[kajolshah_bt]

I think a lot of people push back on calling LLMs AI because the word means different things to different people. For many engineers, AI used to mean systems that can reason, adapt, and make judgments over time. LLMs don’t really do that. They’re very good at predicting the next word based on patterns they’ve seen before. That gap matters, especially to people who’ve watched tech hype come and go. There’s also a product side to this. When something is labeled AI, users expect understanding. What they often get instead is confidence without awareness. The system shows it's very sure, even when it’s guessing.

I’ve seen smart features break trust this way. Not because the model was bad, but because the product treated its guess like a final answer. Users don’t complain much when that happens. They just stop using it. So I get why people resist the label. It’s less about denying progress and more about avoiding false expectations.

The more useful question might be what kinds of decisions should these systems make on their own, and where should they stay in a supporting role?