Dublin, Oct. 26, 2026 (GLOBE NEWSWIRE) -- "The Global Neural Networks Market 2027-2040" has been added to ResearchAndMarkets.com's offering.
Neural networks have become a central force in the global computing industry, accelerating demand for advanced AI hardware, software, models and infrastructure. The report delivers an in-depth assessment of the global neural network market, covering technology developments, commercial opportunities and market forecasts through 2040.
Analysis spans transformers, mixture-of-experts systems, post-transformer architectures and emerging AI models, alongside data-centre GPUs, custom accelerators, edge NPUs, neuromorphic processors, photonic systems and analog computing. Forecasts are provided by market segment, architecture family, deployment environment and region, enabling organisations to evaluate the technologies and applications expected to influence the next phase of artificial intelligence.
Complete neural network value chain analysis
The report maps the full AI value chain, from neural network architectures and algorithms to hardware, software, platforms and commercial applications. It examines demand across hyperscale cloud computing, enterprise AI, consumer electronics, automotive, healthcare, robotics, financial services, defence, telecoms and scientific research.
Regional coverage includes North America, China, South Korea, Japan, Taiwan, Europe, the UK and the rest of the world. The research also evaluates the competitive landscape, investment activity, M&A, regulatory developments, technical standards and strategic risks associated with energy consumption, memory supply, geopolitics, capital intensity, talent and AI safety.
Key areas covered in the report include:
- Market outlook: Global market size and forecasts for 2025-2040, supported by scenario analysis, growth drivers, market restraints, technology inflection points and strategic recommendations.
- Neural network architectures: Coverage of MLPs, CNNs, RNNs/LSTMs, transformers, attention mechanisms, mixture-of-experts, liquid networks, graph and topological approaches, GANs, VAEs, diffusion models, world models, spiking systems, neural operators, KANs, quantum architectures, biological and organoid computing, and memory-augmented systems.
- Model optimisation: Assessment of model compression, on-device AI, training and inference requirements, scaling laws and changing compute demand.
- AI hardware: Analysis of GPUs, hyperscaler custom ASICs, challenger accelerators, edge NPUs, neuromorphic systems, photonic computing, optical interconnects, analog and in-memory processing, thermodynamic computing, advanced packaging, memory and interconnect technologies.
- Software and platforms: Examination of AI frameworks, compilers, foundation model developers, open-weight and proprietary models, MLOps, inference serving, token demand, training data, data labelling, agent infrastructure and memory systems.
- Applications and end markets: Forecasts and commercial analysis for cloud providers, AI laboratories, enterprise and agentic AI, consumer devices, autonomous and connected vehicles, healthcare, industrial automation, robotics, physical AI, finance, defence, telecoms and science.
- Regional ecosystems: Evaluation of established and emerging AI markets, including the hardware ecosystems developing across China, South Korea and Japan.
- Investment and competition: Review of market structure, venture funding, strategic compute partnerships, licensing, IPO activity, M&A and the global start-up landscape.
- Policy and risk: Coverage of the EU AI Act, US export controls, UK and China policy, sector regulation, standards, liability, copyright, litigation and broader market risks.
Detailed forecasts and company intelligence
The report provides global forecasts for 2025-2040 by hardware and software, training and inference, cloud and edge deployment, architecture family, end-use market and region. Scenario-based projections help decision-makers assess how technical progress, infrastructure constraints, regulatory intervention and shifts in AI adoption could affect future revenue opportunities.
A technology roadmap and long-term outlook identify potential developments through 2040. The research also profiles 195 companies across the neural network ecosystem, with each organisation rated on commercial readiness. Supporting appendices include year-by-year forecasts, references and additional market data.
Designed for technology companies, semiconductor manufacturers, cloud providers, investors, enterprise decision-makers and policymakers, the report offers a structured view of the global neural network industry and the competitive, technical and economic factors shaping its future.
Key Topics Covered:
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
LIST OF TABLES [160]
LIST OF FIGURES [32]
A selection of companies mentioned in this report includes, but is not limited to:
- 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...
For more information about this report visit https://www.researchandmarkets.com/r/9p1gdv
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