It’s time to address the looming crisis in entry-level work.

是时候解决入门级工作岗位迫在眉睫的危机了。

Artificial intelligence has not so far produced a clean story of mass unemployment. Aggregate employment in developed countries remains broadly stable, and recent assessments have found limited evidence that AI has shifted the headline numbers. But a troubling change may be hiding beneath the surface: the quiet weakening of the first rung of the career ladder. 到目前为止，人工智能尚未导致大规模失业。发达国家的总体就业情况基本保持稳定，近期的评估也发现，几乎没有证据表明人工智能改变了整体就业数据。但一个令人不安的变化可能正潜伏在表面之下：职业阶梯的第一级正在悄然松动。

The most worrisome evidence is showing up exactly where we should expect it first: in early-career hiring. A working paper released in November 2025 by the Stanford Digital Economy Lab found that workers aged 22 to 25 in the most AI-exposed occupations experienced a 16% relative decline in employment after the spread of generative AI, even after controlling for other factors that might affect firms’ employment decisions. 最令人担忧的证据恰恰出现在我们预料之中的地方：职业生涯早期的招聘。斯坦福数字经济实验室（Stanford Digital Economy Lab）于2025年11月发布的一份工作论文发现，在生成式人工智能普及后，受人工智能影响最严重的职业中，22至25岁员工的就业率相对下降了16%，即便在排除了其他可能影响企业招聘决策的因素后，这一结论依然成立。

An Anthropic report from March 2026 provides suggestive evidence that led to a similar conclusion. More experienced workers in those same occupations did not suffer the same decline. Employment is not also declining in the entry-level jobs with low AI exposure. The concern is specific to early-career jobs that are exposed to AI. That is not a minor signal. It suggests that firms may be using AI to substitute for the junior tasks through which people traditionally gain their first foothold—at least for those in jobs where generative AI is used extensively, like software developers, customer service representatives, computer programmers, and information systems managers. Anthropic公司2026年3月的一份报告提供了类似的佐证。在同一职业中，经验更丰富的员工并未经历同样的下滑。而在人工智能影响较小的入门级岗位中，就业率也并未下降。这种担忧专门针对那些受人工智能影响的早期职业岗位。这绝非一个小信号。它表明，企业可能正在利用人工智能替代那些人们传统上用来获得职业立足点的初级任务——至少在生成式人工智能被广泛使用的岗位上是这样，例如软件开发人员、客户服务代表、计算机程序员和信息系统经理。

The time is now to make changes in the way we train, prepare, and support young people who are about to enter the workforce. Educational institutions need to reorient for the era of an AI-augmented workforce. Governments must incentivize businesses to hire and train early-career workers. Businesses, in turn, need to recognize the importance of developing a long-term workforce experienced in AI—a process that begins with entry-level workers. And students themselves should take on the responsibility of not only becoming AI fluent but learning how to apply that knowledge in various fields. 现在是时候改变我们培训、准备和支持即将进入职场的年轻人的方式了。教育机构需要为人工智能增强型劳动力的时代重新定位。政府必须激励企业招聘和培训处于职业生涯早期的员工。反过来，企业也需要认识到培养具备人工智能经验的长期劳动力的重要性——这一过程始于入门级员工。而学生们自己也应承担起责任，不仅要精通人工智能，还要学习如何将这些知识应用于各个领域。

In short, we must change the way we have traditionally thought of entry-level work. This is especially true because the broader labor market for recent graduates is also softening. The Federal Reserve Bank of New York reported that in the fourth quarter of 2025, the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%, its highest level since the covid pandemic. 简而言之，我们必须改变传统上对入门级工作的看法。这一点尤为重要，因为应届毕业生的整体就业市场也在走软。纽约联邦储备银行报告称，2025年第四季度，应届大学毕业生的失业率升至5.6%，而就业不足率（即从事通常不需要大学学位的工作的毕业生比例）达到42.5%，为新冠疫情以来的最高水平。

No single statistic can prove that AI is the sole cause of that deterioration. Hiring in general is way down post-pandemic, and young people are particularly vulnerable to the slowdown. But it would be a mistake to ignore the possibility that AI is accelerating an already difficult transition from school to work. Behind these statistics is a great deal of personal distress. Recent graduates today often submit hundreds of applications before they receive a single offer, and surveys consistently find elevated rates of anxiety, financial precarity, and burnout among young workers in extended job searches. 没有任何单一统计数据能证明人工智能是导致这种恶化的唯一原因。疫情后整体招聘需求大幅下降，而年轻人对这种放缓尤为敏感。但如果忽视人工智能正在加速这一本已艰难的从学校到职场过渡的可能性，那将是一个错误。这些统计数据背后是大量的个人痛苦。如今的应届毕业生往往在投递数百份申请后才能收到一份录用通知，调查也一贯显示，在漫长的求职过程中，年轻员工的焦虑感、财务不稳定性以及职业倦怠感比例居高不下。

If AI quietly closes the door on typical early jobs, people will pay the price in delayed independence, postponed family formation, and the sense that their first serious professional efforts have been refused. It also matters because entry-level jobs are part of the economy’s training system. Junior analysts learn which numbers can be trusted. Young software developers learn how production systems fail. New marketers learn how customers behave outside the neat language of dashboards. Early-career legal and financial staff learn how rules, judgment, deadlines, and human relationships actually interact. 如果人工智能悄然关闭了典型的入门级工作之门，人们将付出代价：独立时间推迟、组建家庭延后，以及感受到自己最初的职业努力被拒之门外。这也很重要，因为入门级工作是经济培训体系的一部分。初级分析师学习哪些数据值得信赖；年轻的软件开发人员学习生产系统如何失效；新的营销人员学习客户在仪表盘整洁的数据之外的真实行为；早期的法律和金融人员学习规则、判断、截止日期和人际关系是如何实际相互作用的。

If AI absorbs more of the drafting, triage, coding, summarizing, and administrative preparation that once helped train entry-level workers, firms may become more efficient in the short run while society becomes less capable in the longer run. The right way to improve the skills of young workers is not to tell them, “Learn to code.” That advice, which shaped more than a decade of federal initiatives and university expansion, rested on the premise that coding was a stable, scalable skill almost anyone could learn and parlay into a middle-class job. The premise no longer holds. 如果人工智能吸收了更多曾经有助于培训入门级员工的起草、分类、编码、总结和行政准备工作，企业在短期内可能会变得更高效，但社会在长期内可能会变得能力不足。提高年轻员工技能的正确方法不是告诉他们“去学编程”。这一建议曾塑造了十多年的联邦倡议和大学扩张，其前提是编程是一项稳定、可扩展的技能，几乎任何人都可以学习并将其转化为中产阶级的工作。但这一前提已不再成立。

The layer of work AI handles well—translating a specification into routine code, reproducing standard patterns, debugging predictable errors—is precisely the layer that “learn to code” programs were built around. Supervising AI systems in their work is now a much more relevant skill. So understanding the outputs AI systems produce will become very important. To help people develop such skills, we should require universities, community colleges, and professional programs to embed AI literacy, data literacy, prompt-based workflow skills, verification skills, and domain judgment into ordinary degrees. 人工智能擅长处理的工作层面——将规范转化为常规代码、复制标准模式、调试可预测的错误——恰恰是“学编程”项目所围绕的核心。监督人工智能系统的工作现在是一项更相关的技能。因此，理解人工智能系统产生的输出将变得非常重要。为了帮助人们培养这些技能，我们应该要求大学、社区学院和职业项目将人工智能素养、数据素养、基于提示的工作流技能、验证技能和领域判断力融入到普通学位中。

Every graduate should know how to use AI tools, check their output, understand their limits, and combine them with human expertise. This matters even for graduates entering occupations that look relatively safe from AI, such as those in health care. Almost every job contains tasks—drafting, summarizing, scheduling, research, basic data work, routine communication—for which AI is already a substantial productivity tool. The competition most young workers will experience is not human versus machine but colleague versus AI-augmented colleague. 每一位毕业生都应该知道如何使用人工智能工具、检查其输出、了解其局限性，并将它们与人类专业知识相结合。即使对于那些看起来相对不受人工智能影响的职业（如医疗保健行业）的毕业生来说，这一点也很重要。几乎每一份工作都包含起草、总结、日程安排、研究、基础数据工作和日常沟通等任务，而人工智能已经是这些任务中重要的生产力工具。大多数年轻员工将面临的竞争不是人与机器的竞争，而是同事与“人工智能增强型同事”之间的竞争。

For most young workers, the realistic path to making themselves valuable is not to avoid AI but to become fluent in the technology and combine that with domain judgment, contextual reasoning, and human relationship skills. To this end, schools should emphasize paid co-ops, apprenticeships, and employer-linked projects so students build judgment in real workplaces before they graduate. Governments should also create targeted tax credits, wage subsidies, and training grants for… 对于大多数年轻员工来说，让自己变得有价值的现实途径不是回避人工智能，而是精通这项技术，并将其与领域判断力、情境推理和人际关系技能相结合。为此，学校应强调带薪实习、学徒制和与雇主挂钩的项目，以便学生在毕业前就能在真实的工作场所建立判断力。政府也应为……创造有针对性的税收抵免、工资补贴和培训补助。