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The COVID-19 pandemic and accompanying policy steps triggered financial disruption so plain that sophisticated statistical techniques were unnecessary for lots of questions. For example, unemployment jumped greatly in the early weeks of the pandemic, leaving little room for alternative explanations. The impacts of AI, however, might be less like COVID and more like the internet or trade with China.

One common approach is to compare results between more or less AI-exposed workers, firms, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is generally defined at the job level: AI can grade homework but not handle a classroom, for instance, so teachers are considered less disclosed than employees whose whole job can be performed from another location.

3 Our technique combines information from three sources. Task-level direct exposure price 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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Some jobs that are theoretically possible may not show up in use because of design limitations. Eloundou et al. mark "License drug refills and supply prescription info to drug stores" as fully exposed (=1).

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

Our new procedure, observed direct exposure, is meant to quantify: of those tasks that LLMs could in theory speed up, which are actually seeing automated use in expert settings? Theoretical ability incorporates a much wider range of jobs. By tracking how that gap narrows, observed direct exposure supplies insight into financial changes as they emerge.

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

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We then change for how the job is being carried out: completely automated executions receive complete weight, while augmentative use gets half weight. Lastly, the task-level coverage measures are averaged to the profession level weighted by the fraction of time invested on each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We compute this by very first averaging to the profession level weighting by our time portion step, then averaging to the profession category weighting by overall work. For example, the measure shows scope for LLM penetration in the bulk of jobs in Computer & Math (94%) and Workplace & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical abilities. For instance, Claude currently covers simply 33% of all tasks in the Computer system & Math category. As abilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a large uncovered area too; many tasks, naturally, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal tasks like representing customers in court.

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

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At the bottom end, 30% of workers have zero protection, as their tasks appeared too rarely in our information to fulfill the minimum threshold. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the profession level weighted by existing work finds that development projections are rather weaker for jobs with more observed direct exposure. For every 10 percentage point increase in protection, the BLS's development forecast stop by 0.6 percentage points. This supplies some recognition because our steps track the separately obtained estimates from labor market analysts, although the relationship is small.

Each strong dot reveals the average observed direct exposure and predicted employment modification for one of the bins. The dashed line reveals an easy direct regression fit, weighted by present employment levels. Figure 5 shows characteristics of workers in the top quartile of direct exposure and the 30% of workers with absolutely no direct exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Present Population Survey.

The more revealed group is 16 percentage points more likely to be female, 11 portion points more likely to be white, and nearly two times as likely to be Asian. They earn 47% more, usually, and have higher levels of education. People with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, an almost fourfold difference.

Scientists have taken various techniques. Gimbel et al. (2025) track changes in the occupational mix utilizing the Existing Population Study. Their argument is that any important restructuring of the economy from AI would appear as changes in distribution of jobs. (They discover that, so far, changes have actually been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize job posting data from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on joblessness as our top priority outcome since it most directly catches the potential for financial harma worker who is unemployed wants a job and has not yet discovered one. In this case, task postings and employment do not necessarily signify the need for policy actions; a decrease in task postings for a highly exposed role may be combated by increased openings in an associated one.