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The COVID-19 pandemic and accompanying policy procedures caused economic disturbance so stark that advanced analytical methods were unneeded for numerous concerns. For instance, unemployment leapt greatly 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 technique is to compare results in between more or less AI-exposed employees, companies, or markets, in order to separate the effect of AI from confounding forces. 2 Exposure is typically defined at the task level: AI can grade research but not manage a classroom, for example, so teachers are considered less unwrapped than workers whose whole task can be carried out from another location.

3 Our technique combines information from 3 sources. The O * NET database, which identifies jobs connected with around 800 distinct professions in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level exposure quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a task at least two times as fast.

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4Why might real use fall short of theoretical ability? Some jobs that are theoretically possible might disappoint up in use due to the fact that of design restrictions. Others might be slow to diffuse due to legal restrictions, specific software application requirements, human confirmation actions, or other difficulties. Eloundou et al. mark "Authorize drug refills and offer prescription info to drug stores" as completely exposed (=1).

As Figure 1 programs, 97% of the jobs observed across the previous 4 Economic Index reports fall into classifications rated as theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage distributed throughout O * NET tasks grouped by their theoretical AI exposure. Tasks ranked =1 (completely feasible for an LLM alone) represent 68% of observed Claude usage, while tasks rated =0 (not practical) account for simply 3%.

Our new procedure, observed direct exposure, is meant to quantify: of those jobs that LLMs could in theory accelerate, which are actually seeing automated usage in expert settings? Theoretical ability includes a much more comprehensive series of tasks. By tracking how that gap narrows, observed direct exposure offers insight into economic changes as they emerge.

A task's exposure is higher if: Its jobs are theoretically possible with AIIts jobs see significant use in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a relatively higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a bigger share of the total role6We provide mathematical information in the Appendix.

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The task-level coverage measures are balanced to the profession level weighted by the fraction of time invested on each job. The measure reveals scope for LLM penetration in the bulk of tasks in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

Claude currently covers just 33% of all tasks in the Computer & Math category. There is a large uncovered location too; many tasks, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal jobs like representing clients in court.

In line with other information showing that Claude is extensively 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. Finally, Data Entry Keyers, whose main task of reading source files and going into data sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have no coverage, as their jobs appeared too infrequently in our information to meet the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Statistics (BLS) publishes regular employment forecasts, with the most recent set, published in 2025, covering anticipated changes in work for each occupation from 2024 to 2034.

A regression at the profession level weighted by current employment finds that growth forecasts are rather weaker for tasks with more observed direct exposure. For every 10 percentage point increase in protection, the BLS's development projection visit 0.6 percentage points. This supplies some recognition because our procedures track the separately obtained price quotes from labor market analysts, although the relationship is minor.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the typical observed exposure and projected employment modification for among the bins. The dashed line reveals a simple linear regression fit, weighted by current work levels. The little diamonds mark private example occupations for illustration. Figure 5 shows attributes of employees in the leading quartile of exposure and the 30% of employees with zero direct exposure in the three months before ChatGPT was launched, August to October 2022, utilizing information from the Present Population Study.

The more exposed group is 16 percentage points more most likely to be female, 11 portion points more likely to be white, and almost two times as most likely to be Asian. They make 47% more, typically, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most uncovered group, an almost fourfold distinction.

Researchers have actually taken different approaches. Gimbel et al. (2025) track changes in the occupational mix using the Current Population Survey. Their argument is that any important restructuring of the economy from AI would reveal up as changes in distribution of tasks. (They find that, up until now, modifications have been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task publishing 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 catches the capacity for financial harma worker who is unemployed wants a task and has actually not yet discovered one. In this case, job posts and work do not necessarily signal the requirement for policy actions; a decrease in task postings for an extremely exposed role may be counteracted by increased openings in a related one.