Recently, according to an article published by the Chief Investment Officer of One River Asset Management, entrepreneur and well-known investor Sparks believes that there is a misconception in the current market regarding Large Language Models (LLMs), and the true long-term investment value lies not in the construction of these models themselves, but in the application ecosystem on them. This statement sounded the horn of a turning point in the logic of AI investment. When the Large Language Model (LLM) rapidly becomes a "technological tap", the essence of this transformation has quietly surfaced: true value creation is shifting from an arms race in underlying technology to a disruptive restructuring of traditional industries at the application level. The rapid commercialization of big language models is essentially an inevitable law of technological evolution. Just as broadband has transformed from a scarce resource to an infrastructure, the training cost of AI models is rapidly decreasing due to increased computing power and open-source ecosystems. OpenAI's GPT series, Meta's Llama, and other open-source models enable small and medium-sized companies to access cutting-edge technologies at an extremely low cost. This trend of "freeization" has certainly lowered the threshold for innovation, but it has also exposed a cruel reality: it is difficult to build a moat solely based on the model itself. But 'free' does not mean 'worthless'. The popularization of big language models is actually a catalyst for transforming technological dividends into industry innovation. When the underlying technology becomes' hydro coal ', the imagination of the application layer becomes the key to victory. For example, in the medical field, AI assisted diagnostic systems can quickly analyze patient medical records and the latest medical literature by combining the semantic understanding ability of large language models, and provide personalized treatment plan recommendations for doctors. This kind of application layer innovation is the true value sink after the "free" technology. The core of the "application layer opportunity" referred to by Sparks lies in using AI to reconstruct the value chain of traditional industries. This process follows the "three-step" logic: data integration - process reengineering - scenario innovation. Taking the financial industry as an example, traditional banks rely on manual review of loan applications, which is inefficient and prone to errors. The AI driven intelligent credit system, by integrating the text analysis capabilities of big language models and traditional risk control models, can evaluate enterprise financial reports, industry trends, and even social media public opinion in real time, reducing approval time from weeks to minutes. This kind of disruption not only improves efficiency, but also gives rise to new business models such as "dynamic pricing" and "risk warning". For example, in the manufacturing industry, the combination of AI and the Internet of Things is reshaping production processes. A certain automobile factory has deployed an AI visual inspection system to analyze the quality of parts on the production line in real time, increasing the defect recognition rate from 90% manual to 99.9%, while reducing rework costs by 30%. This type of application does not rely on the most advanced large language models, but achieves "small incisions, large profits" through deep optimization of vertical scenes. Transitioning from the infrastructure layer to the application layer means a thorough restructuring of the investment logic. The infrastructure layer competes with "technological progressiveness", while the application layer pays more attention to "scenario adaptation" and "commercial closed-loop capability". Taking the education industry as an example, the success of an AI tutoring tool depends not only on the fluency of its model's dialogue, but also on its ability to accurately match students' knowledge blind spots and stimulate learning interest. A startup company analyzed millions of student question data and found that "breaking down problem-solving steps" is more effective in improving learning outcomes than "providing answers directly", thereby optimizing product design and doubling user retention rates. The ability of data-driven iteration is the core competitiveness of application layer companies. The 'greatest opportunities' predicted by Sparks will focus on three major areas. One is vertical industry solutions. Traditional industries such as healthcare, law, and manufacturing are urgently in need of the efficiency revolution brought about by AI. For example, AI legal assistants can automatically generate contract terms, analyze precedents, liberate lawyers from repetitive labor, and focus on strategic work. The second is human-machine collaborative innovation. AI is not replacing humans, but rather amplifying human capabilities. Designers use AI tools to quickly generate draft proposals, and then optimize them based on professional judgment, reducing the time for creative implementation by more than 50%. The third is the combination of edge computing and AI. With the popularization of 5G, AI capabilities are migrating from the cloud to terminals. The real-time quality inspection of smart factories and the real-time decision-making of autonomous driving rely on the low latency processing capability of edge AI. Sparks' insight is essentially a reminder to the market that the ultimate value of AI lies not in the number of model parameters, but in its ability to solve real-world pain points. When the big language model becomes a "technical tap water", the focus of investment must shift to "how to use this water to irrigate a new business ecosystem". In the next two to three years, application layer companies that can deeply understand industry pain points, build data loops, and achieve commercial monetization will become true winners. This AI revolution is shifting from a "technology competition" to a "value creation competition", and the real disruptors are often those innovators who stand on the shoulders of giants but can solve specific problems down-to-earth. (New Society)