Can Real-Time Data Reshape Industry Strategy?

The COVID-19 pandemic and accompanying policy measures caused economic interruption so stark that sophisticated statistical approaches were unnecessary for lots of questions. Joblessness jumped greatly 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 web or trade with China.

One typical technique is to compare results in between basically AI-exposed employees, 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 research but not handle a class, for example, so teachers are considered less unwrapped than employees whose whole task can be carried out remotely.

3 Our method combines information from 3 sources. The O * internet database, which specifies jobs associated with around 800 special professions in the US.Our own use data (as measured in the Anthropic Economic Index). Task-level direct exposure estimates from Eloundou et al. (2023 ), which determine whether it is theoretically possible for an LLM to make a job a minimum of twice as quick.

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Some jobs that are in theory possible might not show up in usage because of design limitations. Eloundou et al. mark "License drug refills and provide prescription details to pharmacies" as totally exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous 4 Economic Index reports fall under classifications rated as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude use distributed across O * web tasks organized by their theoretical AI exposure. Jobs ranked =1 (totally possible for an LLM alone) account for 68% of observed Claude usage, while jobs ranked =0 (not practical) represent simply 3%.

Our brand-new step, observed direct exposure, is indicated to quantify: of those tasks that LLMs could in theory speed up, which are actually seeing automated use in professional settings? Theoretical capability incorporates a much wider variety of jobs. By tracking how that gap narrows, observed exposure provides insight into economic changes as they emerge.

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

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We then change for how the job is being carried out: completely automated applications get full weight, while augmentative usage gets half weight. Lastly, the task-level protection steps are balanced to the profession level weighted by the portion of time invested in each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the occupation level weighting by our time portion step, then balancing to the occupation classification weighting by overall employment. For example, the step shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.

The protection reveals AI is far from reaching its theoretical abilities. Claude presently covers simply 33% of all tasks in the Computer & Mathematics category. As abilities advance, adoption spreads, and deployment deepens, the red location will grow to cover the blue. There is a large exposed area too; many jobs, of course, stay beyond AI's reachfrom physical agricultural work like pruning trees and running farm machinery to legal tasks like representing customers in court.

In line with other information revealing that Claude is extensively utilized for coding, Computer system Programmers are at the top, with 75% coverage, followed by Customer support Representatives, whose primary tasks we significantly see in first-party API traffic. Data Entry Keyers, whose primary job of reading source files and entering information sees significant automation, are 67% covered.

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At the bottom end, 30% of workers have zero protection, as their jobs appeared too occasionally in our data to meet the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The US Bureau of Labor Statistics (BLS) publishes routine employment projections, with the latest set, published in 2025, covering forecasted changes in work for each profession from 2024 to 2034.

A regression at the profession level weighted by current employment discovers that development projections are somewhat weaker for jobs with more observed exposure. For every single 10 portion point boost in coverage, the BLS's growth forecast stop by 0.6 percentage points. This provides some validation because our measures track the independently derived price quotes from labor market experts, although the relationship is minor.

step alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the average observed exposure and predicted work modification for one of the bins. The rushed line shows a simple direct regression fit, weighted by existing employment levels. The small diamonds mark private example occupations for illustration. Figure 5 shows characteristics of workers in the top quartile of direct exposure and the 30% of employees with zero direct exposure in the 3 months before ChatGPT was released, August to October 2022, using information from the Current Population Survey.

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

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job utilize task publishing Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our concern outcome since it most straight records the potential for financial harma employee who is out of work wants a task and has actually not yet found one. In this case, task posts and work do not always signal the requirement for policy responses; a decline in job posts for an extremely exposed role may be combated by increased openings in an associated one.