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Equation 29 · Part 1 · The Economics and Physical Limits of Running AI Agents at Scale

Symbol E

E[Cn]≈∑i=1nci1−qi\mathbb{E}[C_n] \approx \sum_{i=1}^{n} \frac{c_i}{1-q_i}
E\mathbb{E}

What this part means

The expected value operator: the probability-weighted average of the quantity inside its brackets.

Its job in the formula

E is part of the quantity the equation computes from the expression on the right.

The passage around this formula

If a step fails and is retried with probability qiq_i , independent of other steps, the expected cost of the trajectory becomes E[Cn]≈∑i=1nci1−qi\mathbb{E}[C_n] \approx \sum_{i=1}^{n} \frac{c_i}{1-q_i}. Holding qiq_i = q constant across the trajectory — a simplification, since real failure rates are neither constant nor independent — the expected extra cost from retries is q1−q\frac{q}{1-q}∑i\sum_i cic_i , and because cic_i increases with i , that extra cost is weighted toward the trajectory’s most expensive, latest steps rather than distributed evenly across it. A one-percent per-step retry rate is a rounding error at step five. Applied at step two hundred of an uncached or lightly cached loop, it is a rounding error on a much larger number.

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Learn the underlying idea

Probability assigns a number from 0 to 1 to an event under a stated model. Zero means impossible within that model; one means certain.

Open the illustrated probability: a quantified chance guide →

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Sources cited in the article section

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