Token prices keep falling, yet devs burn more: Jevons paradox hits AI coding agents
daniel_mac8 · x · 2026-10-11
Developer danielmac8 shares a counterintuitive reality: despite falling token prices, he spent a week tuning cache TTL, autocompact, and subagent models just to stay under his Codex and Claude Code usage limits.
The reason: cheaper tokens mean agents run longer and spawn more instances, so total spend keeps climbing. His one-line summary — Jevons paradox, in real life: efficiency gains translate into more consumption, not lower costs. For agent engineering teams, cost optimization should focus on usage control rather than per-token price.
More from coding & agent
- RL post-training Qwen 27B as a Cypher agent lifts graph-query accuracy 6.2 points for $119 — sophiamyang · 2026-10-11
- Same Qwen3-Coder 30B: instant success via LM Studio, 57-minute fix loop via Ollama — Proof_Nothing_7711 · 2026-10-11
- Insomnia keeps Mac agents running with the lid closed — engineers warn it overheats — emax · 2026-10-11
- Why most companies shouldn't be using AI agents yet: hype wastes millions daily — DavidLinthicum · 2026-10-11
- Typedef workshop: building a context graph layer for data and coding agents — AI Engineer · 2026-10-11
- Legwork: an MCP server that lets your AI find and sandbox-install GitHub tools — Status_Record5772 · 2026-10-11