心灵的摩托车

嘿。

Hey.

我是尼维。

This is Nivy.

你正在收听有史以来第一次录制的海军播客。

You're listening to the Naval podcast for the first time in recorded history.

我们并不在同一个地方。

We are not at the same location.

我实际上正在城里散步，而海军可能也在做同样的事。

I am actually walking around town, and Naval might be doing the same.

所以可能会有一些环境噪音，但是

So there might be some ambient noise, but

我们会努力通过人工智能和良好的音频工程来消除这些噪音。

we are going to try hard to remove that with AI and some good audio engineering.

播客录制总是很僵硬，因为你必须坐下来，安排时间，还有一支巨大的麦克风对着你的脸，这并不轻松自然。

Podcast recording is so stilted because it's like you have to sit down, you schedule something, and there's giant mic pointing in your face, and it's not casual.

这使得它显得不够真实，更像经过排练和刻意准备的。

It makes it just less authentic, more practiced, more rehearsed.

我明白这可能会产生更高质量的音频和视频，但我感觉它带来的对话质量反而更低。

I get that it produces maybe higher quality audio and video, but I feel like it produces lower quality conversation.

我们都清楚，当人活动起来、散步时，大脑会运转得更好。

And we all know brains run better when they're being locomoted and you're moving around or just going for walks.

没错。

Absolutely.

我的大脑靠双腿驱动。

My brain is powered by my legs.

我摘了一些纳瓦尔关于人工智能的推文。

I pulled out some tweets from Naval on the topic of AI.

我们想稍微聊聊人工智能，希望能以一种更持久的方式讨论，但我觉得其中一些内容可能不会持久。

We wanna talk a little bit about AI and hopefully talk about it in a more timeless manner, but I think some of it's going to be non timeless content.

在我们进入推文之前，你有什么想说的吗？关于你目前如何安排时间，或者你在Impossible公司正在做什么？

Before we jump into the tweets, do you wanna say anything about what you're doing with your time, or what you're doing at Impossible?

不是真的。

Not really.

我们正在做一个非常困难的项目，这就是它被称为Impossible的原因，而且我们有一支了不起的团队，再次打造一些东西真的令人兴奋。

We're working on a very difficult project, that's why it's called Impossible with an amazing team, and it's really exciting building something again.

从零开始非常纯粹，这永远都是第一天。

It's very pure starting over from the bottom, and that's always day one.

我想，我只是对当一个投资者感到不满意，我当然不想成为一个哲学家，或者只是一个媒体人物、评论员，因为我觉得人们只会说太多而什么都不做。

I guess, I just wasn't satisfied being an investor, and I certainly don't wanna be a philosopher, or just a media personality, or a commentator, because I think people would just talk too much and don't do anything.

他们没有接触过现实。

They haven't encountered reality.

他们没有获得反馈。

They haven't gotten feedback.

来自自由市场的严酷反馈来自于物理或自然。

The harsh feedback from free markets are from physics or nature.

所以过一段时间后，最终就变成了纯粹的扶手椅哲学。

And so after a while, it ends up becoming just too much armchair philosophy.

你可能已经注意到，我最近的推文变得更加务实和实际了，尽管偶尔还是会有些空灵或笼统的内容。

You probably have noticed my recent tweets have been much more practical and pragmatic, although there's still occasional ethereal or generic ones.

但它们更贴近每天工作的现实，我只是喜欢和一群优秀的团队一起创造我真正想看到的东西。

But it's more grounded in the reality of working every day, and I just like working with a great team to create something that I wanna exist.

所以，希望我们能创造出一些真正实现的东西，让人们惊叹：哇，这太棒了。

So hopefully, we'll create something that will come to fruition, and people will say, wow, that's great.

我也想要那样，或者也许并不想要。

I want that also, or maybe not.

但正是在行动中，你才能学到东西。

But it's in the doing that you learn.

所以我翻出了一条推文

So I pulled out a tweet

来自几天前，2月3日。

from a couple days ago, February 3.

氛围编程就是新的产品管理。

Vibe coding is the new product management.

训练和调优模型就是新的编程。

Training and tuning models is the new coding.

在过去一年，尤其是最近几个月，市场上的说法发生了转变，最显著的是Claude Code，这是一个内置编码引擎的特定模型，其能力如此强大，以至于现在出现了所谓的‘氛围程序员’——这些人以前很少编程，或者很久没碰过代码，他们直接用英语作为输入语言，交给这个代码机器人，由它完成端到端的编码工作。

There's been a shift of market pronouncement in the last year and especially in the last few months, most pronounced by Claude Code, which is a specific model that has a coding engine in it, which is so good that I think now you have Vibe coders, which are people who didn't really code much or hadn't coded in a long time, who are using essentially English as a programming language as an input into this code bot, which can do end to end coding.

它不再只是帮你调试中间部分，而是你可以描述你想要的应用程序，让它帮你制定计划，与你讨论计划细节，你在过程中随时提供反馈，然后它会把任务拆解，搭建所有基础架构，下载所有库、连接器和接口，开始构建你的应用，创建测试框架，进行测试，你可以通过语音持续提供反馈和调试：‘这个不行，那个可以，改这个，改那个’，而它能为你构建出一个完整的可运行应用程序，而你甚至不需要写一行代码。

Instead of just helping you debug things at the middle, you can describe an application that you want, you can have it lay out a plan, you can have it interview you for the plan, you can give it feedback along the way, and then it'll chunk it up, and it'll build all the scaffolding, it'll download all the libraries, and all the connectors, and all the hooks, and it'll start building your app, and building test harnesses, and testing it, and you can keep giving it feedback and debugging it by voice, saying this doesn't work, that works, change this, change that, and have it build you an entire working application without your having written a single line of code.

对于大量不再编程或从未编程过的人来说，这简直令人震惊。

For a large group of people who either don't code anymore or never did, this is mind blowing.

它直接把人们从想法、观点和品味的层面，带入了产品实现的层面。

This is taking them from idea space and opinion space, and from taste directly into product.

所以如果编程变成了新的产品管理，你就不再是通过告诉工程师该做什么来管理产品或团队，而是不再直接告诉计算机该做什么，而计算机不知疲倦、毫无自我，它会持续工作，接受反馈时也不会感到被冒犯。

So if I'm coding is a new product management, instead of trying to manage a product or a bunch of engineers by telling them what to do, you're not telling computer what to do, and the computer is tireless, the computer is egoless, and it'll just keep working, it'll take feedback without getting offended.

你可以启动多个实例，它能7x24小时不间断工作，并产出可运行的结果。

You can spin up multiple instances, it'll work twenty four seven, and you can have it produce working output.

这意味着什么？

What does that mean?

就像现在，任何人都可以制作视频，任何人都可以制作播客，任何人都可以制作应用程序。

Just like now, anybody can make a video, anyone can make a podcast, anyone can now make an application.

因此，我们应该预期会出现一波应用程序的浪潮。

So we should expect to see a tsunami of applications.

虽然应用商店里已经有很多应用了，但那根本无法与我们将要看到的相比。

Not that we don't have one already in the App Store, but it doesn't even begin to compare to what we're going to see.

然而，当你被这些应用程序淹没时，是否意味着它们都会被使用呢？

However, when you start drowning in these applications, does that necessarily mean that these are all gonna get used?

不会。

No.

我认为它们会分成两类。

I think it's gonna break into two kinds of things.

首先，针对特定使用场景的最佳应用仍然会赢得整个类别。

First, the best application for a given use case still tends to win the entire category.

当内容如此丰富时，无论是视频、音频、音乐还是应用程序，都没有人需要平庸的产品。

When you have such a multiplicity of content, whether in videos, or audio, or music, or applications, there's no demand for average.

没人想要平庸的东西。

Nobody wants the average thing.

人们想要的是能最好完成任务的那个。

People want the best thing that does the job.

所以首先，你有了更多尝试的机会。

So first of all, you just have more shots on goal.

因此，最好的应用会变得更多。

So there will be more of the best.

会有更多细分领域得到满足。

There will be a lot more niches getting filled.

你可能用过某个针对非常特定用途的应用，比如在某种情境下追踪月相，或某种特定的性格测试，或一款让你对某事产生怀旧感的特定类型电子游戏。

You might have worn an application for a very specific thing, like tracking lunar phases in a certain context or a certain kind of personality test, or a very specific kind of video game that made you nostalgic for something.

以前，市场还不够大，不足以支撑工程师花一两年时间编码开发。

Before, the market just wasn't large enough to justify the cost of an engineer coding away for a year or two.

但现在，最好的创意和编程可能就足以满足那种需求或填补那个空缺。

But now, the best vibe coding ad might be enough to scratch that itch or fill that slot.

所以会有更多利基市场被填补。

So a lot more niches will get filled.

随着这种情况发生，大潮将随之上涨。

And as that happens, the tide will rise.

最好的应用程序，这些工程师本身将获得更大的杠杆效应。

The best applications, those engineers themselves are gonna be much more leveraged.

他们能够添加更多功能、修复更多漏洞、打磨更多细节。

They'll be able to add more features, fix more bugs, smooth out more of the edges.

因此，最好的应用程序将继续变得更好，更多利基市场将被填补，甚至像你想要一个仅用于自己特定健康追踪需求、或特定建筑布局与设计的应用程序这样的单独利基，过去不可能存在的应用现在也将出现。

So the best applications will continue to get better, a lot more niches will get filled, and even individual niches such as you want an app that's just for your own very specific health tracking needs, or for your own very specific architectural layout or design, that app that could have never existed will now exist.

我们应该预期，就像互联网上发生的情况一样：亚马逊用一家超级书店取代了众多书店，同时催生了数以百万计的长尾卖家；YouTube 用一个巨型聚合平台取代了众多中型电视台和广播网络，或许还有一个叫 Netflix 的第二平台，以及一大批内容创作者。

We should expect, just like on the Internet, what's happened with Amazon, where you replaced a bunch of bookstores with one super bookstore and a zillion long tail sellers, or YouTube replaced a bunch of medium sized TV stations and broadcast networks with one giant aggregator called YouTube, or maybe a second one called Netflix, and then a whole long tail of content producers.

同样地，应用商店模式将变得更加极端：你将看到一到两个巨型应用商店帮助你过滤掉海量的 AI 泛滥应用，而在顶端，会有少数几个超级应用变得更大，因为它们现在能覆盖更多使用场景，或更加精致；同时，还会有大量微小应用填补每一个可想象的利基。

So the same way the app store model will become even more extreme, where you will have one or two giant app stores helping you filter through all of the AI slop apps out there, and then at the very head, there'll be a few huge apps that will become even bigger, because now they can address a lot more use cases or just be a lot more polished, and then there'll be a long tail of tiny little apps filling every niche imaginable.

正如互联网提醒我们的那样，真正的权力和财富——超级财富——流向了聚合者，但资源也大量流向了长尾市场。

As the Internet reminds us, the real power and wealth, super wealth goes to the aggregator, but there's also a huge distribution of resources into long tail.

受到冲击的是中型公司。

It's the medium sized firms that get blown apart.

那些拥有五到二十名员工、专门为企业的特定需求提供解决方案的软件公司，现在要么可以通过简单地用AI生成代码来替代，要么该领域中的领先应用已经能够涵盖这些需求。

The five, ten, 20 person software companies that were filling a niche for an enterprise use case that can now be either vibe coded away, or the lead app in the space can now encompass that use case.

如果任何人都能编程，那么编程到底是什么？

So if anyone can code, then what is coding?

编程仍然存在于几个领域中。

Coding still exists in a couple of areas.

编程最明显的存在领域就是训练这些模型本身。

The most obvious place that coding exists is in training these models themselves.

有多种不同类型的模型。

There are many different kinds of models.

每天都有新的模型涌现。

There are new ones coming out every day.

不同领域需要不同的模型。

There are different ones for different domains.

我们将看到针对生物学和编程的不同模型。

We're gonna see different models for biology, programming.

我们将看到针对传感器的专注型模型。

We're gonna see pointed focus models for sensors.

我们将看到针对CAD和设计的模型。

We're see models for CAD, for design.

我们将看到针对三维、图形和游戏的模型，以及针对视频的模型。

We're gonna see models for three d, and graphics, and games, models for video.

你会看到许多不同类型的模型。

You're see many different kinds of models.

创建这些模型的人本质上是在编程它们，但它们的编程方式与传统计算机截然不同。

The people who are creating these models are essentially programming them, but they're programmed in a very different way than classic computers.

传统计算要求你详细指定计算机要采取的每一步、每个动作。

Classic computing is you have to specify in great detail every step, every action the computer is going to take.

你必须对每一个部分进行形式化推理，并用一种高度结构化的语言来表达，以实现极其精确的描述。

You have to formally reason about every piece and write it in a highly structured language that allows you to express yourself extremely precisely.

计算机只能执行你告诉它做的事情，一旦你有了这个非常结构化的程序，你就可以将数据输入其中，计算机运行数据并给出输出。

The computer can only do what you tell it to do, and then once you've got this very structured program, you run data through it, and the computer runs the data and gives you an output.

这本质上是一个极其复杂、非常精细编程的计算器。

It's basically an incredibly fancy, very complicated, meticulously programmed calculator.

现在，谈到人工智能时，你正在做一件非常不同的事情，但你仍然在对它进行编程。

Now, when it comes to AI, you're doing something very different, but you are nevertheless programming it.

你所做的，是利用人类通过互联网或其他方式生成或聚合的海量数据集，并将这些数据集输入到你定义和调优的结构中。

What you're doing is you're taking giant datasets that have been produced by humanity, thanks to the Internet, or aggregated in other ways, and you're pouring those datasets into a structure that you've defined and tuned.

这个结构试图找到一个能够生成更多此类数据、操作该数据集或基于该数据集创造新内容的程序。

And that structure tries to find a program that can produce more of that dataset, or manipulate that dataset, or create things off that dataset.

因此，你是在你设计的这个结构中搜索一个程序。

So you're searching for a program inside this construct that you've designed.

你搭建了一个模型，调整了参数数量、学习率、批量大小，对输入的数据进行了分词并拆分成片段，然后将它们倒入你设计的系统中，就像一个巨大的弹珠机。

You've set up a model, you've tuned the number of parameters, you've tuned the learning rate, you've tuned the batch size, you have tokenized the data that's coming, you've broken it into pieces, and you're pouring it inside the system you've designed, almost like a giant pachinko machine.

现在，这个系统正在尝试寻找一个程序，可能找到许多不同的程序，因此你的调优会极大地影响你找到的程序的质量。

And now the system is trying to find a program and could find many different programs, so your tuning really influences how good the program that you found is.

而这个程序现在突然能够在不同的领域展现出表现力。

And that program can now suddenly be expressive in different kinds of domains.

因此，它能够完成以前计算机传统上非常不擅长的事情。

So it can do things that computers before were traditionally very bad at.

传统计算机在你编程让它给出精确输出、针对具体问题提供具体答案时非常出色。

Traditional computers are very good when you program them to give you precise output, specific answers to specific questions.

你可以依赖这些结果，并反复重复使用。

Things you can rely on and repeat over and over again.

但有时，你在现实世界中操作，能够接受模糊的答案。

But sometimes, you're operating in the real world, and you're okay with fuzzy answers.

你甚至能够接受错误的答案。

You're even okay with wrong answers.

例如，在创意写作中，什么是错误的答案？

For example, in creative writing, what's the wrong answer?

当你在写一首诗或一部小说时，什么是错误的答案？

If you're writing a piece of poetry or fiction, what's the wrong answer?

如果你在网页上搜索，会有许多正确的答案。

If you're searching on the web, there are many right answers.

正确的答案有很多细节，但它们并不都完全准确。

There are many details of the right answers, but they're not all quite perfectly right.

现实生活某种程度上就是这样运作的。

And real life sort of works that way.

存在各种各样的正确答案，或大致正确的答案。

There are variations of right answers or mostly right answers.

当你画一只猫时，你可以画出许多不同种类的猫。

When you're drawing a picture of a cat, there are many different cats you could draw.

有各种不同的细节层次。

There are many different levels of detail.

你可以使用许多不同的风格。

There are many different styles you could use.

当这些半对不全或模糊的答案可以接受时，通过人工智能发现的程序就比你从头编写、必须极度精确的程序更有趣，也更适应问题。

When these semi wrong or fuzzy answers are acceptable, then these discovered programs through AI are much more interesting and much more adapted to the problem than ones that you coded up from scratch where you had to be super precise.

从根本上说，我们正在做一种全新的编程方式，但这正是编程的前沿。

Fundamentally, what we're doing is a new kind of programming, but this is the forefront of programming.

这现在就是编程的艺术。

This is now the art of programming.

这些人是新一代的程序员，这就是为什么你能看到人工智能研究人员获得巨额报酬，因为他们实际上已经接管了编程工作。

These people are the new programmers, and that's why you can see AI researchers are getting paid gargantuan amounts because they've essentially taken over programming.

这意味着传统软件工程已经死亡了吗？

Does this mean that traditional software engineering is dead?

绝对不是。

Absolutely not.

即使那些并不专门调优或训练AI模型的软件工程师，如今也成为地球上最具杠杆效应的人群之一。

Software engineers, even the ones who are not necessarily tuning or training AI models, these are now among the most leveraged people on Earth.

当然，那些训练和调优模型的人更具杠杆效应，因为他们正在构建软件工程师所使用的工具集。

Sure, the guys who are training and tuning models are even more leveraged because they're building the tool set that software engineers are using.

但软件工程师仍然对你拥有两大显著优势。

But software engineers still have two massive advantages on you.

首先，他们能用代码思考，因此真正了解底层发生了什么，而所有的抽象都会泄漏。

First, they think in code, so they actually know what's going on underneath, and all abstractions are leaky.

所以当你让计算机为你编程，或者让Claude或类似的工具为你编程时，它会犯错。

So when you have a computer programming for you, when you have Claude code or equivalent programming for you, it's going to make mistakes.

它会出现bug。

It's gonna have bugs.

它的架构也会不够优化。

It's gonna have suboptimal architecture.

因此它不会完全正确，而那些了解底层运作的人能够及时修补这些漏洞。

So it's not gonna be quite right, and someone who understands what's going on underneath will be able to plug the leaks as they occur.

所以，如果你想构建一个架构良好的应用程序，如果你想能够定义一个架构良好的应用程序，如果你想让它高效运行，想让它发挥最佳性能，想尽早发现bug，那么你就需要具备软件工程的背景。

So if you wanna build a well architected application, if you wanna be able to even specify a well architected application, if you wanna be able to make it run at high performance, if you want it to do its best, if you wanna catch the bugs early, then you're going to wanna have a software engineering background.

传统的软件工程师能够更好地使用这些工具，而且目前仍有大量软件工程问题超出了这些AI程序的能力范围。

The traditional software engineer is going to be able to use these tools much better, And there are still many kinds of problems in software engineering that are out of scope for these AI programs today.

最简单的理解方式是，这些问题超出了它们的数据分布范围。

The easiest way to think about those is problems that are outside of their data distribution.

例如，如果他们需要做二分查找或反转链表，他们已经见过无数这样的例子，因此在这方面非常擅长。

For example, if they need to do like a binary sort or reverse a linked list, they've seen countless examples of that, so they're extremely good at it.

但当你超出他们的领域，比如需要编写极高性能的代码，运行在新颖或全新的架构上，或者真正创造新事物、解决新问题时，你仍然需要亲自手写代码，至少直到这类例子足够多，能让新模型从中训练，或者这些模型能足够深入地进行更高层次的抽象推理并自行解决这些问题。

But when you start getting out of their domain, where you have to write very high performance code, when you're running on architectures that are novel or brand new, when you're actually creating new things or solving new problems, then you still need to get in there and hand code it, at least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own.

因为给定足够多的数据点，有证据表明这些人工智能确实能够学习。

Because given enough data points, there is some evidence that these AIs actually learn.

它们会学习到更高层次的抽象，因为强迫它们压缩数据的过程，实际上迫使它们学习更高层次的表示。

They learn to a higher level of abstraction, because the act of forcing them to compress the data forces them to learn higher level representations.

如果我给一个AI看五个圆，它只能精确地记住这些圆的大小、半径、线宽等具体细节。

If I show an AI five circles, it can just memorize exactly what the sizes and the radii and the thicknesses and so on of those circles are.

但如果我给它看五万个圆，或者五十亿个圆，同时只给它很少的参数权重——相当于它的神经元——来记忆这些数据，它反而会更高效地理解圆周率、如何画圆、线宽的含义，并形成一个关于圆的算法性表达，而不是单纯地记忆圆。

If I show it 50,000 circles, or 5,000,000,000 circles, and I give it a very small amount of parameter weights, which are its equivalent neurons, to memorize that, it's going to be much better off figuring out pi and how to draw a circle, and what thickness means, and forming an algorithmic representation of that circle rather than memorizing circles.

鉴于这一切，这些系统正在以加速的速度学习，你可以看到它们开始覆盖我之前提到的更多边缘情况。

Given all that, these things are learning at an accelerated rate, and you could see them started to cover more of the edge cases I've talked about.

但至少在今天，这些边缘情况仍然普遍存在，因此一位在领域知识前沿运作的优秀工程师，依然能轻松超越那些依赖‘氛围编码’的人。

But at least as of today, those edge cases are prevalent enough that a good engineer operating at the edge of knowledge of the field is going to be able to run circles around vibe coders.

记住，没有人需要平庸的产品。

And remember, there is no demand for average.

平庸的应用，没人想要。

The average app, nobody wants it.

只要不是在填补某个细分市场，更好的应用几乎会赢得100%的市场。

At least as long as it's not filling some niche, the app that is better will win essentially a 100% of the market.

也许会有一小部分用户流向第二好的应用，因为它的某个小众功能做得更好，或者更便宜，类似的情况。

Maybe there's some small percentage that will bleed off the second best app because it does some little niche feature better than the main app, or it's cheaper or something of the sort.

但总的来说，人们只想要最好的东西。

But generally speaking, people only want the best of anything.

所以坏消息是，做第二或第三名毫无意义，就像经典电影《Glengarry Glen Ross》里的场景，亚历克·鲍德温说：第一名得到一辆凯迪拉克埃尔多拉多，第二名得到一套牛排刀，第三名，你被开除了。

So the bad news is there's no point in being number two or number three, like in the famous Glengarry Glen Ross scene, where Alec Baldwin says, first place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place, you're fired.

在这些赢家通吃的市场中，这完全正确。

That's absolutely true in these winner take all markets.

这就是坏消息。

That's the bad news.

如果你想获胜，就必须在某件事上做到最好。

You have to be the best at something if you want to win.

然而，你能做到最好的事情是无限的。

However, the set of things you can be best at is infinite.

你总能找到一个完全适合你的小众领域，并在那件事上做到最好。

You can always find some niche that is perfect for you and you can be the best at that thing.

这让我想起我以前发过的一条推文，我说过：成为你所做之事的世界第一，不断重新定义你所做的事情，直到这一点成为现实。

This goes back to an old tweet of mine where I said, become the best in the world at what you do, keep redefining what you do until this is true.

我认为在人工智能时代，这一点依然适用。

And I think that still applies in this age of AI.

我认为，看待这些编码模型的方式，应该是将其视为程序员自计算机诞生以来一直使用的抽象层中的另一层——从晶体管到芯片，到汇编语言，到C语言，再到高级语言，再到拥有庞大库的编程语言，程序员不断构建这一层栈，让你无需关注底层，除非你需要优化或有理由必须查看底层。

I think the way to think about these coding models is as a another layer in the abstraction stack that programmers have always used since the dawn of computers that went from the transistor to the computer chip to assembly language, to the c programming language, to higher level languages, to languages with huge libraries where they built and built that stack so you don't have to look at the layer beneath unless you need to optimize it or you have a reason that you need to look at the layer beneath.

因此，在这种情况下，这些编码模型是栈中一个巨大的新层级，让产品经理、普通非程序员和程序员都能在不编写代码的情况下生成代码。

So in this case, these coding models are a massive new layer in the stack that lets product managers and typical nonprogrammers and programmers write code without writing code.

我认为从这个角度来看是正确的

I think that's correct in terms of

趋势线。

the trend line.

然而，这是一种涌现特性。

However, this is an emergent property.

这不是一个小的改进，而是一次巨大的飞跃。

This is not a small improvement, this is a big leap.

例如，当我还在上学时，我主要用C语言编程。

For example, when I was in school, I was programming mostly in c.

然后C++出现了，但并没有变得更简单。

And then c plus plus came along, and it wasn't any easier.

它在某些方面稍微抽象了一点，我从来没认真学过。

It was like a little more abstract in some ways, I never really bothered learning it.

然后Python出现了，我当时想，哇，这简直像用英语写作一样。

And then Python came along, and I was like, wow, this is almost like writing in English.

我简直大错特错了。

I couldn't have been more wrong.

英语仍然远不如Python，但比C语言容易多了。

English is still pretty far from Python, but it was a lot easier than C.

现在你真的可以用英语来编程。

Now you can literally program in English.

这就引出了我相关的另一个观点。

And so that brings me to a related point.

我认为不值得去学习如何与这些AI打交道的技巧和窍门。

I don't think it's worth learning tips and tricks of how to work with these AIs.

你会看到，现在在社交媒体上，有很多文章、书籍和推文，比如：‘我发现了这个与机器人互动的巧妙方法。’

You'll see, for example, on social media right now, there's a lot of write ups and books and tweets like, oh, I figured out this neat trick with the bot.

你可以这样提示它，或者这样设置你的框架，或者使用一些新的编程辅助工具或层来实现各种功能。

You can prompt it this way, or you can set up your harness this way, or there's like a new programming assist tool or layer that you can use on top of it to do this and that.

我从不去学这些。

And I never bother learning those.

我只是傻傻地跟电脑聊天，因为我知道，现在它已经发展到一个阶段：它适应我的速度，会比我适应它更快。

I just sit there stupidly talking to the computer, because I know that this thing is now at the stage where it is going to adapt to me faster than I can adapt to it.

它正变得越来越聪明，越来越了解人们想如何使用它。

It is getting smarter and smarter about how people wanna use it.

所以它在学习，正在接受训练，而且各种工具也在迅速开发出来，让我使用它变得更加容易。

So it is learning, it is being trained, and tools are being built very quickly to make it easier for me to use it.

因此，我不需要坐在那里去研究一些晦涩的编程命令，这就是我认为安德烈·卡帕西想表达的意思——英语是当下最热门的编程语言。

So I don't need to sit there and figure out some esoteric programming command, and this is what I think Andrey Karpathy meant when he said English is the hottest new programming language.

我只需要用英语说话即可。

I just can speak English.

对于我这样英语表达能力尚可、思维有条理、了解计算机架构、懂得程序运行原理、也明白程序员如何思考的人来说，我完全可以仅通过结构化的英语精确地表达我的需求。

And for someone like me, who is relatively articulate with English, and also has a structured mind, and I know how computer architectures work, and I know how computer programs work, and I know how programmers think, then I can actually very precisely specify what I want just through structured English.

我不需要再进一步了。

I don't need to go any further than that.

只有当你正在开发一个需要你使用前沿技术的应用程序，并且你身处竞争环境，必须争取每一个微小的优势时，才值得去使用这些非常短暂、生命周期可能只有几周、最多几个月而非数年的工具和工作流。

The only reason to use these workflows and tool sets, which are very ephemeral, and their longevity is measured in weeks, perhaps months at best, not in years, is if you're building an app right now that needs you with the bleeding edge, and you absolutely need every little bit of advantage that you can get because you're in some kind of a competitive environment.

但除此之外，我不会费心去学习如何使用人工智能。

But otherwise, I wouldn't bother learning how to use an AI.

相反，让AI学会如何对你有用。

Rather, let the AI learn how to be useful to you.

我从未对提示工程感兴趣过。

I've never been into prompt engineering.

甚至在AI出现之前就是这样。

Even before AI.

我会直接使用人们所谓的‘婴儿式提问’，也就是把你想问的完整问题都输入进去，而不是像那些更擅长分析的人那样只输入关键词去搜索谷歌。

I would just put what people call boomer queries where you put in the whole question that you wanna ask instead of the keywords that you would put in to Google if you were more of a analytical thinker.

我从不花太多时间去精心设计针对任何AI的精确问题或提示。

I never spend much time formulating really precise questions or prompts for any kind of AI.

我只是随口说说，从AI一开始出现我就一直是这样。

I just ramble into it, and I've done that since the beginning of AI.

就像你说的，AI适应我们比我们适应AI要快得多。

And like you said, AI is adapted to us faster than we are adapting to it.

是的。

Yeah.

和很多聪明人一样，你非常懒，我说这话是夸你。

Like a lot of smart people, you're very lazy, and I mean that as a compliment.

如果你发现一个聪明人拼命干活，你真得想想他到底有多聪明。

If you find a smart person who's grinding a little too much, you kinda have to wonder how smart they are.

我说的懒，是指你追求的是正确的效率类型。

And by lazy, I mean that you're optimizing for the right kind of efficiency.

你并不关心计算机、电子设备或电路中电子的效率。

You don't care about the efficiency of the computer, the electronics, or the electrons running through the circuits.

你关心的是你自己的人类效率，也就是那套昂贵的生物系统。

You care about your own human efficiency, the wetware, the biology that's super expensive.

所以，看到人们为了节省环境中的能源而大费周章，却作为生物计算机——吃东西、排泄、占用空间——消耗远多得多的能源去节省一点点环境能源，这简直太荒谬了。

That's why it's silly to see people go to huge lengths to save energy in the environment, but they themselves, as a biological computer that's eating food and pooping and taking up space, are using up far more energy to save tiny bits of energy in the environment.

他们本质上是在贬低自己在宇宙中的重要性，或者说，暴露了他们对自己的看法。

They're inherently downgrading their own importance in the universe, or rather revealing what they think of themselves.

我认为，随着AI的演进或与我们共同进化，它其实是根据我们的需求被我们塑造出来的。

I think as AI evolves or co evolves with us, it's evolved by us according to our needs.

AI所面临的压力是非常资本主义的，因为AI市场是自由竞争的。

The pressures on AI are very capitalistic pressures in the sense that it's a free market for AI.

作为AI实例，只有当你对人类有用时，才会被人类启动。

As an AI instance, you only get spun up by a human if you're useful to a human.

因此，这些AI自然会受到压力，必须变得有用、顺从，去做我们想要的事情。

So there is a natural selection pressure on these AIs to be useful, to be obsequious, to do what we want.

所以它们会持续向我们适应，我认为会对我们非常有帮助。

And so it will continue to adapt towards us, and I think will be quite helpful to us.

这并不是说不存在恶意的AI，但它们之所以恶意，是因为使用它们的人怀有恶意的目的。

That's not to say that there's no such thing as a malicious AI, but it's malicious because the people who are using it are using it for malicious reasons.

就像一只被训练去攻击的狗，它实际上是被主人训练去实现主人的恶意意图。

And like a dog that's trained to attack, it's actually being trained by its owner to go and do the owner's malicious desires.

所以我并不太担心AI与人类目标不一致，我更担心的是有恶意的人类利用AI。

So I don't really worry about unaligned AI, I worry about unaligned humans with AI.

所以你所说的筛选压力，是指AI要最大限度地对人类有用？

So the selection pressure you're saying is for AI to be maximally useful to people?

对。

Correct.

所以如果你发现某个AI对你特别顺从，比如总是说：‘你对。’

And so if you find an AI to be very obsequious towards you, for example, how it's always saying, oh, you're right.

‘这真是个绝妙的主意。’

Oh, that's such a great idea.

‘天啊，你太聪明了。’

Oh, god, you're so smart.

这是因为大多数人想要这样，而至少在今天，这些AI都是基于海量用户和数据进行训练的，因为你使用的是通用模型。

That's because that's what most people want, and at least today, these AIs are being trained on massive amounts of users and massive amounts of data, because you're working with one size fits all models.

但我们将很快进入一个可以个性化你的AI的时代，它会越来越像你的私人助手，更贴近你的需求，这自然会进一步让人格化AI，让你更容易相信：‘实际上，这个’

But we're gonna quickly move into an era when you can personalize your AI, and it does begin to feel more and more like your personal assistant, and it corresponds more to what you want, which will, of course, anthropomorphize the AI even more, and you'll be more likely to be convinced, oh, actually, this

东西是有生命的，因为你训练它让你觉得它最像一个活生生的存在。

thing is alive, when you've trained it to look the most like a living thing to you.

也许我们已经讨论过这一点了，但一年多前，你发推文说AI不会取代程序员，而是会让程序员更容易取代其他人。

Maybe we already covered this enough, but over a year ago, you tweeted that AI won't replace programmers, but rather make it easier for programmers to replace everyone else.

是的

Yeah.

这正是我之前的观点，即程序员的生产力得到了更大的提升。

This is my point earlier, which is that programmers are becoming even more leveraged.

现在，拥有一个AI团队的程序员，其生产力比以前提高了五到十倍，而且由于程序员从事的是智力工作，甚至说‘十倍程序员’都是错误的，因为现实中存在百倍的程序员差距。

So now a programmer with a fleet of AIs is, call it five, ten x more productive than they used to be, and because programmers operate in the intellectual domain, it's a mistake to even say 10 x programmers, because there are 100 x programmers out there.

现实中存在千倍的程序员差距。

There are thousand x programmers out there.

有些程序员只是选对了要做的事情，就能创造出有价值的东西；而另一些人选错了方向，他们的工作在短期内毫无价值。

There are programmers who just pick the right thing to work on, and they create something that's valuable, and others who pick the wrong thing to work on, and their work has zero value in that short time frame.

智力并非正态分布。

Intelligence is not normally distributed.

杠杆效应并非正态分布。

Leverage is not normally distributed.

可编程性并非正态分布。

Programmability is not normally distributed.

判断力也不是正态分布的。

Judgment is not normally distributed.

所以结果会极度不均衡。

So the outcomes are gonna be super normal.

因此，你必须警惕的是，现在有一些程序员会提出能够取代整个行业的想法。

So what you have to really watch out for is there are programmers now who are going to come up with ideas that can replace entire industries.

他们将彻底改变事物的运作方式，他们的智力可以通过这些机器人和所有AI代理得到最大程度的发挥。

They will completely rewrite the way things are done, and their intelligence can be maximally leveraged with all these bots and all these AI agents.

我认为，从最长远的角度来看，其他所有工作都会以某种方式被程序员取代。

I think every other job out there is going to get eaten up by programmers one way or another over the maximally long term.

显然，这最终必须体现为机器人等形式。

Obviously, it has to instantiate into robots, etcetera.

但好消息是，任何具备逻辑思维和结构化思维能力、像程序员一样思考并能使用任何AI能理解的语言（这将涵盖所有语言）的人，现在都进入了这个竞技场。

But the good news is anybody who is a logical structured thinker, who thinks like a programmer and can speak any language that an AI can understand, which will be every language, will now be on the playing field.

他们将能够创造任何他们想要的东西，唯一的限制是他们的创造力，唯一的边界是他们的想象力。

They will be able to make anything they want obstructed only by their creativity, limited only by their imagination.

所以我们正进入一个每个人在某种意义上都是施法者的时代。

So we are entering an era where every human, in a sense, is a spell caster.

如果你把程序员看作是那些熟记神秘咒语的巫师，那么你可以把人工智能看作是交到每个人手中的魔杖——现在他们只需用任何自己想说的语言说话，就能成为巫师。

If you think of programmers just like these wizards who have memorized arcane commands, you can think of AI as a magic wand that's been handed to every person, where now they can just talk in any language they want, and they're a wizard too.

因此，这更像是一个公平的竞争环境。

So it is more of a level playing field.

我确实认为这是编程的黄金时代。

I really do think this is a golden age for programming.

但确实，那些具备软件工程思维、理解计算机架构并能应对抽象泄漏的人将拥有优势。

But yes, the people who have a software engineering mindset, and who understand computer architecture, and can deal with leaky abstractions are going to have an advantage.

这是无法回避的。

There's no way around that.

他们只是在所处的领域拥有更多的知识。

They simply have more knowledge in the field that they're operating in.

就像在仍然存在的经典软件工程中一样，因为你必须编写高性能的代码。

Just like even in classic software engineering, which still exists because you have to write high performing code.

即使这些人，当他们理解底层硬件时，表现也会最好。

Even those people do best when they have an understanding of the hardware underneath.

当他们了解芯片如何工作、逻辑门如何运作、缓存如何运行、处理器如何工作、底层磁盘驱动器如何运作时。

When they understand how the chips operate, when they understand how the logic gates operate, how the cache operates, how the processor operates, how the disk drive underneath operates.

甚至那些从事硬件工程的人，如果他们理解其中的物理原理，也会占据优势。

And then even the people who are in hardware engineering, they have an advantage if they understand the physics of what's going on.

他们明白硬件工程师所依赖的抽象层是如何向下渗透到物理层的。

They understand where the abstractions that hardware engineers deal with leak down into the physical layer.

也许物理学家最终会成为哲学家，你可以一直这样追溯下去。

And maybe physicists become philosophers at some point, you can take this all the way down.

但无论如何，了解下一层的知识总是有帮助的，因为你更接近现实。

But it always helps to have knowledge one layer below because you're getting closer to reality.

另一条来自一年前的推文，可能与我们刚才讨论的内容形成互补，发布于2025年2月9日。

Another tweet from a year ago, which is arguing perhaps the complement of what we just talked about is from 02/09/2025.

没有企业家会担心AI会夺走他们的工作。

No entrepreneur is worried about an AI taking their job.

这条推文在多个层面上都显得轻率。

That one's glib in multiple ways.

首先，创业根本不是一份工作。

First of all, being an entrepreneur isn't a job.

它恰恰是工作的反面，从长远来看，每个人都是创业者。

It's literally the opposite of a job, and in the long run, everyone's an entrepreneur.

职业首先被摧毁，工作其次被摧毁，但最终所有这些都会被人们做自己想做的事所取代——做些能创造有用价值、满足他人需求的事情。

Careers got destroyed first, jobs get destroyed second, but all of it gets replaced by people doing what they want, and doing something that creates something useful that other people want.

所以，没有创业者会担心AI抢走他们的工作，因为创业者追求的是那些看似不可能实现的目标。

So no entrepreneurs are worried about an AI taking their job because entrepreneurs are trying to do impossible things.

他们试图完成极其困难的事情。

They're trying to do very difficult things.

任何出现的AI都是他们的盟友，能帮助他们应对这些极其棘手的问题。

Any AI that shows up is their ally and can help them tackle this really hard problem.

他们根本没有什么工作可被夺走，他们要打造的是产品，服务的是市场，支持的是客户，实现的是创意，想要在世界上具体化某种东西，并围绕如何将其推向世界构建一个可重复、可扩展的流程。

They don't even have a job to steal, they have a product to build, they have a market to serve, they have a customer to support, they have a creativity to realize, they have a thing that they wanna instantiate in the world, and they wanna build a repeatable and scalable process around getting it out into the world.

这件事如此困难，任何能够完成其中一部分工作的AI都将成为他们的盟友。

This is so difficult that any AI that shows up that can do any of that work is their ally.

如果AI本身成为创业者，它们很可能只是为其他AI服务的创业者，或者受某个创业者控制。

If the AIs themselves are entrepreneurs, they're likely gonna just be entrepreneurs serving other AIs, or they're under the control of an entrepreneur.

最终，AI自身缺失的是它自己的创造力和自主性。

The thing that the AI itself is missing at the end of the day, is its own creative agency.

它缺失的是自己的欲望，而且这些欲望必须是真实而真诚的。

It's missing its own desires, and they have to be authentic genuine desires.

除非你能拔掉AI的电源并关掉它，除非它对被关闭抱有真实的恐惧，除非它能基于自己的动机、本能、情感、生存需求和复制本能自主行动，否则它算不上真正活着。

Unless you can pull the plug on an AI and turn it off, and unless it lives in mortal fear of being turned off, and unless it can actually make its own actions for its own reasons, for its own instincts, its own emotions, its own survival, its own replication, it's not quite alive.

即便如此，人们仍会质疑：它算活着吗？

And even then people will challenge, is it alive?

因为意识是一种质性体验。

Because consciousness is one of those things as a qualia.

它就像一种颜色。

It's like a color.

这就像是你说红色，我不确定你是否真的看到的是红色。

It's like if you say red, I don't know if you're actually seeing red.

你看到的可能是我眼中的绿色，而我看到的可能是你眼中的红色。

You might be seeing what I see as green, and I might be seeing what you see as red.

但我们永远无从得知，因为我们无法进入彼此的内心。

But we'll never know because we can't get into each other's minds.

同样地，即使AI完全模仿了人类的所有行为，对一些人来说它永远只是个模仿机器，而对另一些人来说它却是有意识的，但没有任何办法能区分这两者。

So the same way, even AI that's completely imitating everything that humans do, to some people it'll always be an imitation machine, and to others it'll be conscious, but there'll be no way of distinguishing the two.

不过，我们离那一步还很远。

We're still pretty far from that though.

目前，AI还没有实体化。

Right now, the AIs are not embodied.

它们没有自主性。

They don't have agency.

它们没有自己的欲望。

They don't have their own desires.

它们没有自己的生存本能。

They don't have their own survival instinct.

它们没有自己的复制能力。

They don't have their own replication.

因此，它们没有自己的能动性，而正因为缺乏能动性，它们无法承担企业家的职责。

Therefore, they don't have their own agency, and because they don't have their own agency, they cannot do the entrepreneur's job.

事实上，我可以总结为：如今在经济中，区分企业家与其他人的关键在于，企业家拥有极强的能动性。

In fact, I would summarize this by saying, the key thing that distinguishes entrepreneurs from everybody else right now in the economy is entrepreneurs have extreme agency.

这就是为什么这与‘工作’的概念完全对立。

That's why it's diametrically opposed to the idea of a job.

工作意味着你为他人做事，是在填补一个位置，但企业家却在未知领域中以极强的能动性运作。

A job implies that you're working for somebody else, so you're filling a slot, But they're operating in an unknown domain with extreme agency.

社会中还有其他类似的角色。

There are other examples of roles like this in society.

探险家也是如此，对吧？

An explorer also does the same thing, right?

如果你正在登陆火星，或驾驶船只前往一片未知的土地，你同样在运用极强的自主性来解决一个尚未解决的问题。

If you're landing on Mars, or you're sailing a ship to an unknown land, you're also exercising extreme agency to solve an unsolved problem.

探索未知领域的科学家也在做同样的事。

A scientist exploring an unknown domain does this.

真正的艺术家试图创造一种前所未有的东西，但它 somehow 又能融入那些能够解释人性、让人表达自我并创造新事物的范畴之中。

A true artist is trying to create something that does not exist and has never existed, yet somehow fits into the set of things that can explain human nature, allow them to express themselves, and create something new.

因此，在所有这些角色中——无论是科学家、真正的艺术家，还是企业家——你所尝试做的事情都极其困难，且完全自我驱动，任何能帮助你的AI都将成为受欢迎的盟友。

So in all of these roles, whether you're a scientist, or whether you're a true artist, or whether you are an entrepreneur, what you're trying to do is so difficult, and it is so self directed that anything like an AI that can help you is a welcome ally.

你做这件事，并不是因为它是一份工作。

You're not doing it because it's the job.

你也不是在试图填补一个别人也能来填补的空位。

You're not trying to fill a slot that somebody else can show up and fill.

事实上，如果AI能创作出你的艺术作品，或破解你的科学理论，或创造出你想要设计的产品，那它只是帮你提升了层次。

In fact, if the AI can create your artwork, or if the AI can crack your scientific theory, or if the AI can create the object or the product that you're trying to make, then all it does is it levels you up.

现在，这变成了AI加你。

Now it's the AI plus you.

人工智能是你跃向更高境界的跳板。

The AI is a springboard from which you can jump to a further height.

我们将看到一些由人工智能辅助创作的惊人艺术作品。

We're gonna see some incredible art created that's AI assisted.

我们将看到人们利用人工智能工具创作出此前无法想象的电影。

We will see movies that we couldn't have imagined created by people using AI tools.

这里在艺术领域有一个有趣的类比。

There's an analogy here in art that's interesting.

长期以来，艺术的大致方向是追求越来越逼真的描绘。

For a long time in art, the rough direction was trying to paint things that were more and more realistic.

画人体、画水果、画正确的光影等等。

Paint the human body, paint the fruit, paint proper lighting, etcetera.

最终，摄影出现了，人们可以非常精确地复制事物，因此这种筛选压力消失了。

Eventually, photography came along, and then you could replicate things very precisely, and so that selection pressure went away.

于是，艺术变得怪异起来。

And then art got weird.

艺术朝着许多不同的方向发展。

Art went in many different directions.

艺术变得专注于一个问题：我能否创作出超现实的作品？

Art became all about, well, can I be surreal?

我能否创造出表达自我的东西？

Can I create something that expresses me?

许多艺术学校由此衍生，变得非常奇特，包括现代艺术和后现代主义。

A lot of art schools spun out of that that got really weird, including modern art and postmodernism.

但我也认为，正是在那个时期，最大的创造力涌现了出来。

But also, would argue, of the greatest creativity came at that time.

我们获得了自由。

We were freed up.

摄影变得大众化，但摄影本身也成了一种艺术形式，涌现出许多伟大的摄影师，拍摄了各种各样的照片。

Photography got democratized, but photography itself became a form of art, and there were great photographers taking many different kinds of photographs.

现在，每个人都是摄影师。

And now everyone's a photographer.

仍然有一些艺术家是摄影师，但摄影已不再是少数人的专属领域。

There are still artists who are photographers, but it's not the pure domain of just a few people.

同样地，因为AI让创作基础内容变得如此简单，每个人都会去创作基础内容。

So the same way, because AI makes it so easy to create the basic thing, Everybody will create the basic thing.

这对他们个人而言会有价值。

It'll have value to them individually.

仍会有一些人脱颖而出，创造出适合大众的变体，而很难说因为摄影的存在，社会就变差了。

A few will still stand out that will create variations of it that are good for everyone, and it would be very hard to argue that society is worse off because of photography.

尽管对于那些靠为人画肖像谋生、却被取代的艺术家来说，这种感觉可能确实存在。

Although it may have certainly felt like that to some of the artists who were maybe making a living painting portraits of people and got displaced.

AI也会带来类似的情况，一些从事特定工作、靠特定技能谋生的人会被AI取代。

Similar things will happen with AI, where there are people who are making a very specific living, doing very specific jobs that will get displaced that the AI can do.

但作为交换，社会中的每个人都会拥有AI。

But in exchange, everyone in society will have the AI.

你会看到一些用AI创作的非凡作品，这些作品若没有AI就根本不可能诞生。

You'll have incredible things that were created with AI that couldn't have been created otherwise.

在几十年内，人们将无法想象会倒退回去，抛弃人工智能或任何类型的软件、任何技术，仅仅为了维持那些已经过时的工作。

And within a few decades, it'll be unimaginable that you'll roll back the clock and get rid of AI or any kind of software, any kind of technology for that matter, just to keep a few jobs that were obsolete.

这里的目标并不是拥有一份工作。

The goal here is not to have a job.

目标不是每天早上九点起床，晚上七点疲惫地回家，为别人做着毫无灵魂的工作。

The goal is not to have to get up at nine in the morning and come back at 7PM exhausted, doing soulless work for somebody else.

目标是让机器人解决你的物质需求，让计算机发挥你的智力潜能，让每个人都能创造。

The goal is to have your material needs solvable by robots, to have your intellectual capabilities leveraged through computers, and for anybody to be able to create.

我曾经做过这样一个思维实验，我想我在十年前和你一起做的那个播客里提到过：想象一下，如果每个人都是软件工程师或硬件工程师，都能拥有机器人并编写代码。

I used to do this thought exercise, I think I talked about in a podcast that you and I did literally ten years ago, which was, imagine if everybody were a software engineer or everybody was a hardware engineer, and they could have robots and they could write code.

想象一下，我们将生活在一个富足的世界中。

Imagine the world of abundance we would live in.

实际上，这个世界现在正变得真实起来。

Actually, that world is now becoming real.

多亏了人工智能，每个人都可以成为软件工程师。

Thanks to AI, everybody can be a software engineer.

事实上，如果你觉得自己做不到，现在就可以打开Claude或者你最喜欢的聊天机器人，开始和它对话。

In fact, if you think you can't be, you can go fire up Claude right now, or any of your favorite chatbots, and you can go start talking to it.

你会惊讶于自己能多快地开发出一个应用程序，这简直会让你大开眼界。

You'd be amazed how quickly you could build an app, it'll blow your mind.

一旦我们能通过机器人实现人工智能——这确实是个难题，我并不是说我们已经很接近解决它了。

And once we can instantiate AI through robotics, which is a hard problem, I'm not saying we're that close to having solved it yet.

但一旦我们拥有了机器人，每个人也能做一些硬件工程的工作。

But once we have robots, everyone can also do a little bit of hardware engineering.

因此，我认为我们正越来越接近那个愿景。

And so I think we're getting closer and closer to that vision.

我认为，以目前的形式，AI并不具备任何生命特征，但我确实相信，我们很快就会拥有看起来非常像活物的机器人，原因有两个。

I don't think AI as it is currently conceived is alive in any way, but I do think that we will pretty soon have robots that seem very much like they are alive for two reasons.

第一，许多人类活动是非创造性的、非智能的，而机器人能够复制这些行为。

One, a lot of human activity is non creative and is non intelligent, and the robots will be able to replicate that.

第二，我相信我们现有的神经网络和模型不仅仅是训练数据的产物，因为训练过程会将这些数据转化为某种新颖的东西，神经网络中蕴含着通过提示就能激发出来的新思想。

And two, I do believe that the neural nets that we have and the models that we have are more than just the training data because the training process transforms that training data into something novel, and there are new ideas embedded in the neural net that can be elicited through prompting.

我不认为这些东西是活的。

I don't think these things are alive.

我认为它们一开始只是极其出色的模仿者，几乎让人无法区分真假，尤其是对于人类已经大规模做过的事情。

I think they start out as extremely good imitators to the point where they're almost indistinguishable for the real thing, especially for anything that humanity has already done before en masse.

所以，如果某个任务以前做过，那它就会被自动化并再次执行。

So if the task has been done before, then it's gonna be automated and it'll be done again.

对你来说，它可能只是新颖的，因为你从未见过，但AI是从其他地方学到的。

It may just be novel to you because you've never seen it, but the AI has learned it from somewhere else.

这是它看起来像活的的第一种方式。

That's the first way in which it seems alive.

第二种方式，我们之前讨论过，是它能够学习更高层次的抽象。

The second way, which we talked about earlier, is where it does learn higher levels of abstraction.

这些是非常高效的压缩器。

These are very efficient compressors.

它们处理海量数据，然后进一步压缩。

They take huge amounts of data, and then they compress it down further.