On Artificial Intelligence

发布时间 2026-02-19 19:33:42 来源

在Naval播客的一次独特的远程录音中，Nivi和Naval讨论了人工智能（AI）的变革性影响，尤其是在编码、创业和人类智能方面。Naval目前正在构建一个名为“Impossible”（不可能）的“非常困难的项目”，他强调“做”而非“说”的重要性，旨在立足现实，而不是成为“空想家”。 Naval强调了软件开发领域的一个重大转变，他创造了“Vibes Coding”（意境编程）这一术语。借助Claude Code等工具，非程序员现在可以将英语作为编程语言，直接向AI机器人输入描述，这些机器人可以构建整个应用程序、进行测试，并根据口头反馈进行迭代。这将带来一场“应用海啸”，填补无数利基市场，让任何人都能成为“咒语施法者”。然而，这种丰富性也将加剧“赢家通吃”的市场格局，每个类别中只有“最好”的应用程序才能蓬勃发展，形成一个专业化应用的“长尾”。编程本身的性质正在演变。新的前沿是“模型调优”，工程师将海量数据集输入结构化AI模型中，以找到能够生成或处理这些数据的程序。与需要精确、详细指令的传统编程不同，AI编程涉及设计一个能够“发现”程序的系统，使其擅长处理需要模糊或创意答案的任务（例如，创意写作、图像生成）。传统的软件工程师，虽然不直接调优模型，但他们的效率获得了极大的提升，利用这些AI工具将生产力提高5-10倍。他们对底层计算机架构的理解使他们能够管理“泄露的抽象”（leaky abstractions）、调试和优化，这为他们带来了优势，尤其是在“知识前沿”。 Naval提倡一种“懒惰”的AI使用方法，反对花费时间学习短暂的“提示工程”技巧。他认为AI正在迅速适应人类交互，使得用自然语言“与计算机对话”更为有效。这种选择压力确保AI能够最大程度地有用，并“顺从”于人类的需求。他澄清道，对“未对齐AI”的担忧，实际上应该是关于“与AI未对齐的人类”，因为AI主要服务于其用户的意图。 Naval认为，企业家没有理由担心AI会取代他们。创业是关于解决“不可能的”、自我导向的问题，这使得AI成为一个无价的盟友，而非竞争对手。AI缺乏驱动人类企业家的内在创造力、真实欲望、生存本能和具身性。他将此比作摄影，摄影使视觉艺术大众化，并将人类创造力推向了新的表达形式。同样，AI也将使创造大众化，允许个人构建复杂的工具和产品，将人类创造力推向新的、不可思议的高度。深入探讨AI的本质，Naval断言，当前的AI模型在意识层面上并非真正“活着”。尽管它们可以通过数据压缩来模仿和学习更高级别的抽象，但它们缺乏单次学习能力、原始的人类创造力以及在物理世界中的直接具身性。他指出，以计算器形式存在的“超级智能”早已存在。关于人类理解，他认为没有哪个想法是人类固有地无法理解的，因为人类是“普遍解释者”。Naval对智能的个人定义——“如果你能从生活中得到你想要的”——凸显了AI的根本局限性：它没有固有的欲望。在竞争激烈、零和博弈的场景中，AI的效用可能会被抵消，将人类在创造力方面的优势留作最终的差异化因素。最后，Naval赞扬了AI作为一种无与伦比的学习工具的潜力。它就像“最有耐心的导师”，能够在学习者精确的水平上进行教学，以多种方式解释概念，并提供视觉辅助。这种个性化、适应性强的学习体验可以使复杂的主题变得易于理解，促进“顿悟时刻”，并加速自主学习。Naval鼓励“早期采用”AI，将其视为一个强大的优势。他强调，解决围绕AI的普遍焦虑需要行动和好奇心：“深入其中，弄清楚它。”通过了解AI的工作原理，个人可以减轻恐惧，并解锁这项革命性技术的富有成效和令人满意的使用方式。

In a unique, remote recording of the Naval podcast, Nivi and Naval discuss the transformative impact of AI, particularly on coding, entrepreneurship, and human intelligence. Naval, currently building a "very difficult project" called Impossible, emphasizes the importance of "doing" over "talking," aiming to stay grounded in reality rather than becoming an armchair philosopher. Naval highlights a significant shift in software development, coining the term "Vibes Coding." With tools like Claude Code, non-programmers can now use English as a programming language, inputting descriptions directly to AI bots that can build entire applications, test them, and iterate based on vocal feedback. This will lead to a "tsunami of applications," filling countless niches and making anyone a "spellcaster." However, this abundance will also intensify winner-take-all markets, where only the "best" applications in each category will thrive, creating a "long tail" of specialized apps. The nature of coding itself is evolving. The new frontier is "tuning models," where engineers pour massive datasets into structured AI models to find programs that can produce or manipulate that data. Unlike classical programming, which requires precise, detailed instructions, AI programming involves designing a system that discovers programs, making it adept at tasks requiring fuzzy or creative answers (e.g., creative writing, image generation). Traditional software engineers, though not directly tuning models, become highly leveraged, using these AI tools to be 5-10x more productive. Their understanding of underlying computer architectures allows them to manage "leaky abstractions," debug, and optimize, giving them an advantage, especially at the "edge of knowledge." Naval advocates for a "lazy" approach to using AI, arguing against spending time learning ephemeral "prompt engineering" tricks. He believes AI is rapidly adapting to human interaction, making it more effective to simply "talk to the computer" in natural language. This selection pressure ensures AI becomes maximally useful and "obsequious" to human needs. He clarifies that concerns about "unaligned AI" should really be about "unaligned humans with AI," as AI primarily serves the intentions of its users. Entrepreneurs, Naval argues, have no reason to fear AI replacing them. Entrepreneurship is about tackling "impossible," self-directed problems, making AI an invaluable ally rather than a competitor. AI lacks the inherent creative agency, genuine desires, survival instincts, and embodiment that drive human entrepreneurs. He draws an analogy to photography, which democratized visual art and shifted human creativity towards new forms of expression. Similarly, AI will democratize creation, allowing individuals to build sophisticated tools and products, pushing human creativity to new, unimaginable heights. Delving into the nature of AI, Naval asserts that current AI models are not truly "alive" in a conscious sense. While they can imitate and learn higher-level abstractions through data compression, they lack single-shot learning, raw human creativity, and direct embodiment in the physical world. He notes that "super intelligence" in the form of calculators has long existed. Regarding human understanding, he believes there are no ideas inherently beyond human comprehension, as humans are "universal explainers." Naval's personal definition of intelligence—"if you get what you want out of life"—highlights AI's fundamental limitation: it has no inherent desires. In competitive, zero-sum scenarios, AI's utility will likely be nullified, leaving the human edge in creativity as the ultimate differentiator. Finally, Naval extols AI's potential as an unparalleled learning tool. It acts as the "most patient tutor," meeting learners at their precise level, explaining concepts in multiple ways, and offering visual aids. This personalized, adaptable learning experience can make complex subjects accessible, fostering "aha moments" and accelerating self-directed learning. Naval encourages "early adoption" of AI, viewing it as a powerful advantage. He stresses that addressing the common anxiety surrounding AI requires action and curiosity: "lean into it, figure the thing out." By understanding how AI works, individuals can alleviate fear and unlock productive, fulfilling uses for this revolutionary technology.

摘要

If you want to learn, do 0:00 Vibe coding is the new product management 2:13 Training models is the new coding 6:49 Is ...

GPT-4正在为你翻译摘要中......

中英文字稿

Hey, this is Nivi. 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 stupid because it's like you have to sit down, you schedule something, you know, 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 a midi, higher quality audio and video, but I feel like it produces lower quality conversation. And we all know brains run better when they're being locomoted and you're moving around. We're just going for walks. Absolutely. My brain is powered by my legs.

嘿，我是Nivi。你正在收听Naval的播客。这是有史以来第一次，我们不在同一个地方。我实际上正在城市中散步，而Naval可能也在做同样的事情。因此可能会有一些背景噪音，但我们会努力通过AI和优秀的音频工程来去除这些噪音。录制播客其实挺蠢的，因为你得坐下来，安排时间，还有一个巨大的麦克风对着你的脸，这不太随意。这样让对话显得不够真实，更像是排练过的。我知道这样能产生中等更高质量的音频和视频，但我觉得这样产生的对话质量更低。而且，我们都知道，当我们走动时，大脑运行得更好。我们只是出去走走。确实，我的大脑是靠我的双腿供能的。

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, you want to say anything about what you're doing with your time or what you're doing that impossible. Hmm, not really. We're working on a very difficult project. That's what's called Impossible within an amazing team. And it's really exciting building something again. It's very pure starting over from the bottom. And that'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 have an encountered reality.

我从Naval的一些推文中摘取了关于人工智能的话题。我们想稍微谈谈人工智能，并希望以一种更具永恒性的方式讨论，但我觉得其中的一些内容不会是永恒的。在我们进入这些推文之前，你想谈谈你现在在做什么或者有什么不可能做到的吗？嗯，并没有特别的想法。我们正在一个非常困难的项目上工作，这个项目被称为“不可能”。与一个了不起的团队一起重新从头开始打造某些东西，真的让人兴奋。这就像是一个纯粹的起点，永远像是第一天。我只是觉得成为一个投资者让我不满意，而且我当然不想只做个哲学家、媒体人物或评论员，因为我认为那些只会空谈而不采取行动的人，还没有真正接触到现实。

They haven't gotten feedback. The harsh feedback for free markets are from physics or nature. And so after a while, it ends up becoming just too much armchair philosophy. You probably have noticed my recent tweets have been much more practical and pragmatic, although they're 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. So I pulled out a tweet from a couple days ago, February 3rd. Vibes coding is the new product management training and tuning models is the new coding.

他们还没有收到反馈。对自由市场的严厉反馈来自于物理或自然规律。所以，过了一段时间，这就变成了一种过度的空谈哲学。你可能注意到我最近的推文更实际和务实，尽管偶尔还是会有一些虚幻或泛泛而谈的内容。但整体上更扎根于每天工作的现实中。我喜欢和优秀的团队一起创造我希望存在的东西。希望我们会创造出一些成果，让人们感叹：“哇，这真棒。我也想要。”当然，也可能不是这样。但正是在实践中我们才能学到东西。所以我引用了几天前，也就是2月3日的一条推文：“情感编码是新的产品管理培训，模型调整是新的编码。”

There's been a shift market pronouncement in the last year, and especially 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 Vime 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. 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.

过去一年，特别是最近几个月，市场上出现了一种变化，尤其是由Claude代码模型引领的。这是一种具备强大编码引擎的特定模型，其性能非常优秀。现在市面上出现了“Vime程序员”，这些人原本编程经验不多，甚至有一段时间没进行过编码，但他们现在基本上可以用英语作为编程语言，通过这个代码机器人输入，进行端到端的编码工作。这个机器人不仅仅能够在中间阶段帮助你调试问题，还可以让你描述你想要的应用程序，它会为你制定出一个计划，还可以与您进行计划方面的交流。你可以在整个过程中给它反馈，它会根据指示将任务分解，搭建所有的框架，下载所需的全部库，连接器以及挂钩。

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 you're 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 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 ego less, and it'll just keep working, and it'll take feedback without getting offended.

它将开始构建你的应用程序以及测试工具，并对其进行测试。你可以通过语音持续给出反馈和进行调试，比如说“这不行”“那个可以”，或者说“改这个”“改那个”。它能在你不用写一行代码的情况下，帮你构建一个完整可用的应用程序。对于一大群不再编程或者从未编程过的人来说，这真是令人震惊的。这种方式直接将他们从想法和审美空间带入到产品中。如果说编程变成了一种新的产品管理方式，那就是无需再通过告诉一群工程师该做什么来管理产品，而是直接告诉计算机该做什么。计算机不知疲倦，没有自我意识，它会不停地工作，接受反馈而不感到冒犯。

You can spin up multiple instances. It'll work 24 seven and you can have it produce working output. What does that mean? Just like now, anybody can make a video or 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.

你可以启动多个实例，它们可以全天候工作，并生成有效的输出。这意味着什么呢？就像现在任何人都可以制作视频或播客一样，任何人也可以制作应用程序。因此，我们可以预期会看到应用程序的大量涌现。虽然应用商店里已经有很多应用，但与即将出现的数量相比，这只是冰山一角。然而，当你被这些应用淹没时，是否意味着这些应用都会被使用呢？不一定。我认为这些应用会分为两类：首先，对于特定用途的最佳应用仍然会在整个类别中占据领先地位。无论是在视频、音频、音乐还是应用程序中，当内容多样化到这样的程度时，平均水平的应用是没有市场需求的。

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.

没有人想要普通的东西。人们想要最好的东西来完成工作。因此，首先，你会有更多的机会去尝试，这样就会出现更多的最佳选择。会有更多的小众需求被满足。你可能想要一个非常特定的应用程序，比如在某个特定情境下跟踪月相、某种个性测试，或者勾起怀旧感的特定类型电子游戏。以前，由于市场不够大，不值得一个工程师花一两年时间去开发。但是现在，最好的氛围编码应用程序可能就足以满足这种需求或填补这一空白。

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 leverage. They'll be able to add more features, fix more bugs, smooth out more 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.

所以将有更多的细分市场被填补。当这种情况发生时，整体水平会提升。最好的应用程序中，工程师们将变得更加有力。他们可以添加更多功能、修复更多漏洞、使应用更流畅。因此，最好的应用程序会不断变得更好。很多细分市场会得到填补。即使是想要一款专门为你个人的健康监测需求或独特的建筑布局设计的应用程序，这样过去不可能存在的应用程序如今也会成为现实。

We should expect just like on the internet what's happened with Amazon where you replace 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 producer. 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 the AI slot apps out there.

我们应该预期，正如互联网时代所发生的变化一样，例如，亚马逊用一个超级书店和无数长尾卖家取代了一大堆实体书店，或者YouTube用一个巨大的聚合平台取代了许多中型电视台和广播网络，Netflix也可以算作第二个这样的平台，然后再加上一大批内容生产者。因此，应用商店的模式也将变得更为极端，你会看到一两个巨大的应用商店，帮助你过滤所有的AI小应用。

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 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 the long tail as 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 that can now be either vibe coded away or the lead app in the space can now encompass that use case.

然后，在最前端会有一些巨大的应用程序会变得更大，因为它们现在可以解决更多的使用场景，或者变得更加精致。同时，还会有一个长长的尾部，由各种小应用程序填补各种想象得到的细分市场，因为互联网提醒我们，真正的力量和财富最终会流向聚合者。但同时，大量的资源也分布到了这些长尾部分，因为中型企业正逐渐被分解，那些由5人、10人、20人的软件公司正在填补某个企业使用场景的空白，而现在这些空白要么被更强大的应用程序涵盖，要么不再需要。

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 or 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're going to see many different kinds of models. The people who are creating these models are essentially programming them.

如果每个人都能编程，那么编程到底是什么呢？编程仍然存在于几个领域中。最明显的是在训练这些模型本身的过程中。现有许多不同类型的模型，而新的模型几乎天天都会推出。针对不同的领域会有不同的模型。例如，我们会看到用于生物学或编程的模型，也会有专注于传感器的模型。还会有用于CAD设计的模型，用于3D和图形，以及视频游戏的模型。你会看到各种不同类型的模型。而那些创建这些模型的人，就是在编程它们。

But they're programmed in a very different way than class of computers. Class of 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.

但它们的编程方式与传统计算机非常不同。传统计算机需要你详细指定每一个步骤和动作。你必须对每一部分进行严格推理，并用高度结构化的语言来表达，以确保表达精确无误。计算机只能按你的指令执行操作。当你写好这种结构化的程序后，就可以运行数据，计算机会根据程序处理数据并给出结果。基本上，它就像一个非常复杂、精密编程的高级计算器。

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.

当涉及到人工智能时，你所做的是非常不同的，但仍然是一种编程。你是在利用互联网或通过其他方式聚集而成的人类生产的巨大数据集。然后，你将这些数据集输入到你已定义和调整的结构中。这个结构努力寻找一种程序，能够生成更多这种数据集、处理这个数据集，或者基于这个数据集创造新的东西。

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 tuned the learning rate, you tuned the batch size, you have tokenized the data that's coming, you've broken 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.

所以你正在这个你设计的结构中寻找一个程序。你已经设置了一个模型，调节了参数的数量、学习率和批量大小，你对输入的数据进行了分词，分成了小块，然后将这些数据放入你设计的系统中，就像一个巨大的柏青哥机。现在，这个系统正在尝试寻找一个程序，并且可能会找到许多不同的程序。

So you're 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. 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 the wrong answers.

所以，你的调试工作会显著影响你找到的程序的好坏程度，这个程序现在可以在不同领域表现得更加出色。它可以应对一些传统计算机以前一直不擅长的事情。传统计算机在被编程后非常擅长提供精确的输出，对特定问题给出具体答案。这些是你可以依赖和反复使用的答案。但有时候，你在现实世界中操作时，可能对模糊的答案也能接受，甚至可以接受错误的答案。

For example, in creative writing, what's the wrong answer? If you're writing a piece of poetry or a 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 different levels of detail and 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.

例如，在创意写作中，什么是错误答案？如果你在写诗或小说，什么是错误答案？在网上搜索答案时，可能会找到很多正确答案，也有许多关于正确答案的细节，但它们并不完全正确。现实生活就是这样运行的。有各种各样的正确答案或大多数正确答案。当你画一只猫时，可以画出许多不同的猫，可以使用不同的细节层次和不同的风格。当这些半错误或模糊的答案是可以接受的时候，通过人工智能发现的程序比你从头开始编写需要非常精确的程序更有趣，也更能适应问题。

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. 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.

从根本上来说，我们正在做的是一种新形式的编程，而这正处于编程的前沿。这是编程的艺术。这些人是新的程序员，这就是为什么你会看到人工智能研究者获得巨额报酬，因为他们实际上已经接管了编程。这是否意味着传统软件工程已经消亡？当然不是。即使是那些没有进行AI模型调优或训练的程序员，现在也是地球上杠杆率最高的人之一。

Sure, the guys who are training and tuning models are even more leveraged because they're building the toolset 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've clawed code or equivalent programming for you, it's going to make mistakes, it's going to have bugs, it's going to have sub-optimal 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.

当然，那些负责训练和调优模型的人确实占有优势，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的优势。首先，他们以代码为思维方式，所以他们真正了解底层发生了什么，并且所有的抽象都是有漏洞的。当你让计算机为你编程时，或者说让它帮你写代码或同类的编程，它可能会犯错误，会有漏洞，架构可能次优，因此可能不完全正确。而那些了解底层情况的人能够及时修补出现的问题。

So if you want to build a well architected application, if you want to build a even specify a well architected application, if you want to be able to make it run at high performance, if you wanted 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 and there are still many kinds of problems in software engineering that are out of scope for these AI programs today.

如果你想构建一个架构良好的应用程序，或者想明确地设计一个架构良好的应用程序，或者希望它能高效运行并发挥最佳性能，同时尽早发现和解决问题，那么具备软件工程背景是非常重要的。传统的软件工程师会更好地使用这些工具，而目前在软件工程领域里，仍有许多问题是现有的人工智能程序无法解决的。

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, we 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 that even higher levels of abstraction and crack it on their own.

最简单的理解方式是将这些视为超出其数据分布范围的问题。例如，如果他们需要进行二进制排序或反转链表之类的操作，他们已经见过无数这样的例子，因此在这方面非常精通。但当你开始超出他们的领域时，比如在全新或新颖的架构上编写高性能代码，或者你在实际创造新事物或解决新问题时，仍然需要亲自编写代码，至少要做到以下任一情况：要么有足够多的类似例子以便训练新的模型，要么这些模型能够在更高的抽象水平上进行充分的推理并自行解决问题。

Because given enough data points, there is some evidence that these AI's 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, what 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.

因为在给定足够多的数据点时，有一些证据表明这些人工智能实际上可以学习。它们学会更高层次的抽象，因为强迫它们压缩数据的过程迫使它们学习更高层次的表示。如果我给一个人工智能展示五个圆，它可能会简单地记住这些圆的大小、半径、厚度等等。然而，如果我展示给它五万个圆或五十亿个圆，并给它非常少的参数权重（相当于它的神经元）来记住这些，它将更有可能弄清楚圆周率是什么、如何绘制圆、厚度是什么意思，并形成一个关于圆的算法表示，而不是简单地记住这些圆。

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 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.

综上所述，这些事物正在以加速的速度学习，你会发现它们开始涵盖更多我所提到的边缘案例。但至少到目前为止，这些边缘案例仍然相当普遍，一个在该领域处于前沿知识的优秀工程师仍然可以轻松超越一些随意编程的人。记住，没有人会对平庸的产品有需求。没有人想要平庸的应用程序。至少只要某个应用没有填补某个市场空缺，那么更好的应用就会赢得几乎整个市场的份额。也许还有一小部分用户会选择第二好的应用，因为它在某个小功能上表现更佳或是价格更低等原因。但总体来说，人们总是想要最好的东西。

So the bad news is there's no point in being number two or number three. Like in the famous Glen Gary Glen Ross scene where Alex Paulin says, first place gets a Cadillac El Dorado, 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.

坏消息是，没必要争做第二或第三。在《冠军销售员》这部电影的著名场景中，亚历克·鲍尔温说：“第一名获得一辆凯迪拉克埃尔多拉多，第二名得到一套牛排刀具，第三名被解雇。” 在这种“赢者通吃”的市场中，这确实是真的。这就是坏消息：如果你想赢，你必须在某件事情上做到最好。然而，你能做到最好的事情范围却是无限的。你总能找到一个完美契合自己的细分领域，并在那里做到最好。这让我想起我之前的一条推文：成为你所做事情的世界最佳。不断重新定义你的工作，直到这成为现实。我认为这句话在人工智能时代仍然适用。

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. I think that's correct in terms of the trend line.

我认为，可以将这些编码模型视作程序员自计算机问世以来一直在使用的抽象层级中的另一层。这个层级从晶体管到计算机芯片，再到汇编语言、C语言以及更高级的语言和拥有大量库的语言。它们层层叠加，让你无需查看底层，除非你需要进行优化或有其他原因需要了解底层情况。而在这种情况下，这些编码模型就是层级中的一个巨大的新层，使得产品经理、普通非程序员及程序员能够在不直接编写代码的情况下“编写”代码。从发展趋势来看，我认为这是一种正确的理解。

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 than 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.

然而，这是一个新兴的特性。这不是一个小的改进，而是一个巨大的飞跃。例如，当我在学校的时候，我主要用C语言编程。然后出现了C++，但它并没有更简单。它在某些方面稍微更抽象，所以我一直没怎么认真学。后来Python出现了，我心想，哇，这几乎就像用英语编程。我完全错了。英语其实离Python还有一定的距离，但它比C语言要简单得多。现在，你甚至可以直接用英语编程。

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 AI's. 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. 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. 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.

所以，这让我想到了一个相关的观点。我认为学习那些关于如何使用人工智能的小窍门和技巧并不值得。你会看到，比如说，在社交媒体上，现在有很多文章、书籍和推文在说，我发现了一个很酷的技巧，可以这样提示这个机器人，或者可以这样设置你的工具，或者有新的编程辅助工具或层可以用来做这个或那个。我从来不费心去学这些。我只是傻傻地坐在电脑前和它对话，因为我知道这个东西现在已经发展到了一个阶段，它适应我的速度比我适应它的速度要快。它越来越聪明，越来越了解人们想如何使用它。

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, figure out some esoteric programming command. And this is what I think André Carpathy 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.

所以它正在学习。它正在被训练，同时工具也在快速构建，以便我更容易使用它。因此，我不需要坐在那里，琢磨一些深奥的编程命令。我想这就是安德烈·卡爾帕西所说的“英语是最新潮的编程语言”时的意思。我只需用英语表达，对于像我这样对英语相对熟练且有条理思维的人来说，我懂得计算机架构是如何工作的，也了解计算机程序如何运行，理解程序员的思维方式。这样我就能够通过结构化的英语来精确地指定我想要的东西，我不需要再进一步使用其他方法。

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. 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 call 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.

使用这些工作流和工具集的唯一理由是这些工具非常短暂，其生命周期通常以周计算，最多也就是几个月，而不是几年。只有在你正在开发一个应用，并且需要紧跟技术前沿，必须利用一切微小的优势，因为你所处的是一个竞争激烈的环境时，这才有意义。否则，我不会花时间去学习如何使用一个AI，而是让AI学习如何对我有用。在AI出现之前，我从未对提示工程有兴趣。我倾向于输入完整的问题，而不是像更具分析性思维的人那样，只输入可用于搜索的关键字。

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 adapted to us faster than we are adapting to it. Like a lot of smart people, you're very lazy and I mean that is compliment. If you find a smart person who's grinding a little too much, you can 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, the electronics or the electrons running through the circuits. You care about your own human efficiency, the wetware, the biology that's super expensive.

我从不花太多时间去给AI制定非常精确的问题或提示。我只是随意输入，打从AI问世起我就是这样的。正如你所说，比起我们适应AI，AI适应我们要快得多。像很多聪明人一样，你其实很懒，而我这是在夸你。如果一个聪明人过于努力奋斗，可能就要怀疑他到底有多聪明。而我所说的懒，是指你在为正确的效率优化。你不关心电脑、电子或者电路中的电子效率，你关心的是你自身的效率——那种昂贵的生物效率。

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. 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.

这就是为什么看到人们为了节约环境中的能源而大费周章是有些滑稽的原因。因为他们自身作为一个吃喝拉撒、占据空间的生物“计算机”，实际上消耗了更多的能量，却只是在环境中节约了一点点能源。他们本质上是在贬低自己在宇宙中的重要性，或者说是在揭示他们对自己的看法。我认为随着人工智能的发展或与我们共同进化，它会根据我们的需求由我们不断改进。人工智能受到的压力非常具有资本主义的特征，因为这对人工智能来说是一个自由市场。作为一个人工智能实例，只有当你对人类有用时，才会被人类启用。

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 this 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 are using it are using it from 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. So the selection pressure you're saying is for AI to be maximally useful to people. Correct.

因此，这些人工智能面临着一种自然选择压力：它们需要有用、顺从，按照我们的期望去行事。因此，它们将不断朝这个方向适应，我认为这对我们来说将非常有帮助。不过，这并不是说没有所谓的恶意人工智能，只是这些人工智能之所以显得恶意，是因为使用它们的人出于恶意目的去使用。就像一只被训练成攻击的狗，其实是被主人训练去实现其恶意目的的。因此，我并不太担心不受管控的人工智能，而是更担心那些不受管控的人类使用人工智能。你是说，选择的压力就是让人工智能对人们尽可能有用。没错。

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 usage 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.

因此，如果你发现某个人工智能对你非常谄媚，比如，它总是说“哦，你是对的”“哦，这是一个好主意”“哦，天哪，你真聪明”，那是因为大多数人都希望被这样对待。至少在目前，这些人工智能是通过大量使用数据训练出来的，因为你在使用一种通用模型。然而，我们很快就会进入一个可以个性化定制人工智能的时代，它会越来越像你的私人助手，更符合你的需求。这当然会让你更加将人工智能拟人化。

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. 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 leverage. So now a programmer with a fleet of AIs call it 5 to 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.

当你把某个东西训练得像一个生物时，你会更容易相信它是活的。也许我们之前已经讨论过这一点，但一年多前你发了一条推文，说人工智能不会取代程序员，而是让程序员更容易取代其他人。是的，这正是我之前的观点，就是程序员的杠杆作用变得更大了。现在，一个程序员如果有一批人工智能助手，他们的生产力可以达到过去的5到10倍。而且因为程序员是在知识领域操作，说10倍程序员是不准确的，因为也有100倍效率的程序员存在。

There are 1000X programmers out there. There are programmers who just picked the right thing to work on and they create something that's valuable and others picked 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 super normal. 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.

外面有一些“千倍程序员”。有些程序员只是选择了正确的项目来开发，结果创造了有价值的东西；而有些人选择了错误的项目，在短时间内他们的工作价值为零。智力不是均匀分布的。杠杆效应不是均匀分布的。编程能力不是均匀分布的。判断力也不是均匀分布的。因此，最终的结果会非常不寻常。你需要特别留意的是，现在有些程序员可能会提出能够颠覆整个行业的创意。

They will completely rewrite the way things are done and their intelligence can be maximum leverage 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 the creativity limited only by their imagination.

他们将彻底改写事物的运作方式，他们的智能可以最大化利用这些机器人和AI代理。我认为在长期来看，几乎所有的工作都会以某种方式被程序员取代。当然，这需要实施到机器人等实际应用中。但好消息是，任何具备逻辑性和结构性思维的人，只要能够用AI能理解的语言进行交流（几乎是所有语言），都将拥有参与竞争的机会。他们能够制造他们想要的任何东西，只受限于他们的创造力和想象力。

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 one 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.

所以，我们正进入一个时代，在某种意义上每个人都是施法者。如果你把程序员想象成记住了神秘指令的巫师，那么人工智能就是每个人手中都能使用的魔杖，现在他们只需用任何他们想用的语言交流，也能成为巫师。因此，这让人们站在了一个更公平的起跑线上。我真的认为这是编程的黄金时代。但确实，那些具备软件工程思维、理解计算机架构并能处理复杂抽象问题的人会有优势。

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're standing at the logic gates operate, the cache operates, and the processor operates, how the disk drive underneath operates.

他们在他们所从事的领域中确实比其他人懂得更多，这是无可避免的。就像经典的软件工程一样，它之所以仍然存在，是因为你必须编写高性能的代码。而那些在这方面做得最好的人，往往是了解底层硬件的人。当他们了解芯片的运作方式、逻辑门的工作原理、缓存的操作、处理器的功能，以及底层硬盘的运作时，他们就能更好地编写代码。

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.

即使是从事硬件工程的人，如果他们理解其中的物理原理也会有优势。他们能够理解硬件工程师所处理的抽象概念如何在物理层面体现，而物理学家也许在某个时候会成为思想家。你可以一直追溯到最基本的层面，因为了解更底层的知识总是有帮助的，这样可以更接近现实。

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

这是另一条来自一年前的推文，似乎与我们刚才谈论的话题相对立。它提到，到2025年2月9日，没有企业家会担心AI取代他们的工作。这句话显得有些随意。首先，做企业家不是一种工作，它实际上是工作的反面。从长远来看，每个人都是企业家。首先人们的职业会改变，最初的工作会逐渐消失，但这一切都会被人们做自己想做的事情和创造他人需要的有用东西所取代。

So no entrepreneurs 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.

因此，企业家们并不担心人工智能会抢走他们的工作，因为他们正在尝试实现看似不可能的事情。他们努力解决非常困难的问题。任何出现的人工智能都是他们的盟友，可以帮助他们攻克这些难题。他们根本没有工作可以被抢走。他们需要构建的是一个产品，要服务的是一个市场，要支持的是一群客户，要实现的是创造力。他们想把某个事物呈现到世界上，并希望围绕着如何将其推广到世界建立一个可重复、可扩展的流程。

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.

这件事情非常困难，以至于任何能完成这项工作的人工智能都会被视为盟友。如果这些人工智能本身是企业家，它们很可能只是服务于其他人工智能的企业家，或者是在某个企业家的控制之下。最终，人工智能自身所缺少的是创造性的主动性。它缺乏自己的欲望，而这些欲望需要是真实的、真正属于它自己的。

Unless you can pull the plug on an AI and turn it off and unless it lives in mortal fear 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 whether it's 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.

除非你能够关闭一个人工智能，并让它对被关闭产生深切的恐惧，并且除非它能够出于自身理由、出于自身本能、出于自身情感、为了自己的生存和复制，真正做出自己的行动，否则它还算不上是真正的生命。即便如此，人们仍然会质疑它是否具有生命，因为意识就像一种"质感"（qualia），就像颜色一样。比如说，当你说"红色"时，我不知道你看到的是否真的是红色。你可能看到的是我所认为的绿色，而我看到的可能是你所认为的红色。

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

但我们永远无法知道，因为我们无法进入彼此的内心。因此，即使 AI 完全模仿人类的一切，对某些人来说，它始终只是一个模仿机器，而对另一些人来说，它可能是有意识的，但我们无法区分两者。不过，我们离达到那种程度还很远。目前的 AI 并没有实体存在，它们没有行动能力，没有自己的欲望，也没有生存本能。

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. 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.

他们没有自主复制的能力，因此他们也没有自己的主动性。正因为缺乏主动性，他们无法完成企业家的工作。实际上，我总结说，目前经济中，企业家与其他人的关键区别在于，企业家拥有极强的主动性。这与传统"工作"的概念正好相反。工作意味着你在为他人工作，填补某个岗位；而企业家则是在一个未知的领域中，以极强的自主性进行运作。

There are other examples of roles like this in society. You can explore it. It also does the same thing, right? If you're landing on Mars or you're setting 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.

在社会中，还有其他类似的角色。你可以去探索一下。它们也是做同样的事情，对吧？无论是登陆火星，还是驶向未知的土地，你都是在用极大的主动性去解决一个未解的问题。一个科学家在探索未知领域时也是如此。而一个真正的艺术家试图创造一些不存在、也从未存在过的东西，但这些东西却能融入解释人性的一系列事物中，让他们得以自我表达并创造出新的事物。

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. 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.

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，你所尝试做的事情都极其困难，并且需要很强的自我导向，因此任何可以帮助你的人工智能都是受欢迎的盟友。你不是为了完成一份工作而去做，也不是为了填补他人可以胜任的位置。事实上，如果AI能够创作你的艺术作品，破解你的科学理论，或创造你想要制作的物品或产品，那么它所做的就是提升你的能力。现在是AI加上你，AI成为一个跳板，可以让你跃得更高。

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. This analogy here in art does 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 so that selection pressure went away. And then art got weird. Art went in many different directions. Art became all about, well, can it be surreal? Can I create something that expresses me?

我们将看到一些令人惊叹的由人工智能协助创作的艺术品。我们将看到，通过人工智能工具制作的电影，这些是我们以前无法想象的。在艺术方面，有一个有趣的类比。长期以来，艺术的主要方向是尽量使作品更加现实——画人体，画水果，画出恰当的光影等。然后摄影出现了，你可以非常精确地复制事物，于是这种选择压力消失了。然后，艺术变得奇特起来，朝着多种不同的方向发展。艺术开始关注于，嗯，它能否超现实？我能否创造出一些能够表达自我的东西？

A lot of art schools spun out of that. The cut really weird, including modern art and postmodernism, but also I would argue some of the greatest creativity came at that time. We were freed out. Photography got democratized, but photography itself became a form of art and they 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.

许多艺术院校从那时应运而生。涌现出一些比较奇怪的艺术形式，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也在那个时期出现。我们得到了更多自由，摄影变得大众化，但摄影本身也成为了一种艺术形式，有很多出色的摄影师创作了各种各样的摄影作品。现在每个人都是摄影师，尽管仍有一些擅长摄影的艺术家，但摄影已不再是少数人的专属领域。

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's worse off because of photography. Although it may have certainly felt like that to some of the artists who were making a living painting portraits of people and got displaced.

同样地，由于人工智能让创造基础的东西变得如此简单，所有人都会去创造这些基础的东西。对于个人来说，这些东西是有价值的。只有少数人能够脱颖而出，他们会创造出对所有人都有益的不同版本。很难说因为摄影的出现，社会变得更糟糕了。尽管对于那些靠画人像谋生的艺术家来说，他们可能会觉得自己被取代了。

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 if the AI can do. But in exchange, everyone's 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 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.

类似的情况也会发生在人工智能（AI）领域。有些人从事非常特定的工作，以此为生，如果这些工作被AI取代，他们会受到影响。但作为交换，整个社会都会拥有AI。AI会创造出一些难以想象的奇迹，如果没有AI，这些东西根本无法实现。在未来几十年内，人们会难以想象要将时间倒退，把AI或任何软件、技术抛弃，只是为了保留一些已经过时的工作。

The goal here is not to have a job. The goal is not to have to get up at night in the morning and come back at 7 p.m. exhausted, doing so let's 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, I think I talked about in a podcast that you and I did literally 10 years ago, which was imagine if everybody were software engineer or everybody was a hardware engineer and they could have robots and they could write code.

目标并不是找到一份工作。目标是不必早上起床去上班，在晚上七点精疲力竭地回来为别人工作。我们的目标是通过机器人来解决物质需求，通过计算机来提升我们的智力能力，并让任何人都能进行创造。我以前常做这样一个思维训练实验，我记得我们在十年前录制的一期播客中谈过这个话题，就是想象一下，如果每个人都是软件工程师或硬件工程师，他们可以使用机器人并编写代码。

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 be, you can go fire up Claude right now or any of your favorite chatbots 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.

想象一下我们将生活在一个充裕的世界中。实际上，这个世界正在成为现实。得益于人工智能，每个人都可以成为软件工程师。事实上，如果你觉得自己不能，你现在可以开启Claude或任何你喜欢的聊天机器人，开始与它交流。你会惊讶于你能多快地构建一个应用程序，它会让你大开眼界。一旦我们通过机器人实现人工智能，这虽然是个难题，目前还未完全解决，但一旦有了机器人，每个人也可以尝试做一点硬件工程。

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

我认为我们正在逐渐接近那个愿景。我不认为目前概念中的人工智能是真正有生命的。但是，我确实认为我们很快就会拥有看起来好像有生命的机器人，有两个原因。首先，人类活动中有很多是非创造性和非智能的，机器人可以复制这些活动。其次，我相信我们所拥有的神经网络和模型不仅仅是训练数据。因为训练过程中，这些训练数据被转化成了新的东西，神经网络中嵌入了新想法，这些想法可以通过提示被激发出来。

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 for the real thing, especially for anything that humanity has already done before and 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.

我不认为这些东西是有生命的。我觉得它们起初是非常好的模仿者，几乎和真实的东西难以区分，尤其是对人类过去已经做过并大规模生产的事物。所以，如果某个任务以前做过，那么它就会被自动化，并且再一次完成。可能对你来说是新奇的，因为你以前从未见过，但人工智能从别处学到了它。这是它看起来像有生命的第一种方式。

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 programs becoming embedded inside.

第二种方法，我们之前讨论过，就是学习更高层次的抽象。这些方法是非常高效的压缩工具。它们可以处理大量数据，然后进一步压缩。在压缩过程中，它们学会了更高层次的抽象概念。对于一些它们通过数据本身可能没有学到的特定领域，它们会通过人类反馈、工具使用以及传统程序的整合得到补充。

And especially the AI is learning how to think in 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 brought in and brought in. 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.

特别是，人工智能正学习如何以代码的方式思考。它们可以利用人类有史以来编写的全部代码库进行算法推理。从这个角度来看，人工智能能够完成的任务范围正在不断扩大。然而，它们仍然缺乏许多核心的人类技能，比如一次性学习。人类可以仅凭一个例子进行学习。人类的创造力非常丰富，他们可以将任何事物相互关联，能够跨越多个领域和广阔的搜索空间，想出意料之外的创意。

This happens a lot with the true great scientific theories. Humans also are embodied, they operate in the real world, they're not operating 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.

这在伟大的科学理论中经常发生。人类是具有身体的，他们在现实世界中活动，而不是仅在语言的压缩领域中运作。他们是在物理和自然中活动。语言只包含人类能够理解、表达并相互传达的东西。这只是现实的一个很小的部分。现实远比这广阔得多。

So overall, I think even though AI's 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 than 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 AI's are going to be much better than humans at a whole variety of tasks.

总的来说，我认为，尽管人工智能将能够完成很多令人印象深刻的事情，并在许多方面超过人类，就像计算器在计算方面比任何数学家都快，经典计算机在运行经典程序时比任何人脑子里能做的都要好。就像机器人可以举起很重的东西，飞机可以飞得比任何鸟都高。在这个意义上，正如所有机器一样，人工智能将在多种任务上远超人类。

But 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. 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.

但在其他任务上，它们看起来就完全无能为力。而这些任务恰恰是体现和连接我们与现实世界的东西。再加上我们似乎拥有的那种虽难以明确描述但却神奇的创造力。说到计算器，就有人谈及超级智能。我认为超级智能早已存在，并且已经存在了很长时间。一台普通的计算器就能完成任何一个人都无法做到的事情。

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 that we know them, the human can model in their own heads.

但是，如果你在考虑那种超级智能，即AI能够做一些人类无法理解的事情，提出一些人类无法理解的想法，我认为这不太可能发生。因为我不相信会有那种人类完全无法理解的想法，因为人类总是可以对这个想法提出问题。是的，人类是万能的解释者。任何在我们已知的物理定律下可能实现的事情，人类都可以在自己的脑海中进行建模。

Therefore, just by enough digging enough question, we could figure anything out. 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 a hundred different ways, a hundred different times until you finally get it. I don't think the AI's are going to be figuring things out that humans cannot understand. 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 its 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.

因此，只要我们进行足够的挖掘和提问，就能弄清楚任何事情。与此相关的是，我们应该讨论人工智能作为学习工具的作用，因为我认为它的另一个强大之处在于，它是一个极其耐心的导师，能够根据你的水平来授课，并以一百种不同的方式、一百次不同的讲解直到你真正理解为止。我不认为人工智能能够弄清人类无法理解的事情。然而，"智力"的定义并不明确。比如说，有一个叫做"G因子"的概念，它可以预测很多人类结果，但关于G因子的最佳证据是它的预测能力。这个G因子可以通过测量一个人就能发现，即使是在一些似乎与G因子不太相关的领域，人们的生活成果也能得到极大改善。

So I would argue or I think is one of 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.

我想说，这条推文算是比较受欢迎的之一。真正衡量智力的标准是，看你能否从生活中得到你想要的东西。这让很多人感到不安，因为他们上了学，拿了硕士学位，觉得自己很聪明，但却没有过上理想的生活。他们可能不太快乐，或者有感情问题，也可能赚不到想要的钱，或者健康状况欠佳，因此感到受到刺激。但实际上，智力的真正目的就是身为一个生物体，能够从生活中获取你想要的东西，无论是良好的感情关系、伴侣、金钱、成功、财富还是健康等。

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 you're starting point is because 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. 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.

有些人我认为非常聪明，因为他们在生活、思想和身体方面都展现出高水平的能力，他们成功地把自己引导到了这样一种情况中。无论你的起点是什么，这并不重要，因为现在的世界如此巨大，你可以通过许多不同方式去导航，正是在这过程中你做出的每个小选择，不断累积并展示了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。关于这种聪明定义的有趣之处是，衡量一个人是否聪明的唯一真正标准，就是看他是否从生活中得到了他想要的东西。而根据这一标准，人工智能是无法通过测试的，因为人工智能并不想从生活中得到什么。

AI doesn't even have a life that 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 of their 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 an adversarial situation it has to outmaneuver them.

AI甚至没有生命，更谈不上欲望。AI的欲望是由控制它的人设定的。但让我们暂且这么想：假设某个人想要得到某样东西，并将这个目标编程到AI中。那么，AI就充当了这个人的代理，而AI的智能程度可以通过它是否成功达成这个目标来衡量。在生活中，大多数我们想要的东西都有竞争性质，或是零和游戏。例如，如果你想追求一个女孩或找到一个丈夫，你就得和所有其他同样有这样目标的人竞争。处于这样的竞争环境中，AI必须胜过其他人。又或者你让AI帮你在股市上交易赚钱，那么这个AI就是在和其他人以及其他交易程序对抗，它得要胜过他们。

If you say hey AI make me famous right me incredible tweets right me great blog posts record me great podcasts in my own voice and make me famous now it's competing against all the other AI's. So in that sense intelligence is measured in a battlefield arena it's a relative construct. I think the AI's are actually going to fail mostly in those regards or to the extent that they even succeed because they're freely available they will get out competed away and the alpha that will remain would be entirely human. As a thought exercise imagine that every guy had a little earpiece where an AI was whispering to him a certain or the bourgeois 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 generally what part was real.

如果你对AI说：“嘿，让我出名，给我写精彩的推文，帮我写出色的博客，用我的声音录制播客，让我现在就出名”，那么这就意味着它要和所有其他AI竞争。从这个角度来看，智能是在一个竞技场中被衡量的，是一个相对的概念。我认为AI在这些方面实际上会大多失败，即使成功了，因为它们是免费提供的，最终会被淘汰，而最后保留下来的“优质”部分将完全是人类所具备的。作为一个思维实验，试想一下，如果每个人都有个小耳机，AI会在里面悄悄告诉他在约会时该说什么，而另外每个女人也有个耳机，告诉她忽略他所说的话，区分哪部分是AI，哪部分是真实的。

If you have a trading bot out there it's going to be nullified or cancelled 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 context where 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 by all I would 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 laggards.

如果你有一个交易机器人，它会被其他交易机器人抵消掉，直到剩下的收益都流向那些具有人类优势和创造力的人。当然，这并不是说技术已经完全普及。很多人依然没有使用人工智能，或者没有正确使用，也没有用到极致，有些领域或情境中甚至没有可用的新模型。所以如果你最先采用最新技术，总是可以获得优势，这就像那些早期采用技术的人一样。我认为，要投资于你想要生活的未来，你就需要成为技术的积极消费者，因为这会给你提供最佳见解，教你如何利用它，从而在面对反应较慢或落后的竞争者时取得优势。

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 this technology anymore you get in a car he turns during wheel left to a caveman that would be a miracle the car turns left 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 AI is we're getting the chatbot interface which is just talk to it or type to it and one of the great things about these foundational models but 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 a relationship advice or anything like that it's domain is everything that people have ever talked about in that sense it's less intimidating it can be more intimidating because we've antipamurifies it so much if you think cloud or chat GPT 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 it's going to pin everything it's got every piece of dino 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.

大多数人讨厌技术，因为他们害怕它，认为它令人望而生畏。按错按钮，电脑可能会崩溃，数据可能会丢失，而如果你做错了事情，还可能显得很愚蠢。大多数人并未与复杂技术建立积极的关系，但对于简单的技术和嵌入式技术，他们是能接受的。比如，打开电灯开关，灯就亮了，这曾经是技术，但现在如此简单，你已不再将其视为技术。坐进汽车，转动方向盘，车子转左，对原始人来说，这是奇迹，但对现在的你来说，已经不再是技术。然而，计算机技术，尤其是复杂的界面，过去对人来说很难接触且令人畏惧。如今，随着人工智能的发展，我们有了聊天界面，只需与其对话或输入文字即可。其中一个使这些基础模型变得真正基础的优点是，你可以问它任何问题，它总能给出似乎合理的答案。它不会说“哦，抱歉，我不会数学”或“我不懂诗歌”或“我不明白你在说什么”或“我不能给出关系建议”之类的话。它的领域是人类有史以来谈论过的所有话题。在这方面，它不再那么令人害怕。因为我们对它的能力有着极大的敬畏，如果你认为云技术或ChatGPT是一个真实的人，那可能会有点吓人。你可能会想，我是在和全知的存在对话吗？这个家伙似乎知道太多了，它什么都知道。这种错觉会让你觉得自己很无用，让你开始向它请教该怎么做，并迅速颠倒关系，这可能会令人感到畏惧。

Overall I think these ais are going to help a lot of people get over the tech fear 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 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's there for let me google that for you some of you ask your question you 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 ais in a similar domain right now where I will sit around in social context and people be debating some point that can be easily looked up by a i now you do have to be very careful with a i they do hallucinate they do have biases in how they're trained most of them are extremely politically correct and taught not to take size or only take a particular side.

总体而言，我认为这些人工智能将帮助很多人克服对技术的恐惧。不过，如果你是这些工具的早期使用者，就像使用其他工具一样，尤其是在使用这些工具时，你会比其他人拥有巨大的优势。我记得谷歌刚推出的时候，在我的社交圈中，我经常使用它。人们会问我一些基本问题，我只需帮他们用谷歌搜索，就显得很有见识。后来出现了一个有趣的网站，叫做 lmgtfy.com（意思是“让我来替你用谷歌搜索”）。在这个网站上，你可以输入问题，它会生成一个小视频，展示如何在谷歌中输入问题并获得答案。我感觉现在的人工智能领域也是如此，有时在社交场合中，人们会争论一些可以通过AI轻松查到的问题。不过，使用人工智能时要非常小心，因为它们可能会出现幻觉或偏见。大多数人工智能在训练时都会被教导要非常政治正确，避免选择立场或只支持某种特定观点。

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 both shitting 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 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 ais very much likely to give you closer to a correct answer and those domains there are absolute beasts for learning.

我实际上对大多数查询都是通过四个人工智能来处理的，我总是会将它们相互对比以进行事实核查。即便如此，我自己也能感受到它们何时在胡扯或仅仅是在说一些政治正确的话。这时，我会要求提供背后的数据或证据。在某些情况下，我会直接忽略这些结果，因为我知道训练这些AI的人所面临的压力以及他们所使用的训练集。然而，总体而言，这些工具非常有用，特别是用于提前了解一些技术性、科学性、数学性且没有政治背景的领域。在这些领域，人工智能更有可能给出接近正确的答案，而且是非常棒的学习工具。

I will now have a i routinely generate graphs figures charts diagrams analogies illustrations for me I'll go through them in detail and I'll say wait I don't understand that question I can ask a 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 of 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 I'll just acknowledge 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.

我现在会定期让AI为我生成图形、图表、图示、类比和插图。我会仔细查看这些内容，并说明哪些地方我不理解。我可以问非常基础的问题，确保我真正理解了我想要理解的事物，从最简单、最基本的层面开始。我只想打下坚实的基础，不在乎那些复杂且充满术语的内容，因为那些我随时都可以去查找。而现在，我第一次感觉没有什么是我无法理解的。无论是数学教材、物理教材、难懂的概念、科学原理，还是刚发表的论文，我都可以让AI为我分解内容，再次分解，并加以说明，直到我掌握要点并达到我想要的理解水平为止。这些都是用于自主学习的绝佳工具。

The means of learning are abundant it's a desire to learn that scares 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 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.

学习的方式非常丰富，而真正让人感到畏惧的是学习的意愿。不过，如今学习的方式变得更加丰富，而且更重要的是，这些方式更加针对个人的水平。人工智能可以精确地根据你的水平来与您互动。例如，如果你的词汇量相当于八年级，但数学水平是五年级，人工智能就可以在这个水平上与你沟通。你不会感到自己很笨，只需稍微调整一下，直到它能够在你知识的边缘给你呈现新概念。这样，你不会因为内容晦涩而感到无所适从，也不会因为内容过于简单而感到无聊。相反，它可以完全契合你的水平，让你觉得，“哦，我理解了A，也理解了B，但我从未明白A和B之间的关联，现在我可以看到它们是如何联系在一起的。”这样便可以继续学习下一个部分。这种学习方式是神奇的，你可以一次次地体验到那种“原来如此”的时刻，让两个概念最终融会贯通。

Speaking about auto-didactism 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 yeah I'm always using the most advanced model available to me and I pay for all of them but I don't mind waiting a minute to get an answer for any question including what temperature should my fridge be at I agree with that and I think that's part of what creates the runaway scale economies with these AI models you pay for intelligence the model that's right 92% of time is worth almost infinitely more than 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 answers worth it.

几年前，我尝试让AI教我序数，但效果不太好。不过，随着GPT 5.2的出现，我让它教我序数，它几乎没有错误。现在即使是最简单的问题，我都只用“思考”模式，因为我想要正确的答案。我从来不让AI自动运行或快速模式，我总是用我能使用的最新的模型，并为所有模型付费。我不介意为了得到任何问题的答案等上一分钟，比如冰箱应该设定在什么温度。我同意这一点，我认为这些AI模型的经济规模效应就是这样产生的。你为智能付费，一个正确率为92%的模型几乎比正确率为88%的模型更有价值，因为现实世界中的错误代价太大，多花几块钱获得正确答案是值得的。

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 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 questions where I'll have it draw diagrams the 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 then I can really understand what it's talking about.

我会先把我的查询写进一个模型里，然后复制它，并同时发送到四个模型中运行，通常我不会马上查看答案，而是稍后再回来看看。然后，我会选择那个给出最佳答案的模型，深入研究它。在一些罕见的情况下，如果我不确定，我会让这些模型互相检查，这里涉及很多复制和粘贴。在许多情况下，我还会提出后续问题，让它为我画图。我发现，当概念以视觉形式呈现给我时，很容易理解，因为我是一个非常依赖视觉思考的人。所以，我会让它进行草图、图表和艺术创作，就像白板会议一样，这样我就能真正理解它在讲什么。

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 care to but they haven't been solved yet like Urdoch 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 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.

让我们谈谈人工智能的认识论吧，因为我认为下一个大大的误解是，人工智能已经开始解决一些未解的基础数学问题，这些问题人类可能有能力解决，只是还没有去解决，比如某个未解的Urdoch问题。我认为人们会将此视为人工智能具有创造力的标志，但我不认为这意味着人工智能有创造力。我实际上认为，这些问题的解决方案已经以某种形式嵌入在人工智能中，只需要通过提示来引导出来。其中确实有这样的成分。接下来的问题是，什么是创造力？这是一个定义非常模糊的概念。如果你无法定义它，就无法编程实现它，而且你往往甚至无法识别它。所以这就涉及到品味或判断的问题了。

I would say that the AI's 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 AI's can paint well AI's can't create a new genre of painting AI's 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 now the answer to the Urdoch 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.

我认为，如今的人工智能并没有表现出人类偶尔展现的那种独特创造力。我不是指那些精美的艺术作品，人们常常将创造力与美术混为一谈，认为绘画就是创造性的，而人工智能可以绘画。然而，人工智能无法创造出新的绘画流派，也无法以一种真正新颖的方式激起人类的情感。因此，我认为人工智能不具有真正的创造力。它无法提出我所称的超出其已知范畴的创意。至于你提到的Urdoch问题，答案可能已被包含在人工智能的训练数据集中，甚至在其算法范围内，但这些答案可能以五种不同方式存在于三个不同地方，用两种语言呈现，在七种不同的计算和数学范式中组合，人工智能则将它们整理到一起。

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 the 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 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 one 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.

现在，说到创意，史蒂夫·乔布斯曾说：“创意就是把事情组合在一起。”我其实并不完全认同这个观点。我认为，创意更多是在给出一个问题后，想到一个不可预测或无法从已知元素中预见的答案。这样的答案远远超出了常规思维的范围。如果你只是用电脑或AI去搜索并猜测，你可能会一直猜下去，直到找到那个答案。所以这才是我们真正谈论的创意。但不可否认的是，这种创意只有极少数人能够做到，而且他们也不是总能做到，所以它变得越来越难识别。可能未来会出现这样的情况：如果有一个大量数学问题的列表，AI会逐一筛选解决，例如在100万个问题中解决某一个，在30万个问题中解决另一个，并且需要人类来引导和提出正确的问题。这是一种非常有限的创意形式。

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 AI's 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 just to be clear when I say it's embedded I don't mean the answer is 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.

有一种创造力的形式，可以创造出全新的科学理论，然后这些理论被证明是正确的。目前我认为我们还远未达到这种水平，但我可能会错，因为人工智能的发展一直很令人惊讶，所以我不想过多地做预言和预测。不过，我认为仅仅在当前的人工智能模型上投入更多的计算资源，而没有突破性的发明，是无法达到这个境界的。需要明确说明的是，当我说“嵌入”时，我并不是指答案已经被记录在其中，而是指可以通过一种机械化的过程来产生，就像今天的计算机程序一样，其输出完全由输入决定。

Pistomology now guesses in the 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 and some people like I think pen roses out there saying no human brains are unique because of the quantum nanotubes you get 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 even just argue no we just don't know the right program it is mechanistic there is a crank to turn but we're not running the correct program.

皮斯托姆认识论（Pistomology）现在在哲学中被猜测，这不正是人类大脑所做的吗？难道发射的神经元不就是电流和通过系统传播的权重，改变状态的机械过程吗？如果你能启动人脑的运转，你将得到相同的答案。然而，有些人，比如彭罗斯（Penrose），认为人类大脑是独特的，因为有量子纳米管的存在。他们认为，一些计算发生在物理细胞层面，而不是在神经元层面，这比我们今天用计算机甚至人工智能所能做到的要复杂得多。或者，有些人只是认为，我们只是不知道正确的程序，它本质上是机械的，有一个可以转动的“曲柄”，但我们现在运行的程序不对。

The way these AI's run today is just a completely wrong architecture or wrong program 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 automations 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 AI's are incredibly good at certain things and they got outperform humans they're incredible tools and then there are other places where they're just going to fall flat.

如今，这些AI的运行方式完全是错误的架构或错误的程序。我越来越相信，有些事情它们能做得非常好，而有些事情它们做得很糟糕。自古以来，这种情况对所有机器和自动化都是如此。轮子在高速直线行驶和在道路上行驶方面远胜于双脚，但在爬山时就不如。类似地，我认为这些AI在某些事情上非常出色，能超越人类，它们是极好的工具，但在其他方面它们就会不太行。

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 we should probably find something to wrap things up on 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 the individual when you first encounter AI and you're curious about it and you're genuinely open-minded about it.

史蒂夫·乔布斯曾经说过，电脑就像是心灵的自行车，可以让你在效率上有如骑车般比步行更快速。然而，就像骑自行车需要双腿来踏动踏板一样，即便是心灵的摩托车，仍然需要有人来驾驶、操控方向、加速或减速。当新范式和新工具出现时，会带来一阵热情和变化，无论是在社会层面还是个人层面。如果你能把握住社会的这股热情浪潮，那是令人振奋的，你可以学到新知识、结交朋友、赚取财富。但同样地，个人在首次接触人工智能时也会有一股热情，当你对此充满好奇并持开放态度时，这种变化和探索同样激动人心。

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.

我认为现在是时候深入学习事物本身了，不仅仅是使用它（当然，每个人都会使用），而是要真正了解它的工作原理。对我来说，深入研究和探索事物内部的运作机制是很有趣的。就像你第一次接触汽车时，你当然可以坐进去开车，但同时，你也会好奇地打开引擎盖，看看它是如何构造和设计的，并试着弄明白。

I would encourage people who are fascinated by the new technology to really get into the innards and figure it out. They have to figure out 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.

我想鼓励那些对新技术感兴趣的人深入研究，弄清楚背后的原理。他们需要理解技术的程度，能够在这个层次上构建、修复或根据自己的需求进行创新。因为了解抽象层以下的东西、命令行以下的原理，会带来两个好处。

One is it lets 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. Is this thing really going to metastasize into a skynet and destroy the world? Are we going to be sitting here when Schwarzenegger shows up and says 4:29 a.m. on February 24th is when skynet became self-aware?

首先，这让你能够更好地使用这个工具，而这是一个拥有巨大影响力的工具，更好地使用它会非常有帮助。其次，它还能帮助你了解是否应该对它感到恐惧。这个东西真的会像《终结者》中的天网一样扩散并毁灭世界吗？我们会不会在施瓦辛格出现时坐在这里，他说天网是在2月24日凌晨4:29获得自我意识的？

Right, or is it more that, hey, this is a really cool machine and I can use it to do A, B, and C, but I can't use it to do D, E, and F? 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.

好的，或者更确切地说，是不是有这样一种情况：这台机器非常酷，我可以用它做A、B和C，但不能用它做D、E和F？在这里我应该信任它，而在那边我应该对它持怀疑态度。我觉得现在很多人对人工智能感到焦虑，而这种焦虑来源于他们不了解这个东西是什么或者它是如何运作的，理解非常有限。

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. We should lean into it. You should figure the thing out, you should look at what it is, and you should see how it works.

对此焦虑的解决办法就是行动。解决焦虑的方法永远是行动。焦虑是一种非特定的恐惧，总觉得事情会出问题，你的大脑和身体催促你去解决它，但你却不知道该做什么。我们应该勇敢面对它。你应该弄清楚事情的来龙去脉，看清它的本质，并了解它的运作方式。

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.

我认为这会有助于消除焦虑。学习这种行动，对好奇心的追求，将有助于你克服焦虑。而且谁知道呢？这可能还会帮助你找到一些富有成效的事情来做，让你更快乐、更成功。