EP 37: Neurons: Future of AI Processing

About this episode

What if the next generation of computers wasn't made of silicon — but of living human neurons? Not simulated neurons, not artificial neural networks inspired by biology, but actual brain cells grown in a lab, connected to electrodes, and used to process information. That's not science fiction anymore. It's happening right now at FinalSpark, a Swiss startup building the world's first remotely accessible biocomputing platform.

In this episode, Sam talks with Dr. Ewelina Kurtys, a neuroscientist with a PhD in brain imaging and a postdoctoral researcher at King's College London, about how living neurons could revolutionise computing — and why they use one million times less energy than silicon-based AI hardware.

 

▸  WHAT YOU'LL LEARN

▪  How FinalSpark was founded in 2014 by Fred Jordan and Martin Kutter — and why they pivoted from digital AI to biological computing when they realised the energy and cost problem was unsolvable with silicon

▪  Why 20 watts powers the human brain while silicon-based AI requires megawatts — and what that means for AI's sustainability crisis

▪  The difference between neurons as processors (not power sources) — a crucial distinction most people get wrong

▪  Why biological neural networks learn continuously while digital systems require full model updates — and what that means for energy efficiency

▪  The honest challenge: nobody yet knows exactly how neurons encode information — the biggest scientific hurdle in biocomputing right now

▪  How the I/O interface works: electrodes measuring neural spikes, analog-to-digital converters, researchers writing Python code to control neurons remotely

▪  The remote access breakthrough: researchers in Tokyo or Bristol can log in and control living neurons in Switzerland in real time via browser

▪  Why neurons won't outperform GPUs on speed: biocomputing specialises in efficiency and adaptability, not clock cycles

▪  FinalSpark's current stage: they've stored 1 bit of information and are collaborating with 9 universities on fundamental research

▪  The cost argument: even at 10× lower price than NVIDIA, biocomputers would still generate billions in profit due to energy and infrastructure savings

▪  Bioethics, consent, and regulation: how FinalSpark is working with philosophers now to establish ethical frameworks before biocomputing scales

▪  Why human-machine integration is not new: prosthetics, pacemakers, and smartphones are already blending biology and technology

▪  The hybrid computing future: silicon, quantum, and biocomputing will coexist, each doing what they do best

▪  The real game-changer: cheap, accessible AI for everyone — Ewelina's vision for what biocomputing