Cut your AI cost IN HALF (EASY)

Matthew Berman18m 34s
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    Video summary

    Matthew Berman separates software work into research, specification, implementation and review. The proposed workflow uses a stronger model to decide what should be built, transfers a written specification to another coding model, then returns the pull request for review and targeted corrections. The emphasis is on matching capability to the task rather than using one expensive model for every stage.

    Matthew Berman illustrates the idea with assumed token volumes and prices, then describes manual handoffs and delegation between coding harnesses. The savings example is hypothetical, and reported quota savings are personal estimates rather than a controlled measurement. Actual results depend on task difficulty, context transfer, model quality, pricing and the extra cost of review or rework.

    Matthew Berman extends routing beyond coding to knowledge work and reasoning-effort settings. The transferable lesson is to understand the models and settings being used, preserve a useful specification and check implementation quality. The talk does not establish that every deployment task is low risk or that a particular model will always be the cheapest adequate choice.

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    Matthew Berman on a black background with the headline MATCH MODEL TO TASK in blue and white. Framed in blue with WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 7 July 2026 and duration 18m 34s.

    Matthew Berman argues that AI coding costs can fall when stronger models handle planning and review while less expensive models execute a sufficiently detailed specification.