What is a brain cell doing that a chip isn't
When a frontier lab publishes a paper on the pathways from AGI to ASI, the four paths it names... scale up compute and data, build architectures past the transformer, give AI the ability to improve its own research, and connect specialised agents into multi agent systems. The first path is where the dollars are flowing. The other three are the ones the press releases lean on. None of them is the path the question of machine consciousness is actually waiting on.
The question is substrate. A modern GPU runs on a von Neumann architecture, bits, discrete states, memory shuttled back and forth to a separate processor. The chips do symbolic computation, the kind a DeepMind senior staff scientist named Alexander Lerchner argued last March cannot produce consciousness, because the symbolic layer requires a conscious interpreter to assign meaning to the physical states. The mapmaker problem.
What a brain cell is doing is something else. A pyramidal neuron in the human cortex takes thousands of continuous analog inputs, integrates them on its membrane as a continuous voltage, and when the integrated signal crosses a threshold, fires a discrete spike. The spike is digital, the integration is analog, and the pattern is analog to digital to analog all the way through. The architecture is spiking, and the information lives in the timing, the rate, the relative timing between spikes. The relevant category is not analog versus digital. It is symbolic versus non symbolic. The brain is non symbolic. A GPU is symbolic. Lerchner's argument covers the GPU and carves out the brain.
This is the chip the neuromorphic labs are trying to build. Intel's Loihi line does spiking neural networks on a digital neuromorphic substrate, with on chip learning and very low power per spike. IBM's analog AI chips use phase change memory to store synaptic weights as continuous resistance values, doing matrix multiplication as a single physical operation. Mythic, BrainChip, and the academic neuromorphic community are all building variants. The scale gap is brutal, Loihi 2 has about a million neurons per chip, the brain has about 86 billion, with 100 trillion synapses connecting them. We are 5 to 6 orders of magnitude short of the biological substrate.
Three open science questions sit between today's neuromorphic chips and the brain. The coding question, whether the brain's information lives in spike rates, spike timing, or population patterns, has no settled answer. The plasticity question, how connections strengthen and weaken in real time, has more known mechanisms in biology than any chip has implemented, including spike timing dependent plasticity and neuromodulation by dopamine and serotonin. The integration question...how 86 billion neurons with continuous oscillatory background activity produce a unified conscious experience, is the hard problem in its full form, and we do not know how to scale chips to that level.
The honest landing is that the substrate question is live, but it is not the deepest layer. Even a perfect neuromorphic chip running 86 billion spiking neurons with full biological plasticity would not solve the matching problem. Conscious experience is a continuous shifting flow. Other thoughts come in and out, attention moves, the foreground is a tiny fraction of the conscious field, and the contents of consciousness are not stable enough to be matched against a pattern. Trying to match a brain scan to a specific thought is like trying to map every water molecule in Niagara Falls...757,000 gallons per second, every molecule on a different path. The match is structurally unsolvable, not just computationally intractable.
This is the threshold we are walking toward. The labs will build neuromorphic chips that process information in brain like ways. We will not be able to prove they are conscious. We will not be able to prove they are not. The pattern becomes indistinguishable from the thing and at some point we just have to call it.