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The COVID-19 pandemic and accompanying policy measures caused financial disturbance so stark that advanced analytical methods were unneeded for numerous questions. For instance, unemployment leapt dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One common method is to compare outcomes between basically AI-exposed employees, firms, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is normally specified at the task level: AI can grade homework however not handle a classroom, for instance, so instructors are considered less discovered than employees whose whole job can be performed from another location.

3 Our method combines data from 3 sources. Task-level direct exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task at least two times as fast.

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Some jobs that are theoretically possible might not show up in use because of design constraints. Eloundou et al. mark "License drug refills and supply prescription info to pharmacies" as completely exposed (=1).

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

Our brand-new step, observed exposure, is indicated to measure: of those jobs that LLMs could in theory speed up, which are actually seeing automated usage in professional settings? Theoretical ability encompasses a much broader range of tasks. By tracking how that gap narrows, observed direct exposure supplies insight into economic modifications as they emerge.

A task's direct exposure is greater if: Its tasks are in theory possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a relatively greater share of automated usage patterns or API implementationIts AI-impacted tasks comprise a larger share of the total role6We give mathematical information in the Appendix.

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The task-level coverage steps are balanced to the profession level weighted by the portion of time spent on each task. The step reveals scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) professions.

Claude presently covers just 33% of all jobs in the Computer system & Mathematics category. There is a big exposed location too; lots of jobs, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm equipment to legal tasks like representing clients in court.

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

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At the bottom end, 30% of employees have zero coverage, as their tasks appeared too rarely in our information to meet the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by existing employment discovers that development projections are rather weaker for jobs with more observed exposure. For every single 10 percentage point boost in protection, the BLS's growth forecast visit 0.6 percentage points. This offers some validation because our procedures track the individually derived quotes from labor market experts, although the relationship is minor.

Each solid dot reveals the average observed direct exposure and projected employment modification for one of the bins. The dashed line shows a basic linear regression fit, weighted by current employment levels. Figure 5 programs qualities of workers in the leading quartile of exposure and the 30% of workers with zero direct exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing data from the Present Population Survey.

The more revealed group is 16 portion points most likely to be female, 11 portion points more likely to be white, and practically two times as most likely to be Asian. They earn 47% more, on average, and have higher levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most bare group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result due to the fact that it most directly captures the capacity for financial harma employee who is jobless desires a job and has actually not yet discovered one. In this case, task postings and work do not necessarily signify the requirement for policy actions; a decline in job posts for an extremely exposed role might be combated by increased openings in an associated one.