在任何给定时间, 技术对就业有两个作用: 它取代了传统工作, 并且创造了新的工作领域。机器取代了农民,，但使, 说, 航空工程师得以存在。所以, 如果科技创造了新的就业机会, 谁获得了这些就业机会? 他们的工资如何? 新就业机会保持新鲜状态需要多长时间, 在它们成为任何工人都可以做的另一项常见任务之前?
At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?
麻省理工学院劳工经济学家大卫·奥托领导的一项关于美国就业的新研究揭示了所有这些问题。在战后的美国,，正如 Autor 和他的同事详细展示的,，新的工作形式往往使 30 岁以下的大学毕业生比其他任何人都受益更多。
A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else.
“我们以前从未确切地知道谁在做新工作,” Autor 说。 “It 在城市环境中，年轻人和受过教育的人, 做得更多。”
“We had never before seen exactly who is doing new work,” Autor says. “It的 done more by young and educated people, in urban settings.”
该研究还包含强大的大规模洞察: 很多基于创新的新工作都是由需求驱动的。 1940 年代, 为应对第二次世界大战,，政府支持的研究和制造扩张产生了大量新工作, 和新形式的专业知识。
The study also contains a powerful large-scale insight: A lot of innovation-based new work is driven by demand. Government-backed expansion of research and manufacturing in the 1940s, in response to World War II, accounted for a huge amount of new work, and new forms of expertise.
“这表明，无论我们在哪里进行新投资,，我们最终都会获得新的专业化,” Autor 说。 “如果您创建一个大型活动,，的总是有机会获得与其相关的新专业知识的。我们认为这令人兴奋。”
“This says that wherever we make new investments, we end up getting new specializations,” Autor says. “If you create a large-scale activity, there的 always going to be an opportunity for new specialized knowledge that的 relevant for it. We thought that was exciting to see.”
是的,了解新工作,以及获得它的工人类型,可能与人工智能的传播相关—尽管,在Autor的的估计,中现在判断人工智能将如何影响工作场所还为时过早。
And yes, learning about new work, and the kinds of workers who obtain it, might be relevant to the spread of artificial intelligence — although, in Autor的 estimation, it is too soon to tell just how AI will affect the workplace.
“人们真的很担心基于人工智能的自动化会更快地侵蚀特定任务,” Autor 观察到。 “侵蚀任务与侵蚀工作,不同，因为许多工作涉及大量任务。但我们’都在说:新作品从哪里来?它’如此重要,而我们对此知之甚少。我们不 不知道它会是什么, 它会是什么样子, 以及谁能够做到这一点。”
“People are really worried that AI-based automation is going to erode specific tasks more rapidly,” Autor observes. “Eroding tasks is not the same thing as eroding jobs, since many jobs involve a lot of tasks. But we’re all saying: Where is the new work going to come from? It的 so important, and we know little about it. We don’t know what it will be, what it will look like, and who will be able to do it.”
“如果每个人都是专家,那么没有人是专家”
“If everyone is an expert, then no one is an expert”
四位合著者还合作完成了一项于 2024 年发表的新工作, 的主要研究,，该研究发现，从 1940 年到 2018 年，美国大约有十分之六的工作属于自 1940 年以来才广泛发展的新专业。这项新研究通过更精确地考察谁担任新工作领域，扩展了该研究范围。
The four co-authors also collaborated on a previous major study of new work, published in 2024, which found that about six out of 10 jobs in the U.S. from 1940 to 2018 were in new specialties that had only developed broadly since 1940. The new study extends that line of research by looking more precisely at who fills the new lines of work.
为此,，研究人员使用了美国人口普查局 1940 年至 1950 年, 的数据以及人口普查局的 美国社区调查 (ACS) 2011 年至 2023 年的数据。在第一种情况, 中，因为人口普查局的记录在大约 70 年后完全公开,，学者们可以检查有关职业的个人数据,工资, 和更多,，并且可以跟踪 1940 年至 1950 年人口普查计数期间更换工作的相同工人。
To do that, the researchers used U.S. Census Bureau data from 1940 through 1950, as well as the Census Bureau的 American Community Survey (ACS) data from 2011 to 2023. In the first case, because Census Bureau records become wholly public after about 70 years, the scholars could examine individual-level data about occupations, salaries, and more, and could track the same workers as they changed jobs between the 1940 and 1950 Census enumerations.
通过与美国人口普查局, 的合作研究安排，作者还获得了对个人级 ACS 记录的安全访问。这些数据使他们能够分析新职业专业— 工人的收入, 教育, 和其他人口特征，并将其与长期职业专业的工人进行比较。
Through a collaborative research arrangement with the U.S. Census Bureau, the authors also gained secure access to person-level ACS records. These data allowed them to analyze the earnings, education, and other demographic characteristics of workers in new occupational specialties — and to compare them with workers in longstanding ones.
新作品, Autor 观察到, 总是与新形式的专业知识联系在一起。起初, 这种专业知识很稀缺; 随着时间的推移, 它可能会变得更加普遍。无论如何，, 专业知识通常与新技术形式相关。
New work, Autor observes, is always tied to new forms of expertise. At first, this expertise is scarce; over time, it may become more common. In any case, expertise is often linked to new forms of technology.
“它需要掌握一些能力,” Autor 说。 “使劳动力变得有价值的不仅仅是做事的能力,而是专业知识。这通常将高薪工作与低薪工作区分开来。” 此外, 他补充道, “ 它必须是稀缺的。如果每个人都是专家,，那么没有人是专家。”
“It requires mastering some capability,” Autor says. “What makes labor valuable is not simply the ability to do stuff, but specialized knowledge. And that often differentiates high-paid work from low-paid work.” Moreover, he adds, “It has to be scarce. If everyone is an expert, then no one is an expert.”
通过检查人口普查数据,，学者们发现，早在 1950 年,，大约 7% 的员工从事 1930 年以来出现的工作类型。最近,，2011-2023 年期间，大约 18% 的工人从事 1970 年以来引入的工作。(这恰好与每十年新增工作岗位的比例大致相同,，尽管 Autor 认为这不是一个 hard-and-fast trend.)
By examining the census data, the scholars found that back in 1950, about 7 percent of employees had jobs in types of work that had emerged since 1930. More recently, about 18 percent of workers in the 2011-2023 period were in lines of work introduced since 1970. (That happens to be roughly the same portion of new jobs per decade, although Autor does not think this is a hard-and-fast trend.)
在这些时间段, 新工作在城市地区出现的频率更高,，30 岁以下的人比任何其他年龄段的人受益更多。从事新工作似乎会产生持久影响: 1940 年从事新工作的人在 1950 年, 从事新工作的可能性是一般人口的 2.5 倍。大学毕业生从事新工作的可能性比高中毕业生高2.9个百分点。
In these time periods, new work has emerged more often in urban areas, with people under 30 benefitting more than any other age category. Getting a job in a line of new work seems to have a lasting effect: People employed in new work in 1940 were 2.5 times as likely to be in new work in 1950, compared to the general population. College graduates were 2.9 percentage points more likely than high school graduates to be engaged in new work.
新工作还有工资溢价,，总体工资比现有工作形式高,。然而，研究表明, 随着时间的推移，工资溢价也会逐渐消失,，因为许多形式的新工作中的特定专业知识被更广泛地掌握。
New work also has a wage premium, that is, better salaries on aggregate than in already-existing forms of work. Yet as the study shows, that wage premium also fades over time, as the particular expertise in many forms of new work becomes much more widely grasped.
“The scarcity value erodes,” Autor says. “It becomes common knowledge. It itself gets automated. New work gets old.”
毕竟, Autor 指出, 驾驶汽车曾经是一种稀缺的专业知识。就此而言，, 直到 20 世纪 90 年代才能够使用 WordPerfect 或 Microsoft Word, 等文字处理程序。一段时间后,虽然,能够处理文字处理工具成为使用计算机的最基本部分。
After all, Autor points out, driving a car was once a scarce form of expertise. For that matter, so was being able to use word-processing programs such as WordPerfect or Microsoft Word, well into the 1990s. After a while, though, being able to handle word-processing tools became the most elementary part of using a computer.
回到人工智能一分钟
Back to AI for a minute
Studying who gets new jobs led the scholars to striking conclusions about how new work is created. Examining county-level data from the World War II era, when the federal government was backing new manufacturing in public-private partnerships throughout the U.S., the study shows that counties with new factories had more new work, and that 85 to 90 percent of new work from 1940 to 1950 was technology-driven.
In this sense there was a great deal of demand-driven innovation at the time. Today, public discourse about innovation often focuses on the supply side, namely, the innovators and entrepreneurs trying to create new products. But the study shows that the demand side can significantly influence innovative activity.
“技术不像, ‘Eureka!’，它只是发生,” Autor 说。 “创新是有目的的活动。创新是累积的。如果你走得足够远,，它就会有自己的动力。但如果你不’t,，’将永远不会到达那里。”
“Technology is not like, ‘Eureka! where it just happens,” Autor says. “Innovation is a purposive activity. And innovation is cumulative. If you get far enough, it will have its own momentum. But if you don’t, it’ll never get there.”
这让我们回到人工智能,，这是 2026 年很多人关注的话题。人工智能会创造良好的新就业机会, 还是会夺走工作? 嗯, 这可能取决于我们如何实现它, Autor 认为。考虑一下庞大的医疗保健行业,，如果人们有兴趣创造就业机会，那里可能会有多种类型的技术驱动的新工作,。
Which brings us back to AI, the topic so many people are focused on in 2026. Will AI create good new jobs, or will it take work away? Well, it likely depends how we implement it, Autor thinks. Consider the massive health care sector, where there could be a lot of types of tech-driven new work, if people are interested in creating jobs.
“There are different ways we could use AI in health care,” Autor says. “One is just to automate people的 jobs away. The other is to allow people with different levels of expertise to do different tasks. I would say the latter is more socially beneficial. But it的 not clear that is where the market will go.”
另一方面, 也许政府以各种形式驱动需求, 人工智能可以以最终提高医疗保健部门生产力的方式应用, 从而创造新的就业机会。
On the other hand, maybe with government-driven demand in various forms, AI could get applied in ways that end up boosting health care-sector productivity, creating new jobs as a result.
“ 美国一半以上的医疗保健费用是公共资金,” Autor 观察到。 “我们在那里有很大的影响力,我们可以朝这个方向推动事情。有不同的使用方法。”
“More than half the dollars in health care in the U.S. are public dollars,” Autor observes. “We have a lot of leverage there, we can push things in that direction. There are different ways to use this.”
这项研究的支持,部分,由休利特基金会,谷歌技术与社会访问学者计划, NOMIS基金会,施密特科学AI2050奖学金,史密斯理查森基金会,詹姆斯·M.和凯瑟琳·D.斯通基金会,和Gak研究所支持。
This research was supported, in part, by the Hewlett Foundation, the Google Technology and Society Visiting Fellows Program, the NOMIS Foundation, the Schmidt Sciences AI2050 Fellowship, the Smith Richardson Foundation, the James M. and Cathleen D. Stone Foundation, and Instituut Gak.