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

Funding to physical AI companies is booming in 2026.

Venture investors appear to increasingly see physical AI as the next leg of the broader AI boom. Notably, according to a recent article in , many firms known for early bets on software, internet services and social media companies are writing more checks to companies building 鈥減hysical technologies and materials tied to the artificial-intelligence boom.鈥�

兔子先生传媒 data backs this up.

In the first half of 2026, global venture funding in the space totaled $47.4 billion across 521 deals, per our data. That鈥檚 up dramatically 鈥� almost 4x 鈥� compared to the second half of 2025 when physical AI startups raised $12 billion across 470 deals. It鈥檚 also up significantly 鈥� by nearly 80% 鈥� from the $26.4 billion raised across 436 deals in the first half of 2025.

To give you an idea of just how much more money is going into physical AI companies, here’s a comparison. In the three years spanning 2022 to 2024 combined, venture investors put a total of $41.9 billion into physical AI companies 鈥� still several billion less than we鈥檝e seen raised in just the first half of this year alone.

And before we go any further, I should clarify that by our criteria, physical AI includes industries such as robotics, autonomous vehicles, aerospace, drones, industrial automation and sensors.

Noteworthy 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 鈥檚 raised in February. , , and 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 raised another $5 billion in funding at a $61 billion valuation 鈥� double the $30.5 billion valuation it received less than a year earlier.
San Diego-based in March landed a $2 billion Series G round co-led by and . Its valuation jumped to $12.7 billion.
In March, Austin-based , 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. led the round, which set Saronic鈥檚 valuation at $9.25 billion 鈥� more than double its Series C level in 2025.

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.

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 , which raised $416 million, and Arlington, Virginia-based autonomous drone maker , which raised $320 million. On the M&A side, one of the most notable deals was roughly $900 million acquisition of Tel Aviv鈥檚 humanoid robotics startup , a transaction the company explicitly tied to its push into physical AI.

Investor POV

, general partner at , told 兔子先生传媒 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.

鈥淓ven our mobile phones now have LIDAR scanners on them,鈥� he noted, 鈥渄emocratizing the ability to map objects and spaces.鈥�

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 鈥渁ttractive unit economics, large deal values and multi-year deployments,鈥� 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.

鈥淭he costs to build these companies have come down, and AI infrastructure and multi-modal tech to do so is now available,鈥� 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 software and data, with 鈥渉ardware [as] the distribution model for creating a data intelligence flywheel.鈥�

, partner and head of growth at , told 兔子先生传媒 News via email that while physical industries remain capital intensive, AI and other enabling technologies are changing how efficiently companies can build and scale. Historically, the capital required to reach meaningful scale made investors wary, but he argues that 鈥渢ech barriers are plummeting, experienced talent is pouring in, and market demand is rising.鈥�

That convergence is driving more investment into areas including energy, robotics and autonomy, inference, chips and compute, and data center infrastructure. As a result, he said, funding is increasingly shifting away from experimentation and toward companies that can hit production milestones, land customers and scale efficiently.

For Eclipse, physical AI is not a new theme but a core investment thesis dating back to the firm鈥檚 founding in 2015. Fath said the opportunity has become more compelling because 鈥渢he technical and economic conditions are now catching up to that longstanding conviction,鈥� allowing companies to iterate, deploy products and reach customers faster.

Eclipse defines physical AI broadly as 鈥渋ntelligence embedded in systems that perceive, reason, and act in the real world,鈥� while generally avoiding investments in standalone large-language-model providers. Fath described the firm鈥檚 focus as investing on the 鈥渟houlders,鈥� rather than the 鈥渉ead.鈥� This means that Eclipse backs both the infrastructure that enables generative AI, such as chips, compute, energy and data centers, and the companies applying AI to build new businesses in the physical world.

He views the current landscape as the result of technology, talent, capital, demand and policy finally aligning. More powerful compute, foundation models, simulation and developer tools are allowing smaller teams to build faster with less capital and labor, Fath points out. Looking ahead, he expects value to accrue throughout the physical AI stack, but believes the strongest moats will belong to companies that vertically integrate and own multiple layers.

Ultimately, he said, 鈥渃ustomers value operational efficiency, reliability, and revenue, not technical sophistication alone.鈥� The companies that can turn technical capability into dependable systems at commercial scale 鈥� and then use their data and infrastructure to expand into additional products 鈥� are likely to capture the most value.

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