Inference Engineering: The New Tokenomics of AI

The article, a paid newsletter piece, argues that the AI industry is shifting from a training-centric to an inference-centric phase. It explains that as models become more capable, the economic focus moves to the continuous operation of AI across enterprise workflows. The piece details the technical and economic distinctions between prefill (reading) and decode (writing) stages of inference, and introduces concepts like KV cache management, batching, prefix caching, and latency considerations. The author predicts that enterprise inference will become economically as important as pretraining, and that the optimization goal changes from lowest token cost to lowest cost per accepted outcome at required latency. The article is primarily an analytical commentary, not a news report, and is likely paywalled as indicated by 'Subscribe to Premium to Gain Access'.