AI company PhysicsX raises $300m in Series C funding round

PhysicsX has its headquarters in London, with an additional office in New York, and a presence in California’s Bay Area and Singapore.

PhysicsX, an AI company for industrials, has announced an oversubscribed $300m Series C financing round that brings the approximate value of the organisation to around $2.4bn. 

The round was led by Temasek, with participation from additional investors such as M&G Investments, Intrepid Growth Partners, Applied Materials, Atomico, General Catalyst, July Fund, NGP, Nvidia, Radius and Siemens.

PhysicsX has its headquarters in London, with an additional office in New York, and a presence in California’s Bay Area and Singapore. The company aims to deliver deep physics AI enablement across the engineering life cycle, working with organisations in the areas of aerospace, defence, automotives, semiconductors, materials, energy and renewables.

To meet growing demand, the company has doubled its team in the last 12 months to more than 300 people. There are plans, the company said, to further accelerate growth with the expansion of its platform capabilities and research, including the development of larger, more powerful pre-trained physics AI models, known as ‘large physics models’.

Commenting on the announcement, Jacomo Corbo, the co-founder and CEO of PhysicsX, said, “Almost every hard problem in the physical economy – better aircraft, better chips, better engines, better energy systems – comes down to how fast and how well engineers and machine operators can work through the underlying physics. 

“For decades, that has been the binding constraint on hardware innovation. Physics AI removes it. We are giving engineers the ability to explore thousands of designs where they once managed a handful, in seconds rather than weeks, across the most demanding industries in the world.” 

He added, “We are also enabling more reliable, more efficient and altogether new ways of doing engineering, manufacturing and production. This financing lets us put that capability in the hands of more engineers and push the frontier toward ever larger and more capable large physics models.”

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