Fundamental Computational Limits and the Trajectory of Technological Development

Contemporary technological development increasingly relies on computational scaling: deploying larger models, more parameters, and greater energy expenditure to achieve incremental improvements. This approach assumes that sufficient resources can overcome any computational challenge. However, recent theoretical work on the P vs NP problem suggests intrinsic operational limits that cannot be circumvented through brute-force methods. This paper examines the implications of the Intrinsic Operational Gradient Axiom for current technological trajectories, particularly in artificial intelligence, and analyzes the temporal and economic constraints these fundamental limits impose.