Source: The Next Web
The gap between what AI coding assistants can do (produce syntactically correct, runnable code) and what they should do (write secure code) is hardening into a structural problem rather than a temporary growing pain. If security defect rates in AI-generated code remain flat even as compilation success climbs, the models are optimizing for the wrong objective function—rewarding completion over safety—and human code review is becoming a mandatory tax on any production deployment, not an optional quality gate. Companies adopting AI coding tools don't save labor proportionally; they shift the bottleneck from writing to auditing.