First-order effects

• Recent graduates face fewer openings at startups and especially at Big Tech, as employers use AI for some entry-level task categories rather than staffing them with new hires.
• Big Tech and startups can redirect recruiting budgets and manager time from graduate pipelines toward roles that build, deploy, or oversee AI systems.

Second-order effects

• Universities, boot camps, and early-career candidates will face stronger pressure to demonstrate AI-adjacent skills and work readiness before hiring, potentially narrowing the traditional on-the-job entry path.
• The split between reduced graduate hiring and demand for AI specialization could widen pay and opportunity differences across technical roles, consistent with the earlier AI/ML senior-pay premium.

Third-order effects

• If employers keep substituting AI for routine starter tasks, firms may need to redesign apprenticeship and training models to preserve a pipeline of experienced workers rather than relying on large graduate cohorts.
• The evidence points to a labor-market transition, not a settled verdict on net job loss: later research linking AI exposure to weaker entry-level employment makes the graduate pipeline a key measure of whether displacement persists. Entry-level workers in AI-exposed fields have already seen employment fall.