UK Startup Led by Former F1 Engineer Reinvents Manufacturing with A.I.
PhysicsX, a London-based startup applying cutting-edge A.I. to solve some of the toughest challenges in industrial engineering, has raised $135 million in a series B funding round. The company’s software uses large physics models (LPM) to simulate and optimize complex systems, promising faster innovation and performance gains in fields from aerospace to medical devices. The latest funding round was led by London VC firm Atomico, with additional backing from Siemens, Temasek (Singapore’s state-owned investment company), Applied Materials, and Berlin- and New York-based venture capital firm July Fund.
Founded in 2019 by Robin Tuluie and Jacomo Corbo, PhysicsX aims to tackle complex design and performance challenges in manufacturing. Tuluie previously led R&D for Formula One teams, including Renault (Alpine) and Mercedes. Corbo is a physicist who co-founded the A.I. firm QuantumBlack, which was acquired by McKinsey & Co. in 2015. Their experience in high-performance industries exposed a persistent challenge: as systems become more advanced, even experienced engineers struggle to navigate trade-offs and push performance forward using traditional tools and instincts.
“We’re setting out to change engineering, shifting physics simulation from physical testing to inference with A.I.,” Corbo told Observer. “The last few years have seen remarkable advances in A.I., allowing us to build models capable of predicting the complex physics and chemistry relevant to some of the most critical applications in advanced manufacturing industries.”
PhysicsX aims to solve industrial problems that conventional computer-aided design (CAD) and simulation tools cannot address. Its enterprise software platform combines A.I., deep learning, and domain expertise to enhance the entire engineering lifecycle—from early concept development to design optimization and deployment.
“Innovators often struggle with the choices, or hit a wall of performance that they cannot break through,” Tuluie told Observer. “Wouldn’t it be great to have a capability that could iterate incredibly fast, give answers to the optimum design and manufacturing implications, and last without breaking or wearing out? This superpower is exactly what we are seeking to provide to our users.”
PhysicsX uses large physics models (LPMs), or specialized algorithms built for physics-intensive research that draw on the power of large language models. These models can process any type of data and unify insights across the entire engineering lifecycle, significantly reducing the time spent on complex tasks that can otherwise take weeks. According to Tuluie and Corbo, the PhysicsX’s platform can unlock entirely new categories of hardware, from next-generation semiconductors to sustainable aircraft. While the company did not name specific clients, its A.I. platform is already being integrated into the workflows of leading manufacturing and engineering firms.
Tuluie said his company has already made significant breakthroughs, such as cutting the scrap rate of jet engine turbine blades by 70 percent for an aerospace client by integrating learned physics models into the quality assurance process. In another case, PhysicsX improved the energy efficiency and reduced blood damage of an artificial heart by 42 percent using the same approach, with generative A.I. used to design the pump’s blade shapes—achieving flawless performance in medical trials. He added that even more complex problems, once considered unsolvable, may soon be within reach.
“The manufacturing world knows it needs to reinvent itself,” Corbo said. “For decades, industries like aerospace and defense, automotive, semiconductors, materials and energy have been held back by the limitations of legacy tooling.”
He added that the most competitive hardware companies of the next decade will be those that operate more like software firms: agile, data-driven and continuously optimizing.
PhysicsX previously raised $32 million in a Series A round in November 2023. The company did not disclose its current valuation following the latest funding.
