Full Episode: The AI Industrial Revolution

频道: Naval
视频: https://www.youtube.com/watch?v=v6MWNrVbM4E
原文语言: en
统计: 共 117 轮 · 主持人 2 · Naval 44 · Guillermo 25 · Max 33 · Blake 12

[0:00] 主持人

Welcome. You're listening to the Naval podcast, your authoritative source for new knowledge. We're trying something new today. Uh I have three frontier founders with us. Three good-looking guys actually, and a fourth good-looking guy, Naval. And let me just introduce everybody. Gumo the G Roush. Um he's building Versel into an AI cloud for the world of agents and whatever comes after that.

欢迎收听。你正在收听 Naval 播客——你获取新知的权威来源。今天我们要尝试点新东西。我请来了三位前沿创业者，三个长得都挺帅的家伙，再加上第四个帅哥 Naval。我先来介绍一下大家。Guillermo Rauch，他正在把 Vercel 打造成一朵面向 agent 世界——以及之后无论会出现什么的——AI 云。

[0:25] 主持人

Good to be here. Blake Shawl, he's building supersonic aircraft in his own factory and jet engines as well. Blake's company, Boom Supersonic. And then Max Hodak from science. He's building a biohybrid brain interface that grows living neurons on silicon to restore sensory functions like sight, but then eventually to explore new parts of the brain and new senses. All three of these guys are not composing their products with off-the-shelf parts. They're building their own factories and you know we don't care as much about what they're building exactly as we do about what they're learning about how they're building. What's the new knowledge they're generating? What's their alpha? What principles are they discovering that other founders can learn from? What are they trying to figure out right now? And also what are the cutting edge or crazy ideas that they haven't even talked about yet and they're still forming in their brains. Naval, do you have any reactions to any of that before I jump into Gummo? Yeah, let's just have fun.

很高兴来到这里。Blake Scholl，他在自己的工厂里造超音速飞机，同时也造喷气发动机。Blake 的公司是 Boom Supersonic。还有来自 Science Corp 的 Max Hodak，他在做一种生物混合脑机接口——在硅片上培养活体神经元来恢复视觉之类的感官功能，最终还要去探索大脑里的新区域和全新的感官。这三位都不是用现成零件来拼凑自己的产品，他们是在自己造工厂。我们其实不太在意他们具体在造什么，更在意的是他们在「怎么造」这件事上学到了什么。他们正在产出哪些新知？他们的 alpha 是什么？他们发现了哪些其他创业者也能借鉴的原则？他们眼下正在琢磨什么问题？还有，哪些前沿到疯狂、连他们自己都还没公开聊过、还在脑子里酝酿的想法？Naval，在我开始问 Guillermo 之前，你对这些有什么想说的吗？嗯，咱们就好好玩一把吧。

[1:25] Naval

Yeah, you guys should just jump in.

对，你们几个直接聊就行。

[1:27] Guillermo

Yeah. So, I can't remember my exact quote, by the way, but I've been really pilled uh with this idea of software factories and the job of the engineer being something that you just show up to work. You used to used to ship the output directly and everything inside the company was, you know, how good is person A at shipping output B. And now what's happening is the way that I'm judging you as an engineer is like are you producing the factory that would produce multiplicative outputs B through Z, right? Um and that's a that's a pretty significant change because basically like we used to believe and it should be somewhat controversial that there's 10x engineers like now clearly there's 100x or a thousandx engineers and the world hasn't fully adjusted to this. I used to get flamed on Twitter for saying they're 10x engineers that it flies in the face of so much like equality philosophy that everyone's equal. But the reality is when you're operating in idea domains, when you're operating intellectual domains and virtual digital domains, it's not even 10x, it's 100x or thousandx and it always has been. Satoshi Notch, you know, the guy who invented JavaScript, the Brendan I of the world, uh John Carmarmac, I mean these are thousandx programmers. Not to even mention if you choose the right thing to work on versus the wrong thing to work on, that's an infinity difference and it could just be not nemer, just one who had a better judgment on what to work on in the first place. And now obviously it's less controversial because of uh AI leverage. What's controversial is that the token leaderboards, right? Like people are still getting a little confused because now they think, well, I have a bunch of 100x engineers. Look at all these tokens that I'm paying for. I'm curious if you guys have seen the same like how do you measure ROI?

好。顺便说一句，我记不清我原话是怎么说的了，不过我最近特别着迷于「软件工厂」这个概念——工程师的工作正在变成你来上班这件事本身。过去你是直接交付产出物，公司内部一切的衡量标准就是：A 这个人交付 B 这个产出的能力有多强。而现在正在发生的是，我评判你作为一名工程师，看的是：你是不是在搭建那座能批量产出 B 到 Z 的工厂？这是个相当大的转变。基本上，过去我们相信——这一点本该有些争议——存在所谓的 10 倍工程师；而现在显然已经有 100 倍、1000 倍工程师了，世界还没完全适应过来。我以前在 Twitter 上说有 10 倍工程师，会被喷得很惨，因为这跟很多「人人平等」的哲学完全相悖。但现实是，当你在思想领域、智识领域、虚拟数字领域里工作时，差距根本不止 10 倍，而是 100 倍、1000 倍，而且向来如此。Satoshi Nakamoto，发明 JavaScript 的人 Brendan Eich 那一类人，John Carmack——这些都是 1000 倍程序员。更别说，如果你挑对了要做的事而不是挑错了，那差距是无穷大；可能只是一念之差，只是某人一开始在「该做什么」上判断得更好。现在因为 AI 带来的杠杆，这话已经没那么有争议了。真正有争议的是 token 排行榜——大家还有点糊涂，他们会想：我手下有一堆 100 倍工程师，看我为这些 token 付了多少钱啊。我很好奇你们是不是也看到了同样的情况，你们怎么衡量 ROI？

[3:07] Max

It's like the old measuring lines of code, you know, token consumption with lines of code feel like similarly not direct paradigms.

这就像过去拿代码行数来衡量一样，用 token 消耗量来衡量，感觉同样不是一个直接对得上的范式。

[3:15] Max

I mean, my observation has been that claude or cha GPT um or GPT is about is basically as good as you are in a domain. And so, uh if you're if you're a really capable developer, then these things are really powerful. And if you're a junior developer, then you'll kind of find it to be like more of a junior developer. Like on the one hand, these models are incredibly capable. On the other hand, the feedback that you give them sporadically seems to be incredibly important. And these little updates seem to totally determine the types of uh performance you get out of them.

我的观察是，Claude 或者 ChatGPT、GPT，基本上在某个领域里跟你水平差不多。所以如果你是个能力很强的开发者，这些工具就威力惊人；如果你是个初级开发者，那你大概会觉得它也就是个初级开发者的水平。一方面，这些模型能力强得难以置信；另一方面，你时不时给它们的反馈似乎又极其关键。这些小小的修正，好像完全决定了你能从它们身上榨出什么样的表现。

[3:45] Guillermo

There's a new kind of support that I give which is you come to me and like you didn't get good output out of the model and I tell you what to prompt the model with. So like the idea of like the quality of the reprompting which I think you're alluding to is is extremely important.

我现在会提供一种新的支持：你来找我，说你从模型那儿没得到好的产出，然后我告诉你该用什么 prompt 去喂它。所以你刚才暗示的那个——「重新 prompt 的质量」——这一点极其重要。

[4:00] Max

But I mean and to be clear I think that this will become less important over time like as the models get much much smarter then you'll be able to put in less and get more out. Um but at least at this stage it really seems to kind of reflect back the judgment that the user brings in. In my experience,

不过得说清楚，我觉得随着时间推移这件事会越来越不重要——等模型变得聪明得多，你就能输入更少、得到更多。但至少在现阶段，它真的很像是在把用户带来的判断力反射回去。这是我的经验。

[4:14] Naval

I've kind of resisted learning all the ticks and tricks and tips like, you know, there was a, oh, use Ralph Wigum, use Open Claw, use Hermes, use this prompt engine, use this scaffolding, plug in this piece, you know, uh, always use plan mode. I just ignored all of that. I just assumed the model's just going to get better faster than I would figure out how to use it. It would figure out how to use me faster than I would figure out how to use it. And so I've just been completely hamfisted with them and I get frustrated at them and just sort of I I found myself typing less and less information and doing less and less work as time goes on with the models because I just assume I can brute force my way through it and I'll throw Codex Claw and Gemini at the same problem over and over and just waste tokens to save time. And I think no matter how expensive these models might seem, they're still way cheaper than a human. So I would say just waste tokens, save time. Don't look at the tokens either as inputs or outputs. Just look at your time and look at the final output. And even if they're writing lowquality code, which I know in many cases they are, it's not necessarily production quality or scalable code. When the time comes and I want to ship it to production, I'll just throw more tokens at it. I'll say, "Okay, now go through look at it, rewrite it, and they're just going to get better every generation." Uh, so yeah, I don't I don't see where this necessarily stops. As long as we have verifiable domains and solve problems, they're going to resolve those problems. That's in the unsolved problems domain where maybe you're terren tower you're at the cutting edge of creativity that you need to be you know working very collaboratively and carefully and closely with the model but I'm not in that I'm not at that level in software engineering and gear but you're probably the most extreme software engineer in the team right like out of this set you're probably the one who most hardcore came up from a software background like how are you finding these models at the edge of their capability? Well, there's one thing that's happened recently that uh what you're saying resonates strongly with, which is it used to be that you would give a prompt to the model and it kind of does it like classic like next token prediction thing and it like runs away with your idea. And models now have been doing this like intuitive planning mode without to your point not even having to plan where it comes back to you and says look what you're asking me for there's these three routes we can take. there's this set of tradeoffs that we're going to go down. That's a moment where like you know people do the whole thing on X like oh now we have a PhD level engineer model like that's very clear that the models at some point graduated. They used to be junior engineers now they're principal engineers because they come back to you with a set of tradeoffs and obviously sometimes they [ __ ] which is hilarious. It tells you this one is going to take three weeks and this many interest it make really bad predictions but clearly it's now this like I respect the models a lot more as a as a peer like that I'm going back and forth intellectually with but there there are a lot of gaps still so like if you're really really proficient engineer or architect you I think you're still extracting more juice so the question sort of that Max was positing of like if you're junior do you get junior back. Well, clearly not because a junior gets more advanced knowledge in code that they would have never been able to write by themselves, but doesn't an experienced architect get 10x whereas a junior engineer gets 2x. That's what I'm kind of trying to figure out still.

我一直有点抗拒去学那些花招、技巧和小窍门。你知道，曾经有过这种风潮：用 Ralph Wiggum、用 Open Claw、用 Hermes、用这个 prompt 引擎、用那套脚手架、插上这个组件、永远开 plan mode。我把这些全忽略了。我就假定模型变好的速度会比我搞清楚怎么用它的速度更快——它搞清楚怎么用我，会比我搞清楚怎么用它更快。所以我对它们一直特别粗手粗脚，会冲它们发火，而且随着时间推移，我发现自己输入的信息越来越少、干的活也越来越少，因为我就假定我可以靠蛮力硬闯过去：我会把 Codex、Claude 和 Gemini 同时丢到同一个问题上，反复试，纯粹浪费 token 来省时间。我觉得不管这些模型看上去多贵，它们还是比一个人便宜太多。所以我会说，浪费 token，省时间。别盯着 token 看，无论是输入还是输出。只看你自己的时间，只看最终产出。哪怕它们写的是低质量代码——我知道很多情况下确实如此，不一定是生产级或可扩展的代码——等到我真要推上生产环境的时候，我就再多砸点 token：跟它说「好，现在过一遍，重写一下」，而且它们每一代都会更好。所以，对，我看不出这条路非得在哪儿停下来。只要我们手里有可验证的领域、可解的问题，它们就会把这些问题解掉。真正困难的是那些未解问题的领域——也许你站在创造力的最前沿，需要非常协作、谨慎、紧密地跟模型一起工作。但我不在那个领域，我在软件工程上没到那个层次。不过你大概是这帮人里最极致的软件工程师，对吧？这一群人里，你应该是最硬核、最纯粹从软件背景里走出来的那个。你在模型能力的边缘地带，觉得它们表现得怎么样？嗯，最近发生了一件事，跟你说的特别能产生共鸣：以前你给模型一个 prompt，它就像经典的「下一个 token 预测」那样直接干，然后顺着你的想法一路狂奔。而现在的模型会进入一种直觉式的 plan mode——按你的说法，甚至不用刻意去规划——它会回过头来跟你说：「看，你让我做的这件事，我们有这三条路可以走，会面临这一组取舍。」那一刻就是大家在 X 上常说的「我们现在有了博士级工程师模型」——很明显模型在某个时间点毕业了。它们以前是初级工程师，现在是首席工程师，因为它们会带着一组取舍方案回来找你；当然有时候它们也会胡说八道，挺好笑的，它会告诉你「这条要花三周、这么多依赖」，预测错得离谱。但显然它现在已经是那种——我开始把模型当成一个我可以来回切磋的智识同侪去尊重了。不过还有很多缺口，所以如果你是个真正非常精通的工程师或架构师，我觉得你还是能榨出更多汁水。所以 Max 抛出的那个问题——如果你是初级，你拿回来的是不是也是初级水平？显然不是，因为一个初级开发者能从中拿到他自己永远写不出来的更高阶的代码知识。但是，难道一个资深架构师拿到的不是 10 倍、而初级工程师拿到的是 2 倍吗？这就是我还在琢磨的事。

[7:35] Max

Yeah. Yeah. But I mean, I think there's there's architectural decisions. So, when you think about the development, I'm seeing this now with some of our the junior software engineers on the team of like what is the next step in their career progression. It's going from like writing implementation for a feature to picking technologies like choosing between Postgress versus some other database or picking between ZMQ versus some other message Q or like some other queuing system and those I mean the models can suggest them but that's the thing where you'll see it and you'll be like no no I want to use this other thing. That's the type of little feedback that I'm saying really matters in the types of output that you seem to get at this point.

对，对。我觉得这里头有架构层面的决策。所以谈到开发，我现在在我们团队里几位初级软件工程师身上就看到了——他们职业发展的下一步是什么？是从「给一个功能写实现」转向「选技术栈」：在 Postgres 和别的数据库之间做选择，在 ZeroMQ 和别的消息队列、别的排队系统之间做选择。这些事模型是能给建议，但恰恰是这种时候，你会看一眼然后说「不不不，我要用另外那个」。这就是我说的那种小小的反馈，它在现阶段对你能拿到什么样的产出真的很关键。

[8:07] Guillermo

Taste taste and judgment right? Taste and judgment. That said you can ask them which one should I use and why and they know everything. they'll give you really good trade-offs. That's the change I was saying has happened recently where you would say hey go and um put this super high cardality telemetry data into Postgress and it's like no bro like we don't put that kind of data into Postgress like you should consider click house or Athena or whatever like that's happened to me a lot which is really impressive um but I the thing I'm still like kind of struggling with is clearly the human is still completing the model like at one point is it the other way about like the the human is the one sort of getting the instructions back on like go get me this API key because it's something that only you can do uh or get me this amount of capital for my next set of investments that I need to make. Uh you just watch like that clearly we're still not there yet. That's a temporary aberration. Pretty soon every good SAS company or hosting provider will have a CLI and API interface that the models can meet directly. they don't even necessarily need an API like as long as it's like textbased Unix based the agent can hack its own API

品味和判断力，对吧？品味和判断力。话虽如此，你也可以直接问它们「我该用哪个、为什么」，它们什么都懂，会给你非常好的取舍分析。这就是我说的最近发生的变化：你以前会说「去把这种超高基数的遥测数据塞进 Postgres」，它会说「不行兄弟，这种数据我们不往 Postgres 里塞，你应该考虑 ClickHouse 或者 Athena 之类的」。这种事在我身上发生很多次了，相当惊艳。但我还在纠结的一点是：显然现在还是人在补全模型。到哪个时间点会反过来——变成人才是那个接收指令的一方，比如「去帮我拿这个 API key，因为这事只有你能干」，或者「去帮我筹到我下一轮投资所需的这笔资金」。你就这么看着——显然我们还没到那一步。那只是暂时的反常现象。很快每一家像样的 SaaS 公司或托管服务商都会有 CLI 和 API 接口，让模型可以直接对接；它们甚至不一定需要 API——只要是基于文本、基于 Unix 的，agent 就能自己黑出一套 API 来。

[9:20] Naval

and then the money part you insert crypto tokens you know put in bitcoin put in whatever and the model goes and just pays for whatever it needs and I think like

然后是钱的部分，你塞进去加密代币——你知道，放进比特币，放进随便什么——模型就自己去把它需要的东西付掉。我觉得吧……

[9:27] Naval

you know there people working on this

你知道，已经有人在做这件事了。

[9:29] Guillermo

but the thing I am now thinking through is is pure software dead like is pure software engineering like an obsolete thing it's like saying speaking English right the models now speak English we had to learn code to communicate with the models. Now the models speak English and they speak fuzzy sloppy English like a human and they understand things. So where's the moat like for a founder? Hardware it's a boon you know like now if you had to build hardware it was hard to build a software company alongside like Patrick Cullison says software is art and it's hard to hire artists. So now as a hardware founder great you can have really good software develop fairly quickly. Um, if you're creating models, maybe that's the new software engineering, training models and tweaking models and post- trainining and fine-tuning models. But classic software engineering is that dead is pure software investable is pure software something organize a company, a team around and try to get some leverage. Did you guys see the uh there was an article an X by Mitchell Hashimoto called the block economy or the building block economy something like that like his argument is that the most useful thing for agents to have now is really powerful reusable building blocks because to Max's example you wouldn't expect your clanker to reinvent a Q infrastructure system every time he needs to send an email it needs to bring in the right building block that's right size for the task that you're asking for and say, "Well, okay, for this one, it's B MQ." I challenge the notion that I would want the agent to reinvent the entire universe from first principles in a way that's incompatible with the rest of society and civilization. Like it's almost like reinventing highways, laws, policies, etc. just for you. Even if there's a potential for extra optimization, extra juice that you can get out of it, there's a still a um sort of like cooperation at large scale value of saying we're both depending on Postgress 13.2 and so that's still really really really valuable. I would say like the category of infrastructure software and building blocks that these agents are going to use obviously embias is this what we're building seems extremely valuable and I don't see the agent anytime soon and by the way you could even another metaphor I've been using is like agent's already been created that the models can reuse is like a token cache because you you don't want to churn through a trillion tokens to reproduce what's already existing. Uh and so there's always starting points that the model can fork off from, but it's going to change things quite profoundly.

但我现在反复在想的一个问题是：纯软件是不是死了？纯粹的软件工程是不是已经过时了？这就好比说「讲英语」这件事——模型现在会讲英语了。我们当初得学代码才能跟模型沟通，而现在模型会讲英语，会讲那种模糊、随意、像人一样的英语，而且它们能听懂。那么，对一个创业者来说护城河在哪儿？硬件是个福音。你知道，过去如果你要造硬件，同时再去搭一家软件公司是很难的——就像 Patrick Collison 说的，软件是一门艺术，而招到艺术家很难。所以现在作为一个硬件创业者，太好了，你可以相当快地拥有很不错的软件开发能力。如果你是在造模型，也许那才是新的「软件工程」——训练模型、调模型、做后训练、做微调。但经典的软件工程，是不是死了？纯软件还值不值得投？纯软件还能不能成为一家公司、一个团队去围绕它组织、并从中拿到一些杠杆的东西？你们看到 Mitchell Hashimoto 在 X 上那篇文章了吗，叫《积木经济》还是《building block 经济》之类的——他的论点是，现在对 agent 来说最有用的东西，是真正强大、可复用的积木块。因为照 Max 举的例子，你不会指望你的 clanker（机器人助手）每次要发封邮件都重新发明一套队列基础设施，它需要为你提的任务调用那块尺寸合适的积木，然后说「好，这个用 RabbitMQ」。我质疑「我会想让 agent 从第一性原理出发把整个宇宙重新发明一遍」这个想法——那样发明出来的东西会跟社会和文明的其余部分都不兼容。这几乎就像专为你一个人重新发明高速公路、法律、政策等等。就算其中真有额外优化、额外能榨出来的汁水的潜力，「我俩都依赖 Postgres 13.2」这种大规模协作的价值仍然在，这依然非常非常非常宝贵。我会说，这些 agent 将要用到的那一类基础设施软件和积木块——显然我这么说有偏见，因为这正是我们在做的——看起来极其有价值，而且我看不出 agent 短期内能取代它。顺便说，我一直在用的另一个比喻是：agent 已经造出来、可供模型复用的东西，就像一个 token 缓存——因为你不想为了重新产出本就已经存在的东西，去消耗掉万亿级的 token。所以总有些起点是模型可以从那儿 fork 出去的。但这会相当深刻地改变很多东西。

[11:57] Naval

So these are like libraries and dependencies, but for models.

所以这些就像是库和依赖，只不过是给模型用的。

[12:00] Guillermo

Yes. For agents specifically.

对。具体说是给 agent 用的。

[12:03] Max

To Naval's question though, I mean I learned a program when I was really little. And I like that was the thing that through all of like being a teenager and in my 20s like I get like sucked into it and just like code for like 20 hours and it was super fun and I knew all this stuff about programming languages. I haven't written a single line of code in quite a while now. And I mean, partly that's because my job is different, but also since December, I've built a huge amount of software that I now use every day. There's all these projects that I've kind of fantasized about for years that now I'm like using um that I've actually built and I didn't write any of that. And I just can't imagine going back to like actually writing code by hand anytime. Like I mean, I'm unlikely to do that anyway, but just like in general, I see that I have a hard time seeing that as part of the future.

不过回到 Naval 的问题。我很小的时候就学了编程。整个青少年时期、还有二十几岁那阵，我都会一头扎进去，连着写 20 个小时代码，超级好玩，我对编程语言的方方面面都门儿清。但我已经有挺长一段时间没写过一行代码了。一部分原因是我的工作变了，但另外，从去年十二月以来，我造了海量我现在每天都在用的软件。有好多我幻想了好多年的项目，现在我真的在用了，是我真造出来的，而那些代码没有一行是我自己写的。我实在没法想象未来某个时候再回去手写代码。我是说，反正我本来也不太可能再去写，但总体上，我很难把「手写代码」看成未来的一部分。

[12:46] Guillermo

Yeah. There's something really cool is that you understand how the pieces click together. Like I feel like anyone that understands what an API is and how data flows, inputs and outputs, performance because you kind of you have to orient the model around like this is a certain level of expectation that I have out of this operation like that's that's always been infinitely more useful than um than writing code. Like I feel like a really good a proficient engineering leader has been quote unquote like vibe coding through people on Slack or one-on- ones because you're transmitting your will, your intent, your experience and you're letting others run with it. Uh it's just that now we do the same but with agents. Uh and so I think that's why you've been successful with it. But I don't know that everyone sees the same level of success. I

对，有件很酷的事是，你懂得这些零件是怎么咬合在一起的。我觉得任何一个懂 API 是什么、懂数据怎么流动、懂输入输出、懂性能的人——因为你得围绕模型去定向，告诉它「我对这个操作有这样一个程度的期望」——这种能力一直都比写代码本身有用得多。我觉得一个真正优秀、精通的工程负责人，一直以来都在通过 Slack、通过一对一会议对着人「vibe coding」，因为你是在传递你的意志、你的意图、你的经验，然后让别人带着它跑。只不过现在我们换成了对 agent 做同样的事。所以我觉得这就是你能用得这么成功的原因。但我不确定每个人都能看到同样程度的成功。

[13:36] Naval

mean I went from not having written code in 20 years to I'm coding all the time now. but through agents and I'm building tons of software and it turns out that just understanding the basic principles of software engineering and algorithms actually gets you a long ways because the reason I stopped coding was because I didn't have time to figure out the latest language latest architecture infrastructure pieces to plug into and I know Verscell makes it a lot easier but even then just getting started was a bare like just plugging pieces together assembling infrastructure was just so annoying. The thing that really changed is I mean it used to be that you could build a lot like you like there's a lot that was straightforward but then you would hit some random thing and then you could spend

我是从 20 年没写过代码，到现在天天在写代码——但是是通过 agent 在写，而且我在造一大堆软件。结果发现，只要你懂软件工程和算法的那些基本原理，其实就能让你走得很远。因为我当初不写代码了，是因为我没时间去搞清楚最新的语言、最新的架构、要插进去的那些基础设施组件。我知道 Vercel 让这件事简单多了，但即便如此，光是「上手开始」都很费劲——光是把那些零件拼起来、组装基础设施，就烦人得要命。真正改变的是——过去你能很顺地造出很多东西，有很大一块都是直截了当的，但接着你会撞上某个莫名其妙的坎，然后……

[14:15] Max

kind of some indefinite period of time debugging some narrow thing and now with the agents what happens is you just don't get stuck anymore which is pretty amazing

……然后你可能要花上不知道多久的时间去 debug 某个特别狭窄的小问题。而现在有了 agent，发生的情况是你再也不会卡住了，这相当惊人。

[14:23] Naval

or they get stuck

或者是它们自己卡住。

[14:24] Max

it's removed well no I mean like relatively quickly they can find like the right way to do things and it used to be that like I remember when their friends learned a program be like nope it's just like intrinsically frustrating like if like that's part of the feel that's how you learn and that just isn't true anymore.

那种卡顿被消除了。不，我是说，相对来说它们能很快找到正确的做法。过去就像——我记得我朋友们学编程的时候会说「不行，这东西本质上就是让人抓狂」，仿佛挫败感是这门手艺感觉的一部分、是你学习的方式。而那一点现在已经不成立了。

[14:39] Naval

Blake, how are you applying all the stuff at uh Boom Supersonic?

Blake，你在 Boom Supersonic 是怎么把这些东西用起来的？

[14:43] Blake

Yeah. What I found is it completely changes the role of software and hardware developers. The thing that we did from day one was uh try to take a lot of traditional engineering workflows and I mean hardware engineering workflows and turn them into software. And so if you haven't been around hardware engineering, let me see if I can make this more clear. there uh there's a lot of engineering hardware engineering that happens in Excel spreadsheets on engineers laptops in a silo and it's very complex uh spreadsheets sometimes like VBScript code and all of this is actually software but it's it's treated as if it's not software there's no there's no source control there's no automated testing if you want to hand something off from like an aerodynamicist to a structures engineer that's done manually with like a spreadsheet over email like it's the 1990s. It's terrible. And so we we started building these kind of like software frameworks that can automate and make repeatable hardware engineering flows. The idea we could reduce the cost of iteration. Um but it was it was slowgoing because we could never get enough we could never like afford enough software engineers. And what we've gotten into is this uh mind-blowingly different model where the software engineers actually create the architectures because they understand systems, they understand algorithms, they they understand, you know, division of concerns. Uh and then the hardware engineers can vibe code their pieces because what they know about hardware engineering and the result is just like mindblowingly different productivity for small teams. like give an example like like if you're designing a turbine blade like classically so a turbine blade starts like uh cold but when it runs it's hot so it gets bigger and so you have to design both the aerodynamics and the structural design of the thing to work on it cold shape and this hot shape and so you have to convert between cold and hot and you convert between structures and aerodynamics and this takes like one engineer one day for one blade for one piece of the analysis and there are like a thousand blades in a jet and and so you can't do much and we literally now with a combination of software and hardware people created the solution you can change blade geometry you can see in real time the structures and aerodynamics results and so it allows two engineers to design an entire jet engine which is just wildly different. One of the things you mentioned is that you have software engineers creating the tools and architectures for the rest of the engineers. That to me is the biggest u the cataclysm of enterprise software is that there's no like startup that builds hardware collaboration tools that can sell you anything anymore because in internally you're just coding the right things that you need at any given time. Even spreadsheets are kind of cooked, right? Because the reason spreadsheets were successful is that no one could build custom software. So the thing that approximates custom software the most is a spreadsheet with a bunch of EV script functions. I personally have moved almost entirely from uh Excel to Python models where I can actually like get like believable simulations of things.

嗯。我发现它彻底改变了软件开发者和硬件开发者的角色。我们从第一天起做的事，就是试着把大量传统工程流程——我是说硬件工程流程——转化成软件。如果你没在硬件工程圈待过，我试着说得更清楚些：有大量硬件工程是发生在工程师笔记本上、孤岛式的 Excel 表格里的，非常复杂，有时表格里还藏着 VBScript 代码，这些其实都是软件，但大家根本不把它当软件对待：没有版本控制，没有自动化测试；如果你想把东西从气动工程师交接给结构工程师，那是靠人工——拿个表格通过邮件发过去，像在过 1990 年代一样，糟透了。所以我们开始搭建这种软件框架，能把硬件工程流程自动化、可重复化，想法是把迭代成本降下来。但进展一直很慢，因为我们永远招不够、永远雇不起足够多的软件工程师。后来我们进入了一种思路完全不同、令人脑洞大开的模式：软件工程师负责创建架构——因为他们懂系统、懂算法、懂关注点分离；然后硬件工程师可以「vibe code」出他们自己那块东西，因为他们懂硬件工程。结果就是小团队的生产力截然不同、令人震惊。举个例子：你设计一片涡轮叶片——经典做法是，涡轮叶片在冷态下是一个形状，但一运转它会变热、会膨胀变大，所以你得让它的气动设计和结构设计在「冷态形状」和「热态形状」下都成立，于是你要在冷态和热态之间换算，还要在结构和气动之间换算，这一整套，针对一片叶片、一项分析，就要一个工程师干上一天，而一台喷气发动机里有大约一千片叶片，所以你根本干不了多少。而现在，我们真的靠软件人和硬件人的组合做出了一个解决方案：你可以改叶片几何形状，实时看到结构和气动的结果。这让两名工程师就能设计出一整台喷气发动机，这简直是天差地别。你刚才提到的一点是，你让软件工程师为其余工程师创建工具和架构。在我看来，企业软件最大的剧变就是这个：再也没有哪家做「硬件协作工具」的创业公司能卖给你任何东西了，因为在内部，你随时都能直接编出你当下需要的那个对的东西。连电子表格基本上也完蛋了，对吧？电子表格之所以成功，是因为过去没人能造定制软件，而最接近定制软件的东西就是一张带一堆 VBScript 函数的表格。我个人已经几乎完全从 Excel 转到了 Python 模型，在那里我真的能跑出对事物可信的模拟。

[17:47] Max

Yeah. I mean the thing that that AI hasn't come to yet that I think it it will within the next year like probably within 26 that will be very very exciting is right now it can generate software but soon it'll be it will generate step files and PCB layouts. And when it comes for mechanical and electrical engineering, that will be a whole other thing that we haven't seen yet. That'll be very, very cool.

对。我觉得 AI 还有一块没触及到、但我认为它会在未来一年内——很可能就在 2026 年内——做到的事，会非常非常令人兴奋：现在它能生成软件，但很快它就能生成 STEP 文件和 PCB 布局。等到它能搞机械工程和电气工程的时候，那会是一件我们还没见过的、全新维度的事。那会非常非常酷。

[18:07] Naval

Yeah. On the hardware side, I think it's really a boon for like all these little gadget companies and part companies that write really bad software because they can't make great software and now they're going to be able to make good enough software or it may not even software that is a human front end. It might just be completely agentic for an agent to access and you just talk to it through voice and control hardware. And I this is why one of the reasons why I think for example China is big into open- source models right they're basically going all in on it because they have hardware superiority they have these very complex supply chains and component chains and they're basically saying hey if I can just generate software on demand then I don't have this disadvantage anymore against Silicon Valley. So that's not the only reason why they're doing open source. I think they're also behind. They're distilling models. They're catching, you know, they're collaborating on resources. But I think the Chinese government has a history of funding efforts that then sort of help their entire ecosystem along, especially in network effect businesses.

对。在硬件这一侧，我觉得它对那些做小玩意儿、做零部件、但软件写得很烂——因为他们做不出好软件——的公司来说，真是个福音。现在他们能做出「够用」的软件了，甚至可能根本不是给人用的软件前端，而可能完全是 agentic 的、供一个 agent 来访问的，你只需通过语音跟它对话来控制硬件。这也是为什么——比如说——我认为中国大举投入开源模型的原因之一：他们基本上是在 all in 这件事，因为他们有硬件上的优势，有这些极其复杂的供应链和元器件链，他们等于在说「只要我能按需生成软件，我对硅谷就不再有这个劣势了」。当然这不是他们做开源的唯一原因。我觉得他们也确实落后，他们在蒸馏模型，在追赶，在协作共享资源。但我认为中国政府一向有出资去扶持那种最终能带动整个生态系统的项目的传统，尤其是在有网络效应的业务上。

[19:05] Guillermo

And so I think they want to like uh pull all their resources, catch up on AI, and use it to give their hardware stuff an advantage. And ironically, they're doing all the open source stuff because Open AI is not open. You know, Gro publishes models, but I think they're a model or two behind. Uh, Google has some local models, but nothing really that competitive. Anthropic, to my knowledge, I don't even know of any open source models from them. So, all the open source heft is coming from China. It helps all our hardware founders, but it helps their hardware founders and factories and so on that much more. But all all the crappy little software that goes with all the little random knickknacks and thingamajigs that you buy off of Amazon for to tinker with a lazy Saturday afternoon, that software is getting a lot better very quickly. I think everyone's had the wakeup call that without great frontier coding models, you don't have self-improvement. And so imagine China as a whole not having the ability to produce frontier everything, right? It's not just producing software is in any piece of this hardware pipeline like Blake was saying like you need to generate software. If you fall behind on your ability to generate software, you fall behind on the ability to generate everything. One thing I'm curious about from you guys is like cuz everyone loves to talk about Chinese models like do you use Chinese models? Do you know anybody that uses Chinese models? This is an argument I had yesterday actually which is uh one person at the table dinner was claiming that uh you know you'll just use deepseek for 97% of things because it's so cheap and if you need more intelligence you'll just run it over and over again the same problem and you'll only use the open AI anthropic etc models for the most advanced tasks and I was kind of like I don't know I think intelligence is an unalloed good you always want more intelligence and when these models make a mistake you don't know it

所以我觉得他们想把所有资源调动起来，在 AI 上追上来，再用 AI 去给他们的硬件那一块带来优势。而讽刺的是，他们之所以做这一切开源的东西，是因为 OpenAI 并不开源。你知道，Grok 会发布模型，但我觉得他们落后一两代。Google 有一些本地小模型，但没什么真正有竞争力的。Anthropic 据我所知，我甚至不知道他们有任何开源模型。所以所有开源的分量都来自中国。这帮到了我们这边所有硬件创业者，但它帮到他们那边的硬件创业者、工厂等等就更多了。不过，那些跟你周六下午闲来无事、从 Amazon 上买来瞎折腾的各种零碎小玩意儿配套的烂软件，正在以非常快的速度变好。我觉得大家都被敲响了警钟：没有顶尖的前沿编码模型，你就没有自我改进的能力。所以想象一下整个中国如果不具备生产「前沿一切」的能力会怎样——这不只是生产软件，而是这条硬件流水线里的每一环，就像 Blake 说的，你都需要生成软件。如果你在生成软件的能力上落后了，你就会在生成一切的能力上落后。我对你们几位很好奇的一件事是——因为人人都爱聊中国模型——你们用中国模型吗？你们认识任何用中国模型的人吗？这正是我昨天吵的一架：饭桌上有个人声称，你 97% 的事情都会用 DeepSeek，因为它太便宜了；如果你需要更高的智能，你就把同一个问题反复跑很多遍；只有最高阶的任务你才会用 OpenAI、Anthropic 之类的模型。我当时的反应是——我不知道，我觉得智能是一种纯粹的好东西，你永远想要更多智能；而且当这些模型犯错时，你是不知道的。

[20:51] Guillermo

and it's always cheaper than a real person and real time. So, you'll just use the most intelligent model available, which isn't great news necessarily because it means that, you know, you're going to end up creating a monopoly or igopoly kind of situation in AI. But, uh, I always want the most intelligent programmer. I always want the most correct answer. I always want the best judgment. And given the amount of leverage that I'm going to pour into it through capital and code and people and, you know, marketing, I want to make the right decision every time. And often when between two models, let's say like I have one model that I know is a little smarter than the next one and they both give me answers. Often I actually don't know which is the correct answer, right? So if I know one model's a little smarter, I'm going to go with that answer and eventually I'm going to stop asking the model that I think is less intelligent. But I don't know, have you guys found a use for the these, you know, so-called less intelligent models? We see uses so that so we have the AI gateways uh data that basically like every application agent, etc. goes through. And so there's definitely usage of open models, but the top is like heavily dominated by the frontier intelligence. And there's a subcategory or there's like a caveat to that, which is that frontier intelligence at reasonable cost and performance like slaps at scale. So like people don't get really excited about Gemini, but they put out these models that are like super smart at the right performance cost combination and for a lot of tasks other than co coding actually interestingly enough. Uh they're the best models. They're like the best like industrial production models. Uh you can throw them at like support tasks or browser automation. Like I would always put a Gemini model there. Uh and I would look to Chinese models for those kinds of things. But anytime I'm working to push the frontier, you need the best possible coding model. And that's basically now like two or three models and uh and the Chinese are not certainly not in it.

而它永远比一个真人便宜、而且是实时的。所以你只会用手头能拿到的最聪明的那个模型——这未必是好消息，因为这意味着你最终会在 AI 领域造出一种垄断或寡头垄断的局面。但我永远想要最聪明的程序员，永远想要最正确的答案，永远想要最好的判断力。考虑到我要通过资本、代码、人、还有市场营销往里灌注的杠杆有多大，我希望每一次都做出正确的决定。而且常常在两个模型之间——比方说我知道有一个模型比另一个稍微聪明一点，它俩都给了我答案——很多时候我其实并不知道哪个才是正确答案，对吧？所以如果我知道某个模型聪明一点，我就会采纳它的答案，最终我会干脆不再去问那个我认为比较笨的模型。但我不确定，你们有没有给这些所谓「比较笨」的模型找到过什么用途？我们能看到一些用途——我们手里有 AI Gateway 的数据，基本上每个应用、agent 等等都从这儿过。所以开源模型确实有使用量，但顶部明显是被前沿智能重度主导的。这里有个子类别，或者说有个但书：在合理成本和性能下的前沿智能，在大规模场景里是真的能打。比如大家对 Gemini 没那么兴奋，但他们推出的这些模型在「性能/成本」这个组合的甜点上超级聪明，而且对很多任务来说——有意思的是，恰恰是编码以外的任务——它们是最好的模型，它们是最好的那种「工业级生产模型」。你可以把它们丢到客服任务或者浏览器自动化上，这种我一定会放一个 Gemini 模型，这种事我也会去看中国模型。但只要我是在做推进前沿的工作，你就需要可能拿到的最好的编码模型，而那基本上现在就只有两三个模型，中国的肯定不在其列。

[22:44] Naval

Hey Max, you're pushing pretty hard into vertical integration and extreme urgency. Do you want to talk about that?

嘿 Max，你在「垂直整合」和「极端紧迫感」这两件事上推得相当狠。你想聊聊这个吗？

[22:50] Max

Yeah, I mean for many things we um you can't buy it so you got to make it somehow. Our preference would always be to buy something um like if there's a vendor that offers a service at a great price like for example like PCBs like we don't make PCBs like those are they're basically free you can buy them in unlimited quantity from Asia but the the closer that our products get to being like a single block of coalently bonded matter the better they'll be lower power smaller higher performance last longer and um there's just like there like the components aren't available and in order to do that type of integration be able to actually innovate beyond things just piecing together things that you can buy off the shelf which really is is very very limiting. I guess you have to like learn it to do it yourself and that shows up as vertical integration. So we own a captive MEMS foundry on the east coast which we bought because there was really no other way to do the type of packaging and assembly stuff that we wanted to do and I think that all of this is going to be affected heavily by AI over the next few years. It's not quite there yet. In fact, ironically, one of the biggest impacts that we've seen of AI inside the companies in regulatory interactions because if we can do things like generate documentation or if we can ask like we want to change, we want to evolve this product like there's thousands of ISO standards that might apply which ones do we have to comply with and like trace this through. This used to be like you're like you're following a whole regulatory quality team for several months as they trace this and now the AI just kind of knows. Um, but when I think about stuff like the the surgical program or the MEMS fab, I think ultimately the software still needs hands. Like it's going to be smarter than us, but if it can't make things, then like those are real real boundaries. And so we've instrumented our foundry as well as many other parts of the company in in ways where um as these models get better uh that should show up pretty immediately in in things like the the cell engineering that we're doing and the material science that we're that we're developing.

好。我是说，很多东西你压根买不到，所以你只能想办法自己造。我们的偏好其实永远是「能买就买」——比如有哪个供应商能以很好的价格提供某项服务，像 PCB，我们就不自己做 PCB，那玩意儿基本上等于免费，你能从亚洲不限量地买到。但是，我们的产品越接近「一整块共价键合在一起的物质」，它就越好——功耗更低、体积更小、性能更高、寿命更长。而问题在于，那些元器件根本买不到；要做那种程度的整合，要真正能在「把买来的现成东西拼凑在一起」之外做出创新——而拼凑现成件其实非常非常受限——我想你就得自己把它学会、自己上手做，而这体现出来就是垂直整合。所以我们在东海岸自有一座专属的 MEMS 晶圆厂，是我们买下来的，因为实在没有别的办法去做我们想做的那种封装和组装的活儿。我觉得未来几年，这一切都会被 AI 大幅影响，只是现在还没完全到位。事实上，讽刺的是，我们在公司内部看到 AI 影响最大的领域之一，是跟监管机构的互动：如果我们能做到比如自动生成文档，或者我们可以问——我们想改动、想演进这款产品，可能有成千上万条 ISO 标准会适用，我们到底必须符合哪些、把这条线一路追踪下来——这件事过去你得让一整个监管/质量团队跟好几个月去梳理，而现在 AI 基本上就直接知道了。但当我想到像外科手术项目，或者那座 MEMS 晶圆厂这样的东西时，我觉得归根结底软件仍然需要「双手」。它会比我们更聪明，但如果它造不出实体东西，那些就是实打实的边界。所以我们已经给我们的晶圆厂、以及公司里其他很多部分都装上了仪表（数据采集），方式是这样的：随着这些模型变得更好，这种提升应该会相当即时地反映在我们正在做的细胞工程、正在研发的材料科学这类事情上。

[24:42] Naval

It sort of makes me realize that like it's been a while since I've generated a basic legal document using a lawyer, right? I stopped asking lawyers for NDAs and you know agreement for this and sign that and research this and like all the basic legal tasks are gone too because you know there's the old joke that law is like spaghetti code you know they have this very complicated code that they try to put in English and it contradicts this code over here and has to fit into that code over here and there no real APIs for it. Um but for just like junior engineers and junior engineering I should say junior engineers basically got a promotion to senior engineers and junior engineering got taken over by agents and so the same way I think in a way the downside is you can look at law and say you know parallegals just got fired or you could say parallegals just got promoted to senior lawyers and now they can spend their time thinking about the law. It's actually kind of interesting to think about the parallels of how software engineering is evolving with lawyers because lawyers, you never know what they put into these documents exactly. You just trust them. Like, hey lawyer, can you look at this document? Can you tell me if it's legit? Can you do red lines? Whatever. Like, at the end of the day, you're what you're valuing in the relationship with a lawyer is that is that they're a trusted authority. They went to law school and they're putting their reputation on the line. Cool.

这让我意识到，我已经很久没有通过律师去生成一份基础的法律文件了。我不再找律师起草 NDA、各种协议、签这个、查那个——所有这些基础的法律活儿也都消失了。有个老笑话说，法律就像意大利面条式代码（spaghetti code）：他们有一套极其复杂的「代码」，试图用英文写出来，这边一条跟那边一条相互矛盾，还得塞进另一处的框架里，而且根本没有真正的 API。但对初级工程师来说——我应该说，初级工程师基本上被提拔成了高级工程师，而初级工程的活儿被 agent 接管了。同样地，从某个角度看，坏处是你可以盯着法律行业说「律师助理（paralegal）刚被裁了」；但你也可以说「律师助理刚被提拔成了高级律师」，现在他们可以把时间花在真正思考法律上。其实软件工程的演进和律师行业的演进有一些很有意思的相似之处：因为律师，你永远不知道他们到底往这些文件里塞了什么，你就是信任他们。比如「嘿律师，你能看看这份文件吗？能告诉我它靠不靠谱吗？能帮我做 red line 标注吗？」诸如此类。说到底，你在和律师的关系里真正看重的，是他们是一个值得信任的权威——他们上过法学院，他们拿自己的声誉做担保。很酷。

[26:01] Guillermo

I think there's a parallel par parallel with like the biggest problem in software engineering today is these mountains of slob that end up as a PR and then people are say like there's all these memes on Twitter like way back in the day we used to read every line of code of a PR. Well, in my world infrastructure I want engineers to be able to say I understand doesn't necessarily mean that you've read every line of the of the PR. You need to be able to say I am signing off on understanding the consequences of this PR or I wrote the test harness the simulations the proofs the type checkers etc to be able to say even without reading this I have confidence I can sign off on it's going to be safe in production and so it's it's kind of interesting because there's a world in which we embrace that everything is going to be spaghetti code and that we don't fully understand it but we write the basically evaluator that give us confidence and then we rely on like people uh like the infrastructure production engineers to say okay I'm fine uh sending this into prod you know at the end of the day like someone is going to get paged if your systems go down and I think another thing that people are underestimating is that creating software is really easy 0ero to one but think about a thousand days from now what does what does your software look like is it secure is it tested is it production grade uh is it performant and are you still motivated to invest all of those tokens in maintaining it in prod

我觉得这里有个类似的对应：今天软件工程最大的问题，就是堆积如山的垃圾代码最后变成一个 PR，然后大家就会说——Twitter 上一堆这样的梗——「想当年我们可是会逐行读完一个 PR 的每一行代码」。但在我所处的基础设施领域，我希望工程师能够说「我理解了」，而这并不一定意味着你读过这个 PR 的每一行。你需要能够说：我对这个 PR 后果的理解是负责的；或者我写了测试框架、模拟、证明、类型检查器等等，所以即便没逐行读，我也有信心签字，确认它上生产是安全的。这其实挺有意思的：有一个世界是我们接受「所有东西都会是意大利面条式代码、我们并不完全理解它，但我们写出能给我们信心的评估器（evaluator）」，然后我们依靠像生产基础设施工程师这样的人来说「好，我没问题，可以把它发到生产环境」。毕竟说到底，系统宕机时总得有人被 page（呼叫）。我觉得还有一件事大家低估了：造软件其实很容易，从 0 到 1 很简单，但想想一千天之后——你的软件会是什么样子？它安全吗？经过测试吗？是生产级的吗？性能好吗？而你还有没有动力继续投入所有这些 token 去把它维护在生产环境里？

[27:33] Naval

I mean humans are becoming verifiers right and and that's kind of how we train these models with good verification data and now we need human verifiers so yeah I think a lot of the a lot of the old function of people lawyers engineers operations people move to verifying the stack and saying yeah this is roughly correct and I I'll roughly stand behind it and I'll support you if it goes wrong

我是说，人类正在变成验证者（verifier）对吧？而我们训练这些模型，靠的就是优质的验证数据，现在反过来我们需要人类验证者。所以，是的，我觉得很多过去由人来承担的职能——律师、工程师、运营人员——都在转向验证整个技术栈，去说「嗯，这大致是对的，我大致愿意为它背书，如果出事我会支持你」。

[27:53] Blake

one of the things we see related to the regulatory is it massively reduces change aversion and improves iteration. So to give you an example like let's let's say you're going to go certify an airplane. One of the zillions of things you have to do is prove that it could withstand a lightning strike and the the regulatory documentation for the test plan for such a thing stretches on for say 200 pages. And what you would classically do is hire a, let's be honest, not super bright engineer who's willing to be there monkey at keyboard writing 200 pages of regulatory compliance documentation. And it takes a couple months. And and by the way, if you change the airplane now, you want to cry because there's another like two months of rework of this like wrote kind of regulatory compliance documentation. And what we found is you we can build a rag that will enable us to basically prompt our way through all of that work you know in let's call it minutes. The first order effect is oh you save a lot of time. The the the second order effect is if you change the specification of the airplane uh it now takes you know uh minutes not months. So you can actually be willing to change. And the third order effect is you can now you know basically get rid of the not very great engineers and have a small number of really creative ones. They can iterate rapidly because the cost of change goes down and in a certain sense like the entire regulatory burden which really hurts the ability to iterate drops away. I think that this is a really undersold story in AI right now. I think the consensus in Silicon Valley is that like regulation sucks like any like we want to go faster, we want to rec realize this amazing future. We want abundance. We want just like prosperity and stuff that slows down that future is just kind of to be avoided. And certainly I think we've overregulated. We've made it impossible to build stuff. It's just like it's totally crazy what goes into getting building any type of thing in a lot of places either physical or otherwise. But you know like a lot of the regulations themselves are not the problem. Like if you've actually read a lot of these things like like having non smog choked cities is great. Being able to swim in like many rivers is great. Like having like a lot of these things were progress. The problem is that it is really difficult for humans to deal with understanding and complying with this and that every time you have to exchange a letter with the government, you wait months. And if you could take a lot of the things that we've learned and kind of make them like totally frictionless, that would actually pretty cool. And I think that um that I think is an under an underold story in in AI right now.

我们在监管这块看到的一件事是：它能大幅降低对变更的抗拒，并提升迭代速度。举个例子，假设你要去给一架飞机做适航认证。你必须做的无数件事之一，是证明它能扛住一次雷击，而针对这种测试方案的监管文档动辄长达约 200 页。传统做法是，老实说，你得雇一个不算特别聪明、但愿意当「键盘上的猴子」的工程师，去写这 200 页的合规文档，要花上好几个月。顺便说一句，如果你之后改了飞机的设计，你简直想哭，因为这种死记硬背式的合规文档又得返工差不多两个月。而我们发现，我们可以搭一个 RAG，让我们基本上靠 prompt 就把所有这些活儿干完——姑且说，几分钟搞定。一阶效应是「哦，省了好多时间」。二阶效应是，如果你改了飞机的规格，现在重做只要几分钟而不是几个月，所以你就真的愿意去改了。三阶效应是，你现在基本可以摆脱那些不太行的工程师，只留一小撮真正有创造力的人，他们可以快速迭代，因为变更成本降下来了。从某种意义上说，那套严重拖累迭代能力的整个监管负担，就此消失了。我觉得这是当下 AI 里一个被严重低估的故事。硅谷的共识好像是「监管很烂」——我们想跑得更快，想实现这个美妙的未来，想要丰裕，想要繁荣，任何拖慢这个未来的东西都该被避开。我当然也认为我们监管过度了，把造东西这件事搞得几乎不可能。在很多地方，要建任何一类东西——不管是实体的还是别的——所要付出的代价简直是疯了。但很多监管本身并不是问题。如果你真去读过这些东西，你会发现：让城市不被雾霾掐住喉咙是好事，能在很多河里游泳是好事，这些东西里有很多是进步。问题在于，人类要去理解并遵从这些东西真的很难，而且每次你要和政府往来一封公文，就得等上几个月。如果你能把我们学到的很多东西变得完全没有摩擦，那其实会相当酷。我觉得这是当下 AI 里一个被低估的故事。

[30:19] Naval

Yeah. until the regulators start spewing tokens back at us and then you start getting huge amounts of documents from the regulators that you have to comply and it's agent on agent wars. But

是啊，直到监管机构也开始朝我们「喷 token」——然后你就会从监管方那里收到海量需要遵从的文件，变成 agent 对 agent 的战争。不过——

[30:28] Blake

but that's basically what we have now.

但这基本上就是我们现在的处境了。

[30:30] Naval

Yeah. But but there is a fair fight. Yeah.

对。不过，至少这是一场公平的较量。是的。

[30:33] Blake

I'd argue that's an improvement from where we are now. Like one of the terrible things right now is if you build anything physical, you have to get a building permit. It's like you're guilty until proven innocent. And the worst thing I' that we've run into is the fire department because they have like the moral impremature of, you know, people pulling people out of burning buildings. And yet what they actually do is just like screw with your design for buildings for months. And I, you know, if we could replace the fire marshall with with an agent that would critique your your building plan quickly,

我倒觉得这比我们现在的状况是个进步。现在最糟的一点是，你要造任何实体的东西，都得拿到建筑许可，就好像「有罪推定、要自证清白」。我们碰到过最糟的是消防部门，因为他们占据着道德高地——你懂的，从燃烧的大楼里把人救出来。可他们实际干的事，就是拿你的建筑设计折腾你好几个月。我就想，如果我们能用一个 agent 取代消防督察，让它能快速点评你的建筑方案——

[31:03] Guillermo

um, even even if it's feedback was overdone, it would be massively better than the delays that exist today. When Max was talking about this potentially being a good thing to have all this regulation, my my head went to the things that make agents successful is uh humans or other agents setting up the right testing guard rails. Uh a lot of people are really excited about SLGOLO. I don't know if you guys have played with that or like Ralph loops where you tell the model go do this and this is your exit criteria. Well, I'm telling Blake go make us all supersonic. your exit criteria is that you've complied with all of these regulations. So there's totally a world in where we say like the regulations are great. They're like our testing, our test suite. As long as this p passing these tests for one that's not incurring contradictions and the regulations are actually reasonable, etc. Like they're actually an awesome guard rail to have. Otherwise, we would be shipping slop directly into into the air.

嗯，哪怕它给的反馈做过头了，也会比今天存在的那些拖延要好太多。刚才 Max 谈到「这一堆监管说不定其实是好事」时，我脑子里冒出来的是：让 agent 成功的关键，是由人类或其他 agent 设好正确的测试护栏。很多人对 SLGOLO 特别兴奋，不知道你们玩过没有，还有 Ralph loop——你告诉模型「去干这件事，这是你的退出标准」。那么，我就可以告诉 Blake：去把我们都变成超音速吧，你的退出标准就是你已经满足了所有这些监管要求。所以完全可以有这样一个世界：我们说监管挺好的，它们就像我们的测试、我们的测试套件。只要它能通过这些测试——首先这些测试不能自相矛盾，监管本身也得是合理的等等——那它们其实是一套很棒的护栏。否则，我们就会把垃圾代码直接发射上天。

[32:01] Naval

Yeah. But this is going to turn into a red queen race, right? They're going to have agents. We're going to have agents. I think we might have better agents, which is good, as opposed to have to do human versus human, but if anything, their cycle time, their response time may get lower. Like the app store is drowning in spam. I'm sure the patent office right now is drowning in spam. And so these agencies, they're going to be slow adopters of AI. They're going to get DDoSed, right, by clever entrepreneurs just overloading them with documents. It's possible that the approval time for this stuff might extend out as this suddenly get flooded. It creates the opportunity to um I think really shift the model, the regulatory model. Imagine if we drove around a city the way we build things today. Before you could go anywhere, you'd have to write a plan up, ship it to some regulator, you know, and your plan would have to specify we're going to take such and such a route and we're going to drive this speed limit. We're going to use our blinker and we're going to stop at every stop sign and we're never going to run a red light, blah blah blah blah blah. And then 3 months later, you get back critique. It's like, well, we think you should like drive on this other street. And eventually you get approval. didn't go drive somewhere. It's insane. You can never go anywhere. And yet that is absolutely the way we build physical infrastructure in this country. It's guilty until proven innocent. And and what we should actually do is make more of these things enforcementbased rather than preapproval based.

是的。但这会演变成一场红皇后竞赛（red queen race）对吧？他们会有 agent，我们也会有 agent。我觉得我们可能会有更好的 agent，这是好事，总比人对人地干要强；但话说回来，他们的周期时间、响应时间反而可能变长。比如 App Store 现在被垃圾内容淹没了，我敢肯定专利局现在也被垃圾申请淹没了。所以这些机构会是 AI 的慢采纳者，他们会被 DDoS——被聪明的创业者用海量文件压垮。完全有可能，随着这些东西突然被洪水般灌入，审批时间反而会被拉长。这其实创造了一个机会，我觉得是真正去改变那套模式、那套监管模式。想象一下，如果我们今天开车出行的方式，跟我们今天造东西的方式一样：你去任何地方之前，都得先写一份方案，递交给某个监管机构，方案里得写明「我们将走某某路线、开多少限速、会打转向灯、每个 stop sign 都停、绝不闯红灯」等等等等。然后三个月后你拿回批注：「嗯，我们觉得你应该改走另一条街。」最后你终于拿到批准，结果哪儿也没去成。这太疯狂了，你永远哪儿都去不了。可这恰恰就是我们这个国家建设实体基础设施的方式——有罪推定。我们真正该做的，是让更多这类事情变成「基于事后执法」而不是「基于事前审批」。

[33:18] Max

I mean, I don't know. I mean, I I don't want to be under too much. Like, if I ship a medical device to a lot of people, there needs to be it's like there's unknowns there. It's like we were responsible. We did clinical trials. We reported all the data, but Max, this is this is why there's so little innovation in medical right now because the FDA approval process is a nightmare. Uh, in fact, the two biggest advancements in tech in Silicon Valley in the last decade, AI and before that, crypto, they're both in the math domain because it's the last unregulated domain. And when they started regulating frontier models and started regulating GPUs, that stops as well. You know, Peter Teal laments about how there's no innovation in the physical domain. what's been held back by just the huge regulatory barriers and you can always find a scare version like you vaccine or medical like famous ones, right? But the regulations spread everywhere. The tentacles are everywhere and there's all these different contradictory regulatory bodies. You saw how uh was it SpaceX, they got sued first for for not having enough I forget what it was migrants or refugees or whatever, but they're not allowed to hire them by government regulation on the other side because they're not citizens. This is not like logical code that has to compile in one place. These are madeup random regulations all over the place. You might comply with one state, you violate another state, you violate federal over here, you annoy this guy over here, that guy chooses to prosecute one out of 50 people who are his friend. It's it's very arbitrary. It's very capricious.

我是说，我也不好讲。我不想太轻佻。比如，如果我要把一款医疗器械投放给很多人用，那里面确实有些未知风险。我们是负责任的，我们做了临床试验，我们如实上报了所有数据。——但 Max，这正是为什么现在医疗领域几乎没什么创新，因为 FDA 的审批流程就是个噩梦。事实上，过去十年硅谷科技领域最大的两项进展——AI，以及在那之前的加密货币——都属于数学领域，因为那是最后一个不受监管的领域。而当他们开始监管前沿模型、开始监管 GPU 时，那也会随之停滞。Peter Thiel 一直哀叹实体领域没有创新，被巨大的监管壁垒拖住了。你总能找到一个吓人的例子——比如疫苗、医疗这些著名案例对吧？但监管已经渗透到方方面面，触手无处不在，而且存在一大堆相互矛盾的监管机构。你看到了吧，是 SpaceX 吗？他们先是因为没招够——我忘了具体是什么了——移民还是难民之类的而被起诉，但另一边，因为这些人不是公民，政府监管又规定他们不许雇这些人。这不像那种必须在一处编译通过的逻辑代码，而是散落各处、临时拍脑袋的随机规定。你可能合规了某个州，却违反了另一个州，又违反了这边的联邦规定，还惹毛了这边某个人，而那个人会从五十个人里挑一个——挑他朋友的对头——去起诉。这太武断、太反复无常了。

[34:39] Blake

And and moreover, like the idea that this makes like things safer, I think it's just a complete mythology. Like just watch, you know, watch watch Boeing as an example. They certified the 737 Max, which had a single sensor that had complete authority over the nose up, nose down attitude of that airplane. No intern is dumb enough to think that's a good idea. And yet, it got all the way through the certification system. This stuff doesn't actually make us safer. It just makes us slower. Well, I mean, there's definitely dysfunction here. I mean, I think that some of this makes us safer in the sense that the NRC makes us safer, which is that their job was to make sure that nuclear energy was safe. They did this by permitting zero plants until I think like a year ago since the 70s. It will be perfectly safe if we never build any of it. And I want to be really clear that I'm on the side of deregulation in on a lot of this. I agree with Blake that a lot of this can be done a lot more efficiently. But I also think it's a little too dismissive just to say it's like oh this is like the FDA or like even it's it's in the reg it's in the agencies in general. And the problem is deeper to the degree that when the if the FDA approves 10 really important drugs, they don't get any credit for that. One patient dies and they get hauled before Congress and yelled at. And so they have very negatively biased incentives here. And I I think the reality is is that this is reflective of the beliefs of the American people. There's this trade-off here between the perception of risk taken in human subjects research and the rate at which we get new medicines. And it is absolutely true that if we move faster on this, we would learn. It's totally asymmetric and I think you're totally right, Max. If you approve a bad thing, your career is over. If you block a good thing, nobody notices, right? So, it so it creates this asymmetric slowdown. And I think this is um I think that is the most important problem to solve in the regulatory state.

而且更进一步说，认为这套东西让事情更「安全」了，我觉得纯属神话。你就看看波音（Boeing）这个例子。他们给 737 Max 做了认证，而那架飞机有一个单一传感器对机头上仰、下俯的姿态拥有完全的控制权。没有哪个实习生会蠢到觉得那是个好主意，可它却一路通过了整个认证体系。这套东西其实并没让我们更安全，只是让我们更慢。——嗯，这里确实存在功能失调，我也认为其中有些东西确实让我们更安全，就像 NRC（核管理委员会）让我们更安全——他们的工作是确保核能安全，他们的做法是从七十年代到大约一年前为止，批准了零座核电站。如果我们永远一座都不建，那当然会绝对安全。我想说得非常清楚：在很多事情上我是站在去监管这一边的，我同意 Blake 说的，很多事情可以高效得多。但我也觉得，把它简单一句话打发成「哦这就是 FDA」或者「就是监管机构整体的问题」，有点太轻率了。问题更深一层：当 FDA 批准了 10 款真正重要的药，他们得不到任何功劳；而一个病人死了，他们就被拖到国会被一顿痛骂。所以他们的激励是带着强烈负面偏向的。我觉得现实是，这反映了美国民众的信念。这里存在一个权衡：人类受试者研究中所承担风险的「观感」，与我们获得新药的速度，二者之间的取舍。如果我们在这件事上跑得更快，我们确实会学到更多，这一点千真万确。它是完全不对称的——Max，我觉得你说得完全对：你批准了一个坏东西，你的职业生涯就完了；你拦下了一个好东西，没人会注意到，对吧？所以它就造成了这种不对称的拖慢。我觉得这是监管型国家里最重要、最该解决的问题。

[36:21] Max

But but this is a very deep problem because it is this is where the voters are like and we we go and poll some of the stuff that we're working on in the future to understand kind of like where where the American people are on it. And if you push too hard on this, like there are there are all kinds of ways you could work around it. You go to prosper. There's all kinds of ways to try to go faster. But if you're seen as being a bad actor, then you're rejected from the society that we live in. That is the thing that you need an answer for, which is deeper than just saying like, "Oh, well, we need regulatory reform."

但这是个非常深层的问题，因为这恰恰是选民所在的位置。我们会就一些我们未来在做的事情去做民调，来搞清楚美国民众到底站在哪一边。如果你在这件事上推得太狠，其实有各种各样的办法可以绕过去——你可以去 Próspera，有各种办法去试着跑得更快。但如果你被视为一个「坏行为者」，那你就会被我们所生活的这个社会所排斥。这才是你必须给出答案的东西，它比单纯说「哦，我们需要监管改革」要深得多。

[36:49] Naval

You you have a deep point there, Max, which is uh it's the voters, right? Yeah.

Max，你这一点说得很深刻——问题在选民，对吧。是的。

[36:54] Naval

This where the citizens are. Like we like to blame politicians. You'll see an X all the time, right? When people are like, "Oh, this politician, that politician, you a politician." like they're elected, they're voted, majority vote, right? This is where the people literally are. That's the package. That's the bundle they've chosen. And you may not like this constantiation, but if you were to remove this one, something very similar would take its place because the voters would just vote them right back in. And I think culturally it's very hard for most people to understand what we lost, what we missed, right? So for example, like France, you know, there's a French entrepreneur on X lamenting that 57% of GDP gets sucked up by the government and so you can't create companies. But to the average French citizen, that's not visible. They don't notice what they're missing. They just know they're slightly poorer than the US. The economist just did a little piece on economist is finally coming back around to being capitalists after 30 years. And they just did a little piece on how the US is outstripping everybody and growing faster and getting bigger. But then they immediately turn around and say, well, it's because of the oceans, because of natural resources, everything but capitalism, right? They don't want to say the dirty sea word. uh because you know for some reason all of these all of these uh magazines became Marxist at some point um but they can't they can't envision or imagine what could have been if we had just been a little more lazare a little more open

这是公民所在的位置。我们喜欢怪政客，你在 X 上总能看到，对吧？大家老是说「哦，这个政客、那个政客，你这个政客」——可他们是被选出来的，是投票选出来的，多数票，对吧？这恰恰就是人民真实所在的位置。这就是那个「打包套餐」，是他们自己选的那个组合。你也许不喜欢这个具体的化身，但就算你换掉这一个，会有一个非常相似的取而代之，因为选民会直接把他们再投回来。而且从文化上讲，让大多数人理解「我们失去了什么、我们错过了什么」是非常难的。比如法国，X 上有个法国创业者哀叹说，57% 的 GDP 被政府吸走了，所以你没法创办公司。但对一个普通法国公民来说，这是看不见的，他们感受不到自己错过了什么，只知道自己比美国人稍微穷一点。《经济学人》（The Economist）刚做了一篇小文章——他们在沉寂三十年后终于又开始回归资本主义了——他们刚做了一篇小文，讲美国如何把所有人甩在身后、增长更快、体量更大。但他们紧接着又转头说，那是因为海洋、因为自然资源，把所有原因都算上，就是不算资本主义，对吧？他们不愿说出那个肮脏的「资本主义」字眼，因为不知怎的，这些杂志在某个时间点都变成马克思主义者了。但他们就是无法设想或想象：如果我们当初再放任自流一点（laissez-faire）、再开放一点，本可以是什么样子。

[38:08] Naval

so I would love to see a true experiment among the 50 states you know different regulations different tax structures not because right now the federal tax structure and federal regulations dominate everything but imagine you know you could go to some small state if you had cancer and you could try every drug that everyone was cooking up in caviat mtor and you got to do your research and blah blah blah but this is known as the experimental zone um same way for drones same way for well aircraft is a little harder because you got to cross a lot of areas but

所以我特别想看到在五十个州之间做一场真正的实验——不同的监管、不同的税制。倒不是因为现在能这么干，毕竟现在联邦税制和联邦监管主宰一切。但想象一下，如果你得了癌症，你可以去某个小州，把所有人正在捣鼓的药都试一遍——「使用者自负风险」（caveat emptor），你自己去做研究等等——这就是所谓的「实验区」。无人机也一样，飞机就难一点，因为你得跨越很多区域，但是——

[38:35] Max

do you think there's something magical in there the notion of like innovation zones because we have we have a huge like nimi problem right uh but if you if you create like you know opt-in yimi zones they create some experimentation framework and by definition it happens where people are consenting and you can try different rules or no rules or different ways of enforcing or you know innocent until proven guilty and then see what actually happens and what are the innovation consequences and what are the safety consequences and then then the successes can spread but I mean to Naval's to Naval's point an innovation zone would not solve the problem in drug discovery um there so there was the right to try act passed a little while ago we've had this pathway called single patient IND for a lot longer than that um the FDA like if if your doctor calls the FDA and says hey I want to give this my patient an approved drug they give over 99% of those over like they approve over 99% of those. They can even grant them over the phone. Um the problem is that in order to dose a patient, you still need clinical grade drug and the only entity with that is typically the IP owner who's in the middle of running a clinical trial. Like they're investing hundreds of millions of dollars into like making this thing. And the problem is that the FDA, they'll draw an adverse inference if something bad happens to your patient who's probably really sick to begin with. And that's going to be seen as a property of the drug which is global, not related to your innovation zone. And so there's kind of two problems. One is you need to get the IP owner to give you some of your drug which they're not going to do. And then you need to prevent the global regulator from casting doubt on what might happen with their clinical trial if they give you some.

你觉得「创新区」这个概念里有什么神奇之处吗？因为我们有一个巨大的 NIMBY（邻避）问题，对吧？但如果你设立那种自愿加入的 YIMBY（拥护建设）区，搞出某种实验框架，按定义这些就发生在人们自愿同意的地方，你可以试不同的规则、或者干脆没规则、或者不同的执法方式、或者「无罪推定」，然后看实际会发生什么——创新后果是什么、安全后果是什么，然后成功的做法就能推广开。但是，回到 Naval 的那个观点，「创新区」并不能解决药物研发里的问题。前阵子通过了一个「尝试权法案」（Right to Try Act），我们还有一条叫「单一患者 IND」的通道，存在的时间比那还久。FDA——如果你的医生打电话给 FDA 说「嘿，我想给我的病人用一款已获批的药」，他们超过 99% 都会放行，甚至可以在电话里当场批准。问题在于，要给病人用药，你仍然需要临床级别的药品，而唯一拥有它的实体通常是那个正在做临床试验的 IP 持有者——他们正往里砸数亿美元来生产这东西。麻烦在于，FDA 会「推定不利」：如果你的病人——他本来可能就病得很重——出了什么坏事，那会被视为这款药本身的特性（这是全局性的），而跟你的「创新区」无关。所以这里基本有两个问题：一是你得让 IP 持有者分点药给你，而他们不会干；二是你得防止那个全局监管机构因为分了药给你，而对他们自己临床试验的结果蒙上怀疑的阴影。

[40:06] Naval

How would you address I mean I don't know your field. How would you address that in medicine?

你会怎么解决——我不懂你这个领域——在医学上你会怎么解决这个问题？

[40:10] Max

Oh well I mean that in particular I mean this is just like a very inside baseball. I think the FDA has to be prohibited from drawing adverse inferences across different users of a capsit, for example. There's these like a bunch of specific ways that you could really accelerate innovation with a relatively light regulatory touch by just um preventing this this kind of paranoia from driving our decisions.

哦，我是说，具体到那个问题——这其实非常「圈内行话」了。我觉得必须禁止 FDA 在同一款药（比如某个胶囊剂）的不同使用者之间做「推定不利」。有一堆这样具体的办法，只要用相对轻的监管干预——就是阻止这种偏执去主导我们的决策——你就能真正地加速创新。

[40:31] Naval

Is there anything better than the FDA out there? Like what are we benchmarking these regulators against or is it not an interesting question because we don't have everyone follows the FDA.

外面有没有比 FDA 更好的东西？我们拿这些监管机构去对标什么？还是说这压根不是个有意思的问题，因为我们没有别的可比——所有人都跟着 FDA 走？

[40:40] Max

So I'll give two two expansions to that. The first is um Europe which is not really better than the FDA but they have a different system in that they've got these these notified bodies which are basically private businesses that are blessed by their host governments to certify things whether this is trains or planes or medical devices and the notified body system uh creates slightly better incentives at the review layer because they can hire people they can grow there's competition among the notified bodies they themselves have to be compliant with the conditions placed by their host governments for certification but it means that they can there can be many thousands more reviewers than you might have in the US. The second thing I'll say is there actually is one approved getting paid implantable BCI today which is in China and the CFDA is thinking for itself. And they really do have a system that I think is going to give us a run for our money if we're not if we're not careful. And they they they handle it very differently.

那我给两个延伸的方向。第一个是欧洲——它其实并不比 FDA 好，但他们有个不同的体系：他们有所谓的「公告机构」（notified body），本质上是一些私营企业，由各自的所在国政府授权去认证东西，不管是火车、飞机还是医疗器械。这套公告机构体系在审查这一层创造了稍微更好的激励，因为他们可以招人、可以扩张，公告机构之间存在竞争；他们自身又必须遵守所在国为认证设定的条件，但这意味着评审员的数量可以比美国多上几千人。第二点我想说的是，今天其实真有一款已获批、且能拿到付费的植入式 BCI（脑机接口）——在中国，CFDA（中国的药监机构）是有自己独立思考的。他们确实有一套体系，我觉得如果我们不当心，会给我们带来真正的压力。而且他们处理方式非常不一样。

[41:35] Naval

How do they handle it?

他们怎么处理的？

[41:36] Max

I mean the costs to bring a drug to market or a device to market are just much lower. I mean you can try things in humans and you can try things on market like the so the problem the one of the things that I've spent a lot of time recently thinking about is like 20 years ago we were buying far fewer laptops and phones each one was much more expensive now there's they're cheaper there's far more of them we buy more of them the total spending has gone up this is great stock prices of things like Qualcomm and Samsung and Apple are way up everybody's happy they're using kind of the excess wealth generated by the phones and laptops to buy the phones and laptops um this doesn't happen in healthcare. In healthcare, because you've got this reimbursement mechanism in the way where there's this kind of enterprise sale happening, the bucket of money that we use to buy healthcare is basically fixed. It is not increasing as there is more stuff that is producing better healthcare outcomes like we see in technological growth industries. And so this means that the rate of spending on healthcare grows at roughly the rate of of growth of tax receipts. And so if let's say that like AI is booming and there's major advances that are happening and two years from now we're spending 10 times as much on AI as we are now, this could be great, but if in two years we're spending 10 times as much on healthcare, this would be a catastrophe. And this is fundamentally at odds with being a technological growth industry. And so as time goes on and there's more things to spend money on that extend and improve quality of life for patients, like we can restore vision to people go blind in their 80s. We might be able to extend life in like far past where it's been before. we can restore capability to patients that are older and in worse condition, but like how do you pay for that? There's kind of this like omni problem in healthare which is all really related the same problem which is just too expensive to bring these things to market and that's what China is getting at. The way out of this is not singlepayer or some revision to health to health insurance. It's to bring down the costs so that someone can buy this with a credit card finance maybe like a car worst case. And to do that, we have to make it cheaper to bring these things to market. And China's doing that. That that will allow them to sell these things for $10,000 on $100,000. There's no private market in healthcare. And because there's no private market, what was the analogy people make sometimes? Like imagine instead of going to restaurants and paying, you would basically go to all the restaurants and then at the end of the month, you would send all the receipts and all the bills to your insurer to the government and they would reimburse you. Well, there'd be a line outside every good restaurant. Every bad restaurant, you know, would be available. Um, the weights would be terrible. The product wouldn't improve. You're basically running a small communist society inside a larger capitalist society. And that's what we're doing in healthcare.

我是说，把一款药或一款器械推向市场的成本，要低得多。你可以在人身上试，也可以在市场上试。我最近花了很多时间思考的一件事是：二十年前我们买的笔记本和手机要少得多，每一台都贵得多；现在它们便宜了，数量多得多，我们买得更多，总支出反而上升了。这很棒——像高通（Qualcomm）、三星（Samsung）、苹果（Apple）这些公司的股价都涨了不少，大家都很开心，人们用手机和笔记本所创造出的多余财富，又拿去买更多的手机和笔记本。但这在医疗里不会发生。在医疗里，因为你中间夹着这套报销机制——是一种类似企业级销售的东西——我们用来购买医疗的那笔钱，基本上是固定的。它并不会因为出现了更多能带来更好健康结果的东西就增长，不像我们在那些技术增长型行业看到的那样。这意味着医疗支出的增速大致只跟税收的增速持平。所以假设 AI 正在爆发，发生了重大进展，两年后我们在 AI 上的花费是现在的十倍，这可能是好事；但如果两年后我们在医疗上的花费是现在的十倍，那就是一场灾难。这从根本上和「成为一个技术增长型行业」是相冲突的。随着时间推移，能花钱去延长并改善病人生命质量的东西越来越多——比如我们能让八十多岁失明的人重见光明，我们也许能把寿命延长到远超以往，我们能让年纪更大、状况更差的病人恢复机能——但这些怎么付得起钱？医疗里有一种「无所不在」的问题，归根结底都是同一个问题：把这些东西推向市场实在太贵了，而这正是中国在攻克的点。出路不是单一支付方（single-payer），也不是对医保做某种修补，而是把成本降下来，让一个人能用信用卡就买得到，最坏情况像买车那样去做分期。要做到这点，我们必须让把这些东西推向市场变得更便宜。中国正在这么做，这会让他们能把这些东西卖到一万美元，而不是十万美元。医疗里没有真正的私人市场，而正因为没有私人市场——人们有时打的那个比方是怎么说来着？想象一下，你不是去餐厅吃完直接付钱，而是去所有餐厅吃，到月底把所有收据和账单都寄给你的保险公司、寄给政府，然后他们给你报销。那结果就是每家好餐厅外面排长队，每家烂餐厅都有空位，等待时间糟透了，产品也不会改进。你基本上是在一个更大的资本主义社会里运行一个小型的共产主义社会，而这正是我们在医疗里干的事。

[44:07] Naval

It's also what we're doing on roads, which is why we have traffic. Like, it's the exact same situation on roads. It's why there's, you know, there's no variable pricing for getting on the highway. It's why it's always clogged.

这也是我们在道路上干的事，这正是我们会堵车的原因。道路上的情况一模一样：这就是为什么上高速没有可变定价，为什么它总是堵得水泄不通。

[44:17] Naval

If you want to step on the third rail of healthcare for for a moment, think about this healthcare plan. Tell me what's wrong with it. Right? Imagine that the first 20% of your uh annual income was your healthcare deductible. Doesn't matter like if if you're broke and homeless, it's zero. If you're rich, you know, it's millions of dollars. Uh but whatever your annual income is, the first 20% is your healthcare deductible. And then the rest is paid by the government, the insurance system up to the usual caps that they have today. You would create a private market pretty quickly. And so like in dental and plastic surgery and sort of a lot of optional medical procedures, you would actually get a competitive situation. You get improvement. Like if you look at optometry, you know, with LASIC, you look at dental with like veneers and uh braces and all that stuff and kind of all the dental surgery stuff that they do. Or if you look at plastic surgery, like those fields do seem to be advancing because they're private payers. They have people who are, you know, voting with their money. So we need to do some equivalent of that in the normal healthare system. But people lose their minds. They don't want to think one step ahead. They're like, "No, no, no. Well, what about the broke person?" Well, the broke person has no income. So, they're like, "Well, 20% is too much for some people." Okay, you can put some deductible in there. But generally, if you don't have some private market where people are paying a lot of the times for what are medical procedures, you're just not going to get this feedback loop that you're talking about. You're not going to get this ability to spend more money into the system. Right now, like very wealthy people can't spend voluntarily into the system, but the prices aren't anywhere. The rate cards aren't anywhere. the system's not designed for it. It's like if you go shopping for medical care and you want to pay out of your pocket, sometimes they'll quote you a price that's 10x what they charge the insurance company.

如果你愿意暂且踩一踩医疗这条「高压线」，想想这个医保方案，告诉我它哪里有问题，行吗？想象一下：你年收入的前 20% 是你的医疗免赔额（deductible）。无所谓——如果你一贫如洗、无家可归，那就是零；如果你很富，那可能是几百万美元。但不管你年收入多少，前 20% 是你的免赔额，剩下的由政府、由保险体系按今天那套常规上限来付。这样你会很快催生出一个私人市场。就像在牙科、整形外科以及一大堆可选的医疗项目里，你会真正出现一种竞争局面，你会看到改进。你看眼科——比如 LASIK（激光近视手术）；你看牙科——比如贴面、牙套以及他们做的各种牙科手术；或者你看整形外科——这些领域确实在进步，因为它们是私人付费者，是用自己的钱「投票」的人。所以我们需要在正常的医疗体系里搞出某种等价物。但人们会抓狂，他们不愿往前多想一步，他们就会说「不不不，那破产的穷人怎么办？」——可破产的穷人没有收入啊，所以（前 20% 就是零）。然后他们又说「对有些人来说 20% 太多了」——好吧，你可以在里面再设点免赔额。但总体而言，如果你没有一个私人市场、没有人在很多时候自掏腰包为医疗项目付费，你就压根得不到你刚才说的那种反馈循环，你也得不到那种把更多钱投进系统里的能力。现在，连非常有钱的人都没法自愿往系统里花钱，因为价格根本无处可寻，费率表也根本没有，整个系统就不是为此设计的。这就好比，你去为医疗服务「购物」、想自费支付，有时候他们给你报的价是他们向保险公司收费的 10 倍。

[45:57] Guillermo

Have you heard Sid's story from GitLab? Do you know Sid?

你们听过 GitLab 那位 Sid 的故事吗？你们认识 Sid 吗？

[46:02] Guillermo

So, he was uh I mean

他当时——我是说——

[46:04] Guillermo

had a massively successful IPO then was diagnosed with a rare cancer and has achieved has lived way past the prognosis, has really taken it into his own hands. I think he went from kind of he did frontline chemo and then there was one alternative that was available. He exhausted it and the doctors were like, "We've got nothing for you." Since I think like six or seven companies have come out of it, there's now 20 or 30 drugs in his escalation ladder. He's still alive. Um years later,

经历了一次极其成功的 IPO，之后被诊断出一种罕见癌症，而他活得远远超过了医生给的预期，真的把命运掌握在了自己手里。我记得他先是做了一线化疗，然后当时有一个可选的替代方案，他把它也用尽了，医生们就说「我们对你已经没辙了」。从那以后，我想大概有六七家公司因此诞生，现在他的「升级阶梯」里有二三十款药。他到现在还活着——好多年过去了。

[46:31] Guillermo

he's doing great. I saw him the other day and he he basically created his own like personalized medicines and uh treatment plan.

他状态很好。我前几天还见到他，他基本上是给自己打造了一套个性化的药物和治疗方案。

[46:39] Max

Yeah, there's there's a handful of these anecdotes that I've heard now. I it is really clear to me that at the high end if you just kind of have like you're not dealing with insurance you have the resources you're like I want the full toolbox of modern science outcomes are possible that that like your normal like if you go and ask your doctor like oh what will happen if I do this they will just start shouting and throwing things but it is clear that that much that like that crazy things are possible at the high end and I think that this type of like end of one medicine is actually going to end up being a really rich source of research for understanding how to build more translatable things.

是啊，这类我现在听过的轶事有好几个了。对我来说很清楚的一点是：在高端这一头，如果你不用跟保险打交道、你有资源、你说「我要现代科学的全套工具箱」，那是可能出现各种结果的。而你那种正常情况——如果你去问你的医生「哦，我要是这么做会怎样」，他们只会开始大喊大叫、摔东西。但很清楚的是，在高端这一头，疯狂的事情是可能发生的。我觉得这种「n 等于 1 的个体化医疗」最终其实会成为一座非常丰富的研究金矿，帮助我们理解如何打造出更多可推广（translatable）的东西。

[47:12] Max

It requires a ton of agency from the patient in a moment where they're at their weakest, which is pretty uh ironic. My friend passed away from cancer and like last thing he wanted to do was research n equals one medicine uh because he was just, you know, like like dying by the week. But this is where AI should really shine and come up with the right solutions and democratization of like what can you actually do when you find yourself in that situation. It's kind of crazy how few people get access to this just from a knowledge perspective, not just monetarily speaking.

这要求病人在自己最虚弱的时刻拿出极强的主动性，挺讽刺的。我有个朋友死于癌症，他临终前最不想做的事就是去研究那种 n=1 的个体化疗法——因为他当时就是，你懂的，一周一周地在往下垮。但这恰恰是 AI 真正该大显身手的地方：给出对的方案，把这种能力民主化——当你真陷进那种处境时，到底还能做点什么。光从知识获取的角度看，能接触到这些的人少得离谱，这跟有没有钱还是两码事。

[47:49] Guillermo

How much autonomous software do you guys have in your organizations that's running on its own or near autonomous and improving on its own? For us it's the a lot of the infrastructure is already autonomous because we have uh we have this capability uh that fires off uh upon finding anomalies which I recommend everyone creates a version of this or Verscell offers a version of this but upon anything happening that's anomalous today the mo most engineering organizations are responding to this by setting up alarms or uh like monitoring thresholds by hand which is pretty insane but that's actually how the entire industry works you say if my error rate increases by this amount uh at this API endpoint do this so we've actually uh automated a lot of the S sur job uh site reliability engineering so anything uh uh any metric that uh slows down speeds up uh throughput changes whatever fires off an anomaly alert an agent investigates that an agent can decide to create an incident if the incident is filed, people get looped in and the agent begins the process of remediation. We're doing everything except for like the actual like giving the tools for the agent to like you know change prod but we're basically like serving solutions on a silver platter to engineers and then the other thing that's working really well for us is just autonomous optimization processes um and autonomous security research. So the other we open source this tool called deepseac it's [ __ ] incredible. was like mythos, but you get it today. Uh we run it against our entire monor repo using 10,000 concurrent agents in the cloud and it found basically several quarters worth of security research uh progress was made in um basically a couple days and $14,000 worth of tokens. So I'm talking about like months worth of red teaming, security research, entire teams of people. Uh and so we're basically now running like this periodic because the the other problem with AI is that cyber security is becoming a nightmare. Um there's way too many vulnerabilities, way too much work to do. There's too powerful adversaries. So you have to like basically be investing very proactively. We're running a lot of autonomous security research. Um so S sur and then optimization work are very obvious. Uh you've probably seen on Twitter there's people translating code bases from language A to language B. Like a lot of the work that if you if you already put in the work to get a working program optimizing it or rewriting it in a native programming language or things like that is now becoming quite um quite doable uh with with Frontier models.

你们各自的组织里，有多少软件是完全自主运行、或接近自主、还能自我改进的？对我们来说，很多基础设施已经是自主的了，因为我们有这么一个能力：一旦发现异常就自动触发——我建议每个人都做一个这样的东西，Vercel 也提供了一个版本。今天大多数工程团队应对异常的方式，是手动设报警、手动设监控阈值，这其实挺离谱的，但整个行业就是这么干的：你规定『如果这个 API 端点的错误率涨了多少，就执行某某操作』。我们其实已经把大量 SRE（站点可靠性工程）的活儿自动化了——任何指标变慢、变快、吞吐量变化，不管什么，只要触发异常告警，就有一个 agent 去排查，agent 可以决定要不要建一个事故工单；工单一开，相关人员被拉进来，agent 就开始走修复流程。除了真正给 agent 改生产环境的工具，其它我们基本都做了——我们等于是把解决方案盛在银盘子上端给工程师。另一件对我们效果特别好的，是自主优化流程和自主安全研究。我们开源了一个叫 deepseac 的工具，简直牛逼炸了，有点像神话，但你今天就能用上。我们让它在云端用 1 万个并发 agent 跑我们整个 monorepo，几天之内、花了 1.4 万美元的 token，就做出了相当于好几个季度的安全研究进展。我说的是好几个月的红队演练、安全研究、整支整支团队的工作量。所以我们现在基本是周期性地跑这个，因为 AI 带来的另一个问题是：网络安全正在变成一场噩梦。漏洞太多了，要干的活儿太多了，对手又太强了。所以你必须非常主动地投入。我们跑了大量自主安全研究。SRE 和优化工作是最显而易见的两块。你大概在 Twitter 上见过有人把代码库从 A 语言翻译成 B 语言——很多这种活儿，只要你前期已经做出了一个能跑的程序，现在用前沿模型去优化它、或用某种原生编程语言重写它之类的，已经相当可行了。

[50:33] Blake

I mean just for my own vibe coded app I built a uh bug reporting queue for my test flight users and they can report bugs from inside the app. It uploads the logs in a screenshot. And of course, they use for feature requests, too. So then I just have a simple Damon go through, compile all the bug reports. It actually proactively analyzes and fixes them in the background. And then it ships me a test flight version to try out before I ship it to the testers. And then for feature requests, I just have it right now compile them, but I could see an app in the future could literally be built by the users. Now, I'm not saying that's a good idea. It might be a mess, but at least it can take the bug reports and stuff.

就拿我自己 vibe coding 出来的 app 来说，我给我的 TestFlight 用户做了一个 bug 上报队列，他们可以直接在 app 里报 bug，会自动上传日志和一张截图。当然他们也用来提功能需求。然后我就让一个简单的守护进程（daemon）跑一遍，把所有 bug 报告汇总起来，它其实会在后台主动分析并修复这些 bug，然后给我推一个 TestFlight 版本，让我在发给测试者之前先试一下。至于功能需求，我现在只是让它汇总起来，但我能想象未来的 app 完全可以由用户自己来构建。我可不是说这是个好主意，可能会乱成一锅粥，但至少它能把 bug 报告这些东西接管下来。

[51:10] Naval

We should ship that, by the way, just to see what happens to the social experiment.

话说回来，我们真该把那个发出去，纯粹为了看看这个社会实验会怎么收场。

[51:14] Blake

Yeah. Yeah. The social experiment, right? You you end up with like that Homer Simpson car where it's got an umbrella and like a flashlight, you know, a clown horn and so on where it's got every feature. But definitely for bug fixing, you could do that. We did in a way a version of that experiment where uh I stopped all project work across the entire company for a week and said everybody from the receptionist to the engineers uh build whatever you think is the most important thing to build. Uh the only requirements you have to use AI and you have to demo it for the whole company when you're done. I expected we would get a large number of silly projects and a small number of needle movers. And what we got was a large number of needle movers and a very small number of silly projects. And wow. Yes.

对对。社会实验，是吧？最后你会搞出个像 Homer Simpson 那辆车一样的东西——上面装了把伞、一个手电筒、一个小丑喇叭等等，啥功能都有。但用来修 bug 绝对是可行的。我们其实在某种意义上做过一版这个实验：我让整个公司停掉所有项目工作一周，告诉大家，从前台到工程师，所有人都去做你认为最重要的那个东西。唯一的要求是必须用 AI，而且做完之后要给全公司演示。我本以为会收获一大堆瞎搞的项目、少数几个真能推动业务的。结果恰恰相反：我们收获了一大堆能推动业务的，只有极少数是瞎搞的。哇，真的。

[51:54]

Yeah,

对。

[51:55] Naval

that's a great experiment.

这是个很棒的实验。

[51:56] Blake

Yeah, two or three are like trajectory changing like they would absolutely change the direction of the company. But the what this surprised me the most was literally the receptionist like the shipping and receiving associate whose job it was to like take packages off a truck and like email people when their like stuff came into inventory uh build an automation for that and uh and that that we're actually using. The conclusion I kind of is like, wow, like everybody has some idea of what could exist that would make the world better, but that many times their first order ideas are stupid and they don't have the they don't have the ability to project that out and kind of see that it's stupid. But if they have the ability to go from idea to an actual thing, if it's not working, they can react. They can iterate. And if you give them a week, by the time they're at the end of the week, they've actually built something that makes sense.

对，其中两三个简直是改变公司轨迹级别的——它们绝对会改变公司的方向。但最让我意外的，是字面意义上的前台、那个收发货的同事——他的工作就是把包裹从卡车上卸下来、东西入库后给相关人发邮件——他给这事儿做了个自动化，而且我们现在还真在用。我得出的一个结论大概是：哇，原来每个人心里都有点想法，知道有什么东西要是存在了就能让世界更好一点，但很多时候他们的第一直觉想法是蠢的，而他们自己又没那个能力把这想法推演出来、看出它蠢在哪。可一旦他们有能力把想法变成一个真东西，如果它跑不通，他们就能反应、能迭代。给他们一周时间，到周末的时候，他们其实真做出了一个说得通的东西。

[52:42] Guillermo

But imagine if all work was like that. like how can you set up a workforce that does not do the work directly? All they do is train the agent that does the work for them. And we've done this as well like you have to remind folks and you have to like create hackathons and hey let's build agents. Uh and obviously there's a lot of people there's a culture change happening like there are a lot of people that are just coming in who intuitively know their job is to not work on the thing is to actually train the agent that works on the thing. But I'm curious about like, you know, what what does the autonomous company of the future look like?

但想象一下，如果所有工作都是这样呢？比如，你该怎么组建一支不直接干活的队伍？他们做的全部事情，就是训练那个替他们干活的 agent。我们也做过类似的事——你得不停提醒大家，得搞 hackathon，喊『来，咱们一起搭 agent』。当然这里头有很大的文化转变正在发生：现在有很多人一进来就本能地知道，自己的工作不是去做那件事，而是去训练那个做那件事的 agent。但我很好奇，未来那种自主运转的公司到底会长什么样？

[53:18] Max

It could get a lot crazier. Maybe you just turn on all cameras and the agents just watching everything that's happening. It see the shipping and receiving thing is very inefficient and it creates the app presents the app.

可以变得比这还疯狂得多。也许你只要把所有摄像头都打开，让 agent 盯着发生的一切。它看到收发货那套流程效率很低，就直接把那个 app 做出来、端到你面前。

[53:28] Guillermo

Did you see that? Zach installed this thing into everyone's machines. He's thinking about it. It's like we saw this too like we're we're um we're we're likely going to ship a feature into AI gateway that allows people to opt in into preserving inputs and outputs and then you can say for all of my inputs and all my outputs can you extract the skills of the things that I like learn from my work and then dump it as skills uh so that I can even download them for myself. But you could imagine people in in companies wanting to to share and and pull this together. It's funny because for me that's so unimaginable for my own work because my own work is not repetitive. I look for things to automate. There's almost nothing left for me to automate for my own work. And I and I hope that's where kind of everybody ends up, right? You just work in your maximum zone of creativity and interest at all times. And like if there is anything left to automate, you should automate it. Get it out of your life. It'll free you up to be creative and that's where you generate all the value. But I think that's very hard to see in the job career mindset because you hire people to do the same thing over and over and that's going away and that's really scary because people like, well, what am I going to do? Well, you're going to do creative things. You're going to come up with new things and you don't have to come up with a new thing every day. That's impossible, right? But you're going to come up with a new thing once in a while that will then create something else, some point of leverage for you. But it it is it is a scary time for people for sure. If you've been doing the same thing over and over for 10 years and now all of a sudden it's like, well, now you're going to train an agent and automate it away, that's scary.

你看到了吗？Zach 把这玩意儿装进了每个人的电脑里。他正琢磨这事儿呢。我们也看到了同样的趋势——我们很可能会在 AI Gateway 里上线一个功能，让大家可以选择开启『保留输入和输出』，然后你就能说：把我所有的输入和所有的输出拿来，从我的工作里把那些我喜欢、想学的东西提炼成 skills，再 dump 出来，让我自己都能下载下来用。但你可以想象，公司里的人会想把这些共享出来、汇集到一起。有意思的是，对我自己的工作来说这反而难以想象，因为我自己的工作不是重复性的。我一直在找能自动化的东西，可我自己的活儿几乎已经没什么可自动化的了。我希望大家最终都能走到这一步：你随时都待在自己创造力和兴趣的最高区间里。如果还剩下什么能自动化的，就该把它自动化掉，把它从你生活里清出去——它会把你解放出来去搞创造，而那才是你产生全部价值的地方。但我觉得在『打工/职业』那套心态里，这点很难看清，因为你雇人就是来一遍遍重复做同一件事的，而这种活儿正在消失，这确实很吓人，因为人们会想『那我以后干嘛？』——你会去做有创造力的事，会去想出新点子，而且你不用每天都想出一个新东西，那不可能，对吧？但你会时不时想出一个新东西，它又会孕育出别的东西，成为你的某个杠杆点。但对很多人来说，这确实是个让人害怕的时代。如果你过去十年一直在重复做同一件事，现在突然变成『行了，你现在得去训练一个 agent，把这活儿自动化掉』，那是挺吓人的。

[54:54] Max

I think historically it was the returns were like 70% intelligence, 30% agency and now it's going to be 70% agency, 30% intelligence and that will that will shift further as the models get better and better.

我觉得从历史上看，回报的构成大概是 70% 靠智力、30% 靠主动性（agency），而现在要变成 70% 靠主动性、30% 靠智力了；随着模型越来越强，这个比例还会继续往那边偏。

[55:08] Naval

I'm actually not sure about that, Max. I'll take the counterpoint on that. I think it's 99% intelligence and 1% agency because then the agents will exercise the agency,

Max，这点我其实不太确定，我来唱个反调。我觉得是 99% 靠智力、1% 靠主动性，因为主动性（agency）以后会由 agent 来执行，对吧？

[55:17] Naval

right? You will literally be like, "Hey agent, I'm making smart decisions and thinking big thoughts. Just go implement stuff." In fact, sometimes I want to build features on apps uh that I'm flowing out of vibe coding. I'll ask the agent, "What features should I build next?" You know, go look at the logs, go look at the users, what should I do?

你会真的就只是说：『嘿 agent，我来做聪明的决策、想那些宏大的事，你只管去把东西实现出来。』事实上，有时候我想给那些 vibe coding 弄出来的 app 加功能，我会直接问 agent：『接下来我该做哪些功能？去看看日志、去看看用户，我该干嘛？』

[55:33] Max

To be clear, I'm talking about the returns to humans. um the the humans that will be best fit for the future will be the ones that are more agentic which is to say like the ones that can come in and just have the thought of like I'm going to open cloud and be like what should I build versus watch YouTube and here's a fun experiment I'll bet you we all know a lot of people now who are coding who weren't coding before including many cases ourselves right so the number the percentage of coders in the ecosystem has probably gone up by might be 10x right yeah it might literally be 10 times as many people are coding now than we're coding a year ago

说清楚一点，我说的是对人类的回报。最适应未来的那批人，会是更有主动性（agentic）的人——也就是那种能一进来就直接冒出『我要打开 Claude，问它我该做点什么』这种念头的人，而不是去刷 YouTube 的人。这儿有个好玩的实验：我敢打赌，我们现在都认识一大堆以前不写代码、现在却在写代码的人，很多时候包括我们自己，对吧？所以整个生态里写代码的人数比例大概涨了，可能有 10 倍，对吧？真有可能，现在写代码的人数，字面意义上是一年前的 10 倍。

[56:03] Guillermo

it's wild our signup numbers are through the roof and there's this new class of people who are not engineers. They just use the infrastructure. But I think it might be like podcasters and YouTubers and like people posting on X. The majority of people are still not creating code. Like I go to people and I'm like, "Oh man, vibe coding is so much fun. It's more fun than like I I had a little gaming group that I used to play video games and FPS's to blow off steam." I completely stopped playing. All that time went into vibe coding instead. It's more entertaining. you get something real out of it and but the feedback loop is just as tight or even better. And I went to my other friends and I was like, "Hey, you should be vibe coding instead." And they just gave me this blank look and I'm like, "No, no, you don't understand. Building things is so much easier." But I think to them it was always like some blackbox process in the background. They never understood it. They assume maybe you were just talking to computer all along. So they don't see what's changed. They don't realize it's a lot easier. to them just that starting to Max's point the starting is so impossible to imagine and hard they don't do it. So we might have taken you know 0.01% of the population writing code to maybe now it's 1% call it a 100x increase but 99% still never going to write code. So we are in this weird space.

这太疯狂了，我们的注册数据直接爆表。冒出来一类全新的人，他们不是工程师，就只是用这些基础设施。不过我觉得这批人更像是播客主、YouTuber、在 X 上发帖的那种人。大多数人还是不写代码的。我去跟人安利，我说『天哪，vibe coding 太好玩了，比打游戏还好玩』——我以前有个小游戏圈子，会一起打 FPS 来发泄解压，现在我彻底不玩了，那些时间全砸进 vibe coding 里了。它更有娱乐性，你还能从中得到真实的产出，而且反馈循环一样紧凑、甚至更紧。我去跟其他朋友说『嘿，你也该来 vibe coding』，他们就一脸茫然地看着我，我说『不不不，你没懂，做东西现在变得太容易了』。但我觉得对他们来说，写代码一直是后台某个黑箱过程，他们从没真正理解过它，他们大概以为你从头到尾就只是在跟电脑说说话而已。所以他们根本看不出发生了什么变化，意识不到现在已经简单太多了。对他们来说——就回到 Max 那个点上——那个『起步』这件事本身太难想象、太难了，于是他们干脆不做。所以我们可能是把写代码的人从占人口的 0.01% 提到了现在的 1%，算它涨了 100 倍，但仍有 99% 的人永远不会去写代码。所以我们正处在这么一个很微妙的阶段。

[57:19] Blake

It's crazy. It's like it's a video game and it's a great video game but real stuff comes out.

这太疯狂了。它就像一款电子游戏，而且是款很棒的游戏，但产出的却是真东西。

[57:25] Max

Yeah. My fiance was up all night last night because she couldn't go to sleep because she was hacking on something and of course she wasn't writing any of the code. But it's just like it's addictive in a way that programming hasn't been for me for like over a decade. It's amazing because it's like a lottery for people. I think the normies normies have gotten a little more into the vibe coding but through uh models that are more media models, video models for example, right? More people probably fooled around making videos and images than they did writing code and apps. The problem is like I don't video has its own issues, right? Maybe someday we like make me a great movie about X and I'll just spit out a good documentary, but right now they don't have the taste or the judgment. This is a bet that I have with uh Andre Carpathy was like what's the year that you'll be able to just dump in a book and get a movie out? I think closer although I think he has come down substantially in timeline since we made this bet a few years ago. By 2030 we're going to have like dozens of Lord of the Rings. Like there's going to be some fan who's like he did it wrong. I'm going to make my own take. Um like the famous stories or like the one of my other benchmarks for progress in AI is I'm a huge fan of of a series called The Expanse. Um there's a series there's a TV series and there's there's nine books and they've made the first six books but they haven't made the last three books and there's meaningful divergences and I just I haven't gotten in I haven't read the books. Like I'm looking forward to I can dump in the last three books conditioned on the TV series and be like generate the last three seasons.

对。我未婚妻昨晚一宿没睡，因为她在捣鼓一个东西，睡不着——当然代码一行都不是她写的。但它就是有种让人上瘾的劲儿，这种感觉编程已经十多年没带给我过了。这太神奇了，因为它对普通人来说就像一张彩票。我觉得那些『小白』（normies）确实也开始更多地接触 vibe coding 了，不过是通过那些偏媒体类的模型，比如视频模型，对吧？玩着做视频、做图片的人，大概比写代码、做 app 的人要多。问题是视频有它自己的麻烦，对吧？也许将来某天我们能说『给我做一部关于某主题的好电影』，它就吐出一部不错的纪录片，但现在它们还没那个品味和判断力。我跟 Andrej Karpathy 打过一个赌：哪一年你能直接丢进去一本书、就产出一部电影？我觉得这天不远了——不过自从我们几年前打这个赌以来，他的时间表已经大幅提前了。到 2030 年我们会有好几十个版本的《指环王》（Lord of the Rings）。会有那么个粉丝跳出来说『他拍错了，我要拍我自己的版本』。就拿那些著名故事来说——我衡量 AI 进展的另一个标尺是：我是《苍穹浩瀚》（The Expanse）这个系列的铁粉，它有一部电视剧，还有九本书，他们只拍了前六本，没拍最后三本，而且剧和书有不少明显的分歧。我一直没去读那几本书，我特别期待能把最后三本书丢进去、再以电视剧为基准条件，让它『生成最后三季』。

[58:47] Naval

This is coming.

这快来了。

[58:48] Naval

That's a great feature. Yeah. But that's in a way it's easy because there's already all this reference material. When you said get me the next Lord of the Rings, I was really excited because we haven't really had a breakthrough in imagination.

那是个很棒的功能。对。但某种意义上这反倒容易，因为已经有了这么多参考素材。你刚说『给我整出下一部《指环王》』的时候，我其实特别兴奋，因为我们在想象力上已经很久没有真正的突破了。

[59:01] Guillermo

Oh, we're going to see that

哦，这个我们一定会看到的。

[59:02] Guillermo

in culture the likes of Harry Potter and Lord of the Rings. I I'm really excited about that

在文化层面，会出现《哈利·波特》（Harry Potter）和《指环王》量级的东西。我对这个真的特别兴奋。

[59:06] Naval

and that will be the I agree that that will be the more exciting one.

而那会是——我同意，那会是更让人兴奋的那一种。

[59:09] Naval

What can humans uniquely do? This gets back this gets to the core issue. What are humans going to be able to uniquely do, right? And I think Max, you're an AGI maximalist. So for you it's nothing. Agents will do everything.

人类能独一无二地做什么？这就回到了核心问题。人类到底将能独一无二地做什么，对吧？Max，我觉得你是个 AGI 极大主义者，所以对你来说答案是『没有』——agent 什么都能干。

[59:20] Max

I'm not like antihuman, but I just like I think it's going to be we will have to find like if your identity is how smart and creative you are, you're going to have a bad time.

我倒不是反人类，我只是觉得，我们将不得不去重新找到点别的——如果你的身份认同建立在你有多聪明、多有创造力上，那你以后会很难受。

[59:28] Naval

Yeah. I I guess I'm still on the other side of that. I think that creativity is still the thing in the environment that surprises you. You step out of the system and do something that wasn't even imaginable within the system. It's outside of the training data. It's out of the out of the distribution of data that was fed into the system. And I think there'll always be room for that. Have you noticed that every cla website looks the same? And and people like basically like dial in what a cla website looks like once you get enough generations out of the model. Like there's a look it's this serif font. It's brown and cream and they use monospace fonts with a certain amount of spacing. Like after a while you get this this distribution that you say well this this is not creative. This is slop that came out of claude. It's not going to be human versus computer. It's going to be human with computer versus just computer.

嗯，这点我大概还是站在另一边。我认为创造力依然是这个环境里那个能让你意外的东西：你跳出系统，做出一件在这个系统内部根本无法想象的事。它在训练数据之外，在喂给系统的那堆数据的分布之外。我觉得这种东西永远会有它的空间。你们有没有注意到，每一个 Claude 做出来的网站长得都一个样？人们基本上把『一个 Claude 网站该长啥样』给固化下来了——你从模型里跑出足够多次之后，就会发现有那么一种固定的样子：衬线体（serif font），棕色和奶油色，再配上某种固定间距的等宽字体（monospace）。跑久了你就得到这么一个分布，于是你会说，这一点都不创新，这就是 Claude 吐出来的『AI 垃圾』（slop）。所以未来不会是『人类对决电脑』，而会是『人 + 电脑 对决 纯电脑』。

[1:00:20] Max

Just computer will eventually happen, but we're pretty far away.

纯电脑那天最终会到来，但我们离它还挺远的。

[1:00:23] Max

But the computer is going to be able to produce these crazy super stimuluses that like it's going to be it's going to make the entertainment. And I mean, we kind of see a weak form of this in in Tik Tok. Um, and so when you think about the going like my my personal definition of art is meaningful out of distribution behavior. And so this is something that kind of is surprising in some way. Feels like you're kind of moving in the Z-axis. Like you're surprised that the thing was realized,

但电脑将有能力制造出那种疯狂的『超常刺激』（super stimuli），它会去做那些娱乐内容。我是说，我们在 TikTok 上已经能看到这种现象的一个微弱版本了。所以当你想到——我个人对艺术的定义是『有意义的、超出分布（out of distribution）的行为』。也就是那种在某种程度上会让你意外的东西。感觉就像你在沿着 Z 轴移动：你惊讶于这东西竟然被实现了出来。

[1:00:51] Naval

but meaningful. Yeah.

但要有意义。对。

[1:00:52] Max

Meaningful means that like it it somehow to me means that it somehow changes your like future trajectory through the universe. Like your life is somehow different for having thought about it and and reflected on it. Well, my definition of art is completely different and leads to a completely different outcome. Sorry to interrupt.

『有意义』对我来说意味着，它以某种方式改变了你今后在这个宇宙中的人生轨迹。就是说，正因为你想过它、并对它有所反思，你的人生从此变得有些不同了。嗯，我对艺术的定义跟这个完全不同，而且会导向一个完全不同的结论。抱歉打断一下。

[1:01:06] Naval

No, it's interesting how just by your definition, you get to a different premise. That's the extrapolation of the axiom.

不，挺有意思的——单凭你给出的定义，就能推出一个完全不同的前提。这就是把公理一路外推的结果。

[1:01:12] Max

Yeah. I mean, one of the things I like about my definition is that it's so broad. Like, there can be like military maneuvers that you can be like, that was art.

是啊。我喜欢自己这个定义的一点就是它特别宽泛。比如一次军事调度，你都可以说，那简直是一件艺术品。

[1:01:18] Max

And I think we're going to see this all the time. We're going to see move 37s all over the place. Although, I'm curious what your definition of art is.

而且我觉得这种情况会越来越常见。我们会到处看到类似 AlphaGo 第 37 手那样的神来之笔。不过我也很好奇，你对艺术的定义是什么。

[1:01:24] Naval

I mean, I have multiple definitions, but so it's not like a concrete I haven't packaged into one thing. But I do think of art as something where you convey emotion. You convey something you felt to another person. And so you create some object or something or that that creates that that takes an emotion that you felt inside. And so to me, a computer almost by definition is incapable of doing it. The exact same piece of art uh without intent behind it is sort of meaningless. Now you can also argue nature is art like beauty in nature like you see a sunset, right? Not let's say human. So that one I would call it's pure intelligence working without motive. There's beauty for example in a sunset because there's an intelligence there. There's a complex system at work there and your brain recognize it and there's no motive there. So no ego gets involved. But art in kind of the more human sense I think of as someone felt something and they wanted you to feel that thing or they wanted to feel that thing again or they wanted to capture the feeling they had with that thing and so they created the thing. Attribution to who created it is going to be really important. So like for example a beautiful photo, right? If a person takes the photo versus AI generates the exact same photo down to the last pixel, the person taking the photo will have more meaning for me.

我其实有好几套定义，没有把它打包成一个固定说法。但我确实认为艺术是一种传递情感的东西——你把自己感受到的东西传递给另一个人。于是你创造出某个物件或某样东西，把你内心曾经的某种情绪承载出来。所以在我看来，计算机几乎从定义上就做不到这件事。同样一件作品，如果背后没有意图，某种意义上就是没有意义的。当然你也可以说，自然本身就是艺术，比如自然之美——你看一场日落，对吧？这里先不谈人。那种我会称之为纯粹的智能在无动机地运作。日落之所以美，是因为那里存在一种智能，存在一个复杂系统在运转，你的大脑识别出了它，而它背后没有任何动机，所以也就没有自我（ego）牵涉其中。但更偏人类意义上的艺术，我理解为：某个人感受到了某种东西，他想让你也感受到那个东西，或者想再次感受到那个东西，或者想把当时的那份感受捕捉下来，于是创造了这件作品。所以「是谁创作的」这件归属问题会变得非常重要。举个例子，一张美丽的照片，对吧？如果是一个人拍的，跟 AI 生成一张连最后一个像素都一模一样的照片相比，那个人拍的对我来说更有意义。

[1:02:39] Guillermo

I just invested in a startup that does verifiability with hardware at the station that someone some human actually took a photo which is going to have a lot of really cool use cases.

我刚投了一家初创公司，它用硬件在拍摄端做可验证性，证明某张照片确实是某个真人拍的——这会有很多非常酷的应用场景。

[1:02:48] Naval

It will we will be drowned in slop. No question.

我们一定会被各种垃圾内容（slop）淹没，这毫无疑问。

[1:02:50] Max

Do you remember the control net stuff from like a year or two ago? There was like there's one particular scene of like it was like a medieval village. It had like a swirl in it. Do you remember?

你还记得一两年前那个 ControlNet 的东西吗？有一个特别经典的场景，好像是一个中世纪村庄，画面里藏着一个漩涡。你记得吗？

[1:03:00] Naval

Yeah.

记得。

[1:03:00] Max

That was AI generated and that was the one of the first times I looked at this and

那是 AI 生成的，而那是我第一次看到这种东西的时候……

[1:03:05] Max

thought it was really cool. Like whether you want to call it R.

……觉得它真的很酷。不管你想不想把它叫作艺术。

[1:03:07] Guillermo

But that one doesn't that one break your your premise because some human came up with the the training and the prompt to arrive to that really cool riddle. By the way, it's totally possible that an AI can also do that in the future. But I give whoever came up with that idea of the optic optical illusion control net, I give them more credit than the

但那个例子不正好打破了你的前提吗？因为是某个人想出了那套训练方法和提示词，才得到那么酷的「视觉谜题」。顺便说一句，AI 将来完全有可能自己也做到这一点。但对于那个想出这种 ControlNet 视错觉点子的人，我会给他比……更高的评价。

[1:03:29] Naval

I think the bar is going to be raised massively. Like it's going to take more and more to surprise you. It's going to have to be more and more impressive. Like Studio that's already happened.

我觉得门槛会被大幅抬高。也就是说，要让你感到惊艳会越来越难，作品得越来越令人印象深刻才行。就像那个吉卜力风格，这种事其实已经发生了。

[1:03:37] Naval

Yeah. Like like OpenAI destroyed Studio Ghibli for everybody. Nobody wants to see that Studio Ghibli work ever again, right? It's been done. And so

对啊。比如 OpenAI 等于把吉卜力工作室的风格给所有人「玩坏」了。现在没人想再看那种吉卜力风格的东西了，对吧？已经被玩烂了。所以——

[1:03:45] Max

Oh, that one also has I have a counter point to that one. Like have you watched real Studio Giblly? It actually looks so much [ __ ] better than the soft that open put out. Like watch it again now. It's impressive.

哦，关于这一点我也有个反驳。你有没有去看真正的吉卜力作品？它真的比 OpenAI 弄出来的那些破玩意儿好太多了。现在再去重看一遍，你会发现那才叫惊艳。

[1:03:55] Naval

Yeah. At the point where you've seen tons of Studio Ghibli things everywhere all over the internet. It is now in distribution. It's no longer surprising. The art value has been around.

是啊。但当你已经在互联网上到处看过海量的吉卜力风格图片之后，它现在已经进入了「分布之内」，不再让人惊讶了。那种艺术价值已经被稀释掉了。

[1:04:02] Naval

That's right. That's right. No, your surprise definition still works. I just think that that humans are the ones who can generate surprise completely out of the data distribution. And I think they can do it with intent and I do think intent matters for meaning. So to your meaning point, right, you said meaning and surprise, right? Right. And I guess what I would say is that humans can steal the ones generate surprise out of the system. For example, let's say you took an AI and you trained it to be perfect at mathematics, right? The perfect mathematics AI and it's within the formal system of mathematics. And then Kurt Goodle comes along and he has something completely outside of the system, right? Girdle's in completeness theorem. It was completely stepped out of the system and used uh attributes of physics to basically break the system. So that kind of thing I don't think an AI could get to. So there's always room for creativity outside. surprise and then the meaning comes from the fact that a human was involved that they did it for a purpose and they conveyed something. So maybe I can interpret your definition my way, but we'll see how it plays out. I'm a little more optimistic about humans.

没错，没错。不，你那个「惊讶」的定义依然成立。我只是认为，能彻底跳出数据分布、制造出真正惊讶的，是人类。而且我觉得人类是带着意图去做的，我确实认为意图对意义很关键。所以回到你刚才说的那个点——你提到「意义」和「惊讶」，对吧？我想说的是，只有人类能从系统之内跳出去制造惊讶。打个比方，假设你拿一个 AI，把它训练成数学上完美无缺，对吧？一个完美的数学 AI，它完全在数学这套形式系统之内运作。然后哥德尔（Kurt Gödel）出现了，他拿出了某个彻底在系统之外的东西，对吧？也就是哥德尔不完备定理。他完全跳出了那套系统，借用物理学之类的属性，从根本上把这套系统给「攻破」了。这种事我觉得 AI 是做不到的。所以系统之外永远存在创造的空间——惊讶。而意义则来自「有一个人参与其中」这件事：他是为了某个目的去做的，他传递了某种东西。所以也许我可以用我的方式去解读你的定义，但具体怎么发展，咱们走着瞧。我对人类要更乐观一些。

[1:05:00] Naval

So if you train an AI model, it's trained on some data distribution. It's trained on some tokens. It then learns some some distribution of language and the structure within that. Um, is it possible for an LLM or transformer to kind of go out of distribution, have like a new idea that was not present in the training set somehow? Well, the training sets are so large that it is hard to imagine ideas that are not within the training sets somewhere. But, uh, if they exist, they probably lie in the natural domain in physics and interaction and feeling and emotions and evolution in things that the it's not subject to. So, I do think that there's still things outside of language, but language does encapsulate a lot. Language is a great compressor and we've got a lot of it.

你训练一个 AI 模型，它是在某个数据分布上训练出来的，是在某些 token 上训练出来的。它于是学到了语言的某种分布，以及其中的结构。那么问题是，一个 LLM 或者 transformer，有没有可能跳出分布、凭空冒出一个训练集中根本不存在的新想法？嗯，训练集已经大到很难想象有什么想法是某处的训练集里完全没有的了。但如果真有这种想法存在，它们大概率落在自然领域里——物理、互动、感受、情绪、演化，这些是 AI 接触不到的东西。所以我确实认为，在语言之外仍然有东西存在，只不过语言确实封装了非常多的内容。语言是一个极好的压缩器，而我们手里又有海量的语言。

[1:05:40] Max

But I mean you can get to these other things through selfplay that

但你其实可以通过自我对弈（self-play）去触及这些别的东西——

[1:05:44] Max

selfplay and sensors like cameras are sensors like our eyes are sensors.

自我对弈，还有传感器，比如摄像头就是传感器，就像我们的眼睛也是传感器一样。

[1:05:48] Guillermo

Yeah. I mean I think the question is how do you what how do you go out of distribution without randomness? So in the case of like RL you can get randomness like you can sample an action from a distribution of an action space and you can get randomness that can take you down these walks into new territory. But I think the the real to kind of turn this around is like can humans go out of distribution? Where does any new idea come from? Are we also dependent on randomness to get us into these new territories?

对。我觉得问题在于：你怎么在没有随机性的情况下跳出分布？拿强化学习（RL）来说，你可以引入随机性——你可以从动作空间的某个分布里采样一个动作，由此得到随机性，把你带向那些通往新领域的「随机游走」。但反过来想，真正的问题是：人类能跳出分布吗？任何新想法到底从哪儿来？我们是不是也得依赖随机性，才能闯进这些新领域？

[1:06:13] Naval

We're not dependent on pure randomness like like natural selection works through pure randomness, right? Where you just mutate a gene and then see what happens. But with humans, we seem to have this ability to cut through infinite space and get, you know, just eliminate huge swats. And so our creativity makes sense within the larger scheme of things. That seems to be one of our unique capabilities. And um maybe AI is starting to do at the edges as we're seeing with solving some of these math problems. But even math is a very bounded domain, but it's a big one. I'm not I'm not saying it'll never get there. I don't have that confidence. But I think at least at the moment, I would say that uh truly stepping outside surprising people, that's still a domain of humans. And I think humans plus AI is where it's all moving to. Like human without AI, forget it. Pure AI, I don't think is there yet. But I think human plus AI, we're in that era. How long we stay there, I'm betting is longer than people think. I think humans will have an enormous amount of value. Um, in fact, more value. All of us, everyone here, our productivity has gone through the roof. And basic economics normally says that when someone's productivity is higher, they're wealthier. They're better off. You actually hire more of them, not less of them. Maybe some of you are not hiring junior people anymore, although I don't know if that's necessarily true. I don't think of it as junior versus senior. If someone is really good with AI and they're really smart and creative, I want to hire them more than ever because the leverage I'm going to get out of them is incredible.

我们并不依赖纯粹的随机性。像自然选择就是靠纯随机性运作的，对吧？你只是随机让一个基因突变，然后看会发生什么。但对人类来说，我们似乎有一种能力，能在无限的可能空间里直接「切」出一条路，一下子排除掉巨大的成片区域。所以我们的创造力在更大的图景里是讲得通的，这似乎正是我们独有的能力之一。也许 AI 已经开始在边缘地带做到一点点了——就像我们看到它能解决某些数学难题那样。但即便是数学也是一个边界非常清晰的领域，尽管它是个很大的领域。我并不是说 AI 永远到不了那一步，我没有那种笃定。但至少在当下，我会说：真正跳出框架、给人带来惊讶，这仍然是人类的专属领域。而且我认为，一切正在往「人类 + AI」的方向走。光靠人不用 AI，算了吧；纯 AI，我觉得还没到那一步。但「人类 + AI」，我们正处在这个时代。至于我们会在这个阶段停留多久，我赌的是——比大多数人想象的更久。我认为人类会拥有巨大的价值，事实上是更高的价值。我们所有人，在座的每一个人，生产力都直线飙升。而基本的经济学原理通常告诉我们：当一个人的生产力更高时，他更富有、处境更好，你实际上会雇用更多这样的人，而不是更少。也许你们当中有些人已经不再招初级岗位了，不过我不确定这是不是真的。我倒不会用「初级 vs 资深」来看待这件事。如果一个人真的很会用 AI，又聪明又有创造力，那我比以往任何时候都更想雇他，因为我能从他身上获得的杠杆效应实在太惊人了。

[1:07:32] Guillermo

That's a new requirement. And we're hiring juniors and super seniors as long as they're really good with agents and really good with AI and and quick to adapt.

这是一项新的硬性要求。我们既招初级的，也招超资深的——只要他们真的擅长用 agent、真的擅长用 AI、并且适应得很快。

[1:07:40] Blake

And a lot of them don't need to be hired anymore. They can create their own thing.

而且他们当中很多人根本不再需要被雇用了，他们可以自己搞出一番事业。

[1:07:44] Blake

My my hypothesis is we end up with a larger number of smaller teams. Like the number of people required to accomplish the given task drops by a lot. And you know people who only see first order of facts say oh my gosh all the jobs are disappear because I can do I can do a jet engine with two people. I don't need a thousand you know 998 jobs are gone. But what it actually means is you can create a lot of different chat engines.

我的假设是，最终我们会走向「更多、但更小的团队」。完成某项任务所需的人数会大幅下降。你知道，那些只看到一阶效应的人会说：「天哪，所有工作都要没了，因为我两个人就能造一台喷气发动机，我不再需要一千个人了，那 998 个岗位就消失了。」但它实际意味着的是：你可以造出非常多种不同的喷气发动机。

[1:08:03] Guillermo

I think that's exactly right.

我觉得说得太对了。

[1:08:04] Blake

I think there will be goes back to Naval's point. I I think the thing that's uniquely human is the creativity and what's been missing for you know a lot of people can be creative but they don't know how to turn their vision into a real thing that's changing. So I think we're have an explosion of an of entrepreneurship explosion of founders and a very large number of very small teams because you don't need many people to accomplish something.

我觉得这又回到了 Naval 刚才那个观点。我认为人类独有的东西就是创造力，而一直以来很多人缺的是——他们可以很有创意，却不知道怎么把自己的愿景变成一个真正能改变世界的东西。所以我觉得我们会迎来一场创业的大爆发、创始人的大爆发，以及数量极其庞大的超小型团队，因为你不再需要很多人就能做成一件事。

[1:08:29] Naval

Yeah. I think like look AI provided uh base level intelligence and uh domain knowledge uh and cut through all the jargon and then now agents actually provide a lot of agency. So the main things left are creativity, taste, and yes, you need enough agency to get started, agency to stick with it, but you don't necessarily need the agency to like spend 20 years learning one thing before you can dive into it, make a contribution. And so that barrier going down, generalists are having a field day. And at the end of the day, we're all generalists. All of us like to think about everything. We don't like to be just trapped in one thing. Like Max is here talking about consciousness and the FDA and brain science and creativity. Like all of us are trying to think about everything all the time. And so, uh, people in Twitter who are always fond of saying like experts, credentials, sources, right? Those are the guys getting hurt because the expertise doesn't matter. You spent 5 years, 10 years getting a PhD in XYZ, you know, hopefully develop your creativity and your instincts and your taste and your judgment because if all it did was help you memorize a whole bunch of things and jargon and, you know, learn some scaffolding stuff, well, AI will cut right through that. It's like a, you know, calculator times a billion or, you know, bicycle for the mind but accelerated. So I I think it's about people with AI versus people without AI. And so the single best thing you can be doing right now for yourself is just getting really good with these tools, getting comfortable with them and always knowing the edges of the boundaries of what they're capable and what they're not capable of. And that is a moving target.

对。我是这么看的：AI 先提供了基础层面的智能、领域知识，帮你穿透所有那些行话黑话；而现在 agent 又实实在在地提供了大量的「能动性（agency）」。所以剩下真正属于人的，就是创造力、品味，当然你还需要足够的能动性去起步、去坚持下去，但你已经不再需要那种「先花 20 年钻研一件事，然后才能扎进去做出贡献」的能动性了。随着这道门槛降下来，通才（generalists）正迎来他们的黄金时代。说到底，我们每个人本质上都是通才，我们所有人都喜欢思考一切事情，没人愿意被困死在一件事里。比如 Max 在这儿可以聊意识、聊 FDA、聊脑科学、聊创造力——我们所有人都在试图随时随地思考所有事情。所以那些在 Twitter 上特别爱说「专家、证书、信源」的人，恰恰是最受冲击的一群，因为专业资历不再那么重要了。你花了 5 年、10 年读了个某某专业的博士，但愿你顺带培养出了自己的创造力、直觉、品味和判断力——因为如果这一切只是帮你死记硬背了一大堆知识和行话、学了一些脚手架式的套路，那 AI 会直接把它们一刀切穿。它就像一个放大十亿倍的计算器，或者说一辆「思维的自行车」，只不过被极大地加速了。所以我觉得，关键就在于「用 AI 的人 vs 不用 AI 的人」。因此，你现在能为自己做的最好的一件事，就是把这些工具用得炉火纯青、用得得心应手，并且时刻清楚它们能力的边界在哪里、不能做什么。而这个边界本身，是一个不断移动的靶子。