AI is predominantly replacing outsourced, offshore workers

Original Article ↗ Hacker News Discussion ↗

What the cited report actually says

  • Discussion centers on a pulled PDF from an MIT-affiliated report rather than the Axios summary.
  • Reported findings: GenAI-driven cuts are concentrated in “non-core” standardized work—customer support, admin processing, and templated dev tasks—often already outsourced.
  • Interview-based: 52 execs; some reported 5–20% headcount reduction in those functions, but this is self-reported belief, not audited data.

Where AI is displacing work

  • Many see AI as a swap for low-quality offshore labor: similar interaction model (spec → output → review) but cheaper and more controllable.
  • Examples given: “support engineering” grunt work (upgrades, certs), call centers, content moderation, basic customer/tech support.
  • Several posters already use LLMs exactly as they previously used offshore juniors: for drafts and routine implementation with local oversight.

Remote work, offshoring, and full automation

  • Some argue: on-site → remote → offshoring → AI is a logical progression; anything that can move to Bozeman can move to Bangalore, and then to automation.
  • Others reject the “inevitable” jump to automation, citing long-lived human factory work (e.g., sewing) despite decades of offshoring.
  • Debate over whether in‑person work provides durable advantage, with side-thread on whiteboards vs online tools and the value of domain knowledge.

Returns on GenAI investment

  • The “95% of orgs see zero return” claim sparks argument:
    • One camp: this is normal early-stage CapEx for transformative tech (analogies to PCs; productivity paradox).
    • Opposing camp: PCs had clearly demonstrable ROI from day one; GenAI resembles blockchain/“digital transformation” fads with unclear business value and subsidized, loss-making pricing.

Outsourcing economics and Indian IT

  • Widespread criticism of large offshore agencies: low quality, babysitting costs, possible perverse incentives, even speculation about money-laundering–like dynamics.
  • Expectation that low-value “body shops” and parts of the Indian IT sector will be heavily hit as AI does the same low-end work at scale.

Customer support bots

  • Split views:
    • Pro: LLMs already outperform many bad call centers, especially for simple issues and doc navigation.
    • Anti: most real support problems are complex, require empowerment (refunds, account changes), and current AI front-ends mainly act as frustrating gatekeepers.

Broader social and labor impacts

  • Long subthread on inequality: some foresee AI/automation intensifying class conflict and making peaceful reform unlikely; others argue overall material conditions have improved despite inequality.
  • Shared concern that AI lets a small number of “domain-savvy experts” replace large numbers of junior and offshore workers, raising questions about career ladders and how many such experts the economy needs.