The $100B megadeal between OpenAI and Nvidia is on ice

Original Article ↗ Hacker News Discussion ↗

Market / Bubble Sentiment

  • Many see the paused $100B deal as part of a broader “GPU/AI bubble” fueled by cheap money, hype, and non‑binding megadeal press releases used to pump valuations.
  • Others argue these deals are mostly hedging and positioning among big players, not outright scams, though some companies (e.g., Oracle) are cited as abusing AI-partnership PR to goose stock.
  • There’s a strong expectation of an eventual crash; disagreement is mostly on timing and trigger.

Nvidia’s Role and Competition

  • Several commenters think this is fundamentally a GPU bubble: Nvidia’s margins and valuation are seen as unsustainably high and vulnerable to:
    • Hyperscalers’ own chips (Google TPUs, AWS Trainium, Microsoft/Amazon/Meta custom silicon).
    • AMD/Intel and Chinese accelerators.
    • Increasing ability to avoid CUDA lock‑in.
  • Some think the paused deal is actually good for OpenAI (avoiding overpaying for Nvidia capacity), bad for Nvidia as customers diversify.
  • Others note Nvidia is also training its own models and building software stacks, but mostly as a way to sell more hardware, not to compete head‑on with frontier model providers.

OpenAI’s Position and Business Model

  • OpenAI is portrayed as cash‑hungry, with shrinking market share, weak product-market fit in consumer (lots of free use, limited willingness to pay), and heavy capex needs.
  • Comparison to Anthropic: Anthropic is perceived as more B2B/coding-focused, with a clearer monetization path.
  • There’s skepticism that $100B‑scale model training investments can be recouped via subscriptions or ads; several see frontier models becoming a commodity.

Leadership and Trust

  • Extensive hostility toward OpenAI’s leadership: described as manipulative, undisciplined, and excessively promotional.
  • Some argue this reputational risk may be influencing partners like Nvidia; others think big investors don’t care about ethics as long as returns are possible.

Commoditization, Open Models, and Local AI

  • Thread consensus leans toward:
    • Rapid commoditization of LLMs: open‑weight models catch up quickly; quality differences are narrow and ephemeral.
    • Long‑term advantage likely in tooling, integration, and distribution rather than in any single “frontier” model.
  • Power users report strong experiences with local and open models, suggesting a pathway that undermines expensive centralized offerings.

Infrastructure Constraints

  • Commenters highlight physical limits (DRAM, fabs, power, datacenter build‑out) and historical boom‑bust cycles in semiconductors as reasons current AI/GPU spending can’t scale indefinitely.