The AI engineering landscape is shifting from coding to orchestration as teams battle code overload. Meta's Global Head of Autonomous ML, Shah Rahman, cuts through the hype to define what AI-native engineering actually requires: context engineering, spec-driven development, critical verification, and disciplined problem decomposition. This framework separates productive 10x engineering from faster failure.

AI-generated code quality approximates early-career developers, with studies showing 45% contains security flaws. Successful teams implement spec-driven workflows that break problems into discrete milestones with clear validation points. The bottleneck has permanently shifted from writing code to proving it works at scale with reliability and security.

Context engineering emerges as the single most important skill for AI-native engineers, involving systematic curation of project-specific information. Teams practicing rigorous context engineering report 40-50% speed increases and dramatically reduced alignment overhead. Avoid over-trusting AI with large problems; instead, break tasks into AI-manageable chunks where humans handle edge cases.