Key Expansion Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy measures triggered economic disruption so plain that sophisticated analytical methods were unnecessary for numerous concerns. Unemployment jumped greatly 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 web 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 separate the effect of AI from confounding forces. 2 Direct exposure is normally specified at the task level: AI can grade homework but not handle a classroom, for example, so instructors are considered less discovered than workers whose entire job can be carried out from another location.

3 Our approach integrates data from three sources. The O * web database, which identifies tasks associated with around 800 distinct occupations in the US.Our own usage information (as determined in the Anthropic Economic Index). 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 job at least two times as fast.

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4Why might actual use fall brief of theoretical capability? Some jobs that are theoretically possible may not reveal up in use due to the fact that of model restrictions. Others may be sluggish to diffuse due to legal restrictions, specific software requirements, human verification actions, or other difficulties. For instance, Eloundou et al. mark "Authorize drug refills and provide prescription information to pharmacies" as completely exposed (=1).

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

Our new measure, observed exposure, is implied to measure: of those jobs that LLMs could in theory accelerate, which are actually seeing automated usage in professional settings? Theoretical capability includes a much more comprehensive variety of tasks. By tracking how that gap narrows, observed exposure offers insight into financial modifications as they emerge.

A task's exposure is higher if: Its jobs are in theory possible with AIIts tasks see significant use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted tasks make up a bigger share of the total role6We provide mathematical information in the Appendix.

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

The coverage shows AI is far from reaching its theoretical abilities. For example, Claude currently covers just 33% of all tasks in the Computer system & Math classification. As capabilities advance, adoption spreads, and release deepens, the red area will grow to cover heaven. There is a big exposed location too; many tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal tasks like representing clients in court.

In line with other information showing that Claude is extensively utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Client Service Representatives, whose primary tasks we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source files and entering information sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have no protection, as their jobs appeared too rarely in our information to satisfy the minimum limit. This group consists of, for instance, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Stats (BLS) publishes regular employment projections, with the most current set, published in 2025, covering forecasted modifications in employment for every single profession from 2024 to 2034.

A regression at the profession level weighted by current work discovers that development projections are rather weaker for tasks with more observed exposure. For every single 10 percentage point boost in coverage, the BLS's growth forecast drops by 0.6 percentage points. This supplies some recognition in that our procedures track the independently obtained estimates from labor market analysts, although the relationship is minor.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the average observed direct exposure and projected work modification for among the bins. The dashed line reveals a simple direct regression fit, weighted by present work levels. The small diamonds mark specific example professions for illustration. Figure 5 programs attributes of employees in the top quartile of direct exposure and the 30% of workers with no direct exposure in the three months before ChatGPT was launched, August to October 2022, utilizing information from the Present Population Survey.

The more uncovered group is 16 portion points most likely to be female, 11 portion points more most likely to be white, and nearly twice as most likely to be Asian. They earn 47% more, typically, and have greater levels of education. For example, individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most reviewed group, an almost fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority result because it most directly catches the potential for economic harma worker who is out of work wants a task and has actually not yet found one. In this case, task postings and work do not necessarily signify the requirement for policy actions; a decline in job posts for an extremely exposed function may be neutralized by increased openings in a related one.