The Global Neural Networks Market 2027–2040

October 2026 | 687 pages | ID: G476C23EEB78EN
Future Markets, Inc.

Abstracts

Neural networks have become the organising principle of the global computing industry. Every frontier language model, every image and video generator, every autonomous-driving perception stack and most modern speech, vision and recommendation systems is a neural network. The hardware that trains and runs them, the models themselves and the software used to deploy them now make up one of the fastest-growing and most heavily capitalised markets in technology. The scale of investment is unprecedented. OpenAI closed a US$122 billion round at an US$852 billion valuation in April 2026, and Anthropic raised US$65 billion at a US$965 billion valuation in May 2026. Mistral AI raised €3 billion, AMI Labs US$1.03 billion and SambaNova US$1 billion. Cerebras raised US$5.55 billion in the largest US IPO of 2026. NVIDIA licensed Groq’s inference technology for about US$20 billion. Consolidation is reshaping edge AI and photonics, through onsemi–Synaptics (about US$7 billion), Marvell–Celestial AI (US$3.25 billion), ADI–Alif (US$1.35 billion) and Microchip–Hailo.
NVIDIA remains the reference platform for data-centre accelerators. Custom chips from Google, Amazon, Meta and OpenAI are gaining ground, as are inference specialists such as Cerebras, Groq, Etched and Rebellions, and China’s domestic GPU makers.

Five shifts define the outlook:

• Inference overtakes training as the dominant workload. Reasoning models and agentic AI multiply the compute used per task.
• Architectures diversify. Mixture-of-experts transformers dominate today. State-space, liquid, diffusion and other post-transformer designs from Liquid AI, Inception, Pathway and others are gaining traction.
• Neural networks move into the physical world. World models and robot foundation models are carried into vehicles, robots and devices.
• New computing substrates begin to commercialise. These include neuromorphic, photonic, analog, thermodynamic and biological computing.
• Value migrates towards models, software and Asia. Sovereign AI programmes in Korea, Japan, India, the Gulf and Europe build national capacity.

There are also serious constraints:

• grid power;
• HBM memory and advanced packaging capacity;
• export controls dividing the US and Chinese markets;
• EU AI Act obligations;
• the gap between infrastructure spending and end-user revenue.

How companies handle memory bandwidth, power, custom silicon and inference economics will decide who captures value through 2040.
The Global Neural Networks Market 2027–2040 is an analysis of the hardware, models, software and applications behind modern AI. It contains more than 160 tables and 28 figures. Detailed forecasts to 2040 are broken down by:

• market segment;
• hardware type;
• training vs. inference;
• cloud vs. edge;
• architecture family;
• end-use sector;
• region.

Bull, base and bear scenarios are included, and the report reconciles its figures with third-party estimates. The report goes beyond market sizing to assess how close each company is to delivering working commercial products. Every one of the 194 profiled companies has a full one-page profile covering:

• company overview;
• neural network technology and products;
• dated developments for 2025–2026;
• market position and outlook.

Each profile also carries a commercial readiness rating on a five-stage scale: Commercial at scale, Commercial – early, Integrating, Transitioning or Development. Alongside it are an estimated time to a working commercial product or model and a statement of the readiness evidence. This lets investors, strategists and procurement teams see at a glance which companies are shipping today, which are piloting with customers, which are in transition after acquisitions or pivots, and which remain years from market. Summary tables and a chart break readiness down across seven company groups. They show that established chipmakers and leading model developers are overwhelmingly commercial, while neuromorphic, photonic, analog and thermodynamic computing developers are mostly still integrating with customers or in development.

The report covers:

• data-centre GPUs and custom ASICs;
• challenger and Chinese accelerators;
• edge neural processors;
• neuromorphic, photonic, analog and biological computing;
• HBM memory, packaging and interconnect;
• foundation and open-weight models;
• post-transformer architectures;
• token economics;
• applications in automotive, healthcare, robotics, defence and finance.

It also includes regional analysis, a full record of funding, M&A and IPOs, regulation and a 2027–2040 technology roadmap.

Report contents include:

• Global market forecasts 2025–2040 by segment, hardware type, workload, deployment, architecture, end use and region, with bull, base and bear scenarios
• Data-centre accelerators: NVIDIA and AMD roadmaps, hyperscaler custom chips, challengers, China’s chip ecosystem, Korean and Japanese developers
• Edge neural processors for smartphones, AI PCs, vehicles, industrial systems and robots, plus edge AI consolidation
• Neuromorphic, photonic, analog and in-memory, thermodynamic, quantum and biological computing
• HBM roadmap, the memory wall, advanced packaging and optical interconnect
• Architectures: CNNs, transformers, mixture of experts, state-space models, liquid, graph and topological networks, spiking networks, world models, quantum and biological networks
• Foundation model developers, valuations, open-weight models, token economics, MLOps and AI memory infrastructure
• Applications: cloud, enterprise and agentic AI, consumer devices, automotive, healthcare, robotics and physical AI, finance, defence, telecoms, science
• Regional analysis: North America, China, South Korea, Japan, Taiwan, Europe, UK, India, Middle East, Russia and Australia
• Funding rounds, M&A, IPOs and strategic compute partnerships for 2024–2026
• Regulation: EU AI Act, US export controls, UK, China, ISO/IEC 42001, FDA–EMA principles, litigation
• Risks: power, memory supply, geopolitics, capital intensity, safety
• Technology commercialisation timeline and ten predictions to 2040
• Commercial readiness assessment of 194 companies, with stage, time to product and evidence
• 194 one-page company profiles including 1X Technologies, Agibot (Zhiyuan Robot), Aidoc, Akhetonics, Alchip Technologies, Aleph Alpha, Alibaba Group (Qwen, T-Head), Alif Semiconductor, Ambarella, AMD, AMI Labs, Ampere Computing, Analog Devices, Anduril Industries, Anthropic, Arago, Arlequin AI, Arm Holdings, Aspinity, Astera Labs, Axelera AI, Axera Semiconductor, Ayar Labs, Baidu (incl. Kunlunxin), Baseten, Biren Technology, Black Forest Labs, Black Sesame Technologies, Blaize, Blumind, BrainChip, Broadcom, ByteDance, Cambricon Technologies, Cartesia, Celestial AI, Cerebras Systems, ChipAgents, Chipmind, CogniFiber, Cohere, CoreWeave, Cortical Labs, Crusoe, d-Matrix, Darktrace, Databricks, DeepSeek, DeepX, EdgeCortix, ElevenLabs, EnCharge AI, Enflame Technology, Engramme, Etched, Extropic, Ferroelectric Memory Company (FMC), Field AI, Figure AI, FinalSpark, Fireworks AI, Fractile and more.....

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 2030
  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, 2025
  10.2.3 The H200/MI325X rule and 25% duty, 2026
10.3 United Kingdom
10.4 China
10.5 Standards and certification
  10.5.1 ISO/IEC 42001
  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 2037
12.2 Ten predictions for 2040
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

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

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