We'll explore how the AI hype cycle has created a tendency to reach for complex solutions where simpler ones would work better. Drawing from real-world deployment experiences, this talk examines the common practice of deploying AI agents and LLMs when a pseudorandom number generator or basic rule engine would be more appropriate and maintainable. We'll consider how this "AI-first" approach often leads to unnecessary complexity and systems that are harder to debug and maintain. The difference between experienced engineers and newcomers isn't knowing how to build sophisticated AI systems—it's knowing when not to build them. We'll look at practical approaches to technology selection, where complexity is driven by actual requirements rather than trends. The talk will demonstrate how to identify the right point on the spectrum from simple randomization to advanced neural networks, examining the trade-offs between over-engineering and under-engineering. These concepts will be illustrated through a basic insurance system analysis, showing how different architectural decisions performed in practice and what we learned from both successful and failed approaches.