AI coding assistants have mostly sped up code generation, but the failures that actually sink software projects—contradictory requirements, weak architecture decisions, blind spots in test coverage, and murky accountability during incidents—happen elsewhere in the lifecycle. The next phase of AI-native development means letting AI participate in requirements analysis, risk-based test selection, and production deployment/observability decisions, not just writing code faster. Organizations that redesign their workflows around AI's actual strengths, rather than bolting AI onto existing human-centric processes, will see fundamentally different outcomes than those chasing velocity metrics alone.
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