I am guessing you mean AGI? They usually mean they’re not generally intelligent, i.e., displaying cognitive flexibility for different types of tasks without training or extensive fine-tuning. Think of a human intern, you don’t need to tell that human intern beyond a simple phrase when you need something. The intern will figure out how to do it, including figuring out what they don’t know, and what they need to learn to do that thing.
While the definition of intelligence is speculative, the way that LLMs work is well known. They don't apply any thinking or reasoning at all. It's not even conceptualized or attempted. Instead, they predict the most likely response to the prompt given the training data. We can all agree that statistical predictions alone are not intelligence.
It's a mix of trying to look blasé, of believing that a superficial knowledge of how they work is enough to dismiss them as purely mechanical, and probably genuine fear and denial for the implications of their existence.
LLMs are not just intelligent, they're general intelligences in that they're not limited to a single task (such as a chess-playing AI or a voice-recognition AI) but they're capable of any task that can be performed with text as input and output (which doesn't mean it's just text manipulation, the internals are not limited to text).
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?
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LLMs are not just intelligent, they're general intelligences in that they're not limited to a single task (such as a chess-playing AI or a voice-recognition AI) but they're capable of any task that can be performed with text as input and output (which doesn't mean it's just text manipulation, the internals are not limited to text).
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?