Global Trade Insights for Emerging Regions

The COVID-19 pandemic and accompanying policy measures caused economic disturbance so plain that advanced statistical approaches were unnecessary for numerous questions. For example, joblessness jumped dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common method is to compare results between basically AI-exposed employees, companies, or markets, in order to isolate the result of AI from confounding forces. 2 Exposure is typically defined at the job level: AI can grade homework but not manage a class, for instance, so teachers are thought about less uncovered than employees whose entire job can be carried out from another location.

3 Our approach combines information from three sources. The O * NET database, which specifies tasks connected with around 800 unique occupations in the US.Our own use data (as measured in the Anthropic Economic Index). 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.

Evaluating Offshore Models and In-House Hubs

Some tasks that are in theory possible might not show up in use because of model restrictions. 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 throughout the previous four Economic Index reports fall into classifications rated as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed across O * NET tasks organized by their theoretical AI exposure. Tasks rated =1 (completely possible for an LLM alone) represent 68% of observed Claude use, while jobs rated =0 (not practical) represent simply 3%.

Our brand-new measure, observed exposure, is meant to quantify: of those tasks that LLMs could in theory accelerate, which are in fact seeing automated use in expert settings? Theoretical capability includes a much more comprehensive series of tasks. By tracking how that space narrows, observed direct exposure offers insight into economic changes as they emerge.

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

Managing Global Capability Centers for Better ROI

The task-level protection measures are balanced to the profession level weighted by the portion of time spent on each task. The measure reveals scope for LLM penetration in the bulk of tasks in Computer & Mathematics (94%) and Workplace & Admin (90%) occupations.

The protection reveals AI is far from reaching its theoretical abilities. For instance, Claude currently covers simply 33% of all tasks in the Computer & Mathematics category. As capabilities advance, adoption spreads, and release deepens, the red location will grow to cover the blue. There is a large exposed location too; many tasks, of course, remain 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 data revealing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Customer support Agents, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose main task of reading source files and going into information sees significant automation, are 67% covered.

International Market Outlook for Emerging Economies

At the bottom end, 30% of employees have absolutely no coverage, as their jobs appeared too rarely in our information to fulfill the minimum limit. This group consists of, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The United States Bureau of Labor Stats (BLS) publishes routine employment forecasts, with the most recent set, released in 2025, covering anticipated changes in employment for every single profession from 2024 to 2034.

A regression at the occupation level weighted by existing work finds that growth forecasts are somewhat weaker for tasks with more observed direct exposure. For every 10 percentage point boost in protection, the BLS's development forecast come by 0.6 portion points. This supplies some validation because our measures track the independently derived estimates from labor market analysts, although the relationship is small.

Each strong dot shows the typical observed direct exposure and predicted work change for one of the bins. The rushed line reveals a basic linear regression fit, weighted by current employment levels. Figure 5 shows characteristics of employees in the top quartile of direct exposure and the 30% of employees with no exposure in the 3 months before ChatGPT was launched, August to October 2022, using data from the Present Population Study.

The more exposed group is 16 percentage points most likely to be female, 11 percentage points more most likely to be white, and practically twice as most likely to be Asian. They earn 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 bare group, a practically fourfold distinction.

Scientists have actually taken various techniques. For example, Gimbel et al. (2025) track changes in the occupational mix utilizing the Existing Population Survey. Their argument is that any important restructuring of the economy from AI would appear as modifications in circulation of tasks. (They find that, so far, changes have actually been average.) Brynjolfsson et al.

Key Growth Statistics to Track in 2026

( 2022) and Hampole et al. (2025) utilize job publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result since it most directly catches the potential for financial harma employee who is out of work desires a job and has not yet found one. In this case, job posts and employment do not necessarily signify the need for policy actions; a decline in job posts for an extremely exposed function might be neutralized by increased openings in a related one.