Can Deep Data Reshape Industry Growth?

The COVID-19 pandemic and accompanying policy procedures caused financial disturbance so plain that advanced statistical methods were unneeded for numerous concerns. For example, unemployment jumped sharply 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 approach is to compare results between basically AI-exposed employees, companies, or markets, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is generally specified at the task level: AI can grade research but not manage a class, for example, so instructors are considered less disclosed than workers whose entire job can be carried out remotely.

3 Our approach combines data from 3 sources. The O * web database, which enumerates jobs associated with around 800 special professions in the US.Our own usage information (as determined in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job at least two times as fast.

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Some tasks that are in theory possible might not reveal up in usage because of design restrictions. Eloundou et al. mark "License drug refills and supply prescription details to drug stores" 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 possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage distributed across O * internet tasks grouped by their theoretical AI exposure. Jobs ranked =1 (completely possible for an LLM alone) account for 68% of observed Claude use, while tasks rated =0 (not practical) account for just 3%.

Our new step, observed exposure, is suggested to measure: of those jobs that LLMs could in theory accelerate, which are actually seeing automated usage in expert settings? Theoretical capability includes a much broader series of jobs. By tracking how that space narrows, observed exposure supplies insight into financial changes as they emerge.

A job's direct exposure is higher if: Its tasks are in theory possible with AIIts jobs see considerable use in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a relatively higher share of automated use patterns or API implementationIts AI-impacted jobs comprise a bigger share of the total role6We provide mathematical details in the Appendix.

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We then change for how the job is being performed: completely automated implementations get full weight, while augmentative use gets half weight. Finally, the task-level coverage measures are averaged to the occupation level weighted by the portion of time invested in each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by very first averaging to the profession level weighting by our time portion procedure, then averaging to the profession category weighting by total work. For instance, the step shows scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Office & Admin (90%) professions.

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

In line with other data revealing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client Service Representatives, whose main tasks we significantly see in first-party API traffic. Data Entry Keyers, whose primary task of checking out source documents and entering data sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have absolutely no coverage, as their tasks appeared too occasionally in our data to fulfill the minimum limit. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Stats (BLS) publishes regular work projections, with the most recent set, published in 2025, covering forecasted changes in employment for each profession from 2024 to 2034.

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

measure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the average observed exposure and predicted work change for one of the bins. The dashed line reveals an easy linear regression fit, weighted by existing employment levels. The little diamonds mark individual example professions for illustration. Figure 5 programs characteristics of employees in the leading quartile of direct exposure and the 30% of employees with no exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing information from the Present Population Study.

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

Researchers have actually taken various methods. Gimbel et al. (2025) track changes in the occupational mix using the Existing Population Survey. Their argument is that any crucial restructuring of the economy from AI would appear as changes in circulation of tasks. (They find that, up until now, modifications have actually been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job posting information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our concern outcome since it most straight captures the potential for economic harma employee who is unemployed desires a task and has actually not yet discovered one. In this case, task posts and work do not always signal the need for policy actions; a decrease in task postings for an extremely exposed role might be counteracted by increased openings in a related one.