Blog: AI Could Build Your MVP. That's Why You Need Better Engineers
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Research from DORA, METR, and GitClear's analysis of 211 million lines of code reveals a consistent pattern: AI coding tools accelerate output but correlate with less stable delivery, increased code duplication, and deferred technical debt. DORA's 2024 report found AI adoption dragging both throughput and stability down, while the 2025 report shows throughput gains but continued stability degradation. A METR study found experienced developers expected to be 20% faster with AI but were measured as 19% slower. The core argument is that AI moved the hard parts of software development rather than eliminating them — production discipline, architectural judgment, incident ownership, and reviewing AI-generated code for subtle errors remain human responsibilities. Senior engineers become more valuable, not less, because they know what to ask for, what to discard, and what will fail at scale. The conclusion is that companies need senior engineers embedded in their workflows to own what AI produces.
