Key Growth Statistics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps triggered economic disruption so stark that advanced statistical methods were unnecessary for numerous concerns. Unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, however, might be less like COVID and more like the internet or trade with China.

One common method is to compare results between more or less AI-exposed workers, companies, or markets, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is generally defined at the task level: AI can grade research however not manage a class, for instance, so teachers are thought about less unveiled than employees whose entire job can be carried out from another location.

3 Our technique integrates data from 3 sources. Task-level direct 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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4Why might real usage fall short of theoretical ability? Some tasks that are theoretically possible may not reveal up in usage due to the fact that of model restrictions. Others might be slow to diffuse due to legal restrictions, particular software application requirements, human confirmation steps, or other hurdles. Eloundou et al. mark "Authorize drug refills and offer prescription info to drug stores" as completely exposed (=1).

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

Our brand-new step, observed direct exposure, is indicated to measure: of those tasks that LLMs could theoretically accelerate, which are in fact seeing automated use in expert settings? Theoretical capability encompasses a much wider range of jobs. By tracking how that space narrows, observed direct exposure provides insight into economic changes as they emerge.

A job's direct exposure is higher if: Its jobs are in theory possible with AIIts tasks see significant use 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 tasks comprise a larger share of the total role6We provide mathematical information in the Appendix.

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We then adjust for how the task is being carried out: completely automated applications receive full weight, while augmentative use gets half weight. Lastly, the task-level protection measures are averaged to the occupation level weighted by the portion of time invested in each job. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the occupation level weighting by our time fraction procedure, then balancing to the profession category weighting by overall work. The step shows scope for LLM penetration in the majority of jobs in Computer & Math (94%) and Office & Admin (90%) occupations.

Claude currently covers just 33% of all jobs in the Computer & Mathematics classification. There is a big exposed area too; many tasks, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal jobs like representing clients in court.

In line with other information showing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% protection, followed by Consumer Service Agents, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose primary job of reading source documents and going into data sees significant automation, are 67% covered.

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

A regression at the profession level weighted by present work discovers that growth forecasts are rather weaker for jobs with more observed exposure. For every 10 portion point increase in protection, the BLS's growth forecast come by 0.6 portion points. This provides some recognition because our steps track the independently obtained quotes from labor market analysts, although the relationship is slight.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the typical observed exposure and forecasted employment modification for one of the bins. The dashed line reveals an easy linear regression fit, weighted by existing work levels. The little diamonds mark private example professions for illustration. Figure 5 programs attributes of workers in the top quartile of direct exposure and the 30% of workers with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Existing Population Study.

The more bare group is 16 portion points more likely to be female, 11 percentage points more most likely to be white, and practically twice as likely to be Asian. They earn 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 exposed group, an almost fourfold distinction.

Researchers have actually taken different approaches. For instance, Gimbel et al. (2025) track modifications in the occupational mix utilizing the Present Population Study. Their argument is that any crucial restructuring of the economy from AI would reveal up as modifications in distribution of jobs. (They discover that, so far, modifications have actually been average.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome because it most straight records the capacity for financial harma employee who is unemployed desires a task and has not yet discovered one. In this case, task postings and employment do not always indicate the need for policy actions; a decline in task postings for a highly exposed role may be combated by increased openings in a related one.