피지컬 AI 세계 시장(2027-2040년)

Physical AI - systems that sense, decide, and act in the physical world - is the defining technology market of the coming two decades, extending machine intelligence from screens into machines, vehicles, and environments. It spans nine primary vertical sectors - industrial automation, autonomous vehicles and mobility, humanoid and service robots, smart infrastructure, healthcare, agritech, defence, space, and consumer systems - plus a wearable-electronics interface layer and the semiconductor foundation underpinning all of them. In aggregate the market surpasses $430 billion by 2030, crosses $1 trillion around 2035–2040, and approaches $1.6 trillion by 2040, as compute shifts from data-centre training toward real-time edge inference and safety-critical embedded control.

2025 marked an inflection - the first year the full deployment stack matured simultaneously, spanning perception, foundation models, actuation, edge compute, and simulation. The frontier problem has shifted from single-unit capability to fleet-scale coordination: orchestrating heterogeneous, multi-vendor robot fleets toward shared objectives, where vendor-agnostic orchestration software is becoming as strategically valuable as any individual robot. Maturity varies sharply by application - warehouse robots, cobots, surgical systems, and wearables are commercially deployed and reliable, while general-purpose humanoids and domestic robots remain years from the reliability and cost viability that mass adoption requires. Safety certification is emerging as the binding deployment gate, with initiatives such as NVIDIA's Halos and BlackBerry QNX formalising certification to functional-safety standards for robots operating uncaged alongside humans - shifting the constraint on commercial deployment from "capable enough" to "safe enough and certifiable.

The market's defining feature is that no single geography has yet combined the four ingredients that decide leadership: frontier intelligence, low-cost manufacturing, certified trustworthiness, and deployment density. The United States leads on intelligence and orchestration software; China on manufacturing cost and volume; Japan on deployment density, now reinforced by a $65 billion sovereign Physical AI commitment targeting over 30% of the global robotics market by 2040; and Europe on an industrial-physics wedge exemplified by Mistral's acquisition of Emmi AI. Public markets have begun repricing the theme, with LG Electronics tripling on its robotics pivot and legacy operating-system and silicon vendors recast as physical-AI infrastructure. The result is an open race whose outcome remains genuinely undetermined - and in which value is rotating toward the deployment, safety, and hardware layers, where revenue is nearest-term and the structural advantages of incumbency and scale are only beginning to assert themselves.

The Global Physical AI Market 2027-2040 provides complete strategic intelligence on the global Physical AI market, with long-horizon forecasts across nine primary vertical sectors plus the wearable-electronics interface layer and the foundational semiconductor stack. It applies a Three-Wave Adoption Framework - Industrial Proving Ground, Cross-Sector Expansion, and Consumer and Sovereign Deployment - and includes TRL assessments by application, regional competitive analysis, regulatory and sovereignty mapping, and detailed profiles of 650+ companies across the value chain.

This 2027 edition captures the developments that reshaped the market over the past year:

• Record capital: Physical AI raised a record $75 billion plus in 2025 as the full deployment stack matured simultaneously.
• The coordination frontier: competition has shifted from single-unit capability to fleet-scale orchestration of heterogeneous, multi-vendor robots.
• The safety-certification gate: certified functional safety - through systems such as NVIDIA Halos and BlackBerry QNX - is now the binding constraint on uncaged, human-adjacent deployment.
• Consolidation: frontier labs are acquiring industrial-physics capability (Mistral/Emmi AI) and fleet operators are buying manipulation (Bear Robotics/Kinisi).
• Public-market repricing: LG Electronics tripled on its robotics pivot as markets reprice physical-AI infrastructure.

Report contents include:

• Executive Summary - The Physical AI moment; top-line findings; the Three-Wave adoption framework; the past year's decisive shifts; the investment surge; and the Open Race thesis
• Introduction - What Physical AI is; the Sense-Decide-Act triad; why deployment is viable now; and the strategic market arenas
• Core Technology Architecture - Sensing and perception; Vision-Language-Action and foundation models; actuation and dexterous manipulation; edge computing and fleet orchestration; simulation and digital twins; and energy
• The Embodiment Gap and Technology Maturity - Where Physical AI surpasses humans; where the gap remains; and TRL assessment by application
• Semiconductor and Hardware Ecosystem - AI chip market and architectures; the component value chain; and supply chain and geopolitical risk
• Global Physical AI Market: Aggregate Size and Growth 2027–2040 - Market scope; value pool distribution; and three-scenario forecasts
• Industrial Automation and Smart Manufacturing - Robotics, cobots, quality inspection, predictive maintenance, warehouse automation, and company profiles
• Autonomous Vehicles and Mobility Systems - Self-driving cars, freight, drones, delivery robots, maritime, eVTOL, and company profiles
• Humanoid and Service Robots - Pilots to production; competitive landscape; pricing; and company profiles
• Smart Infrastructure and the Built Environment - Building AI, energy management, security and patrol robots, and company profiles
• Healthcare and Medical Physical AI - Surgical robotics, exoskeletons, hospital robots, diagnostics, and company profiles
• AgriTech and Environmental Physical AI - Autonomous field equipment, precision agriculture, drones, and company profiles
• Defence, Security and Dual-Use Physical AI - UAVs, UGVs, maritime systems, counter-UAS, and company profiles
• Space Robotics and Extreme Environments - Planetary exploration, in-space servicing, extreme-environment robots, and company profiles
• Consumer Physical AI and Smart Home - Robot vacuums, smart home, outdoor and companion robots, and company profiles
• Wearable Electronics and Physical AI Integration - Market segmentation; XR, smartwatches, medical wearables, hearables, rings, e-textiles; and company profiles
• Regional Markets - North America, Europe, China, and Rest of World
• Competitive Landscape and Investment - Investment themes; competitive dynamics; and the M&A landscape
• Key Barriers - Technical, economic, and regulatory barriers
• Regulatory Frameworks - US, EU, and China; and the emerging safety-certification regime
• Physical AI Sovereignty and Geopolitics - US-China competition; Europe's dilemma; and the middle-power opportunity
• Emerging Physical AI Frontiers (2028–2040) - Brain-computer interfaces, quantum sensing, and climate Physical AI

Companies profiled include ABB Robotics, ABB Smart Buildings, Abbott, Abbott (FreeStyle Libre CGM), Accuray, Activ Surgical, Adidas, Advanced Farm Technologies, AeiRobot, Aeolus Robotics, AeroFarms, AeroVironment, Aethon (TUG), AGCO Corporation, AgEagle, Agersens, Agibot / Zhiyuan Robot, Agility Robotics, AgriTask, Agrobot, AGROINTELLI, Agtonomy, AI SpaceFactory, AiCure, Aidoc, AiQ Smart Clothing, Airbus (Zephyr HAPS), AirMap, AirSeed Technologies, AliveCor (KardiaMobile), ALS (Automated Laboratory Systems), Altitude Angel, Altius Space Machines, AltoVolo, Amazfit / Zepp Health, Amazon Prime Air, Amazon Robotics, Ambi Robotics, Analog Devices, Andromeda, Anduril Industries, ANRA Technologies, ANYbotics, Aohang Intelligent Technology, APOTECAchemo, Apple, Apple (Vision Pro), Applied Intuition, Apptronik, Aquabotix, Arable, Archer Aviation, ARX Robotics, ARxIUM, Ascendance Flight Technologies, Asensus Surgical (KARL STORZ), ASI Mining, Astribot, Astrobotic Technology, Astroscale, Atlas Elektronik, AUBO Robotics, Aurora Innovation, Auterion, AutoFlight, AutoGrid, Automated Packaging Systems, AutoStore, Ava Robotics, Avidbots, Axibo, Axis Communications, Axon Enterprise (TASER / AI Defence), Baidu Apollo Go, Balyo, Bang & Olufsen, Barnstorm AgTech, Bastian Solutions, BD (Becton Dickinson) / BD Rowa / BD Pyxis, Bear Robotics, Bedrock Robotics, BeeHero, Bell Flight (Bell Textron) - Nexus / APT, Berkshire Grey, BETA Technologies, Beyond Imagination, Biofourmis, BioIntellisense, Bionik Labs, Blue In, Blue Origin, Blue River Technology (John Deere), Blue White Robotics, Boardwalk Robotics, Boart Longyear, Boeing (space systems), Boeing / Wisk Aero, Bonsai Robotics, Booster Robotics, Borg Robotics, Bosch, Bosch Sensortec, Bose, Boston Dynamics, Boston Dynamics (Spot), Brain Corp, Brainbox AI, Brainlab, Breaker Industries, BridgeDP Robotics, Bright Machines, BrightFarms, BROKK, BuildingIQ, Built Robotics, Bureau Veritas, Burro, BXI Robotics, C3.ai, CACI International, Camus Energy, Carbon Robotics, CardieX, Carrier Global, Caterpillar, Cattle Eye, Circular, Clearpath Robotics, ClearSpace, Clone Robotics, CNH Industrial (Case IH / New Holland), Cobalt Robotics, Coco, Cognex, Comau, Connecterra, Contoro Robotics, CopperTree Analytics, Corindus (Siemens Healthineers), Covariant, Cowlar, CropX, Cubic Farm Systems, Current Health, Cyberdyne, Cyberdyne (HAL), CycloTech, D-Orbit, Daikin, DARPA (RSGS program), Dascena, Dataa Robotics, Dedrone (Axon), Deep Robotics, Deep Trekker, DEKA Research, DeLaval, Denso Robotics, Devanthro, Dexcom, Diehl Aviation, Digger DTR, Diligent Robotics, DistalMotion, DJI, Dobot Robotics, Doccla, Dogtooth Technologies, DOK-ING, Doosan Robotics, Doroni Aerospace, Dreame Technology, Dronamics, DroneDeploy, Dufour Aerospace, Dusty Robotics, Dwbrobot, Dyna Robotics, Dynium Robot, Durr, EarthSense, ECA Group, Ecobee, Ecorobotix, Ecovacs, Eden Green Technology, Ehang, Einride, Eka Robotics, Ekso Bionics, Elbit Systems, Electron Robots, Elephant Robotics, Elevate Farms, Elexon Mining, Elroy Air, Embodied Inc, Emerson Electric / AspenTech, Emesent, Emmi AI, Emotiv, Enchanted Tools, EngineAI, Engineered Arts, Enkel Energi, Enlighted (Siemens), Enline Energy, Epi-Watch, Epiroc, Epirus, Epoch Robotics / ROBOTERA, Epson (Moverio), Epson Robots, Equivital, ERC System, Eureka Robotics, Eve Air Mobility, Eviation, Exyn Technologies, FANUC, Farm-ng, FarmWise, FDROBOT, Ferrovial Vertiports, Fetch Robotics / Zebra Technologies, FFRobotics, Field AI, Figure AI, Fitbit (Google), FLIR Systems / Teledyne FLIR, FLSmidth, Flyability, Flytrex, Formic, ForwardX Robotics, Foundation Robotics, Four Growers, Fourier Intelligence, Foxglove, Freight Farms, Furhat Robotics, Galaxea AI, Galbot, Garmin, Gather AI, Gatik, Gauzy, GE Aerospace (eVTOL / AAM Division), GE HealthCare, GEA, Geek+, Generalist, Generation Robots, Generative Bionics, Genetec, Ghost Robotics, GITAI, Globus Medical, Gradient Comfort, Gravis Robotics, Greeneye Technology, GreyOrange, Hanson Robotics, Harvest Automation, Harvest CROO Robotics, Heart Aerospace, Hexagon Manufacturing Intelligence, HID Global, Hirebotics, Hitachi, Hocoma, Holiday Robotics, Honda, Honeywell Building Management, Honeywell Intelligrated, Horizon Aircraft, HTC (Vive XR), Huawei, Hullbot, Humanoid (SKL Robotics), Huntington Ingalls Industries (REMUS), Hylio, i.v.STATION, IBM, IBM Maximo, ICON (extraterrestrial construction), Inceptio, iniVation AG, InOrbit.AI, InstantEye Robotics, IntBot, Interactive Wear, InteraXon (Muse), InTouch Health (Teladoc), Intuitive Machines, Intuitive Surgical, Inuktun, InVia Robotics, iRhythm, Iron Ox, Isansys Lifecare, Ispace, Itron, Jabra (GN Audio), JAKA Robotics, Jaunt Air Mobility, JBT Corporation, Joby Aviation, John Deere, Johnson & Johnson MedTech (OTTAVA), Johnson Controls, JOZ-Tech, JuneBrain, K-Scale Labs, K.U.L.T, KAKTI, Kaman (KMAX), Kawada Robotics, Kawasaki Heavy Industries, Kawasaki Heavy Industries Robotics, Keenon Robotics, Kepler Exploration Robotics, Kernel, Keybotic, Kinestral Technologies, KION Group, Kiwibot, KNAPP, Knightscope, KODE Labs, Kodiak Robotics, Koidra, Komatsu, KONE, Kratos Defense, Kuafu, Kubota, KUKA, kyDrive, L3Harris Technologies, Landis+Gyr, Laronix, Leju Robotics, Lely, Leonardo DRS, Levi Strauss (Project Jacquard), Levita Magnetics, Liebherr, LifeSignals Group, Lilium, LimX Dynamics, Lockheed Martin, Locus Robotics, Loki Robotics, Lucid Audio, Lumos Robotics, Lunar Outpost, Macco Robotics, MacLean Engineering, Magic Leap, MagicLab, Malloy Aeronautics, Manna Drone Delivery, MANUS Technology Group, MARTAC, Masimo and more......

Table of Contents

1 EXECUTIVE SUMMARY

• 1.1 The Physical AI Moment
  • 1.1.1 The Macro Pull: Why Physical AI Is Structurally Inevitable
• 1.2 Top-Line Market Findings
  • 1.2.1 Semiconductor Foundation
• 1.3 Three-Wave Adoption Framework
  • 1.3.1 The Cost Compression Curve: From Enterprise to Consumer
• 1.4 The Investment Surge: Capital Follows Conviction
• 1.5 Strategic Imperatives: The Window Is Narrow
• 1.6 THE OPEN RACE: Who Leads the Physical AI Era?
  • 1.6.1 What Victory Looks Like: The Three Combinations That Win
• 1.7 The Past Twelve Months
  • 1.7.1 Funding crossed a record — and changed shape.
  • 1.7.2 The frontier problem became coordination, not capability
  • 1.7.3 Safety certification emerged as the deployment gate
  • 1.7.4 Sovereign capital entered, led by Japan
  • 1.7.5 The frontier labs moved into the physical economy
• 1.8 Public Markets Reprice Physical AI
• 1.9 Market revenues to
• 1.10 Technology Readiness Level (TRL)
• 1.11 What This Report Covers

2 INTRODUCTION: THE PHYSICAL AI REVOLUTION

• 2.1 What Is Physical AI?
• 2.2 The AI Technology Stack: Where Physical AI Sits
• 2.3 What Has Changed: Why Physical AI Is Deployable Now
• 2.4 The Defining Thesis: An Open Race
• 2.5 Market Architecture: The Seven Strategic Arenas

3 CORE TECHNOLOGY ARCHITECTURE

• 3.1 The Physical AI Triad: Sense, Decide, Act
• 3.2 Sensing and Perception Layer
  • 3.2.1 Multimodal Sensor Fusion
  • 3.2.2 Wearable and On-Body Sensor Integration
  • 3.2.3 Event-Based Cameras: The Next Perception Frontier
• 3.3 Decision-Making and Reasoning Layer
  • 3.3.1 Vision-Language-Action Models: The New Architectural Standard
  • 3.3.2 Physical Intelligence (π) and the Foundation Model Race
  • 3.3.3 World Models and Physical Simulation: The NVIDIA Cosmos Platform
  • 3.3.4 Reinforcement Learning and Imitation Learning
  • 3.3.5 Open-Source and Democratization of Robotics AI
• 3.4 Actuation and Control Layer
  • 3.4.1 Actuator Technologies
  • 3.4.2 The Dexterous Manipulation Problem: The 31% BOM Challenge
  • 3.4.3 Wearable Actuators: Haptics, Exoskeletal, and Therapeutic
• 3.5 Flexible, Stretchable, and Biointegrated Electronics: The New Materials Platform
  • 3.5.1 Advanced Materials: The Enabling Layer
    • 3.5.1.1 Graphene and 2D Materials
    • 3.5.1.2 Silver Nanowires and Conductive Inks
    • 3.5.1.3 Piezoelectric Polymers (PVDF)
    • 3.5.1.4 Electroactive Nanomaterials
    • 3.5.1.5 Cellulose and Sustainable Biopolymers
    • 3.5.1.6 Magnetically Responsive Materials
    • 3.5.1.7 Ionic Conductors and Hydrogel Electronics
    • 3.5.1.8 Phase Change Materials for Thermal Management
    • 3.5.1.9 Metamaterials and Architected Materials
  • 3.5.2 Flexible Hybrid Electronics (FHE) for Physical AI
  • 3.5.3 Stretchable Artificial Skin and Electronic Skin Systems
  • 3.5.4 E-Textiles and Smart Apparel
• 3.6 Onboard and Edge Computing for Physical AI
  • 3.6.1 The Real-Time Processing Imperative
  • 3.6.2 NVIDIA Jetson Thor: The Physical AI Compute Standard
    • 3.6.2.1 2026 developments
  • 3.6.3 Fleet Intelligence and Distributed Learning Networks
  • 3.6.4 Connectivity
• 3.7 Simulation and Digital Twin Infrastructure
  • 3.7.1 The Simulation Imperative
  • 3.7.2 The Sim-to-Real Gap: The Remaining Challenge
• 3.8 Energy Storage and Harvesting
  • 3.8.1 Battery Technology Roadmap
  • 3.8.2 Triboelectric Nanogenerators (TENGs): Body-Powered Physical AI
  • 3.8.3 Smart Building Energy Integration

4 THE EMBODIMENT GAP AND TECHNOLOGY MATURITY

• 4.1 Where Physical AI Surpasses Human Performance
• 4.2 Where the Embodiment Gap Remains Critical
• 4.3 Technology Readiness Level (TRL) Assessment by Application

5 SEMICONDUCTOR AND HARDWARE ECOSYSTEM

• 5.1 The Physical AI Chip Landscape
  • 5.1.1 AI Chip Market Size and Growth 2024–2034
  • 5.1.2 Architecture Breakdown: GPU, FPGA, ASIC
  • 5.1.3 Key Players by Category
• 5.2 Component Market Value Chain
  • 5.2.1 Actuators and Transmissions
  • 5.2.2 Sensors and Perception Hardware
  • 5.2.3 Computing and Control Systems
  • 5.2.4 Power Systems and Batteries
• 5.3 Supply Chain Risk and Geopolitical Exposure
  • 5.3.1 Government Funding — The Strategic Investment Race
• 5.4 Market Challenges to AI Chip Deployment

6 GLOBAL PHYSICAL AI MARKET: AGGREGATE SIZE AND GROWTH 2026–2040

• 6.1 Framing the Market
• 6.2 The Three-Wave Adoption Framework: Detailed Structure
  • 6.2.1 Wave 1: Industrial Proving Ground (2026–2030)
  • 6.2.2 Wave 2: Cross-Sector Expansion (2030–2040)
  • 6.2.3 Wave 3: Consumer and Sovereign Deployment (2035–2040)
• 6.3 Value Pool Distribution Across the Stack
• 6.4 Three-Scenario Revenue Forecasts 2026–2040
• 6.5 The Robotics Market Specifically: Sizing the Core

7 INDUSTRIAL AUTOMATION AND SMART MANUFACTURING

• 7.1 Market Overview and Strategic Context
• 7.2 Robotic Arms and Pick-and-Place Automation
  • 7.2.1 Industrial Robot Market Structure
  • 7.2.2 The AI Transition in Industrial Robotics
  • 7.2.3 Pick-and-Place: From Fixed Programs to Foundation Models
• 7.3 Computer Vision Quality Inspection
  • 7.3.1 Performance Capabilities
  • 7.3.2 Application Domains
  • 7.3.3 The AI Revolution in Quality: From Fixed Templates to Adaptive Learning
• 7.4 Collaborative Robots (Cobots) Working Alongside Humans
  • 7.4.1 The Cobot Market: Structure and Growth
  • 7.4.2 Safety Standards and the ISO/TS 15066 Framework
  • 7.4.3 Human-Robot Collaboration ROI: Case Studies
• 7.5 Predictive Maintenance on Physical Equipment
  • 7.5.1 Market Size and Value Proposition
  • 7.5.2 Sensor-Fusion Monitoring: Vibration, Thermal, Acoustic
  • 7.5.3 Industrial Wearables for Worker Safety and Ergonomics
• 7.6 AI-Driven Warehouse and Supply Chain Automation
  • 7.6.1 The Warehouse: Physical AI's Commercial Proving Ground
  • 7.6.2 Autonomous Mobile Robots (AMRs): The Foundation Layer
  • 7.6.3 Fleet Intelligence: The Data Flywheel in Action
• 7.7 Smart Building AI: Physical AI in the Built Environment
  • 7.7.1 What Makes a Building "Smart" from a Physical AI Perspective
  • 7.7.2 HVAC Optimization: The Largest Energy Reduction Opportunity
  • 7.7.3 Smart Glazing: AI-Integrated Electrochromic Windows
  • 7.7.4 Security Patrol Robots in Smart Buildings
• 7.8 Digital Twins and Smart Factory Orchestration
  • 7.8.1 The Digital Twin Revolution in Manufacturing
• 7.9 Market Drivers and Challenges
• 7.10 Company Profiles 107 (92 company profiles)

8 AUTONOMOUS VEHICLES AND MOBILITY SYSTEMS

• 8.1 Market Overview and Strategic Context
• 8.2 Self-Driving Cars: From Proof of Concept to Commercial Reality
  • 8.2.1 The SAE Autonomy Framework: Where We Stand
  • 8.2.2 Waymo: The Unambiguous Leader
  • 8.2.3 Tesla Full Self-Driving: The Vision-Only Alternative
  • 8.2.4 Chinese Autonomous Vehicle Leaders: Baidu Apollo and Pony.ai
• 8.3 Autonomous Freight: The Commercial Logic is Compelling
  • 8.3.1 The Economics of Driverless Trucking
  • 8.3.2 Aurora Innovation: First Commercial Driverless Freight Service
  • 8.3.3 The Autonomous Freight Market Opportunity
• 8.4 Autonomous Drones: Three Commercial Markets
  • 8.4.1 Delivery Drones: Last-Mile Economics and Regulatory Progress
  • 8.4.2 Surveying, Inspection, and Industrial Drones
  • 8.4.3 Military and Surveillance Drones
• 8.5 Last-Mile Delivery Robots: Sidewalk Autonomy
  • 8.5.1 The Sidewalk Robot Market
• 8.6 Autonomous Maritime Systems
  • 8.6.1 Commercial Shipping: The Port-to-Port Opportunity
  • 8.6.2 Autonomous Underwater Vehicles (AUVs) in Commercial Applications
• 8.7 Air Taxis and Urban Air Mobility: The eVTOL Market
  • 8.7.1 Market Context and the Shakeout to Date
  • 8.7.2 eVTOL Market Forecast 2026–2040
  • 8.7.3 The Certification Landscape: Who Is Winning the Race
  • 8.7.4 Joby Aviation: The Consensus Frontrunner
  • 8.7.5 Archer Aviation and the Stellantis Manufacturing Partnership
  • 8.7.6 Vertiport Infrastructure: The Missing Link
• 8.8 Regulatory Landscape: The Jurisdiction-by-Jurisdiction Challenge
  • 8.8.1 SAE Level 4/5 Regulation by Jurisdiction
• 8.9 Company Profiles
  • 8.9.1 SELF-DRIVING CARS AND ROBOTAXIS 214 (12 company profiles)
  • 8.9.2 AUTONOMOUS TRUCKING AND FREIGHT 227 (8 company profiles)
  • 8.9.3 LAST-MILE DELIVERY ROBOTS (SIDEWALK) 235 (5 company profiles)
  • 8.9.4 DELIVERY DRONES 241 (7 comapmy profiles)
  • 8.9.5 AUTONOMOUS MARITIME SYSTEMS 250 (3 company profiles)
  • 8.9.6 eVTOL AIRCRAFT MANUFACTURERS 253 (37 company profiles)
  • 8.9.7 VERTIPORT AND UAM INFRASTRUCTURE 291 (4 company profiles)
  • 8.9.8 AIRSPACE MANAGEMENT AND UTM PLATFORMS 295 (4 company profiles)

9 HUMANOID AND SERVICE ROBOTS

• 9.1 Market Overview: From Pilots to Production
• 9.2 The Three-Wave Adoption Framework
  • 9.2.1 Wave 1: Industrial Applications (2025–2030)
  • 9.2.2 Wave 2: Consumer/Developer (2027–2033)
  • 9.2.3 Wave 3: Medical/Elder Care (2030–2040+)
• 9.3 Competitive Landscape
• 9.4 Average Selling Price Trajectory
• 9.5 Regional Dynamics
• 9.6 Company Profiles 303 (110 company profiles)

10 SMART INFRASTRUCTURE AND THE BUILT ENVIRONMENT

• 10.1 Market Overview
• 10.2 AI-Driven HVAC and Energy Management
  • 10.2.1 The AI HVAC optimization stack
• 10.3 Smart Grid and Energy Infrastructure AI
• 10.4 Digital Twins for Infrastructure
• 10.5 Physical Security and Patrol Robots in Infrastructure
• 10.6 Smart Building AI: Physical AI Perspective
• 10.7 Company Profiles
  • 10.7.1 BUILDING AI PLATFORMS AND MANAGEMENT SYSTEMS 419 (22 company profiles)
  • 10.7.2 SECURITY, ACCESS CONTROL AND SURVEILLANCE AI 441 (5 company profiles)
  • 10.7.3 PATROL AND SECURITY ROBOTS 446 (2 company profiles)
  • 10.7.4 CLEANING AND DISINFECTION ROBOTS 448 (9 company profiles)
  • 10.7.5 SMART ELEVATORS, ESCALATORS AND VERTICAL TRANSPORT 457 (4 company profiles)
  • 10.7.6 SMART ENERGY AND GRID AI 461 (12 company profiles)
  • 10.7.7 AI-CONTROLLED SMART GLAZING 473 (4 company profiles)
  • 10.7.8 SMART HVAC AND CLIMATE AI 477 (6 company profiles)

11 HEALTHCARE AND MEDICAL PHYSICAL AI

• 11.1 Market Overview: The Healthcare Physical AI Opportunity
• 11.2 Robotics: The Fastest-Growing Medical Device Segment
• 11.3 Medical Exoskeletons
• 11.4 Hospital Logistics and Clinical Support Robots
  • 11.4.1 Deployed platforms
• 11.5 AI Diagnostic and Clinical Decision Support
• 11.6 Company Profiles 488 (63 company profiles)

12 AGRITECH AND ENVIRONMENTAL PHYSICAL AI

• 12.1 Market Overview: Agriculture's Physical AI Inflection Point
• 12.2 Autonomous Tractors and Field Equipment
  • 12.2.1 The Autonomous Tractor: From Autosteer to Full Autonomy
  • 12.2.2 Weeding Robots: The Killer App for Specialty Crops
  • 12.2.3 Robotic Harvesting
• 12.3 Precision Agriculture: Sensors, Analytics, and AI
  • 12.3.1 Crop Monitoring and Analytics
  • 12.3.2 Precision Livestock Farming
• 12.4 Agricultural Drones and Aerial Platforms
• 12.5 Farmgate to Fork: AgriTech in Supply Chain and Environment
  • 12.5.1 Smart Logistics and Cold Chain
  • 12.5.2 Environmental Monitoring Physical AI
• 12.6 Company Profiles 558 (73 company profiles)

13 DEFENSE, SECURITY AND DUAL-USE PHYSICAL AI

• 13.1 Market Overview: The Militarization of Physical AI
• 13.2 Unmanned Aerial Vehicles (UAVs): The Dominant Platform
• 13.3 Unmanned Ground Vehicles (UGVs)
• 13.4 Unmanned Maritime Systems
• 13.5 Counter-UAS: The Fastest-Growing Segment
• 13.6 Defense AI Software and Command Enablement
• 13.7 Ethical and Legal Framework for Lethal Autonomous Weapons
• 13.8 Company Profiles 636 (36 company profiles)

14 SPACE ROBOTICS AND EXTREME ENVIRONMENTS

• 14.1 Market Overview: Physical AI Beyond Earth
• 14.2 Planetary Exploration: Rovers and Landers
  • 14.2.1 Lunar Exploration: The Physical AI Proving Ground
  • 14.2.2 Mars: The Long-Duration Autonomy Frontier
  • 14.2.3 Asteroid Mining and Deep Space
• 14.3 In-Space Servicing, Assembly, and Manufacturing (ISAM)
• 14.4 Extreme Environment Robots Beyond Space
• 14.5 Company Profiles
  • 14.5.1 SPACE & ORBITAL 676 (23 company profiles)
  • 14.5.2 UNDERWATER & DEEP SEA 699 (9 company profiles)
  • 14.5.3 NUCLEAR & RADIATION HAZARD 708 (2 company profiles)
  • 14.5.4 MINING & SUBSURFACE 710 (27 company profiles)
  • 14.5.5 HAZARDOUS ENVIRONMENT INSPECTION (OIL & GAS, INDUSTRIAL, INFRASTRUCTURE) 737 (7 company profiles)

15 CONSUMER PHYSICAL AI AND SMART HOME

• 15.1 Market Overview
• 15.2 Robot Vacuums: Physical AI's Mass-Market Success Story
• 15.3 Smart Home Automation: The Physical AI Control Layer
• 15.4 Consumer Outdoor Robots
• 15.5 Personal and Companion Robots
• 15.6 Consumer AI in the Physical World: The Software Layer

16 WEARABLE ELECTRONICS AND PHYSICAL AI INTEGRATION

• 16.1 The Wearable Electronics Market: The Largest Near-Term Physical AI Segment
• 16.2 Market Segmentation: Products and Revenue
• 16.3 Consumer Wearables: Units by Category 2020–2040
• 16.4 Market Leaders: Global Wearable Electronics Shipments
• 16.5 Extended Reality (AR/VR/MR): The Dominant Growth Driver
  • 16.5.1 Current XR Landscape
  • 16.5.2 XR Technology Roadmap
• 16.6 Smartwatches and Fitness Trackers: The Proven Platform
• 16.7 Medical and Healthcare Wearables
• 16.8 Hearables: AI Audio at Scale
• 16.9 Smart Rings: Physical AI's Most Intimate Form Factor
• 16.10 Smart Clothing and E-Textiles
• 16.11 Key Wearable Technology Trends 2026–2040
• 16.12 Wearables as Physical AI Integration Layer
• 16.13 Company Profiles
  • 16.13.1 SMARTWATCH AND GENERAL WEARABLE AI PLATFORMS 754 (10 company profiles)
  • 16.13.2 SMART RINGS 764 (7 company profiles)
  • 16.13.3 AR / VR / XR AND SMART GLASSES 771 (16 company profiles)
  • 16.13.4 AI HEARABLES 787 (7 company profiles)
  • 16.13.5 MEDICAL AND CLINICAL AI WEARABLES 794 (20 company profiles)
  • 16.13.6 EXOSKELETONS AND PHYSICAL ASSISTANCE WEARABLES 814 (8 company profiles)
  • 16.13.7 NEURAL INTERFACES AND BCI WEARABLES 822 (8 company profiles)
  • 16.13.8 INDUSTRIAL AND WORKER SAFETY WEARABLES 830 (5 company profiles)
  • 16.13.9 SMART CLOTHING AND AI E-TEXTILES 835 (8 company profiles)
  • 16.13.10 WEARABLE AI CHIPS AND COMPUTE PLATFORMS 843 (5 company profiles)
  • 16.13.11 SPORTS AND PERFORMANCE AI WEARABLES 848 (3 company profiles)
  • 16.13.12 REMOTE PATIENT MONITORING PLATFORMS 851 (5 company profiles)

17 REGIONAL MARKETS

• 17.1 NORTH AMERICA
  • 17.1.1 Market Position
  • 17.1.2 Strategic Advantages
  • 17.1.3 Constraints
• 17.2 EUROPE
  • 17.2.1 Market Position
  • 17.2.2 Strategic Advantages
  • 17.2.3 Constraints
  • 17.2.4 Country-Level Dynamics
• 17.3 CHINA
  • 17.3.1 Market Position
  • 17.3.2 Structural Advantages
  • 17.3.3 Constraints
• 17.4 ASIA-PACIFIC (EX-CHINA)
  • 17.4.1 Japan
  • 17.4.2 South Korea
  • 17.4.3 India
  • 17.4.4 Singapore and Southeast Asia
• 17.5 REST OF WORLD
  • 17.5.1 Middle East
  • 17.5.2 Latin America
  • 17.5.3 Africa

18 COMPETITIVE LANDSCAPE AND INVESTMENT

• 18.1 The Investment Surge
• 18.2 Investment Themes
• 18.3 Competitive Dynamics by Layer
• 18.4 Key Strategic Battlegrounds
• 18.5 Leading Physical AI Investors
• 18.6 M&A Landscape
  • 18.6.1 Consolidation in
• 18.7 The AMI Labs Strategic Position

19 KEY BARRIERS TO PHYSICAL AI ADOPTION

• 19.1 Technical Barriers
• 19.2 Economic Barriers
• 19.3 Regulatory Barriers

20 REGULATORY FRAMEWORKS

• 20.1 United States
• 20.2 European Union
• 20.3 China
• 20.4 The Regulatory Divergence Risk
• 20.5 The Emerging Physical AI Safety-Certification Regime

21 PHYSICAL AI SOVEREIGNTY AND GEOPOLITICS

• 21.1 The New Geography of Technological Power
• 21.2 The US-China Physical AI Competition
• 21.3 Europe's Strategic Dilemma
• 21.4 The Middle Power Opportunity
• 21.5 Physical AI and the Future of Industrial Sovereignty
• 21.6 The Sovereign Investment Race Intensifies (Japan)

22 EMERGING PHYSICAL AI FRONTIERS (2028–2040)

• 22.1 The Convergence Horizon
• 22.2 Brain-Computer Interfaces and Physical AI
• 22.3 Quantum Sensing and Physical AI Perception
• 22.4 Biological-Physical AI Integration
• 22.5 Climate Physical AI

23 CONCLUSIONS AND OUTLOOK

• 23.1 The Decade Ahead
• 23.2 The Three Decisive Variables
• 23.3 The Fundamental Insight

24 APPENDIX

• 24.1 RESEARCH METHODOLOGY
  • 24.1.1 Report Scope and Definitions
  • 24.1.2 Data Sources
  • 24.1.3 Market Sizing Methodology
  • 24.1.4 Limitations and Key Assumptions
• 24.2 GLOSSARY OF PHYSICAL AI TERMS

25 REFERENCES