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Genesis AI

Category: Robotics / Embodied AI

Genesis AI is building a universal robotics foundation model (RFM) and horizontal platform for general-purpose physical AI, using a data-centric approach combining physics simulation, generative modeling, and real robot data. Genesis AI was founded in 2023. The company is led by Zhou Xian. Based in Paris, France and Palo Alto, CA, USA. Team size: 20. Total funding raised: $105M (Seed, July 2025). Latest round: Seed. Key investors include ["Eclipse Ventures", "Khosla Ventures", "Bpifrance", "HSG", "Eric Schmidt"].

Founded
2023
Headquarters
Paris, France and Palo Alto, CA, USA
Team size
20
Total funding
$105M (Seed, July 2025)

Value proposition

To unlock unlimited physical labor by providing a universal AI brain for robots, making it possible to deploy general-purpose robots that can perform essential physical work, thereby reducing the complexity, cost, and time of developing and deploying robotic solutions.

Products and solutions

A proprietary, high-speed physics and rendering simulation engine., A universal robotics foundation model (RFM) for general-purpose robots., A horizontal robotics platform for developing and deploying robotic skills.

Unique value

The core of its innovation lies in its proprietary data engine that generates high-fidelity, large-scale synthetic data. This allows Genesis AI to train and validate its universal robotics foundation model to control any robot for any task, fusing simulation with real-world interaction and internet-scale data.

Target customer

Robotics companies, research institutions, and enterprises seeking to deploy general-purpose robots for automating a wide range of physical tasks that require dexterity, mobility, and reasoning.

Industries served

Lab Automation, Logistics, Manufacturing, General Household and Service Automation, Any industry seeking to automate physical tasks

Technology advantage

A full-stack approach combining a proprietary, high-speed simulation engine with real-world data collection. This creates a closed-loop system that provides a unique data advantage for training robust, generalizable AI models for robotics, significantly reducing development time and cost compared to real-world-only training.

How they differentiate

Genesis AI differentiates itself by creating a universal foundational model applicable to various robots, unlike competitors focusing on specific hardware or tasks. Its key technological edge comes from a proprietary, high-fidelity physics and simulation engine that is reportedly much faster than standard tools like NVIDIA's Isaac Sim, enabling rapid training on synthetic data.

Main competitors

NVIDIA Isaac Sim Ecosystem, Figure AI, Tesla Bot, Sanctuary AI

Key partnerships

Originated from an open-source research collaboration involving over 20 research labs and 18 universities., Strategic backing and advisory from key figures in AI and academia.

Notable customers

N/A (Company is in early development and pre-launch)

Major milestones

Raised a $105 million seed round. Emerged from stealth in July 2024. Established dual headquarters in Paris and Palo Alto. Assembled a founding team from top AI and robotics research institutions.

Growth metrics

Recruited a team of over 20 leading researchers in six months and secured one of the largest seed rounds in the robotics sector.

Market positioning

Positioned as a core technology provider aiming to supply the foundational AI layer for a wide range of general-purpose robots across multiple industries, rather than being a niche, application-specific vendor.

Geographic focus

Global, with primary research and business operations in the United States and France.

Patents and IP

Information on specific patents is not publicly available. The company's primary intellectual property is its proprietary simulation engine and the foundational models it develops.

About Zhou Xian

Zhou Xian is the Founder and CEO of Genesis AI. He holds a PhD in Robotics from Carnegie Mellon University and a Bachelor's degree from Nanyang Technological University. His experience includes research internships at Meta AI and the MIT-IBM AI Lab, with a focus on robotics, computer vision, and world model learning.

Official website: