Dev Interrupted Stop measuring AI adoption. Start measuring AI impact. | LinearB’s APEX framework
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Apr 7, 2026 They introduce APEX, a framework for measuring AI's real impact on software delivery. Conversations cover why DORA and SPACE fall short for AI-driven work. They explain treating AI as a first-class SDLC contributor and avoiding the illusion of coding speed. The four APEX pillars—AI Leverage, Predictability, Efficiency, and Developer experience—shape where teams should focus first.
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Faster Coding Is Often An Illusion
- AI tools create a strong sense of individual speed by increasing volume of work developers can produce.
- Ben Lloyd Pearson notes executives see adoption and expect 10x speed, but downstream systems often can't absorb the volume.
Measure Impact Not Just Adoption
- Adopt APEX to balance AI adoption with predictability, efficiency, and developer experience rather than tracking AI usage alone.
- Dan Lines explains APEX's four pillars: AI Leverage, Predictability, Efficiency, and DevX as a balanced operating model.
Upstream AI Gains Get Lost Downstream
- Upstream acceleration from AI is frequently lost to downstream chaos like slow reviews and deployment bottlenecks.
- Dan Lines cites benchmark data showing many AI-created PRs either take longer to merge or never get merged at all.