‘Nothing Ever Happens’ Is Over
频道: Naval
视频: https://www.youtube.com/watch?v=lIUEJqIDPcA
原文语言: en
统计: 共 13 轮 · Nivi 3 · Naval 10
[0:00] Nivi
You're listening to the Naval podcast. This is Nivei. There's no set topic for this episode. It will be a popuri. 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? 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 through him and 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 has reorganized the square around AI and maybe Toby at Shopify is doing that. You know, there's some guys who are very good at organizational management and they do these kinds of experiments. I've never been good at organizational management. I actually hate organizational management because 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.
你正在收听 Naval 播客。我是 Nivi。这一期没有固定主题，会是个大杂烩。Naval，你在 Impossible，也就是你现在这家公司里，是怎么用 AI 来改变你管理业务的方式的？还是说你们规模太小，又是一群各自独立的顶尖贡献者，AI 其实并没有影响到你们实际运营公司的方式？更多是后一种情况。我们是一个中心辐射式（hub and spoke）的架构。我的联合创始人是 CEO，所有人某种程度上都向他汇报。他就像那个唯一的产品经理，脑子里装着所有事情到处跑，努力把这个不可能完成的任务整合到一起，大家都通过他来对接，而且大家都挺聪明的。我们保持一个非常扁平的结构，我们尽量推动大家彼此直接沟通。我们甚至都不用 Slack，你大概能体会到那种感觉。所以我们没有明确地把 AI 当成内部的沟通方式，但隐性地说，AI 仍然非常有帮助。所以我们不像 Square。我知道 Jack Dorsey 已经围绕 AI 重组了 Square，可能 Shopify 的 Toby 也在这么做。你知道，有些人非常擅长组织管理，他们会做这类实验。我从来都不擅长组织管理。我其实讨厌组织管理，因为我讨厌组织。我讨厌大群体。我觉得在大群体里把事情做成实在太难了，你打交道的也不是最优秀、最聪明的人，而且总是有办公室政治。所以我就是更喜欢把团队保持得小一点，我们指望每个人都能独立运作，并在需要时彼此沟通。
[1:22] Naval
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 who 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 they have a bunch of VPs or SVPs reporting to them. and then you have a bunch of VPs below that then middle man measures 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 is 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 to fully interconnected graph and that's insane. Fully interconnected graph is everyone talking to anyone with a light hub in 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.
就像我说的，我们甚至都不用 Slack。我们不用任何项目管理软件。我想就只有 GitHub。然后当大家想互相交流时，他们就直接发短信。真的就是一对一地谈。有时候会很混乱，他们得自己琢磨该去找谁、怎么找到对的路径。但这本身就是技能的一部分。这有点像计算机网络。你怎么组织一个网络才能高效？因为到某个点上，沟通的开销会变得非常高。传统的答案是层级制，是树状系统。就是顶端有一个人，CEO，然后他们手下有一堆 VP 或 SVP 向他们汇报，再往下又有一堆 VP，然后是中层管理者，以此类推，这能让事情保持有序、朝一个方向前进，但它很压抑，有很多办公室政治，你没法跟低你两三级的人说话，除非你进入「创始人模式」，像 Elon 或 Brian Chesky 那样，而且这还被当成某种了不起的成就来庆祝——突然之间 CEO 居然被允许跟一个工程师说话了。你能听出来我在讽刺。我就是觉得那是一种很糟糕的运作方式，但它是规模的必然要求，而我们还没到那个规模，所以我不喜欢它。相反，我喜欢完全互联的图（fully interconnected graph），而那是疯狂的。完全互联的图就是每个人都能跟任何人交流，配上一个轻量的中心辐射结构，中间有一个人努力把所有事情记在脑子里。在网络里，完全互联图的特点是每个节点都必须高度智能。所以你要做的就是这个——你雇用高度聪明的人，他们能在一个完全互联的图里运作。
[2:41] Naval
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 hierarchal organization where they're going to be more comfortable. So, we don't really rely on any tools. This episode is presented by USV, 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. USVC 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 Versell. As USVC 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 illquid. 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/mpodcast.
如果他们没法自己找到路径、去找到那个能解决某个具体问题的人，或者他们没法跟其他人协作或沟通，那他们就不属于这种组织，他们应该去找一个层级制的组织，在那里他们会更自在。所以我们其实不怎么依赖任何工具。本期节目由 USV 呈现，这是一家每个美国人都能投资的公开风险投资基金。风险投资对大多数投资者来说一直遥不可及。你只能把鼻子贴在玻璃上，眼看着那些公司在私募市场里复利增长到万亿美元估值，而你只能等着它们上市。USVC 就是为改变这一点而建的。它是一篮子横跨各个阶段的高成长风险投资，经 SEC 注册、由专业团队管理，没有合格投资者认证要求，最低门槛只要 500 美元。目前只面向美国投资者。这只基金包含 OpenAI、Anthropic、xAI 和 Vercel。随着 USVC 加入更多公司，投资者也会持有那些公司的一份。而 Naval 是投资委员会的主席。风险投资不适合用你明天就要用的钱。它有风险、流动性差。不要投入任何你输不起的钱。但如果你有这个胃口，要让一美元发挥作用，可能没有比真正的风险投资更拼命的方式了——世界上最聪明的年轻人以疯狂的工作时长去构建未来。投资很简单，去 usvc.com/npodcast。
[3:54] Naval
Before investing, carefully read the objectives, risks, fees, and expenses in the funds perspectus at usvc.com. Investing in USVC is speculative, high-risisk, 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. 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 a codebase 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 internet as much anymore. You don't need the explicit marking down of things because the AI can figure out where you are. You could even unleash the AI on the codebase on the designs. Like say you have 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 Gant 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 divisions lacking resources. AI can constantly be doing this data analysis and digging and reporting for you. Reports on demand.
在投资之前，请在 usvc.com 上仔细阅读基金募集说明书里的投资目标、风险、费用和支出。投资 USVC 是投机性的、高风险的，份额流动性差，没有公开交易市场。这只基金是新的，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红，投资者可能损失全部或大部分投资。过往业绩不保证未来收益。由 Alps Distributors, Inc. 分销。回过头说，AI 在组织内部隐性地仍然是个非常有帮助的工具，我可以给你举两个例子，虽然还有更多。一个就是，如果你在读别人写的、非常复杂的代码，你可以直接让 AI 帮你读，给你一个摘要。论文也一样，它们能读别人的论文给你做总结。它其实可以遍历一个代码库，告诉你组织里谁可能是某个主题的专家，并把你引导到那个人那里。所以 AI 可以替你做很多这种挖掘工作。你不再那么需要显式的内部网了，你不再需要把东西显式地记录下来，因为 AI 能弄清楚你处在什么位置。你甚至可以把 AI 放到代码库上、放到设计图上。比如说你有硬件设计，你可以把它放到设计图上。如果你有供应商和厂商，你可以把它放到那个存放所有供应商和厂商文档的数据库或文件夹上。如果你愿意，你甚至可以把它放到公司邮箱上，然后问：我们现在到哪儿了？我们离真正出货到底还有多远？根据你认为我们在预估和时间线上实际所处的位置，给我画一张甘特图，谁落后了、谁领先了、哪个部门缺资源。AI 可以持续地替你做这种数据分析、挖掘和汇报。按需出报告。
[5:26] Naval
You don't need specific charts and dashboards and business integration systems. You can just have AI literally recreate it on the fly. You maybe don't want doing 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. The other is that traditionally in a company you would have the hardware people in a company like RG, the hardware people, you the software people, you' be 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 touch points 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 you just bypass the AI and 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.
你不需要特定的图表、仪表盘和商业集成系统。你可以直接让 AI 实时把它重新生成出来。你也许不想每次都这么做，因为可能太慢，但你可以让它按需搭建这些仪表盘，也可以让它按需更新。所以这是一个巨大的好处。另一个是，传统上在一家公司里，你会有硬件的人、软件的人、AI 的人，他们基本上不会去做彼此的工作。但现在有了 AI，他们至少能做到彼此工作的百分之二三十。所以这让他们之间的衔接稍微容易一些。比如说 AI 的人，如果需要测试什么东西，他们可以自己创建软件测试框架。也许不适合用于生产部署，但这总比干坐着等一个软件的人过来给你写点定制代码要好。同样地，硬件的人也可以写一点软件来启动一个新的硬件设备，而原本他们可能得等软件的人。所以让 AI 介入，能让每个人都做一点点各种事。它让大家更通才化。而通过变得更通才，意味着你跟其他人对接时有了更好的接触点。你不一定非要让别人给你显式地写一个 API 来配合他们的代码。你其实可以直接让 AI 去发现一个 API，或者创建它自己的 API，或者你绕过 AI，在任何它想要的层面上直接连接——无论是在数据库里还是在代码库里。所以它天然就是一个力量倍增器，但我们并没有针对它做任何明确的部署。
[6:55] Nivi
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. 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 igopoly business? Does it top out at some point? Do they run out of 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 this just mag 2 or mag one 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 worlds 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.
你现在正试图搞清楚什么问题？我问这个的原因是，你很少有机会看到聪明人正在进行中的工作成果。我的执念之一就是试图发掘聪明人的秘密和内心想法。这个世界跟几年前已经非常不一样了。有两家、可能四家公司正在主导 AI，如果把硬件领域的 Nvidia 算进去就是五家。问题是，这是一个稳定的局面吗？这会变成一门大宗商品化的生意，还是会变成一门垄断生意，或者会是寡头垄断的生意？它会在某个点上见顶吗？他们会用尽数据、模型会停止改进吗，还是我们会一路走到 AGI？实验室内部的人当然是 AGI 的信徒，他们认为所有价值都会被吸进 AI 实验室里。这最终会变得比「Mag 7」的世界还要更集中，变成「Mag 2」甚至「Mag 1」吗，还是说它会以某种方式碎片化？开源真的有机会吗，还是说人们永远只想要最聪明的模型，为此他们会放弃隐私、放弃开源，乖乖在云端付费？所以我觉得这些都是巨大的问题，巨大无比，是足以撼动世界的问题，但我不知道答案。你能以分布式的方式训练 AI 吗？分布式训练是可能的吗，还是说这些东西会越来越越来越集中化？我觉得现在的主流观点是会走向集中化训练，两到四家公司主导，数据中心和电力是瓶颈，而所有人都在朝那个方向冲。
[8:21] Naval
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 in 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 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 and I can wander around it." 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 it 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 Yan Lakun famously did one recently with Japa. 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?
但万一那是错的呢？那会是一个有意思的逆向押注。但我还没看到证据。我是说，我觉得 AI 这一部分正在形成的主流观点是对的。至于 AGI，我不知道。我不想干预言未来这一行。前沿实验室里的人当然相信它，他们相信这件事已经有相当一段时间了。我看到的 AI 有一种「参差不齐的智能」（jagged intelligence）。它在多模态推理上也相当糟糕。我不认为它对世界有一个好的模型，尽管现在冒出了一大堆「世界模型」公司。不过我觉得他们把某种东西搞混了——那种东西看起来像一个你可以在里面穿行的世界，人们就说：「哦，那是个世界模型，因为看起来你在生成一个看起来像世界的东西，我还能在里面到处逛。」那不是世界模型。世界模型是指你有一个智能体，它脑子里有一个关于世界的模型，这让它能采取行动，然后预测它行动的后果，再根据发生的事情调整自己的行为，无论它是否学到了东西。所以要有一个像强化学习那样的循环。那才是世界模型。所以我们正在看到世界模型公司涌现。我记得 Yann LeCun 最近就很有名地做了一个，叫 JEPA。所以我们将会看到新种类的模型、新种类的智能体、新种类的智能。我们会走到 AGI 吗？我不知道。而这正是每个人都在试图搞清楚的同一件事，对吧？
[9:35] Nivi
But this world is changing. The famous meme I think on 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 postcoavid the world is changing a lot faster. There was some dislocation around COVID or perhaps it was just we were in unstable equilibrium and CO 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 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, maybe 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. Is there anything you're trying to figure out in the world of hardware?
但这个世界正在变化。X 上有个很有名的梗，叫"什么都不会发生"，对吧？我觉得那个时代已经结束了。我还没法完全说清楚为什么，但我想任何留心观察的人都会告诉你，疫情之后世界变化的速度快了很多。COVID 前后出现了某种错位，又或许只是我们原本就处在一种不稳定的均衡里，而 COVID 恰好打破了那个均衡。然后我们经历了一次相变，但现在这个世界似乎运转得快多了——地缘政治上如此，经济上如此，技术上也是如此。VC 现在被迫去投更多硬科技，火箭、无人机、AI，你可以说是那些科幻级的技术。所以我觉得现在科幻级的技术需求很旺盛，而科幻级的科学家、科幻级的作家供给却很少，科幻级的工程师供给也很少。所以我们看到这个世界正在转变，也许变得更好，也许变得更糟，但现在事情变化得非常非常快。我们正活在那句中国诅咒里——"愿你生活在有趣的时代"。在硬件领域，有没有什么是你正想搞明白的？
[10:43] Naval
I think drones are still underleveraged. Even though they've come to prominence in the battlefield recently, we still haven't seen anywhere near the endgame 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 surprise where the attacker can mass all the attack drones in one area. Whereas 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. Then 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. So those seven to nine call the shots whether in the security council or elsewhere. And so nuclear weapons were the new logic of violence after 1945. Now the US logic of 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? 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 corona virus 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 or 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 or thousands of times what could have gotten access to them before. So that's a pretty scary thought. 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.
我觉得无人机的潜力还远远没被挖出来。尽管它们最近在战场上崭露头角，但我们离无人机的终局形态还差得很远。倒没有什么特别的东西是我正想搞明白的。我的意思是，我觉得无人机防御会非常困难，因为发起攻击的无人机同时占了两个便宜：一是动能优势，因为它是俯冲下来打你的；二是突袭优势，攻击方可以把所有攻击无人机集结到一个区域，而防御方总是兵力分散。防御方只有一个优势，就是射程近——防御方往上拦截要覆盖的距离，比攻击无人机打进来要覆盖的距离小得多。但我觉得无人机战争会改变社会中暴力的结构，所以它实际上会从根本上改变军队乃至整个国家的架构方式。你可以说，现代国家的崛起是步枪带来的结果，因为步枪让一个昔日的农民能在战场上撂倒一个封建骑士。然后你需要工厂来造步枪，你得操练火枪手、给他们配枪、训练他们。于是民族国家应运而生，并取代封建国家成为支配性的形态，因为它是干这件事的正确组织结构。再到核时代之后，真正独立主权的国家只剩七到九个，其他所有国家都活在别人的核保护伞之下。所以是那七到九个国家说了算，无论在安理会还是别的地方。所以 1945 年之后，核武器成了暴力的新逻辑。而现在美国的暴力逻辑是无人机。这又会从根本上改变这盘棋，因为无人机把"相互保证毁灭"的逻辑下沉到了个体层面。未来如果你真的恨某个人，一架无人机就能够得着他。这是一种正在出现的诡异暴力形式，它基本上会重构我们所认识的社会。我不知道它会往哪个方向走。会不会是少数几个非常庞大、非常强大的国家控制所有无人机？还是说无人机被极度民主化，任何个人都能变得致命？另外，我觉得人们对 AI 的恐惧之一是生物武器。我不想把大家搞得人心惶惶，但理论上，过去如果你足够聪明，你也能琢磨出怎么造一件生物武器。只是同时具备专业知识和获取渠道、能真正做成这件事的人非常少——尽管还是太多了，因为那个碰巧在武汉生物武器实验室隔壁被释放出来的冠状病毒就把这事搞成了。所以现在这种能力会被民主化，就像 vibe coding 被民主化一样。如今能 vibe coding 的人数，是过去会写代码的人数的几百倍甚至几千倍。同样的道理，能接触到生物武器或病毒的人数，也会是过去能接触到的人数的几百倍甚至几千倍。所以这是个相当吓人的念头。当然我们也可以反过来做，希望现在同样的这些 AI 也能去研究怎么造疫苗、怎么造出阻止它们的东西。但问题在于，所有官方研究、所有"好人"做的研究，永远都被卡在监管后面。而世上几乎没有哪种监管像医疗监管那么糟糕。我觉得现在真正的机会之一，是让 AI 去攻克医学、生物学和疗法。但要做到这点，你需要数据，你得能看到每个人的数据集，你得能看到所有的治疗结果。
[14:08] Naval
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 the right to try then I think you could have reasonable defenses. But my fear is this will only happen in an 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 had a bunch of healthy young volunteers would have said sure give me this vaccine and then give me co I'll take one for the team. But now because of quote unquote bioethicists we don't even allow that. There 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. 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 highquality software. You know most companies do one or two things well. Apple does two things really well.
你想要尽可能多的数据。可这些数据被藏在层层数据孤岛、层层监管和规则之下——而且是有正当理由的，你不想针对到具体的个人。但如果你能把那个数据集匿名化、清洗干净并让它流通出来，然后允许人们在"试药权"的前提下去测试疗法，那我觉得你就能建立起像样的防御。但我担心的是，这只会在紧急状况下才发生。即便在 COVID 那种我们身处紧急状况的时候，我们搞疫苗也花了很长时间——而那些疫苗后来证明也没那么有效。但搞疫苗之所以花那么久，就是因为我们不让人们在自愿和试药权的框架下行动，实在拖得太久了。我觉得换作过去，会有一群健康的年轻志愿者站出来说"行，把疫苗给我，再让我感染 COVID，我为团队牺牲一下"。可现在因为所谓的"生物伦理学家"，我们连这都不允许。官僚程序实在太多了，能对那少数几个想把事情做成的人说"不"的人实在太多了。所以就这一点，我对未来确实有点担心。硬件方面还有什么有意思的？我觉得硬件会迎来一场文艺复兴。因为历史上很多硬件的问题在于，写出好的软件太难了。所以你看到各种了不起的硬件冒出来，但软件烂得一塌糊涂，结果设备本身就用得不顺。Apple 做得特别好，是因为他们把硬件和高质量的软件整合在了一起。你知道大多数公司只能把一两件事做好，Apple 能把两件事都做得非常好。
[15:36] Naval
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 very 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. or my AI agent will interact with the hardware directly and I don't need software anymore. So if you're someone for example who was making security cameras or you were 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 cloud 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. 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 encouraging 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 compliment. Same thing for Nvidia.
他们造出色的硬件，他们也做出色的软件。他们在云和 AI 上不太行。Google 在云上很强，在 AI 上很强，但比如说在硬件上就不太行。软件嘛，我会说他们某些类型的软件做得好——他们擅长云端软件，但不擅长消费级软件。而现在，突然之间，冒出来一大批硬件做得很好、但软件做不好的公司。它们可以做出"够用就行"的软件，或者它们甚至根本不需要做软件——又或者是我的 AI agent 会直接和硬件交互，我不再需要软件了。所以比如说你是个做安防摄像头的，或者你是做儿童玩具的，或者你做可编程的灯，突然之间这些东西的软件就变得容易多了。你可以找个聪明的小孩用 Claude Code 钻进去，把你需要的所有软件都搭出来；又或者你压根不需要任何软件，因为你的安防摄像头不再由每个人各自的 agent 来控制，也不再需要定制软件了。所以我觉得硬件本身正在通过软件被解锁。我想这也是为什么中国如此热衷开源的原因之一。眼下他们落后，所以当你落后的时候，你会试图通过开源来追赶。我觉得这里面也有一点他们的民族自豪感——"我们是一条船上的"。也许是政府在资助他们、鼓励他们做开源。但这同时也很好地契合了他们在硬件上的主导地位。中国制造了大部分的消费电子产品，所以对他们来说，开源是极其有利的，因为它把他们的"互补品"商品化了。Nvidia 也是同样的道理。
[17:01] Naval
Nvidia just wants to sell as many cards as possible. They want people to use as 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.
Nvidia 只想尽可能多地卖卡。他们希望人们尽可能多地使用 AI 模型，所以他们希望这一切都开源。所以你就有了一批硬件玩家——包括大半个中国和 Nvidia——他们的动机都是"嘿，这一切都该开源"。超大规模云厂商也一样，他们也希望全都开源。所以是他们在推动 AI 模型的开源，而这又把软件商品化了，软件再去解锁更多硬件。所以我觉得我们会看到越来越多有意思、好用的硬件，因为现在软件已经搞定到一定程度，那些硬件就被解锁了，变得相当好用。
[17:35] Naval
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. 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 a 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. There is no one no one 200 years ago who could have imagined how we would end up where we are today in terms of technological advancement and capitalism and economics and you know 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. 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 CO 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 II where we start exchanging nukes, that would be a very bad scenario. So these things are easier to imagine. They're more legible to our mind. 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 try 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.
我不会对未来感到害怕或焦虑，一部分是因为我是个盲目的乐观主义者，一部分是因为我生活在第一世界。是啊，我不会为它焦虑，因为我觉得想象末日场景实在比想象正面场景容易太多了——因为乐观需要创造力。比如说失业这件事就是个很清楚的例子。盯着现有的工作、看出它们会怎么消失，这很容易；但要预测下一份工作会是什么，就非常难——可不可避免地，总会有下一份工作。正因为如此，我觉得人们往往会执着于末日场景。想象毁灭的途径，比想象崛起的途径要容易得多。200 年前没有任何一个人能想象出，我们今天会在技术进步、资本主义、经济以及各种社会的兴起方面走到这一步。他们就是想象不出来。他们想象不出今天存在的工作里有 10% 是什么样的，因为那时候每个人都在农场里干活。但不管怎样，我们还是走到了今天。所以同样地，我觉得他们当年想象的末日场景，其实和我们今天想象的末日场景非常相似——哪怕一百年前也是。我活着的每一个十年，都会冒出来一场新的环境灾难，总有人在讲世界要因为环境而终结；然后每个十年又总有一场灾难要来，是因为一场会终结世界的战争。是啊，有时候确实会非常接近。COVID 是吓人的。如果 COVID 真的变成一种凶险得多的病毒，我们可能就陷入糟糕的境地了。如果爆发了一场二战那样的战争、我们开始互扔核弹，那会是非常糟糕的场景。所以这些事更容易被想象出来，它们对我们的头脑来说更"可读"，所以我们把它们抱得更紧。再加上那种结局如此灾难性，人们自然会执着于它。但我觉得创造力是很难想象的，乐观是很难做到的。所以我觉得我们必须去培育乐观，必须去奖励乐观，我们必须不理性地乐观——因为反正这也是唯一的出路。所以每当有人玩那种"桶里的螃蟹"的把戏，想把乐观主义者拽回去、不停地喊"末日、末日、末日"，他们也许是对的，但这肯定无助于事。那不是你想和他一起待在散兵坑里的那种人。