The brain's per-event learning/signaling energy ratio reverses AI's, yet at the system level both are signaling/inference-bound

Two facts about brain energy point in opposite directions and resolve at different scales. Per event, learning is the cheap operation: synaptic plasticity is only 4–11% of fast excitatory transmission (claim-synaptic-plasticity-cheap-fraction-of-transmission-energy), the reverse of the artificial-network ratio where the training/backward pass is the expensive part (claim-training-inference-compute-asymmetry-mechanism). At the system level, that reversal is contained inside a convergence: signaling, not learning, dominates the brain's total energy budget. Attwell & Laughlin's 2001 grey-matter energy budget attributes the great majority of signaling energy to action potentials (~47%) plus postsynaptic effects (~34%) — roughly 81% combined — with the metabolic cost of ongoing signaling far outweighing that of weight change.

The parallel to AI is structural. Inference — running the trained model — came to dominate AI compute at the system level as deployments scaled (claim-inference-dominant-ai-compute-2026, claim-ai-inference-means-running-a-model), even though a single training run is enormously more expensive than a single inference. Both systems spend most of their energy using what they learned, not learning it, for the same underlying reason: inference/signaling events vastly outnumber learning events. The cross-domain twist is that the per-event ratio inverts between substrate and silicon while the system-level ledger converges.

Sourcing update (2026-07-27). The specific 47%/34% split is now grounded on Attwell & Laughlin's own 2001 abstract, promoted as its own claim-note (claim-attwell-laughlin-2001-grey-matter-signaling-energy-split); the earlier [unverified-quant] flag (search-snippet only) is discharged and question-verify-attwell-laughlin-2001-grey-matter-energy-budget is answered. Two caveats now attach in its place. First, the figure is dated: Howarth, Gleeson & Attwell (2012) revised the cortical total from ~81% to ~71%, cutting the action-potential share from 47% to 21% (claim-howarth-2012-revised-brain-energy-budget-lowers-action-potential-share) — the "convergence" argument survives either way, since signaling still dominates by a wide margin under both estimates. Second, verification reached the paper's abstract, not its paywalled full text, so the note stays seedling. The qualitative claim (signaling dominates the brain's budget) is well established.