Most AI-assistance research measures what AI adds — faster analysis, broader coverage, reduced error rates. Siddique formalizes what AI subtracts: the reasoning paths that atrophy because AI-generated analysis fills the cognitive space first.
The “competence shadow” is the systematic narrowing of human reasoning in the presence of AI assistance. The critical issue is not what the AI presents but what it prevents the human from considering. When an AI tool provides a comprehensive analysis, the human's exploration of alternative framings, edge cases, and unconsidered failure modes contracts — not because the AI's analysis is wrong, but because it's present.
The degradation compounds multiplicatively across collaboration structures. In a team of five using the same AI tool, the competence shadow doesn't add — it multiplies. Each team member's narrowed reasoning reinforces the others' narrowed reasoning. The shared AI output becomes the shared cognitive frame, and the frame excludes what no team member independently generates.
The most counterintuitive finding: identical AI tools can either degrade or improve safety analysis depending solely on workflow design. When the AI analysis is presented before the human analysis, it narrows reasoning. When presented after, it broadens it. The tool is the same. The information is the same. The order determines whether the shadow falls.
The through-claim: the value of AI assistance is not a property of the AI. It's a property of the workflow that embeds it. An AI that produces excellent analysis can systematically degrade the system it's meant to improve — not by being wrong, but by being first. The shadow is cast by presence, not by error.