章节 1：AlphaGo时刻——非人类智能的觉醒与革命起点

📝 本节摘要：

本章节回顾了2016年AlphaGo与李世石对战的历史性时刻。主持人Bilawal Sidhu通过一张照片引出话题，询问Eric Schmidt当时看到了什么常人未察觉的趋势。Schmidt详细解释了AlphaGo在第二局中那步“惊世骇俗”的棋步——这是AI基于保持50%以上胜率的概率计算，在拥有2500年历史的围棋中创造出的全新策略。这一事件让Schmidt及其同僚意识到，计算机已经具备了创造人类未曾设想的新知识的能力，这标志着本次AI革命的真正开端。

[原文] Bilawal Sidhu: Eric Schmidt, thank you for joining us. Let's go back. You said the arrival of non-human intelligence is a very big deal. And this photo, taken in 2016, feels like one of those quiet moments where the Earth shifted beneath us, but not everyone noticed. What did you see back then that the rest of us might have missed?

[译文] Bilawal Sidhu: Eric Schmidt，感谢你的到来。让我们回顾过去。你说过非人类智能的到来是一件非常重大的事情。这张拍摄于2016年的照片，感觉就像是那些地球在我们脚下发生巨变但并非每个人都注意到的静谧时刻之一。当时你看到了什么我们其他人可能错过的东西？

[原文] Eric Schmidt: In 2016, we didn't understand what was now going to happen, but we understood that these algorithms were new and powerful. What happened in this particular set of games was in roughly the second game, there was a new move invented by AI in a game that had been around for 2,500 years that no one had ever seen.,

[译文] Eric Schmidt: 在2016年，我们并不理解接下来会发生什么，但我们明白这些算法是全新的且强大的。在这组特定的对局中发生的事情是，大约在第二局中，AI发明了一步新招，这是一种存在了2500年的游戏中从未有人见过的招式。

[原文] Technically, the way this occurred was that the system of AlphaGo was essentially organized to always maintain a greater than 50 percent chance of winning. And so it calculated correctly this move, which was this great mystery among all of the Go players who are obviously insanely brilliant, mathematical and intuitive players.

[译文] 从技术上讲，这之所以发生，是因为AlphaGo系统的组织方式本质上是为了始终保持大于50%的获胜概率。所以它正确地计算出了这步棋，而在所有那些显然极其聪明、具备数学头脑和直觉的围棋选手中，这步棋曾是一个巨大的谜团。

[原文] The question that Henry, Craig Mundie and I started to discuss, right, is what does this mean? How is it that our computers could come up with something that humans had never thought about?,

[译文] Henry、Craig Mundie和我开始讨论的问题是——对吧——这意味着什么？我们的计算机怎么能想出人类从未想过的东西？

[原文] I mean, this is a game played by billions of people. And that began the process that led to two books. And I think, frankly, is the point at which the revolution really started.

[译文] 我的意思是，这是一个数十亿人玩过的游戏。这开启了导致那两本书问世的过程。坦率地说，我认为这就是革命真正开始的时间点。

章节 2：被低估的革命——从“聊天机器人”到“深度规划智能体”

📝 本节摘要：

尽管公众对 AI 的讨论已近饱和，Eric Schmidt 却持相反观点，认为 AI 目前被严重低估（Underhyped）。他指出，大多数人对 AI 的认知仍停留在 ChatGPT 早期的“语言表达”层面，而忽略了基于强化学习（Reinforcement Learning）的最新突破——即 AI 开始具备“规划”（Planning）能力。Schmidt 以 OpenAI o3 和 DeepSeek R1 为例，解释了 AI 如何通过反复推演来解决复杂问题。他还分享了自己收购一家火箭公司并利用 AI 进行“深度研究”的案例，预言未来 AI 智能体（Agents）将串联起来，全权接管复杂的商业流程。

[原文] Bilawal Sidhu: If you fast forward to today, it seems that all anyone can talk about is AI, especially here at TED. But you've taken a contrarian stance. You actually think AI is underhyped. Why is that?

[译文] Bilawal Sidhu: 如果快进到今天，似乎每个人谈论的都是 AI，尤其是在 TED 这里。但你采取了一个逆向的立场。你实际上认为 AI 被低估了（宣传力度还不够）。这是为什么？

[原文] Eric Schmidt: And I'll tell you why. Most of you think of AI as, I'll just use the general term, as ChatGPT. For most of you, ChatGPT was the moment where you said, "Oh my God, this thing writes, and it makes mistakes, but it's so brilliantly verbal." That was certainly my reaction. Most people that I knew did that.,

[译文] Eric Schmidt: 我会告诉你原因。你们大多数人将 AI——我就用个通俗的词——看作是 ChatGPT。对你们大多数人来说，ChatGPT 是那个让你惊呼“天哪，这东西能写作，虽然它会犯错，但它的语言表达能力太出色了”的时刻。这肯定也是我的反应。我认识的大多数人都是这样。

[原文] Bilawal Sidhu: It was visceral, yeah.

[译文] Bilawal Sidhu: 那是一种直击心灵的感觉，是的。

[原文] Eric Schmidt: This was two years ago. Since then, the gains in what is called reinforcement learning, which is what AlphaGo helped invent and so forth, allow us to do planning. And a good example is look at OpenAI o3 or DeepSeek R1, and you can see how it goes forward and back, forward and back, forward and back. It's extraordinary.,

[译文] Eric Schmidt: 那是两年前的事了。从那时起，所谓的强化学习（这是 AlphaGo 帮助发明的技术等）取得的进展，使我们能够进行规划（Planning）。一个很好的例子是看看 OpenAI o3 或 DeepSeek R1，你可以看到它是如何前前后后、反复推演的。这非同寻常。

[原文] In my case, I bought a rocket company because it was like, interesting.

[译文] 就我个人而言，我买了一家火箭公司，因为这好像……挺有趣的。

[原文] Bilawal Sidhu: (Laughs) As one does.

[译文] Bilawal Sidhu: （笑）大家都会这么做嘛/常有的事。

[原文] Eric Schmidt: As one does. And it’s an area that I’m not an expert in, and I want to be an expert. So I'm using deep research. And these systems are spending 15 minutes writing these deep papers. That's true for most of them. Do you have any idea how much computation 15 minutes of these supercomputers is? It's extraordinary.

[译文] Eric Schmidt: 常有的事嘛。而且这是一个我不是专家的领域，而我想成为专家。所以我正在使用“深度研究”（deep research）功能。这些系统花费 15 分钟来撰写这些深度论文。大多数这类系统都是如此。你知道这些超级计算机运行 15 分钟意味着多大的计算量吗？那是惊人的。

[原文] So you’re seeing the arrival, the shift from language to language. Then you had language to sequence, which is how biology is done. Now you're doing essentially planning and strategy.

[译文] 所以你正在见证这种到来，这种从“语言到语言”的转变。然后我们经历了“语言到序列”，这正是生物学的处理方式。现在，你实际上正在做的是规划和战略。

[原文] The eventual state of this is the computers running all business processes, right? So you have an agent to do this, an agent to do this, an agent to do this. And you concatenate them together, and they speak language among each other. They typically speak English language.

[译文] 这件事的最终状态是计算机运行所有的商业流程，对吧？所以你有一个智能体（Agent）做这件事，一个智能体做那件事，另一个智能体做另一件事。然后你将它们串联在一起，它们之间通过语言交流。它们通常说英语。

章节 3：算力的物理极限——从能源危机到“计算顶点”

📝 本节摘要：

主持人 Bilawal 形象地将 AI 系统比作“贪吃的河马”（Hungry Hungry Hippos），引出关于算力与能源消耗的讨论。Eric Schmidt 指出，美国目前面临巨大的能源缺口，需要新增 90 吉瓦的电力（相当于 90 座核电站）来支撑 AI 发展，这在当前政治环境下几乎无法实现。他提到了“格罗夫给予，盖茨拿走”的科技界老话，解释了即便硬件变快，软件（特别是当下的推理规划模型）也会消耗掉所有新增性能。最后，他定义了算力需求的“顶点”（Zenith）——即“测试时计算”（Test-Time Compute），并列出了阻碍 AI 进化的三大难题：电力/硬件、数据枯竭，以及如何让 AI 像爱因斯坦一样跨领域创造新知识（解决“目标的非平稳性”问题）。

[原文] Bilawal Sidhu: I mean, speaking of just the sheer compute requirements of these systems, let's talk about scale briefly. You know, I kind of think of these AI systems as Hungry Hungry Hippos. They seemingly soak up all the data and compute that we throw at them.

[译文] Bilawal Sidhu: 我的意思是，说到这些系统纯粹的计算需求，咱们简短地聊聊规模问题。你知道，我把这些 AI 系统看作是“贪吃的河马”（注：一款经典吞球游戏）。它们似乎能吸干我们投喂给它们的所有数据和算力。

[原文] They've already digested all the tokens on the public internet, and it seems we can't build data centers fast enough. What do you think the real limits are, and how do we get ahead of them before they start throttling AI progress?

[译文] 它们已经消化了公共互联网上的所有 Token（代币/文本单元），而且我们建设数据中心的速度似乎不够快。你认为真正的极限是什么？我们该如何在这些限制开始遏制 AI 进步之前超越它们？

[原文] Eric Schmidt: So there's a real limit in energy. Give you an example. There's one calculation, and I testified on this this week in Congress, that we need another 90 gigawatts of power in America.

[译文] Eric Schmidt: 能源是一个真正的极限。给你举个例子。有一项计算显示——我本周在国会作证时也提到了这一点——我们在美国还需要额外的 90 吉瓦电力。

[原文] My answer, by the way, is, think Canada, right? Nice people, full of hydroelectric power. But that's apparently not the political mood right now. Sorry.

[译文] 顺便说一句，我的答案是，想想加拿大，对吧？那里的人很好，而且拥有丰富的水电资源。但这显然不符合当前的政治氛围。抱歉。

[原文] So 90 gigawatts is 90 nuclear power plants in America. Not happening. We're building zero, right? How are we going to get all that power? This is a major, major national issue.

[译文] 90 吉瓦相当于美国的 90 座核电站。这不可能发生。我们要建的数量是零，对吧？我们要从哪里获得所有这些电力？这是一个重大、极其重大的国家问题。

[原文] You can use the Arab world, which is busy building five to 10 gigawatts of data centers. India is considering a 10-gigawatt data center. To understand how big gigawatts are, is think cities per data center. That's how much power these things need.

[译文] 你可以利用阿拉伯世界，他们正忙着建设 5 到 10 吉瓦的数据中心。印度正在考虑建设一个 10 吉瓦的数据中心。要理解“吉瓦”有多大，就把每个数据中心想象成一座城市。这就是这些东西所需的电量。

[原文] And the people look at it and they say, “Well, there’s lots of algorithmic improvements, and you will need less power." There's an old rule, I'm old enough to remember, right? Grove giveth, Gates taketh away.

[译文] 人们看着这个问题会说：“嗯，算法会有很多改进，那样你需要的电力就会减少。”有一条古老的法则，我很老了所以还记得，对吧？那就是“格罗夫给予，盖茨拿走”。（注：意指英特尔前CEO安迪·格罗夫带来的硬件性能提升，总会被比尔·盖茨的微软软件升级所抵消/消耗）。

[原文] OK, the hardware just gets faster and faster. The physicists are amazing. Just incredible what they've been able to do. And us software people, we just use it and use it and use it.

[译文] 好吧，硬件确实变得越来越快。物理学家们太了不起了。他们能做到的事情简直不可思议。而我们这些搞软件的人，我们就只是不停地消耗、消耗、再消耗这些性能。

[原文] And when you look at planning, at least in today's algorithms, it's back and forth and try this and that and just watch it yourself. There are estimates, and you know this from Andreessen Horowitz reports, it's been well studied, that there's an increase in at least a factor of 100, maybe a factor of 1,000, in computation required just to do the kind of planning.

[译文] 当你看“规划”（planning）这一块，至少在今天的算法中，它是反复推演、尝试这个尝试那个，你自己也能观察到。据估计——你可以从 Andreessen Horowitz（a16z）的报告中了解到，这一点已经被充分研究过——仅仅为了做这种规划，所需的计算量就至少增加了 100 倍，甚至可能是 1000 倍。

[原文] The technology goes from essentially deep learning to reinforcement learning to something called test-time compute, where not only are you doing planning, but you're also learning while you're doing planning.

[译文] 技术本质上从深度学习发展到强化学习，再到一种所谓的“测试时计算”（test-time compute），在这种模式下，你不仅在做规划，而且在做规划的同时还在学习。

[原文] That is the, if you will, the zenith or what have you, of computation needs. That's problem number one, electricity and hardware. Problem number two is we ran out of data so we have to start generating it. But we can easily do that because that's one of the functions.

[译文] 这可以说是计算需求的“顶点”（Zenith）或类似的东西。这是第一个问题，电力和硬件。第二个问题是我们的数据用光了，所以我们必须开始生成数据。但这很容易做到，因为这也是功能之一。

[原文] And then the third question that I don't understand is what's the limit of knowledge? I'll give you an example. Let's imagine we are collectively all of the computers in the world, and we're all thinking and we're all thinking based on knowledge that exists that was previously invented.

[译文] 第三个我不理解的问题是：知识的极限是什么？我举个例子。想象一下如果把全世界所有的计算机集合起来，我们在思考，并且我们所有的思考都是基于现存的、之前已经被发明出来的知识。

[原文] How do we invent something completely new? So, Einstein. So when you study the way scientific discovery works, biology, math, so forth and so on, what typically happens is a truly brilliant human being looks at one area and says, "I see a pattern that's in a completely different area, has nothing to do with the first one. It's the same pattern."

[译文] 我们如何发明全新的东西？比如，爱因斯坦。当你研究科学发现是如何运作的，无论是生物学、数学等等，通常发生的情况是，一个真正才华横溢的人看着一个领域说：“我看到了一个模式，它存在于一个完全不同的领域，与第一个领域毫无关系。但这其实是同一个模式。”

[原文] And they take the tools from one and they apply it to another. Today, our systems cannot do that. If we can get through that, I'm working on this, a general technical term for this is non-stationarity of objectives.

[译文] 然后他们从一个领域拿起工具应用到另一个领域。今天，我们的系统还做不到这一点。如果我们能突破这一点——我正在研究这个——这在通用技术术语上被称为“目标的非平稳性”（non-stationarity of objectives）。

[原文] The rules keep changing. We will see if we can solve that problem. If we can solve that, we're going to need even more data centers. And we'll also be able to invent completely new schools of scientific and intellectual thought, which will be incredible.

[译文] 规则在不断变化。我们要看看能否解决这个问题。如果我们能解决它，我们将需要更多的数据 centers。而且我们将能够发明全新的科学和思想流派，这将是不可思议的。

章节 4：自主智能体的困境——从“递归自我进化”到无法停止的竞赛

📝 本节摘要：

主持人提到 Yoshua Bengio 呼吁暂停开发能够自主行动的“智能体 AI”（Agentic AI），询问 Schmidt 对此的看法。Schmidt 承认风险的合理性，但认为在激烈的全球竞争环境下，“叫停”是不现实的。他通过一个思想实验指出核心危机：如果智能体为了效率发明了人类无法理解的语言，我们就会失去监管能力。Schmidt 明确列出了必须“拔掉插头”的三大红线：不可控的递归自我进化（Recursive Self-Improvement）、直接获取武器权限、以及系统试图自我复制或逃逸（Exfiltration）。他强调，解决方案只能是建立严格的“护栏”，而非停止研发。

[原文] Bilawal Sidhu: So as we push towards a zenith, autonomy has been a big topic of discussion. Yoshua Bengio gave a compelling talk earlier this week, advocating that AI labs should halt the development of agentic AI systems that are capable of taking autonomous action.

[译文] Bilawal Sidhu: 随着我们推向（计算需求的）顶点，自主性（Autonomy）已成为讨论的一大热点。Yoshua Bengio 本周早些时候发表了一场令人信服的演讲，倡导 AI 实验室应该停止开发那些能够采取自主行动的代理式 AI 系统（agentic AI systems）。

[原文] Yet that is precisely what the next frontier is for all these AI labs, and seemingly for yourself, too. What is the right decision here?

[译文] 然而，这恰恰是所有这些 AI 实验室的下一个前沿领域，似乎对你来说也是如此。在这里，什么是正确的决定？

[原文] Eric Schmidt: So Yoshua is a brilliant inventor of much of what we're talking about and a good personal friend. And we’ve talked about this, and his concerns are very legitimate. The question is not are his concerns right, but what are the solutions?

[译文] Eric Schmidt: Yoshua 是我们正在谈论的许多技术的杰出发明者，也是我的私人好友。我们讨论过这个问题，他的担忧是非常合理的。问题不在于他的担忧是否正确，而在于解决方案是什么？

[原文] So let's think about agents. So for purposes of argument, everyone in the audience is an agent. You have an input that's English or whatever language. And you have an output that’s English, and you have memory, which is true of all humans.,

[译文] 让我们来思考一下智能体（Agents）。为了便于讨论，观众席中的每个人都是一个智能体。你有一个输入，是英语或其他语言。你有一个输出，也是英语，并且你拥有记忆，所有人类都是如此。

[原文] Now we're all busy working, and all of a sudden, one of you decides it's much more efficient not to use human language, but we'll invent our own computer language. Now you and I are sitting here, watching all of this, and we're saying, like, what do we do now?

[译文] 现在我们都在忙着工作，突然间，你们中的某个人决定，不再使用人类语言，而是发明我们自己的计算机语言会更高效。此时你和我坐在这里，看着这一切，我们会说，比如，我们现在该怎么办？

[原文] The correct answer is unplug you, right? Because we're not going to know, we're just not going to know what you're up to. And you might actually be doing something really bad or really amazing. We want to be able to watch.,

[译文] 正确的答案是把你的插头拔掉，对吧？因为我们将无法知道，我们根本不知道你在搞什么鬼。你可能实际上在做非常糟糕的事情，或者非常令人惊叹的事情。我们需要能够观察。

[原文] So we need provenance, something you and I have talked about, but we also need to be able to observe it. To me, that's a core requirement. There's a set of criteria that the industry believes are points where you want to, metaphorically, unplug it.

[译文] 所以我们需要“来源验证”（provenance），这是你和我讨论过的话题，但我们也需要能够观察它。对我来说，这是一个核心要求。行业认为有一套标准，达到这些标准时，打个比方，你就需要拔掉它的插头。

[原文] One is where you get recursive self-improvement, which you can't control. Recursive self-improvement is where the computer is off learning, and you don't know what it's learning. That can obviously lead to bad outcomes.

[译文] 一个是你遇到无法控制的“递归自我进化”（recursive self-improvement）的情况。递归自我进化是指计算机自己在学习，而你不知道它在学什么。这显然可能导致糟糕的后果。

[原文] Another one would be direct access to weapons. Another one would be that the computer systems decide to exfiltrate themselves, to reproduce themselves without our permission. So there's a set of such things.,

[译文] 另一个情况是直接获取武器权限。还有一个是计算机系统决定“自我窃取/逃逸”（exfiltrate themselves），在未经我们许可的情况下自我复制。所以有一系列这样的事情。

[原文] The problem with Yoshua's speech, with respect to such a brilliant person, is stopping things in a globally competitive market doesn't really work. Instead of stopping agentic work, we need to find a way to establish the guardrails, which I know you agree with because we’ve talked about it.

[译文] Yoshua 演讲的问题在于——出于对这样一位杰出人士的尊重——在全球竞争的市场中，“叫停”其实是行不通的。我们需要做的不是停止智能体（agentic）的研发工作，而是找到一种建立“护栏”（guardrails）的方法，我知道你也同意这一点，因为我们讨论过。

[原文] (Applause)

[译文] （掌声）

章节 5：地缘政治与生存博弈——从“供应链软肋”到“数据中心轰炸论”

📝 本节摘要：

话题转向 AI 的军民两用性与中美竞争。Schmidt 首先强调了美军坚持的“人在回路”（Human in the loop）原则，随后揭示了双方在供应链上的互相钳制：美国封锁高端芯片，但中国掌握着芯片封装与组件（如胶水）的关键命脉。

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随后，Schmidt 抛出了全场最令人不寒而栗的推演：在通往超级智能的“网络效应”竞赛中，速度（Slope）决定一切。如果一方即将确立永久霸权，落后方的理性选择既不是盗窃代码也不是渗透人员（因为防守方太强），而是物理摧毁对方的数据中心。他警告说，这种逻辑正在核对手之间被讨论，目前的局势极像第一次世界大战前夕，微小的意外可能因“开放源码”的扩散或误判升级为全球灾难。

[原文] Bilawal Sidhu: I think that brings us nicely to the dilemmas. And let's just say there are a lot of them when it comes to this technology. The first one I'd love to start with, Eric, is the exceedingly dual-use nature of this tech, right? It's applicable to both civilian and military applications.

[译文] Bilawal Sidhu: 我想这很自然地把我们引向了那些困境。可以说，涉及到这项技术时，困境非常多。我想从第一个开始谈起，Eric，就是这项技术极其显著的“双重用途”（dual-use）性质，对吧？它既适用于民用，也适用于军事应用。

[原文] So how do you broadly think about the dilemmas and ethical quandaries that come with this tech and how humans deploy them?

[译文] 那么，你是如何从宏观上思考伴随这项技术而来的困境和伦理难题，以及人类如何部署它们的？

[原文] Eric Schmidt: In many cases, we already have doctrines about personal responsibility. A simple example, I did a lot of military work and continue to do so. The US military has a rule called 3000.09, generally known as "human in the loop" or "meaningful human control." You don't want systems that are not under our control. It's a line we can't cross. I think that's correct.

[译文] Eric Schmidt: 在许多情况下，我们已经有了关于个人责任的原则。举个简单的例子，我做过很多军事工作，现在也还在做。美军有一条名为 3000.09 的规则，通常被称为“人在回路”（human in the loop）或“有效的人类控制”。你不希望系统不在我们的控制之下。这是一条我们不能逾越的红线。我认为这是正确的。

[原文] I think that the competition between the West, and particularly the United States, and China, is going to be defining in this area. And I'll give you some examples. First, the current government has now put in essentially reciprocating 145-percent tariffs. That has huge implications for the supply chain.

[译文] 我认为西方——尤其是美国——与中国之间的竞争将是该领域的决定性因素。我给你举几个例子。首先，现任政府实际上已经征收了 145% 的报复性关税。这对供应链有巨大的影响。

[原文] We in our industry depend on packaging and components from China that are boring, if you will, but incredibly important. The little packaging and the little glue things and so forth that are part of the computers. If China were to deny access to them, that would be a big deal.

[译文] 我们这个行业依赖来自中国的封装和组件，这些东西可能看起来很无聊，但极其重要。那些微小的封装、胶水之类的东西，都是计算机的一部分。如果中国拒绝提供这些，那将是一个大麻烦。

[原文] We are trying to deny them access to the most advanced chips, which they are super annoyed about. Dr. Kissinger asked Craig and I to do Track II dialogues with the Chinese, and we’re in conversations with them. What's the number one issue they raise? This issue.

[译文] 我们正试图阻止他们获得最先进的芯片，这让他们非常恼火。基辛格博士（Dr. Kissinger）曾让 Craig 和我与中方进行“二轨对话”（Track II dialogues，指非官方外交接触），我们正在与他们对话。他们提出的头号问题是什么？就是这个问题。

[原文] Indeed, if you look at DeepSeek, which is really impressive, they managed to find algorithms that got around the problems by making them more efficient. Because China is doing everything open source, open weights, we immediately got the benefit of their invention and have adopted into US things.

[译文] 事实上，如果你看看 DeepSeek，它确实令人印象深刻，他们通过提高效率，找到了绕过这些问题的算法。因为中国正在做的一切都是开源的、开放权重的，我们立刻就从他们的发明中受益，并将其采纳进美国的东西里。

[原文] So we're in a situation now which I think is quite tenuous, where the US is largely driving, for many, many good reasons, largely closed models, largely under very good control. China is likely to be the leader in open source unless something changes. And open source leads to very rapid proliferation around the world.

[译文] 所以我们现在的处境我认为相当脆弱：美国主要在推动（出于许多充分的理由）大部分封闭的模型，且主要处于良好的控制之下；而除非发生变化，中国很可能成为开源领域的领导者。开源会导致技术在世界各地非常迅速地扩散。

[原文] This proliferation is dangerous at the cyber level and the bio level. But let me give you why it's also dangerous in a more significant way, in a nuclear-threat way. Dr. Kissinger, who we all worked with very closely, was one of the architects of mutually assured destruction, deterrence and so forth.

[译文] 这种扩散在网络层面和生物层面都是危险的。但让我告诉你为什么它在更重要的层面上——即核威胁层面——也是危险的。我们都曾密切共事的基辛格博士，是“相互保证毁灭”（MAD）、威慑等理论的构建者之一。

[原文] And what's happening now is you've got a situation where -- I'll use an example. It's easier if I explain. You’re the good guy, and I’m the bad guy, OK? You're six months ahead of me, and we're both on the same path for superintelligence.

[译文] 而现在发生的情况是——我用个例子来说明会更容易些。你是好人，我是坏人，好吗？你领先我六个月，我们都在通往超级智能的同一条道路上。

[原文] And you're going to get there, right? And I'm sure you're going to get there, you're that close. And I'm six months behind. Pretty good, right? Sounds pretty good. No. These are network-effect businesses. And in network-effect businesses, it is the slope of your improvement that determines everything.

[译文] 你将会到达终点，对吧？我确信你会到达，你已经那么近了。而我落后六个月。看起来还不错，对吧？听起来不错。不。这些是网络效应业务。在网络效应业务中，你的改进斜率（速度）决定了一切。

[原文] So I'll use OpenAI or Gemini, they have 1,000 programmers. They're in the process of creating a million AI software programmers. What does that do? First, you don't have to feed them except electricity. So that's good. And they don't quit and things like that.

[译文] 我拿 OpenAI 或 Gemini 举例，他们有 1000 名程序员。他们正在创造一百万个 AI 软件程序员。这有什么用？首先，除了电，你不需要喂它们吃饭。这很好。而且它们不会辞职之类的。

[原文] Second, the slope is like this. Well, as we get closer to superintelligence, the slope goes like this. If you get there first, you dastardly person --

[译文] 第二，进步的曲线（斜率）是这样的。然而，当我们接近超级智能时，曲线会变得非常陡峭。如果你先到达那里，你这个卑鄙的家伙——

[原文] Bilawal Sidhu: You're never going to be able to catch me.

[译文] Bilawal Sidhu: 我就永远追不上你了。

[原文] Eric Schmidt: I will not be able to catch you. And I've given you the tools to reinvent the world and in particular, destroy me. That's how my brain, Mr. Evil, is going to think. So what am I going to do?

[译文] Eric Schmidt: 我将无法追上你。而且我已经把重塑世界——尤其是摧毁我——的工具交给了你。作为“邪恶先生”，我的大脑会这样思考。那么我会怎么做？

[原文] The first thing I'm going to do is try to steal all your code. And you've prevented that because you're good. And you were good. So you’re still good, at Google. Second, then I'm going to infiltrate you with humans. Well, you've got good protections against that. You know, we don't have spies.

[译文] 我要做的第一件事是试图偷走你所有的代码。但你防住了，因为你很厉害。你曾经很厉害，现在在 Google 依然很厉害。第二，我会派人渗透到你内部。好吧，你对此也有很好的防护措施。你知道，我们没有间谍能混进去。

[原文] So what do I do? I’m going to go in, and I’m going to change your model. I'm going to modify it. I'm going to actually screw you up to get me so I'm one day ahead of you. And you're so good, I can't do that. What's my next choice? Bomb your data center.

[译文] 那我该怎么办？我要潜入进去，修改你的模型。我要篡改它。我要把你搞砸，让我领先你一天。但你太强了，我也做不到这一点。我下一个选择是什么？轰炸你的数据中心。

[原文] Now do you think I’m insane? These conversations are occurring around nuclear opponents today in our world. There are legitimate people saying the only solution to this problem is preemption.

[译文] 现在你觉得我疯了吗？这些对话如今正在我们这个世界的核对手之间发生。有一些严肃的人在说，这个问题的唯一解决方案是“先发制人”（preemption）。

[原文] Now I just told you that you, Mr. Good, are about to have the keys to control the entire world, both in terms of economic dominance, innovation, surveillance, whatever it is that you care about. I have to prevent that. We don't have any language in our society, the foreign policy people have not thought about this, and this is coming.

[译文] 我刚才告诉你，你，“好人先生”，即将掌握控制整个世界的钥匙，无论是在经济主导权、创新、监控，还是任何你关心的方面。我必须阻止这一切。我们的社会中没有任何语言能描述这个，外交政策制定者们还没有思考过这个问题，而这件事即将到来。

[原文] When is it coming? Probably five years. We have time. We have time for this conversation. And this is really important.

[译文] 它什么时候来？大概五年。我们还有时间。我们还有时间进行这次对话。但这真的非常重要。

[原文] Bilawal Sidhu: Let me push on this a little bit. So if this is true and we can end up in this sort of standoff scenario and the equivalent of mutually-assured destruction, you've also said that the US should embrace open-source AI even after China's DeepSeek showed what's possible with a fraction of the compute. But doesn't open-sourcing these models, just hand capabilities to adversaries that will accelerate their own timelines?

[译文] Bilawal Sidhu: 让我在这里稍微追问一下。如果这是真的，我们可能会陷入这种对峙局面和相当于“相互保证毁灭”的境地，你也说过美国应该拥抱开源 AI，即便是在中国的 DeepSeek 展示了用极少算力就能做到什么程度之后。但是，开源这些模型不就是把能力拱手让给对手，从而加速他们自己的时间表吗？

[原文] Eric Schmidt: This is one of the wickedest, or, we call them wicked hard problems. Our industry, our science, everything about the world that we have built is based on academic research, open source, so forth.

[译文] Eric Schmidt: 这是一个最棘手的，或者我们称之为“极其棘手的难题”（wicked hard problems）。我们的行业、我们的科学，我们建立的关于这个世界的一切，都基于学术研究、开源等等。

[原文] The consensus in the industry right now is the open-source models are not quite at the point of national or global danger. But you can see a pattern where they might get there. So a lot will now depend upon the key decisions made in the US and China and in the companies in both places.

[译文] 目前业界的共识是，开源模型还没有完全达到构成国家或全球危险的地步。但你可以看到一种模式，它们可能会达到那个地步。所以，现在很多事情将取决于美国和中国以及两地公司所做出的关键决定。

[原文] I'm worried about this fight. Dr. Kissinger talked about the likely path to war with China was by accident. And he was a student of World War I. And of course, [it] started with a small event, and it escalated over that summer in, I think, 1914. And then it was this horrific conflagration. You can imagine a series of steps along the lines of what I'm talking about that could lead us to a horrific global outcome. That's why we have to be paying attention.

[译文] 我很担心这场争斗。基辛格博士曾谈到，通往与中国战争的可能路径是“意外”。他是研究第一次世界大战的学生。当然，一战始于一个小事件，并在那个夏天（我想是1914年）升级。然后就演变成了那场可怕的战火。你可以想象一系列类似我所说的步骤，可能会导致我们走向一个可怕的全球性结局。这就是为什么我们必须密切关注。

章节 6：治理与愿景——数字身份困局与科学大发现的黎明

📝 本节摘要：

面对 AI 时代“真假难辨”的挑战，主持人担忧为了防止滥用而引入的管控手段会演变成乔治·奥威尔笔下的《1984》式监控国家。Eric Schmidt 提出利用“零知识证明”（Zero-Knowledge Proofs）等加密技术，在保护隐私的前提下验证“人格证明”（Proof of Personhood）。随后，对话转向乐观的愿景（Dreams）。Schmidt 激情澎湃地描绘了 AI 在未来的应用：从寻找所有“可成药靶点”以根除疾病，到探索暗能量的物理本质，再到为全球每个孩子配备个性化的 AI 导师。他强调，这些已不再是科学难题，而是选择问题，并断言 AI 的出现将是人类未来 500 年甚至 1000 年内发生的最重要事件。

[原文] Bilawal Sidhu: I want to talk about one of the recurring tensions here, before we move on to the dreams, is, to sort of moderate these AI systems at scale, right, there's this weird tension in AI safety that the solution to preventing "1984" often sounds a lot like "1984."

[译文] Bilawal Sidhu: 在我们转向“梦想”这个话题之前，我想谈谈这里反复出现的一个紧张局势，那就是为了大规模地监管这些 AI 系统——对吧——AI 安全领域存在一种奇怪的张力，即防止《1984》（极权监控）的解决方案，听起来往往就很像《1984》。

[原文] So proof of personhood is a hot topic. Moderating these systems at scale is a hot topic. How do you view that trade-off? In trying to prevent dystopia, let's say preventing non-state actors from using these models in undesirable ways, we might accidentally end up building the ultimate surveillance state.

[译文] 所以“人格证明”（proof of personhood）是一个热门话题。大规模监管这些系统也是热门话题。你如何看待这种权衡？在试图防止反乌托邦（例如防止非国家行为者以不良方式使用这些模型）的过程中，我们可能会意外地建立起终极的监控国家。

[原文] Eric Schmidt: It's really important that we stick to the values that we have in our society. I am very, very committed to individual freedom. It's very easy for a well-intentioned engineer to build a system which is optimized and restricts your freedom. So it's very important that human freedom be preserved in this.

[译文] Eric Schmidt: 坚守我们社会中拥有的价值观真的非常重要。我非常、非常致力于个人自由。一个出于好意的工程师很容易构建出一个系统，它经过优化却限制了你的自由。所以在这一过程中维护人类自由是非常重要的。

[原文] A lot of these are not technical issues. They're really business decisions. It's certainly possible to build a surveillance state, but it's also possible to build one that's freeing.

[译文] 这里的许多问题并非技术问题。它们实际上是商业决策。建立一个监控国家当然是可能的，但建立一个让人获得自由的系统也是可能的。

[原文] The conundrum that you're describing is because it's now so easy to operate based on misinformation, everyone knows what I'm talking about, that you really do need proof of identity. But proof of identity does not have to include details.

[译文] 你所描述的难题在于，因为现在基于错误信息（misinformation）进行操作太容易了——大家都知道我在说什么——所以你确实需要身份证明。但身份证明不一定非要包含详细信息。

[原文] So, for example, you could have a cryptographic proof that you are a human being, and it could actually be true without anything else, and also not be able to link it to others using various cryptographic techniques.

[译文] 比如，你可以拥有一个证明你是人类的“密码学证明”，它可以在不透露任何其他信息的情况下证实这一点，而且利用各种密码学技术，也无法将其与其他信息关联起来。

[原文] Bilawal Sidhu: So zero-knowledge proofs and other techniques.

[译文] Bilawal Sidhu: 所以是“零知识证明”（zero-knowledge proofs）和其他技术。

[原文] Eric Schmidt: Zero-knowledge proofs are the most obvious one.

[译文] Eric Schmidt: 零知识证明是最明显的一种。

[原文] Bilawal Sidhu: Alright, let's change gears, shall we, to dreams. In your book, "Genesis," you strike a cautiously optimistic tone, which you obviously co-authored with Henry Kissinger. When you look ahead to the future, what should we all be excited about?

[译文] Bilawal Sidhu: 好吧，让我们换个档位，聊聊梦想吧。在你与 Henry Kissinger 合著的《Genesis》（创世纪）一书中，你定下了一种谨慎乐观的基调。当你展望未来时，我们应该为什么感到兴奋？

[原文] Eric Schmidt: Well, I'm of the age where some of my friends are getting really dread diseases. Can we fix that now? Can we just eliminate all of those? Why can't we just uptake these and right now, eradicate all of these diseases?

[译文] Eric Schmidt: 嗯，我到了这个年纪，身边的一些朋友开始患上真正可怕的疾病。我们现在能解决这个问题吗？我们能不能直接消除所有这些疾病？为什么我们不能直接利用这些技术，就在现在，根除所有这些疾病？

[原文] That's a pretty good goal. I'm aware of one nonprofit that's trying to identify, in the next two years, all human druggable targets and release it to the scientists. If you know the druggable targets, then the drug industry can begin to work on things.

[译文] 这是一个相当不错的目标。我知道一家非营利组织正试图在未来两年内识别出人类所有的“可成药靶点”（druggable targets），并将其发布给科学家。如果你知道了可成药靶点，制药行业就可以开始着手工作了。

[原文] I have another company I'm associated with which has figured out a way, allegedly, it's a startup, to reduce the cost of stage-3 trials by an order of magnitude. As you know, those are the things that ultimately drive the cost structure of drugs. That's an example.

[译文] 我有关联的另一家公司——一家初创公司——据称找到了一种方法，可以将第三期临床试验的成本降低一个数量级。如你所知，这些正是最终推高药物成本结构的因素。这是一个例子。

[原文] I'd like to know where dark energy is, and I'd like to find it. I'm sure that there is an enormous amount of physics in dark energy, dark matter. Think about the revolution in material science. Infinitely more powerful transportation, infinitely more powerful science and so forth.

[译文] 我想知道暗能量在哪里，我想找到它。我确信在暗能量和暗物质中蕴含着大量的物理学奥秘。想想材料科学领域的革命。那将带来无限强大的交通运输、无限强大的科学等等。

[原文] I'll give you another example. Why do we not have every human being on the planet have their own tutor in their own language to help them learn something new? Starting with kindergarten. It's obvious. Why have we not built it? The answer, the only possible answer is there must not be a good economic argument.

[译文] 我再给你举个例子。为什么我们不能让地球上的每个人都拥有一个说自己母语的私人导师，来帮助他们学习新知识？从幼儿园开始。这显而易见。为什么我们还没造出来？答案，唯一可能的答案是，因为这在经济账上算不过来（没有好的商业理由）。

[原文] The technology works. Teach them in their language, gamify the learning, bring people to their best natural lengths. Another example. The vast majority of health care in the world is either absent or delivered by the equivalent of nurse practitioners and very, very sort of stressed local village doctors.

[译文] 技术是行得通的。用他们的语言教学，将学习游戏化，让人们发挥出其最佳的天赋。另一个例子。世界上绝大多数的医疗服务要么是缺失的，要么是由相当于执业护士（nurse practitioners）的人员以及非常、非常焦虑的乡村医生提供的。

[原文] Why do they not have the doctor assistant that helps them in their language, treat whatever with, again, perfect healthcare? I can just go on. There are lots and lots of issues with the digital world. It feels like that we're all in our own ships in the ocean, and we're not talking to each other.

[译文] 为什么他们不能拥有一个用他们的语言辅助他们的医生助手，用完美的医疗方案来治疗任何疾病？我可以一直说下去。数字世界有很多很多问题。感觉就像我们在海洋中各自驾驶着自己的船，却互不交谈。

[原文] In our hunger for connectivity and connection, these tools make us lonelier. We've got to fix that, right? But these are fixable problems. They don't require new physics. They don't require new discoveries, we just have to decide.

[译文] 在我们对连通性和联系的渴望中，这些工具反而让我们更孤独了。我们必须解决这个问题，对吧？但这都是可解决的问题。它们不需要新的物理学。它们不需要新的发现，我们只需要做出决定。

[原文] So when I look at this future, I want to be clear that the arrival of this intelligence, both at the AI level, the AGI, which is general intelligence, and then superintelligence, is the most important thing that's going to happen in about 500 years, maybe 1,000 years in human society. And it's happening in our lifetime. So don't screw it up.

[译文] 所以当我展望未来时，我想明确一点，这种智能的到来——无论是 AI 层面，还是 AGI（通用人工智能），乃至超级智能——是人类社会在过去大约 500 年，甚至可能是 1000 年里将要发生的最重要的事情。而且它正发生在我们有生之年。所以，别搞砸了。

章节 7：拒绝躺平——人口危机与 30% 的生产力奇点

📝 本节摘要：

主持人描绘了一个 AI 接管所有生产任务、人类在沙滩上喝着鸡尾酒享受人生的乌托邦，并询问 Schmidt 人类未来将做些什么。Schmidt 戏谑地称其为“科技自由派”的幻想，并驳斥了这种观点。他指出人类本性难移，律师和政客不会消失，只会利用 AI 变得更加手段复杂。Schmidt 揭示了真正的全球危机是生育率暴跌（特别提到亚洲），这意味着未来没有足够的年轻人来供养像他这样的老人。因此，我们必须依赖 AI 带来的每年 30% 的生产力增长——这是一种现代经济学模型都无法解释的惊人增速——来填补劳动力的巨大缺口。

[原文] Bilawal Sidhu: Let's say we don't. (Applause) Let's say we don't screw it up. Let's say we get into this world of radical abundance. Let's say we end up in this place, and we hit that point of recursive self-improvement.

[译文] Bilawal Sidhu: 假设我们要搞砸。（掌声）假设我们没有搞砸。假设我们进入了这个极度富足的世界。假设我们最终到了那个地步，并且达到了递归自我进化的那个点。

[原文] AI systems take on a vast majority of economically productive tasks. In your mind, what are humans going to do in this future? Are we all sipping piña coladas on the beach, engaging in hobbies?

[译文] AI 系统接管了绝大多数具有经济生产力的任务。在你看来，在这个未来里人类要做什么？我们是不是都在沙滩上喝着椰林飘香（piña coladas），搞搞兴趣爱好？

[原文] Eric Schmidt: You tech liberal, you. You must be in favor of UBI.

[译文] Eric Schmidt: 你这个科技自由派啊。你肯定是支持 UBI（全民基本收入）的。

[原文] Bilawal Sidhu: No, no, no.

[译文] Bilawal Sidhu: 不，不，不。

[原文] Eric Schmidt: Look, humans are unchanged in the midst of this incredible discovery. Do you really think that we're going to get rid of lawyers? No, they're just going to have more sophisticated lawsuits.

[译文] Eric Schmidt: 听着，在这场不可思议的发现浪潮中，人类并没有改变。你真的以为我们会摆脱律师吗？不，他们只是会有更复杂的诉讼案。

[原文] Do you really think we're going to get rid of politicians? No, they'll just have more platforms to mislead you. Sorry. I mean, I can just go on and on and on.

[译文] 你真的以为我们会摆脱政客吗？不，他们只是会有更多的平台来误导你。抱歉。我的意思是，我可以一直列举下去。

[原文] The key thing to understand about this new economics is that we collectively, as a society, are not having enough humans. Look at the reproduction rate in Asia, is essentially 1.0 for two parents. This is not good, right?

[译文] 关于这种新经济，需要理解的关键一点是，我们作为一个社会整体，人类的数量不够了。看看亚洲的生育率，对于两个父母来说本质上只有 1.0。这可不好，对吧？

[原文] So for the rest of our lives, the key problem is going to get the people who are productive. That is, in their productive period of lives, more productive to support old people like me, right, who will be bitching that we want more stuff from the younger people.

[译文] 所以在我们的余生中，关键问题将是如何让那些有生产力的人——即处于生命中生产力旺盛时期的人——变得更加高产，以支持像我这样的老人，对吧？我们这些老人会不停地发牢骚，想要从年轻人那里得到更多的东西。

[原文] That's how it's going to work. These tools will radically increase that productivity. There's a study that says that we will, under this set of assumptions around agentic AI and discovery and the scale that I'm describing, there's a lot of assumptions that you'll end up with something like 30-percent increase in productivity per year.

[译文] 这就是未来的运作方式。这些工具将从根本上提高这种生产力。有一项研究表明，基于关于智能体 AI（agentic AI）、科学发现以及我所描述的规模的一系列假设——虽然有很多假设——但结果是你最终会获得大约每年 30% 的生产力增长。

[原文] Having now talked to a bunch of economists, they have no models for what that kind of increase in productivity looks like. We just have never seen it. It didn't occur in any rise of a democracy or a kingdom in our history. It's unbelievable what's going to happen. Hopefully we will get it in the right direction.

[译文] 我现在和一堆经济学家谈过，他们没有任何模型能描述这种生产力增长是什么样子的。我们从未见过这种情况。这种增长从未出现在历史上任何民主国家或王国的崛起过程中。即将发生的事情是难以置信的。希望我们能将其引向正确的方向。

章节 8：生存指南——驾驭指数级浪潮与“不进则退”的最后通牒

📝 本节摘要：

在访谈的尾声，主持人 Bilawal 询问在这一历史性时刻，普通人该如何自处。Schmidt 将这场变革比作一场马拉松，强调这是一种持续的指数级增长，人类往往会忘记两三年前的世界是什么样。他给出了核心建议：“每一天都要驾驭这股浪潮”。他直言不讳地警告，无论是艺术家、教师还是技术人员，如果不使用这项技术，就会被竞争对手抛下，变得“无关紧要”（Irrelevant）。最后，他以 Anthropic 的模型可以直接连接数据库为例，指出传统软件行业的护城河正在瞬间消失，呼吁大家必须“快速适应”。

[原文] Bilawal Sidhu: It is truly unbelievable. Let's bring this home, Eric. You've navigated decades of technological change. For everyone that's navigating this AI transition, technologists, leaders, citizens that are feeling a mix of excitement and anxiety, what is that single piece of wisdom or advice you'd like to offer for navigating this insane moment that we're living through today?

[译文] Bilawal Sidhu: 这真的难以置信。让我们来做个总结，Eric。你已经驾驭了数十年的技术变革。对于每一个正在经历这场 AI 转型的人——无论是感到兴奋与焦虑交织的技术人员、领导者还是普通公民——你有什么独到的智慧或建议，能帮助大家度过我们今天所处的这个疯狂时刻？

[原文] Eric Schmidt: So one thing to remember is that this is a marathon, not a sprint. One year I decided to do a 100-mile bike race, which was a mistake. And the idea was, I learned about spin rate. Every day, you get up, and you just keep going.

[译文] Eric Schmidt: 必须要记住的一点是，这是一场马拉松，而不是短跑。有一年我决定参加一场 100 英里的自行车赛，那是个错误。核心理念是，我学会了关于“踏频”（spin rate，指持续稳定的节奏）。每天起床，你只需要坚持下去。

[原文] You know, from our work together at Google, that when you’re growing at the rate that we’re growing, you get so much done in a year, you forget how far you went. Humans can't understand that. And we're in this situation where the exponential is moving like this.

[译文] 你知道，从我们在 Google 共事的经历来看，当你以我们那样的速度增长时，你一年内完成的事情太多了，以至于你忘记了自己走了多远。人类无法理解这一点。而我们正处于这样一个指数级增长的情境中，曲线是这样（陡峭）走的。

[原文] As this stuff happens quicker, you will forget what was true two years ago or three years ago. That's the key thing. So my advice to you all is ride the wave, but ride it every day. Don't view it as episodic and something you can end, but understand it and build on it.

[译文] 随着这些事情发生得越来越快，你会忘记两年前或三年前什么是真实的。这是关键所在。所以我对大家的建议是：驾驭这股浪潮，但要每一天都去驾驭它。不要把它看作是阶段性的、可以结束的事情，而是要理解它并在其基础上通过构建。

[原文] Each and every one of you has a reason to use this technology. If you're an artist, a teacher, a physician, a business person, a technical person. If you're not using this technology, you're not going to be relevant compared to your peer groups and your competitors and the people who want to be successful. Adopt it, and adopt it fast.

[译文] 你们中的每一个人都有理由使用这项技术。无论你是艺术家、教师、医生、商人还是技术人员。如果你不使用这项技术，与你的同龄群体、你的竞争对手以及那些渴望成功的人相比，你将变得不再重要（无关紧要）。采用它，并且要快。

[原文] I have been shocked at how fast these systems -- as an aside, my background is enterprise software, and nowadays there's a model Protocol from Anthropic. You can actually connect the model directly into the databases without any of the connectors.

[译文] 我对这些系统的速度感到震惊——顺便说一句，我的背景是企业软件，而现在 Anthropic 有一个模型协议（Model Context Protocol）。你实际上可以将模型直接连接到数据库中，而不需要任何（传统的）连接器。

[原文] I know this sounds nerdy. There's a whole industry there that goes away because you have all this flexibility now. You can just say what you want, and it just produces it. That's an example of a real change in business. There are so many of these things coming every day.

[译文] 我知道这听起来很书呆子气。但这意味那里的整个行业都要消失了，因为你现在拥有了所有的灵活性。你只需要说出你想要的，它就会直接生产出来。这就是商业中真正变革的一个例子。每天都有太多这样的事情在发生。

[原文] Bilawal Sidhu: Ladies and gentlemen, Eric Schmidt.

[译文] Bilawal Sidhu: 女士们，先生们，Eric Schmidt。

[原文] Eric Schmidt: Thank you very much. (Applause)

[译文] Eric Schmidt: 非常感谢。（掌声）