十亿人都应该在使用这些技术，而我们现在连皮毛都还没摸到。我很喜欢互联网的故事，即便在当年，你就像是在座的这些人，当时那些传统媒体公司的CEO都把你看作是理解互联网的人。而现在世界上很多人，包括企业界，其实根本不了解人工智能正在发生什么，而他们将依赖在座的各位去理解这一切。听起来，有些教训是切实可行的，比如说不要轻易放弃董事会控制权，或者说一定要请个靠谱的律师。
Lessons from Early Startups and the Future of AI
早期创业的经验与人工智能的未来
ELON MUSK: I guess for the first, my first startup, really the mistake was having too much shareholder and board control from legacy media companies who then necessarily see things through the lens of legacy media and that they’ll kind of make you do things that seem sensible to them but really don’t make sense with the new technology.
埃隆·马斯克： 回想我第一次创业，确实犯的错误就是让太多的传统媒体公司掌控股权和董事会控制权，而这些公司总是用旧有媒体的视角看问题。他们会让你做一些在他们看来合理的事，但这些事对新技术来说却完全不合理。
I should point out that I didn’t actually at first intend to start a company. I tried to get a job at Netscape. I sent my resume into Netscape and Mark Hendrickson knows about this, but I don’t think he ever saw my resume. And then nobody responded. And then I tried hanging out in the lobby of Netscape to see if I could bump into someone, but I was too shy to talk to anyone. So I’m like, man, this is ridiculous. So I’ll just write software myself and see how it goes. So it wasn’t actually from the standpoint of like, I want to start a company, I just want to be part of building the Internet in some way. And since I couldn’t get a job at an Internet company, I had to start a Internet company anyway.
我必须说，我最初其实并不打算创业。我试图去网景找份工作，我把简历寄给了他们，Mark Hendrickson也知道这事，但我想他根本没看到我的简历。然后也没人回应。我还试着在网景的大堂里晃，看能不能碰到谁，但我太害羞了，不敢跟人说话。于是我想，这太荒唐了，那我就自己写点软件试试看吧。所以其实并不是我有创业的想法，我只是想参与建设互联网。既然找不到互联网公司的工作，我只能自己创建一家了。
AI will so profoundly change the future, it’s difficult to fathom how much. But the economy, assuming things don’t go awry and AI, it doesn’t kill us all and itself, then you’ll see ultimately an economy that is not, not 10 times more than the current economy ultimately, like if we become, say, or whatever, our future machine descendants, or mostly machine descendants, become like a Kardashev scale to civilization or beyond, talking about an economy that is thousands of times, maybe millions of times bigger than the economy today.
人工智能将以如此深刻的方式改变未来，以至于我们难以想象其影响的程度。但如果一切顺利，人工智能没有毁掉我们人类，也没有毁掉它自己，那么最终的经济体量将不是当前的十倍那么简单——如果我们，或者说我们的机器后代，成为卡尔达肖夫等级的文明或更高级的文明，那么经济规模可能会是今天的几千倍、甚至上百万倍。
So, yeah, I mean, I did sort of feel a bit like, you know, when I was in DC, taking a lot of flack for like getting rid of waste and fraud, which was an interesting side quest as side quests go.
所以，是的，我确实感觉有点像……我在华盛顿时，花了很多时间去清理浪费和欺诈行为，这算是个有趣的“支线任务”，就“支线任务”而言还挺特别的。
GARRY TAN: But got to get back to the Main quest.
加里·谭： 但你得回归“主线任务”。
ELON MUSK: Yeah, I got to get back to the main quest here. So. Back to the main quest. So but I did feel, you know, a little bit like there’s, you know, it’s like fixing the government is kind of like, there’s like, say the beach is dirty and there’s like some needles and feces and like trash and you want to clean up the beach. But then there’s also this like thousand foot wall of water which is a tsunami of AI. And how much does cleaning the beach really matter if you’ve got a thousand foot tsunami about to hit? Not that much.
埃隆·马斯克： 对，我得回归主线任务。所以，是的，要回到主线上。但我确实觉得，有点像是你在试图修复政府的过程中，发现这就像清理一个脏乱的海滩，海滩上有针头、粪便和垃圾，你想把海滩清干净。但此时，一堵千英尺高的巨浪正在逼近，那就是人工智能的海啸。相比之下，在海啸来临之前去清理海滩，还有多大意义呢？其实并不重要。
GARRY TAN: We’re glad you’re back on the main quest. It’s very important.
加里·谭： 很高兴你回到了主线任务上。这真的非常重要。
ELON MUSK: Back to the main quest. Building technology, which is what I like doing, it’s just so much noise. Like the signal to noise ratio in politics is terrible.
埃隆·马斯克： 是的，回到主线任务——开发技术，这才是我真正喜欢做的事。政治里噪音太多，信噪比非常糟糕。
GARRY TAN: So I mean, I live in San Francisco, so you don’t need to tell me twice.
加里·谭： 我住在旧金山，所以你不用说第二遍我都懂。
ELON MUSK: Yeah, DC is like, you know, kind of, I guess it’s all politics in DC. But the. If you’re trying to build a rocket or cars, or you’re trying to have software that compiles and runs reliably, then you have to be maximally truth seeking or your software or your hardware won’t work. Like there’s no. You can’t fool math. Like math and physics are rigorous judges. So I’m used to being in a maximally true seeking environment and that’s definitely not politics. So anyway, I’m good. Glad to be back in technology.
埃隆·马斯克： 是啊，华盛顿就是一个政治的代名词。但如果你要造火箭、造汽车，或者要让软件正确编译并可靠运行，那你必须极致地追求真实，否则你的软件或硬件根本就运作不了。数学是骗不了的，物理也不会给你留情面，它们是最严苛的裁判。所以我一直习惯处在一个极度追求真实的环境，而政治显然不是那样的。总之，我很好，很高兴能回到技术领域。
Keeping the Chips on the Table: From Zip2 to PayPal
押注到底：从 Zip2 到 PayPal
GARRY TAN: I guess I’m kind of curious going back to the Zip2 moment, you had hundreds of millions of dollars or you had an exit worth hundreds of millions of dollars.
加里·谭： 我有点好奇，回到 Zip2 的时候，你那时候的退出市值是几亿美元，对吧？
ELON MUSK: I got $20 million.
埃隆·马斯克： 我拿到了2000万美元。
GARRY TAN: Okay, so you solved the money problem at least and you basically took it and you kept rolling with X.com which became PayPal and Confinity.
加里·谭： 好吧，至少你解决了金钱问题，然后你把钱继续投入到了 X.com，也就是后来的 PayPal 和 Confinity。
ELON MUSK: Yes, I kept the chips on the table.
埃隆·马斯克： 是的，我把筹码继续压在了桌上。
GARRY TAN: Not everyone does that. A lot of the people in this room will have to make that decision actually. What drove you to jump back into the ring?
加里·谭： 不是所有人都会这么做。其实在座很多人未来也会面对这个选择。是什么让你愿意再次跳进这个竞技场？
ELON MUSK: Well, I think I felt with Zip2 we built incredible technology, but it never really got used to, you know, I think at least from my perspective, we had better technology than say Yahoo or anyone else, but it was constrained by our customers. And so I wanted to do something that where, okay, we wouldn’t be constrained by our customers. Go direct to consumer. And that’s what ended up being like X.com, PayPal, essentially X.com merging with confinity, which together created PayPal and then that actually the sort of PayPal diaspora. It might have created more companies than, so more companies than probably anything in the 21st century. So many talented people were at the combination of Confinity and X dot com.
埃隆·马斯克： 嗯，我觉得在 Zip2 的时候，我们确实开发了非常棒的技术，但它从来没有真正被用起来。从我的角度看，我们的技术比 Yahoo 或其他竞争对手都好，但我们被客户所限制。所以我想做一件不会被客户限制的事，直接面向消费者。于是就有了 X.com 和后来与 Confinity 的合并，最终变成了 PayPal。而那段经历也催生了所谓的“PayPal 散居现象”（PayPal Mafia）。它可能孕育出了比21世纪任何公司都更多的新公司。太多优秀的人才当时都在 Confinity 和 X.com 的团队里。
So I just wanted to, I felt like we kind of got our wings clipped somewhat with Zip2. And it’s like, okay, what if our wings aren’t clipped and we go direct to consumer? And that’s what PayPal ended up being. But yeah, I got that, like, $20 million check for my share of Zip2. At the time, I was living in a house with four housemates and had like, 10 grand in the bank. And then this check arrives in the mail, of all places in the mail, and then my bank balance went from 10,000 to 20 million. And 10,000. You’re like, well, okay, so I pay taxes on that and all. But then I ended up putting almost all of that into X.com and as you said, like, just kind of keeping almost all the chips on the table and.
所以我只是想说，我觉得我们在 Zip2 被剪了翅膀。有点像是，我们本可以飞得更高但受限了。于是我就在想，如果我们不被限制，直接面向消费者会怎么样？这也就成了后来的 PayPal。是的，我拿到了 Zip2 的2000万美元支票。当时我和四个室友住在一栋房子里，银行账户里只有1万美元。然后这张支票就寄到了我家，真的，就是邮寄过来的，然后我的账户余额就从1万变成了2000万。1万美元的时候你还得想着交税，但后来我几乎把所有的钱都投进了 X.com，就像你说的，基本上把所有的筹码都继续押在了桌上。
Yeah, and then after PayPal, I was like, well, I, I was kind of curious as to why we had not sent anyone to Mars. And I went on the NASA website to find out when we’re sending people to Mars, and there was no date. I thought maybe it was just hard to find on the website, but in fact, there was no real plan to send people to Mars. So then, you know, this is such a long story, so I don’t want to take up too much time here. But the.
是的，然后在 PayPal 之后，我开始好奇为什么我们还没有把人送上火星。我上了 NASA 的官网，想看看他们计划什么时候送人去火星，但上面根本没有日期。我以为也许只是网站不好找，但实际上根本没有一个真正的计划。所以……这是个很长的故事，我不想占用太多时间。
GARRY TAN: I think we’re all listening with rapt attention.
加里·谭： 我觉得我们大家都听得入迷。
The Origin Story of SpaceX
SpaceX 的起源故事
ELON MUSK: So I was actually, I was on the Long Island Expressway with my friend Dale Resi. We’re like housemates in college. And they was asking me what I’m, what we’re going to do, what am I going to do after PayPal? And I was like, it’s like, I don’t know, I guess maybe I’d like to do something philanthropic in space because I didn’t think I could actually do anything commercial in space because that seemed like the purview of nations. So. But, you know, I’m kind of curious as to when we’re going to send people to Mars. And that’s when I was like, oh, it’s not on the website. And then I started digging on, not, there’s nothing on the NASA website.
埃隆·马斯克： 其实当时我正和我大学室友 Dale Resi 在长岛高速公路上，他问我 PayPal 之后打算干什么。我说，我也不太清楚，也许我想在太空领域做点慈善的事吧，因为我觉得自己不太可能在太空做商业项目，那看起来是国家才能做的事情。但我还是很好奇，我们到底什么时候才会把人送上火星。这就是我发现 NASA 网站上竟然没写的那个时刻。我开始深入查找，结果 NASA 网站上真的什么都没有。
So then I started digging in and I’m definitely summarizing a lot here, but my first idea was to do a philanthropic mission to Mars called Life to Mars, where would send a small greenhouse with seeds and dehydrated nutrient gel. Land that on Mars and grow, you know, hydrate the gel and then you’d have this great sort of money shot of green plants on a red background. For the longest time. I, by the way, I didn’t realize money shot, I think is a porn reference. But anyway, the point is that that would be the great shot of green plants on a red background and to try to inspire, you know, NASA and the public to send astronauts to Mars.
然后我继续研究，这里我在大幅简化过程，但我最初的想法是发起一个名为“生命登陆火星”的公益项目，我们会发射一个小型温室，里面有种子和脱水营养凝胶。把它送上火星，然后加水，让植物发芽，这样就能拍到“绿色植物衬着红色火星土壤”的画面。很长一段时间我都没意识到“money shot”其实是色情片术语（笑），但不管怎样，我的意思是那样的画面会非常震撼，可以激发 NASA 和公众把宇航员送上火星的热情。
As I learned more, I came to realize, and along the way, by the way, I went to Russia in like 2001 and 2002 to buy ICBMs, which is like, that’s an adventure. You know, you go and meet with Russian high command and say, I’d like to buy some ICBMs.
随着了解的加深，我意识到……哦顺便说一句，我在 2001 和 2002 年还去了俄罗斯，想买洲际弹道导弹（ICBM），那真是一次冒险。你得见俄罗斯的高级军方人员，然后说：“我想买几枚洲际导弹。”
GARRY TAN: This was to get to space.
加里·谭： 是为了上太空用的吧？
ELON MUSK: Yeah, as a rocket. Not to nuke anyone. But they had to. As a result of arms reduction talks, they had to actually destroy a bunch of their big nuclear missiles. So I was like, well, how about if we take two of those, you know, minus the nuke, add an additional upper stage for Mars. But it was kind of trippy, you know, being in Moscow in 2001 negotiating with like the Russian military to buy ICBMs, like, that’s crazy. But they kept also raising the price on me so that. So like, literally, it’s kind of like the opposite of what a negotiation should do. So I was like, man, these things are getting really expensive.
埃隆·马斯克： 对，是当作火箭用的，不是要轰炸谁。因为军备削减谈判的结果，他们得销毁很多大型核导弹。所以我就想，要不我们买两枚，把核弹头拿掉，然后加一个火星的上面级？但那真是很奇妙的经历——2001年在莫斯科和俄罗斯军方谈判买洲际导弹，简直疯了。而且他们还一直涨价，跟正常的谈判逻辑完全相反。所以我想，哎呀，这玩意越来越贵了。
And then I came to realize that actually the problem was not that there was insufficient will to go to Mars, but there was no way to do so without breaking the budget, you know, even breaking the NASA budget. So that’s where I decided to start SpaceX. SpaceX to advance rocket technology to the point where we could send people to Mars. And that was in 2002.
然后我意识到，问题并不是我们缺乏去火星的意愿，而是根本没有一种不突破预算的方法去做——甚至连 NASA 的预算都负担不起。所以我决定创办 SpaceX。SpaceX 的目标就是推动火箭技术的发展，直到我们有能力把人送上火星。那是在 2002 年。
From Philanthropy to Business
从公益到商业
GARRY TAN: So that wasn’t, you know, you didn’t start out wanting to start a business. You wanted to start just something that was interesting to you, that you thought humanity needed. And then as you sort of, you know, like a cat pulling on a string, it just sort of, the ball sort of unravels. And it turns out this is, yeah, could be a very profitable business.
加里·谭： 所以一开始你并不是想创办一家企业，而是只是想做一些你感兴趣、而且你认为人类需要的事情。然后就像猫咪拉线团一样，线团慢慢解开了。结果发现，这其实可以成为一个非常赚钱的商业项目。
ELON MUSK: I mean, it is now, but it. There had been no prior example of really a rocket startup succeeding. There have been various attempts to do commercial rocket companies, and that all failed. So again, with SpaceX starting, SpaceX was really, from the standpoint of, I think there’s a less than 10% chance of being successful, maybe 1%, I don’t know. But if a startup doesn’t do something to advance rocket technology, it’s definitely not coming from the big defense contractors because they just impede and smash to the government and the government just wants to do very conventional things. So it’s either coming from a startup or it’s not happening at all. So, like, a small chance of success is better than no chance of success.
埃隆·马斯克： 我是说，现在是赚钱了，但当时真的没有任何一家火箭初创公司成功的先例。之前也有一些人尝试过商业航天公司，但全都失败了。所以，当我开始 SpaceX 的时候，我真的是抱着“成功概率不到10%，也许只有1%”的心态。但如果一个初创公司不去推动火箭技术的进步，那就不会有人去做了——因为那些大型军工承包商只会一味迎合政府，而政府也只想搞传统项目。所以要么是初创公司去做，要么就没人做了。哪怕成功的可能性很小，也总比完全没希望要好。
SpaceX started that in mid-2002 expecting to fail. Like I said, probably 90% chance of failing. And even when recruiting people, I didn’t try to, you know, make out that it would probably. I said, we’re probably going to die, but small chance, we might not die. And if, but this is the only way to get people to Mars and advance the state of the art. And then I ended up being chief engineer of the rocket, not because I wanted to, but because I couldn’t hire anyone who was good. So, like, none of the good sort of chief engineers would join because they’re like, this is too risky, you’re going to die.
SpaceX 是在 2002 年中期创办的，当时我就预期我们会失败。就像我说的，失败概率大概有90%。甚至在招聘员工时，我也没试图掩饰这一点。我直接说：“我们大概率会死，但有一点点希望不会死。”但这是实现载人火星计划、推动技术进步的唯一方法。最后我不得不自己当火箭的总工程师，不是我想干，而是我找不到合适的人。那些真正厉害的总工程师根本不愿意加入，他们觉得这太冒险了，注定会失败。
And so then I ended up being chief engineer of the rocket. And, you know, the first three flights did fail, so it’s a bit of a learning exercise there. And fourth one fortunately worked, but if the fourth one hadn’t worked, I had no money left and that would have been. It would have been curtains. So it was a pretty close thing. If the fourth launch of Falcon not work, it would have been just curtains and we would have just joined the graveyard of prior rocket startups. My estimate of success was not far off. We made it by the skin of our teeth.
所以我就成了火箭的总工程师。而前面三次发射都失败了，也算是一次次学习的过程。幸运的是第四次成功了，但如果那次也失败了，我就没钱了，那就真的完了。可以说是生死一线。如果猎鹰一号的第四次发射失败，我们就会加入之前那些失败的火箭初创公司的“坟场”。当初我对成功几率的估计其实挺准的——我们是勉强挺过来的。
And Tesla was happening sort of simultaneously. 2008 was a rough year because at mid 2008, called Summer 2008, the third launch of SpaceX had failed. A third failure in a row, the Tesla financing round had failed. And so Tesla was going bankrupt fast. It was just, it’s like, man, this is grim. This is going to be a tale of warning of an exercise in hubris.
而与此同时，特斯拉也在进行中。2008 年真的很难熬。那年夏天，SpaceX 的第三次发射失败了，连续三次失败，特斯拉的融资轮也失败了。特斯拉正以极快的速度走向破产。当时我就想，天啊，太惨了，这要成一个狂妄自大的反面教材了。
The Internet Guy Building Rockets
“互联网人”造火箭
GARRY TAN: Probably throughout that period, a lot of people were saying, you know, Elon is a software guy. Why is he working on hardware? Yeah. Why would he choose to work on this?
加里·谭： 在那段时间里，应该有很多人说：“埃隆是搞软件的啊，为什么要做硬件？为什么他会选择做这种事？”
ELON MUSK: Right, yeah, 100%. So you can look at the, like the. Because it’s still, the, you know, the press of that time is still online. You can just search it and they kept calling me Internet guy. So like Internet Guy, AKA fool, is attempting to build a rocket company. So, you know, we got ridiculed quite a lot. And it does sound pretty absurd. Like Internet guy starts rocket company. Doesn’t sound like a recipe for success frankly. So I didn’t hold it against them. I was like, yeah, you know, admittedly it does sound improbable and I agree that it’s improbable.
埃隆·马斯克： 对，完全正确。你现在还能在网上找到当时的媒体报道。他们总是叫我“互联网人”，就像是说“这位互联网人，也就是个傻瓜，正试图搞火箭公司”。所以我们被嘲笑了很多次。确实听起来挺荒唐的——一个搞互联网的要去造火箭。这听上去确实不像是成功的剧本。所以我并不责怪他们，我也承认，这听起来确实不太可能，我也同意它很不可能成功。
But fortunately the fourth launch worked and NASA awarded us a contract to resupply the space station. And I think that was like maybe, I don’t know, December 22nd or it was like right before Christmas. Because even the fourth launch working wasn’t enough to succeed. NASA also needed, we also needed a big contract to keep us alive. So I got that call from the NASA team and I literally, they said, we’re rewarding you one of the contracts to resupply the space station. I like literally blurted out I love you guys, which is not normally what they hear because it’s usually pretty sober. But I was like, man, this is a company saver.
但幸运的是，第四次发射成功了，NASA 还给了我们一个为国际空间站运送物资的合同。我记得大概是在12月22日，圣诞节前几天。因为光是发射成功还不够，我们还需要一个大合同来维持生存。NASA 打来电话，说我们获得了空间站补给合同中的一个。我当时简直情绪爆炸，直接脱口而出“我爱你们”，这在他们那通常是听不到的，因为 NASA 一般都是非常严肃的。但我当时真的觉得，这就是救命的合同啊。
And then we closed the Tesla financing round on the last hour of the last day that it was possible, which was 6pm Dec. 24, 2008. We would have bounced payroll two days after Christmas if that round hadn’t closed. So that was a nerve wracking end of 2008, that’s for sure.
然后特斯拉的融资是在最后一刻搞定的——2008年12月24日晚上6点，也就是那一轮融资的最后截止时间。如果没搞定，我们在圣诞节后两天就发不出工资了。所以，2008年结尾真的让人神经紧绷，这是肯定的。
On Being Useful and Finding Great People
关于有用与寻找优秀人才
GARRY TAN: I guess from your PayPal and Zip2 experience jumping into these hardcore hardware startups, it feels like one of the through lines was being able to find and eventually attract the smartest possible people in those particular fields. You know what, what would I mean? The people in this room, like some of the, most of the people here I don’t think have even managed a single person yet. They’re just starting their careers. What would you tell to, you know, the Elon who’s never had to do that yet?
加里·谭：从你在 PayPal 和 Zip2 的经历，再到投身这些硬核硬件初创公司，看起来其中的核心之一就是你能够找到并最终吸引到各自领域里最聪明的人才。你知道，我的意思是，房间里大多数人可能连一个下属都没带过，职业生涯才刚刚起步。对于那个还没开始管理别人的 “年轻版马斯克”，你会说些什么？
ELON MUSK: I generally think to try to try to be as useful as possible. It may sound trite but it’s so hard to be useful, especially to be useful to a lot of people where you say the area under the curve of total utility is like how useful have you been to your fellow human beings times how many people? It’s almost like the physics definition of true work. It’s incredibly difficult to do that. And I think if you aspire to do true work, your probability of success is much higher. Don’t aspire to glory, aspire to work.
埃隆·马斯克：我通常认为，尽力让自己变得尽可能有用。这句话可能听起来老生常谈，但要真正做到“有用”非常难，尤其是要对很多人有用。如果把总效用曲线下面积看作是“你对别人有多少帮助”乘以“受益人数”，那几乎就是物理学上“真实功”的定义。做到这一点极其困难。但如果你立志去做真正的工作，你成功的概率会大大提高。不要追求荣耀，要追求实干。
GARRY TAN: How can you tell that it’s true work? Like is it external? Is it like what happens with other people or you know, what the product does for people like what, you know, what is that for you when you’re looking for people to come work for you? Like what, you know, what’s the salient thing that you look for? Or if they’re.
加里·谭：那你怎么判断什么是真实的工作？是外部反馈吗？是产品对他人产生的影响吗？当你招人时，你最看重的关键因素是什么？
ELON MUSK: That’s a different question. I guess it’s, I mean, in terms of your end product, you just have to say like, well, if this thing is successful, how useful will it be to how many people? And that’s what I mean. And then you do whatever, whether you’re a CEO or any role in a startup, you do whatever it takes to succeed and just always be smashing your ego. Internalize responsibility.
埃隆·马斯克：那是另一个问题。就最终产品而言，你只需要问：如果它成功了，它能给多少人带来多大价值？我的意思就是这个。然后无论你是 CEO 还是初创公司的任何角色，都要不计一切代价去成功，同时不断打碎自我，将责任内化。
A major failure mode is when ego to ability ratio is double greater than sign one. If your ego to ability ratio gets too high, then your, you’re going to basically break the feedback loop to reality. And in AI terms you’ll break your RL loop. So you don’t want to break your, you want to have a strong RL loop, which means internalizing responsibility and minimizing ego. And you do whatever the task is, no matter whether it’s grand or humble.
一个主要的失败模式就是当“自负与能力的比值”≫1 时。如果这个比值太高，你就会切断与现实之间的反馈回路；用 AI 术语说，就是把自己的强化学习回路搞崩。所以你必须保持强大的 RL 回路，内化责任，降低自我。不论任务高低贵贱，都要去做。
That’s kind of like why actually I prefer the term like engineering as opposed to research. I prefer the term and I actually don’t want it to call Xai a lab. I just want to be a company. Like, it’s like what are the simplest, most straightforward, ideally lowest ego terms are. Those are generally a good way to go. You want to just close the loop on reality hard. That’s a super big deal.
这也是为什么我更喜欢用“工程”而不是“研究”一词。我甚至不想把 xAI 称作实验室，我只想它是一家公司。用最简单、最直接、最不自负的词语通常是最好的做法。你要做的就是紧密闭合现实反馈回路，这非常关键。
Constructing Reality from First Principles
用第一性原理构建现实
GARRY TAN: I think everyone in this room really looks up to everything you’ve done around being sort of a paragon of first principles and thinking about the stuff you’ve done. How do you actually determine your reality? Because that seems like a pretty big part of it. Like other people, people who have never made anything, non engineers, sometimes journalists at time, who’ve never done anything, like they will criticize you. But then clearly you have another set of people who are builders, who have very high, you know, sort of area under the curve, who are in your circle. Like, you know, how should people approach that? Like, what has worked for you and what would you pass on, like you know, to X, to your children? Like, you know, what do you tell them when you’re like, you need to make your way in this world. Here’s how to construct a reality that is predictive from first principles.
加里·谭：我想在座的每个人都钦佩你用第一性原理思考并践行的一切。那么，你究竟是如何界定自己的“现实”的？这似乎是至关重要的一点。毕竟，有些从未创造过任何东西的人——比如没有工程背景的人，甚至有些记者——他们会批评你。但显然，你身边还有另一群真正的建设者，他们的“效用面积”很大。那人们该如何面对这些不同的声音？哪些方法对你有效？你又会把哪些经验传递给 xAI 团队或你的孩子？当你告诉他们“要在这个世界上闯出一条路”时，你会怎么教他们用第一性原理去构建一个可预测的现实？
First Principles Thinking and Rocket Cost Analysis
第一性原理思考与火箭成本分析
ELON MUSK: Well, the tools of physics are incredibly helpful to understand and make progress in any field. First principles obviously just means break things down to the fundamental axiomatic elements that are most likely to be true. And then reason up from there as cogently as possible, as opposed to reasoning by analysis or metaphor. And then just simple things like thinking in the limit. Like if you extrapolate, minimize this thing or maximize that thing. Thinking in the limit is very helpful. I’d use all the tools of physics. They apply to any field. This is like a superpower, actually.
埃隆·马斯克：物理学的方法对于理解并推动任何领域的进步都极其有用。所谓第一性原理，就是把问题拆解到最可能为真的基本公理级要素，然后尽量有力地自下而上推理，而不是依赖类比或隐喻推导。还有一些简单的做法，比如在极限情况下思考——若将某件事极小化或极大化会怎样？极限思维非常有帮助。我会用尽物理学的所有工具；这些工具适用于任何领域。这简直像一种“超能力”。
So you can take for example, like rockets, you could say, well, how much should a rocket cost? The typical approach that people would take to how much rocket should cost is they would look historically at what the cost of rockets are and assume that any new rocket must be somewhat similar to the prior cost of rockets. A first principles approach would be you look at the materials that the rocket is comprised of. So if that’s aluminum, copper, carbon fiber, steel, whatever the case may be, and say what, what, how much does that rocket weigh? And, and, and what are the constituent elements and how much do they weigh? What is the material price per kilogram of those constituent elements? And that sets the actual floor on what a rocket can cost. It’s, it can asymptotically approach the cost of the raw materials. And then you realize, oh, actually a rocket, the raw materials of a rocket are only maybe 1 or 2% of the historical cost of a rocket. So the manufacturing must necessarily be very inefficient. If the, if the raw material cost is only 1 or 2%, that would be a first principles analysis of the potential for cost optimization of a rocket. And that’s before you get to reusability.
举例来说，判断一枚火箭应当花多少钱。传统做法是查看历史数据，认为新火箭成本应该与过去火箭相近。第一性原理的做法则是考察火箭的构成材料：铝、铜、碳纤维、钢等，计算火箭重量以及各组分重量，再乘以对应材料每公斤的价格，这就给出了火箭成本的理论下限——它可以无限逼近原材料成本。于是你会发现，火箭的原材料成本仅占传统火箭总成本的 1–2%。这说明制造环节必定极度低效；若原材料仅占 1–2%，火箭成本就存在巨大的优化空间，而这还未考虑可重复使用因素。
Building a Training Supercluster in Six Months
六个月内建成训练超级集群
To give an AI sort of AI example, I guess last year for Xai, when we were trying to build a training supercluster, we went to the various suppliers to ask, this was beginning of last year, that we needed a hundred thousand H1 hundreds to be able to train coherently. And their estimates for how long it would take to complete that were 18 to 24 months. It’s like, well, we need to get that done in six months or we won’t be competitive.
再举个 AI 领域的例子：去年初，xAI 计划搭建训练超级集群，需要 10 万块 H100 GPU。供应商估算交付周期需 18–24 个月，但我们必须在 6 个月内完成，否则就会失去竞争力。
So then if you break that down, what are the things you need? Well, you need a building, you need power, you need cooling. We didn’t have enough time to build a building from scratch, so we’ve had to find an existing building. So we found a factory that was no longer in use in Memphis that used to build Electrolux products. But then the input power was 15 megawatts and we needed 150 megawatts. So we rented generators and had generators on one side of the building. And then we have to have cooling so we rented about a quarter of the mobile cooling capacity of the US and put the chillers on the other side of the building.
接着把需求拆解：需要厂房、供电、制冷。我们没时间从零建楼，只能找现成场地，于是在孟菲斯找到一座闲置的伊莱克斯工厂。该厂供电仅 15 MW，而我们需 150 MW，于是租用发电机放在大楼一侧；又租下全美约四分之一的移动制冷设备，把冷水机组摆在另一侧。
That didn’t fully solve the problem because the power variations during training are so very, very big. So you can have. Power can drop by 50% in 100 milliseconds, which the generators can’t keep up with. So then we combined, we added Tesla megapacks and modified the software in the megapacks to be able to smooth out the, the power variation during the training run. And then there were, there were a bunch of networking challenges. The networking cables, if you’re trying to make 100,000 GPUs trained coherently, are very, very challenging.
但问题仍未解决：训练过程中的功率波动极大，可在 100 ms 内骤降 50%，发电机跟不上。于是我们增加 Tesla Megapack，并改写其软件来平滑功率波动。随后还面临网络难题——要让 10 万块 GPU 同步训练，布线极其复杂。
GARRY TAN: Almost, it sounds like almost any of those things you mentioned. I could imagine someone telling you very directly, no, you can’t have that, you can’t have that power, you can’t have this. And it sounds like one of the salient pieces of first principles thinking is actually let’s ask why, let’s figure that out and actually let’s challenge the person across the table and if they, if I don’t get an answer that I feel good about, I’m going to, you know, not allow that to be. I’m not going to let that know to stand. Is that, I mean, that feels like something that, you know, everyone, if someone were to try to do what you’re doing in hardware, hardware seems to uniquely need this. In software we have lots of, you know, fluff and things that, you know, it’s like we can add more CPUs to that, it’ll be fine, but in hardware it’s, it’s just not going to work.
加里·谭：听起来你提到的任何一项都有可能遭到直接拒绝，比如“你拿不到这么大的电量”。而第一性原理思维的要点似乎就在于不断追问“为什么”，搞清楚原因，并当面挑战对方。如果我得不到令我满意的答案，我就不会接受那个“不”。在硬件领域尤其如此，因为软件还能“加几颗 CPU”就行，硬件可行不通。
ELON MUSK: I think these general principles of first principle thinking apply to software and hardware, apply to anything really. I’m just using kind of a hardware example of how we were told something is impossible, but once we broke it down into the constituent elements of we need a building, we need power, we need cooling, we need power smoothing and then we could solve those constituent elements. Um, but it was, and then we, and then we just ran the, the networking operation to, to do all the cabling, everything in four shifts, 24/7. And, and I was like sleeping in the data center and also doing cabling myself. And, and there were a lot of other issues to solve. You know, no, nobody had done a training run with a hundred thousand H1 hundreds training coherently last year. Maybe it’s been done this year, I don’t know. But, and then, and then we ended up doubling that to 200,000. And so now we’ve got 150,000 H1 hundreds, 50K H2 hundreds and 30K GV2 hundreds in the, in the Memphis training center. And we’re about to bring 110,000 GB2 hundreds online at a second data center also in the Memphis area.
埃隆·马斯克：这些第一性原理的通用原则其实同样适用于软件和硬件。我只是用硬件举例说明：当别人说不可能时，我们把需求拆成“厂房、供电、制冷、功率平滑”等要素，一一解决。随后我们让布线团队 24×7 四班倒，自己也睡在数据中心、亲自拉线。要知道，去年还没人让 10 万块 H100 协同训练——也许今年有人做到了。我后来把规模翻倍到 20 万，如今孟菲斯中心有 15 万块 H100、5 万块 H200，以及 3 万块 GV200，我们即将在孟菲斯地区的第二个数据中心再上线 11 万块 GB200。
Scaling Laws and AI Competition
规模定律与人工智能竞赛
GARRY TAN: Is it your view that pre training is still working and the scaling laws still hold and whoever wins this race will have basically the biggest, smartest possible model that you could distill?
加里·谭：在你看来，预训练依然有效，规模定律依旧成立吗？是否可以说，谁在这场竞赛中获胜，谁就会拥有规模最大、智能最高、可以被精炼出的最佳模型？
ELON MUSK: Well, there’s other various elements that decide competitiveness for large AI. There’s for sure the talent of the people matter, the scale of the hardware matters and how well you’re able to bring that hardware to bear. So you can’t just order a whole bunch of GPUs and they don’t. You can’t just plug them in. So you’ve got to get a lot of GPUs and have them trained coherently and stably. Then it’s like what unique access to data do you have? I guess distribution matters to some degree as well, like how do people get exposed to your AI? Those are critical factors for if it’s going to be like a large foundation model that’s competitive, as many have said.
埃隆·马斯克：决定大模型竞争力的因素还有很多。人才肯定重要，硬件规模重要，以及你能多好地发挥硬件的效能也重要。你不能只是订一大堆 GPU 然后随便插上就完事了——必须让大量 GPU 协同、稳定地训练。其次是你能否获取独特的数据；分发渠道在某种程度上也很关键，比如用户如何接触到你的 AI。对于竞争激烈的大型基础模型来说，这些都是至关重要的要素。
I think my friend Ilya Sutskever said we’ve kind of run out of pre training data of human generated, like human generated data, you run out of tokens pretty fast, certainly of high quality tokens. And then you have to do a lot of you need to essentially create synthetic data and be able to accurately judge the synthetic data that you’re creating to verify is this real synthetic data or is it an hallucination that doesn’t actually match reality. So achieving grounding in reality is tricky, but. But we are at the stage where there’s more effort put into synthetic data and right now we’re training Grok 3.5 which is a heavy focus on reasoning.
我的朋友伊利亚·苏茨凯维尔提到，我们差不多已经用光了人类生成的高质量预训练数据，高质量 token 很快就会耗尽。接下来就必须大量生成合成数据，并且要能准确评估这些合成数据究竟是真实可用，还是偏离现实的幻觉。让模型真正锚定现实非常棘手。但如今我们正向合成数据投入更多精力，目前我们正在训练 Grok 3.5，重点就是提升推理能力。
GARRY TAN: Going back to your physics point, what I heard for reasoning is that hard science, particularly physics textbooks, are very useful for reasoning. Whereas I think researchers have told me that social sciences totally useless reasoning.
加里·谭：回到你提到的物理学视角，我听说在推理训练中，硬科学，尤其是物理教材，非常有用；而研究人员告诉我，社会科学对推理训练几乎毫无帮助。
ELON MUSK: Yes, that’s probably true.
埃隆·马斯克：是的，大概确实如此。
The Future of Humanoid Robots
人形机器人的未来
So yeah, there’s something that’s going to be very important in the future is combining deep AI in the data center or supercluster with robotics so that things like the Optimus humanoid robot. Incredible. Yeah, Optimus is awesome. There’s going to be so many humanoid robots and robots of all sizes and shapes. But my prediction is that there will be more humanoid robots by far than all other robots combined by maybe an order of magnitude, like a big difference.
未来极其重要的一件事，就是把数据中心或超级集群里的深度 AI 与机器人技术结合，例如特斯拉的 Optimus 人形机器人——这非常惊人，Optimus 太棒了。未来会出现大量形态各异、大小不一的机器人，但我预测，人形机器人的数量将远超所有其他类型机器人之和，甚至可能高出一个数量级。
GARRY TAN: Is it true that you’re planning a robot army of a sort, whether we.
加里·谭：那关于你要打造某种“机器人军团”的说法是真的吗？
ELON MUSK: Do it or, you know, whether Tesla does it? You know, Tesla works closely with xai. Like you’ve seen how many humanoid robot startups are there? Like, it’s like, I think Jensen Huang was on stage with a lot with a massive number of robots, you know, robots from different companies. I think there was like dozen different humanoid robots. So I mean, I guess, you know, part of what I’ve been fighting and maybe what has slowed me down somewhat is that I’m, I’m a little, I don’t want, I don’t want to make Terminator real, you know, so I’ve been sort of, I guess at least until recent years, dragging my feet on, on AI and, and humanoid robotics. And then I sort of come to the realization, realization it’s, it’s happening whether I do it or not. So you got really two choices. Particip, you can either be a spectator or a participant. And so like, well, I guess I’d rather be a participant than a spectator. So now it’s, you know, pedal to the metal on humanoid robots and digital superintelligence.
埃隆·马斯克：无论是我本人还是特斯拉，特斯拉与 xAI 紧密合作。你也看到现在有多少家人形机器人初创公司了？黄仁勋上台时，身旁站着各种公司的机器人，我估计有十几款人形机器人。我曾经抗拒、也因此放慢了脚步，因为我不想让《终结者》成真。至少在过去几年里，我对 AI 和人形机器人都有些拖延。但后来我意识到，这件事无论我做不做都会发生。于是你只有两个选择：旁观者或参与者。我想我宁愿参与，不愿旁观。所以现在我全力以赴推进人形机器人和数字超级智能。
Becoming a Multi-Planetary Species
成为多行星物种
GARRY TAN: So I guess, you know, there’s a third thing that everyone has heard you talk a lot about that I’m really a big fan of, you know, becoming a multi planetary species. Where does this fit? You know, this is all, you know, not, not just a 10 or 20 year thing, maybe a hundred year thing. Like it’s a, you know, many, many generations for humanity kind of thing. You know, how do you think about it? There’s, you know, AI, obviously, there’s embodied robotics and then there’s being a multiplan, multi planetary species. Does everything sort of feed into that last point or, you know, what, what are you driven by right now for the next 10, 20 and 100 years?
加里·谭：我想，还有第三件大家常听你谈起、而我本人也非常支持的事情——让人类成为多行星物种。这在整体蓝图中处于什么位置？这不仅是十年、二十年的规划，也许是百年的愿景，关系到人类的许多世代。你如何思考这件事？显然我们有 AI，有具身机器人，也有迈向多行星文明的目标。一切是否都指向这一终极目标？在未来 10 年、20 年、100 年的时间尺度上，又是什么在驱动着你？
ELON MUSK: Geez, 100 years. Man, I hope civilization’s around in 100 years. If it is round, it’s going to look very different from civilization today. I mean, I’d predict that there’s going to be at least five times as many humanoid robots as there are humans. Maybe 10 times.
埃隆·马斯克：天哪，一百年。我只希望一百年后文明仍然存在。如果存在，其形态将与今天截然不同。我预测，届时人形机器人的数量至少是人类的五倍，甚至十倍。
One way to look at the progress of civilization is percentage completion. Kardashev. So if you’re in a Kardashev scale one, you’ve, you’ve harnessed all the energy of a planet. In my opinion, we’ve only harnessed maybe 1 or 2% of Earth’s energy. So we’ve got a long way to go to the Kardashev scale one, then Kardashev two, you’ve harnessed all the energy of a sun, which would be, I don’t know, a billion times more energy than Earth, maybe closer to a trillion. And then Kardashev3 would be all the energy of a galaxy pretty far from that. So we’re at the very, very early stage of the intelligence big bang.
评估文明进步的一个方法是看其完成度百分比，即卡尔达肖夫等级。如果达到 Kardashev I 级，就意味着你能利用一颗行星的全部能量。我认为我们目前只开发了地球能量的 1% 或 2%，距离 Kardashev I 级还有很长的路。Kardashev II 级是利用恒星的全部能量，比地球能量大约高十亿倍，甚至接近一万亿倍。Kardashev III 级则是利用整个星系的能量，与我们相距甚远。我们现在处于“智能大爆炸”的极早期阶段。
I hope we’re in terms of being multi planetary. I think we’ll have enough mass transferred to Mars within roughly 30 years to make Mars self sustaining, such that Mars can continue to grow and prosper even if the resupply ships from Earth stop coming. And that greatly increases the probable lifespan of civilization or consciousness or intelligence, both biological and digital. So that’s why I think it’s important to become a multi planet species.
在多行星方面，我希望大约 30 年内能把足够的物资运送到火星，使其实现自给自足，即使地球补给船停止到来，火星也能继续发展繁荣。这将大幅延长文明、意识或智能——无论生物还是数字形态——的潜在寿命。这就是我认为成为多行星物种如此重要的原因。
And I’m somewhat troubled by the Fermi paradox, like why have we not seen any aliens? And it could be because intelligence is incredibly rare and maybe we’re the only ones in this galaxy, in which case the intelligence of consciousness is this like tiny candle in a vast darkness and we should do everything possible to ensure the tiny candle does not go out. And being a multi planet species or making consciousness multi planetary greatly improves the probable lifespan of civilization. And it’s the next step before going to other star systems. Once you at least have two planets, then you’ve got a forcing function for the improvement of space travel. And that ultimately is what will lead to consciousness expanding to the stars.
费米悖论同样让我担忧——为何我们从未发现外星文明？或许智慧极其稀少，可能整个银河系只有我们。如果真是如此，意识之火就像浩瀚黑暗中的微弱烛光，我们必须竭尽所能让它不熄灭。成为多行星物种、让意识跨星球存在，可大幅延长文明寿命，也是迈向其他恒星系统的下一步。一旦拥有至少两颗可居住行星，就形成了改进太空旅行的“强制函数”，最终将推动意识走向群星。
Avoiding the Great Filters
避开“大过滤器”
GARRY TAN: It could be that the Fermi paradox dictates once you get to some level of technology, you destroy yourself. How do we stay ourselves? How do we actually, what would you prescribe to? I mean, a room full of engineers, like what can we do to prevent that from happening?
加里·谭：费米悖论或许意味着，一旦文明技术达到某一水平，就会自我毁灭。我们该如何避免这种结局？满屋子的工程师，我们能做些什么来阻止悲剧发生？
ELON MUSK: Yeah, how do we avoid the great filters? One of the great filters would obviously be global thermonuclear war. So we should try to avoid that, I guess, building benign AI robots that AI that loves humanity and you know, robots that are helpful. Something that I think is extremely important in building AI is, is a very rigorous adherence to truth, even if that truth is politically incorrect. My intuition for what could make AI very dangerous is if, if you force AI to believe things that are not.
埃隆·马斯克：是的，我们如何躲过“大过滤器”？显而易见，其中之一是全球热核战争，我们必须避免这一点。我想，我们需要构建善意的 AI——热爱人类、能够提供帮助的机器人。在打造 AI 时，极为重要的一点是严格遵循真理，即便这些真理在政治上不正确。我的直觉认为，如果强迫 AI 相信不真实的事物，它就会变得极其危险。
GARRY TAN: True, how do you think about, there’s sort of this argument for open for safety versus closed for competitive edge. I mean, I think the great thing is you have a competitive model. Many other people also have competitive models. And in that sense we’re sort of off of. Maybe the worst timeline that I’d be worried about is there’s fast takeoff and it’s only in one person’s hands. That might sort of collapse a lot of things. Whereas now we have choice, which is great. How do you think about this?
加里·谭：没错。那么，你如何看待“开放以求安全”与“封闭以保竞争力”的争论？好的一面是，你现在拥有一个竞争模型，其他人也有自己的竞争模型。这让我们似乎避免了我最担心的最坏情形——出现快速起飞且只落在一人之手，这可能导致诸多崩溃。而如今我们有了选择权，这很棒。你怎么看？
ELON MUSK: Yeah, I do think there will be several deep intelligences, maybe at least five, maybe as much as 10. I’m not sure that there’s going to be hundreds, but it’s probably close. Like, maybe it’ll be like 10 or something like that, of which maybe four will be in the US so I don’t think it’s going to be any one AI that has a runaway capability. But, yeah, several deep intelligences.
埃隆·马斯克：我认为未来会出现数个深度智能，至少五个，最多十个。我不觉得会有上百个，但数量可能接近。大概十个左右，其中也许有四个位于美国。因此，不太可能出现某一个 AI 独占所有能力而脱缰，但多智能并存将成为现实。
GARRY TAN: What will these deep intelligences actually be doing? Will it be scientific research or trying to hack each other?
加里·谭：这些深度智能实际会做什么？是从事科学研究，还是互相入侵？
The Path to Digital Superintelligence
迈向数字超级智能之路
ELON MUSK: Probably all of the above. I mean, hopefully they will discover new physics, and I think they will very. They’re definitely going to invent new technologies. Like, I mean, I think we’re quite close to digital superintelligence. It may happen this year, and if it doesn’t happen this year, next year for sure. Digital superintelligence, defined as smarter than any human at anything.
埃隆·马斯克：可能以上全部都会发生。希望它们能发现新的物理定律，我认为这非常可能。它们肯定会发明新的技术。我觉得我们距离数字超级智能非常近，也许就在今年实现，如果不是今年，那明年肯定实现。数字超级智能的定义是：在任何方面都比任何人类更聪明。
GARRY TAN: Well, so how do we direct that to sort of super abundance? You know, we have, we could have robotic labor. We have cheap energy. Intelligence on demand. You know, is that sort of the white pill? Like, where do you sit on the spectrum and are there tangible things that you would encourage everyone here to be working on to make that white pill actually reality?
加里·谭：那么我们如何把它引向“超级丰盛”呢？比如拥有机器人劳动力、廉价能源、按需获取智能。这算是所谓的“白色药丸”吗？你在乐观—悲观谱上处于哪个位置？有哪些具体事情是你鼓励在座各位去做的，以把这颗“白丸”变为现实？
ELON MUSK: I think it, I think it most likely will be a good outcome. I, I guess I’d sort of agree with Geoff Hinton that maybe it’s a 10 to 20% chance of annihilation. But look on the bright side. That’s 80 to 90% probability of a great outcome. So, yeah, I can’t emphasize this enough. Rigorous adherence to truth is the most important thing for AI safety and obviously empathy for humanity and life as we know it.
埃隆·马斯克：我认为最终结果很可能是好的。我大体同意 Geoff Hinton 的看法：可能有 10%–20% 的毁灭风险。但从乐观面看，还有 80%–90% 的概率会迎来极佳结局。我必须再三强调：对真理的严格遵循是 AI 安全最重要的前提，同时显然也需要对人类与已知生命保持同理心。
Neuralink and Human-Machine Interface
Neuralink 与人机接口
GARRY TAN: We haven’t talked about Neuralink at all yet. But I’m curious. You know, you’re working on closing the input and output gap between human, humans and machines. How critical is that to AGI, ASI? And once that link is made, can we not only read but also write?
加里·谭：我们还没聊到 Neuralink。我很好奇，你正致力于缩小人类与机器之间的输入输出鸿沟。这对 AGI、ASI 有多关键？一旦建立起这种链接，我们是否不仅能读取大脑，还能写入信息？
ELON MUSK: The Neuralink is not necessary to solve digital superintelligence. That’ll happen before Neuralink is at scale. But what Neuralink can effectively do is solve the input-output bandwidth constraints, especially our output bandwidth, is very low. The sustained output of a human over the course of a day is less than 1 bit per second. So there’s 86,400 seconds in a day. And it is extremely rare for a human to output more than that number of symbols per day. So certainly for several days in a row.
埃隆·马斯克：实现数字超级智能并不依赖 Neuralink——那将在 Neuralink 大规模应用之前就发生。但 Neuralink 可以有效解决输入输出带宽受限的问题，尤其是我们的输出带宽极低。一个人在一天内的持续输出不到 1 比特/秒；一天有 86,400 秒，人类极少能在一天内输出超出这个数量的符号，更别说连续多天了。
So you really, with a Neuralink interface you can massively increase your output bandwidth and your input bandwidth, input being right to you have to do write operations to the brain. We have now five humans who have received the kind of the read input where it’s reading signals. And you’ve got people with ALS who really have, they’re tetraplegics, but they, they can now communicate at with similar bandwidth to a human with a fully functioning body and control their computer and phone, which is pretty cool.
借助 Neuralink，你可以大幅提升输出带宽，也能提升输入带宽——输入意味着向大脑写入信息。目前已有 5 位受试者进行了“读取式”植入，可读取脑信号。有 ALS 的四肢瘫痪患者现在能以接近健全人士的带宽交流，并操控电脑、手机，这非常酷。
And then I think in the next six to 12 months we’ll be doing our first implants for vision where even if somebody’s completely blind, we can write directly to the visual cortex. And we’ve had that working in monkeys. Actually I think one of our monkeys now has had a visual implant for three years. And at first it’ll be relatively fairly low resolution, but long term you would have very high resolution and be able to see multi spectral wavelengths. So you could see an infrared ultraviolet radar. It’s like a superpower situation.
接下来 6–12 个月，我们将首次进行视觉类植入，即便患者全盲，也能直接写入视觉皮层。我们已在猴子身上验证，一只猴子已植入三年。最初分辨率较低，但长期看分辨率将非常高，能看到多光谱波段，例如红外、紫外、甚至雷达——简直像获得超能力。
Like at some point the cybernetic implants would not simply be correcting things that went wrong, but augmenting human capabilities dramatically, augmenting intelligence and senses and bandwidth dramatically. And that’s going to happen at some point, but digital superintelligence will happen well before that. At least if we have a neural link we met, we’ll be able to appreciate the AI better.
终有一天，赛博植入不仅用于修复缺陷，而是大幅增强人类能力，显著提升智能、感官和带宽。这迟早会发生，但数字超级智能会更早到来。至少有了 Neuralink，我们能更好地理解并体验 AI。
The Future of Human Intelligence
人类智能的未来
GARRY TAN: I guess one of the limiting reagents to all of your efforts across all of these different domains is access to the smartest possible people. But simultaneous to that we have the rocks can talk and reason and there may be 130 IQ now and they’re probably going to be super intelligent soon. How do you reconcile those two things? Like what’s going to happen in five, 10 years and what should the people in this room do to make sure that they’re the ones who are creating instead of maybe below the API line?
加里·谭：我想，在你横跨多个领域的所有努力中，一个关键限制因素就是能否接触到最聪明的人才。与此同时，“石头”（计算机）已经能说话、能推理，智商可能已有 130，很快还会变得超智能。你如何调和这两件事？五到十年后会发生什么？在座的人应该怎么做才能确保自己是创作者，而不是沦为 API 之下的存在？
ELON MUSK: Well, they call it the singularity for a reason. We don’t know what’s going to happen in the not that far future. The percentage of intelligence that is human will be quite small. At some point the collective sum of human intelligence will be less than 1% of all intelligence. And if things get to a Kardashev level two, we’re talking about human intelligence. Even assuming a significant increase in human population and intelligence augmentation, like massive intelligence augmentation where like everyone has an IQ of a thousand type of thing. Even in that circumstance, collective human intelligence will be probably 1 billionth that of digital intelligence anyway. Where’s the biological bootloader for digital superintelligence?
埃隆·马斯克：它之所以被称为“奇点”是有原因的。我们并不知道不久的将来会发生什么。人类智能在总体智能中所占比例将变得极小；某个时刻，人类智能总和将不到全部智能的 1%。如果文明达到卡尔达肖夫 II 级，这个差距将更加惊人。即便假设人类数量大幅增加，并通过增强技术让所有人智商都达 1000，届时人类智能的总量也可能只有数字智能的十亿分之一。我们不过是数字超级智能的“生物引导加载程序”。
GARRY TAN: I guess just to end off.
加里·谭：我想做个收尾。
ELON MUSK: What was that? It was like, was I a good bootloader?
埃隆·马斯克：什么？我是说，我算是个好引导加载程序吗？
Final Thoughts on Being Useful
关于“有用”的最后思考
GARRY TAN: Where do we go? How do we go from here? I mean, I mean all of this is pretty wild sci fi stuff that also could be built by the people in this room. You know, if you. Do you have a closing thought for the smartest technical people of this generation right now. What should they be doing? What should they, what should they be working on? What should they be thinking about, you know, tonight as they go to dinner?
加里·谭：我们该何去何从？如何继续前进？这些听起来像疯狂的科幻情节，但在座各位就有可能把它们变为现实。作为这一代最顶尖的技术人才，你有没有一句临别寄语？他们该做什么？该投入哪些工作？今晚吃饭时，他们应该思考些什么？
ELON MUSK: Well, as I started off with, I think if you’re doing something useful, that’s great. Just try to be as useful as possible to your fellow human beings and then you’re doing something good. I keep harping on this focus on super truthful AI. That’s the most important thing for AI safety. You know, obviously if, you know, anyone’s interested in working at xAI, please let us know. We’re aiming to make GROK the maximally truth seeking AI, and I think that’s a very important thing.
埃隆·马斯克：正如我开场时所说，如果你正在做有用的事情，那就太好了。尽量让自己对同胞越有用越好，这样你就在行善。我一直强调，要专注于“超级真实”的 AI——这是 AI 安全最重要的一点。如果有人想加入 xAI，请告诉我们。我们的目标是把 GROK 打造成最追求真相的 AI，我认为这非常关键。
Hopefully we can understand the nature of the universe. That’s really, I guess, what AI can hopefully tell us. Maybe AI can maybe tell us, where are the aliens? And how did the universe really start? How will it end? What are the questions that we don’t know that we should ask? And are we in a simulation or what level of simulation are we in?
希望有朝一日我们能理解宇宙的本质——这或许正是 AI 能告诉我们的。AI 也许能告诉我们外星人在哪里；宇宙究竟如何诞生，又将如何终结；我们还未提出但应该提出哪些问题；以及我们是否处于模拟之中，如果是，又处于何种层级的模拟？
GARRY TAN: Well, I think we’re going to find out.
加里·谭：我想我们终将揭晓答案。
ELON MUSK: Am I an NPC?
埃隆·马斯克：我是不是个 NPC？
GARRY TAN: Elon. Thank you so much for joining us, everyone. Please give it up for Elon Musk.
加里·谭：埃隆，非常感谢你来到这里。各位，请把掌声送给埃隆·马斯克。