AI coding speed doesn't guarantee business results

The article highlights a paradox in AI adoption: despite faster development, organizational productivity and profits may not improve. The problem lies not in the tools but in the lack of human evaluation. When developers skip critical thinking and rely on AI or no-code templates, they miss essential checks, leading to failures like unset alerts. This erodes their evaluation skills, creating a dependency loop.

Sugimoto's analysis suggests that bridging the gap requires deliberate processes for review and feedback. Companies must ensure that AI-generated code or configurations are scrutinized for quality and fit. Without this, speed becomes counterproductive, as issues emerge downstream. The data from McKinsey and METR underscore the disparity between perceived and actual benefits, emphasizing the need for a balanced approach that preserves human judgment.