Code reasoning practice when autocomplete is everywhere.

Modern AI coding tools are excellent at filling in syntax, drafting scaffolds, and proposing edits. Used well, they make engineers faster. Used without practice, they can slowly hollow out the "read it, predict it, test it in your head" habit that separates a good code review from a rubber stamp. The Code Skill exists so that habit gets daily reps.

The early evidence on this is real but narrow. The Anthropic study (52 participants, a single unfamiliar Python library, 2026) measured skill formation across an AI-assisted and an unassisted cohort and found a ~17-point comprehension-quiz gap (50% vs 67%) favouring the hand-coding group — coverage at InfoQ called this "reduced skill mastery" (InfoQ summary). Practitioners writing in adjacent venues — Addy Osmani on skill atrophy (Substack), VirtusLab on what they call "cognitive debt" in shipped code (VirtusLab blog) — describe a consistent on-the-ground pattern: engineers shipping more code they would not have shipped if asked to write it themselves.

Trade and tech press has carried the same story in less measured form. CIO covered "AI use may speed code generation, but developers' skills suffer" ( CIO); Futurism ran an interview piece titled "Software Engineers Say They're Losing the Ability to Code Now That AI Does It for Them" ( Futurism). These are anecdote-heavy and should be read as signal-of-direction, not proof. Senwitt's framing is the cautious one: when the easiest path is to outsource a thinking task, the skill behind it benefits from being practised on purpose.

Senwitt does not claim that completing Code reps will make you a senior engineer, ship better production systems, or compensate for not learning a language properly. It claims that reading code and predicting behaviour is a skill, and that skills you practice tend to stay sharper than skills you do not.