Big Tech's AI agents still struggle with real-world autonomy, fueling demand for reinforcement learning (RL) environments—simulated training grounds crucial for multi-step task mastery. Startups like Mechanize Work and Prime Intellect are racing to provide these environments, backed by growing investments from AI labs like Anthropic. While RL drives breakthroughs (e.g., OpenAI's o1), critics warn of scalability challenges and "reward hacking" risks. The sector mirrors the data-labeling boom but faces skepticism over its long-term viability amid rapid AI evolution.