Key Expansion Metrics to Track in 2026

The COVID-19 pandemic and accompanying policy measures triggered financial interruption so stark that sophisticated analytical methods were unnecessary for many concerns. For example, joblessness leapt sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, however, might be less like COVID and more like the internet or trade with China.

One common method is to compare results in between basically AI-exposed workers, companies, or industries, in order to isolate the impact of AI from confounding forces. 2 Exposure is typically specified at the job level: AI can grade homework but not manage a class, for instance, so teachers are considered less unwrapped than workers whose entire task can be carried out remotely.

3 Our method combines information from 3 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 task at least two times as fast.

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

As Figure 1 programs, 97% of the tasks observed across the previous 4 Economic Index reports fall under categories rated as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed throughout O * internet tasks grouped by their theoretical AI exposure. Tasks rated =1 (totally possible for an LLM alone) account for 68% of observed Claude use, while tasks ranked =0 (not possible) account for just 3%.

Our brand-new procedure, observed exposure, is suggested to measure: of those jobs that LLMs could theoretically speed up, which are in fact seeing automated usage in expert settings? Theoretical capability encompasses a much more comprehensive series of tasks. By tracking how that gap narrows, observed direct exposure offers insight into economic modifications as they emerge.

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

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The task-level protection procedures are balanced to the profession level weighted by the portion of time spent on each task. The procedure shows scope for LLM penetration in the majority of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) occupations.

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

In line with other data revealing that Claude is extensively utilized 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 primary task of reading source files and entering data sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have zero coverage, as their tasks appeared too rarely in our information to fulfill the minimum threshold. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present work discovers that growth forecasts are somewhat weaker for tasks with more observed exposure. For each 10 percentage point boost in coverage, the BLS's growth projection visit 0.6 portion points. This provides some recognition because our steps track the separately derived price quotes from labor market analysts, although the relationship is slight.

Each solid dot reveals the average observed direct exposure and forecasted employment modification for one of the bins. The dashed line shows an easy direct regression fit, weighted by present employment levels. Figure 5 programs characteristics of workers in the leading 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, using data from the Existing Population Survey.

The more reviewed group is 16 portion points more likely to be female, 11 percentage points most likely to be white, and almost twice as likely to be Asian. They earn 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 unveiled group, a nearly fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority result because it most directly captures the capacity for financial harma employee who is out of work wants a job and has actually not yet found one. In this case, job postings and employment do not necessarily signify the requirement for policy reactions; a decrease in job posts for a highly exposed role might be combated by increased openings in an associated one.