How to Analyze the 2026 Economic Landscape

The COVID-19 pandemic and accompanying policy measures triggered economic interruption so stark that advanced analytical techniques were unneeded for many questions. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little room for alternative explanations. The impacts of AI, however, may be less like COVID and more like the web or trade with China.

One typical method is to compare outcomes between basically AI-exposed employees, firms, or markets, in order to separate the result of AI from confounding forces. 2 Exposure is generally defined at the job level: AI can grade research but not manage a class, for instance, so instructors are thought about less disclosed than employees whose entire job can be performed remotely.

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

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Some tasks that are theoretically possible may not show up in use due to the fact that of model limitations. Eloundou et al. mark "Authorize drug refills and offer prescription information to pharmacies" as totally exposed (=1).

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

Our brand-new measure, observed direct exposure, is meant to quantify: of those tasks that LLMs could theoretically accelerate, which are in fact seeing automated usage in expert settings? Theoretical capability encompasses a much more comprehensive variety of jobs. By tracking how that space narrows, observed direct exposure supplies insight into financial changes as they emerge.

A job's exposure is higher if: Its tasks are in theory possible with AIIts jobs see considerable use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a reasonably higher share of automated usage patterns or API implementationIts AI-impacted jobs make up a bigger share of the general role6We give mathematical details in the Appendix.

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We then adjust for how the task is being performed: completely automated applications get complete weight, while augmentative usage gets half weight. The task-level coverage steps are balanced to the profession level weighted by the fraction of time spent on each job. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We determine this by first averaging to the occupation level weighting by our time portion step, then averaging to the occupation classification weighting by overall employment. For instance, the procedure reveals scope for LLM penetration in the bulk of tasks in Computer & Mathematics (94%) and Office & Admin (90%) occupations.

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

In line with other information showing that Claude is thoroughly utilized for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Representatives, whose primary jobs we progressively see in first-party API traffic. Data Entry Keyers, whose main task of reading source files and entering data sees considerable automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their jobs appeared too rarely in our data to fulfill the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.

A regression at the occupation level weighted by present work finds that growth projections are rather weaker for tasks with more observed direct exposure. For every 10 portion point increase in protection, the BLS's development projection drops by 0.6 portion points. This offers some validation in that our steps track the independently obtained price quotes from labor market experts, although the relationship is minor.

procedure alone. Binned scatterplot with 25 equally-sized bins. Each strong dot shows the typical observed exposure and predicted work modification for one of the bins. The rushed line reveals an easy linear regression fit, weighted by current work levels. The small diamonds mark specific example professions for illustration. Figure 5 shows characteristics of employees in the top quartile of direct exposure and the 30% of employees with absolutely no direct exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Present Population Survey.

The more reviewed group is 16 percentage points most likely to be female, 11 portion points more likely to be white, and almost two times as most likely to be Asian. They make 47% more, typically, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, but 17.4% of the most uncovered group, a nearly fourfold distinction.

Researchers have taken different approaches. For example, Gimbel et al. (2025) track changes in the occupational mix using the Current Population Survey. Their argument is that any essential restructuring of the economy from AI would appear as changes in circulation of jobs. (They discover that, up until now, modifications have actually been plain.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task posting information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our concern outcome due to the fact that it most straight captures the capacity for financial harma employee who is jobless wants a job and has not yet discovered one. In this case, task postings and employment do not necessarily signal the need for policy reactions; a decline in job posts for a highly exposed role may be counteracted by increased openings in an associated one.