Key Expansion Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy measures caused economic disturbance so plain that advanced analytical approaches were unnecessary for many questions. Joblessness leapt sharply in the early weeks of the pandemic, leaving little space for alternative descriptions. The effects of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One typical approach is to compare outcomes between more or less AI-exposed workers, companies, or industries, in order to isolate the result of AI from confounding forces. 2 Exposure is normally defined at the job level: AI can grade research however not handle a class, for instance, so instructors are thought about less bare than employees whose entire task can be carried out from another location.

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

Can Predictive Analytics Transform Global Growth?

Some tasks that are theoretically possible may not show up in usage since of model limitations. Eloundou et al. mark "Authorize drug refills and provide prescription info to drug stores" as completely exposed (=1).

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

Our brand-new procedure, observed exposure, is meant to quantify: of those jobs that LLMs could theoretically accelerate, which are really seeing automated usage in professional settings? Theoretical capability incorporates a much more comprehensive variety of jobs. 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 tasks are theoretically possible with AIIts tasks see substantial usage in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a reasonably greater share of automated use patterns or API implementationIts AI-impacted tasks comprise a bigger share of the total role6We offer mathematical details in the Appendix.

Evaluating Offshore Outsourcing and In-House Units

We then change for how the job is being performed: completely automated applications receive complete weight, while augmentative use gets half weight. Lastly, the task-level protection procedures are balanced to the occupation level weighted by the portion of time invested in each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We determine this by first balancing to the occupation level weighting by our time fraction step, then averaging to the profession classification weighting by total employment. For instance, the measure shows scope for LLM penetration in the majority of jobs in Computer system & Math (94%) and Office & Admin (90%) occupations.

Claude currently covers simply 33% of all tasks in the Computer system & Mathematics classification. There is a large exposed location too; lots of jobs, of course, remain beyond AI's reachfrom physical agricultural work like pruning trees and operating farm equipment to legal tasks like representing clients in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose main jobs we significantly see in first-party API traffic. Finally, Data Entry Keyers, whose main job of reading source documents and getting in data sees considerable automation, are 67% covered.

Charting Economic Shifts of Enterprise Trade

At the bottom end, 30% of workers have absolutely no coverage, as their tasks appeared too infrequently in our information to fulfill the minimum limit. This group consists of, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants. The US Bureau of Labor Statistics (BLS) publishes routine employment forecasts, with the newest set, published in 2025, covering forecasted changes in work for each occupation from 2024 to 2034.

A regression at the profession level weighted by current work finds that development projections are rather weaker for jobs with more observed direct exposure. For each 10 percentage point increase in protection, the BLS's growth projection drops by 0.6 percentage points. This provides some validation because our procedures track the independently obtained estimates from labor market analysts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the typical observed exposure and projected work change for one of the bins. The rushed line shows an easy linear regression fit, weighted by current employment levels. The little diamonds mark individual example professions for illustration. Figure 5 programs attributes of employees in the leading 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 data from the Current Population Study.

The more exposed group is 16 percentage points more likely to be female, 11 portion points more most likely to be white, and almost two times as most likely to be Asian. They earn 47% more, usually, and have higher levels of education. For instance, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most uncovered group, a nearly fourfold difference.

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 concern result because it most directly records the capacity for financial harma worker who is jobless desires a task and has not yet found one. In this case, job posts and work do not necessarily indicate the need for policy reactions; a decline in task posts for an extremely exposed function may be neutralized by increased openings in a related one.