Naval 谈扁平化组织与 AI 的隐性作用

你正在收听 Naval 播客。我是 Nivi。这期没有固定主题，会是个大杂烩。Naval，你在现在的公司 Impossible 是如何利用 AI 来改变业务管理方式的？还是说你们公司太小，都是一群才华横溢的独立贡献者，所以 AI 对实际运营没什么影响？

You're listening to the Naval podcast. This is Nivi. There's no set topic for this episode. It will be a potpourri. Naval, how are you using AI at Impossible, your current company, to change how you manage the business? Or are you guys just too small and a bunch of brilliant independent contributors where it's not having an effect on how you actually run the company?

更偏向后者。我们是中心辐射式架构。我的联合创始人兼 CEO 是核心，所有人都向他汇报。他就像一个产品经理，把所有事情记在脑子里，试图把这项不可能的任务整合起来。每个人都与他对接，大家都很聪明。我们保持非常扁平的结构，鼓励人们直接沟通。我们甚至不用 Slack，这能说明问题。所以，我们并没有明确地在内部使用 AI 作为沟通工具。但隐性地，AI 仍然很有帮助。我们不像 Square，我知道 Jack Dorsey 围绕 AI 重组了 Square，也许 Shopify 的 Tobi 也在做类似的事。有些人在组织管理上很擅长，他们会做这类实验。我从来都不擅长组织管理，实际上我讨厌组织管理，因为我讨厌组织，讨厌大群体。我觉得在大群体里很难做成事，你接触不到最优秀的人，而且总有政治斗争。所以我更喜欢保持小团队。我们依靠人们独立运作，按需沟通。就像我说的，我们甚至不用 Slack，也不用任何项目管理软件，只用 GitHub。人们想交流时就互相发短信，一对一地聊。有时会很混乱，他们得自己想办法协调，但这本身就是一种技能。这有点像计算机网络：如何高效组织网络？因为到某个点，通信开销会变得非常高。传统的答案是层级结构，也就是树形系统：最上面是 CEO，下面是一群 VP 或 SVP 向他汇报，再下面是 VP、中层经理等等。这样能保持组织有序、方向一致，但也很压抑。政治斗争很多。你不能和低你两三级的人说话，除非你像 Elon 或 Brian Chesky 那样进入创始人模式，然后 CEO 能和工程师说话就被当作了不起的成就。你能听出我在讽刺。我觉得这种运作方式很糟糕，但这是规模带来的要求。我们还没到那个规模，所以我不喜欢。相反，我喜欢完全互联的图结构。这听起来很疯狂。完全互联的图就是每个人都可以和任何人交流，而中心辐射式则是有一个人居中协调，把所有事情记在脑子里。在计算机网络中，完全互联的图要求每个节点都非常智能。所以我们就这么做：雇佣非常聪明的人，让他们在完全互联的图中运作。如果他们无法找到合适的人来解决具体问题，或者无法与他人合作沟通，那他们就不适合这种组织，应该去找层级结构，在那里他们会更舒服。所以我们并不依赖任何工具。

It's more the latter. We're a hub and spoke architecture. My co-founder is the CEO and everyone kind of reports into him. He's just kind of the one product manager who runs around with everything in his head to try to bring this whole impossible task together. And everybody interface to him and people are pretty smart. We keep a very flat structure. We try to push people to communicate with each other directly. We don't even use Slack if that gives you a sense. So, we're not using AI as a communication method explicitly inside. But implicitly, AI is still very helpful. So, we're not like Square. I know Jack Dorsey reorganized Square around AI and maybe Tobi at Shopify is doing that. You know, there's some guys who are very good at organizational management. They do these kinds of experiments. I've never been good at organizational management. I actually hate organizational management cuz I hate organizations. I hate large groups. I think it's just so hard to get things done and you're not dealing with the best and the brightest and there's always politics. So, I just prefer keeping groups small. And we count on people to just operate independently and communicate with each other as needed. Like I said, we don't even use Slack. We don't use any project management software. I think it's just GitHub. And then when people want to talk to each other, they just text each other. Literally, they talk one-on-one. And sometimes it's chaotic and they have to figure out how to navigate their way towards, but that's part of the skill set. It's sort of like in computer networks, how do you organize a network for efficiency? Because at some point, the communication overhead gets very high. The traditional answer is hierarchy. It's a tree system. It's like there's one person at the top, the CEO. Then there were a bunch of VPs or SVPs reporting to them. Then you have a bunch of VPs below that, then middle managers and so on. And that keeps things organized and marching in one direction, but it's stifling. There's a lot of politics. You can't talk to people two or three levels below you unless you go founder mode like Elon or Brian Chesky and it's celebrated as some wonderful achievement that all of a sudden the CEO is allowed to talk to an engineer. You can tell I'm being sarcastic there. Like I just think that's a terrible way to operate, but it's a requirement of size. And we're just not at that size. So, I don't like it. Instead, I like the fully interconnected graph. And that's insane. Fully interconnected graph is everyone talking to anyone with a light hub and spoke with one person in the middle who's trying to keep everything in their heads. The thing about a fully interconnected graph in networking is that every node has to be highly intelligent. So, that's what you do. You hire highly intelligent people who can operate in a fully interconnected graph. And if they can't navigate their way to the person they need to talk to to solve a specific problem, or if they can't cooperate or communicate with other people, then they don't belong in this kind of an organization, and they should just go and find a hierarchical organization where they're going to be more comfortable. So, we don't really rely on any tools.

本期节目由 USV C 呈现，这是一支每个美国人都可以投资的公开风险基金。风险投资对大多数投资者来说遥不可及。你只能眼巴巴地看着公司在私募市场估值达到万亿美元，而你要等它们上市。USV C 就是为了改变这一点而成立的。它是一个跨阶段的单一高增长风险投资组合，在 SEC 注册，由专业管理，无需认证资格，最低投资额仅 500 美元。目前仅限美国投资者。该基金包括 OpenAI、Anthropic、xAI 和 Vercel。随着 USV C 增加新公司，投资者也将拥有这些公司的一部分。Naval 是投资委员会主席。风险投资不适合你明天需要的钱。它有风险且流动性差。不要投资任何你输不起的钱。但如果你有胃口，可能没有比真正的风险投资更努力工作的方式来部署一美元了。世界上最聪明的年轻人以疯狂的时间工作，建设未来。投资很简单。访问 USVC.com/podcast。在投资前，请仔细阅读基金招股说明书中的目标、风险、费用和支出，网址为 USVC.com。投资 USV C 具有投机性、高风险，且股票具有流动性，但没有公开交易市场。该基金是新的，运营历史和财务信息有限。它可能无法实现其投资目标或提供分配，投资者可能损失全部或大部分投资。过往业绩不保证回报。由 Alps Distributors Inc. 分销。

This episode is presented by USV C, a public venture fund that every American can invest in. Venture capital has been unreachable for most investors. You've got your nose up to the glass, watching companies compound to trillion-dollar valuations in the private markets while you wait for them to go public. USV C was built to change that. It's a single basket of high-growth venture capital across stages, SEC registered, professionally managed, with no accreditation requirement, and a low $500 minimum. It's for US investors only for now. The fund includes OpenAI, Anthropic, xAI, and Vercel. As USV C adds companies, investors will own a piece of those, too. And Naval is chairman of the investment committee. Venture is not for the money you need tomorrow. It's risky and illiquid. Don't invest anything you can't afford to lose. But if you have the appetite, there may be no harder working way to deploy a dollar than true venture capital. The smartest young people in the world working insane hours to build the future. Investing is easy. Go to USVC.com/podcast. Before investing, carefully read the objectives, risks, fees, and expenses in the fund's prospectus at USVC.com. Investing in USVC is speculative, high risk, and shares are liquid with no public trading market. The fund is new with limited operating history and financial information. It may not achieve its investment objectives or provide distributions, and investors may lose all or a substantial portion of their investment. Past performance does not guarantee returns. Distributed by Alps Distributors Inc.

现在，AI 在组织内部仍然是一个隐性的非常有用的工具，我可以举两个例子，虽然还有很多。一个是，如果你在阅读别人写的非常复杂的代码，你可以让 AI 帮你阅读并给出摘要。论文也一样，它可以阅读别人的论文并给出摘要。它甚至可以遍历代码库，告诉你组织里谁是某个主题的专家，并引导你去找他们。所以 AI 可以帮你做很多挖掘工作。你不再那么需要明确的内网，也不需要明确的标记，因为 AI 能搞清楚你在哪里。你甚至可以把 AI 释放到代码库和设计上，比如硬件设计。如果你有供应商和供应商，你可以把它释放到数据库或存放所有供应商文档的文件夹里。如果你愿意，甚至可以把它释放到公司邮件上，然后问：'我们在哪里？离发货还有多远？根据你的判断，基于估算和时间线，给我画一张甘特图，看看谁落后谁领先，哪个部门缺资源。' AI 可以持续为你做这些数据分析、挖掘和报告。按需报告。你不需要特定的图表、仪表盘和业务集成系统。你可以让 AI 实时重新创建。你可能不想每次都这样做，因为可能太慢，但你可以让它按需构建这些仪表盘，并按需更新。所以，这是一件大事。

Now, AI is implicitly still a very helpful tool within the organization, and I can give you two examples, although there are more. One is just if you're reading code that was written by somebody else that's very complicated, you can just have the AI read it for you and give you a summary. Papers, they can read other people's papers and give you a summary. It can actually go through the code base and tell you who in the organization is likely to be an expert on what topic and guide you to them. So, AI can do a lot of that digging for you. You don't need the explicit intranet as much anymore. You don't need the explicit marking down of things because AI can figure out where you are. You could even unleash the AI on the code base and on the designs, like let's say your hardware designs, you can unleash them on the designs. If you have suppliers and vendors, you can release them on the database or the file folder in which all the documents with suppliers and vendors are kept. You could even unleash it on the company email if you wanted to and just say, 'Where are we? How far are we actually from shipping? Draw me a Gantt chart based on where you think we're actually are in terms of the estimates and the timelines and who's behind and who's ahead, which division's lacking resources.' AI can constantly be doing this data analysis and digging and reporting for you. Reports on demand. You don't need specific charts and dashboards and business integration systems. You can just have AI literally recreate on the fly. You maybe don't want to do it every time because it might be too slow, but you can have it build these dashboards on demand and you can have it update them on demand. So, that's one huge thing.

另一点是，传统上在一家公司里，硬件人员、软件人员、AI 人员各司其职，基本不会互相插手。但现在有了 AI，他们至少能接手彼此 20% 到 30% 的工作，这让跨团队协作变得更顺畅。比如，AI 人员可以自己写测试用的软件框架，虽然不适合生产部署，但总比干等软件人员来写定制代码强。同样，硬件人员也能写点软件来启动新硬件设备，否则他们可能得等软件人员。所以，AI 让每个人都能做一点其他领域的事，让他们变得更通才。通才意味着有更好的接口去跟别人协作——你不需要别人为你写一个明确的 API 才能用他们的代码，你可以让 AI 去发现或创建 API，甚至绕过 API，直接连接数据库或代码库的任意层级。这自然是一个力量倍增器，但我们并没有刻意用它做什么。

The other is that traditionally in a company, you would have the hardware people and a company like ours, you have the hardware people, you have the software people, you have the AI people. And they kind of wouldn't be doing each other's work. But now with AI, they can at least get to 20%, 30% each other's work. So it makes the gluing between them a little easier. The AI people, for example, can create their own software harnesses if they need to test something. May not be good for production deployment, but it's better than having to sit around and wait for a software person to come by and write you some custom code. Same way, the hardware people can also write a little bit of software to bring up a new hardware device, where otherwise they might have needed to wait for software people. So, having AI just lets everybody do a little bit of everything. It makes them more generalist, and by being more generalist, it means that you have better touchpoints to interface with other people. You don't necessarily need to have someone write you an explicit API to work with their code. You can actually just have the AI go and discover an API or create its own API, or just bypass the AI, connect directly at whatever level it wants to, whether in the database or within the code base. So, it's naturally a force multiplier, but we haven't done anything explicit with it.

你现在想弄明白什么？我问这个是因为很少能看到聪明人的工作过程。我特别着迷于挖掘聪明人的秘密和内心想法。

What are you trying to figure out right now? The reason I ask is because you rarely get to see work product from smart people while it's in motion. One of my obsessions is trying to excavate the secrets and inner thoughts of smart people.

世界跟几年前大不相同了。现在有两家，可能四家公司主导着 AI，如果算上英伟达的硬件，那就是五家。问题是，这是稳定状态吗？这会变成商品业务、垄断业务还是寡头业务？它会在某个点见顶吗？他们会耗尽数据，模型停止改进吗？还是我们会一路走到 AGI？实验室里的人当然是 AGI 的信徒，他们认为所有价值都会流入 AI 实验室。最终会不会比七巨头更集中，变成两巨头甚至一家独大？还是会碎片化？开源真的有机会吗，还是人们永远只想要最聪明的模型？为此他们愿意放弃隐私、放弃开源，直接在云端付费。所以我认为这些都是巨大的问题，是颠覆世界的问题，但我不知道答案。能否以分布式方式训练 AI？分布式训练可行吗？还是这些东西会越来越集中？现在的主流观点是集中式训练，两到四家公司主导，数据中心和电力是瓶颈，所有人都在往这个方向冲。但如果这错了呢？那会是一个有趣的反向押注，但我还没看到证据。我觉得这部分 AI 的新兴主流观点是对的。至于 AGI，我不知道，我不想做未来学家。前沿实验室的人当然相信，他们已经相信很久了。我看到的 AI 有锯齿状智能，多模态推理也很差。我不认为它有好的世界模型，尽管有很多世界模型公司出现。但我觉得他们混淆了那种看起来像你可以漫游的世界——人们说“哦，那是世界模型，因为它生成了一个看起来像我能逛的世界”——那不是世界模型。世界模型是智能体内部有一个世界模型，让它能采取行动、预测后果、根据结果调整行为，就像一个强化学习循环。那才是世界模型。我们看到世界模型公司正在涌现，Yann LeCun 最近用 Jeppa 做了一个著名的例子。我们会看到新型模型、新型智能体、新型智能。我们会达到 AGI 吗？我不知道。这也是大家都在想的问题，对吧？但世界在变。X 上有个著名梗是“什么都没发生”，我觉得那已经过去了。我还没完全搞明白为什么，但任何留心的人都会告诉你，后疫情时代世界变化快得多。疫情带来了一些错位，或者只是我们处于不稳定平衡，疫情打破了平衡，然后发生了相变。现在世界似乎快得多，地缘政治、经济、技术都是如此。风投现在被迫投资更多硬件、火箭、无人机、AI，也就是科幻技术。所以科幻技术需求很高，科幻科学家和科幻作者供应不足，科幻工程师供应不足。我们看到世界在转变，也许更好也许更糟，但事情变化非常非常快。我们正活在“愿你生活在有趣的时代”这个中国诅咒里。

The world is very different than it was a few years ago. There are two, maybe four companies that are dominating AI, or five, if you count hardware with Nvidia. And the question is, is that the stable situation? Is this going to be a commodity business, or is this going to be a monopoly business, or is it going to be an oligopoly business? Does it top out at some point? Do they run out data and do the model stop improving, or do we go all the way to AGI? Certainly the people inside the labs are believers in AGI and think that all value is going to disappear into the AI labs. Does this end up even more consolidated than the mag 7 world where there's just mag 2 or mag 1? Or does it somehow fragment? Does open source really have a chance or do people just always want the smartest model? And so for that they'll give up privacy, they'll give up open source, and they'll just pay up in the cloud. So I think these are huge questions. Huge. These are world-shattering questions. But I don't know the answer to this. Can you train AI in a distributed way? Is distributed training possible? Or are these things going to centralize more and more and more? I think now the conventional wisdom is going centralized training, two to four companies dominating, data centers and power are the limits, and everyone is rushing towards that. But what if that's wrong? That would be an interesting contrarian bet. But I don't yet see the evidence. I mean, I think the emerging conventional wisdom for that part of AI is right. As for AGI, I don't know. I don't want to be in the futurist business. Certainly the people in the frontier labs believe it. They believed it for quite a while. The AI that I'm seeing at has jagged intelligence. It's also pretty bad at multimodal reasoning. I don't think it has a good model of the world, although there are all these world model companies coming up. Although I think they confuse something that looks like a world that you navigate in, which people are like, "Oh, that's a world model cuz it looks like you're generating something that looks like a world that I can wander around in." That's not a world model. A world model is when you have an agent that has a model of the world inside its head, which allows it to take actions and then predict the consequences of its actions and then adjust its own behavior based on what happened, whether it learned or not, so have like a reinforcement learning loop. That's a world model. And so we're seeing world model companies emerging. I think Yann LeCun famously did one recently with Jeppa. And so we are going to see new kinds of models, new kinds of agents, new kinds of intelligence. Are we going to get to AGI? I don't know. Now that's the same thing that everybody's trying to figure out, right? But this world is changing. The famous meme I think at X was like nothing ever happens, right? I think that's over. I haven't quite been able to put my finger on why. But I think anyone who is paying attention would tell you that post COVID the world is changing a lot faster. There was some dislocation around COVID or perhaps it was just we were in unstable equilibrium and COVID just broke that equilibrium then we had a phase shift. But the world seems to be moving a lot faster now. And that's true geopolitically, that's true economically, that's true technologically. VCs are now being forced to fund more hardware, rockets, drones, AI, you know, sci-fi technologies if you would call it. So I think sci-fi technologies are in high demand. Sci-fi scientists and sci-fi authors are in low supply. Sci-fi engineers are in low supply. So we are seeing the world shift and maybe it's for the better or it's for the worse, but things are changing very very fast now. We are living within that Chinese curse of may you live in interesting times.

在硬件领域，你有什么想弄明白的吗？

Is there anything you're trying to figure out in the world of hardware?

我认为无人机仍然被低估了，尽管它们最近在战场上崭露头角。我们离无人机的终局还差得远。我没什么特别想弄明白的。我的意思是，无人机防御会非常困难，因为攻击无人机既有动能优势（它从上方俯冲），又有突袭优势（攻击方可以把所有攻击无人机集中在一个区域，而防御方总是分散的）。防御方有一个优势是短距离——防御方上升的航程比攻击无人机飞来的航程小得多。但我认为无人机战争改变了社会中的暴力结构，实际上会从根本上改变军队和整个国家的架构。你可以说现代国家是步枪的产物，因为步枪让一个前农民能在战场上干掉一个封建骑士。然后你需要工厂造步枪，训练火枪手，武装他们，训练他们，所以民族国家兴起并取代了封建国家，成为合适的组织形式。核武器之后，只有七到九个真正独立的主权国家，其他人都生活在别人的核保护伞下。

I think drones are still under leveraged even though they've come to prominence in the battlefield recently. We still haven't seen anywhere near the end game of drones. There's nothing in particular I'm trying to figure out there. I mean I think drone defense is going to be very difficult because a drone that's attacking has the advantage of both kinetic energy cuz it's coming down on you and it's got the advantage of surprise where the attacker can mass all the attack drones in one area where the defender is always spread thin. The defender has one advantage which is short range. The defender has to traverse a much smaller range going up than the attacking drone probably had to cover coming in. But I think that drone warfare changes the structure of violence in society so it's going to actually fundamentally change how militaries and entire states are architected. You could argue that the modern state rose up as a consequence of the rifle because a rifle allowed a former peasant to take down a feudal knight on the battlefield. Then you need a factory to make rifles and you had to drill musket men and arm them and train them and so nation-states sprung up and became dominant instead of feudal states as the right structure to do that within. And then post-nuclear, there's only seven to nine really independent sovereign nations and everybody else lives underneath someone else's nuclear umbrella.

所以，无论是在安理会还是其他地方，那七到九个国家说了算。1945 年后，核武器成了新的逻辑暴力。现在，最新的逻辑暴力是无人机，这将再次从根本上改变游戏规则。因为无人机把相互确保摧毁的逻辑降到了个人层面。如果你真的恨某人，未来无人机就能干掉他。这是一种奇怪的暴力形式，将从根本上重构我们所知的社会。我不知道它会走向何方。是少数几个非常庞大、非常强大的国家控制所有无人机，还是无人机变得如此民主化，以至于任何个体都能致命？

So those seven to nine call the shots whether in the Security Council or elsewhere. And so nuclear weapons were the new logical violence after 1945. Now the newest logical violence is drones and that's going to fundamentally shift the game again. Because drones bring the logic of mutually assured destruction down to the individual level. If you really hate somebody, in the future, a drone will be able to get them. That's a weird form of violence coming up that's going to basically restructure society as we know it. I don't know which way it goes. Is it going to be the case that you have a few very large, very powerful countries that control all the drones or is that drones get so democratized that any individual can be deadly?

另外，我认为 AI 带来的一个恐惧是生物武器。我不想让大家紧张，但理论上，过去如果你足够聪明，你可能能搞明白如何制造生物武器，但能做到的人——既有专业知识又有渠道的人——非常少。不过，这个数量还是太高了，因为新冠病毒恰好就在武汉生物武器实验室附近被释放了，它自己就搞定了。所以，现在这种能力将被民主化，就像 vibe coding 被民主化一样。现在能 vibe coding 的人比过去能编程的人多几十万倍，同样，能接触到生物武器或病毒的人也比以前多了几十万倍。这真是个可怕的想法。

Also, I think one of the fears with AI is biological weapons. I don't want to get people worked up but in theory, if you were smart in the past, you could have figured out how to make a biological weapon but the number of people who could have done it, who had both the expertise and had the access were very low. Although, it was still too high because the coronavirus that coincidentally got unleashed right next to the bioweapons lab in Wuhan figured it out. So, now that power is going to be democratized just like vibe coding is democratized. Now the number of people who can vibe code is hundreds of thousands of times greater than the number of people who were coding and so the same way, the number of people who can get access to biological weapons or viruses is hundreds of thousands of times what could have gotten access to them before. So, that's a pretty scary thought.

现在，我们也可以反其道而行之，希望同样的 AI 也能研究如何制造疫苗或如何制造阻止它们的东西。但问题是，所有官方研究，所有好人研究，总是被法规挡在门外。几乎没有比医疗法规更糟糕的法规了。我认为，真正的机会之一是 AI 解决医学、生物学和疗法。但要做到这一点，你需要数据。你需要能够查看每个人的数据集。你需要能够查看所有结果。你想要尽可能多的数据。而这些数据隐藏在无数的孤岛和法规规则之后。出于充分的理由，你不想针对个人。但如果你能匿名化、清理并允许这些数据集公开，然后让人们有权尝试测试疗法，那么我认为你就能有合理的防御。但我担心这只会发生在紧急情况下。即使在新冠疫情期间，我们有紧急情况，疫苗也花了很长时间，结果还不太有效，但疫苗花了很长时间，因为我们不允许人们在自愿和尝试权的情况下操作。这花了太长时间。而我认为在过去，比如会有一群健康的年轻志愿者说：“当然，给我这种疫苗，然后给我新冠。我为大家牺牲。”但现在，因为所谓的生物伦理学家，我们甚至不允许那样做。官僚主义太多了。太多的人可以对少数试图做事的人说不。因此，我确实对未来有点担心。

Now, we can also do the opposite, which is hopefully now the same AIs can also research how to create vaccines or how to create things to stop them. But, the problem is that all the official research, all the good guy research, is always gated behind regulations. And there are almost no regulations out there as bad as medical regulations. One of the real opportunities out there, I think, is for AI to solve medicine and biology and therapies. But, to do that, you need the data. You need to be able to look at everyone's data set. You need to be able to look at all the outcomes. You want as much data as possible. And this data is hidden behind so many silos and so many regulations and rules. And for good reason, you don't want to target individuals. But, if you could anonymize, clean up, and allow that data set to get out there, and then you could let people test therapies with a right to try, then I think you could have reasonable defenses. But, my fear is this will only happen in emergency situation. Even during COVID, when we had the emergency situation, we took a long time with the vaccines, which turned out not to be that effective anyway, but it took a long time with the vaccines because we just didn't let people operate under volunteer situations and right to try. It just took way too long. Whereas, I think in the old days, like you would have a bunch of healthy young volunteers would have said, "Sure, give me this vaccine and then give me COVID. I'll take one for the team." But, now because of quote-unquote bioethicists, we don't even allow that. There's just too much bureaucracy assistant. Too many people who can say no to the few people who are trying to get things done. And so, for that, I do worry a little bit about the future.

硬件还有什么有趣的？我认为硬件将经历一场复兴，因为历史上很多硬件的问题在于很难编写好的软件。所以，你看到各种不可思议的硬件出现，但软件很糟糕，设备本身运行不佳。苹果做得很好，因为他们将硬件与高质量软件整合在一起。你知道，大多数公司只做好一两件事。苹果把两件事做得非常好：他们制造出色的硬件，他们开发出色的软件。但他们不太擅长云和 AI。谷歌非常擅长云，非常擅长 AI，但他们在硬件方面并不擅长，例如。而软件，我认为他们擅长某些类型的软件。他们擅长云软件，但不擅长消费软件。现在，突然之间，所有这些非常擅长硬件但不擅长软件的公司，可以做出足够好的软件。或者他们甚至不需要做软件。我的 AI 智能体将直接与硬件交互，不再需要软件。所以，如果你是一个制造安防摄像头的人，或者制造儿童玩具的人，或者制造可编程灯具的人，突然之间，这些软件变得容易多了。你可以让一个聪明的孩子用云代码直接进去，为你构建所有你需要的软件。或者你可能根本不需要任何软件，因为你的安防摄像头由每个人的智能体控制，不再需要定制软件。所以，我认为硬件本身正通过软件被解锁。

What else is interesting in hardware? Hardware, I think, is going to undergo a renaissance because historically, the problem with a lot of hardware is that it's very hard to write good software. And so, you get all this incredible hardware coming out, but the software is terrible, so the device itself doesn't function well. Apple has done really well because they integrate hardware with high-quality software. You know, most companies do one or two things well. Apple does two things really well. They build great hardware, they build great software. They're not that good at cloud and AI. Google is very good at cloud, very good at AI, but they're not really good at hardware, for example. And software, I would say they're good at certain kinds of software. They're good at cloud software, they're not good at consumer software. Now, all of a sudden, you have all these companies that are very good at hardware, but not good at software, they can make good enough software. Or they don't even need to make software. My AI agent will interact with the hardware directly and not need software anymore. So, if you're someone, for example, who is making security cameras, or you're making like toys for kids, or you were making programmable lamps, all of a sudden the software for that just got a lot easier. You can have some bright kid with Claude Code just get in there and build you all the software that you need. Or maybe you don't need any software because your security cameras are not controlled by each person's agent and don't need custom software any longer. So, I think that hardware itself is getting unlocked through software.

我认为这也是中国如此热衷于开源的原因之一。现在，他们落后了。所以，当你落后时，你试图通过开源来追赶。我认为这也有一点民族自豪感，我们在一起。也许政府资助他们并鼓励他们做开源。但这也很符合他们的硬件主导地位。中国制造了大部分消费电子产品。所以，对他们来说，开源非常有利，因为它使他们的互补品商品化。英伟达也一样。英伟达只想尽可能多地销售显卡，所以他们希望人们尽可能多地使用 AI 模型。所以他们希望一切都是开源的。所以，你有一堆硬件玩家，包括中国大部分公司和英伟达，他们的动机是：“嘿，一切都应该是开源的。”超大规模云服务商也希望一切都是开源的。所以他们推动 AI 模型的开源，然后这使软件商品化，而软件解锁了更多硬件。所以，我认为我们将看到越来越多有趣、可用的硬件，因为现在软件已经足够成熟，硬件变得解锁且相当可用。

And this is, I think, one of the reasons why China is so big into open source. Now, they're behind. So, when you're behind, you try to catch up through open source. I think also it's a little bit of their nationalist pride that we're in it together. Maybe the government's funding them and encourage them to do open source. But it also plays well into their hardware dominance. China is manufacturing most of the consumer electronics goods. And so, for them, open source is hugely beneficial because it commoditizes their complement. Same thing for Nvidia. Nvidia just wants to sell as many cards as possible, so they want people to use many AI models as possible. So, they want it all to be open source. So, you have a bunch of hardware players, including most of China and Nvidia, whose incentive is, "Hey, it should all be open source." Hyperscalers also, they want it all open source. So, they drive open source on the AI models, and then that commoditizes software, and the software unlocks more hardware. So, I think we're going to see more and more interesting, usable hardware because now the software is figured out enough that that hardware becomes unlocked and quite usable.

我对未来并不感到害怕或焦虑，部分原因是我是一个盲目的乐观主义者，部分原因是我生活在第一世界。

I don't get scared or worked up about the future partly because I'm a blind optimist and partly because I live in the first world.

是的，我不为此焦虑，因为我认为想象末日场景比想象积极场景容易得多，因为乐观需要创造力。例如，失业问题就是一个明显的例子。很容易看到现有工作如何消失，但很难预测下一个工作是什么。然而，不可避免地，总会有下一个工作。正因为如此，我认为人们倾向于关注末日场景。想象毁灭的方法比想象崛起的方法容易得多。

Yeah, I don't get worked up about it because I think it's just so much easier to imagine doom scenarios than it is to imagine positive scenarios because optimism requires creativity. For example, the job loss thing is a clear example. It's very easy to look at existing jobs and see how they will go away, but it's very hard to predict what the next job will be. But yet, inevitably, there's always the next job. Because of that, I think people tend to fixate on the doom scenarios. It's much easier to imagine the methods of doom than to imagine the methods of rising up.

200 年前，没有人能想象我们今天在技术进步、资本主义、经济以及各种社会崛起方面会走到哪一步。他们根本无法想象。他们想象不到今天存在的 10%的工作，因为那时每个人都在农场干活。但尽管如此，我们还是走到了今天。

There is no one 200 years ago who could have imagined how we would end up where we are today in terms of technological advancement, capitalism, economics, and the rise of various societies. They just couldn't have imagined it. They couldn't have imagined 10% of the jobs that exist today because back then everybody was working on a farm. But nevertheless, here we are.

同样地，我认为他们想象的末日场景实际上和我们今天想象的非常相似。比如，即使 100 年前，在我活着的每十年里，总会有新的环境灾难出现。有人谈论环境导致的世界末日。然后每十年，又会有战争导致的世界末日。是的，有时候确实很接近。新冠疫情很可怕。如果新冠病毒真的变成一种更恶性的病毒，我们可能会陷入困境。如果发生第三次世界大战，开始交换核武器，那将是一个非常糟糕的场景。

So, the same way, I think the doom scenarios they imagined are actually very similar to the same doom scenarios that we imagine today. Like, even 100 years ago, every decade I've been alive, there's been a new environmental catastrophe to come along. Someone's talking about the end of the world because of the environment. And then, every decade, there's a catastrophe coming along because of a war that's going to end the world. And yeah, sometimes you get really close. COVID was scary. If COVID had actually turned out to be a much more nasty virus, we could have been in a bad spot. If there was a World War III where we start exchanging nukes, that would be a very bad scenario.

所以，这些事情更容易想象。它们对我们的思维来说更清晰，所以我们更倾向于关注它们。此外，结果如此灾难性，人们显然会纠结于此，但我认为很难想象创造力。很难保持乐观。所以我认为我们必须培养乐观精神。我们必须奖励乐观。我们必须非理性地乐观，因为无论如何这是唯一的出路。所以每当人们像桶里的螃蟹一样试图把乐观者拉下来，不断说末日末日末日时，他们可能是对的，但这肯定无济于事。那不是你想在战壕里一起待的人。

So, these things are easier to imagine. They're more legible to our minds, so we hold them closer to us. Plus, the outcome there is so catastrophic that people obviously fixate on it, but I think it's very hard to imagine creativity. It's very hard to be optimistic. And so I think we have to nurture optimism. We have to reward optimism. We have to be irrationally optimistic because that's the only way out of this anyway. So whenever people sort of do the crabs in a bucket thing where they're trying to pull the optimist back down and they keep saying doom doom doom, they might be right, but it's certainly not helping matters. That's not the person you want to be in a foxhole with.