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The COVID-19 pandemic and accompanying policy steps caused financial disturbance so plain that advanced statistical methods were unneeded for many questions. For example, joblessness jumped dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One common approach is to compare results between basically AI-exposed employees, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is generally defined at the job level: AI can grade research but not manage a classroom, for instance, so teachers are thought about less exposed than employees whose entire task can be carried out remotely.

3 Our method integrates information from 3 sources. Task-level exposure quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least two times as fast.

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4Why might real usage fall brief of theoretical capability? Some tasks that are theoretically possible might not show up in use because of design limitations. Others might be slow to diffuse due to legal restrictions, particular software application requirements, human confirmation actions, or other difficulties. Eloundou et al. mark "Authorize drug refills and offer prescription details to drug stores" as completely exposed (=1).

As Figure 1 shows, 97% of the jobs observed across the previous 4 Economic Index reports fall into categories ranked as theoretically practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed across O * web jobs organized by their theoretical AI direct exposure. Jobs rated =1 (totally feasible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not possible) represent just 3%.

Our brand-new step, observed exposure, is meant to measure: of those jobs that LLMs could theoretically accelerate, which are in fact seeing automated use in professional settings? Theoretical ability encompasses a much more comprehensive variety of tasks. By tracking how that gap narrows, observed direct exposure provides insight into economic changes as they emerge.

A task's exposure is higher if: Its tasks are in theory possible with AIIts tasks see considerable use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a fairly greater share of automated use patterns or API implementationIts AI-impacted tasks comprise a bigger share of the total role6We provide mathematical information in the Appendix.

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The task-level protection measures are balanced to the profession level weighted by the portion of time invested on each job. The step reveals scope for LLM penetration in the bulk of tasks in Computer system & Mathematics (94%) and Workplace & Admin (90%) professions.

Claude currently covers simply 33% of all jobs in the Computer system & Math classification. There is a big uncovered area too; numerous tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal jobs like representing clients in court.

In line with other information showing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose main tasks we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of reading source files and going into data sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their tasks appeared too occasionally in our information to fulfill the minimum threshold. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Statistics (BLS) publishes routine work forecasts, with the latest set, released in 2025, covering anticipated changes in employment for each profession from 2024 to 2034.

A regression at the profession level weighted by existing work finds that growth projections are rather weaker for jobs with more observed direct exposure. For every single 10 percentage point boost in coverage, the BLS's development forecast drops by 0.6 portion points. This offers some recognition in that our procedures track the separately derived price quotes from labor market experts, although the relationship is slight.

Each strong dot reveals the average observed exposure and forecasted work change for one of the bins. The dashed line shows a basic linear regression fit, weighted by existing employment levels. Figure 5 shows qualities of workers in the leading quartile of exposure and the 30% of employees with no exposure in the three months before ChatGPT was launched, August to October 2022, utilizing information from the Existing Population Survey.

The more disclosed group is 16 portion points most likely to be female, 11 percentage points most likely to be white, and almost twice as most likely to be Asian. They earn 47% more, typically, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, a practically fourfold distinction.

Scientists have taken various techniques. Gimbel et al. (2025) track modifications in the occupational mix utilizing the Existing Population Survey. Their argument is that any crucial restructuring of the economy from AI would appear as changes in circulation of jobs. (They discover that, so far, changes have been plain.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on unemployment as our priority outcome since it most directly captures the capacity for economic harma worker who is unemployed desires a task and has actually not yet found one. In this case, task postings and employment do not necessarily signal the requirement for policy responses; a decline in job posts for an extremely exposed function might be counteracted by increased openings in an associated one.