River AI, a startup founded by xAI co-founder Igor Babuschkin, has raised $1.1 billion in a combined seed and Series A round led by General Catalyst and AMP PBC, with participation from Nvidia, AMD Ventures, Y Combinator, and Temasek. The company, which emerged from stealth in June, is aiming to rebuild the AI stack from the ground up to create personally trainable AI agents.
AMP PBC, an AI-focused investment firm founded in 2026 by former Andreessen Horowitz general partner Anjney Midha, is a notable participant. Midha previously backed companies like Black Forest Labs, Mistral AI, LMArena, and OpenRouter during his time at a16z.
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A mission to reinvent AI training
Babuschkin, whose resume includes stints at DeepMind and OpenAI, has outlined a vision that diverges from the trajectory of other major AI labs. Instead of focusing on human worker replacement, River AI intends to turn agents into personally trainable assistants that are owned by the user.
“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,” Babuschkin wrote in his launch blog. He envisions agents as “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.”
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River AI’s first product is an API that allows developers to apply reinforcement learning (RL) and low-rank adaptation (LoRA) fine-tuning to open models. The company positions this 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 states.
Timing and market context
The size of this round — eye-popping for a company that is only two months old — is another sign of the heated AI investment climate. But River’s premise arrives at a time when enterprises are increasingly seeking control over their AI model destiny by mixing open-weight models with proprietary ones.
River AI claims its neocloud offering can solve the post-training expertise problem. “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.
The broader vision is that everyone will have their own agents, trained by themselves and working on their behalf. This concept is already emerging with the rise of personal, locally-running agents like OpenClaw and its derivatives. Nvidia has also been partnering with PC makers such as Dell, Microsoft, and HP to produce AI-capable hardware.
How River’s technology will differentiate itself from these efforts remains to be seen, but the company now has a substantial war chest to pursue its ambitious roadmap. The funding round is a strong validation of the personal AI thesis, even as questions about the sustainability of such large early-stage investments persist.
This article is for informational purposes only and does not constitute financial advice. The AI startup funding market is volatile and uncertain; readers should conduct their own research before making any investment decisions.
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