The original title is: "Bottleneck Scoring: Why Weighted Averages Fail AI Readiness Assessments"

Short summary

The article argues that weighted-average scoring for AI readiness assessments is structurally flawed because it dilutes catastrophic weaknesses — a team without Git can still score high. The author designed a bottleneck-scoring model that caps total scores when fatal preconditions are missing, reducing the mechanism to Math.min(baseScore, cap). Caps sit just below level boundaries to encode the highest achievable level while carrying a specific gap, with the lowest cap winning when multiple fire simultaneously.

• •Weighted-average scoring dilutes fatal weaknesses — a team without Git can still score high on AI readiness
• •Bottleneck scoring caps total scores when preconditions are missing, using Math.min(baseScore, cap)
• •Caps are placed below level boundaries to encode maximum achievable level while carrying a gap; lowest cap wins

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