Global Trade Outlook for Future Regions

The COVID-19 pandemic and accompanying policy steps triggered financial disruption so stark that sophisticated statistical methods were unneeded for numerous concerns. For instance, unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, nevertheless, may be less like COVID and more like the internet or trade with China.

One common method is to compare outcomes between more or less AI-exposed workers, companies, or industries, in order to separate the result of AI from confounding forces. 2 Direct exposure is typically defined at the task level: AI can grade homework however not manage a classroom, for instance, so instructors are considered less exposed than workers whose entire job can be performed from another location.

3 Our approach 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 task at least two times as quick.

Key Growth Statistics to Track in 2026

4Why might actual use fall short of theoretical ability? Some jobs that are in theory possible may not show up in usage due to the fact that of model constraints. Others may be sluggish to diffuse due to legal restrictions, particular software requirements, human confirmation actions, or other hurdles. Eloundou et al. mark "Authorize drug refills and provide prescription info to drug stores" as fully exposed (=1).

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

Our brand-new procedure, observed exposure, is meant to measure: of those jobs that LLMs could in theory speed up, which are in fact seeing automated use in expert settings? Theoretical capability incorporates a much broader series of jobs. By tracking how that space narrows, observed exposure offers insight into economic modifications as they emerge.

A task's direct exposure is greater if: Its jobs are theoretically possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its tasks are carried out 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 overall role6We provide mathematical details in the Appendix.

Evaluating Offshore Outsourcing and In-House Hubs

We then change for how the job is being performed: totally automated implementations get full weight, while augmentative usage gets half weight. The task-level protection procedures are averaged to the occupation level weighted by the portion of time spent on each job. Figure 2 shows observed exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the occupation level weighting by our time fraction measure, then averaging to the occupation classification weighting by total employment. The procedure reveals scope for LLM penetration in the majority of tasks in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

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

In line with other information showing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer care Representatives, whose primary jobs we progressively see in first-party API traffic. Data Entry Keyers, whose primary task of checking out source files and going into data sees considerable automation, are 67% covered.

Leveraging AI to Improve Predictive Forecasting

At the bottom end, 30% of employees have no coverage, as their jobs appeared too occasionally in our data to meet the minimum limit. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

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

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the typical observed exposure and predicted employment change for one of the bins. The rushed line reveals a simple direct regression fit, weighted by present employment levels. The small diamonds mark individual example professions for illustration. Figure 5 shows attributes of workers in the leading quartile of direct 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 Current Population Survey.

The more revealed group is 16 percentage points more likely to be female, 11 percentage points more likely to be white, and practically two times as most likely to be Asian. They earn 47% more, typically, and have greater levels of education. For instance, people with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most bare group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result since it most straight records the capacity for economic harma worker who is out of work desires a task and has not yet discovered one. In this case, job posts and work do not always signal the requirement for policy actions; a decline in job posts for a highly exposed function may be neutralized by increased openings in a related one.