Summary

The generative AI hardware materials market covers the materials and components used to build AI training and inference systems, from raw substrates and gases through to finished server racks. Demand is driven by hyperscaler, enterprise and sovereign capital expenditure on AI datacentres, and by the growing share of AI compute performed at the edge.

System performance is limited by a set of physical constraints rather than by model design. Compute throughput is limited by reticle area and transistor density. Memory bandwidth is limited by HBM stack height and pin width. Interconnect bandwidth is limited by signal attenuation in copper traces above roughly 224 Gbps per lane. Heat removal is limited by thermal interface conductivity and coolant flow rate. Power delivery is limited by IR drop and voltage regulator efficiency. Progress against each constraint depends on a specific materials or packaging development, which is why this layer determines how quickly AI compute capacity can be added.

The market is structured in nine layers: raw materials, gases and photoresists; power semiconductors and delivery; thermal materials and cooling; photonics packaging; substrates and interposers; advanced packaging; HBM and the memory subsystem; AI accelerator silicon; and AI servers and racks. A related but separate segment is the datacentre construction supply chain, covering power infrastructure, cooling plant, buildings and engineering services.

Growth rates differ substantially by layer. AI accelerator silicon is the largest segment but grows more slowly than several of the layers below it. HBM and advanced packaging gain share over the forecast period, as stack heights increase and CoWoS-class packaging capacity remains tight. Photonics packaging and datacentre power semiconductors grow fastest from a smaller base, as co-packaged optics moves into volume production and rack power levels rise above 300 kW. Cooling shifts away from air: direct liquid cooling and immersion account for more than 60% of new AI deployments by 2030.

Two structural characteristics are relevant to suppliers and buyers. The first is geographic concentration. Taiwan, South Korea and Japan account for most leading-edge silicon, memory, packaging, substrate and specialty materials capacity, and CoWoS-class packaging is the most constrained single step. The second is the effect of export controls, which have led to a separate hardware supply chain in China with its own suppliers and process node limits. The capability gap between the two is narrowing fastest in small-model inference and slowest in frontier model training.

Report contents include:

• Executive summary - headline findings, the hardware bottleneck, value-chain map and implications by stakeholder
• The compute stack - training and inference economics, cloud, edge and sovereign AI, and the cost weighting of memory and packaging
• AI accelerator silicon - GPUs, hyperscaler ASICs, alternative architectures, the Chinese ecosystem, foundry roadmaps and wafer-level integration
• AI-driven chip design (EDA) - incumbent vendor initiatives, the startup cohort and AI-EDA market forecasts
• High bandwidth memory and beyond - HBM roadmap and capacity, custom HBM, compute-in-memory, CXL pooling and 3D DRAM
• Advanced packaging and substrates - 2.5D and 3D integration, CoWoS capacity, ABF and FC-BGA substrates, interposers, hybrid bonding and OSAT capacity
• Co-packaged optics and silicon photonics - CPO architecture, optical I/O chiplets, photonics foundries and packaging supply chain
• Thermal management - thermal interface materials, vapour chambers, cold plates, immersion and microfluidic cooling
• Power delivery and the GaN/SiC transition - 48V and 800V HVDC architectures, SiC and GaN device and substrate supply, VRMs, server PSUs and backside power delivery
• Networking and optical materials - switch silicon, pluggable transceivers, DSP and SerDes, III-V materials, NICs, DPUs and connectors
• Datacentre construction - power infrastructure including SMRs, facility cooling, modular designs and site selection
• Edge GenAI hardware - AI smartphones and PCs, automotive silicon, robotics compute and edge accelerator start-ups
• Regional analysis - Taiwan, South Korea, Japan, China, Southeast Asia, the United States, Europe and Israel
• Supply chain and geopolitics - China's domestic supply chain, CHIPS Act implementation, critical materials and single-point-of-failure analysis
• Sustainability and embodied carbon - operational emissions, fab embodied carbon, water use, renewable procurement and disclosure standards
• Market forecasts and strategic outlook - segment, regional and customer-tier forecasts, choke-point analysis, M&A and investment framework
• Appendix - methodology, definitions, abbreviations, sources and year-by-year forecast outputs

Companies profiled include: 1X Technologies, 3M, Acbel Polytech, Accelink Technologies, Achronix Semiconductor, Advanced Micro Devices (AMD), AGC (Asahi Glass), Agility Robotics, AheadComputing, Ajinomoto FineTechno (ABF), Akhan Semiconductor, Alibaba T-Head (PingTouGe), Alpha Assembly Solutions (MacDermid Alpha), Alphabet Inc. (Google), Amazon Web Services (AWS), Ambarella, Amber Semiconductor (AmberSemi), Amkor Technology, Amphenol Corporation, Anduril Industries, Apple Inc., Applied Materials, Apptronik, Arago, ASE Group, ASE Technology Holding (incl. SPIL), Asetek, Asia Vital Components (AVC), ASMPT, Asperitas, Astera Labs, Astrus, AT&S (Austria Technologie & Systemtechnik), Auras Technology, Avalanche Technology, Axelera AI, Axera Technology, AXT Inc., Ayar Labs, BE Semiconductor Industries (BESI), Biren Technology, Black Sesame Technologies, Blaize, Broadcom Inc., Cambricon Technologies, Cambridge GaN Devices (CGD), Carbice Corporation, Celero Communications, Cerebras Systems, Chemours Company, ChipAgents, Chipmind, ChipMOS Technologies, Chiral, Ciena, Cisco Systems, Claros, Coherent Corp., ColorChip, Cooler Master Co., CoolIT Systems, CoreWeave Inc., Corintis, Corning Incorporated, Crossbar Inc., Crusoe Energy Systems, CXMT (ChangXin Memory Technologies), DEEPX, Delta Electronics, d-Matrix, DOW Inc., Dust Photonics, Eaton Corporation, EdgeCortix, EFFECT Photonics, Efficient Computer, Efficient Power Conversion (EPC), Element Six (e6), Eliyan, Empower Semiconductor, Engineered Fluids, Eoptolink Technology, Eridu, Etched.ai, Ethernovia, EuQlid, EV Group (EVG), Everspin Technologies, Fabric8Labs, Fabrinet, Femtum, Ferroelectric Memory Company (FMC), Figure AI, Fourier Intelligence, Foxconn Industrial Internet (FII), Foxconn Interconnect Technology (FIT), Frore Systems, FSP Group, Fujipoly, Furiosa AI, G42, Gaianixx, Galatek, Gigalight, Google, Great Sky, Green Revolution Cooling (GRC), GreenWaves Technologies, Groq Inc., GS Microelectronics (GSME), Hailo Technologies, Henkel AG, Heraeus, Hesheng Silicon Industry, Hisense Broadband, HiSilicon (Huawei), Hitachi Energy, Hon Hai (Foxconn), Honeywell International, Horizon Robotics, Hua Tian Technology (HT-Tech), Huawei Technologies, Hummink, Ibiden Co. Ltd., Iceotope Technologies, Iluvatar CoreX, Indium Corporation, Infineon Technologies AG, Innolight Technology, Innoscience Technology, Intel, Intel Foundry, IQE plc, JCET Group, JetCool Technologies, Kandou AI, Kaneka Corporation, Kinsus Interconnect Technology, Kioxia Holdings, Kneron, Kulicke & Soffa Industries (K&S), Kyocera Corporation, and more.

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Table of Contents

1 EXECUTIVE SUMMARY
1.1 Key Findings
1.2 The Generative AI Hardware Bottleneck
1.3 Materials Value Chain at a Glance
1.4 Eleven-Year Forecast Highlights
1.5 Strategic Implications for Asian Foundries, OSAT, Memory, Substrate, and Cooling Vendors
1.6 Differentiation vs. Adjacent Coverage
1.7 Major Market Players
2 THE COMPUTE STACK BEHIND GENERATIVE AI
2.1 Training vs. Inference Economics
2.2 Cloud, Edge, and Sovereign AI
2.3 Why Memory Bandwidth and Packaging Dominate Cost
2.4 Materials and Components as the New Bottleneck
2.5 Hyperscaler vs. Enterprise vs. Sovereign Capex
2.6 Company Profiles (9 company profiles)
3 AI ACCELERATOR SILICON
3.1 GPUs
3.2 Custom Hyperscaler ASICs
3.3 Domain-Specific and Challenger Architectures
3.4 Chinese AI Chip Ecosystem
3.5 Process Nodes and Foundry Roadmaps
3.6 Wafer-Level Integration and Reticle Stitching
3.7 Company Profiles (53 company profiles)
4 AI-DRIVEN CHIP DESIGN (EDA)
4.1 The EDA Bottleneck in the AI Hardware Era
4.2 The Recursive Loop: AI Designing AI Hardware
4.3 The Incumbent EDA Vendors' AI Initiatives
4.4 The Startup Cohort: Four Distinct Approaches
4.5 Geographic Distribution
4.6 Market Forecast: AI-EDA Tools 2026-2037
4.7 Strategic Implications
4.8 Company profiles (6 company profiles)
5 HIGH BANDWIDTH MEMORY AND BEYOND
5.1 HBM Architecture and TSV Stacking Fundamentals
5.2 HBM Generation Roadmap
5.3 Memory Makers and Capacity Outlook
5.4 Custom HBM (cHBM) and Base-Die Innovation
5.5 Compute-in-Memory and Processing-in-Memory at Scale
5.6 Emerging Memory for AI Datacenters
5.7 Memory Pooling and CXL Fabrics
5.8 3D DRAM - The Post-2030 Path
5.9 Company Profiles (23 company profiles)
6 ADVANCED PACKAGING AND SUBSTRATE MATERIALS
6.1 The 2.5D / 3D Architecture Continuum
6.2 TSMC CoWoS and the Capacity Constraint
6.3 Intel and Samsung Advanced Packaging
6.4 Substrate Technologies (ABF, FC-BGA)
6.5 Interposer Materials (Silicon TSV, Glass, Organic RDL)
6.6 Hybrid Bonding and Copper-to-Copper Interconnect
6.7 OSAT Capacity and Asian Dominance
6.8 Advanced Packaging Materials Suppliers
6.9 Company Profiles (56 company profiles)
7 CO-PACKAGED OPTICS AND SILICON PHOTONICS FOR AI
7.1 The Optical Interconnect Imperative
7.2 CPO Architecture and the Two Network Layers
7.3 TSMC COUPE, CoWoS-Photonics, iOIS
7.4 ASE VIPack and the Merchant Photonics Packaging Layer
7.5 Optical I/O Chiplets: AyarLabs, Lightmatter, Celestial AI
7.6 Switch Silicon and Co-Packaged Optical Engines
7.7 Silicon Photonics Foundries
7.8 Photonics Packaging Materials and Supply Chain
7.9 Market Sizing for Photonics Packaging 2026-2037
7.10 Company Profiles (28 company profiles)
8 THERMAL MANAGEMENT FOR AI DATA CENTERS
8.1 The Thermal Crisis: Power Density at the Package Level
8.2 Thermal Interface Materials (TIMs)
8.3 Heat Spreaders, Vapor Chambers, and Heat Pipes
8.4 Cold Plates and Direct-to-Chip Liquid Cooling
8.5 Immersion Cooling
8.6 Microfluidic and In-Package Cooling
8.7 Market Forecast: AI-Tied Thermal Management 2024-2037
8.8 Company Profiles (40 company profiles)
9 POWER DELIVERY AND GAN/SIC TRANSITION
9.1 The Power Crisis: From 12V to 48V to 800V HVDC
9.2 The Power Hierarchy: System, Board, Package, Die
9.3 SiC Devices and Substrate Supply
9.4 GaN Devices: Lateral, Vertical, Cascode
9.5 Voltage Regulator Modules and Multi-Phase Point-of-Load
9.6 Server Power Supply Units and Rack Rectifier Shelves
9.7 Backside Power Delivery (BSPDN)
9.8 Market Forecast: AI Datacenter Power Semiconductors 2024-2037
9.9 Company Profiles (42 company profiles)
10 NETWORKING AND OPTICAL MATERIALS
10.1 The Three Network Layers in an AI Datacenter
10.2 Switch Silicon Roadmap
10.3 Pluggable Optical Transceivers
10.4 DSP and SerDes for Optical Transceivers
10.5 III-V Materials Layer: InP, GaAs, GaN-Photonics
10.6 NICs, DPUs, and SmartNICs
10.7 Cables, Connectors, and Direct Attach Copper
10.8 Market Forecast: AI-Tied Networking and Optical 2024-2037
10.9 Company Profiles (36 company profiles)
11 DATA CENTER CONSTRUCTION AND SUSTAINABILITY
11.1 The AI Datacenter Buildout: Scale and Scope
11.2 Power Infrastructure: Grid, On-Site Generation, and SMRs
11.3 Facility-Level Cooling Architecture
11.4 Construction Supply Chain and Modular Datacenter Architecture
11.5 Geographic Concentration and Site Selection
11.6 PUE, WUE, and Sustainability Metrics
11.7 Regulatory Framework
11.8 Market Forecast: AI Datacenter Construction Supply Chain 2024-2037
12 EDGE GENAI HARDWARE
12.1 The Edge AI Taxonomy
12.2 AI Smartphones
12.3 AI PCs
12.4 NVIDIA Jetson and the Embedded AI Platform
12.5 Automotive AI Silicon
12.6 Humanoid Robotics: The Emerging Edge AI Compute Frontier
12.7 Edge AI Accelerator Start-ups
12.8 Edge AI Memory: LPDDR5X, On-Chip SRAM, eMRAM
12.9 Market Forecast: Edge AI Silicon 2024-2037
12.10 Company Profiles (51 company profiles)
13 REGIONAL ANALYSIS: GEOGRAPHY OF THE GENAI HARDWARE SUPPLY CHAIN
13.1 The Asian Concentration
13.2 Taiwan
13.3 South Korea
13.4 Japan
13.5 China
13.6 Southeast Asia and India
13.7 The United States
13.8 Europe and Israel
13.9 The Rest of World: Niche Capabilities and Sovereign Ambitions
13.10 Aggregate Regional Capture: Scenario Analysis 2026-2037
14 SUPPLY CHAIN AND GEOPOLITICS
14.1 The Defining Tensions
14.2 The China Strategy: Sovereign Stack and Domestic Substitution
14.3 US CHIPS Act Implementation and Domestic Reshoring
14.4 European Chips Act and Strategic Autonomy
14.5 The Critical Materials Layer
14.6 Single-Point-of-Failure Analysis
14.7 Scenarios for Supply Chain Resilience
14.8 Sovereign AI as a Strategic Demand Driver
15 SUSTAINABILITY AND EMBODIED CARBON
15.1 The Sustainability Stakes
15.2 Operational Emissions: Training, Inference, and the Cooling Energy Tax
15.3 Embodied Carbon in Semiconductor Manufacturing
15.4 Water, Chemicals, and Resource Intensity
15.5 Renewable Energy Procurement at Hyperscaler Scale
15.6 Heat Recovery, Circular Economy, and End-of-Life
15.7 Carbon Accounting Standards and Corporate Disclosure
15.8 Green Manufacturing Practices at Major Suppliers
15.9 Market and Regulatory Outlook 2026-2037
16 MARKET FORECASTS: GENAI HARDWARE 2026-2037
16.1 Forecast Methodology and Framework
16.2 Total GenAI Hardware Market - Base Case Forecast
16.3 Bull/Base/Bear Scenarios at Aggregate Level
16.4 AI Accelerator Silicon Sub-Segment Forecast
16.5 HBM and Memory Sub-Segment Forecast
16.6 Advanced Packaging Sub-Segment Forecast
16.7 Photonics Packaging Sub-Segment Forecast
16.8 Thermal Management Sub-Segment Forecast
16.9 Power Delivery Sub-Segment Forecast
16.10 Networking and Optical Sub-Segment Forecast
16.11 Datacenter Construction Supply Chain Sub-Segment Forecast
16.12 Edge AI Silicon Sub-Segment Forecast
16.13 Regional Capture Forecast
16.14 Customer Tier Forecast
16.15 Key Forecast Risks and Sensitivities
17 STRATEGIC OUTLOOK
17.1 The Five Defining Themes of the GenAI Hardware Decade
17.2 The Choke-Point Map
17.3 The Strategic Investment Framework
17.4 M&A Landscape and Strategic Consolidation
17.5 Sensitivity Analysis
17.6 Strategic Implications by Stakeholder
17.7 What Could Change This Forecast
18 APPENDIX
18.1 Forecast Methodology
18.2 Definitions and Terminology
18.3 Abbreviations
18.4 Sources and References
18.5 Forecast Scope, Limitations, and Disclaimers
18.6 Detailed Year-by-Year Forecast Outputs

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List of Tables/Graphs

List of Tables TABLE 1 Headline Findings Summary (Base Case) TABLE 2 Eleven-Year Forecast Summary: GenAI Hardware Materials Market 2026-2037 (US $B, Base Case) TABLE 3 Top Ten Strategic Conclusions Mapped to Stakeholder Type TABLE 4 Training vs. Inference Hardware Mix Comparison TABLE 5 Silicon Content per 100 MW AI Training Facility (Reference BoM) TABLE 6 Cost-per-Token by Model Size and Hardware Configuration 2024-2040 (USD per million output tokens) TABLE 7 Sovereign AI Build-Outs by Country 2025-2030 TABLE 8 AI Accelerator Memory Requirements 2024-2030F TABLE 9 US and Chinese Hyperscaler Capex Summary 2021-2026 (US $B) TABLE 10 GPU Specifications: NVIDIA Blackwell, Rubin; AMD MI350X, MI450 (2024-2026) TABLE 11 Rack-Scale GPU Platform Comparison TABLE 12 AI ASIC Specifications: Google, AWS, Microsoft, Meta (2024-2026) TABLE 13 AI ASIC Technology Specification Database (All Major Vendors) TABLE 14 Chinese Data Center Processor Manufacturer Overview TABLE 15 China AI Chip Capability Gap Assessment by Workload Type TABLE 16 Semiconductor Process Node Roadmap 2024-2030 TABLE 17 TSMC Node Roadmap: N3, N2, A16, A14 Specs and Timeline TABLE 18 Wafer-Scale Accelerator Yield Economics: Cerebras WSE-3 and Tesla Dojo TABLE 19 Incumbent EDA Vendor AI Initiatives vs. Startup Cohort TABLE 20 AI-EDA Approaches by Design-Flow Stage TABLE 21 AI-EDA Market Forecast 2026-2037 TABLE 22 HBM Generation Technical Specifications HBM2E to HBM5 TABLE 23 HBM Bonding Integration Roadmap and Vendor Mapping TABLE 24 HBM Market Share by Supplier 2022-2028F (%) TABLE 25 HBM Customer Demand Breakdown: NVIDIA, Google, AMD, Hyperscalers 2024-2028F TABLE 26 Custom HBM Players, Products, Design Roadmaps TABLE 27 Standard vs. Custom HBM Revenue Forecast 2024-2030F (US $M) TABLE 28 Near-Memory and In-Memory Computing Landscape TABLE 29 Resistive Non-Volatile Memory Technologies TABLE 30 Storage-Class Memory Technology Comparison TABLE 31 CXL Switch Silicon Vendors and Capability Matrix TABLE 32 3D DRAM Technology Readiness Assessment by Player 2026 TABLE 33 Advanced Packaging Technology Comparison: 2.5D and 3D Options TABLE 34 CoWoS Capacity Forecast by Sub-Variant 2024-2037 (k wafers/month equivalent) TABLE 35 TSMC SoIC Variants: Specifications and AI Customer Adoption TABLE 36 Comparative Advanced Packaging Roadmap: TSMC vs. Intel vs. Samsung TABLE 37 Substrate Suppliers for AI Accelerator Packages TABLE 38 Substrate Demand Forecast for AI Packages 2024-2037 (k units/month) TABLE 39 Interposer Material Comparison: Silicon TSV vs. Glass vs. Organic RDL TABLE 40 Hybrid Bonding Adoption Roadmap for DRAM Applications 2023-2030 TABLE 41 OSAT Capacity and Revenue Concentration 2024-2030 TABLE 42 Advanced Packaging Materials Suppliers TABLE 43 Migration Trajectory from Copper to Optical Across the Two Network Layers TABLE 44 Key Technology Building Blocks for Co-Packaged Optics TABLE 45 TSMC Photonics Packaging Capabilities TABLE 46 Merchant Photonics Packaging Platform Comparison TABLE 47 Optical I/O Chiplet Vendor Comparison TABLE 48 AI-Switch Silicon Roadmap with CPO Integration TABLE 49 Silicon Photonics Foundry Capability Matrix TABLE 50 CPO Supply Chain Critical Materials and Suppliers TABLE 51 Photonics Packaging Revenue Forecast for AI Applications 2024-2037 (US $B) TABLE 52 Cooling Technologies for High-Performance AI Processors TABLE 53 Thermal Interface Material Categories and Suppliers TABLE 54 TIM Properties for AI Accelerator Applications TABLE 55 TIM Revenue Forecast for AI Datacenter Applications 2024-2037 (US $M) TABLE 56 Heat Spreader and Vapor Chamber Suppliers TABLE 57 Heat Spreader and Heat Sink Revenue Forecast 2024-2037 (US $M) TABLE 58 Cold Plate Suppliers for AI Servers TABLE 59 Liquid Cooling Adoption Share in New AI Datacenter Deployments TABLE 60 Immersion Cooling Fluid Categories and Suppliers TABLE 61 Immersion Cooling System Suppliers TABLE 62 Microfluidic Cooling Technology Comparison TABLE 63 Facility Liquid Cooling Infrastructure Suppliers TABLE 64 AI-Tied Thermal Management Revenue Forecast 2024-2037 (US $B) TABLE 65 Power Delivery Hierarchy in AI Servers TABLE 66 Comparison of 48V and 800V HVDC Rack Architectures TABLE 67 SiC vs. GaN vs. Silicon Power Device Comparison TABLE 68 SiC Substrate and Device Suppliers TABLE 69 GaN Device Manufacturers and Application Focus TABLE 70 AI VRM Controller and Power Stage Suppliers TABLE 71 Server Power Supply Unit Suppliers TABLE 72 Backside Power Delivery Adoption Roadmap TABLE 73 AI Datacenter Power Semiconductor Revenue Forecast 2024-2037 (US $B) TABLE 74 The Three Networking Layers in an AI Datacenter TABLE 75 AI Switch Silicon Roadmap TABLE 76 Optical Transceiver Form Factor and Data Rate Roadmap TABLE 77 Optical Transceiver Module Suppliers for AI Datacenters TABLE 78 Optical DSP Suppliers and Application Mapping TABLE 79 III-V Substrate Materials Suppliers for AI Optical Transceivers TABLE 80 NIC, DPU, and SmartNIC Suppliers TABLE 81 Cable, Connector, and Fiber Suppliers for AI Datacenters TABLE 82 AI-Tied Networking and Optical Revenue Forecast 2024-2037 (US $B) TABLE 83 AI Datacenter CAPEX Breakdown (100 MW Training Facility, 2026 Reference) TABLE 84 Hyperscaler Power Procurement Strategies (2025 Snapshot) TABLE 85 Major Switchgear, Transformer, and Power Infrastructure Suppliers TABLE 86 Facility Cooling Infrastructure Suppliers TABLE 87 Major AI Datacenter Construction Companies and Operators TABLE 88 Construction Engineering and EPC Firms with Major AI Datacenter Practice TABLE 89 PUE Targets and Achievement at Major Hyperscalers (2025) TABLE 90 AI-Tied Datacenter Construction Supply Chain Revenue Forecast 2024-2037 (US $B) TABLE 91 Edge AI NPU Performance by Application Segment TABLE 92 Flagship Smartphone AI Processor Comparison (2026) TABLE 93 Evolution of Apple Neural Engine AI Performance (2017-2026) TABLE 94 AI PC Silicon Platform Comparison (2026) TABLE 95 AI PC On-Device LLM Inference Capability (2026) TABLE 96 NVIDIA Jetson Product Line (2026) TABLE 97 Automotive AI Silicon Platforms (2026) TABLE 98 Humanoid Robot Compute Platforms (2026) TABLE 99 Edge AI Start-up Landscape TABLE 100 Edge AI Memory Suppliers and Categories TABLE 101 Edge AI Silicon Revenue Forecast 2024-2037 (US $B) TABLE 102 Regional Capture of GenAI Hardware Bill of Materials, 2026 Base Case TABLE 103 Taiwan AI Hardware Supply Chain by Capability Layer TABLE 104 Korea AI Hardware Supply Chain by Capability Layer TABLE 105 Japan AI Hardware Supply Chain by Capability Layer TABLE 106 China AI Hardware Supply Chain by Capability Layer TABLE 107 Southeast Asia and India AI Hardware Supply Chain TABLE 108 United States AI Hardware Supply Chain by Capability Layer TABLE 109 Europe and Israel AI Hardware Supply Chain TABLE 110 Regional GenAI Hardware BoM Capture by Scenario (% of Global BoM Value) TABLE 111 Major US Export Control Actions Affecting AI Hardware (2019-2026) TABLE 112 Chinese Wafer-Fab Equipment Companies and Capability Status TABLE 113 Major CHIPS Act-Funded Semiconductor Projects TABLE 114 Critical Materials Supply Chain Concentration for AI Hardware TABLE 115 Top Single-Point-of-Failure Risks in the GenAI Hardware Supply Chain TABLE 116 Supply Chain Diversification Scenario Outcomes 2030 TABLE 117 Lifecycle Carbon Footprint by AI Chip Type TABLE 118 AI Carbon Footprint Examples and Mitigation Strategies TABLE 119 Estimated Embodied Carbon Across the AI Hardware Hierarchy TABLE 120 Water Consumption Profile for AI Hardware Manufacturing and Operations TABLE 121 Hyperscaler Renewable Energy and Nuclear Procurement (2025 Snapshot) TABLE 122 Lifecycle and End-of-Life Treatment for AI Hardware TABLE 123 Major Corporate Carbon Commitments Affecting AI Hardware Procurement TABLE 124 Green Manufacturing Initiatives by Major Semiconductor Suppliers TABLE 125 Forecast Methodology and Key Assumptions TABLE 126 Total GenAI Hardware Market by Major Segment, Base Case (US $B) TABLE 127 GenAI Hardware Aggregate Market Across Three Scenarios, 2026-2037 (US $B, excl. construction supply chain) TABLE 128 AI Accelerator Silicon Sub-Segment Forecast 2024-2037 (US $B) TABLE 129 HBM and AI-Tied Memory Sub-Segment Forecast 2024-2037 (US $B) TABLE 130 Advanced Packaging Sub-Segment Forecast 2024-2037 (US $B, AI-tied) TABLE 131 Photonics Packaging Sub-Segment Forecast 2024-2037 (US $B) TABLE 132 Thermal Management Sub-Segment Forecast 2024-2037 (US $B, AI-tied) TABLE 133 Power Delivery (AI Datacenter Tied) Sub-Segment Forecast 2024-2037 (US $B) TABLE 134 Networking and Optical (AI-Tied) Sub-Segment Forecast 2024-2037 (US $B) TABLE 135 Datacenter Construction Supply Chain Sub-Segment Forecast 2024-2037 (US $B) TABLE 136 Edge AI Silicon Sub-Segment Forecast 2024-2037 (US $B) TABLE 137 Regional GenAI Hardware BoM Capture Forecast, 2026-2037, Base Case (%) TABLE 138 Total GenAI Hardware Demand by Customer Tier, Base Case 2026-2037 (US $B, excl. construction supply chain) TABLE 139 The Five Defining Themes: Strategic Implications by Layer TABLE 140 The Top 15 Strategic Choke Points in the GenAI Hardware Supply Chain TABLE 141 Strategic Tier Classification of GenAI Hardware Sub-Segments TABLE 142 Notable GenAI Hardware M&A and Strategic Investments 2020-2026 TABLE 143 Sensitivity of Base Case 2030 Forecast to Key Assumptions TABLE 144 Detailed Year-by-Year Total Forecast, Base Case (US $B, excl. DC construction supply chain) TABLE 145 Detailed Year-by-Year Total Forecast Across All Three Scenarios (US $B, excl. DC construction supply chain) List of Figures FIGURE 1 Five Compute-Scaling Walls and Their Material Solutions FIGURE 2 Generative AI Hardware Materials Value-Chain Layer Map FIGURE 3 Base-Case Forecast Stacked-Area Visualisation 2026-2037 FIGURE 4 Bull, Base, and Bear Scenario Comparison 2026-2037 FIGURE 5 Asia-Pacific Capture Rate of GenAI Hardware Value 2026-2037 FIGURE 6 AI Data Centre Silicon Content Map FIGURE 7 Inference Token Economics by Model Size FIGURE 8 Sovereign AI Capex Pipeline 2024-2030 by Geography FIGURE 9 Generative AI Compute Demand Scaling vs. Electrical Interconnect Capacity FIGURE 10 AI Accelerator BoM Decomposition: Where the Dollars Go FIGURE 11 Annual GenAI-Driven AI Hardware Demand Pool 2024-2030 FIGURE 12 NVIDIA GPU Architecture Evolution: Volta to Post-Blackwell Timeline FIGURE 13 Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms FIGURE 14 Hyperscaler ASIC Roadmap Comparison FIGURE 15 Hyperscaler ASIC vs. Merchant GPU Share of Datacenter AI Compute 2024-2037 FIGURE 16 AI ASIC Start-Up Landscape by Funding Stage FIGURE 17 GPU vs. AI ASIC Performance per Watt Comparison 2022-2026 FIGURE 18 China Semiconductor Capability Map: Node vs. Supply-Chain Layer FIGURE 19 China AI Chip Roadmap vs. NVIDIA / AMD: Parity Distance by Generation FIGURE 20 Leading-Edge Foundry Roadmap Comparison 2023-2037 (Gantt) FIGURE 21 HBM Architecture: Die-Stack Cross-Section FIGURE 22 HBM Bandwidth Evolution HBM1 to HBM5 FIGURE 23 HBM4 Die-to-Wafer Bonding Integration Scheme FIGURE 24 HBM Market Share by Supplier 2022-2028F FIGURE 25 SK hynix HBM Strategy and Roadmap FIGURE 26 Samsung HBM Strategy and Roadmap FIGURE 27 Micron HBM Strategy and Roadmap FIGURE 28 HBM Customer Demand Breakdown by AI Accelerator FIGURE 29 Custom HBM Architecture: Co-Design Concept FIGURE 30 Custom HBM Share of Total HBM Bit Demand 2026-2037 FIGURE 31 Near-Memory vs. PIM Architecture Comparison FIGURE 32 CXL Memory Pooling Architecture and Vendor Map FIGURE 33 3D DRAM Concept Architectures FIGURE 34 Monolithic Die vs. Chiplet Architecture: Yield and Cost FIGURE 35 Chiplet Interconnect Technology Spectrum FIGURE 36 CoWoS Integration: GPU + HBM on Silicon Interposer FIGURE 37 CoWoS Capacity Expansion Roadmap FIGURE 38 OSAT Revenue Concentration by Geography 2024-2037 FIGURE 39 Compute Demand vs. Interconnect Bandwidth Gap FIGURE 40 Photonics Packaging Revenue Forecast for AI Applications 2024-2037 FIGURE 41 AI Accelerator TDP and Cooling Architecture Trajectory 2022-2037 FIGURE 42 Liquid Cooling Adoption Trajectory in AI Datacenter Deployments FIGURE 43 Power Density at AI Server Rack: From 30 kW to 600 kW per Rack FIGURE 44 Wide-Bandgap Power Semiconductor Material Properties Comparison FIGURE 45 Edge AI Performance and Power Envelope Map FIGURE 46 Total GenAI Hardware Market 2024-2037 by Segment, Base Case FIGURE 47 GenAI Hardware Market Bull/Base/Bear Scenarios 2024-2037 FIGURE 48 Sensitivity of 2030 Forecast to Key Variables