1 Introduction
Generative artificial intelligence (AI) has rapidly moved from a consumer-facing technology to a general-purpose tool used inside organizations. Since the public release of ChatGPT in late 2022, large language models have become increasingly embedded in writing, coding, data analysis, customer service, marketing, legal support, education, and managerial work (Hartley et al., 2026). This diffusion has renewed a long-standing question in the social sciences: how does a major new technology reshape labor demand? The question has become especially urgent because generative AI differs from many earlier automation technologies. Earlier waves of automation were often associated with routine manual and rule-based work (Acemoglu and Autor, 2011; Autor, 2015; Acemoglu and Restrepo, 2019). Generative AI, in contrast, appears especially relevant for language-intensive, analytical, and creative tasks, i.e., activities that are central to many white-collar occupations.
The public debate has therefore focused heavily on displacement. Business leaders have warned that AI systems may reduce the need for some corporate roles.[^1] Media accounts have described fewer openings for entry-level white-collar workers (Iscenko and Millet, 2026). Recent academic studies have asked whether employment or hiring has declined in occupations that appear more exposed to generative AI (Brynjolfsson et al., 2025a). Yet the labor-market effects of a new technology need not appear only as fewer jobs in exposed occupations. Firms may also respond by changing the organization of work itself. They may shift hiring away from jobs whose baseline task content is more exposed to generative AI. They may also continue hiring within the same broad job categories while rewriting job descriptions, changing task requirements, and moving workers toward activities that are less substitutable by AI or more complementary to it. The first margin is a reallocation of hiring demand across jobs. The second is redesign of the tasks within jobs.
Distinguishing these two margins is central to understanding how generative AI affects work. If adjustment occurs only through hiring reallocation, the main empirical question is which occupations, industries, or seniority groups gain or lose labor demand. This is the perspective implicit in much of the emerging evidence linking occupation-level exposure to employment, productivity, wages, or job postings (Brynjolfsson et al., 2025b; Hampole et al., 2025; Chandar, 2025; Chen et al., 2025; Johnston and Makridis, 2025; Liu et al., 2025). But if firms also redesign jobs, then the task content of work becomes endogenous to technological diffusion. In that case, exposure is not simply a fixed property of an occupation. It changes as employers revise what they ask workers to do. Labor-market adjustment then takes place even when broad occupational labels remain unchanged.
This distinction is also important for interpreting recent debates over the job ladder. Several studies and public reports have raised concerns that generative AI may be especially harmful to junior workers, whose tasks often involve drafting, summarizing, coding, analysis, and other activities that AI systems can assist or automate (Felten et al., 2023; Gmyrek et al., 2023; Tomlinson et al., 2025). Other evidence questions this interpretation, arguing that declines in entry-level hiring may reflect broader macroeconomic forces rather than AI-driven substitution (Iscenko and Millet, 2026). A key challenge is that many existing studies measure exposure at the occupation level and then compare employment or hiring across more- and less-exposed occupations (Eloundou et al., 2024; Brynjolfsson et al., 2025a). Such approaches are useful for identifying where generative AI may matter most, but they cannot observe whether firms are changing the content of junior and senior jobs within the same occupation. As a result, they leave open a more general question: does generative AI merely change the composition of jobs firms post, or does it also change what those jobs contain?
This paper studies how generative AI reorganizes labor demand. We focus on three questions. First, how does exposure to generative AI in posted jobs evolve over time as the technology diffuses? Second, to what extent do changes in aggregate exposure reflect hiring reallocation across jobs versus redesign of task content within jobs? Third, do these adjustment margins differ across the job ladder?
Answering these questions requires data that observe labor demand at scale and contain information about the content of jobs. We use a nationwide dataset from Lightcast that contains all online job postings in the United States and covers all sectors of the economy from January 2021 through June 2025. The raw data contain more than 188 million postings during our study period. We implement a repeated random sampling procedure within occupation-by-seniority-by-industry cells, yielding a final sample of 9,373,092 postings. This nationwide coverage is important because the relevance of generative AI depends on the task content of work, which varies across occupations, industries, and seniority levels. Studies based on selected sectors or occupations may therefore capture only part of the labor-market adjustment. By covering the full U.S. economy, our data allow us to examine how changes across different parts of the labor market combine into aggregate patterns of labor-demand reorganization.
We construct a dynamic, posting-level measure of generative AI exposure using a two-stage large language model (LLM) pipeline. In the first stage, we extract tasks from each job description and match those tasks to skill groups (specialized or common). In the second stage, we classify each task according to whether current generative AI tools can substantially reduce the time required to complete it at equivalent quality. We then aggregate task-level labels into a posting-level exposure index. This design adapts the task-based logic of prior exposure measures to the level at which firms actually describe vacancies. It allows exposure to vary across postings within the same occupation, across industries and seniority levels, and over time as firms revise job content.
We then use two complementary decomposition methods to separate the margins of adjustment. The first is a three-fold extension of the Kitagawa decomposition (Kitagawa, 1955). We consider aggregate generative AI exposure as a weighted average of exposure across occupation-by-industry-by-seniority cells. Aggregate exposure can change because firms alter the mix of jobs they post, because the task content of similar jobs changes over time, or because both adjustments occur simultaneously. We interpret these components as hiring reallocation, job redesign, and their interaction. The second method is a weighted Oaxaca–Blinder decomposition comparing the pre- and post-GPT periods (Oaxaca, 1973; Oaxaca and Sierminska, 2025). This regression-based decomposition allows us to examine which observable job characteristics, especially occupation, industry, seniority, location, remote-work status, internship status, and employment type, account for the compositional change in exposure.
The analysis yields three main findings. First, generative AI exposure in posted jobs is dynamic. Mean exposure rises through early 2022, declines through 2023, and partially recovers thereafter. This pattern is difficult to reconcile with the view that exposure is a fixed attribute of occupations. It suggests instead that firms’ stated task requirements evolve over time. The decline is especially concentrated among high-exposure occupations, while low- and medium-exposure occupations are more stable. Exposure also varies sharply across sectors. It is highest in finance and insurance, professional services, and information, and lowest in accommodation and food services, retail trade, and transportation and warehousing. Senior positions have higher exposure than junior or intermediate positions on average, revealing a seniority gradient that occupation-level measures cannot capture.
Second, labor demand adjusts through both hiring reallocation and job redesign. The composition effect turns negative after the third quarter of 2023, indicating that firms shift hiring away from jobs with higher baseline exposure. The within-cell exposure effect also becomes negative around the same period and grows in magnitude thereafter, indicating that firms reduce exposure within continuing job categories. From the third quarter of 2023 onward,[^2] hiring reallocation accounts for 52% of the decline in aggregate generative AI exposure, while within-cell job redesign accounts for 39.46%. The interaction term accounts for the remaining 8.54%. Thus, the decline in aggregate exposure is not simply a story of fewer postings in highly exposed occupations. A large share reflects changes in the task content of jobs that firms continue to post.
Third, adjustment differs across the job ladder. Senior jobs adjust earlier and primarily through hiring reallocation. From the third quarter of 2023 onward, the composition effect accounts for 70.80% of the aggregate contribution among senior postings. Junior jobs show a broader pattern: hiring reallocation, job redesign, and their interaction all contribute meaningfully. Intermediate jobs track the aggregate pattern most closely, with reallocation and redesign contributing almost equally. These patterns suggest that generative AI does not simply move labor demand up or down the job ladder. It changes the margins through which firms adjust different layers of work. For senior roles, firms appear to adjust first by changing the structure of vacancies. For junior roles, firms simultaneously change both the types of jobs they post and the tasks embedded in those jobs.
The Oaxaca–Blinder decomposition reinforces these conclusions while clarifying the observable dimensions behind compositional adjustment. Average exposure declines after GPT diffusion, and both the explained and unexplained components are negative. The explained component accounts for roughly two-thirds of the aggregate decline, and occupational shifts account for about 90% of the exposure decline attributable to observed job characteristics. Other posting-level characteristics, including remote-work arrangement, industry, employment type, and internship status, also contribute, although more modestly. These results show that occupational reallocation is the dominant observable source of compositional change, but it is not the whole story. The posting-level data reveal additional vacancy-design margins that are not visible in occupation-level analyses.
This paper contributes to research on technology and labor markets in three ways. First, it develops a dynamic, posting-level measure of generative AI exposure. Existing measures have been valuable for identifying which occupations are most exposed ex ante, but they generally assign a fixed score to occupations using standardized task taxonomies (Eloundou et al., 2024; Felten et al., 2023; Gmyrek et al., 2023; Tomlinson et al., 2025). Our approach shows that exposure itself changes over time and varies within occupations. This matters because the diffusion of generative AI is not merely a shock to a fixed set of jobs; it is also a process through which firms update the task content of those jobs.
Second, the paper shifts the empirical focus from job displacement to labor-demand reorganization. Much of the current debate asks whether highly exposed occupations decline (Brynjolfsson et al., 2025a; Chandar, 2025; Chen et al., 2025; Johnston and Makridis, 2025; Liu et al., 2025). We ask how the structure of labor demand changes. This broader framing is important because firms can adapt to generative AI without eliminating an occupation or even reducing total hiring in a category. They can rewrite jobs, reweight tasks, and alter the mix of skills requested from workers. Our evidence suggests that such redesign is quantitatively large and becomes more important as generative AI tools become more organizationally deployable.
Third, the paper provides new evidence on generative AI and the job ladder. Prior work has debated whether junior workers are more exposed or more adversely affected than senior workers (Brynjolfsson et al., 2025a; Hosseini Maasoum and Lichtinger, 2025). We show that the relevant heterogeneity is not only about the magnitude of adjustment but also about its mechanism. Junior and senior jobs adjust through different combinations of hiring reallocation and task redesign. This distinction has implications for how new workers enter the labor market and acquire skills. If entry-level roles are being redesigned at the same time that firms are reallocating hiring away from exposed positions, then early-career workers may face not only fewer opportunities in some job categories but also a changing set of tasks within the opportunities that remain.
More broadly, the findings suggest that generative AI is reorganizing the architecture of work. The labor-market response is not captured by a simple substitution narrative in which exposed jobs decline and less-exposed jobs expand. Nor is it captured by a purely augmentation narrative in which the same jobs become more productive without changing their content. Instead, firms appear to adjust along multiple margins: they change where they hire, what jobs contain, and how these adjustments differ across hierarchy levels. For researchers, this implies that exposure measures should be treated as dynamic objects rather than fixed occupation-level characteristics. For firms, it suggests that workforce planning requires attention not only to headcount but also to task design. For policymakers and educators, it highlights the need to monitor how the entry points into professional work are changing as generative AI diffuses.
The remainder of the paper proceeds as follows. The next section reviews related research on AI exposure, labor demand, and the job ladder. Section 3 describes the Lightcast job-posting data and sampling procedure. Following that, Section 4 presents the construction of the posting-level generative AI exposure measure. Section 5 introduces the decomposition framework. Section 6 then reports the findings. Lastly, Section 7 concludes.