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

Physical AI Funding Booms in 2026

Funding to physical AI companies is booming in 2026, with venture investors increasingly seeing physical AI as the next leg of the broader AI boom. Global venture funding in the space totaled $47.4 billion across 521 deals in the first half of 2026, up dramatically from $12 billion across 470 deals in the second half of 2025.

This trend is not limited to a few large deals. Several multibillion-dollar megadeals drove the spike in H1 investment. One very large deal in particular accounted for nearly one-third of all venture dollars: Mountain View, California-based Waymo's $16 billion Series D raised in February. Alphabet, Dragoneer Investment Group, DST Global, and Sequoia Capital co-led the financing, which was raised at a staggering $126 billion valuation.

Other companies that have brought in large rounds this year include In May, defense tech startup Anduril Industries raised another $5 billion in funding at a $61 billion valuation — double the $30.5 billion valuation it received less than a year earlier. San Diego-based Shield AI in March landed a $2 billion Series G round co-led by Advent International and JP Morgan Chase. Its valuation jumped to $12.7 billion. In March, Austin-based Saronic, a defense tech startup focused on autonomous sea vessels, raised $1.75 billion in Series D funding, bringing its total funding to around $2.6 billion. Kleiner Perkins led the round, which set Saronic's valuation at $9.25 billion — more than double its Series C level in 2025.

Notable Exits

The physical AI space has also produced several notable exits so far in 2026, although activity has been more concentrated in aerospace, defense, and drones than in areas like robotics. SpaceX has been the clear outlier, raising $75 billion in its June IPO at a $1.77 trillion valuation. Other notable public debuts include Herndon, Virginia-based space intelligence company HawkEye 360, which raised $416 million, and Arlington, Virginia-based autonomous drone maker Aevex, which raised $320 million. On the M&A side, one of the most notable deals was Mobileye's roughly $900 million acquisition of Tel Aviv's humanoid robotics startup Mentee Robotics, a transaction the company explicitly tied to its push into physical AI.

Investor Perspective

Ryan Ziegler, general partner at Edison Partners, told Crunchbase News via email that while funding in physical AI has historically been concentrated in robotics and humanoids, defense, and foundational models, he sees the opportunity as much broader. Physical AI, in his view, represents the convergence of software, hardware, sensors, and IoT, and services across a wide variety of real-world applications. What is changing, according to Ziegler, is AI's ability to process data from those systems at such a scale and speed to generate useful operational insights, while the underlying hardware becomes cheaper and more accessible. "Even our mobile phones now have LIDAR scanners on them," he noted, "democratizing the ability to map objects and spaces." For Edison Partners, the appeal is particularly strong in high-value, traditionally analog industries where physical AI can become mission-critical infrastructure. Ziegler pointed to manufacturing, supply chain, utilities, agriculture, transportation, government, and physical and spatial intelligence as areas of interest. Many of these companies resemble vertical software businesses, he said, with "attractive unit economics, large deal values, and multi-year deployments," while their combination of software, sensors, and hardware can generate proprietary datasets that become increasingly valuable over time. Edison is especially interested in applications where the return on investment is measurable through predictive maintenance, risk management, asset integrity, security, and autonomous operations. The economics of building these companies have also improved considerably over the past two years. Ziegler compared the shift to what cloud infrastructure did for SaaS. "The costs to build these companies have come down, and AI infrastructure and multi-modal tech to do so is now available," he said. Meanwhile, compute and foundation-model capabilities have become more accessible, reusable models and physics-based simulation have improved, training data is more plentiful, and sensor and hardware costs have declined. At the same time, companies are increasingly bundling hardware into recurring or mixed-revenue models and moving toward outcome- or usage-based pricing. That combination, Ziegler said, makes the hardware itself a distribution mechanism for these companies.