2 Related Literature
This section situates our study in two related streams of research. The first develops measures of exposure to AI in general and, more specifically, generative AI. We build on this work by moving from static occupation-level exposure measures to a dynamic, posting-level measure that captures variation within occupations and over time. The second examines the labor-market effects of generative AI. We contribute to this literature by shifting attention from whether exposed jobs decline to how labor demand is reorganized through two margins: hiring reallocation and job redesign. We further examine how these margins differ across the job ladder.
2.1 AI and Generative AI Exposure Metrics
A large literature measures which types of work are likely to be affected by AI. The conceptual foundation of this literature is task-based: technology affects labor demand not by acting directly on occupations, but by changing the tasks performed within them (Acemoglu and Autor, 2011; Autor, 2015; Acemoglu and Restrepo, 2019). Because occupations are bundles of tasks, technological change may automate, augment, or otherwise reshape some parts of an occupation more than others. Consistent with this view, many AI exposure measures combine occupational task information from O*NET with external assessments of AI capabilities to construct occupation-level exposure indices (Frey and Osborne, 2017; Felten et al., 2018; Felten et al., 2021; Webb, 2019; Pizzinelli et al., 2023; Hampole et al., 2025).
Recent work extends this approach to generative AI and LLMs. Much of this work continues to rely on ONET task or ability information, but adapts the exposure concept to the capabilities of language models. Eloundou et al. (2024), for example, develop a task-based rubric that evaluates whether LLMs and LLM-powered software can substantially reduce the time required to perform specific tasks, and then aggregate these assessments to the occupation level. Felten et al. (2023) adapt an ability-based occupational exposure framework to advances in language modeling. Gmyrek et al. (2023) use task-level evaluations to distinguish automation from augmentation based on the distribution of exposure across tasks within occupations. Benítez-Rueda and Parrado (2024) use synthetic AI surveys to construct a more holistic occupation-level measure based on occupations’ characteristic tasks. Other studies use real-world interaction data. The Anthropic Economic Index (Handa et al., 2025) analyzes millions of Claude.ai conversations and maps them to ONET tasks and occupations to identify where AI use is concentrated. Tomlinson et al. (2025) similarly use anonymized Microsoft Bing Copilot conversations, classify user goals and AI actions into O*NET work activities, and aggregate these classifications into occupation-level AI applicability scores.
These measures have been valuable for identifying which occupations are plausibly exposed to generative AI. However, most are constructed at the occupation level and are therefore fixed within occupations and largely time-invariant.[^3] This feature limits their ability to capture an important implication of the task-based view itself: if firms revise the tasks required in a job as technology diffuses, then exposure need not remain fixed even within the same occupation. Occupation-level measures can identify exposure ex ante, but they cannot observe whether employers subsequently rewrite job requirements, adjust skill demands, or change the task content of otherwise similar jobs.
We depart from this approach by constructing a dynamic, posting-level measure of generative AI exposure. Using job postings as the unit of analysis allows exposure to vary across postings within the same occupation and over time as employers revise task requirements. This granularity is central to our research design. It allows us to distinguish between two margins of labor-demand adjustment: hiring reallocation, in which firms change the mix of jobs they post, and job redesign, in which firms change the task content of comparable jobs. Static occupation-level measures assign a single exposure value to each occupation and therefore cannot separate these two channels.
2.2 Effects of Generative AI in the Labor Market
A rapidly growing literature examines whether generative AI has begun to affect labor-market outcomes. The emerging evidence is mixed, reflecting differences in data sources, exposure measures, outcomes, and empirical designs. We organize this literature around two issues most closely related to our analysis: changes in labor demand for exposed work and heterogeneity across the job ladder.
Effects on employment and labor demand: Several studies report evidence consistent with reduced demand for highly exposed or substitutable work. Using administrative payroll records, Brynjolfsson et al. (2025a) document sizable relative employment declines in the most AI-exposed occupations after the release of ChatGPT. Liu et al. (2025) find corresponding declines in job postings for more substitutable roles. Evidence from online labor markets points in a similar direction: studies generally find that generative AI reduces demand or earnings in highly automatable freelance tasks such as writing and translation (Qiao et al., 2023; Hui et al., 2024; Demirci et al., 2025; Teutloff et al., 2025). Other work emphasizes heterogeneity in whether generative AI substitutes for or complements labor. Chen et al. (2025) show that occupations prone to automation experience declining labor demand and simplified skill requirements, whereas occupations prone to augmentation experience increases in demand and skill complexity. Johnston and Makridis (2025) find average gains in wage bills and employment in more exposed sectors, alongside declines where AI is more directly substitutive.
At the same time, other studies question whether observed declines in exposed work can be attributed to generative AI. A central concern is that AI exposure is correlated with sensitivity to macroeconomic conditions. Iscenko and Millet (2026) show that highly AI-exposed occupations are concentrated in rate-sensitive sectors such as information, finance, and professional services, and that postings for these occupations began declining before the release of ChatGPT, around the onset of the Federal Reserve’s sharp monetary tightening cycle. This suggests that part of the observed decline in demand for exposed work may reflect macroeconomic contraction rather than technological displacement. Related studies that attempt to detect employment effects directly also find limited evidence of aggregate labor-market effects. Chandar (2025) find no systematic differences in employment patterns across more- and less-exposed occupations in Current Population Survey (CPS) data. Humlum and Vestergaard (2026) link survey-based chatbot adoption to Danish administrative records and report precisely estimated zero effects on individual earnings and hours worked, even as adopting workplaces experience task reorganization and create new AI-related roles.
This debate motivates our focus on mechanisms rather than only levels of employment or hiring. If changes in exposed labor demand partly reflect macroeconomic shocks, then aggregate declines in exposed occupations are difficult to interpret on their own. Our decomposition framework separates changes in aggregate exposure into shifts in the mix of posted jobs and changes in the task content of comparable jobs. The latter margin is especially informative because it captures variation within occupation-by-sector-by-seniority cells, rather than reallocation across these broad dimensions.
Effects along the job ladder: A related literature asks whether generative AI affects workers differently across career stages. Brynjolfsson et al. (2025a) provide prominent evidence in this direction, documenting a 16 percent relative employment decline among early-career workers ages 22–25 in the most AI-exposed occupations after the release of ChatGPT. Simon (2025) and Eisfeldt et al. (2023) similarly document especially pronounced declines in postings for entry-level roles. Using firm-level data, Hosseini Maasoum and Lichtinger (2025) identify a seniority-biased pattern in which junior employment declines relative to senior employment within AI-adopting firms, driven mainly by slower junior hiring rather than increased separations.
The interpretation of these seniority-related patterns remains contested. Iscenko and Millet (2026) show that within highly AI-exposed occupations, postings for junior and senior roles have declined roughly in parallel since their peak in spring 2022, with little evidence that junior roles experienced disproportionately larger declines. They argue that some estimates of entry-level employment loss may reflect cohort dynamics during broad-based hiring slowdowns rather than seniority-targeted displacement. Humlum and Vestergaard (2026) reach a complementary conclusion in a difference-in-differences design, finding that AI adoption is not the main driver of observed declines in early-career employment in Denmark.
Our study contributes to this literature in three ways. First, we move beyond static occupation-level exposure measures by constructing a dynamic measure from the text of individual job postings. This allows us to observe how exposure changes within occupations as firms revise task requirements. Second, we distinguish two mechanisms of labor-demand adjustment. The Kitagawa decomposition separates changes in aggregate exposure into hiring reallocation across jobs and task redesign within comparable jobs. A complementary Oaxaca–Blinder decomposition then identifies which observable job characteristics are most associated with the exposure change. Third, we provide a more granular view of the job ladder. Existing studies often assign the same occupation-level exposure score to junior and senior jobs within an occupation. Our posting-level measure allows exposure to differ by seniority within the same occupation and allows us to examine whether junior and senior roles adjust through different margins. This distinction is important because generative AI may reshape career opportunities not only by changing demand for jobs at different seniority levels, but also by changing the task content through which workers enter, advance, and accumulate expertise.