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The COVID-19 pandemic and accompanying policy steps triggered economic disturbance so plain that advanced analytical techniques were unneeded for lots of concerns. Unemployment leapt dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, may be less like COVID and more like the web or trade with China.

One typical method is to compare results in between basically AI-exposed employees, firms, or markets, in order to isolate the result of AI from confounding forces. 2 Exposure is normally defined at the job level: AI can grade homework but not handle a class, for instance, so teachers are thought about less exposed than employees whose entire task can be carried out from another location.

3 Our technique combines data from three sources. The O * web database, which specifies tasks related to around 800 special occupations in the US.Our own usage data (as measured in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job at least twice as fast.

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Some tasks that are theoretically possible might not reveal up in usage because of model constraints. Eloundou et al. mark "Authorize drug refills and provide prescription info to pharmacies" as fully exposed (=1).

As Figure 1 programs, 97% of the tasks observed across the previous four Economic Index reports fall under categories ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage distributed across O * NET jobs grouped by their theoretical AI direct exposure. Jobs rated =1 (fully feasible for an LLM alone) account for 68% of observed Claude usage, while tasks ranked =0 (not feasible) represent just 3%.

Our new step, observed direct exposure, is meant to measure: of those jobs that LLMs could theoretically accelerate, which are actually seeing automated use in expert settings? Theoretical capability includes a much broader series of tasks. By tracking how that space narrows, observed exposure offers insight into financial modifications as they emerge.

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

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We then change for how the job is being brought out: completely automated implementations receive complete weight, while augmentative usage receives half weight. The task-level coverage procedures are balanced to the profession level weighted by the fraction of time invested on each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We compute this by very first averaging to the occupation level weighting by our time fraction step, then averaging to the profession classification weighting by overall work. For example, the step reveals scope for LLM penetration in the bulk of tasks in Computer system & Mathematics (94%) and Office & Admin (90%) occupations.

The coverage shows AI is far from reaching its theoretical abilities. Claude presently covers simply 33% of all jobs in the Computer system & Math classification. As capabilities advance, adoption spreads, and release deepens, the red location will grow to cover the blue. There is a big exposed area too; many tasks, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal tasks like representing clients in court.

In line with other data revealing that Claude is thoroughly utilized for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer support Representatives, whose main tasks we significantly see in first-party API traffic. Finally, Data Entry Keyers, whose main task of reading source files and entering 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 occasionally in our information to fulfill the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the profession level weighted by present work finds that growth projections are rather weaker for jobs with more observed direct exposure. For each 10 percentage point boost in coverage, the BLS's development forecast visit 0.6 portion points. This supplies some recognition because our procedures track the separately derived quotes from labor market analysts, although the relationship is slight.

Each solid dot reveals the average observed exposure and predicted work modification for one of the bins. The rushed line shows an easy linear regression fit, weighted by existing work levels. Figure 5 shows attributes of workers in the top quartile of direct exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Existing Population Survey.

The more unwrapped group is 16 percentage points more most likely to be female, 11 percentage points most likely to be white, and practically twice as likely to be Asian. They make 47% more, on average, and have higher levels of education. For example, individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most unwrapped group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority outcome since it most straight records the capacity for economic harma worker who is jobless desires a job and has actually not yet found one. In this case, task posts and work do not always signal the requirement for policy actions; a decline in job postings for a highly exposed role may be counteracted by increased openings in an associated one.