The AI bubble is 17 times the size of the dot-com frenzy, analyst says

Original Article ↗ Hacker News Discussion ↗

Is AI a Bubble or a Tectonic Shift?

  • Some see AI as a fundamental technological shift, others say shifts and bubbles often coexist (as with dot-com).
  • Several commenters think AI valuations are clearly frothy, but not uniformly bubble-like across all companies or sectors.

Interest Rates and “Misallocated Capital”

  • The cited “Wicksellian deficit” metric is criticized as mostly an interest-rate story, not AI-specific.
  • People note that the 2022 unwind of ZIRP-era excesses doesn’t show clearly in the chart used, making the analysis feel incomplete or misleading.

Public vs Private Markets

  • One view: the true bubble is in private AI companies burning huge R&D and capex, funded by cash-rich tech giants.
  • Counterview: there are many public companies with little or no profit and extreme valuations, implying a bubble in public markets too.

Dot-Com Comparisons: Scale, Jobs, and Skepticism

  • Those who lived through dot-com say the job market then was far hotter; today money goes more to GPUs and data centers than to engineers.
  • Skepticism dynamics differ: some recall dot-com as wall-to-wall optimism; others recall prominent skeptics even then.
  • One theory: bubbles end when “this time it’s different” becomes the majority view; AI skepticism still feels mainstream, so we may be early.

Hardware, Infrastructure, and Residual Value

  • Key difference vs dot-com: billions going into physical compute, construction, and power infrastructure, not just websites.
  • Debate over how reusable AI/ML hardware is if the bubble pops; some point to crypto farms as precedent for rapidly depreciating assets.

Labor, Class, and Automation Fears

  • Some frame AI as a capital-versus-labor power shift, aiming to reduce dependence on workers.
  • Others reject Marxist framing, arguing executives are driven more by competitive fear than class struggle.

Profitability and Model Economics

  • Concern that ever-larger models require massive capex; if expected ROI dips below training cost, next-gen model development could abruptly halt.
  • Big platforms may be profitable overall yet run AI as massive loss leaders to corner the market.

Developer Experience and “Vibe Coding”

  • Anecdotes of AI-generated front-ends becoming unmaintainable at scale, prompting rewrites by hand.
  • Some see AI as a force multiplier for strong engineers but a liability for the inexperienced.

Macro Impact if It Pops

  • Several argue blast radius will be smaller than dot-com because much spending is from cash flow and leaves useful infrastructure.
  • Others warn that major index levels and a tech-heavy market mean an AI crash could still trigger at least a technical recession.

Where Are the Consumer Products?

  • A few are surprised how little AI tangibly affects their daily lives beyond search, review summaries, and chatbots.
  • Questions remain about whether AI’s current economic value is more enterprise/back-end than consumer-facing.