Last Updated: 8 June 2026
PhysicsX, a UK-based company that uses AI to run engineering simulations far faster than traditional software, has closed a $300 million Series C funding round at a valuation of approximately $2.4 billion. The company was co-founded by Jacomo Corbo and Robin Tuluie, and its software is used across sectors that build complex physical products: aerospace and defense, semiconductors, automotive, energy, and materials manufacturing.
At its core, PhysicsX is trying to fix a problem every hardware engineer knows well. Running a simulation means asking a computer to model how a physical design will behave in the real world — how an engine part handles heat, how a wing holds up under pressure, how a chip moves electricity. Traditional tools do this accurately, but slowly. A single run can take hours or even days. Because each simulation is expensive in time and computing resources, engineering teams can only test a limited number of designs before committing to one. That constraint has real consequences for the quality of what gets built. The engineering simulation software market is valued at over $15 billion in 2026 and projected to double by 2031, driven by AI tools that reduce both cost and waiting time. PhysicsX is building directly into that shift.
The Series C was oversubscribed and led by Temasek, the Singapore investment firm that first backed PhysicsX in 2025. New investors M&G Investments and Intrepid Growth Partners joined the round, alongside returning backers Applied Materials, Atomico, General Catalyst, July Fund, NGP, NVIDIA, Radius, and Siemens.
Every physical product goes through simulation before it is built. Engineers test how materials will behave, how heat will spread, how fluids will flow. The challenge is that the underlying physics is genuinely complex. Traditional simulation tools calculate everything from scratch each time, following the equations of physics step by step. That approach is reliable, but it does not scale. Teams end up testing far fewer design options than they would like, simply because there are not enough hours in the day.
AI offers a different approach. Rather than recalculating physics from first principles on every run, an AI model can learn patterns from thousands of previous simulations and then predict results directly. The output arrives in seconds instead of hours, and the model can also incorporate real-world data to improve its accuracy over time. This means engineering teams can explore far more design options across every stage of a product's life, from early concept through manufacturing and operation.
US aerospace and defense spending on AI is forecast to reach $5.8 billion by 2029, roughly 3.5 times its 2025 level, according to IDC projections cited in Deloitte's 2026 Aerospace and Defense Industry Outlook. PhysicsX is already active in those sectors.
The funding is earmarked for three areas: expanding the company's global reach, developing new platform capabilities, and advancing research into what PhysicsX calls Large Physics Models. These are pre-trained AI models designed to understand physical behavior broadly, in the same way that large language models are trained to understand text. The goal is a physics AI system capable enough to apply across many industries and problem types, not just specific applications it was built for.
"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. For decades, that has been the binding constraint on hardware innovation. Physics AI removes it. 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. We are also enabling more reliable, more efficient, and altogether new ways of doing engineering, manufacturing, and production. This financing lets us put that capability in the hands of more engineers and push the frontier toward ever larger and more capable Large Physics Models."
Jacomo Corbo, Co-Founder & CEO of PhysicsX
The company also plans to open offices in the Bay Area and Singapore, extending its current footprint in London and New York. Growth has been fast. Over the past twelve months, PhysicsX doubled its recognized revenue, tripled its booked revenue, and more than doubled its customer count.
PhysicsX was co-founded by Jacomo Corbo, who serves as CEO, and Robin Tuluie, who serves as Co-Founder and Chairman. The team has grown to more than 300 people, twice the size it was a year ago, and includes physicists, AI researchers, domain engineers, and software specialists.
The platform takes AI models trained on simulation data and real-world measurements and uses them to predict physical behavior on demand. An aerospace engineer can evaluate thousands of wing designs in the time a conventional simulation would take to process one. A semiconductor manufacturer can model heat distribution across chip layouts almost instantly. The platform connects to the software engineering teams already use and works across the full product lifecycle: design, manufacturing, and ongoing operation. It replaces the simulation step in existing workflows rather than sitting alongside it.
"High-fidelity physics simulation has always been powerful, but it has also been slow, costly, and the preserve of a small group of specialists. Physics AI changes that in every dimension. It makes high-fidelity simulation dramatically more efficient, augments and improves on pure simulation results with ingestion of real-world data into our Large Physics Models, and opens it to applications that were never practical before. We believe in the democratization of this technology to broad technical profiles across an industrial organization — engineers, designers, and operators who previously couldn't run these analyses themselves. As that capability spreads, its utility compounds across the business. That's the change we're driving."
Robin Tuluie, Co-Founder and Chairman, PhysicsX
Temasek led the Series C and has been involved with PhysicsX since 2025, supporting the company's expansion into new international markets. M&G Investments and Intrepid Growth Partners came in as new backers, broadening the investor base beyond PhysicsX's existing group.
The returning investors cover the industries PhysicsX sells into. NVIDIA and Applied Materials are central to semiconductor and computing hardware. Siemens is one of the world's largest industrial automation companies and has an active collaboration with PhysicsX on data center power infrastructure. General Catalyst, Atomico, NGP, July Fund, and Radius complete the group, with backgrounds spanning technology investment and energy.
Facts Only
PhysicsX, a UK-based company, closed a $300 million Series C funding round at a $2.4 billion valuation on 8 June 2026.
The company was co-founded by Jacomo Corbo (CEO) and Robin Tuluie (Chairman).
PhysicsX uses AI to accelerate engineering simulations for industries like aerospace, semiconductors, automotive, energy, and materials manufacturing.
Traditional simulations can take hours or days; PhysicsX’s AI models reduce this to seconds.
The Series C was led by Temasek, with new investors M&G Investments and Intrepid Growth Partners joining.
Returning investors include Applied Materials, Atomico, General Catalyst, July Fund, NGP, NVIDIA, Radius, and Siemens.
PhysicsX plans to expand globally, develop Large Physics Models, and open offices in the Bay Area and Singapore.
The company doubled its revenue, tripled booked revenue, and more than doubled its customer count in the past year.
The team has grown to over 300 employees, twice its size from a year ago.
The engineering simulation software market is valued at over $15 billion in 2026 and projected to double by 2031.
US aerospace and defense AI spending is forecast to reach $5.8 billion by 2029.
PhysicsX’s platform integrates with existing engineering software and works across design, manufacturing, and operation phases.
Executive Summary
PhysicsX, a UK-based AI-driven engineering simulation company, has secured $300 million in Series C funding at a $2.4 billion valuation. The round was led by Temasek, with participation from new investors M&G Investments and Intrepid Growth Partners, alongside returning backers like NVIDIA, Siemens, and General Catalyst. PhysicsX’s technology accelerates engineering simulations by using AI to predict physical behaviors in seconds, replacing traditional methods that take hours or days. This enables engineers to test far more design iterations across industries like aerospace, semiconductors, and automotive. The company plans to expand globally, develop Large Physics Models (pre-trained AI systems for broad physical behavior), and open offices in the Bay Area and Singapore. Revenue and customer growth have surged, with the team doubling to over 300 employees in the past year. The broader engineering simulation market is projected to double by 2031, driven by AI adoption, with PhysicsX positioning itself as a leader in this shift.
The company’s approach leverages AI models trained on simulation data and real-world measurements to predict outcomes rapidly, integrating with existing engineering workflows. Co-founders Jacomo Corbo and Robin Tuluie emphasize the democratization of high-fidelity simulation, enabling non-specialists to run complex analyses. While the technology promises significant efficiency gains, its long-term impact depends on adoption across industries and the scalability of Large Physics Models. The funding reflects strong investor confidence in AI’s role in transforming physical product development, though challenges remain in ensuring accuracy and broad applicability.
Full Take
**Steelman:** PhysicsX’s narrative is compelling—it frames AI as a revolutionary tool for engineering, unlocking unprecedented efficiency and innovation. The company’s rapid growth, high-profile investors, and clear market demand (a $15B+ industry projected to double) lend credibility. The focus on "Large Physics Models" mirrors the success of large language models, suggesting a scalable, generalizable approach to physical simulation. The democratization angle—making high-fidelity simulation accessible to non-specialists—aligns with broader trends in AI-driven automation.
**Pattern Scan:** The article leans heavily on **ARC-0012 Authority by Association**, citing marquee investors (Temasek, NVIDIA, Siemens) to bolster credibility without deep technical scrutiny. There’s also a whiff of **ARC-0024 Ambiguity** in the claims about "Large Physics Models"—the term is evocative but lacks concrete benchmarks or peer-reviewed validation. The framing of AI as a universal solver for "almost every hard problem in the physical economy" risks **ARC-0030 Overpromising**, a common pattern in tech hype cycles.
**Root Cause:** The narrative assumes that faster simulations inherently lead to better products, but this ignores the human and systemic factors in engineering—creativity, risk assessment, and the limits of data quality. The unstated assumption is that AI can replace first-principles physics without introducing new biases or errors. Historically, similar claims about automation (e.g., CAD software, digital twins) have delivered incremental gains but rarely the promised revolutions.
**Implications:** If PhysicsX succeeds, it could lower barriers to innovation in capital-intensive industries, but the benefits may accrue unevenly. Large firms with existing simulation infrastructure (e.g., aerospace giants) will adopt faster than smaller players, potentially widening gaps. The reliance on AI also raises questions about accountability—who’s liable when an AI-optimized design fails in the real world?
**Bridge Questions:**
1. How does PhysicsX validate the accuracy of its AI predictions against real-world outcomes, especially in safety-critical applications like aerospace?
2. What are the energy and computational costs of training these Large Physics Models, and do they offset the efficiency gains?
3. If AI democratizes simulation, will it also commodify engineering expertise, devaluing human judgment in design processes?
**Counterstrike Scan:** A coordinated influence campaign would emphasize the inevitability of AI disruption, downplay risks, and use investor endorsements to preempt skepticism. This article aligns with that playbook but stops short of outright manipulation—it’s promotional but not deceptive. The lack of critical voices (e.g., engineers skeptical of AI’s role) is notable but not sinister; it’s a standard startup narrative. No red flags beyond typical tech optimism.
*Patterns detected: ARC-0012 Authority by Association, ARC-0024 Ambiguity, ARC-0030 Overpromising*
Sentinel — Human
The text exhibits high fluency and logical structure typical of advanced AI generation, effectively synthesizing complex financial and scientific data, though it is anchored by verifiable corporate details.