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The COVID-19 pandemic and accompanying policy steps triggered financial interruption so plain that advanced statistical approaches were unneeded for many concerns. Unemployment leapt greatly in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, however, might be less like COVID and more like the web or trade with China.

One common approach is to compare results in between more or less AI-exposed workers, companies, or markets, in order to separate the effect of AI from confounding forces. 2 Direct exposure is normally specified at the job level: AI can grade homework however not manage a classroom, for example, so instructors are considered less unveiled than employees whose whole job can be performed remotely.

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

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Some tasks that are theoretically possible may not reveal up in use because of model limitations. Eloundou et al. mark "License drug refills and offer prescription information to drug stores" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous four Economic Index reports fall into classifications ranked as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed throughout O * web tasks organized by their theoretical AI exposure. Jobs ranked =1 (fully possible for an LLM alone) account for 68% of observed Claude use, while jobs ranked =0 (not feasible) represent simply 3%.

Our new step, observed direct exposure, is implied to quantify: of those jobs that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical ability includes a much broader range of tasks. By tracking how that space narrows, observed exposure provides insight into financial modifications as they emerge.

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

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We then adjust for how the job is being brought out: totally automated applications receive full weight, while augmentative usage receives half weight. The task-level coverage procedures are balanced to the occupation level weighted by the portion of time spent on each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We calculate this by first balancing to the profession level weighting by our time portion measure, then averaging to the occupation category weighting by total employment. The measure shows scope for LLM penetration in the majority of jobs in Computer system & Math (94%) and Office & Admin (90%) professions.

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

In line with other information revealing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% protection, followed by Customer care Representatives, whose main jobs we progressively see in first-party API traffic. Data Entry Keyers, whose main task of reading source documents and going into information sees considerable automation, are 67% covered.

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At the bottom end, 30% of employees have absolutely no coverage, as their jobs appeared too occasionally in our information to meet the minimum limit. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the occupation level weighted by current work discovers that development forecasts are rather weaker for tasks with more observed exposure. For each 10 percentage point boost in coverage, the BLS's growth forecast visit 0.6 portion points. This offers some validation in that our measures track the independently obtained quotes from labor market analysts, although the relationship is minor.

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

The more uncovered group is 16 portion points more likely to be female, 11 percentage points most likely to be white, and practically two times as most likely to be Asian. They make 47% more, on average, and have higher levels of education. For example, individuals with academic degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome because it most straight captures the potential for economic harma employee who is unemployed desires a job and has not yet discovered one. In this case, task postings and employment do not necessarily signify the requirement for policy responses; a decline in task postings for an extremely exposed function might be combated by increased openings in a related one.