Prime Intellect Raises $130M to Open RL Training Labs Once Locked Inside OpenAI

Prime Intellect has raised a $130M Series A, bringing its total funding to over $150M. The round was led by Radical Ventures, with strategic participation from NVIDIA Ventures, Intel Capital, and Dell Technologies Capital. The thesis is blunt: the infrastructure that lets AI labs build self-improving models has been locked behind closed doors, and Prime Intellect wants to hand it to everyone else.

The company is building what it calls the Open Superintelligence Stack, a full-stack platform spanning training, inference, and compute. The angel roster reads like a who's-who of the current AI moment: John Schulman (Thinking Machines), Karim Atiyeh (Ramp), Aaron Levie (Box), Harrison Chase (LangChain), Matthew Prince (Cloudflare), and Milan Kovac (Tesla), among many others.

Why RL changes everything

To understand why this raise matters, you need to understand what reinforcement learning (RL) actually unlocks. Pre-training, the process of feeding a model billions of tokens of internet text, is extraordinarily expensive and requires massive compute clusters. Only a handful of labs can do it. RL post-training is different: it lets you take an existing open model and teach it to get better at your specific task by having it practice in a simulated environment and rewarding correct behavior.

Companies can now own their model optimization loop, training directly on their own product, optimizing for their specific workflows, and building agents that improve continuously in production. Owning this loop is how you build a compounding moat in the agentic era. The missing piece has always been the infrastructure to do this at scale, outside of a lab setting.

RL is now a cornerstone of AI infrastructure tooling, teaching agents complex behaviors without the cost and risk of real-world trial and error. The problem is that until recently, the tooling to run serious RL at scale was entirely internal to labs like OpenAI and Anthropic.