{"slug": "130m-series-a-to-build-the-open-superintelligence-stack", "title": "$130M Series A to Build the Open Superintelligence Stack", "summary": "Prime Intellect raised $130 million in Series A funding led by Radical Ventures, with participation from NVIDIA Ventures, Intel Capital, and Dell Technologies Capital, to build an open superintelligence stack for training and deploying frontier AI models. The company has scaled to over $100 million in annualized revenue with 6,000 customers, including Ramp, which used Prime Intellect's stack to beat closed frontier models on a spreadsheet search task.", "body_md": "# $130M Series A to Build the Open Superintelligence Stack\n\n# $130M Series A to Build the Open Superintelligence Stack\n\nToday, we're announcing that we've raised **$130M**, led by Radical Ventures, with participation from NVIDIA Ventures, Intel Capital, Dell Technologies Capital, and our existing investors. Bringing our total funding to over **$150M** to build the open superintelligence stack.\n\nWe're also joined by angels who are building the frontier themselves: John Schulman (Thinking Machines), Karim Atiyeh (Ramp), Aaron Levie (Box), Dwarkesh Patel, Milan Kovac (Tesla), Winston Weinberg (Harvey), Mike Knoop (Zapier, Ndea), Asher Spector (Flapping Airplanes), Jeff Wang (Cognition), Rohan Anil (Core Automation), Matthew Prince (Cloudflare), Brendan Foody (Mercor), Devansh Pandey (Standard Intelligence), Harrison Chase (Langchain), Nic Ouporov (Fleet) and many more.\n\n## Why now: RL changes who can build frontier AI\n\nPre-training concentrated frontier AI in a handful of labs. RL breaks that open: companies can now own their model optimization loop — train directly on their own product, optimize for their specific workflows, and build agents that improve continuously in production.\n\nOwning this loop is how you build a compounding moat in the agentic era. The only missing piece has been the infrastructure — until now, it lived exclusively inside the labs.\n\n## The Open Superintelligence Stack\n\nWe train open frontier models and ship the same stack to our customers. It spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n## Who's building on Prime Intellect\n\nWe are grateful to over 6k customers working with us including many of the leading AI startups, neolabs and enterprises, who use our stack — across compute, RL and post-training, sandboxes, inference, environments, and evaluations.\n\nIn under a year, that demand has scaled to over $100m in annualized revenue.\n\n**Companies like Ramp beat closed frontier models using our post-training stack.**\nRamp trained a 35B model on [Lab](https://www.primeintellect.ai/blog/lab-is-open) that beat Opus at spreadsheet search, running 27% faster and far cheaper than Haiku. [Read the case study →](https://www.primeintellect.ai/case-study/ramp)\n\n“We worked with Prime Intellect to train Fast Ask on Lab — a small RL-trained subagent that helps the Ramp Sheets agent find answers inside spreadsheets. The result beat the frontier models on accuracy while running at faster speeds and a fraction of the cost. Rather than wait on a better frontier model, we trained our own for the workflow that mattered to us”\n\nKarim Atiyeh\n\nRamp Co-CEO\n\n## What's next\n\nWe're scaling every layer of the stack — ever-larger compute clusters, larger RL runs, and the stack for agentic training, inference and continual learning.\n\nBeyond that, we are placing ambitious bets at the frontier of where the puck is going and build infrastructure for the problems we believe are most consequential, such as:\n\n- Long-horizon agents and\n[Recursive Language Models (RLMs)](https://www.primeintellect.ai/blog/rlm). Today's models break down over long contexts; RLMs manage their own context and coordinate sub-agents. We've been scaling RLM training over the last months — we believe it will be the scaling paradigm for agents that work for days. - Automate AI research and science, across all aspects from\n[pre-training](https://www.primeintellect.ai/auto-nanogpt)([autonomous nanogpt](https://www.primeintellect.ai/auto-nanogpt)), to[RL](https://www.primeintellect.ai/blog/general-agent)([General Agent](https://www.primeintellect.ai/blog/general-agent)) and beyond. - Continual learning. The future is models that learn in production, where training and inference collapse into a single continuous loop. Our stack was built for this world — a tight integration between RL rollouts, training, and serving.\n\n## Join us\n\nWe're a small team racing the most well-funded closed labs in the world to build superintelligence in the open. The same stack that trains our frontier models is now in the hands of thousands of teams.\n\nIf you want to train frontier models you own — on your data, your workflows, your product — we'd love to build with you. [Start training →](https://app.primeintellect.ai/dashboard/home)\n\nIf you want to build the infrastructure of open superintelligence, we're hiring across RL, inference, distributed systems, and compute. [See open roles →](https://jobs.ashbyhq.com/PrimeIntellect)", "url": "https://wpnews.pro/news/130m-series-a-to-build-the-open-superintelligence-stack", "canonical_source": "https://www.primeintellect.ai/blog/series-a", "published_at": "2026-07-09 07:48:34+00:00", "updated_at": "2026-07-09 08:12:24.187238+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-startups", "ai-infrastructure", "ai-products", "ai-research"], "entities": ["Prime Intellect", "Radical Ventures", "NVIDIA Ventures", "Intel Capital", "Dell Technologies Capital", "Ramp", "John Schulman", "Karim Atiyeh"], "alternates": {"html": "https://wpnews.pro/news/130m-series-a-to-build-the-open-superintelligence-stack", "markdown": "https://wpnews.pro/news/130m-series-a-to-build-the-open-superintelligence-stack.md", "text": "https://wpnews.pro/news/130m-series-a-to-build-the-open-superintelligence-stack.txt", "jsonld": "https://wpnews.pro/news/130m-series-a-to-build-the-open-superintelligence-stack.jsonld"}}