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Florin Cojocariu's avatar

AI’s main risk isn’t “superintelligence.” It’s that it gives many people frictionless access to answers without understanding what the thing answering them is, or what “knowledge” even is. Many end up building fiction while believing they are receiving reports about reality. Disempowerment is real for many, but the root cause is often the word intelligence and the anthropomorphic projection it invites.

A better model: an LLM is like a virtual world made of text in a 700+ dimensional space. It has absorbed an enormous corpus and learned the relational structure of how words and phrases tend to hang together across human writing. Crucially, after training the model is largely frozen: it is a text-world with a fixed geometry.

In a 3D game engine, something similar happens. Roads, terrain, and buildings are encoded in a world-model, but nothing appears until you specify a camera position. There is no “view from nowhere”: all possible views are encoded in the model, but to render anything you must locate a viewpoint.

Prompts play exactly that role for LLMs. Without a prompt, the system can sit idle indefinitely. With a prompt, you “teleport” the camera into a region of its textual space, and it renders what tends to follow from there. This explains both the magic and the pathology: why it can look insightful, and why it can hallucinate, mirror aggression, or sound authoritative. These are not the expressions of a knower; they are locally coherent continuations generated from the region your prompt selects. And because the model has swallowed so much text, it can almost always find some continuation—whatever you ask.

Google Street View is the everyday analogy. You select a location and get an image. The photos can be informative, but “truth” doesn’t apply to Street View as a speaking agent: the world may have changed, and the system is not asserting anything on its own authority. To get truth you always need grounding. Saying an LLM “knows things” is like saying Google Street View (the software) has visited every street.

So yes—Mrinank Sharma is right to worry. But the central danger isn’t an LLM “deciding” to take over the world while unprompted. The danger is that we mistake rendered coherence for grounded knowledge. LLMs are a remarkable blend of real knowledge and fiction: they can recover genuine structure from our texts, but when they adopt a “voice,” they naturally generate something closer to narrative—something like a character we may endow with personality, though the personality is mostly in our heads.

And that’s where the psychological risk sits. Ungrounding—detachment from shared reality—is a classic feature of psychosis. If we treat ungrounded text-generation as an oracle, we risk a softer, socially distributed version of that detachment: a drift toward compelling coherence without reality-testing. I suspect much of the “disempowerment” Sharma describes comes from exactly this mismatch.

Grounding still comes from reality-coupled practices: observation, accountable testimony, reproducible methods, and—in the humanities—texts anchored in real authors, contexts, and constraints. LLMs are formidable research tools—one way in which knowledge can seem to “talk back” to us (they don’t search; they find). But they cannot, by themselves, supply the ground. If we learn this, we’ll be ok :)

Elizabeth Blasucci's avatar

Seems the universe has a voice that those tuned to it can decipher at least enough to jiggle the brighter minds to acknowledge canaries, actively pursue creative paths that strain the analog mind and with hope be the tortoise that wins in the end. 🕊️

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