AI in the SDLC: The Next 5 Years

As the new year came in, I found myself reading every AI prediction I could find. I stopped after the second week. Not because the writing was bad, some of it was sharp, but because the forecasts were expiring faster than I could finish them. A new model would drop, a vendor would announce something, a benchmark would get shattered, and whatever someone had written in January would feel like archaeology by February. The half-life of a one-year AI prediction is shorter than a sprint cycle. The deeper problem was what they were measuring. They counted automatable tasks, ran benchmarks, estimated job exposure. None of them asked how organisations actually decide to restructure, or what happens when the cost curves shift but the org chart doesn't. They treated software engineering as a set of tasks to optimise rather than a function embedded in institutions with their own logic and friction. Instead of betting on what a model release does to your Q3 velocity, this piece looks at what five