Forecasting Economic Trends in 2026

The COVID-19 pandemic and accompanying policy steps triggered economic disruption so plain that sophisticated statistical methods were unneeded for numerous questions. For example, unemployment jumped dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One common technique is to compare results between basically AI-exposed workers, firms, or industries, in order to isolate the effect of AI from confounding forces. 2 Direct exposure is typically defined at the job level: AI can grade homework but not manage a classroom, for instance, so instructors are considered less uncovered than workers whose entire task can be performed remotely.

3 Our method combines information from 3 sources. The O * web database, which enumerates jobs related to around 800 special professions in the US.Our own usage information (as determined in the Anthropic Economic Index). 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 two times as fast.

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Some jobs that are in theory possible may not reveal up in use because of design restrictions. Eloundou et al. mark "License drug refills and supply prescription info to drug stores" as fully exposed (=1).

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

Our brand-new measure, observed direct exposure, is indicated to quantify: of those jobs that LLMs could theoretically accelerate, which are in fact seeing automated use in expert settings? Theoretical ability incorporates a much broader series of tasks. By tracking how that gap narrows, observed direct exposure provides insight into financial modifications as they emerge.

A job's exposure is higher if: Its jobs are in theory possible with AIIts tasks see significant use in the Anthropic Economic Index5Its jobs are carried out in job-related contextsIt has a fairly greater share of automated use patterns or API implementationIts AI-impacted jobs comprise a bigger share of the overall role6We offer mathematical information in the Appendix.

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We then change for how the task is being carried out: totally automated executions get full weight, while augmentative usage gets half weight. The task-level protection measures are averaged to the profession level weighted by the portion of time invested on each job. Figure 2 shows observed direct exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the occupation level weighting by our time portion procedure, then balancing to the profession category weighting by overall employment. The measure shows scope for LLM penetration in the majority of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) occupations.

The coverage shows AI is far from reaching its theoretical abilities. For circumstances, Claude presently covers simply 33% of all jobs in the Computer system & Math classification. As abilities advance, adoption spreads, and release deepens, the red location will grow to cover heaven. There is a big uncovered area too; lots of tasks, naturally, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing customers in court.

In line with other data showing that Claude is extensively used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Agents, whose primary jobs we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of checking out source documents and entering information sees considerable 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 information to meet the minimum threshold. This group consists of, for example, Cooks, Bike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Statistics (BLS) releases routine employment forecasts, with the latest set, released in 2025, covering anticipated modifications in work for every occupation from 2024 to 2034.

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

Each strong dot shows the average observed exposure and forecasted work change for one of the bins. The dashed line reveals a simple linear regression fit, weighted by present employment levels. Figure 5 programs attributes of workers in the leading quartile of direct exposure and the 30% of employees with no direct exposure in the 3 months before ChatGPT was released, August to October 2022, utilizing information from the Current Population Study.

The more unveiled group is 16 percentage points most likely to be female, 11 percentage points more most likely to be white, and nearly two times as most likely to be Asian. They make 47% more, typically, and have greater levels of education. For example, people with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, a nearly fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting data from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our priority outcome since it most directly captures the potential for economic harma employee who is out of work wants a task and has actually not yet found one. In this case, task posts and employment do not always indicate the need for policy reactions; a decline in job postings for an extremely exposed role may be neutralized by increased openings in a related one.