Vital Expansion Metrics to Track in 2026

The COVID-19 pandemic and accompanying policy procedures triggered economic interruption so plain that sophisticated statistical techniques were unnecessary for numerous questions. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, however, might be less like COVID and more like the internet or trade with China.

One typical approach is to compare results between basically AI-exposed workers, companies, or industries, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is normally defined at the task level: AI can grade homework however not handle a classroom, for instance, so teachers are thought about less unveiled than workers whose entire task can be performed from another location.

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

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4Why might real usage fall short of theoretical capability? Some jobs that are theoretically possible might disappoint up in use since of design restrictions. Others may be sluggish to diffuse due to legal restrictions, specific software application requirements, human verification actions, or other difficulties. Eloundou et al. mark "Authorize drug refills and provide prescription details to drug stores" as totally exposed (=1).

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

Our new step, observed exposure, is meant to measure: of those tasks that LLMs could in theory speed up, which are actually seeing automated usage in expert settings? Theoretical capability incorporates a much broader variety of jobs. By tracking how that gap narrows, observed exposure provides insight into financial modifications as they emerge.

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

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The task-level protection steps are averaged to the profession level weighted by the fraction of time invested on each task. The procedure reveals scope for LLM penetration in the majority of tasks in Computer & Mathematics (94%) and Workplace & Admin (90%) occupations.

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

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

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At the bottom end, 30% of workers have absolutely no coverage, as their tasks appeared too occasionally in our information to satisfy the minimum limit. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the occupation level weighted by present employment finds that development projections are rather weaker for jobs with more observed direct exposure. For each 10 percentage point boost in protection, the BLS's development projection visit 0.6 portion points. This supplies some validation because our measures track the independently obtained quotes from labor market analysts, although the relationship is minor.

Each solid dot shows the typical observed direct exposure and forecasted employment change for one of the bins. The rushed line shows an easy linear regression fit, weighted by present work levels. Figure 5 programs characteristics of workers in the top quartile of direct exposure and the 30% of employees with no exposure in the 3 months before ChatGPT was launched, August to October 2022, using data from the Present Population Study.

The more uncovered group is 16 portion points most likely to be female, 11 percentage points more likely to be white, and practically twice as most likely to be Asian. They make 47% more, usually, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most uncovered group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome because it most directly records the capacity for economic harma worker who is out of work desires a job and has not yet found one. In this case, task posts and employment do not always indicate the requirement for policy responses; a decrease in job posts for a highly exposed function may be counteracted by increased openings in an associated one.