About
How searching for better AI memory leads to proving why it's impossible.
The starting point: memory for AI agents
I'm not a neuroscientist. I'm a software developer and physicist — and in 2026 I simply wanted to build AI agents with better memory. The question seemed technically solvable: how do you extract the meaning of a text from a language model in order to store it efficiently and retrieve it later?
The first problem: meaning has no fixed location
What I found was frustrating: the final hidden layer of a transformer contains no clean, extractable representation of meaning. Instead, every meaning is distributed as superposition — a simultaneous overlay of many directions — across the entire activation space. You can't cut it out without destroying everything else.
The detour: computational neuroscience
To understand why, I went deeper — into computational neuroscience, graph theory, wave physics on networks. How does the brain solve this problem? It turns out: it doesn't. The brain doesn't store meaning locally either. Meaning *emerges* as a global interference pattern — not in one place, but as a state of the whole system. This led to the AHT equations: a formal model of meaning, memory, and experience as wave dynamics on the connectome.
The proof: what I originally wanted to solve is unsolvable
The strange thing happened at the end: by understanding why meaning in the brain is not locally stored but emerges as a global eigenmode pattern through wave interference, I could apply the same mechanism to transformers — and prove that incremental learning in dense weight matrices is geometrically impossible. Not difficult. Not inefficient. Structurally excluded. That's exactly the question I started with. The detour was the answer.
A further discovery: humans are automata
The same theory leads to a further, uncomfortable conclusion. The human is a system that resonates meaning, avoids pain, flees boredom — and in doing so imagines itself to be deciding. What we call consciousness is not an independent phenomenon but the correlate of attractor dynamics: the experience that arises when a wave field returns to a stable eigenmode state. The question of whether AI can ever achieve consciousness therefore does not arise — because we don't have one either, at least not in the sense the question presupposes.
What now
The theory is developed across several papers, and the mathematical proofs have been verified by two independent proof assistants (Lean 4 and Isabelle/HOL). Whether the brain model is correct will have to be shown by experiments. But the impossibility theorem stands — regardless of what the brain actually does.
Feedback
I welcome any feedback — especially errors in my arguments or proofs. Anyone who finds a mistake helps the theory more than someone who agrees. You can reach me at andreasa.bean@beanbox.at.