DeepSeek V4 launches AI technology for Hong Kong stocks to "distinguish true from false": MARKETINGFORCE (02556) steps onto the "full-stack Token factory" for revaluation.

Policy First Sets the Tone: Technological Innovation is not just about Models, but also about Industrial Application After the launch of DeepSeek V4 preview version, the AI sector in the Hong Kong stock market has heated up again. The market's initial reaction focused on domestic computing power, servers, semiconductors, and cloud companies. However, the underlying logic of this AI trend is not just about "stronger models". On April 24th, the State Council executive meeting discussed matters related to technological innovation, clearly stating the goal of becoming a technological powerhouse by 2035, accelerating the achievement of high-level technological self-reliance, and emphasizing the need to strengthen the role of enterprises in technological innovation, promoting the deep integration of technological innovation and industrial innovation. In the context of the AI industry, this statement points to a clear direction: AI cannot just stay in model rankings, parameter competitions, and concept dissemination, but must ultimately enter business processes, industrial sites, and real operational results. In other words, the policy does not really encourage just "doing AI", but rather making AI a new productive force. This also provides a standard for the differentiation of the Hong Kong AI sector going forward: who just talks about models and concepts, and who can actually turn AI into efficiency, revenue, and ROI that enterprise customers are willing to continuously pay for. External Pressures Strengthen Independent Controllability, Domestic AI Chain Enters a Period of Reevaluation At the same time, the external environment is strengthening the logic of independent controllability of the domestic AI industry chain. After the U.S. House Foreign Affairs Committee passed export control-related bills such as MATCH, the Ministry of Commerce responded by stating that China opposes the generalization of national security and the abuse of export controls, and will resolutely defend the legitimate rights and interests of Chinese companies. This indicates that the logic of the domestic AI industry chain is no longer just a short-term hot topic, but a long-term strategic direction. From computing chips, to domestic large models, to enterprise-level AI application platforms, China's AI industry is forming a more complete industrial chain loop. The significance of DeepSeek V4 lies in the fact that it not only represents the continuous breakthrough of domestic model capabilities but also further strengthens the synergistic expectation of "domestic computing power + domestic models + domestic application ecosystem". However, if one only focuses on computing power, they are only seeing the first half of the game. Computing power solves the question of "can it run"; Models solve the question of "can it generate"; The application layer solves the question of "can it make money". After the Hong Kong AI sector truly enters the "distinguishing real from fake" stage, the market will not just ask who has AI concepts, but who can actually integrate AI capabilities into operational results. DeepSeek V4 Makes Models Cheaper, but also Makes Low-end Immitations More Dangerous One of the biggest changes brought about by DeepSeek V4 is the further democratization of strong model capabilities. As models become stronger, contexts become longer, and costs of use become lower, the barrier for companies to access AI naturally decreases. However, this is not necessarily a good thing for the AI application layer. For low-end imitation applications, this poses a threat. Many AI application companies in the past used to sell on the basis of "we have integrated large models". But when strong model capabilities like DeepSeek V4 rapidly become widespread, the uniqueness of "integrating models" itself is no longer scarce. Ultimately, enterprise customers will not pay long-term for "which model you used", but will ask: Did the number of sales leads increase? Did the conversion rate improve? Did customer service costs decrease? Was operational analysis faster? Did R&D efficiency improve? Were management decisions more accurate? These are practical questions about the effectiveness of actual applications. This is the core of the "distinguishing real from fake" in the AI sector. The stronger the model, the more it will eliminate low-end imitations; the cheaper the model, the more it will amplify the value of the platform's results; the more widespread the token, the more it will test who can improve the commercial output of each token. Therefore, DeepSeek V4 does not weaken the application layer, but rather creates differentiation within the application layer. MARKETINGFORCE's Alignment with Policies: From AI Tool to Enterprise-level AI Production System In this context, MARKETINGFORCE is worth revisiting. The core focus of MARKETINGFORCE is not just about simply "integrating DeepSeek V4", but about building a results-oriented enterprise AI production system around AI native application platforms, enterprise intelligence systems, and full-stack token factories. From a product perspective, MARKETINGFORCE is not just a single AI tool, but a combination of a more complete platform layer: GenAI OS provides the underlying operating environment and model scheduling capabilities; AI-Agentforce Intelligent Agent Middleware 3.0 is responsible for agent production and governance; KnowForce captures industry knowledge, corporate memory, and business context; Products like Data-Agent, GEO, AI Sales Agent, SuperCodeX Agent enter specific business scenarios such as operational analysis, AI-driven marketing, sales conversion, and R&D efficiency. This structure aligns well with the policy emphasis on "strengthening the position of enterprises in technological innovation" and "deeply integrating technological innovation and industrial innovation". It is not just about whether AI has the capability, but about how AI can enter enterprise systems, business processes, and revenue and efficiency chains. This is the core logic of MARKETINGFORCE's "results platform". A "Full-stack Token Factory": Transforming Policy Benefits into a Business Cycle While DeepSeek V4 lowers the cost of general tokens, MARKETINGFORCE aims to enhance the value of scenario tokens. The concept of a "full-stack token factory" can be understood as a processing chain for the AI application layer: The underlying model provides low-cost tokens; GenAI OS manages model scheduling and system integration; KnowForce injects industry knowledge and corporate memory; AI-Agentforce breaks down model capabilities into executable tasks; Products like Data-Agent, GEO, AI Sales Agents ultimately deliver business results in scenarios such as operational analysis, AI-driven marketing, sales conversion, and R&D efficiency. The key to this process is not just to "spend tokens", but to "process tokens". General tokens themselves will become increasingly cheaper, but after being processed through industry knowledge, corporate data, business processes, and intelligent systems, tokens can be transformed into higher-value scenario results. Enterprises will not pay long-term for just model calls, but will pay for customer acquisition, transactions, cost reduction, efficiency improvements, and decision quality. This is the key to MARKETINGFORCE's position in the "full-stack token factory" reevaluation window. Hong Kong's AI Differentiation: Policies emphasize independence, while the market looks at results Looking at the performance of the Hong Kong stock market, after the launch of DeepSeek V4, funds initially focused on domestic computing power, semiconductors, and technology hardware, which aligns with the policy focus on independent controllability. But what is worth observing next is whether the AI application layer can continue the momentum. For the computing power layer, the logic is supply-oriented; for the model layer, the logic is performance-oriented, and for the application layer, the logic is revenue and ROI-oriented. If MARKETINGFORCE continues to demonstrate revenue growth from AI applications, deepening customer usage, and accelerating deployment of multiple scenario intelligent agents, it has the opportunity to prompt a reevaluation of its valuation in the market: Is it ultimately a traditional marketing SaaS company, or an enterprise AI native application platform? Is it selling software functionality, or an enterprise intelligent system? Is it consuming tokens, or processing scenario tokens? Is it talking about AI concepts, or making money from AI? After DeepSeek V4, this is the communication window that MARKETINGFORCE should seize. Policies provide the direction, DeepSeek provides the catalyst, and MARKETINGFORCE needs to demonstrate the results After the launch of DeepSeek V4, the Hong Kong AI sector has entered a new phase of differentiation. At the policy level, technological self-reliance and the position of enterprises in innovation continue to be strengthened; Externally, export control pressures are accelerating the development of the domestic AI ecosystem; Industrially, domestic model capabilities continue to advance, and the cost of general tokens continues to decrease; In the market, the AI sector is transitioning from a universal rise in concepts to "distinguishing real from fake". In this process, the value communication of MARKETINGFORCE can focus on three key words: Results Platform. Ultimately, enterprise customers pay for results, not just for model calls. Full-stack Token Factory. Transforming low-cost model tokens like DeepSeek V4 into scenario tokens that enterprises are willing to continuously pay for. Enterprise Intelligent System. Upgrading from a single-point AI tool to a system that is collaborative, manageable, reviewable, and iterative. While DeepSeek V4 breaks the imagination of the model layer, MARKETINGFORCE needs to prove the business cycle at the application layer. The main theme for the Hong Kong AI sector going forward may no longer be who can talk more about AI, but who can truly turn AI into revenue, efficiency, and profit.