Big Tech Told Kids to Code. The Jobs Didn’t Follow [audio]

Original Article ↗ Hacker News Discussion ↗

Access to the podcast / transcript

  • Commenters note the original article links to a free podcast; archive links don’t help because they don’t capture audio.
  • People want an automatic transcript; some are puzzled why transcripts aren’t available immediately given modern speech-to-text tools.

CS grads, expectations, and hiring bar

  • Multiple anecdotes of recent CS grads unable to find tech jobs, some pivoting to sales or non-tech roles.
  • Others report long-standing patterns: even in earlier decades, classmates who only “did the classes” often failed to land dev jobs, while those with side projects and open‑source work did better.
  • Several say SWE is not a natural outcome of a CS degree; success also requires self‑study, portfolio work, interview prep, and networking.
  • Some argue that with the current glut, employers can demand master’s degrees even for entry‑level roles; others call this lazy gatekeeping.

Immigration, offshoring, and labor supply

  • One camp sees reducing H‑1B approvals as a way to help laid‑off or new US grads.
  • Another camp argues this weakens US tech, reduces competition, and mostly benefits mediocre domestic candidates.
  • There’s a side debate on H‑1B1 visas (Chile/Singapore) and fee exemptions.
  • Several say offshoring (India, Latin America, Eastern Europe) is a far bigger driver of lost US jobs than AI.

Market dynamics vs collective responses

  • Some frame the situation as normal market cycles: demand rises and falls; no degree guarantees stability.
  • Others challenge this fatalism, arguing society chooses policies (e.g., student debt, weak safety nets) and could choose differently.
  • There’s friction between “adapt or fail” rhetoric and calls for stronger collective safeguards, progressive taxation, or industrial policy.
  • Claims that post‑COVID worker “power” was crushed by coordinated corporate behavior are met with counterclaims that any such power was fleeting or media‑driven.

“Learn to code” and responsibility for overselling

  • Debate over whether tech was aggressively oversold as a near‑guaranteed high‑pay path by politicians, media, and industry, versus simply being an objectively strong option at the time.
  • Some point to retraining rhetoric (“learn to code” for laid‑off workers) and argue it implicitly promised more than it could deliver.

Are developers underpaid or overpaid?

  • One view: big tech pushed coding education to flood the labor market, hold down wages, and maximize profits; $300k+ compensation is still underpricing relative to value created.
  • Opposing view: tech salaries at that level are already outliers, comparable to or exceeding doctors/lawyers without equivalent barriers or time investment; dev pay is a bubble.
  • Further nuance:
    • Devs are key but not sole contributors to big‑tech profits; monopoly and network effects matter more than code alone.
    • Businesses are expected in a market system to minimize labor costs, just as workers try to maximize pay; neither side is inherently “evil.”
    • Others dispute simplistic supply‑and‑demand explanations, pointing to geography, politics, and history as major determinants of pay.

AI vs other causes of the downturn

  • Several criticize the AI‑framed headline as clickbait, arguing there’s little concrete evidence that AI coding tools are displacing junior jobs.
  • More commonly cited causes: pandemic over‑hiring and subsequent corrections, offshoring, and saturation of common software niches.

Global competition and China

  • Some claim US culture undervalues technical talent and overvalues sales and legal roles, contrasting this with China’s allegedly engineer‑led model and rising dominance in critical technologies.
  • Others push back, noting accusations of IP theft and questioning China’s originality, while some compare this to historic skepticism about Japan/Taiwan.

Boom–bust and the “cottage industry” phase

  • A broader framing compares software to past infrastructure booms:
    • Early phases need many engineers; once platforms (e‑commerce, social, game engines, cloud) are built, demand shifts to a smaller maintenance/“musical chairs” market.
    • Many domains (e‑commerce, 3D, networks) are now seen as mature, with fewer greenfield opportunities for large cohorts of new developers.