The 3P Framework

In the AI Story

The framework is most useful for terminal value calculation, where uncertainty about the long-term competitive environment is unusually high. A single-point terminal value implies a precision that the analysis does not support. The 3P approach replaces it with scenario-based estimation: calculate terminal value under three or four narratives, weight by probability, use the weighted average. The result will differ from any single scenario, and the difference is a measure of the analytical honesty embedded in the calculation.

Applied to the SaaSpocalypse, the framework structures the ecosystem-vs-code debate. The pessimistic terminal scenario (AI disruption extends to the ecosystem layer; margins compress; growth stalls) is possible but not plausible — the historical evidence suggests ecosystem advantages survive technology transitions. The base case (ecosystem advantages persist; margins hold; growth moderates) is probable. The optimistic case (ecosystem advantages strengthen; AI proliferation increases demand for platform services; margins expand) is plausible but not probable. Weighting these — say, sixty percent base, twenty-five percent optimistic, fifteen percent pessimistic — produces a terminal value that incorporates the uncertainty rather than hiding it.

The framework also disciplines AI-side investment. The narrative that any specific AI company will capture dominant share of a multi-trillion-dollar market is possible — it has happened in prior technology cycles. It is not probable, because the Big Market Delusion framework predicts that aggregate market share assumptions across competitors exceed one hundred percent. Treating possible outcomes as probable produces the kind of overinvestment that defines bubbles. The 3P discipline forces honest probability weighting that reveals which AI investments are priced for plausible outcomes and which are priced for outcomes that are merely possible.

The discipline matters because human cognition systematically conflates the three categories. Vivid possible scenarios feel probable. Recent plausible scenarios feel certain. The 3P framework is procedural rigor against this cognitive tendency, forcing the analyst to articulate which evidence supports the scenario and how much weight that evidence deserves.

Origin

Damodaran has used variants of the framework throughout his career, with the explicit "3P" formulation appearing in his recent commentary on AI investment scenarios. The intellectual lineage runs through Bayesian probability theory and through Philip Tetlock's work on calibrated forecasting.

Key Ideas

Three categories, not two. Possible, plausible, and probable are distinct; conflating them produces specific analytical errors.

Probability weighting is the discipline. Each scenario receives an explicit probability; the weighted average becomes the input to valuation.

Terminal value benefits most. The framework's largest impact is on terminal-value calculation, where uncertainty is highest and single-point estimates most misleading.

Honest uncertainty over false precision. A weighted estimate incorporates the uncertainty rather than concealing it behind a single number.