Vital Expansion Metrics to Track in 2026

The COVID-19 pandemic and accompanying policy measures triggered financial interruption so stark that advanced analytical techniques were unneeded for lots of questions. Unemployment leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, may be less like COVID and more like the internet or trade with China.

One common approach is to compare results in between more or less AI-exposed workers, firms, or industries, in order to separate the result of AI from confounding forces. 2 Direct exposure is typically specified at the task level: AI can grade homework but not handle a class, for example, so instructors are thought about less unwrapped than employees whose entire task can be carried out remotely.

3 Our method integrates information from 3 sources. Task-level 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 fast.

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Some jobs that are theoretically possible may not reveal up in use because of design limitations. Eloundou et al. mark "License drug refills and supply prescription information to pharmacies" as completely exposed (=1).

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

Our new measure, observed exposure, is meant to quantify: of those tasks that LLMs could theoretically accelerate, which are actually seeing automated use in expert settings? Theoretical ability includes a much broader series of jobs. By tracking how that gap narrows, observed exposure offers insight into financial modifications as they emerge.

A task's exposure is greater if: Its jobs are theoretically possible with AIIts tasks see considerable use in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a fairly greater share of automated usage patterns or API implementationIts AI-impacted jobs comprise a larger share of the overall role6We provide mathematical information in the Appendix.

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We then change for how the job is being brought out: fully automated executions get full weight, while augmentative usage receives half weight. The task-level coverage steps are averaged to the occupation level weighted by the portion of time invested on each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We compute this by very first averaging to the occupation level weighting by our time portion measure, then balancing to the profession category weighting by overall employment. The step shows scope for LLM penetration in the bulk of tasks in Computer system & Math (94%) and Office & Admin (90%) professions.

Claude presently covers just 33% of all jobs in the Computer & Mathematics category. There is a large exposed area 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 revealing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer care Representatives, whose main tasks we progressively see in first-party API traffic. Finally, Data Entry Keyers, whose primary task of checking out source files and entering information sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their tasks appeared too rarely in our information to fulfill the minimum threshold. This group includes, for instance, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Data (BLS) publishes routine work projections, with the current set, published in 2025, covering anticipated changes in employment for every single profession from 2024 to 2034.

A regression at the profession level weighted by current work discovers that growth forecasts are somewhat weaker for jobs with more observed exposure. For every 10 portion point increase in coverage, the BLS's development forecast stop by 0.6 percentage points. This supplies some validation because our steps track the independently derived estimates from labor market experts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed direct exposure and forecasted work modification for one of the bins. The rushed line reveals a simple linear regression fit, weighted by existing employment levels. The little diamonds mark individual example professions for illustration. Figure 5 programs characteristics of employees in the top quartile of direct exposure and the 30% of workers with zero direct exposure in the three months before ChatGPT was launched, August to October 2022, using data from the Current Population Study.

The more unveiled group is 16 portion points most likely to be female, 11 percentage points most likely to be white, and nearly two times as most likely to be Asian. They make 47% more, usually, and have higher levels of education. For example, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most unveiled group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data from Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most directly records the capacity for financial harma employee who is unemployed desires a job and has actually not yet found one. In this case, job postings and employment do not always signal the requirement for policy responses; a decline in job posts for a highly exposed function may be counteracted by increased openings in an associated one.