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 the Lightcone 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.
Chapters (Powered by https://ChapterMe.co):
00:00 Intro
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
