Physical AI and robotics-focused vertical AI funding

Physical & Embodied AI Verticals

2026: A Landmark Year for Trustworthy Physical AI and Sector-Focused Robotics Investment

The year 2026 has emerged as a watershed moment in the evolution of Physical AI and embodied robotics, driven by an unprecedented surge in investment, technological breakthroughs, and a collective industry emphasis on trustworthiness, safety, and sector-specific deployment. As autonomous systems increasingly become integral to societal infrastructure—spanning defense, manufacturing, healthcare, logistics, and utilities—the landscape is rapidly shifting toward a resilient, interpretable, and high-reliability ecosystem. This convergence of capital, innovation, and regulatory focus signals a new era of responsible automation that prioritizes human safety and societal trust.

Explosive Growth in Funding and Focus on Trustworthy Physical AI

Building upon previous momentum, 2026 has shattered funding records, emphasizing the industry’s commitment to developing trustworthy embodied AI solutions capable of operating safely and effectively in complex real-world environments. The infusion of capital into startups and established players underscores a paradigm shift: for autonomous systems to reach mass adoption, they must demonstrate robustness, interpretability, and regulatory compliance.

Notable Investment Highlights

Embodied AI and Perception:
- Algorized, specializing in wireless sensor infrastructure for human-aware perception, secured $13 million in Series A funding. Their focus on interpretable perception systems aims to improve safety and regulatory adherence in industrial settings.
- Qianjue Technology has expanded into full-sized robots, emphasizing embodied AI solutions that excel in complex human-robot interactions, particularly within logistics and service industries demanding adaptive, reliable autonomous agents.
- Humand, based in San Francisco, raised $66 million in Series A for its AI-powered operating system designed for frontline and deskless workforces. Their platform fosters human-robot collaboration and frontline decision-making, aligning with broader trends of integrating AI into human-centric workflows.
- FYLD continues scaling AI-powered frontline intelligence for infrastructure projects like construction and utilities, securing $41 million in Series B. Their solutions aim to enhance safety, efficiency, and regulatory compliance in high-stakes environments.
Sector-Specific Robotics and Manufacturing Modernization:
- Squint raised $40 million in Series B to develop AI-driven manufacturing solutions that significantly improve productivity, safety, and regulatory adherence, addressing ongoing global supply chain challenges.
- Freeform, with $67 million in Series B, advances laser-based AI manufacturing solutions, emphasizing precision, interpretability, and safety—especially vital in sectors like aerospace, defense, and industrial production where errors are costly.
- A €52 million initiative, involving Emerald Technology Ventures and Japanese partners, focuses on Physical AI platforms optimized for industrial automation, exemplifying a global effort to develop hardware-software integrated solutions that are reliable and scalable.

Hardware Ecosystem: The Bedrock of Trustworthy, Energy-Efficient Autonomous Systems

Hardware innovation remains critical, with recent breakthroughs emphasizing semiconductor advancements, edge computing, and energy efficiency—all essential for trustworthy, real-time autonomous operation.

MatX Inc., founded by former Google engineers, raised $500 million to accelerate large language model (LLM) chip development, highlighting the importance of specialized hardware for trustworthy, high-performance AI inference.
The AI chip startup ecosystem attracted an additional $1.1 billion in venture capital funding this year, signaling a renewed investor focus on scalable, energy-efficient hardware supporting on-device inference—crucial for privacy, safety, and low-latency responses.
Cerebras Systems secured $1.2 billion to develop next-generation AI chips, explicitly designed for trustworthy training and inference, vital in safety-critical autonomous applications where failures can be catastrophic.
Hardware firms like SambaNova and Positron are advancing scalable hardware solutions tailored for embodied AI robotics, supporting continuous, energy-efficient operation in industrial and defense environments.
Mirai and Algorized focus on on-device inference solutions for edge AI, enabling privacy-preserving, robust capabilities essential for healthcare, defense, and industrial automation.
The Toronto-based startup Taalas secured $169 million to develop advanced AI chips, underscoring semiconductor innovation as foundational for system reliability.
Nvidia has deepened collaborations with venture partners to transfer GPU and AI chip technologies into emerging Physical AI companies, bolstering safety, trustworthiness, and performance in autonomous systems.
Zurich’s Rapidata secured $8.5 million in seed funding, reflecting growing interest in robust AI infrastructure supporting large-scale physical AI deployments globally.

Defense, Security, and High-Reliability Autonomous Systems

Security and defense sectors continue to channel substantial investments into autonomous systems that meet stringent standards of safety, explainability, and resilience:

Overland AI raised $100 million to develop autonomous security and surveillance systems emphasizing reliable perception, explainability, and decision-making—key for safeguarding critical infrastructure against evolving threats.
A Ukrainian-Estonian startup secured over €7 million to develop trustworthy AI tools supporting military operations, specifically targeting resilience, explainability, and robustness in unpredictable environments.
Armadin Security attracted $24 million in seed funding amid over $400 million globally invested in AI-driven security solutions, emphasizing trustworthy AI principles like fault tolerance and explainability for autonomous threat detection.
Shield AI is reportedly in negotiations for up to $1 billion at a $12 billion valuation, underscoring investor confidence in reliable, interpretable autonomous defense systems capable of operating effectively in complex combat scenarios.

Expanding Ecosystem: AI Governance, Observability, and Privacy Tools

As autonomous systems embed deeper into societal infrastructure, AI safety, governance, observability, and privacy tools are gaining critical importance:

Braintrust secured $80 million to develop bias detection, safety, and robustness platforms, ensuring trustworthy deployment of Physical AI across sectors.
OPAQUE raised $24 million to pioneer confidential AI solutions, addressing privacy, security, and regulatory compliance, particularly vital in healthcare, defense, and critical infrastructure.
Platforms like SurrealDB and Goodfire provide interpretable, scalable databases and explainability solutions, fostering trust in autonomous decision-making and sensor data management.
Solid, based in Zurich, raised $20 million in seed funding to develop trustworthy AI infrastructure focused on bias mitigation, safety, and interpretability—directly supporting trustworthy Physical AI systems.
Potpie AI, located in San Francisco, secured $2.2 million pre-seed funding for its context layer service, enhancing autonomous agents’ reasoning and robustness, critical for safe, reliable autonomous operation.

The Rise of Peer-to-Peer Infrastructure for Autonomous AI Agents

A groundbreaking trend in 2026 is the emergence of distributed, peer-to-peer infrastructure enabling autonomous AI agents to operate collaboratively, resiliently, and securely across decentralized networks:

Unicity Labs recently secured $3 million in seed funding to develop peer-to-peer infrastructure platforms focused on resilient, scalable autonomous agents capable of distributed decision-making, fault tolerance, and self-organizing behaviors. Such systems are essential for large-scale infrastructure management, defense applications, and industrial automation.

This development complements ongoing trends of peer-to-peer communication, fault resilience, and distributed reasoning, paving the way for autonomous systems that can adapt, self-heal, and operate reliably in complex, decentralized environments.

New Frontiers: Spatial AI and Sector-Specific Logistics

Beyond core applications, spatial AI and sector-specific logistics solutions are gaining prominence, driven by the need for immersive environment understanding and optimized supply chains:

World Labs secured $1 billion to scale spatial AI models capable of generating immersive 3D environment understanding. These models empower autonomous embodied systems to reason within complex spatial environments, vital for robot navigation, AR/VR, and industrial automation.
Mojro, a SaaS platform specializing in AI-powered logistics, raised $3 million in seed funding. Their solutions aim to streamline enterprise supply chains, supporting autonomous, sector-specific logistics operations with intelligent planning and execution.

Latest Developments Reinforcing Sector-Specific Autonomy

Two recent major developments reinforce the trajectory toward trustworthy, sector-focused AI:

Wayve, the London-based autonomous driving startup, closed a $1.5 billion Series D funding round to scale its autonomous driving AI. This enormous raise emphasizes autonomous mobility as a primary use case, with a focus on safety, interpretability, and scalability in complex urban environments.
Union.ai raised $38.1 million in Series A to expand its open-source AI orchestration stack, enabling production-grade AI infrastructure for large-scale embodied systems. Their platform facilitates scalable, fault-tolerant deployment, critical for autonomous vehicle fleets and industrial automation.

Current Status and Future Outlook

The 2026 investment landscape vividly illustrates a paradigm shift: hardware-software convergence, trustworthy AI principles, and sector-specific deployment are converging to redefine Physical AI’s societal role. The influx of capital—from chip startups like Taalas and Cerebras to sector-specific robotics firms such as Wayve and Squint—is laying a robust foundation for scalable, safe, and reliable autonomous systems.

The focus on hardware innovation—evidenced by MatX’s $500 million raise and the $1.1 billion VC influx into AI chips—underscores that trustworthy, energy-efficient autonomous deployments fundamentally depend on cutting-edge semiconductor and edge hardware technologies.

The emergence of autonomous mobility giants like Wayve, with their expansive funding rounds, signals autonomous driving and robotaxi deployment as core use cases, emphasizing safety, interpretability, and scalability.

Meanwhile, production AI infrastructure platforms like Union.ai are scaling to meet the demands of large-scale embodied systems, ensuring reliability, reproducibility, and fault tolerance in high-stakes deployments.

In summary, 2026 exemplifies how massive capital inflows, technological breakthroughs, and sector-specific innovations are converging to create a trustworthy, resilient, and scalable Physical AI ecosystem. This foundation is poised to shape a future where autonomous systems are safer, more interpretable, and seamlessly integrated into societal infrastructure, driving responsible automation across every sector.

The future of Physical AI in 2026 is one of responsible growth—where technological innovation aligns with safety, trust, and societal benefit, laying the groundwork for widespread, dependable autonomous systems.

Sources (20)