A rational conversation on where AI is actually going | Benedict Evans
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.
我最有争议的观点，是我认为AI的重要程度和互联网、移动互联网差不多，只是差不多而已。
What's your just the coming job apocalypse?
那就业大末日这件事呢？
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.
每次出现新技术，都会自动化掉一批工作，但那次自动化同时会解锁一批新工作，而你不知道那些新工作是什么，因为它们还不存在。
We've had that process over and over again.
这个过程我们经历过一遍又一遍了。
Even just looking at the most advanced AI companies throughout big open AI, everyone's increasing headcount.
就算只看那些最先进的AI公司，OpenAI这些大公司，每家都在增加员工。
You talk to these doomers on Twitter and they would act like every big company is going to buy Chat GBT tomorrow and then in two weeks time they'll fire all their stuff.
你跟那些在Twitter上喊末日的人聊，他们就好像每家大公司明天就会购买ChatGPT，然后两周后就把所有员工都裁掉一样。
These people are morons.
这些人是蠢货。
You can't predict which things are going to be exposed.
你根本没办法预测哪些东西会被颠覆。
You can't look at a senior partner at a law firm and say, "Well, 17% of their work could be automated.
你没法看着一家律所的高级合伙人说，他们17%的工作可以被自动化。
This is horshit."
这纯属扯淡。
I'm curious if you're following the anti- AI sentiment.
我很好奇你有没有关注反AI的情绪。
It's a big fuzzy mess.
这是个很模糊的大杂烩。
Yes, this will change a bunch of stuff and we'll need to worry about it, but that's kind of a constant.
是的，这会改变很多事，我们需要担忧，但这其实是个常态。
We've always had that.
我们一直都有这样的问题。
What would be a couple things you recommend people do to be more successful in this future?
你有没有几点建议，帮助大家在未来更好地立足？
Don't stick your head in the sand and say, "I hate all of this stuff."
不要把头埋进沙子里，说我讨厌这一切。
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.
那会给你一种强烈的道德优越感，你可以去Blue Sky上对所有人喊AI有多邪恶。
Like great, I'm happy for you.
很好，我为你高兴。
But that's not going to help.
但那没什么用。
What helps is you diving into this and coming out understanding what you can do with today.
真正有用的，是你投入进去，钻研透了，搞清楚你现在能做什么。
My guest is Benedict Evans.
我的嘉宾是Benedict Evans。
Benedict was a longtime partner at A16Z as their in-house analyst and resident thinker.
Benedict是a16z的长期合伙人，担任公司的内部分析师和驻场思考者。
Before that, he was a longtime equity researcher.
在此之前，他是一名资深的卖方股票研究员。
And for the past six years, he's been an independent analyst tracking the most important tech trends and sharing what he's learning.
在过去六年里，他作为独立分析师，追踪最重要的科技趋势，并分享他的所见所学。
Most recently, as you'd expect, he's spending all his time on how AI is changing our lives.
最近，如你所料，他把所有时间都花在研究AI如何改变我们的生活上。
And in his words, AI is eating the world.
用他的话说，AI正在吞噬世界。
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 acrue, and tons more.
在这次对话中，我们深入探讨了我们还没充分认识到的AI影响、反AI情绪的兴起、对就业的冲击、价值链中哪里能沉淀最多价值，还有更多。
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.
如果你对AI感到担忧，或者对事情走向感到迷茫，这次对话会让你学到很多，也会让你好受一些。
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.
在进入正题之前，别忘了去lenny'spass.com，免费使用一年那些最惊艳、最热门、做工最精良的AI产品，只对Lenny's Newsletter订阅者开放。
With that, I bring you Benedict Evans.
话不多说，有请Benedict Evans。
Benedict, thank you so much for being here.
Benedict，非常感谢你来到这里。
Welcome to the podcast.
欢迎来到播客。
Thank you for inviting me.
谢谢邀请我。
You just put out this deck called AI is eating the world.
你刚发布了这个叫《AI正在吞噬世界》的演示文稿。
I want to ask you kind of the the flip side of this of we all know it's a big deal like 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?
我想问问另一面：我们都知道这是大事，但你觉得人们在想象这场变革对生活和工作的影响时，还有什么没有充分预期到的？
Um, an interesting way of thinking about it, I did a um, a podcast last year with someone where I said, you know, I 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.
嗯，有个有意思的思考角度，我去年在一个播客里说过，我最有争议的观点，是AI的重要程度和互联网或移动互联网差不多，而且只是差不多而已，因为显然科技圈里有一批人觉得，不对，这更接近工业革命级别的变革。
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?
而下面还有一批人说，他觉得这就只是和互联网一样大，他难道不知道互联网有多大吗？
And I'm like, smartphones were quite a big deal.
我说的是，智能手机可是挺大的事。
The internet was quite a big deal.
互联网可是挺大的事。
We wouldn't be doing this if it wasn't for the internet.
要不是有互联网，我们现在也不会在这里录这期节目。
So there's like one layer of but then if you dig into that like if you're going to make the internet comparison it's like we're in 1997.
所以有那么一层，但如果你深入追问，打个比方，如果要类比互联网的话，那就像是1997年。
Like it's very exciting.
就是特别令人兴奋。
Most stuff kind of doesn't work yet.
大多数东西还运行不好。
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.
大多数人将来要用的那些东西还没被建出来，也不太清楚等它们被建出来的时候会怎么运作。
And the people who have 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.
而那些已经入场、已经吞下那粒药丸的人，忘了是哪粒了，总之他们想象着全世界都已经跟上了，但现实是分布非常广。
So there's people in tech who bought their cluster of Mac minis and you know don't use Google anymore.
科技圈里有些人买了一组Mac mini，也不用Google了。
And then you look outside tech and setting aside the idiots who think that this isn't real.
然后你看科技圈外面，把那些觉得这不是真的傻瓜放一边不谈。
Um you know most people are using who are using this are using this every week or two maybe.
你知道，大多数在用这东西的人，是每周或每两周才用一次左右。
Um so you've got that kind of spread of adoption and that spread of maturity of how well this works.
所以你有这种采用率的分布，和这东西成熟度的分布。
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 open AAI won the whole thing or you know is anthropic
在这之内，你还能具体分析，比如模型会怎么演进，模型实验室有没有定价权，价值会落在哪里，OpenAI赢了一切还是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 a fractual point here there's like the sort a super high level that like this is going to change absolutely everything.
这周又跑在前面了，然后你就会开始押注这些赛道，但就像身处1997年去预测到底是Excite还是Yahoo赢，答案是两个都不是，大体上如此，所以这里有个分形结构，最宏观那层是这会彻底改变一切，
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.
我觉得说它比互联网大20%还是大100%这种对话没什么意义，这是一次根本性的变革，但你不知道它具体会怎么运作。
Um 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 are saying we don't know which is like slightly facitious but also kind of true.
其实我刚发布这个，我每六个月做一个演示，昨天刚发，有个评论说Benedict这80页幻灯片说的全是我们不知道，这话说得有点刻薄，但某种程度上也是真的。
This episode is brought to you by our season's presenting sponsor, WorkOS.
本集节目由本季主赞助商WorkOS呈现。
What do OpenAI, Anthropic, Cursor, Versell, Replet, Sierra, Clay, and hundreds of other winning companies all have in common?
OpenAI、Anthropic、Cursor、Vercel、Replit、Sierra、Clay，以及其他数百家领先公司有什么共同点？
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他们全都是由WorkOS驱动的。
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WorkOS用一个专门为B2B SaaS打造的现代开发者平台，把那些阻碍交易的功能变成开箱即用的API。
Literally every startup that I'm an investor in that starts to expand up market ends up working with Work OS.
我投资的每一家开始向上市场扩张的初创公司，最终都在用WorkOS。
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就因为他们是最好的。
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It's essentially Stripe for enterprise features.
本质上就是企业级功能界的Stripe。
Visit works.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions.
访问works.com开始使用，或者直接加入他们的Slack，里面有真人工程师等着回答你的问题。
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去works.com，今天就让你的应用企业级就绪。
So, if we're in this 1997 timeline uh for AI, I know it's I know so much of your messages we don't know where it's going exactly yet.
所以，如果我们现在处于AI的1997年时刻，我知道你的核心信息就是我们还不知道它会走向哪里。
I don't know.
我不知道。
Now, do you have a sense of just like the timeline to okay, now things are going to be radically changing?
你有没有感觉到，大概什么时候会到真正开始剧烈变化的节点？
Like where are we in that cycle?
我们在那个周期的哪个阶段？
You talk about all these different cycles we've been through.
你谈过我们经历过的各种技术周期。
Like how far far are we from just like wow it's all different now?
距离那个「哇，一切都变了」的时刻，我们还有多远？
Well, unquestionably we're already in that moment in software.
嗯，在软件领域，我们毫无疑问已经进入那个时刻了。
And then there's a conversation about well what does agentic and AI software development two separate things that merge together mean for the future of software industry?
然后还有一个讨论，就是Agent化AI与AI辅助软件开发这两件原本独立的事合流，对软件行业的未来意味着什么？
You know, there's one extreme which is no one really believes which is, you know, hey, you'll just like v code your own stripe and no one actually believes that although you don't believe that, but like 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 and that's, you know, whole that's one whole conversation.
你知道，有一个极端，是没人真正相信的，就是你只要用Vibe Coding就能自己撸一个Stripe出来，没人真的相信这个，虽然你可能不信，但显然有大量问题还悬而未决，比如这对软件行业意味着什么，你自己能做多少，软件总量会增加多少，这本身就是一整个话题。
But the other extreme is, you know, if you're in a law firm, this is all very interesting.
但另一个极端，比如你在一家律所，这一切都很有意思。
Um, but what am I how how exactly do we use this?
但我们到底要怎么用这东西呢？
And how do we work out how not to be the next story that we've submitted something with hallucinations in it?
我们怎么确保不变成那个把含幻觉内容的文件提交上去的反面教材？
And how many associates are we going to hire next year?
明年我们要招多少律师助理？
Uh, what does this mean for us?
这对我们意味着什么？
One of the analogies I used in the presentation is imagine you're seeing imagine you're an accountant seeing the first software spreadsheets in the late '7s.
我在演示里用的一个比喻是，想象你是一名会计师，在70年代末第一次见到软件电子表格。
This is mind-blowing.
这是颠覆性的。
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.
你改一下这里的利率，所有数字都跟着变，原来要一周的工作，30秒就搞定了。
And we can talk about what that meant for the accounting industry, but clearly if you're an accountant, this is obviously mind-blowing.
我们可以谈谈这对会计行业意味着什么，但如果你是会计师，这显然令人震惊。
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.
但如果你是律师或记者看着这东西，你会想，这很聪明，我的会计应该来看看，但这跟我做的事没关系。
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.
我下周可能会用它记工时，如果要买Apple II、显示器和打印机来运行它不需要花一两万美元的话，调整通胀的话差不多就是那个价，但这不是我做的事。
And you need a word processor, which actually came like very shortly afterwards.
而且你需要的是文字处理器，那个实际上很快就出来了。
And so that's sort of the moment that we're in of there's some people like software development are develop software developers are the accountants seeing visi like oh my god this changes everything like before viscal and after visalc before before cl code and after Claude Code a lot of other people are picking it up using it to varying degrees but slightly puzzled
所以那就是我们所处的时刻：有些人，比如软件开发者，就是那些看到VisiCalc的会计师，天啊，这改变了一切，VisiCalc之前和之后，Claude Code之前和之后，而很多其他人在不同程度上用着它，但有点困惑，
so you there's a bunch of survey data that I put in in the in the presentation that like even if you look at like 13 to 18 year olds or something, it's still like kind of 15
你看看那些调查数据，就连13到18岁的年轻人，也还是大概只有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 on you say they are not using this.
到20%是每天活跃用户，另外20%是每周活跃用户，剩下60%说他们根本没在用。
So there's a sort of very widespread of who gets it and a very wide which I think also maps
所以有一种非常广泛的「谁懂了」的分布，也有一种非常广泛的分布，我觉得也对应
this is kind of almost a separate point maps to the sort of jagged frontier
这几乎算是另一个独立的点，对应到那种参差不齐的前沿
question of where does this work
这东西在哪里有用的问题
where does it not work
在哪里没用
can you tell where it's going to work
你能判断它在哪里有用吗
is it intuitive to know where it would work
有没有直觉告诉你它在哪里有用
can you tell after it worked
用了之后你能判断吗
can you can you can you can you work out for yourself
你自己能不能搞清楚
what you would do with this and all of those intersect
你自己能用它做什么，这些都交织在一起
if you're a software developer a lot of other people were like people having moment or they're not or we're in again
如果你是软件开发者，很多其他人要么在经历顿悟时刻，要么还没有，或者我们又回到了
we're in that kind of 1997 moment of okay
那种1997年的感觉，好，