The AI Supercycle

Quantum Fields Markets
  • 0.0 (0)
  • Investing

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.

  1. 2d ago

    AI Data Centres: The $30 Billion Price Tag

    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, Tim Hardwick follows the money from the concrete to the customer: how rising rates change the economics of the build, why chips lose their edge long before the building does, and what CXMT’s memory progress and Microsoft’s Maia 300 mean for pricing power. He also covers diesel diplomacy and why a late transformer can leave billions of dollars of chips sitting idle, and sets out what to watch from US inflation, ASML, TSMC and the banks. Key points Of the reported $30bn cost, $17bn is chips, $3bn is power infrastructure and just $2.4bn is the building and its construction.On an illustrative $18bn of debt, every extra percentage point on rates adds $180m a year to the interest bill. Delays make that worse.The building lasts decades, while the chips start losing their edge as soon as faster ones arrive. The real question is how quickly the equipment earns back its cost.Building owner, cloud operator and AI customer are three separate streams of cash, and the contracts decide who carries the risk.CXMT’s denser memory design is a step forward, but yields, customer approval and shipment volumes have yet to be proven.Microsoft’s Maia 300 claims 40% more output per megawatt. That is a company claim, and it does not mean 40% off the cost of a data centre.Cheaper AI models could expand usage while making pricing harder to hold.The most expensive equipment doesn’t always decide the opening date. A late transformer or grid connection can.Chapters 0:00 Introduction 0:09 Texas Data Centre Economics 1:14 The AI Supercycle Overview 3:19 Financing the Build 5:14 What $30 Billion Buys 8:19 Capital and Competition 10:53 Microsoft’s Custom Chips 13:36 Cheaper AI, Bigger Pressure 14:40 Resources and Geopolitics 17:41 Bringing It Together 21:10 What to Watch Next 22:53 Cash Versus Construction 23:58 Research and Wrap-Up Read more The Weekly Outlook and research behind this episode: https://qfmi.substack.com Everything discussed is for information and education only. It is not investment advice or a recommendation to buy or sell any financial instrument. Tags AI Supercycle, AI data centres, Abilene, cost of capital, interest rates, semiconductors, memory chips, HBM, CXMT, Microsoft Maia 300, Nvidia, inference, diesel, power infrastructure, ASML, TSMC, US CPI, global macro (00:09) - Texas Data Centre Economics (01:14) - AI Supercycle Overview (03:19) - Financing the Build (05:14) - What $30 Billion Buys (08:19) - Capital and Competition (10:53) - Microsoft’s Custom Chips (13:36) - Cheaper AI, Bigger Pressure (14:40) - Resources and Geopolitics (17:41) - The PRISM Framework (21:10) - What to Watch Next (22:53) - Cash Versus Construction (23:58) - Research and Wrap-Up

    AI Data Centres: The $30 Billion Price Tag

  2. Oct 8

    The Price of Speed: Who Pays for the AI Boom?

    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 3.6 gigawatts, just as America's largest power grid proposed that data centres bring their own power. At the same time, the G7 rushed emergency diesel to market within twenty days. Between those two time horizons sits the AI Supercycle. Tim Hardwick asks what moving this quickly really costs, in capital, electricity, physical infrastructure and risk. He follows the ten-year Treasury yield into what Brent Donnelly calls the exosphere, and traces the money behind Anthropic's $518 billion of commitments, SoftBank's high-yield borrowing and SpaceX's reported chip financing. He also looks at New York City putting the leading AI labs under oath, and explains why diesel still matters to a boom that runs on electricity. Key Points The US ten-year Treasury yield reached 5.34 per cent, its highest since 2002. Yields dipped on Friday's weak jobs report, then closed the day higher.Google's 3.6 gigawatt deal with Constellation answers PJM's proposal that data centres bring their own power or risk being cut off at peak demand. It's the power constraint turning into a rule.Morgan Stanley sees a 32 gigawatt US power shortfall through 2028. Delayed sites hit memory, optics and power-management chips harder than Nvidia and Broadcom.AI capital is increasingly circular. Broadcom may lend Anthropic up to $42 billion to lease Broadcom-linked capacity, and SoftBank borrowed at close to ten per cent to complete its OpenAI investment.Software stocks are at 2026 highs, but are up only about 5 per cent this year against nearly 90 per cent for chips. The revenue question is still open.Assurance is becoming an economic layer. The White House accord is voluntary, while New York's proposals would make both companies and validators liable.Diesel reaches AI through construction logistics and through inflation and bond yields, without ever entering the data hall.Chapters 0:06    Price of Speed2:22    Rates and Rockets5:59    Powering the AI Boom14:04  Safety and Assurance18:49  Diesel and Geopolitics21:16  The Framework Check23:38 What Comes Next Links The Market Pulse and In the Spotlight reports are freely availble at qfmi.substack.com Tags AI Supercycle, Treasury yields, bond market, Google, Constellation Energy, nuclear power, PJM, data centres, power shortage, Anthropic IPO, Broadcom, OpenAI, SoftBank, SpaceX, AI financing, software stocks, AI safety, AI assurance, New York City Council, diesel, G7, macro For information and education only. Not investment advice. (00:06) - Price of Speed (02:22) - Rates and Rockets (05:59) - Powering the AI Boom (14:04) - Safety and Assurance (18:49) - Diesel and Geopolitics (21:16) - The Framework Check (23:38) - What Comes Next

    The Price of Speed: Who Pays for the AI Boom?

  3. Sep 30

    The Price of Building AI

    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 paying for AI's story while selling the plumbing that makes it possible. In the first episode of the new weekly format, Tim Hardwick works through five parts: The macro regime. Long-term borrowing costs rose sharply as the US ten-year Treasury yield moved to post-crisis highs. The episode looks at why: strong business surveys, heavy debt supply, and energy costs driven by the US-Iran war and record diesel prices. It also covers what interest-rate futures now expect from the Federal Reserve in October.The AI Supercycle. From Meta's Muse agent at the customer end, through Anthropic's cloud commitment to Akamai and a record wave of hyperscaler borrowing, to Oracle's force majeure notice on the Jupiter data centre campus in New Mexico. Power delivery has become a contract risk, local opposition is holding up projects, and uranium, copper and Starship's first orbital flight all show the gap between strong fundamentals and cautious prices.Natural resources and geopolitics. Brent and European gas in a short-term downtrend now under test after Iran's Hormuz offer was rejected, and the difference between crude supply and the fuel that actually reaches customers. Also covered: the two-month rare earths truce and what it does and does not settle.The PRISM view. Tim's base case in plain language: the cost of capital, not demand for compute, is setting the tone, and so far it has changed nothing structural. The confidence, triggers and evidence that would change his mind.What matters next. Four tests to watch: the rate path, memory demand, data centre delivery, and whether supply and safety agreements become working processes.The episode closes with late news: Anthropic's IPO prospectus, which reveals the company's growth, costs and more than half a trillion dollars in infrastructure commitments, and which warns investors that advanced AI could pose existential risks to humanity. OpenAI, meanwhile, is seeking to raise money privately instead. Episode 12 will go through the filing in detail and return to AI safety. Chapters  0:04   Macro Costs and AI Risk 1:58    Rising Yields Hit AI Builds 6:12    Consumer Agents and Cloud Deals 10:11   Power Delays and Data Centres 16:14  Energy, Rare Earths, and Geopolitics 19:56  Framework for the AI Cycle 24:31  Anthropic Filing and Safety Warnings 28:28 OpenAI Raises More Capital  Read more Articles, reports and the free Market Pulse and In the Spotlight reports: qfmi.substack.com Subscriber research, including this week's Weekly Outlook, The Two-Speed Market: qfmi.substack.com  Tags: AI, AI infrastructure, data centres, power, grid, bond yields, interest rates, Federal Reserve, oil, diesel, natural gas, rare earths, uranium, copper, Oracle, Blue Owl, Anthropic, OpenAI, IPO, AI safety, SpaceX, Starship, capital allocation, macro For information and education only. Not investment advice or a recommendation to buy or sell anything. (00:04) - Macro Costs and AI Risk (01:58) - Rising Yields Hit AI Builds (06:12) - Consumer Agents and Cloud Deals (10:11) - Power Delays and Data Centres (16:14) - Energy, Rare Earths, and Geopolitics (19:56) - Framework for the AI Cycle (24:31) - Anthropic Filing and Safety Warnings (28:28) - OpenAI Raises More Capital

    The Price of Building AI

  4. Sep 21

    Who Checks the Machines?

    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 examines Anthropic chief executive Dario Amodei’s call to pace frontier AI, the three-step assurance framework behind it, and the divided response from the major laboratories, Europe, Washington and Beijing. The episode adds a sixth potential constraint to the AI Supercycle: assurance, the work of proving that models are safe and reliable enough to deploy. Model capability is advancing in months, while the supply of qualified evaluators grows far more slowly. If different jurisdictions impose incompatible requirements, the same thin pool of specialists may have to evaluate the same models repeatedly. Tim separates catastrophic tail risk from observable operational risk, follows the impact through depreciation, time to revenue and return on invested capital, and explains why stronger assurance could unlock adoption in banking and insurance. The QF Markets base case is that pacing fragments rather than coordinates: higher costs, slower deployment, little near-term reduction in physical AI infrastructure spending, and a possible bridge between frontier capability and enterprise revenue. Tim closes with three conditions that would change that assessment and five signals worth watching. In This Episode What Anthropic’s proposed pacing framework actually involvesWhat the semiconductor sell-off did, and did not repriceWhy assurance could become the sixth constraint on the AI build-outThe limited supply of qualified frontier-model evaluatorsThe challenge presented by open-weight modelsCompeting assurance regimes in Europe, the United States and ChinaWhy catastrophic tail risk and operational AI risk require different treatmentHow the assurance clock affects depreciation, revenue and returnsWhy stronger assurance could unlock deployment in banks and insurersHow future frontier-lab IPOs could make assurance costs more visibleThe QF Markets base case, invalidation conditions and five signals to watchChapters AI Slowdown WarningThe Podcast ReturnsWhat “Pace the Frontier” ProposesWhat Markets Actually RepricedAssurance: The Sixth ConstraintThe Strongest ObjectionsEurope, America and China DivergeTail Risk Versus Operational RiskFour Clocks, One Balance SheetHow Assurance Could Unlock Enterprise AIIPOs Could Make the Constraint VisibleBase Case: Fragmented Assurance RegimesFive Signals to WatchAlso in This Episode Tim provides an update on his latest book: Trading the AI Supercycle - A Cross-Asset Framework for Big Tech Infrastructure, Energy and Critical Materials. QF Markets Quantum Fields AI is a decision-intelligence company focused on the strategic, economic and investment implications of the AI Industrial Economy. We connect both sides of the AI investment equation: how enterprises convert AI expenditure into measurable business value, and how investors and capital allocators understand the capital, infrastructure, constraints and returns shaping the wider AI economy.Our work brings together enterprise AI strategy and economics, independent capital intelligence and domain-specific applications of decision intelligence. Read the free Market Pulse and In the Spotlight reports, and explore the subscriber research, at: https://qfmi.substack.com Tags: AI, semiconductors, HBM, memory, energy, nuclear, uranium, copper, rare earths, data centres, photonics, capital allocation, macro, AI infrastructure (00:05) - AI Slowdown Warning (01:10) - Podcast Returns (03:14) - Pace the Frontier (06:42) - Market Repricing (08:38) - Assurance as Constraint (15:33) - Pushback and Objections (19:40) - Global Assurance Models (23:35) - Tail Risk vs Reality (26:56) - Balance Sheet Impact (29:21) - Enterprise Unlock (35:07) - IPOs and Disclosure (36:41) - Fragmented Regimes (38:21) - What to Watch

    Who Checks the Machines?

  5. Aug 15

    Physical AI: The Race for Embodied Intelligence

    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 actually get a machine to do useful work, reliably, in the real world. The episode opens by benchmarking five humanoid platforms against a single standard, not how good the demonstration looks, but whether the robot can complete the same task safely, repeatedly, and at a cost that earns a return. Tesla Optimus, Boston Dynamics' Electric Atlas, Figure 03, Agility Robotics' Digit, and AgiBot A2 each represent a different route into embodied intelligence, from vertical integration to mechanical heritage to state-backed industrial scale. Inflated headline figures are separated from the real numbers: robotics venture funding is measured in the tens of billions, not the hundreds, and NVIDIA's fifty-trillion-dollar framing describes the addressable economy, not addressable revenue. A real industry disagreement, between claims of a "ChatGPT moment" for robotics and the blunter reality that lab performance regularly halves in real-world deployment, sets up the sector's binding constraints: dexterity, power, industrialisation, safety, and rare-earth materials. The second half works through the CFO and COO questions that will actually decide enterprise adoption, the entire physical AI value chain from magnets to orchestration software, and physical AI's emerging role beyond Earth, in orbital maintenance and lunar infrastructure. The episode closes with a three-horizon framework for investors and the QF-MI base case: not a flood of humanoids into every factory and warehouse, but a slower, more uneven build, with the number to watch being the gap between company-reported production and independently verifiable fleet utilisation. This is also the final episode in the current run of solo episodes, with Tim taking a break for the summer before inviting guests on the show to discuss the AI Supercycle. All reports are published at qfmi.substack.com The Market Pulse and In the Spotlight articles are free, and always will be. The Weekly Outlook, the Weekend Debrief, and the Monthly Strategic Research Report sit behind a paid subscription. Subscribe and you get the full picture. Chapters 0:10 Introduction 0:40 Business Update: PRISM and the Book 3:29 Introducing Physical AI 5:25 From Artificial Intelligence to Physical Intelligence 8:32 Why Humanoid Robots? 10:02 Tesla Optimus 12:30 Boston Dynamics Electric Atlas 15:30 Figure 03 18:40 Agility Robotics Digit 20:43 AgiBot A2 and the Chinese Ecosystem 23:00 The Existing Robotics Economy 24:45 Following the Money 27:00 The Physical AI Stack 28:55 The CFO and COO Test 31:52 The Binding Constraints 37:17 From the Factory to Orbit 38:50 What Should Investors Monitor? 43:20 The QF-MI Base Case 46:10 Conclusion 48:55 Close and Forward Look Tags:  AI supercycle, physical AI, embodied intelligence, humanoid robots, Tesla Optimus, Boston Dynamics, Figure AI, Agility Robotics, AgiBot, vision-language-action models, industrial robotics, robotics investment, NVIDIA, rare earth magnets, robotics-as-a-service, orchestration layer, delivery gap, China robotics, space robotics, lunar robotics, enterprise adoption, capital formation  (00:10) - Introduction (00:40) - Business Update: PRISM and the Book (03:29) - Introducing Physical AI (05:25) - From Artificial Intelligence to Physical Intelligence (08:32) - Why Humanoid Robots? (10:02) - Tesla Optimus (12:30) - Boston Dynamics Electric Atlas (15:30) - Figure 03 (18:40) - Agility Robotics Digit (20:43) - AgiBot A2 and the Chinese Ecosystem (23:00) - The Existing Robotics Economy (24:45) - Following the Money (27:00) - The Physical AI Stack (28:55) - The CFO and COO Test (31:52) - The Binding Constraints (37:17) - From the Factory to Orbit (38:50) - What Should Investors Monitor? (43:20) - The QF-MI Base Case (46:10) - Conclusion (48:55) - Close and Forward Look

    Physical AI: The Race for Embodied Intelligence

  6. Aug 8

    The Enterprise AI Payoff: From Tokenmaxxing to Value per Token

    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 enterprise AI spending actually paying off. The episode opens by drawing a sharp line between activity and value, tokens generated, users provisioned and hours saved don't count until they reach the P&L. Strong hyperscaler results from Microsoft, Alphabet and Amazon confirm enterprise demand for AI capacity is real, but are shown to be evidence of commitment, not proof of return. Conflicting survey findings from PwC, McKinsey, Deloitte, Google Cloud and EY are reconciled: the disagreement itself reveals how immature enterprise AI measurement still is, and a concentration effect (20% of companies capturing 74% of the value) suggests returns are polarising rather than spreading evenly. The second half sets out a practical framework: what makes a credible AI business case, a three-level scorecard connecting technical, operational and financial measurement, and the shift from tokenmaxxing toward disciplined token economics, selecting the right model, controlling architecture, and measuring cost per successful outcome. The episode closes with the dashboard of signals worth tracking, the case for and against the current build-out, and the QF-MI base case: not a spending collapse, but a shift toward selective scaling under real financial discipline. All reports are published at qfmi.substack.com The Market Pulse and In the Spotlight articles are free, and always will be. The Weekly Outlook, the Weekend Debrief, and the Monthly Strategic Research Report sit behind a paid subscription. Subscribe and you get the full picture. Chapters 0:19 AI Value Chain Begins3:02 From Compute to Revenue7:57 ROI Surveys Diverge12:25 Capturing Real AI Value19:10 Measuring Across Three Levels22:22 Token Economics Shift26:03 Optimizing for Outcomes29:29 Efficiency and Demand Rebound32:03 Tracking the Key Signals34:45 Optimistic Case, Rising Demand36:23 Selective Scaling Ahead39:47 Closing Thoughts on the Cycle Tags: AI supercycle, enterprise AI, AI ROI, token optimisation, tokenmaxxing, token economics, value per token, FinOps, AI FinOps, Microsoft Copilot, Azure, AWS, Google Cloud, hyperscaler capex, agentic AI, model routing, inference cost, enterprise adoption, business case, benefit realisation, unit economics (00:19) - AI Value Chain Begins (03:02) - From Compute to Revenue (07:57) - ROI Surveys Diverge (12:25) - Capturing Real AI Value (19:10) - Measuring Across Three Levels (22:22) - Token Economics Shift (26:03) - Optimizing for Outcomes (29:29) - Efficiency and Demand Rebound (32:03) - Tracking the Key Signals (34:45) - Optimistic Case, Rising Demand (36:23) - Selective Scaling Ahead (39:47) - Closing Thoughts on the Cycle

    The Enterprise AI Payoff: From Tokenmaxxing to Value per Token

  7. Jul 31

    The Nervous System of the AI Supercycle

    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 possibility of taking infrastructure into orbit. But before any of that gets built, somebody has to raise the money. In this episode, we turn to capital formation: not a new layer in the stack, but the nervous system running through every layer already covered. Hyperscaler capex is now guided toward roughly $805 billion in 2026, climbing toward $1.1 trillion in 2027, and the way that spending gets financed has shifted fast, from internally funded cash flow to a credit market that is starting to ask harder questions. We trace that shift through three stages, place it against the closest historical parallel (the year-2000 telecoms fibre boom), and unpack the parts of this build-out that don't show up cleanly on any balance sheet: special purpose vehicles, private credit exposure, and this week's live example of circular financing involving Nvidia, SK Group and OpenAI. Finally, we present the QF-MI base case, and what a more selective, more expensive capital market could mean for the pace of the AI build-out over the next 12 to 18 months. In This Episode Why capital formation sits above the physical stack as the constraint that funds all the othersThe scale of hyperscaler capex, and what Alphabet's latest earnings reveal about the pace of spendingComparing today's build-out to the year-2000 telecoms fibre boomThe three stages of AI financing: internal cash, external credit, and capital crowdingWhat a falling bond coverage ratio actually signals, and why it moves before spreads doThe rise of off-balance-sheet financing through special purpose vehiclesWho is really holding the risk: Blackstone, Blue Owl, Apollo and Pimco's growing exposureCircular financing explained, and why Nvidia's SK Group and OpenAI commitments matterThe private equity and IPO story: OpenAI, Anthropic and the test still to comeWhere this sits against a Federal Reserve giving markets no forward guidanceThe sceptic's case, and the QF-MI base case for the next 12 to 18 monthsFollow QF-MI on Substack: https://qfmi.substack.com The Market Pulse and In the Spotlight research series are free to read. Subscribers also receive the Weekly Outlook, Weekend Debrief, and the Monthly Strategic Research Report, providing institutional-grade analysis of the capital flows and physical constraints shaping the AI industrial economy. Chapters 0:19    Capital Formation Emerges2:04   The Financing Layer4:23   Telecom Bubble Comparison7:03    Debt Markets Take Over10:29  Demand Weakens for Bonds13:20  Off-Balance-Sheet Leverage16:12   Circular Financing Risks19:39  Private Funding Boom22:09  Capital as the Constraint24:47  Fed Risk Returns27:30  The Skeptics Case29:49  Base Case Outlook33:00  Nervous System of AI Tags: AI, capital formation, hyperscalers, financing stack, bond markets, private credit, special purpose vehicles, circular financing, Nvidia, capital allocation, macro, AI infrastructure (00:19) - Capital Formation Emerges (02:04) - The Financing Layer (04:23) - Telecom Bubble Comparison (07:03) - Debt Markets Take Over (10:29) - Demand Weakens for Bonds (13:20) - Off-Balance-Sheet Leverage (16:12) - Circular Financing Risks (19:39) - Private Funding Boom (22:09) - Capital as the Constraint (24:47) - Fed Risk Returns (27:30) - The Skeptics Case (29:49) - Base Case Outlook (33:00) - Nervous System of AI

    The Nervous System of the AI Supercycle

  8. Jul 22

    Photonic Interconnects: The Next Constraint

    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 simply how much compute we can build, but how quickly data can move between processors, servers, racks and entire AI factories. Increasingly, the constraint is the network itself. In this episode, we explore photonic interconnects and explain why the future of AI infrastructure will be built on light rather than copper. Using a four-layer framework, we examine where photonics fits across the AI Continuum, from connections inside processor packages, through hyperscale data centres, all the way to laser communications between satellites in orbit. We also analyse one of the least understood strategic materials in the AI supply chain: indium phosphide, the foundation of modern optical communications and an emerging geopolitical bottleneck. Finally, we present the QF-MI base case, highlighting where we believe the greatest investment opportunities, and risks, are likely to emerge over the next several years. In This Episode Why the AI bottleneck is shifting from compute to data movementUnderstanding the I/O Wall and why GPUs are waiting for dataWhy copper is approaching its physical limitsHow co-packaged optics could transform AI hardwareThe four layers of the photonics ecosystemThe rapid growth of 800G and 1.6T optical networkingWhy orbital computing depends on laser communicationsIndium phosphide: the hidden material underpinning AI infrastructureChina's strategic position in the optical supply chainThe QF-MI investment framework and key indicators we're monitoring Follow QF-MI on Substack: https://qfmi.substack.com The Market Pulse and In the Spotlight research series are free to read. Subscribers also receive the Weekly Outlook, Weekend Debrief, and the Monthly Strategic Research Report, providing institutional-grade analysis of the capital flows and physical constraints shaping the AI industrial economy. Chapters 0:19 AI Supercycle Begins2:33 The Photonics Bottleneck6:27 Inside the Chip Limits9:08 Co-Packaged Optics Arrive10:51 Transceivers Power the Network14:03 Laser Links in Orbit15:53 Indium Phosphide Risk19:08 Investment Base Case21:52 Light Connects the Stack Tags: AI, semiconductors, HBM, memory, energy, nuclear, uranium, copper, rare earths, data centres, photonics, capital allocation, macro, AI infrastructure (00:19) - AI Supercycle Begins (02:33) - The Photonics Bottleneck (06:27) - Inside the Chip Limits (09:08) - Co-Packaged Optics Arrive (10:51) - Transceivers Power the Network (14:03) - Laser Links in Orbit (15:53) - Indium Phosphide Risk (19:08) - Investment Base Case (21:52) - Light Connects the Stack

    Photonic Interconnects: The Next Constraint
See All (13)

About

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.

Information

  • Creator
    Quantum Fields Markets
  • Years Active
    2026
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © 2026 Quantum Fields AI Ltd
  • Show Website
    The AI Supercycle