Why to Analyze the 2026 Economic Outlook

The COVID-19 pandemic and accompanying policy measures triggered economic disruption so stark that sophisticated statistical methods were unnecessary for many concerns. For instance, joblessness jumped dramatically in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, however, might be less like COVID and more like the internet or trade with China.

One typical technique is to compare results in between basically AI-exposed employees, firms, or markets, in order to separate the result of AI from confounding forces. 2 Exposure is normally defined at the job level: AI can grade homework but not manage a classroom, for example, so teachers are considered less disclosed than workers whose entire task can be carried out remotely.

3 Our technique integrates data from three 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 job at least twice as quick.

Evaluating Offshore Outsourcing and Global Units

Some jobs that are in theory possible might not reveal up in usage because of design limitations. Eloundou et al. mark "License drug refills and offer prescription information to drug stores" as fully exposed (=1).

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

Our new measure, observed direct exposure, is meant to measure: of those tasks that LLMs could theoretically speed up, which are really seeing automated usage in expert settings? Theoretical ability includes a much more comprehensive series of tasks. By tracking how that space narrows, observed exposure provides insight into economic changes as they emerge.

A task's direct exposure is higher if: Its jobs are theoretically possible with AIIts tasks see considerable usage in the Anthropic Economic Index5Its tasks are performed in work-related contextsIt has a relatively higher share of automated use patterns or API implementationIts AI-impacted tasks comprise a larger share of the general role6We give mathematical information in the Appendix.

Analyzing Global Movements in 2026

We then change for how the task is being carried out: completely automated executions receive full weight, while augmentative use gets half weight. Lastly, the task-level protection steps are averaged to the occupation level weighted by the fraction of time invested in each job. 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 measure, then balancing to the occupation classification weighting by overall work. For instance, the step reveals scope for LLM penetration in the majority of tasks in Computer system & Math (94%) and Workplace & Admin (90%) occupations.

The coverage shows AI is far from reaching its theoretical capabilities. Claude currently covers just 33% of all tasks in the Computer & Math classification. As capabilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a large exposed area too; lots of jobs, obviously, stay beyond AI's reachfrom physical agricultural work like pruning trees and operating farm machinery to legal jobs like representing clients in court.

In line with other data revealing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% protection, followed by Customer care Representatives, whose main jobs we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of checking out source documents and going into data sees significant automation, are 67% covered.

Proven Tips for Scaling Global Enterprise Presence

At the bottom end, 30% of employees have zero protection, as their tasks appeared too occasionally in our data to fulfill the minimum limit. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present work finds that development projections are rather weaker for jobs with more observed exposure. For every 10 portion point boost in protection, the BLS's growth forecast stop by 0.6 portion points. This provides some validation in that our steps track the independently obtained estimates from labor market analysts, although the relationship is small.

measure alone. Binned scatterplot with 25 equally-sized bins. Each solid dot shows the average observed direct exposure and forecasted employment modification 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 occupations for illustration. Figure 5 shows qualities of employees in the top quartile of exposure and the 30% of workers with zero direct exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Current Population Survey.

The more revealed group is 16 percentage points more likely to be female, 11 percentage points more likely to be white, and almost two times as likely to be Asian. They earn 47% more, on average, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most reviewed group, a practically fourfold difference.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority result since it most directly records the capacity for economic harma employee who is unemployed desires a task and has actually not yet found one. In this case, task posts and employment do not always signify the need for policy reactions; a decrease in job posts for an extremely exposed role may be neutralized by increased openings in an associated one.