xAI Co-founder's New Startup River AI Raises $1.1B Just Two Months After Launch
River AI, led by former xAI co-founder Igor Babuschkin, has raised $1.1 billion led by General Catalyst and AMP PBC. The company aims to let users personally train their own AI agents rather than following the industry's push toward replacing human workers.
AI startup River AI, founded by former xAI co-founder Igor Babuschkin, has raised $1.1 billion in a seed and Series A funding round. The round was led by venture firm General Catalyst alongside AMP PBC, a firm founded this year by former Andreessen Horowitz general partner Anjney Midha. Nvidia, AMD Ventures, Y Combinator, and Temasek also participated.
Babuschkin, who previously held AI-focused roles at DeepMind and OpenAI, publicly launched the company in June. His vision diverges from the strategy of other leading AI labs, which are largely focused on replacing human workers — River AI instead aims to create personally trainable assistants.
A different approach to training models
In his launch blog post, Babuschkin wrote that the entire technology stack needs to be rebuilt from scratch — training, models, the product layer, and new hardware that allows personal AI to run close to the user. He envisions capable AI agents becoming a normal part of everyday life, functioning more like constant, trusted companions than tools called upon only for specific tasks.
The company already offers an API priced per million tokens processed, with rates depending on the open model used. The offering lets developers apply reinforcement learning (RL) and low-rank adaptation (LoRA) fine-tuning to turn open models into ones they truly own, rather than relying on prompt engineering with a model they don't control or improve.
Enterprise interest
According to the company's funding announcement, businesses using River AI's service can complete a complex reinforcement learning run in 15 to 20 minutes without needing a dedicated infrastructure team, at two to four times the cost savings compared to closed-source alternatives. This comes as enterprises increasingly want control over which AI models they use, including a mix of open-weight options.
The scale of the funding is notable for such a young company, and may also reflect the current level of investor enthusiasm across the AI sector as a whole.
