We usually think of “world” and “language” as two very different things: the world is everything out there, and language is how we talk about it. But Heidegger turned this picture upside down. For him, the world is not just a pile of objects, it is the web of significance in which those objects matter to us at all. And language is not a set of labels pasted onto things, it is the way this web becomes articulated and shared. Both world and language are relational at their core in that they draw meaning from how things connect with one another, not from how they stand in isolation. This is precisely the insight that today’s transformer models accidentally confirm, because their success comes from simulating relationality rather than representing objects.
When a transformer processes language, it doesn’t store “facts” about the world. Instead, it positions words in relation to other words, building a high-dimensional map of co-occurrences and associations. A hammer shows up in proximity to nails, carpentry, and construction; “doctor” shows up near patients, hospitals, and treatment. This web of connections is what allows the model to generate fluent sentences, because it has learned patterns of relational significance. In that sense, transformers are not mimicking knowledge as a static set of truths, but something closer to Heidegger’s point, which is that what matters is the way meaning emerges through networks of relevance.
And yet, there’s a limit to how far this can go. Human worldhood is not just statistical association but lived significance. We inhabit time, care about outcomes, feel moods, and project futures. These existential structures prevent nonsense like saying Aristotle studied under Galileo, or that someone packs sunscreen for a rainy day. Transformers, by contrast, operate in a flat semantic field where all continuations are equally possible as long as they are statistically smooth. They can reproduce the form of worldhood but not its grounding. This is why they can sound so insightful while also producing hallucinations.
So the strange irony is that the transformer reveals something deeply true about the nature of meaning, that language and world are both relational webs of significance, while also demonstrating how fragile that structure becomes without the grounding of lived existence. The model’s fluency is a shadow cast by our own worldhood, captured in text. Its hallucinations remind us that meaning cannot be reduced to language alone, because the web of relations only holds together when tethered to the lived world.