Key Growth Statistics to Watch in 2026

The COVID-19 pandemic and accompanying policy measures caused economic interruption so stark that sophisticated analytical techniques were unneeded for numerous concerns. Unemployment jumped sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One typical method is to compare results between more or less AI-exposed employees, companies, or markets, in order to separate the result of AI from confounding forces. 2 Direct exposure is typically defined at the job level: AI can grade homework but not manage a class, for example, so instructors are considered less uncovered than workers whose entire task can be carried out remotely.

3 Our technique integrates data from three sources. The O * web database, which specifies jobs associated with around 800 distinct professions in the US.Our own usage information (as measured in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a job a minimum of twice as fast.

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4Why might actual usage fall short of theoretical ability? Some tasks that are in theory possible might disappoint up in usage since of design restrictions. Others might be sluggish to diffuse due to legal restraints, particular software application requirements, human confirmation steps, or other obstacles. For instance, Eloundou et al. mark "Authorize drug refills and provide prescription info to pharmacies" as totally exposed (=1).

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

Our brand-new measure, observed direct exposure, is suggested to measure: of those jobs that LLMs could in theory speed up, which are really seeing automated use in professional settings? Theoretical capability includes a much more comprehensive series of tasks. By tracking how that space narrows, observed direct exposure offers insight into economic changes as they emerge.

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

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We then change for how the task is being carried out: totally automated implementations receive complete weight, while augmentative usage receives half weight. The task-level coverage procedures are balanced to the occupation level weighted by the portion of time invested on each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by very first balancing to the profession level weighting by our time fraction measure, then averaging to the profession classification weighting by overall work. For instance, the step shows scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Workplace & Admin (90%) professions.

The coverage reveals AI is far from reaching its theoretical capabilities. For circumstances, Claude presently covers simply 33% of all tasks in the Computer & Mathematics classification. As capabilities advance, adoption spreads, and implementation deepens, the red area will grow to cover the blue. There is a large exposed location too; numerous tasks, obviously, remain beyond AI's reachfrom physical agricultural 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 used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose main jobs we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of reading source documents and getting in data sees substantial automation, are 67% covered.

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At the bottom end, 30% of workers have absolutely no coverage, as their jobs appeared too rarely in our data to meet the minimum limit. This group includes, for instance, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The US Bureau of Labor Stats (BLS) releases routine work projections, with the current set, released in 2025, covering anticipated changes in employment for every occupation from 2024 to 2034.

A regression at the profession level weighted by existing work discovers that development forecasts are rather weaker for tasks with more observed direct exposure. For each 10 portion point increase in protection, the BLS's growth projection come by 0.6 portion points. This offers some recognition because our measures track the individually obtained quotes from labor market experts, although the relationship is minor.

Each strong dot shows the typical observed direct exposure and projected work change for one of the bins. The dashed line reveals an easy direct regression fit, weighted by existing work levels. Figure 5 shows attributes of employees in the leading quartile of direct exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Current Population Study.

The more bare group is 16 percentage points more most likely to be female, 11 portion points most likely to be white, and almost two times as most likely to be Asian. They earn 47% more, on average, and have higher levels of education. For example, individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most disclosed group, a nearly 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 due to the fact that it most directly captures the potential for economic harma worker who is out of work desires a job and has not yet discovered one. In this case, task postings and work do not always indicate the need for policy actions; a decrease in job posts for a highly exposed function might be combated by increased openings in an associated one.