AI is not valuable just because it can "chat". The AI employee matrix of MARKETINGFORCE (02556) proves that only those who truly work for the company deserve a "high valuation".

Every time the market heats up for AI, graphics cards, power supplies, optical modules, and servers are all in high demand. This is normal. Because these things are most similar to "industrialization," and are the easiest for the capital market to understand: who is expanding production, who is getting orders, who is raising upstream prices, it's easy to draw up a plan and tell a story. But the problem is, what is first seen in the AI industry chain may not necessarily be the most valuable in the end. In the past two years, the market liked to see AI as a competition of "who can shovel the most data to the big models"; by 2026, this understanding will no longer be sufficient. Policy statements have clearly shifted towards application landing. A recent opinion from the State Council on promoting the expansion and improvement of the service industry clearly proposes to deepen the implementation of the "AI+" action, and support the procurement of large models and intelligent body services; this year's government work report also proposes to deepen and expand "AI+", promote the rapid promotion of intelligent bodies, and promote the commercialization and scaling application of artificial intelligence in key industries. The trend is very clear: the next stage of AI is not just a technical issue, but a procurement issue, a budget issue, and a job issue. That is why the most worthy part of Hong Kong's AI to look at now may not only be the rendering chain, but also the intelligent body and the AI-native application platform. Because rendering sells "electricity," models sell "brains," and what intelligent bodies sell is the part that companies are willing to continuously pay for: results. To put it bluntly: only when AI has truly entered into business processes such as marketing, sales, customer service, business analysis, knowledge management, and research and development collaboration, it is no longer just a "showcase technology", but it starts to become a "measurable productivity." This is the most unique aspect of the intelligent body sector. It is not selling a tool that talks, but a new organizational unit. It is not the kind of good-looking but useless flower stand that answers when you ask it a question, but a framework that can handle work orders, manage processes, organize knowledge, coordinate multiple systems, and ultimately provide a business result. The key is whether it can actually be useful and bring tangible benefits to the enterprise. So, the most valuable part of the intelligent body sector is not who has the biggest model parameters, but who is closer to the thing: transforming AI from a function, into a position, into a complete system. Why is this logic important? Because it determines the underlying structure of valuation. The rendering chain often earns money based on the business cycle, core factors include CAPEX, supply and demand, and the rhythm of major overseas factories; while the intelligent body platform earns more like "penetration rate money", the key is whether customers are actually using it, whether there are repeat purchases, and if it is expanding from one department to multiple departments. These two businesses have completely different valuation languages. The former is more like a cyclical growth stock, and the latter is more like a platform-type application stock. One relies on hardware expansion elasticity, while the other relies on the deep restructuring of enterprise processes into AI applications. From this perspective, Hong Kong's so-called AI triumvirate actually represents three different positions. XUNCE Technology (03317) is more like a "water seller," fundamentally converting raw enterprise data into high-quality fuel for the model; DEEPEXI TECH (01384) is more like a "master craftsman," focusing on embedding industry knowledge and complex processes into AI; while MARKETINGFORCE (02556) is increasingly becoming a "full-stack Token factory," attempting to ultimately convert models, knowledge, platforms, and scenarios into operational results that enterprises are willing to continually pay for. What truly deserves the market's attention in terms of valuation sensitivity is not just "who is also doing AI," but who can prove that AI has become an "employee" or "professional team" in the organization's structure. This is why the intelligent body sector is more like a "profitable" business that many people imagine. Because traditional software often involves a "one-time purchase," while intelligent body platforms often start with one scenario, expand to another department, and eventually become an organizational-level infrastructure step by step, becoming more deeply involved, gradually enhancing efficiency, it is a medium to long-term investment. Today customer service is used, tomorrow sales will also get involved, the day after tomorrow business analysis will also be integrated, finally knowledge base, marketing, training, retrospective, research and development collaboration will all be unified into one platform. Therefore, what companies buy on the surface is an AI platform, but in reality, it becomes buying a whole set of "AI employee expansion plans." This explains a very real issue: why the intelligent body sector may not rise rapidly every time, but it becomes easier to be discussed repeatedly? Because the market will gradually realize that what is most scarce in the AI industry chain is not "who can build a model," but "who can sell AI as a long-term budget." Ultimately, the capital market pays for profit statements, not for science fiction. Those who can turn AI from "chatting" to "working" have a better chance of growing from thematic stocks to platform stocks. After all: in the first half of the Hong Kong AI, it's about who is providing oxygen to the model; in the second half of the Hong Kong AI, it's about who can get AI a job card.