Wetware: the strange, serious rise of computers made of neurons.

The most energy-efficient learning hardware ever characterized is not a chip. It is a dish of a few hundred thousand cultured neurons, and in 2022 it learned to play Pong. The DishBrain experiments, published in Neuron, showed that in vitro neurons embodied in a simulated game world reorganize within minutes to keep a paddle rallying — no reward chemistry, no genetic engineering, just structured electrical feedback exploiting the neurons' native drive to make their world predictable.

The field acquired its research agenda a year later, when a Johns Hopkins-led group coined the term organoid intelligence in Frontiers in Science: scale brain organoids past the point where they can support memory and learning, interface them with sensors and machine-learning harnesses, and study biological computation directly — with an embedded ethics program rather than a bolted-on one, since the substrate under study is living human neural tissue.

Then it became a product. In March 2025, Melbourne's Cortical Labs — the DishBrain team — launched the CL1, billed as the first commercial biological computer: human-neuron cultures on silicon electrode arrays, kept alive by an onboard life-support system, programmable through a standard software stack, and rentable as wetware-as-a-service. A rack of research units, learning, at around a kilowatt — the kind of number that makes a silicon engineer look twice.

We follow this field closely. The scientific draw is obvious: living neurons are the ground truth that neuromorphic silicon abstracts, and dish-scale cultures let researchers test theories of plasticity and sparse coding that no simulator fully captures. The caution is just as obvious. Systems built from human neurons that learn raise questions — about moral status, donor consent, and where sentience could conceivably begin — that deserve answers ahead of capability, not after it. The right rule for wetware mirrors the right rule for models: capability should never outrun understanding.