Summary

Neural networks have become the organising principle of the global computing industry. This report provides a comprehensive analysis of the hardware, models, software and applications behind modern AI—from transformers, mixture-of-experts and post-transformer architectures through data-centre GPUs and custom accelerators to edge NPUs, neuromorphic, photonic and analog computing—with market forecasts to 2040 by segment, architecture family and region.

It maps the full value chain: architectures and algorithms, neural network hardware, software/models/platforms, and the markets and applications driving demand across cloud, enterprise, consumer devices, automotive, healthcare, robotics, finance, defence, telecoms and science. Regional analysis covers North America, China, South Korea, Japan, Taiwan, Europe and the UK, alongside the competitive landscape, investment and M&A, regulation and standards, and the energy, memory, geopolitical and capital-intensity risks shaping the sector.

The report profiles 195 companies, each rated on commercial readiness, and includes scenario-based forecasts, a technology roadmap and an outlook to 2040.

Report contents include:

- Executive summary: scope and market definition, key findings, market size and forecast 2025–2040, growth drivers and restraints, technology inflection points, competitive landscape, deal activity and strategic recommendations.
- Introduction: definitions, historical development, architecture taxonomy, training vs. inference, scaling laws and compute demand, the value chain and methodology.
- Neural network architectures and algorithms: MLPs, CNNs, RNNs/LSTMs, transformers and attention, mixture-of-experts, post-transformer and liquid networks, graph/topological, generative (GANs, VAEs, diffusion, world models), spiking, physics-informed/neural operators/KANs, quantum, biological/organoid, memory-augmented, and model compression/on-device AI.
- Neural network hardware: GPUs, hyperscaler custom ASICs, challenger accelerators, China's and Korea/Japan's ecosystems, edge NPUs, neuromorphic, photonic and optical interconnect, analog/in-memory/thermodynamic computing, memory/packaging/interconnect, and hardware market forecasts.
- Software, models and platforms: frameworks and compilers, foundation model developers, open-weight vs. proprietary models, inference economics and token demand, MLOps and inference serving, training data and labelling, and memory/agent infrastructure.
- Markets and applications: hyperscale cloud and AI labs, enterprise and agentic AI, consumer devices, automotive, healthcare, industrial/robotics/physical AI, financial services, defence, telecoms and science, with end-use forecasts.
- Global market forecasts 2025–2040 by segment, hardware vs. software, training vs. inference, cloud vs. edge, architecture family and region, with scenario analysis.
- Regional analysis: North America, China, South Korea, Japan, Taiwan, Europe, the UK, and rest of world.
- Competitive landscape, investment and M&A: market structure, venture funding, M&A and licensing, IPOs, strategic compute partnerships and the start-up landscape.
- Regulation, standards and ethics (EU AI Act, US export controls, UK and China policy, standards, sector regulation, liability/copyright/litigation); market challenges and risks (energy, memory supply, geopolitics, capital intensity, talent, safety); and future outlook to 2040 with roadmap and predictions.
- 195 company profiles rated on commercial readiness, plus appendices (year-by-year forecasts, glossary) and references.

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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
2.5 Scaling laws and compute demand
2.6 The neural network value chain
2.7 Research methodology

3 NEURAL NETWORK ARCHITECTURES AND ALGORITHMS
3.1 Multilayer perceptrons and feedforward networks
3.2 Convolutional neural networks
3.3 Recurrent networks and LSTMs
3.4 Transformers and attention
3.5 Mixture-of-experts models
3.6 Post-transformer architectures
3.7 Liquid neural networks and continuous-time models
3.8 Graph and topological neural networks
3.9 Generative architectures: GANs, VAEs, diffusion and world models
3.10 Spiking neural networks
3.11 Physics-informed networks, neural operators and KANs
3.12 Quantum neural networks
3.13 Biological neural networks and organoid intelligence
3.14 Memory-augmented neural networks
3.15 Model compression, small language models and on-device AI
3.16 Architecture adoption outlook

4 NEURAL NETWORK HARDWARE
4.1 Hardware landscape
4.2 GPUs
4.3 Hyperscaler custom ASICs
4.4 Challenger accelerator companies
4.5 China's accelerator ecosystem
4.6 South Korean and Japanese accelerator developers
4.7 Edge neural processing units
4.8 Open-source neural network hardware
4.9 Neuromorphic processors
4.10 Photonic neural networks and optical interconnect
4.11 Analog, in-memory and thermodynamic computing
4.12 Memory, packaging and interconnect
4.13 Hardware market forecasts

5 SOFTWARE, MODELS AND PLATFORMS
5.1 Frameworks and compilers
5.2 Foundation model developers
5.3 Open-weight vs. proprietary models
5.4 Inference economics and token demand
5.5 Platforms, MLOps and inference serving
5.6 Training data and labelling
5.7 Memory and agent infrastructure
5.8 Software and model forecasts

6 MARKETS AND APPLICATIONS
6.1 Hyperscale cloud and AI labs
6.2 Enterprise and agentic AI
6.3 Consumer devices
6.4 Automotive and autonomous mobility
6.5 Healthcare and life sciences
6.6 Industrial, robotics and physical AI
6.7 Financial services
6.8 Defence, security and government
6.9 Telecommunications and quantum-secure communications
6.10 Science and materials discovery
6.11 End-use market forecasts

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
7.7 By architecture family
7.8 By region
7.9 Scenario analysis

8 REGIONAL ANALYSIS
8.1 North America
8.2 China
8.3 South Korea
8.4 Japan
8.5 Taiwan
8.6 Europe
8.7 United Kingdom
8.8 India, Middle East, Russia, Australia and rest of world

9 COMPETITIVE LANDSCAPE, INVESTMENT AND M&A
9.1 Market structure
9.2 Venture funding
9.3 M&A and licensing
9.4 IPOs and public markets
9.5 Strategic compute partnerships
9.6 Start-up landscape

10 REGULATION, STANDARDS AND ETHICS
10.1 EU AI Act
10.2 US export controls
10.3 United Kingdom
10.4 China
10.5 Standards and certification
10.6 Sector regulation: healthcare and drug development
10.7 Liability, copyright and litigation

11 MARKET CHALLENGES AND RISKS
11.1 Energy and power
11.2 Memory and packaging supply constraints
11.3 Geopolitics and supply-chain concentration
11.4 Capital intensity and valuation risk
11.5 Talent, data and intellectual property
11.6 Safety, security and reliability
11.7 Risk assessment

12 FUTURE OUTLOOK 2027-2040
12.1 Technology roadmap
12.2 Ten predictions for 2040
12.3 Opportunity assessment
12.4 Wildcards and discontinuities

13 COMPANY PROFILES (195 company profiles)

14 APPENDICES
14.1 Detailed year-by-year forecasts
14.2 Glossary

15 REFERENCES

 

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List of Tables/Graphs

LIST OF TABLES
Neural network market segments and scope definitions
Key findings summary
Global neural network market by segment, 2025-2040 (US$ billion)
Key market drivers
Key market restraints and challenges
Leading companies by market segment
Largest neural network funding rounds, M&A and licensing deals, 2025-2026
Strategic implications by stakeholder type
Key milestones in neural network development, 1943-2026
Taxonomy of neural network architectures and primary applications
Training vs. inference: workload, hardware and economics
Selected frontier models: architecture and parameter counts, 2020-2026
Modelled hardware cost per million output tokens by model size, 2024-2040 (US$)
Value-chain layers, value capture and representative companies
Forecast methodology and key assumptions
Reconciliation with related Future Markets reports
CNN architectures and commercial applications
Transformer variants and efficiency techniques
Selected mixture-of-experts models: total and active parameters
Comparison of post-transformer architectures
Post-transformer model developers
Liquid AI: model families, platforms and funding
Graph and topological neural network developers and applications
World-model developers and funding
Spiking vs. conventional artificial neural networks
Spiking neural network hardware: digital, hybrid and analog implementations compared
Neuromorphic photonic architectures for spiking and recurrent neural networks
Scientific machine learning architectures
Quantum neural network and quantum machine learning developments, 2025-2026
Biological computing platforms
Model compression and efficiency techniques
Typical efficiency gains from reduced numerical precision in edge NPUs (relative to FP32)
Sparsity techniques and their impact on neural network inference efficiency
Share of neural network compute by architecture family, 2024-2040 (%)
Neural network hardware categories compared
Neural network hardware energy efficiency by technology category
Data-center GPU roadmaps, NVIDIA and AMD, 2024-2028
NVIDIA and AMD data-center revenue, 2024-2026
Data-center GPU unit shipments and blended ASP, 2025-2040
Hyperscaler custom neural network accelerators
AI ASIC unit shipments by programme, 2025-2040 (thousand units)
Challenger neural network accelerator companies: technology, funding and valuation
Total cost of ownership per accelerator-hour: merchant GPU vs. custom and start-up ASICs, 2025
Wafer-scale and multi-die accelerator yield economics
Chinese neural network accelerator suppliers
Chinese AI chip IPOs, 2025-2026
China AI accelerator capability gap to the NVIDIA flagship by workload type
Korean and Japanese neural network chip developers
Edge NPU suppliers by application
Edge AI consolidation: M&A, 2025-2026
Edge NPU performance and power requirements by application
Edge neural network accelerators: published performance and efficiency figures
Estimated wafer and die costs for edge AI SoCs by process node
Smartphone NPU performance and efficiency evolution, 2017-2024
Robot compute platforms: performance, power and target applications
Open-source and RISC-V neural network accelerator initiatives
Neuromorphic processors: commercial and research
Photonic computing and optical interconnect companies
Optical neural network processor architectures compared
Photonic neural network processors: vendor performance and efficiency claims
Optical switching and light-source suppliers for neural network clusters
Analog, in-memory and probabilistic computing companies
Energy cost of computation versus data movement
Memory technologies for in-memory neural network computing
Processing-in-memory and compute-in-memory products from memory suppliers and specialists
Emerging non-volatile memory devices for neural network synapses and in-memory compute
HBM supplier revenue share, 2024-2026
HBM generations and per-accelerator memory capacity
AI rack bill of materials by component, 2023-2028
Interconnect energy efficiency by technology
Memory bandwidth versus accelerator compute, 2026-2037 (representative flagship accelerator)
HBM stack shipments, pricing and revenue forecast, 2026-2037 (memory report forecast)
Memory tiers for neural network inference
Data-center NN accelerator revenue by type, 2025-2040 (US$ billion)
Edge NPU revenue by device, 2025-2040 (US$ billion)
Neuromorphic and novel NN hardware revenue by technology, 2025-2040 (US$ billion)
Low-power/high-efficiency AI semiconductor market by application, 2025-2036 (US$ billion; scope of the dedicated Future Markets report)
Optical computing report forecast: PIC content in AI accelerator interconnects and processors, 2026-2036 (US$ million)
Optical computing report forecast: optical processors by application, 2026-2036 (US$ billion)
Neural network frameworks and compiler stacks
Leading foundation model developers: models, funding and valuation
Leading open-weight model families
Token-processing indicators, 2024-2026
Inference cost per million output tokens by model class and hardware, 2024-2040 (US$)
Memory consumption by inference workload class
Neural network platform, MLOps and inference-serving vendors
Data labelling and synthetic data providers
AI memory-layer companies
Software, models and platforms revenue, 2025-2040 (US$ billion)
Hyperscaler capital expenditure: 2025 actual and 2026 guidance
Data-center processor demand by facility type, 2025-2040 (US$ billion)
Sovereign AI build-outs by country, 2025-2030
Enterprise neural network applications
On-device neural networks in consumer electronics
Flagship smartphone neural processing units, 2025-2026 generation
On-device LLM inference capability of AI PCs, 2026
Automotive neural network compute platforms and driving stacks
SAE automation levels and in-vehicle neural network compute requirements
Waymo operating metrics, 2022-2026
Neural network applications in healthcare and selected products
Robot foundation models and physical AI platforms
Robot foundation models: architecture, training scale and status
Physical AI technical constraints: current performance versus deployment thresholds
Global humanoid robot shipments, revenue and prices, 2025-2040
Neural network applications in financial services
Neural network companies in defence and security
Neural network applications in telecoms and secure communications
Neural networks in scientific discovery
Neural network market by end-use sector, 2025-2040 (US$ billion)
Future Markets physical AI market forecast by sub-market, 2026-2040 (US$ billion; full-system revenue, not neural network value)
Physical AI value pool by stack layer, 2026-2036 (share of revenue excluding wearables)
Forecast assumptions by segment
Global neural network market, 2024-2040
Segment growth summary
Hardware vs. software, models and platforms, 2025-2040 (US$ billion)
Data-center accelerator spend: training vs. inference, 2025-2040 (US$ billion)
Cloud vs. edge deployment, 2025-2040 (US$ billion)
Future Markets edge AI chip forecast by application, 2026-2036 (US$ billion; dedicated edge AI chips report basis)
Compute-linked market value by architecture family, 2025-2040 (US$ billion)
Neural network market by region, 2025-2040 (US$ billion)
Bear, base and bull scenarios, 2025-2040 (US$ billion)
US neural network policy and infrastructure programmes
China's neural network ecosystem: chips, models and policy
South Korean neural network programmes and investments
Japanese neural network programmes and companies
European neural network companies and funding
UK compute, research and policy initiatives
Sovereign AI programmes outside the US, China and Europe
Data-center NN accelerator vendor share, 2025 and 2026 (estimates)
Neural network funding rounds, 2025-2026
M&A and licensing deals, 2024-2026
Neural network-related IPOs, 2025-2026
Major compute and model partnerships
EU AI Act implementation timeline, including Digital Omnibus changes
US export controls affecting neural network hardware, 2022-2026
UK legislative and policy developments
Chinese AI regulation and procurement measures
AI management and safety standards
FDA-EMA guiding principles for AI in drug development
Notable AI litigation and rulings, 2025-2026
Data-center power demand indicators
AI server rack configuration and power roadmap, 2025-2040
Data-center cooling configurations: density, PUE and overhead
Embodied carbon across the AI hardware hierarchy
AI accelerator memory requirements and cost, 2024-2030
Memory and storage content and cost per AI server node, 2026-2037
Top 15 strategic choke points in the AI hardware supply chain
Capital expenditure vs. revenue indicators
AI-driven EDA approaches by design-flow stage
Risk matrix: likelihood and impact
Technology readiness and commercialisation timeline, 2027-2040
Photonic neural network technology roadmap, 2026-2036
Technology readiness of neural-network-driven physical AI applications, 2026
HBM technology roadmap, per-stack specifications
Ten predictions for the neural network market to 2040
Opportunity matrix by segment
Companies profiled (index)
Commercial readiness stages: definitions
Commercial readiness of profiled companies by company group (number of companies)
Profiled companies by commercial readiness stage

LIST OF FIGURES
Global neural network market by segment, 2024-2040 (US$ billion)
Neural network technology roadmap, 2025-2040
Timeline of neural network milestones, 1943-2026
Neural network architecture taxonomy
Training compute of notable neural network models, 2012-2025 (FLOP, log scale)
Neural network market value chain
Compute cost of attention vs. sequence length: quadratic vs. linear architectures
Share of neural network compute by architecture family, 2025, 2030 and 2040
Data-center NN accelerator revenue by type, 2024-2040 (US$ billion)
Edge NPU revenue by device, 2024-2040 (US$ billion)
Neuromorphic and novel NN hardware revenue by technology, 2024-2040 (US$ billion)
Valuations of leading model developers, 2026
Google monthly tokens processed, 2024-2026 (log scale)
Software, models and platforms revenue, 2024-2040 (US$ billion)
Neural network market share by end-use sector, 2025 vs. 2040
Global neural network market, 2024-2040 (US$ billion, base case)
Segment mix, 2025, 2030 and 2040
Hardware vs. software, models and platforms, 2024-2040 (US$ billion)
Data-center accelerator spend: training vs. inference, 2024-2040 (US$ billion)
Regional share, 2025, 2030 and 2040
Scenario comparison, 2024-2040 (US$ billion)
Data-center NN accelerator vendor share, 2025
Largest neural network funding rounds, 2025-2026 (US$ billion, log scale)
Neural network start-up landscape by layer
Neural network regulatory timeline, 2024-2028
Neural network market risk heat map
Commercialisation timeline for emerging neural network technologies, 2025-2040
Commercial readiness of profiled companies by company group
Agibot A2
Cerebras WSE-3
MemryX MX3
SiMa.ai Modalix