LLMs Aren't AI—They're Fancy Word Predictors

Call it AI if you want. But what's actually running is a predictive algorithm. You feed it a massive pile of text—scraped from the web, books, code repositories, whatever—and it learns patterns about which words tend to follow which other words. That's the whole trick. It's not reasoning. It's not understanding. It's statistical pattern matching at scale, and honestly, that's already pretty wild without pretending it's something it's not.

Why does this matter? Because when you market something as artificial intelligence, people think there's actual intelligence happening. Comprehension. Intent. But an LLM is just very confident at guessing the next token based on what came before. Train it on stolen data, add billions of parameters, and suddenly it sounds smart enough to pass a coding interview. But ask it something outside its training distribution and it hallucinates, confidently throws out fake citations, and confidently does it all again tomorrow.

So when you see someone call their product AI, start asking questions. What's the actual mechanism? Is it a neural network doing what neural networks do, or is there something else happening? Most of the time, it's just an LLM doing what LLMs have always done: predicting the next word, one probability distribution at a time.