Vital Expansion Statistics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps triggered economic disturbance so stark that sophisticated statistical approaches were unneeded for many questions. For example, joblessness leapt greatly in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, however, might be less like COVID and more like the internet or trade with China.

One typical approach is to compare results between more or less AI-exposed workers, firms, or markets, in order to isolate the result of AI from confounding forces. 2 Direct exposure is generally defined at the task level: AI can grade homework however not handle a class, for example, so teachers are considered less uncovered than employees whose whole job can be performed from another location.

3 Our approach integrates data from three sources. Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a job at least two times as fast.

Harnessing AI to Improve Predictive Analysis

Some jobs that are theoretically possible may not show up in use due to the fact that of model limitations. Eloundou et al. mark "Authorize drug refills and supply prescription details to drug stores" as totally exposed (=1).

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

Our brand-new step, observed exposure, is suggested to quantify: of those tasks that LLMs could theoretically speed up, which are actually seeing automated use in professional settings? Theoretical ability encompasses a much wider variety of tasks. By tracking how that gap narrows, observed exposure offers insight into economic modifications as they emerge.

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

Attracting Global Talent in Innovation Markets

We then change for how the task is being performed: fully automated executions get complete weight, while augmentative use receives half weight. Lastly, the task-level protection procedures are balanced to the profession level weighted by the fraction of time invested on each job. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We determine this by very first balancing to the profession level weighting by our time fraction procedure, then balancing to the profession classification weighting by overall employment. For instance, the procedure shows scope for LLM penetration in the majority of tasks in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

Claude currently covers simply 33% of all jobs in the Computer system & Math classification. There is a large uncovered area too; numerous jobs, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing customers in court.

In line with other information showing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Customer care Representatives, whose main tasks we progressively see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source files and entering information sees significant automation, are 67% covered.

Leveraging AI for Market Forecasting

At the bottom end, 30% of workers have zero coverage, as their jobs appeared too infrequently in our data to meet the minimum limit. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Data (BLS) publishes routine work projections, with the most recent set, released in 2025, covering forecasted changes in work for each occupation from 2024 to 2034.

A regression at the profession level weighted by existing employment finds that development forecasts are rather weaker for tasks with more observed direct exposure. For every single 10 portion point boost in coverage, the BLS's development projection drops by 0.6 percentage points. This provides some recognition in that our measures track the separately derived quotes from labor market experts, although the relationship is minor.

Each solid dot reveals the average observed exposure and forecasted employment modification for one of the bins. The dashed line shows a simple direct regression fit, weighted by current employment levels. Figure 5 shows qualities 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, utilizing data from the Current Population Survey.

The more discovered group is 16 percentage points most likely to be female, 11 percentage points more most likely to be white, and nearly twice as likely to be Asian. They make 47% more, usually, and have greater levels of education. For example, people with academic degrees are 4.5% of the unexposed group, but 17.4% of the most bare group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome because it most straight captures the capacity for economic harma worker who is unemployed desires a task and has not yet discovered one. In this case, task posts and employment do not necessarily signify the requirement for policy reactions; a decline in job postings for an extremely exposed role may be combated by increased openings in an associated one.