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Genesis AI Emerges From Stealth with $105M to Build Universal Robotics Foundation Model and Horizontal Platform for General-Purpose Physical AI

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With funding co-led by Eclipse and Khosla Ventures, Genesis will develop breakthrough physical AI infrastructure to power the era of generalist robotics and automate all physical labor

PARIS and PALO ALTO, Calif., July 1, 2025 /PRNewswire/ — Genesis AI, a global physical AI research lab and full-stack robotics company, emerged from stealth today with a mission to unlock unlimited physical labor. The company is building a universal robotics foundation model (RFM) and a horizontal robotics platform, raising $105 million co-led by Eclipse and Khosla Ventures, with participation from Bpifrance, HSG, and visionary leaders Eric Schmidt and Xavier Niel.

Physical labor contributes an estimated $30-40 trillion to the Global GDP, yet over 95% of it remains unautomated due to the limitations of current automation solutions. Today’s robotic systems, such as industrial arms, rely on brittle, rigid, and overfitted software stacks. These systems are narrow in scope, costly to deploy, and challenging to scale. Genesis aims to revolutionize the next generation of general-purpose robots by unlocking unprecedented robustness, flexibility, and cost efficiency – ultimately automating all physical labor.

Genesis brings a data-centric, full-stack approach to physical AI – building a scalable and universal data engine that unifies high-fidelity physics simulation, multimodal generative modeling, and large-scale real robot data collection. Its simulation stack, developed entirely in-house, will generate rich synthetic data at scale, together with a more efficient and scalable real-world data collection system. This dual engine of synthetic and real data bridges historically siloed domains to collect the largest-scale, most diverse, and highest quality data to train RFMs.

“General-purpose robots powered by physical AI will define the next major chapter of human history. While digital AI has made extraordinary progress, physical AI – the intelligence that allows machines to perceive, understand, and interact with the real world – has lagged behind,” said Zhou Xian, CEO of Genesis. “We’re here to change that. By building on the foundations laid by existing digital AI models, we’re bringing human-level intelligence into the physical world. Genesis’s unique approach by fueling digital AI knowledge to drive the emergence of physical AI will deliver unmatched capability, scalability, and cost-efficiency to unlock unlimited physical labor. With 75% of global companies struggling to fill jobs, physical AI is more essential than ever.”

Founded by top academic and industry technical talents from Mistral AI, Nvidia, Google, CMU, MIT, Stanford, Columbia and UMD, with deep expertise across the full stack of physics simulation, graphics, robotics, and large-scale AI model training and deployment, Genesis is well-positioned to rapidly execute its vision through a differentiated approach to physical AI. The company also plans to open-source components of its data engine and foundation model to empower developers, researchers, and partners to build on its breakthroughs and accelerate progress across the broader field of physical AI.

“Even in the most ‘automated’ industries today, the robot-to-human ratio rarely exceeds 1:30, due to the long tail of tasks requiring dexterity, cognition, mobility, and real-world reasoning that current robots simply can’t handle,” said Eclipse Partner, Charly Mwangi. “General-purpose robotics is the breakthrough we’ve been waiting for and stands to impact trillions in labor value across sectors. Genesis has the vision, strategy, and world-class team to define the era of physical AI in order to unlock unlimited physical labor through general-purpose robotics.”

“Physical AI has yet to scale like LLMs because collecting and aligning real-world data can be operationally complex,” said Kanu Gulati of Khosla Ventures. “Genesis is taking a full-stack approach by integrating best-in-class simulation data with real-world robotics data in a continuous, closed-loop system. Owning the entire data pipeline in-house gives them a unique data advantage. We’re excited to back Genesis early as they work to build a universal foundation model for robotics.”

To learn more about Genesis or explore career opportunities to help define the future of robotics, please visit genesis-ai.company.

About Genesis
Genesis is a global physical AI research lab and full-stack robotics company pioneering the world’s first universal robotics foundation model and horizontal robotics platform. With a mission to unlock unlimited physical labor, Genesis empowers the deployment of general-purpose robots to perform the essential physical work that underpins the global economy.

About Eclipse
With ~$5 billion in assets under management, Eclipse is a team of operators and investors partnering with exceptional companies from ideation to all stages of growth to unlock solutions to age-old physical industry problems through the intersection of bits and atoms and the rise of physical AI. For more information, visit www.eclipse.capital.

About Khosla
Khosla Ventures is a venture capital firm focused on investments in artificial intelligence, financial services, healthcare, consumer, enterprise, and sustainability sectors. It is known for making early capital investments in startups such as OpenAI, Instacart, Affirm, DoorDash, and Block.

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I asked ChatGPT to help me pack for my vacation – try this awesome AI prompt that makes planning your travel checklist stress-free

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July 7, 2025

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It’s that time of year again, when those of us in the northern hemisphere pack our sunscreen and get ready to venture to hotter climates in search of some much-needed Vitamin D.

Every year, I book a vacation, and every year I get stressed as the big day gets closer, usually forgetting to pack something essential, like a charger for my Nintendo Switch 2, or dare I say it, my passport.



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Sakana AI: Think LLM dream teams, not single models

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2 hours ago

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July 7, 2025

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Enterprises may want to start thinking of large language models (LLMs) as ensemble casts that can combine knowledge and reasoning to complete tasks, according to Japanese AI lab Sakana AI.

Sakana AI in a research paper outlined a method called Multi-LLM AB-MCTS (Adaptive Branching Monte Carlo Tree Search) that uses a collection of LLMs to cooperate, perform trial-and-error and leverage strengths to solve complex problems.

In a post, Sakana AI said:

“Frontier AI models like ChatGPT, Gemini, Grok, and DeepSeek are evolving at a breathtaking pace amidst fierce competition. However, no matter how advanced they become, each model retains its own individuality stemming from its unique training data and methods. We see these biases and varied aptitudes not as limitations, but as precious resources for creating collective intelligence. Just as a dream team of diverse human experts tackles complex problems, AIs should also collaborate by bringing their unique strengths to the table.”

Sakana AI said AB-MCTS is a method for inference-time scaling to enable frontier AIs to cooperate and revisit problems and solutions. Sakana AI released the algorithm as an open source framework called TreeQuest, which has a flexible API that allows users to use AB-MCTS for tasks with multiple LLMs and custom scoring.

What’s interesting is that Sakana AI gets out of that zero-sum LLM argument. The companies behind LLM training would like you to think there’s one model to rule them all. And you’d do the same if you were spending so much on training models and wanted to lock in customers for scale and returns.

Sakana AI’s deceptively simple solution can only come from a company that’s not trying to play LLM leapfrog every few minutes. The power of AI is in the ability to maximize the potential of each LLM. Sakana AI said:

“We saw examples where problems that were unsolvable by any single LLM were solved by combining multiple LLMs. This went beyond simply assigning the best LLM to each problem. In (an) example, even though the solution initially generated by o4-mini was incorrect, DeepSeek-R1-0528 and Gemini-2.5-Pro were able to use it as a hint to arrive at the correct solution in the next step. This demonstrates that Multi-LLM AB-MCTS can flexibly combine frontier models to solve previously unsolvable problems, pushing the limits of what is achievable by using LLMs as a collective intelligence.”

A few thoughts:

  • Sakana AI’s research and move to emphasize collective intelligence over on LLM and stack is critical to enterprises that need to create architectures that don’t lock them into one provider.
  • AB-MCTS could play into what agentic AI needs to become to be effective and complement emerging standards such as Model Context Protocol (MCP) and Agent2Agent.
  • If combining multiple models to solve problems becomes frictionless, the costs will plunge. Will you need to pay up for OpenAI when you can leverage LLMs like DeepSeek combined with Gemini and a few others? 
  • Enterprises may want to start thinking about how to build decision engines instead of an overall AI stack. 
  • We could see a scenario where a collective of LLMs achieves superintelligence before any one model or provider. If that scenario plays out, can LLM giants maintain valuations?
  • The value in AI may not be in the infrastructure or foundational models in the long run, but the architecture and approaches.

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‘Superintelligence’ Takes Meta Platforms to Record Highs. Should You Buy META Stock Here?

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3 hours ago

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July 7, 2025

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Image of Mark Zuckerberg by Rokas Tenys via Shutterstock

Mark Zuckerberg-led Meta Platforms (META) has proved its critics wrong as its shares have recently climbed to new heights, largely thanks to its artificial intelligence-driven strategy. Central to this AI strategy is “Superintelligence,” a long-term vision Zuckerberg has for creating AI systems that exceed human-level intelligence across many domains.

And although Zuckerberg burned shareholders before with the metaverse, his last passion project, Superintelligence feels different. Unlike the metaverse, AI is a megatrend that is already revolutionizing daily life. And Meta, with its arsenal of popular social media platforms like Instagram, WhatsApp, and Facebook, is betting big on AI to drive growth in the coming years. Meta is hiring big to staff this revolution, with Scale AI founder Alexandr Wang tasked with heading the new Superintelligence unit at Meta.

The market seems to be convinced this time, with Meta stock already up about 23% on a YTD basis.

Can Meta sustain this rally? I believe so, and here is why.

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Meta has been doubling down on its AI ambitions, both by making significant financial commitments and by attracting top talent from rival firms. To that end, the company has reportedly extended compensation offers ranging from $50 million to $100 million to lure engineers away from OpenAI and Anthropic. It also made a $14.3 billion investment for a 49% stake in Scale AI, a startup recognized for its industry-leading data labeling capabilities. This investment positions Meta advantageously when it comes to securing high-quality training datasets.

With such resources in place, Mark Zuckerberg is equipping Meta’s AI models to be not just competitive, but potentially market-leading.

Meta’s powerful cash generation is giving it the flexibility to aggressively invest in AI infrastructure. The company has earmarked $60 billion to $72 billion for capital spending in 2025, much of which will be spent on building and upgrading data centers. This rapid pace of investment demonstrates Meta’s conviction that long-term value can be realized by investing in innovation-driven scale.



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