Why IT specialties are losing their position in the labor market

Statistics from the Federal Reserve Bank of Dallas show that a 10-percentage-point increase in automation reduces the likelihood of employment for graduates by 2% and starting salaries by 5%. Computer science, engineering, and linguistics are most vulnerable, with unemployment among newcomers reaching 7–8%. Meanwhile, medicine, education, and social work maintain unemployment at 1–2%. About two-thirds of IT faculty graduates go on to master's programs, and enrollment in risky specialties is declining.

The rapid development of generative neural networks has disrupted traditional career trajectories for university graduates. Analysts at the Federal Reserve Bank of Dallas have identified a direct correlation: each 10-percentage-point increase in the automation potential of a specific educational program reduces the likelihood of employment in the first year after graduation by nearly 2%. Those who do find work must accept starting salaries that are on average 5% lower than in less automatable fields. The most vulnerable turned out to be classical computer science, computer engineering, management information systems, as well as linguistics and text-based specialties—routine tasks of writing basic code and creating standard content are now performed by AI. Unemployment among beginning IT specialists has reached 7–8%. At the same time, fields requiring personal empathy, emotional intelligence, and physical presence—medicine, education, and social work—maintain unemployment at 1–2%. About two-thirds of IT faculty graduates, having lost hope of quickly finding a junior position, go on to master's programs. In specialties with a high risk of replacement by neural networks, a decline in student enrollment is recorded.