Part 6/16:
Fowler emphasizes that moving from deterministic hardware and software systems to non-deterministic AI environments requires a paradigm shift. Traditional engineering practices, including testing, refactoring, and design patterns, depend on certainties—things that go wrong can often be pinpointed and fixed.
However, AI introduces a level of uncertainty akin to tolerances in structural engineering—accepting variable outcomes within certain limits. This necessitates developing new approaches for quality assurance, risk management, and understanding potential failures.