AI Is Disrupting Entry-Level Jobs, Stanford Study Reveals — Here’s What You Need to Know

Early-career workers in professions that are most exposed to generative AI are experiencing significant employment declines, while older workers and those in less-exposed fields remain relatively unaffected, according to a recent Stanford University study. 

About the Study

Generative AI caused a 13% relative decline in hiring for early-career workers ages 22–25, particularly in highly AI-exposed professions like software engineering, according to the study.  

Jobs with the most significant exposure to generative AI are the most vulnerable, but AI is rapidly improving. The study notes that in 2023, AI could solve only 4.4% of problems in a software engineering database, but by 2024, this had increased to 71.7%.

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The study, using data from payroll services provider ADP, focused on monthly, individual-level payroll records through July.  It notes that changes in employment outcomes are most acute beginning in late 2022, around the time widespread adoption of generative AI tools began.

Key Findings

The study found a 13% relative drop in hiring of early-career workers most exposed to generative AI. The study reported that workers saw a 6% decline from late 2022 to July, while older workers saw a 6% to 9% increase. 

Hiring, Not Compensation

The study also emphasizes that these trends are primarily related to hiring, rather than compensation. Younger workers may face a more challenging job market, but they don’t necessarily earn less when they do find work. The result was consistent across all age groups, so young workers can still have strong earning potential.

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Automation vs. Augmentation

While generative AI is having significant effects on employment, its effects are not universal. Specifically, the study indicates that the impact is greater when observing applications of AI that automate work compared to those that augment it. If AI can do the work of a human on its own, those jobs are more likely to be impacted than those where humans only use AI to supplement their work.

Not Industry- or Company-Specific

The relative drop in hiring of younger workers is not caused by industry- or company-specific events that could impact hiring practices. The example it provides is interest rate shocks, which could affect specific sectors more than others. However, these factors did not appear to have any impact on the study’s results.

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Broader Implications for Hiring Practices

The study presents an early warning sign of AI’s impact on hiring trends, with the authors suggesting that there may be “canaries in the coal mine.”

The “canaries in the coal mine” reference suggests that the study’s findings serve as an early warning sign for younger workers.

Given the already significant change in hiring practices, urgent questions arise for workforce planning, education, and policy.

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