River AI raises $1.1B seed/Series A led by General Catalyst

River AI, an AI startup founded by xAI co-founder Igor Babuschkin, has secured $1.1 billion in a seed/Series A round led by General Catalyst and AMP PBC, with participation from Nvidia, AMD Ventures, Y Combinator, and Temasek. AMP PBC is an AI-focused investment firm founded in 2026 by former Andreessen Horowitz general partner Anjney Midha, a backer of a16z portfolio companies including Black Forest Labs, Mistral AI, LMArena, and OpenRouter.

River came out of stealth in June with a mission to reinvent AI from scratch, starting with how models are trained. Babuschkin — whose résumé includes AI roles at DeepMind and OpenAI — wants to turn agents into personally trainable assistants rather than follow the human-worker-replacement trajectory other AI labs are pursuing. “To get there, we believe the stack has to be rebuilt end to end: training, models, the product layer, and new hardware that lets personal AI live close to you,” he wrote in his launch blog. “Capable agents will be a normal part of everyday life. Less like the assistants you call on today when you need a task done, more like guardian angels: quietly present, on your side, helping with what actually matters to you. They will know you well, and they will be yours, not someone else’s.”

River already offers an API billed per 1 million tokens, with rates depending on the open model used. The API lets developers apply both reinforcement learning (RL) and low-rank adaptation (LoRA) fine-tuning to the models — positioned as an antidote to prompt engineering. “Prompting steers a model you don’t own and can’t improve. River lets you train open models into ones that are truly yours — and serve them like any other endpoint,” the product literature says.

While the round is an eye-popping size for a nascent company — perhaps another indication of the overheated AI atmosphere — River’s premise arrives at a particularly auspicious time, as enterprises increasingly want to control their AI model destiny using a mix of models, including open weight. River says it can solve the post-training expertise part of that problem through its neocloud offering: “Any enterprise can complete a complex reinforcement learning run in 15 to 20 minutes with no infrastructure team required, at two to four times the cost savings relative to closed-source alternatives,” the company wrote in its funding announcement.

Why it matters

River’s bet — that everyone will eventually run their own trained agents rather than prompt someone else’s model — is already visible in the rise of personal, locally running agents like OpenClaw, and in Nvidia’s parallel push with PC makers to put AI-capable hardware in more hands.