---
id: "entity-stanford"
type: "entity"
entityType: "organization"
canonicalName: "Stanford University"
aliases: ["Stanford", "Stanford Digital Economy Lab"]
canonical_url: "https://www.stanford.edu"
source_timestamps: ["¶2"]
tags: ["research-institution"]
related: ["claim-ai-displaces-early-career"]
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-edu-46-perils-replace-entry-level"
sourceUrl: "https://hbr.org/2025/09/the-perils-of-using-ai-to-replace-entry-level-jobs"
sourceTitle: "The Perils of Using AI to Replace Entry-Level Jobs"
---
# Stanford University

**Stanford University** is cited as the source of the study finding that U.S. employment for early-career employees in highly AI-exposed fields (software development, customer service) has fallen substantially in recent years — the empirical backbone of [[claim-ai-displaces-early-career]].

**Enrichment context:** research from Stanford's **Digital Economy Lab** — specifically the working paper *'Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence'* — documents a **13–16% relative employment decline for early-career workers (ages 22–25) in AI-exposed occupations**, with entry-level hiring slowing most where AI automates rather than augments. This is the single most load-bearing empirical source for the vault's thesis, and it also supplies the augmentation-vs-automation distinction that grounds [[claim-hybrid-workflows-outperform]].