Leadership for the Physical AI Age
How does a baby learn faster than an LLM? Not by reading more text, but by touching the world. That analogy from Daniel Dangoor (Investments and Treasury) anchors this episode's thesis: language models are capped by the finite supply of human text, and the next leap in AI depends on machines that can sense the physical world. Host Titto Thomas, Daniel Dangoor, and Nick Shelton unpack why sensors, not chatbots or humanoid robots, are the underserved gold rush of Physical AI. In this episode: Physical AI is bigger than humanoid robots and driverless cars. From rig sensors at Shell that optimized an entire fleet, to in situ soil analysis that maps rare earth deposits in a day instead of 3 months. The compute space race. Dan's macro thesis on why the US treats AI as a race it must win at any cost, and why that makes the compute investment supercycle effectively unlimited. Why sensors are the new Nvidia trade. Sensor stocks lagged every AI basket for 18 months, then rallied 80% between April and June 2026 as real industrial demand, not speculative hype, finally arrived. The ethical scaffolding. Drawing on their backgrounds in theology and philosophy, the panel asks whether governance is mature enough for the productivity and geopolitical stress ahead. Solving humanity's dirty jobs. Why machines should handle the 12 hour pipe inspections in the desert so people never have to. The takeaway: language is only the beginning. The industrial economy needs AI that can feel, and capital is now shifting to build the sensors that make that fusion possible.
8 episoder
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