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Artificial Intelligence

🇺🇸 United States · Engineering
First to lose entry-level jobsAmong the riskiest 22% of 220 majors

My Major Lifespan

1 yr 11 mo
00days 00:00:00:000

until AI starts shrinking hiring of this major’s graduates
Sep 24, 2028 · 728 days left

Likely range now – 3 yrs 11 mo
Class of 2030: the hiring decline may start before you graduate
AI agents are steadily automating modeling, experimentation, and deployment pipelines, but surging demand for AI adoption masks any decline. Graduates who move into general software development already face shrinking junior hiring. The oversupply of graduates from newly created programs is a separate issue from AI.

Major lifespan is the time left until AI is expected to start shrinking hiring of this major’s graduates. It doesn’t mean the major will disappear or that everyone will lose their job.

AI vs Humans: whose side are you on?

Entry-level hiringShrinking
Among majors47 / 220

Where graduates go

2030

Career paths · time left at graduation in 2030

AI / Machine Learning EngineerHigh2 yrs 3 moSep 2032MLOps EngineerHigh3 moSep 2030Data ScientistHighAI replacement may begin before graduationSep 2029AI Researcher (Research Scientist)Medium3 yrs 3 moSep 2033

Artificial Intelligence: how AI affects graduate hiring

AI-major graduates build and test models, a job AI agents are steadily learning to automate the routine parts of. But the current rush by companies to adopt AI in the first place is pulling in graduates faster than automation can push them out, so this major looks stronger than the underlying trend really is.

Why does this major look stable despite AI automating its own work?

AI agents are steadily taking over routine parts of modeling, experimentation, and getting a model into production, the daily grind of this work. Demand from companies racing to adopt AI in the first place is currently outrunning that automation, but graduates who move into general software roles instead already face the shrinking junior hiring hitting that field.

How do AI-research jobs differ from applied AI-engineering jobs?

AI and machine-learning engineer roles, building and deploying models for a product, feel automation pressure sooner, since a lot of that pipeline work is now scriptable. Research-scientist roles, which involve designing new methods and judging which experimental results are worth pursuing, hold up longer, since that kind of judgment is harder for AI to do about itself.

What should an AI-major student focus on beyond coursework?

Publish or ship something that required judgment calls under uncertainty, not just a class project following a tutorial, since employers can otherwise tell an AI agent to attempt the tutorial-level task itself. Learn the basics of deploying and monitoring a model in production, and stay ready to pivot toward general software skills if the degree's newer, less-established programs oversupply this specific job market.

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Sources

Sources describe work, regulations and education. AI replacement dates and scores are AI estimates, not predictions by the cited organizations. Methodology