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The AI race is shifting: instead of asking which model is smarter, the market is increasingly focused on who can put AI into the right workflows, the right tools, and the right work context. For Vietnamese marketers, this is no longer a distant technology story, but a story about productivity, cost, and the ability to deploy in real businesses.
- Key points:
- AI is moving from “the strongest model” to “the easiest ecosystem to integrate.”
- Real value comes from embedding AI into everyday work: reading PRs, automation, search, writing, testing, operations.
- Open standards like Agent Plugins show the market is standardizing how AI capabilities are packaged for reuse across multiple platforms.
- Vietnamese marketers should prioritize testing by workflow, not by single-model trends.
What’s happening
Several signals appearing almost at the same time show that AI is entering a new phase. OpenAI announced Agent Plugins as an open standard for packaging “skills” and configuring shared MCP across clients; Cursor, Vercel and others are also joining this direction, showing that the need is no longer for a standalone tool, but for the ability to carry configuration and behavior across different environments. At the same time, Firecrawl launched a plugin for Codex to bring website search, crawling and interaction capabilities into the agent system, while Cursor continues expanding Agent Plugins to connect skills and MCP servers across platforms.
At the application layer, OpenAI says it is making ChatGPT simpler by unifying the experience across models and expanding access for free users, while Perplexity is bringing GPT 5.6 Terra and Luna into subagent and automation roles. This reflects a reality: end users care less about model names than whether AI can work smoothly within the workflow. Tools are competing by “reducing friction,” not just by benchmark scores.
At the same time, leaks and statements from the community suggest OpenAI is continuing to push its product cycle in both software and hardware: there is news of a new large model called Astra, as well as plans for its first AI hardware product. While the certainty of each piece of news varies, the overall picture is still quite clear: AI is being packaged as products and infrastructure layers that can be plugged into many work contexts, rather than just as a standalone chatbot.
Why the advantage is shifting toward integration
The most notable point in this series of developments is the rise of “reusability.” Agent Plugins are described as a container for agent capabilities, allowing the same set of instructions to run on Codex, ChatGPT, Cursor, GitHub Copilot or VS Code. When AI workflows are standardized this way, value no longer lies in each place building something from scratch, but in a well-designed system spreading across multiple tools and teams.
This is also the logic behind Firecrawl building a plugin for Codex and Vercel working with OpenAI, GitHub, Cursor, AWS to promote an open standard. All platforms understand that the winner is not necessarily the one with the strongest model at a given moment, but the one that gets users to build the habit of using AI most often. When skills, tool-calling permissions and context configuration travel with the user, switching costs rise and platform value becomes more durable.
From a marketing perspective, this explains why AI is no longer “a campaign to talk about,” but “a system to operate.” If a model is only strong in demos, it has not yet created commercial advantage. But if it reads PRs, checks security, automatically finds sources, suggests content, triggers workflows and connects to enterprise tools, it begins to affect real KPIs.
The real cost of waiting is losing your operating standard
Cognizant is mentioned as a fairly practical example: they found Copilot ineffective for their needs and switched to Claude Code, because what matters is not a benchmark snapshot but how well it fits current work, especially legacy modernization and large-scale change. The key point here is that productivity comes not only from a smarter model, but from a larger context window, better task chunking, and fit with real workflows.
OpenAI is also moving toward reducing usage complexity, unifying models for fast chat and deep reasoning while expanding the experience for free users. Perplexity, meanwhile, shows a different direction: splitting models by role into subagents and automation to optimize each task layer. Two different approaches, but both point to the same thing: the market does not reward complexity for its own sake, but rewards the system that makes AI “easier to use and more fit for the job.”
For marketers, the cost of waiting is not just missing a new tool. It is also the team continuing to do manually the tasks that AI is already good enough to support: content review, request classification, insight discovery, message variant testing, or quality-check layers. When competitors learn to integrate AI into operations earlier, they accumulate testing speed and decision speed, two things that are very hard to catch up to with media budget alone.
A view for the Vietnamese market
In Vietnam, most businesses do not need to ask “which model is at the top of the leaderboard,” but rather “which workflow is wasting the most time, and where can AI be plugged in?” The context of international platforms shows that value lies in standardization, reuse and workflow-based deployment. This is especially well suited to small and medium-sized businesses, where resources are limited but the need to accelerate content, customer care, market research and reporting remains very high.
Vietnamese marketers should also see AI as an operational infrastructure layer rather than just a creative tool. If a plugin can help a model find sources more accurately, or an agent framework can attach skills to multiple clients, then the internal question is no longer “whether to use AI,” but “which process standard will allow the team to use AI consistently.” For agencies, this is an opportunity to differentiate through process and quality control; for brands, it is an opportunity to speed up production and reduce dependence on repetitive tasks.
What to do now
- Review the 5–7 most time-consuming marketing workflows: research, drafting, QA checks, reporting, lead classification, customer care.
- Choose one flow that can be automated with an agent or plugin, instead of trying AI in a fragmented, tool-by-tool way.
- Build an internal standard for context, input data and review methods so AI can be reused across platforms.
- Track open standards like MCP/Agent Plugins, as these could be a way to reduce integration costs over the next 6–12 months.
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Read more articles in the same category AI Developments.
This article focuses on the integrated AI ecosystem race with a perspective for the Vietnamese market.
References
- OpenAI Developers – Codex Security Review
- Cursor – Agent Plugins
- OpenAI Developers – Agent Plugins
- Vercel – Agent Plugins
- Firecrawl – Codex plugin
- OpenAI – GPT-5.6 Sol and Luna
- Greg Brockman – ChatGPT simplification
- Perplexity – GPT 5.6 Terra and Luna
- Tech Fund – Cognizant and Claude Code
- Gabriel – why launch now
- Hedgie – OpenAI hardware product
