The AI skills gap is real (but it’s not what you think)

Engineering teams are misidentifying the AI skills gap by focusing on coding speed rather than the real bottlenecks. The true gap lies in understanding architecture, slicing problems into testable increments, connecting code to business value, and building effective developer abstraction layers. The ZIRP era (2008–2022) masked deep engineering dysfunction by allowing teams to hire their way out of systemic problems. Now, with AI amplifying both productivity and defects (a 41% increase in bug rates found in research), leaders must upskill existing engineers, resist the temptation to 'staff up with AI,' and build organizations oriented around outcomes and continuous learning rather than ticket-taking.