Leaked OpenAI financials show $38.5B loss and compute burn

Original Article ↗ Hacker News Discussion ↗

IPO, public markets, and index funds

  • Some argue firms with multi‑billion losses shouldn’t be allowed to IPO, to avoid “bag‑holding” by the public and retirement savers.
  • Others counter that IPOs are voluntary purchases; if a company meets existing regulations it should be free to list, with markets pricing risk/reward.
  • Index inclusion is raised as a complication: once a big loss‑making tech stock joins major indices, many investors are indirectly exposed.
  • Several comments note that index rule changes, not IPOs themselves, are the real problem.

Financials: losses, accounting, and cost structure

  • The headline $38.5B 2025 loss is widely noted as mostly a one‑time, non‑cash accounting charge ($30B) from converting prior investor rights into equity.
  • Stripping that out, commenters cite an ~$8B operational loss, with 2025 revenue around $13B.
  • Reported spending mix (approx): $7.5B cost of revenue, ~$19B R&D, ~$5.7B sales/marketing, ~$1.6B G&A.
  • Some see the adjusted numbers as “better than expected” and compatible with eventual profitability; others remain alarmed by the burn rate.

Inference economics and “subsidy” debate

  • A major thread: revenue ($13B) exceeds cost of revenue ($7.5B), which many take as evidence that inference tokens are not sold below cost.
  • Skeptics respond that ignoring training/R&D is like ignoring depreciation on a factory; “profitable tokens” don’t mean a profitable business.
  • There is disagreement about how much compute is subsidized (e.g., via partners or governments); precise subsidies remain unclear.

R&D, training, and business sustainability

  • Heavy R&D (~$19B) is seen as both a moat (scale, frontier models) and a structural burden (models have short competitive lifetimes).
  • Some argue OpenAI could be profitable if it froze R&D, but most say that would quickly erode its position versus rivals and open‑source.
  • Questions persist over what exactly is in “R&D” versus cost of revenue, especially training vs ongoing engineering.

Moat, competition, and valuation

  • Views diverge sharply: some see strong product–market fit, fast revenue growth, and a plausible path to break‑even at $25–30B revenue.
  • Others argue there’s no durable moat: strong competitors (Anthropic, Google, Chinese labs, open‑weights) and easy switching for enterprises.
  • Valuation debates hinge on whether massive future AI “labor replacement” and market dominance are realistic, or bubble thinking.