From Token Cost to Token Value: DeepSeek V4 Pro and the Economics of Enterprise AI Compared With Marketingforce

The enterprise AI debate is moving beyond model capability and Token pricing toward a more fundamental question: how much business value can AI generate?

DeepSeek V4 Pro illustrates this shift. As stronger models improve agentic execution, tool use and complex-task performance, enterprises are increasingly evaluating AI at the task level rather than simply comparing the cost per Token.

A higher-priced model can still improve overall economics if it completes tasks with fewer retries, less human intervention and lower integration costs. In other words, the relevant metric is moving from cost per Token to value per completed task.

This shift potentially increases the importance of the enterprise application layer.

Foundation models provide general intelligence, but enterprises still need to connect that intelligence with proprietary knowledge, permissions, tools and workflows. Marketingforce (2556.HK) addresses this layer through KnowForce AI, AI-Agentforce 3.0 and GenAI OS, connecting enterprise knowledge, agent execution and operating governance.

Its “Full-Stack Token Factory” strategy can therefore be understood less as a technology label and more as an economic framework:

Model capability → enterprise knowledge → agent execution → business workflow → measurable outcome.

The objective is to convert general-purpose Tokens into what Marketingforce describes as “Scenario Tokens” — outputs tied more closely to specific enterprise tasks and business value.

The financial question is whether this architecture can ultimately produce operating leverage.

According to figures presented by the company for H1 2026, revenue reached RMB1.96 billion, AI application revenue RMB1.13 billion and net profit RMB200 million, while net operating cash inflow was approximately RMB500 million. Customer count increased 25.9%, average monthly revenue per user rose 80.0%, and employee productivity improved 85.6%.

Taken together, these figures suggest a potential progression from customer expansion to deeper monetization, operating efficiency, profitability and cash generation. They should not, however, be attributed to DeepSeek V4 Pro or any single Marketingforce product.

For investors, the next stage of validation is straightforward: whether AI application revenue continues to outgrow the group, customer growth and ARPU remain positive simultaneously, headcount grows materially slower than revenue, and profit continues to convert into operating cash flow.

The broader implication is that enterprise AI may be moving from a Token price war to a Token productivity competition.

DeepSeek and other foundation-model developers are pushing the capability frontier. The enterprise application layer must prove that those capabilities can be converted into repeatable, measurable and cash-generating business outcomes.

That is ultimately where enterprise AI economics will be tested.