The AI Supercycle

Quantum Fields Market Intelligence
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The AI Supercycle podcast from QF-MI provides independent Capital Intelligence for the AI Industrial Economy. From semiconductors, AI factories and data centres to energy markets and power grids, critical materials, orbital compute and embodied intelligence, we track where capital is being deployed, where the binding constraints are emerging, and what it means for traders, investors and the broader economy. Each episode examines the physical infrastructure underpinning artificial intelligence and the investment opportunities emerging from its industrialisation. Published weekly by Quantum Fields Market Intelligence (QF-MI).

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  1. 15 aug.

    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

  2. 8 aug.

    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

  3. 31 juli

    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

  4. 22 juli

    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

  5. 9 juli

    The Orbital Compute Thesis

    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 of the AI Continuum and examines what happens when serious capital proposes moving compute off the planet. The episode tests the engineering claims rigorously, from orbital solar physics and thermal management to the latency gap between training and inference workloads. It reviews the key players: SpaceX's million-satellite FCC filing, Google's Project Suncatcher, Blue Origin's Project Sunrise, Starcloud's GPU in orbit, Axiom Space's operational data centre nodes, Nvidia's Space One module, and China's Three-Body Computing Constellation. The sceptics' case, led by SoftBank's Masayoshi Son, gets equal weight. The economics rest on one variable: launch cost per kilogram. The base case: directionally correct, but on a longer timeline than proponents suggest, with the near-term investable opportunity in the picks and shovels, not orbital compute itself. Chapter Marks  [00:00] Introduction and production note [01:32] The three binding constraints recap: memory, power, materials [02:06] Space trades constraints, it does not bypass them [03:05] The AI Continuum: from underground mines to orbit [05:52] Terrestrial constraints compounding: power, water, land, materials [08:06] What space offers: solar power, thermal management, and the engineering reality [10:37] Constraints that space introduces: radiation, latency, debris, maintenance [11:49] The players: SpaceX/xAI, Google Suncatcher, Blue Origin, Starcloud, Axiom, Nvidia [16:52] China's Three-Body Computing Constellation [17:45] The economics: launch cost per kilogram and the path to cost parity [21:36] Which workloads suit orbital compute: inference, not training [22:29] The sceptics' case: Masayoshi Son and the decisive years argument [23:10] Latency: distance, bandwidth, and why training in orbit is unworkable [24:39] Space debris, maintenance, and the Starship dependency [26:18] Governance: the regulatory void and the SpaceX concentration question [29:22] The QF-MI base case: 40% probability of cost parity within five years [31:17] Global Launch Intelligence Database: 150 orbital launches tracked in 2026 [32:15] The AI Continuum: from the mine to the antenna [35:43] Production note and sign-off Links QF-MI research and subscriptions: qfmi.substack.com

    The Orbital Compute Thesis

  6. 29 juni

    The Critical Materials Constraint

    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 mines, uranium deposits, rare earth refineries and the global supply chains that underpin every transformer, GPU, cable and power station. In this episode we examine why critical materials may become the next major bottleneck in the AI Supercycle, and why markets may still be underpricing the scale of the challenge. We also explore the latest developments shaping the investment landscape, including:  Micron's record earnings and what they reveal about structural shortages  Apple's price increases as supply constraints begin to reprice the value chain  Escalating tensions in the Middle East and implications for energy markets  The G7 Critical Minerals Resilience and Production Alliance  Proposed US tariffs on refined copper  China's export controls on critical materials  The long development timelines that make mining fundamentally different from semiconductors or power generation  Why copper, uranium, rare earths, gallium, germanium and tin all matter to the future of AI infrastructure The episode concludes with my current base case for critical materials over the next three to five years and explains why memory, power and materials should be viewed as one interconnected system rather than three separate investment themes. The AI Supercycle isn't simply a software story. It's becoming one of the largest physical industrial build-outs in modern history. Chapters 00:19 - AI Supercycle Overview 02:30 - The Underground Constraint 05:32 - Iran, Energy Markets & Macro Update 08:40 - The G7 Critical Minerals Alliance 10:18 - Copper Tariffs and Industrial Policy 13:04 - Mining Runs on Geological Time 15:08 - Why Copper Matters 18:07 - Uranium and the Nuclear Supply Chain 21:02 - The Wider Critical Materials Complex 23:52 - China's Strategic Leverage 25:47 - Base Case Outlook 28:48 - Connecting the Three Constraints About The AI Supercycle The AI Supercycle follows the capital flows, infrastructure investment and physical constraints shaping the AI Industrial Economy. From semiconductors, hyperscale data centres and power grids to critical materials, orbital compute and embodied intelligence, each episode examines where capital is being deployed, where bottlenecks are emerging and what this means for investors, businesses and the global economy. (00:19) - AI Supercycle Overview (02:30) - Underground Constraint Begins (05:32) - Iran Shock and Market Moves (08:40) - G7 Mineral Alliance (10:18) - Copper Tariff Watch (13:04) - Mining Runs on Geological Time (15:08) - Copper Under Strain (18:07) - Uranium Supply Tightens (21:02) - Critical Materials Wideview (23:52) - China’s Control Leverage (25:47) - Base Case Outlook (28:48) - The Three Constraints

    The Critical Materials Constraint

  7. 22 juni

    The Power Constraint: When Demand Meets Reality

    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 factories at the right voltage, in sufficient quantity, and on a timescale that can support hyperscale growth. In this episode:  Why the grid, not generation capacity, has become one of the defining bottlenecks of the AI Supercycle.  The US-Iran agreement, energy markets, and why lower oil prices do not solve the power problem.  SpaceX's first week as a public company and what its acquisition of Cursor tells us about AI infrastructure.  Apple's agreement with Intel and the growing reshoring of semiconductor manufacturing.  The "Two-Clock Problem" and why AI demand grows faster than power infrastructure can respond.  Natural gas, nuclear, renewables and the longer-term possibility of orbital compute.  Why transmission infrastructure, interconnection queues and transformer shortages matter.  The investment implications for utilities, independent power producers and nuclear energy.  The QF-MI base case: why power could become the primary binding constraint on the AI Supercycle by 2027. The most valuable asset in the AI industrial economy may not be a chip. It may be a power station. Topics discussed AI infrastructure Power grids and energy markets Data centres and hyperscalers SpaceX IPO and AI capital formation Intel, Apple and semiconductor reshoring Nuclear energy and utilities Orbital compute Critical materials and the AI SupercycleSubscribe 📰 Substack: https://qfmi.substack.com 💼 LinkedIn Newsletter: The AI Supercycle Chapters  00:19 - New Name, New Cycle 01:21 -  The Delivered Power Constraint 02:09 - The Gulf Deal and Oil Flows 05:56 -  SpaceX, Intel and Capital Flows 10:29 -  Why Power Is Different 16:27 -  Four Paths to More Power 20:40 - The Grid Bottleneck 23:34 -  Investing in Power 25:12 -  QF-MI Base Case 29:26 - Power Becomes the Bottleneck Next Episode Episode 4: Critical Materials - copper, uranium, and the physical inputs underpinning the AI industrial economy. Tags AI Supercycle QFMI capital flows physical constraints semiconductors data centres power grids energy markets delivered power electricity demand (00:19) - New Name, New Cycle (01:21) - Delivered Power Constraint (02:09) - Gulf Deal and Oil Flows (05:56) - SpaceX, Intel, and Capital Flows (10:29) - Why Power Is Different (16:27) - Four Paths to More Power (20:40) - Grid Bottlenecks Bite Hard (23:34) - Investing in Power Assets (25:12) - Base Case: Constraint Deepens (29:26) - Power Wins the Bottleneck Race

    The Power Constraint: When Demand Meets Reality

  8. 15 juni

    The Memory Constraint: Why HBM Has Become AI's New Oil

    Episode 2: The Memory Constraint: Why HBM Has Become AI's New Oil Oil Semiconductor Bifurcation, Strategic Partnerships and the Fifth Binding Constraint This week Nvidia locked up future memory supply through a multi-year strategic partnership with SK Hynix, while Alphabet reportedly ordered more than three million TPUs from Intel Foundry, signalling that hyperscalers are diversifying fabrication away from TSMC as demand overwhelms a single manufacturing ecosystem. Tim Hardwick examines High Bandwidth Memory as the first binding constraint on the AI Supercycle. The supply chain runs through three countries and a handful of companies. Demand is accelerating from three directions: more memory per chip, more chips, and more inference workloads. HBM is sold out for the rest of this year. The episode addresses the semiconductor correction, arguing that the sell-off reflects crowded positioning and leveraged ETF amplification rather than a change in the fundamental thesis. The PRISM framework assigns eighty per cent probability to a mid-cycle correction rather than a market top. The sell-off is bifurcated: ASML made all-time highs the same week Nvidia corrected over thirteen per cent, suggesting the market is repricing the most crowded expressions of the AI trade, not the thesis itself. The QF-MI base case is that HBM remains structurally tight through 2027. Supply will grow but demand is likely to outpace it. The principal risks are deteriorating ROI on hyperscaler spending, tighter financial conditions, or geopolitical disruption around Taiwan or Korea. The episode also flags capital formation as a potential fifth binding constraint and previews Episode 3 on delivered power. Chapters 0:17 HBM and the AI Supply Chain 5:54 Capital Becomes the Constraint 10:05 The Three HBM Producers 12:52 Demand Is Accelerating 15:57 Market Rally, Then Correction 19:30 Correction or Top? 23:16 The HBM Base Case 26:58 Watching IPOs and Capital Flows Tags: AI supercycle, HBM, high bandwidth memory, semiconductors, SK Hynix, Nvidia, TSMC, ASML, CoWoS, Intel Foundry, SpaceX IPO, capital formation, semiconductor bifurcation, memory constraint, data centres, inference, Blackwell, Rubin, capital allocation, macro (00:17) - HBM and the AI Supply Chain (05:54) - Capital Becomes the Constraint (10:05) - The Three HBM Producers (12:52) - Demand Is Accelerating (15:57) - Market Rally, Then Correction (19:30) - Correction or Top? (23:16) - The HBM Base Case (26:58) - Watching IPOs and Capital Flows

    The Memory Constraint: Why HBM Has Become AI's New Oil

  9. 8 juni

    The Physical Constraints Behind the AI Supercycle

    Episode 1:  The Physical Constraints Behind the AI Supercycle The AI supercycle is not a single technology trade. It is a multi-year capital allocation event, and the physical infrastructure behind it is being repriced across semiconductors, data centres, power systems, critical materials and macro conditions simultaneously. In this opening episode, Tim Hardwick introduces the central thesis behind QF-MI Market Intelligence: financial markets and physical infrastructure operate on fundamentally different clocks. Stocks can reprice within hours, while fabs, power stations, mines and grid upgrades take years to deliver. The gap between what is announced and what can actually be built is where the main analytical opportunity lies. The episode walks through four binding constraints shaping the AI infrastructure build-out.  Memory, led by high bandwidth memory, is the first: only a handful of suppliers exist, and shortages in HBM can bottleneck the entire AI hardware stack regardless of chip availability. Power is the second, with AI data centres consuming electricity at the scale of small cities, forcing hyperscalers into direct agreements with nuclear operators and disrupting broader energy markets. Critical materials form the third constraint, covering copper (essential for data centres and grids), uranium (part mining story, part energy story) and rare earths (a geopolitical supply issue given China's dominance over processing). The fourth is photonics: as AI clusters scale, moving data between GPUs, racks and data centres becomes as important as the compute itself, making optical networking and silicon photonics increasingly significant. The episode closes with an overview of how QF-MI builds its probability-weighted market views across the AI infrastructure stack, and previews Episode 2, which will focus on memory and HBM in detail. Chapters 0:17 AI Supercycle Introduction 3:40 Infrastructure Beats Software 4:36 Two Clocks, One Market 6:06 Memory Becomes the Bottleneck 7:43 Power and Grid Pressure 9:52 Materials Underpin the Build-Out 11:51 Photonics Joins the Stack 13:37 Space Enters the Conversation 15:23 How QF-MI Builds Its Market Views 17:27 Why QF-MI Exists Tags: AI, semiconductors, HBM, memory, energy, nuclear, uranium, copper, rare earths, data centres, photonics, capital allocation, macro, AI infrastructure (00:17) - AI Supercycle Introduction (03:40) - Infrastructure Beats Software (04:36) - Two Clocks, One Market (06:06) - Memory Becomes the Bottleneck (07:43) - Power and Grid Pressure (09:52) - Materials Underpin the Build-Out (11:51) - Photonics Joins the Stack (13:37) - Space Enters the Conversation (15:23) - QFMIP and Prism Explained (17:27) - Why QFMI Exists

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Om

The AI Supercycle podcast from QF-MI provides independent Capital Intelligence for the AI Industrial Economy. From semiconductors, AI factories and data centres to energy markets and power grids, critical materials, orbital compute and embodied intelligence, we track where capital is being deployed, where the binding constraints are emerging, and what it means for traders, investors and the broader economy. Each episode examines the physical infrastructure underpinning artificial intelligence and the investment opportunities emerging from its industrialisation. Published weekly by Quantum Fields Market Intelligence (QF-MI).

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  • Skapad av
    Quantum Fields Market Intelligence
  • Aktiva år
    2026
  • Avsnitt
    9
  • Åldersgräns
    Barnvänligt
  • Copyright
    © 2026 Quantum Fields AI Ltd
  • Programmets webbplats
    The AI Supercycle
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