Why Physical AI Is the Next VC Gold Rush: Trends in Robotics & Generative AI Investment

The investment landscape for AI has shifted dramatically in 2025. Rather than incremental growth, we’re seeing a consolidation of capital toward both foundational model companies and, increasingly, firms developing “physical AI” systems that operate in the real world—robots, autonomous agents, and neurointerfaces. The stats from Mind the Bridge and Crunchbase show that in Silicon Valley, nearly all scaleup VC dollars are now going into AI, reflecting a tectonic shift in what “tech investment” means. Foundational model players like OpenAI and Anthropic absorbed the bulk of recent funding rounds, but that attention is widening toward physical systems.

Physical AI is still a nascent vertical but growing fast. In just nine months of 2025, scaleups in physical AI have raised over $16 billion. Major deals already include Figure AI’s $1 billion round for humanoid robotics and Neuralink’s $650 million for brain–computer interfaces. These are capital-intensive bets: motion, manipulation, sensing, and interaction with the physical world require heavy R&D, sophisticated hardware, and regulatory pathways—factors that favor established players or companies with deep pockets.

Funding concentration is another key theme. A small number of late-stage, high-valuation companies are capturing a disproportionate share of total AI funding. For example, in Q3 2025, a few foundation model companies accounted for huge fractions of global AI investment. Hardware and physical AI are starting to benefit, but still lag foundational model companies in total dollars absorbed and infrastructure built. Investors are increasingly risk-aware: while foundational models still dominate, physical AI is viewed as the next frontier where the risk is higher but the potential reward might be greater or more durable.

Strategic implications for investment bankers and corporate acquirers are significant. Physical AI is likely to see increasing strategic M&A from industrial incumbents (robotics, automotive, manufacturing, healthcare) looking to de-risk and accelerate adoption. Venture firms should prepare for longer time horizons, higher capital burns, and regulatory and physical supply-chain bottlenecks. Conversely, companies that now focus only on software or generative AI must consider hardware/systems integrations or capital partnerships to avoid being disrupted by vertically integrated physical AI firms.

Open questions include: how fast physical AI will scale manufacturability; supply chain, materials, and component constraints (e.g., sensors, actuators, power); regulatory barriers especially in human-interaction domains; and whether physical AI can escape early false starts (cost, reliability, usability) to reach mass-market scale. Also unresolved is whether funding will continue to flow as freely if valuation growth slows or macroeconomic headwinds rise.