Explore the nuanced role of human data in human-AI interaction systems through this 44-minute lecture delivered at the 2025 Cooperative AI Summer School. Learn from Eugene Vinitsky, Assistant Professor at NYU's Tandon School of Engineering, as he challenges conventional thinking about the centrality of human data in AI systems design. Discover insights from his extensive research on multi-agent systems and reinforcement learning algorithms, drawing from his experience at leading organizations including UC Berkeley, Apple Special Projects Group, DeepMind, Facebook AI Research, and Tesla. Gain understanding of the practical challenges in designing decentralized autonomous agents and the tools that make multi-agent system development more efficient. Examine how human data serves as an enhancement rather than the foundation of effective human-AI interaction, with implications for the future development of cooperative AI systems.