The AI Velocity Trap: When 'Shipping Faster' Actually Means Delivering Less Value
How AI coding assistants create false productivity gains while teams unknowingly sacrifice code quality and business value.
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How AI coding assistants create false productivity gains while teams unknowingly sacrifice code quality and business value.

Why development fundamentals become critical when AI assistants join your workflow – and how to avoid common integration pitfalls.

Technical improvements fail when teams don’t perceive productivity gains. Great teams align actual productivity with perception of productivity through disciplined practices.

Software engineering chases exciting tools over boring measurement discipline. But organizations achieving sustainable productivity gains embrace systematic practices that generate reliable data and build institutional knowledge. The competitive advantage isn’t the most innovative tools—it’s consistently measuring what matters in your specific context while competitors chase trends.

Este articlulo es una mirada al aprendizaje de trabajar un mes codificando con AI.

When observing AI productivity claims, we need to be able to judge the objectivity and skin in the game of our sources.

AI code assistants promise revolutionary productivity gains, with some vendors claiming 50% or higher improvements in developer output.
In this post I do a simple sanity chack ROI to show that the numbers do not add up!

The Code generation tool market is projected to grow to $12 billion by the end of 2028.
But what is the real impact on productivity?

Integrating Shortest Processing Time, Priority Inheritance, and Unplanned Work Management creates systematic high-performance development teams that deliver predictably.

Unplanned work consumes 30-50% of development capacity. Separate capacity allocation and systematic management restore predictable sprint delivery.