New research suggests that jobs relying on codified knowledge are seeing slower entry-level growth as AI adoption increases.

A study from Stanford researchers indicates that artificial intelligence is having a significant impact on entry-level employment. The findings suggest that occupations requiring formal, standardized knowledge—referred to as codified knowledge—are experiencing slower growth for new workers compared to roles that rely on tacit knowledge acquired through experience.

Codified vs. Tacit Knowledge

Researchers utilized the O*NET occupational database to categorize jobs based on their reliance on codified knowledge, which is defined as information that can be taught through textbooks or formal procedures. The study found that roles with high levels of codified knowledge show slower growth for entry-level positions. Conversely, roles requiring tacit knowledge—gained through mentorship and practice—show faster employment growth for mid-career and senior professionals.

Education as a Buffer

The study identified that higher education levels may mitigate some of the negative employment effects associated with AI exposure. Occupations with a higher proportion of college graduates experienced more muted differences in growth between AI-exposed and less-exposed roles. In contrast, sectors with fewer college graduates saw a clear divide, where the most AI-exposed occupations experienced a decline in employment.

Concerns for Future Workers

Lead researcher Erik Brynjolfsson expressed concern that current trends could lead to a labor market where existing jobs remain stable, but the "on-ramp" for new workers starting their careers begins to disappear. He noted that the observed effects on entry-level positions are persistent and widening.