TechSurge: Deep Tech VC Podcast

Dr. Patrick Pilarski on Bionic AI Arms That Learn Their User

8.6K views · 1w ago · 57:40

In this episode, Dr. Patrick Pilarski, co-author of the Alberta Plan alongside Richard Sutton and Michael Bowling, makes the case for a radically different kind of AI: one that never stops learning, builds its own understanding from raw experience, and gets more out of limited compute rather than more data centers.

Topics: Welcome to Edmonton: AI, Free Will, and the Rodeo · Why AI Researchers Name Papers Better Than Other Scientists · Should We Anthropomorphize AI? · The Alberta Plan, Explained · “LLMs Are a Dead End”: Contrasting the Alberta Plan with Today’s AI Labs · Four Years Later: What the Alberta Plan Got Right

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1:00:56

AI Needs $24 Trillion by 2030. GPU Pioneer Says the Money Isn't There

11K views · 1mo ago · 1:00:56

In this episode, host David Goldman speaks with legendary graphics chip architect Raja Koduri, who explains why every gigawatt of AI infrastructure now costs $50 to $60 billion, and why China's goal of doing it for under $10 billion is the real threat to Western AI. Raja twice led graphics at AMD, directed Apple's graphics architecture and was chief architect at Intel. He argues that the real AI race isn't Nvidia vs. Google vs. Broadcom but China vs.

Topics: A Gigawatt of AI Now Costs $60 Billion · Introducing Raja Koduri · What Oxmiq Builds: Electrons In, Tokens Out · The $24 Trillion AI Infrastructure Bill · China vs. the Rest of the World · Memory Is the New Bottleneck

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1:12:26

Senior AI Analyst Exposes the Next $10 Trillion Monopoly in AI

19K views · 1mo ago · 1:12:26

In this conversation, Lyons joins Techsurge to unpack the move towards system selling, the implications of differentiating prefill and decode in inference workloads, and his own four-part framework for spotting the next breakout silicon company.

Topics: No One's Brought a Chip to Market Built for LLMs · Introducing Austin Lyons · Why AI Buyers Now Buy Whole Systems, Not Parts · Nvidia's Margins and the Case for System Simplicity · Can a Startup Compete When You Have to Sell Systems? · Prefill vs. Decode: Splitting the Inference Workload

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1:08:45

Nobel Physics Winner Who Built Google Quantum: Why I'm Building NVIDIA of Quantum Computing

27K views · 1mo ago · 1:08:45

In this episode, Nobel Prize-winning physicist Dr. John Martinis reveals how his breakthrough in superconducting qubits made quantum physics real at macroscopic scale and what it means for the future of technology. The former lead of Google's Quantum AI lab explains why quantum computing is so fragile, why a lot of hype has a low chance to work, and why his fabless company Qolab could be the Nvidia of quantum computing

Topics: The Quantum Computing Hype: Physics vs Engineering · Introducing Nobel Prize Winner John Martinis · Schrödinger's Cat Explained · Can Quantum Effects Exist at a Macroscopic Scale? · The Experiment That Changed Quantum Computing · The Biggest Challenge: Scaling Quantum Computers

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Episodes

Dr. Patrick Pilarski on Bionic AI Arms That Learn Their User

AI Needs $24 Trillion by 2030. GPU Pioneer Says the Money Isn't There

Senior AI Analyst Exposes the Next $10 Trillion Monopoly in AI

Nobel Physics Winner Who Built Google Quantum: Why I'm Building NVIDIA of Quantum Computing

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