恒业资本江一：AI未来核心增长点是“跨技术融合”，将诞生一批独角兽企业

Core Insights - AI has transitioned from a laboratory concept to an omnipresent tool that can write articles, compose music, design, program, schedule in enterprises, inspect in factories, teach in classrooms, and diagnose in hospitals, effectively reducing costs for businesses and creating opportunities for entrepreneurs and investors [1][5] Industry Trends - The logic of profitability has shifted from "scaling" to "efficiency," with AI becoming the commercial core that addresses pain points across various industries, supported by a new synergy among policy, capital, industry, and social acceptance [3][7] - The current phase of AI integration into industries is the third stage, where service applications are central to AI's value release [3][7] - Future technological integrations, such as blockchain with AI, quantum computing with AI, and brain-computer interfaces with AI, are expected to create new business opportunities and potentially lead to the emergence of unicorn companies [3][7] AI Demand and Data Trends - Global AI computing power demand is projected to reach 10^23 FLOPS by 2024, which is 1 million times the total global computing power in 2010, and is expected to grow to 10^26 FLOPS by 2027, a 1000-fold increase in three years [3][7] - Data is viewed as the "oil" of AI, with four key trends anticipated: 1. Data assetization will become a core strategy for companies, with over 50% of listed companies expected to disclose data asset values in their financial reports by 2026 2. The data factor market will mature, transitioning from non-standard to standardized trading 3. Privacy computing technologies like federated learning and differential privacy will be widely adopted to address the "data usable but invisible" issue 4. Synthetic data will become a significant supplement, with its share in AI training expected to exceed 25% by 2027 [3][7] AI Implementation Framework - A five-layer architecture for AI implementation has been proposed, encompassing resource access, data management, Data & AI engineering, intelligent applications, and security operations, which has shown significant effectiveness in large and medium-sized enterprises, reducing project delivery cycles by over 50% and greatly increasing customer renewal rates [4][8] - It is anticipated that over 80% of large and medium-sized enterprises will adopt similar architectural frameworks to build AI infrastructure in the next three years [4][8]