Your brain thinks on about 20 watts. An artificial system doing the same work would need roughly 25 million times more energy, enough, on paper, to supply a quarter of a million homes. Matching the complexity of a human cortex has been a long standing goal in computing, but the power cost is enormous. Researchers have estimated that a human scale model of about 20 billion neurons, run on an exascale machine, would draw around 0.5 gigawatts, or 500 million watts. That is the same order of demand as running hundreds of thousands of average households at once. It is a hard limit on any plan to copy the brain by throwing more conventional silicon at the problem. Biology makes the contrast sharper. The living brain does its work on the power of a dim bulb. An exascale simulator would need the budget of a small city. That gap is why labs have turned to neuromorphic chips, hardware that copies the sparse, event driven style of real neurons instead of burning watts on dense matrix math. Until those architectures are mature, a full, faithful simulation of human cognition stays behind a power bill that no ordinary data center can pay. [Thakur, C. S., et al. (2018). Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain. Frontiers in Neuroscience, 12, 891]