The Global Industrial Metaverse Market 2025-2035
The Industrial Metaverse has the potential to revolutionize sectors such as manufacturing, logistics, transportation, and utilities by making them smarter, more efficient, and more sustainable. The market for industrial metaverse applications could grow to >$150 billion by 2035, with major investments being made in enabling technologies and processes to enhance productivity, accelerate green transitions through VR/AR/MR and 5G technologies supported by AI/ML capabilities, and create additional value for their customers.
The Industrial Metaverse represents the convergence of physical industrial operations with immersive digital technologies, creating a new paradigm for manufacturing, maintenance, training, and collaboration. Unlike consumer-focused metaverse applications, the industrial metaverse prioritizes practical business outcomes and operational efficiency. At its core, the industrial metaverse is a digital ecosystem where physical assets, production processes, and supply chains are mirrored as virtual replicas. These digital twins allow organizations to simulate, monitor, and optimize industrial operations in real-time. Engineers can manipulate virtual models before implementing changes to physical systems, significantly reducing costs and risks associated with physical prototyping.
The technology stack powering the industrial metaverse includes virtual and augmented reality (VR/AR), Internet of Things (IoT) sensors, artificial intelligence, cloud computing, and 5G connectivity. This enables seamless interaction between physical and digital environments, creating immersive experiences where workers can visualize complex data and collaborate across geographical boundaries.
Key applications of the industrial metaverse include:
Remote maintenance and repair, where technicians use AR to receive visual guidance while servicing equipment, improving first-time fix rates and reducing travel costs
Immersive training simulations for dangerous or complex procedures without risking safety or equipment
Virtual design reviews where global teams collaborate on 3D models in shared virtual spaces
Production optimization through real-time monitoring and predictive analytics
Supply chain visualization and management across distributed operations
Major industrial firms like Siemens, GE, and Boeing have already implemented metaverse technologies to achieve significant operational improvements. For example, some manufacturers report 30% reductions in design time and 25% improvements in maintenance efficiency. The industrial metaverse represents a fundamental shift in how industrial operations are conceived, executed, and managed. By creating persistent digital environments that mirror physical operations, organizations can achieve unprecedented levels of collaboration, efficiency, and innovation. As technologies mature and standards evolve, the industrial metaverse will increasingly become an essential competitive advantage rather than a futuristic concept. While challenges remain in areas of interoperability, security, and workforce adaptation, the trajectory is clear: the industrial metaverse is becoming the next frontier of industrial transformation, creating new possibilities for how we design, build, and maintain the physical world.
The Global Industrial Metaverse Market 2025-2035' provides an in-depth analysis of the rapidly evolving industrial metaverse landscape, exploring how this technological paradigm shift is transforming manufacturing, engineering, healthcare, and other key industrial sectors. This 658-page analysis examines the convergence of extended reality (XR), artificial intelligence, digital twins, IoT, and other emerging technologies that are creating immersive, collaborative industrial environments with unprecedented capabilities for optimization, training, and innovation.
Report contents include:
The Industrial Metaverse represents the convergence of physical industrial operations with immersive digital technologies, creating a new paradigm for manufacturing, maintenance, training, and collaboration. Unlike consumer-focused metaverse applications, the industrial metaverse prioritizes practical business outcomes and operational efficiency. At its core, the industrial metaverse is a digital ecosystem where physical assets, production processes, and supply chains are mirrored as virtual replicas. These digital twins allow organizations to simulate, monitor, and optimize industrial operations in real-time. Engineers can manipulate virtual models before implementing changes to physical systems, significantly reducing costs and risks associated with physical prototyping.
The technology stack powering the industrial metaverse includes virtual and augmented reality (VR/AR), Internet of Things (IoT) sensors, artificial intelligence, cloud computing, and 5G connectivity. This enables seamless interaction between physical and digital environments, creating immersive experiences where workers can visualize complex data and collaborate across geographical boundaries.
Key applications of the industrial metaverse include:
Remote maintenance and repair, where technicians use AR to receive visual guidance while servicing equipment, improving first-time fix rates and reducing travel costs
Immersive training simulations for dangerous or complex procedures without risking safety or equipment
Virtual design reviews where global teams collaborate on 3D models in shared virtual spaces
Production optimization through real-time monitoring and predictive analytics
Supply chain visualization and management across distributed operations
Major industrial firms like Siemens, GE, and Boeing have already implemented metaverse technologies to achieve significant operational improvements. For example, some manufacturers report 30% reductions in design time and 25% improvements in maintenance efficiency. The industrial metaverse represents a fundamental shift in how industrial operations are conceived, executed, and managed. By creating persistent digital environments that mirror physical operations, organizations can achieve unprecedented levels of collaboration, efficiency, and innovation. As technologies mature and standards evolve, the industrial metaverse will increasingly become an essential competitive advantage rather than a futuristic concept. While challenges remain in areas of interoperability, security, and workforce adaptation, the trajectory is clear: the industrial metaverse is becoming the next frontier of industrial transformation, creating new possibilities for how we design, build, and maintain the physical world.
The Global Industrial Metaverse Market 2025-2035' provides an in-depth analysis of the rapidly evolving industrial metaverse landscape, exploring how this technological paradigm shift is transforming manufacturing, engineering, healthcare, and other key industrial sectors. This 658-page analysis examines the convergence of extended reality (XR), artificial intelligence, digital twins, IoT, and other emerging technologies that are creating immersive, collaborative industrial environments with unprecedented capabilities for optimization, training, and innovation.
Report contents include:
- Market Growth Projections: Detailed forecasts of the industrial metaverse market from 2025 to 2035, including compound annual growth rates, regional analysis, and segment-specific growth patterns.
- Market Overview: Detailed examination of market evolution, size, growth rate by component/technology/industry/region, investment landscape, drivers, challenges, and opportunities.
- Technology Landscape: Comprehensive examination of core enabling technologies including XR (AR/VR/MR), artificial intelligence, industrial IoT, 5G/6G networks, edge computing, blockchain, and 3D scanning/modeling.
- Industry Adoption Analysis: Sector-by-sector breakdown of industrial metaverse implementation across automotive, aerospace, chemicals, energy, healthcare, construction, supply chain, and retail industries.
- End Use Markets: Comprehensive breakdown by hardware components, AI tools, and industry-specific applications with current commercial examples.
- Investment Trends: Analysis of venture capital, corporate investments, and government funding initiatives driving industrial metaverse development globally.
- Technological Challenges: Critical assessment of current technological limitations, integration complexities, skill gaps, security concerns, and cost barriers.
- Future Opportunities: Exploration of emerging business models, sustainability applications, enhanced customer experiences, and novel use cases in non-traditional industries.
- Regulatory Landscape: Analysis of data privacy, intellectual property, standards development, and environmental regulations affecting industrial metaverse deployment.
- Implementation Case Studies: Real-world examples of successful industrial metaverse applications across manufacturing, product development, training, maintenance, and quality control.
- Market Evolution Timeline: Projected adoption curves from 2025-2035 across short-term, medium-term, and long-term implementation horizons.
- Societal and Economic Impact: Assessment of workforce transformation, economic growth potential, sustainability implications, and ethical considerations.
- Challenges and Risk Factors: Critical examination of technological, implementation, cybersecurity, and economic barriers to adoption.
- Company Profiles: Detailed analysis of over 500 companies including AAC Technologies, ABB, Accelink, Acer, Acuity, Advantech, Aeva, AEye, Ag Leader, Airy3D, Aistorm, Aize, Akselos, Alphabet (Google), Altair, Amazon Web Services (AWS), AMD, AnonyBit, Ansys, Apple, Arm, ArborXR, Artec 3D, Artilux, Axelera AI, Axera Semiconductor, Baidu, Balyo, Baraja, Basemark, Beamagine, BenQ, bHaptics, BlackShark.ai, Blaize, Blippar, BlockCypher, Bosch, BrainChip, Cambridge Mechatronics, Cambricon, Casper Labs, Celestial AI, Cepton, Cerebras Systems, Certik, Chainalysis, Circulor, Clique, Cognite, Cognizant, ConsenSys, Cosmo Tech, Coupa Software, CyDeploy, Dassault Systemes, DataMesh, Deep Optics, DeepX, DeGirum, Dexory, Dexta Robotics, DigiLens, Dispelix, d-Matrix, Dune Analytics, EdgeConneX, EdgeCortix, Edge Impulse, Emersya, EnCharge AI, Enflame, Expedera, Expivi, FARO Technologies, Fetch.ai, Finboot, Flex Logix, FuriosaAI, Gauzy, General Electric, GrAI Matter Labs, Graphcore, GreyOrange, Groq, Hailo, HaptX, Headspace, Hexa 3D, Hexagon, Hikvision, HOLOGATE, Hololight, Horizon Robotics, HTC Vive, Huawei, IBM, ImmersiveTouch, Infinite Reality, Inkron, Intel, Intellifusion, IoTeX, JigSpace, Kalima, Kalray, Kentik, Kinara, Kneron, Kongsberg, Kura Technologies, Leica Geosystems, Lenovo, LetinAR, Leucine, Lightmatter, Limbak, Litmus, Locusview, Loft Dynamics, LucidAI, Lumen Technologies, Lumibird, Luminar, Luminous XR, Lumus, Lynx, Magic Leap, MathWorks, Matterport, MaxxChain, MediaTek, Medivis, Meta, MicroOLED, Microsoft, MindMaze, Mojo Vision, Moore Threads, Morphotonics, Mythic, Native AI, NavVis, Neara, Nextech3D, Niantic, NVIDIA, NXP Semiconductors, Oculi, Omnivision, Oorym, Optinvent, Orbbec, Ouster, PassiveLogic, pgEdge, Photoneo, Pimax, Plexigrid, Presagis, Prevu3D, Prophesee, Q Bio, Qualcomm, Quanergy, Rain, Rapyuta Robotics, RealWear, Red 6, RoboSense, Rokid, R3, Rypplzz, Samsung, SambaNova Systems, Sapeon, Sarcos, Scantinel Photonics, Schott AG, Seeq, Sentera, SiLC, Siemens, SiMa.ai, Solitorch, Space and Time, Spherity, Story Protocol, Swave Photonics, Tachyum, Taqtile, TensorFlow, Tenstorrent, Tesla, Threedium, TRM Labs, TruLife Optics, TWAICE, TwinUp, Unity, Varjo, Veerum, vHive, VividQ, VNTANA, VRelax, Vuzix, Web3Firewall, Windup Minds, Worlds, Xaba, Xpanceo, Yizhu Technology, Zama, ZEDEDA, Zebra Technologies, Zivid, zkPass, and Zvision, spanning hardware manufacturers, software developers, system integrators, connectivity providers, AI specialists, blockchain innovators, XR device makers, sensor companies, robotics firms, edge computing providers, and digital twin platforms.
1 EXECUTIVE SUMMARY
1.1 Definition of the Industrial Metaverse
1.1.1 Key Characteristics
1.1.2 Differentiation from Consumer Metaverse
1.2 Evolution of Industry 4.0 to the Industrial Metaverse
1.2.1 Technological Convergence
1.3 Industrial metaverse ecosystem
1.4 Metaverse enabling technologies
1.4.1 Artificial Intelligence
1.4.2 Cross, Virtual, Augmented and Mixed Reality
1.4.3 Blockchain
1.4.4 Edge computing
1.4.5 Cloud computing
1.4.6 Digital Twin
1.4.7 3D Modeling/Scanning
1.4.8 Industrial Internet of Things (IIoT)
1.5 Industrial Metaverse Implementations
1.6 Current Market Landscape
2 MARKET OVERVIEW
2.1 Market Evolution
2.1.1 Precursors to the Industrial Metaverse
2.1.1.1 Virtual Reality in Industrial Design
2.1.1.2 Augmented Reality in Manufacturing
2.1.1.3 Digital Twin Concepts in Industry 4.0
2.1.2 Transition from Industry 4.0 to Industrial Metaverse
2.1.3 Unmet business needs addressed by the metaverse
2.1.4 Convergence of Physical and Digital Realms
2.1.5 Shift from Connectivity to Immersive Experiences
2.1.6 Evolution of Human-Machine Interaction
2.2 Market Size and Growth Rate
2.2.1 Total market
2.2.2 By component
2.2.3 By technology
2.2.4 End-User Industry
2.2.5 Regional Market Dynamics
2.2.5.1 North America
2.2.5.2 Europe
2.2.5.3 Asia-Pacific
2.2.5.4 Rest of the World
2.3 Comparison with Related Markets (e.g., IoT, AR/VR)
2.4 Investment Landscape
2.4.1 Venture Capital Funding
2.4.2 Corporate Investments
2.4.3 Government and Public Funding Initiatives
2.5 Key Market Drivers
2.6 Technological Advancements
2.6.1 Improvements in XR Hardware
2.6.2 Advancements in AI and Machine Learning
2.6.3 5G and Edge Computing Proliferation
2.6.4 Industry 4.0 Initiatives
2.6.4.1 Smart Factory Implementations
2.6.4.2 Digital Transformation Strategies
2.6.4.3 Industrial IoT Adoption
2.7 Demand for Increased Efficiency and Productivity
2.7.1 Cost Reduction Imperatives
2.7.2 Quality Improvement Initiatives
2.7.3 Time-to-Market Acceleration
2.8 Remote Work and Collaboration Trends
2.8.1 Impact of Global Events
2.8.2 Distributed Workforce Management
2.8.3 Cross-border Collaboration Needs
2.9 Sustainability and Environmental Concerns
2.9.1 Carbon Footprint Reduction Goals
2.9.2 Resource Optimization Efforts
2.9.3 Circular Economy Initiatives
2.10 Market Challenges and Barriers
2.10.1 Technological Limitations
2.10.1.1 Hardware Constraints (e.g., Battery Life, Comfort)
2.10.1.2 Software Integration Complexities
2.10.1.3 Latency and Bandwidth Issues
2.10.2 Integration Complexities
2.10.2.1 Legacy System Compatibility
2.10.2.2 Interoperability Standards
2.10.2.3 Data Integration and Management
2.10.3 Skill Gaps and Workforce Readiness
2.10.3.1 Technical Skill Shortages
2.10.3.2 Change Management Challenges
2.10.3.3 Training and Education Needs
2.10.4 Data Security and Privacy Concerns
2.10.4.1 Cybersecurity Risks
2.10.4.2 Intellectual Property Protection
2.10.4.3 Regulatory Compliance Challenges
2.10.5 High Initial Investment Costs
2.10.5.1 Infrastructure Setup Expenses
2.10.5.2 Software Licensing and Development Costs
2.10.5.3 ROI Justification Challenges
2.11 Opportunities in the Industrial Metaverse
2.11.1 New Business Models
2.11.1.1 Industrial Metaverse-as-a-Service
2.11.1.2 Virtual Asset Marketplaces
2.11.1.3 Subscription-based Digital Twin Services
2.11.2 Sustainability and Green Initiatives
2.11.2.1 Virtual Prototyping for Reduced Material Waste
2.11.2.2 Energy Optimization through Digital Twins
2.11.2.3 Sustainable Supply Chain Simulations
2.11.3 Enhanced Customer Experiences
2.11.3.1 Immersive Product Demonstrations
2.11.3.2 Virtual Factory Tours
2.11.3.3 Customized Product Configuration in VR
2.11.4 Emerging Markets and Applications
2.11.4.1 Industrial Metaverse in Developing Economies
2.11.4.2 Integration with Emerging Technologies (e.g., Quantum Computing)
2.11.4.3 Novel Use Cases in Non-Traditional Industries
3 TECHNOLOGY LANDSCAPE
3.1 Core Technologies Enabling the Industrial Metaverse
3.1.1 Extended Reality (XR): AR, VR, and MR
3.1.1.1 Head-Mounted Displays (HMDs)
3.1.1.2 Haptic Devices
3.1.1.3 Companies
3.1.2 Artificial Intelligence and Machine Learning
3.1.2.1 Deep Learning in Industrial Applications
3.1.2.1.1 Convolutional Neural Networks (CNNs)
3.1.2.1.2 Recurrent Neural Networks (RNNs)
3.1.2.1.3 Generative Adversarial Networks (GANs)
3.1.2.2 Natural Language Processing
3.1.2.3 Computer Vision
3.1.2.4 Companies
3.1.3 Internet of Things (IoT) and Industrial IoT (IIoT)
3.1.3.1 Sensor Technologies
3.1.3.2 Data Collection and Analysis
3.1.3.3 Edge Computing in IIoT
3.1.3.4 Companies
3.1.4 5G and Beyond (6G) Networks
3.1.4.1 Ultra-Low Latency Communication
3.1.4.1.1 Network Slicing
3.1.4.1.2 Mobile Edge Computing (MEC)
3.1.4.2 Massive Machine-Type Communications
3.1.4.3 Enhanced Mobile Broadband
3.1.4.4 Companies
3.1.5 Edge Computing and Cloud Infrastructure
3.1.5.1 Hybrid Cloud Solutions in Edge Computing
3.1.5.2 Edge AI in Edge Computing and Cloud Infrastructure
3.1.5.3 Companies
3.1.6 Blockchain and Distributed Ledger Technologies
3.1.6.1 Smart Contracts in Blockchain and Distributed Ledger Technologies
3.1.6.2 Supply Chain Traceability in Blockchain and DLT
3.1.6.3 Decentralized Finance in Industry
3.1.6.4 Companies
3.1.7 3D Scanning/Modeling
3.1.7.1 Overview
3.1.7.2 Companies
3.2 Emerging Technologies and Their Potential Impact
3.2.1 Quantum Computing
3.2.1.1 Companies
3.2.2 Brain-Computer Interfaces
3.2.2.1 Non-invasive BCI Technologies
3.2.2.2 Neural Control of Industrial Systems
3.2.2.3 Cognitive Load Monitoring
3.2.2.4 Companies
3.2.3 Advanced Materials and Nanotechnology
3.2.3.1 Smart Materials for Sensors
3.2.3.2 Nanotech in Manufacturing
3.2.3.3 Self-healing Materials
3.2.4 Human-Machine Interfaces in the Industrial Metaverse
3.2.5 Edge Computing in the Industrial Metaverse
3.2.6 Autonomous Systems and Robotics
3.2.6.1 Collaborative Robots (Cobots)
3.2.6.2 Swarm Robotics
3.2.6.3 Biomimetic Robots
3.2.6.4 Companies
3.3 Technology Adoption Trends and Forecasts
3.3.1 Short-term Adoption (2025-2028)
3.3.1.1 Technology Readiness Levels
3.3.1.2 Early Adopter Industries
3.3.2 Medium-term Adoption (2029-2032)
3.3.2.1 Scaling Successful Implementations
3.3.2.2 Cross-industry Technology Transfer
3.3.2.3 Standardization and Interoperability Efforts
3.3.3 Long-term Adoption (2033-2035)
3.3.3.1 Mainstream Integration
3.3.3.2 Disruptive Business Models
3.3.3.3 Societal and Economic Impacts
4 END USE MARKETS
4.1 Hardware
4.1.1 XR Devices
4.1.2 Sensors and Actuators
4.1.3 Industrial PCs and Servers
4.1.4 Communication Infrastructure for the Industrial Metaverse
4.1.5 AR/VR/MR Solutions
4.2 AI and Analytics Tools
4.3 Quality Control and Inspection
4.4 By industry
4.4.1 Automotive
4.4.1.1 Overview
4.4.1.2 Current commercial examples
4.4.2 Aerospace
4.4.2.1 Overview
4.4.2.2 Current commercial examples
4.4.3 Chemicals and materials manufacturing
4.4.3.1 Overview
4.4.3.2 Current commercial examples
4.4.4 Energy
4.4.4.1 Overview
4.4.4.2 Current commercial examples
4.4.5 Healthcare and life sciences
4.4.5.1 Overview
4.4.5.2 Current commercial examples
4.4.6 Construction and engineering
4.4.6.1 Overview
4.4.6.2 Current commercial examples
4.4.7 Supply Chain Management and Logistics
4.4.7.1 Overview
4.4.7.2 Current commercial examples
4.4.8 Retail
4.4.8.1 Overview
4.4.8.2 Current commercial examples
5 REGULATIONS
5.1 Data Privacy and Security Regulations
5.2 Intellectual Property Considerations
5.3 Standards and Interoperability Initiatives
5.4 Environmental and Sustainability Regulations
6 SOCIETAL AND ECONOMIC IMPACT
6.1 Workforce Transformation and Skill Requirements
6.2 Economic Growth and Productivity Gains
6.3 Sustainability and Environmental Impact
6.3.1 Energy Consumption
6.3.2 E-Waste
6.3.3 Virtual Economies and Blockchain
6.3.4 Reduction in Pollution
6.4 Ethical Considerations and Social Implications
7 CHALLENGES AND RISK FACTORS
7.1 Technological Challenges
7.2 Implementation and Integration Issues
7.3 Cybersecurity Risks
7.4 Economic and Market Risks
8 COMPANY PROFILES
8.1 Virtual, Augmented and Mixed Reality (including haptics) 362 (71 company profiles)
8.2 Artificial Intelligence 421 (135 company profiles)
8.3 Blockchain 519 (36 company profiles)
8.4 Edge computing 548 (35 company profiles)
8.5 Digital Twin 575 (53 company profiles)
8.6 3D imaging and sensing 620 (170 company profiles)
8.7 Other technologies, platforms and services 647 (11 company profiles)
9 RESEARCH METHODOLOGY
10 GLOSSARY OF TERMS
11 REFERENCES
1.1 Definition of the Industrial Metaverse
1.1.1 Key Characteristics
1.1.2 Differentiation from Consumer Metaverse
1.2 Evolution of Industry 4.0 to the Industrial Metaverse
1.2.1 Technological Convergence
1.3 Industrial metaverse ecosystem
1.4 Metaverse enabling technologies
1.4.1 Artificial Intelligence
1.4.2 Cross, Virtual, Augmented and Mixed Reality
1.4.3 Blockchain
1.4.4 Edge computing
1.4.5 Cloud computing
1.4.6 Digital Twin
1.4.7 3D Modeling/Scanning
1.4.8 Industrial Internet of Things (IIoT)
1.5 Industrial Metaverse Implementations
1.6 Current Market Landscape
2 MARKET OVERVIEW
2.1 Market Evolution
2.1.1 Precursors to the Industrial Metaverse
2.1.1.1 Virtual Reality in Industrial Design
2.1.1.2 Augmented Reality in Manufacturing
2.1.1.3 Digital Twin Concepts in Industry 4.0
2.1.2 Transition from Industry 4.0 to Industrial Metaverse
2.1.3 Unmet business needs addressed by the metaverse
2.1.4 Convergence of Physical and Digital Realms
2.1.5 Shift from Connectivity to Immersive Experiences
2.1.6 Evolution of Human-Machine Interaction
2.2 Market Size and Growth Rate
2.2.1 Total market
2.2.2 By component
2.2.3 By technology
2.2.4 End-User Industry
2.2.5 Regional Market Dynamics
2.2.5.1 North America
2.2.5.2 Europe
2.2.5.3 Asia-Pacific
2.2.5.4 Rest of the World
2.3 Comparison with Related Markets (e.g., IoT, AR/VR)
2.4 Investment Landscape
2.4.1 Venture Capital Funding
2.4.2 Corporate Investments
2.4.3 Government and Public Funding Initiatives
2.5 Key Market Drivers
2.6 Technological Advancements
2.6.1 Improvements in XR Hardware
2.6.2 Advancements in AI and Machine Learning
2.6.3 5G and Edge Computing Proliferation
2.6.4 Industry 4.0 Initiatives
2.6.4.1 Smart Factory Implementations
2.6.4.2 Digital Transformation Strategies
2.6.4.3 Industrial IoT Adoption
2.7 Demand for Increased Efficiency and Productivity
2.7.1 Cost Reduction Imperatives
2.7.2 Quality Improvement Initiatives
2.7.3 Time-to-Market Acceleration
2.8 Remote Work and Collaboration Trends
2.8.1 Impact of Global Events
2.8.2 Distributed Workforce Management
2.8.3 Cross-border Collaboration Needs
2.9 Sustainability and Environmental Concerns
2.9.1 Carbon Footprint Reduction Goals
2.9.2 Resource Optimization Efforts
2.9.3 Circular Economy Initiatives
2.10 Market Challenges and Barriers
2.10.1 Technological Limitations
2.10.1.1 Hardware Constraints (e.g., Battery Life, Comfort)
2.10.1.2 Software Integration Complexities
2.10.1.3 Latency and Bandwidth Issues
2.10.2 Integration Complexities
2.10.2.1 Legacy System Compatibility
2.10.2.2 Interoperability Standards
2.10.2.3 Data Integration and Management
2.10.3 Skill Gaps and Workforce Readiness
2.10.3.1 Technical Skill Shortages
2.10.3.2 Change Management Challenges
2.10.3.3 Training and Education Needs
2.10.4 Data Security and Privacy Concerns
2.10.4.1 Cybersecurity Risks
2.10.4.2 Intellectual Property Protection
2.10.4.3 Regulatory Compliance Challenges
2.10.5 High Initial Investment Costs
2.10.5.1 Infrastructure Setup Expenses
2.10.5.2 Software Licensing and Development Costs
2.10.5.3 ROI Justification Challenges
2.11 Opportunities in the Industrial Metaverse
2.11.1 New Business Models
2.11.1.1 Industrial Metaverse-as-a-Service
2.11.1.2 Virtual Asset Marketplaces
2.11.1.3 Subscription-based Digital Twin Services
2.11.2 Sustainability and Green Initiatives
2.11.2.1 Virtual Prototyping for Reduced Material Waste
2.11.2.2 Energy Optimization through Digital Twins
2.11.2.3 Sustainable Supply Chain Simulations
2.11.3 Enhanced Customer Experiences
2.11.3.1 Immersive Product Demonstrations
2.11.3.2 Virtual Factory Tours
2.11.3.3 Customized Product Configuration in VR
2.11.4 Emerging Markets and Applications
2.11.4.1 Industrial Metaverse in Developing Economies
2.11.4.2 Integration with Emerging Technologies (e.g., Quantum Computing)
2.11.4.3 Novel Use Cases in Non-Traditional Industries
3 TECHNOLOGY LANDSCAPE
3.1 Core Technologies Enabling the Industrial Metaverse
3.1.1 Extended Reality (XR): AR, VR, and MR
3.1.1.1 Head-Mounted Displays (HMDs)
3.1.1.2 Haptic Devices
3.1.1.3 Companies
3.1.2 Artificial Intelligence and Machine Learning
3.1.2.1 Deep Learning in Industrial Applications
3.1.2.1.1 Convolutional Neural Networks (CNNs)
3.1.2.1.2 Recurrent Neural Networks (RNNs)
3.1.2.1.3 Generative Adversarial Networks (GANs)
3.1.2.2 Natural Language Processing
3.1.2.3 Computer Vision
3.1.2.4 Companies
3.1.3 Internet of Things (IoT) and Industrial IoT (IIoT)
3.1.3.1 Sensor Technologies
3.1.3.2 Data Collection and Analysis
3.1.3.3 Edge Computing in IIoT
3.1.3.4 Companies
3.1.4 5G and Beyond (6G) Networks
3.1.4.1 Ultra-Low Latency Communication
3.1.4.1.1 Network Slicing
3.1.4.1.2 Mobile Edge Computing (MEC)
3.1.4.2 Massive Machine-Type Communications
3.1.4.3 Enhanced Mobile Broadband
3.1.4.4 Companies
3.1.5 Edge Computing and Cloud Infrastructure
3.1.5.1 Hybrid Cloud Solutions in Edge Computing
3.1.5.2 Edge AI in Edge Computing and Cloud Infrastructure
3.1.5.3 Companies
3.1.6 Blockchain and Distributed Ledger Technologies
3.1.6.1 Smart Contracts in Blockchain and Distributed Ledger Technologies
3.1.6.2 Supply Chain Traceability in Blockchain and DLT
3.1.6.3 Decentralized Finance in Industry
3.1.6.4 Companies
3.1.7 3D Scanning/Modeling
3.1.7.1 Overview
3.1.7.2 Companies
3.2 Emerging Technologies and Their Potential Impact
3.2.1 Quantum Computing
3.2.1.1 Companies
3.2.2 Brain-Computer Interfaces
3.2.2.1 Non-invasive BCI Technologies
3.2.2.2 Neural Control of Industrial Systems
3.2.2.3 Cognitive Load Monitoring
3.2.2.4 Companies
3.2.3 Advanced Materials and Nanotechnology
3.2.3.1 Smart Materials for Sensors
3.2.3.2 Nanotech in Manufacturing
3.2.3.3 Self-healing Materials
3.2.4 Human-Machine Interfaces in the Industrial Metaverse
3.2.5 Edge Computing in the Industrial Metaverse
3.2.6 Autonomous Systems and Robotics
3.2.6.1 Collaborative Robots (Cobots)
3.2.6.2 Swarm Robotics
3.2.6.3 Biomimetic Robots
3.2.6.4 Companies
3.3 Technology Adoption Trends and Forecasts
3.3.1 Short-term Adoption (2025-2028)
3.3.1.1 Technology Readiness Levels
3.3.1.2 Early Adopter Industries
3.3.2 Medium-term Adoption (2029-2032)
3.3.2.1 Scaling Successful Implementations
3.3.2.2 Cross-industry Technology Transfer
3.3.2.3 Standardization and Interoperability Efforts
3.3.3 Long-term Adoption (2033-2035)
3.3.3.1 Mainstream Integration
3.3.3.2 Disruptive Business Models
3.3.3.3 Societal and Economic Impacts
4 END USE MARKETS
4.1 Hardware
4.1.1 XR Devices
4.1.2 Sensors and Actuators
4.1.3 Industrial PCs and Servers
4.1.4 Communication Infrastructure for the Industrial Metaverse
4.1.5 AR/VR/MR Solutions
4.2 AI and Analytics Tools
4.3 Quality Control and Inspection
4.4 By industry
4.4.1 Automotive
4.4.1.1 Overview
4.4.1.2 Current commercial examples
4.4.2 Aerospace
4.4.2.1 Overview
4.4.2.2 Current commercial examples
4.4.3 Chemicals and materials manufacturing
4.4.3.1 Overview
4.4.3.2 Current commercial examples
4.4.4 Energy
4.4.4.1 Overview
4.4.4.2 Current commercial examples
4.4.5 Healthcare and life sciences
4.4.5.1 Overview
4.4.5.2 Current commercial examples
4.4.6 Construction and engineering
4.4.6.1 Overview
4.4.6.2 Current commercial examples
4.4.7 Supply Chain Management and Logistics
4.4.7.1 Overview
4.4.7.2 Current commercial examples
4.4.8 Retail
4.4.8.1 Overview
4.4.8.2 Current commercial examples
5 REGULATIONS
5.1 Data Privacy and Security Regulations
5.2 Intellectual Property Considerations
5.3 Standards and Interoperability Initiatives
5.4 Environmental and Sustainability Regulations
6 SOCIETAL AND ECONOMIC IMPACT
6.1 Workforce Transformation and Skill Requirements
6.2 Economic Growth and Productivity Gains
6.3 Sustainability and Environmental Impact
6.3.1 Energy Consumption
6.3.2 E-Waste
6.3.3 Virtual Economies and Blockchain
6.3.4 Reduction in Pollution
6.4 Ethical Considerations and Social Implications
7 CHALLENGES AND RISK FACTORS
7.1 Technological Challenges
7.2 Implementation and Integration Issues
7.3 Cybersecurity Risks
7.4 Economic and Market Risks
8 COMPANY PROFILES
8.1 Virtual, Augmented and Mixed Reality (including haptics) 362 (71 company profiles)
8.2 Artificial Intelligence 421 (135 company profiles)
8.3 Blockchain 519 (36 company profiles)
8.4 Edge computing 548 (35 company profiles)
8.5 Digital Twin 575 (53 company profiles)
8.6 3D imaging and sensing 620 (170 company profiles)
8.7 Other technologies, platforms and services 647 (11 company profiles)
9 RESEARCH METHODOLOGY
10 GLOSSARY OF TERMS
11 REFERENCES
LIST OF TABLES
Table 1. Comparison of the consumer and industrial metaverses.
Table 2. Metaverse Enabling Technologies.
Table 3. Comparison of Key Features: Major Industrial Metaverse Platforms.
Table 4. Augmented Reality in Manufacturing.
Table 5. Digital Twin Concepts in Industry 4.0.
Table 6. Differences between Industry 4.0 and the Industrial Metaverse.
Table 7. Unmet Business Needs Addressed by the Metaverse.
Table 8. Maturity/development of Industrial Metaverse technology building blocks
Table 9. Global Industrial Metaverse Market Size and Growth Rate, 2025-2035.
Table 10. Market Share by Component (Hardware, Software, Services), 2025-2035
Table 11. Market Share by Technology (AR/VR/MR, Digital Twins, AI, IoT), 2025-2035.
Table 12. Market Share by End-User Industry, 2025-2035.
Table 13. Energy Consumption Comparison: Traditional vs. Metaverse-Enabled Industrial Processes.
Table 14. Regional Market Size and Growth Rates, 2025-2035.
Table 15. Cost Comparison: Traditional Industrial Processes vs. Metaverse-Enabled Processes
Table 16. Investment in Industrial Metaverse by Type (VC, Corporate, Government), 2020-2025.
Table 17. Venture capital funding for industrial metaverse.
Table 18. Venture Capital Funding for Industrial Metaverse, 2021-2025.
Table 19. Corporate industrial metaverse investments, 2021-2025.
Table 20. Government and Public Funding Initiatives.
Table 21. Key Market Drivers for the Industrial Metaverse.
Table 22. Advancements in AI and Machine Learning.
Table 23. Smart Factory Implementations.
Table 24. Digital transformation strategies.
Table 25. Industrial IoT Adoption.
Table 26. Carbon footprint reduction.
Table 27. Resource optimization efforts.
Table 28. Circular economy initiatives.
Table 29. Market challenges and barriers in the Industrial Metaverse.
Table 30. Hardware Constraints (e.g., Battery Life, Comfort).
Table 31. Integration with Emerging Technologies.
Table 32. Novel Use Cases in Non-Traditional Industries.
Table 33. Companies in Extended Reality (XR): AR, VR, and MR.
Table 34. Deep Learning in Industrial Applications.
Table 35. Recurrent Neural Networks (RNNs).
Table 36. Natural Language Processing in Industrial Applications
Table 37. Computer Vision in Industrial Applications
Table 38. Companies in Artificial Intelligence and Machine Learning.
Table 39. Data Collection and Analysis.
Table 40. Edge Computing in IIoT.
Table 41. Companies in Internet of Things (IoT) and Industrial IoT (IIoT) technologies.
Table 42. Ultra-Low Latency Communication in 5G and Beyond (6G) Networks
Table 43. Massive Machine-Type Communications.
Table 44. Enhanced Mobile Broadband in 5G and Beyond (6G) Networks.
Table 45. Companies in 5G and Beyond (6G) Networks.
Table 46. Hybrid Cloud Solutions.
Table 47. Edge AI in Edge Computing and Cloud Infrastructure
Table 48. Companies in Edge Computing and Cloud Infrastructure.
Table 49. Smart Contracts in Blockchain and DLT.
Table 50. Supply Chain Traceability in Blockchain and DLT.
Table 51. Decentralized Finance in Industry.
Table 52. Companies in Blockchain and Distributed Ledger Technologies.
Table 53. Applications of 3D Scanning/Modeling in the Industrial Metaverse.
Table 54. Companies in 3D Scanning/Modeling for Industrial Metaverse Applications
Table 55. Quantum Computing in the Industrial Metaverse.
Table 56. Companies in Quantum Computing.
Table 57. Applications of Brain-Computer Interfaces in the Industrial Metaverse
Table 58. Non-Invasive BCI Technologies Comparison.
Table 59. Examples of Neural Control in Industrial Systems.
Table 60. Companies in Brain-Computer Interfaces.
Table 61. Smart Materials for Sensors.
Table 62. Nanotechnology Applications in Manufacturing.
Table 63. Self-Healing Materials in Industrial Applications
Table 64. Human-Machine Interface Technologies in the Industrial Metaverse
Table 65. Edge Computing Technologies in the Industrial Metaverse
Table 66. Companies in Autonomous Systems and Robotics for the Industrial Metaverse
Table 67. Technology Readiness Levels (TRL) for Industrial Metaverse Applications
Table 68. Adoption Rates of Industrial Metaverse Technologies by Industry, 2025-2035.
Table 69. Advanced materials used in industrial metaverse hardware.
Table 70. Types of Hardware in the Industrial Metaverse
Table 71. XR Devices in the Industrial Metaverse
Table 72. Sensors and Actuators in the Industrial Metaverse
Table 73. Industrial PCs and Servers in the Industrial Metaverse
Table 74. Communication Infrastructure for the Industrial Metaverse
Table 75. AR/VR/MR Solutions in the Industrial Metaverse
Table 76. AR/VR/MR Solutions in the Industrial Metaverse
Table 77. Quality Control and Inspection in the Industrial Metaverse
Table 78. Commercial Examples of the Industrial Metaverse in Automotive
Table 79. Commercial Examples of the Industrial Metaverse in Aerospace
Table 80. Commercial Examples of the Industrial Metaverse in Chemicals and Materials Manufacturing
Table 81. Commercial Examples of the Industrial Metaverse in Energy
Table 82. Commercial Examples of the Industrial Metaverse in Healthcare and Life Sciences
Table 83. Commercial Examples of the Industrial Metaverse in Construction and Engineering
Table 84. Commercial Examples of the Industrial Metaverse in Supply Chain Management and Logistics.
Table 85. Commercial Examples of the Industrial Metaverse in Retail
Table 86. Data Privacy and Security Regulations Impacting the Industrial Metaverse
Table 87. Standards and Interoperability Initiatives for the Industrial Metaverse
Table 88. Environmental and Sustainability Regulations Impacting the Industrial Metaverse
Table 89. Technological Challenges in the Industrial Metaverse
Table 90. Implementation and Integration Issues in the Industrial Metaverse
Table 1. Comparison of the consumer and industrial metaverses.
Table 2. Metaverse Enabling Technologies.
Table 3. Comparison of Key Features: Major Industrial Metaverse Platforms.
Table 4. Augmented Reality in Manufacturing.
Table 5. Digital Twin Concepts in Industry 4.0.
Table 6. Differences between Industry 4.0 and the Industrial Metaverse.
Table 7. Unmet Business Needs Addressed by the Metaverse.
Table 8. Maturity/development of Industrial Metaverse technology building blocks
Table 9. Global Industrial Metaverse Market Size and Growth Rate, 2025-2035.
Table 10. Market Share by Component (Hardware, Software, Services), 2025-2035
Table 11. Market Share by Technology (AR/VR/MR, Digital Twins, AI, IoT), 2025-2035.
Table 12. Market Share by End-User Industry, 2025-2035.
Table 13. Energy Consumption Comparison: Traditional vs. Metaverse-Enabled Industrial Processes.
Table 14. Regional Market Size and Growth Rates, 2025-2035.
Table 15. Cost Comparison: Traditional Industrial Processes vs. Metaverse-Enabled Processes
Table 16. Investment in Industrial Metaverse by Type (VC, Corporate, Government), 2020-2025.
Table 17. Venture capital funding for industrial metaverse.
Table 18. Venture Capital Funding for Industrial Metaverse, 2021-2025.
Table 19. Corporate industrial metaverse investments, 2021-2025.
Table 20. Government and Public Funding Initiatives.
Table 21. Key Market Drivers for the Industrial Metaverse.
Table 22. Advancements in AI and Machine Learning.
Table 23. Smart Factory Implementations.
Table 24. Digital transformation strategies.
Table 25. Industrial IoT Adoption.
Table 26. Carbon footprint reduction.
Table 27. Resource optimization efforts.
Table 28. Circular economy initiatives.
Table 29. Market challenges and barriers in the Industrial Metaverse.
Table 30. Hardware Constraints (e.g., Battery Life, Comfort).
Table 31. Integration with Emerging Technologies.
Table 32. Novel Use Cases in Non-Traditional Industries.
Table 33. Companies in Extended Reality (XR): AR, VR, and MR.
Table 34. Deep Learning in Industrial Applications.
Table 35. Recurrent Neural Networks (RNNs).
Table 36. Natural Language Processing in Industrial Applications
Table 37. Computer Vision in Industrial Applications
Table 38. Companies in Artificial Intelligence and Machine Learning.
Table 39. Data Collection and Analysis.
Table 40. Edge Computing in IIoT.
Table 41. Companies in Internet of Things (IoT) and Industrial IoT (IIoT) technologies.
Table 42. Ultra-Low Latency Communication in 5G and Beyond (6G) Networks
Table 43. Massive Machine-Type Communications.
Table 44. Enhanced Mobile Broadband in 5G and Beyond (6G) Networks.
Table 45. Companies in 5G and Beyond (6G) Networks.
Table 46. Hybrid Cloud Solutions.
Table 47. Edge AI in Edge Computing and Cloud Infrastructure
Table 48. Companies in Edge Computing and Cloud Infrastructure.
Table 49. Smart Contracts in Blockchain and DLT.
Table 50. Supply Chain Traceability in Blockchain and DLT.
Table 51. Decentralized Finance in Industry.
Table 52. Companies in Blockchain and Distributed Ledger Technologies.
Table 53. Applications of 3D Scanning/Modeling in the Industrial Metaverse.
Table 54. Companies in 3D Scanning/Modeling for Industrial Metaverse Applications
Table 55. Quantum Computing in the Industrial Metaverse.
Table 56. Companies in Quantum Computing.
Table 57. Applications of Brain-Computer Interfaces in the Industrial Metaverse
Table 58. Non-Invasive BCI Technologies Comparison.
Table 59. Examples of Neural Control in Industrial Systems.
Table 60. Companies in Brain-Computer Interfaces.
Table 61. Smart Materials for Sensors.
Table 62. Nanotechnology Applications in Manufacturing.
Table 63. Self-Healing Materials in Industrial Applications
Table 64. Human-Machine Interface Technologies in the Industrial Metaverse
Table 65. Edge Computing Technologies in the Industrial Metaverse
Table 66. Companies in Autonomous Systems and Robotics for the Industrial Metaverse
Table 67. Technology Readiness Levels (TRL) for Industrial Metaverse Applications
Table 68. Adoption Rates of Industrial Metaverse Technologies by Industry, 2025-2035.
Table 69. Advanced materials used in industrial metaverse hardware.
Table 70. Types of Hardware in the Industrial Metaverse
Table 71. XR Devices in the Industrial Metaverse
Table 72. Sensors and Actuators in the Industrial Metaverse
Table 73. Industrial PCs and Servers in the Industrial Metaverse
Table 74. Communication Infrastructure for the Industrial Metaverse
Table 75. AR/VR/MR Solutions in the Industrial Metaverse
Table 76. AR/VR/MR Solutions in the Industrial Metaverse
Table 77. Quality Control and Inspection in the Industrial Metaverse
Table 78. Commercial Examples of the Industrial Metaverse in Automotive
Table 79. Commercial Examples of the Industrial Metaverse in Aerospace
Table 80. Commercial Examples of the Industrial Metaverse in Chemicals and Materials Manufacturing
Table 81. Commercial Examples of the Industrial Metaverse in Energy
Table 82. Commercial Examples of the Industrial Metaverse in Healthcare and Life Sciences
Table 83. Commercial Examples of the Industrial Metaverse in Construction and Engineering
Table 84. Commercial Examples of the Industrial Metaverse in Supply Chain Management and Logistics.
Table 85. Commercial Examples of the Industrial Metaverse in Retail
Table 86. Data Privacy and Security Regulations Impacting the Industrial Metaverse
Table 87. Standards and Interoperability Initiatives for the Industrial Metaverse
Table 88. Environmental and Sustainability Regulations Impacting the Industrial Metaverse
Table 89. Technological Challenges in the Industrial Metaverse
Table 90. Implementation and Integration Issues in the Industrial Metaverse
LIST OF FIGURES
Figure 1. Example industrial metaverse operations.
Figure 2. Components of the industrial metaverse.
Figure 3. Evolution of Industry 4.0 to the Industrial Metaverse.
Figure 4. Industrial metaverse ecosystem.
Figure 5. VR-based industrial training session.
Figure 6. Use of AR in manufacturing.
Figure 7. 3D Model: Digital twin of a manufacturing plant.
Figure 8. Infographic: IoT sensors in an industrial setting.
Figure 9. Global Industrial Metaverse Market Size and Growth Rate, 2025-2035.
Figure 10. Market Share by Technology (AR/VR/MR, Digital Twins, AI, IoT), 2025-2035.
Figure 11. Market Share by End-User Industry, 2025-2035.
Figure 12. Regional Market Size and Growth Rates, 2025-2035.
Figure 13. Investment in Industrial Metaverse by Type (VC, Corporate, Government), 2020-2025.
Figure 14. Edge computing in industrial applications.
Figure 15. Smart factory ecosystem.
Figure 16. Head-Mounted Display used in on-site operations.
Figure 17. Wearable textile device with haptic technology.
Figure 18. The Differences between IoT and IIoT.
Figure 19. Brain-computer interface for industrial control.
Figure 20. Examples of the commercial non-invasive EEG equipment based on BCI technology.
Figure 21. Swarm of industrial robots in a warehouse.
Figure 22. Adoption Curves of Different Industrial Metaverse Technologies.
Figure 23. BMW iFACTORY.
Figure 24. Enhatch AR headset.
Figure 25. Augmedics’ xvision Spine System®.
Figure 26. Apple Vision Pro.
Figure 27. The ThinkReality A3.
Figure 28. Microsoft HoloLens 2.
Figure 29. Siemens digital native factory.
Figure 30. Cerebas WSE-2.
Figure 31. DeepX NPU DX-GEN1.
Figure 32. InferX X1.
Figure 33. “Warboy”(AI Inference Chip).
Figure 34. Google TPU.
Figure 35. GrAI VIP.
Figure 36. Colossus™ MK2 GC200 IPU.
Figure 37. GreenWave’s GAP8 and GAP9 processors.
Figure 38. Journey 5.
Figure 39. IBM Telum processor.
Figure 40. 11th Gen Intel® Core™ S-Series.
Figure 41. Envise.
Figure 42. Pentonic 2000.
Figure 43. Meta Training and Inference Accelerator (MTIA).
Figure 44. Azure Maia 100 and Cobalt 100 chips.
Figure 45. Mythic MP10304 Quad-AMP PCIe Card.
Figure 46. Nvidia H200 AI chip.
Figure 47. Grace Hopper Superchip.
Figure 48. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below).
Figure 49. Cloud AI 100.
Figure 50. Peta Op chip.
Figure 51. Cardinal SN10 RDU.
Figure 52. MLSoC™.
Figure 53. Grayskull.
Figure 54. Tesla D1 chip.
Figure 1. Example industrial metaverse operations.
Figure 2. Components of the industrial metaverse.
Figure 3. Evolution of Industry 4.0 to the Industrial Metaverse.
Figure 4. Industrial metaverse ecosystem.
Figure 5. VR-based industrial training session.
Figure 6. Use of AR in manufacturing.
Figure 7. 3D Model: Digital twin of a manufacturing plant.
Figure 8. Infographic: IoT sensors in an industrial setting.
Figure 9. Global Industrial Metaverse Market Size and Growth Rate, 2025-2035.
Figure 10. Market Share by Technology (AR/VR/MR, Digital Twins, AI, IoT), 2025-2035.
Figure 11. Market Share by End-User Industry, 2025-2035.
Figure 12. Regional Market Size and Growth Rates, 2025-2035.
Figure 13. Investment in Industrial Metaverse by Type (VC, Corporate, Government), 2020-2025.
Figure 14. Edge computing in industrial applications.
Figure 15. Smart factory ecosystem.
Figure 16. Head-Mounted Display used in on-site operations.
Figure 17. Wearable textile device with haptic technology.
Figure 18. The Differences between IoT and IIoT.
Figure 19. Brain-computer interface for industrial control.
Figure 20. Examples of the commercial non-invasive EEG equipment based on BCI technology.
Figure 21. Swarm of industrial robots in a warehouse.
Figure 22. Adoption Curves of Different Industrial Metaverse Technologies.
Figure 23. BMW iFACTORY.
Figure 24. Enhatch AR headset.
Figure 25. Augmedics’ xvision Spine System®.
Figure 26. Apple Vision Pro.
Figure 27. The ThinkReality A3.
Figure 28. Microsoft HoloLens 2.
Figure 29. Siemens digital native factory.
Figure 30. Cerebas WSE-2.
Figure 31. DeepX NPU DX-GEN1.
Figure 32. InferX X1.
Figure 33. “Warboy”(AI Inference Chip).
Figure 34. Google TPU.
Figure 35. GrAI VIP.
Figure 36. Colossus™ MK2 GC200 IPU.
Figure 37. GreenWave’s GAP8 and GAP9 processors.
Figure 38. Journey 5.
Figure 39. IBM Telum processor.
Figure 40. 11th Gen Intel® Core™ S-Series.
Figure 41. Envise.
Figure 42. Pentonic 2000.
Figure 43. Meta Training and Inference Accelerator (MTIA).
Figure 44. Azure Maia 100 and Cobalt 100 chips.
Figure 45. Mythic MP10304 Quad-AMP PCIe Card.
Figure 46. Nvidia H200 AI chip.
Figure 47. Grace Hopper Superchip.
Figure 48. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below).
Figure 49. Cloud AI 100.
Figure 50. Peta Op chip.
Figure 51. Cardinal SN10 RDU.
Figure 52. MLSoC™.
Figure 53. Grayskull.
Figure 54. Tesla D1 chip.
