Traditional technology investment follows the logic of "tool improvement", while the core of AI investment is "paradigm disruption." As an efficiency tool (such as customer service robots, image recognition), the value of early AI was linear and estimable. However, with the breakthroughs of large models, generative AI and autonomous agent technologies, AI is becoming the "smart core" of products and services. For example, in drug research and development, AI is no longer just an aid in analyzing data, but can independently propose new molecular structure assumptions; in the software field, AI is changing from a tool for writing code to a "developer" that can understand needs, independently design and deploy complete applications. Meaning of capital: This means that the valuation model of the investment object must change. Capital is no longer just investing in "companies that use AI", but should prioritize investing in "AI-driven systems that have the ability to evolve themselves." The growth curve of such companies will show typical non-linear and exponential characteristics, and their valuation center will shift from the traditional price-to-earnings ratio (P/E) to pricing of "data flywheel effect","model iteration speed" and "ecosystem building capabilities".

Historically, general-purpose technologies (GPT) such as steam engines, electricity, and the Internet have all brought productivity booms that have lasted for decades. AI is a new generation of GPT, but its mechanism of action is more profound: it directly empowers and enhances human intelligence, a core factor of production. In manufacturing, AI-driven "smart factories" achieve not only automation, but also global resource optimization based on real-time data, raising overall equipment efficiency (OEE) to physical limits. In the knowledge industry, AI analysts and AI legal assistants can liberate human experts from cumbersome information processing and focus on high-level judgment and innovation. According to Goldman Sachs research, generative AI is expected to boost global labor productivity by about 1.5 percentage points a year over the next decade and create trillions of dollars in global GDP growth. Capital implications: This process will lead to two main investment streams: "shovelers"(cloud computing, dedicated chips, model service providers) who provide AI transformation solutions for industries; and industry leaders and challengers who can most aggressively and successfully integrate AI into core business processes to disrupt cost structures and regain market share.

AI is creating unprecedented new markets. Autonomous driving will redefine the transportation industry, and the "passenger economy" and data service market it brings is huge;AI new drug research and development has compressed the "discovery-clinical" cycle of biomedicine from ten years to grade, opening up a huge pipeline value; AI-driven personalized education and medical diagnostic services are pushing the 100-billion-dollar market to a trillion-dollar market. At the same time, AI is violently redistributing the value of existing industries. In the financial sector, AI quantitative transactions are eroding the profits of traditional investment strategies; in the retail industry, AI-based precise recommendations and supply chain optimization continue to squeeze the living space of those who fail to transform. The key task of capital is to identify which industry value chains will be deconstructed and reconstructed by AI, and invest in "control points" enterprises in the new value chain.

AI is driving a fundamental change in business models: companies are shifting from selling one-time products (software, hardware) to providing AI-based and continuously iterative intelligent services. For example, car companies are using OTA (over-the-air technology) and autonomous driving services to transform one-time car sales into "mobile smart space" operators that continue to charge software subscription fees; industrial equipment manufacturers use AI predictive maintenance to shift their business focus from selling machines to selling "worry-free operating hours." Significance of capital: This shift has shifted the company's revenue from volatile to stable and predictable, significantly increased customer life cycle value (LTV), and reduced discount rates in valuation models. Capital will highly favor companies that successfully complete this business model transition because they establish deeper customer lock-in and continuous data feedback closed-loop.

AI capabilities have become the core of strategic competition among major countries. Investment in AI computing power (high-end chips), algorithms (top talents and frameworks), and data (high-quality corpus and industrial data) has become a "strategic capital" related to the country's long-term competitiveness and security. Governments of various countries have invested heavily in AI infrastructure (such as computing power networks, public data openness, and R & D support). This is not a pure industrial policy, but a proactive layout of future smart society infrastructure. Significance of capital: This points out a track for capital with strong policy certainty and long-term demand rigidity: domestic high-performance computing, private computing, high-quality industry data sets, and AI governance and evaluation tools that meet regulatory requirements. Investing in these areas means resonating with the top-level national development narrative and sharing the certainty dividends of strategic growth.

This is the core of "artificial intelligence moving towards artificial intelligence". Current AI's "intelligence"(calculation, recognition, generation) in specific fields has surpassed humans, but true "wisdom" involves a deep understanding of complex systems, ethical trade-offs, cross-domain analogy creation, and decisions based on long-term value. This is the focus of the next stage of technological breakthroughs and capital opportunities. Capital significance: The future's top "artificial intelligence" system may become a "strategic decision-making partner" of enterprises and even the country. Investing in this direction means laying out AI's "top-level capabilities": including causal reasoning AI (transcending correlation and understanding essence), embodied intelligence (AI deeply interacts with the physical world), and the ability to align complex values with humans and collaborate with AI systems. This is not only at the forefront of technology, but also the commanding height of shaping the future human social form and business ethics. Those who succeed will gain the status and rewards that define the times.

To sum up, the evolution of artificial intelligence to artificial intelligence has put forward unprecedented requirements for capital. The role of capital must be upgraded from a simple financial provider to a "symbiotic investor": it must not only provide funds, but also deeply understand the technological paradigm, actively help invested companies obtain data and computing power resources, and build an AI native organizational culture. And forward-looking layout at the ethical and governance levels. This is a magnificent value transfer. Wherever capital flows, the emergence of wisdom will accelerate. In this process, the greatest rewards will belong to those who were the first to realize that "wisdom" itself has become the scarcest means of production and dare to carry out long-term, systematic and ecological layout around this core. This is not only a path for financial growth, but also a grand journey to participate in shaping a new civilization era that is more efficient, more creative, and more intelligent.