What if the fastest way to cut interest rates for the whole world is to teach machines to mine?

Daniel Dangoor (Investments and Treasury) and Nick Shelton return for Episode 9, and this time we follow the money. Capital has been pouring into the poster children of Physical AI, drones, humanoid robots, and driverless cars, while the real prize sits underneath: machines that sense, extract, and build in the physical economy.

In this episode:

The deflation thesis: when Physical AI cuts the cost of mining and energy, supply rises, commodity prices fall, and the world gets easing no central bank can deliver. The iPhone economy already proved the mechanism.

Why the middle of the commodity supply chain gets crushed in every cycle, and what junior miners teach us about survival.

The hyperscaling question: software was the one sector that could scale 100x, and AI just ended that monopoly. Where do outsized returns come from in a physical world?

Whoever has more robots wins: the case for effectively infinite capital flowing into robotics, and why debt that builds GDP is not the problem people think it is.

Industry 3.0 to 6.0: from the space race that created Intel to the coming era where machines lead.

The people side: why Silicon Valley is hiring problem solvers, because nobody can define an AI engineer yet.

Dan closes with the best analogy of the series so far: when a person loses one sense, the others sharpen. When humanity hands its base skills to machines, watch what the remaining ones do.

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.

While the vision of Physical AI is incredibly optimistic, the actual implementation is going to be messy. As AI native companies see productivity skyrocket, they are also facing a hidden crisis: a massive drop in workplace empathy as managers get used to bossing around AI agents 24/7 and expect the same from their human employees.

In this episode of Leadership for the Physical AI Age, Nick Shelton returns to discuss the critical importance of change management as we enter the next industrial revolution. Host Titto Thomas breaks down Tryfecta's approach to building a "tag team" between human workers and AI agents, and why the ultimate human advantage will always be first-principles critical thinking.

Using the tragic lesson of a 1999 Swissair crash, Titto explains why rigid, checklist-based training is dead, and how human common sense must step in to oversee the AI processes of the future. Finally, Nick and Titto revisit the ultimate question: How do we use this fire to cook our food, instead of burning down our house?

In this episode, we cover:

• The Empathy Deficit: Why interacting with 24/7 AI agents is changing human behavior and creating friction in the workplace.
• The Tag-Team Model: How Tryfecta is designing systems where agents handle the heavy lifting, but call in humans for critical "last mile" process safety.
• The 5-Year Skill Shelf Life: Why continuous learning and education funds are the only way to survive the modern war for AI talent.
• The Swissair Lesson: How rigid checklists fail in crises, and why first-principles thinking is the most important skill to teach the next generation.
• The Good Life: Using AI to automate mundane administrative tasks to reclaim time for family, legacy building, and deep work.

Learn more and connect with us:

• Visit our website: tryfecta.biz
• Follow Titto Thomas on LinkedIn: Titto Thomas
• Follow Nathan Maroney on LinkedIn: Nathan Maroney
• Follow Nick Shelton on LinkedIn: Nick Shelton