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A developer recounts how an AI feature became unexpectedly expensive under real-user usage, with long queries, repeated retrieval chunks, and unbounded conversation history, and suggests techniques like chunking and deduplication to manage token spend.
Meta is scaling back its AI mouse-tracking tool after employee backlash. The thread draws parallels between human and AI workforces, arguing that token waste mirrors headcount bloat.
The tweet presents an animation of token spend from Vercel AI Gateway, illustrating shifts in usage among AI labs, Anthropic's dominance, and the growth of open weight AI.
A Twitter thread outlines four stages of AI coding hype: amazement, expansion, realization, and a final stage. A reply highlights that companies waste over 44% of AI token spend on bug fixes.
The article discusses measuring 'undeclared-intent spend' in agent workflows, quantifying compute tokens spent outside the declared intent to reveal behavioral costs like drift and off-task execution.