Matthew Berman explains tokens as a model's basic units and distinguishes their listed price from the total tokens needed to solve a task. His benchmark examples illustrate how a cheaper token can produce a similar task cost when a model uses more of them.
Matthew Berman proposes assigning difficult planning to a capable frontier model, implementation to a cheaper and faster model, and final review to another strong model. The cost comparison is an illustrative workflow calculation, not a guarantee that every project will achieve the same savings or quality.
Matthew Berman also considers latency and competition between open and closed models. He argues that wider availability of model weights can increase competition in inference serving and shift value toward infrastructure and applications. These are his economic interpretations and forecasts.
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