Multi system interaction leads to the emergence of intelligence: surpassing the limitations of a single formal system The development process of automated machines, from single mechanized production in the early stages of the Industrial Revolution to the exploration of highly intelligent and self-organizing networks today, deeply confirms a core viewpoint: intelligent and nonlinear behavior cannot be generated from a single, deterministic formal system, but requires the interaction and integration of multiple different types of formal systems to emerge intelligent and nonlinear characteristics. This viewpoint is not only a review of the history of automation development, but also an important inspiration for the construction of future intelligent systems. Early automation, represented by steam engines, was a concrete manifestation of reductionism in classical physics, emphasizing the decomposition of complex phenomena into predictable independent units. This kind of thinking has also deeply influenced the field of mathematics, giving rise to the grand goal of building a complete formal system. However, as Kurt G ö del revealed through his incompleteness theorem, any sufficiently complex and compatible formal system has inherent limitations and cannot fully describe itself. This implies that a single, deterministic formal system has fundamental shortcomings in capturing the complexity and potential intelligence of the real world. In contrast to the limitations of formal systems, nature exhibits a continuously emerging complexity, patterns, and organizational structure that far exceeds what any predetermined theoretical framework can fully encompass. This "emergence" is not simply a superposition, but a new characteristic generated by the interaction of multiple different components within the system, and this nonlinearity is the key feature of intelligent behavior. The unique ability of human cognition - self-examination, iterative interaction, and intuitive insights beyond existing formal frameworks - also implies that the emergence of intelligence is not the result of a single logical deduction, but a complex process of multidimensional information processing and feedback. From early independent machines to later automated production lines, technological advancements have improved efficiency while still fundamentally relying on pre-set programs and a single control logic, which can be seen as the engineering implementation of a single formal system. However, with the emergence of computers (Turing's theoretical breakthrough) and the Internet (Shannon's foundation of information theory), machines began to move towards networking and distribution. This transformation not only greatly expands the functionality of machines, but also provides a new perspective for understanding the emergence of intelligence. The significant gap between the ease of verifying solutions and the difficulty of finding solutions in the core challenge of P/NP problems in computer science may be a manifestation of the lack of focus of a single deterministic system when facing complex problems. Solving complex problems often requires long periods of thinking and trial and error ("difficult to solve"), while understanding and disseminating solutions is relatively rapid ("easy to verify"), which implies that the generation and dissemination of intelligence is not a simple linear process, but involves the emergence of complex information. The Bitcoin designed by Satoshi Nakamoto represents an important paradigm shift in the development of automation systems. It is not just a decentralized database or payment network, but a complex system that operates through multiple different types of distributed formal systems and exhibits self-organizing intelligence. The core architecture of Bitcoin includes a UTXO system based on asymmetric encryption and a miner system based on Proof of Work (PoW), which follow different rules and incentive mechanisms and interact and constrain each other through the dynamic and probabilistic mechanism of longest chain consensus. The UTXO system is responsible for the transfer of value and maintenance of state, and its security depends on the determinacy of cryptography; The miner system maintains system security and consensus through competitive computing power investment, and its behavior is influenced by economic incentives and probabilistic outcomes. The intelligence of Bitcoin is not a pre designed fixed program, but rather the decentralized, censorship resistant, and self-sustaining characteristics that emerge from these two distributed formal systems with different properties in long-term interactions and games. The successful practice of Bitcoin strongly proves that beyond the limitations of a single, deterministic formal system, by integrating different types of rules, mechanisms, and participants, and utilizing the advantages of distributed architecture, we can build more robust, adaptive, and "intelligent" systems. The future development of automation technology will inevitably emphasize the collaboration of heterogeneous systems, dynamic consensus formation, and the ability to emerge intelligence from complex interactions. Only by embracing diversity and building multi system architectures that can interact and balance each other can we truly break through the bottleneck of a single formal system and move towards higher levels of automation and intelligent systems.