In the field of scientific and technological innovation, capital under the traditional investment and financing system generally lacks “patience” due to insufficient perception of technological value, difficulties in discovering economic value, and mismatches between capital supply and innovation demand. As a result, patient capital remains scarce. Artificial intelligence (AI) addresses this scarcity by embedding “patience” into capital through three key mechanisms. First, through natural language processing and knowledge graphs, AI enhances the perception of technological value, enabling cognitive expansion. Second, through digital twins and reinforcement learning, AI enables refined and dynamic discovery of economic value. Third, through smart contracts and blockchain, AI automates the alignment between capital investment and R&D processes. Through these mechanisms, patient capital can be generated continuously and systematically, rendering it no longer scarce. AI transforms patient capital generation from a scarce paradigm relying on a few far-sighted investors into a sustainable paradigm featuring large-scale automated output through technological systems. AI is not an exogenous technological tool but an endogenous “institutional technology.” However, the AI-driven generation of patient capital still faces practical obstacles, including data barriers, institutional and regulatory lags, insufficient transparency and trustworthiness of AI models, and cognitive and behavioral constraints of market participants. To address these challenges, it is necessary to strengthen digital infrastructure and build open and shared big data platforms to eliminate data silos. In addition, institutional frameworks should be refined, regulatory mechanisms optimized, technological systems improved, and subject cognition enhanced, while the correct technological pathways and application methods should be adopted to fully leverage the positive effects of AI systems.