Measuring AI’s Unintended Consequences

AI is great for making teams faster. But is it sustainable? What about maintainability, and burnout, and lock-in? This is part 3 of my series on quantifying AI’s impact on software development. In part 1, we looked at why assessing impact is important, and what not to do. In part 2, we looked at how to measure AI’s impact on delivery speed. In this part, we’re looking at how to measure and model the unintended consequences of using AI. Then we’ll wrap up in part 4 (coming September 29th) with an examination of business outcomes. Finally, an epilogue puts it all together with notes you can share with your CFO. To be notified when next week’s update comes out, add my feed to your RSS reader or subscribe to my free mailing list. Details here. Does Maintainability Even Matter? I can hear it now: “Who cares about namby-pamby issues like maintainability and burnout? It’s a job. If jobs were supposed to be fun, they wouldn’t be called ‘work!’ Suck it up!” So let me be clear: we care about…

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