Traditional software development relies on deterministic logic. You write a function with input A, and it returns output B every single time. You can write robust unit tests, deploy with confidence, and sleep soundly. Generative systems and Large Language Models, however, operate entirely on probabilities. Give them input A, and you might receive output B today, B-minus tomorrow, and a bizarre string of formatted text pretending to be a French recipe on Thursday. When teams at organizations like jarvislearn build technical training and real-world curricula, this unpredictability shifts the core challenge from writing code to managing chaos. Anyone exploring production systems must first confront the real disadvantages of artificial intiligence before allocating capital.