What 25 Years of Deterministic Software Engineering Taught Me About Building AI Systems

After decades of traditional software engineering, the author shares insights on the unique challenges of AI development. Unlike conventional software, AI systems often show perfect test results yet degrade in performance, requiring a shift from absolutes to probabilistic thinking. This shift means focusing on distributions, thresholds, and trade-offs rather than binary outcomes, fundamentally altering how AI systems are built, tested, and deployed. The implications are profound, pushing engineers to rethink their entire approach to software development in the age of AI.