Deep Analysis of AI Concept Stock Market: Computing Power and Application as Dual Mainlines Leading the Market, New Logic of Institutional Fund Allocation
On September 24, 2026, in the capital market, AI concept stocks once again became the market focus. With the continuous breakthroughs in artificial intelligence technology and the acceleration of commercialization, AI concept stocks showed a clear differentiated trend, with computing power and application as the two mainlines leading the structural market trend in the second half of the year. This article will conduct an in-depth analysis of the current market dynamics of AI concept stocks, explore the allocation logic of institutional funds, and provide valuable reference information for investors.
I. Overview of the AI Concept Stock Market
As of September 24, 2026, AI concept stocks performed strongly overall, but showed significant internal differentiation. According to market data, the AI sector index has risen by more than 35% year-to-date, far exceeding the broader market index over the same period. However, this rise was not evenly distributed but showed a clear structural feature. Among AI concept stocks, the computing power sector and the application sector became the two leading mainlines, while other sub-sectors performed relatively flat.
From the perspective of fund flow, institutional funds have significantly increased their allocation to AI concept stocks recently. According to incomplete statistics, since September, more than 50 public funds and private institutions have released AI-related investment strategy reports, most of which are optimistic about the opportunities of the dual mainlines of computing power and application. This change in fund flow reflects the market's confidence in the future development of the AI industry.
II. Computing Power Mainline: Continuous Upgrading of Hardware Infrastructure
As the infrastructure of the AI industry, computing power has always been a market focus. In 2026, with the continuous expansion of large model parameter scales and the popularization of AI applications, the demand for computing power has shown explosive growth. This demand has driven the continuous upgrading of the computing power industry chain and also boosted the strength of related concept stocks.
In the computing power sector, sub-sectors such as chips, servers, and optical modules performed particularly well. Taking chips as an example, domestic AI chip companies like Enflame Technology and Cambricon have seen their stock prices hit new highs recently. Their newly launched next-generation inference chips have made breakthroughs in both performance and cost. Especially Enflame Technology's latest T20 chip, which has a 40% performance improvement and a 30% cost reduction compared to the previous generation, greatly lowers the threshold for large model deployment.
The server sector also performed impressively. With the popularization of AI applications, the demand for high-performance servers has surged. Domestic server manufacturers such as Inspur Information and Sugon have successively launched server products optimized for AI scenarios, which have significant improvements in computing power and energy efficiency. Optical modules, as key components for data transmission, have also benefited from the growth in AI computing power demand, and related companies like Accelink have shown strong stock performance.
It is worth noting that the upgrading of computing power infrastructure is not only reflected in the hardware level but also in the software and algorithm levels. In recent years, domestic enterprises have made breakthroughs in software fields such as AI compilers and distributed training frameworks, and these technological advances have further improved the efficiency of computing power utilization and reduced the development costs of AI applications.
III. Application Mainline: Accelerated Commercialization and Implementation
If computing power is the "infrastructure" of the AI industry, then application is the "superstructure". In 2026, the expansion of AI application scenarios and the acceleration of commercialization have become another major mainline driving the strength of AI concept stocks.
In the application field, the commercialization of large models is the hottest topic currently. Domestic large model companies such as Zhipu AI and MiniMax have recently launched commercial products and achieved implementation in multiple industries. Zhipu AI's latest valuation has exceeded 100 billion, and its large model products have made significant progress in applications in fields such as finance, healthcare, and education. MiniMax, with its unique model architecture and user experience, has secured a place in the consumer application market.
Besides large models, the application of AI in various vertical industries is also accelerating. In the manufacturing industry, AI technology is widely used in production optimization, quality inspection, supply chain management, and other fields, improving production efficiency and product quality. In the financial field, AI-driven intelligent investment advisory, risk control, anti-fraud, and other applications are becoming increasingly popular. In the medical field, AI-assisted diagnosis and drug research and development applications have also made breakthroughs.
It is particularly noteworthy that the application of AI in small and medium-sized enterprises (SMEs) is becoming a new growth point. As AI technology matures and costs decrease, more and more SMEs are adopting AI solutions to enhance their competitiveness. This trend brings broad market space for AI application service providers.
IV. New Logic of Institutional Fund Allocation
In the AI concept stock market, the allocation logic of institutional funds also shows new characteristics. Unlike the past focus solely on technological breakthroughs, current institutions pay more attention to the commercialization capability and profit prospects of AI technology.
First, institutions pay more attention to the profitability of AI enterprises. In the past few years, AI enterprises generally faced profit pressure, but with the advancement of commercialization, this situation is changing. More and more AI enterprises are starting to achieve positive cash flow, and even profitability. Institutional investors have given positive evaluations, believing that this is a sign of the AI industry moving from concept to reality.
Second, institutions pay more attention to the actual effect of AI applications. In investment decisions, institutions not only focus on technical indicators but also attach more importance to the effect of AI applications in actual scenarios and user feedback. Those AI applications that can truly solve industry pain points and create actual value are more likely to gain the favor of institutions.
Third, institutions pay more attention to the synergistic effect of the AI industry chain. In investment layout, institutions not only focus on a single enterprise but also attach more importance to the collaborative development of the entire industry chain. For example, when investing in AI application enterprises, they will simultaneously pay attention to their upstream computing power suppliers and downstream user groups, and evaluate the competitiveness and development potential of the entire industry chain.
V. Future Outlook and Investment Recommendations
Looking ahead, the trend of AI concept stocks will continue to follow the structural feature led by the dual mainlines of computing power and application. With the continuous advancement of technology and the expansion of applications, the AI industry is expected to maintain high-speed growth, bringing long-term investment value to related concept stocks.
For investors, the following aspects need to be paid attention to:
- Focus on technologically leading enterprises: In the computing power field, focus on enterprises with technical advantages in chip design, server manufacturing, etc.; in the application field, focus on enterprises with core technologies that can solve practical problems.
- Focus on commercialization capability: Choose enterprises that have achieved commercialization and have stable sources of income, avoiding pure concept speculation.
- Focus on industry chain synergy: Focus on enterprises that can form synergistic effects with upstream and downstream enterprises and build ecosystems.
- Focus on policy support: As a national strategic priority, the AI industry will continue to receive increased policy support; focus on enterprises benefiting from policy dividends.
In conclusion, AI concept stocks, as a core field of future technological development, have long-term investment value. In the current market environment, investors should remain rational, focus on enterprises with both strong technical strength and commercialization capabilities, and seize the long-term opportunities of AI industry development.
