Vital Growth Statistics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps caused economic disruption so stark that sophisticated statistical approaches were unnecessary for numerous questions. For example, joblessness jumped greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, nevertheless, may be less like COVID and more like the internet or trade with China.

One typical approach is to compare results in between more or less AI-exposed workers, firms, or markets, in order to isolate the effect of AI from confounding forces. 2 Exposure is usually specified at the task level: AI can grade homework but not handle a class, for instance, so instructors are considered less unveiled than workers whose entire task can be carried out remotely.

3 Our technique combines data from three sources. Task-level direct exposure price quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a task at least two times as quick.

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4Why might actual use fall short of theoretical capability? Some jobs that are theoretically possible may not reveal up in use since of model restrictions. Others might be sluggish to diffuse due to legal restraints, specific software application requirements, human verification steps, or other obstacles. For example, Eloundou et al. mark "Authorize drug refills and offer prescription details to pharmacies" as completely exposed (=1).

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

Our new measure, observed exposure, is meant to quantify: of those tasks that LLMs could theoretically accelerate, which are really seeing automated usage in expert settings? Theoretical ability incorporates a much more comprehensive variety of jobs. By tracking how that space narrows, observed exposure supplies insight into economic modifications as they emerge.

A task's exposure is higher if: Its jobs are in theory possible with AIIts tasks see substantial use in the Anthropic Economic Index5Its tasks are performed in work-related contextsIt has a fairly greater share of automated usage patterns or API implementationIts AI-impacted tasks make up a bigger share of the general role6We offer mathematical information in the Appendix.

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We then change for how the job is being brought out: completely automated implementations get full weight, while augmentative usage receives half weight. Finally, the task-level coverage procedures are balanced to the profession level weighted by the fraction of time invested in each task. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We determine this by very first averaging to the profession level weighting by our time fraction step, then balancing to the profession classification weighting by overall work. The procedure reveals 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 tasks in the Computer & Mathematics classification. There is a big uncovered location too; many jobs, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal jobs like representing customers in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client Service Agents, whose primary tasks we progressively see in first-party API traffic. Data Entry Keyers, whose primary task of reading source documents and getting in information sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have absolutely no protection, as their jobs appeared too occasionally in our information to meet the minimum threshold. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Stats (BLS) releases routine employment projections, with the most recent set, published in 2025, covering anticipated modifications in employment for each profession from 2024 to 2034.

A regression at the profession level weighted by existing employment finds that growth projections are rather weaker for tasks with more observed direct exposure. For every single 10 percentage point boost in coverage, the BLS's development forecast stop by 0.6 percentage points. This supplies some validation in that our steps track the separately obtained estimates from labor market experts, although the relationship is minor.

Each strong dot reveals the typical observed direct exposure and predicted work modification for one of the bins. The dashed line reveals a simple linear regression fit, weighted by present work levels. Figure 5 shows characteristics of employees in the leading quartile of exposure and the 30% of workers with no direct exposure in the three months before ChatGPT was launched, August to October 2022, utilizing information from the Existing Population Survey.

The more exposed group is 16 percentage points more most likely to be female, 11 portion points most likely to be white, and nearly two times as likely to be Asian. They make 47% more, typically, and have higher levels of education. For example, people with graduate 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 posting task from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority outcome since it most straight records the potential for economic harma worker who is out of work wants a task and has not yet discovered one. In this case, task posts and employment do not always signal the requirement for policy actions; a decrease in job postings for an extremely exposed function may be combated by increased openings in a related one.