The Global Neural Networks Market 2027-2040

Table of Contents

1 EXECUTIVE SUMMARY

• 1.1 Report scope and market definition
• 1.2 Key findings
• 1.3 Market size and forecast 2025–2040
• 1.4 Growth drivers and restraints
• 1.5 Technology inflection points 2027–2040
• 1.6 Competitive landscape summary
• 1.7 Investment and deal activity 2025–2026
• 1.8 Strategic outlook and recommendations

2 INTRODUCTION

• 2.1 Defining neural networks
• 2.2 Historical development
• 2.3 Taxonomy of neural network architectures
• 2.4 Training and inference
  • 2.4.1 Hardware requirements of training and inference workloads
• 2.5 Scaling laws and compute demand
  • 2.5.1 The cost-per-token curve
• 2.6 The neural network value chain
• 2.7 Research methodology
  • 2.7.1 Approach
  • 2.7.2 Reconciliation with related Future Markets reports

3 NEURAL NETWORK ARCHITECTURES AND ALGORITHMS

• 3.1 Multilayer perceptrons and feedforward networks
• 3.2 Convolutional neural networks
  • 3.2.1 3D CNNs in medical imaging: JLK
• 3.3 Recurrent networks and LSTMs
• 3.4 Transformers and attention
  • 3.4.1 Self-attention and its cost
  • 3.4.2 Efficiency techniques
• 3.5 Mixture-of-experts models
• 3.6 Post-transformer architectures
  • 3.6.1 Why replace attention
  • 3.6.2 State-space models and hybrids
  • 3.6.3 Venture-backed alternatives
  • 3.6.4 Diffusion language models
• 3.7 Liquid neural networks and continuous-time models
  • 3.7.1 Liquid AI
• 3.8 Graph and topological neural networks
  • 3.8.1 Arlequin AI
• 3.9 Generative architectures: GANs, VAEs, diffusion and world models
  • 3.9.1 Diffusion models
  • 3.9.2 World models
  • 3.9.3 World foundation models as training infrastructure for physical AI
• 3.10 Spiking neural networks
  • 3.10.1 Digital, analog and hybrid spiking implementations
  • 3.10.2 Photonic spiking and reservoir networks
• 3.11 Physics-informed networks, neural operators and KANs
• 3.12 Quantum neural networks
  • 3.12.1 Neural networks for quantum key distribution: WiMi
  • 3.12.2 Hybrid spiking-quantum networks: MicroCloud Hologram DSQ-Net
  • 3.12.3 Wider quantum machine learning context
• 3.13 Biological neural networks and organoid intelligence
  • 3.13.1 Cortical Labs
  • 3.13.2 FinalSpark and other developers
• 3.14 Memory-augmented neural networks
  • 3.14.1 Engramme and Large Memory Models
• 3.15 Model compression, small language models and on-device AI
  • 3.15.1 Distillation
  • 3.15.2 Quantisation, pruning and sparsity
  • 3.15.3 On-device AI
  • 3.15.4 Precision, efficiency and edge compilation toolchains
  • 3.15.5 Precision and sparsity roadmap, 2026–2028
• 3.16 Architecture adoption outlook

4 NEURAL NETWORK HARDWARE

• 4.1 Hardware landscape
  • 4.1.1 Energy efficiency: TOPS/W tiers and benchmarks
• 4.2 GPUs
  • 4.2.1 NVIDIA
  • 4.2.2 AMD
  • 4.2.3 Intel and other GPU suppliers
  • 4.2.4 GPU unit shipments, average selling prices and generational mix
• 4.3 Hyperscaler custom ASICs
  • 4.3.1 Google TPU
  • 4.3.2 AWS Trainium and Inferentia
  • 4.3.3 Microsoft Maia
  • 4.3.4 Meta MTIA
  • 4.3.5 OpenAI and Broadcom
  • 4.3.6 Broadcom and Marvell
  • 4.3.7 Custom accelerator volumes and the design-services layer
• 4.4 Challenger accelerator companies
  • 4.4.1 Efficiency and total cost of ownership: merchant GPUs, custom and start-up ASICs
  • 4.4.2 Wafer-scale yield economics
  • 4.4.3 Development cost as a barrier to entry
• 4.5 China’s accelerator ecosystem
  • 4.5.1 China’s processor market, domestic unit shares and the memory gap
  • 4.5.2 Capability gap by workload
• 4.6 South Korean and Japanese accelerator developers
  • 4.6.1 South Korea
  • 4.6.2 Japan
• 4.7 Edge neural processing units
  • 4.7.1 Edge NPU architectures and dataflow
  • 4.7.2 Edge accelerator performance and efficiency benchmarks
  • 4.7.3 Process technology and the cost of edge NN silicon
  • 4.7.4 Mobile NPU efficiency and ultra-low-power audio and sensing
  • 4.7.5 Robot compute platforms and the two-tier control hierarchy
• 4.8 Open-source neural network hardware
  • 4.8.1 RISC-V in production edge NN silicon
• 4.9 Neuromorphic processors
  • 4.9.1 Neuromorphic drivers, restraints and near-term outlook
• 4.10 Photonic neural networks and optical interconnect
  • 4.10.1 Optical neural network architectures
  • 4.10.2 Performance, efficiency and the precision problem
  • 4.10.3 Optical switching and light sources for AI fabrics
• 4.11 Analog, in-memory and thermodynamic computing
  • 4.11.1 The data-movement problem in numbers
  • 4.11.2 Memory technologies for compute-in-memory
  • 4.11.3 Precision limits and the analog roadmap to
  • 4.11.4 Processing-in-memory and compute-in-memory products from memory suppliers
  • 4.11.5 Emerging non-volatile memory as synaptic devices
• 4.12 Memory, packaging and interconnect
  • 4.12.1 High-bandwidth memory
  • 4.12.2 Advanced packaging
  • 4.12.3 Interconnect
  • 4.12.4 Rack bill of materials, HBM content and packaging capacity
  • 4.12.5 Energy per bit and the case for optics
  • 4.12.6 The memory wall in numbers
  • 4.12.7 HBM unit and revenue outlook
  • 4.12.8 Inference memory hierarchy and the KV cache
• 4.13 Hardware market forecasts
  • 4.13.1 Data-center neural network accelerators
  • 4.13.2 Edge neural processing units
  • 4.13.3 Neuromorphic and novel neural network hardware
  • 4.13.4 Comparison with Future Markets’ low-power AI semiconductor forecast
  • 4.13.5 Related-report context: optical computing forecasts

5 SOFTWARE, MODELS AND PLATFORMS

• 5.1 Frameworks and compilers
  • 5.1.1 The framework layer
  • 5.1.2 Compilers, kernels and the CUDA moat
  • 5.1.3 Inference engines and on-device runtimes
• 5.2 Foundation model developers
  • 5.2.1 Capital concentration at the frontier
  • 5.2.2 Europe, China, Japan and Russia
  • 5.2.3 Architecture challengers and new labs
  • 5.2.4 Implications
• 5.3 Open-weight vs. proprietary models
  • 5.3.1 The shifting balance
  • 5.3.2 Who uses open weights and why
• 5.4 Inference economics and token demand
  • 5.4.1 From training to inference
  • 5.4.2 Token volumes
  • 5.4.3 Reasoning models and test-time compute
  • 5.4.4 Falling unit costs and rising total spend
  • 5.4.5 Cost per token by model class and hardware
  • 5.4.6 Memory footprint of inference workload classes
• 5.5 Platforms, MLOps and inference serving
  • 5.5.1 Market structure
  • 5.5.2 Consolidation and valuations
  • 5.5.3 Inference-serving economics
• 5.6 Training data and labelling
  • 5.6.1 From labelling to expert data
  • 5.6.2 Scale AI, Surge AI and the 2025 realignment
  • 5.6.3 Crowdsourced platforms
  • 5.6.4 Synthetic data
• 5.7 Memory and agent infrastructure
  • 5.7.1 Why memory matters
  • 5.7.2 Engramme
  • 5.7.3 Mem0, Zep, Letta and LangMem
  • 5.7.4 Outlook
• 5.8 Software and model forecasts

6 MARKETS AND APPLICATIONS

• 6.1 Hyperscale cloud and AI labs
  • 6.1.1 Capital expenditure as the primary demand signal
  • 6.1.2 Neoclouds, Oracle and Stargate
  • 6.1.3 AI labs as buyers
  • 6.1.4 Processor demand by data-center facility type
  • 6.1.5 Sovereign AI build-outs
  • 6.1.6 Chinese hyperscaler capital expenditure
• 6.2 Enterprise and agentic AI
  • 6.2.1 From copilots to agents
  • 6.2.2 Platforms and leading suppliers
• 6.3 Consumer devices
  • 6.3.1 The on-device shift
  • 6.3.2 Small models and edge silicon
    • 6.3.2.1 Smartphone and AI PC NN processor benchmarks
• 6.4 Automotive and autonomous mobility
  • 6.4.1 End-to-end neural driving
  • 6.4.2 Compute platforms
  • 6.4.3 Open-source silicon and efficient models
  • 6.4.4 Compute requirements by level of autonomy
  • 6.4.5 Robotaxi operating data and in-vehicle compute content
• 6.5 Healthcare and life sciences
  • 6.5.1 Medical imaging
  • 6.5.2 JLK and 3D convolutional networks
  • 6.5.3 Drug discovery and regulation
• 6.6 Industrial, robotics and physical AI
  • 6.6.1 Robot foundation models
  • 6.6.2 Humanoids
  • 6.6.3 Industrial applications
  • 6.6.4 On-board neural network compute for humanoid robots
  • 6.6.5 Vision-language-action model lineage and specifications
  • 6.6.6 Training paradigms, sim-to-real transfer and remaining technical gaps
  • 6.6.7 Fleet learning and orchestration
  • 6.6.8 Humanoid unit and price forecasts
• 6.7 Financial services
  • 6.7.1 Established and emerging uses
• 6.8 Defence, security and government
  • 6.8.1 A funding surge
  • 6.8.2 Arlequin AI and topological networks
  • 6.8.3 Government and intelligence
• 6.9 Telecommunications and quantum-secure communications
  • 6.9.1 AI-RAN and network operations
  • 6.9.2 Neural networks in quantum key distribution
• 6.10 Science and materials discovery
  • 6.10.1 Structural biology
  • 6.10.2 Materials informatics
  • 6.10.3 Weather and climate
  • 6.10.4 Neural operators and scientific machine learning
• 6.11 End-use market forecasts
  • 6.11.1 Reconciliation with Future Markets’ physical AI forecast

7 GLOBAL MARKET FORECASTS 2025–2040

• 7.1 Forecast approach
• 7.2 Total market
• 7.3 By segment
• 7.4 Hardware vs. software
• 7.5 Training vs. inference
• 7.6 Cloud vs. edge
  • 7.6.1 Comparison with Future Markets’ edge AI chip forecast
• 7.7 By architecture family
• 7.8 By region
• 7.9 Scenario analysis

8 REGIONAL ANALYSIS

• 8.1 North America
  • 8.1.1 Hyperscaler capital expenditure
  • 8.1.2 Stargate and gigawatt-scale campuses
  • 8.1.3 Industrial policy: CHIPS Act and the Intel stake
  • 8.1.4 Export controls and revenue share
  • 8.1.5 Capital markets and challengers
• 8.2 China
  • 8.2.1 Domestic accelerators
  • 8.2.2 The AI chip IPO wave
  • 8.2.3 Models: DeepSeek and the open-weight ecosystem
  • 8.2.4 Procurement policy
  • 8.2.5 Quantum-neural and holographic computing companies
• 8.3 South Korea
  • 8.3.1 Memory: SK hynix and Samsung
  • 8.3.2 GPUs and national infrastructure
  • 8.3.3 Neural processing units: Rebellions and FuriosaAI
  • 8.3.4 Models and applications
• 8.4 Japan
  • 8.4.1 Rapidus and leading-edge manufacturing
  • 8.4.2 Edge AI chips and the JST programme
  • 8.4.3 Model developers
  • 8.4.4 SoftBank
  • 8.4.5 Physical AI as national industrial policy
• 8.5 Taiwan
• 8.6 Europe
  • 8.6.1 Mistral AI and European compute
  • 8.6.2 Start-ups and novel architectures
  • 8.6.3 EU policy: Chips Act 2.0 and the AI Act
• 8.7 United Kingdom
  • 8.7.1 Compute: AIRR and Isambard-AI
  • 8.7.2 Policy and legislation
  • 8.7.3 Companies
• 8.8 India, Middle East, Russia, Australia and rest of world
  • 8.8.1 India
  • 8.8.2 Saudi Arabia and the UAE
  • 8.8.3 Russia
  • 8.8.4 Australia and Singapore

9 COMPETITIVE LANDSCAPE, INVESTMENT AND M&A

• 9.1 Market structure
  • 9.1.1 Data-center accelerator concentration
  • 9.1.2 The rise of custom silicon
  • 9.1.3 China as a separate market
  • 9.1.4 Concentration elsewhere in the stack
• 9.2 Venture funding
  • 9.2.1 Scale and concentration of capital
  • 9.2.2 Hardware and infrastructure rounds
  • 9.2.3 Edge, neuromorphic and novel computing
  • 9.2.4 Post-transformer and memory start-ups
  • 9.2.5 Assessment
  • 9.2.6 Physical AI and robot foundation model funding
• 9.3 M&A and licensing
  • 9.3.1 Deal patterns
  • 9.3.2 Deals that did not happen
• 9.4 IPOs and public markets
  • 9.4.1 The United States
  • 9.4.2 China’s domestic GPU listings
  • 9.4.3 Pipeline and implications
• 9.5 Strategic compute partnerships
  • 9.5.1 Gigawatt-denominated deals
  • 9.5.2 Backlog, sovereign deals and the edge
  • 9.5.3 Circularity and risk
• 9.6 Start-up landscape
  • 9.6.1 Layers of the landscape
  • 9.6.2 Dynamics across the layers

10 REGULATION, STANDARDS AND ETHICS

• 10.1 EU AI Act
  • 10.1.1 The Digital Omnibus
  • 10.1.2 Market implications
• 10.2 US export controls
  • 10.2.1 From prohibition to tightening, 2022–2024
  • 10.2.2 The H20 episode and revenue sharing,
  • 10.2.3 The H200/MI325X rule and 25% duty,
• 10.3 United Kingdom
• 10.4 China
• 10.5 Standards and certification
  • 10.5.1 ISO/IEC
  • 10.5.2 Other standards and frameworks
• 10.6 Sector regulation: healthcare and drug development
• 10.7 Liability, copyright and litigation
  • 10.7.1 Product liability and design
  • 10.7.2 Copyright

11 MARKET CHALLENGES AND RISKS

• 11.1 Energy and power
  • 11.1.1 Rack density, cooling and the AI share of data-center load
  • 11.1.2 Cooling overhead and the system-level efficiency gap
  • 11.1.3 Embodied carbon and resource intensity
• 11.2 Memory and packaging supply constraints
  • 11.2.1 Memory requirements by accelerator generation
  • 11.2.2 Memory cost per AI server and the 2029–2030 correction risk
• 11.3 Geopolitics and supply-chain concentration
  • 11.3.1 Choke points in the neural network hardware supply chain
• 11.4 Capital intensity and valuation risk
  • 11.4.1 Capital intensity ratios and the shift from capex to opex
• 11.5 Talent, data and intellectual property
  • 11.5.1 Chip design capacity and AI-driven EDA
• 11.6 Safety, security and reliability
• 11.7 Risk assessment

12 FUTURE OUTLOOK 2027–2040

• 12.1 Technology roadmap
  • 12.1.1 Three waves of commercialisation
  • 12.1.2 Readiness and commercialisation timeline
  • 12.1.3 Architecture and hardware co-evolution
  • 12.1.4 Energy-efficiency roadmap
  • 12.1.5 Photonic neural network roadmap
  • 12.1.6 Physical AI readiness by application
  • 12.1.7 HBM and memory technology roadmap to
• 12.2 Ten predictions for
• 12.3 Opportunity assessment
• 12.4 Wildcards and discontinuities
  • 12.4.1 Upside discontinuities
  • 12.4.2 Downside discontinuities
  • 12.4.3 Unconventional discontinuities
  • 12.4.4 Physical AI scenario variables

13 COMPANY PROFILES (195 company profiles)

14 APPENDICES

• 14.1 Detailed year-by-year forecasts
• 14.2 Glossary

15 REFERENCES

List of Tables

• Table 1. Neural network market segments and scope definitions
• Table 2. Key findings summary
• Table 3. Global neural network market by segment, 2025–2040 (US$ billion)
• Table 4. Key market drivers
• Table 5. Key market restraints and challenges
• Table 6. Leading companies by market segment
• Table 7. Largest neural network funding rounds, M&A and licensing deals, 2025–2026
• Table 8. Strategic implications by stakeholder type
• Table 9. Key milestones in neural network development, 1943–2026
• Table 10. Taxonomy of neural network architectures and primary applications
• Table 11. Training vs. inference: workload, hardware and economics
• Table 12. Selected frontier models: architecture and parameter counts, 2020–2026
• Table 13. Modelled hardware cost per million output tokens by model size, 2024–2040 (US$)
• Table 14. Value-chain layers, value capture and representative companies
• Table 15. Forecast methodology and key assumptions
• Table 16. Reconciliation with related Future Markets reports
• Table 17. CNN architectures and commercial applications
• Table 18. Transformer variants and efficiency techniques
• Table 19. Selected mixture-of-experts models: total and active parameters
• Table 20. Comparison of post-transformer architectures
• Table 21. Post-transformer model developers
• Table 22. Liquid AI: model families, platforms and funding
• Table 23. Graph and topological neural network developers and applications
• Table 24. World-model developers and funding
• Table 25. Spiking vs. conventional artificial neural networks
• Table 26. Spiking neural network hardware: digital, hybrid and analog implementations compared
• Table 27. Neuromorphic photonic architectures for spiking and recurrent neural networks
• Table 28. Scientific machine learning architectures
• Table 29. Quantum neural network and quantum machine learning developments, 2025–2026
• Table 30. Biological computing platforms
• Table 31. Model compression and efficiency techniques
• Table 32. Typical efficiency gains from reduced numerical precision in edge NPUs (relative to FP32)
• Table 33. Sparsity techniques and their impact on neural network inference efficiency
• Table 34. Share of neural network compute by architecture family, 2024–2040 (%)
• Table 35. Neural network hardware categories compared
• Table 36. Neural network hardware energy efficiency by technology category
• Table 37. Data-center GPU roadmaps, NVIDIA and AMD, 2024–2028
• Table 38. NVIDIA and AMD data-center revenue, 2024–2026
• Table 39. Data-center GPU unit shipments and blended ASP, 2025–2040
• Table 40. Hyperscaler custom neural network accelerators
• Table 41. AI ASIC unit shipments by programme, 2025–2040 (thousand units)
• Table 42. Challenger neural network accelerator companies: technology, funding and valuation
• Table 43. Total cost of ownership per accelerator-hour: merchant GPU vs. custom and start-up ASICs, 2025
• Table 44. Wafer-scale and multi-die accelerator yield economics
• Table 45. Chinese neural network accelerator suppliers
• Table 46. Chinese AI chip IPOs, 2025–2026
• Table 47. China AI accelerator capability gap to the NVIDIA flagship by workload type
• Table 48. Korean and Japanese neural network chip developers
• Table 49. Edge NPU suppliers by application
• Table 50. Edge AI consolidation: M&A, 2025–2026
• Table 51. Edge NPU performance and power requirements by application
• Table 52. Edge neural network accelerators: published performance and efficiency figures
• Table 53. Estimated wafer and die costs for edge AI SoCs by process node
• Table 54. Smartphone NPU performance and efficiency evolution, 2017–2024
• Table 55. Robot compute platforms: performance, power and target applications
• Table 56. Open-source and RISC-V neural network accelerator initiatives
• Table 57. Neuromorphic processors: commercial and research
• Table 58. Photonic computing and optical interconnect companies
• Table 59.Optical neural network processor architectures compared
• Table 60. Photonic neural network processors: vendor performance and efficiency claims
• Table 61. Optical switching and light-source suppliers for neural network clusters
• Table 62. Analog, in-memory and probabilistic computing companies
• Table 63. Energy cost of computation versus data movement
• Table 64. Memory technologies for in-memory neural network computing
• Table 65. Processing-in-memory and compute-in-memory products from memory suppliers and specialists
• Table 66. Emerging non-volatile memory devices for neural network synapses and in-memory compute
• Table 67. HBM supplier revenue share, 2024–2026
• Table 68. HBM generations and per-accelerator memory capacity
• Table 69. AI rack bill of materials by component, 2023–2028
• Table 70. Interconnect energy efficiency by technology
• Table 71. Memory bandwidth versus accelerator compute, 2026–2037 (representative flagship accelerator)
• Table 72. HBM stack shipments, pricing and revenue forecast, 2026–2037 (memory report forecast)
• Table 73. Memory tiers for neural network inference
• Table 74. Data-center NN accelerator revenue by type, 2025–2040 (US$ billion)
• Table 75. Edge NPU revenue by device, 2025–2040 (US$ billion)
• Table 76. Neuromorphic and novel NN hardware revenue by technology, 2025–2040 (US$ billion)
• Table 77. Low-power/high-efficiency AI semiconductor market by application, 2025–2036 (US$ billion; scope of the dedicated Future Markets report)
• Table 78. Optical computing report forecast: PIC content in AI accelerator interconnects and processors, 2026–2036 (US$ million)
• Table 79. Optical computing report forecast: optical processors by application, 2026–2036 (US$ billion)
• Table 80. Neural network frameworks and compiler stacks
• Table 81. Leading foundation model developers: models, funding and valuation
• Table 82. Leading open-weight model families
• Table 83. Token-processing indicators, 2024–2026
• Table 84. Inference cost per million output tokens by model class and hardware, 2024–2040 (US$)
• Table 85. Memory consumption by inference workload class
• Table 86. Neural network platform, MLOps and inference-serving vendors
• Table 87. Data labelling and synthetic data providers
• Table 88. AI memory-layer companies
• Table 89. Software, models and platforms revenue, 2025–2040 (US$ billion)
• Table 90. Hyperscaler capital expenditure: 2025 actual and 2026 guidance
• Table 91. Data-center processor demand by facility type, 2025–2040 (US$ billion)
• Table 92. Sovereign AI build-outs by country, 2025–2030
• Table 93. Enterprise neural network applications
• Table 94. On-device neural networks in consumer electronics
• Table 95. Flagship smartphone neural processing units, 2025–2026 generation
• Table 96. On-device LLM inference capability of AI PCs, 2026
• Table 97. Automotive neural network compute platforms and driving stacks
• Table 98. SAE automation levels and in-vehicle neural network compute requirements
• Table 99. Waymo operating metrics, 2022–2026
• Table 100. Neural network applications in healthcare and selected products
• Table 101. Robot foundation models and physical AI platforms
• Table 102. Robot foundation models: architecture, training scale and status
• Table 103. Physical AI technical constraints: current performance versus deployment thresholds
• Table 104. Global humanoid robot shipments, revenue and prices, 2025–2040
• Table 105. Neural network applications in financial services
• Table 106. Neural network companies in defence and security
• Table 107. Neural network applications in telecoms and secure communications
• Table 108. Neural networks in scientific discovery
• Table 109. Neural network market by end-use sector, 2025–2040 (US$ billion)
• Table 110. Future Markets physical AI market forecast by sub-market, 2026–2040 (US$ billion; full-system revenue, not neural network value)
• Table 111. Physical AI value pool by stack layer, 2026–2036 (share of revenue excluding wearables)
• Table 112. Forecast assumptions by segment
• Table 113. Global neural network market, 2024–2040
• Table 114. Segment growth summary
• Table 115. Hardware vs. software, models and platforms, 2025–2040 (US$ billion)
• Table 116. Data-center accelerator spend: training vs. inference, 2025–2040 (US$ billion)
• Table 117. Cloud vs. edge deployment, 2025–2040 (US$ billion)
• Table 118. Future Markets edge AI chip forecast by application, 2026–2036 (US$ billion; dedicated edge AI chips report basis)
• Table 119. Compute-linked market value by architecture family, 2025–2040 (US$ billion)
• Table 120. Neural network market by region, 2025–2040 (US$ billion)
• Table 121. Bear, base and bull scenarios, 2025–2040 (US$ billion)
• Table 122. US neural network policy and infrastructure programmes
• Table 123. China’s neural network ecosystem: chips, models and policy
• Table 124. South Korean neural network programmes and investments
• Table 125. Japanese neural network programmes and companies
• Table 126. European neural network companies and funding
• Table 127. UK compute, research and policy initiatives
• Table 128. Sovereign AI programmes outside the US, China and Europe
• Table 129. Data-center NN accelerator vendor share, 2025 and 2026 (estimates)
• Table 130. Neural network funding rounds, 2025–2026
• Table 131. M&A and licensing deals, 2024–2026
• Table 132. Neural network-related IPOs, 2025–2026
• Table 133. Major compute and model partnerships
• Table 134. EU AI Act implementation timeline, including Digital Omnibus changes
• Table 135. US export controls affecting neural network hardware, 2022–2026
• Table 136. UK legislative and policy developments
• Table 137. Chinese AI regulation and procurement measures
• Table 138. AI management and safety standards
• Table 139. FDA–EMA guiding principles for AI in drug development
• Table 140. Notable AI litigation and rulings, 2025–2026
• Table 141. Data-center power demand indicators
• Table 142. AI server rack configuration and power roadmap, 2025–2040
• Table 143. Data-center cooling configurations: density, PUE and overhead
• Table 144. Embodied carbon across the AI hardware hierarchy
• Table 145. AI accelerator memory requirements and cost, 2024–2030
• Table 146. Memory and storage content and cost per AI server node, 2026–2037
• Table 147. Top 15 strategic choke points in the AI hardware supply chain
• Table 148. Capital expenditure vs. revenue indicators
• Table 149. AI-driven EDA approaches by design-flow stage
• Table 150. Risk matrix: likelihood and impact
• Table 151. Technology readiness and commercialisation timeline, 2027–2040
• Table 152. Photonic neural network technology roadmap, 2026–2036
• Table 153. Technology readiness of neural-network-driven physical AI applications, 2026
• Table 154. HBM technology roadmap, per-stack specifications
• Table 155. Ten predictions for the neural network market to 2040
• Table 156. Opportunity matrix by segment
• Table 157. Companies profiled (index)
• Table 158. Commercial readiness stages: definitions
• Table 159. Commercial readiness of profiled companies by company group (number of companies)
• Table 160. Profiled companies by commercial readiness stage
• Table 161. Global neural network market by segment, 2024–2032 (US$ billion)
• Table 162. Global neural network market by segment, 2033–2040 (US$ billion)
• Table 163. Glossary of terms

List of Figures

• Figure 1. Global neural network market by segment, 2024–2040 (US$ billion)
• Figure 2. Neural network technology roadmap, 2025–2040
• Figure 3. Timeline of neural network milestones, 1943–2026
• Figure 4. Neural network architecture taxonomy
• Figure 5. Training compute of notable neural network models, 2012–2025 (FLOP, log scale)
• Figure 6. Neural network market value chain
• Figure 7. Compute cost of attention vs. sequence length: quadratic vs. linear architectures
• Figure 8. Share of neural network compute by architecture family, 2025, 2030 and 2040
• Figure 9. Data-center NN accelerator revenue by type, 2024–2040 (US$ billion)
• Figure 10. Edge NPU revenue by device, 2024–2040 (US$ billion)
• Figure 11.Neuromorphic and novel NN hardware revenue by technology, 2024–2040 (US$ billion)
• Figure 12. Valuations of leading model developers, 2026
• Figure 13. Google monthly tokens processed, 2024–2026 (log scale)
• Figure 14. Software, models and platforms revenue, 2024–2040 (US$ billion)
• Figure 15. Neural network market share by end-use sector, 2025 vs. 2040
• Figure 16. Global neural network market, 2024–2040 (US$ billion, base case)
• Figure 17. Segment mix, 2025, 2030 and 2040
• Figure 18. Hardware vs. software, models and platforms, 2024–2040 (US$ billion)
• Figure 19. Data-center accelerator spend: training vs. inference, 2024–2040 (US$ billion)
• Figure 20. Regional share, 2025, 2030 and 2040
• Figure 21. Scenario comparison, 2024–2040 (US$ billion)
• Figure 22. Data-center NN accelerator vendor share, 2025
• Figure 23. Largest neural network funding rounds, 2025–2026 (US$ billion, log scale)
• Figure 24. Neural network start-up landscape by layer
• Figure 25. Neural network regulatory timeline, 2024–2028
• Figure 26. Neural network market risk heat map
• Figure 27. Commercialisation timeline for emerging neural network technologies, 2025–2040
• Figure 28. Commercial readiness of profiled companies by company group
• Figure 29. Agibot A2
• Figure 30. Cerebras WSE-3
• Figure 31. MemryX MX3
• Figure 32. SiMa.ai Modalix