PhysicsX Raises $300 Million Series C at $2.4 Billion Valuation to Scale Physics AI for Industrial Engineering

PhysicsX, the physics AI company for industrials, has raised an oversubscribed $300 million Series C at a valuation of approximately $2.4 billion, led by Temasek with participation from new investors M&G Investments and Intrepid Growth Partners alongside existing investors including Applied Materials, Atomico, General Catalyst, NVIDIA and Siemens. The London-based company has doubled year-over-year recognised revenue, tripled booked revenue and more than doubled its customer count over the past year, with its team growing to more than 300 people. The capital will accelerate global growth, platform capability expansion and frontier research including the development of larger pre-trained physics AI models known as Large Physics Models, deployed across aerospace and defence, semiconductors, industrial machinery, automotive, energy and materials manufacturing.

 

The Physics AI Platform and Its Industrial Applications

 

PhysicsX's AI-native engineering platform addresses a fundamental bottleneck in hardware development: the simulation workflows that underpin industrial engineering are slow, expensive and difficult to scale, constraining the number of design variants engineers can evaluate and limiting how quickly organisations can bring better products to market. The company's AI models predict physical behaviour in seconds rather than hours or days, enabling engineering teams to evaluate orders of magnitude more design variants and carry physics insight across the full product lifecycle from early-stage design and manufacturing through to real-time digital twins in operation. This compression of simulation timelines from days to seconds fundamentally changes the economics of hardware iteration in sectors including aerospace, semiconductors and energy systems.

Jacomo Corbo, Co-Founder and Chief Executive Officer of PhysicsX, said almost every hard problem in the physical economy, from better aircraft and chips to better engines and energy systems, comes down to how fast and well engineers can work through the underlying physics. He said physics AI removes the binding constraint on hardware innovation, giving engineers the ability to explore thousands of designs in seconds where they once managed a handful over weeks. Robin Tuluie, Founder and Chairman, said physics AI makes high-fidelity simulation dramatically more efficient, augments simulation results with real-world data and opens it to applications that were never previously practical, democratising the technology across broader technical profiles within industrial organisations.

 

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Relevance to Energy Transition and Industrial Decarbonisation

 

The deployment of PhysicsX across energy sectors including power generation, propulsion systems and materials manufacturing creates direct relevance to industrial decarbonisation that extends beyond the company's core engineering productivity proposition. Faster, cheaper physics simulation accelerates the development of more efficient turbines, heat exchangers, fuel cells and battery systems by allowing engineers to explore a larger design space before committing to physical prototypes. In the semiconductor sector, where the energy efficiency of chips directly determines the power consumption of data centres running AI workloads, faster simulation of thermal and electromagnetic behaviour can contribute to more energy-efficient chip designs.

The involvement of NVIDIA as an existing investor and the reference to GPU economics becoming mature enough to support physics AI at production scale reflects the intersection of the accelerated computing infrastructure buildout with the demands of industrial simulation at scale. Large Physics Models trained on engineering data from across multiple industrial sectors represent a potential step change in the capability available to hardware development teams, analogous in concept to the impact of large language models on software development but applied to physical systems design.

 

Investor Rationale and Growth Metrics

 

Temasek's leadership of the Series C round, having first invested in PhysicsX in 2025, reflects the sovereign wealth fund's strategic interest in deep technology platforms with applications across critical industrial sectors. The combination of Temasek's international growth support and the participation of industrial technology investors including Applied Materials and Siemens alongside pure venture investors including Atomico and General Catalyst provides both capital and strategic ecosystem relationships relevant to PhysicsX's expansion into global industrial customer bases. M&G Investments and Intrepid Growth Partners add institutional and growth equity perspectives to the investor base.

The tripling of booked revenue alongside doubling of recognised revenue and customer count suggests a company at an inflection point where commercial momentum is accelerating significantly beyond the initial customer base that demonstrated early adoption. The doubling of the team to more than 300 people over the past year reflects the investment in engineering talent needed to expand the platform's capabilities and serve a growing global customer base across multiple industrial sectors simultaneously.

 

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Outlook for Physics AI in Industrial Engineering

 

The PhysicsX Series C reflects a broader recognition that AI applied to physical simulation represents one of the most commercially significant opportunities in industrial technology, addressing sectors including aerospace, energy, semiconductors and manufacturing that collectively account for a substantial share of global economic output and industrial emissions. Whether PhysicsX can sustain its growth trajectory while expanding the Large Physics Models programme and maintaining platform quality across an increasingly diverse set of industrial applications will determine whether it can establish a durable competitive position in this emerging category. Sustained execution would establish PhysicsX as the defining platform for physics AI in industrial engineering and demonstrate that AI-accelerated hardware development can deliver both commercial productivity gains and, through better energy systems and more efficient designs, meaningful contributions to industrial decarbonisation.

 

 

Source: PhysicsX