Transformers work because they accidentally approximated an ontological structure.
At the heart of every large language model lies the self-attention mechanism. Each word, or token, gets to “attend” to every other word in the sequence, weighted by relevance. This is what allows a transformer to capture context and coherence with such uncanny fluency. But looked at from a Heideggerian perspective, what the transformer is really doing is not logical deduction but something closer to disclosure, that is, meaning does not arise in isolation but within a field of significance where some elements solicit our attention more than others.
For human beings, this field is structured by mood, care, and readiness-to-hand. A hammer shows up as meaningful not because of its shape or material, but because it belongs to the activity of building, repairing, dwelling. Transformers succeed not because they solved representation, but because they mimicked contextuality, i.e., the way things show up in relation. What self-attention builds is a kind of formal skeleton of world-disclosure.
But it is only a skeleton. Transformers do not inhabit a world. They have no care, no embodiment, no moods that open or close possibilities. Their salience is statistical, not existential. The urgency of meaning, the way a tool matters to a task, or a word to a conversation never enters the system. What the architecture reveals, in other words, is how much can be achieved by approximating contextual significance without actually living in it.
This irony becomes sharper when we remember that transformers were developed within the old representational paradigm of AI. Intelligence was still assumed to be symbol manipulation. Learning was still framed as statistical optimization. Meaning was reduced to patterns of token co-occurrence. The astonishing thing is that, by building machines that act as if they grasp significance, researchers inadvertently stepped into Heidegger’s territory while still thinking in Cartesian terms.
That is why hallucination is not a bug but a structural feature. The model performs as if it knows, but under the hood it is only predicting the next token. The breakthrough was ontological in effect, but accidental in design. Self-attention is not thought, but a ghostly outline of what makes thought possible.
Which is why the transformer is both a revelation and a dead end. It shows that intelligence requires contextuality, but it cannot supply the deeper structures, such as worldhood, projection, and care that make contextuality matter. To assume that scaling alone will produce selfhood is to miss the question entirely. If intelligence is being-in-the-world, then transformers only gesture at the surface of a world they can never enter.
This is where World Mind begins. The transformer showed us, by accident, that intelligence is not about stacking representations but about living in relations of significance. Yet because transformers lack world, mood, and care, they can only simulate the surface of meaning without ever touching its depth. World Mind takes this lesson seriously. Instead of hoping that scale will somehow conjure selfhood, it seeks to build an architecture grounded in the ontological structures that make understanding possible in the first place. The promise of AI lies not in ever-larger statistical engines, but in discovering how to give machines a share, however partial, in world-disclosure itself.