Sorry, grads: Entry-level tech jobs are getting wiped out

Economic pressures and the entry‑level squeeze

• Many say AI is overemphasized; the core drivers are: COVID over‑hiring and layoffs, higher interest rates, R&D tax changes (Section 174), and broad cost-cutting.
• Oversupply of CS / data grads plus easier “CS-adjacent” degree paths has expanded the entry-level pool while positions shrink.
• Some report entry roles not “wiped out” but largely offshored; multiple grad cohorts now compete for a small set of onshore jobs, and “stale” grads are penalized.

AI’s role: accelerator or scapegoat?

• AI tools can make mid‑career devs 10–20%+ more productive and erase language/writing disadvantages for offshore teams.
• Several argue AI mostly augments offshore talent, enabling companies to say “offshoring to AI” rather than “replacing with AI.”
• Others insist current LLMs still behave like weak juniors needing supervision; talk of fully replacing junior coders is seen as executive hubris and investor theater.

Offshoring, visas, and control

• Strong consensus that more junior work is shifting to India/CEE/LatAm via captive centers and outsourcers, amplified by tax incentives and post‑pandemic comfort with remote work.
• Disagreement around H1B: one side says it’s not cheaper than domestic hiring; another says it’s about control over “fragile” workers tied to visas.
• Some want extreme measures (tariffs, minimum salaries for visa workers, offshoring bans); others warn this would damage the wider economy and immigrant-driven innovation.

Education, debt, and “just work harder” narratives

• Arguments to avoid heavy debt and choose cheaper schools, “rigorous” majors, trades, or EU public universities; pushback that even those who followed this advice now face poor prospects.
• Debate over whether $40k in student debt is survivable on low wages; critics stress housing costs, immobility of the poor, and how “move somewhere cheaper” often fails economically and socially.
• Many attack the idea you can reliably “work your way up” through bad jobs; gig and low-wage work can trap people, not launch them.

Hiring practices and skills signaling

• A hiring manager’s story: applicant volumes were normal, not huge; half of finalists were new grads, but some ghosted or underperformed in interviews.
• Processes still heavily favor experience with a specific stack over general ability; this hurts adaptable generalists.
• A cluster of MS/PhD ML/AI applicants had very narrow “tweak model on dataset” profiles and weak general software skills, raising questions about their employability in non-ML roles.

Long‑term pipeline and industry health

• Widespread worry: if everyone refuses to hire juniors, there will be too few experienced engineers in a decade, especially for critical systems.
• Older models of long-term employer–employee loyalty that justified training investments are gone; firms fear trained juniors will leave for better-paying players.
• Some describe tech as “mature in a bad way”: dominated by giant incumbents, less frontier energy, and more risk-averse hiring that starves the next generation.

Political and social responses

• Several commenters see this as a policy/design-of-incentives problem: deregulated gig work, offshoring subsidies, weak labor protections, and political apathy.
• Proposals include: onshore requirements for critical infra, harsher penalties for offshore-caused data breaches, and stronger worker organization.
• Underneath the pragmatism vs. idealism debate runs a growing sense of generational betrayal and fear that locking one cohort out of stable paths will have serious social consequences.