Key Expansion Statistics to Track in 2026

The COVID-19 pandemic and accompanying policy steps caused financial interruption so stark that advanced analytical approaches were unnecessary for many questions. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, might be less like COVID and more like the web or trade with China.

One typical approach is to compare results between basically AI-exposed workers, companies, or industries, in order to separate the effect of AI from confounding forces. 2 Exposure is generally specified at the task level: AI can grade research however not handle a class, for instance, so instructors are considered less revealed than employees whose entire job can be carried out from another location.

3 Our technique combines information from 3 sources. Task-level exposure estimates from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job at least twice as fast.

Leveraging AI for Market Forecasting

Some tasks that are in theory possible may not show up in usage because of design restrictions. Eloundou et al. mark "Authorize drug refills and supply prescription information to drug stores" 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 theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed across O * NET jobs grouped by their theoretical AI exposure. Jobs rated =1 (totally possible for an LLM alone) represent 68% of observed Claude use, while jobs ranked =0 (not feasible) account for just 3%.

Our brand-new procedure, observed direct exposure, is indicated to quantify: of those tasks that LLMs could theoretically accelerate, which are actually seeing automated use in professional settings? Theoretical ability encompasses a much more comprehensive variety of tasks. By tracking how that space narrows, observed direct exposure supplies insight into financial changes as they emerge.

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

Maximizing Operational Efficiency for AI Systems

We then change for how the job is being performed: fully automated executions get complete weight, while augmentative usage receives half weight. Finally, the task-level coverage measures are balanced to the occupation level weighted by the portion of time invested in each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the profession level weighting by our time portion measure, then balancing to the profession category weighting by overall employment. For example, the measure shows scope for LLM penetration in the bulk of tasks in Computer & Math (94%) and Office & Admin (90%) professions.

Claude currently covers just 33% of all tasks in the Computer system & Mathematics category. There is a big exposed area too; lots of tasks, of course, stay beyond AI's reachfrom physical agricultural 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 extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client Service Representatives, whose main tasks we significantly see in first-party API traffic. Lastly, Data Entry Keyers, whose main task of checking out source documents and entering information sees substantial automation, are 67% covered.

Charting Future Trends of Enterprise Commerce

At the bottom end, 30% of workers have no protection, as their tasks appeared too infrequently in our information to meet the minimum limit. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The US Bureau of Labor Stats (BLS) publishes routine employment forecasts, with the current set, released in 2025, covering predicted modifications in employment for each profession from 2024 to 2034.

A regression at the profession level weighted by present work finds that growth forecasts are rather weaker for tasks with more observed exposure. For each 10 percentage point increase in protection, the BLS's growth projection drops by 0.6 percentage points. This provides some recognition because our steps track the separately derived price quotes from labor market experts, although the relationship is minor.

Each strong dot shows the average observed direct exposure and projected employment modification for one of the bins. The dashed line shows an easy direct regression fit, weighted by present work levels. Figure 5 shows characteristics of workers in the leading quartile of direct exposure and the 30% of employees with zero exposure in the 3 months before ChatGPT was released, August to October 2022, utilizing information from the Existing Population Study.

The more unwrapped group is 16 portion points more likely to be female, 11 percentage points more most likely to be white, and almost two times as most likely to be Asian. They make 47% more, typically, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most exposed group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result because it most directly records the capacity for economic harma worker who is out of work wants a task and has not yet found one. In this case, job posts and work do not always indicate the need for policy responses; a decrease in job posts for a highly exposed role may be counteracted by increased openings in a related one.