LLM Authority Index

Modeled AI Authority Value

Overview

Modeled AI Authority Value is a comparative benchmark measure used by LLM Authority Index to represent relative AI discovery opportunity.

It is a modeled estimate, not a measurement of realized commercial performance.

Modeled AI Authority Value is not:

  • revenue;
  • attributable sales;
  • website traffic;
  • advertising-equivalent value;
  • profit;
  • booked pipeline; or
  • a guaranteed future outcome.

The metric is most useful for comparing relative opportunity inside the benchmark when its inputs and interpretation limits are understood.

Why a Modeled Value Exists

Recommendation metrics such as valid recommendation coverage and rank-one rate describe how often and how prominently a brand appears.

Modeled value adds a different layer: it attempts to express the relative economic weight of the measured AI discovery opportunities rather than treating every source query as economically identical.

That makes it a benchmark-comparison tool rather than a revenue-attribution system.

Documented Input Context

The public benchmark uses query-demand inputs as part of its modeled-value calculations.

For the Kids & Family Graphic Apparel benchmark:

  • median monthly query-volume input was 100 in July 2026;
  • median monthly query-volume input was 80 in August 2026;
  • zero-volume prompts represented 3.9% of the July query set; and
  • zero-volume prompts represented 8.0% of the August query set.

These changing demand inputs are important when reading month-over-month modeled dollar values.

A change in modeled value can reflect a combination of:

  • AI recommendation outcomes;
  • recommendation placement;
  • the relative weight assigned to measured opportunities; and
  • changes in the demand inputs used by the model.

For that reason, modeled-value movement should not be interpreted as a pure visibility-change metric.

Current Interpretation Rule

Use Modeled AI Authority Value to answer:

How much relative modeled AI discovery opportunity does the benchmark assign to this brand or category under the active model?

Do not use it to answer:

How much money did AI recommendations generate for this brand?

Those are different questions.

Category-Level vs. Brand-Level Value

The benchmark can publish:

  • total modeled category opportunity for the measurement period; and
  • brand-level modeled AI Authority Value.

Why Demand Changes Matter

Suppose recommendation coverage stays stable while the demand inputs assigned to the measured source queries decline.

The model's dollar output may decline even if recommendation behavior does not materially worsen.

The reverse can also occur.

Therefore:

  • use recommendation coverage, top-three rate, and rank-one rate to describe recommendation behavior;
  • use Modeled AI Authority Value to describe relative modeled opportunity; and
  • do not treat dollar-value movement as a standalone causal explanation.

Formula Transparency

The public research program should disclose enough information for a reader to understand:

  • the principal inputs;
  • the purpose of each input;
  • how demand is incorporated;
  • how recommendation placement affects value, if applicable;
  • how zero-volume source queries are handled;
  • whether sentiment affects value;
  • whether values are normalized; and
  • when the model version changes.

If the complete weighting formula is proprietary, the benchmark should say so explicitly rather than implying full independent reproducibility.

The active public interpretation should therefore treat Modeled AI Authority Value as a comparative model output governed by the current methodology version.

Publication Rules

Where Modeled AI Authority Value appears:

  1. use the word modeled;
  2. never label the value as revenue;
  3. link to this page;
  4. disclose material demand-input changes when comparing periods;
  5. do not overstate precision;
  6. preserve the model version internally; and
  7. annotate longitudinal reports when a model change affects comparability.

Versioning

A change to any principal modeled-value input or weighting rule should be treated as a methodology change.

Examples include:

  • changing demand source;
  • changing demand normalization;
  • changing position weights;
  • changing recommendation-validity weights;
  • changing zero-volume handling;
  • adding or removing sentiment from the model; or
  • changing the economic conversion logic.

Where possible, historical values should be recalculated using the new model. Otherwise, the relevant industry page should mark the break in comparability.

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