AI to hit 40% of jobs and worsen inequality, IMF says

Original Article ↗ Hacker News Discussion ↗

An IMF warning that AI could affect up to 40% of jobs and worsen inequality prompts sharp disagreement over how transformative current systems really are and how fast they’ll improve. Commenters cite early disruption in fields like commercial photography, law and white-collar “digital” work, while noting that many low-paid manual jobs are harder to automate but may still vanish if high earners lose income. There is broad concern that productivity gains will accrue mainly to capital owners, with proposals ranging from UBI to housing and tax reforms, and skepticism that markets or regulation are ready to manage the social fallout.

Overall uncertainty and timelines

  • Many argue long‑term AI impacts are highly uncertain; precise job‑loss forecasts (e.g., “40%”) are seen as speculative.
  • Others say even current systems are already powerful enough to damage large job segments, regardless of future advances.
  • Disagreement over how quickly remaining technical issues (reasoning, continuity, physical world robustness) will be solved.

Which jobs are hit first

  • Strong view that digital, mid/high‑skill work is most exposed in the near term: junior analysts, copywriters, some programmers, legal contract work.
  • Example: major law firms deploying AI platforms for contract creation, analysis, and revision, likely shrinking entry‑level legal roles.
  • Several argue low‑wage physical jobs (cleaning, cooking, childcare, basic labor) are relatively safe until robotics catches up.
  • Others predict self‑driving and robotics will eventually automate many manual jobs, just on a slower timeline.

Creative work, photography, and authenticity

  • Generative image models (e.g., Midjourney v6) are seen as a direct threat to:
    • Stock photography and blog/news header images.
    • Product shots and ad imagery.
    • Studio‑style portraits via face‑conditioning tools.
  • Debate over weddings and life events: some envision AI‑enhanced camera rigs generating infinite composed shots; others insist people will still prefer “authentic, in‑the‑moment” human‑taken photos.
  • Broader theme: AI may flood markets with cheap visuals, devaluing imagery but leaving niches for “handmade”/authentic work and premium brands.

Inequality, productivity, and UBI/housing

  • Many doubt productivity gains will raise most workers’ incomes, citing recent decades where extra profits accrued to capital, not labor.
  • Some note global gains (e.g., new middle classes) but others say that doesn’t negate rising within‑country inequality.
  • Concern that AI will widen skill mismatches faster than people can retrain.
  • UBI is frequently mentioned but criticized as insufficient without housing reform; suggestions include de‑financializing housing, steep taxes on multiple homes, restricting short‑term rentals, and building more “missing middle” housing.

Quality, hype, and market behavior

  • Some see current AI as “half‑broken garbage” that firms and governments will still deploy to cut costs, degrading service (analogies: offshored support, IVR trees, self‑checkout).
  • Others argue that:
    • This dismissal is partly coping; tools are already transformative in some workflows (e.g., replacing StackOverflow/Google for coding help).
    • We are at an “early mobile phone” stage and quality will improve substantially.
  • Skeptics compare AI hype to past over‑promised tech (crypto, VR, Watson, Siri, perpetual “battery breakthroughs”). Supporters respond that even narrow AI (summarization, large‑scale search over documents) is already practically useful.

Social stability and policy

  • Some foresee AI as a new “resource curse”: concentrated control over productive capacity could push societies toward greater inequality and potential conflict.
  • Speculation that, without intervention, many will be pushed out of the labor‑based economy, raising risk of unrest or violent attempts to seize AI‑controlled resources.
  • Others emphasize that “progress” isn’t binary; the challenge is steering AI deployment and timing policy so as to mitigate externalities rather than trying to stop the technology.