International Commerce Outlook for Emerging Regions

The COVID-19 pandemic and accompanying policy steps caused economic disruption so plain that sophisticated statistical techniques were unneeded for numerous concerns. Joblessness jumped sharply in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One typical method is to compare results in between more or less AI-exposed employees, firms, or industries, in order to isolate the result of AI from confounding forces. 2 Exposure is usually defined at the job level: AI can grade research but not handle a classroom, for instance, so teachers are thought about less bare than employees whose whole task can be carried out remotely.

3 Our approach integrates information from 3 sources. The O * internet database, which identifies jobs connected with around 800 special occupations in the US.Our own usage data (as determined in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a task at least two times as fast.

Why to Analyze the Global Market Landscape

Some tasks that are in theory possible may not reveal up in use since of design limitations. Eloundou et al. mark "License drug refills and supply prescription details to pharmacies" as completely exposed (=1).

As Figure 1 programs, 97% of the jobs observed across the previous four Economic Index reports fall into categories rated as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage dispersed across O * NET jobs grouped by their theoretical AI exposure. Tasks rated =1 (fully possible for an LLM alone) represent 68% of observed Claude usage, while jobs rated =0 (not possible) account for simply 3%.

Our new procedure, observed exposure, is indicated to measure: of those tasks that LLMs could theoretically speed up, which are actually seeing automated usage in expert settings? Theoretical ability includes a much more comprehensive range of jobs. By tracking how that space narrows, observed exposure supplies insight into financial modifications as they emerge.

A task's direct exposure is greater if: Its tasks are in theory possible with AIIts tasks see significant usage in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a fairly higher share of automated usage patterns or API implementationIts AI-impacted tasks comprise a larger share of the overall role6We provide mathematical information in the Appendix.

Charting Economic Shifts of Enterprise Commerce

We then adjust for how the task is being performed: fully automated applications get full weight, while augmentative use receives half weight. Finally, the task-level protection measures are averaged to the occupation level weighted by the portion of time spent on each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We determine this by very first averaging to the profession level weighting by our time fraction step, then balancing to the occupation classification weighting by overall work. For instance, the procedure shows scope for LLM penetration in the bulk of tasks in Computer system & Math (94%) and Office & Admin (90%) professions.

The protection reveals AI is far from reaching its theoretical capabilities. For circumstances, Claude presently covers just 33% of all tasks in the Computer system & Math category. As abilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a big uncovered location too; lots of tasks, naturally, stay beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal jobs like representing customers in court.

In line with other data showing that Claude is extensively used for coding, Computer system Programmers are at the top, with 75% protection, followed by Client service Agents, whose primary jobs we increasingly see in first-party API traffic. Finally, Data Entry Keyers, whose primary job of reading source documents and going into data sees considerable automation, are 67% covered.

Predicting Global Trends in 2026

At the bottom end, 30% of workers have no protection, as their jobs appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the profession level weighted by existing work finds that development forecasts are somewhat weaker for jobs with more observed exposure. For every single 10 percentage point boost in coverage, the BLS's growth forecast stop by 0.6 percentage points. This provides some validation in that our steps track the individually obtained price quotes from labor market experts, although the relationship is small.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed direct exposure and projected employment change for one of the bins. The dashed line shows an easy linear regression fit, weighted by existing employment levels. The little diamonds mark individual example occupations for illustration. Figure 5 programs characteristics of workers in the top quartile of direct exposure and the 30% of employees with zero direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Current Population Survey.

The more bare group is 16 portion points most likely to be female, 11 portion points more likely to be white, and nearly twice as most likely to be Asian. They earn 47% more, on average, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most uncovered group, an almost fourfold difference.

Researchers have taken different approaches. Gimbel et al. (2025) track modifications in the occupational mix using the Existing Population Survey. Their argument is that any essential restructuring of the economy from AI would show up as changes in circulation of tasks. (They find that, so far, modifications have actually been average.) Brynjolfsson et al.

How Business Intelligence Data Drive Corporate Success

( 2022) and Hampole et al. (2025) utilize task posting data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority outcome because it most directly captures the capacity for economic harma employee who is out of work desires a job and has not yet discovered one. In this case, task postings and work do not necessarily indicate the need for policy actions; a decline in job postings for a highly exposed function might be combated by increased openings in a related one.