It’s time to address the looming crisis in entry-level work. | MIT Technology Review

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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioArtificial intelligenceIt’s time to address the looming crisis in entry-level work.Here’s how young job seekers, businesses, and society should adapt to the AI revolution.
By Georgios PetropoulosMay 26, 2026Stephanie Arnett/MIT Technology Review | Adobe Stock 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. 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. 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.
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. 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. 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. 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. 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. 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. 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. 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 employers that hire early-career workers into structured, AI-augmented roles. The architecture for this kind of conditional, behavior-linked subsidy already exists in US tax policy. What is missing is a version of these instruments built specifically around early-career AI-augmented work.

Firms, for their part, should stop making hiring decisions based only on short-run cost savings from AI. Young workers are not valuable only for the tasks they perform this quarter. Their value lies in learning, skill formation, institutional memory, and future productivity. Entry-level hiring is not just an expense. It is an investment in the future stock of judgment inside the firm. The most effective AI-augmented senior workforce of the late 2030s will be drawn overwhelmingly from the junior cohort of today. Firms that automate away the learning stage may improve their immediate margins but find themselves, a decade from now, without anyone who understands how their own AI-driven workflows actually behave. Students graduating this spring and next face a tough labor market in transition. AI fluency is becoming a commodity. Domain expertise without AI fluency is being outpaced. The combination is what is genuinely scarce. The mechanical engineer with knowledge of manufacturing and AI proficiency; the software programmer with knowledge of financial services who is also a whiz at AI—these are the types of people who will be in demand. Georgios Petropoulos is an assistant professor at the USC Marshall School of Business. His research focuses on the implications of information technologies for innovation, competition policy, and labor markets. by Georgios PetropoulosShareShare story on linkedinShare story on facebookShare story on emailPopularA woman’s uterus has been kept alive outside the body for the first timeJessica HamzelouWant to understand the current state of AI? Check out these charts.Michelle KimInside the stealthy startup that pitched brainless human clonesAntonio Regalado10 Things That Matter in AI Right NowAmy NordrumDeep DiveArtificial intelligenceWant to understand the current state of AI? Check out these charts.According to Stanford’s 2026 AI Index, AI is sprinting, and we’re struggling to keep up.
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The emergence of artificial intelligence presents a challenge to the entry-level workforce, posing a threat to the traditional career ladder despite stable aggregate employment figures in developed countries. Research, including a working paper from the Stanford Digital Economy Lab and a report from Anthropic, suggests a worrying trend: workers aged 22 to 25 in occupations exposed to generative AI experienced a relative employment decline of sixteen percent, even when controlling for other employment factors. This indicates that firms may be utilizing AI to substitute for the junior tasks essential for early-career professional development, particularly in fields such as software development, customer service, and information systems management.

The concern is narrowly focused on early-career jobs where AI exposure is high, not experienced workers, suggesting a structural shift in how foundational skills are acquired. This situation is compounded by existing labor market softening, as evidenced by rising unemployment and underemployment rates among recent college graduates. If AI subsumes the work that traditionally trains entry-level staff—such as drafting, summarizing, coding, and administrative preparation—societal capability risks diminishing in the long run. This dynamic creates significant personal distress for young workers facing prolonged job searches and precarity.

To address this, adjustments are necessary across education, government policy, and corporate behavior. Educational institutions must reorient their focus to cultivate an AI-augmented workforce. This requires embedding AI literacy, data literacy, prompt-based workflow skills, verification methods, and domain judgment into standard academic degrees. The focus must shift from teaching specific, easily automated tasks, like rudimentary coding, to teaching supervision of AI systems, understanding their outputs, and integrating them with human expertise.

The competition moving forward will be between colleagues augmented by AI rather than strictly against machines. Therefore, the path to increased worker value involves fluency in technology combined with contextual reasoning and human relationship skills. Institutions should support this transition by emphasizing experiential learning through paid co-ops and apprenticeships, allowing students to develop judgment in real workplaces prior to graduation. Governments should implement targeted incentives, such as tax credits and wage subsidies, to encourage employers to invest in training early-career staff in AI-augmented roles.

Firms must also reevaluate their hiring philosophy, recognizing that entry-level positions are not merely short-term cost savings but vital investments in the future stock of institutional judgment. The most capable senior workforce of the late 2030s will likely be drawn from today's junior cohort, composed of individuals who possess both deep domain knowledge and AI proficiency. The future demand lies in rare combinations, such as a mechanical engineer proficient in AI, or a financial professional adept at AI, demonstrating that the convergence of domain expertise and AI fluency represents the most scarce and valuable commodity.