AI isn't replacing jobs. AI spending is

Original Article ↗ Hacker News Discussion ↗

AI Spending, Hardware Bubble, and Repurposing Concerns

  • Several comments see a GPU/datacenter overbuild: massive capex, fast obsolescence, and unclear revenue to justify it.
  • Some expect a classic bubble: infrastructure written off in a few years if LLMs remain “fancy autocomplete.” Others argue even failed AI buildouts leave surplus compute that will find other (possibly better) uses.
  • Debate over whether this spending is irrational hype or a normal pattern where infrastructure investment precedes profits (railroads, internet, etc.).

Offshoring, Not AI, as Immediate Job Killer

  • Many anecdotes: senior US/EU engineers laid off and replaced by cheaper offshore teams (India, Poland, Latin America), often via big outsourcing firms or new “global capability centers.”
  • Some report entire departments moved, US headcount cut while Indian headcount and offices explode, including in big tech and finance.
  • Quality is disputed: some say offshore talent can be excellent at a fraction of US pay; others report severe skill gaps, churn, and “bait-and-switch” practices.
  • Several argue AI is a PR-friendly cover for cost-cutting and offshoring that would be happening anyway.

Remote Work as Enabler of Offshoring

  • Strong view from some that the COVID-era push to prove remote productivity effectively “sold” management on fully distributed teams, making it easy to move work abroad.
  • Others counter that tools and offshoring existed long before; what changed was culture, not technology.
  • Time zones, culture, and legal risk remain friction points, but are seen as manageable relative to labor savings.

Where AI Is Actually Replacing or Reshaping Work

  • Concrete examples:
    • Translation and transcription teams reduced or eliminated (LLM-based translation, call documentation).
    • Internal tooling projects, low-code/iPaaS workflows, and coding agents replacing outside consultants or shrinking project teams.
  • In many orgs AI is framed as an “enhancer”: same or fewer people expected to do more; hiring slows rather than immediate mass replacement.
  • Skeptics note hallucinations and sloppiness still require strong human oversight; proponents say experienced devs get enormous leverage.

Psychological and Educational Effects

  • Concern that “AI will take your job” narratives plus LLM cheating are making students disengaged and graduates less skilled, potentially becoming a self‑fulfilling prophecy.
  • Reports of tech workers feeling despair and devalued skills, even when they don’t use AI themselves.
  • Some argue this “dumbing down” of humans is itself a path to AGI-like dominance (“smarter AI and dumber humans”).

Capital Allocation, Inequality, and Policy Responses

  • Several see AI capex (hundreds of billions) as misallocation compared to training people, manufacturing, or social needs; others note stock buybacks are even larger and more damaging to wages.
  • Discussion of proposals like the HIRE Act (taxing outsourcing, funding domestic apprenticeships), tariffs, and stronger labor protections.
  • Contrast between countries with redundancy protections vs. the US, where an AI bust could cause “generational” damage with little safety net.