AI Labor Market Tracker: July 2026
Decline in Computer Science and IT enrollment since 2022
+9pp yoy
Decline in demand for the most AI-exposed roles, vs. least-exposed since Oct 2022
−8.6pp yoy
Overall headcount growth at adopting firms, vs. non-adopters since Oct 2022
+9pp yoy
Change in work content year-over-year
+4.7pp yoy
Number of job postings required per hire
+264% yoy
Artificial intelligence is beginning to reshape labor markets, but its effects cannot be captured by a single measure. The AI Labor Market Tracker follows how AI is changing labor supply, employer demand, employment and wages, the activities performed within jobs, and the process through which workers and employers find one another.
A central challenge in studying AI's labor market effects is distinguishing anticipation from adoption. Firms may adjust hiring plans before deploying AI tools, while workers may change skill investment decisions in response to expectations about future demand. Conversely, measurable labor market effects may emerge only after organizations formally adopt AI technologies. Throughout this report, we therefore track both exposure-based measures that capture anticipated effects and firm-level adoption measures that capture realized deployment.
The goal of this tracker is not to provide a definitive assessment of whether AI will ultimately increase or decrease employment. Instead, it is to establish a consistent empirical guide for measuring how AI is changing labor markets in real time. We will continue to update the same metrics each month, regardless of whether each metric tells a compelling story. Some results may not change at all and others may surprise us. We'll leave the interpretation of the magnitudes of the changes to further downstream analysis, in order to keep the publishing of these results as objective as possible.
- SupplyHow AI is impacting labor supplyAI certifications are surging even as Computer Science enrollment has passed its recent peak.
- DemandHow AI is impacting labor demandHiring demand has weakened in highly AI-exposed occupations, particularly at junior levels.
- Equilibrium: Employment and wagesHow AI is affecting employment and wagesEmployment is growing more slowly in exposed occupations, while AI-adopting firms continue to expand.
- Work activitiesHow AI is changing work activitiesThe work inside occupations is recently changing quickly, while the share of job postings with high AI exposure is declining.
- MatchingHow AI is changing the hiring processJob postings are producing fewer hires, while candidates report more recruiter ghosting and poor communication.
Computer Science enrollment peaked in 2022. Workers are upskilling rapidly through online certifications, particularly in generative AI. This section tracks the pace and composition of how students and workers are upskilling.
Loading Figure 1.1 — Bachelor's-degree field shares at selected U.S. schools, indexed to starting year 2022. Source: Revelio Labs workforce data from professional online profiles.Figure 1.1 — Bachelor's-degree field shares at selected U.S. schools, indexed to starting year 2022. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 1.2 — The share of all certifications earned on worker profiles each month that are AI-related. Source: Revelio Labs workforce data from professional online profiles.Figure 1.2 — The share of all certifications earned on worker profiles each month that are AI-related. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 1.3 — The composition of AI-related certifications by type over time, from generative AI and LLMs to broader machine learning. Source: Revelio Labs workforce data from professional online profiles.Figure 1.3 — The composition of AI-related certifications by type over time, from generative AI and LLMs to broader machine learning. Source: Revelio Labs workforce data from professional online profiles.
One of the most visible and established effects from AI on the labor market is an anticipatory one: firms are reducing hiring in AI-exposed occupations, whether they have deployed any AI tools or not. AI exposure measures a potential rather than realized automation: the share of activities in a job that AI can credibly do. Job postings in the most exposed occupations have fallen relative to less exposed ones since late 2022 — and the effect is heavily concentrated at junior seniority levels. The results below follow the event-study design of Brynjolfsson, Chandar and Chen (2025). This section considers employer demand from job postings.
Loading Figure 2.1 — Event-study estimate of the change in posting volumes for the most AI-exposed occupations compared to the least exposed (highest versus lowest quintile exposure occupations), relative to October 2022. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. Using Revelio Labs AI exposure score. See appendix for robustness with other AI scores. Source: Revelio Labs job postings data.Figure 2.1 — Event-study estimate of the change in posting volumes for the most AI-exposed occupations compared to the least exposed (highest versus lowest quintile exposure occupations), relative to October 2022. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. Using Revelio Labs AI exposure score. See appendix for robustness with other AI scores. Source: Revelio Labs job postings data.
Loading Figure 2.2 — Event-study estimate of the change in posting volumes for the most AI-exposed occupations compared to the least exposed (highest versus lowest quintile exposure occupations), relative to October 2022; by seniority. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. Junior: Revelio seniority levels 1–2. Senior: 3–7. Using Revelio Labs AI exposure score. See appendix for robustness with other AI scores. Source: Revelio Labs job postings data.Figure 2.2 — Event-study estimate of the change in posting volumes for the most AI-exposed occupations compared to the least exposed (highest versus lowest quintile exposure occupations), relative to October 2022; by seniority. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. Junior: Revelio seniority levels 1–2. Senior: 3–7. Using Revelio Labs AI exposure score. See appendix for robustness with other AI scores. Source: Revelio Labs job postings data.
How much of a job AI could plausibly perform and whether an employer has actually adopted AI tell different employment stories. Employment is growing more slowly in occupations containing the most AI-exposed work. At the same time, firms that have actually adopted AI continue to expand employment overall, although the gains appear to be more concentrated in senior roles. We measure adoption using language from job postings, following Hosseini Maasoum and Lichtinger (2025).
These patterns are not contradictory: employers across the economy can adjust hiring in anticipation of what AI may soon be able to do, while the specific firms that successfully deploy the technology grow and reorganize their workforces.
We compare employment trends in occupations with different levels of AI exposure. This approach builds on the event-study approach of Brynjolfsson, Chandar, and Chen's (2025) Canaries in the Coal Mine. Exposure measures how much of the work in an occupation current AI systems could plausibly perform; it does not indicate whether individual employers or workers have adopted AI. The results therefore capture potential anticipatory effects: employers may adjust hiring because of what they expect AI to do, before they have fully deployed the technology. This section also looks at general employment in AI-related roles, spanning all roles that touch AI; ranging from AI engineers to data center construction workers.
Loading Figure 3.1 — Headcount in AI-related roles compared to all other roles indexed to October 2022. Source: Revelio Labs workforce data from professional online profiles.Figure 3.1 — Headcount in AI-related roles compared to all other roles indexed to October 2022. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 3.2 — Event-study estimate of employment in the most AI-exposed occupations compared to the least exposed, relative to October 2022. Top versus bottom quintile AI exposure, using Revelio Labs AI exposure score. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. See appendix for robustness with other scores. Source: Revelio Labs workforce data from professional online profiles.Figure 3.2 — Event-study estimate of employment in the most AI-exposed occupations compared to the least exposed, relative to October 2022. Top versus bottom quintile AI exposure, using Revelio Labs AI exposure score. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. See appendix for robustness with other scores. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 3.3 — Event-study estimate of employment in the most AI-exposed occupations compared to the least exposed, relative to October 2022; split by age, isolating early-career workers aged 22–25. Top versus bottom quintile AI exposure, using Revelio Labs AI exposure score. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. See appendix for robustness with other scores. Source: Revelio Labs workforce data from professional online profiles.Figure 3.3 — Event-study estimate of employment in the most AI-exposed occupations compared to the least exposed, relative to October 2022; split by age, isolating early-career workers aged 22–25. Top versus bottom quintile AI exposure, using Revelio Labs AI exposure score. Two-way fixed effects regression (occupation and month), standard errors clustered by occupation. See appendix for robustness with other scores. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 3.4 — Difference-in-differences estimate of WARN layoff notices for the most versus least AI-exposed firms. Source: Revelio Labs layoff notice data from WARN.Figure 3.4 — Difference-in-differences estimate of WARN layoff notices for the most versus least AI-exposed firms. Source: Revelio Labs layoff notice data from WARN.
Quantities — adoption effects on headcount
Following Hosseini Maasoum and Lichtinger (2025), we identify AI-adopting firms from job postings. A firm is considered AI adopting, when it posts jobs for AI integrator roles. Note that this approach in identifying adopters is different from Kharazian, Simon and Stevens (2026), which identified adoption from spending data. Results are broadly similar.
Over the period shown, adopting firms grow headcount 27% more than non-adopting firms. Adopting firms were growing faster pre-adoption. The growth is uneven by seniority. Senior headcount grows by 31%, compared with only 6% for junior roles. While lower compared to senior level employment growth, junior growth is still higher at adopting firms compared to non-AI adopting firms.
Loading Figure 3.5 — New AI-adopting firms each month, and the cumulative share of adoption across a rolling panel of eligible U.S. hiring firms. Firms enter the panel with at least 20 new U.S. positions in the preceding 48 months. Source: Revelio Labs job postings and workforce data.Figure 3.5 — New AI-adopting firms each month, and the cumulative share of adoption across a rolling panel of eligible U.S. hiring firms. Firms enter the panel with at least 20 new U.S. positions in the preceding 48 months. Source: Revelio Labs job postings and workforce data.
Loading Figure 3.6 — Change in the headcount gap between AI integrator adopters and non-adopters relative to October 2022, with month fixed effects. Source: Revelio Labs workforce data and classified job postings.Figure 3.6 — Change in the headcount gap between AI integrator adopters and non-adopters relative to October 2022, with month fixed effects. Source: Revelio Labs workforce data and classified job postings.
Loading Figure 3.7 — Estimates of the change in log employment at AI-adopting firms compared to non-adopters relative to October 2022, estimated separately by seniority level with month fixed effects. Source: Revelio Labs workforce data from professional online profiles.Figure 3.7 — Estimates of the change in log employment at AI-adopting firms compared to non-adopters relative to October 2022, estimated separately by seniority level with month fixed effects. Source: Revelio Labs workforce data from professional online profiles.
Whether AI raises or lowers wages depends on whether productivity complementarity or substitution dominates. We estimate the salary premium associated with AI exposure in activities from job postings, weighting occupations by headcount so the result reflects the labor market as workers experience it rather than as postings are distributed.
Loading Figure 3.8 — The headcount-weighted salary premium per one-standard-deviation increase in a posting's AI exposure. Source: Revelio Labs job postings data.Figure 3.8 — The headcount-weighted salary premium per one-standard-deviation increase in a posting's AI exposure. Source: Revelio Labs job postings data.
AI is changing not just which workers are hired, but what workers do. The mix of activities performed across the economy has shifted sharply in recent months, and most of that change is occurring within occupations rather than through changes in the occupation mix. This distinction matters: job titles may remain stable and people may remain within the same occupations, even as the activities performed inside those jobs change, so analyses focused only on occupation-level employment can miss a substantial part of the transformation. At the same time, the share of job postings with high AI exposure is declining, consistent with employers beginning to automate — or reduce their reliance on — some of the work AI can perform.
Loading Figure 4.1 — A treemap of activity-level labor demand in the current month, with area proportional to posting volume. Source: Revelio Labs job postings data.Figure 4.1 — A treemap of activity-level labor demand in the current month, with area proportional to posting volume. Source: Revelio Labs job postings data.
Loading Figure 4.2 — The top five fastest growing and shrinking activities, holding the occupational mix fixed. Source: Revelio Labs job postings and workforce data from professional online profiles.Figure 4.2 — The top five fastest growing and shrinking activities, holding the occupational mix fixed. Source: Revelio Labs job postings and workforce data from professional online profiles.
Loading Figure 4.3 — Year-over-year dissimilarity in the headcount-weighted mix of activities performed across the economy. Source: Revelio Labs job postings and workforce data from professional online profiles.Figure 4.3 — Year-over-year dissimilarity in the headcount-weighted mix of activities performed across the economy. Source: Revelio Labs job postings and workforce data from professional online profiles.
Loading Figure 4.4 — Decomposition of the within- and between-occupation contributions to year-over-year activity dissimilarity. Source: Revelio Labs workforce data from professional online profiles.Figure 4.4 — Decomposition of the within- and between-occupation contributions to year-over-year activity dissimilarity. Source: Revelio Labs workforce data from professional online profiles.
Loading Figure 4.5 — Share of job postings in the highest AI-exposure quintile over time. The decline is consistent with employers beginning to automate — or reduce their reliance on — some highly exposed work, although it may also capture anticipatory changes or other shifts in occupational demand. Source: Revelio Labs job postings and workforce data from professional online profiles.Figure 4.5 — Share of job postings in the highest AI-exposure quintile over time. The decline is consistent with employers beginning to automate — or reduce their reliance on — some highly exposed work, although it may also capture anticipatory changes or other shifts in occupational demand. Source: Revelio Labs job postings and workforce data from professional online profiles.
The impact of adoption on sentiment
Changing work content shows up in how employees talk about their jobs, not just in postings data. Comparing employee reviews at AI-adopting firms compared to non-adopters isolates how sentiment shifts once a firm actually adopts AI.
Loading Figure 4.7 — Difference-in-differences estimates of firm-level AI adoption on business outlook, job security, and senior-leadership sentiment. Comparisons are made within industry and October 2022 firm-size groups, with each measure standardized by pre-ChatGPT dispersion. Source: Revelio Labs employee reviews, workforce data, and classified job postings.Figure 4.7 — Difference-in-differences estimates of firm-level AI adoption on business outlook, job security, and senior-leadership sentiment. Comparisons are made within industry and October 2022 firm-size groups, with each measure standardized by pre-ChatGPT dispersion. Source: Revelio Labs employee reviews, workforce data, and classified job postings.
Loading Figure 4.8 — The difference in layoff-related language in reviews between AI-adopting and non-adopting firms. Source: Revelio Labs sentiment data from employee reviews.Figure 4.8 — The difference in layoff-related language in reviews between AI-adopting and non-adopting firms. Source: Revelio Labs sentiment data from employee reviews.
The hiring process is producing fewer successful matches. Job postings per external hire have risen for six years, while candidates increasingly report recruiter ghosting and poor communication. These trends predate ChatGPT, so they cannot be attributed entirely to generative AI. But AI may be making an existing problem worse by enabling candidates to submit more polished applications, weakening the signals employers use to identify strong candidates.
Job Postings Are Producing Fewer Hires
Loading Figure 5.1 — Job postings per external hire over time, shown as a trailing 12-month ratio. Postings are matched to external hires at the same company, state, role, and seniority level over the posting month and following five months. Source: Revelio Labs workforce and job-posting data.Figure 5.1 — Job postings per external hire over time, shown as a trailing 12-month ratio. Postings are matched to external hires at the same company, state, role, and seniority level over the posting month and following five months. Source: Revelio Labs workforce and job-posting data.
Recruiter Ghosting Is Rising in No-Offer Interviews
Loading Figure 5.2 — Mentions of recruiter ghosting in no-offer interview reviews over time. Source: Revelio Labs interview-review data.Figure 5.2 — Mentions of recruiter ghosting in no-offer interview reviews over time. Source: Revelio Labs interview-review data.
Poor Communication Is Becoming a Larger Source of Candidate Dissatisfaction
Loading Figure 5.3 — Share of no-offer interview complaints mentioning communication, process speed, and other hiring issues. Source: Revelio Labs interview-review data.Figure 5.3 — Share of no-offer interview complaints mentioning communication, process speed, and other hiring issues. Source: Revelio Labs interview-review data.
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