Key Growth Statistics to Track in 2026

The COVID-19 pandemic and accompanying policy measures triggered financial interruption so plain that sophisticated statistical approaches were unnecessary for numerous questions. Unemployment leapt sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, may be less like COVID and more like the internet or trade with China.

One typical method is to compare outcomes in between basically AI-exposed workers, companies, or industries, in order to isolate the impact of AI from confounding forces. 2 Exposure is usually specified at the job level: AI can grade research however not handle a class, for example, so teachers are thought about less uncovered than workers whose whole job can be carried out remotely.

3 Our method combines data from 3 sources. Task-level 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 quick.

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4Why might actual use fall short of theoretical ability? Some jobs that are theoretically possible might disappoint up in usage due to the fact that of design constraints. Others might be sluggish to diffuse due to legal restraints, specific software requirements, human verification actions, or other obstacles. For instance, Eloundou et al. mark "License drug refills and supply prescription information to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 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 across O * NET jobs organized by their theoretical AI exposure. Tasks rated =1 (completely feasible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not practical) represent just 3%.

Our new measure, observed exposure, is suggested to measure: of those jobs that LLMs could in theory accelerate, which are really seeing automated usage in professional settings? Theoretical capability encompasses 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 direct exposure is greater if: Its jobs are in theory possible with AIIts jobs see significant usage in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a relatively greater share of automated usage patterns or API implementationIts AI-impacted tasks make up a larger share of the total role6We give mathematical information in the Appendix.

Key Growth Statistics to Watch in 2026

The task-level protection steps are balanced to the profession level weighted by the fraction of time invested on each task. The procedure shows scope for LLM penetration in the bulk of jobs in Computer & Math (94%) and Office & Admin (90%) occupations.

The coverage reveals AI is far from reaching its theoretical abilities. Claude currently covers just 33% of all tasks in the Computer system & Math classification. As capabilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a large exposed area too; numerous tasks, naturally, stay beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose main jobs we significantly see in first-party API traffic. Data Entry Keyers, whose main task of checking out source files and going into information sees considerable automation, are 67% covered.

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

A regression at the occupation level weighted by current employment discovers that development projections are somewhat weaker for jobs with more observed direct exposure. For each 10 percentage point increase in coverage, the BLS's development forecast come by 0.6 percentage points. This provides some recognition because our steps track the independently obtained price quotes from labor market analysts, although the relationship is small.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the typical observed direct exposure and forecasted work modification for one of the bins. The dashed line reveals an easy linear regression fit, weighted by current work levels. The little diamonds mark individual example occupations for illustration. Figure 5 shows characteristics of workers in the leading quartile of exposure and the 30% of workers with absolutely no direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Present Population Study.

The more unveiled group is 16 portion points most likely to be female, 11 portion points most likely to be white, and nearly twice as likely to be Asian. They make 47% more, usually, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most revealed group, a practically fourfold difference.

Researchers have taken various techniques. Gimbel et al. (2025) track modifications in the occupational mix using the Present Population Study. Their argument is that any important restructuring of the economy from AI would reveal up as modifications in distribution of tasks. (They discover that, up until now, changes have been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task posting information from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on unemployment as our priority outcome due to the fact that it most directly captures the potential for financial harma employee who is out of work desires a task and has not yet discovered one. In this case, task postings and employment do not necessarily signal the requirement for policy responses; a decrease in task postings for an extremely exposed function may be neutralized by increased openings in an associated one.