AI Trend Tracker | H2 2026: From 'Brute Force Computing' to 'Algorithmic Equality', Hong Kong AI Application Industry Faces Revaluation Inflection Point
\n\n2026 has quietly entered its second half, and the global artificial intelligence industry is undergoing a profound transformation. From the computing power competition at the beginning of the year to the current application implementation, the AI industry is gradually moving from the technological enthusiasm stage to the business rationality stage. This transformation is not only reshaping the industry landscape but also having a profound impact on the capital market. Especially in the Hong Kong market, the AI application industry is facing a historic opportunity for value revaluation.
\n\nI. Dynamic Balance of Computing Power Demand and Supply
\n\nIn the first half of 2026, the global AI computing power market experienced unprecedented fluctuations. On one hand, the launch of large models like ChatGPT-5 and GPT-5 has driven continuous demand for high-end computing power; on the other hand, capacity expansion by chip manufacturers such as NVIDIA and AMD has led to a rapid increase in computing power supply. This supply-demand imbalance peaked in the second quarter, causing significant fluctuations in computing power prices.
\n\nAccording to industry data, in Q2 2026, the global AI computing power utilization rate has dropped from 85% at the beginning of the year to 65%, while computing power prices have fallen by about 30%. This change marks the transition of the AI industry from the era of "brute force computing" to the era of "computing power efficiency first." Investors are beginning to reassess the value logic of AI companies, shifting from pure computing power scale to computing power utilization efficiency and commercialization capabilities.
\n\n1.1 Differentiation of Computing Infrastructure
\n\nAt the computing infrastructure level, the market shows a clear differentiation trend. On one hand, the cloud computing power market is shifting from general computing to specialized computing, with the market share of customized chips such as ASIC (Application-Specific Integrated Circuit) and FPGA (Field-Programmable Gate Array) continuing to expand. On the other hand, the demand for edge computing devices is growing rapidly, especially in fields such as smart cars and industrial IoT.
\n\nComputing infrastructure companies in the Hong Kong market, such as SMIC and Semiconductor Manufacturing International Corporation, are occupying favorable positions in this transformation through technology upgrades and capacity expansion. According to industry analyst predictions, in the second half of 2026, the market size of ASIC customized chips is expected to exceed $30 billion, with a compound annual growth rate of over 45%.
\n\n1.2 Decentralization Trend of Computing Networks
\n\nNotably, AI computing networks are showing a clear decentralization trend. Traditional large-scale cloud computing centers are facing challenges from distributed computing networks. The combination of blockchain technology and AI makes computing power resource sharing and trading possible, greatly improving computing power utilization efficiency.
\n\nIn the Hong Kong market, companies focusing on computing blockchain such as Weimob Group and Kingsoft Software are being re-recognized by the market. These companies not only provide computing power services but also achieve efficient allocation of computing power resources through technological innovation, and are expected to stand out in the wave of AI commercialization.
\n\nII. Transition from "Brute Force Computing" to "Algorithmic Equality"
\n\nThe most significant trend in the AI industry in 2026 is the transition from "brute force computing" to "algorithmic equality." This transition is reflected in three aspects: the increasing importance of algorithmic innovation, model miniaturization and lightweighting, and AI application democratization.
\n\n2.1 Increasing Importance of Algorithmic Innovation
\n\nWith the increase in computing power supply, companies relying solely on computing power scale advantages are facing increasing challenges. In contrast, companies with core algorithmic innovation capabilities are gaining market favor. Especially in large model training, algorithm optimization has become key to reducing computing power costs.
\n\nAI algorithm companies in the Hong Kong market, such as SenseTime and Megvii, are maintaining their competitive advantages through continuous algorithmic innovation. According to industry analysis, in Q2 2026, algorithm optimization by these companies has reduced large model training costs by over 40%, significantly improving commercialization feasibility.
\n\n2.2 Model Miniaturization and Lightweighting
\n\nModel miniaturization and lightweighting are another important trend in AI technology in 2026. With the improvement of edge-side computing capabilities, large AI models are developing in the direction of miniaturization and specialization. This trend enables AI applications to be implemented in more scenarios, especially on mobile devices and IoT devices.
\n\nIn the Hong Kong market, companies focusing on model miniaturization technologies such as Xiaomi Corporation and Sunny Optical are facing development opportunities. These companies have achieved efficient operation of AI models on resource-constrained devices through technological innovation, laying the foundation for the large-scale popularization of AI applications.
\n\n2.3 AI Application Democratization
\n\nAI application democratization refers to the process of AI technology popularizing from large enterprises and professional institutions to SMEs and ordinary users. In 2026, with the rise of low-code AI platforms and AI-as-a-Service (AIaaS) models, SMEs and ordinary users can also use AI technology at a lower cost.
\n\nAI platform companies in the Hong Kong market such as Alibaba-SW and Tencent Holdings are promoting the democratization process of AI applications by opening up AI capabilities and providing AI services. According to industry data, in the first half of 2026, the number of users of these platforms' AI services has increased by 300%, with SME customers accounting for over 60%.
\n\nIII. Value Revaluation of Hong Kong AI Application Industry Chain
\n\nAs the AI industry shifts from technological enthusiasm to business rationality, the Hong Kong AI application industry chain is facing a historic opportunity for value revaluation. This revaluation is mainly reflected in three aspects: accelerated AI commercialization, deepening of industry vertical applications, and reconstruction of valuation logic.
\n\n3.1 Accelerated AI Commercialization
\n\nIn the first half of 2026, the commercialization process of AI companies has significantly accelerated. According to industry data, the commercialization revenue of major AI companies in Hong Kong has increased by over 120% year-on-year, with gross margins rising to over 45%. This change indicates that AI companies are shifting from the "burning money for growth" stage to the "profit-driven" stage.
\n\nParticularly noteworthy is that the business models of AI companies are shifting from single software licensing to diversification, including AI-as-a-Service, data monetization, solutions, and other models. This business model innovation has greatly improved the profitability and cash flow levels of AI companies.
\n\n3.2 Deepening of Industry Vertical Applications
\n\nVertical applications of AI technology in various industries are continuously deepening. In the financial sector, AI technology has been widely used in risk control, intelligent investment advisory, quantitative trading and other scenarios; in the medical field, AI-assisted diagnosis, drug development and other applications have achieved significant results; in the manufacturing industry, AI-driven smart manufacturing and predictive maintenance are becoming popular.
\n\nIndustry-specific AI companies in the Hong Kong market such as Ping An Good Doctor and AAC Technologies are building differentiated competitive advantages by focusing on specific industries. According to industry analysis, the vertical AI solutions of these companies have helped customers improve operational efficiency by 20-30%, showing huge commercial value.
\n\n3.3 Reconstruction of Valuation Logic
\n\nWith the deepening of AI commercialization, the market's valuation logic for AI companies is being reconstructed. It is shifting from the past "dream rate" to "sales rate" and "P/E ratio", with more emphasis on commercialization capabilities, cash flow quality and profit prospects.
\n\nAccording to market data, in the first half of 2026, the average P/E ratio of Hong Kong AI application companies has decreased from 50 times at the beginning of the year to 25 times, but the sales rate remains at 8-10 times. This change indicates that the market is shifting from focusing on "future possibilities" to focusing on "current commercial value", providing a better valuation environment for AI companies with real commercialization capabilities.
\n\nIV. AI Investment Opportunities in A-share Market
\n\nCompared with the Hong Kong market, the AI investment opportunities in the A-share market show different characteristics. On one hand, A-share AI companies pay more attention to technological self-control and domestic substitution; on the other hand, A-share AI companies are closer to domestic market demand and have obvious advantages in specific fields.
\n\n4.1 Technological Self-control
\n\nUnder the current international situation, technological self-control has become the core competitiveness of A-share AI companies. Especially in key areas such as chip design, operating systems, and industrial software, A-share companies are breaking foreign monopolies through technological innovation.
\n\nAccording to industry data, in the first half of 2026, the market share of A-share AI chip companies has increased from 15% at the beginning of the year to 30%, and the computing power performance of domestic AI chips has reached 80% of the international advanced level. This change has provided huge development space for A-share AI companies.
\n\n4.2 Domestic Market Demand Driven
\n\nA-share AI companies are closer to domestic market demand and have obvious advantages in specific fields. For example, in fields such as smart cities, smart manufacturing, and digital healthcare, A-share AI companies have established strong competitive advantages based on their deep understanding of the domestic market.
\n\nAccording to industry analysis, in the first half of 2026, A-share AI companies have a market share of over 60% in the smart city field and over 50% in the smart manufacturing field. The rapid growth in these fields has provided continuous development momentum for A-share AI companies.
\n\nV. Risks and Challenges
\n\nAlthough the AI industry has broad prospects, it still faces many risks and challenges. In terms of technical risks, the rapid iteration of AI technology may lead to rapid obsolescence of existing technologies; in terms of market risks, the AI commercialization process may be slower than expected; in terms of policy risks, the tightening of global AI regulation may increase compliance costs for enterprises.
\n\n5.1 Technical Risks
\n\nThe rapid iteration of AI technology is the main technical risk facing the industry. According to industry data, the average iteration cycle of AI technology has shortened from 18 months in 2023 to 9 months in 2026. This rapid iteration requires companies to continuously invest in R&D, otherwise they may face the risk of falling behind.
\n\nEspecially in the large model field, the rapid iteration of technology may cause models developed with a large amount of resources to quickly become obsolete. According to industry analysis, in the first half of 2026, about 30% of AI companies face the risk of product elimination due to too fast technological iteration.
\n\n5.2 Market Risks
\n\nThe AI commercialization process may be slower than expected, which is the main market risk facing the industry. Although AI technology has huge potential in theory, it still faces many challenges in actual applications, such as data quality, scenario adaptation, and user acceptance.
\n\nAccording to industry data, in the first half of 2026, about 40% of AI companies face the problem that the commercialization process is slower than expected, especially in the SME market and consumer market. This situation may lead to tight cash flow for companies and increase operational risks.
\n\n5.3 Policy Risks
\n\nThe tightening of global AI regulation is the main policy risk facing the industry. Since 2026, governments around the world have successively introduced AI regulatory policies, such as the EU's Artificial Intelligence Act and China's Measures for the Administration of Generative AI Services. These policies may increase compliance costs for enterprises and limit technology application scenarios.
\n\nAccording to industry analysis, in the first half of 2026, the compliance costs of AI companies have increased by an average of 30%, and about 20% of AI companies have adjusted their product strategies due to regulatory policy adjustments. This situation may have a negative impact on the profitability and market expansion of enterprises.
\n\nVI. Conclusion and Outlook
\n\nIn 2026, the AI industry is experiencing a transition from technological enthusiasm to business rationality, which brings both challenges and opportunities. For investors, focusing on the commercialization capabilities of AI companies, the depth of vertical applications, and the rationality of valuation will become key.
\n\nIn the Hong Kong market, the AI application industry is facing a historic opportunity for value revaluation. Those AI companies that truly have commercialization capabilities, focus on vertical fields, and have reasonable valuations are expected to gain market favor. Especially companies focusing on algorithmic innovation, model miniaturization, and AI application democratization will stand out in this round of transformation.
\n\nLooking to the future, the AI industry will continue to maintain rapid growth. According to industry forecasts, the global AI market size will reach $1.5 trillion in 2026, with a compound annual growth rate of over 30%. In this process, AI technology will be deeply integrated with various industries, creating huge economic and social value.
\n\nFor investors, paying attention to the long-term development trends of the AI industry and grasping the key nodes of commercialization transformation will be the key to success in the wave of AI. At the same time, it is also necessary to be vigilant about risks such as technological iteration, market expansion, and policy regulation, and do a good job of risk management and asset allocation.
\n\nIn conclusion, the AI industry in the second half of 2026 is in a critical period of transition from "brute force computing" to "algorithmic equality," and this transition will bring new investment opportunities and valuation logic to the capital market. Investors should closely follow industry dynamics and grasp the historic opportunity of AI commercialization.
