Forecasting Economic Movements in 2026

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

One typical approach is to compare outcomes in between basically AI-exposed employees, companies, or markets, in order to separate the impact of AI from confounding forces. 2 Exposure is usually specified at the task level: AI can grade homework however not handle a classroom, for instance, so instructors are considered less revealed than workers whose whole job can be carried out remotely.

3 Our approach integrates information from three sources. Task-level exposure estimates from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job at least twice as quick.

Key Expansion Statistics to Track in 2026

Some tasks that are in theory possible may not reveal up in use due to the fact that of design limitations. Eloundou et al. mark "Authorize drug refills and offer prescription details to pharmacies" as totally exposed (=1).

As Figure 1 programs, 97% of the jobs observed throughout the previous four Economic Index reports fall under classifications rated as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude use distributed across O * web tasks grouped by their theoretical AI exposure. Tasks rated =1 (fully feasible 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 indicated to measure: of those tasks that LLMs could theoretically speed up, which are in fact seeing automated usage in professional settings? Theoretical ability includes a much more comprehensive variety of tasks. By tracking how that space narrows, observed direct exposure provides insight into financial modifications as they emerge.

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

Global Commerce Insights for Future Regions

The task-level protection measures are balanced to the occupation level weighted by the fraction of time spent on each task. The measure reveals scope for LLM penetration in the bulk of jobs in Computer & Math (94%) and Office & Admin (90%) occupations.

Claude presently covers simply 33% of all tasks in the Computer & Mathematics classification. There is a big exposed location too; many jobs, of course, remain beyond AI's reachfrom physical farming 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% protection, followed by Customer care Agents, whose main tasks we progressively see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of reading source files and getting in information sees considerable automation, are 67% covered.

Harnessing AI to Improve Predictive Analysis

At the bottom end, 30% of workers have zero protection, as their jobs appeared too occasionally in our data to fulfill the minimum limit. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present employment finds that development projections are rather weaker for jobs with more observed exposure. For every single 10 percentage point boost in protection, the BLS's growth projection drops by 0.6 portion points. This provides some recognition in that our measures track the individually obtained estimates from labor market experts, although the relationship is small.

measure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the average observed exposure and forecasted employment change for one of the bins. The dashed line reveals a simple direct regression fit, weighted by present work levels. The small diamonds mark individual example occupations for illustration. Figure 5 programs characteristics of employees in the top quartile of exposure and the 30% of workers with no exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Existing Population Survey.

The more unwrapped group is 16 percentage points more most likely to be female, 11 percentage points most likely to be white, and almost two times as likely to be Asian. They make 47% more, usually, and have greater levels of education. For example, individuals with academic degrees are 4.5% of the unexposed group, however 17.4% of the most reviewed group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our top priority outcome because it most directly catches the capacity for financial harma worker who is unemployed wants a task and has not yet discovered one. In this case, task posts and employment do not necessarily indicate the need for policy reactions; a decrease in task postings for a highly exposed function might be counteracted by increased openings in a related one.