If we could have success with spiking neural networks in silico they would take even less energy, because they don't require global co-ordination. Co-ordination is information and "information = energy by the second law of thermodynamics" is my crank proof
Also the brain has way more parameters than LLMs and also has different neurotransmitters, loops, branching etc so they probably have WAY more capacity than LLMs.
But coding output/W LLMs have us beat
I frankly don't believe in spiking neural networks giving any advantages over what we have. It's a different way to implement ANNs, but "different" isn't "better". It's how the brain does things, sure, but the answer to "why the brain does what it does" is "workarounds for being made of flesh issues" at least half the time.
I can believe in brain having more capacity than frontier LLMs quite easily. We know a single BNN neuron can have the expressiveness of many ANN neurons. And well leveraged overparametrization + compute overhang could explain a decent chunk of the apparent sample efficiency edge.
But that apparent "extra capacity" could also be tied up in things like neurons having to contend with metabolism, in brain's learning algorithms being noisy, in brain having to use neuron circuits to implement "hot memory", etc - instead of contributing only to performance.