From Software to Hardware: The Next Wave of AI | Atomico x PhysicsX | RAISE Summit 2026

Physics X (Series C) recently raised $300 million at a $2.4 billion valuation.

• Series C was led by Temasek.
• The company is currently expanding with a new office in Singapore.
• Laura Connell (Atomico) led the company's Series B the previous year.

Core Value Proposition

• Aims to enable hardware innovation at the speed of software using AI-native physics models.
• Claims simulation and design speeds of 100,000 to 1 million times faster than traditional methods.
  • Example: A mining simulation requiring 5,000 years on HPC was reduced to 5 seconds using a trained model.
• Founded by Robin (theoretical physics) and Giacomo (computer science/ML) based on their prior work in Formula 1.

Market and Industry Focus

• Targets large organizations with complex engineering, manufacturing, and operational challenges.
• Primary verticals include aerospace and defense, automotive, mining, and semiconductors.
• Selects markets based on sectors outpacing global GDP growth, specifically noting strong demand in North America and Asia.
• Prioritizes industries addressing critical global issues such as climate change, the energy transition, and strategic defense.

Go-to-Market and Trust Architecture

• Operates on a "contact sport" model involving forward-deployed engineers rather than a self-service SaaS approach.
• Builds trust through a three-part value proposition:
  • Frontier AI research (the "engine").
  • Deployable platform infrastructure (the "chassis").
  • Embedded technical teams (the "driver").
• Validates models via uncertainty quantification against experimental data and legacy numerical software.
• Maintains compliance with infrastructure standards including ISO 27001.

Organizational Growth and Culture

• Headcount expanded from ~100 at the start of last year to ~350 currently.
• Global footprint includes offices in the UK, US, and Singapore.
• Hiring strategy focuses on high talent density to ensure cohesion between diverse archetypes (e.g., data scientists, sales, and mechanical engineers).
• Corporate culture aims to balance high challenge with high support to avoid bureaucracy or chaos ("surfing the edge of chaos").

Product Evolution and Platform Democratization

• Recently transitioned from a command-line/Python-dependent interface to a graphical user interface with drag-and-drop pipelines.
• Introduced LLM front-ends for querying design constraints and agents for automating workflows.
• Enables non-technical engineers (design, process, operations) to self-serve on the platform.
• First self-service deployments include connecting the platform to live operating systems, such as aluminum smelters.

Competitive Landscape and Future Priorities

• The European industrial AI space has seen increased capital and new entrants, including Project Prometheus and Mistral.
• Strategic priorities for the coming year focus on maintaining technical and talent leadership against new competitors.
• Significant focus on scaling the customer base and engineering team in Asia.
• Co-founders and leadership emphasize the need for accelerated "building" in physical industries to solve complex problems within a decade rather than a century.