Alexandr Wang (@alexandr_wang) started Scale AI to help machine learning teams label data faster. It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning. Just last week, Meta agreed to invest over $14 billion in Scale, valuing the company at $29 billion. Alexandr joined us on @LightconePod to share how Scale evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier. 01:15 - Alexandr’s early days at YC 07:25 - Dialing in on what worked 10:24 - Model improvements, evals 19:18 - The techno-optimist view of work 27:47 - The turning points for Scale AI 37:37 - Agentic workflows 41:55 - “Humanity’s Last Exam” 47:48 - U.S. vs China in AI and hard tech 56:57 - How to be hardcore
