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The AI Supercycle Podcast from QF Markets provides independent capital intelligence for the AI industrial economy. From semiconductors, AI factories and data centres to power grids, energy markets, critical materials, orbital compute and embodied intelligence, we follow where capital is being deployed, where the binding constraints are emerging, and what it costs to build through them.
Each weekly episode connects the latest developments to the multi-year forces behind the AI build-out, and sets out a clear base case along with the evidence that would change it. For traders, investors and anyone trying to understand where the capital in this cycle is really going. Published weekly by QF Markets.
Each weekly episode connects the latest developments to the multi-year forces behind the AI build-out, and sets out a clear base case along with the evidence that would change it. For traders, investors and anyone trying to understand where the capital in this cycle is really going. Published weekly by QF Markets.
میزبانها:
فرکانس به روز رسانی:
every 9 days
میانگین طول صدا:
33 minutes
مصاحبه های مهمان
انگلیسی
ایالات متحده آمریکا
12 قسمت
از سال 8 ژوئن 2026
episodic
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آخرین اپیزود 🔗
Episode 12: The Price of Speed: Who Pays for the AI Boom? Google Goes Nuclear, Bond Yields Hit the Exosphere and New York Asks for an AI Off Switch
Summary
Twenty years, twenty days. This week Google signed a twenty-year nuclear power agreement with Constellation Energy, part of a deal for nearly 3…
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Episode 11: The Price of Building AI Why Rising Borrowing Costs Are Splitting the AI Build-Out in Two
Demand for AI looks strong. The cost of building the power, computing capacity and infrastructure to meet it is rising fast. Last week those two stories began to pull apart, and the market started …
Episode 10: Who Checks the Machines?
Why Proving AI Is Safe May Be the Next Bottleneck
AI’s leaders asked the industry to slow down. Markets wobbled, then shrugged. But the real story may not be whether frontier AI slows—it may be what it costs to prove that it has.
In Episode 10, Tim Hardwick exa…
Episode 9: Physical AI: The Race for Embodied Intelligence
The AI Supercycle has been built in data centres and financial models so far. In Episode 9, Tim Hardwick moves the thesis into the physical world: robots, factories, materials, and the question of whether artificial intelligence can actuall…
The Enterprise AI Payoff: From Tokenmaxxing to Value per Token
The AI infrastructure build-out only matters if enterprises can turn compute into durable economic value. In Episode 8, Tim Hardwick moves from the supply-side story of GPUs, data centres and power to the harder demand-side question: is…
Episode 7: The Nervous System of the AI Supercycle
Capital Formation and the Race to Fund an $805 Billion Build-Out
For six episodes, this show has tracked the physical stack of the AI supercycle. The chips. The power. The materials. Most recently, the photonics connecting it all, and the possibi…
Episode 6: Photonic Interconnects – The Next AI Bottleneck
For the past several years, the AI industry has been focused on one constraint: compute. More GPUs. Larger clusters. Faster processors.
But that bottleneck is changing.
As AI systems continue to scale, the limiting factor is no longer simpl…
Episode 5: The Orbital Compute Thesis: Trading Terrestrial Constraints for Orbital Ones
Space does not bypass constraints. It trades them. After three episodes documenting the terrestrial binding constraints on the AI Supercycle, memory, power, and critical materials, this episode reaches the top o…
Over the past two episodes we've explored the first two binding constraints shaping the AI Industrial Economy: high-bandwidth memory and delivered power. This week we move further down the supply chain. To the ground itself.
The AI revolution doesn't begin inside a data centre. It begins in copper …
Episode 3: The Power Constraint: When Demand Meets Reality
This week on The AI Supercycle, we move from high-bandwidth memory to the second major bottleneck shaping the AI industrial economy: delivered power. Generating electricity is not enough. The real constraint is getting reliable power to AI …
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