このポッドキャストについて 🔗
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.
ホスト:
更新頻度:
every 9 days
平均音声長:
32 minutes
ゲストインタビュー
英語
アメリカ合衆国
15 エピソード
2026年6月8日から
episodic
このポッドキャストの内容 🔗
最新エピソード 🔗
The AI Cash Test: Who Passed and Who Didn’t Microsoft Passes, Alphabet Stumbles, Meta Turns to the Capital Markets, and Memory Sells Out to 2029
Key points
Demand for AI infrastructure remains strong, but funding is becoming more selective.
Meta’s free cash flow turned negative for the quarter, an…
以前のエピソード 🔗
Episode 14: AI Financing Squeeze: Who Can Afford to Wait? Buy the Dip or a Temporary Relief Rally?
Summary
Chipmakers are reporting record demand, yet some AI infrastructure borrowers face nervous lenders, and Monday’s tech rebound has run into a five-month high in oil. In this episode, Tim Hardwic…
Episode 13: AI Data Centres: The $30 Billion Price Tag Why the Chips, Not the Building, Decide Whether the AI Build Pays Back
Summary A one-gigawatt AI data centre in Abilene, Texas, is estimated to cost around $30 billion, and more than half of that goes on chips rather than the building. In this …
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…
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…
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