Why to Analyze the Global Market Landscape

The COVID-19 pandemic and accompanying policy measures triggered financial disruption so plain that sophisticated analytical approaches were unneeded for many concerns. Unemployment leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, however, might be less like COVID and more like the internet or trade with China.

One typical method is to compare results in between more or less AI-exposed employees, companies, or markets, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is normally specified at the task level: AI can grade research but not handle a classroom, for instance, so instructors are considered less discovered than workers whose entire task can be performed remotely.

3 Our approach combines data from three sources. Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a task at least two times as fast.

Key Expansion Metrics to Track in 2026

4Why might real usage fall brief of theoretical capability? Some jobs that are in theory possible may disappoint up in use since of model restrictions. Others might be slow to diffuse due to legal restrictions, specific software requirements, human confirmation steps, or other hurdles. Eloundou et al. mark "License drug refills and supply prescription information to pharmacies" as totally exposed (=1).

As Figure 1 programs, 97% of the tasks observed across the previous four Economic Index reports fall under classifications ranked as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage distributed across O * web jobs organized by their theoretical AI exposure. Tasks 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 new step, observed direct exposure, is meant to quantify: of those tasks that LLMs could in theory accelerate, which are actually seeing automated usage in professional settings? Theoretical capability includes a much wider variety of jobs. By tracking how that space narrows, observed exposure supplies insight into financial modifications as they emerge.

A task's exposure is greater if: Its tasks are in theory possible with AIIts jobs see substantial use in the Anthropic Economic Index5Its jobs are performed in job-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted jobs make up a larger share of the overall role6We offer mathematical details in the Appendix.

Vital Expansion Metrics to Track in 2026

We then change for how the job is being performed: completely automated executions get full weight, while augmentative use gets half weight. Lastly, the task-level coverage measures are averaged to the profession level weighted by the portion of time invested in each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by very first averaging to the occupation level weighting by our time fraction procedure, then balancing to the profession classification weighting by overall work. The measure shows scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

The coverage reveals AI is far from reaching its theoretical abilities. Claude presently covers just 33% of all tasks in the Computer system & Mathematics category. As capabilities advance, adoption spreads, and implementation deepens, the red location will grow to cover the blue. There is a large uncovered location too; many tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm equipment to legal tasks like representing clients in court.

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

Global Market Trends for Emerging Regions

At the bottom end, 30% of workers have absolutely no coverage, as their tasks appeared too occasionally in our information to meet the minimum threshold. This group includes, for instance, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Data (BLS) releases regular work projections, with the newest set, published in 2025, covering predicted changes in work for each profession from 2024 to 2034.

A regression at the occupation level weighted by present employment discovers that growth forecasts are somewhat weaker for tasks with more observed direct exposure. For every 10 percentage point boost in coverage, the BLS's growth projection stop by 0.6 portion points. This offers some recognition in that our procedures track the independently obtained price quotes from labor market experts, although the relationship is small.

Each solid dot reveals the typical observed exposure and predicted work modification for one of the bins. The dashed line reveals a simple direct regression fit, weighted by present employment levels. Figure 5 programs attributes of workers in the top quartile of direct exposure and the 30% of workers with no exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing data from the Existing Population Study.

The more unveiled group is 16 percentage points most likely to be female, 11 percentage points more 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, but 17.4% of the most disclosed group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task from Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome because it most straight catches the capacity for financial harma worker who is jobless wants a task and has not yet found one. In this case, job postings and employment do not always indicate the need for policy reactions; a decline in task postings for an extremely exposed function might be combated by increased openings in a related one.