超越聊天机器人：马克·安德森与本·霍洛维茨谈人工智能的未来

我认为我们尚未了解最终产品的形态和形式。有一个明显的历史类比，你知道，个人电脑从1975年发明到1992年基本上都是文本提示系统。十七年后，整个行业转向图形用户界面并一去不返。顺便说一句，五年后，行业又转向网络浏览器且再未回头。对吧？

I think we don't yet know the shape and form of the ultimate products. There's one just obvious historical analogy as, you know, the personal computer from sort of invention in 1975 through to, you know, basically 1992 was a text prompt system. Seventeen years in, you know, the whole industry took a left turn into GUI's and never looked back. And then by the way, you know, five years after that, the industry took a left turn in in the web browsers and never looked back. Right?

而且，你看，我确信二十年后仍会有聊天机器人，但我相当肯定当前所有聊天机器人公司和许多新公司将开发出各种我们甚至尚未想象的、截然不同的用户体验。

And, you know, look, I'm I'm sure there will be chatbots twenty years from now, but I'm pretty confident that both the current chatbot companies and many new companies are going figure out many kinds of user experiences that are radically different that we don't we don't even know yet.

每次重大技术变革都会带来新能力、新压力以及关于发展进程的新问题。在a16z的Runtime大会上，我与马克·安德森和本·霍洛维茨探讨了AI现状、推理与创造力的演变、市场如何适应新技术，以及这一时刻对塑造未来的创始人和机构意味着什么。现在有请马克和本。

Every major technology shift brings new capabilities, new pressures, and new questions about how progress unfolds. At a16z's Runtime Conference, I sat down with Marc Andreessen and Ben Horowitz to discuss the current state of AI, how reasoning and creativity are evolving, how markets adjust to new technology, and what this moment means for founders and institutions shaping what comes next. Now to Mark and Ben.

请大家欢迎马克·安德森和本·霍洛维茨，以及普通合伙人埃里克·索恩伯格。

Please join me in welcoming Mark Andreessen and Ben Horowitz with general partner, Eric Thornburg.

独奏。进入状态后，你能想象一幅画面。美好使人柔和，持续为说唱歌手创作旋律，激发重点。某些永恒之物。感谢

Solo. Get in the flow, and you can picture like a photo. Beauty makes mellow, maintains, and make melodies for emcees, motivates the point. Something everlasting. Thank

本·洛克姆的表演。音乐是本选的。

you for the Ben rock him who did that. Ben picked the music.

马克，最近有很多关于大语言模型局限性的讨论，比如它们无法真正发明新科学，无法实现真正的创造性天才，只是组合或包装。你对此有何看法？

Mark, there's been a lot of talk lately about the limitations of LLMs that they can't do true invention of, say, new science, that they can't do true creative genius, that is just combining or packaging. You have thoughts here. I would say you.

是的，对我来说就是这样。你会遇到所有这些问题，而且通常它们会以某种形式出现：语言模型是否具有智能，即它们能否真正处理信息并像人类那样实现概念性突破？

Yeah. So for me, it's yeah. So you get all these questions, and, yeah, they usually come in either sort of are language models intelligent in the sense of can they actually process information and have sort of conceptual breakthroughs the way that people can?

然后

And then

还有语言模型或视频模型是否具有创造力？它们能创造新艺术吗？是否真的能实现真正的创造性突破？当然，我对这两个问题的回答是：那么人类能做到这些吗？我认为这里有两个问题，好吧。

there's are language models or video models creative? Can they create new art? Actually have genuine creative breakthroughs. And of course my answer to both of those is, well, can people do those things? And I think there's two questions there, which is okay.

即使有些人被称作‘聪明’，指的是能实现原创性的概念突破，而不仅仅是——姑且这么说——复述训练集或按脚本行事。实际上有多少比例的人能做到这点？我只见过少数几个。其中一些人就在这个房间里，但并不多。大多数人从未做到过。

Even if some people are quote unquote intelligent as in having original conceptual breakthroughs and not just, let's just say regurgitating the training set or following scripts. What percentage of people can actually do that? I've only met a few. Some of them are here in the room but not that many. Most people never do.

然后是创造力。真正具有创造力的人有多少？对吧？你会指向贝多芬或梵高这样的人。你会说，好吧，这就是创造力。

And then creativity. Mean how many people are actually genuinely creative? Right? And so you kind of point to a Beethoven or a Van Gogh or something like that. You're like okay that's creativity.

是的，这就是创造力。那么世界上有多少个贝多芬和梵高？显然不多。所以如果一个事物能超越99.99%的人类标准，那本身就很有意思了。但如果你进一步深究，会发现人类历史上真正意义上的概念性突破有多少？相比之下，更多的是对已有想法的重新组合。

And yeah, that's creativity. And then how many Beethoven's and van Gogh's are there? Obviously not very many. So one is just like, okay, like if these things clear the bar of 99.99% of humanity, then that's pretty interesting just in and of itself. But then you dig into it further and you're like okay like how many actual real conceptual breakthroughs have there ever been actually ever in human history as compared to sort of remixing ideas?

如果你观察技术发展史，几乎总是如此：重大突破通常是至少四十年积累的结果，四个十年。事实上语言模型本身就是八十年研究的集大成者。艺术领域也是如此，小说、音乐等一切创作。虽然存在明显的创造性飞跃，但前人的影响无处不在。即使是贝多芬这样的创造力，他的作品中也有大量莫扎特、海顿等前辈作曲家的影子。所以本质上存在大量的重组与融合。

If you look at the history of technology it's almost always the case that the big breakthroughs are the result of usually at least forty years of sort of work ahead of time, four decades. Right in fact language models themselves are the culmination of eight decades right of previous work and so there's remixing. And then in the arts it's the exact same thing you know novels and music and everything. There are clearly creative leaps but there's just tremendous amounts of influence from people who came before and even if you think about like somebody with the creativity of a Beethoven, like there was a lot of Beethoven in Mozart and Haydn and in the composers that came before. And so there's just tremendous amounts of remixing and combination.

这个问题有点像‘针尖上能站几个天使’的哲学命题——如果你能达到世界顶尖世代创造力智能的0.01%，那基本上就登峰造极了。情感上，我仍想保留对人类创造力独特性的期待。我确实相信这点，也非常愿意相信，但当我使用这些工具时，总忍不住感叹它们展现出的惊人智慧与创造力。

And so it's got a little bit of an angels dancing on the head of a pin question which is like if you can get within you know point 01% of kind of world beating generational creativity intelligence, like you're probably all the way there. So emotionally, I wanna like hold out hope that there is still something special about human creativity. And I certainly believe that. And I very much wanna believe that, but I don't know. When I use these things, I'm like, wow, they seem to be awfully smart and awfully creative.

所以我非常确信它们能跨越这个门槛。

So I'm pretty convinced that they're going clear the bar.

是的。这似乎是你分析中的常见主题——当人们讨论语言模型的局限性时，比如它们能否进行迁移学习或广义学习？你总在追问：人类能做到吗

Yeah. I think that seems to be a common theme in your analysis when people talk about the limitations of LMs. Can they do transfer learning or just learning in general? You seem to ask, can people do

这些？没错。人类能做到这些吗？这就像横向思维，对吧？确实如此。

that? Yes. Can people do these things? Well, it's like lateral thinking, right? So, yeah.

这就像在分布内或分布外进行推理，对吧？没关系。我认识很多擅长分布内推理的人。但真正擅长分布外推理和迁移学习的呢？老实说，我只认识寥寥几个。

So it's like reasoning in or out of distribution, right? And so it's okay. I know a lot of people who are very good at reasoning inside distribution. How many people do I actually know who are good at reasoning outside of distribution and doing transfer learning? And the answer is like, I know a handful.

我认识少数这样的人——每次提问都能得到极其原创的答案。这些答案通常涉及从相邻领域引入概念，实现跨领域联结。比如问金融问题，他们可能从心理学角度解答；问心理学问题，又可能援引生物学理论。

I know a few people where whenever you ask them a question, you get an extremely original answer. And usually that answer involves bringing in some idea from some adjacent space and basically being able to bridge domains. And so you'll ask them a question about, I don't know, finance, and they'll bring you an answer from psychology. Or you ask them a question about psychology and they'll bring you answer from biology, right? Or whatever it is.

以我目前所知，大概就三个人。我的通讯录里有一万人，这个比例实在不高。不过说真的，这反而让我备受鼓舞。

And so I know, don't know sitting here today, probably three. I probably know three people who can do that reliably. I've got 10,000 in my address book. And so three out of 10,000 is not that high a percentage. By the way, find this very encouraging.

是啊，房间里的气氛立刻跌到了谷底。我觉得这很鼓舞人心，因为看看人类在种种限制下依然能创造出这么多东西，对吧？看看我们展现的所有创造力，那些惊人的艺术、电影、小说，还有那些技术发明和科学突破。我们能在自身局限下做到这些已经很了不起了。

Yeah, immediately the mood in the room has gone completely to hell. I find this very encouraging because look at what humanity has been able to build, right, despite all of our limitations. Right? I look at all the creativity that we've been able to exhibit and all the amazing art and all the amazing movies and all the amazing novels and all the amazing technical inventions and scientific breakthroughs. And so we've been able to do everything we've been able to do with the limitations that we have.

所以我觉得，你真的需要百分百确定自己正在进行原创思考吗？我不这么认为。当然能做到最好，最终我们可能会得出这个结论。但即便没有这个确定性，也能取得巨大的进步。

And so I think that do you need to get to the thing where you are 100% positive that's actually doing original thinking? I don't think so. I think it'd be great if you did, and I think ultimately we'll probably conclude that that's what's happening. But it's not even necessary for just tremendous amounts of improvement.

本，上周我们还在你的'全额支付'活动上庆祝几位嘻哈传奇人物，你经常思考创造性天才的问题。你对这个问题怎么看？

Ben, we were just celebrating some some hip hop legends at your paid in full events last week, and so you think a lot about creative genius. How do you think about this question?

是的。我同意马克的观点，不管它是什么，即使没达到那个水平，它也非常有用。我觉得人类对实时体验有种特别的执着，尤其是在艺术领域。以目前的技术水平，预训练数据还不足以完全呈现我们真正想看的东西。不过已经相当不错了。

Yeah. I mean, I think that I agree with Mark that it's whatever it is, it's very useful even if it isn't all the way that level. I think that there's something about the actual, like, real time human experience that humans are very into, at least in art where, you know, with the current state of the technology kind of the pre training doesn't have quite the right data to get to what you really wanna see. But, you know, it's pretty good.

本的非营利活动之一是'全额支付基金会'，旨在表彰并实际上为说唱和嘻哈领域的伟大创新者提供养老金。上周活动上，他请来了过去五十年里这个领域的许多领军人物表演。见到他们并交谈很有趣。但整个领域里有多少人能称得上是真正的概念创新者呢？

One of Ben's nonprofit activities is something called the Pain in Fall Foundation, which is honoring and actually providing essentially a pension for sort of the great innovators in rap and hip hop. And so he knows and has many of we were just at the event and he has many of the kind of leading lights of that field for the last fifty years perform. And it's really fun to meet them and talk to them. But like how many people in that entire field over the course

过去五十年里？这要看你怎么定义，但上周六现场就有好几位。比如洛克威。

of last fifty years did you classify as like a true conceptual innovator? Yeah. Well, you know, it's interesting. It depends how broadly you define it, but there were several of them there on Saturday. So Rock way.

不，我觉得...是的，洛克姆肯定算这个类别。德瑞博士绝对算，乔治·克林顿也肯定算。

No. I think we yeah. Rock Em, you'd certainly put in that category. Doctor Dre, you'd certainly put in that category. George Clinton, you'd certainly put in that category.

从狭义上讲，像Cool G Rap确实有过新创意。但这要看情况。要说音乐上的根本性突破，你大概会提名Brock Kim和George Clinton。

In a narrower sense, like Cool G Rap certainly had a new idea. But, you know, it depends. Like, a fundamental kinda musical breakthrough, you probably just say, Brock Kim and George Clinton.

他们很兴奋吗？

Are they excited?

所以两个里

So two out of

嗯，我是说，那些在场的家伙

Well, I mean, those of the guys who were there

哦，是的。

Oh, yeah.

对，对。但这比例很小。非常小。极小。

Yeah. Yeah. But, yeah, it's a tiny percentage. Tiny. Tiny.

极小。极小。极小。

Tiny. Tiny. Tiny.

昨晚我们与杰瑞德·莱托进行了炉边谈话。他谈到好莱坞许多人对这里发生的事感到恐惧或反对。当你与那些医生、杰斯们、纳斯们、坎耶们交谈时，你看到他们是什么态度？他们对此感到兴奋吗？他们在使用这项技术吗？

We had the fireside last night with Jared Leto. He was talking about how many people in Hollywood are really scared or against what's happening here. What do you see in you know, when you talk to the Doctor. Jays, the Nas, the Kanye's, are they excited? Are they using it?

他们在用吗

Are they

根据我的交流对象来看，音乐界确实有人感到恐惧，但也有很多人对此非常非常感兴趣。尤其是嘻哈音乐人，因为这几乎像是他们当年做法的重演。对吧？他们当年就是借用其他音乐元素创作出新音乐。我认为AI对他们来说是个绝妙的创作工具。

So everybody who I speak to, there are definitely people who are scared in music, but there are a lot of people who are very, very interested in it. And particularly, the hip hop guys are interested because it's almost like a replay of what they did. Right? They just took other music and they kind of built new music out of it. And I think that AI is a fantastic creative tool for them.

它极大地拓展了创作调色板。而且嘻哈音乐的很多精髓在于讲述特定时空的独特故事，对这种事物拥有深刻认知并专门训练，反而是优势，而不是像那种通用型智能音乐模型。

It, like, way opens up the palette. And then for a lot of what hip hop is is it's kind of telling a very specific story of a specific time and place, which having intimate knowledge and being trained just on that thing is actually an advantage as opposed being, like, a generally smart music model.

与此同时，人们也用同样的逻辑说：更智能的终将统治不够智能的。马克，你最近...

At the same time, people also use the same logic of, hey. Whatever is more intelligent will rule whatever is less intelligent. And, Mark, you recently

养猫的人可不会这么说。

Not said by anybody who owns a cat.

马克，你最近发推说：'顶级图形旋转器只能旋转图形，但顶级文字处理器却能旋转图形旋转器'。现场还有人鼓掌。你还说'高智商专家为中智商通才工作'。这是什么意思？

Mark, you recently tweeted, a supreme shape rotator can only rotate shapes, but a supreme word cell can rotate shape rotators. And also someone's clapping here. And also high IQ experts work for mid IQ generalists. What means?

什么意思？是啊。所以博士们都为MBA工作对吧？所以这没什么。

What means? Yeah. So so so the PhDs all work for MBAs. Right? So it's okay.

是啊。嗯，我只是觉得他们把层次提升了。就像你看当今世界，你觉得我们是被聪明人统治的吗？对吧？这就是你从时事中得出的重大结论吗？

So yeah. Well, I just It's just they take it up a level. It's just like when you look at the world today, do you think we're being ruled by the smart ones? Right? Is that your big conclusion from, like, current events current affairs?

对吧？好吧。我们让天才们掌权。

Right? Okay. We put the geniuses in charge.

你是说Kamala和Schromb并不是最优秀的咯？

You mean Kamala and Schromb aren't the best then?

嗯，这甚至不是特指美国。我们放眼全球看看。是的，我认为有两件事是真实的：一是我们可能都低估了智慧的重要性。

Well, that's not even be specific towards The US. Let's just look all over the world. Yeah. And so I think two things are true. One is we probably all kind of underwrite the importance of intelligence.

实际上，关于智慧有一整个背景故事——过去百年间，由于种种原因，它竟成了一个极具煽动性的话题，我们可以详细讨论。甚至仅仅是‘有些人比其他人更聪明’这个观念就会让人不安，人们不愿谈及。作为社会，我们确实对此很挣扎。而事实上，在人类中，智慧几乎与所有积极的人生成果都相关，对吧？

And actually, there's a whole kind of backstory here of intelligence actually turns out to be this, like, incredibly inflammatory kind of topic for lots of reasons over the last hundred years, which we could talk about in great detail. And even the just very idea that like some people are smarter than other people, it just like really freaks people out and people don't like to talk about it. We really struggle with that as a society. So and then it is true that intelligence is like in humans, intelligence is correlated to almost every kind of positive life outcome. Right?

因此在社会科学中，他们通常所说的流体智力（G因素或智商）与几乎所有事物都有约0.4的相关性。它与教育成果、职业成就、收入呈0.4相关，顺便说一句，还与生活满意度、非暴力（能用非暴力方式解决问题）等有关。一方面我们可能都低估了智慧，另一方面那些从事智慧相关领域的人可能又高估了它。你甚至可能造个新词，比如‘智慧至上主义者’之类的，就是那种认为智慧至高无上的人。

And so intelligence generally in the social sciences what they'll tell you is what they call fluid intelligence, the G factor or IQ is sort of 0.4 correlated to basically everything. And so it has 0.4 correlation to like educational outcomes and professional outcomes and income and by the way, also like life satisfaction and by the way, non violence, being able to solve problems without physical violence and so forth. And so like, on the one hand, like we probably all underrate intelligence. On the other hand, the people who are in the fields that involve intelligence probably overrate intelligence. And you might even might even coin a term like maybe like intelligence supremacist or something like that, where it's just like, oh, like intelligence is very important.

所以也许它就像是最重要或唯一重要的事情。但当你审视现实时，你会意识到，显然情况并非如此。

And so therefore maybe it's like the most important thing or the only thing. But but then you look at reality and you're like, okay, that's clearly not the case.

是啊。它仍然只有0.4。对吧？是的。

Yeah. It's still zero only 0.4. Right? Yeah.

首先，它只有0.4。要知道在社会科学领域，0.4已经是个巨大的相关因子了。大多数可关联的因素——无论是基因、可观察行为还是其他——在社会科学中的相关性都远小于这个值。所以0.4虽然很小，但它仍然只有0.4。

Well, so to start with, it's only 0.4. And, you know, in the social sciences, 0.4 is a giant correlation factor. Right? Like most most things that where you can correlate whether it's, know, genes or observed behavior or whatever, anything in the social sciences, the correlations are much smaller than that. So 0.4 is tiny, but it's still only 0.4.

即使你是个彻头彻尾的基因决定论者，认为基因智商决定一切结果，它仍然无法解释0.6的相关性。这还只是个体层面。当你观察群体层面时——这是个著名现象——把一群人聚集成乌合之众，群体的判断会优于平均水平。但把聪明人聚集成群体时，他们反而会变得更蠢，这种现象屡见不鲜。

So even if you're like a full on if you even if you're like a full on genetic determinist and you're just like, you know, genetic IQ just like drives all these outcomes, like it's it's still doesn't explain, you know, 0.6 of the correlation, and so that leaves it. But but but that's just on the individual level. Then you just look at the collective level. Well, you just look at the collective level. It's it's like a famous famous observation as you take a you take a bunch of you you take any group of people, you put them in a mob, and the mob is right, than the average and and and you put a bunch of smart people in a mob and they definitely turn dumber, like and and you see that all the time.

对吧？人们组成群体后的行为会大不相同。然后就会产生诸如'谁在掌权'的问题——无论是公司还是国家。无论筛选机制是什么，显然不仅取决于智商，甚至可能主要不取决于智商。因此，某些AI圈子里流传的'聪明事物必然统治愚昧事物'的假设——

Right? And so you you put people in groups and they they they behave very differently. And then you you create and then you create these, you know, questions around like who's in charge, whether who's in charge at a at a company or who's in charge of a of a country. And like it it's whatever the filtration process, it's clearly not it's not it's it's not it's certainly not only on IQ and it may not even be primarily on IQ. And so so therefore, it's just like this assumption that you kinda hear in some of the AI circles, which is like inevitably the smart, you know, kind of thing is gonna govern the dumb thing.

我觉得这种观点很容易被证伪。智力并不足够。看看这个房间里的人——我们很幸运认识很多聪明人，你只要观察他们就会发现——

Like, I I just think that's, like, very easily. It's just sort of very easily and obviously falsified. Like, intelligence isn't sufficient. And then you just convey it. You know, we're all in this room lucky enough to know a lot of smart people and you just kind of observe smart people.

有些聪明人确实能整合资源取得成功，但很多聪明人始终做不到。所以显然还存在许多与成功、与掌权者相关的其他因素，远不止原始智力这么简单。

And like some smart people, you know, really figure out how to have their stuff together and become very successful and a lot of smart people never do. And so there there's there there must be there obviously are, and there and there, in fact, must be many other factors that have to do with success and have to do with, like, who's in charge than just raw intelligence.

这引出了后续问题：具体有哪些例子呢？就是那些超出智力范畴的技能，特别是为什么人工智能系统无法学习这些技能？

It begs the follow-up question of what are what are some examples of what that might be, you know, skills sort of outside of intelligence, and more particularly, specifically, why couldn't AI systems, you know, learn them?

是的，本，那么根据你的经验，除了智力因素外，还有哪些因素决定了领导力、创业成功、解决复杂问题或组织团队的能力？确实存在这些因素。

Yeah. So Ben, like, what what other than intelligence, what what, in your experience, determines, for example, success in leadership or in entrepreneurship or in solving complex problems or organizing people? Yeah. There

有很多方面。比如很大程度在于能否以正确方式进行对抗。这其中虽然需要一些智力，但更多在于真正理解对话对象——要能解读对方的所有思考方式，学会从公司员工而非自己的视角来看待决策。这种能力需要通过不断与人交流、理解他们的话语来培养。这些都不是智商能解决的，也不是说AI通过训练个体就能完全掌握该说什么。还需要把这些能力与企业的实际业务需求相结合。

are many things. Like a lot of it is being able to have a confrontation in the correct way. And they're there's some intelligence in that, but a lot of it is just under really understanding who you're talking to, you know, being able to interpret everything about how they're thinking about it and just kinda generally seeing decisions through the eyes of the people working in the company, not through your eyes is a skill that, you know, you develop by talking to people all the time, understanding what they're saying, so forth, these kinds of things. And it's just, it's certainly not an IQ thing and not that like I could imagine an AI training on any individual and like figuring it all out and knowing what to say and so forth. But then you also need that integrated with, like, whatever the business ought to be doing.

所以重点不是做受欢迎的事，而是引导人们做正确的事——即使他们不喜欢。这就是管理的精髓。虽然目前没人专门研究这个问题，但未来可能会。

So you're not trying to do what's popular. You're trying to get people to do what's correct even if they don't like it. And, you know, that's a lot of management. So it's not a problem anybody's working on currently, but maybe they will.

确实如此。这是勇气、动力、情感理解和心理理论等多种因素的结合。

It's some right. Some combination of, like, courage, Yeah. Some combination of motivation. Some combination of of emotional understanding, theory of mind.

是啊。要明白人们想要什么？既要考虑必须完成的任务，又要评估他们的才能——哪些人你可以放心用？就算他们跳窗也没关系的那种。

Yeah. What we you know, what do people want? Like, you know, married to, you know, what needs to be done and then, like, how talented are they? Like, which ones can you afford? Like, if they jump out the window, it's fine.

哪些人不行？这类事情有很多微妙的细节，而且高度情境化。我认为管理类书籍之所以糟糕，就是因为它们忽略了情境特殊性——你们公司的产品、人员、组织架构都独一无二，远不是'制定战略五步骤'这种套路能解决的。

You know, which one's not fine? You know, this kind of thing. It's a there's a lot of, like, weird subtleties to it, and it's very situational. I I think the hardest thing about it and why management books are so bad is because it's situational. Like your company, your product, your people, your org chart is very, very different than, know, here are the five steps to building a strategy.

这就像，好吧，这是我读过最他妈没用的东西，因为它跟你半毛钱关系都没有。

It's like, well, that's the most useless fucking thing I ever read because it has nothing to do with you.

所以这其中一件有趣的事，就是心智理论这个概念非常重要，对吧？心智理论指的是你和你的大脑能否模拟他人头脑中的活动，

So one of the interesting things on this, like on this is the concept of theory of mind is really important, right? So theory of mind is can you and your head model what's happening in a person's head,

对吧？

right?

你可能会认为，也许那些更聪明的人在这方面应该更擅长。但事实证明可能并非如此。不相信的理由如下：美军是最早采用并持续成为美国社会中智商测试的主要推行者。他们基本上通过所谓的ASVAB（职业能力倾向测验）来实施。

And you would think that maybe that, you know, maybe obviously people who are smarter should be better at that. It turns out that that may not be true. And the reason to believe that that's not true, which is as follows. The US military was the early adopter and has continued to be sort of the leading adopter in US society of actually IQ testing. They basically launder it through something called the ASVAB, which is what they call vocational aptitude battery test.

但这本质上就是个智商测试。他们仍然使用明确的智商测试，并根据智商将人们分配到不同专业和岗位，包括领导职位。他们知道每个人的智商，并据此进行组织。多年来他们发现，如果领导者的智商与追随者相差超过一个标准差，就会成为真正的问题。而且双向都是如此，对吧？

But it's basically an it's essentially an IQ test. And so they still use basically explicit IQ tests and they they slot people into different specialties and roles, you know, in in part with according to IQ, including into leadership roles. And and so they they know what everybody's IQ is, and they they kinda organize organize around that. And one of the things that they found over the years is if the leader is more than one standard deviation of IQ away from the followers, it's a real problem. And that's true in both directions, right?

如果领导者不够聪明，就无法有效管理。让一个不太聪明的人去模拟更聪明人的思维行为本身就极具挑战性，甚至可能无法实现。但事实证明反过来也一样——如果领导者的智商比他管理的组织平均水平高出两个标准差，他也会失去心智理论能力。非常聪明的人其实很难模拟中等聪明人士的内在思维过程。因此确实需要建立某种程度的连接，而不仅仅是...由此可以推断，如果存在一个智商1000的人或机器，它可能会变得如此陌生，它对现实的认知将与它管理的人或事物如此迥异，以至于根本无法建立任何现实的联系。

If the leader is not smart enough to be able to manage, to be able to For somebody who is less smart to model the mental behavior of somebody who's more smart is inherently very challenging and maybe impossible. But it turns out the reverse is also true, which is if the leader is two standard deviations above the norm of the organization that he's running, he also loses theory of mind. It's actually very hard for very smart people to model the internal thought processes of even moderately smart people. And so there's a real need to have a level of connection there that's not just And therefore by inference, if you had a person or a machine that had a thousand IQ or something like it, it may just be, it would be so alien. Its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way.

所以这再次有力地证明，没错，未来几个世纪内世界远不会按照智商来组织。

So again, this is a very good argument that like it, yeah, the world is gonna be far from organized by IQ for centuries to come.

是的。扎克伯格有句名言说得很好：智能不等于生命。生命具有许多与智能无关的维度。我觉得，如果只专注于研究智能，就会忽略这些本质。

Yeah. And Zuckerberg had a great line, which is intelligence is not life. Life life has a lot of dimensionality to it that is independent of intelligence. I think that, you know, if you spend all your time working on intelligence, you lose track of that.

我们有时会说某些人聪明过头了——他们要么过度预设他人的理性，要么对事情过度思考或合理化。这正好印证了你说的观点：智能并非万能。

We we sometimes say about some specific people that they're too smart to properly model or or or, you know, to they sort of assume too much rationality on other people or they just overthink things or over rationalize them. Yeah. Just to to your point that it's it's not everything.

没错。应该说，人们很少会做出最符合自身利益的选择。

Yeah. Yeah. Yeah. People often people seldom do what's in their best interest, I should say. Yeah.

对。

Yeah.

我认为这其实更偏向生物学范畴。越来越多的科学证据表明，人类的认知——或者说自我意识、信息处理、决策体验——并非仅由大脑完成。传统的心物二元论根本站不住脚。这其实是对智商至上论的驳斥：人类并非仅通过理性思维来感知存在，更不是仅靠大脑的理性思维，而是全身心的综合体验。

You know, I also suspect this kind of gets more into the biology side of things. I you know, there's more and more scientific evidence that basically also that like human human cognition human cognition or human, I don't whatever you wanna call it, self awareness, information processing, decision making sort of experience is is not purely a brain. Like, the basically the the sort of my same as mind body dualism is just not correct. Like and again, this is an argument against sort of IQ supremacism or intelligence supremacism is it's not Human beings don't experience existence just through the rational thought. And specifically not through just the rational thought of the brain, but rather it's a whole body experience.

对吧？我们的神经系统、肠道菌群、嗅觉感知、荷尔蒙等各类生化机制都在参与。追踪最新研究就会发现，人类认知是远比想象更完整的全身性体验。这也正是当前AI领域的核心挑战——现有AI完全是心物二元论的产物，就像没有躯体的孤零零大脑。机器人技术革命到来后情况才会改变。

Right? And there's aspects of our nervous system and there's aspects of everything from our gut biome you know, to, you know, to smells, you know, to the olfactory senses and, you know, and hormones and like all kinds of like biochemical kind of aspects to life. If you just kinda track the research, I suspect we're gonna find is human cognition is a full body experience much, much more than much more than people thought. And and so therefore to actually this, you know, this is like a and this is, you know, one of the kind of big fundamental challenges in the AI AI field right now, which is, you know, the form of AI that we have working is is the is the fully mind body dual version of it, which is it's just a disembodied you know, like a disembodied brain. You know, the robotics revolution for sure is coming when that happens.

当AI被植入可移动的物理实体，配备各类传感器收集现实数据时，才可能逐步实现智识与物理体验的融合。届时我们或许能构建更高级的认知模型，同时深化对人类和机器认知机制的理解。但就目前研究来看，这些构想都处于萌芽阶段，还有大量工作要做。

When we put AI in physical objects that move around the world, you know, you're gonna be able to get closer to having that kind of, you know, integrated intellectual physical experience. You're gonna have sensors and the robots are gonna be able to gather a lot more real world data. And so maybe you can start to actually think about synthesizing a more advanced model of cognition. Maybe we're gonna actually discover more both about how the human version of that works and also how the machine version of that works. But that it's just to me, least reading the research like that, all those ideas feel very nascent, we have a lot of work to do to try to figure that out.

你

Do you

你是否能感知——抱歉，在《今日心智理论》中——它们的状态？或者你能察觉到局限所在吗？你很喜欢和它们交谈。在这个过程中有什么特别让你惊讶的事情吗？

have a sense for how they are, I'm sorry, at Theory of Mind Today? Or do you have a sense of where the limitations are? You you like to talk to them a lot. Are there any particular things that are particularly surprising to you as you do?

是的。总体而言它们表现非常出色。我觉得最迷人的方式之一就是让语言模型创建角色扮演，然后...其实我特别喜欢苏格拉底式对话。我喜欢让观点在辩论中展开的那种对话形式。

Yeah. I would say generally, they're really good. Yeah. And so, like, one of the one one of the more I I find one of the more fascinating ways, you know, to to to work with language models is actually have them create personas and and then, you know, basically have well, actually, so the way I like I like, you know, I like basically I like Socratic dialogues. I like when things are argued out and like a Socratic dialogue.

你让当今任何先进的大语言模型生成苏格拉底对话，它要么会虚构角色。效果不错，但它有个特别烦人的特性——它希望所有人都开心，所以会让所有角色达成一致。

And so, you know, tell a tell a any advanced LLM today to create a Socratic dialogue, and it'll either make up the personas. You can tell what it is. It does a good job. It has this very, very annoying property, which is it wants everybody to be happy. And so it wants all of its personas to agree.

默认情况下，它会先进行一段浅显有趣的讨论，然后就像在看PBS特别节目似的，想方设法让所有人达成共识。对话结束时皆大欢喜。当然我他妈最讨厌这点，简直让我发疯。

And so by default, it will have a it will have a briefly interesting discussion and then it will sort of figure out, you know, basically, it like, you're watching, I don't know, PBS special or something. It'll it'll kinda figure out how to bring everybody in agreement. Everybody's happy at the end of the discussion. And, of course, I fucking hate that. Like, it drives me nuts.

我不想要这种结果。所以我会命令它：让对话更紧张些，充满火药味，让参与者在交流中逐渐暴怒。这样才开始真正有趣。接着我会要求加入大量脏话。

I don't want that. So instead, I I tell it. I'm like, make the conversation more tense, right, and, like, fraught with, like, anger and, like, you know, people's, you know, gonna get, like, increasingly upset throughout the conversation. And then it starts to get really interesting. And then I and then I tell it, you know, bring it you know, use introduce a lot more cursing.

让它们彻底撕破脸皮，互相攻击名誉，展开殊死搏斗。

You know, really have them go at it. Like, all the gloves come off. They're going for pull pull, you know, reputational destruction of each other.

你经常做这类短剧。

You do a lot of these skits.

是啊。然后我就有点忘乎所以了，结果发现他们其实都是秘密忍者，接着他们就打起来了，爱因斯坦拿着双节棍打尼尔斯·玻尔。顺便说一句，它也很乐意这么做。所以你必须控制自己。不过它在心智理论方面确实很出色。

Yeah. Then I I get carried away, then I'm like, it turns out they're all, like, secret ninjas, and then they all start fighting, and you've got Einstein, you know, know, you know, hitting, you know, Niels Bohr with nunchucks. And by the way, it's happy to do that too. So you have to control yourself. But it is very good at theory of mind.

我再举个例子。英国有个政治领域的初创公司发现，现在的语言模型已经足够优秀了。特别是在政治这个子领域里，这个理念很重要。在政治中，人们总是组织选民焦点小组。

Then I'll give you another example. There's a startup actually in The UK in the world of politics. What they found is that they found that language models now are good enough. So specifically for politics, which is sort of a subcategory where this idea matters. So in politics, people do focus groups of voters all the time.

顺便说一句，很多企业也这么做。就是把一群不同背景的人聚在一个房间里，引导他们讨论，试图获取他们的观点。焦点小组常常出人意料，比如那些组织焦点小组的政客们——如果你和他们交谈就会发现，他们经常对选民真正关心的事情感到意外，这些事往往和他们预想的不同。通过这种方式确实能学到很多。

By the way, many businesses also do that. You know, so you get a bunch of people together from different backgrounds in a room and you kind of guide them through a discussion and try to get their points of view on things. Focus groups are often surprising like politicians who If you talk to politicians who do focus groups, they're often surprising. They're often surprised by the things that they thought voters cared about is actually not the things that voters care about. And so you can actually learn lot by doing this.

但组织焦点小组成本很高，而且耗时很长，因为需要实际组织人员、招募筛选参与者等等。现在最先进的模型已经足够胜任这项工作，能准确模拟真实人群的焦点小组。换句话说，你可以在模型里创建角色来模拟焦点小组——比如一个肯塔基州的大学生、一个田纳西州的主妇等等，只要指定这些特征就行。

But focus groups are very expensive to run and then there's a long lag time because they have to be actually physically organized and you have to recruit people and vet people and so forth. And so it turns out that the state of the art models now are good enough at this so they can correctly accurately reproduce a focus group of real people inside the model. So they're good enough to clear that bar. In other words, you can basically have a focus group actually happening in the model where you create personas in the model and then it accurately represents, you know, a college student from, you know, Kentucky is contrasted to a housewife from Tennessee is contrasted to a, you know, whatever, whatever. You you just like specify this.

所以它们已经达到了这个标准。我们会继续观察它们能走多远。

And so, know, they're good enough to clear. They're good enough to clear that bar and, you know, we'll we'll see how far they get.

我想转到泡沫话题。阿明、G2的詹森和马特谈到了正在建设的庞大物理基础设施——AI资本支出占GDP的1%。我们该如何理解和思考这个泡沫问题？

I wanna segue to the bubble conversation. Amin and g two Jensen and Matt spoke about the enormous scale physical infrastructure being built out. AI CapEx is 1% of GDP. How should we understand and think about this bubble question?

我认为这成为一个问题本身就说明我们不在泡沫中。首先要明白的是，泡沫本质上是一种心理现象。要形成泡沫，必须所有人都相信这不是泡沫——这才是其核心机制。

Well, I think the fact that it's a question means we're not in a bubble. That's the first thing to understand. I mean, a bubble is a psychological phenomenon as much as anything. And in order to get to a bubble, everybody has to believe it's not a bubble. That's sort of the core mechanic of it.

我们称之为'投降时刻'。所有人都放弃抵抗，心想'算了，我再也不做空这些股票了，我受够一直亏钱，我要做多'。我们确实见证过这种情况。

And we call that capitulation. Everybody just gives up like, okay, I'm not gonna short these stocks anymore. I'm tired of losing all my money. I'm gonna go long. And we saw that actually.

我其实有点疑惑，当年的科技泡沫究竟是什么？就在互联网泡沫巅峰期，当股价冲上云霄时，巴菲特开始投资科技股——要知道他曾发誓绝不碰科技股，因为他自认看不懂。连他都投降了，当所谓'泡沫'形成时，反而没人说这是泡沫了。现在回头看，互联网显然不是泡沫。

And, you know, and I had a little bit of question, like, really what was the tech bubble? But in the kind of .com era, right as the prices went through the roof, Warren Buffett started investing in tech. So like and he swore he would never invest in tech because he didn't understand it. And so if he capitulated, nobody was saying it was a bubble when it became like a quote unquote bubble. Now if you look at that phenomenon, the internet clearly was not a bubble.

它是真实存在的。短期内确实出现了价格扭曲，因为当时市场网络用户基数还不足以支撑那些产品。后来价格确实超出了市场实际。但AI领域更难出现这种情况，因为短期需求实在太旺盛了，对吧？

You know, it was a real thing. It was in the short term, there was a kind of price dislocation that happened because the market, you know, there were just not enough people on the network to make those products go at the time. And then the prices kind of outran the market. You know, in AI, it's much harder to see that because there's so much demand in the short term. Right?

目前我们根本不存在需求不足的问题。要说五年后会出现需求危机，我觉得相当荒谬。当然可能会出现些奇怪的瓶颈，比如某个时点冷却系统跟不上之类的。但就目前来看，供需关系、增长倍数这些指标，在我看来完全不像泡沫，不过我也说不准。

Like, we don't have a demand problem right now. And, like, the idea that we're gonna have a demand problem five years from now to me seems quite absurd. You know, could there be, like, weird bottlenecks that that appear, you know, like, just, at some point, we just don't have enough cooling or something like that. You you know, maybe. But, like like, right now, if you look at demand and supply and what's going on and multiples against growth, It doesn't look like a bubble at all to me, but I don't know.

马克，你觉得这是泡沫吗？

Do you think it's a bubble, Mark?

嗯...这么说吧，其实没人能确定。我的意思是，连专家们也说不准。

Yeah. Look. I I would just say this. Yeah. Like, nobody knows so nobody knows in the sense of, like, the experts.

比如说，如果你跟对冲基金或银行之类的人交谈，他们肯定不懂。通常CEO们也不懂。所以就这样吧。

Like, if you're talking to anybody at, like, a hedge fund or a bank or whatever, like, they definitely don't know. Generally, the CEOs don't know. So it the By

话说回来，很多风投也不懂。他们只会生气。当你们估值更高时，风投就会情绪激动。这让他们非常愤怒。我经常遇到这种情况，我就想，你到底在气什么？

the way, a lot of VCs don't know. They just get upset. Like, VCs get, like, emotionally upset when you guys have higher valuations. Like, it it makes them, like like, angry. And, you know, and I I get it all the time, and I'm like, what are you mad about?

兄弟，这明明运作得很好啊。高兴点行不行？但确实有很多人带着情绪，巴不得这是个泡沫。

Like, the shit is working, man. Be happy. Come on. But so so, like, there's a lot of emotion around, like, people wanting it to be be a bubble.

是啊。最糟的莫过于错过一桩交易后，那家公司却大获成功。就像我刚才说的，就我个人而言，那个估值太离谱了。在我们这行，你三十年都别想认真对待那种估值。

Yeah. No nothing's worse than passing on a deal than having the company become a great success. Like, I just said, it's just me. It's like That that valuation is outrageous. You you can be serious about that for thirty years in our business.

这简直太神奇了。然后你就能找到各种理由来应对，解释这不是你的错。但你知道，错的是这个世界，不是我。对吧？

It's it's it's amazing. Then you can find yeah. You come up with all kinds of reasons to cope and and explain why it wasn't your mistake. But it's, you know, it's the world of it's the world that's wrong, not me. Right?

所以这种情况很多。我会一直强调要把话题拉回基本面真相。两大基本面真相是：第一，这项技术真的有效吗？

So there there's a lot of that. Yeah. Yeah. So I I just I would just I would just say, like, I would always say bring bring the conversation back to ground truth fundamentals. And the the two big ground truth fundamentals are, number one, does the technology actually work?

它能兑现承诺吗？第二，客户是否愿意买单？如果这两点成立，那么只要这两点站得住脚，一般来说事情就会按正轨发展。

Can it deliver on its promise? And then number two is our customers bang for it. And if those two things are true, then it's very hard to As long as those two things stay grounded, you know, gen generally generally, things are gonna are I think are gonna be on track.

是的。当Gavin和DG在这里时，他说ChatGPT对谷歌来说是个珍珠港时刻，是巨人觉醒的时刻。当我们回顾历史和平台变迁时，是什么决定了现有巨头能赢得下一波浪潮还是新进入者胜出？或者说在AI领域我们该如何思考这个问题？

Yep. When Gavin was up here with DG, he said ChatGPT was a Pearl Harbor moment for Google, the moment when the giant wakes up. When when we look at history and and platform shifts, what determine whether the incumbent actually wins the next wave versus versus new entrants, or how should we think about that in in AI?

嗯，你知道，应对很重要，但这不意味着这就是珍珠港时刻。想想谷歌终于清醒了——那声音就是证明。所以他们不会被彻底碾压。不过话说回来，我认为OpenAI也不会消失。

Well, you know, reacting to it is important, but that doesn't mean like like, I it's a Pearl Harbor moment. Think Google got their head out of their ass. That was the sound of it. So, you you know, they're not gonna get completely run over. But nonetheless, like, I don't think OpenAI is going away.

所以他们确实让这种情况发生了。部分原因是速度问题。长期来看关键在于执行力。而这些大公司在不同程度上已经丧失了执行力。

So, like, they they definitely let that happen. Yeah. Some of it to speed. Then just look, it's execution over a long period of time. And some of these very large companies to varying degrees have lost their ability to execute.

所以如果你在谈论一个新平台，谈论长期建设，就像微软当年对谷歌措手不及一样。微软依然强大，但他们错过了整个机会。他们也错过了移动计算的机会——当时苹果还微不足道，而微软曾坚信自己将主宰移动计算。

And so if you're talking about a brand new platform and you're talking about kind of building for a long time, it's like Microsoft got caught with their pants down on Google. Microsoft's still very strong, but they missed that whole opportunity. They also missed the opportunity. Apple was nothing. And Microsoft fully believed that they were gonna own mobile computing.

他们完全错过了那次机会。但由于Windows垄断地位，他们仍然足够强大可以拓展其他领域。总体而言我认为新公司赢得了新市场，但这不意味着上一代的最大垄断企业就会很快消亡——它们往往能持续很长时间，这是我的看法。

They completely missed that one. But they were still so big from their Windows monopoly they could build into other things. You know, I think generally the new companies have won the new markets, and that doesn't mean the big company the biggest companies, the biggest monopolies from the prior generation just lasts a long time is the way I would look at it.

是啊。我也觉得我们还不太确定。这一切发生得太快了。实际上我认为我们还不清楚最终产品会是什么形态。

Yeah. I I also think we don't quite know. Like, it's all happened so fast. We we actually don't I think we don't yet know the shape and form of the ultimate products. Yeah.

对吧？所以因为这种诱惑总是存在——就像台上这些人做的那样——人们很容易陷入非此即彼的简化思维：要么是聊天机器人，要么是搜索引擎，对吧？

Right? And so and so, like because it's it's tempting, and this is kind of what what always happens. It's kinda it's kinda tempting to look at I don't know I'm saying that's what these guys did on stage, but it's kinda tempting to look. Sometimes you hear the kind of reductive version of this, which is basically it's like, oh, there's either gonna be a chatbot or a search engine. Right?

这场竞争是聊天机器人与搜索引擎之间的较量。谷歌面临的问题正是典型的颠覆性挑战——你是否会颠覆传统的'十条蓝色链接'模式，转而采用AI生成的答案，甚至可能动摇整个广告模式？而OpenAI的问题在于，他们虽然拥有完整的聊天产品，但尚未建立广告业务，也缺乏谷歌级别的分发网络。所以你会觉得，好吧，这局面确实耐人寻味。

The the competition is between a chatbot and a search engine. And the the problem Google has is the classic problem of, you know, disruption. Are you gonna disrupt the 10 blue links model and swap in a you know, at, you know, sort of AI answers and, you know, potentially disrupt the advertising model? And then the problem OpenAI has is they have the the full, you know, the full chat product, but, you know, they don't have the advertising yet, and they don't have the distribution Google scale distribution. And so, you know, you kinda say, okay.

这简直就像直接从《创新者的窘境》这类商业教科书里搬出来的案例。非常典型的1v1竞争态势。但这种思维的危险在于，它假设未来五到二十年内人们主要使用的产品形态仍将是搜索引擎或聊天机器人。要知道，历史总是充满惊人的相似之处。

That's a fairly it's a fairly like that'd be straight out of like, you know, the innovator's dilemma, you know, business textbook. Like this is just a very clear, you know, one versus one, you know, kind of dynamic. But that assumes that, you know, the mistake that you could make in thinking that way is that assumes that the forms of the product in five, ten, fifteen, twenty years that are gonna be the main things that people use are gonna be either a search engine or a chatbot. Right? And, you know, there's just obvious historical analogies.

最明显的历史类比就是个人电脑——从1975年发明到1992年左右，本质上都是文本提示系统。要知道在当时，交互式文本提示相比穿孔卡片系统、分时系统已是巨大进步。直到1992年...

One just obvious historical analogy is, you know, the personal computer from sort of invention in 1975 through to, you know, basically 1992, you know, was a was a text prompt system. Right? You know? And at the time, by the way, an interactive text prompt was a big advance over the previous generation of like punch card systems, time sharing systems. And then, you know, it was, you know, 1992.

大约经过17年发展后，整个行业突然转向图形用户界面且再无回头。更值得注意的是，五年后行业又转向网页浏览器并彻底改变方向。用户体验的形态、本质及其在我们生活中的角色，我认为至今仍未定型。

So it was about seven seventeen years in, you know, the whole industry took a left turn into GUI's and never looked back. You know? And and then by the way, you know, five years after that, the industry took a left turn into web browsers and never looked back. Right? And so the very shape and form and nature of the user experience and how it and how it fits into our lives, you know, is is is I think still unformed.

我确信二十年后仍会有聊天机器人存在，但我更相信现有公司和新兴企业都将创造出我们目前无法想象的颠覆性用户体验。这正是科技行业——尤其是软件领域——的魅力所在：产品形态充满不确定性，创新空间无比广阔。

And so, like and and, know, look, I'm I'm sure there will be chatbots twenty years from now, but I I'm I'm pretty confident that, you know, both the current chatbot companies and many new companies are gonna figure out many kinds of user experiences that are radically different that we don't we don't even know yet. And by and by the way, that's one of the things, of course, that keeps the tech industry fun, which is, you know, especially on the especially on the software side, you know, it's it's not it's not it's not obvious what the shape and form of the products are. And there's just I think there's just tremendous headroom for invention.

在指导在场创业者时，您认为这个时代还有哪些独特之处？或是您经常给出的其他建议？无论是关于正在进行的人才争夺战，还是这个时代特有的其他方面，您还想给创业者们哪些重要建议？

As as you're coaching on entrepreneurs and the entrepreneurs in this room, what what else feels different about this era or or or other advice that you find yourself spent, whether it's around sort of the talent wars that are going on or other aspects that feel unique to this era? What what other advice do you wanna be leaving our entrepreneurs with it?

说到这个时代的独特性...其实你刚才点出了关键——这确实是个独特时代。过度借鉴过去的组织设计经验或上一代的教训可能会产生误导，因为很多方面都不同了。如今公司的构建方式大不相同，就像我们观察到AI博士研究员与传统全栈工程师的行为模式就存在显著差异。

That's unique to this era. Well, like, I I actually think you said the right thing, which is this is a unique era. And so trying to learn the organizational design lessons of the past or trying to learn kind of too much from the last generation can be deceptive because things really are different. Like the way your companies are getting built is quite different in many aspects. And the types of what just like our observation on PhD AI researchers is just very different than like a traditional engineer, full stack engineer or something like that.

因此我认为确实需要从第一性原理出发思考许多问题，因为情况确实不同。从外部观察来看，这确实很不一样。

So I think you do have to think through a lot of things from first principles because it is different. And like observing from the outside, it's really different.

是的，是的。我想补充的是，我认为事情会发生变化。我之前提到过，我认为产品的形态会改变。所以我觉得这里仍然存在大量创新空间。

Yeah. Yeah. And I would just offer, like, I do think things are gonna change. So I already talked about, I think the shape and form of products is gonna change. And so like, I think there's still a lot of creativity there.

我还认为——我本该早些指出——在供需关系的世界里，造成过剩的恰恰是短缺。对吧？当某样东西变得过于稀缺时，就会产生巨大的经济动力去解锁新的供应。所以当前这代AI公司正面临严重的人才短缺问题，特别是顶尖AI研究员和工程师。同时他们也面临着基础设施能力的短缺，包括芯片、数据中心和电力。

I also think, and I was supposed to say, I think that like in a world of supply and demand, the thing that creates glut is shortages. Right? So, like, when something becomes too scarce, there becomes a massive economic incentive to figure out how to unlock new supply. And so the the the current generation of AI companies are really struggling with particularly shortages of the really talented AI researchers and engineers. And then they're really challenged with a shortage of infrastructure capacity chips and data centers and power.

我不想预测具体时间节点。但终有一天这两者都会出现过剩。所以我们很难为此做规划。不过我想说几点：第一，在研究工程领域，中国现在涌现出许多优秀模型的现象非常引人注目，这些模型来自多家企业，特别是DeepSeek、Quinn和Kimmy等公司。

I don't wanna call timing on this. There will come a time when both of those things become gluts. And so I don't know that we can plan for that. Although I would just say the following. Number one, researcher engineer side of things, it is striking to the degree to which there are excellent, you know, outstanding models coming out of China now in, you know, in in in a for multiple companies and, you know, specifically, you know, DeepSeek and and Quinn and and Kimmy.

令人惊讶的是，开发这些模型的团队大多并非知名品牌。基本上，这些都不是那些论文署名的大牌学者。这说明中国已经成功破解了如何培养年轻人才进入这个领域的密码。

It is striking how the teams that are making those are not, you know, the name brand. You know, for the most part, these are not like the name brand people with their names on all the papers. And and so, like, China is successfully decoding how to, like, basically take young people and train them up in the field.

其实xAI在很大程度上也是如此。

Well, in x AI to a large extent too.

没错。所以我认为未来会...听着，在某个阶段这完全合理——短期内这会是极其专业的技能，人们会为此付出高昂代价。但毫无疑问，相关知识正在快速传播普及。

Yeah. Yeah. And so I I think that I think there's gonna be and, look, it makes sense up until it it it makes sense that for a while, it's gonna be the super esoteric skill set, and people are gonna pay through the nose for it. But, like, you know, there's no question. The information is, right, being transferred into the environment.

人们正在学习如何做这件事。你知道，大学生们正在摸索。所以，你知道，存在这样的情况——我不认为未来会出现人才过剩本身，但我确信未来会有更多人当然懂得如何构建这些东西。然后顺便说一句，当然也懂得用AI来构建AI。对吧？

People are learning how to do this. You know, college kids are figuring it out. And so, you know, there's there's and I don't know that there's ever gonna be a talent glut per se, but, like, I I think for sure there's gonna there's gonna be a lot more people in the future who, of course, know know how to build these things. And then and then, by the way, also, of course, know, AI building AI. Right?

所以工具本身会越来越擅长助力这个过程。我认为这是好事，因为当前工程师和研究人员的短缺程度确实限制太大。而在芯片方面——我不是芯片专家也不想具体点名——但芯片行业从未出现过长期短缺，每次短缺最终都导致过剩，因为短缺带来的利润池和利润空间过大，会吸引其他人进场将功能商品化。英伟达虽然拥有芯片史上最强势的地位，但我仍难以相信五年后基础设施还会承受这种压力。

So the the the the the tools themselves are gonna be better better at contributing to that. And so and and I think that think this is good because I think that, you know, the current level of of shortage of of engineers and researchers is is is too constraining. And then and then on the chip side, I don't I don't wanna I'm not a chip guy, and I don't wanna call call it specifically, but, like, it it's never been the case. It's never been the case in the chip industry that there's ever know, every every shortage in the chip industry has always resulted in a glut because the the the profit the profit pool of a shortage, the the margins get too big, the incentive for other people to come in and figure out how to commoditize the function get too big. And so, you know, NVIDIA has, like, know, the best position probably anybody's ever had in chips, but notwithstanding that, I I find it hard to believe that there's gonna be this level of pressure on infrastructure in five years.

没错。即使基础设施的瓶颈转移——比如变成电力、散热或其他问题——届时芯片肯定会过剩。

Yeah. I mean, even if the bottleneck within the infrastructure moves. So if it becomes power, if it becomes cooling or anything else, then you'll have a chip glut for sure. Yeah.

对。我想说的是：五年后我们面临的挑战很可能是完全不同的挑战。

Right. So I think over the I would just say this. It's likely the challenges that we all have in five years from now are gonna be different challenges. Yeah.

确实。在所有行业中，这个行业最不能用静态眼光看待。要知道，局势可能瞬息万变。

Yeah. Yeah. Like, don't don't definitely, this industry of all industries, don't look at us as static. Like, you know, positions could change very, very fast.

让我们以更宏观的视角收尾。马克，你提到中国——上个月我们在华盛顿，参议员最关注的问题之一是如何理解当前中美AI竞赛的态势？能否简要分享你当时的观点？

Let's actually close on more of this macro note. Mark Mark, you mentioned China. Last month, we were in DC, and one one of the big questions the senator has is how should we make sense of sort of the state of the AI race vis a vis China? Do you wanna share just the the high level summary of what what you shared with them?

我的观察是：当前像深度求索、清源等中国模型涌现的情况下，西方特别是美国仍在概念创新上领先——那些重大概念突破多源自美国/西方。中国极其擅长吸收创意并快速实现、规模化、商品化，这在其制造业已得到验证，现在AI领域也做得非常成功。可以说他们正在完美执行'追赶游戏'。

Yeah. So my sense of things, and I and I think the current I think the current if you just observe currently specifically like deep sea quenic chimney and and these models coming out of China, my my sense basically is like, I would say The US specifically in the West generally, but, know, more and more specifically The US is, like, the conceptual innovations are are, you know, have have been, you know, coming out coming out of coming out of The US, coming out of the West, you know, kind of the the big kind of conceptual breakthroughs. China is extremely good at picking up ideas and implementing them and and scaling them and and commoditizing them. And, you know, that's they do that obviously throughout the manufacturing world, and and they're doing it, but now very, I think, successfully sort of in AI. And so I would say they're they're running they're running the ketchup game, like, really well.

你知道吗？总会有这样一个问题：有多少成就是真正通过努力和聪明才智获得的，又有多少是借助了一点外力。比如深夜里的一个U盘，你懂的，那种帮助。所以总是存在一些疑问。但无论如何，他们确实做得非常出色。

You know? And then there's there's sort of always this question of, like, how much of that is, like, being done, let's just say, like, authentically, you know, through hard work and smart people, and then how much is being done with maybe a little bit of help. Maybe a little USB stick in the middle of the night, you know, kinda help. So, you know, there's always a little bit of question. But, like, either either way, you know, they're they're they're doing a great job.

显然，他们的抱负不止于此，中国有许多非常聪明且富有创造力的人才。现在值得关注的是，概念性突破会在多大程度上从那里涌现，以及他们是否会领先。我们在华盛顿对人们说的是：看，这已经是一场全面的竞赛，一场短跑比赛。

Obviously, they they aspire to, you know, more than that, and there are many very smart and creative people in China. And so, you know, it will be interesting now to see, you know, the level to which the conceptual breakthroughs start to come from there and whether they whether they pull ahead. And so but, like, I would say, like, what we tell people in Washington is like, look. This this is a foot this is now this is a full on race. It's a foot race.

这是一场寸土必争的游戏。我们不会有五年的领先优势，可能只有六个月。我们必须加速奔跑，必须赢得比赛。

It's a game of inches. Like, we're not gonna have a five year lead. We're gonna have, like, maybe a six month lead. Like, we have to run fast. We have to win.

我们必须这么做。我们不能给自己的公司施加那些中国政府没有施加给本国企业的限制，否则我们只会输掉。你愿意醒来生活在一个由中国AI主导的世界吗？大多数人会说不。

Like, we we have to we have to do this. We we can't and then we can't put constraints on our companies that the the the Chinese government isn't putting on their own companies. And so, you know, we'll just lose. And, you know, do do you really want do you really wanna wake up in the morning and live in a world, you know, really controlled and run by Chinese AI? Most of us would say, no.

我们不愿生活在那样一个世界。对此我持谨慎乐观态度，因为我们在软件领域确实很强。但一旦AI以机器人形态具身化，情况就会变得可怕得多。这正是我在华盛顿努力向人们阐述的问题：过去四十年美国与西方选择去工业化，而中国已建立起涵盖机械、电子、半导体及各类软件设备的庞大工业生态。

We we don't wanna live in that world. And so so so that so there's that. And I would say I feel moderately good about that just because I think I think we're we're really good at software. You know, the minute this goes into, you know, embodied AI in the form of robotics, I think things get a lot scarier. And this is the thing I'm now spending time in DC trying to really educate people on, which is because The US and the West have chosen to de industrialize to the extent that we have over the last forty years, China specifically now has this giant industrial ecosystem for building mechanical, electrical, and semiconductor and now software devices of all kinds including phones and drones and cars and robots.

AI革命将迎来第二阶段：机器人技术。这个转折会很快到来。届时即便美国在软件上保持领先，机器人的制造仍是难题，这不是单靠几家公司就能完成的。

And so there's gonna be a phase two to the AI revolution. It's gonna be robotics. It's gonna happen pretty quickly here, I think. And when it does, like even if The US stays ahead in software, like the robots gotta get built and that's not an easy thing. And it's not just like a company that does that.

这需要整个生态系统支撑。就像汽车工业不只是三家车企，而是成千上万的零部件供应商；飞机制造、计算机产业也是如此。机器人领域也将遵循同样的规律。

It's gotta be an entire ecosystem. And it's gonna be you know, it's gonna be know, like, I mean, you know, the car industry was not three car companies. It was thousands and thousands of of of component suppliers building all the parts. And it's been the same thing for airplanes, the same thing for computers and everything else. It's gonna be the same thing for robotics.

要知道，今天坐在这里默认这一切都将在中国发生。即便他们在软件领域永远无法真正赶上我们，也可能会在硬件领域超越我们，事情就会这样发展。好消息是，我认为美国政界正逐渐形成一种共识——去工业化确实走得太远了。人们越来越渴望找到逆转这一趋势的方法。可以说，我对此持谨慎乐观态度，认为我们会取得进展，但仍有大量工作要做。

And, you know, by default sitting here today, that's all gonna happen in China. And so even if they never quite catch us in software, they might just like lap us in hardware and that'll be that. The good news is I think there's a growing awareness in there's a growing awareness, I would say, across the political spectrum in The US that like deindustrialization went too far. And there's a growing desire to kinda figure out how to reverse that. And, you know, I'd say I'm guardedly optimistic that we'll be making progress on that, but I think there's a lot of work to be done.

在这个动员号召下，我们结束会议吧。谢谢马克和本。最后，我想欢迎

On that call to arms, let's wrap. Thank you, Mark and Ben. To to wrap up, I'd like to welcome

谢谢。

Thank you.

抱歉

Sorry about the

谢谢大家。

Thank you, everybody.

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As a reminder, the content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a sixteen z fund. Please note that a sixteen z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a 16z.com/disclosures.