Goldman Sachs: AI Is overhyped, expensive, and unreliable

Original Article ↗ Hacker News Discussion ↗

Goldman Sachs’ research note arguing that generative AI spending is high while tangible benefits remain limited has triggered a broader reassessment of the current AI boom. Commenters weigh whether large language models and related tools are genuinely transformative or merely another oversold tech trend, comparing them to past bubbles like crypto and dot-coms while also acknowledging real, if incremental, productivity gains in areas such as coding assistance, search, and content summarization. Many see a mismatch between trillion‑dollar valuations and today’s practical ROI, warning of a possible “AI winter,” even as AI quietly becomes embedded in everyday software and financial firms’ own workflows.

Goldman Sachs’ Motives and Credibility

  • Some argue financial firms never share valuable insights for free; public reports are seen as market signaling or propaganda.
  • Others counter that large institutions need accurate pricing and can be both self‑interested and broadly correct.
  • Several note the report’s headline language (“overhyped, wildly expensive, unreliable”) does not appear verbatim in the PDF.
  • There is debate over whether GS is expressing a genuine view, talking its book, or arriving late after already positioning.

AI vs Traditional Quant/Algo Trading

  • Commenters stress there’s no contradiction between GS using quant algorithms and criticizing current “AI.”
  • Algorithmic trading and ML/LLMs are framed as different categories; sophisticated models need not be “AI” in the buzzword sense.

Hype, Trajectory, and Historical Analogies

  • One camp: current generative AI is expensive, unreliable, and may never justify the massive capex; parallels drawn to crypto, web3, and self‑driving.
  • Another camp: the important factor is long‑term slope (2030–2040), comparing AI to early aviation or the internet in the 1990s, with transformative potential still ahead.
  • Skeptics respond that similar “it’s early days” narratives were used for bubbles that never delivered.

ROI, Investment Horizon, and AI Winter

  • Institutional investors are said to care about payoff within a few years, not distant decades; discounting makes far‑future gains less compelling.
  • Many foresee an “AI winter” in funding if near‑term returns disappoint, though AI as a deployed technology would persist.
  • Some argue current valuations resemble bubble assumptions (very high multiples, unrealistically perfect execution).

Current Usefulness and Limitations

  • Positive experiences: code autocomplete, text summarization, semantic search, translation, TTS/STT, classification, and productivity boosts for some users.
  • Negative experiences: hallucinations, generic answers, buggy code, weak search replacement, and “AI‑washed” products adding little value.
  • Distinction made between generative AI and long‑standing ML (recommendation, decision trees, moderation); the latter is already ubiquitous and impactful.

Societal and Business Effects

  • Concerns include job displacement as firms use AI for efficiency and potential over‑investment to justify layoffs.
  • Some see AI also enabling more powerful exploits, forcing simpler, more privacy‑preserving systems.
  • Pricing debates: some expect assistant tools to get much cheaper; others would pay high subscription fees for current capability.