River AI Raises $1.1B in Funding Across Series Seed and Series A

River AI, building a new stack for personal AI led by Igor Babuschkin and the team, has announced $1.1 billion in funding across Series Seed and Series A, led by General Catalyst and AMP PBC, with strategic investment from NVIDIA and AMD Ventures and participation from Y Combinator and Temasek.

The round accelerates the development of powerful personal AI that understands users and works on their behalf, while putting ownership of that intelligence in the hands of the people and organizations using it.

The approach starts with tools that allow developers and companies to train, tune, and own their own AI models.

Most companies rely on general-purpose AI models built for broad audiences rather than specific organizational needs. Building custom models has traditionally required specialized infrastructure, hardware, and months of engineering work.

The River API simplifies that process, enabling enterprises to run reinforcement learning workloads in 15–20 minutes without a dedicated infrastructure team. The platform provides LoRA fine-tuning and reinforcement learning for frontier open-weight models, while managing compute, weight transfers, and training infrastructure. Models can be deployed directly to production, with usage-based billing that avoids the cost of idle GPU capacity.

AI development is moving toward a more open, accessible, and affordable model. Intelligence should work for the people and organizations using it, rather than remain controlled by the labs that build it.

River AI was founded around this principle: enabling people and companies to own their intelligence.

The ambition extends beyond the API toward powerful personal AI that learns from individuals, remains under their control, and can act in their interests. Today, the API gives developers and enterprises ownership over their AI. Over time, the same control will extend to individuals.

Achieving this vision requires a full-stack approach spanning accessible training infrastructure, personalized and continually learning AI products, and hardware designed to keep personal AI close to its users rather than in centralized data centers.

American AI leadership requires strength in both open-weight and closed frontier models. River AI’s focus on open-weight models and user-owned intelligence is positioned as an important part of that broader ecosystem and a priority for long-term American resilience.

— Hemant Taneja, CEO, General Catalyst

The founding team brings hands-on experience from xAI, Tesla, Google DeepMind, and OpenAI, spanning deep learning, reinforcement learning, and large-scale AI training.

Igor previously worked on generative modeling and reinforcement learning at Google DeepMind and led large-scale training efforts at OpenAI before joining xAI.

There remains a gap between AI’s capabilities and what most companies can practically deploy. River addresses that gap by providing a cost-efficient way to train, tune, and own custom AI models built on proprietary data and tailored to specific workflows.

— Marc Bhargava, Managing Director, General Catalyst

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