Marc Andreessen: The Real AI Boom Hasn't Even Started Yet

If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy.

如果没有 AI，我们现在会对经济接下来会发生什么感到恐慌。

We've actually been in a regime for 50 years of very slow technological change in the face of declining population growth.

过去 50 年，在人口增长放缓的背景下，我们其实一直处在技术变化非常缓慢的状态。

The timing has worked out miraculously well.

这个时机简直好得像奇迹。

We're going to have AI and robots precisely when we actually need them.

我们会在真正需要 AI 和机器人时，恰好拥有它们。

The remaining human workers are going to be at a premium, not at a discount.

剩下的人类劳动者会变得更抢手，而不是被打折看待。

How big of a deal is the moment in time that we are living through right now?

我们现在正在经历的这个时刻，到底有多重要？

This is a very, very historic time.

这是一个非常非常具有历史意义的时代。

AI is the philosopher stone.

AI 就是哲人石。

Now, we have a technology that transfers the most common thing in the world, which is sand, converted into the most rare thing in the world which is thought.

现在，我们有了一种技术，能把世界上最常见的东西，也就是沙子，转化成世界上最稀缺的东西，也就是思想。

Just spent a lot of time with the most cutting edge AI forward founders.

我刚花了很多时间和最前沿、最 AI-first 的创始人在一起。

The most leading edge founders are thinking of, can you have entire companies where the founder does everything?

最前沿的创始人在思考的是，能不能有一种公司，创始人一个人就做完所有事？

There's all this concern that young people, jobs are not going to be there for them, AI is replacing them.

现在大家都很担心，年轻人以后没有工作，AI 会取代他们。

Everybody wants to talk about job loss, but really, what you want to look at is task loss.

所有人都想谈失业，但真正该看的其实是任务消失。

The job persists longer than the individual tasks.

岗位会比其中的单个任务存在得更久。

What's your sense of just the future of three very specific roles, product manager, engineer, designer?

你怎么看三个非常具体角色的未来：产品经理、工程师、设计师？

There's like a Mexican standoff happening between those three roles.

这三个角色之间有点像在三方僵持。

Every coder now believes they can also be a product manager and a designer because they have AI.

现在每个程序员都相信，因为有了 AI，自己也能做产品经理和设计师。

Every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder.

每个产品经理都觉得自己能写代码、做设计，而每个设计师也知道自己能做产品经理和程序员。

They're actually all kind of correct.

他们其实某种程度上都没错。

What happens is, the additive effect of being good at two things is more than double.

结果是，擅长两件事的叠加效果不只是翻倍。

The additive effect of being good at three things is more than triple.

擅长三件事的叠加效果也不只是三倍。

You become a super relevant specialist in the combination of the domains.

你会成为这些领域组合中的超相关专家。

People aren't fully grasping how much this is changing.

人们还没有完全理解这件事正在带来多大的变化。

People who really want to improve themselves and develop their career should be spending every spare hour, in my view, at this point, talking to AI, being like, "All right, train me up."

在我看来，现在真正想提升自己、发展职业的人，应该把每一个空闲小时都用来和 AI 对话，比如说：“好，训练我吧。”

Today, my guest is Marc Andreessen, one of the most seminal figures in tech and in business.

今天，我的嘉宾是 Marc Andreessen，科技和商业领域最有开创性的人物之一。

He invented the web browser, built the world's largest venture firm.

他发明了网页浏览器，创办了世界上最大的风险投资公司。

He's also a multi-time founder and an investor in essentially every generational tech company, and is also one of the most clear-minded, lateral, and insightful thinkers about both the past and the future of technology.

他也是多次创业者，几乎投资了每一家划时代的科技公司，同时也是少数能非常清晰、横向且深刻地思考技术过去与未来的人。

In this very special conversation, we chat about how unique and significant the moment that we are all living through right now is, what skills he's teaching his kids to thrive in the AI future, what happens to product managers, designers, and engineers in the coming years, where moats exist in AI, what the most AI native founders are doing differently, and so much more that is just scratching the surface of this very deep and important conversation.

在这场非常特别的对话里，我们聊了很多：我们当下共同经历的这个时刻有多独特、多重要；他正在教孩子哪些技能，以便在 AI 未来中蓬勃发展；未来几年产品经理、设计师和工程师会发生什么；AI 里的护城河在哪里；最 AI-native 的创始人有什么不同做法；还有更多内容，而这些只是这场深刻且重要对话的冰山一角。

You are going to walk away from this chat being smarter about what is going on in the world right now and where things are heading.

听完这场对话，你会更理解当下世界正在发生什么，以及事情将走向哪里。

A huge thank you to my newsletter community and folks on X for suggesting topics and questions for this conversation.

非常感谢我的 newsletter 社区和 X 上的朋友们，为这次对话提供话题和问题建议。

If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

如果你喜欢这档 podcast，别忘了在你常用的 podcast app 或 YouTube 上订阅和关注。

It helps tremendously.

这会有很大帮助。

And if you become an insider subscriber of my newsletter, you get a year free of over 20 incredible products, including a year free of Lovable, Replit, Bolt, Gamma, Innate and Linear, Superhuman, Dev and PostHog, Descript, Wspr Flow, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chappy, RD, MOB and Stripe Atlas.

如果你成为我 newsletter 的 insider 订阅者，你还能免费使用 20 多款很棒的产品一年，包括 Lovable、Replit、Bolt、Gamma、Innate 和 Linear、Superhuman、Dev 和 PostHog、Descript、Wspr Flow、Perplexity、Warp、Granola、Magic Patterns、Raycast、Chappy、RD、MOB 和 Stripe Atlas 的一年免费权益。

Head on over to lennysnewsletter.com and click Product Pass.

请前往 lennysnewsletter.com，点击 Product Pass。

With that, I bring you Marc Andreessen after a short word from our sponsors.

接下来，在一小段赞助商信息之后，请听我和 Marc Andreessen 的对话。

Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers.

本期节目由 DX 赞助，DX 是由顶尖研究人员打造的开发者智能平台。

To thrive in the AI era, organizations need to adapt quickly, but many organization leaders struggle to answer pressing questions like, which tools are working?

要在 AI 时代取得成功，组织需要快速适应，但很多组织领导者很难回答一些紧迫问题，比如哪些工具真的有效？

How are they being used?

它们是如何被使用的？

What's actually driving value?

真正创造价值的是什么？

DX provides the data and insights that leaders need to navigate this shift.

DX 提供领导者需要的数据和洞察，帮助他们应对这场转变。

With DX, companies like Dropbox, Booking.com, Adient, and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity.

借助 DX，Dropbox、Booking.com、Adient 和 Intercom 等公司能够深入了解 AI 如何为开发者创造价值，以及 AI 对工程生产力产生了什么影响。

To learn more, visit DX's website at getdx.com/lenny.

想了解更多，请访问 DX 网站 getdx.com/lenny。

That's getdx.com/lenny.

网址是 getdx.com/lenny。

If you're a founder, the hardest part of starting a company isn't having the idea, it's scaling the business without getting buried in back office work.

如果你是创始人，创业最难的部分不是有想法，而是在不被后台运营工作淹没的情况下把业务规模做起来。

That's where Brex comes in.

这正是 Brex 能帮上忙的地方。

Brex is the intelligent finance platform for founders.

Brex 是面向创始人的智能财务平台。

With Brex, you get high limit corporate cards, easy banking, high yield treasury, plus a team of AI agents that handle manual finance tasks for you.

使用 Brex，你可以获得高额度公司卡、便捷银行服务、高收益现金管理，以及一组能替你处理手动财务任务的 AI agents。

They'll do all the stuff that you don't want to do, like file your expenses, scour transactions for waste, and run reports all according to your rules.

它们会处理所有你不想做的事情，比如提交报销、扫描交易中的浪费，并按照你的规则生成报表。

With Brex's AI agents, you can move faster while staying in full control.

借助 Brex 的 AI agents，你可以更快行动，同时保持完全掌控。

One in three startups in the United States already runs on Brex.

在美国，每三家 startup 中就有一家已经在使用 Brex。

You can too at brex.com.

你也可以访问 brex.com 开始使用。

Marc Andreessen, thank you so much for being here and welcome to the podcast.

Marc Andreessen，非常感谢你来到这里，欢迎做客本节目。

Awesome, Lenny.

太好了，Lenny。

Thank you.

谢谢。

It's great to be here.

很高兴来到这里。

I want to start with just a big picture question.

我想先从一个大问题开始。

I have a billion directions I want to go, but I think this is going to give us a little bit of a frame of reference.

我有无数个方向想聊，但我觉得这个问题能先给我们一个参照框架。

How big of a deal is the moment in time that we are living through right now?

我们现在正在经历的这个时刻，到底有多重要？

This is a very, very historic time.

这是一个非常非常具有历史意义的时代。

I think 2025 was maybe the most interesting year in my entire career and probably life, and I think I would expect 2026 to exceed that.

我觉得 2025 年可能是我整个职业生涯，甚至可能是我一生中最有意思的一年，而且我预计 2026 年会超过它。

Wow, that says a lot.

哇，这分量很重。

Yeah, I've seen some stuff.

是啊，我也算见过一些事了。

So it feels like two things are happening.

所以感觉像是有两件事正在发生。

One is, the trust that a lot of people have had and kind of what you described as kind of legacy institutions around the world is, I think, in kind of full-scale collapse right now.

第一，很多人对你刚才所说的那种全球 legacy institutions 的信任，我认为现在正在全面崩塌。

By the way, there's a lot of data to support that.

顺便说一句，有很多数据支持这一点。

And so I think there's a lot of structures, orders, and institutions that people have just relied on for a long time that have just proven to not be up for the challenge.

所以我觉得，人们长期依赖的很多结构、秩序和机构，已经证明无法应对眼前的挑战。

And then kind of corresponding with that is, the national and global conversation have become, let's say, liberated.

与此相对应的是，国家和全球层面的对话，可以说被释放出来了。

And so this sort of incredible revolution that we have in what I've described as freedom of speech, freedom of thought, ability for people to openly discuss things that maybe they couldn't discuss even a few years ago has just dramatically expanded.

所以我们正在经历一场不可思议的革命，也就是我所说的言论自由、思想自由，以及人们公开讨论事情的能力。那些也许几年前还不能讨论的事，现在讨论空间已经大幅扩大。

And I think that's now on a one way train for just a much broader range of discourse.

我认为这已经是一列单向列车，会驶向更广泛得多的公共讨论。

And then there's also just these incredibly massive geopolitical shifts that are happening.

另外，还有一些极其巨大的地缘政治变化正在发生。

And obviously, the US is changing a lot, Europe is changing a lot, China's changing a lot.

显然，美国正在发生很大变化，欧洲正在发生很大变化，中国也在发生很大变化。

Latin America, by the way, is changing a lot.

顺便说一句，拉丁美洲也在发生很大变化。

Very dramatic events playing out down there right now.

那里现在正在上演非常剧烈的事件。

Kind of all over the world, I think a lot of assumptions are being pulled out into the daylight and reexamined.

放眼全球，我认为很多假设都正在被摆到阳光下重新审视。

And then it's kind of the fact that all these things are happening at the same time.

然后关键在于，所有这些事都在同一时间发生。

And so you've got all of these countries and industries where things are kind of increasingly upheaval, but you have AI as this kind of new technology that's going to really affect things.

所以你看到这么多国家和行业都越来越动荡，同时 AI 作为一种新技术，也将真正影响一切。

And then you've got people, citizens being able to fully participate and being able to argue things out.

与此同时，人们和公民也能够充分参与，并把事情争论清楚。

And so it's kind of like those three big mega things are all colliding at the same time.

所以就像这三件超级大事正在同一时间碰撞。

And I think we're probably just at the very beginning of all three of those.

而且我觉得我们可能才刚刚处在这三件事的起点。

And those all feel like historical moment shifts, comparable in magnitude to maybe default the Berlin Wall in 1989, maybe the end of World War II, kind of moments like that.

这些都感觉像历史性时刻的转折，规模或许可以类比 1989 年柏林墙倒塌，或者二战结束这样的时刻。

It certainly feels like that.

至少感觉确实如此。

Good God.

天哪。

What a time to be alive.

真是一个活着见证历史的时代。

Yeah.

是啊。

In terms of the AI piece, which is where a lot of people are trying to figure out what to do, what do you think isn't being priced in yet in terms of the impact AI is going to have on, say, the world or just people listening?

就 AI 这部分而言，很多人都在努力弄清楚该怎么办。你觉得 AI 将对世界，或者对正在收听的人产生的影响里，还有哪些没有被充分计入预期？

I think, at this point, it's pretty clear, with our technology hats on, that this stuff is really working now.

我觉得现在，从技术视角看，已经相当清楚了：这些东西真的开始有效了。

There was a ChatGPT moment three years ago.

三年前出现了 ChatGPT 时刻。

By the way, only three years ago, was the ChatGPT moment.

顺便说一句，ChatGPT 时刻也才三年前。

And the big question was, all right, this is incredibly fun and creative.

当时最大的问题是，好吧，这东西非常有趣，也很有创造力。

And we have machines now that can compose Shakespearean sonnets and rap lyrics, and this is amazing.

现在我们有了能写莎士比亚十四行诗和 rap lyrics 的机器，这太惊人了。

But then there was this big question, can you harness this technology for reasoning and for problem solving in domains that really matter, medicine, science, law and so forth?

但随后出现了一个大问题：你能不能把这项技术用于推理，以及用于医学、科学、法律等真正重要领域的问题解决？

And it turns out the answer to that is yes.

结果证明，答案是可以。

And the last 12 months, and especially even just the last three months have really proven that AI can really do... I mean, you're seeing it all now.

过去 12 个月，尤其甚至只是过去 3 个月，已经真正证明 AI 确实能做到……我是说，你现在已经都看到了。

AI is now developing new math theorems.

AI 现在正在发展新的数学定理。

Over the holiday break, it feels like the AI coding thing really hit critical mass and the world's best programmers, including Linus Torvalds, for the first time over the holiday break basically said, "Yeah, AI is now coding better than we can."

在假期期间，AI 编程这件事感觉真正达到了临界规模，世界上最优秀的程序员，包括 Linus Torvalds，第一次在假期期间基本上说：“是的，AI 现在写代码已经比我们更好了。”

And so that's incredibly powerful.

所以这非常强大。

And I think we all assume that AI now is going to get really good at reasoning in any domain in which there are verifiable answers.

我想我们现在都默认，AI 会在任何有可验证答案的领域里变得非常擅长推理。

And so that's going to include many very important domains.

这将包括许多非常重要的领域。

So the technology feels like it's moving fast and it's going to be working really well.

所以这项技术感觉发展很快，而且会运作得非常好。

I think the thing that is not well understood, I think a lot of people in the industry have kind of what I would describe as this one dimensional thing, which is, okay, as a result of the technology now working, AI just kind of sweeps the world and changes everything.

但我认为还没有被很好理解的是，行业里很多人有一种我会称为一维的想法：好，既然技术现在有效了，AI 就会席卷世界、改变一切。

And I think that's kind of the wrong framer.

我觉得这其实是错误的 framing。

I think it's based on an incomplete understanding of the world that we live in or the world that we've been living in for the last 80 years.

它基于一种不完整的理解，没有完整理解我们所生活的世界，或者说过去 80 年我们一直生活其中的世界。

And I would call out two things in particular.

我尤其想指出两点。

So one is, I think it's felt to us, in the US and the West, for the last whatever, 30 years or 50 years, it's felt like we've been in a time of great technological change.

第一，我觉得对美国和西方的我们来说，过去不管是 30 年还是 50 年，都感觉像是一个技术巨变的时代。

But actually, if you look for actually evidence of that, in statistical evidence of that, analytical evidence of that, you basically can't find it.

但实际上，如果你去找真正的证据，统计证据、分析证据，基本上找不到。

And in particular, economists have a way of measuring the rate of technological change in the economy that is productivity growth, which we could talk about what that means, but basically, it's sort of the mathematical expression of the impact of technology on the economy.

特别是，经济学家有一种衡量经济中技术变化速度的方法，叫生产率增长。我们可以展开讲它是什么意思，但基本上，它是技术对经济影响的一种数学表达。

And productivity growth for the last 50 years has actually been very low, not very high.

而过去 50 年的生产率增长其实一直很低，并不高。

So we all feel like it's been very high.

所以我们都觉得它很高。

There's been lots of technological change.

确实发生了很多技术变化。

What's actually happening is, it's been very low.

但实际发生的是，它一直很低。

And in fact, the pace of productivity growth, like in the US, is running at a half of what it... In my lifetime, in our lifetimes, it's been running at about half the pace that it ran between 1940 and 1970, and it's been running at about a third the pace that it ran between about 1870 to about 1940.

事实上，生产率增长的速度，比如在美国，只有过去的……在我这一生、我们这一代人的一生里，大概只有 1940 到 1970 年那段时期的一半，也只有大约 1870 到 1940 年那段时期的三分之一。

And so statistically, in the US, in the West, technology progress in the economy, technology impact in the economy has actually slowed way down.

所以从统计上看，在美国、在西方，经济中的技术进步、技术影响其实已经大幅放缓。

And so the AI thing is going to hit, but it's hitting an environment in which we have actually had almost no technological progress in the actual economy for a very long time.

所以 AI 会到来，但它进入的是这样一个环境：在真实经济里，我们已经很长时间几乎没有技术进步了。

So we could talk about that.

这个我们可以展开聊。

And then there's this other just incredible thing that's happening, which is the demographic collapse, it's sort of a Western phenomenon and increasingly global phenomenon, which is the rate of reproduction of the human species is in rapid decline.

然后还有另一件非常不可思议的事情正在发生，就是人口结构崩塌。它有点像西方现象，也越来越成为全球现象，也就是人类的繁殖率正在快速下降。

And there are many countries, including the US, where the rate of reproduction is under two, meaning that many, many countries around the world, by the way, including China, which is a really big deal, are actually going to depopulate over the next century.

很多国家，包括美国，繁殖率都低于 2，这意味着世界上很多很多国家，顺便说一句也包括中国，这是一件大事，在接下来一个世纪里实际上都会人口减少。

And so you have this kind of precondition that says there's actually been very little technological progress happening in the world and the world is going to depopulate.

所以有这样一个前提：世界上其实已经很少有技术进步在发生，而且世界人口将会减少。

And so AI is going to enter a world in which those two things are true.

AI 将进入一个这两件事都成立的世界。

And I think this is incredibly important because we actually need AI to work in order to get productivity growth up, which is what we need to get economic growth up.

我觉得这极其重要，因为我们确实需要 AI 发挥作用，才能提高生产率增长，而这是我们提高经济增长所需要的。

And we actually need AI to work because we're going to need machines to do all the jobs that we're not going to have people to do because we're literally going to depopulate the planet over the next 100 years.

我们也确实需要 AI 发挥作用，因为我们会需要机器去做那些没有人手去做的工作，因为未来 100 年里，地球人口真的会减少。

And so I think the interplay of these factors is going to be much more interesting, and frankly, more complex than a lot of people have been thinking.

所以我认为这些因素之间的相互作用会比很多人想的更有意思，坦率说也更复杂。

I'm going to follow this thread about kids.

我想顺着孩子这个话题继续问。

I know you have a kid, and my favorite lenses into how people think and what they value is what they're teaching their kids, what they're steering their kids towards.

我知道你有一个孩子，而我最喜欢用来理解别人怎么思考、看重什么的视角，就是看他们在教自己的孩子什么、把孩子引向哪里。

Are there specific skills or even careers that you're steering your kid towards?

有没有一些具体技能，甚至职业方向，是你在引导孩子走向的？

The way I think about this, we have a 10-year-old, we actually homeschool and so we think a lot about this.

我是这么想的，我们有一个 10 岁的孩子，而且我们其实是在家教育，所以我们对这件事想得很多。

So I think the way to think about the impact of AI on, specifically, people as individuals, it's actually, a lot of people just focus on this kind of very, I would say, straightforward or overly simplistic view of just literally job losses, which we can talk about.

我认为，要理解 AI 对个人，尤其是作为个体的人，会产生什么影响，很多人只是盯着一种非常直接，或者说过于简单的看法，也就是字面意义上的失业，这个我们可以聊。

But there's two specific things at the level of an individual person or an individual kid.

但在一个个体、一个孩子的层面上，有两件具体的事。

So I think it's pretty clear that AI is going to take people who are good at doing things and it's going to make them very good at doing things.

我觉得很明显，AI 会让原本擅长做事的人，变得非常擅长做事。

And so it's going to be a tool that's going to raise the average across the board.

所以它会是一种把整体平均水平都抬高的工具。

And look, you see that playing out already.

而且你现在已经能看到这一点在发生了。

Anybody who's in a position where they need to write something, design something, write code or whatever, if they're pretty good at it today, they use AI and all of a sudden they're very good at it.

任何需要写东西、做设计、写代码或类似工作的人，如果他今天已经做得不错，用上 AI 之后，突然就会变得非常厉害。

And so there's sort of that aspect to it.

所以这是其中一个方面。

And I think the way the education system at large is going to teach AI is going to be based hopefully a lot on that.

我觉得整个教育系统将来教 AI 的方式，希望会很大程度上基于这一点。

But then there's this other thing that's happening, which we're also starting to see, and we're really seeing it particularly in coding right now, where the really great people are becoming spectacularly great.

但还有另一件事也在发生，而且我们现在也开始看到了，尤其是在编程领域：真正顶尖的人正在变得极其顶尖。

And so you kind of use the term, you think about the super empowered individual.

你可以用这个词来理解，就是超级赋能的个体。

So the individual who is really good at coding, really good at making movies, really good at making songs, really good at making art, or whatever those things are, or podcasting or hopefully venture capital.

也就是那些真的很会写代码、很会拍电影、很会做音乐、很会做艺术，或者做其他事情的人，比如 podcasting，或者希望也包括 venture capital。

If you're very good at it and you can really harness AI, you can become spectacularly great and super productive.

如果你已经非常擅长，并且真的能驾驭 AI，你就能变得极其出色、极其高产。

I'm sure you have a lot of friends in this category as well, but the really, really good coders are experiencing this right now.

我相信你也有很多朋友属于这一类，但那些真正非常厉害的程序员现在正在经历这个。

My friends who are really good coders are like, "Oh my God, all of a sudden, I'm not twice as good as I used to be, I'm 10 times as good as I used to be."

我那些很会写代码的朋友都在说：“天啊，突然之间，我不是比以前强 2 倍，而是比以前强 10 倍。”

And so I think, at the unit of N=1 of an individual kid, I think the question is, how do you get them into position where they're kind of this super empowered individual such that they're going to be really kind of deep in whatever it is they're going to do, but they're going to be deep in a way that's going to let them fully use the power of AI to be not just great, but to be spectacularly great?

所以我觉得，在 N=1 的单个孩子这个单位上，问题是：你怎么让他们进入这样一种位置，成为这种超级赋能的个体，让他们无论将来做什么，都能钻得很深，而且是以一种能充分利用 AI 力量的方式去钻深，不只是优秀，而是极其优秀。

I think that's the real opportunity, and at least that's what we're shooting for and that's what I would encourage parents to shoot for.

我认为这才是真正的机会，至少这是我们的目标，也是我会鼓励父母们追求的方向。

So when I heard there is essentially agency, this word that we see on Twitter all the time is building agency, them not waiting for someone to tell them what to do, figuring out what to do.

所以我听到的核心其实是 agency，这个词我们在 Twitter 上经常看到，就是培养 agency，让他们不要等别人告诉他们该做什么，而是自己搞清楚该做什么。

Yeah.

对。

Yeah.

对。

So this term agency that's become very, very, very popular, certainly in California for the last couple of years, it's really interesting because I had a lot of trouble with this early on, because I'm like, "Agency?

agency 这个词过去几年在加州当然变得非常非常流行，这很有意思，因为一开始我对它很困惑。我当时想：“agency？”

Okay, what are they talking about?"

“好吧，他们到底在说什么？”

And what they're kind of talking about is initiative, you could just do things.

他们说的其实有点像主动性，就是你可以直接去做事。

What is it?

那是什么呢？

The [inaudible 00:14:25] has the great term, live player, you can be a primary participant in events.

[听不清 00:14:25] 有一个很棒的说法，live player，也就是你可以成为事件中的主要参与者。

And at first, I was like, "Well, yeah, that's kind of obvious, of course."

一开始我想：“嗯，对啊，这有点显而易见，当然了。”

And then I'm like, "Oh, actually, it's not so obvious anymore."

然后我又想：“哦，其实现在已经没那么显而易见了。”

Because to your point, I think so much of our society is based on, there are all these rules and everybody gets taught kind of by default, you're supposed to follow all these rules.

因为就像你说的，我觉得我们的社会很大程度上建立在各种规则之上，而且每个人默认都会被教育成，你应该遵守所有这些规则。

And then if you break the rules, everybody gets freaked out.

然后如果你打破规则，所有人都会紧张起来。

It's like, "Oh my God, he broke the rules."

就像：“天啊，他破坏规则了。”

And so we have somehow worked our way kind of psychologically, sociologically into a state in which I guess the natural assumption for a lot of people is the thing that you... For example, the thing you want to train kids to do is follow all the rules.

所以我们不知怎么在心理上、社会学上进入了这样一种状态：对很多人来说，默认假设似乎是，你要做的事……比如，你应该训练孩子做的事，就是遵守所有规则。

And you could argue that, for example, K through 12 school system or whatever has gotten more and more focused on that over time.

你甚至可以说，比如 K-12 学校系统之类的东西，随着时间推移越来越专注于这一点。

And again, especially unit N=1 of your kid, it's like... And look, there's something to be had.

再说一次，尤其是对你自己孩子这个 N=1 单位来说，这就像……而且说实话，这里面也有价值。

I just had this conversation with my 10-year-old last night, actually, I rolled out the concept of, in order to lead, you must first learn to obey.

实际上我昨晚刚和我 10 岁的孩子聊过这个，我给他讲了一个概念：要想领导别人，必须先学会服从。

In order to issue orders, you must learn how to follow orders and trying to keep him with some level of structure in his life, and not just pure agency.

要想发号施令，你必须先学会听从命令。我是在努力让他的生活里保留一定程度的结构，而不是只有纯粹的 agency。

But yeah, and so look, some rules are important and so forth.

所以是的，有些规则很重要，等等。

But yeah, no, look, there's just a huge premium in life on being somebody who is able to fully take responsibility for things, fully take charge, run an organization, lead a project, create something new.

但确实，人生中有一种巨大溢价，给那些能够完全承担责任、完全掌控局面、运营组织、领导项目、创造新事物的人。

And maybe that has been maybe a little bit diminished in our culture over the last 30 years.

也许在过去 30 年里，这一点在我们的文化中有点被削弱了。

It's healthy that there's now a term for that that is coming back into vogue.

现在有一个词重新流行起来描述它，我觉得这是健康的。

And again, that's how I view AI for kids is like, okay, AI should be the ultimate lever on the world for a kid with agency to be able to say, "Okay, I can actually be a primary contributor, whether that's I can be a primary contributor in everything from developing new areas of physics to writing code, to being an artist, to writing novels, whatever that thing is, I can fully participate in the world.

再说一次，我就是这样看待 AI 对孩子的意义：AI 应该成为一个有 agency 的孩子影响世界的终极杠杆，让他说：“好，我真的可以成为主要贡献者，无论是开辟物理学的新领域，还是写代码、做艺术家、写小说，或者任何事情，我都可以充分参与这个世界。

I can really change things."

我真的可以改变事情。”

And the combination of that idea combined with this technology feels very healthy to me.

这个想法和这项技术结合在一起，在我看来非常健康。

What is that quote about, "Give me a lever and I'll move the world"?

那句名言怎么说来着，“给我一根杠杆，我就能撬动世界”？

And I'll move the world.

我就能撬动世界。

Yeah, that's exactly right.

对，完全没错。

Well, so it's actually funny you mentioned that.

嗯，所以你提到这个其实很有意思。

So the early scientists, including Isaac Newton, were super obsessed with this concept of alchemy.

早期科学家，包括 Isaac Newton，都非常痴迷于炼金术这个概念。

Like Newton, he developed Newtonian physics and he developed calculus and all these things.

比如 Newton，他发展了 Newtonian physics，也发展了 calculus 和所有这些东西。

But the thing he was really obsessed with was alchemy, which was the thing he could never get to work.

但他真正痴迷的是炼金术，也就是他一直没能成功的东西。

And alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was lead into something that was very rare and valuable, which was gold.

炼金术是把铅转化为金，也就是把一种很常见的东西，铅，转化成一种很稀有、很有价值的东西，金。

He spent decades trying to figure out this thing called the philosopher stone, which would be basically the machine or the process that would be able to transmute the common thing into the rare thing, lead into gold, and he never figured it out.

他花了几十年试图弄明白一个叫 philosopher stone 的东西，基本上就是能把常见之物转化为稀有之物、把铅变成金的机器或过程，但他从来没有弄出来。

It was incredibly frustrating.

这让人极其沮丧。

Nobody ever figured that out.

也从来没有人真正弄出来。

And now, we literally with AI have a technology that transfers sand into thought.

而现在，我们真的有了 AI 这项技术，可以把沙子转化为思想。

Right?

对吧？

That just blew my mind.

这句话直接把我震住了。

The most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought.

世界上最常见的东西，也就是沙子，被转化成世界上最稀有的东西，也就是思想。

And so AI, it is the philosopher stone.

所以 AI 就是 philosopher stone。

It is that.

它就是那个东西。

It actually is that.

它真的就是那个东西。

And it's just this incredibly powerful tool.

而且它是一个极其强大的工具。

And that's where I get so excited.

这就是让我如此兴奋的地方。

And again, this is what we're doing with our 10-year-old, which is like, all right, primary thing that we want to make sure to do is, to make sure that he knows fully how to leverage and get benefit out of the philosopher stone, which is to say AI.

再说一次，这也是我们正在和 10 岁孩子做的事：好，我们最重要的是要确保他完全知道如何利用 philosopher stone，并从中获益，也就是说 AI。

And then that's certainly central to everything we're teaching him.

这当然也是我们教他的所有东西中的核心。

There's this meme going around that Silicon Valley people don't let their kids use computers.

现在有个 meme 在流传，说 Silicon Valley 的人不让自己的孩子用电脑。

And there may be a handful of people who are like that.

也许确实有少数人是那样。

I don't know.

我不知道。

I think it's more, honestly, the other way around, which is, the more you're plugged into stuff in Silicon Valley, the more important it is to make sure that your kids actually fully understand this and know how to use it.

我真诚地认为，情况更像是反过来：你越深入参与 Silicon Valley 的事情，就越会觉得必须确保你的孩子真正充分理解这些东西，并知道怎么使用它。

And that's certainly the mode that we're in.

这当然就是我们现在的模式。

And that's certainly the mode that I would encourage parents to think about.

这也当然是我会鼓励父母们去思考的模式。

I did not know your kid was homeschooled.

我之前不知道你的孩子是在家教育。

That is super interesting.

这非常有意思。

It's almost a statement on education in today's day.

这几乎是在对当下的教育做一种表态。

Maybe, is there any thoughts there?

也许，你对此有什么想法吗？

And just for folks that maybe aren't in your tax bracket that want to help their kids be successful, maybe homeschooled, maybe not, what advice would you have?

另外，对于那些可能不在你这个收入阶层、但想帮助孩子成功的人来说，无论是在家教育还是不在家教育，你会有什么建议？

This is the challenge.

这就是挑战所在。

And again, this kind of goes to your original question, which is education, there's two completely different ways to think about education.

而且这也回到你最初的问题：教育有两种完全不同的思考方式。

The way that it's usually thought about and talked about is kind of at the level of a nation.

通常人们思考和讨论教育，是在国家这个层面上。

So it's like a national level issue or maybe a state level issue in the US, which is basically, how do you educate all the kids?

也就是说，它像是一个国家层面的问题，或者在美国也可能是州层面的问题，基本上就是：你如何教育所有孩子？

And of course, that's incredibly important.

当然，这极其重要。

And of course, you're going to need some level of large scale system, like the national K through 12 school system or something like that in order to do that.

当然，为了做到这一点，你会需要某种大规模系统，比如全国性的 K-12 学校系统，或类似的东西。

But then there's this other question, which is like, N=1, for an individual kid, what can you do with an individual kid?

但还有另一个问题，也就是在 N=1 的层面上，对于一个具体的孩子，你能做什么？

And so I'll just give you the ultimate answer to that question, which is, it's been known for centuries that the ideal way to teach a kid at the unit of N=1, by far the ideal way to do it is with one-on-one tutoring.

我直接给你这个问题的终极答案：几个世纪以来人们都知道，在 N=1 的单位上教一个孩子，理想方式，而且远远最理想的方式，就是一对一辅导。

If you just have an individual kid and the goal is to maximize an individual kid, by far you get the best results with one-on-one tutoring.

如果你只有一个孩子，目标是让这个孩子最大化发展，那么一对一辅导会带来远远最好的结果。

And this is something that every royal family knew in history.

这是历史上每个王室都知道的事。

It's something that every aristocratic class knew in history.

也是历史上每个贵族阶层都知道的事。

There's all these amazing examples.

有很多很精彩的例子。

Alexander the Great was tutored by Aristotle.

Alexander the Great 曾由 Aristotle 辅导。

He took over the world.

然后他征服了世界。

Many of the great kings, queens, royal families, aristocrats and so forth over the course of centuries kind of always had this approach.

几个世纪以来，许多伟大的国王、女王、王室、贵族等等，基本上一直都采用这种方式。

There's actually also statistical evidence, analytical evidence that this is correct.

事实上，也有统计证据、分析证据表明这是正确的。

There's this massive question in the field of education, which is, how do you improve educational outcomes?

教育领域有一个巨大问题：你如何改善教育成果？

And basically, it turns out it's very hard to improve educational outcomes except there's one method that always does it, which is called the Bloom's 2 Sigma effect, which is there's one method of education that routinely raises student outcomes by two standards of deviation and will take a kid from the 50th percentile to the 99th percentile and that's one-on-one tutoring.

基本上，结果显示，改善教育成果非常困难，只有一种方法总是有效，它叫 Bloom's 2 Sigma effect，也就是有一种教育方法通常能把学生成果提高 2 个标准差，把一个孩子从第 50 百分位带到第 99 百分位，那就是一对一辅导。

So again, if you go back to N=1, you have a kid and a tutor and they're in this very tight loop with each other, where the kid is able to constantly kind of be on the leading edge of what they're capable of doing and they can move incredibly fast and they get kind of correction in real time, you get these better outcomes.

所以再回到 N=1：你有一个孩子和一个导师，他们之间处在非常紧密的反馈循环里，孩子可以不断处在自己能力边界的前沿，进展可以非常快，并且能实时获得纠正，于是就会得到更好的结果。

But to your question, it's never been economically feasible for anybody other than the richest people in society to be able to provide one-on-one tutoring for kids.

但正如你问的，除了社会上最富有的人之外，让孩子接受一对一辅导在经济上从来都不可行。

AI provides the very real prospect of being able to do that, because obviously now, if you have a kid that's super interested in something and they can talk to an LLM about it and they can ask an infinite number of questions and they can get instantaneous feedback.

AI 提供了一个非常现实的可能性，让这件事变得可行。因为很明显，现在如果一个孩子对某件事非常感兴趣，他可以和一个 LLM 聊这件事，可以问无限多的问题，并且得到即时反馈。

And in fact, you can even tell an LLM, it's like, "Teach me how to do the following."

事实上，你甚至可以直接告诉一个 LLM：“教我怎么做下面这件事。”

And you can say, "Wow, I don't quite understand what you're saying.

然后你可以说：“哇，我不太明白你在说什么。

Numb it down for me a little bit."

给我讲简单一点。”

"Okay, now quiz me, do I actually understand this?"

“好，现在考考我，看看我是不是真的理解了？”

People can just do this today.

人们今天就可以这么做。

And so I think there's this massive opportunity for parents in many walks of life, with a little bit of time at focus, to be able to say, " Okay, my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring."

所以我认为，对很多不同生活处境的父母来说，这是一个巨大的机会：只要花一点时间和精力，就可以说，“好，我的孩子可能仍然会接受传统教育，但我要用 AI 辅导来增强它。”

And of course, there's going to be tons of startups, and there already are, that are going to try to build on all the products and services for this.

当然，会有大量创业公司，而且已经有了，试图围绕这件事打造各种产品和服务。

Khan Academy, on the nonprofit side, has a big push to do this.

在非营利领域，Khan Academy 正在大力推进这件事。

And so I think the broad answer might be a hybrid approach with schools plus one-to-one tutoring through AI.

所以我觉得，大方向的答案可能是一种混合模式：学校教育加上通过 AI 实现的一对一辅导。

You may have heard, there's this great new private school system called Alpha, in which everything I just described is kind of the basis of their philosophy, which is, it's a combination of in person schools and teachers, but it's also heavily based on AI and AI tutoring.

你可能听说过，有一个很棒的新私立学校体系叫 Alpha，我刚才描述的一切基本上就是他们理念的基础：它结合了线下学校和老师，但也高度依赖 AI 和 AI 辅导。

And so I think there is a magic formula in here that I think is going to apply much more broadly.

所以我认为这里面有一个神奇公式，而且会更广泛地适用。

And really, for parents interested in this, now it'd be a great time to really start to think hard about that and to look at the options.

对于对此感兴趣的父母来说，现在正是认真思考并看看有哪些选择的好时机。

It's interesting because there's all this concern that young people, jobs are not going to be there for them, AI is replacing them.

这很有意思，因为大家现在都很担心年轻人未来没有工作，AI 会取代他们。

On the flip side, there's what you're describing here.

但另一面，就是你刚才描述的情况。

It feels like people coming in learning today are going to move so fast and learn so much more.

感觉今天开始学习的人会进步得非常快，也会学到多得多的东西。

And where do you sit on this divide of, young people are in big trouble or they're actually going to be the ones winning in the end?

你怎么看这个分歧：年轻人会陷入大麻烦，还是他们最终反而会成为赢家？

Yeah.

是的。

So the job substitution, job loss thing is just, it's very reductive.

所以，岗位替代、失业这个说法其实非常简化。

I think it's an overly simplistic model.

我认为这是一个过度简单化的模型。

And again, it goes back to what I said at the very beginning, which is, we've actually been in a regime for 50 years of very slow technological change in the economy.

这又回到我一开始说的：过去 50 年里，我们的经济其实处在一个技术变化非常缓慢的状态。

And so again, like I said, it's at half the rate of the previous era and then a third of the rate of 100 years ago.

就像我说的，它只有上一个时代一半的速度，也只有 100 年前三分之一的速度。

And so we're coming out of this kind of phase where we've had almost no technological progress in the economy, we've had remarkably little job churn as a result of that relative to any historical period.

所以我们正从这样一个阶段走出来：经济中几乎没有技术进步，相比任何历史时期，由此带来的岗位流动都少得惊人。

And so even if AI ticks up, even if AI triples productivity growth in the economy, which would be a massively big deal, it would take us back to the same level of job churn that was happening between 1870 and 1930.

因此即使 AI 让经济中的生产率增长上升，哪怕 AI 让生产率增长翻三倍，那也会是非常重大的事，但它也只是把我们带回到 1870 到 1930 年间那种岗位流动水平。

And if you go back and you read accounts of 1870 to 1930, people just thought the world was a watch with opportunity.

如果你回头读 1870 到 1930 年的记载，人们会觉得整个世界都充满机会。

At that rate of technological transformation, kids were able to develop new careers into new areas of the economy, building new kinds of products and services.

在那种技术转型速度下，年轻人能够在经济的新领域里发展新的职业，打造新的产品和服务。

I mean, a huge part of everything in our modern world today was kind of invented and proliferated during that period.

我的意思是，我们今天现代世界里的很大一部分东西，都是在那个时期被发明并普及的。

And so even if AI triples the pace of economic change in the economy, it's going to just translate to a much higher rate of economic growth, it's going to translate to a much higher rate of job growth.

所以即使 AI 让经济变化的速度翻三倍，它也只会转化为高得多的经济增长率，以及高得多的就业增长率。

And there'll be some level of task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation that will happen.

确实会发生某种任务层面和岗位层面的替代，但这会被经济增长和创新带来的宏观影响淹没。

And then corresponding to that, there'll be hiring booms, quite honestly, I think all over the place.

相应地，说实话，我认为各个地方都会出现招聘热潮。

And then again, go back to the other thing, which is like, this is all happening in the face of declining population growth and increasingly population shrinkage.

然后再回到另一点：这一切都发生在人口增长下降、并且越来越多地方出现人口萎缩的背景下。

And so human workers in many, many, many countries over the next 10, 20, 30 years are going to be at more and more of a premium, literally because you're going to have shrinking population levels.

所以在未来 10 年、20 年、30 年里，许多许多国家的人类劳动者会变得越来越稀缺、越来越值钱，原因很简单：人口规模会缩小。

We don't really want to get into politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration that we've had for the last 50 years.

我们不太想特别深入政治，但总体来看，世界似乎会扭转过去 50 年的移民率趋势。

It seems to be kind of a broad-based thing happening with rise to nationalism, concerns about the rate of immigration and immigration historically in countries like the US, it's kind of ebbed and flowed over time based on how the national mood shifts.

这似乎是一种广泛发生的现象，伴随着民族主义抬头，以及对移民速度的担忧；而在美国这样的国家，历史上移民本来就是随着国民情绪变化而起伏的。

And so if you sort of combine in a country like the US or any country in Europe, if you combine declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount.

所以，如果在美国或欧洲任何国家，把人口下降和移民减少结合起来看，剩下的人类劳动者会更值钱，而不是更廉价。

And so I think that combination of faster productivity growth, faster economic growth, and then slower population growth and less immigration actually means there's going to be much less of this kind of dystopian no jobs' thing.

因此我认为，更快的生产率增长、更快的经济增长，再加上更慢的人口增长和更少的移民，实际上意味着那种反乌托邦式的“没有工作”情景会少得多。

I just think it's probably totally off pace.

我只是觉得这种看法可能完全跑偏了。

That is extremely interesting.

这太有意思了。

So what I'm hearing is, you're not super worried about job loss.

所以我听到的是，你并不是特别担心失业。

Is the key here that the timing kind of just works out, this population decrease, all these kind of have to line up for there not to be this massive job loss with AI?

关键是不是在于时间点刚好对上了？人口下降这些因素都必须刚好排列在一起，AI 才不会造成大规模失业？

Yeah.

是的。

Well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy.

嗯，你看，如果没有 AI，我们现在就会因为经济接下来会怎样而陷入恐慌。

Because what we'd be staring at is a future of depopulation.

因为我们面对的将是一个人口减少的未来。

And depopulation without new technology would just mean that the economy shrinks.

而没有新技术的人口减少，只会意味着经济收缩。

So it would mean that the economy kind of itself kind of shrinks over time.

也就是说，经济本身会随着时间逐渐萎缩。

The opportunity diminishes.

机会会减少。

There are no new jobs, there are no new fields, there's no new source of consumer demand for spending on things.

没有新工作，没有新领域，也没有新的消费者需求来源来支撑人们在各种东西上的支出。

And so you would be very worried about going into a period of severe decline of stagnation.

所以你会非常担心进入一个严重衰退和停滞的时期。

And essentially, you'd be looking at these very dystopian scenarios of an economy kind of self-euthanizing itself over time.

本质上，你会看到一些非常反乌托邦的情景：经济随着时间推移逐渐自我安乐死。

And so you'd be very worried about the opposite of what everybody thinks that they're worried about.

所以你真正该担心的，恰恰是大家以为自己在担心的那件事的反面。

The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth and then also for the lack of immigration that's likely.

我们现在之所以不担心这一点，唯一原因是我们知道已经有技术可以弥补人口增长不足，以及很可能出现的移民不足。

And so I would say the timing has worked out miraculously well in the sense of, we're going to have AI and robots precisely when we actually need them, to keep the economy from actually shrinking.

所以我会说，从这个意义上讲，时间点奇迹般地对上了：我们会在真正需要 AI 和机器人来防止经济实际收缩的时候，刚好拥有它们。

And I just think that's just a fundamentally good news story.

我认为这从根本上说是个好消息。

To get to the mass job loss thing that people are worried about on the other side of things, you'd have to look at far, far, far higher rates of productivity growth.

至于大家从另一个方向担心的大规模失业，你得看到远远高得多的生产率增长率才会出现。

You'd have to look at rates of productivity growth that are 10, 20, 30, 50% a year, something like that, which are orders of magnitude higher than we've ever had in an economy in the history of the planet.

你得看到每年 10%、20%、30%、50% 这样的生产率增长率，那比人类历史上任何经济体曾经有过的水平都高出好几个数量级。

It's possible that we get that.

我们有可能达到那个水平。

I mean, look, I have my utopian temptation along with everybody else.

我的意思是，你看，我和其他人一样，也会有乌托邦式的诱惑。

If AI radically transforms everything overnight, then maybe let's play out the kind of utopian-

如果 AI 一夜之间彻底改变一切，那也许我们可以推演一下那种乌托邦式的……

... radically transforms everything overnight.

……一夜之间彻底改变一切。

Then maybe let's play out the kind of utopian scenario.

那也许我们可以推演一下那种乌托邦情景。

You get to a much higher level of productivity growth, you get to much higher level of technological change.

你会达到高得多的生产率增长水平，也会达到高得多的技术变化水平。

Corresponding to that, you'll have a massive economic boom.

与之相应，你会看到一场巨大的经济繁荣。

You'll have a massive growth in the economy.

经济会出现巨大增长。

And then corresponding with that, you'll have a collapse in prices.

然后与之相应，价格会崩塌。

And so the price of goods and services that are, whatever you're going to call it, affected by or commoditized by AI, the prices of those goods and services will collapse.

所以那些被 AI 影响，或者不管你怎么说，被 AI 商品化的商品和服务，它们的价格都会崩塌。

There'll be price deflation.

会出现价格通缩。

And then as a consequence of price deflation, everything that people are buying today gets a lot cheaper.

而价格通缩的结果是，人们今天购买的一切都会便宜得多。

And that's the equivalent of a gigantic increase in wealth across the society, right?

这就相当于整个社会的财富出现了巨大的增加，对吧？

Take it this way.

可以这样理解。

This is actually worth talking about because people, I think, get sideways on this issue.

这其实值得讲一下，因为我觉得人们在这个问题上很容易想偏。

So if AI is going to transform the economy as much as the, whatever, utopians or dystopians or whatever kind of thing that it will, the necessary economic calculation of what happens is massive productivity growth.

所以，如果 AI 真会像那些乌托邦派、反乌托邦派或者其他什么人所说的那样改变经济，那么必然的经济计算结果就是大规模生产率增长。

The consequence of massive productivity growth, what that literally means mechanically is more output requiring less input.

大规模生产率增长的后果，按机制来说，字面意思就是用更少的投入产生更多的产出。

So, you get more economic output for less input.

也就是说，你用更少的投入得到更多的经济产出。

So you're substituting in AI for human workers or whatever.

所以你是在用 AI 替代人类劳动者，或者类似的东西。

And as a consequence, you get this massive boom in output with much lower input costs.

结果就是，在投入成本大幅降低的情况下，产出出现巨大繁荣。

The result of that is you get gluts of goods and services in all those affected sectors.

其结果是，所有受影响的领域都会出现商品和服务过剩。

The result of those gluts is you get collapsing prices.

这些过剩的结果就是价格崩塌。

The collapsing prices mean that the thing today that costs you $100, now costs you $10, and now costs you $1.

价格崩塌意味着，今天花你 $100 的东西，现在只要 $10，然后只要 $1。

That's the equivalent of giving everybody a giant raise because now they have all this additional spending power.

这就相当于给每个人大幅加薪，因为他们现在有了大量额外购买力。

That additional spending power then translates to economic growth, the development of new fields.

这些额外购买力又会转化为经济增长，以及新领域的发展。

Everybody's materially much better off very quickly.

每个人的物质生活都会很快变好很多。

And then by the way, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the social safety net to prevent people from being immiserated because the prices of all the goods and services that a welfare program has to pay from, they're all collapsing.

顺便说一句，即便在这个过程的另一端确实出现了失业，现在提供社会安全网、避免人们陷入贫困也会便宜得多，因为福利项目需要支付的那些商品和服务，价格都在崩塌。

And so, the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses because of this incredible impact that AI is having.

所以，医疗价格会崩塌，住房价格会崩塌，教育价格会崩塌，其他一切的价格都会因为 AI 带来的这种惊人影响而崩塌。

And so in this kind of utopian/dystopian scenario that people have, there's no scenario in which everybody's just poor.

因此，在人们想象的这种乌托邦/反乌托邦情景里，不存在所有人都变穷的情况。

In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse.

事实上，情况正好相反：因为价格崩塌，每个人都会富裕很多。

And then it's actually much easier to pay for the social safety net for the people who, for some reason, can't find a job.

然后，为那些由于某些原因找不到工作的人支付社会安全网，实际上也会容易得多。

And so maybe we end up in that scenario.

所以也许我们最终会进入那种情景。

I mean, the optimistic part of me says, "Yeah, maybe AI is that powerful, and maybe the rest of the economy can actually change to accommodate that, and maybe that'll happen."

我的意思是，我乐观的一面会说：“是的，也许 AI 确实那么强大，也许经济的其余部分真的能够改变来适应它，也许这会发生。”

But the result of that is going to be a much better news story than people think it's going to be.

但结果会比人们以为的要好得多。

And again, everything I've just described, by the way, is just a very straightforward extrapolation of very basic economics.

再说一次，顺便说一句，我刚才描述的一切，都只是对非常基础的经济学做出的非常直接的外推。

I'm not making any bold predictions of what I just said.

我刚才说的并不是什么大胆预测。

This is just a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher rates of technological growth.

这只是一个直接的机械过程：如果你有更高的生产率增长率，而这必然来自更高的技术增长率，它就会自行展开。

And so, I think we're looking at... And to be clear, I think we're looking at a world that's not radically transformed the way that, maybe, the utopians think that it will be or the dystopians think it will be.

所以我认为我们正在看到……说清楚一点，我认为我们看到的世界，不会像乌托邦派或反乌托邦派想象的那样被彻底改变。

I think it'll be more incremental for reasons we can discuss, but I think that incremental, overwhelmingly, I think that process is going to be a good news process.

我认为出于一些我们可以讨论的原因，它会更渐进；但总体上，我认为这个渐进过程会是一个好消息。

And then even if it's much faster, it's also going to be a good news process.

即使它快得多，也仍然会是一个好消息。

It'll just be a good news process in the other way that I just described.

只不过会是我刚才描述的另一种好消息。

I love hearing optimism and good news.

我喜欢听到乐观和好消息。

I will also add that you've been... I was researching you ahead of this chat, and you've been right so many times about where the world is heading.

我还想补充一点，你一直以来……我在这次聊天前研究了你，你很多次都准确判断了世界走向。

That's why I'm especially excited to talk to you.

这也是我特别兴奋能和你聊的原因。

I'll give you a short list.

我简单列几个。

I imagine there are many more things.

我想肯定还有更多。

So one, you were right about the Web and web browsers becoming important.

第一，你正确判断了 Web 和 web browsers 会变得重要。

You were right about software eating the world.

你也正确判断了 software eating the world。

Check.

对了。

In 2011, you said that in 10 years, we're going to have 5 billion people using smartphones.

2011 年，你说 10 年后会有 50 亿人使用 smartphones。

And I believe the actual number ended up being six billion.

我记得实际数字最后是 60 亿。

Also, you had this debate with Peter Thiel that I came across, where you were debating whether technologies stop progressing or if new technology will continue to emerge.

另外，我还看到你和 Peter Thiel 有一场辩论，讨论技术是否停止进步，还是新技术会继续出现。

And you were arguing there's progress.

你的论点是进步还在。

Progress will continue.

进步会继续。

And he was like, "No, I think we're done with cool technology."

而他的观点是：“不，我觉得酷的技术已经结束了。”

You were right.

你是对的。

I imagine there are many more things you were right about.

我想你判断对的事情还有很多。

So again, I love hearing your predictions because I feel like they're actually going to turn out to be correct.

所以我真的很喜欢听你的预测，因为我感觉它们最后真的会被证明是对的。

I was going to start by saying, I've been wrong about tons of things, but I buried those out back behind the shed.

我本来想先说，我错过很多事，只是都被我埋在后院棚子后面了。

Delete them from the internet.

从互联网上删掉。

No browser can discount them.

让任何浏览器都找不到。

Yes.

对。

Yes, I have them nuked out of the internet archives so that they're never seen again.

对，我已经把它们从互联网档案里彻底清掉了，再也不会被人看见。

So, I'm wrong plenty of times also.

所以，我也经常会错。

But yeah, look, I think some of those, I got right.

但确实，有些事我觉得自己判断对了。

By the way, I will say on the Peter one, I've come much more around to Peter's point of view.

顺便说一句，关于 Peter 那件事，我现在越来越认同 Peter 的观点了。

I would probably argue that one quite a bit differently today than I did, and I would give his view, I think, a lot more credit.

如果今天再讨论那件事，我大概会用很不一样的方式来论证，而且我会更认真看待他的观点。

And it actually goes to the discussion that we did, the conversation we just had, which is the... The real form of what Peter was arguing was we have lots of process in bit, we have lots of progress in bits, but we have very little progress in atoms.

这其实也回到我们刚才的讨论。Peter 真正想说的是：我们在 bits 上有很多进步，但在 atoms 上几乎没有进步。

And that's the real core of what he was arguing.

这才是他论点的真正核心。

And I think I was a little bit, I don't know, missing that or glossing that over a little bit because I was so focused on making sure people understood, "No, there actually is still progress happening in bits."

我当时可能有点没抓住这一点，或者轻描淡写了，因为我太专注于让大家明白：“不，bits 领域确实仍然在进步。”

But I think a lot of his critiques around the lack of progress in atoms is real.

但我认为，他关于 atoms 领域缺乏进步的很多批评是成立的。

And again, this goes back to this thing of like... And he's talked about this for a long time.

这又回到那件事，而且他已经谈这个谈了很久。

In the last 50 years, there has just been very little technological innovation in most of the economy.

过去 50 年里，经济中大多数领域几乎没有什么技术创新。

There's been very little technological innovation, in particular, anything involving atoms.

尤其是任何涉及 atoms 的东西，技术创新都非常少。

There's been very little real-world technological change.

现实世界里的技术变化非常少。

There just hasn't been.

确实就是没有。

The built world is just not that different today than it was 50 years ago.

今天的建成环境，和 50 年前相比并没有那么不同。

And again, if you contrast that, if you compare and contrast 1870 to 1930, it was a dramatically different world.

如果你对比 1870 年和 1930 年，那是一个截然不同的世界。

If you contrast 1930 to 1970, it was a dramatically different world.

如果对比 1930 年和 1970 年，也同样是一个截然不同的世界。

If you contrast 1970 today, it's not that different.

但如果对比 1970 年和今天，差别并没有那么大。

And look, you just see that you could just walk around and it's just like, "Oh yeah, there's a bunch of buildings that were built in 1960, and there's a bridge that was built in 1930, and there's a dam that was built in like 1910, and there's a city that was founded in 1880."

你只要到处走走就能看到：哦，这里有一堆 1960 年建的楼，那边有一座 1930 年建的桥，还有一座大概 1910 年建的大坝，还有一座 1880 年建立的城市。

And like, "What have we done?

那我们到底做了什么？

Where are our new cities?

我们的新城市在哪里？

Where are new dams?

新的大坝在哪里？

Where's the California High-Speed Rail?

California High-Speed Rail 在哪里？

What's going on here?"

这到底是怎么回事？

And so, I think he is right about a lot of that.

所以我认为，他在很多方面是对的。

Again, this is also why I think that AI is not going to have as rapid... It's not going to be, again, this kind of utopian or dystopian view of everything changes overnight.

这也是为什么我认为 AI 不会那么快产生影响，不会是那种一夜之间全都改变的乌托邦或反乌托邦图景。

I think it just can't happen because of the reasons that Peter articulates, which is there's so much about how the world works that's basically just like wrapped up in red tape: like bureaucratic process, rules, restrictions, the politics.

我觉得这不可能发生，原因正如 Peter 所说：这个世界的运转方式，有太多东西基本都被繁文缛节包裹住了，比如官僚流程、规则、限制和政治。

By the way, unions, cartels, oligopolies, there's all these structures in the world that are economic or political or regulatory structures that basically prevent things from changing.

顺便说，还有工会、卡特尔、寡头垄断。世界上有各种经济、政治和监管结构，基本上都在阻止事情发生改变。

And so let's take a great example: AI's impact on the healthcare system.

我们举一个很好的例子：AI 对医疗体系的影响。

By rights, AI is going to have a dramatic impact on the healthcare system, and in very positive ways.

按理说，AI 会对医疗体系产生巨大影响，而且是非常积极的影响。

But large parts of the medical system today, they are cartels.

但今天医疗体系的很大一部分其实是卡特尔。

And so the doctors are a cartel, and nurses are a cartel, hospitals are a cartel.

医生是卡特尔，护士是卡特尔，医院也是卡特尔。

Then there's this push to nationalize all the healthcare systems, and then you've got a government monopoly.

然后又有人推动把整个医疗体系国有化，那你就得到一个政府垄断。

And guess what cartels of monopolies don't like, is they don't like rapid change.

而垄断卡特尔最不喜欢的是什么？它们不喜欢快速变化。

And so you show up as a kid and you're like, "Wow, I've got this new technology to do AI medicine."

所以你作为一个年轻人出现，说：“哇，我有一种做 AI 医疗的新技术。”

And they're like, "Oh, does it threaten doctor jobs?

他们会说：“哦，它会威胁医生的工作吗？”

In that case, we're going to block it."

“如果是这样，那我们就要阻止它。”

And I think a lot of consumers, by the way... I see this in my life, and you'll probably see this in your life also, which is ChatGPT is almost certainly a better doctor than your doctor today, but ChatGPT can't get a license to practice medicine.

顺便说，我觉得很多消费者都会感受到这一点。我在自己生活里也看到了，你可能也会看到：ChatGPT 今天几乎肯定比你的医生更会看病，但 ChatGPT 拿不到行医执照。

So, it can't substitute for a doctor.

所以它不能替代医生。

It can't prescribe medications.

它不能开药。

It can't perform procedures.

它不能做手术或操作。

And so, there are these...

所以，这里面有这些……

Anyway.

总之。

So Peter, I think, was very articulate, and has been for a long time on like, "No, there are actually real structural impediments in the economy and in the political system that we have, that actually prevent..." The rates of change, that are anywhere near the rates of change that people have in the past.

所以我认为 Peter 一直非常清楚地指出：经济和政治系统里确实存在真实的结构性障碍，会阻止变化达到过去那种速度。

And you can maybe say, optimistically, maybe the presence of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions for the first time in decades, to really say, "Okay, is this really the world we want to live in?

乐观一点说，也许 AI 这种新魔法技术的出现，会让我们几十年来第一次重新审视许多假设，认真问一句：“好吧，这真的是我们想生活的世界吗？”

Don't we actually want to get to the future faster?"

“我们难道不想更快抵达未来吗？”

So maybe, that would be the optimistic view.

所以，也许这就是乐观的看法。

"It's time to build," somebody famously said. In my calendar, I actually have that as my... When I start to work, "It's time to build."

“It's time to build”，有人很有名地这么说过。我的日历里其实也有这句话，作为我开始工作时的提醒：“It's time to build。”

Yes.

对。

That's my block in the morning of the day.

那是我每天早上的一个时间块。

Thank you for that.

谢谢你写了那句话。

Okay.

好。

I love the way you go from just macro to just like N-of-1, and I want to go to N-of-1.

我很喜欢你从宏观一下子切到 N-of-1 的方式，我也想聊聊 N-of-1。

A lot of the listeners of this podcast are product managers, they're engineers, they're designers.

这个 podcast 的很多听众是产品经理、工程师、设计师。

There's a lot of founders, but there's also a lot of non-founders.

有很多创始人，但也有很多不是创始人的人。

There's a lot of people building product that aren't founders.

有很多人在做产品，但他们不是创始人。

And obviously, a lot of people are worried about where their career is going.

显然，很多人都在担心自己的职业会走向哪里。

"Is one of these roles going to disappear?"

“这些角色里会不会有一个消失？”

"Is one of these roles are going to do really well?"

“这些角色里会不会有一个发展得特别好？”

" How do I stay up to date?"

“我该怎么保持跟上时代？”

You're close with a lot of teams, a lot of product teams.

你和很多团队、很多产品团队都很接近。

What's your sense of just the future of these three very specific roles: product manager, engineer, designer?

你怎么看这三个非常具体角色的未来：产品经理、工程师、设计师？

This, I think, is a really funny question.

我觉得这是一个特别有意思的问题。

These three roles in particular, obviously, are the central roles for building for tech companies.

这三个角色显然是科技公司做产品的核心角色。

So, the way I've been describing it is... You know the concept of the Mexican standoff, right?

我最近是这样描述的：你知道 Mexican standoff 这个概念吧？

Which is the movie scene where the two guys have guns point at each other's heads?

就是电影里两个人拿枪指着对方脑袋的场景。

Mm-hmm.

嗯。

And then there's... If you watch John Woo movies, he loves to have... He does the three-way Mexican standoff, where you've got like a triangle, people.

如果你看 John Woo 的电影，他很喜欢拍三方 Mexican standoff，就是三个人形成一个三角。

And of course, John Woo movies, they've got guns in both hands.

当然，在 John Woo 的电影里，他们两只手都拿着枪。

So, each is aiming at the other two and you've got this kind of standoff situation.

所以每个人都瞄准另外两个人，形成一种僵持局面。

And so the way I've been describing this is there's like a Mexican standoff happening between those three roles: between product manager, designer, and coder.

我一直用这个来描述这件事：产品经理、设计师和 coder 这三个角色之间，正在发生一种 Mexican standoff。

Specifically, the following, which is every coder now believes they can also be a product manager and a designer because they have AI, every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder.

具体来说就是：现在每个 coder 都认为自己有了 AI，也可以做产品经理和设计师；每个产品经理都觉得自己可以做 coder 和设计师；每个设计师也知道自己可以做产品经理和 coder。

And so, people in each of those roles now know or believe that with AI, they don't need the other two roles anymore.

所以，这些角色中的每个人现在都知道或相信，有了 AI，他们不再需要另外两个角色。

They can do that because they can have AI do that.

他们可以做到，因为可以让 AI 去做。

And then of course, there's the real irony, which is all three of them are going to realize that AI can also be a better manager.

当然真正讽刺的是，这三类人都会意识到，AI 也可以成为更好的管理者。

So, they're going to be aiming the guns up the org chart, but that's the next phase.

所以他们接下来会把枪口往组织架构上方瞄准，那就是下一阶段。

And what I think is so fascinating about this Mexican standoff is they're actually all kind of correct, I think.

这个 Mexican standoff 最有意思的地方在于，我觉得他们其实某种程度上都是对的。

Which is, AI is actually a pretty good... It's actually now a really good coder, it's actually now a really good designer, and it's also a really good product manager.

AI 现在确实是相当好的 coder，也确实是非常好的设计师，同时也是非常好的产品经理。

It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs.

它确实擅长做这三件事，或者至少擅长完成这三类工作中的很多任务。

And so again, this goes back to this idea of the super-empowered individual.

这又回到“超级赋能个体”这个想法。

Where if I'm a coder, step one is I need to make sure that I really understand AI coding, and what that means, and how coding is going to change in the future.

如果我是一个 coder，第一步就是要确保自己真正理解 AI coding，理解它意味着什么，以及未来写代码会如何变化。

I need to understand specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates a dozen instances of coding bots.

我需要具体理解，如何从一个完全手写代码的 coder，转变成一个协调十几个 coding bot 实例的 coder。

There's a change in the actual job of coding itself, which is happening right now.

coding 这份工作本身正在发生变化，而且就是现在正在发生。

But the other part of it is, "Okay, how do I become that super-empowered individual?

但另一部分是：“好，我怎么成为那个超级赋能的个体？”

How do I become a coder that also then harnesses AI so that I can also be a great product manager, and I can also be a great designer?"

“我怎样成为一个 coder，同时利用 AI，让自己也能成为很好的产品经理，也能成为很好的设计师？”

And then the same thing for the product manager, which is, "How do I make sure that I can now use coding tools?

产品经理也是一样：“我怎样确保自己现在能使用 coding 工具？”

How do I make sure I can also do AI-based design?"

“我怎样确保自己也能做基于 AI 的设计？”

And the same thing for the designer, which is, "How do I use AI to also become a coder, and also become a product manager?"

设计师也是一样：“我怎样用 AI 让自己也成为 coder，同时也成为产品经理？”

And then what you get is maybe, those individual roles change.

然后你会看到，也许这些单独的角色会发生变化。

Maybe, those are not any more sort of stovepipe roles the way that they have been for the last 30 years or whatever.

也许它们不再像过去 30 年那样，是那种烟囱式的独立角色。

But what happens is that the talented people in any of those roles become super powered, and they become good at doing all three of those things.

但结果是，这些角色中有才华的人会被超级赋能，并且会擅长同时做这三件事。

And then, those people become incredibly valuable, because then those are people who can actually build and design new products from scratch, which is the most valuable thing.

然后，这些人会变得极其有价值，因为他们是真正能够从零开始构建和设计新产品的人，而这是最有价值的事情。

And so, I think that's the opportunity.

所以我认为，这就是机会。

I love this answer.

我很喜欢这个回答。

So what I'm hearing is, essentially, if you're amazing at any of these three roles, you will do well.

所以我听到的是，本质上，如果你在这三个角色中的任何一个里非常出色，你都会发展得不错。

Number one, if you're amazing at these roles, that's great.

第一，如果你在这些角色里非常出色，那当然很好。

But also, part of being amazing in these roles is also being able to fully harness the new technology.

但同时，在这些角色里做到出色的一部分，也包括能够充分利用新技术。

So if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, at some point you are going to hit an issue.

所以，如果你今天是一个顶尖 coder，却一直没有学会如何用 AI 放大自己的 coding 能力、做更多事情，那么到某个时候你会遇到问题。

Here's another way economists talk about this, which is there's the concept of the job, but the job is not actually the atomic unit of what happens in the workplace.

经济学家还有另一种说法：有“工作”这个概念，但工作并不是职场中发生事情的最小单位。

The atomic unit of what happens in the workplace is the task.

职场中发生事情的最小单位是任务。

And then the way the economists think about it is a job is a bundle of tasks.

经济学家的理解是，一份工作是一组任务的组合。

Everybody wants to talk about job loss, but really, what you want to look at is task loss, the tasks changing.

大家都想讨论工作流失，但真正应该看的，是任务流失，以及任务如何变化。

The classic example of task changing.

任务变化的经典例子。

Classic example of task changing was once upon a time, executives never used typewriters or personal computers themselves.

任务变化的经典例子是，曾经有一段时间，高管从不亲自使用打字机或个人电脑。

If you were a vice president of a company in 1970 or whatever, you did not have a typewriter or a computer on your desk typing things.

如果你在 1970 年左右是某家公司的副总裁，你桌上不会有打字机或电脑让你自己打字。

You had a secretary who you dictated memos to.

你会有一位秘书，你口述备忘录给对方。

And then there was this change where emails started to show up.

然后出现了一个变化：email 开始出现。

And what would happen was the job of the secretary, it went from... The job of the secretary changed from sending out letters with stamps on them to sending or receiving emails with the other admins.

当时的情况是，秘书的工作从贴邮票寄信，变成和其他行政人员收发 email。

Then the secretary would print out the email and bring it into the executive's office.

然后秘书会把 email 打印出来，拿进高管办公室。

And the executive office would read the email and paper, scroll the reply and give that message back to the secretary, who would go back and type it into the computer on his or her desk, and send it as an email.

高管会读纸上的 email，草草写下回复，再把那条消息交回给秘书；秘书再回到自己桌前，把它输入电脑，然后作为 email 发出去。

Fast-forward to today, none of that happens.

快进到今天，这些都不会发生了。

Now, executives just do all their own email.

现在，高管都自己处理所有 email。

They still have secretaries or admins, but they're now doing different tasks.

他们仍然有秘书或行政助理，但这些人现在做的是不同的任务。

They're travel planning and orchestrating events, and doing all of these other things that the great admins do.

他们做差旅规划、统筹活动，以及优秀行政人员会做的各种其他事情。

And then the task set, ironically, of the executive, has expanded to do actually more of the clerical work themselves actually.

而讽刺的是，高管的任务范围反而扩大了，实际上要自己做更多文书工作。

Like, sit there and type their own memos.

比如坐在那里自己打备忘录。

Which again, 50 years ago, they never would've done that.

而在 50 年前，他们绝不会这么做。

And so the executive job still exists, the secretary job still exists, but the tasks have changed.

所以高管这个职位还在，秘书这个职位也还在，但任务变了。

And I think that's a great example of what's going to happen.

我觉得这是一个很好的例子，说明接下来会发生什么。

In coding, the tasks are going to change.

在 coding 里，任务会改变。

Product management, the tasks are going to change.

Product management 也是，任务会改变。

Designer, tasks are going to change.

Designer 也是，任务会改变。

And so, the job persists longer than the individual tasks.

所以，职位会比其中的单个任务存在得更久。

And then as the tasks change enough, then that's when the jobs change.

然后当任务变化到足够大的程度，职位才会随之改变。

And so at the level of individual, you want to think of like, "Okay.

所以在个人层面，你要这样想：“好。

I have this job, the job is a bundle of tasks.

我有这份工作，而这份工作是一组任务的组合。

I need to be really good at making sure that I can swap the tasks out.

我需要非常擅长确保自己能把这些任务替换掉。

I can really adapt, use the new technology."

我真的能适应，能使用新技术。”

Get really good at AI coding, for example.

比如，非常擅长 AI coding。

And then you want to add skills.

然后你还要增加技能。

"I can also get really good at design.

“我也可以变得非常擅长 design。

I can also get really good at product management because I've got this new tool."

我也可以变得非常擅长 product management，因为我有了这个新工具。”

So, you want to pick up more and more scope as you do that.

所以在这个过程中，你要接住越来越大的范围。

And then 10 years from now, is your job title coder or coder/designer/product manager?

那么 10 年后，你的职位名称是 coder，还是 coder/designer/product manager？

Or is it just, "I build products"?

或者只是，“我做产品”？

Or is it just, "I tell the AI how to build products"?

又或者只是，“我告诉 AI 怎么做产品”？

It's like whatever that job is called, who even knows what it's going to be, but it's going to be incredibly important because the people doing that job are going to be orchestrating the AI.

不管那份工作叫什么，谁知道它最后会叫什么，但它会极其重要，因为做这份工作的人会在编排 AI。

And so that's the track that the best people are going to be on, and I think that's the thing to lean hard into.

所以这会是最优秀的人所在的轨道，我觉得这就是应该大力投入的方向。

I think people aren't fully grasping just, specifically, software engineering and how much that is changing.

我觉得大家还没有完全理解，尤其是 software engineering 正在发生多大的变化。

It's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think, a year ago, we would not have thought.

很明显，我们很快会进入一个工程师实际上不再写代码的世界；我觉得一年前我们还不会这么想。

And now it's just, clearly, this is where it's heading.

而现在很明显，这就是方向。

It's like there's going to be this artisanal experience of sitting there writing code, which is so crazy how much that job is going to change.

坐在那里写代码会变成一种手工艺式的体验；这份工作的变化之大，真的太疯狂了。

Yeah.

是的。

So again, here, I go back.

所以这里我又要回到过去。

And again, pardon maybe the history lesson, but I go back coding.

还是那句话，可能又要讲点历史了，但我回到 coding 这件事。

So, the first...

所以，最早的……

Do you know the original definition of the term calculator?

你知道 calculator 这个词最初的定义吗？

Do you know what that referred to?

你知道它指的是什么吗？

No.

不知道。

It referred to people.

它指的是人。

So back before there were like electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating... Like the way that an insurance company would calculate actuarial tables or the military would like calculate, I don't know, whatever troop logistics formulas or whatever it was.

所以在还没有电子计算器、电脑或这些东西之前，你真正进行 computing、进行 calculating 的方式，比如保险公司计算精算表，或者军方计算什么部队后勤公式之类的。

The way that you would do it is you would actually have a room full of people.

做法就是你真的会有一整个房间的人。

And by the way, these are like big rooms.

顺便说一句，那些房间还很大。

You could have hundreds or thousands or tens of thousands of people doing this.

可能有几百、几千，甚至几万人在做这件事。

And you would actually figure out... Somebody at the head of the room was responsible for whatever the mathematical equation was.

然后你会把事情拆出来……房间最前面有个人负责那条数学公式，不管它是什么。

And then, they would parcel out the individual mathematical calculations to people sitting at desks, who were doing them all by hand.

然后他们会把单个数学计算分派给坐在桌前的人，这些人全都手算。

And that job title was those people were calculators.

这些人的职位名称就是 calculator。

And so, we've gone from a world in which you literally have people doing mathematical equations by hands.

所以我们曾经生活在一个真的有人用手做数学方程的世界。

Then, we got the first computers.

然后，我们有了第一批 computer。

The first computers, of course, didn't have programming languages.

当然，最早的 computer 没有 programming language。

They only had machine code.

它们只有 machine code。

So, the first computers were programmed with 1s and 0s.

所以最早的 computer 是用 1 和 0 编程的。

And so the task of the programmer became, "Do the 1s and 0s," and then that became punch cards.

于是 programmer 的任务变成了“处理 1 和 0”，然后又变成了 punch card。

And you can still... There's still people, kicking today, whose job as a programmer was to build the punch cards.

你现在仍然能见到一些还健在的人，他们当 programmer 的工作就是制作 punch card。

And then you got, actually, this big breakthrough, which was called assembly language, which was basically the way to do machine code but with some level of English added to it.

然后出现了一个真正的重大突破，叫 assembly language，基本上就是用 machine code，但加入了一定程度的英文。

And then the best programmers did assembly language.

然后最优秀的 programmer 会写 assembly language。

And then when I was coming up, it was higher level languages like C, that compiled into machine code, and that's what programmers did.

到我成长起来的时候，已经是 C 这样的 higher-level language，它会编译成 machine code，那就是 programmer 做的事。

And then I still remember when scripting languages... We developed JavaScript at Netscape, and then Python took off, and Pearl, and these other scripting languages.

我仍然记得 scripting language 出现的时候……我们在 Netscape 开发了 JavaScript，后来 Python 火了，Perl 以及其他 scripting language 也起来了。

When scripting language took off in the 2000s, there was this big fight in the technical community, which is, "Scripting, real programming or not?"

当 scripting language 在 2000 年代兴起时，技术社区里有一场很大的争论：“Scripting 算不算真正的 programming？”

because it's like it's kind of cheating.

因为它有点像作弊。

Because real programmers write code that compiles to machine code, and real programmers do memory management themselves, and they do all of this whole craft of writing a C code.

因为真正的 programmer 写的是会编译成 machine code 的代码，真正的 programmer 自己做 memory management，掌握写 C code 的整套手艺。

And these JavaScript or Python programmers are just doing this kind of lightweight things.

而这些 JavaScript 或 Python programmer 只是在做这种轻量级的东西。

Does it even really count as coding?

这真的算 coding 吗？

And of course, the answer is yes, it very much counted.

当然，答案是算，而且非常算。

And now, most coding is done with the scripting languages, which have...

而现在，大多数 coding 都是用 scripting language 完成的，它们已经……

You see my point.

你懂我的意思。

The scripting languages have abstracted away, like, five layers of detail underneath that, that people used to do by hand, and they don't anymore.

scripting language 已经把底下大概五层细节都抽象掉了，那些细节过去人们要手动做，现在不用了。

And then to your point, AI coding is the next layer on that.

然后就像你说的，AI coding 是在这之上的下一层。

AI coding actually abstracts the way the process of actually writing the scripting code.

AI coding 实际上把编写 scripting code 的过程本身抽象掉了。

And so in one sense, this is a really big deal for all the obvious reasons.

所以从一个意义上说，出于所有显而易见的原因，这是一件大事。

But on the other hand, it's like, "Okay, this is the next layer of the task redefinition under the job of programmer."

但另一方面，也可以说：“好吧，这是 programmer 这个职位下，任务重新定义的下一层。”

Now, what's the job of the programmer?

现在，programmer 的工作是什么？

To your point, it's not necessarily to write the code by hand.

正如你说的，不一定是亲手写代码。

But what it is now is, all right, if you talk to the world's best programmer of yesterday, what they'll tell you is, "Oh, my job is I'm sitting there and I'm orchestrating 10 code bots, coding bots that are running in parallel."

但现在它变成了：如果你去问昨天世界上最优秀的 programmer，他们会告诉你：“哦，我的工作就是坐在那里，编排 10 个 code bot，10 个并行运行的 coding bot。”

And literally, they sit there and they shift from browser to browser, or terminal to terminal.

他们真的就坐在那里，在一个个 browser 之间，或者一个个 terminal 之间切换。

Their day job now is arguing with the AI bots to try to get them to write the right code, and then debug it and fix the problems, and change this back, and do all of these things.

他们现在的日常工作就是和 AI bot 争论，试图让它们写出正确的代码，然后 debug、修问题、把这个改回去，做所有这些事情。

And so now, the job of the programmer is to argue with the coding bots.

所以现在，programmer 的工作就是和 coding bot 争论。

But if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you.

但如果你自己不知道怎么写代码，你就不知道怎么评估 coding bot 给你的东西。

And so, you asked about the 10... Our 10-year old is super into computers and super into programming.

所以你刚才问到 10 岁……我们 10 岁的孩子非常喜欢电脑，也非常喜欢 programming。

He's using Claude, and ChatGPT, Copilot, and all of these things.

他在用 Claude、ChatGPT、Copilot，还有所有这些东西。

And what I'm telling him is like, "Look..." And by the way, he loves vibe coding.

我告诉他的是：“听着……”顺便说一句，他很喜欢 vibe coding。

He's on Replit all of the time doing vibe coding, doing games.

他一直在 Replit 上做 vibe coding，做游戏。

He's sitting there.

他就坐在那里。

It's hysterical because he's sitting there.

这很好笑，因为他就坐在那里。

It's a 10-year old basically, who spends two hours at dinner arguing with an AI for fun.

一个 10 岁的孩子，基本上会在晚饭时花两个小时和 AI 争论，而且是为了好玩。

But what I'm telling him is, "No, look, you need to still fully understand and learn how to write and understand code, because the coding bots are giving you code.

但我告诉他的是：“不，听着，你还是需要彻底理解并学习如何写代码、如何理解代码，因为 coding bot 给你的是代码。

If it doesn't work, or if it's not doing what you expect, or it's not fast enough or whatever, you need to be able to understand the results of what the AI is giving you."

如果它不能运行，或者没有按你预期的方式运行，或者速度不够快之类的，你需要能够理解 AI 给你的结果。”

In the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works.

这就像写 scripting language code 的人，最终确实也需要理解微处理器是怎么工作的。

And so again, it's kind of this up leveling of capability where you actually want the depth to be able to go down and be able to understand what the thing is actually doing, even if you're not spending your day actually doing that by hand.

所以这又是一种能力的向上提升：你其实需要有足够深度，能够下探并理解这个东西到底在做什么，即使你一天的工作并不是亲手做那些事。

And again, I look at that and I'm like, "Okay.

而我再看这件事，就会想：“好。

Now, programmers are going to be 10 times or 100 times or a thousand times more productive than they used to be."

现在，programmer 的生产力会比过去高 10 倍、100 倍，甚至 1000 倍。”

And that is, overwhelmingly, a good thing.

而这压倒性地是一件好事。

The tasks are definitely changing.

任务肯定在变化。

The nature of the job is changing.

工作的性质也在变化。

But are human beings going to be involved in the coding process and overseeing the AI coding and all of that?

但人类会不会参与 coding 过程，监督 AI coding 以及所有这些事？

And the answer is, of course, absolutely 100%.

答案当然是，绝对会，100%。

No question.

毫无疑问。

So you're in the camp of still learning to code is still a valuable skill?

所以你属于那个阵营：仍然认为学习 coding 依然是一项有价值的技能？

Oh yeah, totally.

哦，是的，完全是。

Again, if you want to be one of these super... Look, if you just want to put yourself on autopilot, and like, "I can't be bothered.

还是那句话，如果你想成为这种超级……听着，如果你只是想让自己进入自动驾驶状态，然后说：“我懒得管。

I'm just going to have AI write the code, and it's going to generate whatever it does and that's fine.

我就让 AI 写代码，它生成什么就是什么，也没问题。

And I'm going to be..." If the goal is to be a mediocre coder, then just let the AI do it.

而我会……”如果目标只是做一个平庸的 coder，那就让 AI 来做吧。

It's fine.

没关系。

The AI is going to be perfectly good in generating infinite amounts of mediocre code.

AI 完全有能力生成无限多的平庸代码。

No problem.

没问题。

It's all good.

都没事。

If the goal is, "I want to be one of the best software people in the world, and I want to build new software products and technologies that really matter," then yeah, you, 100%, want to still... You want to go all the way down.

如果目标是，“我想成为世界上最优秀的软件人之一，我想打造真正重要的新软件产品和技术”，那是的，你百分之百还是要……你要一路钻到底。

You want your skillset to go all the way down to the assembly, to assembly and machine code.

你的技能要一路深入到汇编、汇编语言和机器码。

You want to understand every layer of the stack.

你要理解技术栈的每一层。

You want to deeply understand what's happening at the level of the chip, and the network, and so forth.

你要深入理解芯片层面、网络层面等等到底在发生什么。

By the way, you also really deeply want to understand how the AI itself works, because you want to... If people understand how the AI works, they're clearly able to get more value out of it than somebody who doesn't understand how it works.

顺便说一句，你也真的要深入理解 AI 本身是怎么工作的，因为你会想要……如果人们理解 AI 的工作原理，他们显然能比不理解的人从中获得更多价值。

You're always more productive if you know how the machine works when you use the machine.

使用机器时，如果你知道机器怎么工作，你总会更高效。

And so the super-empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way down the stack because you want to be able to understand what it's giving you.

所以，在这一切另一端那个被超级赋能、想用新技术做大事的个人，没错，你百分之百要从头到尾理解这东西的整个技术栈，因为你要理解它到底给了你什么。

And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is.

而当某个东西不工作，或者哪里不对时，你要能非常快速地理解原因。

By the way, again, this goes back to education.

顺便说，这又回到了教育。

AI is your best friend at helping you learn all of that because it's like, "Oh, I need to understand.

AI 是帮你学习这一切的最佳朋友，因为你会说，“哦，我需要弄明白。

I don't know, this isn't fast enough."

我不知道，这个还不够快。”

I need to figure out... As a coder, I need to figure out how to do a different approach to memory management or something.

我得想清楚……作为程序员，我得想出一种不同的内存管理方式之类的。

And you can be like, "Well, shit.

然后你可以说，“好吧，糟了。

I don't quite know how to do that.

我不太知道该怎么做。

Okay, AI, let's spend 10 minutes.

好，AI，我们花 10 分钟。

Teach me how to do this.

教我怎么做这个。

Teach me what this all means."

教我这一切是什么意思。”

So all of a sudden, you have this incredibly synergistic relationship with the AI, where it's also helping you get better at the same time that's doing a lot of work for you.

所以突然之间，你和 AI 有了这种极其协同的关系：它在为你做大量工作的同时，也在帮你变得更强。

By the way, I was going to say, I was a big Pearl programmer.

顺便说一句，我刚想说，我以前是个重度 Perl 程序员。

I was an engineer for 10 years, and that was my language of choice.

我做了 10 年工程师，那是我最喜欢用的语言。

Do you remember?

你还记得吗？

I don't know when you were doing it, but do you remember... At least early on, did you ever hit this where C coders were looking down their nose at you and being like-

我不知道你是什么时候写的，但你记不记得……至少早期，你有没有遇到过这种情况：C 程序员会看不起你，然后说——

For sure.

当然有。

It was like, "This is so slow.

他们会说，“这太慢了。

It's not going to scale.

它扩展不起来。

What are you spending all your time on this thing?"

你为什么把所有时间都花在这东西上？”

Yeah, exactly.

对，完全是这样。

And of course, and again, it started this thing where they were sort of correct.

而且当然，同样，这件事一开始是他们某种程度上是对的。

Which is, at the beginning, it wasn't fast enough or whatever.

也就是说，刚开始它不够快，或者类似的问题。

By the end, they were definitely wrong, which is it got much better, much faster.

到后来他们肯定错了，因为它变得好很多、快很多。

And it swept the world.

然后它席卷了全世界。

Most coding today happens as scripting languages.

今天大多数编码都是用脚本语言完成的。

And then by the way, along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones who were able to actually make the scripting languages actually work really well.

顺便说，在这个过程中，那些真正理解脚本语言的人，以及那些理解所有底层系统的人，才是真正能把脚本语言做得很好用的人。

And so, that was a great example of this kind of adaptation.

所以，这是这类适应过程的一个很好例子。

And again, the result of that was a far higher number of people writing code with scripting languages than were ever writing code with lower level languages.

同样，它的结果是，用脚本语言写代码的人数远远超过了曾经用底层语言写代码的人数。

And I think this will just be a more dramatic version of that.

我认为这次只会是一个更戏剧化的版本。

I love that Pearl was designed by a linguist.

我很喜欢 Perl 是由一位语言学家设计的。

I don't know if you remember that part.

不知道你还记不记得这一点。

And that's what made it so nice to code with.

这也是它写起来这么舒服的原因。

That's funny because, of course, it was so notorious for being impossible to understand.

这很有意思，因为它当然也出了名地难以理解。

How ironic.

多讽刺啊。

Yes.

是的。

This episode is brought to you by Datadog, now home to Eppo, the leading experimentation and feature flagging platform.

本期节目由 Datadog 赞助，现在 Eppo 也已加入 Datadog，Eppo 是领先的实验和功能开关平台。

Product managers at the world's best companies use Datadog, the same platform their engineers rely on every day to connect product insights to product issues like bugs, UX friction, and business impact.

全球顶尖公司的产品经理都在使用 Datadog，也就是他们的工程师每天依赖的同一个平台，用来把产品洞察和 bug、UX 摩擦、业务影响等产品问题连接起来。

It starts with product analytics where PMs can watch replays, review funnels, dive into retention, and explore their growth metrics.

它从产品分析开始，PM 可以查看回放、审视漏斗、深入留存，并探索增长指标。

Where other tools stop, Datadog goes even further.

其他工具止步的地方，Datadog 还能走得更远。

It helps you actually diagnose the impact of funnel drop-offs, and bugs, and UX friction.

它能帮你真正诊断漏斗流失、bug 和 UX 摩擦带来的影响。

Once you know where to focus, experiments prove what works.

一旦你知道该关注哪里，实验就能证明什么有效。

I saw this firsthand when I was at Airbnb, where our experimentation platform was critical for analyzing what worked and where things went wrong.

我在 Airbnb 时亲眼见过这一点，当时我们的实验平台对分析什么有效、哪里出错至关重要。

And the same team that built the experimentation at Airbnb built Eppo.

而在 Airbnb 搭建实验体系的同一支团队，后来做出了 Eppo。

Datadog then lets you go beyond the numbers with session replay.

Datadog 还让你通过会话回放超越数字本身。

Watch exactly how users interact with heat maps and scroll maps to truly understand their behavior.

你可以通过热力图和滚动图，精确观察用户如何互动，从而真正理解他们的行为。

And all of this is powered by feature flags that are tied to real-time data, so that you can roll out safely, target precisely, and learn continuously.

这一切都由与实时数据绑定的功能开关驱动，让你可以安全发布、精准定向，并持续学习。

Datadog is more than engineering metrics.

Datadog 不只是工程指标。

It's where great product teams learn faster, think smarter, and ship with confidence.

它是优秀产品团队更快学习、更聪明思考、更有信心发布的地方。

Request a demo at datadoghq.com/lenny.

请访问 datadoghq.com/lenny 申请演示。

That's datadoghq.com/lenny.

网址是 datadoghq.com/lenny。

Coming back to this kind of triad, the other element that I hear more and more of is just the skill of taste, and design, and user experience, it feels like that's a very hard skill to learn.

回到这个三元组合，我越来越常听到的另一个要素，就是品味、设计和用户体验的能力；感觉这是一种很难学习的技能。

And to me, it tells me design is going to be much more valuable in the future.

在我看来，这说明设计未来会变得更有价值。

Yeah, that's right.

是的，没错。

And again, here, this is a great example.

同样，这也是一个很好的例子。

So again, the task level of, like, "Design the perfect icon," is going to be, all right, the AI's going to do that all day long.

所以，在任务层面，比如“设计一个完美图标”，好吧，AI 可以整天做这件事。

If it gives you a thousand icon designs, it's going to be great.

如果它给你 1000 个图标设计，效果会很棒。

It's going to be fantastic, whatever.

会非常好，诸如此类。

And by the way, there will still be some level of human icon design or whatever, but AI is going to get really good at that.

顺便说，当然仍然会有某种程度的人类图标设计之类的工作，但 AI 会非常擅长这件事。

But what are we trying to do, kind of capital D design of, like, "All right, what is this thing for, and how is this going to function in a world of human beings?

但我们真正要做的，是大写 D 的 Design，比如，“好吧，这东西是用来做什么的？它在一个由人类构成的世界里要如何运作？

And is this going to make people happy when they use it?

人们使用它时会开心吗？

Is this going to make people feel good about themselves?

它会让人们对自己感觉更好吗？

Is it going to fit into the rest of their life?

它能融入他们生活的其他部分吗？

Is it going to, I don't know, challenge them in the right way?"

它会不会，以合适的方式挑战他们？”

All of these kinds of higher level questions that the great designers have always thought about.

所有这些更高层次的问题，伟大的设计师一直都在思考。

The job of designer will involve much more of those higher level, more important components, and then again, with AI doing a lot more of the underlying tasks.

设计师的工作会更多包含这些更高层、更重要的部分，而 AI 则会承担更多底层任务。

And so one way to think about it is, I don't know, you think of the world's best designers, Jony Ive or whatever, and you could be like, "Wow."

所以一种思考方式是，比如你想到世界上最优秀的设计师，Jony Ive 之类的人，你可能会说，“哇。”

Like, if I'm a designer today, if I'm a 25-year-old designer and I aspire to be Jony Ive in a decade, it's all of a sudden, I have a new path that I can use to get there, which is... Because Jony did everything.

比如，如果我是今天的一名设计师，一个 25 岁、希望十年后成为 Jony Ive 的设计师，突然之间，我有了一条新的路径可以走到那里，因为……Jony 当年什么都自己做。

He did it without AI.

他是在没有 AI 的情况下做到的。

Now, a young designer tends to be like, "Wow, if I really harness AI in a decade, I'm going to be like the best designer of the world's ever seen because it's not just going to be me.

现在，一个年轻设计师往往会想，“哇，如果我真的把 AI 用好，十年后我会成为世界有史以来最优秀的设计师，因为那不只是我自己。

It's going to be me, plus being so super empowered by this technology to be able to do so much more.

而是我，加上被这项技术超级赋能，所以能做多得多的事情。

And then so much more of my time and attention is going to be able to be focused on these higher-level things that most designers never get to."

然后我的大量时间和注意力，就能投入到这些多数设计师从来没机会触及的高层次问题上。”

I think that's going to be another great example of that.

我认为这会是另一个很好的例子。

So maybe what I'm hearing here is kind of this T-shaped strategy of if you want to be successful in any three of these roles, be very, very, very good at that specific role: product management, engineering design.

所以我听到的也许是一种 T 型策略：如果你想在这三个角色中的任何一个里成功，就要在那个具体角色上非常非常非常强：product management、engineering、design。

And then get good enough at these other two roles.

然后在另外两个角色上达到足够好的水平。

I think that's great.

我觉得这说得很好。

I think that's really relevant.

我觉得这非常相关。

And then Scott Adamson, firstly, just passed away, which is a real tragedy.

然后 Scott Adams 首先，刚刚去世了，这真是一场悲剧。

But I referred for years to, actually, Scott Adams.

但多年来我一直提到的，其实是 Scott Adams。

He had this famous career advice he would give people, which I think makes a lot of sense.

他有一条很有名的职业建议，我觉得非常有道理。

Which dovetails with what you're saying, which-

它和你说的正好吻合，就是——

... advice he would give people which I think makes a lot of sense, which dovetails with what you're saying, which is he used to say it's like, look, he said, "I could have been a pretty good cartoonist or I could have been pretty good at business, but the fact that I was a cartoonist who understood business made me spectacularly great at making Dilbert."

……他给人们的建议，我觉得很有道理，也和你说的吻合。他过去会说，你看，他说：“我本来可以成为一个相当不错的漫画家，也可以在商业上做得不错，但正因为我是一个懂商业的漫画家，我才能把 Dilbert 做得极其出色。”

Because even the world's best cartoonist who didn't understand business could have never written Dilbert, and then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert.

因为即便是世界上最好的漫画家，如果不懂商业，也不可能写出 Dilbert；而世界上最好的商业人士，如果不会画漫画，也做不出 Dilbert。

It took somebody who actually had both of those skills to be able to make Dilbert which is one of the most successful cartoons in history.

必须是一个真正同时拥有这两种技能的人，才能做出 Dilbert，而它是历史上最成功的漫画之一。

And so the way Scott always described it was that from a career development standpoint, the additive effect of being good at two things is more than double.

所以 Scott 一直以来的说法是，从职业发展角度看，擅长两件事带来的叠加效应不只是两倍。

The additive effect of being good at three things is more than triple because you become a super relevant specialist in the combination of the domains, and, look, I mean, you see this all over the economy.

擅长三件事带来的叠加效应也不只是三倍，因为你会成为这些领域组合中的超级相关专家。而且你看，这种现象在整个经济里到处都是。

I mean, you see this all over the economy, but I'll give you an example.

我是说，你在整个经济里都能看到这一点，但我给你举个例子。

Hollywood, just Hollywood as an example.

Hollywood，就拿 Hollywood 来说。

There are a lot of writers who can't direct a movie and they can be very successful writers.

有很多编剧不会导演电影，但他们可以是非常成功的编剧。

There are a lot of directors who can't write a movie.

也有很多导演不会写电影剧本。

They can be very successful directors.

他们可以是非常成功的导演。

But the superstars in the entertainment industry are the people who can write and direct.

但娱乐行业里的超级明星，是那些既能写又能导的人。

They don't have a term for those.

他们没有专门的词来称呼这些人。

They call us auteurors, and those are the people who are the real creative forces that move the field.

他们把这类人称为 auteur，而这些人才是真正推动这个领域的创作力量。

And so again, and by the way, Hollywood, actually it's really funny, I've been spending a lot of time talking to Hollywood people about AI.

所以同样，顺便说，Hollywood 其实很有意思，我最近花了很多时间和 Hollywood 的人聊 AI。

Hollywood has the same Mexican stand-off going right now that we describe in tech, except in Hollywood, for example, for filmmaking, it's the director, it's the writer, and the actor.

Hollywood 现在也有和我们在科技行业描述的一样的三方僵局，只是在 Hollywood，比如电影制作里，是导演、编剧和演员。

Because the director is now thinking, "Wow, I don't need the writer anymore because the AI can write the script and I don't need the actor anymore because I can have AI actors."

因为导演现在会想，“哇，我不再需要编剧了，因为 AI 可以写剧本；我也不再需要演员了，因为我可以有 AI 演员。”

The writer is saying, "Well, I don't need the director because I can direct the movie and the AI can do the actors."

编剧会说，“好吧，我不需要导演，因为我可以导演电影，演员也可以由 AI 来做。”

And the actor is saying, "I don't need either one of these guys.

演员会说，“这两个人我都不需要。

I can have the AI direct the thing, I can have the AI write the thing and I'm just going to show up and do my performance."

我可以让 AI 来导演，让 AI 来写剧本，而我只要出场完成我的表演就行。”

And so it's the same kind of triangular configuration, and again, what's great about it is they're all correct.

所以这是同一种三角结构。而且同样，妙就妙在他们都对。

Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills, and then as a consequence, you're going to have more people who can write and direct or write and act or direct and act or do all three.

这三个领域里的每个人，都将能够横向扩展，掌握那些额外技能。结果就是，会有更多人能写又能导，能写又能演，能导又能演，或者三者都能做。

I think to your point, your T-shaped thing, I think that's going to be true basically across the entire economy.

我觉得顺着你的说法，你那个 T 型能力模型，基本上会适用于整个经济。

And if you think about the T, if you think about the T configuration, it's like, yeah, the breadth, the top of the T is like how many individual domains are you familiar enough with to be able to use the AI tools to be able to do really good work.

如果你想想这个 T，想想 T 型结构，它的横向宽度，也就是 T 的上面那一横，代表你对多少个不同领域足够熟悉，能用 AI 工具在这些领域里做出很好的工作。

And then this part of the T is how deep can you go in at least one of those domains so that you really, really deeply know what you're doing.

然后 T 的竖向部分，就是你在其中至少一个领域能深入到什么程度，能不能真正非常深入地知道自己在做什么。

But if you're super deep on coding and you can use AI to do design and you can use AI to do product management, that's your T right there, and you're a triple threat at the top of the T, but with this level of technical grounding underneath that.

但如果你在 coding 上特别深，又能用 AI 做设计，也能用 AI 做 product management，那你的 T 就在那里了；在 T 的上面那一横，你就是三项全能，而下面还有这种技术根基支撑。

I mean, at that point, again, you're the super-powered individual, you're going to be able to just perform like sheets of magic, for example, in terms of designing and building your products that people in my generation couldn't have even dreamed of.

我的意思是，到那一步，你就是一个被超级增强的个体。比如在设计和构建产品这件事上，你能像变魔术一样完成很多事情，这是我这一代人连想都不敢想的。

And so I think that this is a universal kind of theory that I think can apply across the entire economy.

所以我认为这是一套普适理论，可以应用到整个经济里。

I'm going to invent a new framework right now.

我现在要发明一个新框架。

Okay, forget the T framework.

好，先忘掉 T 型框架。

I'm picturing an F sideways or an E where there's three, two or three, I don't know, downward parts.

我脑子里想的是一个横过来的 F，或者一个 E，有三条，或者两三条向下的部分，我也说不准。

And so what I'm hearing is get good at least two or three.

所以我听到的是，至少要在两三个方面变强。

Yeah, I think that's right.

对，我觉得是这样。

I think that's right.

我觉得是这样。

Yeah, the combination, yeah.

对，关键是组合，对。

My friend, Larry Summers, had a different version of the Scott Adams thing, which is he used to tell people, he said, " The key for career planning is," he said, "don't be fungible."

我的朋友 Larry Summers 对 Scott Adams 那套说法有另一个版本。他以前会告诉别人：“职业规划的关键是，”他说，“不要让自己变得可替代。”

He's an economist and so that was economic speaking.

他是经济学家，所以这是经济学式的说法。

What that means essentially is don't be replaceable.

它本质上的意思就是，不要成为可以被替换的人。

And so don't be a cog, and what that meant was don't just be one thing.

所以不要当一颗螺丝钉，也就是说，不要只是一种单一角色。

So if you're, quote unquote, again, just a designer, just a product manager, just a coder, then in theory you can be swapped in or out.

所以如果你只是所谓“一个设计师”、只是一个 PM、只是一个 coder，那理论上你就可以被换进来或换出去。

But if you have this E or F laying on the side kind of thing, and if you have this combination of things that's actually quite rare, then all of a sudden you're not fungible.

但如果你有这种横放的 E 或 F 结构，如果你拥有这种其实很少见的能力组合，那你一下子就不再可替代了。

Not only you're not fungible, you're actually massively important because you're one of the only people in the world who can actually do that combination of things.

不只是不可替代，你实际上会变得极其重要，因为世界上只有很少人真正能做这种组合的事情。

And yeah, your ability to not become one of those people is just titanically enhanced with AI as compared to anything we've ever seen before.

而且，和我们以前见过的任何东西相比，AI 会极大增强你成为这种人的能力。

This is so interesting because I've worked with people that are good at these two skills and they were always called unicorns at the company.

这太有意思了，因为我合作过一些同时擅长这两种技能的人，公司里总会叫他们独角兽。

She can code and design, oh my god.

她既会 coding 又会 design，天哪。

And what I'm hearing here is this is what you need to become.

而我在这里听到的是，这就是你需要成为的人。

You need to become really good at at least two things there.

你需要在其中至少两件事上真的很强。

I think you used the term smoke stack or something where it's like PM over here, engineer design, and what I'm hearing here is you need to get good at at least two of these skills.

我记得你用了 smoke stack 之类的词，就是这边是 PM，那边是 engineer、design。而我听到的是，你至少要在这些技能中的两个上变强。

The silos of these two roles are disappearing.

这两个角色之间的筒仓正在消失。

That's right.

没错。

That's right.

没错。

And again, I can't overstress the following, for anybody listening to this, the thing about AI that I think people are just not getting enough benefit out of yet is just it will teach you.

而且我再强调也不为过。对所有正在听的人来说，我觉得大家还没有从 AI 里获得足够收益的一点是：它会教你。

This is amazing.

这太了不起了。

There's never been a technology before where you could ask it, "Teach me how to do this thing."

以前从来没有一种技术，你可以问它：“教我怎么做这件事。”

And so I always feel like it's like people spend too much... it's one of these things where it's like so much focus on figuring out how to use a large language model is like, "Okay, what am I going to try to get it to do for me?"

所以我总觉得，人们花了太多时间……这就是那种情况：大家非常关注怎么使用大语言模型，比如“好，我要让它帮我做什么？”

which is of course very important.

这当然很重要。

But the other side of it is, what can I get it to teach me how to do, and it's just as good at that.

但另一面是，我能让它教会我做什么？它在这方面同样出色。

And so again, this is this level of latent superpower.

所以这又是一种潜在的超级能力。

People who really want to improve themselves and develop their career should be spending every spare hour in my view at this point talking on AI, being like, "All right, train me up.

在我看来，那些真正想提升自己、发展职业的人，现在应该把每一个空闲小时都用来和 AI 对话，比如：“好，训练我。

Super empower me.

给我超级能力。

Train me, train me how to be... I'm a coder.

训练我，教我怎么成为……我是一个 coder。

Train me how to be a product manager."

教我怎么成为 PM。”

It will happily do that.

它会很乐意这么做。

It knows exactly how to do that.

它非常清楚该怎么做。

Run me, make me problems... yeah, make me assignments, then evaluate my results.

给我出题，给我布置任务，然后评估我的结果。

It will do that just as happily as it will do work, quote unquote, for you.

它会像帮你“工作”一样乐意做这些事。

Two tricks I've heard along those lines.

沿着这个方向，我听过两个技巧。

One is to watch the output, what the agent is doing and thinking as it's doing the work.

一个是观察输出，也就是 agent 在做工作时具体在做什么、在想什么。

So if you're not an engineer, just sit there and watch it think and make decisions, and it's almost become this layer on top of learning to code is learning to see what the agent is doing and thinking because that teaches you about architecture.

所以如果你不是 engineer，就坐在那里看它思考和做决策。这几乎成了学习 coding 之上的一层能力：学会看 agent 在做什么、想什么，因为这会教你 architecture。

And the other is, a couple podcast guests have mentioned this, when you get stuck and then you figure out how to unstuck yourself, you ask it, "What could I have done differently?

另一个是，有几位播客嘉宾提到过，当你卡住了，后来又想办法让自己脱困时，你可以问它：“我本可以怎么做得不一样？

What could I have said that would've avoided this error in the first place?"

我本来可以说什么，才能一开始就避免这个错误？”

Yeah, that's right.

对，没错。

That's right.

没错。

Yeah, look, on that first one, and again, this is what I'm doing with my 10-year-old.

对，关于第一个，其实这也是我在和我 10 岁孩子一起做的事。

Yeah, look, if you ask me, yeah, this is a really good point.

是的，你要是问我，这一点非常好。

So if you ask an AI, "Write me this code," and then it does it and it comes back and it doesn't work right, if all you know is single function, I asked it and it gave me back something that's not good, what do you even do with that?

如果你让 AI：“帮我写这段代码。”然后它写完回来，结果不能正常工作。如果你只知道一个单一功能：我问了它，它给了我一个不好的结果，那你接下来能怎么办？

You don't understand why it gave you that result.

你不理解它为什么给出那个结果。

Do you even understand what to tell it to try to get it to do something different?

你甚至知道该对它说什么，才能让它尝试做出不同的东西吗？

But to your point, if you actually watch what it's doing and then you have the grounding, kind of that leg of your E or your F, if you have that grounding, then you can be like, "Oh, I see what it's doing.

但就像你说的，如果你真的观察它在做什么，而且你有基础，也就是你的 E 或 F 的那一条腿，如果你有那种 grounding，你就会说：“哦，我看懂它在做什么了。

I see where it made the mistake.

我知道它哪里犯错了。

I see where it went sideways."

我知道它哪里走偏了。”

And then you're all of a sudden able to intervene and be able to say, "No, no, that's not what I meant.

然后你突然就能介入，并且说：“不，不，我不是这个意思。

Do this other thing."

做另一件事。”

And again, this is a big part of having the actual kind of synergistic relationship is that you understand.

而且，这也是和 AI 形成真正协同关系的重要一部分：你得理解。

And by the way, look, I mean, like everything I'm saying is... everything that we're saying right now also is the same as if you're working with human beings.

顺便说一句，其实我们现在说的所有这些，也同样适用于和人类一起工作。

If you and I are colleagues and I would ask you to do something, you'd come back with something completely different, I do need to understand what was happening in your head in order to be able to give you feedback.

如果你和我是同事，我请你做一件事，你回来交了一个完全不同的东西，我确实需要理解你脑子里发生了什么，才能给你反馈。

If I just tell you, "Oh, that's wrong," nothing happens.

如果我只是告诉你：“哦，这不对。”那什么也不会发生。

I need to actually understand.

我需要真正理解。

I need to have theory of mind.

我需要有 theory of mind。

I need to understand what you were thinking in order to really give you the right feedback.

我需要理解你当时在想什么，才能真正给出正确的反馈。

And again, the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing.

而 AI 的好处在于，它会非常愿意坐在那里，一整天解释自己为什么这么做。

It'll happily critique itself.

它也会很乐意自我批评。

By the way, this is a very fun thing where you can have one AI critique the other AI which is another thing which is you have one AI write the code, you have another AI debunk the code.

顺便说一句，这是一件很好玩的事：你可以让一个 AI 批评另一个 AI。也就是说，你让一个 AI 写代码，再让另一个 AI 反驳这段代码。

And so you can actually, you can play the AIs off against each other and get them to argue with each other.

所以你实际上可以让这些 AI 互相较劲，让它们彼此争论。

And yeah, these are all the kinds of skills that are going to become, I think, incredibly valuable.

而这些能力，我认为都会变得极其有价值。

I think people call those LLM councils-

我觉得人们把这种叫作 LLM councils——

Yes.

对。

... where they're talking to each other.

……就是它们互相对话。

Yeah, that's right.

对，没错。

That's right.

没错。

I do feel like if I were... I have no design background.

我确实觉得，如果是我……我没有设计背景。

I've always wanted to design.

我一直想学设计。

I've always wanted to be a great designer.

我一直想成为一个优秀的设计师。

It feels like that's the hardest one to learn of all these three by just watching and talking because there's a lot of exposure hours as folks have used this term, just like how do you learn to be a great designer.

但在这三项里，design 感觉是最难只靠观察和对话学会的，因为它需要大量 exposure hours，很多人用这个说法，就是你到底怎么学会成为一个优秀设计师。

That feels like that's going to be really hard and valuable.

我觉得这会非常难，也非常有价值。

So my true confession is I've always kind of wanted to be a cartoonist, but I have no art skills.

那我坦白一下，我其实一直有点想当 cartoonist，但我完全没有美术能力。

But as we're talking, I'm like, "Hmm, it might be time."

不过我们聊到这里，我在想：“嗯，也许时候到了。”

The time has come, Marc.

Marc，时候到了。

Yes.

是的。

I want to pivot to founders, maybe your bread and butter.

我想转到 founders，也许这是你的老本行。

You spend a lot of time with the most cutting edge, AI-forward founders.

你花了很多时间和最前沿、最 AI-forward 的 founders 在一起。

I'm curious what you see them do, how you see them, some way they operate that's maybe blowing your mind about how the future of starting a company looks, how the future of AI-forward companies look.

我很好奇你看到他们在做什么，你怎么看他们，有没有某种他们的运作方式让你觉得很震撼，关于未来创业会是什么样，未来 AI-forward 公司会是什么样。

Yeah.

对。

So this is a great and very topical topic that's all playing out in real time right now on the leading edge.

这是一个很好的话题，而且非常当下，现在正在最前沿实时展开。

So I think there's like three layers of it and see if this makes sense.

我觉得这里有三层，看看这样说有没有道理。

I think there's like three layers of it.

我觉得这里大概有三层。

I think layer one is they're thinking, "All right, how does AI redefine the products themselves?"

第一层是，他们会想：“好，AI 会如何重新定义产品本身？”

And this is kind of the time-honored kind of thing that happens with technology transitions, and this is kind of what a lot of venture capital is based on which is, okay, there's a new technology that comes out.

这是技术转型中一直会发生的经典情况，也是很多 VC 逻辑的基础：好，一项新技术出现了。

Maybe it's the personal computer or the iPhone or the internet or now it's AI, and it's like, all right, is this a new capability that gets added to existing products.

也许是 personal computer，也许是 iPhone，也许是互联网，现在则是 AI。问题就变成：这是不是一种会被加到现有产品里的新能力？

So all of a sudden you've got, I don't know, an existing software business and now you've got your PC version of it and now you got your iPhone version of it and you just keep on going and the new technology kind of gets added into the mix with another ingredient to an existing formula, and of course, a lot of new technologies are like that.

比如突然之间，你有一个现有的软件业务，现在有了 PC 版，又有了 iPhone 版，然后一路继续下去，新技术就像现有配方里又加入了一种新成分。当然，很多新技术就是这样的。

I don't know when flash storage came out or something, it didn't really redefine the software industry because people just went from using hard disk using flash storage or something.

比如 flash storage 出现的时候，它并没有真正重新定义软件行业，因为大家只是从使用硬盘转向使用 flash storage 之类。

But when the internet came out, like basically old school on-prem software for the most part, not entirely, but a lot of it died and it just got replaced by web software.

但互联网出现时，老派的 on-prem software 基本上大部分死掉了，不是全部，但很多都死了，然后被 web software 取代。

And so sometimes you get the kind of, it's additive to an existing thing.

所以有时候，新技术只是给现有事物做增量叠加。

Sometimes you get the actually it redefines an entire product category, redefines an industry.

有时候，它确实会重新定义整个产品类别，重新定义一个行业。

In many cases, the companies themselves turn over it.

很多情况下，公司本身也会因此被换掉。

So there's sort of this question, and an example you just mentioned, Nano Banana.

所以这里就有这样一个问题。你刚才提到的 Nano Banana 就是一个例子。

So a great example is there are these businesses, like just take Adobe.

一个很好的例子是这类公司，比如就拿 Adobe 来说。

Photoshop is built a, whatever, 40-year franchise in image editing.

Photoshop 在 image editing 上建立了一个大概 40 年的业务版图。

Okay, is AI a sort of a feature now that gets added to Photoshop to be able to do AI-based image editing, or do you just stop editing images entirely because you're using Nano Banana and all images are just being generated and it's just easier to just have AI generate a new image than it is to try to edit an old one?

那么，AI 是不是现在作为一种功能被加入 Photoshop，用来做 AI-based image editing？还是说你干脆完全停止编辑图片，因为你在用 Nano Banana，所有图片都直接生成了，直接让 AI 生成一张新图，比试图编辑旧图更容易？

And so I think there's many areas of tech in which that question is being asked and the answers I think will vary by domain.

所以我认为，在 tech 的很多领域里，大家都在问这个问题，而答案会因领域而异。

But obviously as a venture firm, we're betting hard on many of these categories being totally reinvented, and a lot of the best founders are trying to figure out how to do that.

但显然，作为一家 venture firm，我们在大量押注很多品类会被彻底重塑，而很多最优秀的 founders 都在试图弄清楚怎么做到这一点。

So that's kind of AI changing the definition of the product.

所以这就是 AI 在改变产品的定义。

I think the next layer is actually a lot of what we've already talked about which is AI changing the jobs.

我觉得下一层，其实就是我们前面已经聊了很多的：AI 正在改变工作。

And so it's a lot of what we already talked about, but, okay, if I'm a founder of a company and if I have room in my budget for 100 coders, how do I get those coders to be super-empowered AI coders, not the kind of coders I used to have, and if they're super-empowered AI coders, then does that mean, do I still need the 100?

所以这还是我们已经聊过的很多内容，但问题是：如果我是一家公司的创始人，预算里能容纳 100 个程序员，我怎样让这些程序员变成被 AI 极大增强的程序员，而不是我过去那种程序员？如果他们都变成了超强的 AI 程序员，那是否意味着我还需要这 100 个人？

Maybe now I only need 10.

也许现在我只需要 10 个。

Or does that mean I still want 100 but now they're doing 10 times more?

或者说，我仍然想要 100 个，但他们现在能做 10 倍的事？

And so, as you know, a lot of the best founders are working on that right now.

所以你也知道，很多最优秀的创始人现在都在研究这个问题。

And then I think the third shoe to drop hasn't quite dropped yet, but it's kind of the big one which is, all right, the basic idea of having a company, does that change.

然后我觉得第三个变化还没真正落地，但它可能是最大的一个：拥有一家公司这个基本想法，会不会改变。

And again, here you've got this concept of the super-powered individual which is, okay, can you have entire companies where you have basically the founder does everything.

这里又会回到“超级个体”这个概念：你能不能拥有一家完整的公司，基本上由创始人一个人做所有事。

Because what the founder's doing is overseeing an army of AI bots.

因为创始人做的事情，是管理一支 AI bot 大军。

There's kind of this holy grail in our industry that's been running for a long time which is can you have the one-person billion-dollar outcome.

我们这个行业长期以来有一个圣杯式目标：能不能出现一个人做出十亿美元级结果。

We've had a few of those over the years.

这些年我们确实见过几个。

Bitcoin is probably the most spectacular example with Ethereum right behind it which wasn't quite one person but a very small team.

Bitcoin 可能是最惊人的例子，Ethereum 紧随其后，虽然它不完全是一个人，但团队非常小。

You had Instagram and WhatsApp that had very big outcomes with very small teams.

Instagram 和 WhatsApp 也是非常小的团队做出了非常大的结果。

Every once in a while you get one of these things where you just, something hits, and you just have a very small number of people associated with it.

偶尔你会看到这种事：某个东西突然爆了，而真正参与的人数非常少。

But that said, most software companies obviously end up with huge numbers of employees.

但话说回来，大多数软件公司最后显然都会有大量员工。

And so I think the most leading-edge founders are thinking of, okay, how do I reconstitute the actual very definition or idea of having a company and can you have a company that's literally basically just all AI.

所以我觉得最前沿的创始人在想的是：我怎样重新定义“公司”本身，能不能有一家字面意义上几乎全是 AI 的公司。

If you're doing anything in the real world, that's hard, but if you're doing software, that seems like it might be feasible in some cases.

如果你做的是现实世界里的事情，这很难；但如果你做的是软件，在某些情况下看起来可能可行。

And then there's the ultimate example of that which is can you have like autonomous AI economy stuff happening where you have AI bots on the blockchain or something that are basically out there functioning as a business and making money and just literally where the AI does all the work itself and just issues me dividends.

然后还有一个终极版本：能不能出现某种自主 AI 经济活动，比如区块链上的 AI bot，基本上在外面像一家公司一样运转、赚钱，真正由 AI 自己完成所有工作，然后直接给我分红。

Maybe that's the final outlier result.

也许那就是最终的离群结果。

We have a few founders who are chasing that kind of thing.

我们有几个创始人正在追这个方向。

So I would describe that as kind of the latter that the best founders are on.

所以我会把这描述成最优秀的创始人正在推进的后续阶段。

Super interesting.

特别有意思。

This whole idea of a one-person billion-dollar company, I think it depends on your definition of what this is, like an outcome I could see.

一个人做出十亿美元公司这整个想法，我觉得取决于你怎么定义它；某种结果我是能想象的。

Running my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with just support tickets and issues and bugs.

我一个人运营 newsletter，再加一些 contractor，就已经有特别多琐碎烦人的事要处理，比如 support tickets、各种问题和 bug。

It's hard for me to imagine actually a one-person billion-dollar company, even if AI is handling so much of your support because there's just so many random-edge cases that I'm just... like filling out forms.

即使 AI 已经处理了你大量的支持工作，我也很难想象真的能有一个人做出十亿美元公司，因为总有太多随机的边缘情况，比如填表这种事。

And so I guess depends on, do you have contractors, does that count, what does it mean to be a one person.

所以我想这取决于：你有没有 contractor？这算不算？一个人到底是什么意思？

But I'm just like, "I can't see that happening."

但我就是觉得：“我看不到这会发生。”

Yeah.

是。

I mean, look, Bitcoin, Satoshi pulled it off.

我是说，你看，Bitcoin 这件事，Satoshi 确实做成了。

But the open source community now, does that count?

但现在有 open source community，那算不算？

I don't know.

我不知道。

Yeah.

是。

I guess it counts.

我想算吧。

Okay.

好吧。

Yeah, exactly.

对，没错。

Right?

对吧？

So yeah, I would say I don't propose to have answers here, but more just like the smartest people I know or many of the smartest people I know are thinking hard about this.

所以我会说，我不是说自己有答案，只是我认识的最聪明的人，或者其中很多人，都在认真思考这个问题。

Yeah.

是。

What do you think about moats?

你怎么看护城河？

A big question constantly in AI, the fact that everything's changing, just what's your guys' thesis on moats in AI?

AI 里大家一直在问这个大问题：一切都在变，你们对 AI 护城河的 thesis 是什么？

Is that even a thing?

这东西还存在吗？

Do you care?

你们在意吗？

My experience with really big technological transformations, and of course, I kind of lived this directly with the internet and I saw this happen, is the really big technological transformations, they take a long time to play out and there's all of these structural implications that just kind of cascade out over time.

我对真正大型技术转型的经验，当然我亲身经历了互联网，也亲眼看到了它的发展，是大型技术转型需要很长时间才会展开，而且会有各种结构性影响随着时间层层扩散。

There's this rush to judgment upfront where people say, "Oh, it's therefore obvious that X, Y, Z. It's therefore obvious that this kind of company is going to be the company of the future, not that kind.

一开始大家会急着下判断，说：“哦，所以 X、Y、Z 显然就是这样。因此显然这种公司会成为未来的公司，而不是那种。”

It's obvious that this incumbent's going to be able to adapt and this other one isn't.

“显然这个既有巨头能适应，而另一个不能。”

It's obvious that there's economic opportunity in this kind of startup and not in these others.

“显然这种 startup 有经济机会，而其他那些没有。”

It's obvious that the moats are going to be in this area of the technology, but not in this other area."

“显然护城河会出现在技术的这个区域，而不是另一个区域。”

What everybody does is they kind of state those things with just an enormous amount of self-assurance where they really sound like they have all the answers.

大家说这些话的时候，通常都带着极强的自信，听起来好像他们已经掌握了所有答案。

And then what happens is these ideas kind of saturate the media because the media naturally prizes definitive answers over open questions because it... it's like when CNBC is booking guests, they want a guest who's going to come on and say, "Yes, this is the way, it's going to be X."

然后这些想法会充满媒体，因为媒体天然更喜欢确定答案，而不是开放问题。比如 CNBC 订嘉宾时，他们想要的是上来说“是的，方向就是这样，会是 X”的人。

Not like, "You know, I think that's a really good question and let's debate it from eight different angles."

而不是那种说“你知道，我觉得这是个很好的问题，我们可以从八个不同角度来讨论”的人。

What I found is if you look back on those predictions a few years later, and you can do this by the way, if you pull up coverage of the internet from 1993 through 1997, or for that matter even through 2005 or 2010, and you look at the kinds of confidence statements people were making in the first 10 or 15 years, I would say almost all of them were wrong, generally quite badly wrong.

我发现，如果你几年后回头看这些预测，其实你可以这么做，调出 1993 到 1997 年关于互联网的报道，或者甚至一路看到 2005 年、2010 年，看看前 10 年或 15 年里人们那些信心满满的判断，我会说几乎全都错了，而且通常错得很厉害。

And so I think the process, I think there's going to be a massive amount of technological change.

所以我觉得，这个过程会伴随着大量技术变化。

It's going to be like, I don't know, five or six layers of structural change that will play out over time.

它会有大概五六层结构性变化，随着时间展开。

And again, we've talked about a lot of this, but the implications on what are the definition of products, what are the definitions of companies, what are the definitions of jobs, what are the definitions of industries.

我们前面也聊了很多，但它会影响：产品的定义是什么，公司定义是什么，工作定义是什么，行业定义是什么。

How does this play out at the national level?

这在国家层面会怎么展开？

How does this play out at the global level?

这在全球层面会怎么展开？

By the way, how does this intersect with politics?

顺便说一句，它会怎样和政治交织？

How does this intersect with unions?

它会怎样和工会交织？

How does this intersect with war?

它会怎样和战争交织？

What's China going to do?

中国会怎么做？

And so there are just a tremendous number of unknowns, a very, very large number of unknowns, and I think it's just like really, really dangerous to prejudge these things.

所以这里有太多未知，数量非常非常大。我觉得提前预判这些事真的非常危险。

I'll just run this as a thought experiment and you can see what you think on this, but it's like, are AI models themselves defensible.

我先把它当成一个思想实验抛出来，你可以看看你怎么想：AI 模型本身有没有防御性？

Is there a moat on AI models?

AI 模型有没有护城河？

And on the one hand, you'd be like, "Wow, it certainly seems like there is or should be," because if something takes billions of dollars to build and you need this incredible critical mass of computing data and there's only a certain number of engineers in the world that know how to do this and they are getting paid like MBA stars.

一方面你会说：“哇，看起来当然有，或者应该有。”因为如果某个东西需要几十亿美元才能做出来，需要极其强大的算力和数据临界规模，全球只有少数工程师知道怎么做，而且他们的薪水高得像 NBA 球星。

And then these companies have to deal with all these crazy political issues and press issues and reputational stuff and regulatory and legal.

然后这些公司还必须处理各种疯狂的政治问题、媒体问题、声誉问题，以及监管和法律问题。

All of that translates to, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like 100%, I don't know, whatever, 50/50 or 30/30/30 or 90/10 and one, or whatever it is, market share and then they're going to have whatever profitability they have and it's going to be kind of a classic oligopoly, or maybe one company's going to win definitively and it'll be a monopoly.

所有这些都会让人推导出：好吧，最后可能会有两三家公司拿下接近 100% 的市场份额，可能是 50/50，也可能是 30/30/30，或者 90/10，或者别的什么比例；然后它们会有相应的利润率，这会变成一种经典寡头格局，或者也可能某家公司明确胜出，形成垄断。

And by the way, those outcomes have happened in software many times before.

顺便说一句，这些结果以前在软件行业发生过很多次。

And so maybe that will be the outcome.

所以也许那会成为结果。

The other side of it is if you had told me three years ago that in the Christmas of ChatGPT that within basically a year to year and a half there would be five other American companies that would have basically exactly capable products, and then there would be another five companies out of China that would have exactly capable products, and then there would additionally be open source that was basically the same, I would have been like, "Wow, the thing that seemed like it was black magic all of a sudden has become like commoditized really fast," which by the way, is exactly what happened.

另一方面，如果三年前，也就是 ChatGPT 那个圣诞假期，你告诉我基本上一年到一年半内，会有另外 5 家美国公司做出能力几乎完全相同的产品，然后中国又会有另外 5 家公司做出同样能力的产品，再加上 open source 也基本达到同样水平，我会说：“哇，原本看起来像黑魔法的东西，突然很快就商品化了。”顺便说一句，事实正是这样。

Within a year of GPT3 coming out, there were there open source GPT3s running on a fraction of the hardware that were available for free.

GPT3 发布后不到一年，就已经有开源版 GPT3 在少得多的硬件上运行，而且免费可用。

And then there were five.

然后就有了 5 个。

Now you've got, fully in the game, you've got Google and you've got Anthropic and you've got xAI and you've got Meta and you've got all these other companies that are... and then DeepSeek and Kimi and all these other Chinese companies.

现在已经全面入局的，有 Google、Anthropic、xAI、Meta，还有所有这些其他公司；然后还有 DeepSeek、Kimi，以及其他这些中国公司。

And so even at the level of LLMs or AI models, you can squint and make that argument either way.

所以即使在 LLM 或 AI 模型这一层，两边的论证都能说通。

By the way, same thing at the level of apps.

顺便说，app 层也是一样。

It's like one school of thought is apps are not a thing because the model's just going to do everything, but another way of looking at it is no, actually adapting the model is kind of the engine into a domain involving human beings where you need to actually have it fit for purpose to be able to function in the medical industry or the legal industry or whatever or coding.

一种观点是 app 不存在，因为模型会做所有事情；但另一种看法是，不，其实把模型这个引擎适配到一个涉及人类的领域里，需要让它真正适配用途，才能在医疗行业、法律行业或其他行业，或者编程领域里发挥作用。

No, you actually need the application level's actually going to matter enormously, and maybe the LLMs commoditize and maybe the value goes to the apps.

不，应用层其实会极其重要，也许 LLM 会商品化，价值会流向 app。

And again, you can kind of squint either way on that one, and I know very smart people who are on both sides of that argument.

这个问题同样两边都能说通，而且我认识两边都有非常聪明的人。

And so my honest answer on this is I think we're in a process of discovery over time.

所以我对这个问题的诚实回答是：我认为我们正处在一个随时间发现答案的过程中。

The way I think about this kind of structurally is it's a complex adaptive system.

我从结构上看这类问题，会把它看成一个复杂适应系统。

The technology itself provides one of the inputs.

技术本身只是其中一个输入。

The legal and regulatory process is another input.

法律和监管过程是另一个输入。

Actual individual choices made by entrepreneurs matter a lot.

创业者个人做出的实际选择也非常重要。

The economics matter a lot.

经济性非常重要。

Availability of investor capital varies over time, that matters a lot.

投资资本的可得性会随时间变化，这也非常重要。

This is a complex system, and so we actually don't know the outcomes on this yet.

这是一个复杂系统，所以我们其实还不知道最终结果会是什么。

We need to be open to surprises at the structural level of what happens.

我们需要对结构层面会发生什么保持开放，准备接受意外。

And of course, as a VC, this is very exciting because it means we're doing this now.

当然，作为 VC，这非常令人兴奋，因为这意味着我们现在就在做这件事。

We should make bets along every one of these strategies and see how this plays out.

我们应该沿着这些策略的每一条都下注，然后看看它会怎么发展。

I would just say, there may be, I don't know, there may be like one particularly brilliant, I don't know, hedge fund manager or something who has this all figured out, but I guess I would say if they exist I haven't met them yet.

我只能说，也许会有一个特别聪明的 hedge fund manager 之类的人已经把这一切都想明白了，但我想说，如果真有这样的人，我还没见过。

So what I'm hearing here is don't over-obsess with moats at this point because we have no idea what'll end up being, and as much as it may feel like, okay, there's no way OpenAI will lose this lead, clearly we're seeing a lot of competition.

所以我听到的是：现阶段不要过度执着于护城河，因为我们完全不知道最后会是什么样。即使现在感觉 OpenAI 不可能失去领先地位，但显然我们已经看到很多竞争。

GPT wrapper point is really great.

GPT wrapper 这个点真的很棒。

It was such a derogatory term, I don't know, a year ago, just like, "You're just a GPT wrapper."

大概一年前它还是个很贬义的词，就像别人说：“你不就是个 GPT wrapper 吗？”

Now it's like the companies that are the biggest companies, the fastest growing companies in the world.

现在这些公司却成了全球最大、增长最快的公司。

Yeah. Well, it's like a little bit like, I don't know, I mean, even just like with... this has been the holiday, three years ago was the holiday of ChatGPT. This last month or whatever has been the holiday of Claude, particularly Claude Code for coding. But it's pretty amazing because it's like, okay, there was Claude which is obviously a great accomplishment, but then there's Claude Code which is an app. It's a Claude wrapper. It's agent harness. And then they did this amazing thing where they came out with, was it Coworker?

对。嗯，这有点像……我不知道，就拿这个来说，三年前那个假期属于 ChatGPT；过去这个月左右则属于 Claude，尤其是用于编程的 Claude Code。但这很惊人，因为你会说，好，有 Claude，这显然是很大的成就；然后有 Claude Code，它是一个 app，是一个 Claude wrapper，是 agent harness。然后他们又做了一件很厉害的事，推出了那个，叫 Cowork 吗？

Cowork.

Cowork。

Cowork.

Cowork。

And remember what they said of Cowork, which is Claude Code worked Cowork in a week.

还记得他们怎么说 Cowork 吗？Claude Code 用一周写出了 Cowork。

Yeah, a week and a half, yep, 100%.

对，一周半，没错，100%。

Well, and there's two ways looking at that which is like, "Wow, that's really..." I mean, obviously that's really impressive that Claude Code was able to build Cowork in a week and a half.

嗯，这有两种看法。一种是：“哇，这真的……”我是说，Claude Code 能在一周半内做出 Cowork，显然非常厉害。

That's great.

这很好。

That's amazing.

这很惊人。

The other way to look at it is Cowork was developed in a week and a half.

另一种看法是，Cowork 是一周半做出来的。

How much complexity could there be?

那里面能有多少复杂度？

How much of a barrier to entry can there be in something that was developed in a week and a half?

一个一周半做出来的东西，进入壁垒能有多高？

And then again, it's this push and this pull thing where it's like, wow, it's incredibly functional, incredibly valuable, and people all over the world and every day now are like, "Wow, I can't believe what I can do with this.

然后这又变成一种拉扯：一方面它功能极强、价值极高，现在全世界每天都有人说：“哇，我不敢相信我能用它做这些事。”

It's like the most magical product ever."

“这简直是史上最神奇的产品。”

But at the same time, it took a week and a half.

但与此同时，它只花了一周半。

And so every other model company, I'm sure, you'd have to expect, is sitting there being like, "Okay, obviously we need to build an Asian artist and then obviously we need to build a Cowork thing for regular people."

所以我敢肯定，其他每一家模型公司大概都会坐在那里想：“好，显然我们需要做一个 agent harness，然后显然我们还需要给普通人做一个类似 Cowork 的东西。”

I'm not even saying I know anything, but just obviously they're all going to do that.

我不是说我知道什么内幕，只是显然他们都会这么做。

And so how defensible is that?

所以这东西有多可防守？

In six months, and we've seen this happen before, is Claude Code going to get lapped the same way that GitHub Copilot got lapped?

六个月后，类似的事情我们以前见过，Claude Code 会不会像 GitHub Copilot 那样被反超？

The history in the last three years has been everything that looks like it's like the fundamental breakthrough gets basically replicated and lapped very quickly.

过去三年的历史说明，任何看起来像根本性突破的东西，基本都会很快被复制并超越。

Many of the smartest people I know in the field, when I really talk to them, kind of get a couple drinks into them, they're like, "Yeah."

我认识这个领域里很多最聪明的人，真正跟他们聊，喝上几杯之后，他们会说：“对。”

One theory is there really aren't any secrets among the big labs.

一种理论是，大实验室之间其实没有什么秘密。

The big labs kind of all have the same information and they kind of have all the same knowledge and they lap each other on a regular basis, but there's not a lot of proprietary anything at this point.

这些大实验室基本都掌握同样的信息和知识，它们经常互相超越，但到现在为止，真正专有的东西并不多。

And then again, evidence of that is DeepSeek came out of left field and basically was like a re-implementation of a lot of the ideas under American big labs and had some original ideas of its own.

再说一个证据，DeepSeek 突然杀出来，基本上重新实现了很多美国大实验室底层的想法，同时也有一些自己的原创想法。

But, wow, it wasn't that hard for some basically a hedge fund in China to do it, and so how much defensibility is there.

但让人惊讶的是，中国一家本质上是 hedge fund 的机构做成这件事并没有那么难，那这里到底有多少防御性？

But on the other side of it, you've got, wow, these big labs are now paying individual engineers like they're rock stars and they're incredibly bright and creative people.

但另一方面，这些大实验室现在给单个工程师的待遇像摇滚明星一样，而这些人也确实极其聪明、有创造力。

Maybe there's a dozen nascent ideas at any one of these labs that is actually going to be a huge breakthrough that's going to be hard to replicate.

也许在任何一家实验室里，都有十几个刚萌芽的想法，最后会变成难以复制的巨大突破。

And so again, it's just like, I think we just need... I don't know, my view is I need to put a big discount on my forecasting ability on this one.

所以还是那句话，我觉得我们只是需要……我不知道，我的看法是，在这件事上，我得大幅打折自己的预测能力。

For me, it's much less interesting to try to say, "Okay, as a consequence, industry structure in five years is going to be X, the big winner and the category is going to be company Y, the big product killer app is going to be Z."

对我来说，没那么有意思的是去说：“好，所以五年后的行业结构会是 X，类别里的大赢家会是 Y 公司，真正的 killer app 会是 Z。”

It's like, I don't think I can predict that.

我觉得我预测不了这个。

I think a much better use of my time is being very flexible and adaptable at a time like this.

我觉得在这种时候，更值得花时间的是保持高度灵活和适应性。

So with all this in mind, do you feel like there's something you're paying attention to more to help you decide, okay, this is where we want to place our bet, or is the answer essentially the strategy you guys have, which is place a lot of bets?

考虑到这些，你现在有没有更关注某些东西，来帮你判断“好，这是我们想下注的地方”？还是答案基本上就是你们现在的策略，也就是下很多注？

You guys raised the largest fund in history.

你们募了史上最大的 fund。

Is that the way you win in this world?

在这个世界里，这就是获胜方式吗？

Yeah.

对。

I mean, for us, yeah.

我是说，对我们来说，是的。

For us, we obviously have a very deliberate strategy.

对我们来说，我们显然有非常明确的策略。

One way to think about this the Peter Thiel... You remember the Peter Thiel formulation of... he said, "There's a two by two, there's optimism and pessimism, and then there's determinant and, is it indeterminate, and indeterminate."

可以用 Peter Thiel 的一个框架来理解……你还记得 Peter Thiel 的说法吗？他说有一个二乘二矩阵，一边是乐观和悲观，另一边是确定性和不确定性。

And so he always argued that Silicon Valley is characterized by too much what he calls indeterminate optimism.

所以他一直认为，Silicon Valley 的特点是他所谓的过多“不确定性乐观”。

What he meant by that is basically, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better but can't explain why.

他的意思基本上是，一个不确定性乐观主义者相信世界会变好，但说不清为什么。

Some combination of things is going to happen to make the world be better even if we don't know what those things are.

某些事情的组合会发生，让世界变得更好，即使我们不知道那些事情是什么。

I think he at least historically would say that's basically... that risks at least being just wishful thinking or delusional thinking.

至少从历史上看，我认为他会说，这基本上有风险变成一厢情愿，甚至是妄想。

What the world needs more is determinant optimists, which are people who are like, "No, the world is going to be better because I'm going to do this specific thing."

他认为世界更需要的是确定性乐观主义者，也就是那些会说：“不，世界会变好，因为我要做这件具体的事。”

He would classify, for example, Elon, he would sort of maybe say VCs are indeterminate optimists and then he would say Elon is the determinant optimist where it's like, no, I'm going to build the electric car, I'm going to do solar, and then I'm going to do Mars and these very concrete things.

比如他可能会把 Elon 归为这一类。他也许会说 VC 是不确定性乐观主义者，而 Elon 是确定性乐观主义者，因为他说，不，我要造电动车，我要做太阳能，然后我要去 Mars，这些都是非常具体的事情。

I think there's a lot to Peter's framework, but the way I would describe it is I think maybe if he and I disagree with part of that it would be I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category, and that's the strategy that we have at a16z which is... and the reason for that is hopefully it's not so much wishful thinking.

我觉得 Peter 的框架有很多道理，但如果说我和他在哪一点上可能不同，那就是我认为不确定性乐观是一种更强的现象，至少比他过去描述的更强。我会明确把自己归在不确定性乐观主义者这一类，这也是我们在 a16z 的策略。原因是，希望这并不只是愿望式思维。

It's more, no, the indeterminate optimism of venture capital or the indeterminate optimism of a16z or Silicon Valley is actually very specific which is there are these extremely bright and capable people, like Elon and many others, who are founders and kind of product creators.

更像是说，venture capital 的不确定性乐观，或者 a16z、Silicon Valley 的不确定性乐观，其实非常具体：有一批极其聪明、有能力的人，像 Elon 和很多其他人，他们是创始人，也是产品创造者。

Each of those individual people is a determinant optimist.

这些人每一个单独来看，都是确定性乐观主义者。

Each of them individually has a very strong view of what they're going to do, but the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley is we don't just have one of those and we don't just have 10 of those.

他们每个人都对自己要做什么有非常强烈的判断。但资本主义体系的巨大优点、美国经济的巨大优点、Silicon Valley 的巨大优点，是我们不只有一个这样的人，也不只有 10 个。

We have 100 and a thousand and then 10,000 of those.

我们有 100 个、1000 个，甚至 10000 个。

The way to optimize the outcome is to have as many of those as possible be as good as possible, run as hard as possible.

优化结果的方式，就是让尽可能多这样的人尽可能优秀、尽可能全力往前跑。

And then just the nature of the future is like we just don't know all the answers and that's okay.

而未来的本质就是，我们不知道所有答案，这也没关系。

And then the right way to deal with that is to run as many experiments as possible and have as many smart people try to do as many interesting things as possible.

应对方式就是尽可能多地跑实验，让尽可能多的聪明人去尝试尽可能多有趣的事情。

And so, yeah, I would put myself firmly on the side of the indeterminate optimistic.

所以，是的，我会坚定地站在不确定性乐观这一边。

I'm wondering if the answer to the question of what you look for now more and more is this determinant optimistic founder that has this massive ambition and is-

我在想，现在你越来越看重的，是不是就是这种确定性乐观的创始人：有巨大野心，而且正在……

... [inaudible 01:21:00] Optimistic founder-

……[听不清 01:21:00] 乐观的创始人……

Yeah.

对。

... and has this massive ambition and is actually working on achieving it.

……有巨大野心，而且真的在努力实现它。

Yeah, yeah.

对，对。

No, that's right, that's right.

没错，是这样。

I mean look, the founders need to be determinate optimists.

我的意思是，创始人需要是确定性乐观主义者。

They need to have a very specific plan.

他们需要有一个非常具体的计划。

And look, the critique always... The critique from the founders is, "Oh UVCs have it easy, because you don't actually have to commit, right?

而且你看，创始人一直会批评说：“哦，你们 VC 太轻松了，因为你们其实不用真正押上全部，对吧？”

You don't actually have to, like, make... You have to make the bed you lay in, you can, like, place multiple bets you can have.

你们其实不用真的……不用自己做了选择就只能承受后果；你们可以下多个注。

Whereas a portfolio, you should have a lot more sympathy for us as founders, because we only get to make the one bet."

而作为一个 portfolio，你们应该更同情我们这些创始人，因为我们只能下那一个注。”

And there's truth to that.

这里面有道理。

The kind of argument on that is the founders get to run their companies, we don't.

对此的反驳是，创始人可以经营自己的公司，而我们不能。

So, we don't get to put our hand on the steering wheel.

所以，我们不能把手放到方向盘上。

And so, the great virtue of being a determinant optimist is you actually get to single-mindedly execute against that goal.

因此，做一个确定性乐观主义者的巨大好处，是你真的可以一心一意地围绕那个目标执行。

And look, in the long run, who does history remember?

而且从长远看，历史会记住谁？

History remembers Henry Ford, right, not whoever was, whatever the seed investor who seeded Ford Motor Company, and 10 other car companies have failed.

历史会记住 Henry Ford，对吧，而不是那个给 Ford Motor Company 播种子轮、同时还投了 10 家失败汽车公司的 seed investor。

Right?

对吧？

And so, the determinant optimist is the founder of the company builder and the engineer, and these are the people who actually use the sign, and deserve 99.9999% of the credit.

所以，确定性乐观主义者就是公司建造者和工程师的创始人，他们才是真正承担风险、也理应获得 99.9999% 功劳的人。

But you know, having said that, I do think there is a role for having some indeterminate optimists in the background, no, helping along the way, and helping keep the whole cycle going.

不过话说回来，我确实认为，背景里有一些不确定性乐观主义者也有作用，他们会在一路上提供帮助，并让整个循环持续运转。

Do you think about AGI in shifting your investment thesis?

你会考虑 AGI 来调整你的投资 thesis 吗？

Like, as we approach AGI and hit AGI, as an investor, how do you think about your investment thesis changing?

比如，当我们接近 AGI 并达到 AGI 时，作为投资人，你会怎么思考自己的投资 thesis 会如何变化？

Yeah, so I've always kind of had a little bit of an issue... I've always kind of struggled with the concept of AGI because at least... Well, let's put it this way.

对，所以我一直对 AGI 这个概念有点问题……我一直有点难以处理 AGI 这个概念，因为至少……这么说吧。

Let's define terms which is where I kind of struggle with it.

我们先定义术语，这也是我觉得棘手的地方。

Which is, there's like the prosaic definition of AGI, and then there's like the cosmic definition.

AGI 有一种世俗定义，也有一种宇宙级定义。

And the way I describe it as, so let me start with the cosmic one.

我会这样描述，先从宇宙级定义说起。

So the cosmic one is basically, is the singularity, right?

宇宙级定义基本上就是 singularity，对吧？

And so, AGI is the moment where you enter the singularity which is to say where the world fundamentally changes.

也就是说，AGI 是你进入 singularity 的那个时刻，也就是世界发生根本性变化的时刻。

And the rules of the old world are gone, we're now operating in a new domain.

旧世界的规则消失了，我们开始在一个新的领域里运转。

And then the full definition of singularity is it's a world in which human judgment is no longer really relevant because you get this self-improvement loop, the AI is improving itself.

而 singularity 的完整定义是，在那个世界里，人类判断不再真正相关，因为你会有一个自我改进循环，AI 在改进自己。

In a sort of race circle takeoff scenarios, you could see if this takeoff thing, where the AI's improving itself, and the machines are making decisions so much faster than people, and people are just sitting there watching the machine do its thing.

在某种快速起飞场景里，你可以想象出现这种起飞：AI 在自我改进，机器做决策的速度远远快于人类，而人类只是坐在那里看着机器做它自己的事。

And I kind of described it, I don't really think we live in that world, whether they could call that utopian or dystopian, I don't think we're lucky or unlucky enough to live in that world.

我会这样描述：我其实不认为我们生活在那样的世界里，不管你把它叫作乌托邦还是反乌托邦，我都不认为我们幸运或不幸到生活在那种世界里。

We could debate that, we could talk about that more.

这个可以再辩论，我们也可以继续聊。

But the prosaic definition of AGI that at least I think the industry purchases but it's kind of conversed on, and tell me if you agree with this, is when the AI could do every economically-relevant task as good as a person.

但 AGI 的世俗定义，至少我认为行业大致接受、也逐渐形成共识的，是 AI 能像人一样做好每一项有经济意义的任务。

The way the co-founder of Anthropic put it is, like, "A basket of the most valuable economic tasks," so it's, like, 10, 15, not every single economically-valuable task.

Anthropic 的 co-founder 的说法是，“一篮子最有价值的经济任务”，也就是 10 到 15 项，而不是每一项有经济价值的任务。

Okay, got it.

好，明白。

Yeah, so it's maybe even a slightly reduced definition.

对，所以这甚至可能是一个稍微收窄的定义。

And by the way, we're clearly getting close to that if we're not already there.

顺便说一句，我们显然已经很接近了，即使还没到。

And so on that one, I kind of feel like, so I kind of feel like the cosmic one overstates what's going to happen.

所以在这一点上，我感觉宇宙级定义夸大了将要发生的事情。

And then I kind of feel like the kind of AGI definition that you just gave, I think it kind of understates what's going to happen.

而你刚才给出的那种 AGI 定义，我又觉得它低估了将要发生的事情。

It's almost too reductionist.

它几乎太还原主义了。

And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything.

原因是，我不认为有任何理由假设人类技能水平就是任何事情的上限。

Right?

对吧？

And so the way we say that is AGI always is the definition you gave, the definition I gave.

所以我们说 AGI 时，总是你给的定义、我给的定义。

It's always kind of relative in comparison to a human worker, right?

它总是相对于一个人类劳动者来比较，对吧？

And it's, like, I don't know, human skill level caps out at a certain point, but that's because of the inherent biological limitations of the human organism.

而问题是，我不知道，人类技能水平会在某个点封顶，但那是因为人类有机体本身固有的生物限制。

Right?

对吧？

Human, I gave you an example.

我给你举个例子。

Human IQ, kind of what they call "fluid intelligence," or the sort of G factor of fluid intelligence, IQ I think tops out in humans as a species, it tops out around 160.

人类 IQ，也就是他们所说的“流体智力”，或者流体智力的 G factor，我认为作为一个物种，人类 IQ 的上限大概在 160。

Right?

对吧？

Where at like 160 it's like Einstein level, Einstein [inaudible 01:25:00]-

到 160 左右，就像 Einstein 水平，Einstein [听不清 01:25:00]……

In terms of IQ.

就 IQ 而言。

Yeah.

对。

... in terms of IQ.

……就 IQ 而言。

Like, it just tops out at 160.

它就是在 160 封顶。

The 160 IQ people are the ones who come up with new physics, there's only a small handful of those.

IQ 160 的人是那些能提出新物理学的人，这样的人只有极少数。

Generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a bestselling author, or like a, you know, one of the world's best, I don't know, research scientists, or one of the world's best doctors, whatever it would be, probably 140 is kind of the IQ that you're looking for there.

一般来说，当我们在世界上遇到一个极其聪明的人，比如畅销书作者，或者世界顶尖的某类研究科学家，或者世界顶尖医生之类，大概 140 就是你要找的 IQ 水平。

If you're looking for a really good lawyer, it's probably 130.

如果你要找一个非常好的律师，大概是 130。

If you're looking for a really good line manager in a business, it's probably 110.

如果你要找一个非常好的企业一线经理，大概是 110。

If you're looking for an accountant, like a small business accountant, who's good at doing the books for small businesses, it's probably 105.

如果你要找一个会计，比如小企业会计，能很好地给小企业做账，大概是 105。

Right?

对吧？

And so the kind of scope of impressive human... The ability of the human organism to do intellectually impressive things, it's sort of that 110 to 160 is kind of the spectrum, and good news is there's a lot of those people running around, but there's not that many at 140, 150, 160.

所以，人类能够做出令人印象深刻的智力事情，其范围大概就在 110 到 160 这个区间。好消息是，这样的人不少，但 140、150、160 的人并不多。

But it's like, that's like the limitations of what can fit in here, right?

但这就像是这里面能装下多少东西的限制，对吧？

And it's like, there's no theoretical limit on where this goes if you release the limitations of human biology, right?

如果解除人类生物学限制，这件事往哪里走，在理论上没有上限，对吧？

And so, can you have a... And you already have people running these experiments to kind of do human-equivalent kind of IQ for existing AI models.

所以，你能不能有一个……而且已经有人在做这些实验，试图为现有 AI 模型测出类似人类等价的 IQ。

And by the way, existing AI models are kind of testing around the 130, 140 level, which means they're going to get to the 160 level.

顺便说一句，现有 AI 模型的测试结果大概在 130、140 水平，这意味着它们会达到 160 水平。

And they're arguably on the math side starting to get to the 160 level now.

而且可以说，在数学方面，它们现在已经开始达到 160 水平了。

But I think we're going to have AI models relatively quickly that are going to be like 160, 180, 200, 250, 300.

但我认为，我们相对很快就会拥有 160、180、200、250、300 水平的 AI 模型。

By the way, and I think that's great, right?

顺便说一句，我认为这很好，对吧？

I feel as great about that as I do about the fact that we occasionally get an Einstein.

我对此的感觉，就像我对我们偶尔会出现一个 Einstein 这件事的感觉一样好。

Right?

对吧？

It's like, would the world be better off or worse off with more or fewer Einsteins?

问题是，这个世界有更多 Einstein 会更好，还是更少 Einstein 会更好？

And the answer is, of course the world would be better off with more Einsteins, and of course the world would be better off with machines that have more IQ like Einstein or greater than Einstein.

答案当然是，世界有更多 Einstein 会更好，当然世界上有 IQ 像 Einstein 甚至超过 Einstein 的机器也会更好。

But I think IQ of the machines is going to exceed that of the humans, I think that's really good.

但我认为机器的 IQ 会超过人类，我觉得这真的很好。

And then the performance, again, it goes back to like the AI coding thing that's happening.

然后表现方面，还是回到正在发生的 AI coding 这件事。

Performance against task is going to get better also.

在具体任务上的表现也会变得更好。

I think this is where Linus Torvalds in particular was like, "Yeah, okay, this thing is starting to generate better code than I can."

我觉得尤其是 Linus Torvalds 当时就像是：“行吧，这东西开始生成比我写得还好的代码了。”

Okay?

对吧？

So now we're going to have AI coders that are actually better coders than the best human coders.

所以现在我们会有 AI 程序员，他们的编码能力真的会超过最顶尖的人类程序员。

I think that's... Right?

我觉得这是……对吧？

I think we're going to have AI doctors that are better than the best human doctors, I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see, which we can talk about.

我觉得我们会有比最顶尖人类医生更强的 AI 医生，会有比最顶尖人类律师更强的 AI 律师，这其实会非常有意思，我们可以聊聊。

Which I think is also great.

我觉得这也很棒。

And so, I don't think there's a... I think we're used to living in a world where we just don't understand how good good can get, because we've been capped by our own biology.

所以我不觉得有一个……我觉得我们习惯生活在一个根本不知道“好”能好到什么程度的世界里，因为我们一直被自己的生物条件限制住了。

And we're going to get to experience what it's like when you have the capability at your fingertips, that's actually better than human in these domains.

而我们将会体验到，当你手边就有在这些领域实际超过人类的能力时，会是什么样子。

So you see what I'm saying, which is, like, I think this idea of human equivalent is just going to be a footnote.

所以你明白我的意思，我觉得所谓“达到人类水平”这个概念，最终只会变成一个脚注。

It's like, "Oh yeah, that was just on Tuesday, in 2026 is when they hit that."

就像：“哦对，2026 年某个周二，他们达到了那个水平。”

And it kind of didn't matter because the next question was, like, "Okay, what do we get to do in a world where we actually have machines that are better than that?"

而这其实没那么重要，因为下一个问题会是：“好，那在一个机器已经比这更强的世界里，我们能做什么？”

Right?

对吧？

And so, I think this is going to be much more of an exploratory process for actually seeding human capability than it's going to be any sort of particular singular singularity moment or whatever that happens, that just happens to coincide with the human threshold.

所以我觉得，这更像是一个真正扩展人类能力的探索过程，而不是某个刚好碰上人类门槛的、特定的奇点时刻之类的东西。

200 IQ, I... Just like that frame of reference is such a mind-expanding way to think about just how fast and how smart these things are going to get, and quickly.

200 IQ，我……这个参照框架真的很开脑洞，让人重新思考这些东西会变得多快、多聪明，而且会很快。

Well, I don't know if you have this experience, I have this experience all the time.

我不知道你有没有这种体验，我一直有。

Well, two experiences I have all the time.

准确说，我一直有两种体验。

One is just like, I know I ought to be able to do this, but I just can't... It's going to take too long, I want to write this thing, or I want to... Whatever, I want to have this theory on this thing, or to have a plan or whatever.

一种是，我知道自己应该能做这件事，但我就是做不了……要花太久了。我想写这个东西，或者我想……不管是什么，想对某件事形成一个理论，或者做个计划什么的。

And it's just like, "Fuck," I don't have the eight hours, or by the way, the eight weeks or the eight years, right?

然后就会想：“操”，我没有这 8 个小时，顺便说，也没有 8 周或者 8 年，对吧？

And I just don't know enough yet, and I'm just, like, I can't do the math in my head, and my memory isn't perfect, and I can't remember, and I read... I don't know if you have this, you get interested in something, you read 10 books.

而且我知道得还不够多，我没法在脑子里把数学算出来，我的记忆也不完美，我记不住。我读……不知道你有没有这种情况，你对某件事感兴趣，读了 10 本书。

And then you're like, "Shit, I forgot almost everything I just read."

然后你会想：“靠，我刚读的东西几乎全忘了。”

I wish I could retain it all but I can't.

我希望自己全都能记住，但我做不到。

It's just like you just have this... I sort of live in this state of endless frustration.

这就像你一直有这种……我有点生活在一种无尽挫败感里。

And so it's like, if I could just be smarter than I was, I'd be much better at what I do, but I'm not.

所以会觉得，如果我能比现在更聪明一点，我就能把自己的工作做得好得多，但我并没有。

So there's that.

这是其一。

And I don't know how often you have this, but I have this on a regular basis.

还有一种我不知道你多常遇到，但我经常遇到。

It's just like, "I," because of what we do, I know a bunch of people who I know for fucking sure are smarter than I am.

因为我们做的事情，我认识很多人，我他妈非常确定他们比我聪明。

And I know it because when I talk to them, I just find myself at a certain point.

我之所以知道，是因为我跟他们聊天时，到某个时候就会发现自己进入一种状态。

It's like for the first half of the conversation, I'm just taking notes the entire time.

前半段对话里，我全程都在记笔记。

And for the second half of the conversation, I'm just like, "Fuck," like, "Fuck me."

后半段对话里，我就只是在想：“操”，“我靠。”

Like, this person is just smarter than I am, and they're just out-thinking me, and they're going to keep out-thinking me, and I just can't, and I'm just like, "All right, goddammit.

这个人就是比我聪明，他就是在思考上压过我，而且会一直压过我，我就是跟不上，然后我就想：“好吧，该死。”

I've got to go home and I've got to have a drink."

我得回家喝一杯。

Because I'm just not... Whatever that is, I'm not that.

因为我就是没有……不管那是什么，我没有那个东西。

And so, we're just so used to having those limitations, that the idea of having machines that work for us that don't have those limitations, I just... I think that's much more exciting than people are giving it credit for.

所以我们太习惯这些限制了，以至于一想到会有为我们工作的机器，而它们没有这些限制，我就……我觉得这比人们现在承认的要令人兴奋得多。

Oh man.

天啊。

I could talk to you for hours, Mark.

Mark，我能跟你聊好几个小时。

I'm thinking to close out the conversation, I want to ask about your media diet and your product diet.

我想在收尾时问问你的媒体摄入和产品摄入。

You just talked about books, 10 books, I think you famously read constantly.

你刚才提到书，10 本书。我觉得你是出了名的一直在读书。

I saw an interview with you where you're just like, "Airpods changed my life, I'm just listening to audiobooks now all the time."

我看过你一个采访，你当时大概说：“AirPods 改变了我的生活，我现在一直在听有声书。”

So in terms of a media diet, what are you reading, what are you paying attention to these days in terms, I don't know, podcasts, newsletters, blogs, things like that, and then any books in particular?

所以就媒体摄入来说，你最近在读什么、关注什么？比如 podcasts、newsletters、blogs 之类的，还有没有特别推荐的书？

Yeah, yeah.

对，对。

So what I read is basically, I mean I read... So I read basically three categories of things.

所以我读的东西基本上……我是说，我读……基本分三类。

So in terms of general media, it's basically I sort of... I always describe it as I have an almost perfect barbell strategy, which is I read X, and I read old books.

就一般媒体来说，基本上我总是把它描述成一种近乎完美的杠铃策略：我读 X，也读老书。

Right?

对吧？

So it's basically either, like, up-to-the minute what's happening right now, or it's like a book that was written 50 years ago that has stood the test of time, and then where presumably there's something timeless in it.

也就是说，要么是当下正在发生的、精确到此刻的东西；要么是 50 年前写的、经受住时间考验的书，里面大概有某种恒久的东西。

And then it's sort of everything in the middle, I'm always much more skeptical about.

至于中间的所有东西，我通常都更怀疑。

And in particular, it's kind of what I already said, which is I think if you go back and you read old... Nobody ever does this, it's actually really funny, there's no market for it.

尤其是我刚才其实已经说过的，如果你回头读老的……没人会这么做，这其实很好笑，也完全没有市场。

But if you go back and you read old newspapers... And by the way, you can do this, just read the last week's newspaper, right?

但如果你回头读旧报纸……顺便说，你完全可以这么做，就读上周的报纸，对吧？

Yeah, today, so we're taping on Friday.

对，今天我们是在周五录制。

So read last Friday's newspaper, right?

那就读上周五的报纸，对吧？

And just go back and read it, and be like, "Oh my God.

你回头去读，就会想：“我的天。”

None of this happened.

这些事一个都没发生。

None of what they predicted played out the way that they said that it would.

他们预测的那些东西，没有一个是按他们说的方式发生的。

None of this turned out to actually be that relevant or correct."

这些内容最后也没有真的那么相关或正确。

They didn't understand, by the way, they had no view of what was going to happen this week.

顺便说，他们当时并不理解，也完全不知道这一周会发生什么。

Then they couldn't know, and so they were making predictions and forecasts and so forth based on not having information.

他们不可能知道，所以他们是在缺乏信息的情况下做预测、做判断等等。

But it's like, "Wow, none of this happened, I wish I had never read this, oh my God."

但你会想：“哇，这些事都没发生，我真希望自己从没读过这个，天啊。”

And then it's kind of the same thing with magazines, I go back and read old magazines, and just the level of just the endless numbers of predictions that they make.

杂志也是类似。我回头读旧杂志，会看到他们做出的无穷无尽的预测。

And kind of, you know, the problem with... Newspapers at least they're going day-to-day, the thing with magazines is it's like a week or month kind of a long cycle.

而且问题在于……报纸至少是一天一天来的，杂志的问题是它有一周或一个月这种更长的周期。

And so by the time an article even hits publication, it's often out of date.

所以一篇文章真正刊出来的时候，往往已经过时了。

So I just have a big problem with kind of everything in the middle.

所以我对中间这一大块东西一直很有意见。

And so it's either of the moment or timeless.

所以要么是当下的，要么是永恒的。

But then yeah, you mentioned newsletters.

不过对，你提到了 newsletters。

I mean, so the other thing, and this is maybe obvious, but I think it's probably still underrated, which is actual practitioners in the field who are actually creating content, I think probably is still dramatically underrated.

另一个事情，也许很明显，但我觉得可能仍然被低估了很多，就是某个领域里真正的实践者在创作内容，这件事可能仍然被严重低估。

And I think this is a huge part of the Substack phenomenon, and the newsletter phenomenon, and the podcast phenomenon, is, like, direct exposure to the people who are actually principals in the field who actually know what they're talking about is probably still dramatically underrated.

我觉得这是 Substack 现象、newsletter 现象和 podcast 现象的一个巨大组成部分：你可以直接接触那些真正身在其中、真正知道自己在说什么的核心人物，这可能仍然被严重低估。

And I think again, the reason for that is like we're used to being in this mass media kind of culture in which basically everything is mediated.

原因我觉得还是，我们习惯了处在一种大众媒体文化里，基本上一切都经过中介。

Right?

对吧？

Everything got filtered through like TV interviews or, like, newspaper interviews, or magazine interviews.

所有东西都要经过电视采访、报纸采访或杂志采访这类过滤。

And obviously now more and more it's just, no, you actually want smart people who are actually working on something explaining themselves.

而现在显然越来越多的情况是，不，你真正想要的是那些聪明、正在做事的人亲自解释自己。

And then you have tons of intermediation, like podcasts, that kind of open that up for people and make that possible.

然后你有大量的中介形式，比如 podcasts，把这件事向人们打开，并让它成为可能。

And so yeah, domain practitioners are really great.

所以对，领域实践者真的很棒。

I mean, yeah, just to state the obvious in AI, it's obviously your stuff, but also, like, the fact that Lex Fridman can have the world's leading... And any of you guys, there's a small handful of you guys who have access to these people, you could have the world's leading experts in the domain actually show up.

我的意思是，对，在 AI 领域说句显而易见的话，当然有你的内容；另外像 Lex Fridman 能请到世界顶尖的……你们这些人也是，有少数几位能接触到这些人，可以让世界级领域专家真的来聊。

And by the way, and look, the critique always is, people talk their book, like if I'm running a startup or whatever I'm just selling.

顺便说，批评意见总是说，人们都是在为自己背书。比如我在经营一家 startup 或者别的什么，我就是在推销。

But it's like... And there's always a little bit of that... But it's also, my experience is people love to talk about what they do.

但其实……当然总会有一点这种成分……可根据我的经验，人们喜欢谈论自己在做的事。

And they fundamentally want to express what they do, and they want to explain it, and they want people to understand it.

他们从根本上就想表达自己在做什么，想把它解释清楚，也希望别人理解。

And everybody kind of enjoys that, and they get to contribute to human knowledge by doing that, and they get ego gratification by doing that.

每个人基本都会享受这个过程，而且他们通过这样做为人类知识做出贡献，也获得自我满足。

And so I think there's actually just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just show up and talk about what they're doing.

所以我觉得，去听这个领域里世界顶尖的专家亲自出现并谈论他们正在做什么，里面其实有巨大的 alpha。

And of course the world is awash in that today in a way that it wasn't as recently as 10 years ago.

当然，今天的世界已经充满了这种内容，而就在 10 年前还不是这样。

So yeah, I do as much of that as I can too.

所以对，我也尽可能多地吸收这些。

And there's also just this culture in tech, Silicon Valley, in particular, of sharing, or not trying to keep these secrets.

而且在 tech 圈，尤其是 Silicon Valley，也确实有一种分享文化，不太会试图把这些秘密藏起来。

Everyone on LinkedIn is always like, "How is this free?"

LinkedIn 上大家总是在说：“这怎么会是免费的？”

Like, it's just the way it works.

但这就是它运转的方式。

Yeah.

对。

Somebody said, "Silicon Valley is a company town, but they company is Silicon Valley."

有人说过：“Silicon Valley 是一座公司城，只不过这家公司就是 Silicon Valley。”

Right?

对吧？

And again, at the loneliest coast, again, is one of these great n equals one.

而且，这在最孤独的海岸，又是一个很典型的 n equals one。

If the level of n equals one is somebody, and I've run startups before, I've run companies before, if the level of n equals one of, like, running a company, that's just a giant pain in the fucking butt.

如果 n equals one 的层面是某个人，而我以前创办过 startups，也经营过公司，如果是在经营一家公司的 n equals one 层面，那真是他妈巨大的麻烦。

Because your secrets are walking out the door, and your employees are walking out the door, and the whole thing sucks.

因为你的秘密会走出门，你的员工也会走出门，整件事都很糟。

But the other side of it is you also benefit from that, right?

但另一面是，你也会从中受益，对吧？

Because you get to hire people with all these skills and experiences, right, and you're in this ecosystem that adapts and channels talents and skill and knowledge and people into the new fields.

因为你可以招到有这些技能和经验的人，而且你身处一个生态系统，它会适应并把人才、技能、知识和人输送到新的领域。

So there's kind of the push and pull of that at the level of just being an individual CEO.

所以在个体 CEO 的层面，这里面有一种拉扯。

At the level of just being in the ecosystem to your point, yeah, it's an absolutely magical phenomenon.

但从身处整个生态系统的层面看，正如你说的，对，这绝对是一个神奇的现象。

And by the way, for all of the issues in Silicon Valley, I did the count once, I think AI is the ninth major technology platform in the history of Silicon Valley.

顺便说，尽管 Silicon Valley 有各种问题，我曾经数过一次，我觉得 AI 是 Silicon Valley 历史上的第九个主要技术平台。

Right?

对吧？

Silicon Valley is still called Silicon Valley, we haven't made Silicon here in decades.

Silicon Valley 现在仍然叫 Silicon Valley，但我们已经几十年没在这里生产 silicon 了。

Right?

对吧？

We used to actually... You know it's called Silicon Valley because they used to make chips, right?

以前我们确实……你知道它叫 Silicon Valley，是因为他们以前在这里做芯片，对吧？

They used to have the actual fabs were in Silicon Valley.

当时真正的 fabs 就在 Silicon Valley。

And then they designed them and they made the chips.

他们会设计芯片，也会制造芯片。

And so, and that was wave one starting in the 19... No, that was actually, no, that was more wave three or whatever.

所以，那是从 19……不对，其实那更像是第三波还是什么。

But that was when the area was named in the 1950s.

但这个地区是在 1950s 被这样命名的。

But now we're on wave nine.

而现在我们已经到了第九波。

Right?

对吧？

And the company town phenomenon where the company is, the industry, again, the indeterminate optimism, nobody had to sit and plan and say, "Okay, in the 1990s Silicon Valley's going to do the internet, in the 2000s they're going to do the smartphone, in the 2010s they're going to do the cloud, in the 2020s they're going to do AI."

这种公司城现象里，公司就是这个行业。再说回 indeterminate optimism，没有人需要坐下来计划说：“好，1990s Silicon Valley 要做 internet，2000s 要做 smartphone，2010s 要做 cloud，2020s 要做 AI。”

It's just, right, the indeterminate optimism of ecosystem flexibility of the ecosystem that they Silicon Valley could morph into all these categories, and again, maybe a testimony to indeterminate optimism.

它就是这样，对吧，生态系统灵活性的 indeterminate optimism，让 Silicon Valley 能够变形成所有这些类别；这也许又一次证明了 indeterminate optimism。

This reminds me of the meme of how we're all just wrappers over sand, everything we're building is just wrapper over wrapper, wrapper, wrapper.

这让我想起那个 meme，说我们都只是沙子的 wrappers，我们构建的一切都是 wrapper 套 wrapper、wrapper、wrapper。

The wrapper thing is hysterical, yeah, yeah.

wrapper 这个说法太搞笑了，对，对。

I'm a software company and I'm a chip wrapper, right?

我是一家软件公司，我就是芯片的 wrapper，对吧？

Yeah.

对。

Yeah.

对。

I'm a business application, I'm a database wrapper.

我是一个 business application，我就是 database wrapper。

Yeah, exactly.

对，没错。

I'm a sand... I mean, you and I, we're all now sand wrappers.

我是个沙……我是说，你和我，我们现在都成了给沙子套壳的人。

Sand wrappers.

给沙子套壳的人。

Perfect.

完美。

Okay.

好。

One more question along the media diet, I asked your partner Ben Horowitz what to talk to you about.

沿着媒体摄入这个话题，我再问一个问题。我问过你的合伙人 Ben Horowitz，该跟你聊什么。

This is a16z if people don't know him.

如果有人不认识他，他也是 a16z 的。

And he said you're really into movies these days.

他说你最近特别迷电影。

Yeah.

对。

And so I don't know, any movies?

所以我想问，有什么电影吗？

Any movies you're really into these days, any movies you've absolutely loved recently?

最近有什么你特别喜欢的电影，或者最近看过、非常喜欢的电影吗？

Yeah, so the movies that blew my socks off last year, which I think is the best movie of the decade for sure and maybe of the last, like, 15 years, is this movie.

有，去年让我震撼到不行的电影，我觉得肯定是这十年最好的电影，甚至可能是过去 15 年最好的，就是这部。

Unfortunately it's one of these things, not a lot of people have seen it, but I would encourage it.

可惜这类东西看过的人不多，但我会强烈推荐。

It's called Eddington.

叫 Eddington。

I've not heard of it.

我没听过。

Have you not heard of it?

你没听过？

Okay, so you're going to really enjoy it.

好，那你肯定会很喜欢。

So, I won't spoil too much of it, so at the surface level the following spoils nothing.

我不会剧透太多，表层设定说出来不算剧透。

So at the surface level, it's set in a small town in New Mexico called Eddington which is a small town about 600 people.

表面上看，故事发生在 New Mexico 一个叫 Eddington 的小镇，大概 600 人。

And there's a sheriff who's played by Joaquin Phoenix who's like an old, crusty, basically right-winger, and then there's a mayor played by Pedro Pascal who's basically a young, hip, progressive.

里面有个警长，由 Joaquin Phoenix 演，是那种老派、粗粝、基本偏右翼的人；还有个市长，由 Pedro Pascal 演，基本是年轻、时髦的进步派。

And then the movie starts I think in March of 2020.

电影我记得是从 2020 年 3 月开始。

And so it starts when COVID first hits.

也就是 COVID 刚爆发的时候。

And then it sort of as it plays out over the next few months, it intersects, and it sort of extends into the summer of 2020.

然后随着接下来几个月的发展，它开始交织，并延伸到 2020 年夏天。

So, kind of the George Floyd moment and then protests and riots and kind of everything.

就是 George Floyd 事件那个时刻，然后抗议、骚乱，各种事情。

So sort of the convergence of COVID and then all the BLM stuff.

所以就是 COVID 和所有 BLM 相关事情的汇合。

And then there's a third kind of element to it which is there's a company which is basically a loosely-disguised version of Meta if you read the backstory of it, which is building an AI data center on the outskirts of town.

然后还有第三个元素：有一家公司，如果你读它的背景设定，会发现基本就是稍微改头换面的 Meta，正在小镇外围建一个 AI 数据中心。

So they kind of pull that in as sort of a thing that looms larger and larger over time.

所以他们把这件事也拉进来，而且它随着时间推移变得越来越有压迫感。

And then the thing it really is great at is it really shows, you know, this is a small town in New Mexico.

这部电影真正厉害的地方在于，它非常好地呈现了一个 New Mexico 小镇。

And so, everybody in the town gets full wrapped up in all the COVID stuff, and they get fully wrapped up in all the BLM stuff, and they get fully wrapped up in all the tech anxiety stuff.

镇上的每个人都完全卷入了 COVID 那套东西，完全卷入了 BLM 那套东西，也完全卷入了对科技的焦虑。

But they're all experiencing it basically through the internet, right?

但他们基本都是通过互联网在经历这一切，对吧？

Which is what actually happened, right?

而这也确实就是当时发生的事，对吧？

So the reason I love the movie so much is one is it's the first movie that directly grapples with 2020, of what happened in 2020, and it just, like, fully, fully engages and grapples with all the dynamics that were playing out in the country.

所以我这么喜欢这部电影，一个原因是它是第一部直接处理 2020 年、直接面对 2020 年发生了什么的电影，而且它真的完全投入、完全正面处理了当时美国社会里所有正在展开的动力。

But the other reason is it's the first movie that does a really good job of showing what it was like especially in that area to live in a world in which there were things happening in the real world, and people were kind of experiencing events online, like in a way that was very central in their lives.

另一个原因是，它是第一部真正拍出了那种生活状态的电影：尤其是在那个地区，现实世界里有事情发生，而人们又在网上经历这些事件，并且这种线上经历在他们生活中占据了非常核心的位置。

Right?

对吧？

And so it does a really good job of pulling in smartphones and social media in a way that movies really, really, really struggle with, and then the whole thing comes together in an incredibly entertaining way.

所以它把智能手机和社交媒体融入得特别好，而这恰恰是电影一直非常非常难处理的东西。最后整部片子又以一种极其好看的方式组合在一起。

And so I wouldn't even say I completely agree with the movie or whatever, and I think the director of the movie and I would probably disagree about a lot, but he really tries hard to really grapple with what it's actually like to live like a human being in the 2020s in America in a way that I think many other filmmakers who are very talented have just been very scared of touching.

我甚至不会说我完全同意这部电影，或者同意它的所有观点。我觉得导演和我可能在很多事情上都会有分歧，但他真的很努力地去处理：在 2020 年代的美国，像一个人一样生活到底是什么感觉。很多非常有才华的电影人一直很害怕碰这个题材，而他做到了。

And this guy, for some reason he's just like, "Yeah, I'm just going find all the third rails and I'm just going to fucking grab them."

而这个人不知道为什么，就是一副“行，我就去找所有高压线，然后他妈的直接上手抓”的态度。

I can see why that's your favorite movie of the year.

我明白为什么这是你今年最喜欢的电影了。

It's great, it's great, it's great.

太棒了，太棒了，太棒了。

Everybody should see it.

每个人都应该去看。

Oh man.

天啊。

Okay, final question, I want to ask about your product diet.

好，最后一个问题，我想问问你的产品饮食。

Are there any products you use that maybe are less known that you love, that you want to recommend?

有没有什么你在用、可能没那么出名但你很喜欢、想推荐的产品？

You can mention products you're investors in if you use them constantly.

如果你经常用，也可以提你们投资的产品。

We have so many that it's really hard to, you know, I always feel it's who's your favorite shoulder?

我们投的太多了，很难挑。我总觉得这就像问你最喜欢哪只肩膀。

And so it's really hard to pull out specific ones.

所以很难单独拎出几个。

But I'll talk about a few.

但我可以说几个。

I mean they're all just observations.

其实都只是一些观察。

So one is my 10-year-old, I have, my 10-year-old is 100% obsessed with Replit.

第一，我 10 岁的孩子，我家 10 岁孩子现在 100% 痴迷 Replit。

And by the way, it was not from me.

顺便说一句，这不是我影响他的。

Do you have kids?

你有孩子吗？

I do, I have one two-and-a-half year old.

有，我有一个两岁半的孩子。

Two-and-a-half.

两岁半。

Okay, so you haven't run into what I'm running into now, which is whatever it is that you do is not cool.

好，那你还没遇到我现在遇到的情况：不管你做什么，都不酷。

Right?

对吧？

Like, it's two-and-a-half, whatever daddy does is like the coolest thing in the fucking world.

两岁半的时候，爸爸做什么都是全世界最酷的事。

I can tell you, by the time he's 10, whatever you do is, like, deeply uncool.

我可以告诉你，等他 10 岁的时候，不管你做什么，都会显得非常不酷。

Right?

对吧？

And I'm highly aware of that.

我对此非常清楚。

And so, like, if I mention, "Oh yeah, we work on XYZ," he's like, "Okay."

所以如果我说，“哦对，我们在做 XYZ”，他就会说，“好吧。”

But when he discovers something, then it's cool, or when his friends tell him about it it's cool.

但如果是他自己发现了某个东西，那就酷；或者他的朋友告诉他，那就酷。

And so he through no interference on my part discovered Replit about three months ago, and discovered Vibe coding, and is completely obsessed with Vibe coding games, and all kinds of things and literally will sit and do it for hours.

所以在完全没有我干预的情况下，他大概三个月前发现了 Replit，又发现了 Vibe coding，现在完全痴迷用 Vibe coding 做游戏和各种东西，真的会坐在那里做上好几个小时。

And so I'm seeing that phenomenon play out, which is super fun.

所以我正在看这种现象展开，特别有意思。

That's one.

这是第一点。

Two is, I am just completely in love with all the AI stuff.

第二，我对所有 AI 相关的东西都完全着迷。

I think it's just absolutely amazing, hysterical.

我觉得它们绝对惊人，也特别搞笑。

My favorite party trick at dinner parties now is to pull out Grok with Bad Rudy, which is, if you've seen, it's a foul-mouthed raccoon avatar in the OS Grok app.

我现在晚宴上最喜欢玩的把戏，就是拿出 Grok 里的 Bad Rudy。你如果见过的话，它是 OS Grok app 里一个满嘴脏话的浣熊头像。

So, I think that's super fun.

所以我觉得那特别好玩。

We have this company, Sesame, that they went viral last year for these just incredibly, like, intimate emotional kind of voice experiences.

我们投了一家公司叫 Sesame，他们去年因为那些极其亲密、情感化的语音体验火了。

So I think the voice stuff is fantastic.

所以我觉得语音这块非常棒。

But I'm also a super fascinated by all the voice input stuff.

但我也对所有语音输入相关的东西非常着迷。

And so Limitless Suite, yeah, Limitless recently, the company recently sold, but all the... I think the pendants, the wearables, all that stuff is going to be big, the Meta glasses.

比如 Limitless Suite，对，Limitless 最近，这家公司最近被卖掉了。但所有这些……我觉得吊坠、可穿戴设备、这些东西都会很大，Meta glasses 也是。

I think there's going to be a whole wearables revolution here.

我觉得这里会出现一场完整的可穿戴革命。

I love the voice input stuff.

我很喜欢语音输入这类东西。

There's this app on my phone now called Whisper Flow, which is voice transcription, which works like staggeringly well.

我手机上现在有个 app 叫 Whisper Flow，是做语音转写的，效果好到惊人。

It's incredible, it's like a voice transcription function, but you can actually talk to the AI model while you're doing voice transcription.

它很不可思议，就像一个语音转写功能，但你在做语音转写的时候，实际上可以跟 AI 模型说话。

So it kind of understands when you're telling it, "No, no, I want bullet points over there and I want this and that."

所以当你对它说，“不不，我想要那边用项目符号，我想要这个、那个”，它能理解。

And it understands that you're not telling it to type in the words "I want bullet points," it just actually understands that you want bullet points.

它明白你不是要它把“我想要项目符号”这几个字打出来，而是真的理解你想要项目符号。

And so that's a great example of a super useful thing.

所以这是一个非常有用的例子。

And so, I think the voice mode stuff is going to be really great.

因此我觉得语音模式相关的东西会非常棒。

Subscribers of my newsletter get a year free of Replit and Whisper Flow, so there we go.

我的 newsletter 订阅者可以免费用一年 Replit 和 Whisper Flow，所以正好。

What's the most memorable thing your son's built with Replit?

你儿子用 Replit 做过的最让你印象深刻的东西是什么？

Oh, well so, he's gotten super into Star Trek.

哦，他最近特别迷 Star Trek。

And so, so far he's writing, like, Star Trek simulators.

所以到目前为止，他在写那种 Star Trek 模拟器。

So like all the, you know, all the... By The Next Generation, they actually have a-

比如所有那些，你知道，所有那些……到 The Next Generation 的时候，他们其实有一个——

Next Generation, okay, I was going to ask which-

Next Generation，好的，我正想问是哪一部——

Well actually, we like them all.

其实我们都喜欢。

We watched the new Star Fleet Academy last night-

我们昨晚看了新的 Star Fleet Academy——

Hm.

嗯。

... which actually is quite good.

……其实还挺不错的。

But we watched the original, we watched them all.

但我们也看了原版，所有的都看了。

But it was in Next Generation where they actually developed an actual design language for the computers.

不过是在 Next Generation 里，他们才真正为电脑发展出了一套设计语言。

If you watch the original series, they just had, like, basically knobs with lights.

如果你看原版剧集，他们基本就是一些带灯的旋钮。

And they didn't really, they just were, like, fucking around on set, and trying to pretend they were doing it.

他们其实也没真做什么，就是在片场瞎摆弄，假装自己在操作。

But by Next Generation, they actually had designed, they actually had a UI design language.

但到了 Next Generation，他们确实设计了一套 UI 设计语言。

And so one of the fun things you can do with Vibe coding is you can say, "Give me a Star Trek Next Generation user interface for whatever, this, that," or whatever.

所以 Vibe coding 有个很好玩的地方，你可以说：“给我做一个 Star Trek Next Generation 风格的用户界面，用来做这个、那个”，随便什么。

And it actually uses the, they called it... I'm just having a nerd-out... They called it LCARS design language.

它真的会用那套……我现在有点 nerd-out 了……他们叫它 LCARS design language。

And it'll actually build you like Star Trek Next Generation bridge consoles using that design language, but with your choice of a Star Trek name, for example.

它真的会用那套设计语言给你做出 Star Trek Next Generation 里的舰桥控制台，还可以按你选的 Star Trek 名字来定制。

And so he's going crazy for that kind of thing.

所以他对这类东西简直着迷。

That sounds extremely delightful.

听起来太让人开心了。

You guys should open source and release that.

你们应该把它 open source 然后发布出来。

Marc, like I said, I could talk to you for hours, well, you've got things to do.

Marc，就像我说的，我能跟你聊好几个小时，不过你还有事要忙。

Anything you want to leave listeners with before we wrap up?

在结束之前，有什么想留给听众的吗？

Anything you want to double-down on or just leave listeners with?

有什么你想再强调一下，或者想留给听众的吗？

Yeah, so a couple things.

有，几点。

So one is, we got super lucky last week, Packy McCormick wrote the best piece ever written about us, actually, which he released.

第一，我们上周特别幸运，Packy McCormick 写了迄今为止关于我们最好的一篇文章，并且发布了出来。

And so it's the best explanation of what we do, and how we think.

这是对我们做什么、以及我们如何思考的最好解释。

And so I would definitely recommend that.

所以我绝对推荐大家去看。

And then we're putting a lot, we have a great team of folks now, we're putting a lot of effort ourselves into video and content.

另外，我们现在有一支很棒的团队，也在把很多精力投入到视频和内容上。

And so I'd definitely recommend our YouTube channel, which I think has a lot of great stuff and is going to be very exciting in the next year.

所以我也绝对推荐我们的 YouTube 频道。我觉得上面有很多很棒的内容，而且明年会非常精彩。

Awesome.

太棒了。

Well link to that, I think it's just youtube.com/a16z, something like that.

我们会放上链接，我记得就是 youtube.com/a16z，差不多是这个。

And you guys have great stuff.

你们的内容很棒。

Okay.

好的。

Marc, thank you so much for being here.

Marc，非常感谢你来做客。

Awesome, thank you for having me.

太好了，谢谢你邀请我。

I really appreciate it.

真的很感谢。

Bye everyone.

大家再见。

Thank you so much for listening.

非常感谢你的收听。

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如果你觉得这期有价值，可以在 Apple podcasts、Spotify，或你常用的 podcast app 上订阅这个节目。

Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast.

另外，也请考虑给我们评分或留下评论，因为这真的能帮助其他听众发现这个 podcast。

You can find all past episodes or learn more about the show at lennyspodcast.com.

你可以在 lennyspodcast.com 找到所有往期节目，或了解更多关于这个节目的信息。

See you in the next episode.

我们下期见。