According to Demis Hassabis, Genie 3 is being used to generate dynamic simulation environments where SIMA agents can be trained to achieve specific goals, with Genie 3 adapting its world in response to SIMA's actions (source: @demishassabis, Twitter). This approach enables scalable, flexible reinforcement learning and opens up business opportunities in automated AI training, synthetic data generation, and advanced simulation platforms for AI development. By allowing one AI to train within the adaptive 'mind' of another AI, organizations can dramatically accelerate real-world deployment of intelligent agents across gaming, robotics, and enterprise automation.

According to Google DeepMind, researchers have accelerated embodied AI agent research by placing the SIMA agent in a Genie 3 simulated environment with a defined goal. In this setup, the SIMA agent interacts with the virtual world while Genie 3 generates dynamic responses, all without explicit knowledge of the agent's objective (source: Google DeepMind, August 5, 2025). This method enables realistic, goal-driven training scenarios for AI agents, improving their adaptability and decision-making in complex environments. Such advancements are crucial for developing more sophisticated embodied AI solutions applicable to robotics, autonomous systems, and interactive virtual assistants.