Analyzing Market Shifts in 2026

The COVID-19 pandemic and accompanying policy measures caused economic interruption so stark that advanced statistical methods were unneeded for many questions. For instance, joblessness leapt sharply in the early weeks of the pandemic, leaving little space for alternative descriptions. The impacts of AI, however, may be less like COVID and more like the web or trade with China.

One typical approach is to compare outcomes in between more or less AI-exposed employees, companies, or markets, in order to isolate the impact of AI from confounding forces. 2 Exposure is usually specified at the task level: AI can grade research but not handle a classroom, for example, so instructors are considered less uncovered than employees whose entire job can be performed remotely.

3 Our method combines data from three sources. The O * NET database, which specifies tasks related to around 800 distinct professions in the US.Our own use information (as measured in the Anthropic Economic Index). Task-level direct exposure 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 fast.

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4Why might real usage fall short of theoretical ability? Some jobs that are in theory possible may disappoint up in use due to the fact that of model limitations. Others might be sluggish to diffuse due to legal restraints, particular software requirements, human confirmation actions, or other hurdles. Eloundou et al. mark "License drug refills and offer prescription details to pharmacies" as completely exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous four Economic Index reports fall under categories ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude usage dispersed throughout O * web tasks organized by their theoretical AI direct exposure. Jobs rated =1 (completely possible for an LLM alone) account for 68% of observed Claude use, while tasks rated =0 (not feasible) account for just 3%.

Our brand-new procedure, observed exposure, is suggested to quantify: of those jobs that LLMs could in theory accelerate, which are really seeing automated usage in expert settings? Theoretical ability includes a much broader variety 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 tasks are in theory possible with AIIts jobs see considerable usage in the Anthropic Economic Index5Its jobs are carried out in work-related contextsIt has a relatively higher share of automated usage patterns or API implementationIts AI-impacted tasks make up a bigger share of the total role6We provide mathematical information in the Appendix.

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

We compute this by first balancing to the profession level weighting by our time fraction procedure, then balancing to the occupation category weighting by overall employment. The procedure shows scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Workplace & Admin (90%) professions.

Claude presently covers simply 33% of all jobs in the Computer system & Mathematics classification. There is a large uncovered location too; numerous jobs, of course, stay beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other information revealing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client Service Agents, whose primary tasks we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary task of checking out source documents and entering data sees substantial automation, are 67% covered.

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At the bottom end, 30% of workers have no protection, as their tasks appeared too infrequently 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 existing employment discovers that development projections are rather weaker for jobs with more observed direct exposure. For each 10 percentage point increase in protection, the BLS's development forecast drops by 0.6 percentage points. This offers some recognition because our procedures track the individually derived estimates from labor market analysts, although the relationship is small.

Each solid dot reveals the typical observed direct exposure and projected work modification for one of the bins. The dashed line shows a simple direct regression fit, weighted by existing work levels. Figure 5 shows attributes of employees in the leading quartile of direct exposure and the 30% of workers with no exposure in the 3 months before ChatGPT was launched, August to October 2022, using information from the Present Population Survey.

The more disclosed group is 16 percentage points more likely to be female, 11 portion points more likely to be white, and practically two times as most likely to be Asian. They earn 47% more, on average, and have greater levels of education. People with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most reviewed group, a practically 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 unemployment as our concern outcome because it most straight catches the capacity for economic harma worker who is unemployed desires a task and has not yet found one. In this case, task posts and work do not always signal the need for policy reactions; a decrease in job posts for a highly exposed role may be neutralized by increased openings in an associated one.