Computer science has one of the highest unemployment rates

Labor‑market data & what it really shows

• Commenters dig into the New York Fed data rather than the article’s framing.
• CS unemployment (~6%) is higher than many engineering fields but far from catastrophic; some majors with very low unemployment have very high underemployment (e.g., nutrition).
• IT‑related majors stand out as having relatively high unemployment but among the lowest underemployment, suggesting once hired they more often get degree‑level work.
• Several note that “employed” doesn’t mean “in‑field,” and definitions of underemployment are contested and hard to measure.

Overproduction, hype, and the “learn to code” era

• Many argue CS enrollment exploded because of salary hype and “critical shortage” narratives, not genuine interest in computing.
• Parents, bootcamps, and colleges are seen as feeding this, leading to many weak graduates and credential inflation.
• Junior roles are reported as hard to get; earlier complaints about age discrimination coexist with today’s junior glut—both pressures may be real at once.

CS education, curriculum choices, and cheating

• A CS professor describes many students arriving unprepared and motivated by money; departments allegedly “dumb down” programs (e.g., heavy Python, less rigor) to retain them.
• Others defend Python as a good teaching language and say universities should teach concepts, not chase job‑market frameworks.
• There’s debate over whether CS should be more theory‑focused while separate “software engineering / development” tracks handle vocational training.
• AI tools are seen as making it much easier for students to cheat their way through, worsening signal/noise among graduates.

Cyclical bust vs structural change (AI, outsourcing, rates)

• Older participants frame this as the latest downturn in a boom/bust pattern seen in 2000 and 2009, amplified by the zero‑interest‑rate hiring bubble and subsequent “cleanup.”
• Others emphasize outsourcing waves and anticipate further AI‑driven job loss; some cite forecasts of large‑scale automation by 2030.
• There’s disagreement whether AI savings will mostly become corporate margin, and how much of this is macroeconomic “noise” versus a lasting reset.

Motivation, job quality, and coding as a general skill

• Several lament a rise of “ticket completers” with little curiosity, and increasingly soul‑crushing Jira‑driven work environments.
• Others argue everyone still benefits from learning to code, but as a broadly useful skill—not a guaranteed path to a high‑paying tech career.