VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape

Venture capitalists are pouring billions into physical AI companies in 2026, as they increasingly view this sector as the next phase of the AI boom. According to The Wall Street Journal, many firms with early software, internet and social media investments are now funding companies developing physical technologies and materials linked to AI advancements.

Crunchbase data reveals that global venture funding in physical AI reached $47.4 billion across 521 deals during the first half of 2026, a significant increase of nearly 4x from the second half of 2025 ($12 billion across 470 deals) and an 80% jump from the first half of 2025 ($26.4 billion across 436 deals).

The surge in funding is evident when comparing the past three years to the current year. From 2022 to 2024, venture investors allocated a combined $41.9 billion to physical AI companies, which is still lower than the $47.4 billion raised in the first half of 2026 alone. Notable deals driving the investment boom include Waymo's $16 billion Series D round in February, raising its valuation to $126 billion.

Other significant investments include Anduril Industries ($5 billion in May), Shield AI ($2 billion in March), and Saronic ($1.75 billion in March).

In addition to new investments, the physical AI sector has experienced several notable exits. SpaceX led the IPO market with a $75 billion offering in June, while HawkEye 360 and Aevex also went public in 2026, raising $416 million and $320 million respectively. Mobileye also made headlines with its $900 million acquisition of Tel Aviv's humanoid robotics startup Mentee Robotics, which is furthering the company's push into physical AI.

Investors such as Ryan Ziegler of Edison Partners see physical AI as a broader opportunity, encompassing software, hardware, sensors and IoT across various real-world applications. He highlights industries like manufacturing, supply chain, utilities, agriculture, transportation, government, and spatial intelligence as attractive areas for these companies, citing their favorable unit economics, large deal values and potential for multi-year deployments.

Ziegler emphasizes that AI's ability to process data from these systems at scale and speed is crucial for generating valuable operational insights, while the costs of underlying hardware continue to drop.

The economics of building these companies have also improved over the past two years, with decreased costs for building AI infrastructure and more accessible compute and foundation-model capabilities. Companies are increasingly bundling hardware into recurring or mixed-revenue models and adopting outcome- or usage-based pricing, turning hardware into a distribution mechanism for software and data.

Joe Fath of Eclipse Capital notes that while physical industries remain capital-intensive, AI advancements are enhancing efficiency in scaling these ventures.