So I wanted to ask your advice and maybe for advice for anybody who's trying to build, who's trying to build something useful in the world, maybe launch a startup or maybe just launch apps, services, whatever, ship software products.

所以，我想問問你的建議，也許是給那些想在世界上建立一些有用的東西的人的建議，也許是啟動一家初創公司，也許只是推出應用程序、服務，不管怎樣，推出軟件產品。

So maybe by way of advice, how do you actually get to shipping?

那麼，或許我可以給你提個建議，你究竟該如何出貨呢？

So I mean a big part of the answer I think is we're in the middle of a legit revolution and I know you've been talking about this on your show, but like AI coding, I mean this is the biggest earthquake to hit software in certainly my life, maybe since the invention of software.

所以，我認為答案的很大一部分是，我們正處在一場合法革命的中間，我知道你在節目中一直在談論這個，但就像人工智能編碼一樣，我的意思是，這肯定是我這輩子，也許是自軟件發明以來，軟件遭遇的最大地震。

And I'm sure we're involved in various of these companies, but these tools from a variety of companies are absolutely revolutionary and they're getting better by leaps and bounds right every day.

我相信我們也參與了這些公司中的許多公司，但這些來自不同公司的工具絕對是革命性的，而且每天都在突飛猛進。

And you know all this, but like the thing with coding, there's like open questions of whether AI can get better at, I don't know, understanding philosophy or whatever, creative writing or whatever, but like for sure we can make it much better at coding, right?

你知道這一切，但就像編碼一樣，人工智能是否能更好地理解哲學或其他什麼，創意寫作或其他什麼，這些都是懸而未決的問題，但可以肯定的是，我們能讓它在編碼方面做得更好，對嗎？

Because you can validate the results of coding.

因為你可以驗證編碼的結果。

And so there's all these methods of synthetic data and self-training and reinforcement learning that for sure you can do with coding.

是以，所有這些合成數據、自我訓練和強化學習的方法，你都可以通過編碼來實現。

And so everybody I know who works in the field says AI coding is going to get to be phenomenally good and it's already great.

是以，我認識的所有從事該領域工作的人都說，人工智能編碼將變得非常出色，而且它已經很棒了。

And you can, I mean, anybody who wants to see this, just go on YouTube and look at AI coding demos, little kids making apps in 10 minutes working with an AI coding system.

你可以，我是說，任何想看這個的人，只要上 YouTube 看看人工智能編碼演示，小孩子們用人工智能編碼系統在 10 分鐘內就能做出應用程序。

And so I think it's the golden age.

是以，我認為這是一個黃金時代。

I mean, I think this is an area where it's clearly the golden age.

我的意思是，我認為這是一個明顯處於黃金時代的領域。

The tool set is extraordinary.

這套工具非常出色。

You know, in a day, as a coder for sure in a day, you can retrain yourself, you know, start using these things, get a huge boost in productivity.

要知道，一天之內，作為一個編碼員，你就可以重新培訓自己，開始使用這些東西，極大地提高工作效率。

As a non-coder, you can learn much more quickly than you could before.

作為一名非編碼員，你可以比以前學得更快。

That's actually a tricky one in terms of learning as a non-coder to build stuff.

作為一個非編碼員，在學習構建東西方面，這實際上是一個棘手的問題。

It's still, I feel like you still need to learn how to code.

我覺得你仍然需要學習如何編寫代碼。

It becomes a superpower.

它成為一種超能力。

It helps him be much more productive.

這有助於提高他的工作效率。

Like you could legitimately be a one person company and get quite far.

就像你可以合法地做一個人的公司，並走得很遠。

I agree with that.

我同意這一點。

Up to your point.

到你的觀點為止。

So I think for sure for quite a long time, the people who are good at coding are going to be the best at actually having AIs code things because they're going to understand what, I mean, very basic, they're going to understand what's happening, right?

是以，我認為在相當長的一段時間內，擅長編碼的人將是讓人工智能編碼的最佳人選，因為他們會理解，我是說，非常基本的，他們會理解發生了什麼，對嗎？

And they're going to be able to evaluate the work and they're going to be able to, you know, literally like manage AIs better.

他們將能對工作進行評估，並能更好地管理人工智能。

Even if they're not literally handwriting the code, they're just going to have a much better sense of what's going on.

即使他們沒有真正手寫代碼，他們也能更好地瞭解發生了什麼。

So I definitely think like 100%, my nine-year-old is like doing all kinds of coding classes and he'll keep doing that for certainly through 18.

是以，我絕對認為，我九歲的孩子百分之百在上各種編碼課程，而且他肯定會一直上到 18 歲。

We'll see after that.

之後再看吧。

And so for sure that's the case.

是以，情況肯定如此。

But look, having said that, one of the things you can do with an AI is say, teach me how to code, right?

不過，話雖如此，你還是可以對人工智能說，教我如何編碼，對嗎？

And so, and, you know, there's a whole bunch of, you know, I'll name names, like there's a whole bunch of work that they're doing at Khan Academy for free.

所以，你知道，有一大堆人，你知道，我會說出他們的名字，比如他們在可汗學院免費做了一大堆工作。

And then, you know, we have this company, Replit, which was originally specifically built for kids for coding that has AI built in.

然後，你知道，我們有一家名為 Replit 的公司，這家公司最初是專門為孩子們設計的，它內置了人工智能編碼功能。

That's just absolutely extraordinary now.

這絕對是非同尋常的。

And then, you know, there's a variety of other systems like this.

然後，你知道，還有各種各樣類似的系統。

And yeah, I mean, the AI is going to be able to teach you to code.

是的，我的意思是，人工智能可以教你編碼。

AI, by the way, is, as you know, spectacularly good at explaining code, right?

順便說一句，眾所周知，人工智能非常擅長解釋代碼，對吧？

And so, you know, the tools have these features now where you can talk to the code base.

是以，你知道，現在的工具都有這些功能，你可以與代碼庫對話。

And so you can like literally like ask the code base questions about itself.

這樣，你就可以像字面意思一樣，向代碼庫提出關於它自己的問題。

And you can also just do the simple form, which is you can copy and paste code into ChatGPT and just ask it to explain it, what's going on, rewrite it, improve it, make recommendations.

你也可以採用簡單的形式，即把代碼複製並粘貼到 ChatGPT 中，然後請它解釋代碼，說明發生了什麼，重寫代碼，改進代碼，提出建議。

And so there's, yeah, there's dozens of ways to do this.

是以，有幾十種方法可以做到這一點。

By the way, you can also, I mean, even more broadly than code, like, OK, you want to make a video game, OK, now you can do AI art generation, sound generation, dialogue generation, voice generation, right?

順便說一下，你還可以，我是說，甚至比代碼更廣泛，比如，好吧，你想做一個視頻遊戲，好吧，現在你可以做人工智能藝術生成、聲音生成、對話生成、語音生成，對嗎？

And so all of a sudden, like you don't need designers, you know, you don't need, you know, voice actors, you know.

所以突然之間，你不需要設計師了，你知道，你不需要配音演員了，你知道。

So, yeah, so there's just like unlimited.

所以，是的，所以就像無限的。

And then, you know, a big part of coding is so-called glue.

然後，你知道，編碼的很大一部分就是所謂的膠水。

You know, it's interfacing into other systems.

你知道，它是與其他系統的接口。

So it's interfacing into, you know, Stripe to take payments or something like that.

是以，它與 Stripe 的接口，你知道，Stripe 接受付款或類似的東西。

And, you know, AI is fantastic at writing glue code.

要知道，人工智能在編寫膠水代碼方面非常出色。

So, you know, really, really good at making sure that you can plug everything together, really good at helping you figure out how to deploy.

所以，你知道，我們真的非常擅長確保你能把所有東西都整合在一起，也非常擅長幫你找出如何部署。

You know, it'll even write a business plan for you.

你知道，它甚至會為你寫一份商業計劃書。

So it's just this, it's like everything happening with AI right now.

就像現在人工智能所發生的一切一樣。

It's just, it's like this latent superpower.

這就像是一種潛在的超能力。

And there's this incredible spectrum of people who have really figured out massive performance increases, productivity increases with it already.

有很多人已經利用它實現了性能的大幅提升和生產力的大幅提高。

There's other people who aren't even aware it's happening.

還有一些人甚至沒有意識到這一點。

And there's some gearing to whether you're a coder or not, but I think there are lots of non-coders that are off the races.

無論你是否是編碼員，都會有一定的匹配度，但我認為有很多非編碼員都不在比賽之列。

And I think there are lots of professional coders who are still like, you know, the blacksmiths were not necessarily in favor of, you know, the car business.

我認為，有很多專業編碼員仍然像鐵匠一樣，不一定支持汽車行業。

So, you know, there's the old William Gibson quote, the future is here.

威廉-吉布森有句名言：未來已來。

It's just not evenly distributed yet.

只是分佈還不均勻。

And this is maybe the most potent version of that that I've ever seen.

這也許是我所見過的最有力的版本。

Yeah, there's, you know, the old meme with the bell curve, the people on both extremes say AI coding is the future.

是啊，你知道的，老掉牙的鐘形曲線，兩個極端的人都說人工智能編碼是未來。

It's very common for programmers to say, you know, if you're any good of a programmer, you're not going to be using it.

程序員通常會說，如果你是個優秀的程序員，你就不會使用它。

That's just not true.

事實並非如此。

I consider myself a reasonably good programmer and my productivity has been just skyrocketed and the joy of programming skyrocketed.

我認為自己是一個相當不錯的程序員，我的工作效率直線上升，編程的樂趣也直線上升。

Every aspect of programming is more efficient, more productive, more fun, all that kind of I would also say code is, you know, code has of anything in like industrial society, code has the highest elasticity, which is to say the easier it is to make it the more of it gets made.

編程的方方面面都更有效率、更有生產力、更有趣，所有這些。我還想說的是，在工業社會中，代碼是任何東西中彈性最大的，也就是說，代碼越容易製作，就會有越多的代碼被製作出來。

Like I think effectively there's unlimited demand for code.

就像我想的那樣，實際上對代碼的需求是無限的。

Like in other words, like there's always some other idea for a thing that you can do, a feature that you can add or a thing that you can optimize.

換句話說，總會有一些其他的想法，比如你可以做的事情，你可以添加的功能，或者你可以優化的東西。

And so, like overwhelmingly, you know, the amount of code that exists in the world is a fraction of even the ideas we have today.

是以，就像絕大多數人一樣，你知道，世界上存在的代碼量甚至只是我們今天所擁有的想法的一小部分。

And then we come up with new ideas all the time.

然後，我們不斷提出新的想法。

And so, I think that like, you know, I was in the late 80s, early 90s when sort of automated coding systems started to come out, the expert systems, big deal in those days.

是以，我認為，在 80 年代末 90 年代初，自動編碼系統開始出現，專家系統也開始出現，這在當時是件大事。

And there were all these, there was a famous book called The Decline and Fall of the American Programmer, you know, that predicted that these new coding systems were going to mean we wouldn't have programmers in the future.

有一本很有名的書叫《美國程序員的衰落與沒落》，書中預言這些新的編碼系統將意味著我們未來將不再有程序員。

And of course, the number of programming jobs exploded by like a factor of 100.

當然，編程工作的數量也以 100 倍的速度激增。

Like my guess will be we'll have more, my guess is we'll have more coding jobs probably by like an order of magnitude 10 years from now.

我的猜測是，10 年後，我們會有更多的編碼工作，可能會是一個數量級。

It will be different.

會有所不同。

There'll be different jobs.

會有不同的工作。

They'll involve orchestrating AI.

它們將涉及人工智能的協調。

But there will be, we will be creating so much more software that the whole industry will just explode in size.

但是，我們將會創造出更多的軟件，整個行業的規模將會爆炸式增長。

Are you seeing the size of companies decrease in terms of startups with the landscapes of little tech?

您是否看到，隨著小技術的發展，初創公司的規模有所縮小？

All we're seeing right now is the AI hiring boom of all time.

我們現在看到的是有史以來人工智能的招聘熱潮。

All for the big tech.

都是為了大科技。

People, and little tech.

人，和很少的技術。

Everybody's trying to hire as many engineers as they can to build AI systems.

每個人都在想方設法僱傭儘可能多的工程師來構建人工智能系統。

It's just, it's 100%.

這只是，這是100％。

I mean, there's a handful of companies, you know, there's a little bit, there's customer service.

我的意思是，有少數幾家公司，你知道，有一點，有客戶服務。

You know, we have some companies and others, I think it's Klarna that's publicizing a lot of this in Europe, where, you know, there are jobs that can be optimized and jobs that can be automated.

你知道，我們有一些公司和其他公司，我想是 Klarna 公司在歐洲宣傳了很多這方面的資訊，你知道，有一些工作可以優化，有一些工作可以自動化。

But like for engineering jobs, like it's just an explosion of hiring.

但就像工程類工作一樣，招聘人數激增。

At least so far, there's no trace of any sort of diminishing effect.

至少到目前為止，還看不出任何遞減效應的跡象。

Now, having said that, I am looking forward to the day, I am waiting for the first company to walk in saying yes, like the more radical form of it.

話雖如此，但我還是期待著這一天的到來，我在等待著第一家公司走進來說 "是的"，就像更激進的形式一樣。

So basically, the companies that we see are basically one of two kinds.

是以，我們看到的公司基本上有兩種。

We see the companies that are basically, sometimes used weak form, strong form.

我們看到的公司基本上都是這樣，有時採用弱形式，有時採用強形式。

So the weak form companies, I sometimes use the term, it's called the sixth bullet point.

是以，弱形式公司，我有時會用這個詞，叫做第六個要點。

AI is the sixth bullet point on whatever they're doing.

無論他們在做什麼，人工智能都是第六個要點。

Sure.

當然。

Right?

對不對？

And it's on the slide, right?

就在幻燈片上，對嗎？

So they've got the, you know, whatever, dot, dot, dot, dot, and then AI is the sixth thing.

是以，他們已經有了，你知道，不管是什麼，點、點、點、點，然後人工智能是第六件事。

And the reason AI is the sixth thing is because they had already previously written the slide before the AI revolution started.

而人工智能之所以是第六件事，是因為在人工智能革命開始之前，他們就已經寫好了幻燈片。

So they just added the sixth bullet point on the slide, which is how you're getting all these products that have like the AI button up in the corner, right?

是以，他們在幻燈片上添加了第六個要點，這就是你如何獲得所有這些在角落裡有人工智能按鈕的產品，對嗎？

The little sparkly button. Right?

閃閃發光的小鈕釦 對不對？

And all of a sudden, Gmail is offering to summarize your email, which I'm like, I don't need that.

突然間，Gmail 提供了郵件摘要功能，我想，我不需要那個。

Like, I need you to answer my email, not summarize it.

比如，我需要你回覆我的郵件，而不是總結它。

Like, what the hell?

搞什麼鬼？

Okay, so we see those and that's fine.

好吧，我們看到了這些，這很好。

That's like, I don't know, putting sugar on the cake or something.

這就像，我不知道，在蛋糕上放點糖什麼的。

But then we see the strong form, which is the companies that are building from scratch for AI, right?

不過，我們也看到了強勢企業的身影，那就是那些為人工智能從零開始的企業，對嗎？

And they're building it.

他們正在建造它。

I actually just met with a company that is building literally an AI email system as an So just good.

實際上，我剛剛見了一家公司，它正在建立一個人工智能電子郵件系統，這樣就很好。

Oh, nice.

哦，不錯。

I can't wait.

我等不及了

Yeah, they're going to completely, right.

是啊，他們會完全，對吧。

So the very obvious idea, very smart team.

所以，這個想法非常明顯，這個團隊非常聰明。

You know, it's going to be great.

你知道，這會很棒的。

And then, you know, Notion just, you know, another, not one of our companies, but just came out with a product.

然後，你知道，Notion 剛剛，你知道，另一家，不是我們的公司，但剛剛推出了一款產品。

And so now companies are going to basically come through, sweep through, and they're going to do basically AI first versions of basically everything.

是以，現在基本上所有的公司都會來做人工智能的第一版。

And those are like companies built, you know, AI is the first bullet point.

你知道，人工智能是第一個要點。

It's the strong form of the argument.

這是論證的有力形式。

Cursor is an example that they basically said, OK, we're going to rebuild the thing with AI as the first citizen.

遊標》就是一個例子，他們基本上是說："好吧，我們要以人工智能為第一公民來重建這個東西。

What if we knew from scratch that we could build on this?

如果我們從零開始就知道我們可以在此基礎上再接再厲呢？

And again, this is like, this is part of the full Employment Act for startups and VCs is just like if a technology transformation is sufficiently powerful, then you actually need to start the product development process over from scratch because you need to reconceptualize the product.

同樣，這也是初創企業和風險投資公司充分就業法案的一部分，就好比如果技術變革足夠強大，那麼你實際上需要從頭開始產品開發過程，因為你需要重新構思產品。

And then usually what that means is you need a new company because most incumbents just won't do that.

這通常意味著你需要一家新公司，因為大多數現有公司都不會這麼做。

And so, yeah, so that's underway across many categories.

是以，是的，這在許多類別中都在進行。

What I'm waiting for is the company where it's like, no, our org chart is redesigned as a result of AI, right?

我期待的是，有一家公司會說，不，我們的組織結構圖是因人工智能而重新設計的，對嗎？

And so I'm looking, I'm waiting for the company where it's like, no, we're going to have like, you know, and the cliche, here's a thought experiment, right?

是以，我在尋找，我在等待一家公司，它就像，不，我們將有像，你知道，和老生常談，這裡是一個思想實驗，對不對？

The cliche would be we're going to have like the human executive team and then we're going to have the AIs be the workers, right?

老生常談的說法是，我們將擁有像人類一樣的執行團隊，然後讓人工智能來當工人，對嗎？

So we'll have a VP of engineering supervising 100 instances of coding agents, right?

所以，我們會讓一位工程副總裁監督 100 個編碼代理實例，對嗎？

Okay, maybe, right?

好吧，也許，對嗎？

By the way, or maybe, maybe the VP of engineering should be the AI, maybe supervising human coders who are supervising AIs, right?

順便說一句，或許，或許工程副總裁應該是人工智能，或許是監督人工智能的人類編碼員，對嗎？

Because one of the things that AI should be pretty good at is managing because it's like not, you know, it's like a process driven.

因為人工智能最擅長的事情之一就是管理，因為它不像，你知道，它就像一個流程驅動的系統。

It's the kind of thing that AI is actually pretty good at, right?

這種事情，人工智能其實很擅長，不是嗎？

Performance evaluation coaching.

績效評估輔導。

And so should it be an AI executive team?

那麼，人工智能高管團隊是否也應該如此呢？

And then, you know, and then of course the ultimate question, which is AI CEO, right?

然後，你知道，當然還有一個終極問題，那就是人工智能首席執行官，對嗎？

And then, you know, and then there's, and then maybe the most futuristic version of it would be an actual AI agent that actually goes fully autonomous.

然後，你知道，還有，也許最未來的版本是一個真正完全自主的人工智能代理。

Yeah, what if you really set one of these things loose and let it, let it basically build itself a business?

是啊，如果你真的把這些東西放任自流，讓它自己創業呢？

And so I will say like, we're not yet seeing those.

所以我要說，我們還沒有看到這些。

And I think there's a little bit of the systems aren't quite ready for that yet.

我認為系統還沒有做好準備。

And then I think it's a little bit of, you really do need at that point, like a founder who's really willing to break all the rules and really willing to take the swing.

然後，我認為在這一點上，你確實需要一個真正願意打破所有規則、真正願意承擔風險的創始人。

And I know those people exist.

我知道這些人是存在的。

And so I'm sure we'll see that.

是以，我相信我們會看到這一點。

And some of it is, as, as you know, with all the startups, this is the execution, the idea that you have a AI first email client.

其中一些原因是，正如你所知道的，對於所有初創公司來說，這就是執行力，也就是你擁有人工智能第一電子郵件客戶端的想法。

This seems like an obvious idea, but actually creating one, executing and then taking on Gmail is really, it's really difficult.

這似乎是一個顯而易見的想法，但要真正創建一個，並付諸實施，然後與 Gmail 一決高下，確實非常困難。

I mean, Gmail, it's fascinating to see Google can't do it because, because why?

我的意思是，Gmail，谷歌不能做到這一點很吸引人，因為，因為為什麼？

Because the momentum, because it's hard to re-engineer the entirety of the system.

因為動力，因為很難重新設計整個系統。

Feels like Google is perfectly positioned to, to do it.

感覺谷歌完全有能力做到這一點。

Same with like your perplexity, which I love, like Google could technically take on perplexity and do it much better, but they haven't, not yet.

就像我喜歡的 "perplexity "一樣，谷歌在技術上可以取代 "perplexity"，而且會做得更好，但他們還沒有，還沒有。

So it's fascinating why that is for large companies.

是以，對於大公司來說，這是一個令人著迷的問題。

I mean, that, that is an advantage for little tech.

我的意思是，這是小技術的優勢。

They can be agile.

它們可以很靈活。

Yeah, that's right.

是的，沒錯。

They can move fast.

它們行動迅速。

Yeah.

是啊

Little companies can break glass in a way big companies can't.

小公司可以打破大公司無法打破的玻璃。

Right.

對

This is sort of the big breakthrough that Clay Christensen had in the innovators dilemma, which is sometimes when big companies don't do things, it's because they're screwing up.

這也是克萊-克里斯坦森（Clay Christensen）在 "創新者困境"（innovators dilemma）中的重大突破，即有時大公司不做事情，是因為他們搞砸了。

And that certainly happens.

這種情況當然會發生。

But a lot of times they don't do things because it would break too much glass.

但很多時候，他們不這樣做是因為會打破太多玻璃。

It was specifically, it would, it would, it would interfere with their existing customers and their existing businesses.

具體來說，就是會干擾他們現有的客戶和業務。

And they just simply won't do that.

他們就是不願意這麼做。

And by the way, responsibly, they shouldn't do that.

順便說一句，負責任地說，他們不應該這麼做。

Right.

對

And so they just get, Clay Christensen's big thing is they often don't adapt because they are well-run, not because they're poorly run, but they're optimizing machines.