General Catalyst leads $1.1B round into 2-month-old River AI

River AI, an AI startup founded by xAI co-founder Igor Babuschkin, has secured $1.1 billion in funding 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, backer of companies at a16z like Black Forest Labs, Mistral AI, LMArena and OpenRouter.)

River came out of stealth in June with a fascinating mission. Babuschkin, whose resume includes AI roles at DeepMind and OpenAI, intends to reinvent AI from scratch, beginning with how models are trained. This is in order to turn agents into personally trainable assistants, rather than following the trajectory other AI labs are on: human worker replacements.

“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,” he envisions.

River already offers an API, billed per 1 million tokens , with rates dependent on the open model used. The API allows developers to use both reinforcement learning (RL) and low-rank adaptation (LoRA) fine-tuning on the models. This first product is intended to be 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 this round is an eye-popping-size investment for a nascent company, and perhaps another indication of the overheated AI atmosphere, River’s premise comes at a particularly auspicious time. Enterprises are waking up to wanting to control their AI model destiny by using a mix of models, including open weight. River is promising to solve the post-training expertise part of that problem with 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,” it wrote in its funding announcement.