AI Added 'Basically Zero' to US Economic Growth Last Year, Goldman Sachs Says

Original Article ↗ Hacker News Discussion ↗

Skepticism about real productivity gains (today)

  • Many commenters report LLMs as unreliable “vibes” tools: hallucinations, lack of guarantees, and high verification cost often erase any time saved.
  • For serious work (important emails, SDKs, workflows), checking and fixing AI output can take as long as doing it manually, especially when correctness matters.
  • Adding more AI-based validation is seen as “a house of cards” built on the same fuzzy machinery.
  • Point raised: if AI can’t reliably do 100% of a job, the job can’t really be removed—only partially assisted.

Hype, AGI, and near‑term expectations

  • Some claim that new “agentic” tools (e.g., OpenClaw/Claude) feel close to AGI and justify beliefs in superintelligence within a few years.
  • Others strongly push back: “feeling” AGI is likened to crypto HODL rhetoric; definitions of AGI are vague and benchmarks missing.
  • Critics see a moving goalpost: when dramatic promises fail, boosters retreat to “all big tech took time” narratives.

Comparisons to past tech & the productivity paradox

  • Many reference the “productivity paradox” of computers and the internet: huge visible change, weak short‑term statistics.
  • Counter‑argument: earlier tech mostly lacked applications and software; with AI the core problem is persistent mistakes, which may be fundamentally harder to solve.
  • Some warn not to assume AI will follow the same arc as PCs/web—many highly hyped technologies (e.g., NFTs) never pay off.

Economics, investment, and measurement issues

  • Several argue GDP and current stats are poor at capturing AI’s impact, especially when firms replace purchases with in‑house AI‑built tools or when benefits flow to foreign chip makers.
  • Others stress that subsidized, loss‑making AI services are a red flag: if users don’t see strong ROI at artificially low prices, full‑price adoption may disappoint.
  • Debate over whether massive AI capex is like the 2000 fiber build‑out (long‑term boon after a bust) or an “innovation black hole” starving other fields.

Labor, workflow, and organizational reality

  • Software dev job market is weak despite AI supposedly boosting output; some link this to lowered quality thresholds and renewed offshoring.
  • AI often speeds isolated tasks but doesn’t solve bottlenecks like meetings, approvals, or organizational inertia, so end‑to‑end gains stay modest.
  • There’s concern about using AI plus cheaper, less‑skilled workers who may not detect subtle errors, versus a few experts supervising many AIs.

Externalities and social costs

  • Beyond GDP: energy use, environmental damage, and e‑waste are highlighted as under‑discussed costs.
  • Loss of social trust is a major worry: deepfakes, AI‑generated slop in science and art, and difficulty verifying anything online could hollow out institutions and push people into small, closed communities.

Enthusiast experiences and cautious optimism

  • Many individual anecdotes of large time and cost savings (e.g., replacing expensive software or consulting with custom tools built via Claude/GPT).
  • Others note equal and opposite stories of AI‑induced mistakes and rework, suggesting a current net effect near zero at macro scale.
  • Broad sense: we are still early; tools are rapidly improving, but sustainable, reliably productive use—and clear economic measurement—lag far behind the hype.