The boundary of computation and the texture of the world: philosophical implications of P vs NP The P and NP problems are the most fundamental puzzles in computer science, not only related to algorithm efficiency, but also revealing the essential boundaries of computation and affecting our understanding of the complexity of the world. G ö del once hinted that if a "computational utopia" could be constructed, all verifiable problems would be efficiently solved, which is the vision of P=NP. However, this promising prospect also harbors enormous energy to disrupt the existing order. P problem refers to a problem that can be solved in polynomial time with efficient algorithms. NP problem refers to a problem that can verify its solution in polynomial time. Obviously, P belongs to NP, and whether P equals NP is still an unsolved mystery. If P=NP holds, many currently difficult problems such as protein folding and drug design will become effortless, greatly promoting technological development and building a highly intelligent world. However, P=NP can also shake the foundation of modern cryptography. Asymmetric encryption algorithms such as RSA have security based on the assumption that certain NP problems are considered computationally difficult. If P=NP, these difficulties will become easy, the encryption system will collapse, and cryptocurrencies such as Bitcoin will also face risks. Therefore, many scientists tend to believe that P ≠ NP. This implies the existence of fundamental computational barriers, where some problems are easy to verify but extremely difficult to solve. P ≠ NP indicates that the world is "asymmetrically emergent and nonlinearly adaptive", and this asymmetry may be the underlying cause of complexity. The complex systems of nature and human society are often difficult to predict by simple models, and the existence of P ≠ NP seems to provide an explanation for this. The Bitcoin designed by Satoshi Nakamoto utilizes the asymmetry between P and NP. Its proof of work mechanism requires solving computationally difficult hash problems (considered NP problems outside of P), but verifying proofs is easy (belonging to P). This asymmetry ensures the security and decentralization of the system. The success of Bitcoin confirms the potential of utilizing computational asymmetry to build innovative systems. In short, the P and NP problems go far beyond theoretical exploration. They reach computational limits, affect information security, and inspire us to understand the complexity of the world. Whether embracing the 'computational utopia' of P=NP or acknowledging the computational boundaries of P ≠ NP, our exploration of this problem will continue to drive technological progress and shape our understanding of ourselves and the universe. Understanding the profound significance of these two possibilities and constantly exploring and innovating within their boundaries may be the true wisdom.