PhysicsX secures $135m in Series B to scale AI-native engineering platform

PhysicsX, a London-based artificial intelligence developer focused on engineering and manufacturing, announced on June 23, 2025, that it has raised $135 million in Series B funding to drive global expansion and deepen product capabilities. The round was led by Atomico, with participation from major strategic and institutional investors including Temasek, Siemens, Applied Materials, July Fund, General Catalyst, NGP, Radius Capital, Standard Investments, and Allen & Co. This latest raise brings the total capital secured by PhysicsX to nearly $170 million.

The fresh capital will be used to accelerate the development of large-scale physics foundation models and scale deployment of the platform in critical industries such as aerospace and defense, semiconductors, energy, automotive, and advanced materials. With industrial AI entering a period of heightened adoption, institutional investors are betting that PhysicsX’s vertically integrated, AI-native platform can help solve long-standing engineering challenges across high-performance and mission-critical environments.

PhysicsX is co-founded by Jacomo Corbo, a former chief scientist at QuantumBlack and ex-chief race strategist at Renault (Alpine) F1, and Robin Tuluie, who previously served as head of R&D at Mercedes F1 and vehicle technology director at Bentley Motors. Their combined expertise positions PhysicsX as a deep-tech enterprise straddling machine learning, simulation, and physical engineering.

What market gaps is PhysicsX addressing in engineering and why is AI integration becoming essential in 2025?

The Series B raise comes at a time when traditional engineering disciplines are struggling to keep pace with increasing design complexity, tighter time-to-market cycles, and growing demands for sustainability. As aerospace, chip manufacturing, and materials science sectors become more computationally intensive, the need for platforms that can combine deep physics models with machine learning has grown urgent.

PhysicsX aims to bridge this gap by offering a software stack that delivers AI enablement across the entire engineering lifecycle—from initial design simulation to iterative product refinement and real-time system optimization. According to institutional investors tracking industrial AI adoption, PhysicsX’s core value lies in reducing design-to-deployment timelines, compressing iteration loops, and offering predictive accuracy beyond the reach of conventional simulation tools.

Analysts suggest that the platform’s ability to ingest massive datasets and learn from historical system behaviors gives it an advantage in fields where traditional finite element modeling has reached scalability limits. Engineers can use PhysicsX tools to simulate dynamic environments, run high-fidelity optimizations, and automate early-stage product configurations—all critical for sectors like defense propulsion systems, electric vehicle thermal management, and semiconductor yield optimization.

How are strategic investors like Siemens and Temasek aligning with PhysicsX’s industrial AI roadmap?

The investor syndicate supporting the Series B includes several entities with deep industrial or geopolitical ties. Siemens, a major European industrial technology firm, highlighted its ongoing collaboration with PhysicsX in the development of AI-based deep physics simulations. Peter Koerte, Chief Technology and Strategy Officer at Siemens, said the investment reflects confidence in PhysicsX’s ability to deliver “trustworthy, industrial-grade AI” in environments where reliability is paramount.

Institutional backers like Temasek and Applied Materials bring regional diversification and manufacturing ecosystem depth. Their participation signals that PhysicsX’s vision resonates not just with software VCs but also with stakeholders embedded in hardware, chips, and global production networks. Analysts suggest that such alignment could facilitate downstream customer acquisition, supply chain integration, and geopolitical access—especially in Southeast Asia and North America.

Atomico, the round’s lead investor, pointed to PhysicsX’s unique positioning at the intersection of AI research and engineering practicality. Partner Laura Connell emphasized that the platform enables engineers to tackle challenges that surpass human intuition, effectively introducing a “software-defined engineering” layer to industrial design.

How does PhysicsX’s platform work and why are foundation models important to its future strategy?

PhysicsX is building a new class of software stack that integrates large-scale machine learning models with physics simulation engines. This architectural choice enables the company to create physics foundation models—massive AI models trained on multidisciplinary datasets that can generalize across systems involving fluid dynamics, structural mechanics, thermal physics, and more.

Unlike narrow-purpose CAE (computer-aided engineering) tools or plug-in ML modules, PhysicsX’s approach involves embedding AI at the infrastructure level. This allows customers to optimize design under multiple constraints simultaneously, such as weight, aerodynamics, heat dissipation, and manufacturability—all while drastically reducing the number of manual trial iterations.

Since its Series A round in November 2023, PhysicsX has grown its team to more than 150 and quadrupled revenue across its enterprise portfolio. Institutional sentiment has turned increasingly bullish as enterprise clients, particularly in Formula 1, defense aviation, and semiconductor fabs, report measurable gains in speed, performance, and cost optimization from the AI-native workflows.

What kinds of use cases and geographies are being prioritized in the next phase of expansion?

PhysicsX intends to deploy the Series B proceeds toward global expansion, with a particular focus on scaling in North America, Europe, and selected Asian markets. Analysts note that U.S. defense contractors, European aerospace firms, and East Asian chip manufacturers are natural targets for AI-native engineering integration, given their intense performance requirements and sensitivity to supply chain fragility.

The platform is already being used in high-stakes environments. In aerospace, it supports simulation-informed optimization of propulsion and heat transfer. In semiconductors, it is applied to model nanoscale material behaviors and optimize manufacturing yield. In electric mobility, it assists with lightweighting strategies and battery system design.

PhysicsX is also expected to commercialize parts of its foundation model architecture into SaaS offerings, allowing enterprise customers to access powerful simulation and design capabilities without building in-house AI stacks. This productization shift could serve as a revenue multiplier while helping enterprises reduce dependency on legacy engineering software vendors.

How does institutional sentiment view PhysicsX compared to other industrial AI platforms?

Compared to generic AI infrastructure providers or horizontal ML-as-a-service vendors, PhysicsX offers a purpose-built platform tailored for the complexities of hardware innovation. Analysts believe that this differentiation is key in retaining pricing power and IP defensibility, especially in segments where regulatory standards and failure tolerance are high.

Investor sentiment reflects optimism around PhysicsX’s commercial traction, technical depth, and cross-sector applicability. General Catalyst, an early backer, noted the platform’s developer-first orientation and ability to scale team capabilities in sync with rising enterprise demand. Given its ability to operate across aerospace, chips, and mobility ecosystems, PhysicsX is increasingly viewed as a foundational player in the emerging industrial AI stack.

Future expansion may also include further integration with industrial metaverse platforms, real-time digital twins, and edge-based control systems—areas that benefit from the convergence of AI models and physical system observability.