Graham McNicoll

Designers and engineers get attached to their work. It makes sense; you spent real effort on it. When someone rewrites it or cuts it, it can feel like a reflection on you. With AI, this is happening more often. I am regularly in the middle of demoing our own product and realize it works differently than it did a week ago. Code has always been a depreciating asset, requiring maintenance and creating tech debt, and that is even more so now. Every line you write has a lifetime, and that lifetime is shrinking. With AI in the loop, it gets rewritten or replaced faster than ever. That is just the nature of the asset now. It says nothing about who built it. The reframing that helps is to focus on the larger goal: are we building something that our users want? What data do we have that proves this? By zooming out to the larger picture, you can spend less time thinking about sunk costs of the projects that are being changed, or of sparing the egos of those involved. What helps with this is showing real evidence that a new version of something is actually performing better. Teams that do this move faster and align better as a group with the shared goal in mind. They hold the users and the problem they are solving tightly and the current solution loosely. When the data says the new version is better, letting go of the old one is easy. What is one piece of work your team is defending right now that the data has not actually backed up?