How Deeptune Helps AI Agents Improve at Useful Work

Deeptune, developing a simulation environment where AI can practice doing work, led by Tim Lupo, and the team, has raised $43 million Series A, led by Andreessen Horowitz with participation from 776, Abstract Ventures, and Inspired Capital, and angels including Noam Brown (Research, OpenAI), Brendan Foody (CEO, Mercor), and Yash Patil (CEO, Applied Compute).

Mastery requires more than knowledge. Operating rooms and cockpits prove that skill comes from practice, not theory; the gap between knowing and doing remains wide.

Current AI models excel at exams but struggle with practical tasks like managing inboxes or closing books, not due to intelligence but a lack of experience.

Deeptune builds high-fidelity digital simulations, sandbox environments where AI agents learn useful work through reinforcement learning. In one year, significant progress has been achieved, yet much remains to be done.

Transforming model capability into real-world performance and revenue demands environments that mirror the economy itself, a pursuit central to advancing AGI.