How to Forecast the Global Market Landscape

The COVID-19 pandemic and accompanying policy measures triggered financial disturbance so stark that advanced statistical techniques were unneeded for numerous concerns. Unemployment jumped dramatically in the early weeks of the pandemic, leaving little space for alternative explanations. The impacts of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common approach is to compare outcomes between more or less AI-exposed employees, firms, or markets, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is generally specified at the task level: AI can grade homework but not manage a class, for instance, so teachers are thought about less unwrapped than employees whose whole task can be performed from another location.

3 Our approach integrates information from three sources. Task-level exposure estimates from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least twice as quick.

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Some tasks that are in theory possible might not show up in usage due to the fact that of model limitations. Eloundou et al. mark "Authorize drug refills and supply prescription information to drug stores" as totally exposed (=1).

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

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

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

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We then adjust for how the task is being carried out: fully automated executions get complete weight, while augmentative use receives half weight. The task-level protection measures are balanced to the occupation level weighted by the fraction of time invested on each job. Figure 2 reveals observed direct exposure (in red) compared to from Eloundou et al.

We determine this by first balancing to the occupation level weighting by our time portion step, then averaging to the profession category weighting by total work. The procedure reveals 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 jobs in the Computer system & Mathematics category. As capabilities advance, adoption spreads, and implementation deepens, the red location will grow to cover heaven. There is a large exposed area too; numerous jobs, 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 system Programmers are at the top, with 75% protection, followed by Customer support Representatives, whose primary tasks we progressively see in first-party API traffic. Lastly, Data Entry Keyers, whose main job of checking out source documents and getting in information sees substantial automation, are 67% covered.

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At the bottom end, 30% of employees have zero coverage, as their tasks appeared too occasionally in our information to satisfy the minimum threshold. This group includes, for instance, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants. The United States Bureau of Labor Data (BLS) releases regular work forecasts, with the most recent set, published in 2025, covering anticipated changes in employment for every single occupation from 2024 to 2034.

A regression at the occupation level weighted by current employment discovers that growth projections are somewhat weaker for tasks with more observed exposure. For every single 10 percentage point increase in coverage, the BLS's growth projection drops by 0.6 percentage points. This supplies some validation in that our measures track the separately obtained quotes from labor market analysts, although the relationship is slight.

step alone. Binned scatterplot with 25 equally-sized bins. Each strong dot reveals the average observed direct exposure and projected employment modification for among the bins. The rushed line reveals an easy direct regression fit, weighted by existing work levels. The small diamonds mark private example occupations for illustration. Figure 5 programs attributes of employees in the top quartile of direct exposure and the 30% of workers with absolutely no exposure in the 3 months before ChatGPT was released, August to October 2022, using data from the Current Population Survey.

The more unwrapped group is 16 percentage points most likely to be female, 11 percentage points most likely to be white, and practically twice as likely to be Asian. They earn 47% more, typically, and have higher levels of education. For example, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most discovered group, a practically fourfold distinction.

Brynjolfsson et al.

( 2022) and Hampole et al. (2025) use job posting task from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our concern outcome because it most straight catches the capacity for financial harma worker who is jobless desires a job and has actually not yet found one. In this case, job postings and employment do not always signify the need for policy reactions; a decrease in job postings for an extremely exposed function might be combated by increased openings in an associated one.