Standard Intelligence, a six-person research lab in San Francisco, raised $75 million from Sequoia Capital and Spark Capital at a $500 million post-money valuation, with Andrej Karpathy participating as an angel. The Information first reported the round on April 30. The team is led by Galen Mead and Devansh Pandey, and the entire raise will go into compute, data, and engineering hires.
What they are building is a 'computer use' foundation model called FDM-1 — an AI system optimized to operate any application through its graphical interface, the way a human does. Most computer-use systems today are wrappers around a general-purpose LLM bolted onto a screenshot-and-click loop. Standard Intelligence trained FDM-1 from the ground up on 11 million hours of screen video, several orders of magnitude larger than any open-source dataset for this task. Demos include extruding a CAD gear in Blender and driving a simulated car through San Francisco after an hour of fine-tuning.
The round arrives in a moment when 'computer use' has become the contested frontier between OpenAI's agent products, Anthropic's Claude, Google's Gemini agents, and a growing cluster of specialized labs. The argument Standard Intelligence is making to investors is that a model trained natively on video of screens — rather than text descriptions of screens — will close the reliability gap that has kept agents stuck below human performance on long-horizon tasks.
For learners: 'computer use' is the next big jump in what AI can actually do for you. Today's chatbots can write code or summarize documents; tomorrow's will book a flight, fix a billing error, or navigate a hospital portal. If you want to be early to the field, learn how to evaluate agent reliability — the labs winning this race will be the ones whose models complete a 30-step task without losing the thread, not the ones with the flashiest demo.