IBM CEO says there is 'no way' spending on AI data centers will pay off

Original Article ↗ Hacker News Discussion ↗

Credibility of IBM’s View

  • Many commenters distrust IBM’s judgment, citing past misses (PCs, cloud, Watson) and its shift to conservative, services‑heavy business.
  • Others argue that IBM’s long survival and deep enterprise exposure give it a realistic view of how hard it is to monetize “cutting‑edge tech” at scale.
  • Some see the CEO’s stance as self‑serving “sour grapes” from a company that largely missed the current AI wave and wants to cool the market.

Capex, ROI, and Depreciation Math

  • IBM’s cited figure: ~$8T in AI capex would require ~$800B/year in profit just to service interest, before replacing hardware.
  • Several threads work through 1‑GW datacenter costs: rough estimates converge around $70–80B/GW once GPUs, power, and cooling are included.
  • Big debate over GPU depreciation: accounting schedules of 5–6 years vs. practical obsolescence in 2–3 years given rapid performance‑per‑watt gains and heavy 24/7 utilization.
  • Some argue future efficiency improvements and lower GPU prices could strand current assets; others counter that revenue per watt or per token could still justify refresh cycles.

Energy, Grid, and Climate Constraints

  • Altman’s suggestion of adding 100 GW of new energy capacity per year is widely viewed as unrealistic, especially in the US, though commenters note China’s aggressive build‑out of solar, wind, dams, and storage.
  • Concerns that data centers will be powered largely by gas, drive up local electricity prices, and externalize costs onto ratepayers, while private firms capture profits.
  • Environmental impact of both training and eventual e‑waste from obsolete accelerators is repeatedly raised.

Bubble vs. Lasting Value

  • Strong camp: this is a classic bubble (compared to fiber overbuild, dot‑com, crypto, telecoms). Hyperscalers are overbuying specialized hardware on optimistic AGI timelines; most investments won’t be recouped.
  • Opposing view: even if many firms fail, AI infra (and cheap surplus compute) will remain valuable for other workloads, much like dark fiber post‑2001. Winners may later buy distressed assets cheaply.
  • Several note that today’s AI services often aren’t profitable; optimism rests on future price elasticity and new use cases, not current unit economics.

AGI Assumptions and Practical Use

  • IBM CEO’s core premise—AGI is very unlikely—underpins his “no way it pays off” claim. Many call that premise unproven.
  • Multiple participants stress that enormous demand could arise without AGI if AI continues to deliver productivity gains (especially for coding and internal tooling).
  • Others emphasize current limitations (hallucinations, lack of determinism, weak PMF outside a few niches) and doubt that spending can scale to the implied per‑capita revenue needed globally.