Three things in AI to watch, according to a Nobel-winning economist

诺贝尔奖得主经济学家：人工智能领域值得关注的三件事

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. 本文最初发表于我们的 AI 每周通讯《算法》（The Algorithm）。若想第一时间在收件箱中获取此类报道，请点击此处订阅。在获得 2024 年诺贝尔经济学奖的几个月前，达龙·阿西莫格鲁（Daron Acemoglu）发表了一篇论文，这使他在硅谷并不讨喜。与大型科技公司首席执行官们所承诺的“彻底重塑所有白领工作”相反，阿西莫格鲁估计，人工智能对美国生产力的提升作用微乎其微，且不会消除对人类工作的需求。他写道，人工智能在自动化某些特定任务方面表现尚可，但许多工作岗位依然会安然无恙。

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. 两年过去了，阿西莫格鲁这种审慎的观点并未流行起来。关于“人工智能引发就业末日”的讨论随处可见，从参议员伯尼·桑德斯的集会，到我在杂货店排队时听到的闲谈，无一例外。一些曾经持怀疑态度的经济学家也开始接受人工智能可能带来剧变的想法。上周，一位加州州长候选人表示，他希望对企业使用人工智能的行为征税，并以此补偿“人工智能驱动的裁员”受害者。一方面，数据依然支持阿西莫格鲁的观点；研究反复表明，人工智能并未影响就业率或裁员情况。但自他做出谨慎预测以来，这项技术已经取得了长足进步。我与他进行了交谈，旨在了解人工智能的最新进展是否改变了他的论点，并探究如果不是迫在眉睫的通用人工智能（AGI），那么真正让他担忧的又是什么。

AI agents

人工智能体（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. 自阿西莫格鲁发表论文以来，人工智能领域最大的技术飞跃之一就是“代理式人工智能”（Agentic AI），即那些超越了聊天机器人、能够自主操作以完成既定目标的工具。由于它们可以独立工作而不仅仅是回答问题，企业正越来越多地将这些智能体宣传为人类员工的“一对多”替代品。阿西莫格鲁认为：“我认为这注定是一个失败的命题。”他认为，与其将智能体视为能够灵活处理一个人全部工作的存在，不如将其视为增强某人特定工作环节的工具。

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 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. 原因之一在于一份工作所包含的各种任务，这也是阿西莫格鲁自 2018 年以来在人工智能研究中一直关注的课题。例如，一名 X 光技师需要处理 30 种不同的任务，从记录病史到整理乳房 X 光检查图像。阿西莫格鲁指出，人类员工可以自然地在各种格式、数据库和工作风格之间切换来完成这些工作，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？智能体是否会极大增强人工智能对就业的影响，归根结底在于它们最终能否处理人类自然完成的任务编排。人工智能公司正在激烈竞争，试图证明其智能体可以在更长时间内独立工作而不出错，有时甚至会夸大结果——但阿西莫格鲁表示，如果智能体无法流畅地在任务之间切换，许多工作岗位将免于被人工智能取代。

The new hiring spree

新的招聘热潮

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.” 多年来，大型科技公司一直以惊人的薪水招募人工智能研究人员。但我向阿西莫格鲁询问了我注意到的另一股招聘热潮：人工智能公司都在组建内部经济学团队。OpenAI 在 2024 年聘请了杜克大学的罗尼·查特吉（Ronnie Chatterji）担任首席经济学家，并于去年宣布查特吉将与哈佛大学经济学家、巴拉克·奥巴马的前顾问杰森·弗曼（Jason Furman）合作，研究人工智能与就业问题。Anthropic 召集了 10 位顶尖经济学家进行类似的工作。就在上周，谷歌 DeepMind 宣布聘请芝加哥大学经济学家亚历克斯·伊马斯（Alex Imas）担任其“通用人工智能经济学总监”。

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. 阿西莫格鲁也注意到他的同事们被这些职位挖走。“这很合理，”他说：人工智能公司非常清楚，公众对人工智能的怀疑情绪正在增长，这在很大程度上源于对就业的担忧。他们有强烈的动机去塑造围绕其技术的经济叙事（看看 OpenAI 最近关于工业政策新时代的提议就知道了）。“我希望我们不会看到的是，”阿西莫格鲁说，“他们聘请经济学家仅仅是为了推销他们的观点或助长炒作。”这种紧张关系笼罩着“人工智能经济学”这一新兴领域；令人担忧的是，一些关于人工智能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能从有利结论中获益最多的公司。

AI apps

人工智能应用

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. 我不认为人工智能很难使用；我们大多数人都是通过使用简单语言的聊天机器人与它交互。但阿西莫格鲁表示，我们应该考虑它与那些开启了早期技术变革的软件（如用于幻灯片的 PowerPoint 和用于文档的 Word）相比如何。“任何人都可以把这些软件安装在电脑上，并让它们完成自己想做的事情，”他说。它们也因此得以普及。“我们还没有看到基于人工智能的、具有同样易用性的应用程序出现，”他说。即使任何人都可以与人工智能模型聊天，普通员工也往往需要一段时间才能从中获得实际的生产力提升。这也是人工智能尚未对就业市场或经济产生剧烈影响的部分原因。

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. 因此，阿西莫格鲁正在观察的关键信号之一，就是那些能让人工智能更易于使用的应用程序的开发。但他承认，在未来一段时间内，我们将看到各种关于人工智能的相互矛盾的证据：例如，一方面有传闻称大学毕业生发现就业市场越来越糟糕，另一方面人工智能对生产力却没有任何明显的影响。“存在巨大的不确定性，”他说。而这正是当前人工智能经济最能说明问题的地方：言论的确定性与一切事物的不确定性并存。