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The COVID-19 pandemic and accompanying policy measures triggered financial interruption so stark that advanced analytical methods were unneeded for many questions. Unemployment leapt sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One typical technique is to compare results between more or less AI-exposed employees, firms, or markets, in order to separate the effect of AI from confounding forces. 2 Exposure is generally defined at the task level: AI can grade homework but not handle a class, for instance, so teachers are considered less reviewed than employees whose whole task can be carried out remotely.

3 Our approach integrates data from three sources. The O * NET database, which mentions jobs related to around 800 special occupations in the US.Our own usage data (as measured in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a job at least twice as quick.

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Some tasks that are theoretically possible might not reveal up in use since of design constraints. Eloundou et al. mark "Authorize drug refills and offer prescription information to drug stores" as totally exposed (=1).

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

Our new step, observed exposure, is meant to measure: of those jobs that LLMs could in theory accelerate, which are really seeing automated usage in expert settings? Theoretical ability encompasses a much broader variety of tasks. By tracking how that gap narrows, observed exposure supplies insight into economic modifications as they emerge.

A task's direct exposure is higher if: Its jobs are theoretically possible with AIIts jobs see significant usage in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a relatively higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a larger share of the overall role6We give mathematical information in the Appendix.

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We then change for how the task is being performed: totally automated applications receive full weight, while augmentative use receives half weight. The task-level protection steps are averaged to the occupation level weighted by the fraction of time invested on each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by very first averaging to the occupation level weighting by our time portion step, then balancing to the profession classification weighting by total employment. For example, the step shows scope for LLM penetration in the bulk of jobs in Computer system & Math (94%) and Office & Admin (90%) occupations.

Claude presently covers simply 33% of all jobs in the Computer system & Math category. There is a big exposed location too; many tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm equipment to legal tasks like representing customers in court.

In line with other information showing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Agents, whose primary tasks we increasingly see in first-party API traffic. Data Entry Keyers, whose main task of checking out source files and going into information sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have no coverage, as their tasks appeared too occasionally in our information to satisfy the minimum threshold. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Data (BLS) publishes regular employment projections, with the current set, released in 2025, covering predicted modifications in employment for every occupation from 2024 to 2034.

A regression at the occupation level weighted by current work finds that growth forecasts are somewhat weaker for tasks with more observed exposure. For every single 10 percentage point boost in protection, the BLS's growth projection stop by 0.6 portion points. This provides some recognition in that our steps track the independently obtained estimates from labor market analysts, although the relationship is small.

Each solid dot reveals the typical observed direct exposure and forecasted work change for one of the bins. The dashed line reveals a simple direct regression fit, weighted by existing work levels. Figure 5 programs qualities of workers in the top quartile of direct exposure and the 30% of workers with absolutely no exposure in the three months before ChatGPT was released, August to October 2022, utilizing data from the Present Population Survey.

The more reviewed group is 16 percentage points more most likely to be female, 11 percentage points more most likely to be white, and nearly two times as likely to be Asian. They earn 47% more, on average, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most unveiled group, a nearly fourfold distinction.

Scientists have taken different techniques. Gimbel et al. (2025) track modifications in the occupational mix using the Existing Population Study. Their argument is that any crucial restructuring of the economy from AI would show up as modifications in circulation of jobs. (They discover that, up until now, changes have actually been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most straight records the potential for financial harma worker who is jobless wants a job and has actually not yet discovered one. In this case, task posts and work do not always signal the need for policy responses; a decline in job posts for an extremely exposed role may be neutralized by increased openings in an associated one.