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Equation 25 · Part 6 · The Economics and Physical Limits of Running AI Agents at Scale

Symbol w

Cncached≈n pout oˉ+n pin w σ+pin γ σ n(n−1)2C_n^{\text{cached}} \approx n\,p_{\text{out}}\,\bar{o} + n\,p_{\text{in}}\,w\,\sigma + p_{\text{in}}\,\gamma\,\sigma\,\frac{n(n-1)}{2}
ww

What this part means

w is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Its job in the formula

w is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

The passage around this formula

…it [ 1 , 2 ] . Substituting those multipliers into the model — reading the accumulated history at the discounted rate γ\gamma ≈\approx 0.1 instead of the full rate, and writing only the newest increment at the write rate w ≈\approx 1.25 — gives Cncached≈n pout oˉ+n pin w σ+pin γ σ n(n−1)2C_n^{\text{cached}} \approx n\,p_{\text{out}}\,\bar{o} + n\,p_{\text{in}}\,w\,\sigma + p_{\text{in}}\,\gamma\,\sigma\,\frac{n(n-1)}{2}. The quadratic term survives. Its coefficient shrinks by the cache-read discount, roughly tenfold, but the shape — cost rising with the square of step count — does not go away, because caching discounts the price of resending the growing history; it does…

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