← Mathematical compendium

Published equation contexts

Tin(k)=∑j=1k(b+ja)=kb+a k(k+1)2T_{\mathrm{in}}(k) = \sum_{j=1}^{k} \left( b + j a \right) = k b + a \, \frac{k (k+1)}{2}

Why this formula appears here

The nonlinearity that surprises people does not live in this equation, which is linear. It lives in agent loops. Suppose a loop runs for k steps, each step appends roughly a tokens of tool output and assistant text, and the context started at b tokens. The total input billed across the whole loop is then Tin(k)=∑j=1k(b+ja)=kb+a k(k+1)2T_{\mathrm{in}}(k) = \sum_{j=1}^{k} \left( b + j a \right) = k b + a \, \frac{k (k+1)}{2}. which is quadratic in the number of steps. Doubling the length of an agent trajectory roughly quadruples its input billing. This is why cache hit rate dominates agentic economics: prefix caching converts most of that quadratic term into reads at one tenth the price, and a workload that loses its cache to a time-to-live expiry or an effort change pays the full quadratic […

Read the full article-specific guide →

Read the representative guide

TinT_{\mathrm{in}}

Symbol T_in

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

Read this term in its guide →
jj

Symbol j

j appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

Read this term in its guide →
j=1j=1

Starting index or lower bound: j=1

This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

Read this term in its guide →
kk

Ending index or upper bound: k

This label says where the repeated addition, multiplication, or accumulation stops. It sets the last term or end of the range.

Read this term in its guide →

How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

Research cited beside this formula

Learn the mathematics behind it

Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

Tin(k)=∑j=1k(b+ja)=kb+a k(k+1)2T_{\mathrm{in}}(k) = \sum_{j=1}^{k} \left( b + j a \right) = k b + a \, \frac{k (k+1)}{2}

Equation 11 · Inference Economics

The Unit Economics of a Token

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The nonlinearity that surprises people does not live in this equation, which is linear. It lives in agent loops. Suppose a loop runs for k steps, each step appends roughly a tokens of tool output and assistant text, and the context started at b tokens. The total input billed across the whole loop is then Tin(k)=∑j=1k(b+ja)=kb+a k(k+1)2T_{\mathrm{in}}(k) = \sum_{j=1}^{k} \left( b + j a \right) = k b + a \, \frac{k (k+1)}{2}. which is quadratic in the number of steps. Doubling the length of an agent trajectory roughly quadruples its input billing. This is why cache hit rate dominates agentic economics: prefix caching converts most of that quadratic term into reads at one tenth the price, and a workload that loses its cache to a time-to-live expiry or an effort change pays the full quadratic […

Meanings in this article

  • kk: the number of steps.
  • bb: the number of tokens.
  • aa: the number of tokens.
Equation guide → · Article →