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AI is crossing an important threshold: from a fast-response tool to infrastructure that can work in the background, handle long-running tasks, and coordinate multiple actions at once. For Vietnamese marketers, this shift is not just about technology, but about productivity, implementation costs, and how internal workflows are designed.
Key points:
- AI is being pushed from “Q&A” toward “execution,” especially for multi-step, background-running tasks.
- Inference costs, agentic capabilities, and deep ecosystem integration are becoming new competitive advantages.
- Small businesses have an opportunity to benefit, but only if they know how to standardize data, processes, and goals.
- Marketers should not wait for a “perfect model”; they should start with a specific problem and measure results clearly.
What is happening
Looking at recent developments, AI is no longer being discussed mainly as a smart chatbot. Instead, new systems are being described as agents that can handle long-term work, operate in the background, and connect with email, documents, calendars, codebases, or operational tools. Gemini Spark is introduced as a personal AI agent running 24/7, while Google has rolled out a series of free tools that touch every stage from content writing, moodboard creation, interface building to programming support and backend management [2][14].
On OpenAI’s side, signals around Astra suggest a similar direction: the new model is rumored to focus on long-running tasks, multi-agent systems, and even math and computer science problems [3][5][11][13]. While much of the information is still speculative, the broader trend is clear: AI’s value is shifting from “can it answer?” to “can it complete complex, multi-step work over a long period of time?”
What is notable is that this race is not only about model capability but also about cost and deployment. One source says 10 mathematical breakthroughs were created at a much lower cost than expected, while tools such as Ollama, DeepSeek-V4-Flash, and efforts to optimize local/streaming execution show that the market is being pulled toward performance per dollar of compute [1][4][10]. In other words, AI is entering a phase of “doing real work” rather than merely “looking smart.”
Why the advantage is shifting from models to ecosystems
In the previous phase, the AI game was usually about answer quality. But current signals show that the advantage is increasingly in deeply integrated ecosystems. Google does not just have Gemini; it also embeds it into Workspace, IDEs, backend management, content creation, and personalized learning [2][14]. For marketers, this matters more than model specs because it determines whether AI truly enters the workflow or remains just an experiment.
On the other side, OpenAI is also being mentioned in the context of new models that not only answer well but can coordinate multiple tasks, support reasoning, handle long projects, and even expand into mathematical research [3][5][11]. When a model can support both thinking and action, the biggest value is no longer in “one good answer” but in its ability to connect to the right data, tools, and access permissions.
This explains why platforms such as Claude, Gemini, and agent tools are racing to publish guides on context engineering, usage rules, and methods for structuring data correctly [6][10][15]. The stronger AI becomes, the more users must know how to “package” tasks properly. The advantage does not automatically go to the best model; it goes to the side that knows how to turn the model into a process.
Lower costs are opening the door to broader automation
A common misconception is that AI only makes a difference when the model is extremely large and expensive. Recent developments suggest the opposite: lower inference costs are making more use cases viable. Some developments mention mathematical proofs being generated at very low API cost, while deployments such as DeepSeek-V4-Flash on Ollama or the MXFP4 build show that demand for cost and speed optimization is rising sharply [1][4][10].
At the same time, the AI startup picture is no longer as simple as “build an app and sell subscriptions.” The fish identifier app story still generates steady revenue, but mainly thanks to ASO, SEO, and product positioning rather than core technology [7]. That proves a very practical point: AI does not erase distribution advantages. It only lowers technical barriers, while business advantage still lies in user acquisition, usage context, and search channels.
That is also why the wave of self-improving agents such as AutoBots or promises about recursively self-improving workflows should be understood as a market signal: businesses want AI to handle repetitive, time-consuming, and measurable work [8]. As the cost per task falls, marketing teams will tend to automate more deeply in areas such as reporting, analysis, content variations, lead nurturing, and sales support.
A perspective for the Vietnamese market
For Vietnamese businesses, the biggest opportunity is not racing to use the “strongest model,” but taking advantage of a tool layer that is becoming cheaper and easier to access. Free or low-cost packages from Google, cloud/local-running models, and agents that can connect Gmail, Docs, Sheets, Calendar open up a new reality: a small team can still run many tasks like a larger one [2][10][14].
But Vietnam also has a clear limitation: data and processes are often fragmented and not standardized, making it hard for AI to create real value if it is used only as a “content-writing machine.” The strongest tools today require good context engineering, meaning the input must be structured, goal-driven, and clearly constrained [6][15]. This is something many local businesses tend to overlook.
For Vietnamese marketers, the biggest impact is the shift from doing each task manually to designing task systems. If a campaign used to require many people to write, analyze, respond, and optimize, AI can now provide the foundation for repetitive steps. Marketers will need to move into roles of editing, quality control, insight definition, and performance measurement. In other words, AI does not replace marketers; it changes how marketers organize work.
What to do now
- Choose one repetitive marketing process to test AI first, such as report summarization, lead classification, ad variant writing, or FAQ responses.
- Standardize input data: briefs, personas, KPIs, brand voice, channel priorities, and legal constraints should be written into reusable forms.
- Evaluate AI by operational efficiency, not just by how good the answer sounds: time saved, error rate, cost per task, and reusability.
- Prioritize tools that can integrate into existing workflows such as email, documents, spreadsheets, CRM, calendars, and content libraries.
The biggest message from the current AI wave is this: value no longer lies in creating an impressive demo, but in turning AI into an everyday operating layer. Whoever does that early will gain advantages in speed, cost, and scalability — things every marketer needs, but not everyone has the time to build from scratch.
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This article focuses on AI shifting from chat to doing the work for you with a perspective for the Vietnamese market.
References
- Gemini Spark is here
- Google has released a stack of free AI tools
- Astra confirmed as OpenAI’s next major model
- Per Aspera Ad Astra OpenAI is preparing “Astra”
- GPT-6 / Astra Leaks
- Anthropic just released NEW rules for context engineering for Claude 5
- My fish identifier app still brought in $902/month in past 30 days
- Announcing AutoBots – Recursively Self-Improving Agents
- DeepSeek-V4-Flash-0731 is now available on Ollama’s cloud
- DwarfStar branchk ds4f-mxfp4 can run the lossless MXFP4 DeepSeek v4 Flash GGUF
- A few things to note about today’s mathematical results announced by OpenAI
- The AI Genius Who Nearly Lost a $45 Billion Portfolio
- SpaceXAI just launched a limited-time discount on Grok credit top-ups
- Hey @thsottiaux, I built an experimental virtual keyboard for Codex
