물리적 AI 생태계 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the physical AI ecosystem market size is projected to expand from USD 22.58 billion in 2025 and USD 26.06 billion in 2026 to USD 51.47 billion by 2031, registering a CAGR of 14.58% between 2026 to 2031.

This report is Segmented by Component (Hardware, Software, and Services), Robot Type and Embodiment (Industrial Robots, Professional Service Robots, Personal and Household Service Robots, and More), Deployment (On-Device, Cloud-Based, and Hybrid), End-User Vertical (Logistics and Supply Chain, Manufacturing, Healthcare, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Physical AI Ecosystem Market Trends and Insights

Low-Latency Edge Inference for Real-Time Autonomy

The physical AI ecosystem market requires fast local decisions when robots operate near people, equipment, or moving materials. A remote cloud connection can introduce delays that are unsuitable for an automotive line, a hospital corridor, or a mobile machine in a busy warehouse. NVIDIA made its Blackwell-powered Jetson Thor generally available in August 2025, with up to 2,070 FP4 teraflops of AI compute in a 130-watt power envelope and a USD 3,499 developer kit price. The company reported 7.5 times higher AI compute and 3.5 times greater energy efficiency than its predecessor. These specifications make edge inference a core design choice for equipment that must respond within a tight operational window. Suppliers without robot-focused inference stacks may therefore face a narrower opportunity to remain relevant in physical AI deployments.

Labor Scarcity in Unstructured Physical Work

Labor constraints support demand in the physical AI ecosystem market because many difficult jobs take place in settings that conventional automation does not handle well. The remaining gaps include picking nonuniform parcels, managing variation across mixed-product lines, and working in wet, confined, or unpredictable spaces. These tasks combine commercial urgency with the type of operating data needed to improve physical AI models. The shortage is not limited to factory roles, as logistics, healthcare support, construction, and field operations also depend on workers to perform varied physical tasks. A system that can adjust to changing layouts or objects can be more useful than fixed automation in these settings. This alignment between unmet labor needs and technical progress increases the value of practical deployments that can be operated safely at the site level.

High Integration Cost and Long Commissioning Cycles

The physical AI ecosystem market faces a near-term constraint because deployment costs extend beyond the robot purchase. Integration services, software customization, safety checks, operator training, and workflow changes can add materially to the initial budget. Complex manufacturing projects can require 12 to 18 months beyond original timelines when systems must be tuned to operating conditions not reflected in simulation. Small and midsize enterprises are particularly exposed because they may lack the integration experience and financial capacity to absorb project overruns. Long commissioning cycles also delay the availability of the operating data that vendors need to refine robot behavior. Commercial models that simplify setup, improve service coverage, or reduce site-specific engineering can therefore have a meaningful advantage.

Other drivers and restraints analyzed in the detailed report include:

1. Flexible Automation in Logistics and Manufacturing
2. Sim-to-Real Digital Twin Pipelines
3. Certification, Liability, and Functional Safety Complexity

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Hardware accounted for 71.08% of the physical AI ecosystem market share in 2025 because every embodied system requires sensors, actuators, manipulators, processors, and power equipment. Industrial robots and mobile platforms remain capital-intensive, so the hardware bill of materials accounts for much of project spending. Reliability, payload, motion precision, operating duration, and environmental fit remain hardware-led considerations. These requirements make platform selection central to deployment decisions and sustain relationships between OEMs, integrators, and end users. The physical AI ecosystem market remains anchored in equipment that can be serviced, supported, and adapted over its operating life.

Software is projected to grow at a 16.32% CAGR through 2031, the highest rate among components. World foundation models, fleet orchestration tools, simulation environments, and digital twin platforms can be sold as separate revenue layers rather than embedded robot features. NVIDIA introduced GR00T N1.7 in early commercial access at GTC 2026, indicating a move toward model software as a distinct commercial layer. Services also remain important because multi-OEM fleets need commissioning, training, optimization, and continuing support. ISO/IEC TR 5469:2024 creates an additional need for AI functional safety evaluation and related specialist services.

Industrial robots accounted for 44.59% of the physical AI ecosystem market size in 2025. Their lead reflects proven use in automotive, electronics, and metals production, where reliability and repeatable motion have been demonstrated over time. The International Federation of Robotics recorded 542,000 global industrial robot installations in 2024, more than twice the level of 10 years earlier. Asia accounted for 74% of new installations, while China installed 295,000 units, and global operational stock reached 4,664,000 units. This installed base gives industrial platforms a practical foundation for adding AI capabilities.

Personal and household service robots are expected to grow at a 17.04% CAGR through 2031. Consumer settings create varied interactions with people, objects, layouts, and changing conditions that can provide broad data for model development. Professional service robots also support surgery, inspection, logistics, and field applications, where AI can extend task flexibility. 1X Technologies began production at its NEO Factory in Hayward, California, with an initial annual capacity of 10,000 units intended for home users from 2026. FANUC reported that it had shipped more than 1,000 robots for physical AI-related applications after its December 2025 product launch.

Complete Report Scope:

• By Component
  • Hardware
  • Software
  • Services
• By Robot Type and Embodiment
  • Industrial Robots
  • Professional Service Robots
  • Personal and Household Service Robots
  • Other Robot Type and Embodiments
• By Deployment
  • On-Device
  • Cloud-Based
  • Hybrid
• By End-User Vertical
  • Logistics and Supply Chain
  • Manufacturing
  • Healthcare
  • Automotive and Mobility
  • Defense and Security
  • Construction, Mining, and Energy
  • Other End-User Verticals
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Turkey
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • Egypt
    • Rest of Africa

Geography Analysis

North America accounted for 35.47% of the physical AI ecosystem market share in 2025. The region combines AI-native robotics firms, substantial corporate investment capacity, and reshoring activity that is increasing the need for flexible production equipment. U.S. robot installations rebounded in 2025 after 2 years of declines, with food production, warehousing, and logistics supporting the recovery. North America had 204 robots per 10,000 manufacturing employees in 2024. This was below Western Europe's 267 and South Korea's 1,220, leaving scope for higher automation density.

Asia-Pacific is projected to expand at an 18.76% CAGR through 2031, the fastest geographic rate in the physical AI ecosystem market. China is the region's central industrial robot base, with 295,000 installations in 2024 and domestic manufacturers holding 57% of its domestic robot market. Japan and South Korea are extending this strength through domestic physical AI programs, and South Korea designated physical AI as a key K-Moonshot mission in February 2026. South Korea deployed a domestic Physical AI Integrated Platform at KAIST for automobiles, precision manufacturing, and shipbuilding. India complements China in the regional industrial robot landscape, recording 9,100 installations in 2024 and drawing manufacturing automation investment linked to production incentive programs.

Europe held the second-largest regional position in 2025, supported by a deep installed base, established automation suppliers, and Western European robot density of 267 per 10,000 manufacturing employees. Germany accounted for 32% of Europe's annual robot installations in 2024, although regional installations declined by 8% that year amid weakening automotive conditions. The European Union's Machinery Regulation and Cyber Resilience Act are shaping procurement requirements for connected robots. The Middle East is advancing robotics in construction, energy, and logistics, while Africa and South America remain early-stage regions focused on mining and agriculture.

1. NVIDIA Corporation
2. ABB Ltd
3. KUKA AG
4. Boston Dynamics, Inc.
5. Tesla, Inc.
6. FANUC Corporation
7. YASKAWA Electric Corporation
8. Agility Robotics, Inc.
9. Figure AI, Inc.
10. NEURA Robotics GmbH
11. Universal Robots A/S
12. Teradyne, Inc.
13. OMRON Corporation
14. Siemens AG
15. Hyundai Motor Company
16. SoftBank Robotics Group Corp.
17. Physical Intelligence, Inc.
18. Covariant, Inc.
19. Dexterity, Inc.
20. Apptronik, Inc.
21. 1X Technologies AS
22. Sanctuary Cognitive Systems Corporation
23. Skild AI, Inc.
24. Google DeepMind

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Low-Latency Edge Inference for Real-Time Autonomy
  • 4.2.2 Labor Scarcity in Unstructured Physical Work
  • 4.2.3 Flexible Automation in Logistics and Manufacturing
  • 4.2.4 Sim-to-Real Digital Twin Pipelines
  • 4.2.5 Proprietary Real-World Action Data Compounding
  • 4.2.6 Sovereign and Safety-Critical Autonomy Programs
• 4.3 Market Restraints
  • 4.3.1 High Integration Cost and Long Commissioning Cycles
  • 4.3.2 Certification, Liability, and Functional Safety Complexity
  • 4.3.3 Scarcity of Long-Tail Physical Interaction Data
  • 4.3.4 Energy, Thermal, and Battery Constraints in Mobile Embodiments
• 4.4 Industry Value-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Rivalry Among Existing Competitors

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Robot Type and Embodiment
  • 5.2.1 Industrial Robots
  • 5.2.2 Professional Service Robots
  • 5.2.3 Personal and Household Service Robots
  • 5.2.4 Other Robot Type and Embodiments
• 5.3 By Deployment
  • 5.3.1 On-Device
  • 5.3.2 Cloud-Based
  • 5.3.3 Hybrid
• 5.4 By End-User Vertical
  • 5.4.1 Logistics and Supply Chain
  • 5.4.2 Manufacturing
  • 5.4.3 Healthcare
  • 5.4.4 Automotive and Mobility
  • 5.4.5 Defense and Security
  • 5.4.6 Construction, Mining, and Energy
  • 5.4.7 Other End-User Verticals
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 Germany
    • 5.5.3.2 United Kingdom
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Russia
    • 5.5.3.7 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 South Korea
    • 5.5.4.4 India
    • 5.5.4.5 Australia
    • 5.5.4.6 Rest of Asia-Pacific
  • 5.5.5 Middle East
    • 5.5.5.1 Saudi Arabia
    • 5.5.5.2 United Arab Emirates
    • 5.5.5.3 Turkey
    • 5.5.5.4 Israel
    • 5.5.5.5 Rest of Middle East
  • 5.5.6 Africa
    • 5.5.6.1 South Africa
    • 5.5.6.2 Egypt
    • 5.5.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 ABB Ltd
  • 6.4.3 KUKA AG
  • 6.4.4 Boston Dynamics, Inc.
  • 6.4.5 Tesla, Inc.
  • 6.4.6 FANUC Corporation
  • 6.4.7 YASKAWA Electric Corporation
  • 6.4.8 Agility Robotics, Inc.
  • 6.4.9 Figure AI, Inc.
  • 6.4.10 NEURA Robotics GmbH
  • 6.4.11 Universal Robots A/S
  • 6.4.12 Teradyne, Inc.
  • 6.4.13 OMRON Corporation
  • 6.4.14 Siemens AG
  • 6.4.15 Hyundai Motor Company
  • 6.4.16 SoftBank Robotics Group Corp.
  • 6.4.17 Physical Intelligence, Inc.
  • 6.4.18 Covariant, Inc.
  • 6.4.19 Dexterity, Inc.
  • 6.4.20 Apptronik, Inc.
  • 6.4.21 1X Technologies AS
  • 6.4.22 Sanctuary Cognitive Systems Corporation
  • 6.4.23 Skild AI, Inc.
  • 6.4.24 Google DeepMind

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment