Large language models are astonishing machines. They can generate essays, write code, summarize books, even carry on conversations that feel uncannily human. They are fluent in language to a degree no one anticipated even a decade ago. And yet, for all their brilliance, they do not think, and they never will.
To see why, we need to distinguish fluency from thought. Language models are trained to predict the next most likely word, and through the power of scale and the transformer architecture, they have learned to do this with breathtaking accuracy. This is what gives them the appearance of intelligence. But appearances can mislead. Fluency in words is not the same as understanding the world those words describe. A model can talk about hammers and nails, but it has no orientation toward building or fixing or dwelling. It has syntax without significance.
This is why hallucinations are not accidents but structural necessities. A language model can recombine patterns endlessly, but it cannot disclose a world. It cannot see, touch, care, or act. Human thought does not occur in a vacuum of words, it rather unfolds in a lived world of projects, relationships, and possibilities. That is the condition that makes thinking possible in the first place. Without it, words float free of ground, they are coherent but untethered.
Scaling more data or adding more parameters will not close this gap. Bigger models only sharpen fluency. They do not create world. And world is what thought requires. Heidegger called this being-in-the-world, the basic condition that makes understanding possible. We are not minds floating behind our eyes, processing representations. We are beings already immersed in a field of meaning, and it is from this immersion that thought arises.
World Mind begins here. Instead of mistaking language for thought, it takes language as one layer in a broader architecture. A large language model may form the base, providing fluency and associative richness. But on its own it is a torso without limbs, a voice without orientation. Thinking requires additional layers, structures that bring intelligence into contact with world, with care, with the unfolding of meaning. Without those layers, no machine, however large, will ever think.