How AI Changes the Engineering Model Itself
Source: SiliconANGLE
The argument that AI coding tools require different engineering practices—not just faster versions of existing ones—is gaining traction. Practitioners are discovering that AI-assisted development creates new failure modes: hallucinated dependencies, brittle abstractions, and unexpected behavior patterns that traditional QA doesn't catch. The industry is still hiring and organizing teams as if AI is a productivity multiplier for existing workflows, rather than recognizing that it changes what needs to be tested, reviewed, and architected at every level. Companies that treat AI as a bolt-on optimization will accumulate technical debt disguised as velocity.