What PhysicsX Does and Why Its Valuation Matters
PhysicsX is an AI engineering simulation company that applies physics-based AI design to compress complex product development workflows, turning months-long design and simulation cycles into seconds and enabling engineers to explore many more options in less time. The company has raised $300m (approx. RM1,380m) in an oversubscribed Series C round, valuing it at $2.4bn (approx. RM11,040m) and more than doubling its nearly $1bn (approx. RM4,600m) valuation from a Series B about a year ago. Founded by former Formula 1 engineers, PhysicsX builds AI-native tools for sectors such as aerospace, defence, automotive, semiconductors, materials, energy and renewables. The startup now has more than 300 employees and is expanding its presence across major engineering hubs. Its rising valuation signals that investors view automated engineering workflows and AI design acceleration as a new strategic layer on top of traditional CAD and simulation software.
Inside the $300M Round and the Investors Behind It
The $300m (approx. RM1,380m) Series C round was led by Temasek and was oversubscribed, a clear sign of strong appetite for physics-based AI design platforms. Existing backers Applied Materials, Nvidia, Atomico, General Catalyst and Siemens all joined again, while new investors such as M&G Investments and Intrepid Growth Partners came on board, alongside others including July Fund, NGP and Radius. According to PhysicsX, the company has now raised around $500m (approx. RM2,300m) in total funding. Capital from the latest round will support expansion of its AI engineering simulation platform, ongoing AI research and geographic growth, including deeper expansion in the US and a new office in Singapore. The breadth of strategic and financial investors suggests that both industrial suppliers and chipmakers see AI-accelerated engineering as central to their future product roadmaps.
From Traditional Simulation to AI-Native Engineering Workflows
PhysicsX is targeting a long-standing bottleneck: traditional CAD and physics simulation tools demand specialist skills and large compute resources, which limit how many design variants engineers can test. By training large physics models on rich simulation and manufacturing data, PhysicsX aims to deliver AI design acceleration where engineers can run thousands of virtual experiments in the time it once took to run a handful. As CEO Jacomo Corbo said, “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.” This shift towards AI-native, automated engineering workflows does not replace CAD and solvers outright, but wraps them with intelligent surrogate models that predict outcomes much faster, changing how teams approach early-stage design and optimization.
Large Physics Models and the Race to Compress Hardware R&D
PhysicsX is building what it calls larger, more powerful pre-trained physics AI models, or “large physics models,” designed to cover the entire engineering life cycle, from concept and design to manufacturing and production. The company says that “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.” AI engineering simulation that can predict fluid flows, heat transfer, structural behavior and process outcomes in seconds could compress hardware R&D cycles, reduce the number of physical prototypes and cut trial-and-error on the factory floor. If successful at scale, this approach may shift value in the simulation software market toward platforms that bundle domain-specific AI models with existing toolchains, rather than selling standalone solvers.
