The term artificial intelligence is broader than many people realize. It doesn’t refer to a single technology or a specific capability, but rather to a category of systems designed to perform tasks that would normally require human intelligence. That includes everything from pattern recognition, language understanding, and problem-solving to more specific applications like recommendation engines or image generation.

When people say something “isn’t real AI,” they’re often working from a very narrow or futuristic definition - usually something like human-level general intelligence or conscious reasoning. But that’s not how the term has been used in computer science or industry. A chess-playing algorithm, a spam filter, and a large language model can all fall under the AI umbrella. The boundaries of AI shift over time: what once seemed like cutting-edge intelligence often becomes mundane as we get used to it.

So rather than being a misleading or purely marketing term, AI is just a broad label we’ve used for decades to describe machines that do things we associate with intelligent behavior. The key is to be specific about which kind of AI we’re talking about - like “machine learning,” “neural networks,” or “generative models” - rather than assuming there’s one single thing that AI is or isn’t.