Silicon Valley robotics funding surges as investors chase physical AI growth

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Silicon Valley robotics funding surges

Highlights

Robotics and physical AI companies raised $33.4 billion globally in H1 2024, exceeding 2023's $28.7 billion and far surpassing 2020 levels, per PitchBook.
Major funding rounds include Anduril's $5 billion in May, Saronic's $1.75 billion in March, Skild AI's $1.4 billion in January, and Physical Intelligence's $1 billion in May.
Despite capital inflows, industry veterans warn of overvaluation as startups like Figure AI achieve $39 billion valuations with limited revenue and primarily pilot deployments.

Funding boom accelerates across robotics

As reported by Business Insider, robotics and physical AI companies raised $33.4 billion globally in the first half of this year, according to PitchBook data, already exceeding the $28.7 billion raised in all of last year and far above the level recorded in 2020.

Large funding rounds are spanning both hardware makers and software developers. Defense company Anduril raised $5 billion in May, co-led by Thrive Capital and Andreessen Horowitz, while autonomous shipbuilder Saronic raised $1.75 billion in March in a round led by Kleiner Perkins. Skild AI raised $1.4 billion in January to build software that controls different types of robots, and Physical Intelligence raised about $1 billion in May for AI models designed to command robots.

Investors are betting that robotics can expand AI beyond screen-based applications into physical labor across factories, construction sites and households. Nvidia chief executive Jensen Huang has described robotics as a major opening for technology companies to address industries that have historically seen limited digital adoption, and several former Nvidia executives have launched their own robotics startups.

Industry participants say recent progress in machine learning is changing the economics of the sector. Better models are helping robots perceive surroundings, make decisions and adapt to new tasks, while lower computing and hardware costs are improving the case for broader deployment.

Valuation risks cloud long-term outlook

Even as capital pours in, some investors and robotics veterans argue that company valuations are running ahead of commercial reality. Startups that are still in research and development are securing multibillion-dollar valuations despite limited revenue and few real-world deployments, with Figure AI cited as one example after reaching a $39 billion valuation last year while remaining in small factory pilots.

Several investors warn that the sector could repeat some of the excesses seen in autonomous vehicles in the 2010s, when heavy venture backing met slower and more expensive commercialization than expected. Robotics companies still face major obstacles, including the lack of large-scale physical-world training data and the operational difficulty of manufacturing, maintaining and deploying complex hardware reliably.

Charly Mwangi of Eclipse says the next few years are likely to be difficult as companies move from laboratory development to live deployments. Sanjay Aggarwal of F-Prime also warns that companies cannot keep raising capital on demonstrations alone and says he would be surprised if all of today's highly funded startups still exist independently within two or three years.

Still, there are early signs of business traction. FieldAI says it has crossed $100 million in revenue and customer contracts from more than 30 customers, and Skild AI says it reached $100 million in annual recurring revenue from more than 60 customers, including projects tied to Nvidia chip assembly and kitchen automation pilots. Supporters of the sector argue that a breakthrough in dexterous manipulation, the ability for robots to handle objects with human-like ease, could become a pivotal moment for wider adoption if it arrives soon enough to justify the current wave of investment.

In our earlier coverage of the sell-off in AI-linked U.S. tech stocks, we noted that investors were reassessing whether lofty valuations and massive AI infrastructure spending can be justified by future revenues. The downturn hit chipmakers and AI computing providers especially hard, while higher oil prices and inflation worries added pressure by raising concerns about tighter financial conditions and higher funding costs.

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