Nobody can know everything, obviously. Except AI. Or can it?
Large
Language Models (LLMs) have trained on an immense amount of texts. From
literature, through nerdy forums to the latest scientific papers. They
possess all this knowledge, which is lying in what experts call latent weights. I have asked ChatGPT to explain latent weights for me:
Once
training is complete, a language model no longer stores knowledge as
explicit facts. Instead, everything it has learned is compressed into
what can be imagined as a vast field of potential — a mathematical space shaped by patterns and relationships found in language. Experts refer to this field as the model's latent weights. These weights don't hold memories as such, but encode the structure of how words, ideas, and contexts relate.
This
field of potential, i.e. latent weights isn't floating in the air; it
exists physically, across thousands of interconnected servers. When you
send a prompt through your app or a website to ask a question, it
travels through this distributed system, and the LLM will generate a
response based on the most likely connections. And since there is an
endless number of possibilities this could be expressed, the response
will always be slightly different (and sometimes even incorrect).
So far this is just a simple explanation of what happens "behind the curtain".
However… what if it's more than that? What if this latent field isn't just a clever engineering trick — but a mirror of something much larger?
In May 2025, Dr. Melvin Vopson published a study
suggesting that gravity may be where underlying information manifests
as matter. According to the study, gravity may not be a fundamental
force but rather an emergent phenomenon arising from the universe's
underlying information structure. In this view, the universe operates
like a vast computational system, where matter and energy are
manifestations of information processing.
Can you see where this is leading? Could it be that large language models — with their latent informational fields and emergent outputs — offer a miniature, computational parallel to the informational structure of the universe itself?
Of
course, this idea is speculative. But so is every great theory before
it gathers evidence. What matters is that we ask the question — and look
closely at the patterns that may already be revealing something deeper.
I
would like to propose this to be studied further because if this is
confirmed, we could use LLMs as a model for our known universe and
possibly even more.
Summary
Large
language models don't store facts — they embody patterns of information
in a latent field. Recent research suggests gravity itself might emerge
from a similar field in our universe. Could LLMs be small-scale mirrors of cosmic structure — offering clues to the nature of reality?