内容来源：https://www.technologyreview.com/2026/05/11/1137090/three-things-in-ai-to-watch-according-to-a-nobel-winning-economist/

内容总结：

诺贝尔经济学奖得主阿西莫格鲁：关注AI对就业影响的三个关键点

2024年诺贝尔经济学奖得主达龙·阿西莫格鲁对人工智能导致“就业末日”的普遍担忧持谨慎态度。他最近在接受采访时指出，尽管关于AI取代人类工作的讨论愈演愈烈，从参议员桑德斯的集会到普通人的日常闲聊中都能听到此类担忧，但目前的数据仍支持他的判断：AI并未显著影响就业率或裁员情况。不过，他承认技术发展迅速，并提出了当前需要重点关注的三个方向：

一、AI代理：目前更应视为“辅助工具”而非“替代者”

阿西莫格鲁认为，近年来AI领域最大的技术飞跃之一是“代理式AI”——能自主完成指定任务的工具。尽管科技公司大力推销其作为“一对多”替代人类员工的能力，但他认为这并不现实。他解释说，一个岗位通常包含数十种不同任务（例如X光技师需要处理30项工作），人类能自然切换工作模式，但AI代理若无法流畅完成这种任务间的协调，许多工作就仍将难以被取代。

二、科技巨头争相招聘经济学家：警惕“为叙事服务”的研究

阿西莫格鲁注意到，OpenAI、Anthropic、谷歌DeepMind等AI公司纷纷组建内部经济学团队，高薪聘请知名经济学家。他认为这反映了行业对公众因就业担忧而日益增长的怀疑态度的回应，但令人担忧的是，这些公司可能希望经济学家“为他们的观点或炒作服务”。最影响就业的研究成果若来自利益相关的企业，其客观性将面临挑战。

三、AI应用缺乏“即插即用”的易用性

阿西莫格鲁将AI与早前的技术变革（如PowerPoint、Word）对比，指出那些软件“任何人都能安装并立即使用”，从而迅速普及。而当前AI工具虽能对话，但普通用户需花费大量时间才能将其转化为实际生产力。他认为，开发更易用的AI应用才是产生经济影响的关键信号。

总结：喧嚣之下，不确定性才是现状

阿西莫格鲁坦言，当前AI经济领域存在巨大矛盾：一边是大学生抱怨就业市场越来越差，另一边却没有AI显著提升生产率的数据。他认为，最值得关注的现象恰恰是——关于AI影响的讨论如此确定，而实际影响却如此不确定。

中文翻译：

一位诺贝尔经济学奖得主认为，人工智能领域有三件事值得关注。
达龙·阿杰姆奥卢对于“工作岗位末日”的预测，态度比大多数人更为谨慎。他真正担忧的其实是以下这些。
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在2024年获得诺贝尔经济学奖的几个月前，达龙·阿杰姆奥卢发表了一篇论文，这篇论文并未为他赢得硅谷的青睐。与大型科技公司CEO们所承诺的“所有白领工作将被彻底颠覆”相反，阿杰姆奥卢估计，人工智能只能给美国生产力带来小幅提升，并且不会消除对人类劳动的需求。他写道，AI在自动化某些任务方面表现尚可，但有些工作完全不会受到影响。
两年后，阿杰姆奥卢这种审慎的看法仍未成为主流。关于AI导致工作岗位末日的话题，到处都能听到——从参议员伯尼·桑德斯的集会，到我排队买杂货时无意中听到的对话。一些此前持怀疑态度的经济学家，如今也越来越倾向于认为AI可能带来某种颠覆性变化。一位加利福尼亚州州长候选人上周表示，他希望对企业使用AI征税，并补偿“AI驱动的裁员”的受害者。
一方面，数据仍然站在阿杰姆奥卢这边；多项研究一再发现，AI并未影响就业率或裁员数量。但自他做出谨慎预测以来，这项技术已经取得了相当大的进展。我与他进行了交流，想了解AI的最新发展是否改变了他的论点，以及如果并非迫在眉睫的通用人工智能，他如今又真正担忧些什么。
AI智能体
自阿杰姆奥卢的论文发表以来，AI领域最大的技术飞跃之一便是智能体AI，也就是那些能够超越聊天机器人范畴、自主运作以完成你交给它的目标的工具。由于它们能够独立工作，而不仅仅是回答问题，企业越来越多地将智能体宣传为人类员工的一对多替代方案。
阿杰姆奥卢认为：“我认为这根本就是一件不划算的事情。”他认为，智能体更应该被看作是增强某人工作中特定部分的工具，而不是灵活到足以处理一个人整个工作的东西。
原因之一在于一项工作所包含的各种不同任务，这也是阿杰姆奥卢自2018年以来一直在其AI相关研究中探讨的课题。例如，一名X光技师要同时处理30项不同的任务，从记录病人病史到整理乳腺X光影像档案。阿杰姆奥卢说，一名员工可以自然地切换格式、数据库和工作风格来完成这些工作，但人工智能需要多少个独立的工具或协议才能做到同样的事情呢？
智能体能否加剧AI对工作岗位的影响，最终取决于它们是否能够处理人类天生就会的任务间的协调安排。AI公司正激烈竞争，以证明它们的AI智能体能够越来越长时间地独立工作而不出错，有时甚至会夸大结果——但阿杰姆奥卢表示，如果智能体无法流畅地在不同任务之间切换，许多工作将免于被AI取代。
新的招聘热潮
多年来，大型科技公司一直以惊人的高薪招募AI研究人员。但我向阿杰姆奥卢问及了我注意到的另一股招聘热潮：AI公司都在组建内部经济学团队。
OpenAI于2024年从杜克大学招募了罗尼·查特吉担任其首席经济学家，并于去年宣布查特吉将与哈佛大学经济学家、前巴拉克·奥巴马顾问杰森·弗曼合作，研究AI与就业问题。Anthropic也召集了一个由10位顶尖经济学家组成的小组开展类似工作。就在上周，谷歌DeepMind宣布已聘请芝加哥大学经济学家亚历克斯·伊马斯担任其“AGI经济学主任”。
阿杰姆奥卢注意到，他的同事们也纷纷被这些职位挖走。“这说得通，”他说：AI公司很清楚，公众对AI的怀疑情绪（很大程度上是由于对就业的担忧）正在加剧。而他们有强烈的动机来塑造围绕其技术的经济叙事（想想OpenAI关于新工业政策时代的最新提案）。
阿杰姆奥卢说：“我希望我们不会看到的是，他们聘请经济学家只是为了宣传他们的观点或进一步炒作。”这种紧张关系笼罩着新兴的“AI经济学”领域；令人担忧的是，一些关于AI对工作影响的最具影响力的研究，可能越来越多地来自那些最能从有利结论中获益的公司。
AI应用程序
我并不认为AI难以使用；我们大多数人通过使用自然语言的聊天机器人与之互动。但阿杰姆奥卢表示，我们应该思考它与开启早期技术变革的软件（如用于制作幻灯片的PowerPoint和用于文档处理的Word）相比如何。
他说：“任何人都可以把这些软件安装到自己的电脑上，并让它们做自己想做的事。”它们也因此得以传播开来。
“我们还没有看到基于AI的应用程序具备同样的易用性，”他说。即使任何人都能与AI模型聊天，普通员工通常也需要一段时间才能从中获得实用和高效的使用体验。这也是AI尚未对就业市场或经济产生颠覆性影响的部分原因。因此，阿杰姆奥卢正在关注的一个关键信号，就是那些让AI更易于使用的应用程序的诞生。
但他承认，在一段时间内，我们会看到各种关于AI的相互矛盾的证据：例如，有传闻说大学毕业生发现就业市场越来越糟，但AI对生产率却没有明显影响。“存在巨大的不确定性，”他说。而这正是当前AI经济最具说明性的一点：言论上的确定性，与其余一切方面的不确定性并存。

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英文来源：

Three things in AI to watch, according to a Nobel-winning economist
Daron Acemoglu is more cautious than most about predictions of a jobs apocalypse. Here’s what’s worrying him instead.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.
Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI. A California gubernatorial candidate said last week that he wants to tax corporate AI use and pay victims of “AI-driven layoffs.”
On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. I spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.
AI agents
One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.
“I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.
One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018. For example, an x-ray technician juggles 30 different tasks, from taking down patient histories to organizing archives of mammogram images. A worker can naturally switch between formats, databases, and working styles to do this, Acemoglu says, but how many individual tools or protocols would an AI require to do the same?
Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally. AI companies are in heated competition to prove that their AI agents can work independently for ever longer periods without making mistakes, sometimes exaggerating the results—but Acemoglu says many jobs will be spared from an AI takeover if agents can’t fluidly switch between tasks.
The new hiring spree
For years Big Tech has been offering staggering salaries to recruit AI researchers. But I asked Acemoglu about a different hiring spree I’ve noticed: AI companies are all building in-house economics teams.
OpenAI hired Ronnie Chatterji from Duke University in 2024 to be its chief economist and announced last year that Chatterji will work with Jason Furman—Harvard economist and former advisor to Barack Obama—to research AI and jobs. Anthropic has convened a group of 10 leading economists to do similar work. And just last week, Google DeepMind announced it had hired Alex Imas, an economist from the University of Chicago, to be its “director of AGI economics.”
Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).
“What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.
AI apps
I don’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents.
“Anybody could install these on their computer and get them to do the things that they want them to do,” he says. They spread accordingly.
“We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use.
But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI: anecdotes that college grads are finding the job market worse and worse, but no noticeable effect of AI on productivity, for example. “There’s a huge amount of uncertainty,” he says. And that’s the most telling thing about the AI economy right now: the certainty of the rhetoric alongside the uncertainty of everything else.
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