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Published equation contexts

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

Why this formula appears here

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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tt

Symbol t

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

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TT

Symbol T

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

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t=1t=1

Starting index or lower bound: t=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.

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TT

Ending index or upper bound: T

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

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Published contexts (1)

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

Equation 1 · AI Economics & Systems

The Jagged Frontier Fallacy: Deconstructing Harvard's Canonical AI Productivity Study

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

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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