The Productivity Paradox: Why More AI Code is Slowing Down Shiptimes

SUMMARY: Discover how AI is transforming software development and what it means for engineering leaders.
GUEST: Jeff Keyes (linkedin.com/in/jeffdkeyes), Field CTO at AllStacks (allstacks.com)
SHOW: 1017
SHOW TRANSCRIPT: The Reasoning Show #1017 Transcript (docs.google.com/document/d/1oGVdDkcLZ4B8k8Fnu5…)
SHOW VIDEO: youtu.be/cXPu8iWeB0k
SHOW SPONSORS:
• ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! (sharegate.com/protect)
• Nasuni - Activate your data for AI and request a demo (nasuni.com/AI)
SHOW NOTES:
Topic 1 - Welcome to the show. Tell us a little bit about your background, and what you focus on these days at AllStacks.
Topic 2 - You’ve been talking to a lot of engineering leaders using AI coding tools—what’s the most surprising gap you’re seeing between increased code generation and actual delivery outcomes?
Topic 3 - Why does increasing developer output with AI often lead to more debugging, duplication, or cleanup instead of faster delivery?
Topic 4 - You’ve described an ‘invisible rework loop’—can you walk us through what that looks like inside a modern engineering team?
Topic 5 - As code generation gets easier, where does the real bottleneck shift in the software delivery lifecycle?
Topic 6 - How do unclear product or engineering specifications get amplified in an AI-assisted development environment?
Topic 7 - If traditional metrics like lines of code or velocity are becoming misleading, what should engineering leaders actually measure to know if AI is improving delivery?
Topic 8 - What does a ‘healthy’ AI-assisted development workflow look like 12–18 months from now?

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