AI 指数级发展下的政策：Anthropic CEO Dario Amodei 万字长文（中英对照全文）

导读

2026 年 6 月，Anthropic 联合创始人兼 CEO Dario Amodei 发表了一篇政策长文《Policy on the AI Exponential》（AI 指数级发展下的政策）。他用《指环王》里那棵迟缓的树胡作比：AI 在指数级狂奔，而政策制定的节奏慢得让人痛苦。这不是一篇泛泛的安全呼吁，而是一份覆盖监管、宏观经济、科学创新、公民自由与地缘政治五大领域的、相当具体的政策框架。^[1]

以下是全文逐段中英对照：每段先是 darioamodei.com 官网的英文原文（引用块），紧跟机智流的中文翻译。译文力求忠实，关键术语保留英文；如有出入，以英文原文为准。原文链接见文末。

In one of the side plots to The Lord of the Rings, two of the Hobbits attempt to rouse Treebeard—a wise but ponderous sentient tree—to defend his forest from an army that is cutting it down. The problem is that Treebeard operates at a very different speed than the Hobbits. It takes him a full day simply to say hello to another tree, so getting him and his peers to act fast enough is nearly impossible.

在《指环王》的一条支线里，两个霍比特人试图唤醒树胡——一棵睿智却迟缓的、有意识的树——让他保卫正被一支军队砍伐的森林。问题在于，树胡的节奏和霍比特人截然不同：他光是跟另一棵树打个招呼就要花上一整天，想让他和同类们足够快地行动起来，几乎不可能。

The intersection of AI and our political institutions feels a bit like the Hobbits and Treebeard. AI is advancing at a lightning pace—in only four years, AI models have gone from barely being able to write a coherent line of code to writing most of the code at major AI companies. Similar gains have been made in biology, physics, math, finance, law, translation, and many other fields. AI’s scaling laws, which predict an exponential increase in general cognitive capabilities with increasing computing power, now have over a decade of empirical evidence behind them. If these scaling laws continue for only a year or two longer, we are likely to get what I’ve called Powerful AI, or “a country of geniuses in a datacenter”.

AI 与我们政治制度交叉的地带，感觉就有点像霍比特人与树胡。AI 正以闪电般的速度前进——短短四年，AI 模型就从勉强写不出一行连贯的代码，发展到写下了各大 AI 公司的大部分代码。在生物、物理、数学、金融、法律、翻译等众多领域，也取得了类似的飞跃。AI 的"规模定律"（scaling laws）——预言随着算力增加、通用认知能力将呈指数级提升——如今背后已有十多年的实证支撑。如果这些规律再延续一两年，我们很可能就会迎来我所说的"强大 AI"（Powerful AI），或者说"数据中心里一个由天才组成的国度"。

By contrast, policy—and especially legislation—moves very slowly. Often this is for good reasons: governments have grave powers, and it’s usually for the best that they aren’t used too hastily. But the mismatch in timescale is nevertheless very painful: in the several years that it can take Congress to act, AI can go from an amusing toy to the full country of geniuses.

与之相对，政策——尤其是立法——走得非常慢。这往往有充分的理由：政府握有重大的权力，通常最好不要太仓促地动用。但这种时间尺度上的错配仍然十分痛苦：在国会可能要花上数年才能行动的这段时间里，AI 就能从一个逗趣的玩具，变成那个完整的"天才之国"。

Over the last few years since AI has become a major commercial technology, those of us who wanted to handle it responsibly have faced a dilemma. We could see clearly where the exponential was going: we strongly suspected that within a few years AI would be one of the rare technologies that fundamentally reshapes the entire policy landscape, in the same way that nuclear weapons reshaped geopolitics and the industrial revolution fundamentally reshaped every economic and social issue. But to those looking only at what AI could do at the time, it looked like a much more mundane technology—similar perhaps to the latest consumer app or cryptocurrency. It was hard to convince most policymakers and companies that anything other than a laissez faire attitude made sense. And to be fair, the fact that AI’s radical effects were not yet present, and that we didn’t know exactly what shape they might take, made it difficult to design the right policies even if there had been the will to act.

自 AI 成为一项重要的商业技术以来的这几年，我们这些想负责任地对待它的人，一直面临一个两难。我们能清楚看到这条指数曲线要去往何方：我们强烈怀疑，几年之内 AI 就会成为那种从根本上重塑整个政策版图的罕见技术——就像核武器重塑了地缘政治、工业革命从根本上重塑了每一个经济与社会议题那样。但在那些只盯着 AI 当时能做什么的人眼里，它更像一项平平无奇的技术——也许跟最新的消费类 App 或加密货币差不多。要说服大多数政策制定者和企业相信"除了放任自流之外还有别的选择是合理的"，很难。平心而论，AI 那些激进的影响尚未显现、我们也不确切知道它们会以何种形式出现——这使得哪怕有行动的意愿，也很难设计出正确的政策。

Given the limits imposed by this situation, many safety advocates (including Anthropic) have so far been focused on advocating for policy actions that preserve optionality, tee up a fast reaction in the future, or give the world better insight into what is coming down the pike – things like transparency legislation, export controls on chips, and data collection on AI’s labor effects. These are not enough, but they have felt like all that was possible.

受制于这种局面，许多安全倡导者（包括 Anthropic）迄今主要把精力放在那些"保留可能性、为将来的快速反应做铺垫、或让世界更看清前方来势"的政策行动上——比如透明度立法、芯片出口管制，以及对 AI 就业影响的数据采集。这些还不够，但当时感觉这已是力所能及的全部。

In the last few months, however, the evidence of AI’s incredible power, as well as its risks, has become undeniable. Perhaps the most emblematic example is Claude Mythos Preview and the discovery that frontier models pose very real risks to cybersecurity, creating the potential for disruption of the financial sector, critical infrastructure, and national security. Mythos Preview scrambled the global cybersecurity landscape. But its broader significance is that it proves beyond doubt that AI models are now tools of global and national strategic consequence. The cyber risks that Mythos-class models present will not be the last that we must face. I believe that biological risks may soon follow, and that serious AI autonomy risks may not be far behind.

然而在过去几个月里，AI 那令人难以置信的力量、以及它的风险，都已变得无可否认。也许最具代表性的例子就是 Claude Mythos Preview，以及由此发现——前沿模型对网络安全构成了非常真切的风险，可能扰乱金融部门、关键基础设施和国家安全。Mythos Preview 搅动了全球网络安全格局。但它更广的意义在于：它无可置疑地证明，AI 模型如今已是具有全球与国家战略意义的工具。Mythos 这一级模型所带来的网络风险，不会是我们必须面对的最后一种。我相信生物风险可能很快接踵而至，而严重的 AI 自主性风险，或许也不会落后太多。

We now, globally and collectively, need to activate a slow and rickety policy apparatus to deal with risks and opportunities that are going to compound surprisingly quickly from here. Many policymakers are showing increased openness to taking action, and it's been encouraging to see our peers come around to the same positions we've been advocating for over the past few years. This is good, but I worry that these early actions are at least a year out of step with AI's rapid progress. This essay is an attempt to close that gap: to lay out where the exponential is now, and the collective action needed to meet the moment.

现在，我们需要在全球范围内集体行动起来，去启动一套迟缓而吱呀作响的政策机器，来应对那些从此刻起会以惊人速度叠加放大的风险与机遇。许多政策制定者正表现出越来越高的行动意愿，看到同行们逐渐认同我们过去几年一直主张的立场，也令人鼓舞。这是好事，但我担心，这些早期行动至少比 AI 的飞速进展慢了一年。这篇文章就是想弥合这道差距：把这条指数曲线如今所处的位置讲清楚，并指出迎接这一时刻所需的集体行动。

I will focus on five perennial policy areas that need re-imagining in an AI world: regulation and public safety, macroeconomics and tax policy, scientific innovation, the balance of power between state and society, and geopolitics. I will speak primarily in terms of US policy since Anthropic is an American company, but most of my recommendations are also relevant to the rest of the world.

我会聚焦于五个在 AI 世界中需要被重新构想的、长期存在的政策领域：监管与公共安全、宏观经济与税收政策、科学创新、国家与社会之间的权力平衡，以及地缘政治。由于 Anthropic 是一家美国公司，我主要以美国政策为例来谈，但我的大多数建议同样适用于世界其他地方。

Along with this essay, Anthropic is releasing a legislative proposal on frontier model testing and a policy framework for job displacement, for which we intend to provide substantial financial backing. We plan to do much more in the future, but we view these as first steps to signal our seriousness.

在发表这篇文章的同时，Anthropic 还发布了一份关于前沿模型测试的立法提案，以及一套针对岗位流失的政策框架——我们打算为后者提供大量资金支持。未来我们会做得更多，但我们把这些视为表明我们认真态度的第一步。

1. Regulation and public safety

一、监管与公共安全

Every new technology or product has both beneficial and harmful uses, and therefore presents a dilemma between innovation and safety. Regulating products makes them less likely to cause harm and has played an important role in improving lives around the world, but it can also directly reduce their benefits and indirectly disincentivize innovation. There is also the Hayekian point that regulators often lack the information needed to make the right decisions about complicated economic tradeoffs, so that regulation is often both ineffective and burdensome. A related idea is the Collingridge dilemma, which states that the impacts of a technology are often hard to anticipate until it is too late to easily manage them.

每一项新技术或新产品，都兼具有益和有害的用途，因此都呈现出创新与安全之间的两难。监管产品能降低它们造成伤害的可能、在改善全球民生方面发挥了重要作用，但它也会直接削减产品的收益、并间接抑制创新。还有哈耶克式的论点：监管者往往缺乏在复杂经济权衡中做出正确决策所需的信息，因此监管常常既无效又累赘。与之相关的还有 Collingridge 困境——一项技术的影响，往往要到难以再轻松驾驭它时，才看得清。

These dynamics loomed large for AI in 2023-2024. It was clear to Anthropic that AI might in the future be capable of producing biological weapons that could threaten millions, or autonomous misbehavior that in extreme cases could even threaten humanity itself. Less clear was the exact form in which the risks would appear, how best to test for them and mitigate them, and how they would play out in practice. There was therefore a high risk that legislation written ahead of time would end up being ineffective—creating pointless or low-value compliance requirements while missing the most crucial sources of actual risk.

这些张力在 2023–2024 年的 AI 身上格外突出。Anthropic 很清楚，AI 未来可能有能力制造出威胁数百万人的生物武器，或在极端情况下甚至威胁人类自身的自主性失当行为。不太清楚的是：这些风险会以什么确切形式出现、如何最好地检测和缓解它们、以及它们在现实中会如何演变。因此，提前写就的立法有很大风险最终沦为无效——制造出毫无意义或低价值的合规要求，却错过了真正风险中最关键的来源。

Ultimately, we concluded that the right approach at that time was transparency. Developers of AI models should have to disclose their safety procedures and the tests that they run on their models and report on any critical safety incidents, so that the public and the scientific community could gain better visibility into risks as they emerge. When and if risks become more definite and their shape is more clear, then the evidence gained through transparency could be used to design smart legislation to precisely target the most concerning risks. Thus, in 2025, Anthropic supported transparency legislation, helping to pass SB 53 in California, RAISE in NY, SB 315 in Illinois (in early 2026), and advocating for a transparency standard at the federal level.

最终，我们得出结论：当时正确的做法是透明度。AI 模型的开发者应当被要求披露其安全流程、对模型所做的测试，并报告任何重大安全事件，好让公众和科学界在风险浮现时能更清楚地看见它们。当风险变得更确定、形态更清晰之时，通过透明度获得的证据，就能用来设计精准瞄准最令人担忧风险的、明智的立法。因此在 2025 年，Anthropic 支持了透明度立法，协助推动加州 SB 53、纽约 RAISE、伊利诺伊 SB 315（2026 年初）通过，并倡导在联邦层面建立透明度标准。

However, now the risks are clearly here. It is time to go beyond transparency to more serious and binding regulation of AI. I believe the best analogy, at least at the current stage of the exponential, is to cars, airplanes, or drugs—powerful technologies essential to the modern economy, but capable of killing large numbers of people if designed or operated poorly. I therefore believe we should model AI regulation on agencies like the Federal Aviation Administration (FAA). Frontier AI models, like airplanes, should be required to go through technical testing and auditing, and their release should be blocked or reversed as a threat to public safety if they do not meet high standards of safety. I am grateful to see the Trump administration’s Executive Order move incrementally towards a greater role for government in AI, though Anthropic’s proposal recommends even further action. Our proposal includes the following elements:

然而，如今风险已经清清楚楚地摆在这里。是时候超越透明度，对 AI 进行更严肃、更具约束力的监管了。我认为最贴切的类比——至少在这条指数曲线的当前阶段——是汽车、飞机或药品：它们都是现代经济不可或缺的强大技术，但若设计或运行不当，就可能致大量人于死地。因此我认为，我们应当参照美国联邦航空管理局（FAA）这类机构来设计 AI 监管。前沿 AI 模型应当像飞机一样，被要求通过技术测试与审计；如果达不到高安全标准，就应作为对公共安全的威胁而被阻止发布或撤回。我很高兴看到特朗普政府的行政命令朝着"政府在 AI 中扮演更大角色"渐进迈步，尽管 Anthropic 的提案建议采取更进一步的行动。我们的提案包含以下要素：

Models above a threshold of compute should undergo mandatory testing by a qualified third party for their level of risk in four specific areas: cybersecurity, biological weapons, loss of control of AI systems, and automated R&D that could accelerate these other risks.

▸ 算力超过一定阈值的模型，应当由合格的第三方就四个特定领域的风险水平进行强制测试：网络安全、生物武器、AI 系统失控，以及可能加速上述其他风险的自动化研发。

The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks. This power must be scoped to the above four specific risks and there must be protective measures against political favoritism or arbitrary decisions.

▸ 如果根据第三方评估认定某个模型构成不可接受的风险，政府应当有权阻止或威慑其部署。这一权力必须严格限定于上述四类特定风险，并且必须设有防止政治偏袒或恣意决策的保护措施。

Third-party evaluation could be done by a government agency (similar to the FAA) or a set of private organizations that are authorized and inspected by the government to evaluate models according to certain standards (a “regulatory markets” approach).

▸ 第三方评估可以由一个政府机构（类似 FAA）来做，也可以由一组经政府授权并接受其检查、按既定标准评估模型的私营组织来做（即"监管市场"思路）。

AI companies that develop advanced AI models must have strong security standards that protect their model weights, should conduct regular red teaming and penetration testing, and should work with the government to defend against major threat actors.

▸ 开发先进 AI 模型的公司，必须具备保护其模型权重的强健安全标准，应定期进行红队演练和渗透测试，并应与政府合作，防御重大威胁行为者。

Safety incidents in the four critical areas must be reported promptly.

▸ 四个关键领域内的安全事件，必须被及时报告。

There may come a time, perhaps relatively soon, when we need to go beyond this, when the most powerful AI systems look less like airplanes or automobiles and more like weaponizable nuclear materials—a threat to humanity rather than “just” a threat to public safety. If that occurs, we may need more aggressive regulatory measures than those I have laid out. But just as it was difficult in 2024 to target and apply the measures I’m suggesting now, I don’t think we should get ahead of ourselves. We should design policies for the dangers that are emerging today, while laying the foundations to ramp up our response even more quickly as new dangers appear.

也许在相对不远的将来，会出现一个时刻——届时最强大的 AI 系统看起来不再像飞机或汽车，而更像可被武器化的核材料：是对人类的威胁，而非"仅仅"对公共安全的威胁。若那一天到来，我们可能需要比我现在所列举的更激进的监管措施。但正如在 2024 年很难精准地瞄准并实施我现在所建议的措施一样，我认为我们不该操之过急。我们应当针对今天正在浮现的危险来设计政策，同时打好基础，以便在新危险出现时能更快地加码应对。

2. Macroeconomics and tax policy

二、宏观经济与税收政策

Governments have long faced the problem of how to encourage economic growth while also providing important public services and ensuring that the least fortunate are taken care of. An important (and generally correct) premise of these debates has been that economic growth is fragile and difficult to achieve—that while reducing inequality might provide important benefits, it has to be traded off against the economic drag of increased taxes or deficits.

长期以来，政府都面临这样一个难题：如何在鼓励经济增长的同时，提供重要的公共服务，并确保最不幸的人得到照顾。这些辩论有一个重要（且通常正确）的前提：经济增长是脆弱的、难以实现的——也就是说，尽管减少不平等或许能带来重要好处，但它必须与"增税或赤字所造成的经济拖累"相权衡。

I suspect that powerful AI may scramble this assumption. If AI achieves the ability to do most cognitive tasks far better than humans, it stands to reason that it could result in extremely rapid and robust economic growth via the acceleration of science, technology, and operational efficiency. The iterative ability of AI to build even better AI may supercharge that growth even further. But for exactly the same reasons, AI may also act as a more general economic substitute for human cognitive abilities than previous technologies have, while also altering the economy far faster than previous technologies have. Thus, it’s reasonable to think that AI could produce much larger disruptions to the labor market than previous technologies, and, potentially, more enduring disruptions. We risk ending up in a world where the economic tradeoff dial is stuck on the hypergrowth, hyper-inequality setting, and is potentially very hard to unstick from that setting. The key challenge in such a world won’t be incentivizing growth, but finding a way for everyone to share in the benefits.

我怀疑，强大的 AI 可能会打乱这个前提。如果 AI 获得了在大多数认知任务上远胜人类的能力，那么按理说，它可能通过加速科学、技术与运营效率，带来极其快速且稳健的经济增长。而 AI 迭代式地构建出更强 AI 的能力，可能会让这种增长更上一层楼。但出于完全相同的原因，AI 也可能比以往的技术更普遍地替代人类的认知能力，同时以远快于以往技术的速度改变经济。因此，有理由认为 AI 可能对劳动力市场造成比以往技术大得多、并且可能更持久的扰动。我们有可能最终落入这样一个世界：经济权衡的旋钮被卡在"超高增长、超高不平等"这一档上，而且可能很难再把它拨回来。在这样的世界里，关键挑战将不再是激励增长，而是找到让每个人都能分享收益的办法。

Of the topics discussed in this essay, macroeconomics and enduring labor displacement are arguably the ones that have attracted the most public attention and the most misunderstanding, so I want to be extremely clear on two points.

在本文讨论的所有话题中，宏观经济与持久性岗位流失可以说是最受公众关注、也最容易被误解的，所以我想把两点讲得极其清楚。

First, enduring job displacement is undesirable and dangerous, and we should do everything we can to minimize or prevent it, not to bring it about. I have warned about job displacement in interviews and essays because I want both policymakers and the private sector to have the best chance to adapt and respond, not because I am trying to be a “prophet of doom”. As a company, Anthropic always does as much as it can to work with customers to find creative new use cases and new sources of revenue that allow them to do more with their existing workforce, rather than focusing solely on cost savings (which often means reducing the workforce). We also constantly try to think of new interaction paradigms that allow humans to have as active a role as possible in collaborating with AI systems as those systems advance. More broadly, it is valuable for the whole world to experiment with using AI in as many new ways as possible, as that is the way for society to discover new possible job configurations. I do think AI will enable a number of new economic opportunities. I’ve predicted that AI will enable single individuals to create billion-dollar companies, and we're already seeing teams of only a few people build businesses with hundreds of millions in revenue. But at the same time we should recognize that there’s a decent possibility that, despite all our efforts, AI still causes significant enduring job loss—and that this may be an intrinsic property of the technology and the way it broadly replicates human cognition.

第一，持久性岗位流失是不可取且危险的，我们应当竭尽所能去减少或防止它，而不是去促成它。我之所以在访谈和文章里就岗位流失发出警告，是因为我希望政策制定者和私营部门都能有最大的机会去适应和应对，而不是因为我想当一个"末日先知"。作为一家公司，Anthropic 总是尽其所能与客户合作，去寻找有创意的新用例和新的收入来源，让他们能用现有的劳动力做更多的事，而不是只盯着省成本（那往往意味着裁员）。我们也不断尝试构思新的交互范式，让人类在与日益强大的 AI 系统协作时，能扮演尽可能主动的角色。更广泛地说，让全世界以尽可能多的新方式去试验 AI 是有价值的，因为这正是社会发现新的可能岗位形态的方式。我确实认为 AI 会催生不少新的经济机会。我曾预言 AI 将让个人也能创办十亿美元级的公司，而我们已经看到只有几个人的团队做出了数亿美元营收的生意。但与此同时，我们也应当承认：很有可能，尽管我们竭尽全力，AI 仍会造成显著而持久的岗位流失——而这或许是这项技术、以及它广泛复刻人类认知方式的一种内在属性。

Second, any response to AI-driven job displacement needs to address both the need to provide for everyone economically, and the need for people to find meaning, purpose, and agency. The latter is ultimately more important, and it depends on deep questions about how society is organized, what people should strive for, and what constitutes the good life. I am actually very optimistic that, even in a world with AI’s that are better than everyone at everything, humans can live lives of deep purpose and strive to build awe-inspiring and beautiful things. But this is something to be collectively worked out by society as a whole, not something policy can directly address. Policy can be most helpful in buying us time to do that work, by slowing down job loss and providing economically for those likely to be affected.

第二，任何对 AI 驱动的岗位流失的应对，都需要同时解决两件事：在经济上供养每一个人的需要，以及人们寻得意义、目的与能动性的需要。后者归根结底更重要，它取决于一些深层问题：社会如何组织、人们应当追求什么、何为美好生活。事实上我非常乐观地相信：即便身处一个 AI 在一切事情上都强过所有人的世界，人类依然可以过上充满深刻意义的生活，依然可以奋力去创造令人惊叹的、美的事物。但这是需要整个社会共同摸索出来的，而不是政策能直接解决的。政策最能帮上忙的地方，是为我们做这件事争取时间——通过放缓岗位流失，并在经济上供养那些可能受影响的人。

In that spirit, some key policy interventions that are likely to be helpful include:

本着这一精神，一些可能有帮助的关键政策干预包括：

Measurement and tracking. It’s easy to dismiss mere data collection and analysis as inadequate to the scale of the problem, but we are unlikely to get good policy if we cannot accurately measure what is happening on the ground. Anthropic has been operating an Economic Index of how people use Claude for nearly a year and a half, but governments have access to types of data we do not, and could greatly expand their economic statistics to more carefully track AI job displacement.

▸ 度量与追踪。人们很容易把"单纯的数据采集与分析"斥为不足以应对问题的规模，但如果我们无法准确测量实际正在发生什么，就不太可能得到好政策。Anthropic 运营一个追踪人们如何使用 Claude 的"经济指数"（Economic Index）已近一年半，而政府能拿到我们拿不到的那类数据，完全可以大幅扩展其经济统计，更细致地追踪 AI 造成的岗位流失。

Pro-employment incentives. A wide range of pro-employment policy incentives can help to slow or reduce job displacement, including: wage insurance policies that compensate people when they have to take a lower-paying job, retention tax incentives to encourage employers not to make layoffs, workforce training grants, or infrastructure to facilitate matching of employers to employees to speed the rate of labor market adaptation. While the particulars of which interventions are best will depend on what kind of labor displacement AI brings, we should readily accept the costs and market inefficiencies that these policies could entail, particularly as they are likely to be offset by AI-driven productivity gains.

▸ 促进就业的激励。一系列促进就业的政策激励，能帮助放缓或减少岗位流失，包括：在人们不得不接受较低薪岗位时给予补偿的工资保险、鼓励雇主不裁员的留用税收激励、劳动力培训补贴，或是促成雇主与雇员撮合、以加快劳动力市场适应速度的基础设施。具体哪些干预最优，取决于 AI 带来何种劳动替代；但我们应当乐于接受这些政策可能带来的成本与市场低效，尤其是因为它们很可能会被 AI 驱动的生产率提升所抵消。

Long-term macroeconomic support. If AI-driven labor displacement ends up being large in magnitude and permanently drives down the demand for labor, it will likely be necessary to go beyond mere incentive programs to long-term income support for a significant fraction of the labor force. Mechanisms such as universal basic income could be financed through taxes on relevant companies or raising the capital gains tax. Universal capital accounts offer another vehicle. Broadly speaking, fast economic growth should create the tax base for shared prosperity.

▸ 长期宏观经济支持。如果 AI 驱动的岗位流失最终规模巨大，并持久地压低对劳动力的需求，那么很可能就有必要超越单纯的激励计划，转向对相当一部分劳动力的长期收入支持。诸如全民基本收入这样的机制，可以通过对相关公司征税或上调资本利得税来筹资。全民资本账户则提供了另一种载体。大致来说，快速的经济增长应当能为共享繁荣创造出税基。

A common focus of economic concern about AI that I haven’t mentioned has been datacenters and particularly their potential to raise energy prices. My view is that AI companies should pay to absorb rate increases—and Anthropic has already made a pledge to do so—but I see public hostility to datacenters as largely a symbol or outlet for broader economic anxieties about AI. It is important we have a direct societal conversation about these wider economic issues and truly have compelling solutions for them, or else they are likely to manifest indirectly, as they have with datacenters.

关于 AI 的经济担忧，有一个我尚未提及的常见焦点，那就是数据中心，尤其是它们推高能源价格的潜在可能。我的看法是，AI 公司应当付费来吸收电价上涨——Anthropic 已就此作出承诺——但我认为，公众对数据中心的敌意，在很大程度上是对 AI 更广泛经济焦虑的一种象征或宣泄口。重要的是，我们要就这些更广泛的经济议题展开一场直接的社会对话，并真正拿出有说服力的解决方案，否则它们很可能会以间接的方式表现出来，就像在数据中心问题上那样。

3. Accelerating AI’s positive impact

三、加速 AI 的正面影响

Just as we must grapple with the balance between innovation and safety for AI itself, we must grapple with the same balance for technologies that are likely to be accelerated by AI, such as biomedicine, energy, or materials science. But while AI itself is likely to present novel challenges that emerge very quickly and that we have no prior experience in handling, other fields accelerated by AI are likely to encounter a very different problem: regulatory systems that were designed for a slower pace of innovation and are not prepared to handle the deluge of new products and advances that AI will bring. AI may also make these downstream technologies safer and more predictable in a way that violates the skeptical assumptions of regulatory agencies like the Food and Drug Administration (FDA).

正如我们必须为 AI 本身去权衡创新与安全，我们也必须为那些很可能被 AI 加速的技术——比如生物医药、能源或材料科学——去权衡同样的关系。但是，AI 本身很可能带来全新的、迅速涌现的、我们毫无应对经验的挑战；而被 AI 加速的其他领域，则很可能遇到一个截然不同的问题：那些为更慢的创新节奏而设计的监管体系，没有准备好应对 AI 将带来的大量新产品与新进展的洪流。AI 还可能让这些下游技术变得更安全、更可预测，其方式恰恰会违背 FDA（美国食品药品监督管理局）这类监管机构的怀疑式假设。

Thus, for downstream applications of AI—in contrast to AI itself—I am more worried about the regulatory apparatus slowing down progress (because it can’t handle the increased pace of change) than I am about it failing to address important risks. The last thing we want is for the benefits of AI to be slowed while its risks loom large, so it’s important to take action on this problem as soon as possible.

因此，对于 AI 的下游应用——与 AI 本身相反——我更担心的是监管机器拖慢进展（因为它应付不了加快的变化节奏），而不是它未能应对重要的风险。我们最不愿看到的，就是 AI 的好处被拖慢、而它的风险却阴云压顶。所以尽早就这个问题采取行动，很重要。

The problem and its solutions will manifest differently in each area of science, commerce, and technology, so I’ll focus on one illustrative area: biomedical innovation. This is both because it will likely be the source of AI’s biggest humanitarian benefits and because it is an area where regulation is especially complex. We don’t know exactly how AI will accelerate biomedical innovation, but it seems likely to:

这个问题及其解法，在科学、商业和技术的每个领域都会以不同方式呈现，所以我聚焦一个有代表性的领域：生物医药创新。这既因为它很可能是 AI 最大人道主义收益的来源，也因为它是一个监管尤其复杂的领域。我们并不确切知道 AI 会如何加速生物医药创新，但看起来很可能会：

Greatly increase the rate at which new drug candidates enter the regulatory pipeline;

▸ 大幅提高新药候选进入监管管线的速度；

Increase the effect sizes and improve the safety profiles of new drugs, because of better optimization and perhaps better understanding of their underlying biology;

▸ 由于更好的优化、或许还有对底层生物学更好的理解，提升新药的疗效、改善其安全性特征；

Develop drug candidates for diseases that have never been successfully treated before;

▸ 为那些此前从未被成功治疗过的疾病，开发出候选药物；

Rapidly create entire new forms of therapies, similar to how antibodies, peptides, and cell therapies have become new categories of treatment over the last few decades.

▸ 迅速创造出全新形态的疗法，正如过去几十年里抗体、多肽和细胞疗法成为了新的治疗门类那样。

Some of these advances will naturally accelerate regulatory timelines without need for structural change. Drugs with larger effect sizes can lead to smaller, less expensive clinical trials, and activate mechanisms for accelerated approval. But the regulatory system is currently designed to apply a high level of scrutiny and many stages of testing, under the assumption that drug candidates often don’t work and often have serious safety problems even when they do. With both the FDA and the European Medicines Agency (EMA), the typical time for a drug candidate to pass through the regulatory pipeline is 7-8 years, in part due to these pessimistic assumptions. Without reforms, AI will simply jam or overload this system.

其中一些进展会自然而然地加快监管时间表，无需结构性改变。疗效更大的药物可以带来更小、更省钱的临床试验，并触发加速审批的机制。但当前的监管体系，是按照"候选药往往无效、即便有效也往往有严重安全问题"的假设，设计成施加高强度审查和多个阶段测试的。无论是 FDA 还是欧洲药品管理局（EMA），一款候选药走完监管管线的典型时间是 7–8 年，部分正是源于这些悲观假设。若不改革，AI 只会把这套系统堵塞或压垮。

Obviously, we don’t want to change things in a way that leads to a crop of snake-oil drugs or widespread safety incidents. But some relatively simple reforms could make the FDA, EMA, and similar agencies more adaptable to a rapid AI-driven scientific acceleration if one were to occur.

显然，我们不希望以一种会催生一堆"江湖郎中神药"或引发大面积安全事故的方式来改变现状。但一些相对简单的改革，可以让 FDA、EMA 及类似机构在"AI 驱动的科学加速"真的发生时，更具适应力。

Many of the steps in the clinical process that previously required expensive and slow experiments may soon be done via AI simulation or analysis. Regulatory agencies should consider developing standards now for what it would take to accept such methods. This would mean they can be adopted quickly once they work, rather than there being an extended period during which unnecessary tests continue to be required. Areas where this could apply include:

临床流程中许多此前需要昂贵而缓慢的实验才能完成的步骤，可能很快就能通过 AI 模拟或分析来完成。监管机构现在就应当考虑，为"接受此类方法需要满足什么条件"制定标准。这样一来，这些方法一旦奏效就能被迅速采纳，而不必经历一段"明知不必要、却仍被要求继续做那些测试"的漫长时期。可能适用的领域包括：

AI-based pharmacodynamics and pharmacokinetics (PD/PK) modeling;

▸ 基于 AI 的药效学与药代动力学（PD/PK）建模；

Prediction of toxicology to avoid the need for multiple species animal toxicology;

▸ 毒理学预测，以避免对多个物种做动物毒理实验的需要；

More accurate dose selection, to reduce to the need for large dose ranges in trials;

▸ 更精准的剂量选择，以减少试验中对大剂量范围的需要；

Biomarker validation via analysis of large datasets;

▸ 通过对大型数据集的分析进行生物标志物验证；

Synthetic control arms in clinical trials, to reduce the need to recruit more participants;

▸ 临床试验中的合成对照组，以减少招募更多受试者的需要；

Developing surrogate endpoints (particularly important in aging and neurodegeneration).

▸ 开发替代终点指标（在衰老和神经退行性疾病中尤其重要）。

Beyond these specific examples, agencies should also consider more radical and flexible mechanisms for accelerated approval. If my predictions about AI are correct, there will soon be many instances of interventions that work really well out of the blue, and the regulatory system should be prepared to take them seriously and not adopt a posture of excessive skepticism.

除了这些具体例子，监管机构还应当考虑更激进、更灵活的加速审批机制。如果我对 AI 的预测是对的，那么很快就会出现大量"凭空冒出来、效果极好"的干预手段，监管体系应当准备好认真对待它们，而不是摆出一副过度怀疑的姿态。

Biomedical acceleration should substantially increase AI’s benefits, but it’s worth noting that it may also help to reduce AI’s risks. Reforming biomedical approvals may help with biodefense, and AI-driven biomedical progress may also improve mental health, which could have a stabilizing effect on society.

生物医药的加速应当能大幅增加 AI 的收益，但值得一提的是，它或许也有助于降低 AI 的风险。改革生物医药审批可能有助于生物防御，而 AI 驱动的生物医药进步也可能改善心理健康，从而对社会产生稳定作用。

4. The state and civil liberties

四、国家权力与公民自由

Every system of government has to confront the question of the state’s power and its limits. The state has a legitimate, often existential, interest in protecting its population from internal and external threats. But granting it too much power is the road to tyranny. Modern democracies have largely managed this balance successfully, but it is an uneasy one at the best of times. Enforcing it has required a great deal of legal and constitutional machinery built up over centuries—for example in the United States the First, Fourth, and Fifth amendments, the Posse Comitatus Act, FISA, and so on.

每一种政体都必须面对国家权力及其边界的问题。国家在保护其人民免受内部和外部威胁方面，有着正当的、往往是攸关存亡的利益。但赋予它过多权力，就是通往暴政之路。现代民主国家在很大程度上成功地驾驭了这种平衡，但即便在最好的时候，这也是一种不安稳的平衡。维系它需要几个世纪里积累起来的大量法律与宪政机器——例如在美国，就有第一、第四、第五修正案，《民兵团法》（Posse Comitatus Act）、《外国情报监视法》（FISA）等等。

AI threatens to upset this balance while also dramatically raising its stakes. But if we react quickly and meet the moment, we can use AI to create a world that has more robust and durable guarantees of liberty and better defense against threats, than we have ever had before.

AI 既威胁要打破这种平衡，又极大地抬高了它的赌注。但如果我们反应迅速、不负这一时刻，我们就能用 AI 创造一个比以往任何时候都拥有更稳健、更持久的自由保障、也更能抵御威胁的世界。

Powerful AI in the wrong hands could be the ultimate tool of autocracy, and our existing legal and constitutional protections are not fully equipped to counter this threat. Fundamentally, the enormous returns to intelligence in terms of power in the world, combined with the rapid pace of AI’s progress, creates a perfect storm for a surprise seizure of power by a range of dangerous actors.

强大的 AI 落到错误的人手里，可能成为专制的终极工具，而我们现有的法律与宪政保护，还没有完全准备好应对这一威胁。从根本上说，智能在世界上转化为权力的巨大回报，加上 AI 进步的飞快节奏，为形形色色的危险行为者"突然夺权"创造了一场完美风暴。

The danger could take a variety of specific technological or operational forms, but what they all have in common is the idea that AI could suddenly confer enormous power while routing around existing mechanisms of democratic oversight. A fully automated drone army that sounds like science fiction today could, in the future, obey unlawful orders and allow governments to unilaterally entrench their power; professionally-trained humans are more likely to object to such illegal direction. A surveillance-focused AI could analyze widely available information at massive scale and use it to infer the innermost details of every citizen’s life—a technological ability not contemplated by current civil liberties law. All of this could happen very quickly, or in secret, so it is important to proactively fortify democracies’ commitment to freedom and civil liberties.

这种危险可能采取各种具体的技术或操作形式，但它们的共同点在于：AI 可能在绕开现有民主监督机制的同时，骤然赋予某一方巨大的权力。一支今天听起来像科幻的全自动无人机军队，未来可能服从非法命令，让政府得以单方面地巩固自身权力——而受过专业训练的人，更有可能拒绝这种非法指令。一个以监控为目的的 AI，可以在海量尺度上分析广泛可得的信息，并据此推断出每一个公民生活中最私密的细节——这是现行公民自由法律未曾设想过的技术能力。这一切都可能发生得非常快，或者在暗中发生，因此提前加固民主国家对自由与公民权利的承诺，至关重要。

The following are some policy ideas we should consider:

以下是我们应当考虑的一些政策构想：

Create reliable accountability rules for fully autonomous weapons. Autonomous weapons, and especially any autonomous systems that coordinate or direct them, should be required to respond to mechanisms of constitutional and command accountability (e.g. court orders, legislation, and accountability to senior human overseers) rather than blindly following orders. This could mean that a suitably-designed legal review panel or the judicial branch have their finger on an “off switch”, that the systems themselves are intrinsically trained to seek out and respond to legitimate oversight authority, or both.

▸ 为完全自主的武器建立可靠的问责规则。自主武器，尤其是任何协调或指挥它们的自主系统，都应当被要求响应宪法与指挥链的问责机制（例如法院命令、立法、以及对高层人类监督者负责），而不是盲目服从命令。这可能意味着：由一个设计得当的法律审查小组或司法部门掌握着"断电开关"；或者系统本身就被内在地训练成去主动寻求并响应合法的监督权威；又或者两者兼具。

Ban the domestic use of fully autonomous weapons. While there is a legitimate case for the necessity of fully autonomous weapons to defend against foreign adversaries (such as Russia invading Ukraine), there is no justification for their use against Americans. The military already has some limits on its ability to operate domestically, but ideally these weapons should be banned in law enforcement as well.

▸ 禁止在国内使用完全自主的武器。尽管在抵御外敌（比如俄罗斯入侵乌克兰）时，完全自主武器的必要性有其正当理由，但用它来对付本国国民却没有任何正当性。军队在国内行动的能力本就已受到一些限制，但在理想情况下，这类武器在执法领域也应被法律禁止。

Close the bulk collection / data broker loophole. Under current law, data that Americans share with private companies (such as internet providers) can be purchased and used for bulk analysis in domestic surveillance and law enforcement. This gap in privacy protections predates AI, but AI will raise the stakes considerably by making mass analysis of such data far more revealing and useful than it has been in the past. This loophole should be closed.

▸ 堵上批量采集／数据经纪的漏洞。根据现行法律，公民分享给私营公司（如网络服务商）的数据，可以被购买并用于国内监控和执法中的批量分析。这一隐私保护上的缺口早于 AI 就已存在，但 AI 会大大抬高其赌注——它让对这类数据的大规模分析，比过去更能揭示隐私、也更有用。这个漏洞应当被堵上。

Public rights to AI advice during adverse government action. As a general principle, it seems important that any person or organization that is the subject of adverse government action (e.g. regulatory or legal action) has access to AI that is at least as capable as whatever the government is allowed to use in that particular action. This would mean not giving the government an unfair advantage, effectively undermining citizens’ legal rights. This could be added as an extension or interpretation of the Administrative Procedure Act, due process protections, or the Sixth Amendment right to legal representation.

▸ 在政府对其采取不利行动时，公众有获得 AI 协助的权利。作为一条普遍原则，任何成为政府不利行动（如监管或法律行动）对象的个人或组织，都应当能够使用至少与"政府在该具体行动中被允许使用的 AI"同等强大的 AI——这一点似乎很重要。这意味着不给政府以不公平的优势，从而实质上架空公民的法定权利。这可以作为对《行政程序法》、正当程序保护、或宪法第六修正案所规定的获得法律辩护权的一种扩展或解释来加入。

Finally, it is worth noting that governments are not the only entities that we should beware of when it comes to AI-driven seizure of power. At various times in history (such as the Gilded Age in the United States or the East India Company in the UK), companies have become powerful enough that they capture the state or adopt quasi-state characteristics. AI will soon become so capable that I worry it cannot safely be fully entrusted to either governments or companies, and there must be checks and balances on each.

最后值得指出的是，在 AI 驱动的夺权问题上，需要提防的并不只是政府。在历史上的不同时期（比如美国的镀金时代，或英国的东印度公司），企业曾强大到足以俘获国家，或具备准国家的特征。AI 很快就会变得如此强大，以至于我担心它无法被安全地完全托付给政府或企业中的任何一方——必须对各方都设有制衡。

Regulation is one answer on how to rein in companies (and my ideas for that are in Section 1), but it’s also important that AI companies have more separation of power and accountability than is typical for private entities. Anthropic’s Long-Term Benefit Trust (an independent governance body designed to hold the company to its mission) is one such structure, and the industry should continue to explore mechanisms that go further. Getting the balance right—so that both companies and the government have meaningful checks on their powers—is essential.

监管是约束企业的一个答案（我在第一节里给出了这方面的想法），但同样重要的是，AI 公司要比一般私营实体拥有更多的权力分立与问责。Anthropic 的"长期利益信托"（Long-Term Benefit Trust，一个旨在让公司恪守其使命的独立治理机构）就是这样一种结构，而行业应当继续探索走得更远的机制。把这种平衡把握好——让企业和政府的权力都受到有意义的制衡——至关重要。

5. Securing leadership by democracies

五、确保民主国家的领先

It has become a common instinct, perhaps developed from recent experience with the internet and telecommunications, to regard new technologies geopolitically as instruments of trade policy, with the aim being to “diffuse our technology stack around the world”. But it is my very strong belief that AI is something much more profound, something that resets the whole game board and around which all future geopolitical strategy must be shaped—like nuclear weapons, but potentially even more so.

如今有一种常见的直觉——也许是从近来与互联网和电信打交道的经验中形成的——倾向于从地缘政治上把新技术视为贸易政策的工具，目标是"把我们的技术栈扩散到全世界"。但我非常坚定地相信，AI 是某种深刻得多的东西，某种会重置整个棋盘、并且未来一切地缘战略都必须围绕它来塑造的东西——像核武器，但潜在的影响甚至更大。

If AI really will soon be “a country of geniuses in a datacenter”, or anything remotely close to it, then AI is likely to be the dominant source of military and economic power for any nation. In a virtual country of 100 million geniuses, 10 million could be applied to military strategy, 10 million to drone manufacture, 10 million to weapons R&D, 10 million to intelligence collection and analysis, 10 million to general scientific advancement, and so on. A nation that possesses powerful AI facing one without it—or even facing one that is behind in AI by 3 years—could be the equivalent of an army of World War II Marines facing an army of medieval swordsmen.

如果 AI 真的很快就会成为"数据中心里一个由天才组成的国度"，或任何接近于此的东西，那么对任何国家而言，AI 都很可能成为军事与经济权力的主导来源。在一个由一亿天才组成的虚拟国度里，可以把 1000 万投入军事战略，1000 万投入无人机制造，1000 万投入武器研发，1000 万投入情报搜集与分析，1000 万投入一般性的科学进步，以此类推。一个拥有强大 AI 的国家，对上一个没有强大 AI（哪怕只是在 AI 上落后 3 年）的国家，可能就相当于一支二战陆战队对上一支中世纪的持剑武士军队。

In addition, if powerful AI enables deeper and potentially permanent forms of autocratic repression (see Section 4), this makes it all the more important that the world’s most powerful nations are democracies—or at least that strong protections exist against AI-driven repression. It also increases the urgency of a focused geopolitical strategy.

此外，如果强大的 AI 能够催生更深、且可能是永久性的专制压迫形式（见第四节），那么"世界上最强大的国家应当是民主国家"——或者至少应当存在针对 AI 驱动压迫的强力保护——就变得愈发重要。这也增加了制定一套有针对性的地缘战略的紧迫性。

Democracies should seek to form a global coalition centered on building AI according to their common values, iteratively trying to draw in the rest of the world by making it more and more attractive to be part of the coalition and less and less attractive to be outside it. The coalition should be a coordinated internationalization of the AI policy ideas discussed in Section 1 through 4, plus an effort to lock down the supply chain critical to building AI by sharing it within the coalition and denying it to those outside it. Some principles and operating goals might include:

民主国家应当寻求组建一个围绕共同价值观来构建 AI 的全球联盟，并通过让"身处联盟之内"越来越有吸引力、"身处联盟之外"越来越无利可图，来不断尝试把世界其他国家拉拢进来。这个联盟应当是把第一节到第四节所讨论的 AI 政策构想协调一致地国际化，再加上一项努力：通过在联盟内部共享、对联盟之外封锁，来锁死构建 AI 所必需的供应链。一些原则和运作目标可能包括：

Managing the AI supply chain. Members of the trusted coalition should freely share chips and semiconductor manufacturing equipment (SME) with each other, while working together to deny it to adversaries. US export controls on frontier chips and SME to China have been a major contributor to the US’s overall lead in AI, and these policies need to be expanded, tightened, and coordinated with other likeminded states. Pending legislation like MATCH and OVERWATCH is a good first step here, and allied democracies need to consider similar measures.

▸ 管理 AI 供应链。受信任联盟的成员之间，应当自由共享芯片和半导体制造设备（SME），同时协同对对手封锁。美国对华出口前沿芯片和 SME 的管制，是美国在 AI 上整体领先的一大原因，这些政策需要被扩大、收紧，并与其他志同道合的国家协调。MATCH 和 OVERWATCH 等待审立法是很好的第一步，而盟友民主国家也需要考虑类似措施。

Coordinate to address AI’s risks. The policies to address biological, cybersecurity, and autonomy risks described in Section 1 will be more effective (as well as less burdensome to industry) if they are coordinated internationally. This would mean companies can comply with compatible standards and regulators can learn from each other how to best measure and mitigate these risks. Law enforcement and intelligence agencies should also work more closely together on tracking and disrupting threats of misuse, such as efforts by terrorists to build biological weapons with AI.

▸ 协调应对 AI 的风险。第一节所述应对生物、网络安全和自主性风险的政策，如果在国际上加以协调，会更有效（对产业的负担也更小）。这意味着企业可以遵循彼此兼容的标准，监管者也能相互学习如何最好地度量和缓解这些风险。执法与情报机构也应当更紧密地合作，去追踪和挫败滥用的威胁，比如恐怖分子试图借助 AI 制造生物武器的图谋。

Share AI’s benefits. Trade and regulatory policy can be used to facilitate a more rapid diffusion of AI’s economic benefits within the coalition, sharing lessons on how to accelerate innovation. Coordinating approaches to beneficial deployment could help bring the benefits of AI to developing countries. For example, harmonization of medical approval regimes could lead to faster and better testing and approval of AI-enabled drugs (as discussed in Section 3 above).

▸ 共享 AI 的收益。贸易与监管政策可以用来促进 AI 的经济收益在联盟内更快地扩散，并分享如何加速创新的经验。协调有益部署的方式，有助于把 AI 的好处带给发展中国家。例如，统一医药审批制度，可以让 AI 赋能的药物更快、更好地完成测试和审批（如上文第三节所述）。

Mutual defense. Countries in the coalition should work together to defend each other with AI and from adversaries’ AI. The coalition should collectively ensure sufficient production of AI-led cyberdefenses, AI-powered drones, AI-driven manufacturing, classified AI compute, AI-driven R&D, and sharing of AI-driven intelligence collection.

▸ 共同防御。联盟内的国家应当携手，用 AI 相互防御，并抵御对手的 AI。联盟应当集体确保足量供给：AI 主导的网络防御、AI 驱动的无人机、AI 驱动的制造、涉密 AI 算力、AI 驱动的研发，以及 AI 驱动的情报搜集的共享。

Rejection of AI-powered repression. Coalition members should have to reject the high-tech, ultra-repressive, AI-powered tyranny that I warned about in The Adolescence of Technology, and must have safeguards similar to those I described in Section 4 above.

▸ 拒绝 AI 驱动的压迫。联盟成员必须拒绝我在《技术的青春期》中所警告的那种高科技、超高压、由 AI 驱动的暴政，并且必须拥有与我在上文第四节所述类似的保障措施。

Macroeconomic cooperation. Crises of employment or job stability, like any other economic crisis, can be contagious across borders. Countries therefore have a mutual interest in working together to coordinate macroeconomic support and stabilization policies, like those described in Section 2, to counter any employment effects.

▸ 宏观经济合作。就业或岗位稳定的危机，与任何其他经济危机一样，可能跨境传染。因此各国有着共同的利益去携手协调宏观经济支持与稳定政策（如第二节所述），以对冲任何就业方面的冲击。

The goal should be to make membership in the coalition as attractive as possible—and the costs of remaining outside it clear. The coalition would rest on coordination among sovereign states, with each nation retaining full authority over its own affairs. It could grow iteratively, starting with ideologically aligned democracies (which will be naturally amenable to joining) and progressively welcoming countries that are less naturally aligned but prepared to meet the coalition's standards in exchange for the enormous benefits of membership. Ideally, the entire world would eventually join. But even if that isn't possible, building the coalition puts democracies in the strongest position to contain and outcompete the regimes that remain committed to repression.

目标应当是让加入联盟尽可能有吸引力——并让留在联盟之外的代价清清楚楚。这个联盟将建立在主权国家之间的协调之上，每个国家都保留对其自身事务的完整权力。它可以迭代式地壮大：从意识形态上相近的民主国家起步（它们会自然乐于加入），再逐步欢迎那些天然不那么相近、但愿意为换取入盟的巨大收益而达到联盟标准的国家。理想情况下，全世界最终都会加入。但即便做不到这一点，建立这个联盟也能让民主国家处在最有利的位置，去遏制并在竞争中胜过那些仍执意于压迫的政权。

A window of opportunity

终、一扇机会之窗

AI’s exponential progress has created an urgency and a pace of change that the policymaking process is ordinarily ill-equipped to handle. But it has also created a unique window of opportunity. The confluence of clear and present evidence of AI’s risks, an early taste of the AI’s potential for both economic value creation and economic disruption, and a remarkable public backlash against unregulated approaches to AI have created a situation where policymakers are unusually open to forward-looking actions. Treebeard and his forest are waking up.

AI 的指数级进步，造就了一种紧迫感和一种变化速度，而政策制定过程通常没有能力去应对。但它同时也开启了一扇独特的机会之窗。几股力量交汇在一起——AI 风险清晰而真切的证据、对 AI 在创造经济价值与造成经济扰动两方面潜力的初尝、以及公众对"放任不管"式 AI 路线的强烈反弹——造就了这样一种局面：政策制定者对前瞻性的行动表现出异乎寻常的开放。树胡和他的森林，正在醒来。

It’s become popular in AI industry circles to view this as a PR problem: to say that AI needs “better marketing”. I reject this framing completely. People are worried about AI because they correctly perceive that its risks are real, not because AI CEOs have been insufficiently Panglossian. I believe it is my duty as an AI leader to continue to be transparent about these risks, and public concern in response to this transparency constitutes democratic accountability working as it should. The key challenge is focusing this concern into constructive solutions and not allowing it to descend into formless anger and violence.

在 AI 业界圈子里，有一种流行的看法，把这视为一个公关问题：说 AI 需要"更好的营销"。我彻底拒绝这种框定。人们之所以担忧 AI，是因为他们正确地察觉到它的风险是真实的，而不是因为 AI 公司的 CEO 们不够盲目乐观。我相信，作为一名 AI 领袖，持续就这些风险保持透明是我的职责所在；而公众针对这种透明所作出的关切回应，正是民主问责机制在正常运转。关键的挑战，是把这份关切聚焦为建设性的解决方案，而不要让它沦为无定形的愤怒和暴力。

I am optimistic about finding solutions because many of these issues—from addressing job displacement, to pre-release testing of models, to export controls on chips, to other AI related policy issues such as energy use—have a common-sense appeal across the political spectrum. There is an aspirational but realistic future world in which a broad nonpartisan coalition, driven by direct recognition of the challenges posed by AI, leads to sane and forward-looking policies being adopted much faster than usual. The sooner we do this, the sooner we can all share in AI’s incredible benefits.

我对找到解决方案持乐观态度，因为这些议题中的许多——从应对岗位流失，到模型的发布前测试，到芯片出口管制，再到能源使用等其他 AI 相关政策议题——在整个政治光谱上都有一种常识层面的吸引力。存在一个虽属憧憬、却也现实的未来世界：在那里，一个广泛的、超越党派的联盟，因对 AI 所带来挑战的直接认知而被驱动，使得清醒而有前瞻性的政策得以比平常快得多地被采纳。我们越早做到这一点，就能越早地共享 AI 那难以置信的好处。

致谢 Acknowledgments

I would like to thank Allan Dafoe, Mariano-Florentino Cuéllar, Richard Fontaine, Buddy Shah, Vas Narasimhan, Matt Yglesias, Nick Beckstead, Jason Matheny, Brad Carson and many of the staff at Anthropic for their comments and feedback on drafts of this essay.

我要感谢 Allan Dafoe、Mariano-Florentino Cuéllar、Richard Fontaine、Buddy Shah、Vas Narasimhan、Matt Yglesias、Nick Beckstead、Jason Matheny、Brad Carson，以及 Anthropic 的许多同事，感谢他们对本文草稿的评论与反馈。

脚注 Footnotes

[1] I discuss biological and autonomy risks, among others, in my essay The Adolescence of Technology. The Anthropic Institute has also released some initial internal data in When AI Builds Itself about the possibility of recursive self-improvement, or models that are autonomously capable of building better models.

[1] 关于生物风险与自主性风险等，我在《技术的青春期》（The Adolescence of Technology）一文中有讨论。Anthropic Institute 也在《当 AI 构建自身》（When AI Builds Itself）中发布了一些初步的内部数据，谈及"递归式自我改进"——即能够自主构建出更好模型的模型——的可能性。

[2] This phenomenon is not theoretical: we’ve observed it multiple times in our own voluntary governance frameworks like our Responsible Scaling Policy. If we give ourselves a fixed or rigid list of safety requirements for future AI models, a very likely outcome is that requirements which turn out to matter very little end up consuming 95% of our compliance efforts, while at the same time we discover that some of the biggest sources of risk weren’t anticipated in our list at all. Voluntary frameworks can be changed and adapted, but this is much harder with legislation. My attempts to wrestle with this dilemma can be seen in my two public letters about SB 1047, a 2024 California law that attempted to address catastrophic risks and about which I had mixed feelings for the reasons above.

[2] 这一现象并非纸上谈兵：在我们自己的"负责任扩展政策"（Responsible Scaling Policy）等自愿治理框架中，我们已多次观察到它。如果我们给自己定下一份固定或僵硬的、针对未来 AI 模型的安全要求清单，一个很可能的结果是：那些事后证明无关紧要的要求，最终耗掉了我们 95% 的合规精力；与此同时，我们却发现一些最大的风险来源，根本没被列进清单里。自愿框架可以被修改和调整，但立法要做到这点就难得多。我试图与这一两难搏斗的努力，可以在我就 SB 1047 所写的两封公开信里看到——那是 2024 年加州一项试图应对灾难性风险的法律，出于上述原因，我对它的感受是复杂的。

[3] For example, truly severe biological risks may be much more difficult to manage than cyber risks, because attackers have a strong advantage relative to defenders and the severity of a catastrophe may be much greater.

[3] 举例来说，真正严重的生物风险可能比网络风险更难管理，因为攻击方相对防守方占据很强的优势，而灾难的严重程度也可能大得多。

[4] See The Adolescence of Technology for a more detailed analysis why the logic that has led to rapid job market recovery and a lack of enduring labor displacement in other technologies may not apply to AI, and in particular why the usual adaptive mechanisms like Jevon’s paradox or comparative advantage may be overwhelmed by the pace of the technology.

[4] 关于"为何那套在其他技术上带来了劳动力市场快速复苏、未造成持久岗位流失的逻辑，可能并不适用于 AI"，尤其是"为何 Jevons 悖论或比较优势等通常的适应机制，可能被这项技术的速度所压垮"，更详细的分析见《技术的青春期》。

[5] As an example, people still devote their lives to playing Chess, or Go, or climbing mountains, and are still revered for these activities, even though all can be done better by machines.

[5] 举例来说，人们至今仍把毕生精力投入下国际象棋、下围棋或攀登高山，并因这些活动而备受敬重，尽管所有这些机器都能做得更好。

[6] This essentially gives people an extra incentive to migrate to a new job and start training for a new career ladder, even when it may be painful in the short run, by paying them the difference between their new and old salaries, if the new one is lower.

[6] 这实质上给了人们一个额外的激励，去迁往一份新工作、开始为一条新的职业阶梯受训——哪怕短期内可能很痛苦——做法是：如果新工作的薪水更低，就把新旧薪水之间的差额补给他们。

[7] See The Adolescence of Technology for more on this topic.

[7] 关于这一话题的更多内容，见《技术的青春期》。

参考来源