We are constantly fed a version of AI that looks, sounds and acts suspiciously like us. It speaks in polished sentences, mimics emotions, expresses curiosity, claims to feel compassion, even dabbles in what it calls creativity.
But what we call AI today is nothing more than a statistical machine: a digital parrot regurgitating patterns mined from oceans of human data (the situation hasn’t changed much since it was discussed here five years ago). When it writes an answer to a question, it literally just guesses which letter and word will come next in a sequence – based on the data it’s been trained on.
This means AI has no understanding. No consciousness. No knowledge in any real, human sense. Just pure probability-driven, engineered brilliance — nothing more, and nothing less.
So why is a real “thinking” AI likely impossible? Because it’s bodiless. It has no senses, no flesh, no nerves, no pain, no pleasure. It doesn’t hunger, desire or fear. And because there is no cognition — not a shred — there’s a fundamental gap between the data it consumes (data born out of human feelings and experience) and what it can do with them.
Philosopher David Chalmers calls the mysterious mechanism underlying the relationship between our physical body and consciousness the “hard problem of consciousness”. Eminent scientists have recently hypothesised that consciousness actually emerges from the integration of internal, mental states with sensory representations (such as changes in heart rate, sweating and much more).
Given the paramount importance of the human senses and emotion for consciousness to “happen”, there is a profound and probably irreconcilable disconnect between general AI, the machine, and consciousness, a human phenomenon.
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The products currently on the marketplace have architectures that are far more sophisticated than just an LLM. Even something as simple as “Deep Research,” which both Anthropic and Claude have available, is using multiple interconnected systems to provide a single response.
Consider Agentic AI, like Claude Code, where they’re using tools, analyzing the results of those tools, iterating, possibly calling out to MCP servers to do other things, etc.. The tools allow them to do things like read or modify files in the working directory, execute programs (i.e., your linter, installing dependencies, running your app), querying against your app itself, and so on.
And of course note that the single “Claude” box in that diagram has an architecture that’s more sophisticated than just being an LLM. At minimum, consumer facing LLMs generally have a supervisor that censors problematic inputs and outputs; this doesn’t make the system more competent but the same concept can be applied to any other sort of transparent wrapper.
It seems to me that we already have consumer systems that are doing what you described, and we’re already working on enhancing their architectures further.