AI 与互联网或移动技术同等重要

我的嘉宾是本尼迪克特·埃文斯。本尼迪克特曾长期担任 A16Z 的合伙人，是他们的内部分析师和常驻思想家。在此之前，他长期从事股票研究。过去六年，他一直是独立分析师，追踪最重要的科技趋势并分享他的见解。最近，如你所料，他所有时间都花在研究 AI 如何改变我们的生活上。用他的话来说，AI 正在吞噬世界。在这场对话中，我们深入探讨了 AI 将对我们的生活和工作的影响中我们仍未充分定价的部分、反 AI 情绪的兴起、对就业的影响、价值链中大部分价值将流向何处，以及更多内容。如果你对 AI 感到担忧，或者只是对未来的走向感到困惑，这场对话会让你学到很多，也会让你感觉更好。在开始之前，别忘了访问 lenny'spass.com，获取一年免费使用全球最令人惊叹、最热门、最精心打造的 AI 产品的机会，仅限 Lenny 的新闻通讯订阅者。话不多说，有请本尼迪克特·埃文斯。本尼迪克特，非常感谢你来到这里。欢迎来到播客。

My guest is Benedict Evans. Benedict was a longtime partner at A16Z as their in-house analyst and resident thinker. Before that, he was a longtime equity researcher. And for the past six years, he's been an independent analyst tracking the most important tech trends and sharing what he's learning. Most recently, as you'd expect, he's spending all his time on how AI is changing our lives. And in his words, AI is eating the world. In this conversation, we go deep on what we're still not pricing in on the impact that AI is going to have on our lives and our work, the rise of anti-AI sentiment, the impact on jobs, where in the value chain most of the value will accrue, and tons more. If you are worried about AI or just confused about where things are heading, this conversation will teach you a lot and also make you feel better. Before we get into it, don't forget to check out lenny'spass.com for a year free of some of the most amazing, hottest, most well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Benedict Evans. Benedict, thank you so much for being here. Welcome to the podcast.

谢谢你的邀请。

Thank you for inviting me.

你刚刚发布了这个名为《AI 正在吞噬世界》的演示文稿。我想问你它的另一面：我们都知道这是一件大事。既然如此，你认为人们在思考他们将经历的生活和工作变化时，仍然没有充分定价的是什么？

You just put out this deck called AI is eating the world. I want to ask you kind of the flip side of this: we all know it's a big deal. Knowing that, what do you think people are still not fully pricing in when they think about the change that they're going to experience to their lives and their work?

一个有趣的思考方式。去年我和某人做了一期播客，我说，我最具争议的观点是，我认为 AI 的重要性与互联网或移动互联网相当，但也仅此而已。因为显然科技界有一群人认为不，这更像是工业革命之类的。而下面还有一群人会说，他觉得这跟……一样重要？他难道不明白这有多重要吗？我的意思是，智能手机相当重要，互联网也相当重要。如果没有互联网，我们根本不会做这个节目。所以这是第一层。但如果你深入挖掘，如果你要做互联网比较，那就像我们处在 1997 年。非常令人兴奋。但大多数东西还不太管用。人们将要做的绝大多数事情还没有被构建出来，而且当它们真的管用时，也不清楚会如何运作。而那些已经拥有它、已经服用了某种药丸的人（我忘了是哪一种），他们想象全世界都已经到达了那个阶段。而事实是，分布非常广泛。所以科技界有人买了他们的 Mac Mini 集群，不再使用谷歌。然后你看科技界之外，抛开那些认为这不是真的的傻瓜，大多数使用它的人可能每一两周才用一次。所以你有这种采用率的分布，以及这种成熟度的分布。然后在此基础上，你可以提出一些具体观点，比如模型将如何运作，模型实验室是否有定价权，价值将流向何处，OpenAI 是否已经赢了，或者 Anthropic 这周是否领先，然后你可以开始预测这些竞赛，这又像是回到 1997 年，说会是 Excite 还是 Yahoo，答案通常是否定的。所以这里有一个分形点。从超级高层面上看，这将彻底改变一切。我认为说它比互联网大 20% 还是 100% 并没有特别大的意义。那些不是有成效的对话。但这是那种根本性的变化之一。但你又不知道它会如何运作。事实上，我刚刚发布了这个。我每六个月做一次演示，昨天刚发布了一个，其中一个评论是：本尼迪克特，这 80 张幻灯片都在说我们不知道，这有点调侃，但也确实如此。

An interesting way of thinking about it. I did a podcast last year with someone where I said, you know, my most controversial opinion is that I think that AI is as big a deal as the internet or mobile and only as big a deal as the internet or mobile. Because clearly there's a bunch of people in tech who think no, this is more like the industrial revolution or something. And there are a whole bunch of people underneath saying, well, he thinks this is just as big as... does he not understand how big this is? And I'm like, smartphones were quite a big deal. The internet was quite a big deal. We wouldn't be doing this if it wasn't for the internet. So there's like one layer. But then if you dig into that, if you're going to make the internet comparison, it's like we're in 1997. It's very exciting. Most stuff kind of doesn't work yet. Most of the stuff that people are going to do hasn't been built yet and it's not really clear how any of it's going to work when it does work. And the people who have already got it, who have already taken whichever pill it is, I forget which, sort of imagine that everybody in the world is already there. And the truth is you've got this kind of very wide distribution. So there's people in tech who bought their cluster of Mac Minis and you know don't use Google anymore. And then you look outside tech, and setting aside the idiots who think that this isn't real, you know most people who are using this are using it every week or two maybe. So you've got that kind of spread of adoption and that spread of maturity of how well this works. And then within that you can make sort of specific points about well how are the models going to work and do the model labs have pricing power and where's the value going to be and you know has OpenAI won the whole thing or you know is Anthropic got it this week and so then you can kind of get into calling those races where again it's like being in 1997 and saying well is it going to be Excite or Yahoo and the answer was no generally. So there's a sort of fractal point here. There's the sort of super high level that this is going to change absolutely everything. I don't think it's particularly productive to say well is it 20% bigger than the internet or 100%? Those aren't productive conversations. But it's one of those fundamental changes. But then you don't know how any of it is going to work. In fact, I just published this. I do a presentation every six months and I just published one yesterday and one of the comments was, Benedict, this is 80 slides saying we don't know, which is slightly facetious but also kind of true.

我最具争议的观点是，我认为 AI 的重要性与互联网或移动互联网相当，但也仅此而已。你所说的即将到来的就业末日呢？每次我们有了新技术，它就会自动化掉一批工作，然后这种自动化又解锁了一批新工作，而你不知道新工作是什么，因为它还不存在。我们一次又一次地经历这个过程。

My most controversial opinion is that I think that AI is as big a deal as the internet or mobile and only as big a deal as the internet or mobile. What's your just the coming job apocalypse? Every time we have a new technology, it automates away a bunch of jobs and then that automation unlocks a bunch of new jobs and you don't know the new job cuz it doesn't exist yet. We've had that process over and over again.

即使只看最先进的 AI 公司，整个 OpenAI 都在增加员工人数。你和 Twitter 上的那些末日论者交谈，他们会表现得好像每家大公司明天都会购买 ChatGPT，然后两周内就会解雇所有员工。这些人都是白痴。你无法预测哪些事情会暴露。你不能看着一家律师事务所的高级合伙人说：“嗯，他们 17% 的工作可以自动化。”这完全是胡说八道。

Even just looking at the most advanced AI companies, throughout big OpenAI, everyone's increasing headcount. You talk to these doomers on Twitter and they would act like every big company is going to buy ChatGPT tomorrow and then in two weeks time they'll fire all their staff. These people are morons. You can't predict which things are going to be exposed. You can't look at a senior partner at a law firm and say, 'Well, 17% of their work could be automated.' This is bullshit.

我很好奇你是否在关注反 AI 情绪。

I'm curious if you're following the anti-AI sentiment.

这是一团巨大的模糊混乱。是的，这会改变很多事情，我们需要担心，但这几乎是常态。我们一直都有这种情况。你会推荐人们做哪几件事，以便在这个未来中更成功？不要把头埋在沙子里说：“我讨厌所有这些。”这会给你一种道德优越感，你可以去 Blue Sky 上对每个人大喊 AI 有多邪恶。很好，我为你高兴。但这没有帮助。有帮助的是你深入其中，并最终理解你今天能用它做什么。

It's a big fuzzy mess. Yes, this will change a bunch of stuff and we'll need to worry about it, but that's kind of a constant. We've always had that. What would be a couple things you recommend people do to be more successful in this future? Don't stick your head in the sand and say, 'I hate all of this stuff.' That gives you a great feeling of moral superiority and you can go on Blue Sky and shout at everybody about how evil AI is. Like great, I'm happy for you. But that's not going to help. What helps is you diving into this and coming out understanding what you can do with today.

那么，如果我们正处于 AI 的 1997 年时间线，我知道你传达的很多信息是我们还不知道它确切会走向何方。你现在有没有一个感觉，就是距离事情发生根本性变化还有多久？我们在这个周期中处于什么位置？你谈到了我们经历过的所有这些不同周期。我们离“哇，一切都不同了”还有多远？

So, if we're in this 1997 timeline for AI, I know it's so much of your message that we don't know where it's going exactly yet. Now, do you have a sense of just the timeline to when things are going to be radically changing? Like where are we in that cycle? You talk about all these different cycles we've been through. How far are we from just 'wow, it's all different now'?

毫无疑问，在软件领域我们已经身处那个时刻了。然后还有一个讨论是关于智能体式和 AI 软件开发——这两件不同的事情融合在一起——对软件行业的未来意味着什么。你知道，有一种极端观点，没人真的相信，就是“嘿，你就能像写代码一样自己做出 Stripe 了”。没人真信这个，尽管你不信，但显然有一大堆问题关于这对软件行业意味着什么，以及你能自己做多少事情，或者会有多少更多的软件。那是一个完整的讨论。但另一个极端是，如果你在一家律所，这一切都很有趣，但我们到底怎么用这个？我们怎么才能避免成为下一个提交了带有幻觉内容的案例？我们明年要招多少律师助理？这对我们意味着什么？我在演讲中用的一个类比是：想象你是一个会计，在 70 年代末看到了第一个电子表格软件。这太震撼了。你在这里改一下利率，所有其他数字都变了，它把一周的工作在 30 秒内做完了。我们可以谈谈这对会计行业意味着什么，但显然如果你是个会计，这绝对是震撼的。但如果你是个律师或记者看到这个，你会想，嗯，这很聪明，我的会计应该看看，但这并不是我做的事。我可能下周用它来填时间表，如果买 Apple 2、显示器和打印机来运行它不需要花 1 万或 1.5 万美元的话——按通胀调整后就是这个价——但那不是我做的事。而且你需要一个文字处理器，那实际上很快就出现了。所以这就是我们现在的时刻：有些人——比如软件开发者——就像看到 VisiCalc 的会计一样，“天哪，这改变了一切”。VisiCalc 之前和之后，Claude Code 之前和之后。很多其他人在使用它，程度不同，但有点困惑。所以我在演讲中放了一些调查数据：即使看 13 到 18 岁的人，也只有大约 15-20% 的人是每日活跃用户，另外 20% 是每周活跃用户，而该人群中其余 60% 的人说他们没用这个。所以谁理解它，分布非常广泛，我认为这也映射到——这几乎是另一个点——所谓的“锯齿状前沿”问题：它在哪有效，在哪无效，你能判断它会在哪有效吗，凭直觉知道它在哪有效吗，你能在它有效之后判断吗，你能自己琢磨出用它做什么吗。所有这些都交织在一起。如果你是一个软件开发者，很多其他人要么正在经历那个时刻，要么没有。我们正处于那种 1997 年的时刻：“好吧，这是什么？”

Well, unquestionably we're already in that moment in software. And then there's a conversation about what agentic and AI software development — two separate things that merge together — mean for the future of the software industry. You know, there's one extreme, which no one really believes, which is, you know, 'hey, you'll just like v-code your own Stripe.' No one actually believes that, although you don't believe that, but clearly there's a whole bunch of questions about what this means for the software industry and how much stuff you'll be able to do yourself or how much more software there will be. That's one whole conversation. But the other extreme is, you know, if you're in a law firm, this is all very interesting, but how exactly do we use this? And how do we work out how not to be the next story that we've submitted something with hallucinations in it? And how many associates are we going to hire next year? What does this mean for us? One of the analogies I used in the presentation is: imagine you're an accountant seeing the first software spreadsheets in the late '70s. This is mind-blowing. You know, you change the interest rate here and all the other numbers change, and it does a week of work for you in like 30 seconds. We can talk about what that meant for the accounting industry, but clearly if you're an accountant, this is obviously mind-blowing. But if you were a lawyer looking at that or a journalist looking at that, you'd think, well, that's very clever and my accountant should see this, but that's not what I do. I might use it for my time sheet next week if it didn't cost 10 or $15,000 to get the Apple 2 and the monitor and the printer to run it, which is what it cost if you adjust, but that's not what I do. And you need a word processor, which actually came very shortly afterwards. And so that's sort of the moment that we're in: there are some people — like software developers — who are the accountants seeing VisiCalc, like 'oh my god, this changes everything.' Before VisiCalc and after VisiCalc, before Claude Code and after Claude Code. A lot of other people are picking it up, using it to varying degrees, but slightly puzzled. So there's a bunch of survey data that I put in the presentation: even if you look at 13 to 18 year olds or something, it's still like kind of 15-20% of people are daily active users and another 20% are weekly active users, and then the other 60% of those people in that demographic say they are not using this. So there's a very wide spread of who gets it, which I think also maps — this is kind of almost a separate point — to the sort of 'jagged frontier' question of where does this work, where does it not work, can you tell where it's going to work, is it intuitive to know where it would work, can you tell after it worked, can you work out for yourself what you would do with this. All of those intersect. If you're a software developer, a lot of other people are having that moment or they're not. We're in that kind of 1997 moment of 'okay, what is this?'

顺着这个思路，你一直在写的一个话题是这种对专业服务、咨询服务、前向部署工程师的意外投资。所有 AI 实验室——至少两大巨头 OpenAI 和 Anthropic——都在投资收购大型咨询公司和 PE 公司。说说那里到底发生了什么，为什么会这样。

Along those lines, something you've been writing a bit about is this unexpected investment in professional services, consulting services, forward-deployed engineers. All the AI labs — at least the two big ones, OpenAI and Anthropic — are investing in buying massive consultancies and PE firms. Talk about just what's happening there, why that's happening.

嗯，这很有趣。昨晚我写新闻通讯时，我一直在想找个笑话，但没完全想好，不过你知道类似这样的：有个笑话是，机器学习科学家是住在旧金山的统计学家，而前向部署工程师就像是住在旧金山或工作在旧金山的埃森哲外包软件开发者。我的意思是，开玩笑了，如果你有任何专业服务的经验，公司并没有很多人闲着等着去建一个大项目，或做一项大的新分析，或构建一项大的新技术或新产品，或想清楚如何重新设计他们的门店，或确定门店应该开在哪里，或试图找出为什么客户流失率太高。所有这类问题都是你聘请贝恩、波士顿咨询、麦肯锡这类公司，或者埃森哲、印孚瑟斯等公司的原因，或者你聘请一个品牌代理公司或一个建筑事务所。而且总是这样：嗯，我们可以雇一些建筑师，但我们为什么要养 15 个建筑师呢？我们直接去找一个建筑事务所就行了。我们直接去找一个广告公司。所以你应该彻底重新构想公司所有的内部工作流程，并找出哪些可以用 AI 快速自动化。那是一个项目。那需要一个项目，需要五到十个人坐下来花一两个月来弄清楚。然后实际执行是另一个项目。所以我们需要把这三个垂直系统接入这两个水平系统，并构建一堆新的工作流程，培训人们去做。那么，你猜怎么着？谁来做这些？因为你没有一堆人闲着没事干。所以，一方面，这是一些 PE 公司的模式的一部分，即他们为投资组合公司提供支持来做事情。另一方面，这就是为什么你根据你要做的事情，聘请贝恩或埃森哲或公关公司来帮你解决。这个趋势真正有趣的地方在于，你会认为 AI 会让咨询顾问消失。“不，我们不再需要这些人了。AI 会做他们的工作。”相反，最前沿的 AI 实验室正是最投资这些人的。我觉得这相当令人惊讶。

Well, it's funny. I was kind of groping for a joke last night when I wrote my newsletter and couldn't quite get to land it, but you know something like: you know the joke that a machine learning scientist is a statistician who lives in San Francisco, and there's something in there of like a forward-deployed engineer is like an Accenture outsourced software developer who lives in San Francisco or works in San Francisco. I mean, joking apart, if you have any experience of professional services, companies do not have lots of people sitting around waiting to build a big new project or do a big new piece of analysis or build a big new piece of technology or a new product or work out how they're going to redesign their stores or work out where the stores should be or try and work out why the churn is too high. All of those kinds of questions are reasons why you hire Bain, BCG, McKinsey on one side, or Accenture, Infosys, whoever on the other, or you hire a branding agency or you hire an architecture firm. And it's always like: well, we could hire some architects, but why on earth would we want to have 15 architects on staff when we just go and hire an architecture firm? We just go and hire an ad agency. And so you're supposed to completely reimagine all of the internal workflows of your company and work out which of them could be automated really quickly with AI. That's a project. That's a project that needs like five or 10 people to sit down and spend a month or two working it out. And then actually doing it is another project. So we need to plug these three vertical systems into these two horizontal systems and build in a bunch of new workflows and train people to do that. Well, guess what? Who's going to do that? Because you don't have a bunch of people sitting around not doing anything. So, on the one side, this is part of the model of some PE firms, which is that they provide support to their portfolio companies to do stuff. And on the other side, that's why you hire, depending on what you're trying to do, you hire Bain or you hire Accenture or you hire publicists to help you work that out. What's really just funny about this trend is you would think AI is going to make consultants gone. 'No, we don't need all these people anymore. AI is going to do their work.' Instead, the most cutting-edge AI labs are the ones most investing in these folks. I think it's pretty surprising.

文章有一节关于资本，主要讨论这些资本支出都流向哪里，以及模型实验室是否会形成差异化？然后有一节关于部署，主要讨论这对软件行业意味着什么？第三节是关于这会如何改变事物？在关于变化的那一节里，我试图梳理的一个线索是：工作的难点到底是什么？难点是一行一行地写代码吗？难点是给你做学校或者做 PowerPoint 吗？还是别的什么？是任务本身还是整个工作？

There's a section on capital, which is basically where is all this capex going and are the model labs going to have differentiation? And then there's a section on deployment, which is basically what does it mean for the software industry? And then the third section is how does this change stuff? And one of the sort of strands I tried to pull together in the section on change is what's the hard part of the job? Is the hard part of the job writing the code line by line? Is the hard part of the job like giving you the school or making the PowerPoint or is the hard part of the job something else? Is it the task or the job?

把这两者拆开看，有时候任务就是工作本身。经典的例子是电梯操作员。我住的大楼里就有专人操作的电梯，是手动电梯，没有按钮，门上有根操纵杆，司机把你送到你要去的楼层，就像旧金山的那种缆车。50 年代以后这些都被自动化了，现在你按个按钮就行，按按钮本身就成了一个工作。所以有些情况下，任务就是工作，然后这个任务被自动化了。

And you know, pulling that apart, sometimes the task is the job. Like the classic example is an elevator attendant. I live in a building that has an attended elevator. We have a manual elevator. There's no button. There's a lever in the door and it drives you to your floor. It's a vertical speed car. It's like one of those trams in San Francisco. They drive you to the store to your floor. And then those all got automated after the 50s and now you press a button and pressing the button is a job. So there were some things where the task was the job and the task got automated.

更常见的情况是价格弹性，这也是为什么人们会提到杰文斯悖论。杰文斯悖论其实就是价格弹性。如果做某件事的成本降低了，会发生什么？你是花更少的钱做同样的事，还是花同样的钱做更多的事，或者因为有了新的投资回报率而花更多的钱做更多的事？看看会计史或者专业服务领域，比如我在 Twitter 上开过的一个玩笑：年轻人可能不信，但在 Excel 出现之前，初级投行分析师工作时间很长；现在有了 Excel，高盛的副经理们周五中午就下班了。但实际情况并不是这样，对吧？

What happens much more, and this is why people talked about the Jevons paradox, is price elasticity. Jevons paradox is just price elasticity applied. If you make it cheaper to do something, what happens? Do you do the same for less money, or do you do more for the same amount of money, or do you do more for more money because you've got new ROI? And if you look at something like the history of accounting or indeed professional services, like this is a joke I made on Twitter back when it was Twitter: young people won't believe this, but before Excel, junior investment bankers worked really long hours, and now thanks to Excel, Goldman's associates all work at lunchtime on Fridays. It's like, well, why is that not what happened?

软件开发领域也是如此。在 IDE、库和操作系统出现之前，开发者必须自己写所有代码。现在，如果你写一个 iPhone 应用，90% 的代码都是 Apple 帮你写好的。Apple 写了调制解调器驱动、图形驱动和文件系统，你完全不需要写这些。那么按理说我们现在只需要十分之一的工程师，但事实并非如此。所以你必须审视一个行业，弄清楚到底是哪种情况？难点又在哪里？

You could make the same point in software development. Before IDEs and libraries and operating systems, developers had to write all the code. Now, if you write an iPhone app, 90% of the code is written for you by Apple. Apple wrote the modem driver and the graphics drivers and the file system. You don't need to write any of that. So we've got like a tenth as many engineers now. Well, no. And so then you kind of have to look at an industry and work out, well, which is it? And what is the hard part?

我想到的一个类比是电商的历史。亚马逊做的事情就是帮你找到那个 SKU。如果你知道自己想要什么 SKU，比如你想要那个麦克风支架，你知道零件号，就可以去亚马逊买到。但如果你不知道该买哪个麦克风，可能就不该从亚马逊开始。把这个逻辑乘以无数产品类别。所以亚马逊做的是帮你拿到 SKU，但知道你想要哪个 SKU 是另一项工作。Claude 可以帮你写代码，但你要写什么代码？它当然可以帮你做功能，但你要什么功能？你的客户是谁？对那个客户来说什么产品是对的？你打算怎么推向市场？

One of the analogies that occurred to me here is to look at the history of e-commerce. What Amazon does is get you the SKU. If you know what SKU you want, you want that microphone stand, you know the part number, you can go to Amazon and get it. If you don't know what microphone to get, you probably shouldn't start on Amazon. Multiply that by many, many product categories. So what Amazon does is get you the SKU, but knowing what SKU you want is another job. Claude can write you the code, but what code do you want? It can make you the features, sure, but what features do you want? Who's your customer? What's the right product for that customer? How are you going to take it to market?

绕了一大圈来回答你的问题：你为什么要雇麦肯锡？是为了拿到一份 75 页的幻灯片吗？严格来说，Claude 可以做一个非常糟糕的版本。然后 LinkedIn 和 Twitter 上那些 AI 骗子会说：“嘿，我用 Claude 做了一份麦肯锡风格的幻灯片。”你一看就会想：“这简直是狗屎，根本不是麦肯锡能给你的东西。”但即使它真的像，那也不是你付钱的目的。你雇贝恩的真正目的是让他们走遍你的企业，弄清楚：你为什么没做那件事？这里的政治因素是什么？你真正需要做什么？然后去和你的客户聊聊，弄清楚他们真实的想法，而不是 Google 首页上的东西。所有这些其他工作才是关键，而 PowerPoint 只是任务，不是你雇他们的原因。

Long way of answering your question, why do you hire McKinsey? Are you hiring them to get a 75-slide deck? Well, narrowly Claude will make a really crappy version of that. And you'll get all these AI grifters on LinkedIn and Twitter saying, 'Hey, I made a McKinsey deck with Claude,' and you look at it and think, 'Yeah, that's a bunch of dog crap. That's not what you'd get from McKinsey.' But even if it was, that's not what you paid them for. What you actually pay Bain to do is to go and walk all over your enterprise, your company, and work out, yes, but why is it that you didn't do that? And how do the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually think as opposed to what's on the first page of Google. All the other stuff, and the PowerPoint is just the task, but that's not what you hired them for.

亚马逊和零售商的关系如此，软件开发也是如此。所以就有了这种分裂。我想到的另一个类比是那些被互联网碾压的行业，因为它们同时拥有这两部分，而且你可以把这两部分拆开。一边是物理制造或物理分销，另一边是其他东西，真正的价值所在。典型的例子是报纸和唱片业。唱片公司不认为自己是在制造小塑料片，但那确实是它们实际在做的事，当这部分消失时，它们就完蛋了。报纸也一样。报纸不认为自己是制造和运输公司。当你把这两者脱钩时，问题就来了。但很多时候你无法脱钩，或者那根本不是问题所在，或者你让那部分变得便宜，然后其他所有事情也跟着变了。

The same with Amazon versus the retailer, the same with software development. So you've got that kind of split. The other analogy that occurred to me here was looking at the class of industry that got steamrolled by the internet because they had those two things and you could split the part. So you had the physical manufacturing or physical distribution and then you had the other thing, the actual thing. Classic examples are newspapers and recorded music. Record companies do not think of themselves as being in the business of manufacturing small pieces of plastic, but that was what they actually did, and when that went away they were screwed. Same thing for newspapers. Newspapers did not think of themselves as manufacturing and trucking companies. When you decouple that, then that becomes a problem. But often you can't decouple that, or that wasn't really the problem, or you make that thing cheap and then all this other stuff happens as well.

所以这一切远比“我们只要把会计师自动化，或者把顾问自动化”要复杂得多。我的演示文稿里有两张图表，显示会计师的就业人数在整个 20 世纪都在上升，进入 21 世纪后又继续上升。所以，从加法机、打孔卡、大型机、数据库、ERP、云计算，到电子表格和 PC，会计师的数量一直在增加。这是为什么？肯定比单纯的自动化更复杂。

And so all of this is just vastly more complicated than saying, 'Hey, we're just going to automate the accountants or we're going to automate the consultants.' I mean, there's two charts in the presentation of the number of people employed as accountants which went up right through the 20th century and has gone up again since the beginning of the 21st century. So you have adding machines and punch cards and mainframes and databases and ERP and cloud with spreadsheets and PCs, and the number of accountants keeps going up. And so why is that? Well, it must be more complicated than automation.

即使只看最先进的 AI 公司，Anthropic、OpenAI，我刚刚在播客里请了 Every 的 Dan Shipper。所有人都在增加员工。那些你本以为最不可能增加人力的公司，反而在大量增加人手。就像你说的，这真的很复杂。你对即将到来的工作末日有什么看法？Dario 在说所有初级岗位都没了。

Even just looking at the most advanced AI companies, Anthropic, OpenAI, I just had Dan Shipper from Every on the podcast. Everyone's just increasing headcount. The companies you would think would be least likely to add humans are adding many, many humans. And to your point, it's really complicated. What's your just kind of gist on the job, the coming job apocalypse? You know, Dario's talking about all the entry-level people, no more jobs.

是的。这里有一个细微的点：我不喜欢诉诸权威的论证。而且我不认为你经营一个 AI 实验室就突然让你……或者说，如果你要用诉诸权威的论证，那它应该与领域相关。所以，我对 Dario 关于模型未来 6 到 12 个月走向的看法很感兴趣。

Yeah. I mean, there's a narrow point here which is that I don't like argument from authority. And I don't think the fact that you run an AI lab suddenly gives you... or rather, if you're going to use argument from authority, then it should be relevant to the field. So like, I'm interested in Dario's opinions on where models are going to go in the next 6 to 12 months.

我对劳动价值论、市场价值和比较优势之类的理论观点不太感兴趣。是啊，也许他在大学里上过这门课，我也上过。所以我觉得对“达里奥说”这种话要谨慎一点，先不说那种愤世嫉俗的看法——认为他只是在炒作股票，我完全不信。这又回到了我关于平台变革的观点。每次出现新技术，都会自动化掉一批工作，然后这种自动化——无论是价格弹性还是自动化带来的赋能——又会释放出一批新工作。回到 1800 年，90% 的人都是农民，我们最担心的是庄稼会不会歉收，因为那样大家就会挨饿甚至更糟。从那时起，我们一直在自动化工作，同时创造新工作。你总能看见哪些工作会消失，却不知道新工作是什么，因为它还不存在，而且听起来很蠢。比如铁路工程师：铁路是什么？为什么会有这种东西？谁会在乎？谁想跑那么快？所以这个过程反复发生。任何经济学大一新生都会告诉你这些。自 1800 年以来，这个过程反复出现，每次都会带来摩擦性痛苦和错位。很多人失业，很多城镇空心化，这很糟糕。但当你走出困境时，我们都更富有了，不再担心庄稼歉收。这就是过去 200 年的历程。

I'm not particularly interested in opinions on theories of labor and market value and competitive comparative advantage. Like, yeah, maybe he had a course on that at university. So did I. So I think one needs to be a little bit cautious on like, well, Dario says, and that's setting aside the cynical view that he's just doing that to pump stock, which I don't believe at all. So this kind of comes back to my point about platform shifts. Every time we have a new technology, it automates away a bunch of jobs, and then that automation, whether it's price elasticity and the enablement of the fact that they became automated, unlocks a bunch of new jobs. So you go back to 1800: 90% of us were peasants, and our major concern was whether the crops were going to fail, because then we'd all go hungry or worse. Ever since then, we've been automating jobs and creating new jobs. You can always see the job that's going to go away, and you don't know the new job because it doesn't exist yet, and it's something that sounds dumb anyway. Like, railway engineer: what's a railway? Why would that be a thing? Who would care? Who would want to go that fast? So we've had that process over and over again. This is what any first-year economics student would tell you. We've had this process over and over again since 1800, and each time you go through it, you get a bunch of frictional pain and dislocation. A bunch of people lose their jobs, a bunch of towns get hollowed out, and it all sucks. But when you come through on the other side, we're all richer, and we're not worried about the crops failing anymore. This is the process of the last 200 years.

那么问题来了，有没有什么先验理由认为这次会不同？因为互联网消灭了一批工作，个人电脑也消灭了一批工作。现在没多少人做排版工、电话接线员或打字员了。互联网消灭了一批工作，回头看，消失的通常都是烂工作，新工作更好，因为 GDP 一直在增长。那么 AI 有什么不同吗？

So then the question is, is there some a priori reason why this would be different from those? Because the internet removed a bunch of jobs, PCs removed a bunch of jobs. There aren't many people working as typesetters anymore, or telephone operators, or typists. The internet removed a bunch of jobs, and generally the jobs that go away are crap jobs, seen retrospectively, and the new jobs are better because GDP keeps going up. So is AI different?

对此有几个答案。一种理论是这次会快得多。确实，AI 的采用比以往技术更快，但这是因为你站在巨人的肩膀上。你不需要等每个人都买昂贵的硬件，比如手机或 PC，也不用等电信公司部署宽带。它已经在那里了。所以 ChatGPT 当然能获得 9 亿聊天用户，因为已经有 9 亿人在网上了。1994 年马克·安德森推出 Netscape 时，地球上大约只有 5000 万到 1 亿台 PC。所以不，那时你没有 9 亿用户。但关键是，他不需要等电话网络或微芯片，再往前你不需要等电力或大规模生产。你总是站在巨人的肩膀上。总是有叠加效应。所以是的，这次更快，但互联网也更快。

There are a couple of answers to this. One theory is that this is going to be way quicker. Certainly the adoption of AI is quicker than previous technologies, but that's because you're standing on the shoulders of giants. You don't need to wait for everyone to buy a piece of expensive hardware, like a phone or a PC, or wait for the telco to deploy broadband. It's already there. So of course ChatGPT can get 900 million chat users because there are already 900 million people on the internet. When Marc Andreessen launched Netscape in 1994, there were like 50 to 100 million PCs on Earth. So no, you didn't have 900 million users then. But the point is, he didn't need to wait for phone networks or microchips, and before that you didn't need to wait for electricity or mass production. You're always standing on the shoulders of giants. There's always a compounding effect. So yeah, this is faster, but the internet was faster too.

另一个答案，这又回到了专业服务的问题。你跟 Twitter 上的那些末日论者聊天，他们表现得好像每个大公司明天就会买 ChatGPT，然后两周内解雇所有员工。这些人都是白痴。这是末日论者愚蠢的众多原因之一：完全不懂世界如何运作。这就是他们什么都不懂的起点。典型的大企业软件销售周期，你比我更清楚，运气好也要 18 个月。这始终是个问题。企业销售周期比风投支持的初创公司融资周期还长。你谈成一笔企业交易的时间比两轮融资之间的间隔还长。这始终是个问题，尤其是航空航天或医疗等行业。所以不，人们不会直接拆掉 SAP 换成 XYZ。也许三、五、十年后，是的，整个架构会彻底改变，所有工作都会变化。但这需要三、四、五、十年，而且需要时间一个行业一个行业地推进，也需要时间让人们想明白“原来可以用这个做那件事”。

I think the other answer to this, and this kind of comes back to the professional services point, is like you talk to these doomers on Twitter and they act like every big company is going to buy ChatGPT tomorrow and then in two weeks they'll fire all their staff. These people are morons. This is one of many reasons why doomers are morons: a complete failure to understand the way the world works. That was the starting point why they then didn't understand anything else. Typical big company enterprise software sales cycle, you'll know this better than me, is like 18 months if you're lucky. This is always the problem. The enterprise sales cycle is longer than the venture-backed startup funding cycle. It takes you longer to get an enterprise deal than it takes you to go between rounds. This was always the problem, particularly for sectors like aerospace or healthcare. So no, people aren't just going to tear out SAP and replace it with XYZ. Maybe in three, five, ten years, yes, that whole estate will look radically different and all those jobs will have changed. But it will take three, four, five, ten years, and it will take time sector by sector, and it will take time for people to work out that you could do that thing with this.

我记得在 Andreessen Horowitz 时我们看过一家公司叫 Frame.io，做视频编辑和协作。那里没有任何东西是至少五年前、甚至十年前做不到的。实际上，这是个糟糕的例子，因为它依赖前沿的 Web 技术。但如果你随便挑 10 家在 ChatGPT 发布前一天成立的 SaaS 公司，有多少家能在之前 15 年的任何时候成立？延迟在于有人意识到“哦，那个行业存在那个问题，而这是我们的解决方式”。这一切并非在 Google Docs 发布后第二天就发生了。人们花了 10、15、20 年才发明出所有这些东西，并想明白“原来可以用这个做那件事”。所以这一切都是在说：是的，它会很快，但实际上不会，人们需要一段时间才能弄清楚如何彻底改变他们的业务运作方式。

One of the companies I always remember that we looked at when I was at Andreessen Horowitz is a company called Frame.io, which is video editing and video collaboration. There's nothing there that you couldn't have done at least five years earlier, and maybe ten years earlier. Actually, that's a bad example because it relies on cutting-edge web technologies. But if you go out and pick ten random SaaS companies that were started the day before ChatGPT launched, how many of them could have been founded at any point in the previous 15 years? The delay was somebody realizing, 'Oh, that problem exists inside that industry, and this is the way we would solve it.' It didn't all happen the day after Google Docs. It took like 10, 15, 20 years for people to invent all that stuff and work out that you could do that with this. So all of that is a way of saying: yes, it is going to be quick, but actually no, it will take a while for people to work out how to completely change how their business works.

你的观点让人很安心，因为基本上就是说，好吧，这事很大，但我们以前经历过很多次变革，一切都会好起来的。

Your view is so comforting because it's basically like, okay, this is a huge deal, but we've been through many transformations before and it's going to be okay.

嗯，我在演示文稿末尾有一张幻灯片，标题大概是“这次会跟以往完全不同，就像以往每次一样”。下一张幻灯片是 50 年代 IBM 的一则广告，上面是一大片穿着白衬衫、打着领带的白人男性，都举着飞行规则手册。广告标语是——这是 IBM 的广告——上面写着“IBM 电子计算器”——那时还不叫计算机——“它只有冰箱大小，却相当于多了 150 名工程师”。有多少公司会把“我们给你 150 名额外工程师”作为口号？我的意思是，这不就是 Claude Code 的全部卖点吗？免费给你 150 名额外工程师，或者不是免费，但那可是一大笔钱。

Well, I have a slide towards the end of the presentation which, and I know the title is something like, 'This is going to be completely different from everything else, just like everything else.' And then the next slide is an IBM ad from the 50s which has got this sea of white men in white shirts and ties all holding up flight rules. The slogan on the ad is, it's an IBM ad, it says 'An IBM electronic calculator' — this is before it was called a computer — 'It's the size of a fridge, it's like having 150 extra engineers.' How many people listening to this company list their company's slogan as basically 'We'll give you 150 extra engineers'? I mean, isn't that the whole pitch of Claude Code? 150 extra engineers for free, or not free. That's a lot of money.

嗯，是的，这就是它给你的结果，所以我们一遍又一遍地重复这个过程，只是为了让它变得具体。我的意思是，显然没有互联网我们做不到这一点。所以我的演示文稿里有一张幻灯片，我们可以聊聊，那是一张图表，显示自 50 年代以来美国超市里库存了多少种商品。这张幻灯片想表达的是，条形码让超市能库存更多商品，因为它们可以追踪库存。但制作那张图表，我必须知道有个叫食品营销协会的东西。我还得发现他们每年都会公布超市里有多少个 SKU。然后我得意识到他们从 50 年代就存在了。如果我挖得够深，也许能做出一个完整的时间序列，然后做出整张图表。现在想象一下在 1994 年做这件事。首先，你根本不知道这东西存在。你真的需要去一个图书馆，那里有他们公布的那个数字，以及报告里的数据。你完全不知道。然后你需要找到一个有这些报告的图书馆。所以你要花大概三天时间打电话，花大概 50 美元的长途电话费，才能找到一个有这些报告的图书馆。或者你打电话给食品营销协会，他们说：'当然可以。如果你买——你知道，我们可以卖给你，每份 500 美元。' 然后你知道你得去——也许你住在纽约，或者某个地方有这些报告——两周后你拿到了图表，你看着它，然后分析师生活的另一面是：你花一整天做图表，看着它说：'这没什么意思。' 所以你花了两周做图表，然后看着它说：'嗯，我不会用这个。' 而对我来说，这就像在谷歌上花了两小时。所以我们忘记了互联网是多么了不起的一件事。这么说有点绕，但我们忘记了我们经历过这些巨大的变化，然后我们看不到它，因为世界一直是这样。这次可能有什么不同？即使你的代码不同——这次一切都会改变，就像上次一样。明显的区别当然是 AGI 可能会出现，超级智能也会出现，它可以做人类的工作，可以为我们做很多事情，实际上可以取代工作。就我们正在经历的这场变革的这个方面，你有什么想法？

Um, so yeah, that's what it gave you, and so yes, we keep going through this over and over and over again just to kind of make that tangible. I mean, obviously we couldn't be doing this without the internet. So there's a slide in my presentation which we could maybe talk about, but it's a slide or chart showing how many products are stocked in supermarkets in America since the 50s. And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it. But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how many SKUs there were in supermarkets every year. And then I had to realize they'd been around since the 50s. And if I dug long enough, I might be able to make a whole time series and I could make a whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. You really need to go and find a library where they publish that number, and then the numbers in that report. You'd have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone and spend like $50 on long-distance phone calls to find a library that has these. Or maybe you call the Food Marketing Institute and they say, 'Yeah, sure. If you buy a—you know, we'll sell them to you for $500 each.' So then you know you're going to get on a—maybe you live in New York or like there's somewhere that has this—and you two weeks later you've got the chart and you look at it and then the other side of this is the life of an analyst: you spend all day making a chart and you look at it and go, 'That's not very interesting.' So you spend two weeks to make the chart and then you look at it and go, 'Yeah, I'm not going to use that.' And for me, this was like two hours in Google. And so we like we forget how big a deal the internet was. That's a long way of saying it, but like we forget we've had these absolutely enormous changes and then we don't see it because it's like that's the world—the world has always been. What's different potentially this time? Just to even though your code is it's different—this is everything's going to change, like just like last time. The big difference obviously is that AGI might emerge and superintelligence, where that is, could you know, does the work of humans, can do a lot of this stuff for us, can actually replace jobs. Just thoughts on that element of this transformation we're going through.

我不知道。这是我在写关于 AI 的文章时遇到的困难之一。当然在 2023 年、24 年初，所有的问题都是你在 12 月就能问的问题。问题并没有真正改变，策略也没有真正改变。我认为 AGI 的问题也是一样。我的意思是，我们可以观察到一点：我们对人类智能是什么没有理论，我们对这些模型为什么工作得这么好没有理论，我们对它们会变得多好也没有理论。所以我们基本上都是在凭感觉预测会发生什么。然后你会有凌晨两点嗑了药的哲学系学生说：'嘿，老兄，这是意识吗？也许我们也没有意识，我们只是以为自己有。' 是啊，太好了，谢谢。我认为今天可以观察到的一点是：我们不知道，我们不清楚，我们可以猜测，但我们并不真正知道这会走向何方。我认为今天可以说的是，有很多术语的重新定义。所以我去年年底在演讲中引用了一句话，来自一位叫 Larry Tesla 的 AI 科学家，他说：'AI 就是机器还做不到的事情，因为一旦机器能做到，人们就会说，"嗯，那只是软件。"' 所以当然，我确实在社交媒体上时不时做个调查，问：'机器学习还算 AI 吗？' 因为我确实听到有人说：'哦，那不是 AI。那只是图像识别。' '那不是 AI，那只是情感分析。' 所以 AI 有点像'技术'这个词。如果它是新的，那就是技术。但在 60 年代，航空公司、喷气式客机是技术。现在喷气式客机属于科技。所以有一种感觉，AI 是一个移动的目标——它就是刚刚开始工作的东西。我认为这里的重点是，现在你可以清楚地看到人们在重新定义 AGI，让它指代那些现在能工作的东西。那么 AGI 现在的定义是什么？就像它能做一定比例的经济上有价值的工作。嗯，这和'它有灵魂，它是活的'是非常不同的。因为数据库也能做到。比如，1975 年的 IBM 大型机就能做相当比例的经济上有价值的工作，这些工作以前是由人做的，结果发现还有一大堆它做不到的事情，而我们当时不知道那些事情存在。所以这里有很多创造性的重新定义。超级智能——我不确定超级智能是比 AGI 更高级还是更低级？因为去年我认为超级智能是非常好，但不如真正的 AGI。现在又变成：'哦不，不，我们已经有了 AGI，但超级智能，那真的很难。' 所有这些术语都像——什么？甚至很有趣，今天早上我在 Hacker News 上争论。你还记得那个争论——你记得那个争论，这从来都不是对时间的好利用——但你还记得那个争论，比如人们会争论加密货币是不是区块链，或者区块链是不是加密货币。这没有正确答案。我们只需要确保你明白你说的是什么意思，但这并没有一个正确的答案。我们会不会得到具有人类水平智能的东西？我们不知道。我认为我们没有办法回答这个问题。也许能，也许不能。你可以从两边论证。与此同时，这意味着我们有了这个东西，它显然是一种完全变革性的技术。也许严肃的一点是：你不需要相信——即使模型明天停止进步，如果就这样了，我们明天撞上了砖墙——这也是一种极其有用的技术，将在未来 10 年内改变世界并得到推广。所以你不需要相信那些东西，也能相信这是一件大事。

I don't know. This is one of the ways I've struggled to write about AI. Like certainly in 2023, early '24, all the questions were questions you could have asked in December. Like the questions didn't really change and the strategies didn't really change. And I think the AGI question is kind of the same. I mean, the observation one can make: you know, we have no theory of what human intelligence is, we have no theory of why these models work so well, we have no theory of how much better they will get. So we're all just kind of vibes forecasting as to what will happen. And then you can have like the 2 a.m. doped-out philosophy students talking about, 'Hey man, is this consciousness? Maybe we aren't conscious either, we just think we are.' Like, yeah, great, thank you. I think the one thing one can observe today is: so we have no idea, we don't know, we can guess but we don't really know how this is going to end up. What I think you can say today is that there's a lot of kind of redefinition of terms. So I think a quote I used in my presentation late last year was an AI scientist called Larry Tesla, who said, 'AI is whatever machines can't do yet, because once machines can do it, people say, "Well, that's just software."' And so certainly, I mean, I did do a poll on social media every now and then asking, 'Is machine learning still AI?' Because I've certainly heard people say, 'Oh, that's not AI. That's just image recognition.' 'That's not AI, that's just sentiment analysis.' So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in the 60s, airlines, jetliners were technology. Now a jetliner is in tech. And so there's a sort of sense of AI is like a moving target—it's whatever just started working. And I think the point here is now clearly you can see people redefining AGI to mean the stuff that works now. So is AGI what's the definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very different thing to 'it has a soul and it's alive.' Because a database can do that. Like, you know, an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people, and it turned out there was a whole bunch of other stuff that it couldn't do that we didn't know existed. So there's a lot of like kind of creative redefinition here. Superintelligence—I'm not sure is superintelligence more than AGI or less than AGI? Because last year I thought superintelligence was like really good but not as good, not actual AGI. And now it's like, 'Oh no, no, we've already got AGI, but superintelligence, that's really hard.' It's like all these terms are like—what? Even it's funny, I was having an argument on Hacker News this morning. You remember the idea—you remember the argument, which is never a good use of time—but you remember the argument of like, you know, people would argue about whether crypto is blockchain or whether blockchain is crypto. There isn't a right answer to that. Let's just be sure you know it's important to understand what you mean when you say that, but there isn't like a correct answer to this. Are we going to get to something that has human-level intelligence? We don't know. I don't think we have any way of answering that question. Maybe, maybe not. You can make arguments either way. Meantime, it does mean in the meanwhile we've got this thing that's clearly kind of a completely transformative technology. And maybe the serious point here is: you don't have to believe—even if the model stopped getting better tomorrow, if this is it and we hit a brick wall tomorrow—this is an incredibly useful technology that's going to change the world and get rolled out over the next 10 years. So you don't have to believe in any of that stuff to believe that this is a giant deal.

有一件事确实变了：我请了你的前老板 Mark Anderson 上播客，但我们实际上没有在对话中聊到这个，他在我们开始录制之前提了一下，但我一直没机会问。他有一个见解，现在公司的机会集比以前大得多。我们以前没有万亿美元的公司。现在我们将有几十家万亿美元的公司。

Something that's definitely changed: I had your former boss Mark Anderson on the podcast, and we didn't actually talk about this during the conversation, and he brought it up before we started recording and I never got to it. He had this insight that the opportunity set for companies now is so much larger. We used to have no trillion-dollar companies. Now we're going to have dozens of trillion-dollar companies.

就像公司能成长到的规模在急剧上升，估值也随之上涨。他的观点是，人们还没有真正理解现在公司能变得多大。比如大家都在五到六个月内达到 1 亿美元的年收入。对此你怎么看？

Just like the size companies can grow to is going up so much and valuations also go up along with that. And his point is just people haven't really grokked just how large companies can get now. Like everyone's hitting 100 million AR in like five months, six months. Just thoughts on that.

是的，这就是他 15 年前提出的“软件吞噬世界”的论点。总可寻址市场（TAM）越来越大，因为你可以覆盖经济中越来越大的部分。所以想想经典的平台迁移框架：大型机——峰值安装量大约在 7 万到 8 万台。然后互联网兴起时，全球有 5000 万到 1 亿台 PC。今天有超过 10 亿台，可能 10 到 15 亿，但很多是企业用的。全球大约有 7 到 8 亿台消费级 PC，以及约 55 到 60 亿部智能手机。这就是为什么 ChatGPT 能有 9 亿周活跃用户。

Yeah, I mean this was his whole 'software is eating the world' thesis from 15 years ago. The TAM gets progressively bigger because you can address larger and larger parts of the economy. So if you think about the classic platform shift framing: mainframes — peak install base was something like 70,000 to 80,000 units. Then when the internet kicks off, there were 50 to 100 million PCs on earth. Today there are over a billion, maybe 1 to 1.5 billion, but a lot are corporate. There are about 700 to 800 million consumer PCs, and about 5.5 to 6 billion mobile smartphones. That's why you can have 900 million weekly active users on ChatGPT.

所以五年前有一种说法：“我们已经用完了人口红利，所以下一件事不可能再大一个数量级。”这在某种程度上是对的，但模型错了。因为现在显然你在向另一个方向移动——你在拓展并自动化经济中全新的、庞大的领域。

So there was this narrative five years ago: 'Well, we've run out of people, so the next thing can't be an order of magnitude bigger.' That was true up to a point, but it was the wrong model. Because clearly what's happening now is you're moving in another direction — you're branching out and automating big new ways of the economy.

现在回到你关于工作的观点——你可以说我们只会用 AI 取代所有人，所有钱都会流向 Sam Altman，Mark 可以再给自己买一架湾流。我认为另一种答案是，这又回到了劳动总量谬误。过去 200 年里，每一项这样的技术都消除了一批工作，创造了一批新工作，创造了一批新价值，并为所有人带来了繁荣。经历这个过程是痛苦的，但它总是创造更多价值。

Now, back to your job point — you could argue we're just going to replace all the people with AI and all the money will go to Sam Altman and Mark can buy himself another Gulfstream. I think the other answer is it's back to the lump of labor fallacy. Over the last 200 years, each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, and unlocks prosperity for all of us. It's painful as you go through it, but it always creates more value.

所以这里你当然可以类比电力行业——电力如何成为一切的一部分，而软件也在慢慢渗透出去。类比是工厂里的电力，然后电力慢慢扩散。这再次说明：它慢慢扩散去做越来越多的事情，为经济贡献越来越多的价值。

So here you could certainly make an analogy to the electricity industry — how electricity became part of absolutely everything, and software has been slowly working its way out. The analogy would be electricity in factories, then electricity slowly spreads out. That would be the point again: it slowly spreads out to do more and more things, contributing more and more value to the economy.

它也会消失在事物内部。另一方面，这是我演讲中资本部分的一个观点。Sam Altman 有句名言：我们将像水或电一样按表计费出售 AI 智能。你看到这个会想：我亲爱的孩子，你需要我给你解释一下公用事业行业的利润率结构。因为你知道吗——当你看电视时，电视公司不会把你月账单的一部分付给电力公司。当你洗衣服时，博世不会把洗衣机价格的一部分付给电力公司。

It also disappears inside things. The other side of this is the point of my capital section in the presentation. There's this quote from Sam Altman where he said we're going to be selling AI intelligence on a meter like water or electricity. And you look at this and think: my dear sweet child, you need me to explain the margin structure of the utility industry to you. Because guess what — when you watch television, the TV company isn't paying a percentage of your monthly bill to the electricity company. When you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine.

显然，当前更具体的战术问题是：我们最终会拥有三个大模型，还是变成数百个模型、开源模型、本地模型等等？即使我们最终拥有，比如说，三到六个到十个每年花费数千亿美元的巨型基础模型，它们能从中获得所有价值吗？

Clearly, the more specific tactical question at the moment is: do we end up with three giant models, or does it become hundreds of models, open models, local models, and so on? And even if we do end up with, say, three to six to ten giant foundation models that cost hundreds of billions of dollars a year, do they get all the value from that?

我职业生涯始于电信分析师，所以还会关注一些。全球移动行业年收入约 1 万亿美元，现在可能更多一点。每年资本支出约 2000 亿美元。整个电信行业约 3000 亿，移动约 2000 亿——约占年收入的 15% 到 20%。如果你看移动数据消费的图表，它是一条直线上升的指数曲线。现在全球的数据量是 2010 年的 1500 到 2000 倍。而股票在 25 年里毫无起色，因为这是一个增长停滞、低利润的商品公用事业。他们在销售这个客观上令人惊叹的全球技术基础设施，极其复杂和精密，但所有酷的东西都是别人做的。

I started my career as a telecoms analyst, so I still pay attention to it a bit. Global mobile industry has revenue of about a trillion dollars a year, maybe a bit more now. It spends about $200 billion a year on capex. Total telecoms is about $300 billion, mobile about $200 billion — about 15 to 20% of revenue every year. If you look at a chart of mobile data consumption, it's an exponential curve going straight up. The number now is about 1,500 to 2,000 times what it was in 2010 globally. And the stocks have gone nowhere in 25 years because it's an ex-growth, low-margin commodity utility. They're selling this objectively amazing piece of global technology infrastructure with enormous complexity and sophistication, but all the cool stuff is made by somebody else.

这是一个关键时刻：电信公司曾以为他们会做你在 iPhone 上做的所有事情。结果不仅他们没做成，苹果也没做成。所有东西都在更上层。所以围绕基础模型的根本问题是：模型能完成所有事情吗？你能直接去聊天机器人那里让它完成所有事情吗？模型公司能继续构建这些“Claude for X”、“Claude for Y”之类的东西吗？在我看来，这很像你在 Excel 里点“文件 > 新建”时看到的东西？就像平板电脑，但所有这些实际上也都是价值数十亿美元的公司。如果不是这样，那么所有东西都必须变成应用——无论“应用”是什么意思？如果所有东西都必须变成应用，谁来构建它们？它们不可能都由模型实验室构建，就像它们不可能都由微软构建一样。

This was that pivotal moment where the telcos thought they would do all the stuff you did on your iPhone. Not only do they not do it, but Apple doesn't do it either. It's all further up the stack. So the elemental question right now around foundation models is: does the model do the whole thing? Can you just go to the chatbot and get it to do the whole thing? Can the model companies keep building these 'Claude for X', 'Claude for Y' things, which to me look very much like what you see if you hit File > New in Excel? It's like the tablets, but all of those are actually billion-dollar companies as well. And if not, does it all have to be apps — whatever 'app' means? And if it all has to be apps, who builds those? They can't all get built by the model labs, just as they didn't all get built by Microsoft.

所以如果它们都由其他公司构建，基础模型会像 Windows 那样拥有向上游的杠杆吗？还是这更像 AWS？如果你是一家工程公司或律师事务所购买软件，你不在乎它运行在哪个云上，你也不必标准化在 AWS 上，因为所有软件都在那里。开发者不会因为所有客户都用 AWS 就都标准化在 AWS 上。这不是它的运作方式。这是 Windows 操作系统的运作方式，但不是云的运作方式。

So if they're all built by other companies, do the foundation models have leverage up the stack the way Windows did? Or is this more like AWS? If you're an engineering company or a law firm buying a piece of software, you don't care which cloud it runs on, and you don't have to standardize on AWS because that's where all the software is. Developers don't all standardize on AWS because all the customers use AWS. That's not how it works. That's how Windows OS works, but that's not how cloud works.

所以在我看来，如果聊天机器人不是用户体验，而需要应用，并且模型公司不会去构建这些，而模型本身基本上是商品——至少从用户角度看是这样——那么模型公司为什么会有定价权？所有价值不都应该在更上层吗？你基本上不是得到三到六家公司以边际成本销售商品吗？当然，半导体分析师们会说：“不不不，定价权将永远无限。”

So it does seem to me that if the chatbot isn't the UX and it needs to be apps, and the model companies aren't going to build that, and the models themselves are basically commodities — at least as you can see them as users — then why would the model companies have pricing power? Wouldn't all the value be further up the stack? Aren't you basically getting three to six companies selling a commodity at marginal cost? Now obviously the semiconductor analyst guys are like 'No, no, no, there's going to be infinite pricing power forever.'

一个非常有趣的结论是，你认为随着时间的推移，基础模型公司——Anthropic、OpenAI 等——它们的利润率会被压缩。它们不会像今天这样成功。更大的机会在应用层，也就是那些基于模型构建应用的人，即封装层。

A really interesting takeaway here is that your sense is over time the foundational model companies, Anthropic, OpenAI, others, will their margins will get squeezed. They will not be as successful as they are today. And the bigger opportunity is in the application layer, the people building on the models, the wrappers.

是的。我的意思是，这是一个非常确定性的论点，关键在于模型公司似乎没有网络效应。所以似乎不存在赢家通吃的效应，即其中一家会远远领先于其他家。因此，竞争应该是无限期的。你会有无限期的竞争。产品之间没有真正根本性的差异化。那么，为什么会有定价权呢？与此同时，如果你需要成千上万种不同的应用，由不同的人构建，这些应用不可能都由模型公司来构建。所以，最终它应该更像云计算，而不是 Windows。现在，这可能是完全错误的，我在演讲中提出的一个观点是，想象一下在 1997 年讨论互联网。你能说对什么？或者，在 2000 年讨论移动互联网。你不可能，大多数人几乎都会错过所有东西。你肯定会说，一家来自库比蒂诺的过气 PC 公司会赢得一切，但没人会这么说。而一家有着奇怪标志的搜索公司，比如搜索，那和移动有什么关系？不，算了吧，你是个白痴。所以，我们应该假设我们不知道，但基本的逻辑是，它们为什么会有定价权？我不知道。我在 99 年还是个初级分析师的时候，去英国看了一家试图在线销售电脑组件的 .com 公司。他们有整套模式、故事和品牌。我们去看他们，在从伯明翰回来的火车上，一位叫 David Tate 的高级银行家，我们坐在一起讨论，Tate 说：“这是一个低利润的转售商，一次性销售。你可以随便说 .com，它就是个低利润的转售商。”我认为这就是问题的关键：它们是同质化的商品基础设施提供商。这里面有很多科学，但移动领域也有很多科学。比如，你为一块平板屏幕付多少钱？平板屏幕有诺贝尔奖，但它们仍然是低利润的商品。我期待被证明是错的，但嘿，这就是现在的情况。

Yeah. I mean, this is a very sort of deterministic thesis which is the models companies crucially what I said is the models don't seem to have network effects. So there doesn't seem to be a winner takes all effect where one of these will run away ahead of the others. So you should have competition indefinitely. You have competition indefinitely. You don't have really radical differentiation in what the product is. Then why would you have pricing power? And meanwhile, if you need to have thousands of applications that are all different built by different people, those can't all be built by the model people. So it should end up looking more like cloud than it looks like Windows. Now that may be completely wrong, and you know one of the points I make in the presentation is like imagine having this conversation about the internet in 1997. What would you have got right? Or indeed having it about mobile in 2000. You would not, most of you would have missed almost all of it. You certainly would have said that a has-been PC company from Cupertino would win the whole thing, and no one would have said that. And a search company with a weird logo, like search, what's that got to do with mobile? No, forget it, you're an idiot. So like we should presume we don't know, but they're all the sort of basic building blocks of like, well, but why would they have pricing power? I don't know. I had a when I was a baby analyst in like '99, went to see a .com company in the UK that was trying to do online selling computer components online. And they had this whole model and this whole story and the brand and the whole thing. And we went up to see them and we're on the train back from Birmingham, and this sort of senior banker called David Tate, we're all sitting talking about it, and Tate says, "It's a low margin reseller, one-time sales. You can say .com all you like. It's a low margin reseller." And I think that's the kind of the crux of this is they're undifferentiated commodity infrastructure providers. There's a lot of science to it, but there's a lot of science in mobile. I mean, what do you pay for a flat panel screen? There are Nobel prizes in flat panel screens. They're still a low margin commodity. I look forward to being proven wrong, but hey, that's what it looks like now.

太好了。我知道你不是投资者。我知道你在 A16Z 工作，但并没有真正做投资，只是个合作伙伴。合作伙伴只是坐着高谈阔论。你会投资哪些公司吗？比如，如果现在有几家公司你会投资，有没有在名单上或者某些类别？

This is great. So I know you're not an investor. I know you didn't actually do investing at A16Z even though you work for A16Z partner. Partner just sit around and pontificate partner. Would you are there companies you would invest in? Like if there are a couple companies you'd invest in now is there some on that list or categories even?

你知道，我简单提过我曾是一名分析师。我是一名卖方股票分析师。我不是一个很好的卖方股票分析师，部分原因是我不喜欢和客户交谈，部分原因是我不关心股价，这似乎是不适合做股票分析师的。而且，正确和早到之间有很大区别，正确的公司和正确的价格之间也有很大区别。现在，确定性地看，你可以纵观市场说，嗯，就像智商钟形曲线 meme，智商 50 的人和智商 200 的人都说：“杰夫·贝佐斯，聪明人，我买股票。”你当然可以过度思考这一切，你可以看看谷歌、苹果、Facebook、亚马逊，说很难看到它们会因为这一切而遇到问题。你当然可以看到它们各自的问题，其中一家可能会掉链子，但值得记住移动领域发生了什么。互联网是一个巨大的、显而易见的平台转变。移动的有趣之处在于，有些公司完全错过了它。而对其中一些公司来说，它真的没有改变什么。比如对谷歌来说，它没有改变什么。对 Meta 来说，这很棒。这是比 PC 更好的社交方式，因为你有了摄像头和通知，手机随时带在身边。亚马逊呢？这改变了什么？并没有改变一切。我在这里大大简化了，但关键是，雅虎邮件未能完成转型。有些本来就在衰落的公司未能完成转型。也许 eBay，你可以争论具体的名字。

You know, I mentioned briefly that I was an analyst. I was a sellside equity analyst. I was not a very good sellside equity analyst, partly because I was not interested in talking to clients, partly because I was not interested in share prices, which would seem to be like a disqualification to be an equity analyst. And you know, there's a huge difference between being right and being early, and there's a huge difference between the right company and the right price. Now, deterministically, you can look across the market and say, well, you know, it's like the bell curve IQ meme, and the guy with 50 and the guy with 200 are both saying, "Jeff Bezos, smart guy, I buy stock." And you can certainly overthink all of this, and you can look at Google, Apple, Facebook, Amazon and say it's hard to see a problem for them really with all of this. You can certainly see questions for all of them, and one of them may drop the ball, but it's worth remembering what happened in mobile. The internet was this big obvious platform shift. The funny thing about mobile is that some companies missed it completely. And for some of them, it really didn't change anything. Like for Google, it didn't change anything. For Meta, this was great. This is a way better way to do social than on PC because you've got a camera and notifications and it's on your phone all the time with you. Amazon, like what does this change? Doesn't change everything. I'm massively oversimplifying here, but the point is, meanwhile Yahoo Mail fails to make the jump. There are companies that were already kind of dying that failed to make the jump. Maybe eBay, you could argue about individual names.

关键在于，我们经历了那次转变，但对半个行业——半个互联网行业——来说，什么都没改变。所以我觉得这里也能看到类似的情况。a16z 的 Steven Sinofsky（他曾负责 Windows 业务）总是说：现有企业总是试图把新事物变成一项功能，有时他们是对的，有时它确实只是一项功能。顺着这个思路，我想听听你的看法。很多嘉宾都提到一个趋势：分发正在成为越来越大的护城河，因为软件越来越容易构建，每个人都在发布产品。每个人都在争夺注意力，这变得越来越难。吸引注意力从来都不容易，但市场噪音正在疯狂上升。在我看来，这说明分发正成为越来越有价值的技能和资产。这也意味着现有企业会更成功，因为它们已经拥有分发渠道，而初创公司却要努力突破。

The point is that we went through that shift and it didn't change anything for half the industry, half the internet industry. So I think you could propose a little bit of that here. It's what Steven Sinofsky at a16z, who used to run Windows, would always say: incumbents always try to make the new thing a feature, and sometimes they're right, sometimes it's a feature. Actually, along those lines, something I wanted to get your take on. There's this thread that's been happening across a bunch of guests, which is around distribution becoming a bigger and bigger moat because as software is easier to build, everyone's launching products. Everyone's trying to compete for attention. It's getting harder and harder. It's always been hard to get people's attention, but the noise in the market is just going up like crazy. To me, that tells me distribution is becoming a more and more valuable skill and asset. And it also tells me incumbents are going to be a lot more successful because they already have distribution versus a startup that's trying to break through.

是的。这有点像 Drake 的那个梗：他说，“我不喜欢那个，我喜欢这个。”就像，我不喜欢看到 GPT-2 说唱歌手，我喜欢的是“马具”。所以，我在去年底的演讲中确实谈到了这一点：如果产品是商品，那么分发就至关重要。我今年早些时候写过一篇关于 Siri 的文章：它们如何竞争？很多人拿它和网页浏览器做对比。浏览器的根本在于，浏览器作为产品和浏览器渲染引擎是有区别的。渲染引擎有好有坏，但浏览器产品只是渲染引擎的一个很薄的包装。有一个输入框和一个输出框，还有什么？浏览器设计的最后一项创新是什么？标签页浏览，那是 20 或 25 年前的事了。时不时有人试图在浏览器设计上创新，但从未成功，因为我们找到了理想形态。就像试图在智能手机设计上创新：它就是一个玻璃矩形，你无能为力。所以，当然，微软利用分发打入了市场。然后，撇开诉讼不谈，事实证明赢得浏览器市场并不重要，因为价值在更上层。微软赢了浏览器市场五六年，但什么也没得到。所以，现在显然谷歌正在利用分发来推动 Gemini。Gemini 和其他产品有什么区别？如果你整天用这些东西，你知道，但对普通人来说，没有区别。Meta 也一样：看看人们使用情况的调查数据，甚至在 Llama 这样的新闻之前，Meta 的使用率就介于 ChatGPT 和 Gemini 之间。如果你在科技圈，人们完全忽略了它，但他们把它铺到了每一个服务表面，效果还不错。所以，当领域基本上是商品时，分发一个足够好的产品：分发和品牌就变得非常重要。你可以从 OpenAI 去年年底的战略中看到这一点：人们称之为“昨天的一切”。他们只是尝试各种方法，试图找到如何获得飞轮效应，如何获得分发，如何获得粘性，如何让人们在使用谷歌、Meta 和亚马逊铺天盖地的产品之前就用上他们的。然后就有了惯性和默认的力量：为什么要切换？苹果在这里是最后一个落地的。有一个有点奇怪的开放理念，现在有一个更奇怪的故事说 OpenAI 要起诉苹果。祝你好运。关于苹果交易的有趣之处是，顺便说一句，如果你回去看 2024 年的 WWDC，整个下半场都是 Apple Intelligence。那是我见过的关于个人 AI 助手最令人信服的愿景。他们后来没能发布，但其他人也没能发布。再看一遍，你会想，“好吧，所以你想要的是一种工具使用、智能体式、设备端 AI，没有提示注入、没有幻觉，并且有一个完全标准化的 API 系统，覆盖 10,000 个应用，意图都能完美运行？”这听起来不错，但我并不惊讶他们没能发布。没有人发布过那个。但那个愿景很棒。我真的很想看看一个月后的 WWDC 会发生什么。他们现在真的会发布由 Gemini 驱动的那个版本吗？但这也是另一点：会有 AI 智能，不管我们怎么称呼它，Android 上的 Gemini Intelligence，然后 iOS 上会有 Apple Intelligence，由 Gemini 驱动，但不会是同一套产品。模型只是底层的“笨东西”，是商品，为关于功能应该是什么以及不同分发的决策提供动力。在这种情况下，当然，苹果有十亿台设备可以在边缘运行这个，而谷歌有一个很棒的营销口号：“即将登陆我们最强大的设备”，意思是它不能在大多数 Android 上运行。所以，又是分发问题。谷歌 I/O 下周举行，我们拭目以待他们会发布什么。

Yeah. I mean, there's a version of the Drake meme: he says, "I don't like that. I do like this." It's like, I don't like seeing GPT-2 rappers. I do like harnesses. So, yeah, I did spend some time talking about this in the presentation I did at the end of last year: if the product is a commodity, then the distribution is what matters. I wrote a thing about Siri earlier this year: how do they compete? Well, there's an obvious comparison a lot of people made with web browsers. The fundamental thing about a web browser is that there's a distinction between the web browser as a product and the web browser rendering engine. The rendering engine can be better or worse, but the browser product is just a really thin wrapper for a rendering engine. There's an input box and an output box, and what else? What's the last innovation in browser design? Tab browsing, which is 20 or 25 years ago. Every now and then somebody tries to innovate in browser design, and it never works because we found the platonic ideal. It's like trying to innovate in smartphone design: it's a glass rectangle, there's nothing you can do. So what happened, of course, is that Microsoft used distribution to break in. Then, setting aside the lawsuit, it turns out that winning browsers doesn't matter anyway because the value is further upstack. Microsoft won browsers for five or six years, and it didn't get them anything. So clearly what's happening now is that Google is using distribution to drive Gemini. What's the difference between Gemini and others? If you're using this stuff all day, you know, but for a normal person, there's no difference. The same thing with Meta: if you look at survey data on which people use, even before the new news thing like Llama, Meta was up there between ChatGPT and Gemini. If you're in tech, people have completely written it off, but they sprayed it on every service surface and it wasn't that bad. It was fine. So distribution of an adequate product when the field is basically a commodity: distribution and brand become a big deal. You could see that in OpenAI's strategy late last year: people called it "everything yesterday." They were just trying everything to figure out how to get a flywheel, how to get distribution, how to get something that sticks, how to get something people use before Google and Meta and Amazon spray it everywhere and get everybody using theirs. Then you have inertia and the power of the default: why would you switch? Apple is kind of the last penny to drop here. There was a slightly weird opening ideal, and now there's an even weirder story that OpenAI wants to sue Apple. Good luck with that. The funny thing about the Apple deal is, just to go off on a tangent, if you go back and watch WWDC 2024, the whole second half is Apple Intelligence. That was the most compelling vision of a personal AI assistant I've seen. They then couldn't ship it, but neither could anybody else. Watch it again and you're like, "Okay, so you want tool-using, agentic, on-device AI with no prompt injection and no hallucinations and a completely standardized API system across 10,000 apps with intents that all work perfectly?" That sounds good to me, but I'm not surprised they couldn't ship it. Nobody else has shipped that. But that vision was great. I really want to see what happens at WWDC in a month. Do they actually ship that now powered by Gemini? But that's also another point: there's going to be the AI intelligence, whatever we call it, Gemini Intelligence on Android, and then there's going to be Apple Intelligence on iOS, which is powered by Gemini, but it's not going to be the same set of products. The model is just the dumb thing underneath, the commodity that powers different decisions about what the feature should be and different distribution. In that situation, of course, Apple has a billion devices that can run this on edge, and Google has a wonderful marketing slogan: "Coming soon to our most powerful devices," meaning it won't work on most Androids. So again, distribution questions. Google I/O is next week, so we'll see what they launch.

哦不，他们发布了，他们发布了 Android……这正好说明我们有多不关注 Android 和 iPhone 了。谷歌上周搞了个大活动。他们要用 Google Books 取代 Chromebooks，还有一个由 Gemini 驱动的全新 Android 智能，将推给那五个买了 Pixel 手机的人。你不是谷歌员工吧。

Oh no, they launched, they launched Android... just shows how we stopped paying attention to Android and iPhone. Google had a whole big thing last week. They're replacing Chromebooks with Google Books and they have a new Android intelligence powered by Gemini that will roll out to the five people who bought a Pixel phone. You don't work for Google.

是的。我想换个方向。我很好奇你是否在关注反 AI 情绪，感觉它正在增长。如果你看过这些调查，AI 的受欢迎程度还不如 ICE（美国移民和海关执法局）。

Yeah. I want to go in a slightly different direction. Something I'm curious if you're following is just the anti-AI sentiment that feels like it's growing. It feels like if you've seen these surveys, AI is less popular than ICE.

人们正在试图阻止数据中心建设。我记得埃里克·施密特刚做了一场毕业典礼演讲，每次他提到 AI 时，台下都一片嘘声。你觉得这到底是怎么回事？长期来看会走向何方？

People are trying to stop data centers from being built. I think Eric Schmidt just did a commencement speech and people were booing him every time he mentioned AI. Just like where do you think this is going? Where do you think this goes over time?

这很有意思，而且是一大堆模糊混乱的不同问题。我觉得有些是实实在在的，比如我的电费账单涨了——当然客观上说这只发生在极少数地方，但确实发生了。而用水问题很奇怪，因为它完全是虚假的。我来解释一下我的意思。数据中心用水冷却，大多是闭环系统，但数据中心相对于美国总用水量的比例极小。我专门查过，劳伦斯利弗莫尔实验室在 2024 年底做了一项研究，估算美国数据中心用水量，结果大约只占美国总用水量的 0.017%。显然，如果你住在一个小镇上，只有一口井，而他们封了井把水全给了数据中心，那你肯定很恼火，但那是规划问题，不是数据中心的问题。总的来说，数据中心约占美国能源消耗的 5%，未来五年可能每年增长 1 个百分点，但用水问题纯属无稽之谈。然后还有更实际的问题，比如这会不会导致失业？你可以看一堆三小时长的经济学家对谈播客，主要结论是：我们真的还不知道。有些图表说会，有些说不会。很明显，18 到 24 岁年轻人的就业率在下降，但无论是否有学位，无论是否在看似受 AI 影响的领域，情况都一样。所以有很多计量经济学争论。这里有一个更广泛的观点：我们几乎没有任何来自任何方面的关于 AI 现状的可靠数据。模型实验室什么都不告诉我们。他们没有提供任何有意义的使用信息。他们只给出一些奇怪的研究，比如多少人用这个做了那个，但没有给出日活跃用户数。我们甚至不知道 ChatGPT 的日活跃用户数。这太疯狂了。所有数据都来自学术经济学家试图从 BLS 调查中反推，或者咨询公司和营销机构花大钱调查两万人，问“你用这东西做什么？”我们没有关于实际情况和真实使用人数的好数据。至于就业问题，很多人翻遍美国人口普查收集的所有数据，试图找出我们能看到的迹象。能看到生产力提升吗？目前答案是，没有明确共识表明我们看到了对就业的影响。但政治上这并不重要。如果你是个找不到工作的学生，那显然是个问题，至于是因为 AI 还是因为特朗普和关税，那是另一回事。然后还有一些小众问题，比如给青少年浪漫小说画封面的人很恼火，因为现在不用付钱就能得到一张裸女骑龙飞越火山的图片。抱歉，我故意说得刻薄了点，但确实有这种情况。小说家、电子书作者，围绕是否可以使用 AI 存在巨大的文化战争。还有整个 AI 垃圾内容的问题。你看到那个数字了，30-40% 的新播客是 AI 生成的。所以有一大堆模糊的问题。其中一些有点像我们当年对社交媒体的抵制，但压缩得更紧。对社交媒体的抵制有些是真的，有些半真半假，有些则完全不是。比如“Facebook 卖你的数据”这件事，a) 不是真的，b) 相信它的人绝对坚信它是真的，并认为你提出异议是疯了。就像乔纳森·斯威夫特说的：你无法用理性说服一个没有理性接受某个观点的人。所以你会看到各种观点，就像社交媒体时代一样。有 20 种不同的事情，有些非常真实，有些非常不真实，还有很多处于模糊的中间地带。与此同时，特朗普说他想要一项关于危险模型的新行政令，我不认为这是抵制情绪的根源。担心导弹网络攻击感觉不像美国主流社会的谈话内容，但正是这件事让特朗普对 AI 产生了兴趣。

It's interesting and it's a big fuzzy mess of different stuff. I think there is tangible stuff like my electricity bill went up, which applies actually in a very small number of places objectively, but it did. And the water thing is weird because it's just completely fake. I should qualify what I mean here. Data centers use water for cooling, mostly closed loop, but the number of data centers relative to the total amount of water use in the USA is tiny. I actually went and dug into this. The Livermore lab did a study at the end of 2024 where they estimated US data center water consumption, and it came out at about 0.017% of US water consumption. Now obviously if you live in a small town and you've got one well and they capped the well and gave all the water to the data center, then you're really pissed off, but that's a planning problem, not a data center problem. In general, yes, data centers are about 5% of US energy and might grow at 1% a year for the next five years, one percentage point a year, but the water stuff is just nonsense. Then you get into more tangible questions like, is this taking jobs away? You can watch a bunch of three-hour podcasts of economists talking to each other, and the main answer is we really don't know yet. There are a bunch of charts that kind of say yes and a bunch that kind of say no. Clearly there's a slowdown in employment of 18 to 24 year olds, but that seems to be the same for people who do and don't have degrees, and the same for fields that look exposed to AI and fields that don't look exposed to AI. So there's a lot of econometric argument about this. There's a broader point here: we have very little data on what's going on in AI from anyone. The model labs don't tell us anything. They don't give us any meaningful usage information. They give us these weird studies of how many people used this for that, but they don't give us a daily active user number. We do not have a daily active user number for ChatGPT. This is crazy. All the data comes from academic economists trying to back stuff out of BLS surveys, or consultancies and marketing agencies spending a bunch of money to survey 20,000 people and saying, "What are you doing with this stuff?" We don't have good data on what's going on and how many people are really using this. To the employment question, there are a lot of people looking through all the stuff the US census collects and trying to work out where we can see this. Can we see productivity? The answer right now is there's no clear consensus that we're seeing an impact on jobs. But politically that doesn't matter. If you're a student and you can't get a job, that's clearly an issue, whether it's because of AI or because of Trump and tariffs is a different question. Then you get niche things like people who draw book covers for young adult romance novels are very upset that now you can get a picture of a naked woman on a dragon flying over a volcano without paying them. I'm sorry, I'm being deliberately unkind, but there's a little of that. Novelists, people who write ebooks, there's a huge culture war over whether it's okay to use AI. There's this whole AI slop question. You saw the number that 30-40% of new podcasts are generated by AI. So there's a big fuzzy mass of questions. Some of this is a little bit like the backlash we had around social media, but much more compressed. Some of the backlash around social was true, some was sort of true, and some wasn't. It's exemplified in the whole "Facebook sells your data" thing, which is a) not true, and b) the people who believe it are absolutely adamant that it's true and you're a lunatic for suggesting otherwise. It's like the line from Jonathan Swift: you can't reason somebody out of an idea they didn't reason themselves into. So you get this wide spread of ideas, just as you did with social. There are 20 different things, some really real, some really not real, and a lot in the middle. Meanwhile, Trump says he wants a new executive order on dangerous models, which I don't think drives the backlash. Worrying about missile cyber doesn't feel like a Main Street America conversation, but that's what got Trump interested in this stuff.

我喜欢问有孩子的人，尤其是那些对趋势思考很深的人。基于你对世界走向和 AI 将如何影响未来的了解，你如何改变抚养孩子的方式？你在教他们什么不同的东西，可能对他们未来有帮助？

Something that I like to ask folks that have kids, especially people that are thinking so deeply about where things are going. Knowing what you know about where the world is heading, what AI is going to do to the future, how are you changing the way you raise your kids? What are you teaching them differently that might help them in the future?

我不知道。我觉得这里有一个曲线。如果你的孩子未来一两年就要进入就业市场，那么一切都悬而未决，没人知道会怎样。如果你的孩子五年后进入就业市场，那谁知道呢？但到那时很多事情应该已经稳定下来，尽管方式可能难以预测。所以如果我有一个 21 岁的孩子，我会更担心。但我没有，我的孩子才十几岁。所以这些问题各不相同。然后还有很多在 ChatGPT 之前就存在的问题，比如把关人的崩溃，你是否应该相信 TikTok 上那个网红说的话，以及你究竟从哪里获取对以色列局势的理解，所有那些社交媒体和互联网媒体消费的问题。我不知道。有些人对自己孩子生活的每一分钟都极其刻意地安排。

I don't know. I think there's a curve here. If you've got kids who are going onto the job market in the next year or two, then everything is up in the air and no one knows how this is going to work. If you've got kids who are going onto the job market in like five years, then who knows? But stuff will have settled down a lot by then in probably unpredictable ways. So I could be a lot more worried if I had a 21-year-old. I don't. I've got a kid in his early teens. So those questions vary. Then you've got a lot of the questions that were the same before ChatGPT around the collapse of gatekeepers, whether you should really believe what that influencer on TikTok says, and where exactly you're getting your understanding of what's going on in Israel, and all those kinds of social media and internet media consumption questions. I don't know. There are people who are super intentional about every minute of their child's life.

我想起乔治·卡林的那句话：开得比你快的是疯子，开得比你慢的是白痴，这绝对适用于育儿。所以每个人都觉得自己在中间某个位置，但我并没有一个非常系统、广泛且连贯的计划，规定我的孩子在未来 3、6、12、18 个月要做什么。我只求他别再弄坏他的 Chromebook 了。

I recall the George Carlin line that anyone who drives faster than you is a maniac and anyone who drives slower is an idiot, and that certainly applies to parenting. So everybody thinks they're somewhere in the middle, but I don't have a deeply systematic and widespread and coherent plan for what my child will be doing in 3, 6, 12, 18 months. I'd settle for him not breaking his Chromebook again.

我喜欢你那种“一切都会好起来的”整体氛围。伙计们，一切都会好起来的。

I like that your general vibe is it's going to be okay, guys. It's going to be okay.

是啊。也许因为我是英国人，我们 500 年没有政治暴力了。如果我来自伊朗，我对未来的平静态度会不同。有一层意思是：是的，这会改变很多事情，我们需要担心，但这几乎是常态。我们一直如此。我记得在社交媒体恐慌期间，我翻出了 70 年代末关于数据库的一堆书。当时也有数据库恐慌，而且其中一半是对的。例如，如果所有警察记录和政府记录都上线，那就不一样了。想想深度伪造问题。一种愚蠢的反应是说：“你没听说过 Photoshop 吗？”这没错，但一个 15 岁的孩子不能用 Photoshop 在一下午做出他们高中每个女孩的硬核色情裸照，然后发给全校。

Yeah. Maybe this is because I'm British and we haven't had political violence in 500 years. If I came from Iran, I'd have a different attitude to being calm about the future. There's a layer of yes, this will change a bunch of stuff and we'll need to worry about it, but that's kind of a constant. We've always had that. I remember during the panic around social media, I dug up a whole bunch of books from the late 1970s about databases. There was a whole panic about databases, and half of it was true. For example, if all police records and all government records are online, that's different. Consider the deepfake issue. A dumb reaction is to say, 'Haven't you heard of Photoshop?' Which is true, but a 15-year-old kid couldn't use Photoshop to make hardcore pornographic nudes of every girl in their high school and send them to the whole school in one afternoon.

还能做成视频。

And turn them into video.

完全正确。甚至更多。现在他们可以了。所以这不一样。社交媒体的挑战：90 年代人们说这很棒，你可以成为村里唯一的同性恋孩子，找到你的同类。但结果你也可以是村里唯一的纳粹，唯一的恋童癖，或者想看儿童色情的人。现在你可以找到其他喜欢看儿童色情的人，他们会告诉你这很棒。所以，哎呀。我们连接了所有人，但不幸的是，这也意味着我们连接了所有坏人、我们自己最糟糕的本能以及社会中的每一个问题。AI 也会如此。深度伪造新闻是我们现在能看到的明显问题。还会有更多这类事情。但也有一些技术受众应该知道的事情。你知道英国邮局丑闻吗？

Exactly. Even more. And now they can. So that is different. The challenge of social media: in the 1990s, people said it's great, you can be the only gay kid in your village and find your tribe. But it turned out you could also be the only Nazi in your village, or the only pedophile, or someone who wanted to look at child porn. Now you can find the other people who like looking at child porn and they'll tell you it's great. So, oops. We connected everybody, and unfortunately that meant we connected all the bad people and all of our own worst instincts and every problem in society. That will happen again with AI. Deepfake news is the obvious thing we can see now. There will be a whole bunch more of this stuff. But there's also something a technical audience should know about. Do you know about the post office scandal in the UK?

不知道。

Nope.

好的，插个话。在英国，邮局大多是特许经营，由小商人经营，通常是药店，典型的是第二代印度移民。大约 15 年前，邮局推出了一套由富士通构建的新销售点计算机系统，存在显示现金短缺的 bug。邮局说：“啊哈，我们就知道这些人在偷我们的钱。”数百人入狱，有人自杀、破产、失去家园。与此同时，邮局和富士通的人上法庭发誓说系统没有 bug，也没有其他人遇到这个问题。这是 1970 年代的技术。每一波技术都会带来有意或无意毁掉人们生活的方式。中国的全民监控是有意的。也许有些人应该进监狱，也许不。但每项技术都如此：有毁掉生活的方式，你必须意识到这一点，同时也不要恐慌。

Okay, sidebar. In the UK, post offices are mostly franchises run by small business people, often pharmacies, typically run by second-generation Indian immigrants. About 15 years ago, the post office rolled out a new point-of-sale computer system built by Fujitsu that had bugs showing cash shortfalls. The post office said, 'Aha, we knew these people were stealing from us.' Hundreds of people were imprisoned, there were suicides, bankruptcies, people lost their homes. Meanwhile, people from the post office and Fujitsu went to court swearing there were no bugs and nobody else had this problem. This is 1970s technology. Every wave of technology comes with ways to ruin people's lives, deliberately or by accident. Chinese mass surveillance is deliberate. Maybe people should go to prison, maybe not. But we have this with every technology: ways to ruin lives, and you have to be conscious of that and also not panic about it.

顺着这条线，回到孩子和工作的话题。有没有你正在引导孩子远离的工作，或者你希望引导他们去做的工作？

Following that thread and coming back to the kids thing and the jobs thing. Is there a job you are steering your kid away from, and is there a job you think you want to steer them towards?

这我不确定，可能还有点早。他还没到“我想当消防员”的阶段。

I don't know about that, it's probably a bit early yet. He's not quite at the 'I want to be a fireman' stage.

那可能是个好工作。

That might be a great job.

是啊。当然，回顾我的职业生涯，我先是做股票分析师，然后进入行业，再后来做顾问。你知道自己职业道路的日子已经过去了。显然有些人想成为建筑师、软件工程师，或者 X 或 Y。我不知道。我唯一的想法是，你慢慢会发现你有一堆技能，一堆能让你擅长的工作，以及一堆别人愿意付钱的事情。你至少需要其中两个，最好三个都具备。

Yeah. Certainly, if I look at my career, I started as an equity analyst, then worked in industry, then was a consultant. The days when you knew what your career would be are over. There were clearly some people who wanted to be an architect, a software engineer, or X or Y. I don't know. The only thinking I have is that you slowly work out there are a bunch of skills you have, a bunch of jobs that make you good at them, and a bunch of stuff people will pay you for. You want to get at least two of those, and preferably all three.

稍微拉远一点，我问你一个元问题。关于 AI，你认为还没有人问，或者问得不够，而我们该问自己的问题是什么？

Zooming out a little bit, let me ask you a meta question. What's a question about AI that you think nobody's asking yet, or not enough people are asking, that we should be asking ourselves?

当然。我们谈到了价值捕获。显然每个人都在问这个，但我不确定有多少人在问模型实验室是否拥有定价权。很多人假设今天的状况会持续，或者当然会有。所以这也许是一个问得不够的问题。我在演讲最后提出的问题是：什么是任务，什么是工作？什么只是变成了一个按钮或宏，而人们实际雇佣你做什么？这是思考这个问题的有用方式。显然有些工作，那只是一个任务，工作就会被自动化掉。但有很多工作不是这样。我在演示文稿最后用一张全球录制音乐收入图表总结了这一点，那是一条 U 形曲线。从 2000 年到 2015 年下降了大约一半，之后回升到峰值的 75% 左右，经通胀调整。这是由流媒体驱动的。

Sure. We talked about value capture. Obviously everyone is asking about that, but I'm not sure how many people are asking whether model labs have pricing power. A lot of people presume the situation today will continue, or that of course they will. So that's maybe a question not enough people ask. The question I posed towards the end of my presentation is: what's the task and what's the job? What is just the thing that becomes a button or a macro, versus what are people actually hiring you for? That's a useful way to think about this. Clearly there will be some jobs where that is just a task and the job gets automated away. But there are a bunch where that isn't the question. I pulled that together at the end of the deck with a chart of global recorded music revenue, which is a U-shaped curve. It dropped by about half from 2000 to 2015, and since then has come back to about 75% of the peak, adjusted for inflation. That's driven by streaming.

我看了看这张图，心想，图表的前半部分是在问：如果我不需要花 15 美元买一张 CD 就能得到那首歌，会发生什么？而后半部分是在问：如果每月 15 美元就能听所有音乐，又会怎样？所以，这完全是两种不同的问题。你可以用同样的方式来看 Uber 或 Airbnb 这类公司。一开始，你用新技术做旧的事情，但做得更多——你把 Flickr 搬到手机上，把邮件打印出来，然后你创造出只有新事物才能实现的新东西。再往后，你甚至可能完全重新定义问题，做出完全不同的东西。Spotify 不是在线音乐商店，它是别的东西。而现在，你只有在问题被提出、并且你建成了一个数十亿美元、被无数人使用的东西之后，才知道问题是什么——因为 Spotify 看起来疯狂，Airbnb 看起来也疯狂。但我觉得，要理解这意味着什么，你必须超越“用新技术做旧事但做得更多”的阶段，去思考：你能做什么不同的事情？因为这项技术，什么改变了？什么以前不可能，现在被解锁了？而不是仅仅用新技术做更多旧事。

And I kind of looked at this and said, well, the first half of this chart is saying, what happens if I don't have to pay $15 to get a CD to get that track? And the second half of the chart is saying, what happens if $15 a month gives you all the music that there is? So, it's kind of a completely different sort of question. And you could, you know, that's the way that you could look at Uber or the way you could look at Airbnb, all these kinds of companies. is that to begin with you do the old thing but more with every new technology you do the old thing but more of it on the new place. So, you know, you put flicker on mobile, you print out your emails, and then you make new things that are only possible with a new thing. And then maybe you go a bit further and you kind of completely redefine the question and you make something that isn't that at all. You know, Spotify is not an online music store. It's something else. And right now, you know, those questions, you only even know what the question is after it's been asked and you built a billion dollar thing that lots of people use because like obviously Spotify look crazy and Airbnb look crazy. But that's a sort of I think the way to get at what this means is you have to get past we do the old stuff but more and you have to get to what do you do that's different that's because of this what does this change what wasn't possible before what gets unlocked as opposed to just doing the old thing but more of it.

是的。这正好支持了你那个总体观点：我们不知道会发生什么，这是前所未有的。如果你把时间拉回到几年前，比如三四年以前，你最不可能想到会被自动化的职业就是工程和编程——那感觉是最难的事情，我们还需要人来构建这些东西。但现在，它成了所有角色中变化最大的一个：从你写所有代码，到你的代码中 0% 是 AI 写的？你几乎没意识到，你没意识到那其实是无聊的体力劳动，可以被自动化。你曾以为它是别的东西。这很有趣。

Yeah. just to support this kind of general theme you have of it's like we don't know what is going to happen like this is unprecedented if you if you were to zoom out like a few years ago maybe three years ago four years ago the last profession you think would be automated is engineering and coding it's like that feels like the hardest thing that's like we're going to need people to build these things now it's like the most transformed role of any role like you went from writing all your code to 0% of your code is AI it's almost like you didn't realize you didn't realize it was boring manual labor that could be automated. You thought it was something else. It's funny.

我的意思是，我看过美国政府的一个叫 Onet 的数据集，它试图分析每一个职业，然后人们给它打分，说这个职业有百分之多少暴露在 AI 之下，AI 今天能完成其中的百分之多少。我认为这完全是一堆荒谬的、自欺欺人的狗屁。原因有两个。第一个原因是，讽刺的是，这恰恰是逻辑系统的问题，专家系统的问题。专家系统的问题在于——给不了解的人解释一下——你试图识别一张猫的图片，然后开始构建逻辑步骤：你做一个边缘检测器，再做一个纹理检测器，做一个眼睛检测器，做一个耳朵检测器……15 年后，你有了 700 个步骤，但它根本不管用。而当你试图分析一个职业，把它拆解成哪些部分可以自动化、哪些不能时，就会发生同样的事情。你无法那样描述一个职业，至少我们做不到。你不能看着一家律师事务所的高级合伙人说，他们 17% 的工作可以被自动化——这完全是胡扯。你做不到。我认为这个谬误的另一面是谈论出租车司机。

I mean, I was looking at this whole there's a sort of US government called own data set called Onet or something like that which tries to kind of analyze every single job and then people try and kind of score it and they try and say, well, you know, this profession is X or Y% exposed to AI and AI can do Z% of it today. I think this is just the most ridiculous bunch of deluded horseshit. And there's two reasons for this. The first reason is that this is like ironically this is the logical systems problem. The expert systems problem. The problem of expert systems is like for anyone who doesn't know like you try to recognize a picture of a cat and say you start building up logical steps. So you make an edge detector and then you make a third detector and you make an eye detector and you make an ear detector and 15 years later you've got 700 steps and it doesn't work. Um, and this is what happens when you try and look at a profession and sort of break it down by which bits can be automated and which can't. You can't describe a profession like that or at anyway we can't. You can't kind of look at a senior partner at a law firm and say, well, 17% of their work could be automated like this is horshit. You can't do that. Um, I think the other side of the fallacy though is to talk about taxi drivers.

所以，如果我们是在 1997 年进行这场对话，那就像 Uber 测试。想象一下，我们在 1997 年，什么会被互联网摧毁？嗯，报纸会没事的，它们会省下印刷费。这听起来像个笑话，但当时人们确实说互联网对报纸有好处，它们的印刷成本会下降。好吧，是也不是。但另一面是，显然出租车司机——你不可能用互联网自动化那个，它跟互联网毫无关系。也许你可以网上订车，但那不会改变任何事情。当然，结果它彻底改变了整个行业。所以，我前几天看到的例子是：不会受 AI 影响的事情——私人教练。好吧，我把我的 iPhone 放在金属架上，摄像头对着我，让 AI 给我制定训练计划，看着我，告诉我动作是否标准。那我为什么还需要私人教练？现在，这可能是完全胡扯。但事情就是这样运作的。那些你认为不会受影响的东西——你无法预测哪些事情必然会被暴露，或者很多大公司当初看起来根本不会成功，也不像是会被颠覆的。

So um you know if we've been having this conversation in 1997 it's like the Uber test. Imagine we were in 1997 what will be crushed by the internet? Well newspapers will be fine. They'll just because they'll save money on the printing bills. This is like a joke but people said that newspaper the internet will be great for newspapers. Their printing bills will go down. Well yes but no. But the other side is well obviously like taxi drivers you couldn't automate that with the internet. It's got nothing to do with the internet. Maybe you'd have internet booking but like no that's not going to change anything. And of course, it completely changes the whole thing. And so, uh, like the example I saw the other day was like things that won't be affected by AI, personal trainers. Okay. So, I take my iPhone and I balance it on the metal piece with the camera pointed at me and I ask an AI to build me a training routine and watch me and tell me if I'm doing it right. Why do I need a personal trainer? Now, that might be complete nonsense. Um, but that's how these things work. Like the stuff that you don't think is ex you can't predict which things are going to be exposed necessarily or you know a lot of the big companies are things that didn't look like that would work and didn't like look like that was exposed.

另一面当然是我演示文稿末尾的一张图表，比较了 Uber 和 Airbnb，因为这是马克·安德森的老生常谈：Uber 不向出租车公司卖软件，Airbnb 不向酒店卖软件。好，现在我们来看看市场影响。在很多城市，Uber 摧毁了出租车业务，同时也把整个市场做得更大——城市变大了，所有人都转向了 Uber。而 Airbnb 对酒店的影响，如果你真的去看数字，其实相当边缘。它们开辟了一个完全不同的业务，也许稍微减缓了酒店的增长。但你知道，我妻子下周飞往密尔沃基，她晚上 8 点降落，想去酒店，想要客房服务，需要浴室洗澡，早上 6 点需要健身房，然后 7 点开车去客户现场。她不会住 Airbnb，绝对零可能。而酒店业务的一半是商务旅行。一旦你深入具体事情，就会变得复杂。我记得有人在社交媒体上说，本尼迪克特的问题是他对每件事的回答都是“看情况”。是的，确实如此，看情况。所以，这又回到了我 1997 年的观点。你可以说一些东西，但你必须保持谦逊。

The other side of this of course is this is one of the charts at the end of my presentation is comparing Uber and Airbnb because this is like the cliche from Mark Andre that like Uber doesn't sell software to taxi companies, Airbnb doesn't sell software to hotels. Okay, now let's go and look at the market impact. Well, a whole bunch of cities where Uber demolished taxi business and made it much bigger as well made the town became much bigger and everyone switched. Airbnbs impact hotel hotels if you actually go and look at the numbers is pretty marginal. They carved out this whole other business and maybe they slowed down the growth of hotels bit. But you know my wife flies to Milwaukee next week. She's going to land at 8:00 at night. She wants to go to a hotel. She wants to have room service. She needs a bathroom bath. She needs, you know, needs a gym at 6:00 in the morning and then she's get 7 in the morning, she's going to drive to the client site. She's not going to stay in an Airbnb. Like absolutely zero chance she's going to stay in an Airbnb. And half of the hotel business is travel is business travel. And you know, you can as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said the problem with Benedict is everything his answer to everything is it depends. It's like yeah, it does. It depends. So there were, you know, it's back to my 1997 point. You can say some of this. Um, but you have to have that humility.

是的。我回到你用的那个短语，“假设根本不确定性”是一个很好的核心论点。所以，知道这一切之后，很难说清楚。我们不知道确切的方向。事情会变化很大，但总体上可能还好。只是很多听众非常担心他们的工作和职业，以及世界变化有多大。根据你所知道的，你会推荐人们做哪几件事，以便在这个未来中更成功？

Yeah. I'm coming back to this uh phrase you use, presume radical uncertainty is a nice uh core thesis here. So knowing all this just it's hard to tell. We don't know exactly where it's going. Uh things are going to change a lot, but it'll probably be okay broadly. just a lot of people listening are pretty worried about their jobs and their careers and how much the world changes. What would be a couple things you recommend people do knowing what you know to be more successful in this future?

嗯，我应该先回到你刚才说的，就像凯恩斯告诉我们的：长期来看，我们都死了。

Well, I I should just kind of wind back on what you just said is like as Kees tells us in the long run we're all dead.

所以，你知道，平均而言第一次世界大战中没有人死亡。很好。但如果你是 1914 年的 19 岁青年，你有三分之一的机会回不来。所以，显然有一批职业面临重大疑问，特别是如果你是一名助理律师，或者曾考虑成为助理律师。这些职业将如何发展，专业服务金字塔结构会变成什么样，都非常不确定。我认为唯一能给出的答案是：不要把头埋进沙子里，说“我讨厌这一切”，因为那会让你产生道德优越感，你可以在 BlueSky 上对每个人大喊 AI 有多邪恶。很好，我为你高兴，但那没有用。有用的是你完全投入其中，沉浸进去，然后理解你能用它做什么，它如何改变事物，以及你如何能成为一个优秀的被雇佣者。这也许仍然没有帮助，但如果你去一家律所面试，他们说“我们去年雇了 100 名助理，今年只打算雇 50 名”，你在面试中说“我认为 AI 是邪恶的，我永远不会用它”，那可能不是正确的态度。这也许不是特别令人安慰，但我认为没有别的选择。你必须深入其中，吸收它，内化它，思考它意味着什么，就像你和我当年对待移动互联网和互联网一样。我认为这是非常可操作且一致的建议：去做事。去构建。不要只是坐着空谈，对正在发生的事情感到愤怒。

So, you know, on average nobody died in World War I. Great. But if you're a 19 year old in 1914, you had a one in three chance of not coming back. So clearly there's a bunch of professions where this is a major question, particularly if you're an associate or would have been thinking about being an associate. It's very unclear how those professions are going to play out, what happens to the pyramid structure of professional services. The only answer I think one can have is don't stick your head in the sand and say I hate all of this stuff because that gives you a great feeling of moral superiority and you can go on BlueSky and shout at everybody about how evil AI is. Great, I'm happy for you, but that's not going to help. What helps is you diving into this completely, submerging yourself in it, and coming out understanding what you can do with it, how this changes things, how you can be a great hire. That may still not help, but if you're going into a law firm and they say, well, we hired 100 associates last year and this year we're only going to hire 50, going to the interview and saying, well, I think AI is evil and I'm never going to use it, is probably not the right move. That may not be particularly comforting, but I don't think there's an alternative. You have to dive into this and absorb it and internalize it and think about what it means, just as you and I did with mobile and with the internet. I think that is actually very actionable and very consistent advice on the podcast: just do stuff. Build it. Don't just sit around and pontificate and be pissed at what's happening.

最后，我们进入 AI 角落，这是播客的固定环节。问你的问题是：你在工作或生活中使用 AI 的一个有趣方式是什么？能启发他人的那种。

To close us out, I'm going to take us to AI Corner, a recurring segment of the podcast. The question to you is: what's one way you've used AI in your work or life that is really interesting, something that other people might be inspired by?

我不知道。我很难回答这个问题，因为我有点像那个看着 ChatGPT 的律师。我会自动化的东西是精确的信息检索任务，而这恰恰是 AI 最不擅长的。这不是批评，只是观察。我想让机器为我做的事情，正是 AI 目前做不好的。我用它来校对。我用它来处理图像。我用它来重新装修我的公寓。效果非常好。给我一张房间的照片，重新粉刷，加上这盏灯、这张桌子和这块地毯。不，把地毯颜色换一下。有些东西它确实擅长。但几年前有人说过，AI 擅长计算机不擅长的事情，却不擅长计算机擅长的事情。我很难找到很多我需要它的例子。但话说回来，我的工作有点独特。我整天坐在办公桌前，试图把一大堆其他东西综合成新想法。这并不常见。我很难找到 AI 的使用案例。我就像那个看着电子表格的会计师，心想：嗯，这很聪明，显然会彻底改变一切。但我实际上并不每天做电子表格。

I don't know. I struggle with this question because I'm sort of the lawyer looking at ChatGPT. So the stuff that I would automate are precise information retrieval tasks, which is precisely the thing that this is kind of worst at. That's not a criticism, just an observation. The kind of stuff I would want a machine to do for me is the stuff AI can't do very well at the moment. I use it for proofreading. I use it for images. I used it redecorating my apartment. That worked fantastically well. Here's a picture of this room. Repaint it. Add this light and this table and this rug. No, change the color of the rug. There are kinds of stuff where it works. But a couple of years ago, somebody said AI is good at stuff that computers are bad at and bad at stuff that computers are good at. And I struggle to find many examples where I need it. But then, I'm a kind of unique weird job. I sit at my desk all day trying to synthesize a whole bunch of other stuff into new ideas. That's not particularly common. I struggle to find AI use cases. I am the accountant looking at a spreadsheet and thinking, well, that's very clever and this is clearly going to completely transform everything. But I actually don't make spreadsheets every day.

我去看了 Pete Holmes 的脱口秀。不知道你认不认识他。他开了个玩笑：我们希望 AI 去清理街上的狗屎，做所有那些没人想做的苦差事，但结果它却说，哦，让我帮你写作，让我帮你创作图像。这就像，不，我不想做那些脏活，我想搞创作，做艺术。

I went to a standup comedy show of Pete Holmes. I don't know if you know him. He made this joke: we want AI to clean the poop off the street and do all these hard things nobody wants to do, but instead it's like, oh, let me help you write, let me help you create imagery. It's like, no, I don't want to do all these ugly things, I want to be creative, make art.

是啊，这有很多变体。就像我不想让 AI 做我为了乐趣而做的事情，我想让它做那些我不觉得有趣的无聊事。

Yeah, well, there are variations of all of this. It's like I don't want the AI to do the stuff I do for fun, I want it to do the boring stuff that I don't do for fun.

找到那个契合点，说笑归说笑，这又回到我的聊天机器人观点：聊天机器人是一块空白屏幕和锯齿状边缘。就像，我该做什么，什么会有效？这是一个大问题，解决方案是把它包装在用例里。部分原因也是 AI 会消失。我现在写的大部分东西都是口述的。我用语音备忘录口述，然后自动转写。那还算 AI 吗？还是只是语音识别？可能里面有个 LLM。好吧，所以那也许是 AI。在某个点上，它只是自动化。

And finding that mesh, joking apart, this is going to come back to my chatbot point: the chatbot is a blank screen and a jagged edge. Like, what am I supposed to do and what will work? That's a big problem, and the solution is to wrap it in use cases. Part of it is also that AI just disappears. So most of what I write now I dictate. I dictate as a voice memo and that's automatically transcribed. Is that still AI or is that just voice recognition? It's probably an LLM. There's probably an LLM in there. Okay, so maybe that's AI. At a certain point it's just automation.

你用什么做语音转写？

What do you use for voice transcription?

我其实觉得 Apple Notes，就是 iPhone 自带的那个应用，就很好用。我知道有人想要别的，但我口述完就出来了。它管用。所以我很满意。

I actually find Apple Notes, the app built into the iPhone, works fine. I'm conscious people want others, but I dictate and there it is. It worked. So I'm happy with that.

好的，最后一个问题，然后进入我们非常激动人心的快问快答环节。你还有什么想分享的吗？还有什么想留给听众的？

All right, final question before we get to our very exciting lightning round. Is there anything else you wanted to share? Anything else you want to leave listeners with?

没有了，我想我已经独白够多了，也讲了很多幻灯片里的内容。去读幻灯片，订阅我的 newsletter，然后你会得到更多精彩的 Benedict Evans 智慧，其中一些甚至可能有用。有人退订了我的 newsletter，说我没有给他任何可操作的股票建议。我说，嗯，从某个层面来说，这完全正确。从另一个层面来说，也许不是。

No, I think I've monologued plenty and gone through a bunch of stuff in the deck. Go read the deck and sign up to my newsletter, and then you will get many more mags of brilliant Benedict Evans wisdom, some of which may even be useful. Someone unsubscribed from my newsletter and said you didn't give me any actionable stock ideas. I'm like, well, on one level that's completely true. On the other level, maybe not.

那么，Benedict，我们到了非常激动人心的快问快答环节。我有五个问题问你。准备好了吗？

Well with that, Benedict, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

当然。

Sure.

第一个问题，你经常向别人推荐哪两三本书？

First question, what are two or three books that you find yourself recommending most to other people?

这对我来说很难，因为我读了很多书，然后记不住读过哪些。我有时开玩笑说，有一部 19 世纪末的经典英国喜剧叫《三人同舟》，那是我的首选。比如，我们挂画遇到困难。嗯，书里有一段讲这个。你知道，我们做这个遇到困难。哦，书里有个故事讲这个。全都很好笑。所以《三人同舟》是我的首选。

It's a tough one for me because I just read an enormous amount of books and then I can't remember which ones I've read. I sometimes joke that there's a classic British comedy from the late 19th century called Three Men in a Boat, which is like my go-to. It's like, we're having trouble hanging a picture. Well, there's a section about that. You know, we're having trouble doing this. Oh, well, there's a story about that. All of which are hilarious. So Three Men in a Boat is my go-to.

有一本大概是威廉·克罗农写的关于芝加哥经济史的书，非常迷人，而且实际上和技术很相关，因为它基本上在讲标准化、打包化、物流、渠道冲突、网络动态和网络中立性。比如当芝加哥的肉类加工商发展到从纽约运一头牛到芝加哥，杀掉、打包再运回纽约比在纽约直接杀更便宜时，还有冷藏车的定价问题，这简直就像在读关于宽带的东西。全都是同样那类商业问题，非常迷人。我还读了什么？我不知道。读书吧。多读不同的书。读成年人的书。请读点别的，别只读《指环王》。如果你要再给公司起名，比如我看到这个，还有最新的彼得·蒂尔的公司？我就想说读点别的书吧。所有东西都从这一本书里取角色名。世界上不止一本书。如果不止一本书，那就全是科幻小说。读点不同的东西。读点你不知道的东西。

There's a book by I think William Cronin about the economic history of Chicago which is fascinating and actually very relevant to technology because it's talking basically about standardization and packetization and logistics and channel conflict and network dynamics and network neutrality. So like when the meat packers of Chicago reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it and then ship it back to New York than to kill it in New York. And the pricing of refrigerator cars and it's exactly like reading about broadband. It's all the same kind of those business issues which is fascinating. What else have I read? I don't know. Read books. Read different books generally. Read books for grown-ups. Please read something other than Lord of the Rings. If you're going to name another company like I saw this and what was the latest like Peter Thiel company? I was like read another book. Everything is named after a character from this one book. There's more than one book in the world. If there is more than one book, then all about science fiction. Read about different things. Read about things you don't know about.

顺着这个思路。你最近有特别喜欢、很享受的电影或电视剧吗？

Kind of along those lines. You have a favorite recent movie or TV show that you've really enjoyed?

我不知道。我已经严重脱离了当前的媒体节奏，大部分时间都在看经典电影，就是那些你总觉得自己应该看过、但又觉得望而生畏的片子，然后你看了之后就会想：“哦，原来真的很好看。”我最近看了《第七封印》，就是那种伍迪·艾伦开玩笑说又恐怖又无聊的电影，但它其实很精彩。真的很有意思，而且才一个小时左右。所以，去看一部你应该看过但还没看的电影吧。

I don't know. I've dropped so badly off the current media treadmill and I just spend most of my time watching classics which are like always the ones that you're supposed to have seen and that all seem intimidating and then you watch them and you're like, "Oh, that was actually really good." I watched The Seventh Seal recently, which is like one of those joke Woody Allen terrifying, boring movies, and it was brilliant. It was really interesting, and it's only like an hour. So, go watch one of those movies that you are supposed to have seen or hadn't seen.

最近发现的最喜欢的、你真的很爱的产品是什么？可以是小工具，也可以是应用。

Favorite recent product that you've recently discovered that you really love. Could be a gadget, could be an app.

我本周早些时候在一个公司的合伙人会议上发言。今天星期几？星期一。不，上周。然后遇到了那家公司的创始人，他名字很有名，我欣赏了他的鞋子，没说什么，但半小时后谷歌了一下，好吧，我要买一双。你想分享品牌还是保密？好吧，保密。我不知道。我觉得新产品是一波一波来的，你会进入一波新东西的浪潮。比如上次出现像 iPhone 应用那样酷的应用是什么时候？所有空白空间都没了？我的意思是，这在一定程度上是产品发布、平台发布的结果，所有空白空间都给了酷炫的新应用，而现在我们还没有真正——这又回到之前的观点——我们还没有突破性的消费者 AI 应用，我认为主要是因为边际成本问题，所以你无法让它免费、获得 5000 万用户然后有收入瓶颈。但我们还没有那些突破性的东西。

I was speaking at a partner meeting for a company earlier this week. What's today? Monday. No, last week. And met the founder of the company who has a very famous name and admired his shoes and didn't say anything but then went on Google like half an hour later yeah okay I'll buy a pair of those. You want to share the brand or you want to keep it secret? Okay we'll keep it secret. I don't know. I think one comes in waves of new products and you get into waves of new things. Like when's the last time there was a cool app like iPhone apps that was all that white space went? I mean it's partly a function of product ships a platform ships like all the white space went for cool new apps and now we haven't quite got actually this is to the earlier point we don't have breakout consumer AI apps yet because I think because of marginal cost more than anything else so you can't make it free and get 50 million users and then have a revenue bottle. But we don't have those breakout things yet.

对，面向消费者。

For consumer yeah.

面向消费者没有。我只是觉得奇怪，一直收到这些录音笔广告，比如有人在卖名片大小的硬件录音笔。我就想，但我没搞懂。我手机上就有录音功能啊。

For consumer no. I just weird I keep getting these ads for voice recorders like somebody's selling like a business card size hardware voice recorder. I'm like but like I didn't get it. Like I've got a voice recorder on my phone.

是啊。各种酷东西都在涌现。好，还有两个问题。你有没有最喜欢的人生格言，在工作或生活中经常想起？

Yeah. All kinds of cool stuff coming. Okay, two more questions. Do you have a favorite life motto that you find yourself coming back to often in work or in life?

我想我之前提到过，显然我经常说“看情况”。

I suppose I've mentioned earlier apparently I mostly say it depends.

这就要成标题了。

That's going to be the title.

不，大概会没事的。

No, it'll probably be okay.

是啊。好吧。这就是我感受到的氛围。我喜欢这个。我喜欢“大概会没事的”这种说法。不是百分百确定。好，最后一个问题。我在某处看到你有很多旧手机。是真的吗？

Yeah. Okay. I that's that's the vibe I get. I like that. I like that it's probably going to be okay. Not for sure. Okay. Final question. I saw somewhere that you own a lot of old phones. Is that true?

是的，没错。作为——我保留着，我是说，我以前是电信分析师和移动分析师，我保留了我所有的手机，直到某个点。现在它们有点无趣了。但你可能记得，在 iPhone 之前，尤其是在美国以外，手机的外观有巨大的创造力和扩展，因为每个人基本上都在围绕一个微小的灰色方块进行创新。所以每个人都试图与其它一切区分开来。在它某种程度上定型之前。这有点像汽车。就像在风洞之前的汽车。汽车看起来都不一样，每个人都在尝试创新，因为你有同样的四个轮子和同样的发动机，每个人都试图基于形状来区分，然后所有东西都收敛到一种形状，手机也一样，所有东西都收敛到一种形状。在那之前有所有这些创新。所以，是的，我有一大堆 PDA 和智能手机。

It is. Yes. As a I kept I mean I was a telecoms analyst and mobile analyst and I kept all my phones up to a point. Now they're kind of uninteresting. But as you may remember like before the iPhone, particularly outside the USA, there was this huge creativity and expansion in what phones look like because everyone was basically innovating around a little teeny tiny gray square. So everyone was trying to differentiate from everything else. Before it kind of result. It's kind of like cars actually. It's like cars before street before like wind tunnels. Cars all look different and everyone's trying to innovate around because you've got the same four wheels and the same engine and everyone's trying to differentiate based on like the shape and then everything converges on one shape and it's kind of the same with phones like everyone everything converged on one shape. Before that there was all this innovation. So yeah, like I have like a whole bunch of PDAs and smartphones.

大概有多少部手机？

How many phones we talking about?

我不知道，大概 20 或 30 部。好吧。不算太疯狂。最老的是哪部？你有的最老的是哪部？

I don't know like 20 or 30. Okay. It's not so crazy. What's like the oldest one? What's the oldest one you got?

所以，我有一部——我应该让你告诉我，我会把盒子拿下来。我有一部爱立信的鲨鱼鳍翻盖手机，大概是 98 年左右的，这再次体现了硬件设计、视觉设计试图差异化。我有一部 2001 年的 iMode 手机和一部 2001 年的 J-Phone，带摄像头。所以，我 2001 年从日本回来时，我的手机有彩色屏幕和摄像头，然后我开了无数客户会议，人们就只是想看看那部带彩色屏幕的手机。简直令人震惊。它在日本以外不能用。我前几天插上电，它还能充电。当然我显然不能用它做什么。这里也有点类比，我们曾以为会有各种不同的形状和尺寸，在 iPhone 之前，人们想象着有些人会有小型口袋 PC，有些人有键盘，还有折叠的，所有这些关于它会长什么样的不同想法，但我们没有意识到所有东西都会收敛到一台设备上。

So, I have one of those I should have you told me I'd have got the box down. I have one of those Ericsson shark fin flip phones from like 98 or something which is very again like hardware design visual design trying to differentiate. I've got an iMode phone from 2001 and a J-Phone from 2001 that has a camera. So, I came back from Japan in 2001 and my phone had a color screen and a camera and like I just had like endless client meetings and people just wanted to see the phone with a color screen. Like it's mind-blowing. Didn't work outside Japan. I plugged it in the other day. It still charges up. I mean clearly I can't do anything with it. And like there's a little bit of an analogy in there as well and that like we thought there'd be all these different shapes and sizes and before the iPhone people kind of imagined like well some people will have like a little pocket PC and some people have a keyboard and you have like folding although all these different ideas for what it would look like and it we didn't realize it was all going to converge on one device.

Benedict，这太棒了。我学到了很多。聊完之后我感觉更好了。最后两个问题。人们可以在网上哪里找到你？他们去哪里找这个演讲？以及听众怎样才能对你有帮助？

Benedict, this was amazing. I learned a ton. I feel better after this conversation. Two final questions. Where can folks find you online? Where do they find this presentation? And how can listeners be useful to you?

如果你能谷歌到我，就像我常说的，我父母做了很好的 SEO。所以，谷歌 Benedict Evans。然后有一个网站，我发布所有我做过的演讲，并注册我的每周通讯。除此之外，他们怎么能对我有用？就像我一直在努力理解事物，一直在尝试问不同的问题。科技领域最糟糕的事情就是继续谈论同样的东西。

If you can Google me, as I always say, my parents had good SEO. So, Google Benedict Evans. And so there's a website where I publish all the presentations that I've done and sign up for my newsletter which comes out every week. Otherwise, how can they be useful to me? Like I'm always trying to understand stuff and I'm always trying to ask different questions. The worst thing in tech is to like carry on talking about the same stuff.

就像，你懂的，当你真正理解某件事的那一刻，就是你不得不转向其他事情的时候。所以我总是在想，不，我是不是一直在重复同样的话题？比如去年我可能花了太多时间说：“但这些模型仍然会幻觉。别告诉我它们不会幻觉。”而它们确实会。它们仍然会幻觉。你知道，你稍微再推它们一下，任何问题，你仍然会得到类似“不，那不是真的”的回答。嗯，但这并不意味着它们没有用。所以我必须不断推动自己。这对我来说始终是个挑战：我该如何推动自己？嗯，然后是的，如果你想让我去加勒比海给你的董事会做演示，嗯，那就告诉我。

It's like, you know, the moment you really understand something is the moment you have to push on to something else. And so I'm always trying to think like, no, am I just talking about the same thing over and over again? Like last year I just spent probably too much time saying, 'But these models still hallucinate. Stop telling me they don't hallucinate.' And they do. They still hallucinate. You know, you push them, push them a little bit further, any question, and you'll still get like, 'Nope, that's not true.' Um, but that doesn't mean they're not useful. So you have to kind of keep pushing keep pushing myself. So that's always the challenge for me is is how do I push? Um, and then yes, if you want me to come and present to your board in the Caribbean, um, then let me know.

顺便说一下，域名是 bend-ans.com，如果大家想了解你的话。还有 evs.com。

And by the way, the domain is bend-ans.com if folks want to check you out. And it's evs.com.

Mendic，非常感谢你来做客。

Mendic, thank you so much for being here.

非常感谢。

Thanks a lot.

大家再见。非常感谢收听。如果你觉得本期内容有价值，可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅本节目。也请考虑给我们评分或留下评论，这真的能帮助其他听众找到这个播客。你可以在 lennispodcast.com 找到所有往期节目或了解更多节目信息。下期再见。

Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennispodcast.com. See you in the next episode.