In a recent episode of Ezra Chapman’s Curious podcast, Tony Kenyon, Director of NeuroWare, explores the future of computing and why the next major advance in AI may depend not on building ever-larger models, but on rethinking the hardware that underpins them.
The discussion covers semiconductors, nanoelectronics and neuromorphic computing, examining how brain-inspired technologies could offer a more energy-efficient and scalable approach to AI. As demand for computational power continues to grow, this interview considers whether today's computing architectures can continue to meet future requirements, and how neuromorphic systems may help overcome challenges relating to power consumption, memory and performance.
The conversation also explores the broader implications of this technological shift, including the countries, companies and innovations that could shape the future of AI and advanced computing.
As AI infrastructure approaches practical limits, one question sits at the heart of the discussion: could the next breakthrough come not from bigger models, but from an entirely different kind of machine?
Image courtesy of Ezra Chapman
