What is happening to jobs? Separating AI hype from reality

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What is really happening to jobs? Separating AI hype from reality

Key takeaways

AI’s effects on overall employment is likely small, though a tough job market for new graduates may be partly due to AI.

AI’s impact on worker productivity is mixed but generally positive.

Firm adoption has accelerated but unevenly across the economy.

Early evidence is hardly the last word on the future of work in an AI world.

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Advances in AI models have sparked fears that rapid disruption of labor markets is imminent, if not already underway. A steady drumbeat of media articles has forecast a grim future for white-collar work due to AI.[1] Fears of an “AI jobs apocalypse” are often amplified by AI leaders themselves. For example, Dario Amodei, CEO of Anthropic, has predicted that AI could wipe out half of white-collar jobs and push unemployment to 20 percent. A labor market upheaval of this magnitude would cause enormous suffering for many households and pose a significant challenge for policymakers.

While the public has been debating AI’s potential impact on firms and their workers, research and data have been catching up. Our goal in this brief is to synthesize the fast-growing body of research on AI’s impact for policymakers and others eager to understand how AI is affecting the labor market right now. For ease of exposition, we organize this empirical evidence into a set of stylized facts, as follows:

AI’s impact on aggregate employment is likely small right now.

A tough market for recent graduates may be partly due to AI.

AI’s impact on worker productivity is mixed but generally positive.

Firm adoption has accelerated but unevenly across the economy.

xEarly evidence is hardly the last word on AI’s impacts.

We will now explore each of these points in greater depth.

AI’s impact on labor market conditions is likely small right now.

No one can predict the future, but there is little evidence that AI is causing significant job losses right now. Unemployment among workers in occupations most exposed to AI-driven disruption is rising, but not faster than among those least exposed.[2] As shown in Figure 1, the unemployment rate for the top quintile of AI-exposed workers has risen by 0.77 percentage points since 2022, while the unemployment rate for the least-exposed workers rose slightly more, by 0.85 percentage points over the same period. These aggregate trends suggest a broadly softening labor market, rather than one characterized by AI-driven job losses.

Figure 1: Unemployment rate by AI-exposure quintile, 2015-2026 (quarterly)

Source: IPUMS-CPS data. Updates and extends Eckhardt and Goldschlag (2025) using replication code. Note: AI exposure from Felten, Raj, and Seamans (2021).[3] Quintile 1 = least exposed, 5 = most exposed; dashed line is the U.S. total.

There is also little evidence of AI depressing employment or job postings in the most highly exposed occupations. Employment trends in occupations with high exposure to AI are fairly stable.[4] While employment growth in coding-heavy occupations has slowed somewhat, it remains positive.[5] There is no evidence that AI adoption has negatively impacted firms’ job postings.[6] Indeed, online job postings for software developers — a very highly exposed occupation — have been growing faster than for other occupations over the last year.[7] Among firms that adopted enterprise AI, employment grew by 10 percent in the two years following adoption, an effect driven by firms with the highest per capita AI spending.[8]

What about the companies announcing layoffs, increasingly citing AI as a driving factor?[9] Both industry leaders and labor economists express some healthy skepticism about these claims.[10] While some narrow layoffs may be connected to AI-related automation, others appear driven by a desire to free up cash flow for AI investments or to reduce headcount after pandemic-era over-hiring. Human resource executives say the impacts of AI are more evident in role consolidation and hiring avoidance in roles where AI can automate many tasks.[11]

This doesn’t mean AI isn't having any negative impact on some workers; rather, the effects so far are more nuanced than an imminent “AI jobs apocalypse” would suggest. AI could be creating pockets of disruption that aren’t easily visible in aggregate economic data. In particular, there is some evidence that AI may be negatively affecting demand for young white-collar workers, as we discuss next.

A tough labor market for young workers may be partly due to AI

Recent graduates are facing the most challenging job market in years, with unemployment rates for new grads reaching 5.6 percent in early 2026, up 1.6 percentage points from three years earlier.[12] This rise has fueled concerns that AI is replacing many of the jobs recent graduates once sought. Junior roles often involve routine research, analysis, and writing tasks that can now largely be done with AI. Consistent with this intuition is empirical evidence that AI may be dampening demand for new hires.

In a widely discussed paper, Brynjolfsson, Chandar, and Chen report a notable decline in employment among early-career workers in AI-exposed occupations, notably software developers and customer service representatives, since ChatGPT’s launch in 2022. As shown in Figure 2, by contrast, employment among older workers in those same occupations remained relatively stable or continued to grow. The authors liken these young workers to “canaries in the coal mine,” the first to experience labor market disruption from AI.[13] Other researchers have since identified similar negative effects on the hiring of young AI-exposed workers in the U.S. and the U.K., beginning in 2022.[14]

Figure 2: Employment by age in two AI-exposed occupations, 2021–2026

(a) Customer service representatives

Source: Stanford Digital Economy Lab and ADP Research. Note: Employment index by age group, from a five-year balanced sample of firms using ADP payroll services; 100 = each group's employment at the November 2022 launch of ChatGPT (Brynjolfsson, Chandar, and Chen 2025).

(b) Software developers

Source: Stanford Digital Economy Lab and ADP Research. Note: Employment index by age group, from a five-year balanced sample of firms using ADP payroll services; 100 = each group's employment at the November 2022 launch of ChatGPT (Brynjolfsson, Chandar, and Chen 2025).

The timing of this impact is somewhat surprising. AI model capabilities were very limited in 2022. Other factors may be at play, including rising interest rates, pandemic over-hiring, and remote work. For example, in response to rising inflation, the Federal Reserve aggressively hiked interest rates beginning in March 2022 — several months before ChatGPT’s public release the following November. Two new papers find that hiring in AI-exposed occupations began to decline after the Fed’s monetary policy shift but before ChatGPT’s debut.[15] Also, the rapid shift to remote work during the pandemic, which can slow on-the-job learning, erodes the value of hiring younger workers. New evidence suggests hiring in remote-friendly occupations began to skew toward more experienced workers after the pandemic.[16]

In response to concerns about these and other possible confounding effects, Brynjolfsson and coauthors added new controls. In these results, employment declines among entry-level workers are not notable until 2024.[17] It thus seems plausible that factors other than AI are driving declines in hiring young workers around 2022. However, by 2024, both AI adoption and model capabilities had advanced significantly, making the direct impacts of AI more plausible.

Hiring of entry-level workers in AI-exposed occupations clearly declined markedly around 2022. What is harder to determine is whether this decline constitutes clear evidence of AI’s impact on demand for young workers, now or in the future. Given other macroeconomic shocks to labor demand around this time, isolating AI’s impact is empirically challenging. This remains an open and active area of research.

The impact of AI on worker productivity is mixed but generally positive.

In experimental settings, generative AI tools — such as chatbots and coding tools — have often been found to disproportionately improve the performance of less experienced and poorer performing workers. In one such study, researchers analyzed the impact of a generative AI assistant on customer support agents in a large call center.[18] The assistant increased overall productivity by 15 percent, with gains highly concentrated among novice and less-skilled workers, who saw a 30 percent improvement in the number of issues resolved per hour. There was no performance improvement among highly skilled customer service agents, whose response quality fell slightly.

Other studies also show that AI tools generally speed up task completion, though the effects vary by task, context, and skill level. Figure 3 summarizes research findings on the impact of AI tools on speed across a variety of tasks. In software development, the use of GitHub Copilot — an AI tool that suggests code and functions — allowed tasks to be completed 56 percent faster, with gains concentrated among less-experienced programmers.[19] More modest impacts on software development were found in a separate paper, with effects ranging from 10 percent to 30 percent, depending on the firm where it was deployed.[20] In writing tasks, access to ChatGPT was found to reduce writing time for workers of all abilities and improve writing quality among low-ability writers.[21] Among young lawyers, the use of an AI tool was found to increase the speed of legal work, such as drafting contracts.[22] In medical settings, an assessment of AI scribes found some evidence that they increase the speed of medical note-taking, although the effects are small and occasional inaccuracies require physician oversight.[23]

Figure 3: Experimental estimates of generative AI's effect on task speed

Source: Authors' compilation of randomized and field experiments. Note: Each point is the estimated effect of giving workers access to generative AI on the time taken to complete representative tasks, colored by the AI model generation used. Choi et al. (2023) is shown as a range (the span across tasks). Cui et al. (2025) and Brynjolfsson et al. (2023) are converted from output-per-hour measures, and Brynjolfsson et al. is a staggered-rollout, quasi-experiment rather than a randomized trial. Estimates are task-level and short-horizon, and tasks differ across studies, so magnitudes are not strictly comparable.

In many workplace settings, employees must determine when and how to deploy AI assistance, which complicates matters considerably. Dell’Acqua and coauthors document that AI’s capabilities are “jagged,” meaning performance can be strongly positive or negative depending on the specific task to which it is deployed.[24] Determining whether AI output is useful or needs further refinement can require skilled judgment. For example, when researchers deployed an AI assistant to help Kenyan entrepreneurs, less-skilled entrepreneurs posted lower revenues and profits from using an AI tool than those who grew their businesses without it.[25] Examining why, the researchers found that less-skilled entrepreneurs were more likely to act on generic advice from the AI tool that was detrimental to their specific situations, while better performers extracted suggestions more tailored to their business needs.

While AI can make some employees more individually productive, it can also narrow the range of creative ideas. The use of AI in creative writing tasks was found to increase the quality of stories produced by less-skilled writers, but AI-generated stories were much more similar to one another than human-generated ones.[26] Hao and coauthors found that, while scientists who adopt AI tools publis

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