Ep 9: Cutting Rates with Robots: Capital Flows and the Deflationary Power of Physical AI
Download MP3What 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.
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.
Creators and Guests
Guest
Daniel Dangoor
Daniel Dangoor is an experienced financial professional including as head of Macro Thematic trading at Goldman Sachs . His extensive career spans over 15 years in asset management and trading, featuring leadership and portfolio management roles across major global financial hubs:Goldman Sachs (London): Head of Macro Thematic Trading (April 2025 – June 2026) SPX Capital (London): Senior Portfolio Manager (December 2023 – March 2025) Brevan Howard (London): Senior Portfolio Manager (March 2019 – December 2023) Bradesco Asset Management (São Paulo): Portfolio Manager (August 2010 – March 2019) Prior to his career in finance, Daniel built a strong quantitative foundation in Brazil. He studied Mechanical Engineering at Universidade Presbiteriana Mackenzie from 2006 to 2010, where he was an active member of the Propulsion Engine Team. He also completed the International Baccalaureate program at St. Paul's School in São Paulo, with high-level focuses in Mathematics, Physics, Chemistry, and Business.
