内容来源：https://www.technologyreview.com/2026/04/06/1135187/the-one-piece-of-data-that-could-actually-shed-light-on-your-job-and-ai/

内容总结：

经济学家呼吁启动“曼哈顿计划”式数据工程，以破解AI对就业的真实影响

随着人工智能技术迅猛发展，关于“AI取代人类工作”的讨论在硅谷乃至全球持续升温，引发了广泛的社会焦虑。然而，多位经济学家指出，当前基于“任务暴露度”的预测模型存在严重缺陷，无法准确评估AI对就业市场的实际冲击。他们呼吁发起一项大规模数据收集计划，为制定有效的应对政策提供关键依据。

当前预测工具的局限性

目前，业界常用美国政府的职业任务数据库来评估某项工作被AI替代的风险程度（即“暴露度”）。例如，有研究指出房地产经纪人职业的AI暴露度为28%。但芝加哥大学经济学家亚历克斯·伊马斯尖锐指出：“仅凭暴露度来预测岗位流失，是一个完全无意义的工具。”他认为，这只能揭示最极端的情况（如全自动替代），却无法解释绝大多数复杂的工作演变。

核心问题：生产力提升与就业需求的悖论

伊马斯以程序员使用AI编码工具为例，说明了问题的复杂性：效率提升可能使公司用同样成本获得更多产出。随后，公司可能因产品降价而刺激需求增长，从而扩大团队；也可能因需求增长有限而缩减用人规模。最终结果取决于具体的“价格弹性”——即价格变化能在多大程度上影响市场需求。这正是当前经济决策面临的核心盲点。

数据缺失：我们仍在“黑暗中摸索”

伊马斯强调，我们拥有如牛奶、谷物等消费品的详细价格弹性数据，但对于教师、网页开发者、营养师等众多正受AI影响的职业，却缺乏系统性的经济数据。这些数据可能散落在私营企业或咨询公司中，未被整合供研究使用。

行动倡议：启动经济数据“曼哈顿计划”

为此，伊马斯发出“行动号召”，主张经济学家及相关机构应启动一项类似“曼哈顿计划”的宏大工程，系统性地收集整个经济体系中各类商品与服务的价格弹性数据。这不仅关乎当前明显受AI影响的行业，也涉及未来可能被波及的领域。他认为，尽管这项工程需要投入大量时间和资金，但能为预测AI如何重塑未来就业市场提供首个现实图景，并让政策制定者有机会未雨绸缪。

当前，立法者尚未就AI时代的就业转型提出连贯计划，而社会焦虑已在推动一些旨在暂停数据中心建设的行动。在此背景下，建立扎实的数据基础以照亮前路，显得尤为紧迫。

中文翻译：

真正能揭示你的工作与人工智能关系的关键数据
一位经济学家表示："我们需要为此启动一项曼哈顿计划。"
本文原载于我们的每周人工智能通讯《算法》。若想第一时间在收件箱中阅读此类报道，请在此处订阅。

在硅谷的辐射圈内，人工智能引发的就业危机已被视为必然。悲观情绪如此弥漫，以至于人工智能公司Anthropic的社会影响研究员在周三回应"对人工智能未来更乐观展望"的呼吁时表示，近期可能出现经济衰退和"初级职业阶梯的崩塌"。该公司首席执行官达里奥·阿莫迪——这位言辞更激进的同事——曾称人工智能是"替代人类劳动的通用工具"，可能在五年内取代所有工作。当然，这类观点并非仅出自Anthropic。

这些讨论不出意外地引发了许多从业者的恐慌（或许也助推了上周兴起的"全面暂停数据中心建设"运动）。立法者们并未缓解这种恐慌——至今无人能清晰阐述应对未来的连贯计划。

就连曾警告"人工智能尚未导致失业、短期内可能不会引发断崖式冲击"的经济学家们，也逐渐认同人工智能可能以独特且前所未有的方式改变我们的工作模式。

芝加哥大学的亚历克斯·伊马斯正是这样的经济学家。周五上午的对话中，他分享了两点：一是直言我们预测未来的工具极其匮乏；二是呼吁经济学家们立即开始收集一类关键数据——唯有依靠这类数据，才可能制定应对人工智能影响劳动市场的可行方案。

关于我们工具的缺陷：任何工作都由具体任务构成。以房地产经纪人为例，其部分工作是询问客户购房需求。美国政府自1998年起持续更新庞大的职业任务数据库，收录了数千项此类任务。OpenAI去年12月正是依据这些数据评估职业受人工智能影响的"暴露程度"（例如发现房地产经纪人暴露度为28%）。今年2月，Anthropic又借助该数据库分析数百万条Claude对话记录，探究用户实际使用AI完成的任务，并与数据库任务进行比对。

但伊马斯指出，仅了解任务暴露度会让人对职业风险产生错觉。"单凭暴露度预测岗位替代毫无意义，"他坦言。

在最极端情况下——当某个岗位的所有任务都无需人工指导即可由AI完成时，这种分析或许具有参考价值。伊马斯解释道，如果AI完成这些任务的成本低于人力成本（这并非必然，因为推理模型和智能体AI可能产生高昂费用），且质量达标，该岗位就可能消失。这让人联想到几十年前常被提及的电梯操作员；如今的类似岗位或许是仅负责电话分流客服。

但对绝大多数职业而言，情况远非如此简单。具体细节至关重要：某些职业前景黯淡，但仅凭暴露度数据无法判断影响方式和时间节点。

以编写代码为例。假设某高端交友应用开发者借助AI编程工具，将原本三天的工作压缩至一天完成。这意味着工作效率提升。雇主支付同等薪酬可获得更高产出。那么，雇主会因此增聘还是裁员？

伊马斯认为，这个问题应让政策制定者彻夜难眠，因为答案因行业而异。而当前我们如同在黑暗中摸索。

就程序员而言，效率提升可能促使交友应用降价（尽管怀疑者认为企业会私吞利润，但在竞争市场中，这种做法可能被对手以低价击垮）。降价总会刺激一定需求增长，但增幅多少？若新增数百万用户，公司可能扩大规模并增聘工程师以满足需求；若需求几乎未增长（或许降价也吸引不了非高端应用用户），则可能引发裁员。

将这种假设推及所有可能受AI影响的职业，便引出了当今最紧迫的经济学问题：价格弹性的具体表现，即价格变动如何影响需求变化。这正是伊马斯上周强调的第二点：目前我们缺乏覆盖全经济领域的相关数据，但我们本可以掌握。

伊马斯指出，我们确实掌握麦片、牛奶等超市商品的数据（芝加哥大学与超市合作获取扫描器价格数据），但对教师、网页开发员、营养师等职业（这些均被列为AI"暴露"岗位）却无此类统计。至少没有经过系统整合或向研究者开放的数据——它们往往散落于私营企业或咨询公司。

"我们需要像曼哈顿计划那样大规模收集数据，"伊马斯强调。且收集范围不应局限于当前明显受影响的职业："现在未暴露的领域未来也会暴露，因此需要追踪整个经济体的统计数据。"

获取这些信息需要时间与资金，但伊马斯论证了其价值：这将帮助经济学家首次切实展望人工智能塑造的未来图景，并为政策制定者创造规划应对的可能。

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英文来源：

The one piece of data that could actually shed light on your job and AI
“We need a Manhattan Project for this,” one economist says.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. The mood is so grim that a societal impacts researcher at Anthropic, responding Wednesday to a call for more optimistic visions of AI’s future, said there might be a recession in the near term and a “breakdown of the early-career ladder.” Her less-measured colleague Dario Amodei, the company’s CEO, has called AI “a general labor substitute for humans” that could do all jobs in less than five years. And those ideas are not just coming from Anthropic, of course.
These conversations have unsurprisingly left many workers in a panic (and are probably contributing to support for efforts to entirely pause the construction of data centers, some of which gained steam last week). The panic isn’t being helped by lawmakers, none of whom have articulated a coherent plan for what comes next.
Even economists who have cautioned that AI has not yet cut jobs and may not result in a cliff ahead are coming around to the idea that it could have a unique and unprecedented impact on how we work.
Alex Imas, based at the University of Chicago, is one of those economists. He shared two things with me when we spoke on Friday morning: a blunt assessment that our tools for predicting what this will look like are pretty abysmal, and a “call to arms” for economists to start collecting the one type of data that could make a plan to address AI in the workforce possible at all.
On our abysmal tools: consider the fact that any job is made up of individual tasks. One part of a real estate agent’s job, for example, is to ask clients what sort of property they want to buy. The US government chronicled thousands of these tasks in a massive catalogue first launched in 1998 and updated regularly since then. This was the data that researchers at OpenAI used in December to judge how “exposed” a job is to AI (they found a real estate agent to be 28% exposed, for example). Then in February, Anthropic used this data in its analysis of millions of Claude conversations to see which tasks people are actually using its AI to complete and where the two lists overlapped.
But knowing the AI exposure of tasks leads to an illusory understanding of how much a given job is at risk, Imas says. “Exposure alone is a completely meaningless tool for predicting displacement,” he told me.
Sure, it is illustrative in the gloomiest case—for a job in which literally every task could be done by AI with no human direction. If it costs less for an AI model to do all those tasks than what you’re paid—which is not a given, since reasoning models and agentic AI can rack up quite a bill—and it can do them well, the job likely disappears, Imas says. This is the oft-mentioned case of the elevator operator from decades ago; maybe today’s parallel is a customer service agent solely doing phone call triage.
But for the vast majority of jobs, the case is not so simple. And the specifics matter, too: Some jobs are likely to have dark days ahead, but knowing how and when this will play out is hard to answer when only looking at exposure.
Take writing code, for example. Someone who builds premium dating apps, let’s say, might use AI coding tools to create in one day what used to take three days. That means the worker is more productive. The worker’s employer, spending the same amount of money, can now get more output. So then will the employer want more employees or fewer?
This is the question that Imas says should keep any policymaker up at night, because the answer will change depending on the industry. And we are operating in the dark.
In this coder’s case, these efficiencies make it possible for dating apps to lower prices. (A skeptic might expect companies to simply pocket the gains, but in a competitive market, they risk being undercut if they do.) These lower prices will always drive some increase in demand for the apps. But how much? If millions more people want it, the company might grow and ultimately hire more engineers to meet this demand. But if demand barely ticks up—maybe the people who don’t use premium dating apps still won’t want them even at a lower price—fewer coders are needed, and layoffs will happen.
Repeat this hypothetical across every job with tasks that AI can do, and you have the most pressing economic question of our time: the specifics of price elasticity, or how much demand for something changes when its price changes. And this is the second part of what Imas emphasized last week: We don’t currently have this data across the economy. But we could.
We do have the numbers for grocery items like cereal and milk, Imas says, because the University of Chicago partners with supermarkets to get data from their price scanners. But we don’t have such figures for tutors or web developers or dietitians (all jobs found to have “exposure” to AI, by the way). Or at least not in a way that’s been widely compiled or made accessible to researchers; sometimes it’s scattered across private companies or consultancies.
“We need, like, a Manhattan Project to collect this,” Imas says. And we don’t need it just for jobs that could obviously be affected by AI now: “Fields that are not exposed now will become exposed in the future, so you just want to track these statistics across the entire economy.”
Getting all this information would take time and money, but Imas makes the case that it’s worth it; it would give economists the first realistic look at how our AI-enabled future could unfold and give policymakers a shot at making a plan for it.
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