How to sell RL envs and data to AI labs: Interview with Sean Cai

The AI data market is shifting toward specialized, long-horizon tasks in domains like biology and cybersecurity, where verification remains a significant bottleneck. High-quality data procurement now requires vendors to possess "research taste"—the ability to align data generation with downstream model capability improvements—rather than just providing raw, menial labeling. Effective data partners must implement rigorous quality control, including n-gram contamination testing and realistic environment modeling, to avoid reward hacking. While frontier labs like Anthropic and OpenAI invest billions annually in data, the industry remains supply-constrained, as few vendors can deliver the research-first, scalable solutions required for next-generation model training. Consequently, labs are increasingly treating data vendors as strategic research partners, sometimes pursuing exclusivity to secure novel, high-performance data streams that directly translate into model intelligence.