by Titto Thomas
A podcast by Tryfecta Capital exploring the intersection of AI, Robotics, and Leadership. We deconstruct the business of Physical AI: how to invest in it, how to build it, and how to lead through it. Featuring interviews with Industrialists, deep tech founders, VC insights, and market analysis on the future of automation and embodied intelligence.
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4/2/2026
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August 2, 2026
<p>Automation does not fail at the top. It fails in the layers underneath.</p><p>Sami Sultan, Vice President at Darcy Partners, ex BCG, and ex Shell wells engineer, returns for part two: a walk through the full Physical AI stack as it actually exists in heavy industry today.</p><p>In this episode:</p><p>The stack, layer by layer. Cameras reading rock as it comes off the shale shaker, computer vision flagging anomalies, AI agents that sense, plan, and act, and the actuation layer where a digital decision finally touches physical equipment.</p><p>Human in the loop. Why the geosteer keeps their job when a few percent of production is worth millions, and why safety critical calls will stay human for a long time yet.</p><p>The strange origin of mission control. How a failed military operation in 1980s Iran gave birth to joint command, then to the remote operations center, now the brain of rigs, mines, and factories everywhere.</p><p>Why full stack automation ventures go bankrupt. One sensor fails and the house of cards comes down. The aerospace lesson is that automation is a management system, not a feature.</p><p>Beyond the buzzword. Why Sami refuses to say digital twin, and why physics informed neural networks are winning the trust that black box AI cannot.</p><p>And the next frontier. Drilling techniques crossing into mining, continuous extraction, and the bridge that could finally make Western rare earth production viable.</p><p>Watch this space.</p><p>About the guest: Sami Sultan is Vice President of Oil and Gas and AI at Darcy Partners, where he leads technology scouting and advisory for the world's largest energy operators and utilities. He spent nearly a decade at Shell as a wells engineer, where he helped found Shell Geodesic, an algorithmic well navigation venture that applied AI to the subsurface years before it was mainstream. He holds seven patents, is a BCG alum, and earned his MBA in sustainability from the Yale School of Management.</p>
July 20, 2026
<p>The AI boom has a power problem, and simply scaling up traditional energy infrastructure will not solve it.</p><p>Sami Sultan, Vice President at Darcy Partners, ex Shell wells engineer, and BCG alum, joins Episode 10 to map where the energy for the machine age could actually come from: microgrids, distributed energy resources, and a pragmatic transition where existing capabilities get repurposed rather than expanded.</p><p><strong>In this episode:</strong></p><p><strong>The power question: </strong>why energy demand compounds as AI spreads, and why the sustainable path runs through distributed generation, not just bigger grids.</p><p>The transition in practice: stranded gas bridging data center demand today, old wells repurposed for energy storage tomorrow, and how capital decides what gets built.</p><p>What heavy industry brings to the table: decades of experience moving liquids, running remote operations, and managing complex infrastructure, now pointed at new problems.</p><p>The Physical AI stack arriving on sites today: red zone cameras, emissions drones, robot dogs, and the climb from sensing to agents to autonomy.</p><p>Sami's origin story: building algorithmic well navigation at Shell in 2017, trained on synthetic wells the way Waymo trained on synthetic miles, before most boardrooms knew what a GPU was.</p><p>And a first look at the three layer architecture we are building at Tryfecta: an agentic base, a physics model at the core, and command and control on top.</p><p>Part one of two. Sami returns next episode for the technology deep dive.</p>
July 13, 2026
<p>What if the fastest way to cut interest rates for the whole world is to teach machines to mine?</p><p>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.</p><p>In this episode:</p><p>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.</p><p>Why the middle of the commodity supply chain gets crushed in every cycle, and what junior miners teach us about survival.</p><p>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?</p><p>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.</p><p>Industry 3.0 to 6.0: from the space race that created Intel to the coming era where machines lead.</p><p>The people side: why Silicon Valley is hiring problem solvers, because nobody can define an AI engineer yet.</p><p>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.</p>
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A podcast by Tryfecta Capital exploring the intersection of AI, Robotics, and Leadership.
We deconstruct the business of Physical AI: how to invest in it, how to build it, and how to lead through it. Featuring interviews with Industrialists, deep tech founders, VC insights, and market analysis on the future of automation and embodied intelligence.
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