# EP 37: Neurons: Future of AI Processing

Page: https://stenobird.com/podcast/data-science-with-sam-4345646/ep-37-neurons-future-of-ai-processing
Text version: https://stenobird.com/podcast/data-science-with-sam-4345646/ep-37-neurons-future-of-ai-processing.md
Podcast: [Data Science With Sam](https://stenobird.com/podcast/data-science-with-sam-4345646)
Published: 2026-04-19T19:36:04+00:00
Episode link: https://rss.com/podcasts/data-science-with-sam/3006412
Audio file: https://content.rss.com/episodes/395137/3006412/data-science-with-sam/2026_07_20_13_47_14_75431501-f3bf-42b3-85d5-cde6c39a22cc.mp3
Processing state: not_requested
JSON: https://stenobird.com/v1/public/podcasts/data-science-with-sam-4345646/episodes/ep-37-neurons-future-of-ai-processing
Duration seconds: 1777

## Resource

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 out…

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## Transcript

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