The "agentic coding" trend positions AI as the primary code generator while developers become "orchestrators" who review outputs and steer AI agents. This approach promises efficiency but comes with significant trade-offs: increased system complexity to handle AI's non-determinism, growing skill atrophy among developers, and dangerous vendor lock-in risks as teams become dependent on specific AI platforms.

A critical paradox emerges: effectively supervising AI-generated code requires the very coding skills that deteriorate through overuse. Studies already show developers experience skill erosion within months of regular AI tool use. Senior engineers report losing their "firm mental model" of applications, while junior developers face steeper learning curves when they're limited to reviewing rather than writing code.

Contrary to industry hype, LLMs prioritize speed over understanding and conciseness—values that conflict with sound development practices. The industry's focus on token usage and rapid generation ignores that coding equals planning for many developers who think better through actual implementation rather than abstract specification. Without the friction of writing and debugging, developers lose the skills needed to effectively manage the tools meant to replace them.