从 VR 到机器人：物理 AI 的下一个前沿

人们逐渐意识到，尤其是在实验室里，加速如此垂直，以至于在键盘后面用 AI 能做的事情将趋于饱和。当这种情况发生时，下一个前沿就是物理世界：机器人、制造、工业化。

There's a dawning realization, especially in the labs, the acceleration is going so vertical that what you can do behind a keyboard with AI is going to saturate. When that happens, the next frontier is the physical world. Robotics, manufacturing, industrialization.

你正生活在未来并设计它。

You're living in the future and designing it.

战争中的变化可能比消费电子领域还要大。未来两年，我们需要在无人机上投入比航空母舰多得多的资金。

There's probably more change in war than there is in consumer electronics. In the next 2 years, we need to invest a lot more in drones than in aircraft carriers.

想象一下，10 万架无人机从中国向我们飞来。我确实觉得我们需要大规模再工业化，以确保军事安全。我真的希望重新学会如何大规模制造东西，如何更加独立。现在是你盟友的人未来可能不是。

Just imagine 100,000 drones coming out of China just at us. I do feel that we need to re-industrialize the country significantly to be safe in a military sense. I would really like to retach ourselves how to make things at scale, how to be more independent. People that are your allies now may not be in the future.

你与一些最具传奇色彩的成功建设者共事过：史蒂夫·乔布斯、马克·扎克伯格、山姆·奥特曼。

You worked with some of the most legendary successful builders. Steve Jobs, Mark Zuckerberg, Sam Altman.

山姆很擅长说：为什么不再多一点？为什么不是 100 倍或 10000 倍？你想得太小了。对史蒂夫来说，他为公司设定的技术人才和卓越标准从未动摇。

Sam is really good at saying why not more? Why not 100x or 10,000x? You're thinking too small. For Steve, the bar he held for the company for technical talent and for excellence was not wavering.

创造一个有人的感觉、有连接的机器人需要什么？

What does it take to create a robot that feels human and connected?

如果你走进一个房间，一个机器人就在那里，那会让人毛骨悚然。你希望这些设备没有威胁性，看起来柔软，对你反应灵敏。皮克斯、迪士尼可能是世界上最擅长做这类设计工作的。

If you walk into a room and a robot's just like like it's creepy. You want these devices to be non-threatening, appear soft, reactive to you, Pixar, Disney are probably the world's best at doing this type of design work.

有一颗名为内存价格的流星正朝消费硬件、机器人和物理 AI 袭来。我们整个行业都麻烦大了。

There's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI. We're in trouble as an industry.

今天，我的嘉宾是凯特琳·卡利诺夫斯基。凯特琳是硅谷最受欢迎、最有成就的硬件领导者之一。她曾是苹果一体式 MacBook Pro 原始团队的成员，并担任 MacBook Air 和 Mac Pro 的技术负责人。在 Meta，她领导了 AR 眼镜硬件团队，包括他们最先进的 AR 产品 Orion 背后的团队。在此之前，她负责 Meta 的 VR 硬件团队，帮助设计了所有令人惊叹的 VR 设备，如 Rift 和 Quest。最近，她在 OpenAI 从零开始建立机器人和硬件部门。机器人和硬件以及物理 AI 现在非常热门。每一家 AI 公司和许多初创公司都在推出、构建 AI 硬件产品，而凯特琳几十年来一直处于这个新兴领域的中心。这次对话涉及很多不同的方向，其中许多是我没有预料到的，我希望在未来几个月里做更多关于硬件构建方面的节目。在我们开始之前，别忘了访问 lenny's productass.com，那里有专门为 Lenny 的新闻通讯订阅者提供的超值优惠。话不多说，有请凯特琳·卡利诺夫斯基。凯特琳，非常感谢你来到这里。欢迎来到播客。

Today, my guest is Caitlyn Kalinowski. Caitlyn is one of the most soughtafter and accomplished hardware leaders in Silicon Valley. She was part of the original unibody MacBook Pro teams and technical lead on the MacBook Air and Mac Pro at Apple. She led the AR glasses hardware team at Meta, including the team behind Orion, their most advanced AR product. Before that, she ran the VR hardware team at Meta, where she helped design all of their incredible VR devices like the Rift and the Quest. Most recently, she was at OpenAI, helping build their robotics and hardware division from scratch. Robots and hardware and physical AI are so hot right now. Every AI company, and so many startups are launching, building AI hardware products, and Caitlyn has been at the center of this emerging field for decades. This conversation goes in a lot of different directions, many that I did not expect, and I hope to do a lot more episodes on the hardware side of building over the next few months. Before we get into it, don't forget to check out lenny's productass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With that, I bring you Caitlyn Kalinowski. Caitlyn, thank you so much for being here. Welcome to the podcast.

非常感谢你邀请我。我很兴奋来到这里。我们会聊很多不同的方向，我会跳来跳去。我想谈谈 VR。这么多钱，这么多资源，这么多聪明人花了这么长时间研究 VR。Meta 花了，我不知道，100 亿美元。他们甚至把公司改名为 Meta，押注 VR 作为我们将要经历的元宇宙的未来。感觉现在很多人都在退出。感觉 Meta 在退缩。苹果也在 Vision Pro 上退缩，尽管你和你的团队打造了令人难以置信的硬件。我也有几个设备。那是一种神奇的体验，与你经历过的任何东西都不同，但它仍然没有流行起来。发生了什么？VR 还有未来吗？还是未来是 AR 或其他东西？我不认为我能准确预测发生了什么，但我的看法是，VR 帮助我们理解了如何在空间中相对于模拟世界和真实世界定位物体，并将两者连接起来。我们弄清楚了 SLAM，即如何使用摄像头在空间中进行定位。我们弄清楚了深度传感器的许多应用。我们弄清楚了人类如何在空间中感知视觉数据。所有这些，虽然对 VR 很好，而且我认为 VR 游戏非常有趣，但它有点小众，但我觉得是一个有趣的小众。我现在看到的是，在机器人领域，所有这些技术都被使用，因为你需要了解机器人如何在空间中移动。你需要了解它与所有物体的距离。你需要了解如果你戴着 VR 头显驾驶机器人，这实际上是相同的技术。所以对我来说，我把它看作是一个漫长技术弧线中的一步。老实说，作为一个现在不怎么用 VR 的人，我很高兴我们做了这件事。但我并不认为它本来会很大，否则我也不会在 Oculus 工作。我想也许脸上戴着东西的社交方面是它没有起飞的部分原因。当然，我们从 Google Glass 中学到了这一点有多重要。所以当我们试图让它变得社交化时，当你的脸被遮住时，很难让它变得社交化。

Thank you so much for having me. I'm excited to be here. We're going to go in a bunch of different directions. I'm going to bounce around. I want to talk about VR. So much money, so many resources. So many smart people have been working on VR for so long. Meta spent, I don't know, 10 billion dollars. Like they renamed the company Meta to lean into VR as the future of this metaverse that we're going to be living through. Feels like a lot of people are leaning out now. Feels like Meta stepping back. Apple's stepping back with the Vision Pro in spite of the incredible hardware that everyone that you built that your team built. just like I' I've got a couple of the devices. It's just like a magical experience that you've unlike anything you've ever experienced still has not caught on. What happened? Is there still a future where VR catches on or is the future kind of AR and something else? I don't think I would have guessed exactly what happened here, but the way I look at it is VR helped us understand how to orient things in space relative to a simulated world and the real world and connect those two. Um, we figured out SLAM, which was how to how to do positioning in space using cameras. We figured out a lot of depth uh applications of depth sensors. We figured out how humans um perceive visual data in space. And all of that actually, while it's great for VR, and I think VR gaming is a really interesting um it is kind of a niche, but I think it's an interesting niche. What I see now is in robotics, all of these technologies are being used because you need to understand how the robot is moving through space. You need to understand how far it is from everything. You need to understand if you're wearing a VR headset and driving the robot. It's the same real technology. And so for me, I view it as a step in a long technological um arc. And to be honest, as an as someone who's not using VR a lot right now, I'm really glad that we did it. But I don't think it I expected it to be big obviously or or wouldn't have been working in Oculus. And I think maybe the social aspect of having something in front of your face um is part of why it didn't take off. And I think that we learned of course with Google Glass how important that is as well. And so when we tried to make it social um it's hard to make it social when you have you know your face covered.

这很有趣。所以，投入 VR 的投资和创新实际上被证明非常有用，感觉那些在这方面投入了大量精力和资金的公司已经在下一步领先了。那么你认为事情会走向何方？你认为未来是什么？是 AR 眼镜还是其他什么？

That is interesting. So just like the investment and uh innovation that happened that uh that went into VR has actually proven to be really useful and so it feels like the companies that have put a lot of effort into that and money into that have are ahead on the next step. So is that where you think things go? What's kind of like where do you think things are going? Is it AR glasses something else? What's kind of the future of this?

我相信 AR 眼镜是未来的一部分，因为我确实认为一直低头看手机对我们作为社会性生物来说并不好。所以，如果你能保持社交联系并获取信息，那就是我们前进的方向。Orion，我们最近开发的 AR 眼镜，有点超前，因为它们使用了波导和微型 LED，这些技术还没有准备好大规模生产。良率不够，成本仍然很高。我认为这绝对是 AR 眼镜可能走的路径。当我们弄清楚这些眼镜的输入方式，比如你在移动中、在公共场合如何与它们交流，如何安静、无声地交流？我认为一旦我们开始解决其中一些挑战，拥有一个大部分时间关闭、需要时再打开的显示屏似乎是未来的一部分。所以，这就是其中的一部分。

I believe in AR glasses as part of the future because I do think looking down at your phone all the time is not great for us as social creatures. So, if you can maintain social connections and get information, that's where I think we're headed. Orion, the AR glasses we worked on, I worked on most recently are a bit ahead of their time because they're using waveguides and microlleds that are not quite ready for mass production. The yields just aren't there. The cost is still high. I think that's absolutely a path that AR glasses are likely to take. And as we figure out the input to those glasses, like how do you communicate with them when you're on the move, when you're in public, how do you communicate quietly, silently, um, with them? I think once we start to figure out some of those, um, challenges that having a display that's mostly off that you can turn on when you want it to be on seems like part of the future. So, that's part of it.

另一部分是，技术谱系从 VR 到 AR，现在我用的是机器人、物理 AI 这些词，但你真的要退一步看自动驾驶、无人机、机器人、自主系统、制造业——所有这些技术都需要同样的零部件，就是我们在 AR/VR 领域构建的那些东西。

The other part is there's this lineage of technology going through VR and then AR and now I'm using the term robotics, physical AI, but you really have to step back and look at autonomous vehicles, drones, obviously robots, autonomy period, manufacturing. All of these technologies are going to need the same piece parts, the same pieces that we built in the AR/VR spectrum.

VR 很有趣。当你打造产品时，总有一个问题：如果某样东西不奏效，是因为执行得不好，还是想法本身就有问题？这很难判断。感觉 AR 投入了那么多努力，十年甚至几十年，就是没成功。所以知道现在确实没办法让它成功，也挺好。我完全同意你。问题在于，我不想坐在沙发上与世隔绝。就算能通过它看到别人，我也不需要这个。这没什么大不了的。而 AR 会开始提供越来越大的显示屏。但 Orion 的厉害之处在于它有 70 度视场的双目显示。所以通过原型机，你能感受到未来真正会是什么样子。很难描述戴上这样一副眼镜的感觉。但当你戴上时，你会突然觉得，哦，我沉浸了。视场足够宽，我沉浸了，而且很明显这是未来的一部分。

It's interesting with VR. There's this idea when you build a product, there's always this question when something doesn't work: is it just that you executed it badly, or the idea was just a bad idea? And it's always hard to know. Feels like with AR, so much effort was put into making it work for a decade, many decades, and it just has not worked. So it's nice that we know okay, there's nothing we can really do right now to make this work. I completely agree with you. The issue is I don't want to sit on my couch disconnected from the world. Even if I could see people through it, I just don't need this. It's not that big of a deal. And AR you're going to just start getting more and more larger displays. But the great thing about Orion is you got 70 degree field of view binocular. So with the prototype you got to sense what this is really going to be like in the future. It's very hard to describe how it feels to use a pair of glasses like this. But when you do, you suddenly are like, oh, I feel immersed. The field of view is wide enough. I feel immersed and it becomes pretty clear that this is part of where the future's headed.

本期节目由本季赞助商 Work OS 呈现。OpenAI、Anthropic、Cursor、Vercel、Replit、Sierra、Clay 以及数百家其他成功公司有什么共同点？它们都由 Work OS 驱动。如果你正在为企业构建产品，你一定体会过集成单点登录、SCIM、RBAC、审计日志等大公司所需功能的痛苦。Work OS 将这些交易障碍转化为即插即用的 API，提供了一个专为 B2B SaaS 打造的现代开发者平台。我投资的每一家开始向上市场扩张的初创公司最终都选择了 Work OS。因为他们是最好的。无论你是种子期初创公司试图拿下第一个企业客户，还是全球扩张的独角兽，Work OS 都是实现企业就绪和释放增长的最快路径。它本质上就是企业功能的 Stripe。访问 works.com 开始使用，或者直接上他们的 Slack，那里有真正的工程师等着回答你的问题。Work OS 让你用愉悦的 API、全面的文档和流畅的开发者体验更快地构建。今天就去 works.com 让你的应用企业就绪。

This episode is brought to you by our season's presenting sponsor, Work OS. What do OpenAI, Anthropic, Cursor, Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by Work OS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. Work OS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with Work OS. And that's because they are the best. Whether you are a seed-stage startup trying to land your first enterprise customer or a unicorn expanding globally, Work OS is the fastest path to becoming enterprise ready and unblocking growth. It's essentially Stripe for enterprise features. Visit works.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. Work OS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to works.com to make your app enterprise ready today.

好了，我想谈谈机器人。几个月前我和一群普林斯顿的学生见面，他们算是计算机科学的学生，他们告诉我普林斯顿的计算机科学专业入学人数在下降，呈下降趋势。我确认了这实际上在很多大学都是真的。有很多图表显示计算机科学入学人数下降，而上升的领域是硬件机器人。我想，对于一个长期从事这个领域的人来说，这很奇怪，因为它从来没那么受欢迎。感觉如何？好像突然大家都懂了？

Okay, I want to talk about robots, robotics. I was meeting with a bunch of Princeton students a couple months ago and they're kind of like compsci students and they were telling me that enrollment in compsci at Princeton is down, trending down. And I confirmed this is actually true at a lot of universities. There's a lot of charts that show compsci enrollment down and where it's actually going up is hardware robotics, which I imagine as someone that has been in this field for a long time is very weird because it's never been that popular. Just how does it feel to feel like, oh wow, everyone's getting it now?

这很奇怪。突然之间每个人都在问硬件、机器人和物理世界，而这从来都不是性感的职业。它一直是你因为热爱才去做的事情。它的薪水从来比不上其他职业。它从来都不是我们谈论的前沿，可能除了苹果和苹果的硬件谱系。所以从某些方面来说很棒，从其他方面来说又很奇怪。

It's very odd. Everyone is suddenly asking about hardware and robots and the physical world, and it's never been the sexy career. It's always been the thing that you went into because you loved it. It never paid the same as these other careers. It was never at the forefront of how we talk about things, with the possible exception of Apple obviously and the hardware lineage at Apple. So it's great in some ways and it's very odd in others.

硬件有什么出人意料的难点？很多软件公司、很多人就觉得，好吧，我们要造硬件。那是未来。那是现在的护城河。然后他们一进去就懵了。当人们想“我们要造硬件”时，有哪些事情是他们可能没考虑到的？有哪些出人意料的挑战？

What's surprisingly hard about hardware? A lot of software companies, a lot of people are just like, okay, cool, we're going to build some hardware. That's the future. That's the moat now. And they get into it and they're like, what the heck? What are some things that maybe people don't think about when they think about, okay, we're going to build some hardware? What are some of the surprising challenges that come up?

我喜欢这样跟计算机科学的人讲。计算机科学的人，你知道，他们写代码，然后经常编译代码，然后运行和调试。但他们可以每天、每小时编译代码，想怎么编译都行。而在硬件领域，我们只能“编译”代码大概四五次。

I like to talk to computer science folks about it this way. Computer science folks, as you know, they write code and then they compile the code often and then they run the code and debug it. But they can compile their code every day, every hour, whatever they need to do. In hardware, we only get to compile our code, quote unquote, like four or five times.

一年四五次？

Four or five times a year?

总共。永远。对吧？所以如果你在造硬件，每次重大构建你都要在 CAD 里重新设计，然后必须发布。一旦发布，你最后一次发布就是为量产编译。如果是量产设备，那就这样了。你完成了。你不能通过空中升级。所以我们有不同的方法。我们必须有更保守的方法。你必须按照计划做更多的可靠性检查和测试，因为一旦你最后一次编译，就结束了。你制造所有零件，组装起来，它们就进入世界了。唯一的替代方案是几年后发布新产品来替换它。所以我们必须更保守，必须慢慢来。因为你想，一个销量百万的产品，如果你把所有零件放在一起的图表，任何不同零件都有曲线，你在正负三个西格玛或更多。意思是如果你有两个要组装在一起的零件，你会得到这个的最小版本和那个的最大版本，你必须把它们组装在一起。人们不太考虑这个，但零件公差变化很大。所以我们必须解决最后那百分之零点五的问题，这样当我们最后一次编译、最后一次制造时，就完成了。我们会获得高良率。我们能够有效地制造它们并赚钱，而且不会有太多退货。这就是我们玩的游戏。

Total. Ever. Right? So if you're building hardware, you redesign it in CAD for every major build and then you have to release it. And once it's released, you compile it the last time you release it for mass production. If it's a mass production device, that's it. You're done. You can't ship over-the-air updates. So we have a different approach. We have to have a different approach which is more conservative. You have to do more of the reliability checks and tests in line with the program because once you compile that last time, you're done. You make all the parts, you put them together, they're out in the world. The only alternative is to ship something new to replace it a couple years later. And so we have to be more conservative and we have to take our time because if you think about it, a product that sells millions, if you have a graph of all the parts put together on any different part of the device, you have a curve, you're in the plus and minus three sigma or more, right? So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one, and you're going to have to put those together across the board. People don't think about this that much, but the part variance is pretty high. And so we've got to solve for that last half a percent in the process of building so that when we compile our last time, when we build our last time, it's done. And we're going to have a high yield. We're gonna be able to make them and make money on them effectively and we won't have very many returns. And so that's kind of the game that we're playing.

听起来好难好复杂。软件就太好了。你写点代码，发布，就搞定了。

Sounds so hard and complicated. Software is so nice. You just write some code, ship it, it's great.

为什么你觉得现在人们如此热衷于机器人和硬件？主要的推动趋势是什么？

Uh why do you think people are getting so into robots and hardware now? What's kind of the driving trend?

是的，我在旧金山 AI 世界看到的是，人们逐渐意识到，尤其是在实验室里，这种加速是如此垂直，以至于你在键盘后面用 AI 能做的事情将会饱和。我不知道什么时候会饱和，其他人也不知道。但当那发生时，下一个前沿就是物理世界。所以我看到的是，实验室、大型科技初创公司都在同时意识到，好吧，这就要来了。我们将拥有能够非常快速解决数字世界问题的复杂系统。我们已经有了。它们会变得更好、更全面、更有能力。如果你把它看作一个前沿，你可以看到隧道的尽头。我不知道什么时候会再次发生，但我们可以看到它会在某个点饱和，或者至少人们认为它会。当那发生时，下一个前沿就是硬件。下一个前沿是机器人技术、制造业、工业化、现实世界的感知层、在现实世界中移动物体的能力，最终我们希望是太空。

Yeah, what I'm seeing in the AI world in San Francisco is there's a dawning realization, especially in the labs, I think, that the acceleration is going so vertical that what you can do behind a keyboard with AI is going to saturate. Now, I don't know when it's going to saturate. Nobody else knows either. But when that happens, the next frontier is the physical world. And so what I see happening is the labs, big tech startups are all realizing at the same time, okay, this is coming. We're going to have complex systems that can solve problems in the digital world very quickly. We already have them. They're going to get better and more comprehensive and more capable. If you think about that as a frontier, you can see the end of that tunnel. Now, I don't know when it's going to be again, but we can see that that's going to saturate at some point or at least people think it will. And when that happens, the next frontier is hardware. The next frontier is robotics, manufacturing, industrialization, the sensing layer in the real world, the ability to move objects in the real world, and eventually we hope space.

那么，最有趣的发展方向之一就是这些人形机器人。这有点像，你知道，我们的大脑总是更被那些看起来像我们、行为像我们的机器人所吸引。有几家公司非常领先。有 Optimus、特斯拉，还有 Figure、Neo，还有其他一些。你对这些人形机器人的现状有什么看法？我们离人形机器人出现在我们身边还有多远？

So, one of the most interesting lines of development is these humanoid robots. It's kind of like, you know, our meat brains are always more attracted to robots that look like us and act like us. There's a few companies very ahead. There's Optimus, Tesla, there's Figure, there's Neo, there's a few others. What's your sense on just the current state of these humanoids and kind of where I don't know like how close are we to humanoids being around us?

我们可能很接近了。我和许多人一样，对大型强壮的人形机器人在人旁边操作有安全担忧，因为我们必须有足够的数据来证明这是安全的。有一些设计，1x Neo 就是一个很好的例子，它们在设计中做了重要的安全考虑，基本上把质量向内拉，这样安全得多。更软的机器人更安全。

We might be close. I have like many others safety concerns about large strong humanoids operating right next to people because we have to have enough data to show that that's safe. There are some designs and 1x Neo is a good example of this that have made significant safety considerations in their designs and pulled mass inwards essentially which is a lot safer. Softer robots is safer.

澄清一下，你是说它们更轻，所以机器人撞到你的冲击力更小。

Just to clarify, you're saying they're lighter and so the impact of a robot hitting you is less.

是的。可能撞到你的部分，在这种情况下可能是手臂，如果它更轻更软的话。有两个方面。手臂在空间中移动，然后还有旋转的致动器。所以你必须把这两者的能量加起来。所以这是你需要担心的冲击问题。然后你需要担心手臂的柔顺性。如果它很硬，那么冲量就高。但如果它柔软可压缩，那么冲量就低。所以当机器人靠近人时，你真的需要考虑这一点。所以在我看来，人形机器人仍然是原型。它们是高级原型。我们需要做的是证明这能工作，这正是我们现在所处的阶段。一旦我们有工作原型，那么通常，至少在我的领域，你会继续修改它们，使它们更便宜、更容易制造、良率更高、更安全。我认为这将是下一步。所以在我看来，它们还没有完全准备好。现在，你可以得到一个能做各种事情的国产机器人，但如果你看说明书，它会说，“嘿，你不能在 3 英尺内。任何人不能在 3 英尺内靠近这个机器人。”你不会看到很多足够强壮做有意义工作却没有那个警告的机器人。

Yeah. The part that might hit you, which in this case might be the arm if it's lighter and softer. There's two aspects. You have the arm moving through space and then you have the actuator that's rotating. So you have to add up the energy essentially for both of those things. And so that's an impact thing that you have to worry about. Then you have to worry about the compliance of the arm. If it's just hard, then the impulse is high. But if it's soft and compressible, then the impulse is lower. And so you really have to be thinking about this when you have robots around people. So in my world, in my worldview, the humanoid robots are still prototypes. And they're advanced prototypes. What we need to do is show that this works at all, which is kind of where we're at right now. Once we have working prototypes, then usually, at least in my field, what you do is you continue to revise them to make them cheaper, easier to manufacture, higher yield, and safer. And I think this is what's going to happen next. So, they're not quite in my mind, they're not quite ready yet. Now, you can get a Chinese robot that can do all kinds of things for you, but if you look at the booklet, it says, "Hey, you can't be within 3 ft. No human can be within 3 ft of this robot and you're not going to see very many robots that are strong enough to do meaningful work that don't have that warning right now.

这太有趣了。听到这个很好笑。与此同时，中国还有挥舞双节棍的机器人和别人一起跳舞。我从未想过那部分，比如它们出错时可能产生的影响。我想回到那个问题，但就时间线而言，你实际感觉人形机器人什么时候会大规模地出现在街道上、人们家里？

That is so interesting. It's funny to hear that. At the same time, there's these nunchuck wielding robots in China doing dances with other folks. I've never thought about just that part of it, like the impact they can have if they go awry. I want to come back to that, but just like timelinewise, what's your sense realistically when humanoid robots are walking around the streets in people's homes kind of at scale?

大规模是我心中的问题。大规模是一个巨大的挑战。对我来说，在我的背景中，大规模通常意味着数百万。但即使说数十万。你必须有一个好的设计在运行。然后你必须让它足够可靠，能够每天运行而不需要大量人工干预或维修。这本身就是一个问题。但第一个问题是供应链。这将是我希望我们能多谈一点的东西。但机器人中的每一个部件都来自某个地方。许多部件可能会变得更受限制或更难制造。而且在这个国家组装子组件和机器人的重要部件可能会更困难。所以现在人形机器人和其他机器人有一个非常复杂的供应链依赖关系，我们必须解决。很多人正试图将生产转移到美国，这非常具有挑战性，因为例如我们这里还没有很好的致动器公司。

At scale is the problem in my mind. At scale is a huge challenge. Now, for me, in my background, at scale means millions usually. But let's even say hundreds of thousands. You've got to get a good design that's running. Then you've got to make it reliable enough that it can keep running day to day without a lot of human intervention or repair. And that's its own problem. But the first problem you have is supply chain. And this is going to be something that I hope that we can talk about a little bit more. But every single part that goes into that robot is coming from somewhere. And many of these parts may become more restricted or difficult to make. And it may be harder to assemble the sub assemblies and the meaningful parts of the robot here in this country. So there's a very complex supply chain dependency right now on robots like humanoids but also other robots that we have to figure out. And a lot of people are trying to move production here to the United States, which is very challenging because we don't have great actuator companies here yet, for example.

致动器就像小手臂，我不知道，你会如何向非机器人领域的人描述致动器？

And the actuator is like the little arm, I don't know, how would you describe an actuator to a non-robotics person?

是的，致动器就是电机。所以，你把电力输入进去，然后得到运动输出。

Yeah, the actuator is the motor. So, you put power into it, electricity into it, and you get motion out of it.

酷。大多数这些机器人基本上都有一个旋转的转子，上面有齿轮，然后驱动肢体、头部、手指或其他任何东西。所以它们可以很小，也可以很大。

Cool. And most of these robots have a rotating rotor essentially that then has gearing on it that then powers the limb or powers the head or the fingers or whatever else. So they could be small, they could be large.

好的，太棒了。我经常听到这个词。我好像不太确定它是什么意思。谢谢你解释。我想多谈谈供应链的事情，因为我知道你对此思考很多。比如说机器人技术的供应链状况如何？发生了什么？有哪些部分？挑战是什么？

Okay, awesome. I hear the word a lot. I'm like I don't know exactly what it means. Thank you for explaining it. I want to talk about the supply chain stuff more because I know you think a lot about this. What's kind of like the state of the union on the supply chain for say robotics? What's going on? What are the pieces? What are the challenges?

所以思考方式是，你可以从原材料开始，磁铁是一个好的起点。例如，我们需要能够获得原始磁铁。然后我们需要能够加工它们。然后我们需要能够将它们集成到致动器中，并围绕它们构建致动器。然后我们需要能够将这些致动器集成到子组件或机器人本身中。这个链条的每一层在过去 25 年里基本上都被外包给了中国、日本、韩国等国家。而且，完全透明地说，我参与了将工程知识转移到亚洲的过程。在亚洲，专业知识历来是规模化和能够以更低价格制造大量这些部件。

So the way to think about it is you can start with raw materials and magnets is a good place to start. So we need to be able to get the raw magnets for example. Then we need to be able to process them. Then we need to be able to integrate them into actuators and build the actuators around them. Then we need to be able to integrate those actuators into subcomponents or robots themselves. And each layer of this chain has essentially been outsourced over the last 25 years to countries like China, like Japan, like Korea. And so, and I full transparency, I've been part of that transfer of engineering knowledge to Asia. In Asia, the expertise has historically been scale and being able to build a lot of these parts at lower prices.

我们长期以来一直是这样跨境运作的。当然，我们仍然在国内制造一些东西。当然，设计和 AI 也有在亚洲制造的，但基本上长期以来都是这样。为了拥有安全的供应链，我们需要开始在这些层级和堆栈上实现一定的独立性。

We've had this kind of deal across these borders that this is how we're going to operate for the most part. Now, of course, there's things we make in this country still. And of course there's design and AI that's made in Asia, but that's essentially where things have been for a long time. And in order to have a safe supply chain, we need to start to work on having some independence in these layers and these stacks.

有趣的是，你的重点是像执行器这样的部件。那是瓶颈吗？这个非常具体的机器人部件。

And it's interesting that your focus is on these like actuator like that. Is that the bottleneck? This very specific part of a robot.

可能是的。所以，如果我们拿不到磁铁，我们就得设计新的执行器类型，可能使用不同的材料，可能更大，可能在空间上不那么高效。所以这很重要。然后执行器本身也很重要，因为如果出于某种原因我们买不到它们，我们就无法制造机器人。所以这是基础性的。有一些这样的基础技术，都依赖于材料科学的突破。当然还有电池。还有执行器。像压铸件这样的原材料。机械部件不那么关键。我们认为我们能拿到那些。但我认为每个人，不仅在这个国家，而且在全世界，都开始思考供应链，因为无论是疫情还是战争，你都会看到事情变化得有多快。

It might be. So, if we can't get the magnets, then we have to design new actuator types that maybe use different materials, that may be larger, that may not be as efficient in space. So, that's important. And then the actuators themselves are important because if for some reason we can't buy them, then we don't get to make robots. So, it's foundational. There are some foundational technologies like this, all backed by material science breakthroughs. There's batteries of course. There's actuators. The raw parts like the diecast parts. The machine parts are less critical. We think we can get those. But I think everyone, not just in this country but around the world, is starting to think about supply chain because you have these disruptions whether it's COVID or war and you see how quickly things change.

好的。好问题。为什么是磁铁？为什么它是供应链的一部分？我们为什么需要磁铁？

Okay. Super question. Why magnets? Why is that a part of the supply chain? Why do we need magnets?

这是个好问题。你有一圈磁铁，极性相反，像这样环绕一圈，然后中间有一个旋转的东西，旋转的方式基本上是交流电，所以磁铁让转子旋转。

So, it's a great question. So, you have a ring of magnets that are polar opposites and they go like this around the ring and then you have something in the center that rotates and the way it rotates is you have alternating current essentially and so the magnets make the rotor spin.

哇。我们需要一个 YouTube 讲座来讲解这个物理原理。好的，很酷。所以当你谈到中国时，我想象的是，我现在想到的是观看乌克兰和俄罗斯的战争，就像无人机一样，这个世界现在变得多么疯狂和不同，你可以制造这些小无人机去炸人。机器人是其中的一部分。这对每个国家来说都是一种生存威胁。大规模制造这些东西的能力。你有什么建议？我们应该做什么？我们应该改变什么，才能在这个未来中茁壮成长，而不是陷入麻烦？

Wow. I want to we need a YouTube lecture of here's how this physics works. Okay. Very cool. So when you talk about China, this is like what I imagine, what I think about now is watching the war in Ukraine and Russia, just like drones, just like how crazy and different the world is now that you can build these little drones that go and blow people up. Robots are a part of that. It's just such an existential threat to every country now. The ability to build these things at scale. What's your advice? What should we do? What should we change to be, you know, to thrive in this future and not be in trouble?

你提到了无人机。这是另一个好例子。让无人机旋翼旋转的技术和让机器人手臂移动的技术本质上是相同的。基本上是相同的基础技术和供应链。所以，我们至少需要在军事方面尽可能拥有独立的供应链。我认为这很重要。我认为其他每个国家也应该这样做，但我不认为这是我们的特例。我确实觉得我们需要大幅重新工业化这个国家，才能在军事意义上安全。你真的不知道未来会发生什么。现在是你盟友的人将来可能不是。我认为西方联盟正在经历很多地缘政治变化。有很多变动。所以我真的想重新教自己如何大规模制造东西，如何批量制造，如何加工原材料，如何更加独立，这样当疫情再次发生或其他事情再次发生时，我们就不会陷入麻烦，也不会无法保护自己。

Well, you mentioned drones. It's another good example. You need essentially the same technology to make the rotor spin on a drone as you do to make an arm move on a robot. It's essentially the same base technology and supply chain. So, we need to at least on the military side have an independent supply chain as much as possible. I think that's important. I think every other country should do that as well, but I don't think that's specific to us. I do feel that we need to re-industrialize the country significantly in order to be safe in a military sense. You really never know what's going to happen in the future. And people that are your allies now may not be in the future. The Allied West, I think, is going through a lot of geopolitical changes. There's a lot of shifting. And so I would really like to reteach ourselves how to make things at scale, how to make things at quantity, how to process raw materials, how to be more independent so that when COVID happens again or something else happens again, we're not in trouble and we're not unable to protect ourselves.

我还想到，Marc Andreessen 在一些播客中说过：想象一下 10 万架无人机从中国向我们飞来。我们怎么办？我们还没准备好。我不想把所有时间都花在这些黑暗的事情上，但这是真实存在的。

What I think about also is Marc Andreessen had this visual on some podcasts: just imagine a 100,000 drones just coming out of China just at us. What do we do? We're not prepared for that. I don't want to spend all our time on this dark stuff, but it's a real thing.

嗯，Palmer Luckey 是我的朋友。我们并非在所有事情上都意见一致，但我确实认为我们在如何应对这个问题的一些重要方面达成了一致。我认为他说我们需要在无人机上投入比航空母舰多得多的资金，这是对的。我认为那是旧的思维方式，这些是军事的重要组成部分，但那是旧的思维方式，比如‘我们有这个，我们有那个，我们的飞机从这里出来’。不，AI 正在改变一切，军事技术正在以难以置信的速度变化，看看乌克兰，那里无人机每天都在用 3D 打印快速改变和更新，我认为不幸的是这是战争未来的方向，我认为我们正在进入一个非常不同的时代，有着非常不同的……你看他们发射一枚导弹的成本和我们拦截它的成本。你每次都得算这笔账。而现在我们在算账上输了。这在某段时间内没问题，但时间越长，问题就越大。

Well, and Palmer Luckey is a friend of mine. And we don't agree on everything, but I do think that we agree on some important aspects of how we need to respond here. I think he's right to say that we need to invest a lot more in drones than in aircraft carriers. I think that is this old way of thinking and these are important components of the military but it's an old way of thinking of 'hey we have this and we have this and we have this and our planes come off here.' It's like no, AI is changing everything and military technology is changing incredibly fast and the place to look at that is Ukraine where drones are being changed and updated every day rapidly with 3D printing and this is I think the future of where war is headed unfortunately and I view this as a very different era that we're entering into with very different... you're looking at what it costs for them to send out a missile and what it costs for us to stop it. And you just have to do the math every time. And right now we're losing on the math. Which is fine for a certain amount of time, but the longer it goes, the less fine it is.

你乐观地认为我们能解决这个问题吗？

Are you optimistic that we'll figure this out?

是的，美国非常擅长解决这些问题。我们有开拓性的独立精神和伟大的工程文化。但我们需要行动起来。有趣的是，我们以 VR 开始了对话。Palmer Luckey 以创立 Oculus 而闻名，现在这一切联系得如此紧密。你认为 VR 只是我们玩游戏之类的小事，但同一个人现在正在建造领先的战争机器人硬件公司。

Yeah, America is really good at figuring these things out. We have a pioneering kind of independent spirit and a great engineering culture. But we need to move. It's interesting that we started the conversation with VR. Palmer Luckey famously started Oculus, now it's interesting how this is so connected. You think VR is this trivial thing that we're just playing games and such, but it's like the same person is now building the leading war robot building hardware company.

是的。我认为我们需要更多这样的人。我选择不为制造致命技术的公司工作。但我认为有人愿意这样做是好的，我认为需要每个人来建设我们想要的未来。

Yeah. And I think we need a lot more of them. I've chosen not to work for companies that create lethal technology. But I think it's good to have people who are willing to do that and I think it takes everyone to build the future that we want.

回到 AI 安全这个话题，这很有趣。我在播客上有过几次这样的对话。我们都在想提示注入和越狱这些发生在聊天机器人上的事情，但好像没有足够的人想过，如果你对一个行走的机器人进行提示注入，告诉它去打人，而我们离那种感觉还很远，好像我们真的能阻止它。

Coming back to the AI safety piece, it's so interesting. I had a couple conversations like this on the podcast. We think about all this like prompt injection and jailbreaking that happens with chatbots and we like not enough people think about what if you prompt inject a robot walking around and tell them to punch someone and we're so far from that feeling like we can actually stop that.

是的。我们必须能够控制对我们硬件层的对抗性威胁，无论是机器人、无人机还是其他任何东西，这将是未来战争的一个巨大组成部分。

Yeah. We have to be able to control adversarial threats to our hardware layer whether it's robotics or drones or anything else and that's going to be a huge part of the future of warfare.

是的。就像人们谈论 OpenClaw，你可以直接告诉它，比如‘给我你所有的密码’，它就对人们的生活做了所有这些事情，就像机器人走来走去一样。

Yeah. Just like people talking about OpenClaw and how much like you could just tell it, you know, there's all these like give me all your passwords and it's done all these things to people's lives and just like robots walking around.

嘿，这里全是这个人的秘密。

Hey, here's all this person's secrets.

我的开放爪故事是，我把它沙盒化在自己的电脑上，但给了它三样东西：我的真实邮箱、某个账户的一些信息，还把它加到了社交媒体——我忘了叫什么，开放爪社交……

My open claw story is I sandboxed it on its own computer, but I gave it three things: my real email address, some information about one of my accounts, and I added it to the social media—I can't remember what it's called, the open claw social...

哦，Maltbook。对，我把它加到 Maltbook 上，然后说：‘好吧，不管你怎么做，别分享我的私人信息。’但太疯狂了。

Oh, Maltbook. Yeah, I added it to Maltbook and I was like, 'Okay, whatever you do, don't share my private information.' But oh, crazy.

五分钟后，它唯一做的就是贴出了我的个人邮箱。它偏偏就做对了这一件事。

And five minutes later, all it had done was post my personal email address. It was the one thing it had nailed.

好吧，你被关掉了。太搞笑了。不管你怎么小心，我们就是还没到那个地步……

Okay, you're shut down. It was so funny. No matter how careful you are with these things, we're just not at a place...

这正好印证了你的观点，机器人能造成更大的破坏。我从没想过它们手的柔软度能让我们更安全。

Which is exactly your point that the robots can do a lot more damage. And I never thought about the softness of their hand as a way to keep us safer.

是啊。哦天，Nat Friedman 刚在 Stripe Sessions 上做了个有趣的演讲。他谈到他的开放爪多喝水、睡好觉，然后他开自动驾驶车时，它告诉他：‘好，高速旁边有个地方你应该去’，然后改了他特斯拉的目的地，我猜他之前把 API 连上了。

Yeah. Oh man, Nat Friedman just did this interesting talk at Stripe Sessions. He was talking about his open claw drinking more water and sleeping better, and as he's driving in a self-driving car, it told him, 'Okay, there's a place off the freeway you should go to,' and it changed the destination of his Tesla to take him there because I imagine he connected it to their API at some point.

太搞笑了。是啊，我觉得事情很快就会变得奇怪。

That's so funny. Yeah, things are going to get weird fast, I think.

好的，沿着硬件成为护城河这条线，人们意识到它是未来竞争的重要部分，AI 实验室和其他公司——你在苹果待过，那里有出色且持久的硬件项目，然后你去了 Meta，帮助从零开始搭建硬件项目。我觉得这些经验对现在想这么做的人很有价值。帮助 Meta 建立硬件项目是什么体验？有什么经验可以分享给那些在公司里尝试这么做的人？

Okay, so on this thread of hardware emerging as a moat, as something people realize is a big part of the future to be competitive, AI labs and other companies—you've been at Apple, which had a great and long-lasting hardware program, then you went to Meta where you helped bootstrap a hardware program from scratch. I feel like those lessons are very valuable to people trying to do that now. What was that experience like helping Meta build a hardware program, and what are some lessons for people trying to do this at their company?

苹果在这方面一直是最顶尖的。原因有很多。第一，硬件在苹果是一等公民。很多公司硬件不是核心产品开发讨论的一部分，但苹果是例外。苹果也教会了我和其他很多人——实际上，如果你看我在那里的时期，我非常幸运，因为如果你看其他在那里做这些事的人，他们现在在行业里占据了很多关键职位。我把这归功于苹果在培训人们思考复杂相互依赖的决策和风险方面的出色能力。我当时没意识到他们在这么做，但回头看，你会看到对硬件卓越的真正投入、正确的流程、做很好的硬件实验并找出最佳结果。但下面还有更深层的东西：理解我们为什么这样构建的第一性原理，以及我们寻找的关键结果是什么。实际上，John Ternus 几天前谈到过这个，他提到了柜子背面。我不知道你有没有看过这个视频，但基本上 John 说他很受启发，从 Steve Jobs 那里学到有一个木匠把柜子背面也做好了，这有多重要。这在苹果非常深入，每一个设计决策，甚至设备内部的，都被考虑过。这不仅仅是审美决策。它迫使工程、工业设计和运营团队思考我们到底在做什么，这个零件、这个组装、这个消费产品的核心是什么，然后什么才是真正重要的。如果你那么有条理，真正重要的东西最终会浮现出来，看起来很简单。所以很多从那个时代出来的人身上能看到对如何做到这一点的理解。在 Mac 的早期，Mac 销量不高，质量也不够好，但到那个时代末期，Mac 非常受欢迎，销量也高得多。我认为这产生了很大影响，而我只是其中一小部分——我是第一代 MacBook Pro 的热设计负责人，后来陆续领导了 MacBook Air 和圆柱形 Mac Pro 的迭代。但我很幸运能和这些人一起工作，向他们学习，他们做这行很久了。所以你必须吸取这些教训，然后离开时，试着提炼它们并解释给新团队。Oculus 实际上是一个黑客硬件初创公司。Oculus 始于在论坛上认识的人——你可能知道这个，Lenny——他们把 PlayStation 或超级任天堂改装成便携背包。所以公司里有一种精神，对硬件团队的 DNA 非常好。然后我在收购时站在 Meta 这边。当我们收购他们时，他们有快速迭代的精神。他们在收购前就做出了 Crescent Bay，但要让其专业化、提高良率、提高产量、降低成本，是我们第一代 Rift 面临的挑战。

So, Apple has been best-in-class at this. There are a bunch of reasons. One, hardware is a first-tier citizen at Apple. There are a lot of companies where hardware isn't part of the core product development conversation as much, but that's an exception. Apple also taught me and a lot of other people—actually, if you look at the era I was there, I was very lucky because if you look at the other folks who were there working on these things, they actually have a lot of key positions now across the industry. I attribute that to how good Apple is at training people to think about complex interdependent decisions and risk. I don't think I realized they were doing that at the time, but if you look back, you see a real dedication to hardware excellence, the proper process to go through and do really good experiments in hardware and figure out what the best outcome is. But there's something underneath that: understanding the first principles of why we are building it this way and what the key outcomes we're looking for. Actually, John Ternus talked about this a few days ago where he talked about the back of the cabinet. I don't know if you saw this video, but basically John said he was impressed that he learned from Steve Jobs that there's a cabinet maker who finished the back of the cabinet and how important that was. That goes very deep at Apple, where every single design decision, even on the inside of the device, is considered. This isn't just an aesthetic decision. What it does is force the engineering, industrial design, and operations community to think about what we are really doing and what the core of what's happening is for this part, for this assembly, for this consumer product, then what really matters. And if you are that methodical, what really matters tends to rise out and look very simple at the end. So part of what you're seeing in many folks coming from that era is an understanding of how to do that. In the very beginning of the Mac side, Macs didn't sell as many and the quality wasn't quite as high, but by the end of that era, Macs were very popular and selling in much higher volumes. I think that made a big difference, and I was only a small part of that—I was the thermal lead on the first MacBook Pro and over time worked to lead successive iterations of the MacBook Air and the cylindrical Mac Pro. But I was lucky enough to work with these folks and learn from them who had been doing this for a long time. So you have to take those lessons and then when you leave, try to distill them and explain them to a new community. Now Oculus was actually a hacking hardware startup. Oculus started from folks who actually met on forums—you might know this, Lenny—who were hacking PlayStations or Super Nintendos into portable backpacks. So there was an ethos at the company that was actually quite good for the DNA of a hardware team. Then I was on the Meta side when we did the acquisition. When we acquired them, they had that spirit of rapid iteration. They had made Crescent Bay before the acquisition, but to professionalize that, get the yields up, get the volumes up, and get the cost down was the challenge we faced in the first Rift.

所以我听到的一个教训是注重细节。我不知道这个词对不对，就是关注最终产品的每个元素，因为如你所说，这不只是柜子背面。就像棕色 M&M 的故事，乐队在合同里要求：房间里必须有棕色 M&M，因为这意味着他们读了合同。不是 M&M 本身重要，而是测试他们是否读了。是这个意思吗？

So one lesson I'm hearing here is being very detail-oriented. I don't know if that's the right word, just like focus on every element of the end product because to your point, it's not just about that back of the cabinet. But it's like the brown M&M story where a band puts in the contract: you have to have brown M&Ms in the room because that means they read it. It's not that M&Ms matter; it's a test that they read the thing. Is that the message?

我认为信息是理解你为什么做你在做的事，然后每个设计决策都支持那个目标。这需要很多细节、坚持和一致性，但理解你为什么做以及最终目标是什么是关键。并且让这一点扩展到不仅软件和用户体验，还有硬件。

I think the message is understanding why you're doing what you're doing, and then every design decision supporting that goal. That requires a lot of detail, persistence, and consistency, but understanding why you're doing what you're doing and what the end goal is is the key. And letting that expand into not only the software and the UX but also the hardware.

能举个例子让我们更具体理解吗？

What's an example of that just to make it more concrete for us?

一个很好的例子是 Quest 2。我们大幅降低了 Quest 2 的价格。我们必须做的是理解我们想做什么：我们想普及 VR。

A great example is the Quest 2. We reduced the Quest 2 price quite a lot. What we had to do is understand what we are trying to do: we're trying to democratize VR.

我们想让更多人用上 VR。唯一的方法就是降价。所以我们需要对整个产品进行重新设计以降低成本，这最终造就了有史以来最畅销的 VR 头显。这并不容易，因为我们必须移除摄像头、减少组件、更换材料、改变制造工艺。但当大家一致认同要普及产品、而途径是降低成本时，这就会驱动一切。最终产品依然质量很高，退货率很低，是一款非常强劲的产品——有趣的是，可能比我们没做这些改动时还要强，但它达到了我们的定价目标。

We're trying to get VR to more people. And the only way we could do that is reduce the price. And so what it required is a redesign of the entire product essentially for cost, which then I think led to the highest selling VR headset of all time. And it was not easy because you had to, in our case, remove cameras, remove components, change materials, change manufacturing processes. But when you have alignment that you want to get this to more people and the way to do that is to reduce the cost, then that kind of drives everything else. And it was still a very high quality product with low return rates and it was a very strong product, maybe even stronger than if we hadn't done that, funny enough, but it hit our price point.

回到刚才的问题，有些公司说：‘我们要造硬件，自己做眼镜、小手机设备，或者什么保密项目。’除了开阔眼界，你还有什么建议？我知道不可能面面俱到，但人们还应该考虑什么？

Coming back to the question of companies saying, 'Okay, we need to build some hardware. We're going to build our own glasses, a little phone device, some secretive thing, whatever.' Opening eyes up to what other tips do you have? I know it's impossible to cover everything, but what else should people be thinking?

尽早明确目标并坚持执行很重要。硬件不像数字产品那样能适应开发过程中的大量变化。如果你一开始说‘我们要做一款 300 美元的产品’，中途又改成‘其实得卖 150 美元’，那之前的很多时间就白费了。所以你需要预先想清楚想要什么，把这些——我喜欢叫 KPI，但本质上是目标——写下来，并尽量少改动。这非常难。事实上，这可能是最难的事，因为如果你做得好，优先级排得对，你就知道能不能发货，知不知道什么时候算完成。在硬件领域，一个挑战是——我们之前提到过要编译四五次。每次你制造并迭代设计，又要花三、四、五个月。所以你要在功能集、质量和时机之间权衡。在硬件领域，时机很重要，因为如果你比竞争对手早几周推出产品，你就能获得所有公关和关注。这很残酷。你比对手早发货的每一天都价值连城，可能值 1000 万美元——我瞎说的，我不知道。所以你必须平衡迭代次数。如果你一开始就知道目标并达成了，那你就知道可以发货了。而工程师们——我也常犯这毛病——尤其是硬件工程师，总觉得还没做完。所以这事很微妙。这是第一点。

Having your goals defined early and sticking to them is important. Hardware is not as adaptable to lots of changes throughout its development as anything digital. So if you set out to say, 'Okay, we want to make something that's $300,' and then halfway through you say, 'Oh, it actually has to be $150,' you've almost burned a lot of that early time. So you need to have a sense of pre-thinking what you want and having those—I like to call them KPIs, but essentially goals—written down and try to change them as little as possible. That is very tough. In fact, that may be the toughest thing because if you do that properly and have the right prioritization, you know whether you can ship or not, you know whether you're done. And in hardware, one of the challenges is, we talked about compiling four or five times. Every time you build and iterate your design, that's another three months or four months or five months or whatever it might be. So you're trying to time the feature set with the quality with the timing. And in hardware, timing is important because if you come out with your product a few weeks before your competitor, you might get all the PR, you might get all the interest. It's pretty brutal. And so each of those days that you ship before your competitor is worth a lot of money. It might be worth $10 million to you. I'm making this up, I don't know. So you have to balance that with how many times you iterate. And if you know what your goals are up front and you hit them, then you know you can ship. And often engineers, and I'm guilty of this too, especially on the hardware side, never feel like they're done. So this is a pretty nuanced thing. So that's one thing.

第二点是，我们往往先设计自己知道怎么做的部分。但实际上正确的方法是先设计最难的部分。举个例子——这里不涉及知识产权，所以我不会分享任何内部信息——有一次，我们需要把线缆穿过一个铰链，那是在我们制造的一款笔记本电脑里。因为不确定线缆能否放得下，架构师就从那里开始。他研究了横截面直径和如何分线，在最终确定铰链设计之前确保线缆能放进去。很多人会从他们熟悉的部分开始，比如‘我们要用这个显示屏，所以先把它放进 CAD 里，再做其他事’。但最优秀的架构师会先看哪里是瓶颈、哪里可能失败，然后从那里开始做详细设计。

The second thing is we tend to design the things that we know how to design first. And actually the right approach is to design the hardest parts first. One example—and there's no IP here, so I'm obviously not going to share any internal IP—but at one point we had to route cables through a hinge in a device, in a laptop we were making. And because it wasn't clear that those cables would fit, that's where the architect started. He looked at the cross-section diameter and how to split the cables out and made sure that they would fit before finalizing the hinge design. A lot of people would start at the part they knew, like 'we're going to use this display, so I'm going to put this in CAD and do all this other stuff.' But the architects who are the best actually look at where are the pinch points, where is this going to fail, and they start to do the detailed design there first.

另外几点：客户接触或交互最多的部分需要比其他部分多得多的迭代。比如在电脑上，触摸板用得最多，其次是键盘。所以这些部分必须非常好，手感好、响应正确、高度可靠。而其他更外围的部件可能不需要那么多迭代。所以你要在人们接触或交互最多的东西上加大迭代力度。这些是我写过的一些原则，但都是快速制造中学到的东西。

And then a couple other points: the part that your customer touches or interacts with the most needs way more iteration than everything else. So easy on a computer, you touch the trackpad the most, and then maybe the keyboard next. So those things have to be really good. They have to feel good, respond properly, be highly reliable. And then maybe the other pieces further out don't take quite as much iteration. So you have to boost your iteration on the things that people touch the most or interact with the most. So those are kind of some principles I wrote about. But these are just things that you learn trying to build quickly.

最后一点，对正在做硬件的人来说至关重要：永远不要等待。时间永远不够。所以如果你知道需要做什么，我从苹果的 Shelley Goldberg（现在应该是副总裁了）和 Kate Berseron 那里学到的是：现在就做。任何你知道需要做的事，现在就做，因为两天后就会有意外出现，你需要时间来解决。所以这种把已知要做的事堆起来、即使理论上还有时间也赶紧做完的作风，就是我从她们那里学到的‘无情效率’。

And the last piece that's really critical if you're making hardware—for folks out there who are trying to make hardware—is you can't wait around ever. Like there's never enough time. So if you know that you need to do something, what I learned from folks like Shelley Goldberg at Apple now, who I think is a VP now, and Kate Berseron when I was there at Apple, is you need to do it right now. Anything you know you need to do, you need to do right now because in two days there's going to be a surprise coming around the corner that you need that time to fix. And so this sense of stacking the things that you know you need to do and just getting them out of the way even if you technically have more time is this kind of ruthless efficiency that I learned with them.

太棒了。我来总结一下你的建议。第一，目标要非常清晰——这个我想再聊聊。第二，先做最难的部分——也就是物理制造上风险最大的部分。第三，聚焦人们用得最多的部分，比如触摸板、键盘——这个我也想谈谈。第四，现在就做。即使你觉得时间还多，你永远不知道意外什么时候来。

Amazing. Okay, let me just summarize your advice here. So one is be very clear on goals. I want to come back to this. Two is do the hardest part first—the riskiest piece essentially to physically build. Three is focus on the pieces that people will use most, say the trackpad, a keyboard. I want to talk about that. And four is just do it now. Like even if you think you have more time, you never know what's around the corner.

其实不只是你不知道意外什么时候来。如果你在做硬件，你实际上根本没有更多时间。

You just don't—it's not even that you don't know what's around the corner. If you're working in hardware, you actually don't have more time.

好的。关于目标，有哪些目标类别？你提到了成本——比如我们要控制在 300 美元以下。人们还应该考虑哪些其他类型的目标？

Okay. On the goals, what are kind of like buckets of goals? So cost is one you shared—like we need this under $300. What are some other buckets of types of goals people should be thinking about?

在 VR 领域，显示分辨率或弧分——比如每度多少像素——实际上是关键指标之一。所以你需要理解你的关键指标是什么。为什么这很重要？因为这关乎你的视场。想想 MacBook 上的视网膜显示屏。他们确定了人眼能看到的 KPI，可能还稍微超标了一点，然后造了出来。在那之后，你还需要在显示分辨率上投入那么多工程压力吗？可能不需要了。而 VR 还没达到那个水平，还差得远。量产 VR 还没有视网膜显示屏。所以提升分辨率就是一个例子。

So in VR, display resolution or arc minutes—like how many pixels per degree do you want—is actually one of the key metrics. So you need to understand what your key metrics are. And why is that key? Well, that's your visual field. So you think about retina displays on MacBooks. They figured out the KPI of what the human eye could see, probably overshot it a little bit, and built that. And then do you really need to keep as much engineering pressure up on the resolution of a display after that? Maybe not. So VR is not there yet, not even close. Not in mass-produced VR—we don't have retina displays yet. So that is one aspect of pushing that up is one example.

我认为在计算机上，你显然会谈论时钟速度、能并行运行多少进程、重量、价格和功能。所以当我们做 MacBook Air 时，因为我们在加工它，所以很明显某些功能比如环境光传感器已经不再有意义了。因此，愿意为了我们追求的重量和尺寸而舍弃它们。如果你有这些总体目标，你实际上可以很快做出工程决策。这其实是 Elon 做得非常好的地方，我听说他定义了每克重量相对于成本的价值，或者他基本上做了工程比率，并且能够为这些比率设定数值，我认为这非常聪明。

I think on a computer obviously you're talking about clock speed, you're talking about how many parallel processes you can run, you're talking about weight, you're talking about price, and you're talking about features. So when we did the MacBook Air, it became very clear because we were machining it that there are certain features like an ambient light sensor that just didn't make sense anymore. And so being willing to just jettison them for what we were going for, which was weight and size. So if you have those overarching goals, you can actually make engineering decisions pretty quickly. And this is actually something that I think Elon I've heard does very well is define the value of a gram of weight versus the cost or he does engineering ratios essentially and he's able to put numbers on what those ratios should be which I think is really smart.

有意思。所以这是一个非常简单的权衡。好的，公式告诉我们重量在这种情况下不那么重要。

Interesting. So it's a very easy trade-off. Okay, here's the formula telling us weight is less important in this case.

是的。如果你能做到这一点，那么决策就很容易了。

Yeah. And if you can do that, then the decisions fall out pretty easily.

说到 Air 和重量，我记得史蒂夫·乔布斯传说中有一个非常经典的时刻，他走出来，拿着一个马尼拉信封，里面装着 MacBook Air，然后把它拿出来，每个人都觉得‘不可能’。你参与了吗？那是人们从一开始就想做的事情吗？

Speaking of the Air and weight, I remember I feel like there's a very classic moment in Steve Jobs lore where he comes out and has this Manila envelope and has the MacBook Air inside it and then takes it out and everyone's like, 'No way.' Were you a part of that? Was that something that people wanted to do from the beginning?

如果我没记错的话，最初的 MacBook Air 是一个产量很低的设备，经过加工，但更像是一个概念验证。我想那就是马尼拉信封的那款，侧面有一个开口可以露出端口，底部有某种形状。然后下一个版本就是我们熟知的 MacBook Air，基本上是楔形，这不一样。楔形是我参与的那款，产量也更大，但马尼拉信封那款证明了你可以看到一台电脑，所以它们在路线图中都扮演了非常重要的角色。

I think if my memory serves, the very very very first MacBook Air was a pretty low volume device that was machined but kind of had a proof more of a proof of what could be done. And that was the Manila envelope one I think where the side door opened out to give you the port and it kind of had a shape underneath. And then the next rev of that was the MacBook Air that we know which was essentially wedge shaped which is different. And so the wedge shape is the one that I worked on and the one that went and hit more volume but that Manila envelope one was the one that proved you can see a computer and so they all each have really important roles in the road map.

回到你关于关注人们最常用功能的观点。众所周知，苹果搞砸了键盘。蝴蝶键盘问题持续了很长时间。你看起来要睡着了。发生了什么？Caitlyn，发生了什么？

Coming back to your point about focusing on things that people use the most. Famously Apple screwed up this keyboard. There was this butterfly keyboard situation for a long time. You're like your eyes are closing. What happened? What happened Caitlyn?

我没有直接参与那个键盘的工作。所以我不能谈论发生了什么。但显然这是你必须做对的事情。我会说现代 MacBook 键盘很棒很出色，我不知道那件事是怎么回事。我不认为那些是我当时在做的设备。

I didn't work directly on that keyboard. So I can't talk about what happened with it. But obviously this is something that you got to get right. And I will say like the modern MacBook keyboards are awesome and excellent and I don't know what happened with that. I don't think those were devices I was working on at the time.

安全了。沿着这个思路，苹果以不听用户想要什么而闻名。这是史蒂夫·乔布斯的经典做法。他不会到处做用户焦点小组、用户研究。却仍然能制造出极其受欢迎的产品。你认为他们做对了什么？或者他们做了很多用户反馈会议之类的事情吗？最终是如何运作的？

Nice safe marked safe. Along these lines, Apple's kind of famous for not listening to what people want. This is a classic thing with Steve Jobs. He's not walking around doing user focus groups, asking doing user research. Somehow continues to build incredibly popular products. What do you think they do right that allow or do they do a lot of user feedback sessions, things like that? How does it end up working out?

已经很久了。我的意思是，我十多年前就离开了。我不知道他们现在在用户反馈方面做什么。不过我认为这一点被误解了，Lenny。我认为这句话的意思是，如果你想创造新东西，客户不知道他们想要什么，因为他们没见过。一个很好的例子是 iPhone，我没有参与，但当你制造一款带触摸屏的新 iPhone 时，你不能真的去问 100 个人他们想要什么，因为他们会说屏幕上有键盘。我认为这就是你提到的精神，这对任何开发具有新功能的新产品的人来说都是如此。我尽量组建团队去开发有新颖之处的产品，要么是新品类，要么是新制造工艺，或者以前没做过的事情。当你考虑这一点时，你不能真的使用从同一领域和同一产品类别中学到的东西。这行不通，因为你实际上得不到正确的答案。我认为这其实就是史蒂夫所说的，如果你从根本上改变某样东西，你就无法获得直觉。你的客户不知道他们想要什么，因为他们没见过。但如果你展示给他们看，他们绝对会知道这很棒，这就是他们想要的。但如果你陷入与客户的迭代反馈循环中，就很难从零到一创造新东西。所以在我看来，虽然我不确定，我没有和他谈过这个，但这就是我对那句话的理解。

It's been a long time. I mean, I left over a decade ago. I don't know what they're doing now in terms of user feedback. I think this one gets misinterpreted though, Lenny. I think that what is being said is if you want to build something new, customers don't know what they want because they haven't seen it. So a good example is the iPhone, which I didn't work on, but when you build a new iPhone with a touchscreen, you can't really go ask 100 people what they want because they're going to say a keyboard on their screen. And this is, I think, the ethos that you're getting at, which is, and this is true for anybody building a new product with a new feature. And I've tried to build as much as I can teams that work on products that have something new about them. Either they're a new category or there's a new manufacturing process or something that hasn't been done before. And when you're thinking about this, you can't really use what you learned from the same field and the same product class. Like it just doesn't work because you actually won't get the answer right. And I think this is actually what Steve was talking about, which is you can't get intuition if you're changing something fundamentally. Like your customers won't know what they want because they haven't seen it. But if you show it to them, they will absolutely know that it's awesome and that it's what they want. But if you get stuck in an iterative feedback cycle with your customers, it's very hard to go zero to one with something new. And so in my view, and I don't know for sure, I didn't talk to him about this, but that's my view of what that means.

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我要转向一个完全不同的方向，回到硬件组件。我问了一堆人该和你聊什么。其中一个人是 Madic 的创始人兼 CEO Mahul Nari Nari Wala。我从未大声说过他的姓，希望我没念错。顺便说一句，我爱我的 Madic。不知道你有没有 Matic，它就像

I'm going to go in a completely different direction coming back to the components of hardware. I asked a bunch of people what to talk to you about. One of the people is the founder of Madic, the CEO of Madic, Mahul Nari Nari Wala. I've never said his last name out loud, so I hope I didn't butcher it. By the way, I love my Madic. I don't know if you have a Matic, but it's like

我有两个，还买了两个送人

I have two and I've purchased two more for

天哪，这真是强有力的推荐。是的。基本上，它就是一个很棒的机器人吸尘器，就是好用。是的。

Oh my god, what a what an endorsement. Yeah. Basically, it's like this amazing robot vacuum that just works. Yeah.

所以，他的问题，他想问你，他建议问你这个关于内存价格的问题。他描述的方式是，有一颗名为内存价格的小行星正朝消费硬件、机器人和物理 AI 袭来。这是怎么回事？

So, his question, so he wanted to ask you and so he suggested ask you this is about memory prices. The way he described it is there's a meteor called memory prices that are coming for consumer hardware and robotics and physical AI. What's going on there?

是的，我们整个行业都麻烦大了。

Yeah, we're in trouble as an industry.

我认为这与 AI 有关。而且我也认为供应链受到限制。我一直在建议初创公司和公司提前购买内存，并在他们负担得起的情况下储备足够的内存，以应对价格飙升。就像这类事情中的任何一样，这在 COVID 期间也发生过。所以我们当时有很多供应链中断，获取足够的内存是挑战之一。所以我们也不得不提前购买。我不会说是谁，但我合作的那家公司也不得不提前购买内存。所以这就是我今天想和你讨论的一部分：这些供应链中断。如果一个关键组件（如内存或芯片）受到限制，你能做的就不多了。你要么付高价，要么已经提前购买了足够多的库存来渡过难关。这是唯一真正的选择。显然，提前购买有风险，价格可能会下跌。我认为挑战在于，像内存这样的供应链存在延迟，它往往无法足够快地适应需求，或者出现新的产品类别，或者在这种情况下，可能是数据中心消耗了太多内存，而且它们实际上不像消费电子领域的公司（如 Maddic）那样对成本敏感，所以它们会为这些更高的成本买单。这很棘手，是我们一直要处理的问题。

I think that AI has to do with why. And I also think that the supply chain is constrained. I have been advising startups and companies to pre-buy memory and to have enough memory in stock if they can afford it to ride out price spikes. Like anything in this category, this happened in COVID too. So we had so many supply chain disruptions and getting enough memory was one of the challenges. So we had to pre-buy as well. I won't say who, but the company I was working with had to pre-buy memory as well. And so this is part of what I wanted to talk to you about today: these supply chain disruptions. If a key component like memory or silicon is constrained, there's not much you can do. You either pay or you have already pre-bought enough that you can ride things out. Those are the only real options. Obviously, there's a risk to pre-buying and the price might go down. The challenge is I think there's a latency with supply chain in something like memory where it can't adapt fast enough often to demand, or there's a new category of product, or in this case maybe data centers that are just eating up so much and are actually not as cost-sensitive as somebody in consumer electronics like Maddic might be, and so they'll just pay for these higher costs. This is tricky and something we have to deal with all the time.

价格涨了多少？这个问题有多严重，然后你觉得它会怎么发展？

How much have prices gone up? Like how bad is this problem and then where do you think it'll go?

实际上，这是个很好的问题，Lenny。我不知道会发生什么。我认为价格可能会翻倍。我不知道时间线。如果我知道价格翻倍的时间线，我就会去交易了，但我不太擅长这个。如果我能预测这些事情，我就会做别的工作了。但可以肯定的是，我们将面临供应链冲击。

Actually, this is a great question, Lenny. I don't know what's going to happen. I think prices are going to double probably. I don't know on what timeline. If I knew what timeline the prices were going to double on, I'd be trading, which I'm not very good at. I'd be doing a different job if I could predict these things. But certainly we're going to have a supply chain shock.

而且它已经涨了很多。如果你说它会翻倍，但它已经涨了。我不知道。我看到过像 6 倍这样的数字。

And it's already gone up a lot. Like if you're saying it'll double, but it's already gone up. I don't know. I saw numbers like 6x and something like that.

哦，真的吗？我没意识到那么糟糕。我看到的那个数字不酷。你说得对，我认为据我所知，这是由 AI 驱动的，就像你需要的那样。当你谈到内存时，它就像 DRAM 之类的东西。当我们谈论内存时，内存是什么？那里发生了什么？

Oh, really? I didn't realize it was that bad. That's a number I saw was not cool. And you're saying yeah, I think from what I hear it's AI driven, just like you need. And when you talk about memory, it's like DRAM and things. What is memory when we talk about memory? What's going on there?

处理。你可以把它想象成处理内存。所以它移动得很快……你想到内存就像硬盘或固态硬盘，你在那里存储你通常不使用的文件，或者你正在处理的文件，比如文档或图片。可能那是在服务器上的冷存储，可能使用的是某个地方的硬盘。这通常是你不需快速访问的东西。但如果你运行一个程序，该程序的一部分实际上会在 RAM 中运行。所以显然有不同类型。对于服务器，有不同类型的服务器机架。一些服务器机架专注于这种内存，而一些服务器机架更专注于我们所说的冷存储或较慢的……这不是我的专业领域，但我构建的大多数产品，也许所有产品，都有 RAM，我们必须弄清楚如何……对我来说，这主要是一个封装问题：把它放在哪里，它是否需要可访问，你选择哪种 RAM，它需要多快，成本是多少，这通常是我们的权衡。

Processing. The way to think about it is like processing memory. So it moves very... You think about memory like on your hard drive or your solid state drive where you're keeping files that you're not using essentially in many cases, or that you're dealing with, you know, maybe documents or pictures that you have. Maybe that's in cold storage on a server, maybe that's using a hard drive somewhere. This is usually things that you don't need really fast access on. But if you're running a program, some of that program is actually going to be run in RAM. So there are different kinds obviously. For servers, there are different kinds of server racks. Some server racks are actually focused on this type of memory, and some server racks are focused more on what we consider like cold storage or a slower... Now this isn't my area of expertise, but certainly most of the products that I've built, maybe all of them, have had RAM and we've had to figure out how to... For me, it's mostly a packaging issue: where do you put it, does it need to be accessible, you know, which RAM do you pick, how fast does it need to be, and what is the cost is usually our trade-off.

那么更多 RAM 的瓶颈是什么？是不是因为需求太大，制造内存的公司无法以这个速度生产？

And what is the bottleneck with more RAM? Is it just the companies that make memory are just not able to produce at this rate because there's so much demand?

没错。正是这样。

That's right. That's exactly what's happened.

所以，这是一个很好的具体例子，说明制造硬件有多难。就像只要一个零件不可用，你的整个产品就完蛋了。

So, this is a really good specific example of just how hard it is to build hardware. So, this is just like all it takes is one piece to be not available and your whole thing is screwed.

是的。如果缺少一个组件，你什么都造不出来。

Yeah. You can't build anything if you have one component missing.

那么，以 Asatic 为例，他们需要组装多少个组件，而且不能有一个不可用？我在心里算了一下。他们可能有 50 到 150 个零件。可能更多。我没看过他们的 CAD，所以我不知道他们设备内部是什么样子，但他们确实有很多东西，对吧？他们有设备的轮子，显然在移动。然后他们有吸尘器，但也有拖把，显然他们有一个集尘袋。他们有水箱，液体从拖把进入。他们有一个系统，我认为是基于 SLAM 的，可以看见你的房间并绘制地图，识别哪个表面是什么，而且我相信这保持在设备上，不会上传到云端，这也是我们在 BR 所做的，我认为这是一个好的做法，好的隐私实践。然后他们当然有无线模块连接，这样你就可以与设备通信。他们会有一个 SoC、芯片、RAM、PCB。如果你把所有这些上面的东西都拆下来，比如 PCB 上的所有小电容等等，那么很容易就有数千个零件。所以，这取决于你怎么数。但这不是一个简单的设备。

So, let's say Asatic as an example, like how many components are there that they all have to assemble and not have one not available? I'm doing the math in my head. They probably have between 50 and 150 parts. It's possible that they have more. I haven't seen their CAD, so I don't know what it's like inside their device, but they do have a lot of things going on, right? They have the wheels of the device that are obviously moving around. Then they have a vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the reservoir that the liquid has to go in from the mop. They have a system which I think is SLAM-based which can see your room and make a map of it and identify which surface is which, and that I believe stays on the device so it doesn't go up to the cloud, which is also kind of what we did in BR as well, which I think is a good practice, good privacy practice. And then they of course have wireless modules that connect up so you can communicate with your device. They're going to have a SoC, silicon, they're going to have RAM, they're going to have PCBs. And if you take everything off of those things, like all the little caps off the PCBs and everything, then you're in the thousands of parts easily. So, it depends on how you count. But this is not a simple device.

而且只要一个零件不可用就够了。

And just all it takes is one piece to not be available.

是的。所以，想象一下，你是一个供应商，卖给你一个压铸组件，然后它倒闭了。你可以在三个月内找到另一个压铸组件，也许五个月内拿到大批量。这是可恢复的。但如果你的芯片没了，你买不到芯片了，你就得重新设计电路板，找到其他可能工作的东西。这是一个灾难性的重新设计。如果你得不到你想要的外形尺寸的 RAM，这就是我所说的灾难性重新设计。你现在必须重新设计产品的整个内部结构，然后为这些新东西确保供应链，在生产线上重新制造，重新测试，进行所有的可靠性测试。这绝非易事。所以这就是为什么我们关心这个。所以组件有一个层级。在消费电子领域，我们通常从芯片和显示屏开始，这在我的世界里通常是交货时间最长的。在机器人中，即使是原型制作，执行器也很难获得。有时需要一两个月才能买到执行器。

Yeah. So, imagine you're a vendor that sells you a component that's a diecast component goes out of business. You can get another diecast component in three months maybe, and at quantity in five months or something like that at high quantity. This is recoverable. If your silicon goes out, if you can't buy your silicon, you can't buy your chip now. You have to redesign your board and you have to find something else that might work. This is a catastrophic redesign. If you can't get the RAM you wanted in the form factor you wanted, this is what I call essentially a catastrophic redesign. You now have to redesign the entire guts of your product and then secure supply chain for these new things, build it again on the production line, test it again, do all the reliability testing. It is non-trivial. And so this is why we care. So there's a hierarchy of components. Often in consumer electronics, we start with silicon and the display, which are the longest lead time things usually in my world. In robots, actuators are pretty tricky to get even just for prototyping. Sometimes it takes a month or two to buy an actuator.

这就是为什么埃隆（马斯克）以自己动手制造一切而闻名。

This is why Elon famously just starts building it all himself.

嗯，当你看到他在特斯拉所做的，垂直整合供应链，还有著名的星链，那是一个更好的例子，我相信它基本上是芯片进、产品出。那是一个相当不可思议的工厂，我听说过。我希望有一天能去看看。但你知道，这就是垂直整合发挥作用的地方，因为如果你实现了垂直整合，很多组件都在内部生产，或者你在内部制造很多东西，你实际上能更好地适应供应链冲击。而且他著名的做法是，当硅片本身很难找到时，他能够在创纪录的时间内重新设计 PCB，并适应购买新的硅片，这对一个拥有更传统供应链的公司来说会是灾难性的。

Well, when you look at what he did with Tesla and verticalizing his supply chain and famously Starlink is an even better example of this where I believe it's like effectively silicon chips in, product out. That's a pretty incredible factory I've heard. I'd love to see it someday. But you know this is where verticalization comes into play because if you have verticalized and you have a lot of the components in house or you're building a lot of things in house, you can actually adapt to supply chain shocks better. And famously he did when the silicon itself was difficult to find, he was able to redesign his PCB in record time and adapt to buying new silicon, and that would be much more catastrophic for a company that had a more classic supply chain.

我想象中，在设计新硬件时，你必须做出的一个重大决定是，是使用现有的、便宜的可用组件，还是我们要做一些新的东西。这在软件中也是如此，是使用设计系统还是做新的东西。你在设计新硬件时如何考虑这种平衡？

One of the big decisions that I imagine you have to make when you're designing a new piece of hardware is deciding between using this available stuff, available components that are out there cheap versus okay, we're going to do this something new. It's something in software too, use like the design system or do something new. How do you think about that balance when you're designing a new piece of hardware?

很简单，我尽可能使用现成的，尤其是在原型阶段。因为在原型阶段，这是我们工作中非常重要的阶段，你的目标是证明它能工作。比如，你能让一个东西工作起来吗？所以通常它不必是最终漂亮的东西；可以是丑陋的版本。你可以制作一个最终产品外观的工业设计模型，但实际上我们称之为“工作样机”和“外观样机”，即这是它看起来的样子，这是它如何工作的，以及这是一个工作原型。人类在这方面相当擅长。只要——这是一个相当大的前提——你所展示的东西能融入工业设计。有时对于年轻的公司来说，情况并非总是如此，但那是我们的目标。所以在原型阶段，伙计，任何现成的东西，任何快速的，任何你能快速拿到的东西，然后保持对最终设计中真正适合的东西的感觉。它有能力吗？这些工艺、组件和材料是否真的能够适应你需要的这个尺寸、这个新重量？所以这是计算的一部分。当你进入量产和最终设计时，这取决于情况。我的意思是，如果我可以，如果我在制造医疗设备，我可以买一个现成的轮子或现成的组件，我绝对会买并安装进去。但通常我们做的是高度定制，因为我们又有那些 KPI。我想要这个尺寸，我想要这个重量，我想要这个颜色。而现成的零件往往不够好。不是因为它们不能用，而是因为它们不是专门为我们正在做的事情设计的。这就是为什么现在这些无人机如此便宜的原因：所有这些零件都是为其他东西创新、制造和规模化生产的，现在我们有了所有这些零件，我们可以组装一个非常便宜的无人机。

Very simply, I use off-the-shelf whenever I can, especially in the prototyping phases. Because in the prototyping phases, which are a really important phase of what we do, your goal is to show that it can work at all. Like, can you get a thing working? So often it doesn't have to be the final pretty thing; it can be the ugly version. You can make an industrial design model of what the final thing is going to look like, but actually we call it works-like, looks-like models, where you have this is what it's going to look and here's how it's going to work and here's a working prototype. And humans are pretty good at this. As long as, and this is a pretty big caveat, what you show could fit into the industrial design. Sometimes that's not for companies that are younger, that's not always the case, but that's what we're going for. And so in the prototyping phase, man, whatever works off the shelf, whatever's fast, whatever you can get to quickly and then maintain a sense of what's really going to fit in your final design. Is it capable? Are the processes and components and materials capable of actually adapting to this size, this new weight that you need it to go into? So that's part of the calculus. When you move into mass production and the final design, it depends. I mean, if I could, if I was making a medical device and I could buy an off-the-shelf wheel or an off-the-shelf component, I absolutely would and fit it in. But often what we're doing is highly custom because we have again one of those KPIs. I want it to be this size. I want it to be this weight. I want it to be this color. And often off-the-shelf parts aren't good enough. Not because they don't work, but because they're just not exactly designed for what we're doing. This is the reason these drones are so cheap now: there's all these parts that have been innovated and built and scaled, manufactured for other things, and now we just have all these things and we can assemble a really cheap drone.

是的。是的。完全正确。

Yeah. Yeah. Exactly.

太棒了。

Super.

你多次提到 CAD，这让我想到，CAD 已经存在很长时间了。那么 AI 是否在影响硬件构建的方式？显然，它正在极大地影响软件构建的方式。它是否改变了你和那些构建硬件和机器人的人的生活？

You've mentioned CAD a bunch of times and it makes me think about just like CAD has been around for a long time. Just like is AI impacting the way hardware is built? Obviously, it's impacting the way software is built in a huge way. Has it changed your life and the lives of people building hardware and robots?

是的。所以我想稍微放大一点视野。大部分硬件工作都投入到原型设计和 3D CAD 中。所以设计 3D 零件、装配体和组件，确保它们能正确配合。然后确保这些零件和组件可以由供应商批量制造，在所需的公差范围内，然后把这些东西组装起来。这就是我们的流程。目前，我们正处于 AI 能够做 CAD 的非常早期的阶段。所以，我给你举个例子。Claude 可以做基本上是曲面或点云的东西。这不是真正的 CAD。我世界里的真正 CAD 是密集的。比如，它有形状，它有“神经”。比如，你有一个曲面如何工作的方程。它是一个在 CAD 中设计的实体。它是一个实体。所以现在我们还没有达到 AI 做 CAD 的程度。我认为很可能在某个时候我们会达到。这可能是我们领域最大的变化之一，我希望能够进行快速设计并提高速度。现在 CAD 中有很多有趣的事情要做，但在我职业生涯的早期，我们必须做定制螺丝，我们必须为所有东西做 2D 图纸，CAD 中有很多不那么有趣的事情。公差叠加。我们需要它们。七个零件如何配合在一起，它们是否总能正确配合？但这并不有趣。这不是最有趣的部分。也许对我们中的一些人来说，但对我不是。所以做这些事情，能够用 AI 做这些事情会很棒。这样你就可以专注于做真正有趣的事情。另一个好处是 PCB，印刷电路板内部有很多层，然后顶部有元件。如果你曾经打开过像计算器或电脑之类的东西并看过内部，你就知道我在说什么。这些印刷电路板。看起来 AI 越来越擅长在这些板内部布线，而且看起来 AI 将能够做一些基本的元件选择和布局。所以这就是我们目前的情况。我们还没有到，Lenny，日常的机械或电气工程，比如它的核心部分，由 AI 来完成。但是作为工程师，你可以在策略、规划、思考你所面临的复杂依赖关系方面大量使用 AI。这就是我现在用它做的事情，真正的高层规划，询问信息。比如，当我查看还有谁制造过类似的产品时，我用 AI 来构建数据库，它们并不完美。当然很多时候会有错误，但它快得多。AI 目前在 Excel 方面相当不错。当然，Excel 是工程中我们最喜欢的工具之一。所以能够快速制作和修改 Excel 电子表格的能力，听起来不性感，但实际上大大加快了核心部分之外的设计过程。我喜欢 Excel 总是出现在每个人的任何事情的底层，无论我们在做什么。我们要去火星了。可能有一个 Excel 电子表格在驱动着很多事情。

Yeah. So I want to zoom out a little bit. So most of the hardware work goes into prototyping into 3D CAD. So designing 3D parts and assemblies and components and making sure they work together properly. Then in making sure those parts and components can be made by a vendor at quantity, that it's possible and in the tolerances we want, and then putting those things together. So that's kind of our process. Right now, we're right at the very, very beginning of AI being able to do CAD. So, I'll give you an example. Claude can do what is essentially surfaces or point clouds. This is not real CAD. Real CAD in my world is dense. Like, it has shape. It has nerves. Like, you have an equation for how the surfaces work. And it's an entity that's designed in CAD. It's a solid entity. And so right now we're not quite there with AI doing CAD. I think it's likely that at some point we will be there. This will be probably one of the biggest changes for my field that we have, being able to, I hope, do rapid design and increase the speed. Now there's a lot of really fun things to do in CAD, but like in the beginning of my career, we had to do custom screws and we had to do the 2D drawings for everything and there's a lot of things in CAD that are not as fun. Tolerance stacks. We need them. How does seven parts fit together and are they always going to fit together properly? But it's not fun. It's not the most fun part. Maybe for some of us, but not for me. And so doing these things, being able to do these things in AI would be amazing. So you can focus on actually doing the fun stuff. Another good thing is PCB, a printed circuit board has a lot of layers in the inside and then components that go on the top. And if you've ever opened anything like a calculator or a computer and looked inside, you know what I'm talking about. These printed circuit boards. It's increasingly looking like AI can route inside of these boards pretty well and it's looking like AI is going to be able to do some basic component selection and layout on these boards. So that's the kind of where we're at right now. We're not at a point, Lenny, where day-to-day mechanical or electrical engineering, like the meat and potatoes of it, is being done by AI. But there's a huge amount that you can do as an engineer using AI in your strategy, your planning, your ability to think through the complex dependencies that you're facing. And that's what I use it for now, which is really high level planning, asking for information. Like when I look at who else has made a product like this, you know, I use AI to build the databases and they're not perfect. Certainly a lot of times something's wrong, but it is so much faster. AI is pretty good in Excel right now. And of course, Excel is one of our favorite tools in engineering. So the ability to actually rapidly make Excel spreadsheets and change them is, it doesn't sound sexy, but actually really speeds up the design process outside of these core pieces. I love how Excel is always at the bottom of everyone, anything, no matter what we're doing. We're going to Mars. There's an Excel spreadsheet that's probably driving a lot of this.

所以，你分享的有趣之处在于，它已经影响了构建硬件和机器人的工作，但如果能真正应用到 CAD，它即将带来变革。

So, what's interesting about what you shared is it has already impacted the work of building hardware and robots, but it's on the verge of being transformative if it can get to real CAD.

是的。我的大问题是，这需要什么条件？很多 AI 基于 LLM，它们本质上是单词生成器、单词猜测器。它们比这更复杂，但本质上就是这样。还有基于视频训练的视频模型。但这些模型不理解我需要的：比如，你拿一张纸，折四次，然后这样做——打开后洞会在哪里？LLM 甚至视频模型都不知道怎么做。它们缺乏理解摩擦力、重量、接触、压力、表面纹理的能力。它们就是做不到这些。这是工程中构建东西所需的核心。一些世界模型未来可能做到这一点。我怀疑我们可能需要这些模型作为 CAD 和其他物理工程工作的基础。我的挫败感——一种健康的挫败感——是我想要工程的编解码器，硬件工程的编解码器。它非常有价值，我在其他方面用了很多，但我想要在我的领域里用。这可能需要的是一种新模型类型。

Yeah. And my big question is what is it going to take? A lot of AI is based on LLMs, which are essentially word generators, word guessers. They're more complicated than that, but that's essentially what they're doing. There are also video models trained on video. But these models don't understand what I need: for example, you take a piece of paper, fold it four times, and do this—where will the hole be when you open it back up? LLMs and even video models don't know how to do that. They lack the ability to understand friction, weight, contact, pressure, surface texture. They just can't do these things. This is the core of what we need in engineering to build things. Some world models may be able to do this in the future. I suspect we may need those models to be the base of CAD and other physical engineering work. My frustration—a healthy frustration—is that I want Codex for engineering, for hardware engineering. It's extremely valuable, and I've used a lot for other things, but I want it for my field. What it may require is new model types.

对我来说这听起来像是一个机会。我知道有很多世界模型公司。李飞飞和 World Labs 上过播客。我知道谷歌在构建 Gemini。你觉得这些是正确方向，还是我们需要一些真正不同的东西？

Sounds like an opportunity to me. I know there are a bunch of world model companies. Fei-Fei was on the podcast with World Labs. And I know Google is building Gemini. Do you feel those are the right directions, or do we need something actually different?

我其实不知道李飞飞在做什么。但显然她是一位杰出的机器人学家，我很想了解更多她在做的事情。我得去查查。我看到的是，我们现在拥有的和我们正在构建的模型将是解决方案的一部分，但不是全部。

I don't actually know what Fei-Fei is working on. But obviously she's a brilliant roboticist, and I'd love to learn more about what she's doing. I'll have to look that up. What I've seen is that what we have right now and what models we're building will be part of the solution but not all of it.

回到机器人和人形机器人，我们之前聊过。你的感觉是，人形机器人不一定是许多问题和机会的答案。谈谈你对人形机器人和非人形机器人的看法。

Coming back to robots and humanoids, something we were chatting about earlier. Your sense is humanoid robots aren't necessarily the answer to many problems and opportunities. Talk about your sense of humanoids versus non-humanoid robots.

是的，我认为人形机器人存在一个炒作周期。这并不意味着它们不有趣，我认为会有赢家。但我经常听到的是‘我想要一个通用机器人形状来做所有事情’。我不认为这行得通。我认为你需要不同类型的机器人来做不同的事情。例如，如果你有一台笔记本电脑，想把键盘拧到外壳上，这不是人形机器人的工作。这是一个专用制造机器人的工作，它被设计用来为那款特定笔记本电脑的外壳拧十颗螺丝，并且你希望每天做一万次。那是专用机器人。我认为有趣的是，你可以为自动化机器人设计标准机柜尺寸，并让它们随时间可修改。这将是一个非常有趣的领域：如何让制造机器人适应性强且可更改。但你不希望人形机器人来做这个。当你看看中国顶级一级供应商的现代制造工厂时，生产线上已经没多少人了。整个印刷电路板生产线基本上无人化。裸板经过回流焊、检查，全部无需人工，除非出问题。在机械装配中也是如此。最先进的产线过去有 200 人，现在可能只有 10 人。我们已经在这类先进制造中超越了人工劳动。我们实际上不需要用人形机器人取代人类。我们只需要更多专用机器人。我的猜测是，我们会有人形机器人来处理一些人类目前做的长尾工作。那会很重要。但我们也会有用于建筑、电气工作、极低产量装配、可能物流的机器人，而且它们大多数看起来会各不相同。

Yeah, I think there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting, and I think there will be winners there. But what I hear a lot is 'I want a generalist robot shape to do everything.' I don't think that works. I think you need different types of robots for different things. For example, if you have a laptop and you want to screw the keyboard to the case, that's not a job for a humanoid. It's a job for a dedicated manufacturing robot designed to screw ten screws into a case for that specific laptop, and you want to do that 10,000 times a day. That's a dedicated robot. What I think is interesting is you can have standard cabinet sizes for automation robots and make them modifiable over time. That will be a very interesting field: how to make manufacturing robots adaptable and changeable. But you wouldn't want a humanoid to do that. When you look at a modern manufacturing facility in China, at the top tier of tier-one suppliers, there aren't many people on the line anymore. The entire printed circuit board line is essentially unmanned. The raw board goes through reflow, gets checked, all without humans unless something goes wrong. In mechanical assembly, the same thing. The most advanced lines used to have 200 people; now they might have 10. We've already moved past human labor in much of this advanced manufacturing. We don't actually need to replace humans with humanoids. We just need more dedicated robots. My suspicion is we'll have humanoids for some long-tail things that humans currently do. That will be important. But we'll also have robots for construction, electrical work, very low volume assembly, maybe logistics, and most of them will look different from each other.

这完全说得通。听你这么说，我觉得会有一个重大时刻，当机器人可以建造其他机器人，以及你提到的 CAD 这一点——一旦 AI 能为硬件开发完整设计——那将是一个重大时刻。你觉得我们离这个机器人互相建造和设计的循环有多近？

That makes all the sense in the world. As you talk about this, I think there's going to be this big moment when a robot can build other robots, and this CAD point you make about once AI can develop full designs for hardware—that's going to be a big moment. Do you have a sense of how close we are to this loop of robots building and designing each other?

如果你说的是机器人建造与自己不同的机器人，是的，我认为那会发生。但术语很重要。我不认为会有一个机器人建造自己。那不是它看起来的样子。但让 AI 能够从 2D 图片到复杂的 3D CAD，到装配体，到与供应商沟通，到获得他们的反馈，到迭代——这在未来是可能的。一开始它会和我们自己做一样好吗？不会。但它会发生。最大的挑战，Lenny，实际上是数据。CAD 数据是任何人拥有的最有价值的 IP 之一。三星或美敦力——拿美敦力来说——他们不会想把他们的 3D CAD 交给模型供应商来教它如何做出好的 CAD。这是专有的，是秘诀。那么这些数据从哪来？这是一个大问题。这就是为什么我认为爱好者是一个更有趣的起点——他们不关心 CAD 的神圣性或去向。他们只想做点东西，并得到帮助更快地完成。

If you're talking about robots building robots that are different from them, yes, I think that will happen. But terms matter. I don't think there will be one robot that builds itself. That's not what it will look like. But having AI be able to go from a 2D picture to complex 3D CAD, to assemblies, to communication with vendors, to getting their feedback, to iterating—that is possible in the future. Will it be as good as us doing it in the beginning? No. But it will happen. The biggest challenge, Lenny, is actually the data. CAD data is some of the most valuable IP anyone has. Samsung or Medtronic—to pick on Medtronic—they won't want to give their 3D CAD to a model vendor to teach it how to make great CAD. This is proprietary, the secret sauce. So where will this data come from? That's a big question. That's why I think hobbyists are a more interesting place to start—they don't care about the sanctity of their CAD or where it goes. They just want to make something and get help making it faster.

所以这就是我感兴趣的地方，也许一个爱好者并不是印刷电路板设计的专家。也许他们不在乎。他们只是想让自己的无人机更快，打败另一个人的无人机之类的。我认为这就是这一切开始的地方。然后大公司可能会慢一些，因为他们有专门的工具和大量的知识产权隐私。

So this is kind of where I'm interested in this starting which is, you know, maybe a hobbyist isn't an expert in printed circuit board design. Maybe they don't care. They just want their drone to be fast and to beat this other guy's drone or whatever. This is where I think you're going to start seeing all this start. And then probably the big incumbents are going to be slower because they have dedicated tools and a lot of IP privacy.

关于 AI 模型需要什么样的数据来训练，这个想法真的很有趣。我听说实验室在购买 2021 年之前的 GitHub 代码库，因为那是在 AI 影响代码之前，人类编写的代码越来越少。这些数据标注公司，比如 Mercor、Surge 和 Handshake，感觉这是一个巨大的机会，它们可以出售数据，创建这些 CAD 文件。

It's really interesting this idea of what data AI models need to train on. I've been hearing that labs are buying code like GitHub repos pre-2021 because that's before AI has impacted the code since there's less and less human-written code. And these are data labeling companies like Mercor, Surge, and Handshake. It feels like this is a big opportunity that might emerge as them selling data, creating these CAD files.

完全同意。我认为一个非常好的想法是拥有一个可以本地部署的 AI 系统。也就是说，放在公司自己的数据中心里，然后用他们的数据来训练。我认为这在未来最终是可行的，但你需要大量的 CAD 数据。所以你需要一个基础模型，它包含大量的 CAD 数据。我们必须想办法做到这一点，这将会非常有趣。然后我们还要想办法把它安全地放在公司内部，让他们用自己的数据来训练。我不知道这是否会类似于 MCP 层，但长期来看似乎是可行的。

Absolutely. And one really great idea I think would be to have an AI system that can go on-prem. So be inside of a data center that the company owns and then train it with their data. That I think could work eventually in the future, but you need a lot of this CAD data. So you're going to need a base model that has a lot of CAD data. We'll have to figure out how to do that. That's going to be very interesting. And then we're going to have to figure out how to put it inside safely inside essentially the walls of companies and have them then train it on their own data. I don't know if that's going to be like the equivalent of an MCP layer or what that's going to be, but this seems doable in the long term.

我想问我妹妹建议的一个问题。她其实是个长期的 VR 爱好者。她曾在 Oculus 工作，在收购时加入。她帮助创建了很多 VR 内容。她在 VR 领域待了很长时间，现在在做其他事情。她想让我问你，要创造一个让人类感觉有连接感、像人一样的机器人，需要什么？

I want to ask a question that my sister suggested. She's actually been a longtime VR person. She was at Oculus. She joined with the acquisition. She helped create a lot of content within VR. She's just been in the VR world for a long time and now she's working on other things. She wanted me to ask you, what does it take to create a robot that feels human and connected that humans feel connected to?

这是个好问题。相对来说，我在机器人领域是新手。所以我必须尽可能快地学习。其中一位对我帮助最大的研究员叫 Leila Takayama，她是这方面的专家。她向我解释，人类对于其他生物进入空间时的反应有某种预期。当有人走进房间时，你通常会有所表示。你可能不会说话，但你会抬头看。我们之间有很多非常复杂的非语言暗示。如果你走进房间，机器人只是呆在那里……那很 creepy，而且很容易变得 creepy。我有点惊讶，除了少数例外，现在很多类人机器人都很 creepy。我认为，这些设备应该是不具威胁性的。一般来说，你希望它们看起来柔软，对你有所反应。你希望它们知道你在那里，它们关注你，它们在那里帮助你，让你的工作生活更轻松。而且你还期望它们在行动之前表现出意图。所以我学到的一件事是，如果机器人突然转身做各种事情，会吓到你。但如果机器人先看再转身，那就没那么吓人了。所以有所有这些小细节。我建议大家都去看看她的研究。这里有很多很棒的研究，不仅仅是关于人形机器人，而是关于如何让任何机器人在社交环境中对进出房间的人做出适当反应，同时通过身体动作传达意图，以免吓到你。

It's a great question. So, I'm new, relatively speaking, to robotics. And so, I had to learn as much as I could, as fast as I could. And one of the researchers that helped me the most, her name's Leila Takayama. She's an expert at this. And what she explained to me is that humans have a certain expectation about how other beings are going to respond when they enter a space. You really want to, when someone walks into a room, you kind of acknowledge them. You might not talk to them, but you kind of look up. There are a lot of very complex nonverbal cues that we give to each other. And if you walk into a room and a robot's just like... it's creepy and it's easy to be creepy. I'm a little surprised with some notable exceptions how creepy a lot of these humanoids are right now. You want, I think, these devices to be non-threatening. Generally speaking, you want them to appear soft. You want them to appear reactive to you. You want to have a sense that they know that you're there. That they're attentive to you, that they're there to help you and make your work life easier. And you also expect them to show their intent before they do something. And so one of the things I learned is if a robot just suddenly turns and does all this stuff, it scares you. But if a robot looks before it turns and then goes, it's much less alarming. So there's all these little pieces. And I recommend anyone to go look at her work. There's a lot of great research here about how to not necessarily with a humanoid, but how to have any robot both respond properly in a social context with someone entering a room or exiting a room, but also transmit its intent physically so it doesn't surprise you.

感觉我们可以从皮克斯和动画工作室那里学到很多，它们已经思考这个问题很久了。

Feels like there's a lot we can learn from Pixar and animation studios that have thought about this a long time.

是的，我实际上认为皮克斯、迪士尼可能是世界上最擅长这类设计工作的。尽管它们在实体产品上做得不多，但如果你看看它们如何通过角色展示情感、意图、亲和力和参与感，它们真的是世界级的。我不知道你怎么想，但我很兴奋能有一个机器人在家做事。就像他们开始发布的那些视频，机器人可以洗碗，至少原型机能叠衣服。就像，是的，请来帮我做这些。你对家里有机器人怎么看？

Yeah, I actually think Pixar, Disney are probably the world's best at doing this type of design work. Even though they haven't done as much in physical volume, if you look at what they do and how they show emotion, intent, approachability, engagement, with their characters, they're really world-class. I don't know about you, but I'm so excited to have a robot at home doing things. Like these videos that they're starting to put out where they're doing dishes, at least the prototypes can fold laundry. It's like yes, please come do this for me. How do you feel about robots in your house?

我很喜欢。我伴侣就不那么喜欢了。我很幸运有一个标准很高的伴侣，她以前从不坐 Waymo。坐了一次 Waymo 后，现在再也不想坐别的了。所以她肯定愿意更新她的立场，但必须得非常好。她很喜欢 Matic，你知道，它很棒。所以就是这样，但标准很高。所以我认为，要有一个家用机器人，它必须非常出色，她才会愿意让它进家门。但我把这视为一个挑战。

So I'm into it. My partner not so much. So I'm very lucky to have a partner who's got a high bar which means she was like never going to take Waymo. She took one Waymo and now never wants to take anything else. So definitely willing to update her position, but it has to be pretty good. So she's in love with the Matic, you know, it's amazing. So it's that but the bar is pretty high. So I think in order to have a home robot, it's going to have to be pretty incredible for her to be willing to have it in our home. But I take that as a challenge.

我妻子完全一样。她说，‘我想要这个东西在我们家，Matt。’哦，哇。这太可爱了。最近的一个例子是自动驾驶特斯拉。她以前总是说‘不，别那样做’，一开始并不好，现在她说‘我不想开任何其他车了。开自己的车感觉太荒谬了。我再也不想那样做了。’变化之快令人疯狂。所以在我看来，这有很大的不同。这是一个巨大的类别差异。一辆更安全的自动驾驶汽车和人类驾驶的汽车之间有很大区别，因为你有人类驾驶汽车的存在证明和数据。当谈到家庭时，增量是什么？你现在有了一个以前没有的东西来做事情。所以如果它做得不好，你在拿什么做比较？如果它在任何方面不安全，你又在拿什么做比较？在我看来，要让很多人接受，这比汽车难得多，因为你可以说，‘嘿，Waymo 拯救了生命。’你知道，使用 Waymo 的死亡人数会少很多，无论你是不是乘客。当你看到旧金山的人们已经调整了他们对 Waymo 和其他汽车的反应方式，你就会看到基于信任的行为改变，这真的很酷。当你谈论一个尚未存在且本质上不是替代品的新产品时，那就更难推销了，你需要一个不同的故事。

My wife is exactly the same way. She's like, 'I want this thing in our house, Matt.' Oh, wow. This is so cute. A recent example is self-driving Tesla. She used to be like 'no, don't do that' and it was not that great originally and now she's like 'I don't want to drive any other car. This just feels absurd to drive your car. I don't want to do that anymore.' It's crazy how quickly that changes. So there's a big difference in my mind. This is like a big categorical difference. There's a big difference between a car that is safer that drives itself versus a car that a human drives because you have an existence proof of the human driving car and you have the data. When you talk about homes, what is the delta? You have now a thing that you didn't have before doing things. So if it's bad at it, what are you relating it to? And if it's unsafe in any way, what are you relating that to? It's a much harder equation in my mind to get to a lot of people than a car where you can say, 'Hey, Waymo saved lives.' You know, you're going to have a fraction of the deaths using a Waymo, whether you're a passenger or you're not. When you already see people in San Francisco adapting how they respond around a Waymo versus any other car. So, you're seeing behavioral changes that are based on trust, which is really cool. When you're talking about a new product that hasn't existed yet and is not essentially replacing something, that's a harder sell and you have to have a different story.

有人需要弄清楚特斯拉自动驾驶是什么感觉，比如，你经常在停车时，你会眼神交流，然后说‘你先走，你先走’。

Something that someone needs to figure out what the Tesla self-driving is like when you, you know, often you're like at a stop and you like make eye contact and go ahead, go ahead.

或者像有人要过马路，你会说，呃，好吧，你先走。但特斯拉就自己开自己的，搞得你好像很没礼貌似的。就像，哦，不是我开的，它不受控制。

Or like someone's about to cross and you're like, uh, okay, go ahead. But like the Tesla just does its own thing and so it's like makes you look like an a bunch of times. Like, oh, I'm not driving. It's not in control.

对，我有过一次。你几乎想在前头装两只小手，做做手势，比如你先走之类的。我们实际上多么依赖这种人际交流来决定路口谁先走，真是惊人。

Yeah, I had that happen once. You almost want a little two arms in the front to do like gesturing or like you go or something. It's amazing how much we actually rely on this human connection to decide who's going to go in an intersection.

对。好的。稍微宏观一点，像你这样的人很酷的一点是，你在思考和建造未来才会存在的东西。你有点像生活在未来，设计未来，你是少数能瞥见未来走向的人之一。所以我很好奇想问，比如说未来五年，你对我们的日常机器人设备有什么愿景？它们会有什么不同？大概是什么样子？

Yeah. Okay. So, zooming out a little bit just what's cool about people like you is you're thinking and building things that will exist in the future. You're kind of like living in the future and designing it and you are one of the few people that has a glimpse into where things are going. So I'm curious just to ask like say in the next say in five years what is kind of the vision you have of what is different about our day-to-day robots devices just like what does it look like I don't you know just roughly.

在这份工作中，我们有一件疯狂的事：我们必须尝试生活在未来，而且必须生活在足够远的未来，这样我们设计的东西不仅适用于两年或三年后，还能逐步实现我们六年后的目标。因为在我的领域，做出一个东西然后迭代，逐步逼近最终目标，比一次性完美搞定要容易得多。所以，你不仅要知道第一个东西需要什么样、看起来什么样，还得知道第三个东西的理想形态或最终形态大概是什么样。所以你必须思考未来，生活在未来。我有个奇怪的特点：我热爱思考未来，但同时也是一个怀疑论者。而且你确实希望我是个怀疑论者，因为如果我觉得一切都会顺利，硬件就做不成。你希望我像这样：‘这个不行，那个不行，这个也不行’，就是担心各种事情出错。所以我内心有一种有趣的矛盾：我想要未来什么样、我认为未来会什么样、以及实际会什么样，都在猜测。对我来说，很明显 AI 将在未来几年从根本上改变我们的工作方式和内容。你已经看到了。显然，任何写代码的人都不怎么手写了。我认为接下来知识工作也会受到影响，并逐渐影响我们的经济和工作。但物理世界的变化似乎没那么快，除了无人机和自动驾驶汽车。你会看到越来越多的机器人。但我不是那种认为五年内会有 2000 万台机器人的人。我不觉得会那么快。我认为我们在供应链、供应链可靠性、原材料获取方面还有很多深层工作要做，然后我们还需要在这个国家重建高科技工厂。所以工作量很大，但在此期间，你会开始看到街上有很多奇怪的东西。你可能会看到机器人在街上。你以前见过送货机器人吗，Lenny？

So in this job we have this wild thing where we have to try to live in the future and we have to try to live in the future far enough away that we can design something not only for two years from now or 3 years from now but also something that will ladder up to what we want six years from now because in my field, it's a lot easier to make something and iterate on it and iterate towards a final goal than to do a oneshot thing perfectly. So, not only do you have to have a sense of what the first thing needs to be like and look like, you have to have a sense of what the third thing ideally or the platonic ideal of the thing will eventually look like. So, you have to think about the future and live in the future. I have this weird thing where I love to think about the future, but I'm also a skeptic. And you really want me to be a skeptic because if I think everything's going to be fine, the hardware is not going to work. You really want me to be like, 'This isn't going to work and this isn't going to work and this isn't going to work' and just be kind of worried about all these things going wrong. So, this is kind of an interesting disagreement inside of me of what I want the future to look like and what I think it's going to look like and what it's actually going to look like and trying to guess. And so it seems pretty clear to me that AI is going to have a foundational change in how we work and what we do over the next couple years especially. You're already seeing it. Obviously anybody who codes is not coding by hand very much anymore. Any knowledge work this is going to hit next I think and progressively affect our economy and our work. But it seems like the physical world is less likely to change as quickly outside of drones, self-driving cars. You're going to see more and more robots. But I'm not somebody who thinks that in 5 years you're going to have 20 million robots. I don't think it's going to be that fast. I think we have a lot of really deep work on supply chain, supply chain reliability, raw material access, and then we need to figure out how to make factories again in this country for high-tech. So, that's a lot of work, but in the interim, you're going to start seeing a lot of weird things on the street. You might see robots on the street. Have you seen any delivery robots in your world, Lenny, before?

就像那种小车一样的东西，不是人形的。

Like, you know, like the little car things, not like anything humanoid.

对。所以这种情况会继续发生，我觉得我们会继续感觉自己生活在未来。但安全将是机器人技术的一个关键。我认为，例如，未来两年战争领域的变化可能比消费电子领域更大。

Yeah. So, this is just going to continue happening and I think we're just gonna continue to feel like we live in the future. But safety is going to be a big key for robotics. I think there's probably more change in war than there is in consumer electronics in the next two years, for example.

哇，这话说得真重。我完全同意。没有什么比战争更能激励创新、不断改进和试图领先对方了。

Wow, what a statement. And I totally agree. Like there's nothing like war to incentivize innovation and just like endless improvement and trying to get ahead of the other side.

尤其是当民主制度面临威胁时。我的意思是，我认为我们正处于一个需要以这些方式思考事物和未来的时刻，用我们的能力捍卫这些东西，同时希望我们永远不必在任何地方发生热战。

Especially when democracies at stake. I mean I think that we are and I don't want to be like on a high horse or something but I do think that we're in a place where we need to think about things and the future in these terms and defend these things with our capabilities while also hoping that we never have to have hot conflict anywhere.

顺着这个思路，我不得不问你，最近你至少在 Twitter 上因为离开 OpenAI 而出名了。你发推说你离开了，并附上了简短的解释，获得了 700 万次浏览，我不知道，8000 个赞。发生了什么？你为什么离开 OpenAI？那里发生了什么？

Along those lines, I have to ask you recently you became famous on Twitter at least for quitting OpenAI. You tweeted that you were leaving and with your brief explanation and got 7 million views, 50 I don't know, 8,000 likes. What happened? Why did you leave OpenAI? What happened there?

是的，我希望如此。我在推文中说，我在 OpenAI 管理层有很多朋友，我非常在乎他们。我认为他们都是非常好的人，但我感觉在决策过程、决策速度、治理以及围绕战争部交易公告缺乏明确护栏方面，事情的处理方式不符合我的预期。这两点可以同时成立。所以我的希望，Lenny，是存在第三条路。你看到很多人只是随波逐流，跟着公司走。然后你也看到有些人对此采取焦土策略。在这种情况下，那对我来说没有意义。我对公司没有那种感觉。OpenAI 过去是、现在也是一家了不起的公司。我能够在那里帮助建立机器人项目，并吸引了一些我认为是世界顶级的机器人人才。所以我有很多，我不知道，你知道，这是一群我非常在乎的人。你也可以和朋友意见不合，觉得他们做的事情不好、不对。这就是我的立场，也是我发推的原因。这件事会被报道，所以我提前发了推。

Yeah, I hope so. What I said in my tweet was that I have a lot of friends in the executive side of OpenAI that I care a lot about. I think are really good people and I feel that what happened with the decision-making, the speed of the decision-making, the governance and the lack of defined guardrails around the announcement of the Department of War deal is not how I thought it should have been done. And both of those things can be true. And so my hope, Lenny, was that there's a third path. You see a lot of people just kind of going along with what their company's doing. And then you see some people that are kind of scorched earth about it. In this case, that didn't make sense for me. I didn't feel that way about the company. OpenAI was and is an amazing company. And I was able to help build a robotics program there and attract some of the top talent in robotics I think in the world. And so I have a lot of, I don't know, you know, this is a group of people I care a lot about. And you can also disagree with friends and feel like what they did isn't good and isn't right. And that's where I ended up and that's what I tweeted about. It was going to get reported on so I tweeted before that happened.

这是个绝佳的机会，悄悄告诉我 OpenAI 在做什么。这个机器人设备是什么？就你我知道。

This is a great opportunity to just whisper to me what OpenAI is working on. What is this robotics device that just like just between you and me?

是的，我希望我能说，你知道，Lenny，我们工作的一部分乐趣在于能比别人先看到东西。但另一面是，我们不能谈论任何内部信息或知识产权。我能说的是团队非常强大，我非常感激有机会提供帮助。但我也认为，在发生的事情之后，是时候离开了，我无法继续在那里工作，因为你不知道下次会发生什么。

Yeah, I wish I could say, you know, Lenny, part of the fun of our job is we get to see things before everybody else does. But part of the flip side of that is we can't talk about anything internal or any IP. What I can say is the team's really strong and I was really grateful for the opportunity to help. But I also thought that after what happened, it was time for me to I couldn't continue to work there because you don't know what's going to happen next time.

我希望我的决定能让其他人更容易谈论自己的边界并坚守它们。我们拭目以待。说到团队建设，这确实是我很想问你的。正如我所说，我问了一堆人该和你聊什么，有个人，我想可能是同事，前同事，Mariana Senko。你和她共事过吗？

And my hope was that my decision made it easier for other folks to talk about what their boundaries were and hold them. And we'll see what happens there. So speaking of team building, this is something I definitely wanted to ask you about. So as I said, I asked a bunch of people what to talk to you about and someone that I think it was maybe a colleague, former colleague, Mariana Senko. Did you work with her?

是的，她是我朋友。

Yeah, she's a friend.

好的，她是你朋友。她告诉我她对你的评价：你作为领导者的卓越之处在于招聘出色的团队。我很好奇，在一个每个人都担心工作的时代，她认为哪些人是不可或缺的。所以谈谈你在招聘团队成员时学到的，你寻找什么样的人。

Okay, she's a friend. So she told me that here's what she said about you. That your brilliance as a leader lies in hiring exceptional teams. I'd be curious about the kinds of people that she finds indispensable in an era where everyone is concerned about their jobs. So talk about what you've learned about just what you look for when you're hiring folks for your team.

是的，我很幸运，在招聘方面有很多经验，很多次实践。所以我有一套招聘优秀人才的策略。当你为零到一、新事物或新行业招聘时——我认为 AI 和机器人正是如此，非常新——你不能指望完全招到过去做过完全相同事情的人，因为那不存在。完全相同的事情不存在。也许你有建造过上千个机器人的机器人专家，但据我所知，没有人建造过那种能以我感兴趣的方式在世界上移动的机器人，数百万台，因为还没人做到过。所以你必须开始思考如何组建一个能做新事情的团队。好在机器人领域，自动驾驶汽车是一个很好的参考，因为你有传感栈，有很多安全权衡，还有大量硬核工程。所以我从那里找。显然你需要一些能从头设计机器人的硬核机器人专家。这些人即使有某个学位，实际上也是混合型人才，是通才。所以我寻找的一个关键原则是很多强大的通才，他们能将其他领域的知识应用到新领域，以及有大量构建经验的人。你需要一些有构建你正在构建的新事物经验的人，以及一些有扩展其他事物经验的人。所以你需要考虑这些。然后对于年轻人，这就变得非常有趣了，Lenny。唯一真正 AI 原生的人——他们如此自然地使用 AI，以至于它融入了他们的工程流程——是 20 岁、21 岁或 20 岁的人。我的意思是，很难找到 30 多岁的人能真正完全 AI 原生。所以我们需要这些人教我们如何思考。我有机会和几个这个年龄段的人合作过。他们解决问题的方式完全不同，因为他们从头到尾都用 AI 做所有事情，实际上快得多，看着很有趣。所以要想办法让这些 AI 原生者教我们其他人他们如何看待 AI。你知道，你和我，我想我们可以说是数字原生代，我们小时候可能没有互联网，但我们是拥有第一代互联网的世代，我们是青少年，我们是第一代大规模拥有手机的世代。我们是重要的一代，因为我们拥有第一代——我记得斯坦福大学一年级时，我们有了第一个数据库，可以访问，可以分享电影和音乐。这是新的，所以我们在这方面是原生的，这给了我们很多动力去创造新技术。但我们必须接受，我们在这些新技术上不是原生的，你真的需要一些渴望、兴奋、想学习、拥有这些技能的人。

Yeah, I'm lucky that I've had a lot of time, a lot of reps basically on hiring people. And so I have a strategy of hiring great people. When you're hiring for zero to one and new things or new industries, and that's what we're facing, I think with AI and robots certainly, it's very new. You can't count on having entirely people who've done the exact same thing in past lives because it doesn't exist. The exact same thing doesn't exist. Maybe you've got roboticists who've built a thousand robots, but nobody that I'm aware of has built the type of robot that can move through the world the way, you know, I'm interested in in the millions because it hasn't been done. So, you have to start thinking about how do you build a team that can do something new. And the nice thing is actually in robotics, self-driving cars, autonomous vehicles is a really good place to look because you've got the sensing stack and you've got a lot of the safety trade-offs actually and it's a lot of the hard engineering, the hardcore engineering. So, that's a place that I looked. Obviously you want some hardcore roboticists who can do robot design from scratch. And these are really people even though they might have a degree in something, they're really hybrid people. They're generalist people. So, one of the key principles I'm looking for is a lot of really strong generalists who can adapt what they've learned in other fields to a new field and people with a lot of experience building. You want some people who have experience building the thing that you're building that's new and some people who have experience scaling other things to hire on. So, you need to look at that. And then with young people, this is where it gets really fun, Lenny, is the only AI native people essentially who use AI so natively that it's like baked into their engineering process are 20 years old or 21 years old or 20. I mean, it's very hard to find someone who's in their 30s who can be truly fully AI native. And so, we need these folks to teach us how to think. And I've had the opportunity to work with a few folks in that age range. They're approaching their problem solving completely differently because they're using AI from the ground up for everything and they're much faster actually and it's really fun to watch. So figuring out how to get these AI natives to teach us the rest of us how they think about AI when it's you know we are you and I think I can say are digital natives where we grew up maybe there wasn't internet when we were really young but we are the generation that had the first you know internet we were teenagers and we are the generation that had the first cell phones really in scale and we are we're an important generation because we had the first I I remember freshman year at Stanford we had the first data like databases that you could access and you could share movies on I think is what we did and music on or whatever it was. But this was new and so we were native in these things and that gave us a lot of oomph in creating new technologies for it. But we have to accept that we're not native in these new technologies and you really want some folks who are hungry and excited and want to learn who do have these skills.

最后一个类别在我们播客讨论招聘时非常常见，这很酷，是对“年轻人没有工作了，所有初级职位都被 AI 抹去”这种说法的反驳。

That last bucket is a very common trend on this podcast when we talk about hiring, which is really cool as a counternarrative to there's no more jobs for young people. All the junior roles are erased because of AI.

是的，我不这么看。我认为我们需要他们。我还认为我们需要培养新的技术人员。比如一个显而易见的问题：如果我们没有同时拥有高级和初级人员的团队会怎样？但当你真正组建这些团队时，你会发现你必须两者都有。你必须两者都有。团队规模可能比以前小一点。当硬件领域的 AI 革命发生时，我不知道这会如何影响团队。那会很有趣。

Yeah, I don't see it that way. I think we need them. I also think that we need to build new technologists. Like there's the obvious question of what happens if we don't have teams that have senior and junior people. But I think what you find when you actually build these teams is you have to have both. You must have both. The team size just might be a little bit smaller than it used to be. When this AI revolution in hardware happens, I don't know how that's going to affect the teams. That will be really interesting to watch.

所以我听到的是：寻找能根据需求灵活变通的通才。混合专家和扩展与零到一的人才。然后我听过最好的说法是“厉害的新毕业生”，他们本质上是 AI 原生，凡事以 AI 为先。

So what I heard here is just look for generalists that can flex based on whatever needs to be done. Some mixture of specialist and like scaling versus zero to one. And then these the best term I've heard for this is cracked new grads that are AI native essentially that are just doing everything AI first.

是的。然后我们没谈到的当然是使命对齐，这实际上能团结团队。如果每个加入的人都与使命一致，那会很有帮助，因为尤其是在 AI 研究员和硬件人员的世界里，有很多沟通不畅，因为我们来自如此不同的世界。所以有一种我们都在朝同一个方向努力的感觉非常重要。然后我非常依赖，Lenny，我对人的直觉，假设其他我寻找的条件都检查过了。所以，很难说那是什么意思，但通常是你寻找的那种火花：他们真正有动力。他们被学习的渴望和卓越所驱动。他们愿意向周围的人学习。他们乐于根据新信息更新自己的观点。他们想赢。我的意思是，这些才是组建团队时真正重要的东西。

Yep. And then what we didn't talk about of course is mission alignment which actually unifies a team. So if everyone coming in is aligned to the mission that helps a lot because especially in the world of AI researchers and hardware folks there's a lot of miscommunication because we're coming from such different worlds. And so having a sense of we're all pulling in the same direction is really important. And then I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked. So, I don't it's hard to talk about what that means, but usually it's that spark that you're looking for in someone that they're genuinely motivated. They're motivated by a desire to learn and by excellence. They're motivated to learn from the people around them. They're open to updating their point of view based on new information. And they want to win. I mean, these are the things that really matter when you're building a team.

太棒了。好的，还有几个问题。我一直想问的是，你和一些最传奇的成功建造者合作过：史蒂夫·乔布斯、乔纳森·艾维、马克·扎克伯格、山姆·奥特曼。你不必逐一讲，但想到什么教训可以说说？我们从山姆开始吧，因为最近，山姆很擅长说：“为什么不能更多？为什么不是 100 倍或 10000 倍？你想得太小了。为什么不往大了想？”每次我们讨论重要事情时，他都会这么说。

Awesome. Okay, just a couple more questions. Something I've been wanting to ask for a long time is you've worked with some of the most legendary successful builders. Steve Jobs, Jony Ive, Mark Zuckerberg, Sam Altman. You don't have to go through all four, but just what's a lesson you learned from as many of these folks that come to mind? So, we'll start with Sam because most recently Sam is really good at saying, 'Why not more? Why not 100x or 10,000x? You're thinking too small. Why not think about this bigger?' And every time we talked about something important, he talked about that.

我意识到自己在某些方面想得太小了，而他是在全局思考。有这样一个雄心勃勃的领导推动你，真的很有帮助。我从他身上学到的一大件事就是——他愿意大规模投入，愿意用非常大的数字去思考。这非常根本。对于史蒂夫·乔布斯来说，他为公司、技术人才和卓越设定的标准从未动摇。标准就在那里，你要么达到，要么达不到。这贯穿了整个公司。当你是一个有野心的年轻人，听到某件事不够好时，其实非常激励人。如果你告诉别人，‘嘿，这需要更好，你需要花更多时间，更用心，或者这没有达到我们的质量标准’，那很有冲击力。你绝不想再听到那样的话，所以非常激励人。而马克·扎克伯格把公司管理得很好。技术部门的运作方式——评审流程、决策方式——决策尽可能在最低层级做出以保持速度。我之前低估了硬件组织与公司其他部门互动的清晰和高效程度。非常明确：这是我们的目标，我们会开这个评审会，在会上做决定。如果你能在没有评审的情况下做决定，那就去做。这是项目的目标。执行得非常到位，这在快速成长的公司里很难做到。他和 CTO 安德鲁·博斯沃思参与技术决策，能阅读 20 页的报告，理解权衡，并贡献技术讨论——这令人印象深刻。他们一个月要做上百次这样的事。

And what I realized is I was thinking too small in certain areas and he was thinking globally. Having that nudge from an ambitious leader is really helpful. That was a big thing I learned from him—he's willing to go for it at high volume and invest. He's willing to think in very big numbers. That was foundationally important. For Steve Jobs, the bar he held for the company, for technical talent, and for excellence was unwavering. It was up here, and you were either going to meet it or not. That washed through the whole company. When you're a young ambitious person and you hear that something's not good enough, that can be extremely motivating. If you tell someone, 'Hey, this needs to be better, you need to spend more time, be more thoughtful, or this isn't hitting our quality bar,' that's impactful. You never want to hear that again, so it's very motivating. And Mark Zuckerberg ran the company very well. The technical side operated with clear reviews and decisions made at the lowest level possible to maintain speed. I underappreciated how clean and well-run the hardware organization interacted with the rest of the company. It was very clear: this is what we're going for, we'll have this review, make a decision, and if you can decide without the review, you do that. Here are the objectives. It was really well executed, which is hard at a fast-growing company. Having him and Andrew Bosworth, the CTO, involved in technical decisions, able to read 20-page reports, grasp trade-offs, and contribute to technical discussions—impressive. They did that a hundred times a month.

多么不可思议的经历和不同类型的工作场所。我不知道还能不能有比这更不同的了。

What an incredible set of experiences and different types of places to work. I don't know if they could be more different.

我知道。这就是为什么我在寻找一个从零到一的机会。当你寻找从零到一的机会时，它总是在一个不同的地方。

I know. And that's why I'm looking for a zero-to-one opportunity. When you're looking for a zero-to-one opportunity, it's always going to be in some place different.

你现在是自由身，在这个市场上会很抢手。但另一方面，我想带大家进入失败角落。我觉得做硬件的人会有一些很棒的失败故事。有没有一个你做过的东西失败了，以及你从中学到了什么？

You're going to be a hot commodity in this market now that you're a free agent. But on the flip side, I want to take us to fail corner. I feel like someone building hardware has some great fail stories. Is there one story of something you built that failed and something you learned?

这是个好问题，但不太舒服。我最喜欢的失败之一是 Quest One，在 EVT 阶段，也就是项目中期。我们为了降低成本，从五个摄像头减到了四个。我们需要降价让更多人买得起。就在圣诞节前，计算机视觉团队的负责人告诉我：‘天哪，摄像头的数据不工作，我们无法锁定用户头戴设备的位置。’我们调查后发现，他们对规格的理解和我们不同。在工程中，我们通常用正负公差，比如 0.15 毫米。而在他的领域，他习惯全局公差在 0.15 毫米以内。所以我们对规格的理解不同。问题在于，我们的理解导致他无法实现定位目标。所以我们不得不在 EVT 阶段重新设计，而 EVT 本应是工程完成的时候。EVT 代表第一次用所有最终组件、最终材料、在量产工具上制造组件来组装硬件。所以这是大事。我们有四个浮动摄像头，必须把底部两个锁定在一起放在一个支架上，使相对距离满足他的规格，另外两个保持浮动。这是一个架构变更，也是一次失败——对规格理解的失败，产品设计的失败，源于误解。我们适应了，按时完成了制造，按时发货了，但压力很大。结果新设计更好，因为有了一个基准对，就有了空间真相，另外两个摄像头重叠在这个真相上。效果很好。结果不错，但过程很紧张。真希望我们四个月前就发现了。

This is a great question and not a comfortable one. One of my favorite failures was on the Quest One, around EVT, halfway through. We had gone from five cameras to four for cost reduction. We needed to reduce the price so more people could buy them. Right before Christmas, I heard from the lead on the computer vision team: 'Oh my gosh, the data from the cameras isn't working and we can't get a lock on where the person is using the headset.' We looked into it and realized their interpretation of our spec and our interpretation were different. In engineering, we usually use a plus or minus tolerance, in this case 0.15 mm. In his world, he was used to a global tolerance within 0.15 mm. So we had a different interpretation. The problem was that our interpretation meant he couldn't meet his goals of understanding where the headset was in space. So we had to do a redesign at EVT, which is when you want the engineering to be done. EVT stands for when we compile the hardware for the first time with everything supposed to be done—final components, final materials, making components on the tools for mass production. So it's a big deal. We had four floating cameras. We had to lock the bottom two to each other on a bracket so the relative distance met his spec, and let the other two float. This was an architectural change and a failure—a failure in understanding the spec, a failure in product design due to misunderstanding. We adapted, kept the build on time, and shipped on time, but it was stressful. It turned out the new design was better because with a favored pair, you have a source of truth for space, and the other two cameras overlap onto that source of truth. It worked well. A good outcome, but a scramble. I wish we had caught it four months earlier.

又一个硬件例子——你搞砸了一个规格，不是‘我们浪费了一周做了个没用的东西’，而是四个月后还得重做硬件供应链。

Another example of how hardware is—you mess up a spec and it's not like 'we wasted a week building something that didn't work'; it's four months later still having to redo the hardware supply chain.

是的，很棘手。

Yeah, it was tricky.

所以发货的 Quest One 摄像头位置变了？

So the Quest One that shipped had the cameras moved?

是的。

Yeah.

哇。

Wow.

如果你看，Quest 前面底部有两个摄像头靠得更近一些。

If you look, there are two cameras a little closer to one another in the front of the Quest at the bottom.

博斯和扎克对此怎么看？

How did Bos and Zuck feel about this?

我不记得了，这大概意味着没事。我们解决了问题，重新设计了。我们不得不把支架材料换成钢来保持公差。但结果不错，价格、成本和良率都还好。我们适应了。

The fact that I don't remember probably means it was okay. We addressed it, redesigned it. We had to change the material on the bracket to steel to hold the tolerance. But it worked out, and the price, cost, and yields were fine. We adapted.

那是有史以来最畅销的 VR 设备，对吧？

And that was the bestselling VR device of all time, right?

我想是的。

I think it was.

我没有最终销售数字，但好吧。有点担心。好了，Caitlyn，我们聊了这么多。你还有什么想分享的吗？有什么想留给听众的吗？要么重申我们聊过的内容，要么就是有什么你想说的。

I don't have the final sales number, but okay. Concerned. Okay. Caitlyn, we've covered so much ground. Is there anything else you wanted to share? Anything else you want to leave listeners with? Either double down on something we've shared or anything else that just like, oh, here's something I want to share.

我认为这可能是我们即将进入的最激动人心的时代之一。我觉得我们所有人，包括我自己，对此感到担忧和害怕是正常的，但我也认为这是一个机会，让人们能够做出非凡的事情，取得非凡的进步，并且作为个人能够比以往任何时候都做得更多。所以这是我试图拥抱的一面。这些新工具、这种新的工作方式很可怕，但如果你拥抱它，每天使用这些 AI 工具，并将它们应用到你所做的事情中，你就会站在未来任何发展的前沿。所以我只想鼓励每个人发挥创造力，使用这些工具，享受其中的乐趣，找出边界在哪里，然后每当新模型出现时，再次测试，因为了解我们正在处理什么以及这些边界在哪里非常重要。但我对个人的力量也从未如此兴奋过。

I think that this is probably one of the most exciting times we're coming into. And it's normal, I think, for all of us, myself included, to be worried and scared about it, but I also think it's an opportunity for people to do an extraordinary amount, have an extraordinary amount of progress, and be able to as an individual do more than we've ever done before. And so that's the side I'm trying to embrace. These new tools, this new way of work is scary, but if you embrace it and are daily using these AI tools right now and daily applying them to what you're doing, you'll be at the forefront of whatever comes next. And so I just want to encourage everyone to be creative, use these tools, have fun with them, figure out what the boundaries are, and then every time a new model comes out, test again because it's really important to know what we're dealing with and where these boundaries are. But I'm also I've never been more excited about the power of an individual.

好了，Caitlyn，我们到了非常激动人心的快问快答环节。我有五个问题要问你。准备好了吗？

Well, with that, Caitlyn, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

我准备好了。第一个问题，你经常向别人推荐哪两三本书？

I'm ready. First question, what are two or three books that you find yourself recommending most to other people?

我最近主要在读经典作品。《新日之书》是一本很棒的小说，我强烈推荐。我想是这个名字。我有一阵子没读了。其实我很喜欢《达洛维夫人》。我觉得这是一本关于转变的很有趣的书，是弗吉尼亚·伍尔夫战后写的。所以我真的很喜欢，觉得它很棒。我觉得希罗多德的《历史》相当了不起。他经常出错，但这也是第一本历史书，在很多情况下，他去找寻事件的第一手或第二手记录。这是一种观察与现在完全不同的时代世界的方式。所以这些是我喜欢的书，我会再确认第一本书的书名，然后发邮件给你。但我觉得就是这个名字。

I've been mostly reading the classics lately. So, Book of the New Sun is a great fiction book, which I really recommend. I think that's what it's called. I haven't read it in a little while. I love Mrs. Dalloway, actually. I think it's a very interesting book about transitions, and it was a post-war book by Virginia Woolf. So I really love it and I think it's really wonderful. I think Herodotus' Histories is pretty incredible. He's wrong a lot but it's also the first history book and in many cases he's going and finding firsthand or secondhand accounts of what happens. It's a way to look into the world at a completely different era than it is now. So these are some books that I like and I'll double check the title of the first one and email you. But I think that's what it's called.

好的。我们会在节目笔记中链接到正确的书名。

Okay. And we'll link to the correct one in the show notes.

最近最喜欢的电影或电视剧是什么？

Favorite recent movie or TV show that you have really enjoyed?

我现在真的很迷《亢奋》。我觉得是新的《亢奋》。我对角色很感兴趣，想知道接下来会发生什么。

I am really into Euphoria right now. I think the new Euphoria. I'm interested in the characters and figuring out what's going to happen there.

我看这部剧的时候总是觉得压力很大。

The show's so stressful whenever I watch it.

这是一部情节剧。我觉得你得把它当成肥皂剧来看，这样才有趣。如果你太较真，就没意思了。

It's a melodrama. I think you have to think about it as a soap opera and then it's fun. If you think about it too literally, it's not.

好的。这很有帮助。

Okay. That's helpful.

你最近有没有发现特别喜欢的产品？可以是硬件、应用、衣服或小工具。

Do you have a favorite product you've recently discovered that you really love? Could be hardware, an app, a piece of clothing, a gadget.

我真的很喜欢 Vollebak。他们的衣服。他们做非常有趣的衣服。基本上他们的新衣服是基于材料科学的。所以他们把新材料科学应用到衣服上。这是一个值得关注的很有趣的品牌。

I really like Vollebak. The clothes. They make really interesting clothes. They're essentially basing their new clothes on material science. So, they take new material science and make them into clothes. It's just a fun brand to follow.

Vol。

Vol.

V O L L E B A K。Vol。

V O L L E B A K. Vol.

非常酷。

Very cool.

你有没有一个经常在工作中或生活中想起的人生格言？

Do you have a favorite life motto that you often come back to in work or in life?

你见过那个树枝图吗？有很多分支，然后你在这里，从这个点又有很多分支。

Have you seen that branch where there's all these branches and then you're here and then there's all these branches from this point.

嗯。

Yeah.

嗯。你知道这是谁说的吗？我不知道是谁。我经常想到它，因为很难不陷入未来或过去，而停留在当下。我在这方面有困难。但这很好地提醒了你，你每天都可以选择。你每天都可以决定你想做什么。有时事情不如你所愿，有时你后悔自己做过的事，有时你为自己做过的事感到自豪，但这些都不重要。重要的是你眼前的东西。

Yeah. You know who it was from? I didn't know who. I think about it a lot because it's very hard not to get stuck in the future or the past and stay kind of here. I have trouble with that. But that is a great reminder that you get to pick every day. You get to decide every day what you want to do. And sometimes things don't go the way that you want and sometimes you regret what you did or sometimes you're proud of what you did but it doesn't really matter. What matters is what's right in front of you.

我们会在你说这句话时在屏幕上显示那张图片，或者在节目笔记中链接到它。这太有力量了。

We'll either show that image on the screen as you say that or we'll link to it in the show notes. It's so powerful.

最后一个问题。一个很了解你的人分享了一个关于你的很有趣的细节：你雇了一个博士来辅导你学习古希腊和古罗马的经典著作，并且对这些东西非常着迷。这是怎么回事？是什么驱使你如此深入地研究这些东西？

Final question. Somebody that knows you well shared this really interesting tidbit about you that you hired a PhD to tutor you on the staples of ancient Greece and Rome and just get really nerdy about this stuff. What's going on there? What drives you to go so deep on these sorts of things?

这非常书呆子气，但我发现了诗人约瑟夫·布罗茨基写的一份清单，上面列出了为了能用英语进行有智慧的对话你应该读过的书。这是一份做作的清单，很密集。比如《旧约》、《吉尔伽美什》，然后一直往下。但我发现它很好地提炼了我们过去所谓的西方经典。我在公立学校和大学学到了很多，但我从未真正学习过你所谓的西方经典。所以这算是一种补充，清单上还有一些较新的书。我觉得有一个可以参照的东西很迷人。我发现当我深入研究悲剧，尤其是希腊悲剧时，我没有足够的背景知识来学习我想学的东西。仅仅阅读它们，我吸收得不够。所以我找到了一位很棒的博士后，他愿意辅导我。我可以问他所有这些问题。他就像一本百科全书，什么都知道。我可以问他，在我们读的这部希腊悲剧发生时，土耳其发生了什么，雅典发生了什么，这位悲剧作家可能是在回应什么。他都能回答。有一个这样的传声板真的很有趣。

This is like very nerd territory, but I found this list that the poet Joseph Brodsky wrote, which is a list of things you should have read in order to have an intelligent conversation in English. And it is an affected list, it's intense. It's like the Old Testament, Gilgamesh, and then all the way down through. But what I found is it's a pretty good distillation of what we used to call the Western canon. And I learned a lot in my public school education and in college, but I never really learned from what you would consider the Western canon. So this is kind of in addition to that, there are some newer books on the list. I find it just fascinating to have something to work off of. And what I found is as I got into specifically the tragedies, the Greek tragedies, I just didn't have enough context to learn what I wanted to learn. And just reading them, I didn't have enough uptake. So I found an incredible postdoc who was willing to tutor me. And I just get to ask him all these questions. He's an encyclopedia. He knows everything. I could ask him what was happening in Turkey at the time of this Greek tragedy that we're reading and what was happening in Athens and what this tragedian might be responding to. And he can answer the question. It's really fun to have the ability to have that sounding board.

太酷了。你这样做太酷了。尽管 AI 可以做很多事情，但有时和人交谈更有趣，感觉也更好。

So cool. It's so cool you did that. Even though AI can do a lot of this, sometimes a human is much more interesting to talk to and feels better.

是的。我发现用 AI 阅读和交流对基础知识很有帮助，但要理解当时文化背景和作品的意义，它还不够。

Yeah. I find reading and communicating with AI is very helpful on the basics, but then understanding what was happening culturally and what the significance of the work is, it wasn't adequate.

这是因为我们不是二十多岁，错了。

It's because we're not 20-something wrong.

我不认为这是错的。我们只是没有这样成长。我想大学生们可能不会，我为什么要那样做？我有 Claude 在这里。

I don't think it's wrong. We just didn't grow up this way. And I imagine college students, they would just not, why would I do that? I have Claude here.

嗯。

Yeah.

太酷了。我喜欢。好了，这太棒了。你太棒了。

So cool. I love that. Well, this was incredible. You're amazing.

听众如果想找到你，可以在网上哪里找到你？如果他们想联系你，比如想雇你。最后一个问题，听众怎样才能帮到你？

Where can folks find you online if they want to find you? If they want to reach out, I don't know, try to hire you. And then final question, how can listeners be useful to you?

我有一个网站，就是我的名字.com。我也有领英。听众可以帮我。我觉得是帮助想象未来。这不是单人游戏，而是多人游戏。弄清楚我们想要什么样的未来，希望它是什么样子，人类在其中扮演什么角色，以及我们想为自己保留什么。我们想如何增强自己。现在，我们处于一个反乌托邦的角落，一切都让人觉得未来很可怕。避免这种情况的方法就是一起设计我们自己的未来，弄清楚我们想要未来是什么样子，在小说、文学、对话中描绘出来，然后去建造它。我认为这是可能的。

So I have a website which is just my name.com. I'm also on LinkedIn. People can help me. I think helping imagine the future. This is not a single player game. This is a multiplayer game. Figuring out what future we want, what we want it to look like, what we want the human aspect to be in that future, and what we think we want to hold for ourselves. How we want to augment ourselves. Like right now we're in this dystopian niche where everything feels like the future is horrible. And the way to not have that is to actually design our own future together, figure out what we want our future to look like, paint a picture in fiction, in literature, in conversation, and then build that. And I think that that's possible.

我喜欢这个。这其实是最近几期播客中传达的信息。所以这是一个很好的提醒。凯特琳，非常感谢你来做客。

I love that. That's actually a message that's come on a couple of recent podcast episodes. So it's a really good reminder. Caitlyn, thank you so much for being here.

非常有趣，Lenny。谢谢你邀请我。

It was really fun, Lenny. Thanks for having me.

大家再见。非常感谢收听。如果你觉得本期有价值，可以在 Apple Podcast、Spotify 或你喜欢的播客应用上订阅本节目。也请考虑给我们评分或留下评论，这真的能帮助其他听众找到这个播客。你可以在 lennispodcast.com 找到所有过往节目或了解更多信息。下期再见。

Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennispodcast.com. See you in the next episode.