Bottleneck Scoring Model Flags Fatal AI Readiness Gaps Weighted Averages Miss

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A software developer has published a write-up detailing a flaw in standard weighted-average readiness assessments, where a team's critical weakness can be masked by strong scores in other areas. To address this, the author designed a bottleneck scoring model that caps the total score whenever a fatal precondition — such as lacking version control or human review — is absent. The mechanism reduces mathematically to Math.min(baseScore, cap), meaning the lowest applicable cap always determines the ceiling. The model is implemented in a fully client-side, open-source AI readiness assessment tool built with TypeScript, React 19, and Vite, with all user data stored locally and never transmitted externally. The article argues that AI-assisted development amplifies the consequences of missing safety practices, making bottleneck-aware scoring more appropriate than simple additive models.

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