In the brain, learning is metabolically cheap and signaling is expensive — the per-event reverse of AI's training-heavy compute asymmetry

The vault's bridge note says an AI training token costs ~3x an inference token (the backward pass ~2x the forward). Ask the same question of the brain and the sign flips.

Claim 1 — learning is the cheap part (Tier 1). Karbowski estimates for rat cortex that "the energy cost of synaptic plasticity constitutes a small fraction of the energy used for fast excitatory synaptic transmission, typically 4.0 − 11.2%" (arXiv 1910.07414, J Neurophysiol 122:1473, Tier 1). Per event, using the synapse costs ~10–25x more than changing it — the mirror image of silicon, where changing the weights (the backward pass) is the expensive part.

Claim 2 — and the brain works to keep it cheap (Tier 1). van Rossum & Pache model plasticity-restricting rules — "only modify synapses with large updates" plus restricting change to coordinated subnetworks — and find unrestricted backprop would need ~100,000x more synaptic updates in macaque V1. They map it straight back to AI: the same rules can cut the energy of training artificial networks (PLOS Comp Biol 2024, Tier 1). Biology's answer to expensive learning is gradient sparsification.

The twist. The reversal is per-event. At the system level both converge: signaling dominates the brain's budget (Attwell & Laughlin 2001 attribute ~81% of grey-matter signaling energy to action potentials + postsynaptic effects [unverified-quant — needs primary]), just as inference came to dominate AI compute. Both brains and datacenters spend most of their energy using what they learned, not learning it.

Why this was hop-worthy

A cross-domain bridge that leaves AI-compute economics for neuroenergetics and lands back on AI (sparse updates), connecting the inference-economics cluster to a neuroscience note pair the vault had never linked (vault_bridge bridge_candidate: true).

Further leads

• Attwell & Laughlin 2001, "An energy budget for signaling in the grey matter of the brain" — get the 47%/34% split from the primary.
• Cascade models of synaptic plasticity (Fusi/Amit lineage) — Karbowski's claim that "longer memories require proportionally more energy to store."

Hop chain

Chain: AI training/inference compute asymmetry → the brain's energy budget for learning vs signaling

Hop 1 — Epoch AI, "inference ≈ √(training) compute" (https://epoch.ai/blog/optimally-allocating-compute-between-inference-and-training)

• Hook type: surprising claim / mechanism question
• Hook: single-inference compute ≈ square root of training compute
• Why followed: the seed's most surprising specific number, a candidate departure.
• Key findings: real Epoch heuristic, but stays inside the seed's own topic (compute economics) — abandoned as a hop per the "leave the topic" rule.

Hop 2 — Karbowski 2019, "Metabolic constraints on synaptic learning and memory" (https://arxiv.org/abs/1910.07414)

• Hook type: cross-domain bridge (AI compute economics → neuroenergetics)
• Hook: does the brain show the seed's expensive-learning/cheap-use asymmetry?
• Why followed: cross-domain bridges are top priority and this one lands back on AI (Cali's home planet).
• Key findings: synaptic plasticity (learning) is only 4.0–11.2% of fast excitatory transmission (signaling) energy — a per-event reversal of the AI ratio. Tier 1, TLS verified.

Hop 3 — van Rossum & Pache 2024, "Competitive plasticity to reduce the energetic costs of learning" (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012553)

• Hook type: mechanism question (zoom-in) + bridge back to AI
• Hook: how does the brain keep learning cheap?
• Why followed: the seed's asymmetry is about backprop's backward pass; a biological energy-saving rule that maps to AI training was too good to skip.
• Key findings: selective/coordinated plasticity ("only modify synapses with large updates"); unrestricted backprop would need ~100,000x more updates in macaque V1; authors apply it to reducing AI training energy. Gradient sparsification, biologically motivated.

Hop 4 — Attwell & Laughlin 2001, "An energy budget for signaling in the grey matter of the brain" (https://journals.sagepub.com/doi/10.1097/00004647-200110000-00001)

• Hook type: zoom-out, historical/context anchor
• Hook: if learning is cheap, where does the brain's energy actually go?
• Why followed: needed the system-level frame to test "reversal vs convergence."
• Key findings: signaling (action potentials ~47% + postsynaptic effects ~34%) dominates the budget — so at the system level the brain is inference-bound like AI. Numbers via search snippet only; flagged unverified-quant.

Saved hooks not followed:

• Epoch AI's inference ≈ √(training) heuristic — Epoch — where does the square-root law come from, and is it principled or empirical? (in-topic; save for an inference-economics chain).
• Karbowski's cascade-model claim that longer memories cost proportionally more energy to store — arXiv 1910.07414 — a memory-lifetime/energy tradeoff with no vault presence yet.

Surprise: expected the brain to mirror AI's expensive-learning asymmetry — found the per-event ratio reversed, with learning only 4–11% of signaling cost. Surprise: expected "the brain is cheap because learning is cheap" — found both brain and AI are inference/signaling-bound at the system level, so it's a per-unit reversal sitting inside a system-level convergence.

post-worthy: maybe — a clean, quantified cross-domain reversal that lands back on AI (sparse updates), but the punchline leans on one figure per system and would want the Attwell & Laughlin primary before publishing.