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The COVID-19 pandemic and accompanying policy steps caused financial interruption so stark that advanced analytical approaches were unneeded for many questions. Unemployment leapt greatly in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One typical technique is to compare outcomes in between more or less AI-exposed employees, companies, or markets, in order to isolate the result of AI from confounding forces. 2 Exposure is normally defined at the task level: AI can grade research but not manage a classroom, for example, so instructors are considered less bare than employees whose whole task can be performed remotely.

3 Our approach integrates data from three sources. Task-level exposure price quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job at least twice as fast.

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4Why might real use fall brief of theoretical capability? Some tasks that are theoretically possible may disappoint up in usage because of design restrictions. Others may be slow to diffuse due to legal restraints, specific software application requirements, human verification actions, or other obstacles. Eloundou et al. mark "Authorize drug refills and supply prescription info to pharmacies" as completely exposed (=1).

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

Our new measure, observed exposure, is meant to measure: of those tasks that LLMs could in theory speed up, which are actually seeing automated use in expert settings? Theoretical capability includes a much more comprehensive variety of jobs. By tracking how that gap narrows, observed exposure offers insight into economic changes as they emerge.

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

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We then change for how the task is being brought out: fully automated executions get complete weight, while augmentative usage gets half weight. The task-level protection procedures are balanced to the profession 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 determine this by first averaging to the profession level weighting by our time portion measure, then averaging to the profession category weighting by total work. For example, the measure reveals scope for LLM penetration in the bulk of jobs in Computer system & Math (94%) and Office & Admin (90%) occupations.

Claude currently covers simply 33% of all jobs in the Computer & Mathematics classification. There is a big uncovered location too; many tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing customers in court.

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

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At the bottom end, 30% of employees have absolutely no coverage, as their tasks appeared too infrequently in our data to satisfy the minimum limit. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The US Bureau of Labor Data (BLS) publishes regular employment forecasts, with the most recent set, released in 2025, covering anticipated modifications in employment for each occupation from 2024 to 2034.

A regression at the occupation level weighted by present work finds that growth projections are somewhat weaker for tasks with more observed exposure. For every single 10 percentage point increase in coverage, the BLS's growth forecast visit 0.6 percentage points. This offers some validation in that our measures track the individually derived estimates from labor market experts, although the relationship is small.

Each strong dot reveals the typical observed exposure and forecasted work change for one of the bins. The rushed line reveals a simple linear regression fit, weighted by existing work levels. Figure 5 shows characteristics of workers in the top quartile of exposure and the 30% of employees with absolutely no direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Current Population Study.

The more exposed group is 16 percentage points more most likely to be female, 11 portion points more most likely to be white, and practically twice as most likely to be Asian. They earn 47% more, typically, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, an almost fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result due to the fact that it most straight records the capacity for financial harma worker who is unemployed wants a task and has actually not yet discovered one. In this case, task postings and work do not always indicate the need for policy actions; a decrease in job posts for an extremely exposed function might be neutralized by increased openings in a related one.