LLMs Are Not Deterministic. And Making Them Reliable Is Expensive (In Both the Bad Way and the Good Way)

Let’s start with a statement that should be obvious but still feels controversial: Large Language Models are not deterministic systems. They are probabilistic sequence predictors. Given a context, they sample the next token from a probability distribution. That is their nature. There is no hidden reasoning engine, no symbolic truth layer, no internal notion of correctness. You can influence their behavior. You can constrain it. You can shape it. But you cannot turn probability into certainty. Somewhere between keynote stages, funding decks, and product demos, a comforting narrative emerged: models are getting cheaper and smarter, therefore AI will soon become trivial. The logic sounds reasonable. Token prices are dropping. Model quality is improving. Demos look impressive. From the outside, it feels like we are approaching a phase where AI becomes a solved commodity. From the inside, it feels very different. There is a massive gap between a good demo and a reliable product. A demo is u