Global Market Outlook for Emerging Economies

The COVID-19 pandemic and accompanying policy measures triggered economic interruption so stark that advanced analytical methods were unneeded for many questions. For instance, joblessness leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common approach is to compare outcomes in between more or less AI-exposed workers, companies, or industries, in order to separate the effect of AI from confounding forces. 2 Exposure is normally defined at the job level: AI can grade homework but not handle a class, for example, so instructors are considered less disclosed than employees whose entire job can be carried out from another location.

3 Our method integrates data from 3 sources. Task-level direct exposure price quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least twice as fast.

Will Deep Data Transform Global Strategy?

Some jobs that are theoretically possible may not reveal up in use since of model restrictions. Eloundou et al. mark "License drug refills and supply prescription information to drug stores" as completely exposed (=1).

As Figure 1 shows, 97% of the jobs observed throughout the previous 4 Economic Index reports fall under classifications ranked as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed across O * internet tasks grouped by their theoretical AI exposure. Jobs ranked =1 (completely possible for an LLM alone) account for 68% of observed Claude use, while jobs rated =0 (not possible) account for just 3%.

Our brand-new procedure, observed direct exposure, is indicated to measure: of those tasks that LLMs could in theory accelerate, which are really seeing automated usage in expert settings? Theoretical capability incorporates a much wider series of tasks. By tracking how that space narrows, observed direct exposure supplies insight into financial modifications as they emerge.

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

How to Forecast the 2026 Economic Landscape

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

The protection reveals AI is far from reaching its theoretical abilities. Claude currently covers just 33% of all tasks in the Computer system & Mathematics category. As capabilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a large uncovered area too; numerous tasks, naturally, remain beyond AI's reachfrom physical farming work like pruning trees and running farm machinery to legal tasks like representing clients 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 support Representatives, whose primary tasks we significantly see in first-party API traffic. Data Entry Keyers, whose primary task of reading source files and going into information sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have zero protection, as their tasks appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by current work discovers that development forecasts are somewhat weaker for tasks with more observed direct exposure. For each 10 percentage point increase in protection, the BLS's development projection drops by 0.6 portion points. This offers 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 strong dot shows the typical observed exposure and forecasted employment modification for among the bins. The rushed line shows an easy direct regression fit, weighted by existing work levels. The small diamonds mark specific example occupations for illustration. Figure 5 programs qualities of employees in the leading quartile of exposure and the 30% of workers with zero exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing information from the Existing Population Study.

The more unwrapped group is 16 percentage points more most likely to be female, 11 percentage points most likely to be white, and practically two times as likely to be Asian. They make 47% more, typically, 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 uncovered group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern result since it most straight captures the capacity for financial harma worker who is out of work wants a job and has not yet discovered one. In this case, job posts and employment do not necessarily signal the need for policy responses; a decrease in task posts for an extremely exposed role might be combated by increased openings in a related one.