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Tokens Too Cheap to Meter

Explores how rapidly decreasing AI token costs across orders of magnitude will transform machine learning from a premium product to ubiquitous computing infrastructure within 1-3 years, examining improvements in GPUs, models, and the implications of supply and demand-side effects.

jyn.dev →

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Related to

Unreal Agent: Cost-Efficient AI Agent Harness Cost-efficient agent harnesses become the primary engineering concern in a world of near-zero token costs, shifting optimization focus from token reduction to orchestration and reliability

Supports

Strands Harness: Frontier Performance with 28% Lower Token Cost Strands Harness achieving 28% lower token costs is a concrete example of the cost reduction trend the article analyzes, showing active optimization pressure even as base costs fall
Kev: Tiny Decision Models on Qwen Tiny decision models on Qwen represent the architectural shift toward cheap, specialized inference that becomes economically viable when tokens approach commodity pricing
Inside the Inference Hardware Revolution Of 2026 Both examine the hardware and economics driving AI inference costs down dramatically, with the inference hardware revolution directly enabling the token price collapse described in Tokens Too Cheap to Meter
SWE-2: Advanced Coding Model with Improved Cost-Capability Tradeoff SWE-2's improved cost-capability tradeoff directly illustrates the economic forces compressing token costs while expanding capability, central to the article's thesis about ML becoming infrastructure
DeepSeek-V4.1-Flash: Smaller, Faster AI Model DeepSeek-V4.1-Flash as a smaller, faster model exemplifies the competitive dynamic pushing token costs toward commodity levels that the article forecasts will become ubiquitous

Type:	link
First published:	Wednesday, 23 September 2026 at 17:01 UTC
Location:	https://jyn.dev/tokens-too-cheap-to-meter/
URL:	https://mattwood.fyi/i/fe1220c9

I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

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