Evaluating Offshore Models and In-House Hubs

The COVID-19 pandemic and accompanying policy steps caused economic disruption so plain that sophisticated analytical techniques were unnecessary for many questions. For instance, unemployment leapt sharply in the early weeks of the pandemic, leaving little space for alternative explanations. The effects of AI, nevertheless, may be less like COVID and more like the web or trade with China.

One typical technique is to compare outcomes in between basically AI-exposed workers, firms, or industries, in order to separate the impact of AI from confounding forces. 2 Direct exposure is generally specified at the task level: AI can grade homework but not handle a classroom, for instance, so instructors are thought about less exposed than employees whose whole task can be carried out remotely.

3 Our method integrates data from 3 sources. The O * internet database, which enumerates jobs associated with around 800 distinct professions in the US.Our own use information (as determined in the Anthropic Economic Index). Task-level exposure quotes from Eloundou et al. (2023 ), which measure whether it is in theory possible for an LLM to make a job a minimum of two times as quick.

Evaluating Offshore Models and In-House Hubs

Some tasks that are theoretically possible may not show up in use since of model limitations. Eloundou et al. mark "License drug refills and provide prescription information to pharmacies" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed throughout the previous 4 Economic Index reports fall under classifications rated as theoretically feasible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude use dispersed across O * NET jobs organized by their theoretical AI exposure. Tasks ranked =1 (totally feasible for an LLM alone) account for 68% of observed Claude usage, while jobs rated =0 (not feasible) account for simply 3%.

Our brand-new procedure, observed direct exposure, is meant to measure: of those tasks that LLMs could in theory speed up, which are really seeing automated use in professional settings? Theoretical ability includes a much more comprehensive variety of tasks. By tracking how that space narrows, observed exposure supplies insight into financial modifications as they emerge.

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

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We then change for how the job is being brought out: fully automated applications get complete weight, while augmentative usage receives half weight. The task-level coverage measures are averaged to the occupation 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 compute this by first averaging to the profession level weighting by our time portion measure, then averaging to the occupation category weighting by overall work. The measure reveals scope for LLM penetration in the majority of tasks in Computer & Mathematics (94%) and Workplace & Admin (90%) occupations.

The coverage reveals AI is far from reaching its theoretical capabilities. Claude presently covers just 33% of all jobs in the Computer & Math category. As abilities advance, adoption spreads, and implementation deepens, the red location will grow to cover the blue. There is a big uncovered location too; lots of jobs, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm equipment to legal tasks like representing customers 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 Representatives, whose primary tasks we progressively see in first-party API traffic. Data Entry Keyers, whose main task of reading source documents and going into information sees considerable automation, are 67% covered.

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At the bottom end, 30% of employees have zero protection, as their jobs appeared too rarely in our data to satisfy the minimum limit. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Stats (BLS) publishes routine work projections, with the most recent set, published in 2025, covering predicted modifications in employment for every profession from 2024 to 2034.

A regression at the profession level weighted by current work discovers that growth forecasts are rather weaker for jobs with more observed exposure. For each 10 percentage point boost in coverage, the BLS's development projection come by 0.6 portion points. This provides some recognition in that our procedures track the independently derived price quotes from labor market analysts, although the relationship is small.

Each strong dot shows the typical observed exposure and forecasted work modification for one of the bins. The rushed line shows a simple direct regression fit, weighted by present work levels. Figure 5 shows attributes of employees in the top quartile of exposure and the 30% of workers with zero direct exposure in the three months before ChatGPT was released, August to October 2022, using information from the Current Population Survey.

The more reviewed group is 16 percentage points most likely to be female, 11 percentage points more most likely to be white, and practically twice as likely to be Asian. They earn 47% more, usually, and have higher levels of education. Individuals with graduate degrees are 4.5% of the unexposed group, however 17.4% of the most uncovered group, a nearly fourfold difference.

Researchers have actually taken different techniques. For example, Gimbel et al. (2025) track changes in the occupational mix utilizing the Present Population Study. Their argument is that any important restructuring of the economy from AI would appear as modifications in distribution of tasks. (They discover that, so far, modifications have been unremarkable.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use task publishing data from Burning Glass (now Lightcast) and Revelio, respectively. We concentrate on unemployment as our top priority result since it most straight captures the capacity for financial harma employee who is out of work wants a job and has not yet found one. In this case, job postings and employment do not necessarily signal the requirement for policy reactions; a decline in task postings for a highly exposed role might be neutralized by increased openings in a related one.