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The COVID-19 pandemic and accompanying policy measures caused financial disruption so stark that sophisticated statistical techniques were unnecessary for many questions. For example, joblessness leapt dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, however, might be less like COVID and more like the web or trade with China.

One typical technique is to compare results in between more or less AI-exposed workers, firms, or industries, in order to separate the effect of AI from confounding forces. 2 Exposure is generally specified at the task level: AI can grade homework but not handle a classroom, for example, so instructors are considered less unwrapped than employees whose whole task can be performed remotely.

3 Our method combines information from three sources. The O * NET database, which mentions tasks related to around 800 special professions in the US.Our own usage data (as determined in the Anthropic Economic Index). Task-level direct exposure price 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 quick.

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Some tasks that are in theory possible might not show up in usage because of design constraints. Eloundou et al. mark "License drug refills and provide prescription info to drug stores" as totally exposed (=1).

As Figure 1 shows, 97% of the jobs observed across the previous 4 Economic Index reports fall under classifications ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed across O * internet jobs organized by their theoretical AI exposure. Jobs rated =1 (completely possible for an LLM alone) account for 68% of observed Claude usage, while jobs ranked =0 (not practical) account for simply 3%.

Our brand-new measure, observed exposure, is suggested to quantify: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated usage in professional settings? Theoretical ability includes a much wider range of jobs. By tracking how that space narrows, observed exposure supplies insight into economic changes as they emerge.

A job's direct exposure is higher if: Its tasks are in theory possible with AIIts jobs see considerable usage in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a fairly greater share of automated usage patterns or API implementationIts AI-impacted jobs make up a larger share of the overall role6We offer mathematical information in the Appendix.

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The task-level coverage procedures are balanced to the profession level weighted by the fraction of time invested on each task. The procedure shows scope for LLM penetration in the bulk of tasks in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

Claude currently covers simply 33% of all tasks in the Computer & Math classification. There is a big uncovered location too; lots of tasks, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other data showing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% protection, followed by Customer support Agents, whose main tasks we significantly see in first-party API traffic. Data Entry Keyers, whose primary job of checking out source documents and getting in information sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have no coverage, as their jobs appeared too occasionally in our information to meet the minimum limit. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Stats (BLS) publishes regular employment forecasts, with the most recent set, released in 2025, covering forecasted changes in employment for each occupation from 2024 to 2034.

A regression at the profession level weighted by existing employment finds that development projections are rather weaker for jobs with more observed direct exposure. For each 10 percentage point increase in protection, the BLS's growth forecast drops by 0.6 percentage points. This offers some recognition because our measures track the separately obtained estimates from labor market experts, although the relationship is slight.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the typical observed exposure and predicted employment change for among the bins. The dashed line shows a simple linear regression fit, weighted by current work levels. The small diamonds mark individual example occupations for illustration. Figure 5 programs qualities of employees in the leading quartile of direct exposure and the 30% of employees with absolutely no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Current Population Survey.

The more exposed group is 16 portion points most likely to be female, 11 percentage points more likely to be white, and almost twice as most likely to be Asian. They earn 47% more, usually, and have greater levels of education. For instance, individuals with academic degrees are 4.5% of the unexposed group, but 17.4% of the most uncovered group, an almost fourfold difference.

Researchers have taken different approaches. For instance, Gimbel et al. (2025) track modifications in the occupational mix using the Present Population Survey. Their argument is that any essential restructuring of the economy from AI would appear as modifications in distribution of jobs. (They find that, so far, modifications have actually been plain.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task posting data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority result due to the fact that it most straight captures the capacity for financial harma worker who is unemployed desires a job and has not yet found one. In this case, task postings and employment do not always signify the requirement for policy responses; a decrease in job posts for an extremely exposed role might be neutralized by increased openings in an associated one.