On March 2, the Financial Times reported that @reflection_ai is raising at least another $2 billion, with its potential valuation approaching $20 billion. This follows a rapid series of raises: the company emerged from stealth in March 2025 with $130 million and a valuation of around $545 million, then raised $2 billion at an $8 billion valuation in October 2025, and is now back in the market again. Reflection frames its mission in the language of openness and open science. The awkward part is that, as of early March 2026, the frontier open-weight model at the center of its pitch still has not been released publicly, its code research agent Asimov remains on a waitlist, and the company’s website features product docs and blog posts but no research papers. The rumors about their recent raise makes Reflection a natural fit for a deep-dive episode in our GenAI Unicorns series. What are they actually building? Why has it all been so slow and secretive if the promise is openness? Is it realistic that their much-discussed open-weight model could outperform the closed labs and Chinese contenders? How are they planning to make money? Is government demand alone enough to sustain the business? There is a lot to unpack: