Workforce policy for the age of AI
The debate over AI and work is often framed as a choice between mass unemployment and widespread human augmentation.
关于人工智能和工作的争论通常被视为大规模失业和广泛的人类增强之间的选择。
The economic evidence supports neither conclusion.
经济证据并不支持这两个结论。
Instead, the literature suggests that AI will likely produce sharply divergent labor-market outcomes on dramatically different timelines, rapidly displacing some forms of work while gradually augmenting others.
相反，文献表明，人工智能可能会在截然不同的时间线上产生截然不同的劳动力市场结果，迅速取代某些形式的工作，同时逐渐增强其他形式的工作。
These effects will also likely vary substantially across occupations, industries, and even employers.
这些影响也可能因职业、行业甚至雇主的不同而存在很大差异。
The economic literature’s central contribution is not merely to show that AI adoption will be divergent and uneven.
经济学文献的核心贡献不仅仅是表明人工智能的采用将是多样化和不平衡的。
It is also that AI exposure is the wrong organizing principle for workforce policy.
此外，人工智能的暴露对于劳动力政策来说是错误的组织原则。
What matters is not just whether a certain occupation is exposed, but also how AI changes the value of human work, and whether it enhances human expertise and/or lowers barriers to economic opportunities.
重要的不仅仅是某种职业是否被暴露，还在于人工智能如何改变人类工作的价值，以及它是否增强了人类的专业知识和/或降低了经济机会的障碍。
Recent economic research offers four key findings that inform these questions and can help shape how policymakers respond to them.
最近的经济研究提供了四个关键发现，为这些问题提供了信息，并有助于制定政策制定者应对这些问题的方式。
First, AI exposure does not necessarily translate into commercially viable automation or augmentation.
首先，人工智能的曝光并不一定转化为商业上可行的自动化或增强。
Second, the central question should not be simply whether AI creates or destroys jobs, but also how it changes the value of human expertise.
其次，核心问题不应该只是人工智能是否创造或摧毁就业机会，而应该是它如何改变人类专业知识的价值。
Third, successful AI adoption requires not only knowing how to use AI but knowing when to trust it.
第三，成功采用人工智能不仅需要知道如何使用人工智能，还需要知道何时信任它。
Fourth, as models become more reliable and capable of completing longer, complex tasks, AI adoption may become more autonomous, and commercially viable across a broader range of workflows.
第四，随着模型变得更加可靠并且能够完成更长、更复杂的任务，人工智能的采用可能会变得更加自主，并且在更广泛的工作流程中具有商业可行性。
These findings argue against both complacency and an immediate economy-wide sweeping intervention.
这些发现反对自满情绪和立即对整个经济进行全面干预。
Workforce policy should instead preempt the worst potential downsides of AI and develop a targeted policy framework that can adapt to evolving labor-market changes.
相反，劳动力政策应该预防人工智能最严重的潜在负面影响，并制定能够适应不断变化的劳动力市场变化的有针对性的政策框架。
It should prioritize the areas where policy can have the greatest economic impact : supporting workers who are actually displaced and expanding access to newly created high-return opportunities.
它应该优先考虑政策可以产生最大经济影响的领域：支持实际失业的工人并扩大获得新创造的高回报机会的机会。
Programs should involve employers directly in their design and promote domain-specific hands-on training, apprenticeships, and wage insurance.
计划应让雇主直接参与其设计，并促进特定领域的实践培训、学徒制和工资保险。
The central challenge is not to forecast every job AI will affect, but to develop a new framework capable of identifying unforeseen disruptions while determining when, where, and how workforce policy should respond.
核心挑战不是预测人工智能将影响的每一项工作，而是开发一个新的框架，能够识别不可预见的干扰，同时确定劳动力政策应在何时、何地以及如何应对。
In the remainder of the paper, we highlight why a public policy response is imperative, examine the tension between policy uncertainty and the costs of doing nothing, discuss four insights from the economic literature and their implications for workforce policy, and propose five policy directions grounded in those findings.
在本文的其余部分，我们强调为什么必须采取公共政策应对措施，研究政策不确定性与无所作为的成本之间的紧张关系，讨论经济文献中的四个见解及其对劳动力政策的影响，并根据这些发现提出五个政策方向。
Specifically, we recommend prioritizing displaced workers and emerging high-productivity opportunities, designing domain-specific training, tailoring programs to shifts in human expertise, expanding apprenticeships, and establishing a federal wage-insurance program.
具体来说，我们建议优先考虑失业工人和新兴的高生产率机会，设计特定领域的培训，根据人类专业知识的变化定制计划，扩大学徒制，并建立联邦工资保险计划。
The economic stakes AI presents for American workers are profound and far-reaching.
人工智能给美国工人带来的经济利益是深远的。
AI is emerging at a moment when U.S. economic mobility has sharply declined and financial insecurity has risen, trends that may reverse decades of shared economic progress and create vulnerabilities that a technological disruption could deepen even if it ultimately delivers substantial gains.
人工智能是在美国经济流动性急剧下降、金融不安全性加剧之际出现的，这种趋势可能会逆转数十年的共同经济进步，并产生技术颠覆可能加深的脆弱性，即使它最终带来了巨大的收益。
The scale of these challenges is not insignificant.
这些挑战的规模并非微不足道。
American households have far greater debt to income ratio today than they did four decades ago, homeownership has become less accessible for younger Americans, wages are increasingly polarized based on education, and incomes are failing to catch up with economic productivity gains (see Figure 1a).
如今美国家庭的债务与收入之比远高于四十年前，年轻美国人越来越难以拥有住房，工资因教育程度而日益两极分化，收入未能赶上经济生产率的增长（见图1a）。
Moreover, and perhaps as a result of these challenges, economic mobility in America is in decline: whereas in the 1940s, roughly 90% of children grew up to earn more than their parents by the 1980s, that number is down to 50% and is still declining (see Figure 1b).
此外，也许由于这些挑战，美国的经济流动性正在下降：而在 20 世纪 40 年代，大约 90% 的儿童长大后收入超过父母，而到 1980 年代，这一数字已降至 50%，并且仍在下降（见图 1b）。
AI could help address these challenges by helping workers develop and leverage expertise, replacing the need for formal education and creating better jobs.
人工智能可以帮助工人发展和利用专业知识，取代对正规教育的需求并创造更好的就业机会，从而帮助应对这些挑战。
But AI may also intensify these challenges by involuntarily displacing workers and causing persistent earnings losses, exacerbating wage disparities, disrupting career ladders, and undermining earnings growth on which economic mobility and homeownership depend (see e.g., Ben-Ishai et al.
但人工智能也可能会不由自主地取代工人，造成持续的收入损失，加剧工资差距，扰乱职业阶梯，并破坏经济流动性和住房拥有率所依赖的收入增长，从而加剧这些挑战（例如，参见 Ben-Ishai 等，2017）。
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There is little doubt that AI will transform the labor market, but the timing, scale, and distribution of that transformation remain uncertain.
毫无疑问，人工智能将改变劳动力市场，但这种转变的时间、规模和分布仍然不确定。
Because AI’s trajectory, commercial applications, and adoption patterns remain unknown, it is impossible to know today with any real certainty which occupations will decline, which new roles will emerge, how occupations will evolve, or which forms of expertise will become more valuable.
由于人工智能的发展轨迹、商业应用和采用模式仍然未知，因此今天不可能真正确定哪些职业将会衰落、哪些新角色将会出现、职业将如何演变，或者哪些形式的专业知识将变得更有价值。
We simply cannot reliably infer future occupational change from current AI capabilities or emerging applications alone.
我们根本无法仅从当前的人工智能功能或新兴应用程序可靠地推断未来的职业变化。
Yet uncertainty about AI’s impacts is not an argument for inaction.
然而，人工智能影响的不确定性并不能成为不采取行动的理由。
Policymaking is about making consequential decisions in an effort to prevent uncertain harms.
政策制定就是做出相应的决定，以努力防止不确定的损害。
AI is no different.
人工智能也不例外。
Policymakers must make consequential up-front decisions today about where, when, and how to prepare for an AI workforce transition whose precise nature will become clear only over time.
政策制定者今天必须就在何处、何时以及如何为人工智能劳动力转型做好准备做出相应的预先决定，其确切性质只有随着时间的推移才会变得清晰。
Similarly, uncertainty in some areas of policy should not prevent action in others.
同样，某些政策领域的不确定性不应妨碍其他政策领域的行动。
In particular, sweeping proposals have been advanced to address the workforce and distributional challenges posed by AI, for example universal basic income, higher taxes on capital, or reforms to social safety-net.
特别是，人们提出了广泛的建议来解决人工智能带来的劳动力和分配挑战，例如全民基本收入、提高资本税或社会安全网改革。
These proposals may have merit, but implementing such ambitious reforms could be fiscally costly, politically difficult, and unlikely to command broad consensus in the near term.
这些提议可能有其优点，但实施如此雄心勃勃的改革可能会带来财政上的高昂成本、政治上的困难，并且在短期内不太可能获得广泛的共识。
Our focus here is not to opine on such large structural changes, but to focus on practical policy changes that could be implemented in the near-term and where there is a direct connection to the economic literature.
我们在这里的重点不是对如此大的结构性变化发表意见，而是关注短期内可以实施的、与经济文献有直接联系的实际政策变化。
While economic research cannot resolve all the uncertainties related to AI workforce policies, it can make policy choices more disciplined and informed.
虽然经济研究无法解决与人工智能劳动力政策相关的所有不确定性，但它可以使政策选择更加规范和明智。
It can identify the mechanisms through which an AI workforce transition may unfold and provide guiding principles for designing effective programs.
它可以确定人工智能劳动力转型可能展开的机制，并为设计有效的计划提供指导原则。
The analysis below draws from these findings and proposes core guiding principles for a workforce transition policy.
以下分析借鉴了这些发现，并提出了劳动力转型政策的核心指导原则。
Our approach begins from the premise that policymakers should establish a new preemptive framework that balances the need for action with the risks of acting under uncertainty, adapts to evolving market shifts in demand, and is continuously informed by real-time empirical evidence.
我们的方法始于这样一个前提：政策制定者应该建立一个新的先发制人的框架，平衡行动的需要与在不确定性下采取行动的风险，适应不断变化的市场需求变化，并不断获得实时经验证据的信息。
Much of the debate over AI and jobs relies on measures of AI exposure, the share of tasks within an occupation that AI may be capable of performing, to identify the occupations most likely to be impacted by AI.
关于人工智能和就业的大部分争论都依赖于对人工智能暴露程度的衡量，即人工智能可能能够执行的职业中的任务份额，以确定最有可能受到人工智能影响的职业。
Gimbel et al.
金贝尔等人。
, for instance, find that several exposure studies identify computer programmers or telemarketers as occupations that are likely to be impacted by AI because they contain higher shares of exposed tasks.
例如，多项暴露研究将计算机程序员或电话营销员确定为可能受到人工智能影响的职业，因为它们包含较高比例的暴露任务。
At the same time, they also find that the studies often produce inconsistent projections as they differ substantially in which occupations they project as highly exposed
与此同时，他们还发现，这些研究经常产生不一致的预测，因为它们在预测高风险职业方面存在很大差异。
Technical capability, however, is not the same as economically viable automation.
然而，技术能力并不等同于经济上可行的自动化。
Identifying which tasks are exposed is an important starting point , but it is insufficient.
确定暴露哪些任务是一个重要的起点，但这还不够。
Whether a task is actually automated in commercial environments depends not only on AI’s technical capabilities, but also on a broader set of practical and economic factors including task completion time, model reliability, error management and mitigation, and workflow or organizational integration costs.
一项任务在商业环境中是否真正实现自动化不仅取决于人工智能的技术能力，还取决于更广泛的实际和经济因素，包括任务完成时间、模型可靠性、错误管理和缓解以及工作流程或组织集成成本。
Mertens et al.
默滕斯等人。
find that although Large Language Models (LLMs) are increasingly capable across a broad range of tasks, their performance remains inconsistent and vulnerable to errors, raising questions about whether they can be reliably integrated into workplace applications.
研究发现，尽管大型语言模型（LLM）在广泛的任务中的能力越来越强，但它们的性能仍然不一致并且容易出错，这引发了关于它们是否可以可靠地集成到工作场所应用程序中的问题。
As of the end of 2025, AI models were capable of completing tasks that took humans three to four hours to complete with only a 65% success rate.
截至 2025 年底，人工智能模型能够完成人类需要三到四个小时才能完成的任务，但成功率仅为 65%。
For many of these tasks, model failures make AI insufficiently reliable for meeting the average error rates that firms would tolerate in real-world commercial settings.
对于其中许多任务，模型失败使得人工智能不够可靠，无法满足企业在现实商业环境中所能容忍的平均错误率。
For comparison, human-reliability frameworks are far more strict in commercial settings relative to AI model performance.
相比之下，相对于人工智能模型的性能，商业环境中的人类可靠性框架要严格得多。
For instance, the Human Error Assessment and Reduction Technique (HEART), a human-reliability assessment framework that was developed to evaluate human performance and measure its likelihood of errors, indicates that human-error probabilities historically range from 2% for routine tasks that require limited skills to 9% for simple tasks performed with limited attention, and 16% for complex tasks requiring substantial comprehension and skill (see Williams and Bell ).
例如，人为错误评估和减少技术 (HEART) 是一种人类可靠性评估框架，旨在评估人的表现并衡量其发生错误的可能性，该技术表明，历史上人为错误概率的范围从需要有限技能的常规任务的 2% 到注意力有限的简单任务的 9%，以及需要大量理解和技能的复杂任务的 16%（参见 Williams 和 Bell）。
While human errors are highly dependent on context, there is no universal human-error standard, and these measures are not directly comparable, they seem considerably below the error rates observed on average for current AI models.
虽然人为错误高度依赖于环境，但没有通用的人为错误标准，并且这些措施不能直接比较，但它们似乎远低于当前人工智能模型观察到的平均错误率。
Mertens et al.
默滕斯等人。
also find that task duration itself matters.
还发现任务持续时间本身很重要。
In fact, longer tasks are less likely to be completed successfully, as a tenfold increase in average task duration reduces model performance by 11%.
事实上，较长的任务成功完成的可能性较小，因为平均任务持续时间增加十倍会使模型性能降低 11%。
For instance, as shown in Figure 1, healthcare support tasks, which have the shortest completion time (just over an hour), generate the highest model performance scores.
例如，如图 1 所示，完成时间最短（仅一个多小时）的医疗保健支持任务生成的模型性能得分最高。
By contrast, legal or architecture and engineering tasks, which take more than 20 hours to complete, generate the lowest performance scores.
相比之下，需要 20 多个小时才能完成的法律或建筑和工程任务的性能得分最低。
This finding is important as it suggests that occupations with longer task completion times may be slower to adopt AI.
这一发现很重要，因为它表明任务完成时间较长的职业采用人工智能的速度可能会较慢。
There is a clear historical precedent for the finding that task duration matters.
任务持续时间很重要这一发现有明显的历史先例。
Automation has often advanced by focusing first on the shortest, most decomposable units of work.
自动化的进步往往首先关注最短、最可分解的工作单元。
In industrial robotics , early commercial viability was achieved in short, repetitive tasks such as spot welding or painting.
在工业机器人领域，早期的商业可行性是通过点焊或喷漆等短期重复性任务实现的。
By contrast, longer-duration tasks, such as installing wires or assembling final products, resisted automation for decades.
相比之下，持续时间较长的任务，例如安装电线或组装最终产品，几十年来一直阻碍着自动化。
This is because these tasks required continuous real-time computer vision and tactile feedback.
这是因为这些任务需要连续的实时计算机视觉和触觉反馈。
In effect, the engineering and error-correction costs of automating these longer tasks exceeded their benefit relative to human labor (see International Federation of Robotics , Nguyen et al.
实际上，自动化这些较长任务的工程和纠错成本超过了相对于人类劳动的收益（参见国际机器人联合会，Nguyen 等人，2017）。
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A similar pattern emerged in digital labor markets.
数字劳动力市场也出现了类似的模式。
Earlier gig economy platforms , such as Mechanical Turk and Taskrabbit, succeeded by decomposing work into small, atomic microtasks, such as five-second image labeling or content verification.
早期的零工经济平台，如 Mechanical Turk 和 Taskrabbit，通过将工作分解为小的原子微任务，如五秒图像标记或内容验证而获得成功。
However, attempts to crowdsource long-form work, such as document writing, often failed due to coordination challenges, inconsistencies or integration costs.
然而，由于协调挑战、不一致或集成成本，众包长篇工作（例如文档编写）的尝试常常失败。
Ultimately, the managerial cost of auditing, correcting, and synthesizing these longer tasks often outweighed the efficiency gains of the technology (see Kittur et al.
最终，审核、纠正和综合这些较长任务的管理成本往往超过了技术带来的效率提升（参见 Kittur 等人，2017）。
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The economic literature has long recognized that task automation does not necessarily lead to a negative outcome for workers .
经济学文献早已认识到任务自动化并不一定会给工人带来负面结果。
Technological progress is often motivated by the desire to replace mundane human work (e.g., sorting through numerous documents or working on a factory line), which could make work more pleasant, more interesting or less dangerous.
技术进步通常是出于取代人类平凡工作的愿望（例如，整理大量文件或在工厂生产线上工作），这可以使工作变得更愉快、更有趣或更少危险。
But it can also replace the more meaningful parts of work, making it less pleasant or less interesting (e.g., automating clerks’ specialized recordkeeping while leaving them with counting stock, and weighing goods responsibilities).
但它也可能取代工作中更有意义的部分，使其变得不那么愉快或不那么有趣（例如，自动化职员的专门记录保存，同时让他们盘点库存和称量货物的责任）。
Autor and Thompson’s expertise framework provides a way to understand these changes.
奥托和汤普森的专业知识框架提供了一种理解这些变化的方法。
It suggests that the impact of AI depends less on the number of exposed tasks or affected occupations, and more on how AI may change the value of expertise—the unique capabilities or know-hows that enable a worker to differentiate herself and generate greater value.
它表明，人工智能的影响较少取决于暴露的任务或受影响职业的数量，而更多地取决于人工智能如何改变专业知识的价值——使工人能够脱颖而出并产生更大价值的独特能力或专业知识。
Expertise creates an economic tradeoff.
专业知识创造了经济权衡。
It enables workers who possess it to command higher pay, but it also serves as a barrier to entry by excluding workers who lack the capabilities required to perform expert tasks.
它使拥有它的工人能够获得更高的工资，但它也通过排除缺乏执行专家任务所需能力的工人而成为进入壁垒。
In accounting, for instance, if AI can automate bookkeeping and data entry but performs inconsistently in more advanced tasks such as tax advice or analysis, it would increase the need for human expertise.
例如，在会计领域，如果人工智能可以实现簿记和数据输入自动化，但在税务建议或分析等更高级的任务中表现不一致，那么就会增加对人类专业知识的需求。
In this case, AI would increase accountants’ wages but reduce the occupation’s employment.
在这种情况下，人工智能会增加会计师的工资，但会减少该职业的就业。
On the other hand, if AI could reliably diagnose and treat the common conditions seen by family physicians, it would reduce the value of their expertise, potentially opening it up to being done by nurse practitioners.
另一方面，如果人工智能能够可靠地诊断和治疗家庭医生所见的常见病症，就会降低他们的专业知识的价值，从而有可能让执业护士来完成这项工作。
1 In this case AI would be reducing entry barriers to the occupation, thereby increasing employment but also lowering pay.
1 在这种情况下，人工智能将减少该职业的进入壁垒，从而增加就业，但也会降低工资。
The expertise framework therefore indicates that some exposed occupations may generate more jobs at lower wages, while others may lead to higher wages but offer fewer job opportunities.
因此，专业知识框架表明，一些暴露的职业可能会以较低的工资创造更多的就业机会，而另一些职业可能会带来更高的工资，但提供的就业机会更少。
The distributional question is hence not simply whether jobs disappear but rather how technology changes the value of human work and expertise.
因此，分配问题不仅仅是工作岗位是否消失，而是技术如何改变人类工作和专业知识的价值。
Because AI systems remain vulnerable to errors, limited interpretability, and non-deterministic outputs, integrating them into workplace tasks is not simply a matter of general AI literacy .
由于人工智能系统仍然容易出错、可解释性有限和输出不确定，因此将它们集成到工作场所任务中不仅仅是一般人工智能素养的问题。
Effective adoption requires task-specific training, experimentation, and feedback in real workplace settings.
有效的采用需要在真实的工作场所环境中进行针对特定任务的培训、实验和反馈。
As Fleming et al.
正如弗莱明等人。
note, deploying AI to automate human work requires substantial last mile customization.
请注意，部署人工智能来自动化人类工作需要大量的最后一英里定制。
This may include adapting general-purpose models to particular business applications, training or customizing them using proprietary data, and achieving the level of accuracy required for performing workflows successfully.
这可能包括使通用模型适应特定的业务应用程序、使用专有数据对其进行培训或定制，以及达到成功执行工作流程所需的准确性水平。
Similarly, Dell’Acqua et al.
同样，Dell’Acqua 等人。
illustrate this challenge: generative AI substantially improved performance on tasks within its capabilities, but actually reduced performance when workers applied AI to tasks that were outside such capabilities.
说明这一挑战：生成式人工智能极大地提高了其能力范围内任务的性能，但当工作人员将人工智能应用于超出其能力范围的任务时，性能实际上降低了。
This suggests that the central training challenge is not simply teaching workers how to use AI tools, but helping them understand when, where, and how those tools can reliably improve performance—and where they are likely to fail.
这表明，培训的核心挑战不仅仅是教员工如何使用人工智能工具，而是帮助他们了解这些工具何时、何地以及如何可靠地提高绩效，以及哪些地方可能会失败。
For this reason, AI integration in the workforce is not merely an educational challenge, but an organizational and strategic one.
因此，人工智能融入劳动力不仅是一项教育挑战，而且是一项组织和战略挑战。
Recent advances in AI capabilities suggest that its impact on work often begins by complementing human workers but gradually evolves toward more autonomous execution.
人工智能能力的最新进展表明，它对工作的影响往往始于补充人类工人，但逐渐演变为更加自主的执行。
The analysis by Mertens et al.
Mertens 等人的分析。
support this idea, showing that while current LLM errors require significant worker adjustment and oversight, model failure rates are halving roughly every 2.5 years.
支持这一观点，表明虽然当前的 LLM 错误需要大量的工作人员调整和监督，但模型失败率大约每 2.5 年减半。
If these trajectories hold, models could achieve an 93% baseline success rate across most professional text-based tasks by 2029, systematically shifting workflows from human-in-the-loop collaboration to more standalone automation.
如果这些轨迹保持不变，到 2029 年，模型可以在大多数基于文本的专业任务中实现 93% 的基线成功率，系统地将工作流程从人机交互协作转变为更加独立的自动化。
Appel et al.
阿佩尔等人。
also find that the share of LLM directive conversations, where users gave Claude a task that was completed with minimal back-and-forth, rapidly increased over time as well.
还发现，LLM 指令对话（用户向 Claude 提供了一项以最少的来回完成的任务）的份额也随着时间的推移而迅速增加。
These findings suggest a pattern in which improvement in model performance is increasingly enabling delegation of tasks that do not require direct human oversight.
这些发现表明，模型性能的改进越来越多地支持不需要直接人工监督的任务委派。
In practice, however, as we noted above, there is a gap between what AI can technically achieve and what is financially and organizationally viable.
然而，在实践中，正如我们上面所指出的，人工智能在技术上可以实现的目标与财务和组织上可行的目标之间存在差距。
Even as technical progress moves very fast, AI integration barriers undermine its adoption in the broader economy.
尽管技术进步非常快，但人工智能集成障碍阻碍了其在更广泛的经济中的采用。
Svanberg et al.
斯文伯格等人。
, for instance, demonstrate that at today’s AI integration costs, U.S. businesses would choose to automate only 23% of the pool of computer vision tasks that can be performed by AI.
例如，证明按照当今的人工智能集成成本，美国企业只会选择自动化人工智能可以执行的计算机视觉任务池中的 23%。
These findings suggest that as model performance improves and model inference costs decline, 2 business adoption is expected to increase across the economy.
这些发现表明，随着模型性能的提高和模型推理成本的下降，2 整个经济体的业务采用率预计将会增加。
It remains unclear, however, where AI would become affordable, feasible and practical or how long it would take to get there.
然而，目前尚不清楚人工智能在哪里变得可以负担得起、可行和实用，或者需要多长时间才能实现。
Drawing on the insights detailed above, we propose five guiding principles for AI workforce policies.
根据上述详细见解，我们提出了人工智能劳动力政策的五项指导原则。
These principles help clarify when public intervention may be warranted, where it should be targeted, and how programs should be designed to address the challenges of the AI transition.
这些原则有助于阐明何时需要公共干预、公共干预的目标应在何处以及应如何设计计划来应对人工智能转型的挑战。
The uneven trajectory of AI automation makes a targeted workforce transition strategy both possible and necessary.
人工智能自动化的不平衡轨迹使得有针对性的劳动力转型战略变得可能和必要。
Because disruption is unlikely to occur across the entire economy at once, public investment should focus on two leading priorities: workers facing actual displacement, and sectors where high-productivity new opportunities are being created.
由于整个经济不太可能同时发生混乱，公共投资应重点关注两个主要优先事项：面临实际失业的工人，以及正在创造高生产率新机会的部门。
Workers facing involuntary displacement should be a top priority because the economic costs of displacement are severe, significant, and persistent.
面临非自愿流离失所的工人应该是首要任务，因为流离失所的经济成本是严重、巨大且持久的。
Beyond the elevated risk of permanent labor-force exit, displacement also produces large earnings losses even among workers who do return to work.
除了永久性劳动力退出的风险升高之外，流离失所还会造成巨大的收入损失，即使是重返工作岗位的工人也是如此。
Davis and von Wachter , for instance, estimated that during periods of high unemployment, displacement has historically caused earnings losses that amount to a staggering 2.8 years of prior earnings.
例如，Davis 和 von Wachter 估计，历史上在高失业率时期，流离失所造成的收入损失相当于之前收入的 2.8 年的惊人损失。
Similarly, supporting transitions into high-productivity, high-impact sectors should be a central priority, not only because these investments may generate greater collective returns, but also because they address two fundamental challenges facing the U.S. economy.
同样，支持向高生产率、高影响力行业转型应该成为重中之重，不仅因为这些投资可能产生更大的集体回报，还因为它们解决了美国经济面临的两个基本挑战。
Employment remains concentrated in slower-productivity sectors, and population aging is increasing the economic burden on a shrinking working-age population (see e.g., Azenui et al.
就业仍然集中在生产力较慢的部门，人口老龄化正在增加不断减少的劳动年龄人口的经济负担（例如，参见 Azenui 等，2017）。
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Future growth may therefore depend increasingly on productivity gains.
因此，未来的增长可能越来越依赖于生产率的提高。
Because workers do not move frictionlessly across sectors, public programs that reduce mobility barriers may generate significant collective gains.
由于工人在不同部门之间的流动并非毫无摩擦，因此减少流动障碍的公共计划可能会产生巨大的集体收益。
The policy response to these two priorities should differ.
对这两个优先事项的政策反应应该有所不同。
For displaced workers, we have a collective interest in supporting individuals who face significant displacement risks before they translate into persistent economic spillovers.
对于失业工人来说，在失业风险转化为持续的经济溢出效应之前，为那些面临重大失业风险的个人提供支持符合我们的集体利益。
Retraining alone is unlikely to be sufficient (see Altman and Schrag ): support should be preemptive and include a broader set of tools , including job-search assistance and wage insurance—although we would also advocate for retraining where realistic employment pathways exist.
仅靠再培训可能是不够的（参见 Altman 和 Schrag）：支持应该是先发制人的，并包括更广泛的工具，包括求职援助和工资保险——尽管我们也主张在存在现实就业途径的情况下进行再培训。
For emerging high-productivity sectors, by contrast, the challenge may be more focused on ensuring that interested workers can access the training needed to pursue new job opportunities in such sectors.
相比之下，对于新兴的高生产率部门，挑战可能更集中于确保感兴趣的工人能够获得在这些部门寻求新工作机会所需的培训。
The priority should be training programs designed to expand access and help more workers obtain high-quality opportunities in such sectors.
优先事项应该是旨在扩大机会并帮助更多工人在这些部门获得高质量机会的培训计划。
Although the economic evidence on workforce training programs suggests that such programs have not produced consistently strong results, particularly when they seek to help displaced workers transition into a new sector or occupation (see Card et al.
尽管劳动力培训计划的经济证据表明，此类计划并未产生持续强劲的成果，特别是当它们寻求帮助失业工人过渡到新的部门或职业时（参见 Card 等，2017）。
, Orrell et al.
，奥雷尔等人。
and Jacobs and Canedy ), the implication is not that governments should avoid workforce investment altogether.
以及雅各布斯和坎尼迪），这并不是说政府应该完全避免劳动力投资。
Rather, it is that they should focus on programs that address the highest societal costs or generate the greatest benefits .
相反，他们应该专注于解决最高社会成本或产生最大效益的计划。
Given the measurable and persistent costs of displacement (see Sullivan and von Wachter ) and the benefits of securing broad access to the highest-impact new jobs, prioritizing these two areas is likely to deliver significant economic spillovers.
考虑到流离失所的可衡量且持续的成本（参见沙利文和冯·瓦赫特）以及确保广泛获得影响最大的新工作的好处，优先考虑这两个领域可能会产生重大的经济溢出效应。
AI adoption involves more than learning how to use new tools : it requires workers to assess reliability, exercise judgment, and integrate AI into existing workflows (see Noy and Zhang and Dell’Acqua et al.
人工智能的采用不仅仅涉及学习如何使用新工具：它要求工作人员评估可靠性、进行判断并将人工智能集成到现有的工作流程中（参见Noy和Zhang以及Dell’Acqua等人，2017）。
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Generic AI literacy or proficiency programs alone are unlikely to be enough for promoting successful use of the technology, especially in occupations where AI applications are high-risk, and outputs are inconsistent or difficult to verify.
仅靠通用人工智能素养或熟练程度计划不太可能足以促进该技术的成功使用，特别是在人工智能应用风险高、输出不一致或难以验证的职业中。
Given the heterogeneity of AI’s effects across tasks and work environments, effective training should be domain-specific and hands-on , enabling workers to test outputs, recognize system limits, exercise judgment, and learn how to integrate AI into the job-relevant workflows.
考虑到人工智能在任务和工作环境中的影响存在异质性，有效的培训应该针对特定领域并进行实践，使员工能够测试输出、认识系统限制、进行判断，并学习如何将人工智能集成到与工作相关的工作流程中。
Additionally, workforce programs should not operate as mere education initiatives; they should be developed in partnership with employers who can help ensure that training is demand-driven and connected to actual hiring and placement opportunities (see Katz et al.
此外，劳动力计划不应仅仅作为教育举措来运作；它们应该与雇主合作开发，雇主可以帮助确保培训是需求驱动的，并与实际的雇用和安置机会联系起来（参见 Katz 等，2017）。
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With respect to AI in particular , because, as we detailed above, business adoption remains uncertain, and dependent on specific workplace conditions, employer participation is needed to ensure that training is aligned with actual deployment , responsive to evolving business needs, and connected to real hiring and placement opportunities.
特别是就人工智能而言，正如我们上面详述的，由于业务采用仍然不确定，并且取决于具体的工作场所条件，因此需要雇主参与以确保培训与实际部署保持一致，响应不断变化的业务需求，并与实际的招聘和安置机会相关联。
That role is, of course, important in preparing to integrate any new technology, let alone one advancing at the extraordinary pace expected of AI.
当然，这一角色对于准备整合任何新技术都很重要，更不用说以人工智能预期的非凡速度前进了。
Workforce transition programs should distinguish between two different risks: labor displacement and wage erosion.
劳动力转型计划应区分两种不同的风险：劳动力流失和工资侵蚀。
In some occupations, AI may reduce employment, generating persistent adjustment costs and social spillovers that extend beyond the workers directly displaced (see Autor et al.
在某些职业中，人工智能可能会减少就业，产生持续的调整成本和社会溢出效应，其范围超出了直接失业的工人（参见 Autor 等，2017）。
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In others, it may place downward pressure on wages, eroding the value of prior skill investments and shifting much of the AI adjustment costs onto those workers (see Acemoglu and Restrepo ).
在其他情况下，它可能会给工资带来下行压力，侵蚀先前技能投资的价值，并将大部分人工智能调整成本转移到这些工人身上（参见 Acemoglu 和 Restrepo）。
These outcomes require fundamentally different responses.
这些结果需要根本不同的反应。
In occupations where AI increases the value of expertise but reduces employment, policy should support displaced workers’ efforts to find new employment in other occupations and new sectors through job-search assistance, wage insurance, and transition to new occupations.
在人工智能增加专业知识价值但减少就业的职业中，政策应支持失业工人通过求职援助、工资保险和向新职业转型等方式在其他职业和新部门寻找新就业机会。
In occupations where AI reduces the value of expertise and hence expands employment but places downward pressure on wages, policy may focus on helping workers within the occupation to preserve or improve earnings through advancement pathways, apprenticeships, or support transitions into higher-paid occupations.
在人工智能降低专业知识价值并因此扩大就业但对工资造成下行压力的职业中，政策可能侧重于帮助该职业中的工人通过晋升途径、学徒制或支持向高薪职业的过渡来保持或提高收入。
Apprenticeships provide an effective mechanism for worker placement .
学徒制为工人安置提供了有效的机制。
They reduce the opportunity cost of training for workers, provide employers with a source of screening and signaling, and align training with real business needs.
它们降低了工人培训的机会成本，为雇主提供了筛选和信号来源，并使培训与实际业务需求保持一致。
They may be especially effective at times of structural changes and heightened workforce uncertainties (see Lerman ).
它们在结构性变化和劳动力不确定性加剧时可能特别有效（参见勒曼）。
This is why apprenticeships may be particularly effective for an AI workforce transition.
这就是为什么学徒制对于人工智能劳动力转型可能特别有效。