Mapping Economic Shifts of Global Commerce

The COVID-19 pandemic and accompanying policy steps caused economic disturbance so plain that advanced analytical approaches were unnecessary for many questions. For example, unemployment jumped dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, however, might be less like COVID and more like the web or trade with China.

One common method is to compare outcomes between more or less AI-exposed workers, firms, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is normally defined at the task level: AI can grade research but not manage a classroom, for example, so teachers are considered less reviewed than workers whose entire task can be carried out from another location.

3 Our approach combines data from three sources. The O * internet database, which specifies tasks related to around 800 distinct professions in the US.Our own usage information (as measured in the Anthropic Economic Index). Task-level direct exposure quotes from Eloundou et al. (2023 ), which determine whether it is in theory possible for an LLM to make a task at least twice as quick.

Attracting Global Teams in Innovation Hubs

4Why might actual use fall short of theoretical capability? Some jobs that are theoretically possible may not show up in usage since of design constraints. Others might be sluggish to diffuse due to legal restraints, particular software application requirements, human confirmation actions, or other hurdles. For instance, Eloundou et al. mark "Authorize drug refills and supply prescription information to drug stores" as totally exposed (=1).

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

Our brand-new procedure, observed exposure, is meant to measure: of those tasks that LLMs could in theory speed up, which are really seeing automated use in professional settings? Theoretical capability includes a much wider range of jobs. By tracking how that space narrows, observed exposure supplies insight into financial modifications as they emerge.

A job's direct exposure is higher if: Its jobs are theoretically possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a fairly higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a bigger share of the total role6We give mathematical information in the Appendix.

Scaling Enterprise Innovation Centers for Future Growth

The task-level protection steps are averaged to the occupation 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 system & Math (94%) and Workplace & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical abilities. Claude currently covers just 33% of all tasks in the Computer system & Mathematics category. As abilities advance, adoption spreads, and release 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 tasks like representing customers in court.

In line with other information 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 progressively see in first-party API traffic. Data Entry Keyers, whose main task of reading source files and getting in data sees significant automation, are 67% covered.

Key Growth Metrics to Track in 2026

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 consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Statistics (BLS) publishes routine employment forecasts, with the current set, published in 2025, covering anticipated changes in employment for every single profession from 2024 to 2034.

A regression at the profession level weighted by existing employment discovers that development projections are rather weaker for tasks with more observed direct exposure. For each 10 percentage point boost in coverage, the BLS's growth forecast visit 0.6 percentage points. This provides some recognition because our steps track the separately obtained price quotes from labor market analysts, although the relationship is small.

Each solid dot shows the average observed exposure and predicted employment change for one of the bins. The dashed line reveals a simple direct regression fit, weighted by existing employment levels. Figure 5 programs characteristics of workers in the top quartile of exposure and the 30% of employees with zero exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Existing Population Survey.

The more exposed group is 16 portion points most likely to be female, 11 portion points more likely to be white, and almost two times as likely to be Asian. They earn 47% more, usually, 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 reviewed group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result because it most straight records the capacity for economic harma worker who is unemployed desires a task and has actually not yet discovered one. In this case, job posts and employment do not necessarily indicate the requirement for policy actions; a decline in job postings for a highly exposed function might be combated by increased openings in a related one.