Tokens too cheap to meter
- ID
- 28027
- Status
- summarized
- Published
- 23 Sep 2026, 5:21 PM
- Fetched
- 24 Sep 2026, 2:03 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://jyn.dev/tokens-too-cheap-to-meter/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.0
- Created
- 24 Sep 2026, 2:03 PM
- Tags
- Audience
- developersai_ml_learnersai_agent_userssaas_founders
What happened
A blog post argues the price of machine-learning intelligence is dropping by orders of magnitude per year, citing GPU power efficiency that doubles roughly every two years (log slope 1.3) and a falling cost per completed task even though per-token prices for frontier models are not consistently declining. The author predicts LLMs become infrastructure rather than a standalone product within 1-2 years, frontier-quality models running locally on commodity hardware in 3-6 years, and quality/access rather than token count becoming the limiting factor. The Hacker News thread drew 273 points and 189 comments.
Why it matters
The concrete decision point is the post's split between cost per token and cost per task: it claims smaller models can be cheaper per token yet consume more tokens than a larger model on the same task because they think more or correct first drafts. If you ship agents, that means benchmarking tokens-per-completed-task on your own workload instead of switching models on sticker price, and treating the 'tokens become cheaper than tool calls' claim as a reason to check whether orchestration, retrieval, or tool-loop overhead now dominates your bill.
Discussion angle
Ask each person to bring one number: tokens consumed per completed task on a small model versus a frontier model for the same job, then debate whether the post's 'tokens cheaper than tool calls' prediction would change how they design retrieval or tool loops today.