Key Growth Metrics to Watch in 2026

The COVID-19 pandemic and accompanying policy steps triggered economic disturbance so stark that sophisticated analytical methods were unnecessary for many concerns. Joblessness leapt dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, however, may be less like COVID and more like the internet or trade with China.

One typical approach is to compare results between more or less AI-exposed employees, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is normally defined at the task level: AI can grade homework but not manage a classroom, for example, so teachers are thought about less bare than workers whose entire job can be carried out from another location.

3 Our approach integrates information from 3 sources. Task-level exposure price quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job at least twice as quick.

Evaluating Offshore Outsourcing and In-House Hubs

4Why might real usage fall short of theoretical capability? Some tasks that are theoretically possible might disappoint up in usage since of model limitations. Others may be sluggish to diffuse due to legal restrictions, specific software requirements, human confirmation actions, or other hurdles. Eloundou et al. mark "Authorize drug refills and offer prescription information to drug stores" as fully exposed (=1).

As Figure 1 programs, 97% of the tasks observed across the previous 4 Economic Index reports fall into categories ranked as in theory possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use distributed across O * internet jobs grouped by their theoretical AI direct exposure. Tasks ranked =1 (fully feasible for an LLM alone) represent 68% of observed Claude usage, while jobs rated =0 (not possible) account for simply 3%.

Our new measure, observed direct exposure, is indicated to quantify: of those jobs that LLMs could in theory accelerate, which are actually seeing automated use in expert settings? Theoretical capability incorporates a much wider range of jobs. By tracking how that space narrows, observed direct exposure offers insight into financial modifications as they emerge.

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

Charting Economic Shifts of Global Commerce

We then adjust for how the task is being performed: totally automated implementations receive full weight, while augmentative use gets half weight. The task-level protection procedures are averaged to the profession level weighted by the fraction of time spent on each task. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

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

Claude presently covers simply 33% of all tasks in the Computer & Math category. There is a big exposed area too; numerous tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and running farm equipment to legal tasks like representing customers 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 Client service Agents, whose main tasks we progressively see in first-party API traffic. Finally, Data Entry Keyers, whose main job of checking out source documents and getting in information sees considerable automation, are 67% covered.

Retaining High-Impact Talent in Innovation Hubs

At the bottom end, 30% of workers have no coverage, as their jobs appeared too rarely in our information to satisfy the minimum limit. This group includes, for instance, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Stats (BLS) publishes regular work 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 current employment discovers that growth projections are somewhat weaker for jobs with more observed exposure. For each 10 portion point increase in protection, the BLS's development forecast stop by 0.6 percentage points. This offers some recognition because our measures track the separately derived quotes from labor market experts, although the relationship is small.

Each strong dot shows the typical observed exposure and forecasted employment change for one of the bins. The dashed line reveals a simple linear regression fit, weighted by existing employment levels. Figure 5 programs attributes of workers in the leading quartile of direct exposure and the 30% of employees with absolutely no exposure in the three months before ChatGPT was released, August to October 2022, using information from the Current Population Survey.

The more disclosed group is 16 portion points most likely to be female, 11 portion points most likely to be white, and nearly two times as most likely to be Asian. They make 47% more, usually, and have higher levels of education. For instance, people with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most disclosed group, an almost fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize data publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our concern outcome because it most directly captures the capacity for financial harma worker who is jobless wants a task and has not yet found one. In this case, job posts and employment do not always signify the requirement for policy reactions; a decline in job posts for an extremely exposed role might be neutralized by increased openings in an associated one.