❯ Labelbox raises more than $100 million as it moves from data labeling to training environments and enterprise agents

Equity plus creditLabelbox announced on October 14 more than $100 million in Series E, including equity and a credit facility, at a $1.25 billion pre-money valuation. Infinitum Partners led, with Wipro Ventures, a16z, B Capital and others.

Profitable this yearLabelbox has more than 400 customers and says it works with over 90% of leading frontier AI labs and hyperscalers. It became profitable this year while growing revenue more than 5x, and runs an expert network of more than 4 million people who write and grade tasks for model companies.

Started with labeling dataFounded in 2018 by Manu Sharma and two co-founders, Labelbox first made labeling tools: people tag images and text to train models. Sharma describes three stages: data, then training environments, now enterprise agents.

Practice grounds for modelsHorizon builds simulated companies full of code, dashboards, spreadsheets and documents where models repeatedly do coding, computer-use and research tasks, scored by programs and experts; the company cites millions of graded attempts. Recursion, launched last week, sells to businesses: agents start from schedules, alerts or tickets, each run is graded, and results improve the next run.

Two businesses feeding each otherLabelbox runs more than 50 agents internally across engineering, research, sales and finance. Models learn work through realistic practice and reliable grading; businesses buying agents care about accuracy. Doing both lets grading experience serve both sides, provided its scores truly reflect whether work was done well.

▮ SIGNALAs AI moves from answering to working, the training data business is upgrading too: it now sells practice grounds and scoring rules, not just labeled data.