What PhysicsX Is and Why Its New Funding Matters
PhysicsX is an AI engineering simulation company that uses physics-based AI design models to automate complex digital prototypes, compressing engineering workflows that once took months of detailed simulations into seconds of automated computation. The company has announced a $300m (approx. RM1,380m) Series C funding round that sets its valuation at about $2.4bn (approx. RM11,040m), more than doubling its previous near-$1bn level. Existing backer Temasek led the round, joined by investors including Applied Materials, Nvidia, Atomico, General Catalyst, Siemens, M&G Investments and Intrepid Growth Partners. PhysicsX says it is addressing resource and skills bottlenecks in advanced manufacturing by providing AI-native engineering software across aerospace, defence, automotive, semiconductors, materials, energy and renewables. The latest capital will support further development of its platform, larger pre-trained physics AI models and expansion, including a new office in Singapore and a larger presence in North America.
Compressing Months of Simulation Into Seconds
At the core of PhysicsX’s pitch is AI that can run engineering simulations at a speed and scale beyond traditional tools, turning AI engineering simulation into a practical daily utility. Its physics-based AI design models learn from high-fidelity simulations and sensor data, then predict how new designs will behave under real-world conditions. Engineers who once tested a handful of configurations over several weeks can now explore thousands of design options in seconds, according to the company’s co-founder and CEO Jacomo Corbo. This shift is not only about speed; it changes how teams plan projects, allocate expertise and manage risk. Automated engineering workflows allow simulations to run continuously in the background, feeding live feedback into design tools and production systems. That means fewer manual iterations, earlier detection of flaws and a shorter path from concept to validated prototype in industries where delays are expensive.
From General-Purpose AI to Vertical Physics AI
PhysicsX reflects a broader move away from generic AI models toward vertical-specific systems tuned for measurable productivity gains. Instead of focusing on language tasks, PhysicsX builds large physics models trained on engineering data, CAD geometries and simulation outputs. These models specialise in tasks such as fluid dynamics, structural analysis and thermal behaviour across sectors like aerospace, energy and semiconductors. The company aims to provide deep physics AI enablement across the entire engineering life cycle, from early concept design to manufacturing and production. This vertical approach aligns incentives: customers care less about AI novelty and more about reduced lead times, better performance and lower failure rates. PhysicsX’s growth — roughly doubling its team to more than 300 people over 12 months — signals investor and customer belief that tailored physics AI can deliver those outcomes in ways general-purpose AI systems cannot match inside engineering toolchains.
Redesigning Engineering Workflows and Skills
The most disruptive aspect of PhysicsX’s platform is how it reshapes everyday engineering work. Automated engineering workflows mean that tasks once assigned to small specialist teams, such as advanced simulation or optimisation, can be embedded into standard design processes. Engineers interact with AI models through familiar tools, receiving instant feedback on stress, aerodynamics or thermal performance while they modify geometry. That shift lets organisations reassign scarce experts from routine simulation runs to higher-value decisions and oversight. Corbo says Physics AI removes the long-standing constraint of how fast and how well teams can work through the underlying physics. In practice, this could enable more experimental designs, faster iterations and new ways of organising collaboration between design, analysis and manufacturing. As large physics models grow in scale and accuracy, they may also serve as common reference systems, standardising how complex physical behaviour is evaluated across global engineering programmes.
