Tracking the Impact of AI on the Labor Market
Key Takeaways
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While the occupational mix is changing more quickly than it has in the past, it is not a large difference and predates the widespread introduction of AI in the workforce.
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Currently, measures of exposure, automation, and augmentation show no sign of being related to changes in employment or unemployment.
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Better data is needed to fully understand the impact of AI on the labor market.
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We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.
The addition of the March 2026 CPS and the introduction of Anthropic’s February usage metrics do not suggest any substantial changes to the analysis TBL released in March. Occupational dissimilarity, industry dissimilarity, and our exposure and usage metrics all remain flat, lie within historical ranges, or continue along the trends they were already exhibiting.1The most notable difference is an uptick in the dissimilarity of occupational mix between older and younger college graduates, though this remains at the high end of the historical range.
Overall, the March 2026 CPS releases do not indicate a meaningful shift from December in most categories. Of course, our analysis is not predictive of the future. We plan to continue monitoring these trends monthly to assess how AI’s job impacts might change. It is important to remember that the effects of new technologies are evolving, and a simple snapshot in time is not enough to explicitly determine what the future holds.
Is this Time Different? Changes in the Occupational Mix
The job mix for AI appears to be changing faster than it has in the past, although not markedly so.
However, shifts in the occupational mix were well on their way during 2021, before the release of generative AI, and more recent changes do not seem any more pronounced, even as the use of AI continues to grow in popularity.
Repeating this analysis by industry similarly suggests a limited effect of AI.
The dissimilarity data we have examined indicates that there is no substantial acceleration in the rate of change in the composition of the labor market since the introduction of ChatGPT. Lacking that, there is nothing meaningful we can either attribute or misattribute to AI.
Recent College Graduates
Figure 8 compares the occupational mix for recent college graduates (ages 20-24) to that of their older counterparts (ages 25-34). If generative AI were in fact substantially changing the labor market for recent college graduates, we would expect to see a growing dissimilarity in the occupational mix between these two groups. However, our results should be interpreted with caution particularly given small sample sizes.
Taking a closer look at the trend since January 2021, the dissimilarity between older and more recent college graduates rarely deviates outside of the 30-33% range (Figure 9). Further, the same caution in interpretation given the small sample sizes holds in this figure as well.
Insights from AI “exposure” and usage
To better understand whether AI is impacting the labor market, we would want to analyze whether the share of workers in occupations that are most impacted by AI usage is changing over time. If AI were automating jobs at scale, we would expect to see a smaller share of workers in some of the jobs that are most negatively impacted.
We use data from OpenAI and Anthropic, respectively, that detail the occupations that are most “exposed” to genAI tools (a theoretical, forward-looking metric across all jobs) and that have the highest actual usage of one specific AI tool, Claude (a more narrow, present-focused metric). (See discussion of limitations in the original analysis. Note that exposure does not equate to automation or job loss.)
Importantly, OpenAI and Anthropic are measuring different things, and we look at them separately.
OpenAI’s “exposure” data
We ask: has the share of workers in occupational exposure quintiles changed since ChatGPT’s launch? Our analysis shows that it has not (Figure 10). The share of workers in the lowest, middle, and highest occupational exposure groups stay stable.
Even when specifically examining the unemployed population, there is no clear growth in exposure to generative AI. Figure 11 depicts the average percentage of tasks exposed amongst unemployed workers by duration of unemployment. AI-driven displacement might suggest a growth in the proportion of exposed tasks amongst recently unemployed workers. Irrespective of the duration of unemployment, however, unemployed workers were in occupations where about 25 to 35 percent of tasks, on average, could be performed by generative AI. Although there is some variation between months, the data demonstrate no clear difference by the duration of unemployment.
Anthropic’s Usage Measure
In March 2026, Anthropic released new usage data corresponding to February 2026. This sample closely follows the previous release covering November 2025. Both of these samples indicate that observed usage is more likely to be associated with automation than augmentation.
Anthropic generates these metrics by sampling one million conversations from both Claude and their enterprise API. Their researchers then attribute each conversation to an O*NET occupational task and categorize the type of usage as either automation (where the model performs the task) or augmentation (where the model enhances the user’s ability to complete the task).
We provide two versions of every usage chart. Panel A displays the results looking only at the tasks observed in the usage data. This method looks at only the subset of O*NET tasks that appear in the Anthropic sample, which necessarily excludes tasks that do not appear. Panel B displays the same results after filling the missing tasks with zeroes for their usage. This method looks across a broader array of tasks, which may provide a better representation of usage, whether in its automated or augmented forms, for a given occupation.
The proportion of employment in occupations with high levels of task AI usage, whether automation or augmentation, has been stable (Figure 14). Repeating a similar analysis as above, Figures 15 and 17 report the occupation-level share of tasks that are associated with automation or augmentation, respectively, amongst unemployed workers by duration of unemployment. Note: for this analysis, we use Anthropic’s most recent data on AI usage, which was released in late March.
Summary
While anxiety over the effects of AI on today’s labor market is widespread, our data suggests it remains largely speculative. The picture of AI’s impact on the labor market that emerges from our data is one that largely reflects stability, not major disruption at an economy-wide level. While generative AI looks likely to join the ranks of transformative, general-purpose technologies, it is too soon to tell how disruptive the technology will be to jobs. The lack of widespread impacts at this early stage is not unlike the pace of change with previous periods of technological disruption. Preregistering areas where we would expect to see the impact and continuing to monitor monthly impacts will help us distinguish rumor from fact.
Footnotes
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“Dissimilarity” in our context refers to the change—relative to a baseline period—in the mix of (for example) employment across occupations. Specifically, a dissimilarity index in that example shows the percent of workers, in any given month, who would have to switch occupations in order to return to the baseline proportions.