The policy direction is clear: technological innovation is measured not just by models but by industrial implementation. Following the launch of DeepSeek V4's preview version, Hong Kong's AI sector has heated up again. The initial market reaction focused on domestic computing power, servers, semiconductors, and cloud providers. However, the underlying logic of this AI rally is no longer simply about "better models." On April 24, the State Council executive meeting studied scientific and technological innovation work, explicitly proposing to anchor the goal of building a technologically strong nation by 2035, accelerate the achievement of high-level technological self-reliance, and emphasize strengthening the role of enterprises as the main body of technological innovation, promoting the deep integration of scientific and technological innovation with industrial innovation. In the context of the AI industry, this message is very clear: AI cannot remain at the level of model leaderboards, parameter competitions, and concept promotion; it must ultimately enter enterprise workflows, industrial sites, and generate real business results. In other words, the policy genuinely encourages not merely "doing AI" but enabling AI to form new, quality productive forces. This also provides a criterion for judging the upcoming differentiation within Hong Kong's AI sector: distinguishing between those who merely talk about models and concepts, and those who can truly turn AI into efficiency, revenue, and ROI that enterprise clients are willing to pay for continuously.
External pressures are reinforcing the logic of self-sufficiency and controllability in the domestic AI supply chain. After the U.S. House Foreign Affairs Committee passed bills related to export controls like MATCH, China's Ministry of Commerce responded by stating its opposition to the generalization of national security and the abuse of export controls, and its firm commitment to safeguarding the legitimate rights and interests of Chinese enterprises. This signifies that the rationale for the domestic AI industrial chain is no longer just a short-term trend but a long-term strategic direction. From computing chips to domestic large language models, and further to enterprise-level AI application platforms, China's AI sector is forming a more complete, closed-loop industrial chain. The significance of DeepSeek V4 is amplified at this juncture: it not only represents the continued breakthrough in capabilities of domestic models but also strengthens the synergistic expectation of a "domestic computing power + domestic models + domestic application ecosystem." However, focusing only on computing power means seeing only the first half of the game. Computing power addresses "whether it can run"; models address "whether it can generate"; the application layer addresses "whether it can make money." As the Hong Kong AI sector truly enters a phase of "separating the wheat from the chaff," the market will not simply ask who has an AI concept, but who can translate AI capabilities into tangible business results.
One of the biggest changes brought by DeepSeek V4 is the further democratization of powerful model capabilities. As models become stronger, context windows longer, and invocation costs lower, the barrier for enterprises to adopt AI naturally decreases. However, this is not a uniformly positive development for the AI application layer. For low-end, superficial wrapper applications, this creates pressure. Previously, the selling point for many AI application companies was simply "we integrated a large model." But as powerful capabilities like those of DeepSeek V4 rapidly become commonplace, "model access" itself ceases to be a scarce resource. Enterprise clients will not ultimately pay long-term for "which model you use," but will ask: Has the number of sales leads increased? Has the conversion rate improved? Have customer service costs decreased? Is business analysis faster? Has R&D efficiency improved? Are management decisions more accurate? These are questions about the practical effects of application. This is the core of the "separating the wheat from the chaff" process in the AI sector. Stronger models will淘汰淘汰 low-end wrappers; cheaper models will amplify the value of results-oriented platforms; more accessible Tokens will test who can increase the commercial output per unit Token. Therefore, DeepSeek V4 does not weaken the application layer but rather creates differentiation within it.
Against this backdrop, MARKETINGFORCE warrants renewed discussion. MARKETINGFORCE's core appeal is not simply "integrating DeepSeek V4," but rather its formation of a results-oriented enterprise AI production system centered around an AI-native application platform, an enterprise agent system, and a full-stack Token factory. From a product structure perspective, MARKETINGFORCE is not a single-point AI tool but possesses a relatively complete platform layer combination: GenAI OS provides the underlying runtime environment and model orchestration capabilities; the AI-Agentforce intelligent agent platform 3.0 is responsible for agent production, collaboration, and governance; the KnowForce knowledge platform accumulates industry knowledge, corporate memory, and business context; products like Data-Agent, GEO, AI Sales Agent, and SuperCodeX Agent then enter specific business scenarios such as business analysis, AI search marketing, sales conversion, and R&D efficiency improvement. This structure aligns highly with the policy emphasis on "the role of enterprises as the main body of technological innovation" and the "deep integration of scientific and technological innovation with industrial innovation." This is because it addresses not whether AI has capability, but how AI enters enterprise systems, business processes, and the chain of revenue and efficiency. This is the core of MARKETINGFORCE's "results platform" logic.
DeepSeek V4 reduces the cost of generic Tokens, while MARKETINGFORCE aims to enhance the value of scenario-specific Tokens. The so-called "full-stack Token factory" can be understood as an AI application layer processing chain: underlying models provide low-cost Tokens; GenAI OS handles model orchestration and system integration; the KnowForce knowledge platform injects industry knowledge and corporate memory; the AI-Agentforce agent platform decomposes model capabilities into executable tasks; scenario applications like Data-Agent, GEO, and AI Sales Agent ultimately deliver business results. The key to this process is not "consuming Tokens" but "processing Tokens." Generic Tokens themselves will become increasingly cheaper, but after processing through industry knowledge, enterprise data, business processes, and the agent system, Tokens can be transformed into higher-value scenario outcomes. Enterprises will not pay long-term for model invocation itself, but they will pay for customer acquisition, deal closures, cost reduction, efficiency gains, and decision-making quality. This is also key to MARKETINGFORCE's position at the "full-stack Token factory" revaluation window.
Judging from market performance in Hong Kong, following the launch of DeepSeek V4, capital initially focused on domestic computing power, semiconductors, and tech hardware, aligning with the policy theme of self-sufficiency and controllability. However, what is more noteworthy to observe subsequently is whether the AI application layer can take the baton. This is because the logic of the computing power layer leans towards supply, the model layer towards performance, and the application layer towards revenue and ROI. If MARKETINGFORCE continues to demonstrate growth in AI application revenue, increased depth of customer usage, and accelerated deployment of multi-scenario agents, it has the opportunity to make the market reconsider its valuation anchor: Is it a traditional marketing SaaS company, or an enterprise AI-native application platform? Is it selling software features, or an enterprise agent system? Is it consuming Tokens, or processing scenario-specific Tokens? Is it talking about AI concepts, or making money from AI? This is the communication window that MARKETINGFORCE should seize most after DeepSeek V4.
Following the launch of DeepSeek V4, Hong Kong's AI sector has entered a new phase of differentiation. At the policy level, technological self-reliance and strengthening the role of enterprises in innovation continue to be emphasized. At the external environment level, export control pressures are accelerating the construction of the domestic AI ecosystem. At the industry level, domestic model capabilities continue to break through, and the cost of generic Tokens keeps falling. At the market level, the AI sector is moving from broad conceptual gains towards "separating the wheat from the chaff." In this process, MARKETINGFORCE's value proposition can focus on three key phrases: Results Platform. Enterprise clients ultimately pay for results, not for model calls themselves. Full-Stack Token Factory. Processing low-cost model Tokens like those from DeepSeek V4 into scenario-specific Tokens that enterprises are willing to pay for continuously. Enterprise Agent System. Upgrading from single-point AI tools to a collaborative, governable, reviewable, and iterable AI employee system. DeepSeek V4 breaks through the imagination at the model layer; MARKETINGFORCE needs to prove the commercial closed-loop at the application layer. The main theme for Hong Kong's AI sector going forward may no longer be about who can talk better about AI, but about who can truly turn AI into revenue, efficiency, and profit.
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