River AI raises $1.1 billion in seed round led by General Catalyst

River AI has raised $1.1 billion in a seed and Series A funding round led by General Catalyst and AMP PBC, with participation from Nvidia, AMD Ventures, Y Combinator, and Temasek. Founded by former DeepMind, OpenAI, and xAI researcher Igor Babuschkin, the startup aims to build a personalized AI infrastructure. River AI claims its platform allows enterprises to complete complex reinforcement learning runs in 15 to 20 minutes, offering two to four times the cost savings of closed-source alternatives.

River AI $1.1 billion funding

• ▪The $1.1 billion funding round for River AI included participation from NVIDIA, AMD Ventures, Y Combinator, and Temasek.
• ▪AMP PBC, which co-led the River AI funding round, is an AI-focused investment firm founded in 2026 by former Andreessen Horowitz general partner Anjney Midha.
• ▪River AI secured $1.1 billion in a seed and Series A funding round led by General Catalyst and AMP PBC.

Personalized AI training infrastructure

• ▪River AI is developing hardware and consumer-facing products alongside its training infrastructure to build a vertically integrated stack where personal AI operates close to users.
• ▪River AI's platform supports low-rank adaptation fine-tuning and reinforcement learning for frontier open-weight models while managing underlying infrastructure automatically.
• ▪River AI is building a personalized AI infrastructure that allows users to customize and continually train open-weight models for their own needs.

Igor Babuschkin background

• ▪River AI was founded by Igor Babuschkin, whose background includes research roles at DeepMind, OpenAI, and co-founding xAI.
• ▪Igor Babuschkin stated that River AI's goal is to shift ownership of AI from the companies and laboratories that train models toward the people and organizations that use them.

Enterprise deployment speed

• ▪River AI claims that enterprises can complete complex reinforcement-learning training runs in 15 to 20 minutes without maintaining an infrastructure team.
• ▪River AI allows customized models to be deployed immediately once training is complete, with token-based billing for actual training and inference usage.

Enterprise cost savings

• ▪River AI's token-based billing charges customers for actual training and inference usage rather than unused GPU capacity.
• ▪River AI claims its platform offers two to four times the cost savings relative to closed-source alternatives.

AI market timing

• ▪The market for personal, locally running agents is growing, as seen with the rise of OpenClaw and Nvidia's partnerships with PC makers like Dell, Microsoft, and HP.
• ▪River AI's launch coincides with an enterprise trend of seeking control over AI models by using a mix of models, including open-weight models.