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Tie demand to product signals

Start at the first move in "Tie demand to product signals" and carry it through to the proof.

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Tie demand to product signalsforecasting, not guessing

A capacity plan that extrapolates last month's GPU-hours misses the things that actually move demand. The forecast should be a function of product reality: how many users, how many requests each and what new capabilities are about to ship.

  1. 1Anchor on a demand model. Tie GPU demand to DAU growth, requests per user and the inference cost of each request type.
  2. 2Run scenarios. Build base, high and aggressive cases - a viral week or a heavy new agent feature can change the answer by a lot.
  3. 3Stress the assumptions. Sensitivity-test the inputs that swing the result most, usually request growth and per-request cost.
  4. 4Convert to a commit decision. Translate the forecast into a reserved baseline plus a burst headroom you can defend to Infra and Finance.