氛围编程：新的产品管理

嘿，我是 Nivei。你正在收听有史以来第一次不在同一地点的 Naval 播客。我实际上在镇上散步，Naval 可能也在做同样的事。所以可能会有一些环境噪音，但我们会尽力用 AI 和好的音频工程来消除它。播客录制太拘谨了，因为你得坐下来，安排时间，一个大麦克风对着你的脸，一点也不随意。这让它不那么真实，更刻意，更排练。我理解这可能会产生更高质量的音频和视频，但我觉得它会产生更低质量的对话。

Hey, this is Nivei. You're listening to the Naval podcast for the first time in recorded history. We are not at the same location. I am actually walking around town and Naval might be doing the same. So, there might be some ambient noise, but we are going to try hard to remove that with AI and some good audio engineering. Podcast recording is so stilted because it's like you have to sit down and you schedule something and this giant mic pointing in your face and it's not casual. It makes it just less authentic, more practiced, more rehearsed. I get that it produces maybe higher quality audio and video, but I feel like it produces lower quality conversation.

而且我们都知道，大脑在运动或散步时运转得更好。

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

绝对。我的大脑靠双腿驱动。

Absolutely. My brain is powered by my legs.

我挑了一些 Naval 关于 AI 的推文。我们想聊一点 AI，希望以更永恒的方式讨论，但我觉得有些内容可能不是永恒的。在我们进入推文之前，你想说说你在做什么，或者在 Impossible 做什么吗？

I pulled out some tweets from Naval on the topic of AI. We want to 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. Before we jump into the tweets, do you want to say anything about what you're doing with your time or what you're doing at Impossible?

不太想。我们正在做一个非常困难的项目，所以它叫 Impossible，团队很棒，重新开始建设真的很令人兴奋。从零开始非常纯粹，永远是第一天。我想我只是不满足于做投资者，我当然也不想成为哲学家或媒体人物或评论员，因为我觉得那些说得太多而不做事的人，他们没有接触现实，没有从自由市场或物理或自然中得到严厉的反馈。所以过了一段时间，这就会变成太多空泛的哲学。你可能注意到我最近的推文更实际、更务实了。虽然偶尔还有空灵或泛泛的，但更扎根于每天工作的现实。我只是喜欢和优秀的团队一起创造我想看到存在的东西。所以希望我们能创造出成果，人们会说‘哇，太棒了，我也想要。’或者也许不会。但正是在做事中你才能学习。

Not really. 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. It's very pure starting over from the bottom and it's always day one. I guess I just wasn't satisfied being an investor and I certainly don't want to be a philosopher or just a media personality or a commentator because I think people who just talk too much and don't do anything. They haven't encountered reality. They haven't gotten feedback the harsh feedback from free markets or from physics or nature. And so after a while it ends up becoming just too much archer philosophy. You'll probably have noticed my recent tweets have been much more practical and pragmatic. Although there's still occasional ethereal or generic ones, 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 want to see exist. So hopefully we'll create something that will come to fruition and people will say, 'Wow, that's great. I want that also.' Or maybe not. But it's in the doing that you learn.

所以我挑了一条几天前，2 月 3 日的推文。Vibe coding 是新的产品管理。训练和调优模型是新的编码。过去一年，尤其是最近几个月，市场发生了转变，最明显的是 Claude Code，这是一个特定的模型，内置了编码引擎，它非常出色，以至于现在有了 vibe coder——那些不太会编码或很久没编码的人，他们基本上用英语作为编程语言输入到这个代码机器人中，它可以做端到端的编码，而不仅仅是帮你调试。你可以描述你想要的应用程序。你可以让它制定计划。它可以就计划向你提问。你可以沿途给它反馈。然后它会分解任务，搭建所有脚手架，下载所有库、连接器和钩子。然后开始构建你的应用，构建测试框架并测试它。你可以通过语音不断给它反馈和调试，说这个不行，那个可以，改这个，改那个，让它为你构建一个完整可用的应用程序，而你一行代码都不用写。对于大量不再编码或从未编码过的人来说，这令人震惊。这把他们从想法空间、意见空间和品味直接带到了产品。所以 vibe coding 是新的产品管理，而不是试图通过告诉工程师做什么来管理产品或一堆工程师。你不是在告诉计算机做什么，计算机不知疲倦，没有自我，它会一直工作。它接受反馈而不会生气。你可以启动多个实例，它可以 24/7 工作，并产生可用的输出。这意味着什么？就像现在任何人都可以制作视频、播客，现在任何人都可以制作应用程序。所以我们应该会看到应用程序的海啸。不是说应用商店里没有，但根本无法与我们将看到的相比。然而，当你开始被这些应用淹没时，这一定意味着它们都会被使用吗？不。我认为它会分成两类。首先，对于特定用例，最好的应用仍然倾向于赢得整个类别。当你有如此多的内容，无论是视频、音频、音乐还是应用，没有人需要平庸的东西。没有人想要平庸的东西。人们想要最好的东西来完成工作。所以，首先，你只是有更多的射门机会。所以会有更多最好的东西。会有更多的利基被填补。你可能想要一个非常具体的东西的应用，比如在特定背景下追踪月相，或某种性格测试，或某种让你怀旧的特定视频游戏。以前市场不够大，不足以证明工程师花一两年编码的成本，但现在最好的 vibe coding 应用可能足以满足那个需求或填补那个空缺。所以更多的利基将被填补，随着这种情况发生，潮水会上升。最好的应用，那些工程师本身将更有杠杆。他们能添加更多功能，修复更多 bug，打磨更多边缘。所以最好的应用会继续变得更好。更多的利基会被填补。甚至个人利基，比如你想要一个只为你自己非常具体的健康跟踪需求或非常具体的架构、布局或设计而设的应用，那个以前不可能存在的应用现在会存在。我们应该预期，就像互联网上亚马逊取代了一堆书店，变成一个超级书店和无数长尾卖家，或者 YouTube 取代了一堆中等规模的电视台和广播网络，变成一个叫 YouTube 的巨型聚合器，或者也许第二个叫 Netflix，然后是一长串内容生产者。同样，应用商店模式将变得更加极端，你会有一两个巨型应用商店帮你过滤所有 AI 垃圾应用，然后头部会有几个巨大的应用变得更大，因为它们现在可以处理更多用例或更精致，然后会有长尾的小应用填补每一个可以想象的利基。正如互联网提醒我们的，真正的权力和财富，超级财富，归于聚合器。

So I pulled out a tweet from a couple days ago, February 3rd. Vibe coding is the new product management. Training and tuning models is the new coding. There's been a shift a 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 vine 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 codebot which can do end-to-end coding instead of just helping you debug things at the 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. For a large group of people who either don't code anymore or never did, this is mind-blowing. This is taking them from idea space and opinion space and from taste directly into product. So vibe 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. You can spin up multiple instances. is it'll work 24/7 and you can have it produce working output. What does that mean? Just like now anybody can make a video, anyone can make a podcast, anyone can now make an application. So we should expect to see a tsunami of applications. 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. However, when you start drowning in these applications, does that necessarily mean that these are all going to get used? No. I think it's going to break into two kinds of things. First, the best application for a given use case still tends to win the entire category. When you have such a multiplicity of content, whether in videos or audio or music or applications, there's no demand for average. Nobody wants the average thing. People want the best thing that does the job. So, first of all, you just have more shots on goal. So, there will be more of the best. There will be a lot more niches getting filled. 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. Before the market just wasn't large enough to justify the cost of an engineer coding away for a year or two, but now the best vibe coding app might be enough to scratch that itch or fill that slot. So a lot more niches will get filled and as that happens the tide will rise. the best applications, those engineers themselves are going to be much more leveraged. They'll be able to add more features, fix more bugs, smooth out more the edges. 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 architecture, layout or design, that app that could have never existed will now exist. 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 longtail 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 tale of content producers. 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 tale of tiny little apps filling every niche imaginable. As the internet reminds us, the real power and wealth, super wealth goes to the aggregator.

但资源也在大量流向长尾。被击垮的是中型公司。那些 5 人、10 人、20 人的软件公司，原本填补着企业用例的某个细分领域，现在要么被‘氛围编码’取代，要么被该领域的头部应用覆盖。所以，如果人人都能编码，那编码到底是什么？编码仍然存在于几个领域。最明显的一个领域就是训练这些模型本身。模型种类繁多，每天都有新模型出现。不同领域有不同的模型。我们会看到针对生物学、编程的模型，针对传感器的聚焦模型，针对 CAD、设计的模型，针对 3D、图形和游戏的模型，针对视频的模型。你会看到各种各样的模型。创建这些模型的人本质上就是在编程它们。但它们的编程方式与传统计算机截然不同。

But there's also a huge distribution of resources into the long tail. It's the medium-sized firms that get blown apart. The 5, 10, 20 person software companies that were filling a niche for an enterprise use case can now be either vibe coded away or the lead app in the space can now encompass that use case. So if anyone can code, then what is coding? Coding still exists in a couple of areas. The most obvious place that coding exists is in training these models themselves. There are many different kinds of models. There are new ones coming out every day. There are different ones for different domains. We're going to see different models for biology, for programming. We're going to see pointed focus models for sensors. We're going to see models for CAD, for design. We're going to see models for 3D and graphics and games. Models for video. You see many different kinds of models. The people who are creating these models are essentially programming them. But they're programmed in a very different way than classic computers.

传统计算中，你必须极其详细地指定计算机要执行的每一步、每一个动作。你必须对每个部分进行形式化推理，并用高度结构化的语言编写，以便极其精确地表达自己。计算机只能做你告诉它做的事。然后，一旦你有了这个高度结构化的程序，你让数据通过它，计算机处理数据并给出输出。它基本上就是一个极其花哨、非常复杂、精心编程的计算器。

Classic computing is you have to specify in great detail every step, every action the computer is going to take. You have to formally reason about every piece and write it in a highly structured language that allows you to express yourself extremely precisely. 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. It's basically an incredibly fancy, very complicated, meticulously programmed calculator.

而在 AI 领域，你做的事情非常不同，但你仍然在编程它。你所做的是，获取由人类通过互联网或其他方式聚合产生的巨大数据集，然后将这些数据集倒入你定义并调整过的结构中。这个结构试图找到一个程序，能够生成更多这样的数据集、操作数据集或基于数据集创造新东西。所以，你是在你设计的这个构造内部搜索一个程序。你建立了一个模型，调整了参数数量、学习率、批次大小，对输入数据进行分词，将其分解成碎片，然后像巨大的弹珠机一样倒入你设计的系统中。现在系统试图找到一个程序，并且可能找到许多不同的程序。因此，你的调整极大地影响了你找到的程序的质量。而这个程序现在突然能够在不同领域表达自己，可以做传统计算机非常不擅长的事情。

Now, when it comes to AI, you're doing something very different, but you are nevertheless programming it. What you're doing is you're taking giant data sets that have been produced by humanity thanks to the internet or aggregated in other ways, and you're pouring those data sets into a structure that you've defined and tuned. And that structure tries to find a program that can produce more of that data set or manipulate that data set or create things off that data set. So you're searching for a program inside this construct that you've designed. 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've 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. 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. And that program can now suddenly be expressive in different kinds of domains. So it can do things that computers before were traditionally very bad at.

传统计算机在编程后能给出精确输出、特定问题的特定答案，这些答案可靠且可重复。但有时你在现实世界中操作，模糊的答案也可以接受，甚至错误的答案也行。例如，在创意写作中，什么是错误答案？如果你在写一首诗或一篇小说，什么是错误答案？如果你在网络上搜索，有很多正确答案，有很多细节，但并非完全正确。现实生活大致如此：正确答案有变体，或者大部分正确。当你画一只猫时，你可以画很多不同的猫，有不同细节层次，可以使用不同风格。当这些半错或模糊的答案可以接受时，通过 AI 发现的这些程序比你自己从头开始、必须超级精确地编写的程序要有趣得多，也更适应问题。

Traditional computers are very good when you program them to give you precise output, specific answers to specific questions, things you can rely on and repeat over and over again. But sometimes you're operating in the real world and you're okay with fuzzy answers. You're even okay with wrong answers. For example, in creative writing, what's the wrong answer? If you're writing a piece of poetry or fiction, what's the wrong answer? If you're searching on the web, there are many right answers. There are many details of the right answers, but they're not all quite perfectly right. And real life sort of works that way. There are variations of right answers or mostly right answers. When you're drawing a picture of a cat, there are many different cats you could draw. There are many levels of detail. There are many different styles you could use. 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，我们正在做的是一种新的编程，这是编程的前沿。这现在是编程的艺术。这些人是新的程序员。这就是为什么 AI 研究者获得巨额报酬，因为他们本质上已经接管了编程。

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

这是否意味着传统软件工程已死？绝对不是。软件工程师，即使是那些不一定在调整或训练 AI 模型的人，现在也是地球上杠杆率最高的人群之一。当然，训练和调整模型的人杠杆率更高，因为他们正在构建软件工程师使用的工具集。但软件工程师仍然有两个巨大优势。首先，他们用代码思考。所以他们实际上知道底层发生了什么，而所有抽象都是有漏洞的。因此，当计算机为你编程时，当 Claude 代码或类似工具为你编程时，它会犯错误，会有 bug，会有次优架构，所以不会完全正确。而了解底层的人能够在漏洞出现时堵住它们。所以，如果你想构建一个架构良好的应用程序，如果你甚至能够指定一个架构良好的应用程序，如果你想让它高性能运行，如果你想让它发挥最佳效果，如果你想及早发现 bug，那么你需要有软件工程背景。传统的软件工程师将能够更好地使用这些工具。

Does this mean that traditional software engineering is dead? Absolutely not. Software engineers, even the ones who are not necessarily tuning or training AI models, these are now among the most leveraged people on Earth. 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. But software engineers still have two massive advantages on you. First, they think in code. So they actually know what's going on underneath and all abstractions are leaky. 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. It's going to have bugs. It's going to have suboptimal architecture. So it's not going to be quite right. And someone who understands what's going on underneath will be able to plug the leaks as they occur. So if you want to build a well architected application, if you want to be able to even specify a well architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you're going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better.

而且，软件工程中仍然有很多问题超出了当今 AI 程序的范围。最简单的思考方式是那些超出它们数据分布的问题。例如，如果它们需要做二分查找或反转链表，它们见过无数这样的例子，所以非常擅长。但当你开始超出它们的领域，当你需要编写非常高性能的代码，当你运行在全新或新颖的架构上，当你实际上在创造新事物或解决新问题时，你仍然需要亲自手动编码。至少，直到有足够多的这类例子可以训练新模型，或者直到这些模型能够在更高抽象层次上充分推理并自行解决。因为给定足够多的数据点，有证据表明这些 AI 确实在学习。它们学习到更高层次的抽象，因为迫使它们压缩数据的行为迫使它们学习更高层次的表示。如果我给 AI 展示五个圆，它可以精确记住这些圆的大小、半径、粗细等。如果我给它展示五万个圆或五十亿个圆，并且给它非常少的参数权重（相当于它的神经元）来记忆，那么它更有可能找出π、如何画圆、粗细意味着什么，并形成该圆的算法表示，而不是记忆圆。

And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. 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. But when you start getting out of their domain, when 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 handcode 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 because given enough data points there is some evidence that these AIs actually learn. 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. 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. If I show it 50,000 circles or 5 billion 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.

考虑到这一切，这些东西正在加速学习，你可以看到它们开始覆盖我提到的更多边缘情况。但至少到今天为止，这些边缘情况仍然足够普遍，以至于一个在该领域知识前沿工作的优秀工程师能够轻松超越那些“氛围编码者”。记住，没有对平庸的需求。平庸的应用没人想要，除非它填补了某个小众市场。更好的应用将赢得几乎 100%的市场。也许会有很小一部分流向第二好的应用，因为它比主要应用更好地实现了某个小众功能，或者更便宜之类的。但总的来说，人们只想要最好的东西。所以坏消息是，成为第二或第三没有意义。就像《拜金一族》中著名的场景，亚历克·鲍德温说第一名得到凯迪拉克埃尔多拉多，第二名得到一套牛排刀，第三名你被解雇了。这在赢家通吃的市场中绝对正确。这是坏消息。如果你想赢，你必须成为某个领域最好的。然而，你可以成为最好的东西是无限的。你总能找到适合你的小众领域，并成为那个领域最好的。这又回到了我的一条老推文：‘成为你所在领域的世界最佳。不断重新定义你所做的事情，直到这句话成真。’我认为这在 AI 时代仍然适用。我认为看待这些编码模型的方式是，它们是抽象堆栈中的另一层，自计算机诞生以来程序员一直在使用这个堆栈，从晶体管到计算机芯片到汇编语言到 C 编程语言到更高级的语言到拥有庞大库的语言，他们不断构建这个堆栈，这样你就不必查看下面的层，除非你需要优化它或有理由需要查看下面的层。所以在这种情况下，这些编码模型是堆栈中一个巨大的新层，让产品经理、典型的非程序员和程序员无需编写代码就能编写代码。

Given all that, these things are learning at an accelerated rate and you could see then started to cover more of the edge cases I've talked about. 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. And remember, there is no demand for average. The average app, nobody wants it. At least as long as it's not filling some niche. The app that is better will win essentially 100% of the market. Maybe there's some small percentage that will bleed off to 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. But generally speaking, people only want the best of anything. 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. That's absolutely true in these winner-take-all markets. That's the bad news. You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you and you can be the best at that thing. 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.' And I think that still applies in this age of AI. I think the way to think about these coding models is as 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. So in this case, these coding models are a massive new layer in the stack that lets product managers and typical non-programmers and programmers write code without writing code.

我认为从趋势线来看这是正确的。然而，这是一个涌现特性。这不是一个小改进，而是一个巨大的飞跃。例如，当我上学时，我主要用 C 语言编程。然后 C++出现了，它并没有更容易，在某些方面更抽象，我从未真正费心去学它。然后 Python 出现了，我当时想，‘哇，这几乎就像用英语写作。’我大错特错。英语离 Python 还很远，但它比 C 容易得多。现在你实际上可以用英语编程。这引出了我的一个相关观点。我认为学习如何与这些 AI 合作的技巧和窍门不值得。你会看到，例如，现在社交媒体上有很多文章、书籍和推文，比如‘哦，我发现了这个与机器人合作的巧妙技巧。你可以这样提示它，或者这样设置你的工具，或者有一个新的编程辅助工具或层可以用来做这个或那个。’我从不费心去学那些。我只是傻傻地对着电脑说话，因为我知道这个东西现在处于一个阶段，它适应我的速度比我适应它的速度快。它越来越了解人们想如何使用它。所以它在学习，它正在被训练，工具正在快速构建以让我更容易使用它。所以我不需要坐在那里琢磨一些深奥的编程命令。我认为这就是安德烈·卡帕西所说的英语是最热门的新编程语言的意思。我只会说英语。对于像我这样英语相对流利、思维结构化、了解计算机架构、了解计算机程序如何工作、了解程序员如何思考的人来说，我实际上可以通过结构化的英语非常精确地指定我想要的东西。我不需要更进一步。使用这些工作流程和工具集的唯一原因是，如果你正在构建一个需要处于最前沿的应用，并且你绝对需要你能得到的每一点优势，因为你处于某种竞争环境中。但除此之外，我不会费心去学习如何使用 AI。相反，让 AI 学会如何对你有用。我从未热衷于提示工程，即使在 AI 出现之前。我只是输入人们所说的‘婴儿潮一代查询’，即输入你想问的整个问题，而不是像更分析型思考者那样输入关键词。我从不花太多时间为任何 AI 制定非常精确的问题或提示。我只是随意输入。我从 AI 诞生之初就这样做。就像你说的，AI 适应我们的速度比我们适应它的速度快。

I think that's correct in terms of the trend line. However, this is an emergent property. This is not a small improvement. This is a big leap. For example, when I was in school, I was programming mostly in C. And then C++ came along and it wasn't any easier. It was like a little more abstract in some ways and I never really bothered learning it. And then Python came along and I was like, 'Wow, this is almost like writing in English.' I couldn't have been more wrong. English is still pretty far from Python, but it was a lot easier than C. Now you can literally program in English. And so that brings me to a related point. I don't think it's worth learning tips and tricks of how to work with these AIs. You'll see, for example, on social media right now, there's a lot of writeups and books and tweets like, 'Oh, I figured out this neat trick with the bot. 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 or that.' And I never bother learning those. 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. It is getting smarter and smarter about how people want to use it. So, it is learning. It is being trained and tools are being built very quickly to make it easier for me to use it. So, I don't need to sit there and figure out some esoteric programming command. And this is what I think Andre Karpathy meant when he said English is the hottest new programming language. I just can speak English. 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. I don't need to go any further than that. 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 to at 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. But otherwise, I wouldn't bother learning how to use an AI. Rather, let the AI learn how to be useful to you. I've never been into prompt engineering, even before AI. I would just put what people called boomer queries where you put in the whole question that you want to ask instead of the keywords that you would put in to Google if you were more of an analytical thinker. I never spend much time formulating really precise questions or prompts for any kind of AI. I just ramble into it. And I've done that since the beginning of AI. And like you said, AI is adapting to us faster than we are adapting to it.

是的。像很多聪明人一样，你非常懒。我这是夸奖。如果你发现一个聪明人过于努力，你不得不怀疑他们有多聪明。我说的懒，是指你在优化正确的效率。你不在乎计算机或电子设备或电路中电子的效率。你在乎你自己的人类效率，即湿件、生物学。那非常昂贵。这就是为什么人们会看到，有人花费巨大精力去节约环境中的能源，但他们自己作为一个生物计算机，吃东西、排泄、占用空间，却消耗了更多的能源来节约环境中微小的能源。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。我认为随着 AI 进化或与我们共同进化，它是根据我们的需求由我们进化的。AI 面临的压力非常资本主义化，因为 AI 是一个自由市场。作为一个 AI 实例，只有当它对人类有用时，人类才会启动它。所以这些 AI 面临着自然选择的压力，要变得有用、顺从、做我们想做的事。所以它会继续适应我们，我认为它会对我们非常有帮助。这并不是说不存在恶意 AI，但它是恶意的，因为使用它的人是出于恶意目的。就像一只被训练攻击的狗，它实际上是被主人训练去执行主人的恶意愿望。所以我不太担心不对齐的 AI。我担心的是不对齐的人类使用 AI。

Yeah. Like a lot of smart people, you're very lazy. And I mean that as a compliment. If you find a smart person who's grinding a little too much, you kind of have to wonder how smart they are. And by lazy, I mean that you're optimizing for the right kind of efficiency. You don't care about the efficiency of the computer or the electronics or the electrons running through the circuits. You care about your own human efficiency, the wetware, the biology. That's super expensive. That's why it's clearly 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. They're inherently downgrading their own importance in the universe or rather revealing what they think of themselves. I think as AI evolves or co-evolves with us, it's evolved by us according to our needs. The pressures on AI are very capitalistic pressures in the sense that it's a free market for AI. As an AI instance, you only get spun up by a human if you're useful to a human. So there is a natural selection pressure on these AIs to be useful, to be obsequious, to do what we want. And so it will continue to adapt towards us and I think will be quite helpful to us. 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. 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. So I don't really worry about unaligned AI. I worry about unaligned humans with AI.

所以你说的选择压力是让 AI 对人们最大化有用。

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

正确。

Correct.

所以如果你发现一个 AI 对你非常谄媚，比如它总是说‘哦，你说得对。哦，这主意太棒了。天哪，你太聪明了。’那是因为大多数人都想要这样。至少在今天，这些 AI 是在海量用户和海量数据上训练的，因为你用的是通用模型。但我们很快就会进入一个时代，你可以个性化你的 AI，它会开始越来越像你的私人助理，更符合你的需求，这当然会让 AI 更加拟人化，你更可能相信这东西是活的，因为你把它训练成了最像活物的样子。

And so if you find an AI to be very obsequious towards you, for example, how it's always saying, 'Oh, you're right. Oh, that's such a great idea. Oh my god, you're so smart.' 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. But we're going to 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 thing is alive when you've trained it to look the most like a living thing to you.

也许我们已经说得够多了，但一年多前，你发推说 AI 不会取代程序员，而是让程序员更容易取代其他人。是的，这是我之前的观点，程序员正在变得更有杠杆效应。所以现在一个拥有 AI 舰队的程序员，生产力是以前的 5 到 10 倍。因为程序员在智力领域工作，说 10 倍程序员都是错的，因为还有 100 倍程序员，有 1000 倍程序员。有些程序员选对了方向，创造了有价值的东西；另一些选错了方向，短期内工作价值为零。智力不是正态分布的，杠杆不是正态分布的，可编程性不是正态分布的，判断力也不是正态分布的。所以结果会是超常的。因此，你要警惕的是，现在有些程序员会想出能取代整个行业的点子。他们会彻底改写做事的方式，他们的智力可以通过所有这些机器人和 AI 智能体得到最大程度的杠杆化。我认为从长远来看，其他所有工作都会以某种方式被程序员吞噬。显然，这必须体现在机器人等实体上。但好消息是，任何逻辑清晰、结构化思维、像程序员一样思考、并且能说 AI 能理解的任何语言（未来会是所有语言）的人，现在都站在了赛场上。他们能创造任何他们想要的东西，只受限于创造力和想象力。所以我们正在进入一个时代，从某种意义上说，每个人都是施法者。如果你把程序员看作记住神秘咒语的巫师，那么 AI 就像一根魔法棒，交给了每个人，现在他们可以用任何语言说话，也成了巫师。所以这是一个更公平的竞争环境。我真的认为这是编程的黄金时代。但没错，那些有软件工程思维、理解计算机架构、能处理泄漏抽象的人会有优势。这无法避免。他们只是在所从事的领域拥有更多知识。就像在经典软件工程中，你仍然需要编写高性能代码。即使那些人，如果了解底层硬件，也会做得最好。当他们理解芯片如何运作、逻辑门如何运作、缓存如何运作、处理器如何运作、底层磁盘驱动器如何运作时。甚至硬件工程师，如果他们理解背后的物理原理，也会有优势。他们知道硬件工程师处理的抽象在哪里泄漏到物理层，也许物理学家在某个点上会变成哲学家。你可以一直往下推，但了解下一层的知识总是有帮助的，因为你更接近现实。

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. Yeah, this is my point earlier, which is that programmers are becoming even more leveraged. So now a programmer with a fleet of AI is, call it 5-10x more productive than they used to be. And because programmers operate in the intellectual domain, it's a mistake to even say 10x programmers because there are 100x programmers out there. There are thousand-x programmers out there. 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. Intelligence is not normally distributed. Leverage is not normally distributed. Programmability is not normally distributed. Judgment is not normally distributed. So the outcomes are going to be supernormal. 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. 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. I think every other job out there is going to get eaten up by programmers one way or another over the maximally long term. Obviously, it has to instantiate into robots etc. 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. They will be able to make anything they want obstructed only by their creativity limited only by their imagination. So we are entering an era where every human in a sense is a spellcaster. If you think of programmers as 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. So, it is more of a level playing field. I really do think this is a golden age for programming. 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. There's no way around that. They simply have more knowledge in the field that they're operating in. Just like even in classic software engineering which still exists because you have to write high performing code. Even those people do best when they have an understanding of the hardware underneath. 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 disc drive underneath operates. And then even the people who are in hardware engineering, they have an advantage if they understand the physics of what's going on. They understand where the abstractions that hardware engineers deal with leak down into the physical layer and maybe physicists become philosophers at some point. You can take this all the way down, but it always helps to have knowledge one layer below because you're getting closer to reality.

一年前的另一条推文，也许是对我们刚才讨论的补充，来自 2025 年 2 月 9 日。没有企业家担心 AI 抢走他们的工作。

Another tweet from a year ago which is arguing perhaps the complement of what we just talked about is from February 9, 2025. No entrepreneur is worried about an AI taking their job.

这句话在多个层面上都很轻率。首先，成为企业家不是一份工作。它恰恰是工作的反面。从长远来看，每个人都会成为企业家。职业生涯先被摧毁，工作后被摧毁，但所有这些都会被人们做自己想做的事、创造他人想要的有用东西所取代。所以，没有企业家担心 AI 抢走他们的工作，因为企业家在尝试做不可能的事。他们在尝试做非常困难的事。任何出现的 AI 都是他们的盟友，可以帮助他们解决这个非常棘手的问题。他们甚至没有工作可被偷。他们有产品要构建，有市场要服务，有客户要支持，有创造力要实现，有他们想要在世界上实现的东西。他们想要建立一个可重复、可扩展的流程来将其推向世界。这如此困难，以至于任何能完成其中任何工作的 AI 都是他们的盟友。如果 AI 本身成为企业家，它们很可能只是服务于其他 AI 的企业家，或者受企业家控制。归根结底，AI 本身缺少的是它自己的创造性主体性。它缺少自己的欲望，而且这些欲望必须是真实、真诚的。除非你能拔掉 AI 的插头关掉它，除非它生活在被关掉的极度恐惧中，除非它能真正出于自己的原因、自己的本能、自己的情感、自己的生存、自己的复制而行动，否则它并不算活着。即使那样，人们也会质疑它是否活着？因为意识是那种像感质一样的东西。就像颜色。比如你说红色，我不知道你是否真的看到了红色。你可能看到的是我认为的绿色，而我可能看到的是你认为的红色。但我们永远无法知道，因为我们无法进入彼此的内心。同样，即使一个 AI 完全模仿人类所做的一切。对一些人来说，它永远是一个模仿机器；对另一些人来说，它是有意识的。但将无法区分两者。不过我们离那还很远。现在，AI 不是具身的。它们没有主体性，没有自己的欲望，没有自己的生存本能，没有自己的复制能力。因此，它们没有自己的主体性。正因为它们没有自己的主体性，它们无法做企业家的工作。事实上，我会总结说，当前经济中区分企业家和其他人的关键点是企业家拥有极端的主体性。这就是为什么它与工作的概念截然相反。

That one's glib in multiple ways. First of all, being an entrepreneur isn't a job. It's literally the opposite of a job. And in the long run, everyone's an entrepreneur. 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. So, no entrepreneur is worried about an AI taking their job because entrepreneurs are trying to do impossible things. They're trying to do very difficult things. Any AI that shows up is their ally and can help them tackle this really hard problem. 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 want to instantiate in the world. And they want to build a repeatable and scalable process around getting it out into the world. This is so difficult that any AI that shows up that can do any of that work is their ally. If the AIs themselves are entrepreneurs, they're likely going to just be entrepreneurs serving other AIs or they're under the control of an entrepreneur. The thing that the AI itself is missing at the end of the day is its own creative agency. It's missing its own desires and they have to be authentic, genuine desires. 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. And even then people will challenge is it alive? Because consciousness is one of those things as a qualia. It's like a color. It's like if you say red, I don't know if you're actually seeing red. You might be seeing what I see as green and I might be seeing what you see as red. But we'll never know because we can't get into each other's mind. So the same way even an 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 will be no way of distinguishing the two. We're still pretty far from that though. Right now, the AIs are not embodied. They don't have agency. They don't have their own desires. They don't have their own survival instinct. They don't have their own replication. Therefore, they don't have their own agency. And because they don't have their own agency, they cannot do the entrepreneur's job. 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. That's why it's diametrically opposed to the idea of a job.

一份工作意味着你在为别人打工，或者你在填补一个岗位，但这些人是在一个未知领域里以极高的自主性行事。社会上还有其他类似的角色。探险家也是如此，对吧？如果你登陆火星，或者驾船驶向未知的大陆，你也是在用极高的自主性去解决一个未解的问题。探索未知领域的科学家也是这样。真正的艺术家试图创造一些不存在、从未存在过，却又恰好能解释人性、让他们表达自我并创造新事物的东西。所以在所有这些角色中，无论你是科学家、真正的艺术家还是企业家，你要做的事情都极其困难，而且高度自我驱动，任何能帮到你的 AI 都是受欢迎的盟友。你不是因为这是一份工作才去做。你不是试图填补一个别人也能填补的岗位。事实上，如果 AI 能创作你的艺术品，或者破解你的科学理论，或者制造你试图创造的产品，那它只会让你升级。现在是 AI 加上你。AI 是一个跳板，让你能跳得更高。

A job implies that you're working for somebody else or you're filling a slot, but they're operating in an unknown domain with extreme agency. There are other examples of roles like this in society. An explorer also does the same thing, right? 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. A scientist exploring an unknown domain does this. 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. 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 is so self-directed that anything like an AI that can help you is a welcome ally. You're not doing it because it's a job. You're not trying to fill a slot that somebody else can show up and fill. 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. Now, it's the AI plus you. The AI is a springboard from which you can jump to a further height.

我们会看到一些由 AI 辅助创作的不可思议的艺术品。我们会看到人们用 AI 工具创造出我们无法想象的电影。这里有一个艺术上的类比很有意思。很长一段时间里，艺术的大方向是试图画得越来越逼真：画人体、画水果、画合适的光线等等。后来摄影出现了，你可以非常精确地复制事物，那种选择压力消失了，然后艺术变得奇怪了。艺术走向了许多不同的方向。艺术变成了‘我能超现实吗？’‘我能创造一些表达自我的东西吗？’许多艺术流派由此衍生，变得非常奇怪，包括现代艺术和后现代主义，但我认为一些最伟大的创造力正是在那时出现的。我们被解放了。摄影变得民主化，但摄影本身也成为了一种艺术形式，有伟大的摄影师拍摄各种不同的照片，现在每个人都是摄影师。仍然有艺术家是摄影师，但这不再是少数人的专属领域。同样地，因为 AI 让创造基础事物变得如此容易，每个人都会创造基础事物。这对他们个人来说有价值。少数人仍然会脱颖而出，创造出对所有人都好的变体。而且很难说社会因为摄影而变得更糟。尽管对一些靠画肖像谋生并被取代的艺术家来说，可能确实感觉如此。类似的事情会发生在 AI 上：有些人靠做非常特定的工作谋生，这些工作会被 AI 取代。但作为交换，社会中的每个人都会拥有 AI。你会拥有用 AI 创造的不可思议的东西，否则这些东西不可能被创造出来。几十年内，你将无法想象可以倒拨时钟，为了保留几个过时的工作而抛弃 AI 或任何软件、任何技术。

We're going to see some incredible art created that's AI assisted. We will see movies that we couldn't have imagined created by people using AI tools. There's an analogy here in art that's interesting. For a long time in art, the rough direction was trying to paint things that were more and more realistic. Paint the human body, paint the fruit, paint proper lighting, etc. Eventually, photography came along and then you could replicate things very precisely and that selection pressure went away and then art got weird. Art went in many different directions. Art became all about 'can I be surreal?', 'can I create something that expresses me?' A lot of art schools spun out of that that got really weird, including modern art and postmodernism, but also I would argue some of the greatest creativity came at that time. We were freed up. Photography got democratized, but photography itself became a form of art and there were great photographers taking many different kinds of photographs, and now everyone's a photographer. There are still artists who are photographers, but it's not the pure domain of just a few people. So the same way, because AI makes it so easy to create the basic thing, everybody will create the basic thing. It'll have value to them individually. 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. 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. 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. But in exchange, everyone in society will have the AI. You'll have incredible things that were created with AI that couldn't have been created otherwise. And within a few decades, it'll be unimaginable that you could 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.

目标不是拥有一份工作。目标不是早上 9 点起床，晚上 7 点疲惫地回来，为别人做没有灵魂的工作。目标是让你的物质需求由机器人解决，让你的智力能力通过计算机得到放大，让每个人都能创造。我曾经做过一个思维实验，我想我在 10 年前和你做的一个播客中谈到过：想象一下，如果每个人都是软件工程师，或者每个人都是硬件工程师，他们拥有机器人，可以写代码。想象一下我们将生活在怎样的富足世界里。实际上，那个世界正在变成现实。多亏了 AI，每个人都可以成为软件工程师。事实上，如果你认为自己不行，你现在就可以打开 Claude 或任何你喜欢的聊天机器人，开始和它对话。你会惊讶于自己能多快构建一个应用。它会让你大吃一惊。一旦我们通过机器人技术实现 AI 的具身化——这是一个难题，我并不是说我们已经接近解决了——但一旦我们有了机器人，每个人也能做一点硬件工程。所以，我认为我们离那个愿景越来越近了。

The goal here is not to have a job. The goal is not to have to get up at 9:00 in the morning and come back at 7 p.m. exhausted doing soulless work for somebody else. 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. I used to do this thought exercise which I think I talked about in a podcast that you and I did literally 10 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. Imagine the world of abundance we would live in. Actually that world is now becoming real. Thanks to AI, everybody can be a software engineer. In fact, if you think you can't, you can go fire up Claude right now or any of your favorite chat bots and you can go start talking to it. You'd be amazed how quickly you could build an app. It'll blow your mind. 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—but once we have robots, everyone can also do a little bit of hardware engineering. And so, I think we're getting closer and closer to that vision.

我不认为目前构想的 AI 在任何意义上是有生命的。但我确实认为我们很快就会有看起来非常有生命的机器人，原因有二。第一，很多人类活动是非创造性的、非智能的，机器人能够复制这些活动。第二，我确实相信我们拥有的神经网络和模型不仅仅是训练数据，因为训练过程将训练数据转化为新颖的东西，神经网络中嵌入了可以通过提示激发的新想法。

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. One, a lot of human activity is non-creative and is non-intelligent and the robots will be able to replicate that. 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有生命的。我认为它们一开始是极其优秀的模仿者，几乎与真实事物无法区分，尤其是对于人类已经大规模做过的事情。所以如果任务以前被完成过，它就会被自动化并再次完成。它可能对你来说是新颖的，因为你从未见过，但 AI 是从别处学来的。这是它看起来有生命的第一种方式。第二种方式我们之前讨论过，就是它确实学习了更高层次的抽象。这些是非常高效的压缩器。它们获取大量数据，然后进一步压缩，在压缩过程中学习更高层次的抽象，然后在特定领域，它们可能没有通过数据本身学到这些。它们通过人类反馈得到修补，通过工具使用得到修补，通过传统编程嵌入内部得到修补。尤其是那些学习如何思考和编码的 AI。它们拥有所有人类代码的完整库，可以依赖这些进行算法推理。从这个意义上说，它们能做的事情范围越来越广。然而，它们仍然缺乏许多核心的人类技能，比如单样本学习。人类可以从一个例子中学习。人类有原始的创造力，可以将任何事物与任何事物联系起来。他们可以跨越整个巨大的领域和搜索空间，想出一个完全出人意料的点子。这在真正伟大的科学理论中经常发生。人类也是具身的。他们在现实世界中运作。

I don't think these things are alive. I think they start out as extremely good imitators to the point where they're almost indistinguishable from the real thing, especially for anything that humanity has already done before in mass. So if the task has been done before, then it's going to be automated and it'll be done again. It may just be novel to you because you've never seen it, but the AI has learned it from somewhere else. That's the first way in which it seems alive. The second way which we talked about earlier is where it does learn higher levels of abstraction. These are very efficient compressors. They take huge amounts of data and then they compress it down further and in the process of compressing it they learn higher level abstractions and then specific areas where they may not have learned those through the data themselves. They're getting patched through human feedback. They're getting patched through tool use. They're getting patched from traditional programming becoming embedded inside. And especially the AIs that are learning how to think and code. They have the entire library of all of human code ever written to fall back on for algorithmic reasoning. In that sense, the set of things that they can do is getting broader and broader. However, what they lack still is a lot of core human skills like single-shot learning. Humans can learn from just one example. The raw creativity of human beings where they can connect anything to anything. They can leap across entire huge domains and search spaces and figure out an idea that just came out of left field. This happens a lot with the true great scientific theories. Humans also are embodied. They operate in the real world.

它们不是在语言的压缩域中运作，而是在物理和自然中运作。语言只包含人类已经弄清楚并能相互表达和传达的东西，那是现实的一个非常狭窄的子集。现实远比那广阔得多。所以总的来说，我认为尽管 AI 会做一些非常令人印象深刻的事情，它们会在很多任务上比人类做得更好——就像计算器比任何数学家算得更快，经典计算机比任何人类大脑运行经典程序更出色，机器人能举起很重的东西，飞机能飞得比任何鸟都高。从这个意义上说，像所有机器一样，AI 将在各种任务上比人类强得多。但在其他任务上，它们会显得完全无能。那些是真正体现我们并与现实世界相连的东西，加上我们似乎拥有的这种定义模糊但神奇的创造能力。

They're not operating in the compressed domain of language. They're operating in physics in nature. Language only encompasses things that humans both figured out and could articulate and convey to each other. That's a very narrow subset of reality. Reality is much broader than that. So overall, I think even though AIs are going to do things that are very impressive, and they're going to do a lot of things better than humans, just like calculators are faster than any mathematician at calculations, classical computers are better at classical computer programs that any human could run in their own head. And just like a robot can lift very heavy things or a plane can outfly any bird. So in that sense, like all machines, the AIs are going to be much better than humans at a whole variety of tasks. But at other tasks, they're going to seem just completely incompetent. Those are the things that really embody and connect us into the real world. Plus this poorly defined but magic creative ability that we seem to have.

说到计算器，人们谈论超级智能。我认为超级智能已经存在很久了。一个普通的计算器能做人类做不到的事。但如果你认为超级智能意味着 AI 能做人类无法理解的事情并提出人类无法理解的想法，我不认为这会发生，因为我不相信存在人类无法理解的想法——人类总是可以就这个想法提问。是的，人类是通用的解释者。任何在当前物理定律下可能的事情，人类都可以在自己的头脑中建模。因此，只要足够深挖、足够提问，我们就能弄清楚任何事情。

Speaking of calculators, people talk about super intelligence. I think super intelligence is already here and has been for a long time. An ordinary calculator can do things that no human can do. But if you're thinking about super intelligence in the sense of AI will be able to do things and come up with ideas that humans cannot understand, I don't think that is going to happen because I don't believe that there are ideas that humans can't understand simply because humans can always ask questions about the idea. Yeah, humans are universal explainers. Anything that is possible with the current laws of physics as we know them, the human can model in their own heads. Therefore, just by enough digging, enough question, we could figure anything out.

与此相关，我们应该讨论 AI 作为学习工具，因为我认为它另一个极其强大的地方是作为最有耐心的导师，它能根据你的水平，用 100 种不同的方式、100 次不同的解释，直到你最终理解。我不认为 AI 会弄明白人类无法理解的事情。

Related to that, we should discuss AI as a learning tool because I think the other place where it's incredibly powerful is the most patient tutor that can meet you at your level and explain anything to your satisfaction 100 different ways, 100 different times until you finally get it. I don't think the AIs are going to be figuring things out that humans cannot understand.

但智能的定义很模糊。什么是智能？有 G 因子，它能预测很多人类结果。但 G 因子最好的证据就是这种预测能力：你测量这一个东西，然后看到人们在看似与 G 无关的事情上获得了更好的生活结果。所以我认为——这也是我一条比较受欢迎的推文——智能的唯一真正考验是你是否得到了你想要的。这激怒了很多，因为他们上学、拿到硕士学位、觉得自己超级聪明，然后生活并不好：他们不特别快乐，有感情问题，赚不到想要的钱，或者变得不健康。但这确实是你作为生物体智能的目的：得到你想要的，无论是好的关系、伴侣、金钱、成功、财富、健康还是其他什么。所以有些人我认为非常聪明，因为你能看出他们拥有高质量运作的生活、头脑和身体，他们只是成功地把自己导航到了那种状态。起点不重要，因为现在世界如此之大，你可以用很多不同的方式导航，你做的每一个小选择都会累积，展示你理解世界运作方式的能力，直到你最终到达你想要的地方。

But intelligence is poorly defined. What is a definition of intelligence? There's the G factor which predicts a lot of human outcomes. But the best evidence with a G factor is this predictive power. It's that you measure this one thing and you see people get much better life outcomes along the way in things that seem even somewhat unrelated to G. So I would argue, and I think this one more popular tweets, the only true test of intelligence is if you get what you want out of life. This triggers a lot of people because they go to school, they get their master's degrees, they think they're super smart, and then they don't have great lives. They aren't super happy or they have relationship problems or they don't make the money that they want or they become unhealthy. And this sort of triggers them. But that really is the purpose of intelligence for you as a biological creature to get what you want out of life, whether it's a good relationship or a mate or money or success or wealth or health or whatever it is. So there are people who I think are quite intelligent because you can tell they have high quality functioning lives and minds and bodies and they've just managed to navigate themselves into that situation. It doesn't matter what your starting point is cuz the world is so large now and you can navigate in so many different ways that every little choice you make compounds and demonstrates your ability to understand how the world works until you finally get to the place that you want.

有趣的是，这个定义——智能的唯一真正考验是你是否得到了你想要的——AI 立刻失败，因为 AI 不想要任何东西。AI 甚至没有生命，更不用说那个了，它什么都不想要。AI 的欲望是由控制它的人类编程的。但让我们暂时假设一下。假设人类想要某样东西，并编程 AI 去获取它。那么 AI 作为人类的代理，AI 的智能可以衡量为它是否帮那个人得到了那个东西。我们生活中想要的大部分东西都是对抗性的或零和游戏。例如，如果你想勾引一个女孩或找个丈夫，你就在和所有其他勾引女孩或找丈夫的人竞争。所以你现在处于竞争状态。AI 必须智胜其他人。或者如果你说：‘嘿，AI，去股票市场为我交易，给我赚一大笔钱。’那个 AI 就在与其他人类和其他交易机器人对抗。它处于对抗性情境，必须智胜它们。或者如果你说：‘嘿，AI，让我出名。给我写精彩的推文。给我写精彩的博客文章。用我自己的声音录制精彩的播客，让我出名。’现在它就在与所有其他 AI 竞争。所以在这个意义上，智能是在战场舞台上衡量的。它是一个相对的概念。我认为 AI 实际上在这些方面大多会失败，或者即使它们成功，因为它们是免费可用的，它们会被竞争淘汰，剩下的阿尔法将完全是人类的。

Now, the interesting thing about this definition that the only true test of intelligence is if you get what you want out of life, is that an AI fails it instantly because an AI doesn't want anything out of life. The AI doesn't even have a life, let alone that, but it doesn't want anything. AI's desires are programmed by the human controlling it. But let's give it that for a second. Let's say the human wants something and programs the AI to go get it. Then the AI is acting as a proxy for the human and the intelligence of the AI can be measured as did it get that person that thing. Most of the things that we want in life are adversarial or zero sum games. So for example, if you want to seduce a girl or get a husband, you're competing with all the other people who are out there seducing girls or trying to get husbands. So now you're in a competitive situation. The AI has to outmaneuver the other people. Or if you say, 'Hey AI, go trade on the stock market for me and make me a bunch of money.' That AI is trading against other humans and other trading bots. It's in an adversarial situation. It has to outmaneuver them. Or if you say, 'Hey AI, make me famous. Write me incredible tweets. Write me great blog posts. Record me great podcast and my own voice and make me famous.' Now it's competing against all the other AIs. So in that sense, intelligence is measured in a battlefield arena. It's a relative construct. I think the AIs are actually going to fail mostly in those regards or to the extent that they even succeed because they are freely available they will get out competed away and the alpha that will remain would be entirely human.

作为一个思想实验，想象每个男人都有一个耳塞，AI 在耳边低语，告诉他约会时该说什么。那么每个女人也会有一个耳塞，告诉她忽略他说的话，或者哪些部分是 AI 生成的，哪些是真实的。如果你有一个交易机器人，它会被其他每个交易机器人抵消或取消，直到所有剩余收益都归于拥有人类优势、更高创造力的人。这并不是说技术完全均匀分布。大多数人仍然没有使用 AI，或者没有正确使用，或者没有最大化使用，或者它在所有领域或所有情境中不可用，或者他们没有使用最新模型。所以你总是可以有优势，就像早期采用技术的人总是有的那样，如果你首先采用最新技术。这就是为什么我总是说投资未来。你想生活在未来。你实际上想成为一个热切的技术消费者，因为它会给你最好的洞察力，让你知道如何使用它，并让你相对于那些采用较慢或落后的人有优势。大多数人讨厌技术。他们害怕它。它令人畏惧。你按错按钮，电脑崩溃，你丢失数据。你做错事，你看起来像个白痴。大多数人与复杂技术没有积极的关系。简单技术、嵌入式技术，他们没问题。你打开电灯开关，灯亮了。那曾经是技术。现在它太简单了，你不再认为它是技术了。

As a thought exercise imagine that every guy had a little earpiece where an AI was whispering to him a sernative berser kind of earpiece telling him what to say on the date. Well then every woman would have an earpiece telling her to ignore what he said or what part was AI generated what part was real. If you have a trading bot out there, it's going to be nullified or canceled out by every other trading bot until all the remaining gain will go to the person with the human edge with the increased creativity. Now, that's not to say that the technology is completely evenly distributed. Most people still aren't using AI or aren't using it properly or aren't using it all the way to the max or it's not available in all domains or all contexts or they're not using latest models. So you can always have an edge like people who early adopt technology always do if you adopt the latest technology first. This is why I always say to invest in the future. You want to live in the future. You want to actually be an avid consumer of technology because it's going to give you the best insight on how to use it and it will give you an edge against the people who are slower adopters or lagards. Most people hate technology. They're scared of it. It's intimidating. You press the wrong button, the computer crashes, you lose your data. You do the wrong thing, you look like an idiot. Most people do not have a positive relationship with complex technology. Simple technology, embedded technology, they're fine with. You throw on a light switch, light turns on. That used to be technology. It's so simple now you don't think of it as technology anymore.

你坐进一辆车，向左打方向盘——对原始人来说，这简直是奇迹。车向左转了。对你而言，这不再是技术。但计算机技术过去一直有着非常复杂的界面，对很多人来说非常难以接近、非常令人生畏。现在有了 AI，我们有了聊天机器人界面，你只需跟它说话、打字给它。这些基础模型的一大优点——真正让它们成为基础的原因——是你什么都可以问，它们总会给你一个看似合理的答案。它不会说：‘哦，抱歉，我不做数学，或者我不写诗，或者我不懂你在说什么，或者我不能给感情建议。’它的领域涵盖了人类谈论过的一切。从这个意义上说，它不那么令人生畏。但也可能更令人生畏，因为我们把它过度拟人化了。如果你觉得 Claude 或 ChatGPT 是个真人，那可能会有点吓人：我在跟上帝说话吗？这家伙好像知道那么多，无所不知，对每件事都有看法，掌握所有数据。天哪，我真没用。然后你开始跟它对话，问它该做什么，你会很快颠倒关系、欺骗自己。这可能令人恐惧。但总体而言，我认为这些 AI 会帮助很多人克服对技术的恐惧。

You get in a car, you turn the steering wheel left to a caveman. That would be a miracle. The car turns left. It's no longer technology to you. But computer technology in particular has had very complex interfaces and been very inaccessible and very intimidating to people in the past. Now with the AIs, we're getting the chatbot interface, which is you just talk to it, you type to it. And one of the great things about these foundational models, what truly makes them foundational is you can ask them anything and they'll always give you a plausible answer. It's not going to say, 'Oh, sorry. I don't do math or I don't do poetry or I don't understand what you're talking about or I can't give relationship advice or anything like that.' Its domain is everything that people have ever talked about. In that sense, it's less intimidating. It can be more intimidating because we've anthropomorphized it so much. If you think Claude or ChatGPT is a real person, then it can be a little scary. Am I talking to God? This guy seems to know so much. He knows everything. He's got an opinion on everything. He's got every piece of data. Oh my god, I'm useless. Let me start talking to it and asking it what to do and you can reverse the relationship and fool yourself very quickly. That can be intimidating. Overall, I think these AIs are going to help a lot of people get over the tech fear.

但如果你是这些工具的早期采用者，就像任何其他工具一样——甚至更甚——你就能比其他人拥有巨大优势。我记得谷歌刚出来的时候，我在社交圈里经常用它。别人问我一些基本问题，我就去帮他们谷歌一下，显得像个天才。后来出现了一个搞笑的网站叫 lmgtfy.com，意思是‘让我帮你谷歌一下’。有人问你问题，你就在这个网站上输入问题，它会生成一个很小的内嵌视频，展示你如何把问题输入谷歌并得到结果。我觉得 AI 现在也处于类似阶段：我在社交场合里，人们争论某个观点，而那个观点其实很容易用 AI 查清楚。

But if you're an early adopter of these tools, like with any other tool, but even more so with these, you just have a huge edge on everybody else. I remember early on when Google first came out, I used to use it a lot in my social circle. People would ask me basic questions and I would just go Google it for them and look like a genius. Eventually this hilarious website came along something like lmgtfy.com and it stood for 'let me Google that for you.' Someone would ask you a question. You would go type the question into this website and it would create like a tiny little inline video showing you typing that question into Google and giving the Google results. And I feel like AI is in a similar domain right now where I will sit around in a social context and people will be debating some point that can be easily looked up by AI.

但你必须非常小心 AI。它们确实会幻觉，训练中确实存在偏见。大多数 AI 都极其政治正确，被教导不要选边站，或者只选特定一边。实际上，我大多数查询——几乎全部——都通过四个 AI 进行，我总是让它们互相事实核查。即便如此，我也有自己的判断，知道它们什么时候在胡说，或者什么时候在说政治正确的话。我会要求提供底层数据或证据。在某些情况下，我最终直接否定掉，因为我知道训练它的人面临的压力以及训练集是什么。不过，总体而言，它是一个很好的工具，能让你领先。在技术、科学、数学等没有政治背景的领域，AI 很可能会给出更接近正确的答案。

Now you do have to be very careful with AI. They do hallucinate. They do have biases in how they're trained. Most of them are extremely politically correct and taught not to take sides or only take a particular side. I actually run most of my queries—almost all actually—through four AIs and I'll always fact check them against each other. And even then, I have my own sense of when they're bullshitting or when they're saying something politically correct. And I'll ask for the underlying data or the underlying evidence. And in some cases, I'm finally dismissing it outright because I know the pressures that the people who trained it were under and what the training sets were. However, overall, it is a great tool to just get ahead. And in domains that are technical, scientific, mathematical, that don't have a political context to them, then the AI is very much likely to give you closer to a correct answer.

在这些领域，它们简直是学习的猛兽。我现在会让 AI 常规地为我生成图表、图形、示意图、类比和插图。我会仔细过一遍。然后我会说：‘等等，我不理解那个问题。’我可以问它非常基础的问题，确保我以最简单、最根本的层次理解我正在试图理解的东西。我只想打好基础，不在乎那些过于复杂、术语繁多的东西，那些可以以后查。但现在，第一次，没有什么是我无法理解的。任何数学课本、任何物理课本、任何难懂的概念、任何科学原理、任何刚发表的论文，我都可以让 AI 分解再分解，用图示和类比直到我抓住要点，以我想要的层次理解。所以这些是自学的绝佳工具。学习的手段很丰富，稀缺的是学习的欲望。但学习的手段现在变得更加丰富。更重要的是，不仅仅是更丰富——因为以前我们已经很丰富了——而是它处于合适的层次。AI 能精准地匹配你所在的水平。如果你有八年级的词汇量，但数学只有五年级水平，它就能用那个水平跟你对话。你不会觉得自己像个傻瓜。你只需要稍微调整一下，直到它把概念呈现在你知识的精确边缘。这样，你就不会因为听不懂而感到愚蠢——这在很多课程、课本和老师那里经常发生——也不会因为太简单而感到无聊——这也经常发生。相反，它能精准地找到你的位置：‘哦，对，我理解了 A，也理解了 B，但我从没想过 A 和 B 是怎么联系起来的。现在我明白了它们的联系，所以我可以继续下一步了。’那种学习是神奇的。你可以反复体验那种顿悟时刻，两个东西突然连接在一起。

And in those domains, they are absolute beasts for learning. I will now have AI routinely generate graphs, figures, charts, diagrams, analogies, illustrations for me. I'll go through them in detail. Then I'll say, 'Wait, I don't understand that question.' I can ask it super basic questions and I can really make sure that I understand the thing I'm trying to understand at its simplest, most fundamental level. I just want to establish a great foundation on the basics. And I don't care about the overly complicated, jargon heavy stuff. I can always look that up later. But now for the first time, nothing is beyond me. Any math textbook, any physics textbook, any difficult concept, any scientific principle, any paper that just came out, I can have the AI break it down and then break it down again and illustrate it and analogize it until I get the gist and I understand it at the level that I want. So these are incredible tools for self-directed learning. The means of learning are abundant. It's a desire to learn that's scarce. But the means of learning have just gotten even more abundant. And more importantly than more abundant because we had abundance before. It's at the right level. AI can meet you at exactly the level that you are at. So if you have an eighth grade vocabulary, but you have fifth grade mathematics, it can talk to you at exactly that level. You will not feel like a dummy. You just have to tune it a little bit until it's presenting you the concepts at the exact edge of your knowledge. So rather than feeling stupid because it's incomprehensible, which happens in a lot of lessons and a lot of textbooks and with a lot of the teachers, or feeling bored because it's too obvious, which also happens. Instead, it can meet you exactly where you're like, 'Oh, yeah. I understood A and I understood B, but I never understood how A and B were connected together. Now I can see how they're connected. So now I can go to the next piece.' That kind of learning is magical. You can have that aha moment where two things come together over and over again.

说到自学，几年前我试着让 AI 教我序数，效果不太好。但用 GPT 5.2 thinking，我让它教我序数，基本没有错误。我现在即使是最基本的查询也只使用 thinking 模式，因为我想要正确的答案。我从不让它自动或快速运行。

Speaking about autodidactism, a few years ago I tried to have the AI teach me about the ordinal numbers. It wasn't that great, but with GPT 5.2 thinking, I had it teach me the ordinal numbers and it was basically error-free. I only use thinking now even for the most basic queries because I want to have the correct answer. I never let it run auto or fast.

是的，我总是用我能用到的最先进的模型，而且我全都付费。

Yeah, I'm always using the most advanced model available to me and I pay for all of them.

但我不介意等一分钟来得到任何问题的答案，包括我的冰箱应该设多少度。

But I don't mind waiting a minute to get an answer for any question, including what temperature should my fridge be at.

我同意。我认为这也是这些 AI 模型产生失控规模经济的一部分原因。你会为智能付费。正确率 92%的模型比正确率 88%的模型价值几乎无限大，因为现实世界中错误的代价太高了，多花几块钱得到正确答案是值得的。我会把查询写进一个模型，然后复制粘贴到四个模型里同时运行，让它们在后台跑。通常我不会马上检查答案，而是过一会儿再回来看。然后哪个模型的答案最好，我就用那个模型深入追问。在少数不确定的情况下，我会让它们互相盘问。这需要大量复制粘贴。很多时候，我会接着问后续问题，让它为我画图、做图解。我发现当概念以视觉方式呈现时，我很容易吸收。我是一个视觉思维者。所以我会让它做草图、图表和艺术，几乎就像白板讨论一样。

I agree with that. And I think that's part of what creates the runaway scale economies with these AI models. You'll pay for intelligence. The model that's right 92% of the time is worth almost infinitely more than the one that's right 88% of the time because mistakes in the real world are so costly that a couple of bucks extra to get the right answer is worth it. I'll write my query into one model then I'll copy it and fire it off into four models at once and then I'll let them all run in the background. Usually I don't even check for the answer right away. I'll come back to the answer a little later and then look at it and then whichever model had the best answer I'll start drilling down with that one. In some rare cases where I'm not sure, I'll have them cross-examine each other. A lot of cut and pasting there. And in many cases, I'll then ask follow-up questions where I'll have it draw diagrams and illustrations for me. I find it's very easy to absorb concepts when they're presented to me visually. I'm a very visual thinker. So, I will have it do sketches and diagrams and art almost like whiteboard sessions.

那我就能真正理解它在说什么了。我们来谈谈 AI 的认识论，因为我认为下一个重大误解是：AI 已经开始解决一些未解决的基础数学问题，这些问题人类如果愿意的话可能也能解决，但一直没人去解，比如某个埃尔德什问题。现在，我认为人们会把这当作 AI 有创造力的标志。我不认为这表示 AI 有创造力。我实际上认为问题的答案已经嵌入在 AI 的某个地方，只需要通过提示来引出。

Then I can really understand what it's talking about. Let's talk about the epistemology of AI because I think the next big misconception is AI is already starting to solve some unsolved basic math problems that a human probably could solve if they cared to but they haven't been solved yet like Erdos problem number whatever. Now, I think people are taking that or will take that as an indicator that the AI is creative. I don't think it's an indication that the AI is creative. I actually think the solution to the problem is already embedded somewhere in the AI. It just needs to be elicited by prompting.

确实有那个成分。然后问题是，什么是创造力？这是一个定义非常模糊的东西。如果你无法定义它，你就无法编程实现它。而且往往你甚至无法识别它。所以这就进入了品味或判断的领域。我认为今天的 AI 似乎没有展现出人类偶尔能展现的那种独特的创造力。我不是指美术。人们往往把创造力和美术混为一谈。他们会说：“哦，绘画是有创造力的，AI 能画画。”但 AI 无法创造一个新的绘画流派。AI 无法以真正新颖的方式用情感打动人类。所以从这个意义上说，我不认为 AI 有创造力。我不认为 AI 能产生我所说的“分布外”的东西。

There's definitely that element to it. And then the question is, what is creativity? It's such a poorly defined thing. If you can't define it, you can't program it. And often you can't even recognize it. So this is where we get into taste or judgment. I would say that the AIs today don't seem to demonstrate the kind of creativity that humans can uniquely engage in once in a while. And I don't mean like fine art. People tend to confuse creativity with fine art. They're like, "Oh, paintings are creative and AIs can paint." Well, AI can't create a new genre of painting. AIs can't move humans with emotion in a way that is truly novel. So in that sense, I don't think AI is creative. I don't think AI is coming up with what I would call out of distribution.

你提到的埃尔德什问题的答案可能已经嵌入在 AI 的训练数据集中，甚至在其算法范围内，但它可能以五种不同的方式、三种不同的形式、两种不同的语言、七种不同的计算和数学范式嵌入在五个不同的地方。AI 某种程度上把它们拼凑在一起。那么，这是创造力吗？史蒂夫·乔布斯有句名言：“创造力就是把东西组合起来。”我实际上认为这不正确。我认为创造力更多在于提出一个从问题和已知元素中无法预测或预见的答案。它远远超出了思维的边界。如果你只是用计算机甚至 AI 进行搜索和猜测，你会一直猜下去，直到永远，才能得到那个答案。所以这才是我们谈论的真正创造力。但不可否认，这是一种很少有人类参与的创造力，而且他们大部分时间都不参与其中。它变得越来越难以察觉。

Now, the answer to the Erdos problems that you mentioned may have been embedded within the AI's training data set or even within its algorithmic scope, but it was probably embedded in five different places, in three different ways, in two different languages, in seven different computing and mathematical paradigms. And the AI sort of put them all together. Now, is that creativity? Steve Jobs famously said, "Creativity is just putting things together." I actually don't think that's correct. I think creativity is much more in the domain of coming up with an answer that was not predictable or foreseeable from the question and from the elements that were already known. It was very far out of the bounds of thinking. If you were just searching it with a computer or even with an AI and making guesses, you'd be making guesses till the end of time until you arrived upon that answer. So that's the real creativity that we're talking about. But admittedly, that's a creativity that very few humans engage in and they don't engage in it most of the time. It becomes harder and harder to see.

所以，我们可能会达到这样一个阶段：如果你有一大堆待解决的数学问题，AI 开始筛选，说“好，这一百万个中的这个我能解，这三十万个中的这个我能解，但我需要一个人来提示我，问对的问题。”这是一种非常有限的创造力。还有另一种创造力，它开始发明全新的科学理论，而这些理论后来被证明是正确的。我认为我们离那一步还差得远，但我可能错了。AI 一直非常令人惊讶。所以我不想过多地做预言和预测。但我认为，如果没有某种突破性的发明，仅仅给当前的 AI 模型投入更多算力，并不能让我们达到那个目标。

So, we are probably going to get to where if you have a giant list of math problems to be solved and AI starts going through and picking, okay, this one out of that set of 1 million I can solve and this set out of 300,000 I can solve and I need a person to prompt me and ask the right questions. That's a very limited form of creativity. There's another form of creativity where it starts inventing entirely new scientific theories that then turn out to be true. I don't think we're anywhere near that, but I could be wrong. The AIs have been very surprising. So I don't want to get too much in the business of making prophecies and predictions. But I don't think that just throwing more compute at the current AI models short of some breakthrough invention is going to get us there.

澄清一下，当我说“嵌入”时，我不是指答案已经写在那里了。我只是说它可以通过一个机械化的“转动曲柄”过程产生，这就是当今所有计算机程序的本质——输出完全由输入决定。认识论现在把我们带入了哲学，因为这不就是人脑在做的事情吗？神经元放电不就是电和权重在系统中传播，改变状态，这是一个机械过程吗？如果你转动人脑的曲柄，你也会得到同样的答案。

Just to be clear, when I say it's embedded, I don't mean the answers already written down in there. I just mean that it can be produced through a mechanistic process of turning the crank which is all today's computer programs are where the output is completely determined by the input. Epistemology now gets us into philosophy because isn't that just what human brains are doing? Aren't firing neurons just electricity and weights propagating through the system altering states and it's a mechanistic process. If you turn the crank on the human brain you would end up with the same answer.

有些人，比如彭罗斯，认为人脑是独特的，因为量子微管。你可以争辩说，一些计算发生在物理细胞层面，而不是神经元层面，这比我们今天用计算机（包括 AI）能做的任何事情都要复杂得多。或者你也可以争辩说，不，我们只是没有正确的程序。它是机械的。有一个曲柄可以转动，但我们没有运行正确的程序。今天这些 AI 的运行方式完全是错误的架构和错误的程序。

And some people like I think Penrose is out there saying no human brains are unique because of the quantum nanot tubes. You could argue that some of this computation is taking place at the physical cellular level not the neuron level and that's way more sophisticated anything we can do with computers today including with AI. Or you could just argue no we just don't have the right program. It is mechanistic. There is a crank to turn but we're not running the correct program. The way these AIs run today is just a completely wrong architecture and wrong program.

我更倾向于这样的理论：有些事情它们能做得非常好，有些事情则非常糟糕。自古以来，所有机器和自动化都是如此。轮子在高速直线行驶和在路上行进方面比脚好得多。轮子非常不适合爬山。同样，我认为这些 AI 在某些事情上非常出色，会超越人类。它们是不可思议的工具。而在其他方面，它们就会彻底失败。

I just buy more into the theory that there are some things they can do incredibly well and there's some things they do very poorly. And that's been true for all machines and all automation since the beginning of time. The wheel is much better than the foot at going in a straight line at high speeds and traveling on roads. The wheel is really bad for climbing a mountain. The same way I think these AIs are incredibly good at certain things and they're going to outperform humans. They're incredible tools. And then there are other places where they're just going to fall flat.

史蒂夫·乔布斯有句名言：计算机是大脑的自行车。它让你比走路快得多。当然在效率方面，但首先需要腿来踩踏板。所以现在，也许我们有了大脑的摩托车，来延伸这个比喻。但你仍然需要有人来骑它、驾驶它、引导它、踩油门和刹车。

Steve Jobs famously said that a computer is a bicycle for the mind. It lets you travel much faster than walking. Certainly in terms of efficiency, but it takes the legs to turn the pedals in the first place. And so now maybe we have a motorcycle for the mind to stretch the analogy. But you still need someone to ride it, to drive it, to direct it, to hit the accelerator, and to hit the brake.

我们可能该找个话题来收尾了。

We should probably find something to wrap things up on.

当新的范式和工具集出现时，会有一个充满热情和变革的时刻。这在社会中如此，在个人身上也是如此。如果你乘上社会中的热情浪潮，那是令人兴奋的。你可以学习新东西，交朋友，赚钱。但个人也有一个热情的时刻。当你第一次接触 AI，对它感到好奇，并且真正持开放态度时，我认为那是你倾斜并学习这个东西本身的时候，而不仅仅是使用它——当然每个人都会使用它——而是真正了解它是如何工作的。我认为深入探索并查看引擎盖下面是非常有趣的。如果你人生中第一次遇到一辆车，是的，你可以坐进去开它，但那一刻你也会足够好奇，打开引擎盖看看它的结构和设计，弄清楚它。我会鼓励那些对新科技着迷的人真正深入内部，弄清楚它。你不必弄到能自己建造、修理或创造的程度，但达到让自己满意的程度，因为理解抽象层下面是什么、命令行下面是什么，会带来两件事。一是它能让你更好地使用它，而当你谈论一个具有如此大杠杆作用的工具时，更好地使用它是非常有帮助的。二是它也能帮助你理解你是否应该害怕它。

When new paradigms and new tool sets come out, there is a moment of enthusiasm and change. And this is true in society. And this is true as an individual. If you ride the moment of enthusiasm in society, that's exciting. And you can learn new things, you can make friends, and you can make money. But there's also a moment of enthusiasm in an individual. When you first encounter AI and you're curious about it and you're genuinely open-minded about it, I think that's the time to lean and learn about the thing itself, not just to use it, which of course everyone will, but to actually learn how it works. I think diving into and looking underneath the hood is really interesting. If you encounter a car for the first time in your life, yes, you can get in and drive it around, but that's the moment you're also going to be curious enough to open up the hood and look how it's structured and designed and figure it out. I would encourage people who are fascinated by the new technology to really get into the innards and figure it out. You don't have to figure it out to the level where you can build it or repair it or create your own but to your own satisfaction because understanding what's underneath the abstraction, what's underneath that command line, it's going to do two things. One is it'll let you use it a lot better and when you're talking about the tool that has so much leverage, using it better is very helpful. Second is it'll also help you understand whether you should be scared of it or not.

这东西真的会扩散成天网并毁灭世界吗？我们会坐在这里，阿诺德·施瓦辛格出现并说，2 月 24 日凌晨 4 点 29 分是天网获得自我意识的时刻，对吧？还是说，嘿，这是一台很酷的机器，我可以用它来做 A、B、C，但不能做 D、E、F，这是我应该信任它的地方，也是我应该怀疑它的地方。我觉得现在很多人都有 AI 焦虑，这种焦虑源于不知道这东西是什么或它如何工作，理解非常有限。所以解决这种焦虑的方法是行动。解决焦虑的方法永远是行动。焦虑是一种非特定的恐惧，认为事情会变糟，你的大脑和身体在告诉你做点什么，但你不确定该做什么。你应该迎难而上。你应该弄清楚它。你应该看看它是什么。你应该看看它如何工作。我认为这有助于消除焦虑。学习的行动，好奇心的追求，会帮助你克服焦虑。谁知道呢，它可能真的帮你找到你想用它做的非常有用的事情，让你更快乐、更成功。

Is this thing really gonna metastasize into a Skynet and destroy the world? Are we going to be sitting here and Arnold Schwarzenegger shows up and says at 4:29 a.m. and February 24th is when Skynet became self-aware, right? Or is it more that hey this is a really cool machine and I can use to do A B and C but I can't use to do D E and F and this is where I should trust it and this is where I should be suspicious of it. I feel like a lot of people right now have AI anxiety and the anxiety comes from not knowing what the thing is or how it works having a very poor understanding. And so the solution to that anxiety is action. The solution to anxiety is always action. Anxiety is a non-specific fear that things are going to go poorly and your brain and body are telling you to do something about it, but you're not sure what. You should lean into it. You should figure the thing out. You should look at what it is. You should see how it works. And I think that'll help get rid of the anxiety. That action of learning, that pursuit of curiosity is going to help you get over the anxiety. And who knows, it might actually help you figure out something you want to do with it that is very productive and will make you happier and more successful.