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Equation 34 · The Economics and Physical Limits of Running AI Agents at Scale

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That exemption changes the shape of the rate-limit exposure the same way it changed the shape of the dollar cost. In the uncached model above, step i sends (i-1)σ\sigma tokens of retransmitted history as ordinary, fully metered input — every one of which counts against the per-minute ceiling, every single step, growing linearly with i within a single trajectory. A long, rapid, uncached loop can approach its own organization’s input-tokens-per-minute ceiling from its own growth alone, with no other caller involved: the trajectory throttles itself. With per-turn caching applied, the accumulated history is read from cache and, per the documentation above, does not count toward the limit at all;…
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That exemption changes the shape of the rate-limit exposure the same way it changed the shape of the dollar cost. In the uncached model above, step i sends (i-1)σ\sigma tokens of retransmitted history as ordinary, fully metered input — every one of which counts against the per-minute ceiling, every single step, growing linearly with i within a single trajectory. A long, rapid, uncached loop can approach its own organization’s input-tokens-per-minute ceiling from its own growth alone, with no other caller involved: the trajectory throttles itself. With per-turn caching applied, the accumulated history is read from cache and, per the documentation above, does not count toward the limit at all; only the newest increment written each turn — a roughly constant wσ\sigma tokens per step, not a growing one — consumes ceiling headroom. Caching is therefore doing two separate jobs that are easy to conflate: it discounts the price of the growing history, and independently, it exempts that same growing history from the throughput governor. A workload that caches for the price discount and does not realize it is also buying rate-limit headroom is undercounting what caching is worth.

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