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Equation 1 · Part 12 · The Jagged Frontier Fallacy: Deconstructing Harvard's Canonical AI Productivity Study

Ending index or upper bound: T

P(Y∣X)=∏t=1TP(yt∣y<t,X).P(Y \mid X) = \prod_{t=1}^{T} P(y_t \mid y_{<t}, X).
TT

What this part means

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

Its job in the formula

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

The passage around this formula

When a knowledge worker asks a bare conversational model to analyze a multi-variable spreadsheet with interdependent business constraints, the model must perform several disjoint cognitive operations within a single autoregressive forward pass: P(Y∣X)=∏t=1TP(yt∣y<t,X)P(Y \mid X) = \prod_{t=1}^{T} P(y_t \mid y_{<t}, X). Because token generation is strictly left-to-right and causal, any early latent error in intermediate arithmetic or structural interpretation compounds multiplicatively across subsequent tokens. If the prompt lacks an explicit scratchpad, state harness, or programmatic tool-execution loop, the model cannot pause, verify an invariant, execute a deterministic calculation, or revise an earlier premise [ 6 , 7 ] .

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Learn the underlying idea

Σ adds a collection of terms. Π multiplies them. The lower and upper labels tell you which terms belong to the collection.

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Sources cited in the article section

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