Core Insights - The article discusses the need for a new way of thinking to effectively harness AI, distinguishing it from traditional internet thinking [1][3] - AI is not merely a tool but can become a value-creating entity through multi-agent systems [1][6] - The concept of "intelligent first" is emphasized as a guiding principle for organizations adopting AI [4][5] AI Thinking - AI thinking is defined as a new problem-solving methodology that applies the "AI First" principle in organizational processes [11] - It involves three core principles: Virtual-First Simulation, Rapid Scalable Trial and Error, and Computational Hedging [11][12][17] Virtual-First Simulation - This principle advocates for creating a digital model of the real world to simulate actions before actual resource investment [12][14] - It allows for low-cost exploration of possibilities, enhancing decision-making [14] Rapid Scalable Trial and Error - AI enables parallel testing of numerous scenarios at minimal costs, significantly speeding up the innovation process [15][16] - This capability transforms the traditional trial-and-error approach into a more efficient and scalable model [16] Computational Hedging - This principle suggests using inexpensive computational resources to mitigate the costs associated with physical resources [17] - AI can simulate complex interactions, reducing the need for extensive physical trials [17] Unmanned Companies - The culmination of AI thinking in organizations leads to the concept of "unmanned companies," where AI agents drive value creation [19][20] - In these companies, human roles shift from execution to design and governance [20] Technical Framework - The operational framework of unmanned companies is based on a universal world model architecture that simulates real-world dynamics [21] - This includes multi-agent behavior and nested models for strategic and operational planning [21][22] Current Applications - AI thinking is already influencing various sectors, such as manufacturing with digital twins and marketing through automated content generation [24][25] - In scientific research, AI accelerates hypothesis testing and validation processes [26] Future Outlook - The transition from an experience-driven to a simulation-driven business landscape is underway, with companies needing to develop high-fidelity world models [27] - Mastery of AI thinking will provide organizations with a competitive edge in agility, efficiency, and scalability [27]