Full Episode: The AI Industrial Revolution
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摘要
Full episode with with 20 minutes of new material at the end. With Guillermo Rauch (Vercel), Blake Scholl (Boom Supersonic), and Max Hodak (Science).
Software factories, vertical integration, the regulatory frontier, and the autonomous company.
Part 1: Waste Tokens, Save Time
0:00 Three Frontier Founders
1:27 AI Software Factories
4:15 Waste Tokens, Save Time
5:47 Models Instructing Humans
9:29 Is Pure Software Dead?
12:03 You Don't Get Stuck Anymore
Part 2: Vibe Coding Hardware
14:39 Vibe Coding a Turbine Blade
18:07 Open Source Compounds China's Advantage
20:15 You Always Want the Smartest Model
22:44 Software Still Needs Hands
24:43 Humans Are Becoming Verifiers
Part 3: The Regulatory Frontier
27:53 The Regulatory Red Queen Race
32:32 Why There's No Innovation in Healthcare
36:49 We Need a True 50-State Experiment
40:31 China's FDA Is Beating Ours
43:37 Healthcare Is a Communist Society Inside Capitalism
45:57 Sid's Story: N-of-1 Medicine
Part 4: The Autonomous Company
47:49 Autonomous Infrastructure
51:25 Your Job Is to Train the Agent
54:54 The Next Lord of the Rings
59:08 What's Your Definition of Art?
1:05:00 Can AI Have New Ideas?
1:07:03 A Very Large Number of Small Teams
Transcript: http://nav.al/industrial
GPT-4正在为你翻译摘要中......
中英文字稿 
欢迎收听Naval播客，这是您获取新知识的权威来源。今天我们尝试一些新事物。我请来了三位顶尖的创始人，三位长得很帅的小伙子，还有第四位帅哥，Naval。让我来介绍一下大家。这位是Guillermo，也叫G. Rausch，他正在将Vercel打造成一个面向智能代理和未来发展的AI云平台。很高兴来到这里。这位是Blake Schall，他在自己的工厂里建造超音速飞机以及喷气发动机，他的公司是Boom Supersonic。最后是来自Science的Max Hodak，他正在开发一种生物混合大脑接口，这种接口将活的神经元生长在硅片上，以恢复视力等感官功能，并最终探索大脑的新区域和新的感知。
▶ 英文原文 ⏱
Welcome. You're listening to the Naval Podcast, your authoritative source for new knowledge. We're trying something new today. 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. Guillermo, the G. Rausch, he's building Vercel into an AI cloud for the world of agents and whatever comes after that. Good to be here. Blake Schall, 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 bio-hybrid 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.
这三个人都不是用现成的零件来构建他们的产品。他们正在建立自己的工厂。我们关注的不是他们在建什么，而是他们在建造过程中学到了什么新的知识？他们的独特之处是什么？他们发现了哪些其他创业者可以学习的原则？他们现在正在尝试解决什么问题？还有哪些前沿或疯狂的想法正在他们的脑海中酝酿，但还没有对外谈论？
▶ 英文原文 ⏱
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，你在我开始讨论Guillermo之前，要对此发表任何看法吗？ 好的，我们就随意一点吧。 你们可以随时插话。 对了，我记不清我确切的说法了，但我对软件工厂这个概念深有体会。工程师的工作就像是每天来上班，把成果直接交付出去。公司内部的运作方式也都是在评估某人交付某项成果的能力。
▶ 英文原文 ⏱
Naval, do you have any reactions to any of that before I jump into Guillermo? Yeah, let's just have fun. Yeah. Yeah. You guys should just jump in. Yeah. So I can't remember my exact quote, by the way, but I've been really pilled with this idea of software factories and the job of the engineer being something that you just show up to work. You used to ship the output directly and everything inside the company was, you know, how good is person A at shipping output B?
现在的情况是，我评判你作为一个工程师的方式是，你是否正在创造一个能够产生乘数效益的工厂，从而能产出B到Z这样的结果，对吧？这是一个相当重大的变化，因为过去我们相信有所谓的10倍工程师，这个观点本来也颇具争议。而现在很明显地存在百倍甚至千倍效益的工程师，但世界对此尚未完全适应。以前我在推特上说有10倍工程师时曾被猛烈抨击。
▶ 英文原文 ⏱
And now what's happening is the way that I'm judging you as an engineer is like, are you producing the factory that will produce multiplicative outputs B through Z, right? 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 a hundred X or a thousand engineers and the world hasn't fully adjusted to this. I used to have flamed on Twitter for saying there are 10X engineers.
是的，完全正确。这种观点与许多关于人人平等的平等哲学相悖。然而，现实情况是，当你在运作理念领域或者在知识和虚拟数字领域工作时，差距不仅仅是10倍，而是100倍甚至1000倍。这种情况一直存在，比如中本聪（Satoshi）、Notch、发明JavaScript的那个人Brendan Eich，以及John Carmack。这些人都是千倍的优秀程序员。如果你选择了正确的工作方向，与选择错误方向相比，那差距就是无限大的。
▶ 英文原文 ⏱
Yeah, exactly. It flies in the face of so much like equality philosophy that everyone's equal. But the reality is when you're operating idea domains, when you're operating intellectual domains and virtual digital domains, it's not even 10X, it's a hundred X or a thousand X. And it always has been Satoshi, Notch, you know, the guy who invented JavaScript, the Brendan Ikes of the world, uh, John Carmack. I mean, these are thousand X programmers, not to even mention if you choose the right thing to work on, which is the wrong thing to work on, that's an infinity difference.
这段文字可以翻译成中文如下：
“其实，不一定是更好的程序员，而是那些首先在选择工作内容上更有判断力的人。而现在，由于人工智能的运用，这一点显得没那么有争议。具有争议的是那些代币排行榜，对吧？很多人仍然有点困惑，因为他们会认为，我有很多能力超强的工程师，看我为这些代币支付的费用。我很好奇你们是否也遇到了同样的问题，比如你们是如何衡量投资回报率的？”
▶ 英文原文 ⏱
And it could just be not necessarily a better programmer, 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 hundred X 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?
这就像以前用代码行数来衡量一样，你知道的，用"标记消耗"来衡量代码行数感觉也不算是直接的比较。我是说，我的观察是，Claude 或 ChatGPT，或者说 GPT，基本上能在某个领域达到你自身的水平。所以，如果你是一个非常有能力的开发者，那么这些工具会非常强大。而如果你是一个初级开发者，你可能会觉得这些工具也像是一个初级开发者。
▶ 英文原文 ⏱
It's like the old measuring lines of code, you know, token consumption lines of code feel like similarly, not direct paradigms. I mean, my observation has been that Claude or ChatGPT, 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.
这些模型一方面非常强大，另一方面，你偶尔给它们的反馈似乎非常重要。小的更新似乎完全决定了它们的性能表现。我现在提供一种新的帮助方式，就是如果你使用模型得不到理想的输出，我会告诉你如何提示模型。
▶ 英文原文 ⏱
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 the little updates seem to totally determine the types of, uh, performance you get out of them. 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.
可以翻译为：所以，你提到的关于重新提示质量的重要性，这确实非常关键。但我认为，随着时间的推移，这个重要性会逐渐降低。随着模型的智能化程度越来越高，你输入的信息可以越来越少，但输出的质量会更高。不过，至少在目前阶段，我的经验是，它在很大程度上反映了用户自身的判断能力。
▶ 英文原文 ⏱
So like the idea of like the quality of the re-prompting, which I think you're alluding to is, is extremely important. 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. But at least at this stage, it really seems to kind of reflect back the judgment that the user brings in, in my experience.
我有点抵触去学习那些小技巧和窍门。比如，有人会说，用拉尔夫·威根姆，用开放爪，用赫尔墨斯，使用这个提示引擎，使用支架，插入这个组件，总是使用计划模式。但我把这些都忽略了。我只是假设模型提升的速度会比我学会如何使用它快。它学会如何使用我的速度会比我学会如何使用它快。所以我对这些模型一直有点笨拙，我对它们感到沮丧，也因此输入的信息越来越少，做的工作也越来越少。因为我认为我可以强行通过。我会反复用Codex、Clawed和Gemini来解决同一个问题，只是为了节省时间而浪费代币。
▶ 英文原文 ⏱
I've kind of resisted learning all the tricks and tips. Like, you know, there was, uh, oh, use Ralph Wiggum, use open claw, use Hermes, use this prompt engine, use the scaffolding, plug in this piece, you know, uh, always use plan mode. I just ignored all of that. I just assumed the model is 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 ham-fisted with them and I get frustrated at them and just sort of, 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 do a codex clawed and Gemini at the same problem over and over and just waste tokens to save time.
我认为，不管这些模型看起来多么昂贵，它们仍然比人类便宜得多。所以我建议不要在意浪费代币，节省时间。不要把代币当作输入或输出资源，只需关注自己的时间和最终结果。即使这些模型有时生成的代码质量不高，我知道很多时候确实如此，这些代码并不是生产级别或可扩展的代码。但是，当我要将它投入生产时，我会投入更多的代币，让模型重新查看、重写。这些模型的每一代都在进步，因此我觉得这个过程不会轻易中止。只要我们有明确的领域和需要解决的问题，它们就会帮助解决这些问题。
▶ 英文原文 ⏱
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 low quality 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.
在未解决问题的领域中，也许你像是数学家陶哲轩，你需要在创意的最前沿进行工作，这时你需要非常协作、仔细而密切地与模型合作。但我不是那个领域的专家，我在软件工程中还达不到那个水平。然后转到设备问题上，但是在这组人中，你可能是最极端的那位软件工程师吧？可能你是唯一一位有着最硬核软件背景的人。你是怎么发现这些模型在其能力边缘的表现的？陶哲轩：嗯，最近发生了一件与你所说内容非常贴切的事情，以前你给模型一个提示，它会像经典的下一个词预测那样进行处理，但它会按照你的思路延续下去。
▶ 英文原文 ⏱
And that's in the unsolved problems domain, where maybe you're Terence Tao, you're 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 then 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? Terence Tao: 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.
模型现在就像直觉计划模式一样工作，正如你所说，它们甚至不需要计划就能回到你这里，然后说：“看，你要我做的事情，我们可以走这三条路线。我们将要面对这些优缺点的权衡。” 这就像人们在某些方面感叹一样，说：“哦，现在我们有了一个博士水平的工程师模型。” 这表明模型在一定程度上已经升级了。以前它们还是初级工程师，现在已经是高级工程师了，因为它们会给你带来一系列的权衡方案。当然，它们有时候也会说出一些荒谬的话，这很有趣。例如，它会告诉你某个方案需要三周时间和多少个代币，预测结果很差。不过，现在显然我更加尊重这些模型，把它们看作是可以进行智力对话的伙伴。但仍然有很多不足之处需要改善。
▶ 英文原文 ⏱
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 trade-offs that we're going to go down. That's the moment where like, you know, people do the whole thing on X. It's 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 trade-offs. And obviously sometimes they say bullshit, which is hilarious. It tells you this one is going to take three weeks and this many tokens. It has 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.
所以，如果你是一位非常非常熟练的工程师或建筑师，我认为你仍然能够榨取更多的价值。Max提到的问题是，如果你是初级工程师，你是否只能得到初级水平的回报？显然不是，因为初级工程师能够获得更高级的代码知识，这些是他们自己永远无法写出的东西。但经验丰富的建筑师会有十倍的收获，而初级工程师可能只有两倍的收获。这仍然是我在努力弄清楚的。但是，我认为在开发过程中需要进行一些架构决策。我现在看到我们团队中一些初级软件工程师面临的就是他们职业发展的下一步是什么，从为一个功能编写实现代码到选择技术，比如选择Postgres还是其他数据库，或者在ZMQ和其他消息队列系统之间做出选择等等。
▶ 英文原文 ⏱
So like, if you're a 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 10 X, whereas a junior engineer gets two X. That's what I'm kind of trying to figure out still. Yeah. Yeah. But I mean, I think there's, there's architectural decisions. When you think about the development, I'm seeing this now with some of our juniors 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 Postgres versus some other database, or picking between ZMQ versus some other message queue, or like some other queuing system.
这段话的意思是：虽然模型可以给你建议，但有时候你会发现这些建议并不符合你的需求，你还是会选择你自己想要的方案。这样的反馈很重要，因为它涉及到品味和判断力。尽管如此，你可以询问模型应该使用哪个方案以及原因，因为它们知道很多信息，可以给出很好的方案对比。这种变化是最近发生的，例如，当你打算把高基数的遥测数据放入Postgres时，模型会指出这不是一个好选择，并建议你考虑使用ClickHouse或Athena等其他方案。我经常遇到这样的情况，真的很令人惊讶。
▶ 英文原文 ⏱
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, no, I want to use this other thing. That's the type of little feedback that I'm saying really matters and the types of output that you seem to get at this point. It's 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 that I was saying has happened recently where you would say, hey, go and put this super high cardinality telemetry data into Postgres. And it's like, no, no, no, no, bro. Like we don't put that kind of data into Postgres. Like you should consider ClickHouse or Athena or whatever. Like that's happened to me a lot, which is really impressive.
但是我仍然有些困扰的事情是，人类目前还是在辅助完成模型的工作。在某个时刻，会不会角色调换呢？比如，人类需要逐步按照指令去做一些事情，比如去获取API密钥，因为只有人类能做到这些，或者去为下一轮投资筹集资金。是的，你只要观望着。显然，我们还没有达到那种境地。这种现象只是暂时的偏差。很快，每个优秀的SaaS公司或托管服务商都会提供一个CLI和API接口，让模型可以直接使用。它们甚至不一定需要API接口，只要是基于技术、基于Unix的环境，代理本身就可以编写自己的API。
▶ 英文原文 ⏱
But the thing I'm still 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 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 or get me this amount of capital for my next set of investments that I need to make. Yeah. You just watch. Like clearly we're still not there yet. That's a temporary aberration. Pretty soon, every good SaaS company or hosting provider will have a CLI and API interface that the models can be directly. They don't even necessarily need an API. Like as long as it's like tech space, Unix space, the agent can hack its own API.
然后在资金方面，你可以插入加密货币，比如比特币或者其他任何币种，模型就会自动为其所需的东西付款。我想，如今已经有人在研究这个问题，但我现在在思考的是，纯软件是否已经过时了？就像说英语一样，现在的模型也能说英语。我们过去必须学习编程语言才能与模型沟通，而现在模型不仅能说英语，还能够用像人类一样模糊的、随意的英语表达，并且理解各种事物。
▶ 英文原文 ⏱
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, you know, there are people working on this, but the thing I am now thinking through is, is pure software dead? Like it's 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.
对于一个创始人来说，护城河在哪里呢？在硬件行业，这是一大福音。你知道，以前你必须构建硬件，同时创建软件公司很困难，就像Patrick Collison所说的，软件是艺术，而雇佣艺术家并不容易。所以现在作为一个硬件创始人，情况非常好。你可以相对快速地开发出优秀的软件。如果你在创建模型，也许那就是新的软件工程，训练模型、调整模型、后期训练和微调模型。但经典的软件工程是否还存在，纯粹的软件是否具有投资价值，能否围绕纯软件组织一个公司和团队，并尝试获得一些杠杆效应。
▶ 英文原文 ⏱
So where's the moat like for a founder? Hardware, it's a boon. You know, like now you had to build hardware. It was hard to build a software company alongside, like Patrick Collison says, software is art and it's hard to hire artists. So now as a hardware founder, great. You can have really good software developed fairly quickly. If you're creating models, maybe that's the new software engineering, training models and tweaking models and post-training and fine tuning models. But classic software engineering is that dead, is pure software investable, is pure software, something you can organize a company, a team around and try to get some leverage.
你们有没有看过一篇文章，是 Mitchell Hashimoto 写的，好像叫《积木经济》或《建筑积木经济》。他的观点是，现在对智能体来说，最有用的是强大且可重复利用的构建模块。就像 Max 举的例子，你不会希望你的机器人每次需要发送电子邮件时都重新发明一个排队基础设施系统，它需要使用适合任务的正确模块。也就是说，对这个任务来说，可能使用队列消息中间件（Ball MQ）就合适。我反对那种每次让智能体从头开始，以一种与社会和文明不兼容的方式重新发明整个世界的想法。
▶ 英文原文 ⏱
Did you guys see the, there's an article, an ex 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 queue infrastructure system every time it 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 you say, well, okay, for this one is ball MQ. I challenged the notion that I would want the agent to reinvent the entire universe and first principles in a way that's incompatible with the rest of society and civilization.
这段英文的大意是，几乎像是为你个人重新设计高速公路、法律、政策等等。即便有进一步优化的可能性，有可能从中获得更多的效益，我们仍然需要在大规模合作的背景下一起依赖于Postgres 13.2。这种大规模合作仍然非常有价值。在我看来，这些代理程序所使用的基础设施软件和构建模块类别显然非常有价值。顺便提一下，我无意中提到了一个比喻：模型已经被创建了，可以被重复使用。
▶ 英文原文 ⏱
Like it's almost like reinventing highways, laws, policies, et cetera, just for you. Even if there is a potential for extra optimization, extra juice that you can get out of it, there's still a sort of like cooperation at large scale value of saying we're both depending on Postgres 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 in biases, this we're building, it seems extremely valuable. And I don't see the agent anytime soon. And by the way, you could even, another metaphor that I've been using is like, it has already been created that the models can reuse.
这就像一个令牌缓存。因为你不希望为了重现已经存在的东西而处理大量的令牌。所以总有一个起点，模型可以从那里开始分叉，但它会显著地改变一些东西。这就像是模型的库和依赖项，特别是针对智能代理来说。回到Naval的问题，我是很小的时候学会编程的，那是我在青春期和二十多岁时一直沉迷的事情，能一头扎进去连续编程20个小时，而且觉得超级有趣。
▶ 英文原文 ⏱
It's like a token cache. Cause you don't, you don't want to churn through a trillion tokens to reproduce what's already existing. Uh, and so there's always a starting point that the model can fork off from, but it's going to change things quite profoundly. So these are like libraries and dependencies, but for models. Yes. For agents specifically. To Naval's question though. I mean, I learned to program when I was really little and I like, that was the thing that through all of like being a teenager and my twenties, like I get like sucked into it and just like code for like 20 hours. And it was super fun.
我以前对编程语言很了解，但已经有很长一段时间没有写过一行代码了。部分原因是我的工作内容已经不同了，但自从去年十二月以来，我已经开发了大量的软件，现在每天都在使用。我一直以来梦想到的项目，我现在正在使用，并且这些项目都是我参与建造的，但我并没有亲自编写任何代码。我现在很难想象会再回去手写代码。即使我不太可能再做这样的事情，我也很难把手写代码视为未来的一部分。
▶ 英文原文 ⏱
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.
当然。有一件很酷的事情是你能理解各个部分是如何连接在一起的。我觉得任何了解API是什么并且知道数据如何流动、输入输出和性能的人都会明白这一点。因为你必须要围绕着这样的模型来定位：我对这个操作有一定的期待。这比直接写代码要有用得多。我觉得一个好的、熟练的工程领导者可以通过 Slack 或一对一交流，以一种所谓的“氛围编码”方式进行引导，因为你是在传递你的意愿、你的意图、你的经验，并让其他人去实践和发展这些想法。
▶ 英文原文 ⏱
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. Cause 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.
嗯，现在我们做的事情和以前一样，只是现在是通过使用智能代理。所以，我认为这就是你取得成功的原因，但我不知道是否所有人都能取得同样的成功。我从20年没有写过代码，到现在经常通过代理编写大量软件的代码。事实证明，只要理解软件工程和算法的基本原理，就能走得很远。我当初停止编程的原因是因为没有时间去研究最新的编程语言、最新的架构和基础设施，而这些代理让这一切变得容易多了。
▶ 英文原文 ⏱
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 mean, I went from not having written code in 20 years to I'm coding all the time now, but through agents and 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 an oversell makes it a lot easier.
但即便如此，刚开始的时候就像是一只难以对付的熊，把各个部分连接起来、建立基础设施都特别让人烦。对，现在真正的改变是，这意味着以前你可以做到很多事情，有些事情很简单，但总会遇到一些不知从何而来的问题，然后可能会花费不定期的时间去调试某个小问题。而现在有了智能代理，你不会再被卡住，真是太棒了。或者说，如果卡住了，也能很快找到正确的解决办法。以前，当我的朋友们学习编程时，经常会觉得特别沮丧，认为那是学习的必经之路，但现在这种情况已经不存在了。
▶ 英文原文 ⏱
But even then just getting started was a bear, like just plugging pieces together, assembling infrastructure was just so annoying. Yeah. 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 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. Or they get stuck. 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 and be like, nope, it's just like intrinsically frustrating. Like if like that's part of the deal, that's how you learn. And that just isn't true anymore.
布雷克，你是如何在Boom Supersonic应用这些东西的？是这样的，我发现它彻底改变了软件和硬件开发人员的角色。从一开始，我们就努力将许多传统的工程工作流程转化为软件流程。我的意思是，将硬件工程的工作流程转变为软件流程。如果你不熟悉硬件工程，容我解释一下，许多工程工作，特别是硬件工程，往往是在工程师个人电脑上的Excel表格中孤立进行的。
▶ 英文原文 ⏱
Blake, how are you applying all this stuff at Boom Supersonic? Yeah. What I found is it completely changes the role of software and hardware developers. The thing that we did from day one was try to take a lot of traditional engineering workflows. 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's a lot of engineering, hardware engineering that happens in Excel spreadsheets on engineers laptops in a silo.
你制作的复杂电子表格，有时就像VBScript代码一样。而这些其实都是软件，但却被当作不是软件来对待。因此，没有源码控制，也没有自动化测试。如果你想把东西从一个空气动力学工程师交给结构工程师处理，就只能像在1990年代那样手动用电子表格通过电子邮件来传递。这种方式很糟糕。于是，我们开始构建这类软件框架，以自动化和使硬件工程流程变得可重复，目的是降低迭代成本。
▶ 英文原文 ⏱
And you're very complex spreadsheets, sometimes like VBScript code. And all of this is actually software, but it's treated as if it's not software. So 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 started building these kind of like software frameworks that can automate and make repeatable hardware engineering flows with the idea we could reduce the cost of iteration.
我们的进展一直很慢，因为我们一直负担不起足够的软件工程师。而现在，我们进入了一种截然不同的模式：软件工程师负责创建架构，因为他们了解系统、算法以及关注点的划分。然后，硬件工程师根据他们对硬件工程的了解，能够更好地完善他们的部分代码。这个模式的结果是，小团队的生产效率令人惊叹地提高了。
▶ 英文原文 ⏱
But it was slow going because we can never afford enough software engineers. And what we've gotten into is this mind-blowingly different model where the software engineers actually create the architectures because they understand systems, they understand algorithms, they understand division of concerns. And then the hardware engineers can vibe code their pieces because what they know about hardware engineering. And the result is just like mind-blowingly different productivity for small teams.
我来举个例子，比如说如果你在设计一个涡轮叶片。传统上，一个涡轮叶片在启动时是冷的，但运转时会变热，从而膨胀。因此，你必须设计叶片的空气动力学和结构设计，使其既适合在冷态下使用，也适合在热态下使用。因此，你需要在冷态和热态之间进行转换，并调整这些结构和空气动力学。这项工作大约需要一个工程师花一天的时间来分析一个叶片。而一个喷气发动机中有大约一千个叶片，所以你能做的工作非常有限。
▶ 英文原文 ⏱
I'll give an example. Like if you're designing a turbine blade, like classically, so a turbine blade starts like 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 its cold shape and this hot shape. And so you have to convert between cold and hot and you can convert these 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 engine. And so you can't do much.
我们现在通过结合软件和硬件，让人们创造出解决方案，你可以改变叶片的几何形状。你可以实时看到结构和空气动力学的结果。因此，这使得两名工程师就能设计出一整个喷气发动机，这简直是天壤之别。你提到的其中一点是，软件工程师为其他工程师创建工具和架构。在我看来，这就是企业软件发展的最大变革。如今已经没有那种专门为硬件协作工具创业的公司能卖给你任何东西了，因为在内部，你可以随时编写所需的合适代码。甚至电子表格都变得不那么重要了，因为电子表格成功的原因是，当时没有人可以构建定制软件。
▶ 英文原文 ⏱
And we literally now with a combination of software and hardware people creating 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, the cataclysm of enterprise software is that there is no like startup that builds hardware collaboration tools that can sell you anything anymore. Because 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.
所以，最接近定制软件的东西就是一个带有许多VV脚本功能的电子表格。我个人已经几乎完全从Excel转向Python模型，因为这样我实际上可以获得更逼真的模拟效果。对，我觉得有一件AI还没有做到的事情，但我相信在明年就会实现，可能在2026年之前，那就是目前AI可以生成软件，但很快它将能够生成STEP文件和PCB布局。当AI可以应用于机械和电气工程领域时，那将会是一个我们从未见过的全新领域，非常令人期待。
▶ 英文原文 ⏱
So the thing that approximates custom software the most is a spreadsheet with a bunch of VV script functions. I personally have moved almost entirely from Excel to Python models, where I can actually like get like believable simulations of things. Yeah. I mean, the thing that that AI hasn't come to yet, that I think 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 will be able to 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.
好的。从硬件方面来看，我认为这对那些制作糟糕软件的小型设备公司和零件公司来说确实是一个福音，因为他们无法创造出优秀的软件。而现在，他们将能够制作出足够好的软件，甚至可能不需要人直接操作的软件，可能只是一个由代理访用的接口。你只需要通过语音与其交流并控制硬件。这也是我认为中国热衷于开源模型的原因之一。中国基本上全力投入其中，因为他们在硬件方面有优势，并且拥有复杂的供应链和零部件链条。
▶ 英文原文 ⏱
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 are the 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 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.
他们基本上在说，如果能按需生成软件，他们就不会再在这方面处于劣势了，尤其是相对于硅谷的竞争。因此，这并不是他们参与开源的唯一原因。我认为，他们也在改进模型的提炼和资源的共享合作。此外，中国政府有资助各种行动的历史，这些行动通常能推动整个生态系统的发展，尤其是在网络效应显著的业务中。所以，我认为他们想集中所有资源，追赶人工智能的发展，并利用它来为他们的硬件提供优势。
▶ 英文原文 ⏱
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 their distilling models or 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. And so I think they want to like pool all their resources, catch up on AI and use it to give their hardware stuff an advantage.
讽刺的是，他们之所以在做所有的开源项目，是因为OpenAI并不“开放”。Grox发布了一些模型，但我认为他们落后了一两个版本。谷歌有一些本地模型，但竞争力不强。据我所知，Anthropic甚至没有任何开源模型。因此，所有的开源力量都来自中国。这对我们的硬件创始人有帮助，但对他们的硬件创始人和工厂的帮助更大。然而，所有那些与您在亚马逊上购买的小玩意儿和物件配套的劣质软件，如今正在飞速提升。
▶ 英文原文 ⏱
And ironically, they're doing all the open source stuff because open AI is not open, you know, Grox publishes models, but I think they're a model or two behind. 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 the crappy little software that goes with all the little random knickknacks and thingamajigs that you buy off of Amazon to tinker with a lazy Saturday afternoon, that software is getting a lot better very quickly.
想象一下整个中国无法生产最前沿的所有东西，对吧？这不仅仅涉及到硬件流水线的某个环节，就像Blake所说的，你需要生成软件。如果你在生成软件的能力上落后了，那么生成任何东西的能力也会落后。我很好奇，你们怎么看待这个问题，因为大家都喜欢讨论这个话题。因为大家都热衷于谈论中国的模式。
▶ 英文原文 ⏱
And so imagine China as a whole, not having the ability to produce frontier everything, right? It's not just in any piece of this hardware pipeline, like Blake was saying, like you need to generate software. If you fall behind in your ability to generate software, you fall behind in the ability to generate everything. One thing I'm curious about from you guys is like, because everyone loves to talk about it. Because everyone loves to talk about Chinese models.
你们会用中文模型吗？你认识谁用过中文模型吗？昨天晚餐时我们有个争论，一个人在桌子上声称，对于97%的情况，你只要用DeepSea就行，因为它非常便宜。如果需要更高的智能，就反复用它解决同一个问题。只有在处理最复杂的任务时才会用Open AI或Anthropic等模型。而我有点不太认同，我觉得智能是一种纯粹的好处，更多智能总是更好的。
▶ 英文原文 ⏱
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 deep sea 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 only use the open AI anthropic, et cetera, models for the most advanced tasks. And I was kind of like, I don't know. I think intelligence is an unalloyed good. You always want more intelligence.
是的。当这些模型出错时，你可能不会察觉。而且使用它们总是比雇用真实的人力和在真实时间中完成任务便宜。所以你会倾向于使用最智能的模型，但这并不一定是好消息。因为这意味着，在人工智能领域，你很可能会最终创造出一个垄断或寡头的局面。不过，我总是希望拥有最智能的程序员，总是希望得到最正确的答案，总是希望做出最佳的判断。考虑到我会通过资本、代码、人员和市场投入大量资源，我每次都想做出正确的决策。
▶ 英文原文 ⏱
Yeah. And when these models make a mistake, you don't know it. And it's always cheaper than a real person and real time. So you just use the most intelligent model available, which isn't great news necessarily. Cause it means that, you know, you're going to end up creating a monopoly or oligopoly 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.
当面对两个模型，例如我知道其中一个模型比另一个稍聪明时，两者经常会给出答案。但我实际上不知道哪个是正确的答案，对吧？所以，如果我知道其中一个模型稍微聪明些，我会选择它的答案。最终，我可能会停止使用我认为不够智能的模型，但我并不确定。你们是否找到这些所谓不够智能的模型的用途呢？我们看到了用途，比如我们有用于人工智能网关的数据，基本上像每个应用程序代理都会通过这些数据。
▶ 英文原文 ⏱
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 is 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 agents that are goes through.
当然，开放模型确实被广泛使用，但是在顶尖领域，前沿智能占据了主导地位。不过，这其中有一个附带的条件，即前沿智能在合理的成本和性能下，能够在大规模应用中表现非常出色。所以，虽然人们对Gemini这个模型并不是特别兴奋，但它们推出的模型在合理的性能成本组合上表现非常聪明。而且，除了编程任务之外，在很多其他任务中，这些模型实际上是最好的工业生产模型。
▶ 英文原文 ⏱
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 this models that are like super smart at the right performance cost combination. And for a lot of tasks other than coding, actually, interestingly enough, uh, they're the best models. They're like the best, like industrial production models.
你可以将它们用于支持任务或浏览器自动化。例如，我通常会在那里使用Gemini模型，并且会考虑使用中国的模型来做这些事情。但每当我致力于推动前沿发展时，我需要最好的编码模型。目前基本上就是两到三个这样的模型，而中国的模型显然不在其中。嘿，Max，你在大力推进垂直整合和极端紧迫性，你想谈谈这个吗？
▶ 英文原文 ⏱
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, they're certainly not in it. Hey, Max, you're pushing pretty hard into vertical integration and extreme urgency. Do you want to talk about that?
是的。我是说，对于很多东西，嗯，我们可能没法买到，所以必须自己想办法做。不过，我们还是更愿意购买现成的产品。比如说，如果有供应商的服务价格很划算，就像印刷电路板（PCB）一样，我们自己不会去生产PCB，因为它们的成本几乎可以忽略不计，你可以从亚洲以无限量购买。但如果我们的产品能更接近一个完整的、共价键结合的实体，那就更好了。这样产品就会功耗更低、更小巧、性能更高，而且使用寿命更长。
▶ 英文原文 ⏱
Yeah. I mean, for many things we, um, maybe 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, it's like, for example, like PCBs, like we don't make PCBs. Like those are, they're basically free. You can buy them an unlimited quantity from Asia, but the closer that our products get to being like a single block of covalently bonded matter, the better they'll be. Lower power, smaller, higher performance, last longer.
嗯，就是说，现在有一些关键的零件是买不到的。为了进行那种类型的整合，并实现真正的创新，超越那些仅仅把现成商品拼凑在一起的方法，你可能需要自己学习如何去做。这就体现为垂直整合。因此，我们在美国东海岸拥有一个自有的MEMS工厂，就是因为我们想做的那种包装和组装工作，没法通过别的方法实现。
▶ 英文原文 ⏱
And, um, there's just like, they're 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 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.
我认为，在未来几年，所有这些都会受到人工智能的严重影响。人工智能目前还没有完全发展到那一步。事实上，有讽刺意味的是，我们已经看到人工智能在公司内部和监管互动中带来了巨大的影响。因为如果我们能生成文档，或者说我们想要更改和发展某个产品，我们就可能需要遵守数以千计的ISO标准。找出我们必须遵循哪些标准，以前这需要整个监管质量团队花费几个月的时间来进行追踪。但现在AI可以帮助我们完成这些事情。
▶ 英文原文 ⏱
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. And in fact, ironically, one of the biggest impacts that we've seen of AI inside the companies and 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.
现在，AI好像已经有了一些智能。但是，当我想到诸如外科手术程序或微机电系统制造工厂时，我认为软件最终仍然需要依赖人类的操作。即便它比我们更聪明，但如果无法实际动手制造东西，那就是一个真正的限制。因此，我们已经为我们的工厂以及公司其他许多部分配备了工具，以便随着这些模型的提升，可以立即体现在我们进行的细胞工程和材料科学的研究中。
▶ 英文原文 ⏱
And now the AI just kind of knows, 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 ways where as these models get better, 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.
这让我意识到，好像我已经很久没有通过律师来制作基本的法律文件了，对吧？我不再要求律师帮我撰写保密协议，也不再让他们帮我签这个、查那个，所有基本的法律事务我都不怎么处理了。因为有个老笑话说，法律就像意大利面条代码。他们把非常复杂的代码试图用英文表达出来，这段代码和那段代码自相矛盾，而且必须和其他代码相符，但并没有真实的接口可以使用。
▶ 英文原文 ⏱
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 are no real APIs for it.
对于初级工程师和初级工程领域，我想说的是，初级工程师基本上相当于晋升为高级工程师，而初级工程工作则被智能代理取代了。同样道理，在法律领域，你可以说法律助理被裁掉了，也可以说法律助理被提升为高级律师，现在他们可以花更多的时间思考法律问题。这种将软件工程的发展与法律职业进行类比的方法其实很有趣，因为法律行业的发展总是充满不确定性。
▶ 英文原文 ⏱
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, paralegals just got fired, or you could say paralegals 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.
你永远无法确切知道这些文件里都包含了什么。你只能信任他们。比如，你说：“嘿，律师，你能看一下这个文件吗？你能告诉我这是否合法？你能做修改建议吗？”不管是什么，归根结底，你在与律师的关系中看重的是他们作为一个值得信任的权威。他们上过法学院，并把自己的声誉置于风险中。我觉得这和现在软件工程中最大的问题有相似之处，那就是那些大量的冗杂代码最终变成了一种开发障碍。
▶ 英文原文 ⏱
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, what you're valuing in the relationship with a lawyer is that they're a trusted authority. They went to law school and they're putting their reputation on the line. I think there's a parallel with, like, the biggest problem in software engineering today is these mountains of slob that end up as a PR.
然后人们在说，像在推特上有很多这样的梗图。就像是在很久以前，我们会阅读PR（合并请求）的每一行代码。不过，在我的领域，即基础设施方面，我希望工程师能够说，我理解了。这并不一定意味着你读过PR的每一行代码。你需要能够说，我同意并理解这个PR的后果。或者说，我编写了测试工具、模拟、证明、类型检查器等等，所以即使没有阅读所有内容，我也有信心地认为它在生产环境中是安全的。
▶ 英文原文 ⏱
And then people are saying, 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. It doesn't necessarily mean that you've read every line 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, et cetera, 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.
这很有意思，因为我们接受了一个这样的世界：一切都会变成乱七八糟的代码，我们并不完全理解它。但是，我们编写了一些基本的评估程序，这些程序让我们对系统有信心，然后依赖于诸如基础设施生产工程师这样的人来决定是否将其投入生产中。毕竟，如果系统崩溃，总会有人需要负责处理。
▶ 英文原文 ⏱
And so it's kind of interesting because there's a world in which we embrace that everything is going to be a spaghetti code and that we don't fully understand it. But we write the, basically, evaluators that give us confidence and then we rely on, like, people, like, the infrastructure production engineers to say, okay, I'm fine sending this into prod. You know, at the end of the day, like, someone is going to get paged if your systems go down.
我认为人们低估的另一点是，从无到有创建软件其实很简单。但是试想一千天后的情况，你的软件会是什么样的？它安全吗？经过测试了吗？达到了生产级别吗？性能如何？你是否还有动力继续投入精力去维护它的生产环境？我的意思是，人类正在成为验证者。而这正是我们用优质的验证数据训练这些模型的方式。现在我们需要人类验证者。
▶ 英文原文 ⏱
And I think another thing that people are underestimating is that creating software is really easy, zero to one. But think about a thousand days from now. What does your software look like? Is it secure? Is it tested? Is it production grade? Is it performant? And are you still motivated to invest all of those tokens in maintaining it in prod? I mean, humans are becoming verifiers, right? And that's kind of how we train these models with good verification data. And now we need human verifiers.
所以，我认为很多传统的角色，比如律师、工程师、运营人员，现在更多的工作是验证技术堆栈，并说："是的，这大致是正确的。" 他们会在出现问题时支持。这种变化在监管方面的一个好处是，它大大减少了对改变的抗拒，提升了迭代速度。举个例子，比如说你要认证一架飞机，其中成千上万的任务之一就是证明飞机可以承受雷击，而这样的测试计划所需的监管文件可能多达200页。
▶ 英文原文 ⏱
So, yeah, I think 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'll roughly stand behind it and I'll support you if it goes wrong. One of the things we see related to the regulatory is it massively reduces change aversion and improves iterations. And to give you an example, like, 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 can withstand a lightning strike and the regulatory documentation for the test plan for such a thing stretches on for, say, 200 pages.
你一般会怎么做呢？老实说，可能会雇一个不是很聪明的工程师来坐在电脑前，机械地编写200页的合规文件。而我们发现，我们可以开发一个系统，可以快速地帮助我们完成所有这些工作，可能只需要几分钟。第一层的效果是，这样会节省大量时间。第二层的效果是，即便你更改了飞机的规格，现在也只需要几分钟，而不是几个月。
▶ 英文原文 ⏱
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 what we found is, you know, 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, that you save a lot of time. The second order effect is if you change the specification of the airplane, it now takes, you know, minutes, not months.
所以，你实际上可能愿意去改变。第三层效应是，你现在可以摆脱那些能力不太出色的工程师，并留下少数非常有创造力的工程师，他们可以快速进行迭代，因为改变的成本降低了。在某种意义上，整个繁重的监管负担减少了，这种负担确实会影响迭代的能力。我认为，这在当前的人工智能领域是一个被低估的故事。
▶ 英文原文 ⏱
So you could 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 that 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.
我认为在硅谷，大多数人的共识是对监管的反感。大家都希望更快地推进科技发展，实现美好的未来，追求繁荣和富足。而那些让这个未来减速的因素，大家都想尽量避免。我确实认为我们已经过度监管，导致建设任何东西变得困难重重。不论是实体建设还是其他方面，在很多地方，要完成一个项目所需的程序都是非常复杂的，令人难以置信。
▶ 英文原文 ⏱
I think the consensus in Silicon Valley is that like regulation sucks. Like any, like we want to go faster. We want to 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 over-regulated. 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.
但是，你知道，很多法规本身并不是问题所在。如果你真的读过这些法规，你会发现能够享受到无雾霾的城市环境或者可以在许多河流中游泳是件很棒的事情。许多这样的法律措施其实都是一种进步。问题在于，人们很难理解和遵守这些法规。并且，每次你需要和政府交换信件时，都要等上好几个月。
▶ 英文原文 ⏱
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 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.
如果你能将我们所学到的许多东西变得完全无障碍，那真的会很酷。而我认为这在目前的人工智能领域是一个被低估的故事。直到监管机构开始疯狂发布新规，而我们就不得不应对大量来自监管机构的合规文件。这就像是代理人与代理人之间的战争。但这基本上就是我们现在所面临的情况。
▶ 英文原文 ⏱
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 be pretty cool. And I think that, that I think is an under, an undersold story in AI right now. 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, but that's basically what we have now.
是的，但是，这是一场公平的竞争。我认为这比我们现在的情况有所改善。当前一个很糟糕的事情就是，如果你想建造任何实体建筑，你必须先获得许可。这就像是在你被证明无辜之前，你已经被判有罪。而我们遇到的最大麻烦就是消防部门，因为他们掌握着道德制高点，总是被认为是英雄，负责从燃烧的建筑中拯救人们。
▶ 英文原文 ⏱
Yeah. But, but there is a fair fight. Yeah. I'd argue that's an improvement from where we are now. Like one of the terrible things right now is if you're going to build anything physical, you have to get a building from it. It's like, you're guilty until proven innocent. And the worst thing that we've run into is the fire department because they have like the moral and premature of, you know, people pulling people out of burning buildings.
他们实际上所做的就是好几个月来干扰你的建筑设计。你知道的，如果我们可以用一个能够快速批评你建筑方案的代理人来替代消防负责人，即使它的反馈有些过度，这也比现在的延误好得多。当马克斯在说这些可能是好事，有这么多的法规时，我想到的就是让代理人成功的一个关键是人类或其他代理人设定合适的测试框架。
▶ 英文原文 ⏱
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 marshal with, with an agent that would critique your, your building plan quickly. Even, even if its 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 humans or other agents setting up the right testing guardrails.
很多人对 "斜杠目标" 感到非常兴奋。我不知道你们是否尝试过这个，或者像 Ralph Loops 这样的模式，你告诉模型去做某件事，并设定好退出标准。比如，我对 Blake 说，去让我们都变成超音速。你的退出标准是你已经遵守了所有这些规定。由此我们可以说，规定就像我们的测试套件，只要通过测试，不产生矛盾，并且这些规定实际上是合理的，等等。
▶ 英文原文 ⏱
A lot of people are really excited about slash goal. 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 where we say like the regulations are great. They're like our testing suite, our test suite. As long as this path, passing this test, one does not incurring contradictions and the regulations are actually reasonable, et cetera.
翻译如下：
其实，它们是很不错的“护栏”。否则，我们可能会把东西毫无节制地排放到空中。是的。但这会变成一场“红皇后竞赛”，对吧？他们会有代理，我们也会有代理。我认为我们可能会有更好的代理，这比人对人较量要好。不过，如果有任何变化的话，他们的周期时间、响应时间可能会变得更短。
▶ 英文原文 ⏱
Like they're actually an awesome guardrail to have. Otherwise we would be shipping slop directly into, into the air. Yeah. But this is going to turn into a red Queens 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.
就像应用商店充斥着垃圾信息一样，我相信专利局现在也正被垃圾信息淹没。因此，这些机构可能会比较慢地采用人工智能。他们会遭遇类似DDoS攻击的情况，因为一些聪明的企业家会用大量文件将其压垮。这可能导致这些东西的审批时间变得更长，因为他们突然被大量文件涌入。这就创造了一个机会，可以真正转变现有的监管模式。
▶ 英文原文 ⏱
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 they suddenly get flooded. It creates an opportunity to, I think, really shift the model, the regulatory model.
想象一下，如果我们像现在建造东西一样在城市中开车。你还没出发，就得先写个计划，发给某个监管部门。计划里得详细说明：我们要走哪条路线，要开多快，要用转向灯，要在每个停车标志前停车，永远不闯红灯，等等等等。
▶ 英文原文 ⏱
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. We're going to drive this speed limit. We're going to use our blinker. We're going to stop at every stop sign, and we're never going to run a red light, blah, blah, blah, blah, blah.
三个月后，你收到反馈：我们觉得你应该走另一条路。最终，你获得批准，可以去某个地方开车。这种方式太荒谬了，让人永远无法自由去任何地方。然而，这正是我们国家建设实体基础设施的方式：有罪推定，直到被证明无辜。我们实际上应该做的是，让更多事情基于执法，而不是基于事先审批。
▶ 英文原文 ⏱
And then three months later, you get that critique. It's like, well, we think you should, like, drive on this other street. And eventually, you get approval. You can 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 what we should actually do is make more of these things enforcement-based rather than pre-approval-based.
我不知道。我是说，我不想承受太大压力。比如说，如果我向很多人提供一款医疗设备，那里面可能会有一些未知因素。我们是负责任的，我们也做了临床试验并报告了所有数据。是的，但这就是为什么现在医疗领域几乎没有创新的原因，因为FDA的审批流程非常繁琐。事实上，过去十年硅谷在科技领域的两个最大突破，人工智能和之前的加密技术，都属于数学领域，因为那是少数尚未被监管的领域。
▶ 英文原文 ⏱
I mean, I don't know. I mean, I don't want to be under too much pressure. 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. Yeah, but Max, this is why there's so little innovation in medical right now, because the FDA approval process is a nightmare. 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.
当他们开始对前沿模型和GPU进行监管时，创新也随之停止。彼得·蒂尔曾感叹，物理领域缺乏创新。这主要是因为巨大的监管障碍在阻碍发展。我们可以看到一些吓人的例子，比如疫苗或医学领域的著名案例。然而，监管无处不在，像触角一样延伸到各个领域。而且，有许多不同且相互矛盾的监管机构。
▶ 英文原文 ⏱
And when they started regulating frontier models and started regulating GPUs, that stops as well. You know, Peter Thiel laments about how there's no innovation in the physical domain. Well, it's been held back by just the huge regulatory barriers. And you can always find a scare version, like your 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.
你看到没有——是什么？SpaceX。对，是SpaceX，他们首先被起诉，因为公司没有雇佣足够的移民或难民，具体是什么我忘了。但是由于政府规定，他们又不能雇用这些人，因为他们不是公民。这不像是一个在某个地方必须编译通过的逻辑代码。各地都是一些随意制定的法规。在一个州你可能遵守法律，却在另一个州违反法律，在联邦层面又会出现问题，你可能会惹恼这个人，而那个人会选择起诉50个人中他不喜欢的一个人。这非常随意，非常任性。
▶ 英文原文 ⏱
You saw how – what was it? SpaceX. SpaceX, they got sued first 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 made-up 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 very arbitrary. It's very capricious.
而且，说这种做法能提高安全性，我认为完全是个神话。看看波音公司就是个例子。他们认证了737 MAX机型，而这个机型只有一个传感器可以完全控制飞机的机头上下姿态。没有哪个实习生会觉得这是个好主意。然而，这居然通过了整个认证系统。这个步骤实际上并没有提高我们的安全性，只是让我们行动更慢。
▶ 英文原文 ⏱
And moreover, like the idea that this makes things safer, I think it's just a complete mythology. Like just watch – you know, 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 step doesn't actually make us safer. It just makes us slower.
好吧，我的意思是，这里确实存在一些功能失调的问题。我认为，这在某种意义上让我们更安全，就像美国核管理委员会（NRC）让我们更安全一样，他们的工作就是确保核能的安全。自上世纪70年代以来，他们一直没有批准新建核电站，直到大概一年前。如果我们永远不建核电，那当然是绝对安全的。我想明确表示，在很多问题上，我支持放松管制。我同意布雷克的观点，这些事情中有很多可以更有效率地完成。
▶ 英文原文 ⏱
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 built any of it. And I want to be really clear that I'm on the side of deregulation on a lot of this. I agree with Blake that a lot of this can be done a lot more efficiently.
但我也认为仅仅说这就像FDA，或者更广泛地说是像其他机构，这种说法有些过于轻率。我觉得问题更加深刻，比如说，如果FDA批准了10种非常重要的药物，他们不会因此得到任何赞扬。然而，只要有一名患者死亡，他们就会被召到国会并受到斥责。所以，他们在这里面临非常负面的激励机制。我认为，这实际上反映了美国民众的信念。
▶ 英文原文 ⏱
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 in the agencies in general. I think the problem is deeper to the degree that 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 think the reality is, is that this is reflective of the beliefs of the American people.
在这里，人类受试者研究中对风险的感知与我们获得新药的速度之间存在一种权衡关系。确实如此，如果我们能更快推进这方面的研究，我们将能学到更多。这种情况非常不对称。我认为你说得很对，Max。如果你批准了一件坏事，你的职业生涯就会结束；如果你阻止了一件好事，没有人会注意到。这种不对称性就导致了速度的减缓。
▶ 英文原文 ⏱
There's this tradeoff 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 creates this asymmetric slowdown.
我认为这是在监管方面需要解决的最重要的问题。但这个问题很复杂，因为它关乎选民的立场。我们需要了解未来工作的方向以及美国人民对此问题的看法。如果我们在这个问题上太过激进，就可能会产生各种各样的规避方式。你可以去Prospera，会发现各种尝试加快进程的方法。
▶ 英文原文 ⏱
And I think this is I think that is the most important problem to solve in the regulatory state. But this is a very deep problem because it is this is where the voters are. Like we go and pull 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 all kinds of ways you could work around it. You go to Prospera. There's all kinds of ways to try to go faster.
但是，如果你被视为一个不良分子，你就会被我们生活的社会所排斥。你需要对这一问题做出更深层次的解答，而不仅仅是简单地说哦，我们需要进行监管改革。你提到的确是一个深刻的观点，马克斯，那就是选民。对，这就是公民所在的地方。
▶ 英文原文 ⏱
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. You have a deep point there, Max, which is it's the voters. Right. Yeah. That's where the citizens are.
我们喜欢责怪政客。你经常会在 X 上看到这种情况。人们总是说，这个政客，那个人都是政客。他们是选举出来的，是多数人投票选出来的。这就是人民的选择，是他们选定的“套餐”和“组合”。你可能不喜欢这个结果，但即便你去掉了这个政客，类似的人物也会取而代之，因为选民还是会通过投票把他们选上来。
▶ 英文原文 ⏱
Like we like to blame politicians. You'll see this on X all the time. Right. When people are like, oh, this politician, that politician, you're a politician. 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.
我认为，从文化上来说，大多数人很难理解我们失去了什么、错过了什么。以法国为例，有一位法国企业家在网上抱怨，说有57%的GDP被政府吸走，因此无法创建公司。但对普通法国公民来说，这些是看不见的。他们只知道自己比美国稍微穷一些。《经济学人》最近发表了一篇文章，指出经济学家们在30年后终于又开始支持资本主义。他们提到，美国的发展速度超过了其他国家，规模日益壮大。可他们紧接着又把这样的成就归因于海洋、自然资源等因素，唯独不提资本主义。他们似乎不愿碰触“资本主义”这个词，可能是因为，这些杂志在某个时候都变得有些偏向马克思主义。但他们无法设想，如果我们能稍微更自由放松一点，会是怎样的局面。
▶ 英文原文 ⏱
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 U.S. The economists just did a little piece on, economists is finally coming back around to being capitalists after 30 years. And they just did a little piece on how the U.S. is outstripping everybody and growing faster and getting bigger. But then they immediately turn 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 C word because, you know, for some reason, all of these magazines became Marxists at some point. But they can't envision or imagine what could have been if we had just been a little more laissez-faire, a little more open.
好的。我想在美国50个州中看到真正的实验情况，比如有不同的法规和税收结构。因为目前，联邦税收结构和法规似乎主导了一切。如果我们能想象一下，可以去一个小州尝试各种新药，尤其是癌症患者，这个地方允许你在做好研究的前提下尝试各种药物，被称为“实验区”。同样的理念可以应用在无人机和飞机上，虽然对飞机来说有些困难，因为它们需要跨越许多区域。我认为这里面有一些神奇的东西，比如创办创新区的想法。因为我们存在一个很大的"不要在我家后院建东西"的问题。但如果你能创建选择性参与的“在我家后院建东西”区域，那就能建立一些实验框架。而这些框架是在人们同意的前提下进行的，可以尝试不同的规则或者没有规则的方案，或者不同的执法方式，甚至是无罪推定。
▶ 英文原文 ⏱
Right. So I would love to see a true experiment among the 50 states, you know, different regulations, different tax structures. 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 and caveat emptor and you've got to do your research and blah, blah, blah. But this is known as the experimental zone. Same way for drones, same way for aircraft a little harder because you've got to cross a lot of areas. I do think there's something magical in there, the notion of like innovation zones. Because we have a huge like NIMBY problem, right? But if you create like, you know, opt-in YIMBY 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.
然后观察实际发生了什么、创新带来了什么后果、安全性又如何，然后成功的经验就可以传播开来。但我想说，正如Naval指出的，一个创新区并不能解决药物发现的问题。前不久通过了"尝试权法案"（Right to Try Act），而"单一患者药物试验"（single patient IND）这一途径已经存在很长时间。如果你的医生打电话给FDA说，他想给他的病人使用一种未经批准的药物，FDA批准的比例超过99%，甚至可以通过电话进行批准。问题在于，给患者用药仍然需要临床级别的药物，而唯一拥有这种药物的通常是正在进行临床试验的知识产权持有者。
▶ 英文原文 ⏱
And then see what actually happens and what are the innovation consequences and what are the safety consequences and then the successes can spread. But I mean, to Naval's point, an innovation zone would not solve the problem in drug discovery. So there's 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. The FDA, like 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, like they approve over 99% of those. They can even grant them over the phone. 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.
他们投入了数亿美元来开发这个东西。问题在于，如果您的病人出现了不良反应，美国食品药品监督管理局（FDA）可能会做出不利的推断，而这些病人可能一开始就病得很重。而这种不良反应会被视为药物的特性，而不是您创新领域的问题。这就带来了两个问题。首先，您需要让知识产权所有者给您一些药物，但他们可能不会这么做。其次，您需要防止全球监管机构对他们的临床试验结果产生怀疑，如果他们给您药物的话。在医学领域，我不知道您会如何应对这个情况？
翻译尽量保持简单明了，解释了药物开发过程中面临的挑战，包括与知识产权和全球监管相关的问题。
▶ 英文原文 ⏱
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 something. How would you address, I mean, I don't know your field. How would you address that in medicine?
哦，我的意思是，特别是这一点，就像棒球中的变化一样。我认为，FDA（美国食品药品监督管理局）不应该被允许从不同的衣壳（capsid）用户中得出不利的推断。实际上，有许多具体的方法可以通过相对宽松的监管来真正加速创新，只要防止这种偏执心理影响我们的决策就行。有没有比FDA更好的机构呢？我们用什么来衡量这些监管机构的表现？或者这个问题不值得问，因为我们没有其他选择？
▶ 英文原文 ⏱
Oh, well, I mean, that in particular, I mean, this is just like a variance at baseball. I think the FDA has to be prohibited from drawing adverse inferences across different users of a capsid, 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 preventing this kind of paranoia from driving our decisions. 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-
所以，我来详细说明两个方面。首先是欧洲。虽然欧洲不一定比美国食品药品监管局（FDA）更好，但他们有一个不同的体系，那就是“指定机构”制度。这些机构基本上是由各自国家政府授权的私人企业，负责认证各种事物，比如火车、飞机或医疗设备。指定机构制度在审查层面上创造了稍微好一些的激励机制，因为这些机构可以雇用更多人、可以扩展业务，而且在指定机构之间存在竞争。这些机构自身需要遵守其所在国家政府所规定的认证条件，但这也意味着，他们可能会拥有比美国多得多的审查员。
▶ 英文原文 ⏱
So I'll give two expansions of that. The first is Europe, which is not really better than the FDA, but they have a different system in that they've got 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 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 that means that they can, there can be many thousands more reviewers than you might have in the US.
我想说的第二件事是，目前世界上确实有一种已获批准且能产生收入的植入式脑机接口（BCI），这个产品在中国。而且中国国家药品监督管理局（CFDA）有自己的思考方式。他们确实拥有一个如果我们不小心，可能会让我们面临挑战的体系。他们的处理方式非常不同。他们是如何处理的呢？就是将药品或设备推向市场的成本要低得多。也就是说，他们可以在人身上进行实验，也可以直接在市场上进行测试。
▶ 英文原文 ⏱
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 careful. And they handle it very differently. How do they handle it? I mean, the cost 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.
近来，我花了很多时间思考这个问题：大约20年前，我们购买的笔记本电脑和手机要少得多，每一台设备都非常昂贵。而现在，这些设备变得更加便宜，数量也多了，我们购买得更多。总的支出也因此增加了。这是个好现象。像高通、三星和苹果这样公司的股价大幅上涨。大家都很高兴，人们正在利用由手机和笔记本电脑带来的财富去购买更多的手机和笔记本电脑。
▶ 英文原文 ⏱
Like the, so the problem that 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.
这在医疗保健领域并不是这样。在医疗保健中，由于有一种报销机制存在，相当于一个企业销售的过程，我们用于购买医疗服务的资金基本上是固定的。即便出现能够改进医疗效果的新技术，这个资金总量也不会增加。这与科技等其他行业的增长有所不同。因此，医疗保健的支出增长速度通常和税收收入增长速度差不多。
▶ 英文原文 ⏱
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 growth of tax receipts.
如果说，人工智能正在快速发展，取得了重大进展，两年后我们在人工智能上的投入是现在的十倍，那这可能是件好事。但如果两年后我们在医疗保健上的花费是现在的十倍，那就会是一场灾难。这与作为一个技术增长行业的理念是根本对立的。随着时间的推移，会有更多东西值得我们投入资金，比如那些能延长和改善病人生活质量的技术，比如可以恢复八十岁时失明的人的视力。
▶ 英文原文 ⏱
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 the quality of life for patients, like we can restore vision to people who go blind in their 80s.
我们可能能够将生命延长到远超以往的程度。我们可以恢复年长且身体状况较差患者的能力。但是，要如何为此买单呢？医护领域有一个普遍的问题，这实际上都与同一个问题有关，就是把这些技术推向市场的成本太高了。这就是中国所关注的问题的实质。解决这一问题的方法不是单纯依靠单一支付系统或是对健康保险做一些修改。
▶ 英文原文 ⏱
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 healthcare, which is all really related to 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 single payer or some revision to health insurance.
这些努力是为了降低成本，使得人们可以用信用卡购买，最坏的情况下可以像买车一样分期付款。为了实现这一点，我们必须降低将产品推向市场的成本，而中国正在实现这一点。这将使他们能够以10,000美元的价格销售这类价值100,000美元的产品。在医疗保健领域并没有私人市场。因为没有私人市场，人们有时会用一个比喻来形容这种情况：想象一下，不是去餐馆付账，而是去所有的餐馆都不需要直接付钱。
▶ 英文原文 ⏱
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 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? Imagine instead of going to restaurants and paying, you would basically go to all the restaurants.
在每个月月底，你需要将所有收据和账单发送给保险公司或政府，然后他们会报销你的费用。这就导致所有好的餐厅门口排起长队，而每个差的餐厅都没人光顾。等待时间会变得很长，但服务质量并不会因此而改进。这样，你基本上是在一个更大的资本主义社会里面运行了一个小型的共产主义社会。这就是我们在医疗保健领域所做的事情。
▶ 英文原文 ⏱
And then at the end of the month, you would send all the receipts and all the bills to your insurer or to the government and they would reimburse you. Well, there'd be a line outside every good restaurant, every bad restaurant would be available. The weights would be terrible. The product wouldn't improve. You're basically running a small communist society inside a larger capitalist society. That's what we're doing in healthcare.
这也是我们在道路上的情况，这就是为什么会有交通堵塞。就像道路上的情况完全一样。这也是为什么高速公路没有动态定价的原因，所以总是堵车。如果你想讨论一下医疗保健领域的敏感话题，想象一下这个医疗保健计划。告诉我这个计划有什么问题，对吧？假设你每年收入的前20%是你的医疗保险自付额。
▶ 英文原文 ⏱
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 no variable pricing for getting on the highway. It's why it's always clogged. If you want to step on the third rail of healthcare for a moment, think about this healthcare plan. Tell me what's wrong with it, right? Imagine that the first 20% of your annual income was your healthcare deductible.
这无关紧要。比如说，如果你身无分文且无家可归，那就等于零。如果你很富有，那就是一百万美元。但无论你的年收入是多少，前20%是你的医疗自付额，其余的由政府和保险体系支付，直到达到他们今天通常设定的上限。这样你就会很快创造出一个私人市场。
▶ 英文原文 ⏱
It doesn't matter. Like if you're broke and homeless, it's zero. If you're rich, you know, it's a million of the dollars, but whatever your annual income is, the first 20% is your healthcare deductible. And then the rest is paid by the government and the insurance system up to the usual caps that they have today. You would create a private market. You would create a private market pretty quickly.
在牙科、整形手术以及许多选择性医疗手术中，通常都会有竞争的情况，这反而推动了进步。就像在眼科手术中，激光矫正视力（LASIK）技术的发展一样；在牙科领域，牙贴面、牙套以及各种牙科手术的进步也十分明显。整形手术领域同样如此，这些领域之所以能够不断发展，是因为它们主要由私人支付者提供资金。
▶ 英文原文 ⏱
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 you look at optometry, you know, with LASIK. You look at dental with like veneers and 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.
他们有一些人是用金钱投票的。所以我们需要在正常的医疗系统中做类似的事情。很多人对此感到不可思议，他们不愿意多想一步。他们会说，不不不，那没钱的人怎么办？如果一个人没有收入，那怎么支付呢？他们还会说，20%的费用对一些人来说太高了。好吧，你可以在这当中设置一些免赔额。但一般来说，如果没有一个私人市场让人们自己支付医疗费用，你就得不到你所说的反馈循环，也无法投入更多资金到系统中。目前，即便非常富有的人也不能自愿在系统中消费，因为价格信息不公开，费率表也找不到，整个系统并不是为此设计的。如果你去为医疗服务支付自费，有时候你会得到一个比他们向保险公司收费高出十倍的报价。
▶ 英文原文 ⏱
They have people who are, you know, voting with their money. So we need to do some equivalent of that in the normal healthcare system. 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, you know, 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 in 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.
你听说过GitLab的Sid的故事吗？你认识Sid吗？他经历了一次极为成功的IPO，然后被诊断出一种罕见的癌症。然而，他不仅活过了医生的预后时间，还自己想办法应对病情。一开始，他接受了常规的前线化疗，之后尝试了所有可用的替代疗法，但医生告诉他已经没有其他办法了。然而，他不放弃，于是从中创造出六七家公司，现在他还有20到30种新药在他的治疗选择中。他仍然活着，并且状态很好。前几天我见到他，他基本上是为自己量身定制了个性化的药物和治疗方案。现在我听说过类似的一些故事，我深刻认识到如果一个人有足够的财力，不必依赖保险，他就能使用现代科学的所有手段，可能会得到普通医护人员难以预见的结果。如果你去问医生这样的事情，他们可能会觉得难以置信。
▶ 英文原文 ⏱
Have you heard Sid's story from GitLab? Do you know Sid? So he was, I mean, he had a massively successful IPO, then was diagnosed with a rare cancer. He 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. 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. He's doing great. I saw him the other day, and he basically created his own like personalized medicines and treatment plan. Yeah. There's a handful of these anecdotes that I've heard now. 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 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.
但是很明显，在高端领域，这种疯狂的事情是有可能发生的。我认为这种"N等于一"的医学类型实际上将成为理解如何构建更多可转化成果的一个非常丰富的研究源泉。这需要患者在他们最虚弱的时候付出极大的自主性，这相当讽刺。我的朋友因癌症去世，最后他希望做的事情就是研究"N等于一"的医学，因为他当时已经无力抗争了。但是，这恰恰是人工智能应该发光发热的地方，找到正确的解决方案，并普及面对这种情况时到底可以做些什么。有点疯狂的是，仅仅从知识的角度，不仅仅是从金钱上，能够获得这种机会的人其实很少。
▶ 英文原文 ⏱
But it is clear that that much, that like that crazy things are possible at the high end. I think that this type of like N of one medicine is actually going to end up being a really rich source of research for understanding how to build more translatable things. It requires a ton of agency from the patient in a moment where they're at their weakest, which is pretty ironic. My friend passed away from cancer, and like last thing he wanted to do was research N equals one medicine because he was just, you know, like dying by the weak. 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.
你们公司中有多少自动运行或接近自动运行的自主软件，并且还能自动改进？对于我们来说，很多基础设施已经是自动化的。因为我们有一个功能，可以在发现异常时自动触发。我建议每个人都创建一个这样的版本，或者使用Vercel提供的版本。但是，实际上目前大多数工程组织在遇到异常情况时，还是通过手动设置报警或监控阈值来应对，这其实比较疯狂，但整个行业基本上就是这么运作的。你会设定规则，例如，如果某个API端点的错误率增加到某个程度，就采取行动。实际上，我们已经自动化了很多网站可靠性工程(SRE)的工作。只要任何指标出现减速、加速、吞吐量变化等，系统就会触发异常警报。
▶ 英文原文 ⏱
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 a lot of the infrastructure is already autonomous because we have this capability that fires off upon finding anomalies, which I recommend everyone creates a version of this or Vercel offers a version of this. But upon anything happening that's anomalous today, the most engineering organizations are responding to this by setting up alarms or 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 at this API endpoint, do this. So we've actually automated a lot of the SRE job, site reliability engineering. So anything, any metric that slows down, speeds up, throughput changes, whatever, fires off an anomaly alert.
一个代理对该情况进行了调查，并可以决定是否创建一个事件报告。如果事件被立案，相关人员就会被通知，代理随后开始进行整改。我们提供了几乎所有的支持，除了让代理直接进行生产环境的更改。但基本上，我们已经为工程师提供了非常简单的解决方案。另外，我们还有效地利用自动优化和自动安全研究。我们开源了一款名为DeepSec的工具，它非常出色，类似于神话传说中的工具，但你今天就能使用。我们使用1万名云端并发代理对整个代码库进行了测试，它在短短几天内就找到了相当于几个季度的安全研究进展，而成本仅为1.4万美元。也就是说，我们完成了几个月的渗透测试、安全研究，相当于一整个团队的工作量。
▶ 英文原文 ⏱
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. And autonomous security research. So the other way, we open source this tool called DeepSec. It's fucking incredible. It's like mythos, but you get it today. We run it against our entire monorepo using 10,000 concurrent agents in the cloud. And it found basically several quarters worth of security research progress was made in basically a couple of days and $14,000 worth of tokens. So I'm talking about like months worth of red teaming, security research, entire teams of people.
我们现在基本上在定期进行这样的操作，因为AI的另一个问题是网络安全正在变成一场噩梦。漏洞太多，要做的工作也太多，对手又过于强大。所以我们必须非常主动地进行投资。我们正在进行大量的自主安全研究。在SRE（网站可靠性工程）、安全和优化工作方面，这些都是很明显的。你可能在推特上见过有人将代码从一种编程语言翻译成另一种语言。类似于你已经努力开发出可以运行的程序，现在用Frontier Models优化它或者用一种原生编程语言重写它，这些任务都变得相当可行。
▶ 英文原文 ⏱
And so we're basically now running like this periodic – because the other problem with AI is that cybersecurity is becoming a nightmare. There's way too many vulnerabilities, way too much work to do. There are too powerful adversaries. So you have to like basically be investing very proactively. We're running a lot of autonomous security research. So SRE, security, and then optimization work are very obvious. 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 you've 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 doable with Frontier Models.
我用自己的 Vibe-coded 应用程序开发了一个供 TestFlight 用户提交错误报告的队列。用户可以在应用内报告问题，同时上传日志和截图。当然，他们也会用这个功能来提交新功能的请求。之后，我让一个简单的守护程序收集并整理所有的错误报告。这个程序还会在后台主动分析并修复这些问题，接着向我发送一个新版的 TestFlight 版本供我测试，确保每次发给测试者的版本是可靠的。至于功能请求，目前我只是让程序整理收集而已。不过，我设想将来可能会有一个应用程序，这个应用就像是由用户自己构建的一样。
▶ 英文原文 ⏱
I mean, just from my own Vibe-coded app, I built a bug reporting queue for my TestFlight users. And they can report bugs from inside the app. It uploads the logs in a screenshot. And of course, they use it for feature requests too. So then I just have a simple daemon go through, compile all the bug reports. It actually proactively analyzes and fixes them in the background. And then it ships me a TestFlight 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. It could literally be built by the users.
现在，我并不是说这是一个好主意。可能会很混乱。但至少它可以接收错误报告等等。顺便说一下，我们应该发布这个项目，看看这个社会实验会产生什么结果。你可能会得到一个像《辛普森一家》里的汽车那样的东西，上面有雨伞、手电筒、滑稽的喇叭等等，功能应有尽有。不过，在修复错误方面，你确实可以这么做。我们确实做过类似的实验，有一次我让整个公司暂停所有项目工作一周，并告诉每个人，从接待员到工程师，都去构建你认为最重要的东西。要求是你必须使用人工智能，并且完成后要向全公司演示。
▶ 英文原文 ⏱
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 so on. We should ship that, by the way, just to see what happens to the social experiment. You end up with that Homer Simpson car where it's got an umbrella and a flashlight, 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 I stopped all project work across the entire company for a week and said, everybody from the receptionist to the engineers, build whatever you think is the most important thing to build. You'll have requirements where you have to use AI and you have to demo it for the whole company when you're done.
我原以为我们会收到大量无关紧要的项目，以及少量真正具有影响力的项目。但结果却是，我们获得了大量的影响力项目，只有很少几个是无关紧要的项目。是的，是的。这是一个很棒的尝试。其中两三个项目可能会改变公司的发展轨迹。让我最意外的，是前台接待员，也就是负责收发货物的员工。她的工作是从卡车上卸货，并在物品入库后给相关人员发邮件。她为这一流程开发了自动化工具，而且我们确实在使用这个工具。
▶ 英文原文 ⏱
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 yes. Yeah. That's a great experiment. Yeah. Two or three are like trajectory changing. Like they'll absolutely change the direction of the company. But the one that 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 they're like stuff came into inventory. Built an automation for that. And, and that, that we're actually using.
我的结论是，每个人或多或少都有一些可以让世界变得更好的想法。然而，很多时候，他们最初的想法并不那么聪明，也没有能力去预见这些想法的问题所在。不过，如果他们有能力将想法变为实践，即使遇到问题，他们也能及时做出反应、反复尝试。给他们一周的时间，到周末时，他们可能真的能创造出一个有意义的东西。试想一下，如果所有的工作都能这样进行，那会是怎样的情景。
▶ 英文原文 ⏱
And the conclusion I kind of that 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 bought something that makes sense. But imagine if all work was like that.
如何建立一支不直接从事工作的团队？他们所做的只是训练那些替他们工作的智能代理。我们也有类似的经验。你需要提醒员工，举办黑客松活动，鼓励大家创建智能代理。显然，这种文化正在转变，很多新加入的人直觉上就知道，他们的工作不是直接做事情，而是训练那些做事情的代理。但我很好奇，未来的自动化公司会是什么样子的？
▶ 英文原文 ⏱
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. 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?
这事情可能会变得更加疯狂。也许你只是把所有的摄像头都打开，让智能代理监视所有正在发生的事情，然后发现运输和接收过程非常低效。于是，它创建了一个应用程序。好吧，Zach转发了这个消息。应用程序展示出来了。你看到了吗？Zach把这个东西安装到每个人的机器上。是的，他在考虑这个，因为我们也看到了这一点，比如我们可能会在AI网关中推出一个功能，允许人们选择是否保留输入和输出。然后你可以要求系统从我的所有输入和输出中提取出我从工作中学到的技能，并把这些技能导出来供我下载。但你可以想象，公司里的人可能会想要共享和整合这些技能。
▶ 英文原文 ⏱
It could get a lot crazier. Maybe you just turn on all the cameras and the agent's just watching everything that's happening and see that the shipping and receiving thing is very inefficient. And it creates the app. Well, Zach shipped that. It presents the app. Did you see that? Zach installed this thing into everyone's machines. Yeah. He's thinking about it because like we saw this too, like we're, we're, 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 of my outputs, can you extract the skills of the things that I like learn from my work and then dump it as skills 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.
这很有趣。对我来说，这种工作方式很难想象，因为我的工作并不是重复性的。我总是寻找可以自动化的事情。目前我自己的工作中几乎没有什么可以再自动化的了。而我希望大家都能达到这样的状态：始终在一个充满创造力和兴趣的最大化区域工作。如果还有什么可以自动化的，就应该去自动化，把它从生活中剔除出去，这样你就可以更多地去发挥创造力，这才是价值的源泉。但在传统的就业观念中，这一点很难被理解，因为人们习惯于雇佣他人来重复同样的工作，而这种方式正在逐渐消失。
▶ 英文原文 ⏱
It's funny. Cause 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 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.
这真让人感到害怕，因为大家会想，我该怎么办？其实，你将会去做一些有创意的事情。你会想出新点子，当然你不需要每天都想出新东西，那是不可能的，对吧？但你偶尔会想到一些新点子，这些点子将会为你创造新的机会。不过，这对于人们来说确实是一个充满挑战的时期。尤其是那些已经重复做了十年同样事情的人，突然之间他们需要去培训一个代理，自动化地替代掉原来的工作，这真让人感到不安。
▶ 英文原文 ⏱
And that's really scary because people are 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 that 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 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 all of a sudden it's like, well, now you're going to train an agent and automate it away. That's scary.
我认为，以前的收益大概是70%靠智力，30%靠执行。而现在则是70%靠执行，30%靠智力。随着模型的不断改进，这个比例还会继续改变。其实，我对这个看法不太确定。Max，我持相反意见。我认为是99%靠智力，1%靠执行，因为执行的部分将由智能代理来完成。你基本上只需要对代理说：“嘿，我做出了明智的决策，并考虑了深远的思路，你只需去执行就可以了。”
▶ 英文原文 ⏱
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. 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, right? You will literally be like, hey, agent, I'm making smart decisions and thinking big thoughts, just go implement stuff.
实际上，有时候我想在应用程序上开发一些功能，但我并不是按传统的方式来编写代码。我会询问智能助手，接下来我应该开发哪些功能呢？你知道的，我会去查看日志，看看用户的使用情况。我应该做些什么？这里我想明确一点，我讨论的是对人类的回报。适应未来的人将是那些更具主动性的人，也就是说，那些能够在思考时决定打开像Claude这样的工具，询问“我应该开发什么功能”，而不是去看YouTube的人。
▶ 英文原文 ⏱
In fact, sometimes I want to build features on apps that I'm floating 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? To be clear, I'm talking about the returns to humans, 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 Claude and be like, what should I build versus watch YouTube?
这是一项有趣的实验。我敢打赌，我们现在认识的很多人都在编程，而他们以前并没有这样做，其中也包括我们自己，对吧？所以，生态系统中编程者的数量，或许增加了10倍，是不是？没错，现在可能比一年前多了10倍的人在编程。与此同时，我们的注册人数激增，还有一类新的用户，他们并不是工程师，只是使用这些基础设施。我想这些人可能是播客制作者、YouTuber，还有在社交平台上发帖的人。然而，大多数人仍然没有在编写代码。
▶ 英文原文 ⏱
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, it might be 10x, right? Yeah, it might literally be 10 times as many people are coding now than we're coding a year ago. It's while 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.
我跟别人说，哎呀，vibe coding 真是太有趣了，比打游戏还要好玩。我以前有一个小游戏小组，常常通过玩电子游戏和FPS游戏来放松，但我完全不玩了，现在所有的时间都投入到了 vibe coding 上。vibe coding 更有趣，而且你还能从中得到实际的东西，它的反馈循环也一样紧凑，甚至更好。我去跟我其他的朋友说，嘿，你们也应该试试 vibe coding。他们只是呆呆地看着我，我说，不不不，你们不懂，构建东西其实容易多了。但对他们来说，这一直像是个神秘的幕后流程，他们从未搞明白，以为你一直在和电脑“对话”，所以没觉得有什么变化。他们没意识到其实已经容易了很多。对他们来说，就像 Max 说的，开始是多么难以想象和困难，所以他们根本不去尝试。
▶ 英文原文 ⏱
Like I go to people and I'm like, oh man, vibe coding is so much fun. It's more fun than, like I had a little gaming group that I used to play video games and FPSs to blow off steam. I completely stopped playing. All that time went into vibe coding. Instead, it's more entertaining and you get something real out of it, 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. 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 black box process in the background. They never understood it. They assumed maybe you were just talking to the 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.
我们可能将会从之前只有0.01%的人口在编写代码，到现在可能有1%的人在写代码，增长了100倍，但仍有99%的人不会编写代码。所以我们现在处于一个很奇怪的状态。这真的很疯狂。就像是在玩一个电子游戏，这个游戏很棒，但却能产生真实的成果。昨晚，我的未婚妻整晚没睡，因为她一直在忙一个项目。当然，她实际上并没有写任何代码，但这就像是一种让人上瘾的体验，而这种感觉我已经有十多年没有在编程中感受到了。这很惊奇，因为对于很多人来说，这就像是一种彩票。我认为普通人对“感觉编程”有了一些兴趣，不过是通过更倾向于媒体的模型，例如视频模型。与写代码和应用程序相比，现在可能有更多的人在尝试制作视频和图像。
▶ 英文原文 ⏱
So we might have taken 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. It's crazy. It's like, it's a video game and it's a great video game, but real stuff comes out. Yeah. My fiance was up all night last night because she didn'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 have gotten a little more into the vibe coding, but through models that are more media models, video models, for example, right? More people are probably fooled around making videos and images than they did writing code and apps.
问题是这样的，我觉得视频也有它自己的一些问题，对吧？也许有一天我们可以说，“给我做一个关于X的好电影”，然后就能自动生成一个不错的纪录片。但目前来说，这还只是一个判断品味的问题。我和Andre Carpathy有一个类似的讨论，就是哪一年我们可以只提供一本书，然后就能输出一部电影？我觉得这个目标离我们更近了。不过我认为自从我们几年前打这个赌以来，他的预期时间表已经大大缩短了。到2030年，我们可能会拥有几十部类似《指环王》的电影。可能会有粉丝说，“他拍错了，我要拍我自己版本。”著名的故事就是这种情况。我还有一个用来衡量AI进步的标杆，就是我非常喜欢一个叫《苍穹浩瀚》的系列。有电视剧版，还有九本书，目前已经拍摄了前六本的内容，但后三本还没有被拍成剧。
▶ 英文原文 ⏱
The problem is like, I don't, video has its own issues, right? Maybe someday we'll be like, make me a great movie about X and I'll just spit out a good documentary. But right now they're at the taste of the judgment. This is a bit that I have with 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 it's a lot 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. Like the famous stories are like that. One of my other benchmarks for progress in AI is I'm a huge fan of a series called The Expanse. 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.
这段话的大意是：
“有一些重要的分歧，我还没有参与其中，也没有读那些书。我期待能有时间在看完电视剧之后再去读最后三本书，然后感叹，原来这就是最后三季的内容。这是一个很美好的未来，因为已经有了很多参考资料。当你说‘给我下一个《指环王》’的时候，我特别激动，因为我们在想象力上真的还没有取得突破。哦，这样的突破也会在文化中出现，比如《哈利·波特》和《指环王》。我对此感到十分兴奋。我同意，这是更加令人兴奋的事情。人类能独特地做些什么？这就回到了核心问题。人类将能够做些什么独特的事情，对吧？我觉得Max，你是一个通用人工智能（AGI）的极端主义者，所以对你来说，人类什么也做不了。”
▶ 英文原文 ⏱
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 the time when I can dump in the last three books conditioned on the TV series and be like, generate the last three seasons. Like this is coming. That's a great future. 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. Oh, we're going to see that too. In culture, the likes of Harry Potter and Lord of the Rings. I'm really excited about that. And that would be the, I agree that that would be the more exciting one. 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.
代理人将会做所有事情。我并不是反人类的，只是我觉得，我们将不得不寻找，如果你的身份认同是基于你的聪明才智和创造力的话，你可能会遇到困难。是的，我想我还是站在另一个角度。我认为创造力是环境中能给你带来惊喜的东西。你可以跳出系统，做一些系统内部甚至无法想象的事情。这种创造力超出了训练数据的范围，它不属于输入到系统的数据分布。我认为这方面总会存在空间。你有没有注意到每个云网站都长得差不多？一旦模型经过足够多的迭代，人们基本上可以确定一个云网站应该是什么样子。这种样子就是它用衬线字体，棕色和奶油色的配色，以及带有一定间距的等宽字体。
▶ 英文原文 ⏱
Agents will do everything. I'm not like anti-human, 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. Yeah, 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 distribution of data that was fed into the system. And I think there'll always be room for that. Have you noticed that every cloud of website looks the same? And people like basically like dial in what a cloud 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.
经过一段时间后，你会发现，这种分布让你觉得，这不具创造性。这是Claude的作品中流出的东西，并不只是单纯的人机对抗。取而代之的是，人类和计算机合作，对抗仅由计算机完成的作品。只有计算机的阶段最终会出现，但离我们还很远。然而，计算机将能够产生无比吸引人的超级刺激，成为娱乐产业的推动力。在TikTok上我们已经看到这方面的初步表现。
我个人对艺术的定义是"有意义的出乎意料的行为"。这意味着某种程度上的惊喜，就像是在某种意外的方向上探秘，让你惊讶于这个东西的实现。但更重要的是，有意义。对我来说，有意义意味着它能在某种程度上改变你在宇宙中的未来轨迹。因为思考和反思它，你的生活如果有所不同，那么它就是有意义的。
▶ 英文原文 ⏱
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. Just computer will eventually happen, but we're pretty far away. But the computer is going to be able to produce these crazy super stimuli that it's going to be, it's going to make the entertainment. And I mean, we, we kind of see a weak form of this in tech talk. Um, and so when you think about the going, 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. It feels like you're kind of moving into the Z axis. Like you're surprised that the thing was realized. But meaningful. Yeah. 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 reflected on it.
好的，我对艺术的定义完全不同，并会导致完全不同的结果。抱歉打断你。不，不，不。只是根据你的定义，你会得到不同的前提。这是公理的推论。是的，我的定义之一的优点是它非常宽泛，比如说军事行动也可以被认为是艺术。我认为我们会经常看到这样的情况，会发现很多"第37步"（move 37s）的出现。不过，我很好奇你的艺术定义是什么。我自己有多个定义，没法概括为一个单一的概念，但我确实认为艺术是一种情感的传递，是把你内心感受到的情感传达给另一个人的东西。通过创造某种对象或事物，以分享你内心的情感。
▶ 英文原文 ⏱
Well, my definition of art is completely different and leads to a completely different outcome. Sorry to interrupt. No, no, no. Just by your definition, you get to a different premise. That's the extrapolation of the axiom. 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. 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. 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 that creates, that takes an emotion that you felt inside.
对我来说，计算机几乎是无法做到这一点的。这种背后没有意图的艺术作品就显得有些无意义。你也可以说大自然就是艺术，比如自然之美，你看到日落，对吧？这不属于人类创作。因此我会称之为纯粹的智能，无动机的运作。日落之美就在于其中蕴含的智能作用，有一个复杂的系统在运作，而你的大脑识别到了这种美，而其中没有任何动机，因此没有自我参与进来。但是人类意义上的艺术，是因为某个人感受到了一种情感，他们希望你也能感受到这种情感，或者他们想再次体验这种情感，或者他们想捕捉到他们当时拥有的感觉，所以他们创作了那个作品。创作者的归属将显得非常重要。
▶ 英文原文 ⏱
And so to me, a computer almost by definition is incapable of doing it. The exact same piece of art 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 recognizes 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.
比如说，一张美丽的照片，对吧？如果这张照片是一个人拍的，与AI生成的像素完全相同的照片相比，我会觉得人拍的照片更有意义。我刚投资了一家创业公司，他们通过硬件可信证书来验证某张照片确实是人拍的，这会有很多很棒的应用场景。我们肯定会被大量劣质内容淹没。你还记得大概一两年前的control net的那些东西吗？有一个特别的场景，比如说一个中世纪的村庄，还有一个漩涡。你记得吗？那是AI生成的，这是我第一次看到这种技术并觉得它真的很酷。不管你想不想称它为艺术。
▶ 英文原文 ⏱
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. I just invested in a startup that does verifiability with hardware attestation that some human actually took a photo, which is going to have a lot of really cool use cases. We will be drowned in slop, no question. Do you remember the control net stuff from like a year or two ago? There's like, there's one particular scene of like, it was like a medieval village. It had like a swirl in it. Do you remember? Yeah. That was AI generated. And that was one of the first times I looked at this and thought it was really cool. Like whether you want to call it art or not.
那一个难道没有打破你的前提吗？因为是某个人设计了训练和提示，才形成那个非常酷的谜题。顺便说一句，AI在未来完全有可能也做到这一点。但我会给予那个提出光学错觉控制网络的人更多的赞赏。我认为标准会大幅提高，让人感到惊讶的门槛会越来越高。它必须越来越令人印象深刻。就像吉卜力工作室，不是吗？这已经发生了。就好像OpenAI摧毁了吉卜力工作室的作品一样。没有人再想看吉卜力工作室的作品了。
▶ 英文原文 ⏱
But that one, doesn't that one break your premise? Because some human came up with 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 with that idea of the optical illusion control net, I give them more credit than the- 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 Ghibli, right? That's already happened. Yeah. Like OpenAI destroyed Studio Ghibli for everybody. Nobody wants to see that if Studio Ghibli work ever again.
已经完成了。虽然那个也完成了，但我对此有一个不同的看法。你看过真正的吉卜力工作室的作品吗？它实际上比OpenAI推出的这些看起来要好得多。再去看一遍吧，真的很令人惊叹。对吧？当你在互联网上已经到处看到大量吉卜力的作品时，它已经进入了普及阶段，就不再令人意外了，艺术价值已经被削弱了。没错，没错。现在你的惊讶定义依然有效，我只是认为人类才是真正能够完全脱离数据分布创造惊喜的，他们可以有意地做到这一点。
▶ 英文原文 ⏱
It's been done. Although that one also has, I have a counterpoint to that one. Like have you watched real Studio Ghibli? It actually looks so much fucking better than this slop that OpenAI put out. Like watch it again now. It's impressive. 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 eroded. That's right. That's right. Now your surprise definition still works. I just think that humans are the ones who can generate surprise completely out of the data distribution. And I think they can do it with intent.
我确实认为意图对于意义很重要。那么回到你提到意义的那个点，对吧？你提到了意义和惊喜，对吧？我想说的是，人类仍然可以是从系统中创造惊喜的人。比如说，假设你用一个人工智能来训练它，使它在数学上达到完美，那就是一个完美的数学AI。这个AI在数学的形式系统之内运作。然后库尔特·哥德尔出现了，他提出了完全超出这个系统的东西，对吧？哥德尔的不完备定理。他完全走出了这个系统，利用物理属性基本上打破了这个系统。我认为AI无法达到这种水平。因此，总是有空间来在创造力之外产生惊喜。而意义来自于人类的参与，他们这样做是有目的的，并且传达了一些东西。所以，也许我可以用自己的方式来解读你的定义，但我们拭目以待。我对人类抱有更多的乐观。
▶ 英文原文 ⏱
And I do think intent matters for meaning. So to your meaning point, right? You said meaning and surprise, right? And I guess what I would say is that humans can still be the ones who 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 Gödel comes along and he has something completely outside of the system, right? Gödel's incompleteness theorem. It was completely stepped out of the system and used 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 in my way, but we'll see how it plays out. I'm a little more optimistic about humans.
如果训练一个AI模型，它会基于某种数据分布进行训练，这些数据包含一些词汇。通过训练，模型学习了语言的某种分布和结构。那么，像大型语言模型（LLM）或Transformer这样的模型有可能跳出这种分布，产生一种在训练集中不存在的新想法吗？其实，训练集的规模如此庞大，很难想象有大量的创意会不在训练集的某个地方被涵盖。但是，如果存在，这些创意可能在于自然领域，比如物理、互动、感受、情感、演化等模型不接触的方面。所以，我认为语言之外仍然存在一些东西，但语言涵盖了很多内容，语言是一个很好的信息压缩器，而且我们有大量的语言数据。通过自我训练和传感器，你可以接触到这些其他的领域，例如像我们眼睛一样工作的相机等传感器。
▶ 英文原文 ⏱
So if you train an AI model, it's trained on some data distribution, it's trained on some tokens. It then learns some distribution of language and the structure within that. Is it possible for an LLM or a 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 set somewhere. But if they exist, they probably lie in the natural domain, in physics, in interaction, in feeling, in emotions, in evolution, in things that 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. But I mean, you can get to these other things through self-play that. Self-play and sensors, like cameras are sensors, like our eyes are sensors.
是啊，我的意思是，我觉得问题在于，没有随机性的话，你怎么能够跳出分布。所以在强化学习中，你可以通过从一个动作空间中采样行动来获得随机性，这种随机性可以带你到新的领域。但是，我认为真正值得我们去思考的是，人类能否跳出分布？任何新的想法从哪里来呢？我们是否也依赖随机性来进入这些新领域？我们并不像自然选择那样完全依赖随机性，自然选择是通过纯粹的随机性来实现的，比如基因突变然后看看结果会怎么样。但对于人类来说，我们似乎有能力在无限的空间中筛选，排除大量不必要的东西，这样我们的创造力在更大的图景中就有了意义。这似乎是我们独特的能力之一。
▶ 英文原文 ⏱
Yeah, I mean, I think the question is, how do you go out of distribution without randomness? So in the case of like RL, you can get randomness. 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 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? We're not dependent on pure randomness, 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.
也许人工智能已经开始在某些边缘领域展现出能力，正如我们在解决一些数学问题时所看到的那样。不过数学是一个范围有限的领域，但它也是一个很大的领域。我并不是说人工智能永远达不到那种水平，只是我没有那种信心。目前，我认为在那些真正能够让人惊讶的领域，仍然是属于人类的领域。我认为未来的发展方向是人类和人工智能的结合。单靠人类是不行的，纯粹的人工智能也还未成熟。但人类加上人工智能，这就是我们现在所处的时代。我们会在这个阶段停留多久呢？我认为会比许多人预想的时间更长。我相信人类将会具备巨大的价值，事实上，价值会更高。我们每个人，所有在座的人，我们的生产力已经大幅提升。从基本经济学来看，当一个人的生产力提高时，他就会更富有，生活得更好。实际上，你雇佣的人会更多，而不是更少。
▶ 英文原文 ⏱
And 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 saying it'll never get there. I don't have that confidence. But I think at least at the moment, I would say that truly stepping outside surprising people, there'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 it's longer than people think. I think humans will have an enormous amount of value. 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.
也许你们中有些人不再招聘初级员工了，但我不确定这是否真是事实。我不认为应把员工分为初级和高级。如果有人在人工智能方面非常出色，并且他们既聪明又有创造力，那么我比以往任何时候都更想雇用他们，因为他们能带来的效益太大了。那么，新要求是什么呢？我们会招聘初级和超高级员工，只要他们在智能代理和人工智能方面非常出色，并且适应能力强。而且，很多人不需要被雇用了，他们可以创建自己的事业。我的假设是，我们最终会有更多的小团队，因为完成任何特定任务所需的人数大大减少。只看到表面现象的人会说，哦天哪，所有工作都没了，因为现在只需要两个人就能制作一台喷气发动机。
▶ 英文原文 ⏱
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. What's the new requirement? We're hiring juniors and super seniors as long as they're really good with agents and really good with AI and quick to adapt. And a lot of them don't need to be hired anymore. They can create their own thing. My hypothesis is we end up with a larger number of smaller teams. Like the number of people it requires to accomplish any given task drops by a lot. People who only see first order of facts say, oh, my gosh, all the jobs are disappeared because I can do a jet engine with two people.
我不需要一千个。你知道，有998个职位已经消失了。但实际上这意味着你可以创造很多不同的喷气发动机。我认为这完全正确。我认为会有这样的发展，并且可以回到Naval的观点，我认为独特的人类特质就是创造力。在过去，很多人可以有创意，但他们不知道如何把他们的愿景变为现实。这种情况正在改变。所以我认为我们会迎来创业热潮，创始人数量激增，以及大量非常小的团队，因为你不需要很多人就可以完成一些事情。
▶ 英文原文 ⏱
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 jet engines. I think that's exactly right. I think there will be, and it goes back to Naval's point, 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 going to have an explosion of entrepreneurship, an explosion of founders, and a very large number of very small teams because you don't need many people to accomplish something.
是的，我认为，AI提供了基础层面的智能和专业知识，能够帮助我们理清各种术语。而现在，智能代理实际上提供了相当多的自主性。因此，主要剩下的就是创造力和品味。是的，你需要足够的自主性来开始，并坚持下去。但你不一定需要花20年时间去学习某个领域才能有所贡献。随着这一障碍的减少，通才现在迎来了大展拳脚的机会。
▶ 英文原文 ⏱
Yeah, I think, like, look, AI provided base level intelligence and domain knowledge 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 and make a contribution. And so that barrier going down, generalists are having a field day.
归根结底，我们都是通才。我们都喜欢思考各种事情，而不仅仅局限于一个领域。比如，马克斯在这里谈论意识、FDA、脑科学和创造力。就像我们所有人一样，我们总是试图同时思考所有事情。
▶ 英文原文 ⏱
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. And, like, all of us are trying to think about everything all the time.
在推特上，那些总是喜欢提到专家、资历和来源的人才是受到冲击的人。因为专家的身份不再那么重要。即使你花了五年、十年去攻读某个领域的博士学位，应该去发展你的创造力、直觉、品味和判断力。因为如果你的博士学习只是帮助你记住一堆东西、术语和一些支撑框架的知识，那么人工智能就会轻易取代这些。
▶ 英文原文 ⏱
And so people on 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 five 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.
这就像是一个，呃，你知道的，计算器乘以十亿，或者说是心灵的自行车，但速度更快。所以我觉得这是关于使用AI的人与不用AI的人的区别。因此，目前你能为自己做的最好的事情就是非常熟练地使用这些工具，与它们相处得舒适，并且始终了解它们的能力和局限。这是一个不断变化的目标。谢谢。
▶ 英文原文 ⏱
It's like a, you know, calculator times a billion or, you know, bicycle for the mind, but accelerated. So 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 or the boundaries of what they're capable of, what they're not capable of. And that is a moving target. Thank you.