Evaluating the impact of AI on the labor market: Current state of affairs

Original Article ↗ Hacker News Discussion ↗

Study methodology & interpretation

  • Several commenters challenge the study’s reliance on OpenAI/Anthropic exposure scores and macro job data, arguing you “can’t see” granular displacement that way.
  • Others defend modeling with “numbers in Excel” as standard scientific practice, but ask about null hypotheses, representativeness, and lag: labor-market effects may take years to show up.
  • Confusion and criticism around headlines like “zero effect”: readers stress the authors actually claim “no discernible disruption in the broader labor market,” which allows for localized harms.

Macro job market vs AI

  • Many argue recent tech layoffs stem more from: interest-rate hikes, post‑COVID demand shifts, R&D tax changes (Section 174), ad-market changes, and prior overhiring during the “free money” era.
  • AI is seen as a convenient narrative to justify cost-cutting that would have happened anyway and to win stock-price bumps.
  • Some note job openings decoupling from the S&P 500 around late 2022, but others say ChatGPT adoption was too small then to be causal amid many concurrent shocks.

Anecdotal displacement and sector-specific pain

  • Multiple anecdotes contradict any literal “zero effect” reading: call‑center staff, older workers near retirement, creatives (film, ads, VFX, writers, actors), and some developers reportedly laid off with AI cited explicitly.
  • Creative fields are repeatedly mentioned as early casualties: lower billing rates, fewer staff, and internal turmoil in media and advertising.

AI, productivity, and software hiring

  • Some small/medium tech firms say LLMs (e.g., code assistants) give ~20%+ productivity boosts, letting them slow dev hiring or work fewer hours while keeping up with workload.
  • Others in big tech say tools aren’t yet replacing “swathes of engineers” or delivering the huge velocity PR claims; they’re better for small teams and greenfield work than for large legacy systems.
  • There’s a noted shift in postings from generic “data science” to “machine learning/AI” roles, which may depress opportunities in adjacent specialties without showing as net job loss.

Work culture, “workslop,” and status anxiety

  • Even if headcounts haven’t collapsed, AI is said to be:
    • Undermining morale (“you’re not using AI well enough”).
    • Enabling underqualified workers to ship superficially functioning outputs.
    • Generating “workslop” (plausible but low‑substance content) that pushes real work downstream.

Historical and distributional concerns

  • One camp cites economic history: major technologies often raise overall employment and create new sectors (e.g., home appliances and female labor-force growth).
  • Critics respond that macro gains obscure who is harmed during transitions, that modern productivity gains no longer reliably reach workers, and that AI plus weak labor power may worsen under‑employment and global inequality.

Offshoring and scapegoating dynamics

  • Several say “AI” is frequently a cover for:
    • Routine rank‑and‑yank performance culls.
    • Moving roles to lower‑cost countries, especially in software.
  • The study’s focus on the “broader labor market” is seen as potentially complacent about specific domains (e.g., Filipino call centers, new CS grads) that could be hit hard even if aggregate stats look stable.