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The COVID-19 pandemic and accompanying policy procedures triggered financial interruption so stark that sophisticated analytical methods were unneeded for numerous concerns. Unemployment 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 internet or trade with China.

One common method is to compare outcomes between more or less AI-exposed employees, companies, or markets, in order to isolate the impact of AI from confounding forces. 2 Exposure is typically defined at the job level: AI can grade homework but not handle a classroom, for instance, so teachers are thought about less discovered than workers whose entire task can be performed remotely.

3 Our technique integrates data from 3 sources. The O * NET database, which mentions jobs associated with around 800 unique occupations in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job a minimum of twice as fast.

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4Why might actual use fall short of theoretical capability? Some tasks that are theoretically possible might not reveal up in usage since of design constraints. Others might be slow to diffuse due to legal restrictions, particular software application requirements, human verification actions, or other difficulties. Eloundou et al. mark "Authorize drug refills and supply prescription details to pharmacies" as totally exposed (=1).

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

Our new measure, observed direct exposure, is implied to measure: of those tasks that LLMs could in theory speed up, which are actually seeing automated use in professional settings? Theoretical ability encompasses a much wider variety of tasks. By tracking how that gap narrows, observed direct exposure offers insight into economic modifications as they emerge.

A task's direct exposure is greater if: Its jobs are in theory possible with AIIts jobs see substantial use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a reasonably greater share of automated usage patterns or API implementationIts AI-impacted jobs comprise a larger share of the overall role6We offer mathematical details 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 procedure reveals scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Workplace & Admin (90%) professions.

The protection reveals AI is far from reaching its theoretical capabilities. Claude presently covers simply 33% of all tasks in the Computer & Math category. As abilities advance, adoption spreads, and implementation deepens, the red location will grow to cover heaven. There is a large uncovered location too; lots of jobs, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal tasks like representing customers in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Agents, whose main jobs we increasingly see in first-party API traffic. Finally, Data Entry Keyers, whose main task of reading source documents and going into information sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their jobs appeared too infrequently in our information to fulfill the minimum limit. This group consists of, for instance, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Statistics (BLS) releases routine work projections, with the current set, published in 2025, covering forecasted modifications in employment for every profession from 2024 to 2034.

A regression at the occupation level weighted by existing employment discovers that growth projections are rather weaker for jobs with more observed direct exposure. For every single 10 percentage point boost in protection, the BLS's growth forecast drops by 0.6 portion points. This provides some validation because our steps track the independently derived quotes from labor market analysts, although the relationship is small.

Each strong dot shows the typical observed direct exposure and predicted employment change for one of the bins. The dashed line reveals a simple linear regression fit, weighted by current employment levels. Figure 5 programs attributes of workers in the leading quartile of direct exposure and the 30% of employees with no exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Present Population Study.

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

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority outcome since it most straight catches the potential for economic harma worker who is jobless desires a task and has not yet discovered one. In this case, job posts and work do not always signify the requirement for policy reactions; a decline in job postings for an extremely exposed role might be neutralized by increased openings in a related one.