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AI race is shifting focus very quickly: it is no longer just about which model is “smarter,” but which is easier to use, cheaper, and better integrated into real workflows. For Vietnamese marketers, this is no longer a purely technological story; it determines content production costs, testing speed, and how competitive advantage will be built over the next 12 months.
- Key points:
- The AI market is leaning toward trial experience, pricing, and ecosystem rather than just showcasing model capabilities.
- Open-weight, free tiers, and developer credits that encourage experimentation are driving prices down and blurring the advantage of the “big players.”
- Real value is gradually shifting to the infrastructure layer: tools, integrations, memory, security, workflows, and distribution.
- Vietnamese businesses need to see AI as an operational capability, not just a content-generation tool.
What’s happening
Looking at recent developments, it is clear that AI is entering a phase of “aggressive commercialization.” On one side, users are complaining that some providers are tightening usage limits, communicating poorly, and making the experience increasingly frustrating; on the other, competitors are pushing a “try now, pay later” strategy with free models, free tiers, or developer credits. These reactions are appearing in parallel in discussions around Anthropic, OpenAI, DeepSeek, and integration platforms such as OpenCode or TokenRouter. [1][2][3]
What stands out is that users no longer judge AI by benchmarks alone. They compare total real cost, convenience, workflow fit, and the feeling of being “treated well” by the product. DeepSeek V4 Flash is praised for competing with top models while being far cheaper; OpenCode makes that model a free option directly in the terminal; TokenRouter also offers Kimi K3 for free along with developer credits. In other words, the game is shifting from “which model is strongest” to “which model creates the best usage habit.” [2][3][5]
At the same time, another signal is emerging: AI is no longer standing alone, but is pulling an entire new ecosystem with it. Security companies, materials firms, defense players, agent tools, and programming platforms are all being drawn into the investment vortex. Some companies are opening offices in Singapore, some are accelerating product expansion, and the infrastructure layers around models are attracting more capital. This shows that the economic value of AI is spreading beyond the model itself. [4][6]
Why the advantage is shifting from model to experience
In AI debates, what usually makes users walk away is not that the model has “gotten worse,” but that the product experience has become less appealing. Complaints about poor communication, reduced limits, and pushing inconvenience onto customers reveal a rather cold truth: if a product does not create a sense of trust, users will leave even if the technology is still strong. Critical remarks aimed at Anthropic reflect exactly this pressure. [1]
On the other hand, OpenAI is mentioned as an example of marketing that is “experience-driven” rather than based on verbal claims. While that perspective comes from community observation, it points to a very important principle: users are willing to stay loyal to the platform that lets them try quickly, use it often, and feel immediate value. By the same logic, platforms like OpenCode or TokenRouter are not selling an “AI dream”; they are reducing friction to the point where users can start in one minute and use a free model right away. [1][3]
For marketers, this is an important signal: AI is no longer a promise used to persuade customers, but an experience that must be designed. Whoever reduces friction better wins the user’s testing time; and in AI, testing time often turns into habit. [1][3][5]
The real cost of the race is no longer promotion, but infrastructure and distribution
As models are increasingly packaged into free offerings, the cost of competition shifts elsewhere: compute, integrations, workflow support, and the ability to reach the right users. DeepSeek V4 Flash is described as a model that is “cheap but powerful,” while OpenCode and TokenRouter make it even more accessible. This shows that value lies not only in the base model, but also in how the model is put into users’ hands. [2][3]
At a deeper level, the list of “AI second-order winners” highlights that the big winners may not be the labs building models, but the companies solving the needs that arise around AI: new materials for chips, security for autonomous agents, or industries that must adapt to automation. This is a familiar economic logic: as core technology gets cheaper, margins shift to the infrastructure, integration, and risk-management layers. [4]
In addition, the fact that AI companies are expanding their presence in Singapore shows that the Asian market is being seen as a hot spot for distribution and operations. This is not just about selling products; it is also about entering an ecosystem where enterprise customers, developers, and partners come together. [6]
A perspective for the Vietnamese market
For Vietnamese businesses, the lesson is not about choosing the “strongest model,” but the “easiest system to use for business goals.” If an AI tool helps a marketing team produce content faster but is difficult to integrate, hard to control in cost, or not suited to approval workflows, the advantage will be very short-lived. By contrast, an AI stack that is powerful enough, has a free tier or low cost, can be tested quickly, and can be connected to CRM, CMS, customer service, or data analytics will have a much bigger impact. [2][3][5]
The Vietnamese market will also be strongly affected by AI becoming commoditized at the model layer. As prices fall and competitors multiply, the real differentiation will lie in localization, proprietary data, system integration, and the ability to turn AI into a measurable process. Marketing teams should treat AI as an operational acceleration layer, not just a tool for writing posts or generating images. [4][6]
What to do now
- Review the entire marketing workflow to identify which steps AI can shorten while still maintaining quality control.
- Prioritize testing models or platforms with free tiers, credits, or low costs before committing long-term budget.
- Build context and an internal knowledge library for AI instead of only teaching prompts; the goal is sustainability when used in real workflows.
- Evaluate AI by total implementation cost: model cost, integration, training, moderation, and data security.
In short, the AI race is leaving the stage of spectacle and entering real life. The winner is not necessarily the one who talks best, but the one that gets users to use it the most, at the lowest cost, and with the least friction.
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References
- Mad respect to DeepSeek… V4 Flash…
- OpenCode just added DeepSeek V4 Flash as a FREE built-in model… / TokenRouter just made Kimi K3 FREE…
- Anthropic should take a page out of OpenAI’s marketing playbook…
- Companies to watch (AI’s second-order winners)…
- Most AI agent courses charging $500 teach you prompting tricks… Google just released a free 1-hour course…
- wow, exciting to see that, in just the last few months, these companies have opened offices in singapore…
