This new study (sciencedirect.com/scien…) tries to resolve a key ambiguity in the consciousness debates: “computation” in brains is not the same as computation in standard digital machines. Living brains “compute” through tightly coupled processes across many scales (molecules → cells → networks) under strict energy limits, mixing continuous signals (fields, gradients, oscillations) with discrete spikes. For instance, this helps explaining why conscious processing may depend on biology’s substrate and multiscale organization rather than only on abstract information flow. 

I think this is relevant for the ongoing discussion about AI and AGI.

In fact, the study also clarifies why today’s AI (including LLMs) is unlikely to be conscious even if it imitates intelligent behavior: current systems run on architectures designed to separate hardware/software and scales, while they mostly approximate continuous dynamics rather than physically realizing them. The relevance is very practical: if one aims for artificial consciousness, scaling existing digital models may be insufficient; instead, one would need new “hybrid” substrates/architectures that integrate continuous dynamics, real-time multiscale coupling, as well as energy-like constraints more like brains.

The scaling hypothesis, alone, cannot work, as I argued 2+ years ago: manlius.substack.com/p/…

The arguments that brain-like computation (and any genuine “semantic control”) depends on multiscale, energetically constrained, hybrid dynamics align with our claim that any realizable “logic of living systems” (royalsocietypublishing.…) requires complementary circuitry. In our recent framework, sparse hierarchical-modular networks provide exactly that circuitry, enabling plastic, interdependent, nonequilibrium dynamics that catalyze evolvability under energetic trade-offs (iopscience.iop.org/arti…).

Any serious discussion of AGI must reckon with the logic of complex biological systems: how multiscale, energetically constrained circuitry turns information processing into robust, adaptive, open-ended evolution. Metaphors are not enough (science.org/doi/10.1126…).

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