In 2016, during a Go match between AlphaGo and Lee Sedol, an unexpected move—Move 37—turned out to be the winning move. This decision, made by the AI, defied 2,500 years of human Go strategy and highlighted the limitations of human thinking, constrained by biology and tradition. The author argues that as we develop AI agents, we often mimic human workflows instead of embracing their potential to think and act differently. While creating "replica" agents that follow human processes builds trust and familiarity, it restricts the effectiveness of AI by confining it to local optimizations. The author introduces the concept of "first-principles agents," which focus on solving problems based on their inherent objectives rather than mimicking human actions. For instance, Amazon’s “Chaos Storage” system places items randomly to optimize retrieval efficiency, contrasting with human categorization. Another example is an experiment where AI agents developed their own shorthand for negotiation, optimizing communication speed beyond human language constraints. The article emphasizes the need for builders to choose between human-centric designs and these more efficient, alien approaches depending on the task at hand. For tasks requiring human oversight or integration with existing systems, replica agents make sense. However, for pure efficiency outcomes, first-principles agents are superior. The author encourages product leaders to rethink their automation strategies. Instead of simply automating standard operating procedures, they should consider the fundamental nature of the problem. By allowing AI to propose unorthodox solutions—like the twisted antenna designed by an evolutionary algorithm—developers can unlock capabilities that are otherwise unimagined.