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The Global Neural Networks Market 2027–2040

The Global Neural Networks Market 2027–2040

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  • Published: October 2026
  • Pages: 687
  • Tables: 163
  • Figures: 32

 

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.....

 

 

 

1             EXECUTIVE SUMMARY            28

  • 1.1        Report scope and market definition               28
  • 1.2        Key findings    29
  • 1.3        Market size and forecast 2025–2040             30
  • 1.4        Growth drivers and restraints              32
  • 1.5        Technology inflection points 2027–2040     33
  • 1.6        Competitive landscape summary   34
  • 1.7        Investment and deal activity 2025–2026    35
  • 1.8        Strategic outlook and recommendations    36

 

2             INTRODUCTION          37

  • 2.1        Defining neural networks       37
  • 2.2        Historical development           38
  • 2.3        Taxonomy of neural network architectures 40
  • 2.4        Training and inference              42
    • 2.4.1    Hardware requirements of training and inference workloads        44
  • 2.5        Scaling laws and compute demand               44
    • 2.5.1    The cost-per-token curve       47
  • 2.6        The neural network value chain         48
  • 2.7        Research methodology           50
    • 2.7.1    Approach         50
    • 2.7.2    Reconciliation with related Future Markets reports              51

 

3             NEURAL NETWORK ARCHITECTURES AND ALGORITHMS              53

  • 3.1        Multilayer perceptrons and feedforward networks               53
  • 3.2        Convolutional neural networks          54
    • 3.2.1    3D CNNs in medical imaging: JLK    54
  • 3.3        Recurrent networks and LSTMs         56
  • 3.4        Transformers and attention  56
    • 3.4.1    Self-attention and its cost     56
    • 3.4.2    Efficiency techniques               56
  • 3.5        Mixture-of-experts models   58
  • 3.6        Post-transformer architectures          59
    • 3.6.1    Why replace attention              59
    • 3.6.2    State-space models and hybrids      60
    • 3.6.3    Venture-backed alternatives                61
    • 3.6.4    Diffusion language models   61
  • 3.7        Liquid neural networks and continuous-time models        63
    • 3.7.1    Liquid AI            64
  • 3.8        Graph and topological neural networks       65
    • 3.8.1    Arlequin AI       65
  • 3.9        Generative architectures: GANs, VAEs, diffusion and world models        66
    • 3.9.1    Diffusion models         67
    • 3.9.2    World models                67
    • 3.9.3    World foundation models as training infrastructure for physical AI            68
  • 3.10     Spiking neural networks          69
    • 3.10.1 Digital, analog and hybrid spiking implementations            70
    • 3.10.2 Photonic spiking and reservoir networks     71
  • 3.11     Physics-informed networks, neural operators and KANs  72
  • 3.12     Quantum neural networks    74
    • 3.12.1 Neural networks for quantum key distribution: WiMi          74
    • 3.12.2 Hybrid spiking-quantum networks: MicroCloud Hologram DSQ-Net        74
    • 3.12.3 Wider quantum machine learning context 74
  • 3.13     Biological neural networks and organoid intelligence         75
    • 3.13.1 Cortical Labs 75
    • 3.13.2 FinalSpark and other developers      76
  • 3.14     Memory-augmented neural networks           77
    • 3.14.1 Engramme and Large Memory Models          77
  • 3.15     Model compression, small language models and on-device AI    78
    • 3.15.1 Distillation       78
    • 3.15.2 Quantisation, pruning and sparsity 78
    • 3.15.3 On-device AI   78
    • 3.15.4 Precision, efficiency and edge compilation toolchains     79
    • 3.15.5 Precision and sparsity roadmap, 2026–2028           80
  • 3.16     Architecture adoption outlook           82

 

4             NEURAL NETWORK HARDWARE       84

  • 4.1        Hardware landscape 84
    • 4.1.1    Energy efficiency: TOPS/W tiers and benchmarks 86
  • 4.2        GPUs   87
    • 4.2.1    NVIDIA                87
    • 4.2.2    AMD     88
    • 4.2.3    Intel and other GPU suppliers             90
    • 4.2.4    GPU unit shipments, average selling prices and generational mix              90
  • 4.3        Hyperscaler custom ASICs   91
    • 4.3.1    Google TPU     92
    • 4.3.2    AWS Trainium and Inferentia               92
    • 4.3.3    Microsoft Maia             92
    • 4.3.4    Meta MTIA        92
    • 4.3.5    OpenAI and Broadcom            93
    • 4.3.6    Broadcom and Marvell            93
    • 4.3.7    Custom accelerator volumes and the design-services layer          94
  • 4.4        Challenger accelerator companies 95
    • 4.4.1    Efficiency and total cost of ownership: merchant GPUs, custom and start-up ASICs   97
    • 4.4.2    Wafer-scale yield economics              98
    • 4.4.3    Development cost as a barrier to entry         99
  • 4.5        China’s accelerator ecosystem         99
    • 4.5.1    China’s processor market, domestic unit shares and the memory gap  102
    • 4.5.2    Capability gap by workload  102
  • 4.6        South Korean and Japanese accelerator developers           103
    • 4.6.1    South Korea    103
    • 4.6.2    Japan  104
  • 4.7        Edge neural processing units              106
    • 4.7.1    Edge NPU architectures and dataflow          108
    • 4.7.2    Edge accelerator performance and efficiency benchmarks           109
    • 4.7.3    Process technology and the cost of edge NN silicon           110
    • 4.7.4    Mobile NPU efficiency and ultra-low-power audio and sensing   111
    • 4.7.5    Robot compute platforms and the two-tier control hierarchy        112
  • 4.8        Open-source neural network hardware        114
    • 4.8.1    RISC-V in production edge NN silicon           115
  • 4.9        Neuromorphic processors    116
    • 4.9.1    Neuromorphic drivers, restraints and near-term outlook 117
  • 4.10     Photonic neural networks and optical interconnect             118
    • 4.10.1 Optical neural network architectures            120
    • 4.10.2 Performance, efficiency and the precision problem            121
    • 4.10.3 Optical switching and light sources for AI fabrics  123
  • 4.11     Analog, in-memory and thermodynamic computing           124
    • 4.11.1 The data-movement problem in numbers  126
    • 4.11.2 Memory technologies for compute-in-memory      127
    • 4.11.3 Precision limits and the analog roadmap to 2030 128
    • 4.11.4 Processing-in-memory and compute-in-memory products from memory suppliers     128
    • 4.11.5 Emerging non-volatile memory as synaptic devices            129
  • 4.12     Memory, packaging and interconnect            130
    • 4.12.1 High-bandwidth memory       130
    • 4.12.2 Advanced packaging 132
    • 4.12.3 Interconnect   132
    • 4.12.4 Rack bill of materials, HBM content and packaging capacity        133
    • 4.12.5 Energy per bit and the case for optics            133
    • 4.12.6 The memory wall in numbers              134
    • 4.12.7 HBM unit and revenue outlook           135
    • 4.12.8 Inference memory hierarchy and the KV cache       136
  • 4.13     Hardware market forecasts 137
    • 4.13.1 Data-center neural network accelerators    137
    • 4.13.2 Edge neural processing units              138
    • 4.13.3 Neuromorphic and novel neural network hardware             139
    • 4.13.4 Comparison with Future Markets’ low-power AI semiconductor forecast             141
    • 4.13.5 Related-report context: optical computing forecasts         141

 

5             SOFTWARE, MODELS AND PLATFORMS     144

  • 5.1        Frameworks and compilers  144
    • 5.1.1    The framework layer  144
    • 5.1.2    Compilers, kernels and the CUDA moat      144
    • 5.1.3    Inference engines and on-device runtimes                145
  • 5.2        Foundation model developers            147
    • 5.2.1    Capital concentration at the frontier              147
    • 5.2.2    Europe, China, Japan and Russia     147
    • 5.2.3    Architecture challengers and new labs        148
    • 5.2.4    Implications   150
  • 5.3        Open-weight vs. proprietary models              150
    • 5.3.1    The shifting balance  150
    • 5.3.2    Who uses open weights and why      150
  • 5.4        Inference economics and token demand   152
    • 5.4.1    From training to inference      152
    • 5.4.2    Token volumes              152
    • 5.4.3    Reasoning models and test-time compute                153
    • 5.4.4    Falling unit costs and rising total spend       153
    • 5.4.5    Cost per token by model class and hardware           154
    • 5.4.6    Memory footprint of inference workload classes   155
  • 5.5        Platforms, MLOps and inference serving     156
    • 5.5.1    Market structure          156
    • 5.5.2    Consolidation and valuations             156
    • 5.5.3    Inference-serving economics             156
  • 5.6        Training data and labelling    157
    • 5.6.1    From labelling to expert data               157
    • 5.6.2    Scale AI, Surge AI and the 2025 realignment            158
    • 5.6.3    Crowdsourced platforms       158
    • 5.6.4    Synthetic data               158
  • 5.7        Memory and agent infrastructure     159
    • 5.7.1    Why memory matters               159
    • 5.7.2    Engramme       160
    • 5.7.3    Mem0, Zep, Letta and LangMem      160
    • 5.7.4    Outlook             161
  • 5.8        Software and model forecasts           161

 

6             MARKETS AND APPLICATIONS           163

  • 6.1        Hyperscale cloud and AI labs             163
    • 6.1.1    Capital expenditure as the primary demand signal              163
    • 6.1.2    Neoclouds, Oracle and Stargate       164
    • 6.1.3    AI labs as buyers          164
    • 6.1.4    Processor demand by data-center facility type       165
    • 6.1.5    Sovereign AI build-outs           165
    • 6.1.6    Chinese hyperscaler capital expenditure    166
  • 6.2        Enterprise and agentic AI       167
    • 6.2.1    From copilots to agents          167
    • 6.2.2    Platforms and leading suppliers        167
  • 6.3        Consumer devices     168
    • 6.3.1    The on-device shift    168
    • 6.3.2    Small models and edge silicon          168
      • 6.3.2.1 Smartphone and AI PC NN processor benchmarks             169
  • 6.4        Automotive and autonomous mobility         171
    • 6.4.1    End-to-end neural driving      171
    • 6.4.2    Compute platforms   171
    • 6.4.3    Open-source silicon and efficient models 171
    • 6.4.4    Compute requirements by level of autonomy          172
    • 6.4.5    Robotaxi operating data and in-vehicle compute content                173
  • 6.5        Healthcare and life sciences               174
    • 6.5.1    Medical imaging          174
    • 6.5.2    JLK and 3D convolutional networks 174
    • 6.5.3    Drug discovery and regulation            175
  • 6.6        Industrial, robotics and physical AI 176
    • 6.6.1    Robot foundation models      176
    • 6.6.2    Humanoids     176
    • 6.6.3    Industrial applications            176
    • 6.6.4    On-board neural network compute for humanoid robots 177
    • 6.6.5    Vision-language-action model lineage and specifications              178
    • 6.6.6    Training paradigms, sim-to-real transfer and remaining technical gaps 179
    • 6.6.7    Fleet learning and orchestration       180
    • 6.6.8    Humanoid unit and price forecasts 180
  • 6.7        Financial services       181
    • 6.7.1    Established and emerging uses         181
  • 6.8        Defence, security and government  182
    • 6.8.1    A funding surge             182
    • 6.8.2    Arlequin AI and topological networks            182
    • 6.8.3    Government and intelligence              183
  • 6.9        Telecommunications and quantum-secure communications      184
    • 6.9.1    AI-RAN and network operations        184
    • 6.9.2    Neural networks in quantum key distribution           184
  • 6.10     Science and materials discovery      185
    • 6.10.1 Structural biology       185
    • 6.10.2 Materials informatics               185
    • 6.10.3 Weather and climate 185
    • 6.10.4 Neural operators and scientific machine learning 186
  • 6.11     End-use market forecasts     187
    • 6.11.1 Reconciliation with Future Markets’ physical AI forecast  188

 

7             GLOBAL MARKET FORECASTS 2025–2040 190

  • 7.1        Forecast approach    190
  • 7.2        Total market    191
  • 7.3        By segment     192
  • 7.4        Hardware vs. software             194
  • 7.5        Training vs. inference 195
  • 7.6        Cloud vs. edge              196
    • 7.6.1    Comparison with Future Markets’ edge AI chip forecast   197
  • 7.7        By architecture family              198
  • 7.8        By region           198
  • 7.9        Scenario analysis       200

 

8             REGIONAL ANALYSIS                201

  • 8.1        North America              201
    • 8.1.1    Hyperscaler capital expenditure       201
    • 8.1.2    Stargate and gigawatt-scale campuses       201
    • 8.1.3    Industrial policy: CHIPS Act and the Intel stake      202
    • 8.1.4    Export controls and revenue share  202
    • 8.1.5    Capital markets and challengers      202
  • 8.2        China  203
    • 8.2.1    Domestic accelerators            203
    • 8.2.2    The AI chip IPO wave 204
    • 8.2.3    Models: DeepSeek and the open-weight ecosystem           204
    • 8.2.4    Procurement policy   204
    • 8.2.5    Quantum-neural and holographic computing companies               204
  • 8.3        South Korea    205
    • 8.3.1    Memory: SK hynix and Samsung       206
    • 8.3.2    GPUs and national infrastructure     206
    • 8.3.3    Neural processing units: Rebellions and FuriosaAI              206
    • 8.3.4    Models and applications       206
  • 8.4        Japan  207
    • 8.4.1    Rapidus and leading-edge manufacturing 207
    • 8.4.2    Edge AI chips and the JST programme           207
    • 8.4.3    Model developers        208
    • 8.4.4    SoftBank           208
    • 8.4.5    Physical AI as national industrial policy       209
  • 8.5        Taiwan 209
  • 8.6        Europe                209
    • 8.6.1    Mistral AI and European compute    210
    • 8.6.2    Start-ups and novel architectures    210
    • 8.6.3    EU policy: Chips Act 2.0 and the AI Act         210
  • 8.7        United Kingdom           211
    • 8.7.1    Compute: AIRR and Isambard-AI      211
    • 8.7.2    Policy and legislation                212
    • 8.7.3    Companies     212
  • 8.8        India, Middle East, Russia, Australia and rest of world       213
    • 8.8.1    India    213
    • 8.8.2    Saudi Arabia and the UAE      213
    • 8.8.3    Russia 214
    • 8.8.4    Australia and Singapore         214

 

9             COMPETITIVE LANDSCAPE, INVESTMENT AND M&A          216

  • 9.1        Market structure          216
    • 9.1.1    Data-center accelerator concentration        216
    • 9.1.2    The rise of custom silicon      216
    • 9.1.3    China as a separate market  217
    • 9.1.4    Concentration elsewhere in the stack           218
  • 9.2        Venture funding           219
    • 9.2.1    Scale and concentration of capital 219
    • 9.2.2    Hardware and infrastructure rounds              219
    • 9.2.3    Edge, neuromorphic and novel computing 219
    • 9.2.4    Post-transformer and memory start-ups     220
    • 9.2.5    Assessment   224
    • 9.2.6    Physical AI and robot foundation model funding   224
  • 9.3        M&A and licensing      224
    • 9.3.1    Deal patterns 224
    • 9.3.2    Deals that did not happen     225
  • 9.4        IPOs and public markets        227
    • 9.4.1    The United States        227
    • 9.4.2    China’s domestic GPU listings           227
    • 9.4.3    Pipeline and implications      228
  • 9.5        Strategic compute partnerships        229
    • 9.5.1    Gigawatt-denominated deals             229
    • 9.5.2    Backlog, sovereign deals and the edge         229
    • 9.5.3    Circularity and risk     230
  • 9.6        Start-up landscape    231
    • 9.6.1    Layers of the landscape         231
    • 9.6.2    Dynamics across the layers 233

 

10          REGULATION, STANDARDS AND ETHICS    235

  • 10.1     EU AI Act           235
    • 10.1.1 The Digital Omnibus 235
    • 10.1.2 Market implications  236
  • 10.2     US export controls      238
    • 10.2.1 From prohibition to tightening, 2022–2024 238
    • 10.2.2 The H20 episode and revenue sharing, 2025            238
    • 10.2.3 The H200/MI325X rule and 25% duty, 2026               238
  • 10.3     United Kingdom           239
  • 10.4     China  240
  • 10.5     Standards and certification  241
    • 10.5.1 ISO/IEC 42001              242
    • 10.5.2 Other standards and frameworks    242
  • 10.6     Sector regulation: healthcare and drug development         243
  • 10.7     Liability, copyright and litigation        244
    • 10.7.1 Product liability and design  244
    • 10.7.2 Copyright          244

 

11          MARKET CHALLENGES AND RISKS 246

  • 11.1     Energy and power        246
    • 11.1.1 Rack density, cooling and the AI share of data-center load            247
    • 11.1.2 Cooling overhead and the system-level efficiency gap      248
    • 11.1.3 Embodied carbon and resource intensity   249
  • 11.2     Memory and packaging supply constraints               250
    • 11.2.1 Memory requirements by accelerator generation  251
    • 11.2.2 Memory cost per AI server and the 2029–2030 correction risk      251
  • 11.3     Geopolitics and supply-chain concentration           252
    • 11.3.1 Choke points in the neural network hardware supply chain           253
  • 11.4     Capital intensity and valuation risk 254
    • 11.4.1 Capital intensity ratios and the shift from capex to opex  255
  • 11.5     Talent, data and intellectual property            256
    • 11.5.1 Chip design capacity and AI-driven EDA      256
  • 11.6     Safety, security and reliability             258
  • 11.7     Risk assessment         258

 

12          FUTURE OUTLOOK 2027–2040         261

  • 12.1     Technology roadmap 261
    • 12.1.1 Three waves of commercialisation  261
    • 12.1.2 Readiness and commercialisation timeline              261
    • 12.1.3 Architecture and hardware co-evolution     263
    • 12.1.4 Energy-efficiency roadmap  264
    • 12.1.5 Photonic neural network roadmap  264
    • 12.1.6 Physical AI readiness by application              265
    • 12.1.7 HBM and memory technology roadmap to 2037   266
  • 12.2     Ten predictions for 2040         267
  • 12.3     Opportunity assessment       268
  • 12.4     Wildcards and discontinuities           270
    • 12.4.1 Upside discontinuities            270
    • 12.4.2 Downside discontinuities      271
    • 12.4.3 Unconventional discontinuities        271
    • 12.4.4 Physical AI scenario variables            271

 

13          COMPANY PROFILES                272 (195 company profiles)

 

14          APPENDICES  572

  • 14.1     Detailed year-by-year forecasts        572
  • 14.2     Glossary           573

 

15          REFERENCES 575

 

List of Tables

  • Table 1. Neural network market segments and scope definitions               28
  • Table 2. Key findings summary          29
  • Table 3. Global neural network market by segment, 2025–2040 (US$ billion)    30
  • Table 4. Key market drivers   32
  • Table 5. Key market restraints and challenges         32
  • Table 6. Leading companies by market segment    34
  • Table 7. Largest neural network funding rounds, M&A and licensing deals, 2025–2026              35
  • Table 8. Strategic implications by stakeholder type              36
  • Table 9. Key milestones in neural network development, 1943–2026      39
  • Table 10. Taxonomy of neural network architectures and primary applications 41
  • Table 11. Training vs. inference: workload, hardware and economics      43
  • Table 12. Selected frontier models: architecture and parameter counts, 2020–2026   45
  • Table 13. Modelled hardware cost per million output tokens by model size, 2024–2040 (US$)              47
  • Table 14. Value-chain layers, value capture and representative companies       48
  • Table 15. Forecast methodology and key assumptions     51
  • Table 16. Reconciliation with related Future Markets reports        51
  • Table 17. CNN architectures and commercial applications           55
  • Table 18. Transformer variants and efficiency techniques               57
  • Table 19. Selected mixture-of-experts models: total and active parameters      58
  • Table 20. Comparison of post-transformer architectures 61
  • Table 21. Post-transformer model developers          62
  • Table 22. Liquid AI: model families, platforms and funding             64
  • Table 23. Graph and topological neural network developers and applications  66
  • Table 24. World-model developers and funding     67
  • Table 25. Spiking vs. conventional artificial neural networks          70
  • Table 26. Spiking neural network hardware: digital, hybrid and analog implementations compared  71
  • Table 27. Neuromorphic photonic architectures for spiking and recurrent neural networks     72
  • Table 28. Scientific machine learning architectures             73
  • Table 29. Quantum neural network and quantum machine learning developments, 2025–2026          75
  • Table 30. Biological computing platforms   76
  • Table 31. Model compression and efficiency techniques 79
  • Table 32. Typical efficiency gains from reduced numerical precision in edge NPUs (relative to FP32)                80
  • Table 33. Sparsity techniques and their impact on neural network inference efficiency              81
  • Table 34. Share of neural network compute by architecture family, 2024–2040 (%)       82
  • Table 35. Neural network hardware categories compared               84
  • Table 36. Neural network hardware energy efficiency by technology category   86
  • Table 37. Data-center GPU roadmaps, NVIDIA and AMD, 2024–2028     89
  • Table 38. NVIDIA and AMD data-center revenue, 2024–2026        90
  • Table 39. Data-center GPU unit shipments and blended ASP, 2025–2040            91
  • Table 40. Hyperscaler custom neural network accelerators           93
  • Table 41. AI ASIC unit shipments by programme, 2025–2040 (thousand units) 94
  • Table 42. Challenger neural network accelerator companies: technology, funding and valuation        96
  • Table 43. Total cost of ownership per accelerator-hour: merchant GPU vs. custom and start-up ASICs, 2025    98
  • Table 44. Wafer-scale and multi-die accelerator yield economics             99
  • Table 45. Chinese neural network accelerator suppliers  100
  • Table 46. Chinese AI chip IPOs, 2025–2026              101
  • Table 47. China AI accelerator capability gap to the NVIDIA flagship by workload type 103
  • Table 48. Korean and Japanese neural network chip developers 105
  • Table 49. Edge NPU suppliers by application            107
  • Table 50. Edge AI consolidation: M&A, 2025–2026               107
  • Table 51. Edge NPU performance and power requirements by application          109
  • Table 52. Edge neural network accelerators: published performance and efficiency figures    109
  • Table 53. Estimated wafer and die costs for edge AI SoCs by process node        111
  • Table 54. Smartphone NPU performance and efficiency evolution, 2017–2024 111
  • Table 55. Robot compute platforms: performance, power and target applications        113
  • Table 56. Open-source and RISC-V neural network accelerator initiatives            114
  • Table 57. Neuromorphic processors: commercial and research 116
  • Table 58. Photonic computing and optical interconnect companies        119
  • Table 59.Optical neural network processor architectures compared       121
  • Table 60. Photonic neural network processors: vendor performance and efficiency claims     122
  • Table 61. Optical switching and light-source suppliers for neural network clusters        124
  • Table 62. Analog, in-memory and probabilistic computing companies  125
  • Table 63. Energy cost of computation versus data movement      126
  • Table 64. Memory technologies for in-memory neural network computing          127
  • Table 65. Processing-in-memory and compute-in-memory products from memory suppliers and specialists       129
  • Table 66. Emerging non-volatile memory devices for neural network synapses and in-memory compute                130
  • Table 67. HBM supplier revenue share, 2024–2026             131
  • Table 68. HBM generations and per-accelerator memory capacity            131
  • Table 69. AI rack bill of materials by component, 2023–2028        133
  • Table 70. Interconnect energy efficiency by technology    134
  • Table 71. Memory bandwidth versus accelerator compute, 2026–2037 (representative flagship accelerator)     134
  • Table 72. HBM stack shipments, pricing and revenue forecast, 2026–2037 (memory report forecast)                135
  • Table 73. Memory tiers for neural network inference           136
  • Table 74. Data-center NN accelerator revenue by type, 2025–2040 (US$ billion)            137
  • Table 75. Edge NPU revenue by device, 2025–2040 (US$ billion) 138
  • Table 76. Neuromorphic and novel NN hardware revenue by technology, 2025–2040 (US$ billion)     140
  • Table 77. Low-power/high-efficiency AI semiconductor market by application, 2025–2036 (US$ billion; scope of the dedicated Future Markets report)        141
  • Table 78. Optical computing report forecast: PIC content in AI accelerator interconnects and processors, 2026–2036 (US$ million)           142
  • Table 79. Optical computing report forecast: optical processors by application, 2026–2036 (US$ billion)                142
  • Table 80. Neural network frameworks and compiler stacks           145
  • Table 81. Leading foundation model developers: models, funding and valuation            148
  • Table 82. Leading open-weight model families       151
  • Table 83. Token-processing indicators, 2024–2026             152
  • Table 84. Inference cost per million output tokens by model class and hardware, 2024–2040 (US$) 154
  • Table 85. Memory consumption by inference workload class       155
  • Table 86. Neural network platform, MLOps and inference-serving vendors          156
  • Table 87. Data labelling and synthetic data providers         158
  • Table 88. AI memory-layer companies          160
  • Table 89. Software, models and platforms revenue, 2025–2040 (US$ billion)    162
  • Table 90. Hyperscaler capital expenditure: 2025 actual and 2026 guidance       163
  • Table 91. Data-center processor demand by facility type, 2025–2040 (US$ billion)       165
  • Table 92. Sovereign AI build-outs by country, 2025–2030 166
  • Table 93. Enterprise neural network applications  167
  • Table 94. On-device neural networks in consumer electronics     169
  • Table 95. Flagship smartphone neural processing units, 2025–2026 generation              170
  • Table 96. On-device LLM inference capability of AI PCs, 2026      170
  • Table 97. Automotive neural network compute platforms and driving stacks     171
  • Table 98. SAE automation levels and in-vehicle neural network compute requirements             173
  • Table 99. Waymo operating metrics, 2022–2026   174
  • Table 100. Neural network applications in healthcare and selected products   175
  • Table 101. Robot foundation models and physical AI platforms  176
  • Table 102. Robot foundation models: architecture, training scale and status    178
  • Table 103. Physical AI technical constraints: current performance versus deployment thresholds     180
  • Table 104. Global humanoid robot shipments, revenue and prices, 2025–2040              181
  • Table 105. Neural network applications in financial services         181
  • Table 106. Neural network companies in defence and security    183
  • Table 107. Neural network applications in telecoms and secure communications        184
  • Table 108. Neural networks in scientific discovery               186
  • Table 109. Neural network market by end-use sector, 2025–2040 (US$ billion) 187
  • Table 110. Future Markets physical AI market forecast by sub-market, 2026–2040 (US$ billion; full-system revenue, not neural network value)                189
  • Table 111. Physical AI value pool by stack layer, 2026–2036 (share of revenue excluding wearables) 189
  • Table 112. Forecast assumptions by segment         190
  • Table 113. Global neural network market, 2024–2040       191
  • Table 114. Segment growth summary           192
  • Table 115. Hardware vs. software, models and platforms, 2025–2040 (US$ billion)      194
  • Table 116. Data-center accelerator spend: training vs. inference, 2025–2040 (US$ billion)      195
  • Table 117. Cloud vs. edge deployment, 2025–2040 (US$ billion) 196
  • Table 118. Future Markets edge AI chip forecast by application, 2026–2036 (US$ billion; dedicated edge AI chips report basis)               197
  • Table 119. Compute-linked market value by architecture family, 2025–2040 (US$ billion)        198
  • Table 120. Neural network market by region, 2025–2040 (US$ billion)    199
  • Table 121. Bear, base and bull scenarios, 2025–2040 (US$ billion)           200
  • Table 122. US neural network policy and infrastructure programmes      202
  • Table 123. China’s neural network ecosystem: chips, models and policy             205
  • Table 124. South Korean neural network programmes and investments                206
  • Table 125. Japanese neural network programmes and companies            208
  • Table 126. European neural network companies and funding       211
  • Table 127. UK compute, research and policy initiatives     212
  • Table 128. Sovereign AI programmes outside the US, China and Europe               214
  • Table 129. Data-center NN accelerator vendor share, 2025 and 2026 (estimates)          217
  • Table 130. Neural network funding rounds, 2025–2026    220
  • Table 131. M&A and licensing deals, 2024–2026   225
  • Table 132. Neural network-related IPOs, 2025–2026          228
  • Table 133. Major compute and model partnerships             230
  • Table 134. EU AI Act implementation timeline, including Digital Omnibus changes       236
  • Table 135. US export controls affecting neural network hardware, 2022–2026 239
  • Table 136. UK legislative and policy developments              240
  • Table 137. Chinese AI regulation and procurement measures      241
  • Table 138. AI management and safety standards  242
  • Table 139. FDA–EMA guiding principles for AI in drug development           243
  • Table 140. Notable AI litigation and rulings, 2025–2026    245
  • Table 141. Data-center power demand indicators 246
  • Table 142. AI server rack configuration and power roadmap, 2025–2040              247
  • Table 143. Data-center cooling configurations: density, PUE and overhead        248
  • Table 144. Embodied carbon across the AI hardware hierarchy   249
  • Table 145. AI accelerator memory requirements and cost, 2024–2030  251
  • Table 146. Memory and storage content and cost per AI server node, 2026–2037          252
  • Table 147. Top 15 strategic choke points in the AI hardware supply chain             253
  • Table 148. Capital expenditure vs. revenue indicators       255
  • Table 149. AI-driven EDA approaches by design-flow stage            257
  • Table 150. Risk matrix: likelihood and impact          259
  • Table 151. Technology readiness and commercialisation timeline, 2027–2040 262
  • Table 152. Photonic neural network technology roadmap, 2026–2036   265
  • Table 153. Technology readiness of neural-network-driven physical AI applications, 2026      265
  • Table 154. HBM technology roadmap, per-stack specifications  266
  • Table 155. Ten predictions for the neural network market to 2040             267
  • Table 156. Opportunity matrix by segment 269
  • Table 157. Companies profiled (index)         272
  • Table 158. Commercial readiness stages: definitions        284
  • Table 159. Commercial readiness of profiled companies by company group (number of companies)                284
  • Table 160. Profiled companies by commercial readiness stage   285
  • Table 161. Global neural network market by segment, 2024–2032 (US$ billion) 674
  • Table 162. Global neural network market by segment, 2033–2040 (US$ billion) 674
  • Table 163. Glossary of terms 675

List of Figures

  • Figure 1. Global neural network market by segment, 2024–2040 (US$ billion)  31
  • Figure 2. Neural network technology roadmap, 2025–2040           34
  • Figure 3. Timeline of neural network milestones, 1943–2026        40
  • Figure 4. Neural network architecture taxonomy   42
  • Figure 5. Training compute of notable neural network models, 2012–2025 (FLOP, log scale)   46
  • Figure 6. Neural network market value chain            50
  • Figure 7. Compute cost of attention vs. sequence length: quadratic vs. linear architectures   60
  • Figure 8. Share of neural network compute by architecture family, 2025, 2030 and 2040          82
  • Figure 9. Data-center NN accelerator revenue by type, 2024–2040 (US$ billion)             138
  • Figure 10. Edge NPU revenue by device, 2024–2040 (US$ billion)              139
  • Figure 11.Neuromorphic and novel NN hardware revenue by technology, 2024–2040 (US$ billion)    140
  • Figure 12. Valuations of leading model developers, 2026 150
  • Figure 13. Google monthly tokens processed, 2024–2026 (log scale)      153
  • Figure 14. Software, models and platforms revenue, 2024–2040 (US$ billion)  162
  • Figure 15. Neural network market share by end-use sector, 2025 vs. 2040          188
  • Figure 16. Global neural network market, 2024–2040 (US$ billion, base case)  192
  • Figure 17. Segment mix, 2025, 2030 and 2040        193
  • Figure 18. Hardware vs. software, models and platforms, 2024–2040 (US$ billion)       194
  • Figure 19. Data-center accelerator spend: training vs. inference, 2024–2040 (US$ billion)       196
  • Figure 20. Regional share, 2025, 2030 and 2040   199
  • Figure 21. Scenario comparison, 2024–2040 (US$ billion)              200
  • Figure 22. Data-center NN accelerator vendor share, 2025            218
  • Figure 23. Largest neural network funding rounds, 2025–2026 (US$ billion, log scale) 223
  • Figure 24. Neural network start-up landscape by layer       233
  • Figure 25. Neural network regulatory timeline, 2024–2028             237
  • Figure 26. Neural network market risk heat map    260
  • Figure 27. Commercialisation timeline for emerging neural network technologies, 2025–2040            263
  • Figure 28. Commercial readiness of profiled companies by company group      285
  • Figure 29. Agibot A2   288
  • Figure 30. Cerebras WSE-3   358
  • Figure 31. MemryX MX3          461
  • Figure 32. SiMa.ai Modalix    525

 

 

 

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The Global Neural Networks Market 2027–2040
The Global Neural Networks Market 2027–2040
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The Global Neural Networks Market 2027–2040
The Global Neural Networks Market 2027–2040
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