Prasma is pioneering a new computing paradigm by using live human neurons, differentiated from stem cells, to perform computational tasks like token prediction. This "brain in a vat" approach leverages biology's inherent power efficiency and continual learning capabilities, offering a potential long-term alternative to silicon-based AI.

Companies are now growing human brain cells on silicon chips and offering cloud API access for developers to code to them. This bio-compute model, which taught neurons to play a video game in a week, is vastly more energy-efficient than traditional GPU clusters, heralding a new computing paradigm.

The next frontier of brain-computer interfaces (BCIs) moves beyond implanting electrodes. Researchers are developing interfaces where a user's own neural stem cells are grown onto a silicon chip. This biological hybrid then integrates with the brain, creating a seamless connection to cloud-based AI.

Challenging Neuralink's implant-based BCI, Merge Labs is creating a new paradigm using molecules, proteins, and ultrasound. This less invasive approach aims for higher bandwidth by interfacing with millions of neurons, fundamentally rethinking how to connect brains to machines.

The primary motivation for biocomputing is not just scientific curiosity; it's a direct response to the massive, unsustainable energy consumption of traditional AI. Living neurons are up to 1,000,000 times more energy-efficient, offering a path to dramatically cheaper and greener AI.

Extending his "Non-Zero" thesis of a developing planetary consciousness (or "noosphere"), Wright now suggests AI could function as non-human neurons within this global brain. This fundamentally alters the trajectory of our species' evolution toward a superorganism.

Companies like Cortical Labs are growing human brain cells on chips to create energy-efficient biological computers. This radical approach could power future server farms and make personal 'digital twins' feasible by overcoming the massive energy demands of current supercomputers.

While today's computers cannot achieve AGI, it is not theoretically impossible. Creating a generally intelligent system will require a new physical substrate—likely biological or chemical—that can replicate the brain's enormous, dynamic configurational space, which silicon architecture cannot.

The core disagreement on AI consciousness is whether it's an emergent property of complex computation (functionalism) or intrinsically tied to biological "messiness." If computation is the key, as functionalists argue, then sufficiently advanced AI consciousness is not just possible, but inevitable.