Falling AI Costs Don't Mean Saving Money
BlackHC · x · 2026-07-16
This post responds to an AI fact-checking article, with the author emphasizing that several points remain valid:
- Memory shortages are a real issue; Gartner noted that sub-$500 PCs might disappear by 2028
- Power grid strain is a genuine concern
- AI's profit model remains unsolved
They also make a sharper point: efficiency gains don't naturally translate to cost savings. Instead, they are reinvested into expanding capabilities—a classic case of the Jevons Paradox—rather than being a case of "engineers doing a bad job."
Related event: Idle GPUs and Power Shortages Highlight AI Economic Bottlenecks(3 posts)→
More from AGI Musings
- Bindu Reddy says GPT-6 is coming soon, with Alibaba, DeepSeek and Kimi close behind — bindureddy · 2026-07-22
- Bindu Reddy says the industry still lacks a way to train 20T models and scale post-training RL — bindureddy · 2026-07-22
- Advanced AI Models Are Becoming Impossible to Plug and Play — emollick · 2026-07-22
- AI suggested a better composition, and that made one user uneasy — Sydde · 2026-07-22
- The Thimble and the Waterfall: AI's Data Bottleneck and Feedback Loops — dyamins · 2026-07-22
- Researcher Admits Kurzweil Was Right About AI Scaling Laws All Along — davidmanheim · 2026-07-22