Part of why the NLP community is so excited about it is because text prediction as an optimization problem eventually necessitates some form of intelligence in order to reduce the loss, and the architectures we're using scale nearly linearly in quality vs size and show no real signs of diminishing returns, meaning you can make them arbitrarily smart just by making them bigger.

I would encourage you to consider what you mean by "real" intelligence and "just text prediction", because AI throws a lot of our assumptions out the window. Talk to GPT-4 in a chatbot cognitive architecture for a few hours and you get a sense of just how intelligent it can be (with the right prompting), but the architecture itself is literally incapable of "thinking" (some wiggle room for inter-layer states) - that is, internal, stateful, causal processes which drive external behavior. A chatbot CA can vaguely approximate it via chain of thought prompting, but without that it essentially has to guess what its thoughts "would" be if it had them, which is very weird and hard to understand intuitively.

In case it isn't clear, what I mean by "cognitive architecture" is the machinery surrounding the language model which lets it interact with the world. A language model in isolation is a causal autoregressive inference engine that will happily autocomplete anything. They are not chatbots, only components in chatbots - that's just the modality we're most familiar with because ChatGPT broke ground, but it's not their only or even most useful form. The LLM is comparable to a human broca's area, which will generate an endless stream of language if you let it. It's the neural circuitry around it which give rise to coherent thoughts and subjective experience.

To be able to accurately discuss these concepts, we need to change the language we use. Words like "intelligence", "consciousness", "sentience", "sapience", etc have always been incredibly vague, approaching completely undefined. They can't be adequately applied to AI until they've been operationalized, such that you could objectively falsify whether or not they apply to a given system.