How to Forecast the 2026 Economic Landscape

The COVID-19 pandemic and accompanying policy steps triggered financial disruption so plain that advanced statistical methods were unneeded for numerous concerns. For instance, joblessness jumped sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, however, might be less like COVID and more like the internet or trade with China.

One typical technique is to compare outcomes in between more or less AI-exposed employees, companies, or markets, in order to separate the result of AI from confounding forces. 2 Direct exposure is normally defined at the task level: AI can grade homework however not handle a class, for instance, so instructors are thought about less reviewed than workers whose entire job can be carried out remotely.

3 Our method integrates information from three sources. The O * NET database, which specifies jobs associated with around 800 distinct professions in the US.Our own use data (as determined 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 task a minimum of twice as fast.

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Some tasks that are in theory possible may not reveal up in use because of design constraints. Eloundou et al. mark "Authorize drug refills and provide prescription info to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous four Economic Index reports fall into classifications rated as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure shows Claude use distributed throughout O * internet jobs organized by their theoretical AI exposure. Jobs rated =1 (completely possible for an LLM alone) represent 68% of observed Claude use, while tasks rated =0 (not possible) account for just 3%.

Our new measure, observed direct exposure, is meant to quantify: of those jobs that LLMs could theoretically speed up, which are in fact seeing automated use in professional settings? Theoretical capability incorporates a much wider variety of tasks. By tracking how that space narrows, observed direct exposure provides insight into financial changes as they emerge.

A task's direct exposure is greater if: Its tasks are in theory possible with AIIts tasks see considerable usage in the Anthropic Economic Index5Its tasks are performed in job-related contextsIt has a reasonably higher share of automated use patterns or API implementationIts AI-impacted tasks comprise a bigger share of the overall role6We provide mathematical details in the Appendix.

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We then adjust for how the job is being performed: completely automated implementations get complete weight, while augmentative usage gets half weight. The task-level coverage procedures 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 very first balancing to the profession level weighting by our time portion procedure, then balancing to the occupation classification weighting by overall work. The procedure reveals scope for LLM penetration in the majority of jobs in Computer & Mathematics (94%) and Office & Admin (90%) professions.

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

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

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At the bottom end, 30% of workers have absolutely no coverage, as their jobs appeared too occasionally in our data 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 Stats (BLS) releases routine employment projections, 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 finds that growth forecasts are somewhat weaker for tasks with more observed exposure. For every 10 percentage point increase in protection, the BLS's growth forecast drops by 0.6 percentage points. This provides some validation in that our procedures track the independently derived estimates from labor market experts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the average observed direct exposure and predicted employment change for one of the bins. The dashed line shows an easy direct regression fit, weighted by current work levels. The small diamonds mark private example occupations for illustration. Figure 5 shows qualities of workers in the leading quartile of exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was launched, August to October 2022, utilizing information from the Existing Population Study.

The more unwrapped group is 16 portion points most likely to be female, 11 portion points more most likely to be white, and almost twice as likely to be Asian. They make 47% more, typically, and have greater levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most bare group, an almost fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task publishing Information Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority result because it most straight captures the potential for economic harma worker who is out of work desires a job and has actually not yet found one. In this case, job posts and employment do not always signify the need for policy actions; a decrease in task postings for an extremely exposed role might be neutralized by increased openings in a related one.