Solving a hard problem and staring blankly out the window cost your brain almost exactly the same.
The brain you are reading this with draws about 20 watts — less than the little bulb inside your fridge. It is under 2% of your body weight yet takes roughly 20% of your energy at rest. The genuinely strange part is something else: that bill barely moves. Whether you are doing algebra or daydreaming, the number differs by a few percent at most. Pull on that thread and the causality flips: it is not that the brain evolved into this shape and happened to be frugal. It is that the energy budget has been making its design decisions from the start — how many neurons it can keep, how many can be lit at once, how the wiring is laid out, and even why you have to cook your food.
Intuition says the expensive part must be the thinking. Open the books and the bulk of it turns out to go on something remarkably dull: restoring the status quo.
A neuron stores a kind of potential energy in the ion concentration difference across its membrane — like a dam holding water. Firing once (an electrical pulse a few milliseconds long) is essentially opening the sluice gate: ions rush through on their own, down the gradient, and that step costs almost nothing. The bill arrives afterwards, when a protein called the sodium–potassium pump burns ATP (the cell's universal energy currency) to haul every displaced ion back behind the dam. Every signal is paid for after it has already happened.
The most interesting line in the budget is that receiving costs more than sending. A neuron's own spikes take roughly a fifth, while being hit by other neurons — thousands of synapses opening ion channels on the receiving side — takes about half, because every one of them has to be cleaned up afterwards. (When Attwell and Laughlin first drew up this budget in 2001, spikes looked like nearly half the cost; a decade of better measurements cut that share dramatically.) And there is a line people forget: doing nothing isn't free — membranes leak, the pump must keep topping up, and that alone is a fifth.
Add it up and the cost structure of "mental work" looks nothing like a factory. It looks like a reservoir with the pumps running year-round. (How a spike actually happens: neurons & action potentials; the synaptic side: synaptic transmission)
Read that budget backwards and it becomes a hard constraint. In 2003 Lennie did a plain piece of division: work out the ATP a single spike plus its postsynaptic consequences costs, then divide the brain's 20 watts by it. The answer stops you short — the cortex can only afford about 1% of its neurons being substantially active at any moment, with average firing rates pushed below one spike per neuron per second.
So sparseness — only a tiny fraction of neurons active at any instant — isn't a taste neuroscientists happen to have. It is discipline enforced by the electricity bill. In 1996 Levy and Baxter reached the same place from pure theory: once energy enters the objective function, the code with the greatest representational capacity is no longer the optimal one, and the optimum slides towards low firing rates. The department enforcing the rule is inhibition, holding everything down and letting only the few that matter through (inhibitory interneurons).
This also resolves the opening paradox: how can a brain own 86 billion neurons and still burn only 20 watts? Because the bill tracks how many are active at once, not how many exist. Capacity and cost are two separate things here — you can own an enormous library as long as you only switch on one lamp at a time.
Artificial networks are the mirror image. In a dense matrix multiplication — the basic move of nearly every model today: multiply the input by a huge table of weights and sum — every single parameter has to be read out and multiplied. The bill tracks size, not how much of the model this particular input actually needed. That's why bigger models cost more, period. The fashionable fix, mixture-of-experts (activating only a small sub-network each time), is precisely a move towards the brain's arrangement: let cost track activity. How wide is the gap? Training GPT-3 once took roughly 1287 MWh (Patterson and colleagues' estimate) — the same electricity would run a 20-watt brain continuously for over seven thousand years.
Here is the most counterintuitive box on the page. If spiking costs money, surely energy use spikes when you concentrate? It doesn't: task-evoked increases are typically under 5%. The other 95%-plus is a baseline that burns regardless of what you are doing. Raichle gave it a name — the brain's dark energy.
That inverts a default assumption. The brain is mostly not an input-driven response machine. Almost all of its effort goes into itself: continuously maintaining an internal model of the world, the body and you, with sensory input arriving mainly to nudge that model. Which lands exactly on the Topic 1 thread — guess first, correct with evidence. The most visible territory of this ongoing activity is the network that lights up when your mind wanders (the default mode network).
Two popular claims can be cleared away in passing. "Thinking burns lots of calories": the increment adds up to a few dozen kcal a day, less than a small handful of nuts — mental work is exhausting, but what it exhausts is mostly not energy (Topic 34 covers this family of misreadings). "We only use 10% of our brains": the energy books rule it out — the organ already takes a fifth of your budget, and evolution does not pay that for tissue idle 90% of the time.
There's a by-product worth knowing: fMRI exists only because the local response overshoots. When a finger is lightly stimulated, blood flow to that patch of cortex rises about 29% while oxygen consumption rises only about 5%. The surplus oxygen stays in the blood and changes its magnetic properties — that difference is what the scanner reads. The bright blob on an fMRI image is blood, not spikes. (The supply machinery: brain energy supply & neurovascular coupling)
Large language models show something that looks similar from outside: whether you ask "what's 1+1" or hand it a proof, one forward pass (running the input through the whole network once) costs a fixed amount of compute, independent of difficulty — just as the brain's bill doesn't track task difficulty. The mechanism is entirely different, though: the model has to push every parameter through every time, whereas in the brain most of the spending was already going to intrinsic activity. What's interesting is that both are loosening. Models have recently learned to think for longer — spend more compute generating intermediate steps on harder problems, known as test-time compute. The brain can't really do that: its budget is hard, so it can only reallocate rather than turn the total power up.
The budget governs something larger still — the shape of the thing.
It has almost no warehouse. Unlike muscle, the brain keeps no meaningful fuel reserve; blood delivers everything just in time. Cut the flow and consciousness goes in about 10 seconds, with irreversible damage in minutes. To keep the supply line matched to demand, the brain runs a fine-grained piece of infrastructure: whichever patch gets busy, its vessels widen within seconds (brain energy supply & neurovascular coupling).
Wiring is charged by the kilometre. An axon (the "cable" a neuron sends out) gets more expensive the longer and thicker it is: it takes volume, it needs myelin, and holding its potential keeps the pumps working. So connectivity everywhere settles into the same pattern — a vast number of short local connections plus a scarce few long-range ones. This isn't simple minimisation: pure thrift would pack everything together and forbid distant contact, and information would crawl. Bullmore and Sporns call it the economics of brain networks: a running negotiation between saving money and taking shortcuts. Out of that negotiation come the few hubs and many local clusters we actually see — along with cortical folding and the myelin on long axons (which both speeds signals up and saves energy).
Finally, it is expensive enough to compete with the rest of the body. In 1995 Aiello and Wheeler proposed the "expensive tissue hypothesis": humans afford a big brain by trading away gut (the specific trade didn't hold up well in later, broader cross-species data, but the core point — this organ is costly enough that something must give — survived). Herculano-Houzel's arithmetic is blunter: roughly 6 kcal per day per billion neurons, so 86 billion runs to around five hundred kcal a day. Raising that on raw food would have great apes chewing eight or nine hours a day, with no room left to add neurons. Cooking — outsourcing part of digestion to fire — is what lifted the ceiling. Part of the reason you have this brain is that somebody learned to cook first.
"Energy is limited" sounds like an engineering footnote. It is actually the shared starting line of several very different disciplines: