Recursive generalization makes neurons 'clean' — agent swarms could crash alignment costs to 7 figures
daniel_mac8 · x · 2026-10-21
Two threads on the implications of Nick Cammarate's big interpretability paper:
- The paper's primary claim is that neural networks learn via recursive generalization: concepts start as large blobs and progressively branch into finer, more selective activations — contradicting the old assumption that semantically unrelated concepts are efficiently packed into neurons. If true, neurons are 'clean', each mainly belonging to a single inference geometry, removing the need for constructs like sparse autoencoders (SAEs) to untangle superposition. The principle may also apply to human brains.
- An underrated implication is collapsing alignment costs: a week ago "fully aligning" any model was impossible at any price; today, with a clear target, you could throw an agent swarm at it for perhaps 9-10 figures; in a few months 8 figures, then 7, and within years maybe bundled into a pro subscription — if agents parallelize understanding and publish their own experiments to build an external knowledge base.
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