马克·安德森：真正的AI热潮尚未开始

这非常有趣。

That is extremely interesting.

所以我的理解是，你并不太担心失业问题。

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

关键在于，这个时间点刚好吻合吗？

Is the key here that the timing kinda just works out?

人口会减少吗？

Does population decrease?

你知道，这些因素必须相互配合，才能避免人工智能导致大规模失业吗？

You know, like, these kind of have to line up for there not to be this massive job loss with AI?

是的。

Yeah.

你看。

Well, look.

如果我们没有人工智能，现在恐怕会因为未来会发生什么而陷入恐慌，因为我们面对的将是人口减少的未来，而没有新技术的人口减少只会导致经济萎缩。

If we didn't have AI, we'd be in a panic right now about what's gonna happen to the Because what we'd be staring at is a future of depopulation and like depopulation without new technology would just mean that the economy shrinks.

所以这意味着经济本身会随着时间逐渐萎缩。

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

机会会减少。

The opportunity diminishes.

没有新的工作，也没有新的领域。

There are no new jobs, there are no new fields.

没有新的消费需求，也没有新的支出来源。

There's no new there's no new source of consumer demand for spending on things.

因此，你会非常担心陷入一段严重衰退或停滞的时期。

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

而且，说白了，你会看到一些非常反乌托邦的场景，比如经济逐渐自我消亡。

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

所以你会非常担心人们以为他们担心的相反情况。

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

我们之所以不担心这一点，仅仅是因为我们现在知道，有技术可以替代人口增长的缺失，以及可能发生的移民减少。

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

因此，我会说，时机简直完美，因为我们正好在经济即将萎缩的时候，迎来了AI机器人来阻止这种情况发生。

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

我只是觉得，这本质上是一个非常积极的消息。

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

要谈到人们所担心的大规模失业问题，你需要看到生产力增长率达到远高于当前的水平。

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

你需要看到每年10%、20%、50%这样的生产力增长率，这比人类历史上任何经济体曾经达到过的水平高出几个数量级。

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

我们也许能达到那样的水平。

It's possible that we get that.

我的意思是，当然，我和所有人一样，也难免有这种乌托邦式的幻想。

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

如果AI突然彻底改变了一切，那我们不妨畅想一下这种乌托邦情景。

If AI like radically transforms everything overnight, then maybe you, let's play out the kind of utopian scenario.

你会达到更高的生产力增长水平，也会经历更剧烈的技术变革。

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

与此相应，将会出现一场巨大的经济繁荣。

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

经济将实现大幅增长。

You'll have a massive growth in the economy.

随之而来的是价格的暴跌。

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

因此，那些受到人工智能影响或商品化的商品和服务，其价格将会暴跌，对吧？

And so the price of goods and services that are sort of, know, whatever you're gonna call it affected by or commoditized by AI, the prices of those goods and services will collapse, right?

这将导致通货紧缩。

It'll be price deflation.

由于价格通缩，今天人们购买的一切都会变得便宜很多。

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

这相当于整个社会财富的大幅增加，对吧？

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

对吧？

Right?

从这个角度来看。

Take it this way.

这实际上值得讨论，因为我觉得人们对这个问题的理解容易出现偏差。

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

如果人工智能真的会像乌托邦或反乌托邦支持者所声称的那样深刻改变经济，那么由此带来的必要经济计算就是生产力的大幅增长。

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

生产力大幅增长的后果，从机械意义上讲，就是用更少的投入获得更多的产出，对吧？

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

所以你用更少的投入获得了更多的经济产出，对吧？

So you get more economic output for less input, right?

因此，你用人工智能替代了人类工人或其他东西，结果就是产出大幅增加，而投入成本却低得多。

So you're substituting in AI for human workers or whatever, and as a consequence, you get like this massive boom in output with much lower input costs.

其结果是，在所有受影响的行业中，商品和服务都会出现过剩。

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

这些过剩的后果就是价格暴跌。

The result of those gluts is you get collapsing prices.

价格暴跌意味着，今天价值100美元的东西现在只值10美元，甚至1美元，这相当于给每个人发了一笔巨额加薪，对吧？

The collapsing prices mean that the thing today that costs you $100 now costs you $10 and now costs you $1 that's the equivalent of giving everybody a giant raise, right?

因为现在他们拥有了更多的可支配收入。

Because now they have all this additional spending power.

这种额外的购买力会转化为经济增长，对吧？

That additional spending power then translates to economic growth, right?

新兴领域的兴起。

The development of new fields.

每个人在很短的时间内物质条件都得到了极大改善。

Everybody's like materially like much better off very quickly.

顺便说一句，如果在这个过程中确实出现了失业，那么提供社会安全网以防止人们陷入贫困的成本也低得多，对吧？

And then by the way, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the kind of social safety net to prevent people from being immiserated, right?

因为福利项目所需支付的所有商品和服务的价格都在暴跌。

Because the prices of all the goods and services that like a welfare program has to pay from, they're all collapsing.

医疗价格暴跌，住房价格暴跌，教育价格暴跌，其他一切价格都在暴跌，这都是人工智能带来的巨大影响。

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

所以在人们所设想的这种乌托邦式的反乌托邦情景中，根本不存在所有人都变穷的情况。

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

事实上，情况恰恰相反——所有人都会变得富裕得多，因为价格暴跌了。

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

而对于那些由于某种原因找不到工作的人，提供社会保障也变得容易得多。

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

所以我们最终会进入这种情景。

And so like, like, we end up in that scenario.

我的乐观一面认为，是的，也许人工智能真的有这么强大，也许经济的其他部分真的能调整以适应这种变化，也许这一切真的会发生。

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

但结果将是，这会是一个比人们想象中好得多的新闻故事。

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

而且，顺便说一句，我刚才描述的一切，只是对基本经济学原理的非常直接的推演。

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

我并没有对我刚才所说的内容做出任何大胆的预测。

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

这只是一个直接的机械过程，只要生产率增长更快，而生产率增长必然源于技术进步的加速，这个过程就会自然发生。

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

我认为我们正面临一个世界，它并不会像乌托邦主义者所想的那样，或像反乌托邦者所担心的那样发生根本性的剧变。

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

我认为，由于我们接下来可以讨论的原因，变化会更加渐进。

I think it'll be more incremental for reasons we can discuss.

但我认为，这种渐进变化是压倒性积极的，我认为这个过程将是一个好消息进程。

But I think that incremental Is a is a is a overwhelmingly, I think that process is going to be a good news process.

即使变化发生得更快，它同样会是一个好消息进程。

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

它将以我之前描述的另一种方式成为好消息进程。

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

我很喜欢听到乐观和好消息。

I love hearing optimism and good news.

我还要补充一点，我在和你聊天前研究过你，你对世界走向的判断已经多次被证明是正确的。

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

这就是我特别期待和你交谈的原因。

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

我给你列个简短的清单。

I'll give you a short list.

我想象还有很多其他事情。

I imagine there are many more things.

好的。

Okay.

首先，你正确预测了网络和网页浏览器会变得重要。

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

你正确预测了软件将吞噬世界。

You were right about software eating the world.

没错。

Check.

你在2011年曾说，十年后将有50亿人使用智能手机，而我认为实际数字最终达到了60亿。

You, in 2011, you said that in ten years we're gonna have 5,000,000,000 people using smartphones, and I believe the actual number ended up being 6,000,000,000.

你还和彼得·蒂尔有过一场辩论，我后来看到了，你们在讨论技术是否已经停止进步，还是新技术会继续涌现，而你坚持认为进步会持续下去。

You also you had this debate with Peter Thiel that I came across where you were debating whether technology has stopped progressing or if new technology will continue to emerge and you're arguing there's progress, progress will continue.

他说：不，我觉得酷炫的技术已经到头了。

He was like, No, I think we're done with cool technology.

你是对的。

You were right.

想象一下，你还有很多其他正确的判断。

Imagine there are many more things you were right about.

所以，我再说一遍，我特别喜欢听你的预测，因为我觉得它们最终都会成真。

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

我先说一句，我其实错了很多事情，但你知道的，我把那些错误都埋在后院的棚子后面了。

So I was just start by saying I've been wrong about tons of things, but, you know, I bury those out back behind the shed.

把它们从互联网上删掉。

Delete them from the Internet.

没有任何网页浏览器能将它们彻底清除。

No web browser can disconnect them.

我已经让它们从互联网档案中彻底消失了，这样就没人再能看到它们了。

I have them nuked out of the Internet archives so that they know they're never seen again.

所以我也经常犯错。

So I'm wrong plenty of times also.

但说实话，我觉得确实如此。

But yeah, I mean, I think yeah.

其中有一些我确实说对了。

Some of those I got right.

顺便说一句，关于彼得的那个观点，我现在更认同他的看法了。

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

如果今天再讨论这个问题，我可能会用完全不同的方式来论证，而且我会更认可他的观点。

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

这其实也引出了我们刚才讨论的那个话题，也就是彼得真正想表达的是：我们在比特层面已经有了很多进展。

And and it actually goes to the kind of the discussion that we did, kind of conversation we just had, which is the the real form of what Peter was arguing was we have lots of process in bit.

我们在比特层面取得了大量进步。

We have lots of progress in bits.

对吧？

Right?

但我们在实体物质方面的进展却非常少。

But we have very little progress in atoms.

对吧？

Right?

而这正是他观点的核心所在。

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

我认为我之前有点忽略了这一点，或者说稍微轻描淡写地带过了，因为我太专注于确保人们明白，不是这样的。

And I think I I I think I I was a little bit, I don't know, missing that or kind of, you know, kind of glossing that over a little bit because I was so focused on making sure people understood, no.

实际上，比特领域仍然在持续取得进展。

There actually is still progress happening in in bits.

但我认为，他对实体物质领域缺乏进展的许多批评是切实存在的。

But I think, you know, a lot of his critiques around the lack of progress in Adams is real.

而且，再次强调，这又回到了他长期以来一直在谈论的这个问题。

And and, again, this goes back to this thing of, like, in the and he you know, he's talking about this for a long time.

在过去五十年里，整个经济领域几乎没有什么技术革新。

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

尤其是在任何涉及现实世界技术变革的领域，几乎没有什么真正的技术进步。

There's been very little technological innovation in particular in anything involving You know, there's been very little real world technological change.

确实没有。

There just hasn't been.

比如，我们所建造的这个世界，今天和五十年前并没有太大不同。

Like, the the the built world is just not that different today than it was fifty years ago.

如果你对比一下1870年到1930年，那是一个截然不同的世界。

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

如果你对比1930年到1970年，那也是一个截然不同的世界。

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

如果你对比1970年到现在，变化却并不大。

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

对吧？

Right?

而且你看，你只要随便走走，就会发现，哦，是啊。

And look, you just see that you could just like walk around and it's just like, oh, yeah.

有很多建筑都是上世纪60年代建的。

There's a bunch of buildings that were built in like 1960.

对吧？

Right?

有一座桥是1930年左右建的，有一座水坝是1910年左右建的，还有一座城市是1880年左右建立的。

And there's a bridge that was built in, like, 1930, and there's a dam that was built in, like, 1910, and there's a city that was founded in, you know, 1880.

那我们到底做了些什么呢？

And, like, what have we done?

对吧？

Right?

我们的新城市在哪里？

Like, where are our new cities?

我们的新水坝在哪里？

Where are our new dams?

你知道在哪吗？

Where you know?

加州高速铁路到底在哪？

Where's where is the California high speed rail?

这到底是怎么回事？

Like what's going on here?

所以，我觉得他在很多方面说得对。

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

再说一遍，这也是为什么我认为人工智能不会产生如此迅速的影响。

Again, this is also why I think that AI is not going to have as rapid an impact.

它不会像人们想象的那样，一夜之间带来乌托邦或反乌托邦式的巨变。

It's not going to be again, this kind of utopian or dystopian view of like everything changes overnight.

我认为这根本不可能发生，因为彼得所指出的原因——世界运作的方方面面都被繁文缛节、官僚流程、规则和限制牢牢束缚，还有政治因素。

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

顺便说一句，你知道吗？工会、卡特尔、寡头垄断，世界上存在各种各样的经济、政治或监管结构，它们本质上都阻止了变革的发生。

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

所以，我们举个很好的例子，比如人工智能对医疗系统的影响。

And so I mean, let's take let's take a great example, like AI's impact on the health care system.

按理说，人工智能将对医疗系统产生深远且积极的影响。

Like, by rights, AI is gonna have a dramatic impact on the health care system and and and in very positive ways.

但你知道，如今医疗系统的很大一部分其实是垄断组织。

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

对吧？

Right?

所以医生是一个垄断集团，护士也是一个垄断集团。

And so there's like a there's the the doctors are cartel and, like, nurses are cartel.

医院也是一个垄断集团。

Like, hospitals are cartel.

然后还有推动将所有医疗系统国有化的趋势，这样一来，你就有了政府垄断。

And then there's this push to, like, nationalize all the health care systems, and then you've got, you know, then you've got a government monopoly.

对吧？

Right?

而垄断集团最不喜欢的就是快速变革。

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

对吧？

Right?

所以，当你作为一个年轻人出现时，你会说：哇。

And so, you know, you show up as a kid, and you're like, wow.

我有了这种新技术，比如人工智能医疗。

I've got, like, this new technology to do like AI medicine.

但他们却说：哦，这会威胁到医生的工作吗？

And they're like, oh, well, does it threaten doctor jobs?

如果是这样，那我们就得阻止它。

Well, in that case, we're gonna we're gonna block it.

而且我认为很多消费者，顺便说一句，我知道我在生活中见过这种情况，你也可能在你的生活中看到过：比如ChatGPT几乎肯定比你现在的医生更优秀。

So and I think a lot of consumers, by the way, you know, I don't I I see this in my life and you you'll probably see this in your life also, is, you know, like, ChechiPT is like almost certainly a better doctor than your doctor today.

但ChatGPT却无法获得行医执照。

But like ChechiPT can't get a license to practice medicine.

对吧？

Right?

所以它不能替代医生。

So it can't substitute for a doctor.

它不能开药。

It can't prescribe medications.

对吧？

Right?

它不能进行手术。

It can't perform procedures.

对吧？

Right?

所以无论如何，我认为彼得一直都非常清晰地表达这一点。

And so there are these anyway, Peter, I think, was very articulate and and has been for a long time.

我觉得不是这样。

I'm like, no.

实际上，我们的经济和政治体系中存在着真正的结构性障碍，这些障碍阻碍了任何接近过去人们所经历的变革速度。

There are actually real structural impediments in the economy and in the political system that we have that actually prevent any the rates of change that are anywhere near the rates of change that people have in the past.

你或许可以乐观地认为，人工智能这种新兴的神奇技术，可能促使我们首次在几十年来重新审视许多假设，真正地问：好吧。

And and you can maybe say optimistically, you know, maybe the presence of it of the new of the new magic technology of AI, maybe it causes us to revisit a lot of these assumption assumptions for the first time in decades to really say, okay.

这真的是我们想要生活的世界吗？

Is this really the world we wanna live in?

我们难道不希望更快地抵达未来吗？

Don't we actually wanna get to the future faster?

所以，这或许是一种乐观的看法。

So maybe that would be the optimistic view.

有人曾著名地说过：是时候去建设了。

It's time to build, somebody famously said.

在我的日历里，我确实把这句话设为我开始工作时的提醒。

I in my calendar, I actually have that as my when I start to work.

是时候去建设了。

It's time to build.

这是我每天早上提醒自己的座右铭。

That's my block in the morning of day.

谢谢你分享这些。

Thank you for that.

好的。

Okay.

我喜欢你从宏观层面直接跳到个体终点的思维方式，我也想从一到一。

I love I love the way you go from just, like, macro to just, like, end of one, and I wanna go to n of one.

这个播客的许多听众都是产品经理。

A lot of the listeners of this podcast are product managers.

他们是工程师。

They're engineers.

他们是设计师。

They're designers.

其中有不少创始人，但也有大量非创始人。

They're not a lot of there's a of founders, but there's also a lot of non founders.

有很多人在做产品，但他们并不是创始人。

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

显然，很多人都在担心自己的职业发展方向。

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

这些角色中会有一个消失吗？

Is one of these roles going to disappear?

这些角色中会有一个发展得特别好吗？

Is one of these roles going to do really well?

我该如何保持与时俱进？

How do I stay up to date?

你和很多团队、很多产品团队都很亲近。

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

你对这三个具体角色的未来有什么看法？

What's your sense of just the future of these three very specific roles?

产品经理、工程师、设计师？

Product manager, engineer, designer?

我觉得这个问题真的很有趣。

This, I think, is really funny question.

这三种角色，尤其是对于科技公司来说，显然是构建产品核心的角色。

So the the the these three roles in particular, obviously, are kind of the central roles for for building you know, for tech companies.

我一直以来是这样描述的：你知道，就像墨西哥式对峙的概念，就是电影里两个人用枪指着对方头部的场景。

So the way I've been describing it is, you know you know, the concept of the Mexican standoff, right, which is the the movie scene where the, you know, the two guys have guns pointing at each other's heads.

嗯。

Mhmm.

如果你看吴宇森的电影，他特别喜欢拍三方对峙，形成一个三角形，每个人都在对峙中。

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

在吴宇森的电影里，他们通常两只手都拿着枪。

There's John Woo movie is they've got, you know, guns in both hands.

所以每个人都在瞄准另外两个人。

So they're all each each is aiming at the other two.

是的。

Yeah.

于是你就遇到了这种对峙局面。

And you got this kind of standoff situation.

所以我一直这样描述：这三者之间——产品经理、设计师和程序员——正发生着一场墨西哥式对峙，具体来说就是，现在每个程序员都认为自己也能胜任产品经理和设计师的角色，对吧？

So the the way I've been describing this is there's like a Mexican standoff happening between those three roles, between product manager, designer, and coder, specifically the following, which is every coder now believes they can also be a product manager and a designer, right?

因为他们有了人工智能。

Because they have AI.

每个产品经理都认为自己能当程序员和设计师。

Every product manager thinks they can be a coder and a designer.

而每个设计师都知道自己也能当产品经理，对吧？

And then every designer knows they can be a product manager, right?

还有程序员，对吧？

And a coder, right?

因此，现在每个角色的人都知道或相信，有了人工智能，他们就不需要另外两个角色了。

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

他们可以做到，因为人工智能可以替他们完成那些工作。

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

当然，真正的讽刺在于，这三者都会意识到，AI 也能成为更好的管理者。

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

所以他们会把枪口对准组织架构的上方，但那可能是下一阶段的事了。

So they're gonna aim me the guns up the org chart, but that's probably the next phase.

我觉得这个墨西哥式对峙如此有趣的是，他们实际上都有一定道理，我认为，对吧？

And what I think is so fascinating about this Mexican stat is they're actually all kind of correct, I think, right?

也就是说，AI 现在确实是个相当不错的程序员。

Which is AI is actually a pretty good, it's actually now a really good coder.

它现在也是一个非常出色的设计师，同时也是一个很棒的产品经理，对吧？

It's actually now a really good designer and it's also a really good product manager, right?

它实际上能很好地完成这三件事，或者至少能完成这三份工作中的大量任务。

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

因此，这又回到了‘超级赋能的个体’这个概念：如果我是个程序员，第一步就是必须真正理解 AI 编程意味着什么，以及编程在未来将如何变化。

And so again, this goes back to this kind of idea of the super empowered individual, where if I'm a coder, step one is I need to make sure that I really understand AI coding and what that means and how coding is gonna change in the future.

我需要具体了解如何从一个完全手动编写代码的程序员，转变为一个能够协调十几个编码机器人工作的程序员。

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

编码本身的工作内容正在发生改变，这种变化正在当下发生。

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

但另一部分是，我该如何成为那个拥有超能力的个体？

But the other part of it is, okay, how do I become that superpower individual?

我该如何成为一名 coder，同时又能利用 AI，成为出色的产品经理和设计师？

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

对于产品经理来说也是如此，我该如何确保自己能使用编码工具？

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

我该如何确保自己也能进行基于 AI 的设计？

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

对于设计师来说也是如此，我该如何利用 AI 来成为程序员和产品经理？

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

于是，你可能会发现这些个体角色发生了变化，比如这些角色可能不再像过去三十年那样是彼此孤立的单一职能。

And then what you get is maybe those individual roles change, like maybe those are not any more sort of stovepipe roles of the way that they have been for the last thirty years or whatever.

但真正发生的是，这些领域中的有才华的人会变得能力超群，能够胜任这三方面的工作。

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

然后这些人会变得极其强大，因为他们真的能够从零开始设计和构建新产品，而这正是最有价值的能力。

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

所以我认为，这就是机遇所在。

And so I I think I think that's I think I think that's the opportunity.

我喜欢这个回答。

So I love this answer.

所以我的理解是，只要你在这三个角色中的任何一个方面出类拔萃，就会表现得很好。

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

第一，如果你在这些角色中表现卓越，那当然很好。

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

而成为这些角色中的佼佼者，还包括能够充分掌握新技术。

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

如果你今天是一个顶尖的程序员，但始终无法学会如何利用AI来提升你的编程能力并创造更多价值，那么迟早你会遇到瓶颈。

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

经济学家还有另一种说法，那就是‘工作’本身并不是职场中实际发生事务的基本单位。

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

职场中真正基本的单位是任务。

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

所以经济学家看待这个问题的方式是，工作是一组任务的集合，大家都谈论失业，但其实你更应该关注的是任务的消失，对吧？

So and and then what what way the economists think about it is a job is a bundle of tasks and everybody wants to talk about job loss but really what you wanna look at is task loss, right?

任务在发生变化。

The tasks changing.

我的意思是，任务变化的经典例子。

I mean, classic example of task changing.

很久以前，高管们自己根本不用打字机或个人电脑，对吧？

Class example of task changing was once upon a time, executives never used typewriters or personal computers themselves, right?

如果你是1970年或那时候的一家公司的副总裁，你的桌上不会有打字机或电脑来自己打字。

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

你有一个秘书，你口述备忘录给她。

You had a secretary who you dictated memos to.

然后发生了一个变化，电子邮件开始出现了。

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

于是，秘书的工作从原本负责贴邮票寄信，转变为发送或接收来自其他行政人员的电子邮件。

What would happen was the job of the secretary then went from, it went from, the job of the secretary changed for sending out letters with stamps on them to like sending or receiving emails with the other admins.

然后秘书会把邮件打印出来，送到高管的办公室。

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

高管阅读纸质邮件后，手写回复，再交给秘书，秘书回到自己的办公桌，把内容打成电子邮件发送出去。

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

而到了今天，这一切都不再发生了。

Fast forward to today, none of that happens.

现在，高管们自己处理所有电子邮件。

Now executives just do all their own email.

他们仍然有秘书或行政人员，但这些人员现在从事的是不同的任务。

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

比如安排旅行、组织活动，以及做所有优秀行政人员该做的其他事情。

You know, they're travel planning and orchestrating events and like doing all these other things that great admins do.

讽刺的是，高管们的任务范围反而扩大了，他们现在要亲自完成更多文书工作，比如坐在那里亲自打备忘录——而这在五十年前，他们根本不会做。

And then the tasks set ironically of the executive has expanded to do actually more of the clerical work themselves actually like sit there and like type their memos, which again, fifty years ago, they never would have done that.

所以高管职位仍然存在，秘书职位也仍然存在，但任务已经改变了。

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

我认为这很好地说明了编程领域即将发生的变化。

And I think that's like a great example of what's gonna happen in coding.

任务将会发生变化。

The tasks are gonna change.

这正是产品管理所经历的情况。

It's what's got product management.

任务将会发生变化。

The tasks are gonna change.

设计师的任务也将发生变化。

Designer tasks are gonna change.

因此，职位的存在时间往往比具体任务更长。

And so the job persists longer than the individual tasks.

当任务变化足够大时，职位才会真正发生转变。

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

所以在个人层面，你得想想，我有这份工作，工作是一系列任务的集合，我需要确保自己能很好地替换这些任务，对吧？

And so at the level of individual, you kind of wanna think of like, okay, I have this job, the job is a bundle of tasks, I need to be really good at making sure that I can like swap the tasks out, right?

我能很好地适应，利用新技术，比如精通AI编程。

I can really adapt, use the new technology, get really good at AI coding, for example.

然后你还想增加技能，我可以变得非常擅长设计，也可以非常擅长产品管理，因为我有了这个新工具。

And then you wanna kind of add skills, I can also get really good at design, I can also get really good at product management because I've got this new tool.

所以随着你这么做，你希望不断拓展自己的职责范围。

So you wanna kind of pick up more and more scope as you do that.

十年后，你的职位头衔是程序员，还是程序员-设计师-产品经理，还是只是‘我构建产品’，或者只是‘我指导AI如何构建产品’？

And then ten years from now, is your job title coder or coder designer product manager, or is it just I build products or is it just I tell the AI how to build products?

不管这个职位叫什么名字，谁知道它将来会变成什么样呢？

It's like whatever that whatever that job is called, who even knows what it's gonna be?

但它一定会变得极其重要，因为做这份工作的人将负责协调AI。

But it's gonna be incredibly important because the people doing that job are gonna be orchestrating the AI.

所以，最优秀的人将会走上这条道路，我认为这就是我们应该全力投入的方向。

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

我认为人们还没有完全意识到软件工程正在发生多大的变化。

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

很明显，我们很快就会进入一个工程师不再实际编写代码的世界，这一点在一年前我们是无法想象的。

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

而现在，这已经清晰地成为趋势。

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

这就像是坐下来亲手写代码变成了一种手工艺术般的体验，真是难以置信，这份工作会发生如此巨大的变化。

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

是的。

Yeah.

所以，我再次回到这个话题，抱歉可能又要讲点历史了，但我想回溯到编程的早期。

So again, here I go back and again, pardon maybe the history lesson, but like I go back like coding.

你知道‘计算器’这个词最初的定义是什么吗？

So the first, do you know that the original definition of the term calculator?

你知道它当时指的是什么吗？

Do know what that referred to?

没有。

No.

我指的是人。

I referred to people.

在电子计算器、计算机或这些设备出现之前，进行计算的方式——比如保险公司计算精算表，军队计算 troop logistics 公式等等——实际上是让一屋子的人来完成的。

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

顺便说一下，这些房间都很大。

And by the way, these are big rooms.

你可能会有数百人、数千人甚至数万人在做这件事。

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

你会安排一个人在房间前端负责整个数学方程，然后把各个计算步骤分发给坐在桌前的人，他们全部手工完成这些计算。

And you would actually figure out you have somebody at the head of the room who was responsible for whatever the mathematical equation was, and then they would parcel out the individual mathematical calculations to people sitting at desks who were doing them all by hand.

这些人的职位名称就是‘计算员’。

And that job title was those people were calculators.

所以我们从一个 literally 由人手工进行数学运算的世界，进入了第一代计算机的时代。

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

最早的计算机当然没有编程语言。

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

它们只有机器码。

They only had machine code.

所以最早的计算机是用一和零来编程的。

So the first computers were programmed with ones and zeros.

因此，程序员的任务变成了处理一和零，后来这演变成了打孔卡片。

And so the task of the programmer became do the ones and zeros and then that became punch cards.

直到今天，仍然有一些程序员的工作是处理打孔卡片。

And there's still people kicking today whose job as a programmer was to like deal with the punch cards.

然后你迎来了一个重大突破，那就是汇编语言，它本质上是用某种类似英语的表达方式来编写机器码。

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

而最优秀的程序员使用的是汇编语言。

And then the best programmers did assembly language.

而当我刚入行时，程序员使用的是像C这样的高级语言，这些语言会编译成机器码。

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

然后我还记得，当脚本语言兴起时，我们开发了JavaScript和Escape，随后Python迅速流行起来，还有Perl和其他这些脚本语言。

And then I still remember when scripting languages, we developed JavaScript and Escape and then Python took off and Perl and these other scripting languages.

当脚本语言在成千上万的开发者中普及时，技术圈里爆发了一场大争论：脚本编程算不算真正的编程？

When scripting languages, you know, took off in the in the the the in the thousands, there was this big fight in the technical community, is, is scripting real programming or not?

对吧？

Right?

因为这感觉有点像作弊，对吧？

Because it's it's like it's kind of cheating, right?

因为真正的程序员写的是能编译成机器码的代码，他们会自己管理内存，精通C语言编程的全套技艺。

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

而这些JavaScript或Python程序员只是在做些轻量级的东西。

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

这甚至都不算真正的编程。

It doesn't even really count as coding.

当然，答案是：当然算，这绝对算数。

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

现在大多数编程都是用脚本语言完成的，对吧？

And now most coding is done with the scripting languages, right?

这就是我要说的，脚本语言抽象掉了以前人们手动处理的五层细节，现在没人再做这些了。

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

正如你所说，AI编程是这一趋势的下一步。

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

AI编程实际上抽象掉了编写脚本代码的过程，对吧？

AI coding actually abstracts away the process of actually writing the scripting code, right?

所以从某种意义上说，这在所有显而易见的方面都是件大事，但另一方面，这也意味着程序员工作的任务定义又进入了一个新层次，对吧？

And so in one sense, this is a really big deal for all the obvious reasons, but on the other hand, it's like, okay, this is the next layer of the task redefinition under the job of programmer, right?

那么，程序员的工作现在到底是什么？

Now, what's the job of the programmer?

正如你所说，现在程序员的工作未必是亲手写代码，而是像这样：如果你去问当今世界上最顶尖的程序员，他们会告诉你，我的工作就是坐在那里，协调十个并行运行的代码机器人。

To your point, it's not necessarily to write the code by hand, but what it is now is all right, now, you know, if you talk to the world's best programmers today, what they'll tell you is, oh, my job is I'm sitting there and I'm orchestrating 10 code bots, right, coding bots that are running in parallel.

对吧？

Right?

事实上，他们整天坐在那里，从一个浏览器切换到另一个浏览器，或从一个终端切换到另一个终端，他们现在的工作本质上就是跟AI机器人争论，试图让它们写出正确的代码。

And literally, they sit there and they shift from browser, you know, browser to browser or terminal to terminal, and their and their their their their their day job now is kind of arguing with the AI bots to try to get them to, like, write the right code.

对吧？

Right?

然后调试、修复问题、修改需求，做所有这些事情。

And then debug it and fix the problems and change the spec and do all these things.

所以现在程序员的工作就是跟编程机器人争论。

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

但如果你自己不会写代码，你就没法评估编程机器人给你的东西对不对？

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

所以，你提到那个10岁的孩子，他特别喜欢电脑和编程。

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

我告诉你，他现在正在用Cloud、ChatGPT、Copilot这些东西。

And what I'm what I'm telling you know, and he's he's using Cloud and ChatGPT and Copilot and all these things.

我告诉他的是：你看。

What I'm telling him is like, look.

顺便说一下，他超爱氛围编程。

And by the way, loves vibe coding.

他整天在Replit上搞氛围编程，做游戏之类的。

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

你知道吗，他坐在那儿，这场景简直太搞笑了，因为他就坐在那儿。

You know, he's sitting there, you know, it's hysterical, right, because he's sitting there.

一个10岁的孩子，居然为了好玩，花两个小时在晚餐时跟AI争论。

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

对吧？

Right?

没错。

Right.

但我告诉他的是：不行，你必须真正理解并学会编写和读懂代码，因为那些编程机器人给你的都是代码。

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

如果代码跑不通，或者没达到你的预期，或者速度不够快，你都得能理解AI给出的结果，对吧？

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

就像编写脚本语言的人最终也需要理解微处理器的工作原理一样。

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

因此，这又是一种能力的提升——你真正需要的是具备深入理解事物本质的能力，即使你日常并不亲手去做这些事。

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

再者，我看到这一点时就想，程序员的生产力现在将比以往高出十倍、百倍甚至千倍，对吧？

And again, I look at that and I'm like, okay, now programmers are gonna be 10 times or a 100 times or a thousand times more productive than they used to be, right?

这绝对是件大好事。

And that is overwhelmingly a good thing.

任务确实在变化，工作的性质也在改变。

The tasks are definitely changing, the nature of the job is changing.

但人类还会参与编程过程，监督AI的编码和相关工作吗？

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

答案当然是绝对的100%，毫无疑问。

And answer is of course, absolutely a 100%, like no question.

所以你是属于仍然认为学习编程是一项有价值技能的阵营。

So you're in the camp of still learning to code, still a valuable skill.

是的，完全没错。

Oh yeah, totally.

再说一遍，如果你想成为那种顶尖人才，好吧，如果你只是想让自己进入自动驾驶模式，觉得懒得动手，就让AI写代码，它生成什么就什么，那也没问题。

Well, again, if you wanna be one of these super, look, if you just wanna put your like self on autopilot and like, I can't be bothered and I'm just gonna have AI write the code and it's gonna generate whatever it does and that's fine.

如果目标只是做个平庸的程序员，那就让AI去做吧。

I'm gonna if the goal is to be a mediocre coder, then just let the AI do it.

没问题。

It's fine.

AI完全能生成海量的平庸代码。

The AI is gonna be perfectly good at generating infinite amounts of mediocre code.

没问题。

No problem.

一切都好。

It's all good.

如果你的目标是成为世界上顶尖的软件人才，想要构建真正重要的新软件产品和技术，那当然，你100%需要继续深入，一直到底。

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

你希望自己的技能能深入到汇编语言和机器码层面。

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

你希望理解栈的每一层。

You wanna understand every layer of the stack.

你希望深入理解芯片层面发生了什么，对吧？

You wanna deeply understand what's happening at the level of chip, right?

还有网络等等。

And the network and so forth.

顺便说一下，你其实也非常希望深入理解AI本身的工作原理，对吧？

By By the way, you also really deeply wanna understand how the AI itself works, right?

因为那些理解AI工作原理的人，显然能从中获得比不了解它的人更多的价值。

Because if people who understand how the AI works are able to, they're clearly able to get more value out of it than somebody who doesn't understand how it works.

我的意思是，当你使用机器时，如果你知道它是如何运作的，你总是会更高效，对吧？

I mean, you're always more productive if you know how the machine works, right, when you use the machine.

因此，那些希望利用这项新技术做出卓越成就的超级赋能者，是的，你100%需要彻底理解这个技术的每一层，因为你希望真正理解它为你提供了什么，对吧？

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

当某些东西出问题或不对劲时，你希望能迅速理解原因。

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

顺便说一下，这又回到了教育问题。

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

AI 是帮助你学习这一切的最好朋友，对吧？

AI is your best friend at helping you learn all that, right?

因为你会想，哦，我需要理解，比如说，这个速度不够快。

Because it's like, oh, I need to understand, I don't know, like this isn't fast enough.

作为程序员，我需要想办法改进内存管理的方式之类的。

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

然后你可能会说，天啊，我真不知道该怎么做到这一点。

And you can be like, well, shit, don't quite know how to do that.

好吧，AI，我们花十分钟，教我怎么做这件事。

Okay, AI, let's spend ten minutes, teach me how to do this.

教我这一切意味着什么。

Teach me what this all means.

对吧？

Right?

所以突然之间，你和AI之间形成了一种极其协同的关系，它在为你做大量工作的同时，也帮助你不断提升。

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

顺便说一下，我之前是个狂热的Pearl程序员。

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

我当了十年工程师，Pearl是我最钟爱的语言。

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

你还记得吗？我不确定你当时是什么时候做的，但至少在早期，你有没有遇到过这种情况：C语言程序员们对你嗤之以鼻，觉得你用的这玩意儿……

You do you remember I don't know when you were doing it, but do do you remember that at the at least early on, do you remember did you ever did you ever hit this where, like, C coders were like looking down their nose at you being like, For

当然有。

for sure.

他们说，这太慢了。

It's like, this is so slow.

根本没法扩展。

It's not gonna scale.

你整天都在忙这个东西吗？

What you spending all your time on this thing?

是的，正是如此。

Yeah, exactly.

当然了，而且当时他们确实有一定道理，因为一开始它确实不够快之类的。

And of course, and again, it was sort of this thing where they were sort of correct, which is at the beginning, it wasn't fast enough or whatever.

但到后来，他们显然是错的。

By the end, they were definitely wrong.

对吧？

Right?

它变得好得多，快得多，而且你知道的，它席卷了全球。

Which is it got much better, much faster, and, you know, it it swept the world.

如今，大多数编程都是用脚本语言完成的。

You know, most coding today happens in scripting languages.

顺便说一句，那些一路走来真正理解脚本语言、同时也懂底层系统的人，才是能让脚本语言真正高效运行的人。

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

对吧？

Right?

所以，这正是这种适应能力的一个绝佳例子。

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

而结果是，使用脚本语言编写代码的人数远远超过了使用底层语言编写代码的人数。

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

我认为这将会是那种情况更显著的版本。

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

我很喜欢Perl是由语言学家设计的这一点。

I love that Perl was designed by a linguist.

我不知道你是否记得这一点，但这正是它编程起来如此舒适的原因。

I don't know if you remember that part, and that's what made it so nice to to code with.

这很有趣，因为当然，Perl以难以理解而臭名昭著。

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

所以

So

多么讽刺啊。

How ironic.

是的。

Yeah.

本集由Datadog赞助播出，Datadog现已整合Epo，领先的实验与功能开关平台。

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

全球顶尖公司的产品经理使用Datadog——与工程师每天依赖的同一平台，将产品洞察与产品问题（如bug、用户体验摩擦和业务影响）联系起来。

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

它从产品分析开始，产品经理可以观看回放、分析漏斗、深入研究留存率，并探索增长指标。

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

当其他工具止步时，Datadog还会更进一步。

Where other tools stop, Datadog goes even further.

它能帮助你真正诊断漏斗流失、bug和用户体验摩擦的影响。

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

一旦你知道该聚焦哪里，实验就能验证哪些方案有效。

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

我在Airbnb工作时亲眼见证了这一点，我们的实验平台对于分析哪些措施有效、哪里出了问题至关重要。

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

而构建Airbnb实验平台的同一团队，也打造了Epo。

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

Datadog还能让你通过会话回放功能超越数字本身。

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

通过热力图和滚动图，精确观察用户如何互动，从而真正理解他们的行为。

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

而这一切都由与实时数据绑定的功能标志驱动，让你能够安全地推出、精准地定位并持续学习。

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

Datadog 不仅仅是工程指标。

Datadog is more than engineering metrics.

它是优秀产品团队更快学习、更智能修复、更有信心发布的地方。

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

请在 datadoghq.com/lenny 申请演示。

Request a demo at datadoghq.com/lenny.

那就是 datadoghq.com/lenny。

That's datadoghq.com/lenny.

回到这个三位一体，我越来越多听到的另一个要素是品味、设计和用户体验的能力。

Coming back to these this kind of triad, the other element that I hear more and more of is just as is the skill of taste and design and user experience.

感觉这是一种很难掌握的技能。

It feels that's a very hard skill to learn.

对我来说，这表明设计在未来将变得更有价值。

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

是的，没错。

Yeah, that's right.

而且同样，这里是一个很好的例子。

And again, here, this is a great example.

所以，再次强调，像设计完美图标这样的任务层级，

So again, the task level of like design the perfect icon, right?

将会被AI全天候自动完成。

Is gonna be like, all right, AI is gonna do that all day long.

它会给你提供一千个图标设计，这会很棒。

It's gonna give you a thousand icon designs, it's gonna be great.

就像，这会非常棒，不管怎样，你知道的。

Like, it's gonna be fantastic, like whatever, know?

不过，仍然会有一些人类设计图标的空间，但AI在这方面会变得非常出色。

There will still, by the way, there will still be some level of human icon design or whatever, but like AI is gonna get really good that.

但我们要做什么呢？

But like, what are we trying to do?

比如，那种大写的D设计，也就是搞清楚这个东西到底是做什么用的？

Like, the the, you know, kind of capital d design of like, alright, what is this thing for?

还有，这个东西该怎么用呢？

And how does this yeah.

它在人类世界中该如何运作？

How is this going to function in a world of human beings?

而且，用户使用它的时候会感到开心吗？

And like, is this gonna make people happy when they use it?

这会让人对自己感觉良好吗？

Is this gonna make people feel good about themselves?

它能融入他们生活的其他部分吗？

Is it gonna fit into the rest of their life?

它会不会以正确的方式挑战他们呢？

Is it gonna, I don't know, challenge them in the right way?

所有这些更高层次的问题，一直是优秀设计师们所思考的——设计师的工作，将更多地涉及这些更高层次、更重要的部分。

All these kinds of higher level questions that the great designers have always thought about, like the job of designer, right, will involve much more of those higher level, more important components.

而AI则会承担更多基础性任务。

And then again, with AI doing a lot more of the underlying tasks.

所以，一种理解方式是，想想世界上最顶尖的设计师，比如乔尼·艾维之类的——如果我现在是个25岁的设计师，渴望十年后成为乔尼·艾维那样的人，那突然间，我有了条新路径可以通往那个目标，因为乔尼·艾维做的一切都没用过AI。

And so, one way to think about it is, I don't know, think of like, I don't know, the world's best designers, Johnny Ive or whatever, it could be like, wow, like if I'm a designer today, if I'm a 25 year old designer and I and I aspire to be, you know, Johnny Ive in a decade, it's it's all of a sudden, have a new path that I can use to kinda get to to to to get there, which is I you know, because Johnny Ive did everything he did without AI.

现在，年轻的设计师可能会想，哇。

Now, you know, a young designer can be like, wow.

如果我在十年内真正善用AI，我将成为世界上有史以来最伟大的设计师，因为那将不只是我一个人的力量。

If I really harness AI in a decade, I'm gonna be like the best designer the world's ever seen because it's not just gonna be me.

这将是我本人，再加上这项技术赋予我的超强能力，让我能够做更多事情。

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

于是，我的大部分时间和精力都能专注于这些大多数设计师从未接触过的更高层次的事情上。

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

我认为这将是另一个很好的例证。

And I think that's gonna be another great example of that.

所以，我听到的可能是这种T型策略：如果你想在这三个角色中的任何一个取得成功，就要在那个特定角色——产品管理、工程或设计——上做到极致，然后对另外两个角色也要达到足够好的水平。

So maybe what I'm hearing here is kind of this t shaped strategy of if you want to be successful in any three of these roles, be very, very, very good at that specific role, product management, engineering, design, and then get good enough at these other two roles.

我觉得这非常好。

Well, so I think that's great.

我觉得这真的非常相关。

I think that's really, really relevant.

可惜的是，斯科特·亚当斯刚刚去世了，这真是一个巨大的悲剧。

Then Scott Adams unfortunately just passed away, which is a real tragedy.

但我多年来一直引用的是斯科特·亚当斯的观点。

But I was always I've always I referred for years to actually Scott's Scott Adams.

他有一套著名的职场建议，经常提供给人们，我觉得非常有道理，这和你所说的不谋而合。他过去常说，你看。

He had this famous he had his famous kind of career advice he would give people, which I I think makes a lot of sense, which which which dovetails with what you're saying, which is he he used to say he used to say it's like, look.

他说，我或许可以成为一个不错的漫画家，或者也可以在商业上做得不错。

He said, I could have been a pretty good cartoonist or I could have been like pretty good at business.

但因为我是一个懂商业的漫画家，这让我在梅根·多布特这件事上变得异常出色。

But the fact that I was a cartoonist who understood business made me like spectacularly great at Megan Dobart.

对吧？

Right?

因为即使世界上最好的漫画家，如果不懂商业，也永远写不出《呆伯特》。

Because even the world's best cartoonists who didn't understand business could have never written Dilbert.

而世界上最好的商业人士，如果不会画漫画，也无法创作出《呆伯特》。

And then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert.

只有同时具备这两种技能的人，才能创造出《呆伯特》。

It took somebody who actually had both of those skills to be able to make Dilbert.

对吧？

Right?

这是历史上最成功的漫画之一。

Which is one of the most successful cartoons in history.

对吧？

Right?

所以斯科特总是这样描述：从职业发展的角度来看，擅长两件事的叠加效应远不止翻倍，对吧？

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

擅长三件事的叠加效应甚至超过三倍，对吧？

The additive effect of being good at three things is more than triple, right?

因为你成为了这些领域结合体中的超级专家。

Because you become a super relevant specialist in the combination of the domains.

而且你看，这种现象在经济中随处可见。

And look, you see this all over the economy.

我的意思是，这种现象在经济中随处可见，我给你举个例子，好。

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

以好莱坞为例。

Just Hollywood as an example.

有很多编剧不会拍电影，但他们依然是非常成功的编剧。

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

有很多导演不会写剧本，但他们依然是非常成功的导演。

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

但娱乐业的超级明星是那些既能写又能导的人。

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

对吧？

Right?

他们并没有一个专门的术语来称呼这些人。

They don't have a term for those.

他们称这些人为作者导演。

They call those auteurs.

对吧？

Right?

而这些人正是真正推动这个领域发展的创意力量。

And that's those are the people who are like the real creative forces that move the field.

所以再说一遍，顺便说一下，好莱坞真是挺搞笑的。

And so again, and by the way, Hollywood it's just really funny.

我花了很多时间和好莱坞的人讨论人工智能。

I've spent spent spending a lot time talking Hollywood people about AI.

好莱坞现在正经历着我们之前在《攻击》中描述的那种僵局，只不过在好莱坞，比如在电影制作中，导演就是编剧和演员。

Hollywood has the same Mexican standoff going right now that we described in Attack except in Hollywood, for example, for filmmaking is the director is the writer and the actor.

因为导演现在在想：天啊，我不再需要编剧了，因为AI可以写剧本，我也不再需要演员了，因为可以用AI演员。

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

编剧则说：我不需要导演，因为我可以自己执导电影，而AI可以负责演员部分。

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

而演员则说：我不需要你们中的任何一个。

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

我可以请AI来执导，让AI来写剧本，我只需要到场表演就行了。

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

所以这同样是那种三角形的僵局。

And so it's the same kind of triangular configuration.

而且again，这一点很棒的是，他们每个人都是对的。

And again, what's great about it is they're all correct.

在这三个领域中的每个人，将来都能横向拓展，掌握这些额外的技能。

Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills.

结果就是，会有更多人能够同时写作和导演，或写作和表演，或导演和表演，甚至三者兼备。

And then as a consequence, you're gonna have more people who can write and direct or write and act or direct and act or do all three.

我认为你提到的T型转变，这一点几乎会在整个经济领域中都成立。

And I think to your point, your T shift thing, I think that's gonna be true basically across the entire economy.

如果你想想T型结构，顶部代表的是：你对多少个不同领域足够熟悉，能够利用AI工具做出出色的工作？

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

而T的这一部分则是：你至少能在其中一个领域深入到什么程度，真正精通你所做的事情。

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

比如，如果你在编程方面非常精通，同时能用AI做设计、用AI做产品管理，那这就是你的T型结构。

But like, if you're like super deep on coding and you can use AI to do design and you can use AI to do product management, that's your T right there.

你在T的顶部是三栖人才，但下方还有扎实的技术根基。

And you're a triple threat at the top of the T, but with this level of technical grounding underneath that.

而且到了那时，你再次成为了一个超级强大的个体。

And mean, at that point, you're again, you're the super powered individual.

你将能够完成如同魔法般的壮举。

You're gonna be able to just perform like feats of magic.

例如，在设计和开发新产品方面，像我这一代人根本无法想象。

For example, in terms of designing and building new products, you know, that the people in my generation couldn't have even dreamed of.

所以我认为，这是一套普适性的理论，可以应用于整个经济领域。

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

我现在要发明一个新的框架。

I'm gonna invent a new framework right now.

好的。

Okay.

忘掉那个T型框架吧。

Forget the t framework.

我想象的是一个横向的F或者E，有两到三个向下的分支。

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

所以，我听到的是至少要变得优秀。

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

我觉得这是对的。

I think that's right.

觉得这是对的。

Think that's right.

然后就是那个，是的。

And then the yeah.

那个组合，是的。

The combination yeah.

让我朋友劳伦斯·萨默斯对斯科特·亚当斯的观点有不同版本，他过去常对人们说。

Let my my friend Larry Summers had a had a different version of the Scott Adams thing, is he he used to tell people.

他说，职业规划的关键是，不要变得可替代。

He said, the key for career planning is he said, don't be fungible.

对吧？

Right?

他是一位经济学家，所以这是从经济学角度来说的。

And he's an economist and so that was economic speaking.

这意味着本质上不要成为可替代的人。

And what that means is essentially is don't be replaceable.

所以不要做一颗螺丝钉。

And so don't be a cog.

对吧？

Right?

这意味着不要只做一件事。

So what that meant was don't just be one thing.

对吧？

Right?

所以如果你仅仅是一个设计师、一个产品经理、一个程序员，理论上你就可以被随意替换。

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

但如果你有这种，嗯，这种像倒置的E或F那样的东西，并且你拥有这种罕见的组合，那么你就不再是可替代的了。

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

你不只是不可替代，实际上你还极其重要，因为你是世界上少数几个能完成这种技能组合的人之一。

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

而且，是的，相比以往任何时代，AI极大地提升了你避免沦为那种单一型人才的能力。

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

这太有趣了，因为我曾经和一些同时具备这两种技能的人共事过，他们总是被公司称为‘独角兽’。

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

她既能做开发，又能做设计。

She can coat and design.

天哪。

Oh my god.

我在这里听到的是，这就是你需要成为的样子。

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

你需要至少在两个领域都变得非常出色。

You need to be come really good at at least two things.

我想你之前用过‘烟囱’这个词，意思是产品经理在这里，工程师和设计师在那里。

They're I think you used the term smokestack or something where it's like PM over here, engineer design.

我在这里听到的是，你需要至少精通其中两项技能。

And what I'm hearing here is you need to get good at at least two of these skills.

这两个角色之间的壁垒正在消失。

The silos of these two roles are disappearing.

没错。

That's right.

没错。

That's right.

而且我要再次强调，对所有听这段话的人而言。

And again, can't overstress the following for anybody listening to this.

我认为人们还没有充分受益于人工智能的一点是，它会教你。

The thing about AI that I think people are just like not getting enough benefit out of yet is just it will teach you.

这太棒了。

Like, This is amazing.

以前从未有过这样的技术，你可以问它：教我怎么做这件事。

There's never been a technology before where you can ask it like teach me how to do this thing.