Company and Round
<cite index="6-2,6-3">Rhoda AI announced its public launch after 18 months in stealth, unveiling FutureVision, a new approach to robotic intelligence based on video-predictive control and designed to operate beyond controlled laboratory demonstrations and into real-world environments. The company also announced it has raised $450 million in Series A funding to accelerate development and industrial deployment.</cite> <cite index="1-3">The investment values the company at $1.7 billion.</cite>
<cite index="1-13">The funding round attracted support from several investment groups, including Khosla Ventures, Temasek, Mayfield, Premji Invest, and Capricorn Investment Group.</cite> <cite index="4-4">The $450 million Series A will support continued research and engineering investment, expansion of industrial deployments and customer pilots, and growth of Rhoda's multidisciplinary team spanning generative artificial intelligence, computer vision, and robotics.</cite>
Leadership
<cite index="22-1">Rhoda is led by chief executive officer and cofounder Jagdeep Singh, a serial deep-tech founder who has built and scaled multiple technology companies.</cite> <cite index="26-3,26-4">Previously, he was cofounder and CEO of QuantumScape (NYSE: QS), where he led the company from early research through public listing, pioneering advances in solid-state battery technology for electric vehicles. Over the course of his career, Singh has founded and scaled multiple deep-technology companies spanning artificial intelligence, communications, and advanced materials, including Infinera (NASDAQ: INFN), Lightera (acquired by Ciena), and Raxium (acquired by Google).</cite>
<cite index="21-9">Chief Science Officer Eric Ryan Chan is a Stanford researcher and leader in computer vision and generative modeling who previously served as a generative model architect at WorldLabs. Gordon Wetzstein, professor at Stanford University and head of the Computational Imaging Lab, also co-founded the company alongside a team drawn from leading generative AI, computer vision, and robotics organizations.</cite>
Technology
<cite index="6-4,6-5,6-6">Traditional industrial robots perform well in structured environments but remain largely limited to pre-programmed trajectories. More recent artificial intelligence approaches — particularly vision-language-action (VLA) models — allow robots to learn from data and have demonstrated impressive results in laboratory settings. However, many still struggle to cope with the variability of the real world, including shifting layouts, previously unseen objects, and unpredictable workflows.</cite>
Rhoda's answer to this problem is what it calls a Direct Video Action (DVA) model. <cite index="8-5">Rather than relying primarily on teleoperated robot trajectories, Rhoda pre-trains its models on internet-scale video — hundreds of millions of videos — to build a strong prior on motion, physics, and physical interaction.</cite> <cite index="2-3,2-4">The system continuously observes its environment, predicts future states using video-based modeling, and converts those predictions into actions. The process runs in a closed feedback loop, updating robot behavior every few hundred milliseconds as conditions change.</cite>
<cite index="9-6">The strong motion prior learned during Rhoda's natively autoregressive video-based pretraining allows the model to learn new tasks efficiently, often requiring as little as ten hours of teleoperation data.</cite> <cite index="6-7">Built on this architecture, FutureVision serves as Rhoda's intelligence layer — a foundation model that powers Rhoda systems today and is expected over time to be licensed to partners across different robotic hardware and software platforms.</cite>
Deployment and Context
<cite index="4-2,4-3">Rhoda's technology has demonstrated autonomous operation in production environments, where robots must handle continuously changing materials, layouts, and workflows. In a recent high-volume manufacturing evaluation, Rhoda said it completed a component-processing workflow in under two minutes per cycle without human intervention, exceeding customer key performance indicators.</cite>
<cite index="1-10,1-11">Rhoda AI stated its platform is built for compatibility with diverse robotic hardware. This design allows manufacturers and logistics firms to implement intelligent robots without needing to overhaul their current infrastructure.</cite>
The announcement arrives amid a broader surge in embodied AI investment. <cite index="16-13,16-14,16-15">The embodied AI market is accelerating sharply, with full-year funding rising from approximately $3.09 billion in 2024 to approximately $4.23 billion in 2025; year-to-date 2026 had already reached approximately $5.60 billion by early July — exceeding the entire 2025 total before the year was half over.</cite>