How the New AI Model Is Changing Marketing Tool Selection

Written by Đội ngũ Marketing365, reviewed under the Content Policy of Marketing365. Last updated 20/08/2026.

Contents

The new AI model is changing the standard for choosing marketing tools
What has changed in the new AI model — and how it affects the way marketing teams use tools
What mechanism is pushing the value of the new AI model toward control, safety and workflow?
In Vietnam, the new AI model will be chosen by how easy it is to test and control
What Vietnamese marketers should do with the new AI model
References

The new AI model is not only making users expect more from answers. It is pulling marketing teams toward a different set of standards: real-world performance, safety, output control, and how well a tool fits internal workflows. For Vietnamese marketers, this is a very practical shift because choosing the wrong model can increase operating costs, data risks, and even moderation workload.

Key points

The AI race is shifting from “sounds good” to “can do it and can be controlled.”
Recent signals show that infrastructure, safety, and workflow are where the advantage is created.
Marketing teams should not buy AI based on instinct; they should choose by task, risk, and process.
In Vietnam, the model that is easiest to test, replace, and control will have the best chance of going further.

The new AI model is changing the standard for choosing marketing tools

Three notable signals are moving together: Stanford has launched a 2.5-hour course explaining the operating layers inside LLMs; Anthropic is said to pay up to $750,000 a year for engineers who understand those layers; and Sam Altman says Astra is a powerful model, but it needs more time before a safe release because of its cyber capabilities. When one side is pouring money into people who understand how models run, and another is tightening safety before scaling, the market’s emphasis has already changed.

The common thread is not the company name, but how the industry is valuing models. Buyers are no longer asking only, “Does it answer well?” They are asking, “Can it be controlled, can it be explained, can it be used in real business work?” For marketing, that is a shift from a tool to try into a system that must be accountable in operations.

At the same time, the AI models and services around it are also showing that the application layer is being pushed closer to real needs. xAI has brought Imagine Image 2.0 to Grok for editing images, removing backgrounds, resizing, and combining multiple images; Pi App Studio lets users build apps with AI tools such as ChatGPT, Claude, Gemini and Copilot; and GeForce NOW has been modded into a Windows desktop to run LM Studio and an AI model directly on a cloud machine. These are not separate stories. Together, they show AI moving out of the “nice demo” zone and into workflow, infrastructure, and real access.

What has changed in the new AI model — and how it affects the way marketing teams use tools

This section is about things that can be seen and checked: learning materials, features, platforms, and access methods. They create very specific pressure on how marketing teams choose tools and assign work to AI.

A course on LLM layers: how much of the model should marketing teams understand?

Stanford has a 2.5-hour course that walks through the foundational layers of LLMs, while Anthropic pays very high salaries to engineers who understand those layers. These two signals say the same thing: understanding models is no longer only the job of technical teams. Marketers do not need to go as deep as writing kernels, but they do need enough understanding to know where a model is strong, where it is weak, and why the same prompt can produce different quality. Reference: https://x.com/RoundtableSpace/status/2086373165509341199

University lecture hall, lecturer pointing to a model-layer diagram, marketing team taking notes

When AI tools enter content, advertising, CRM and analytics, foundational model knowledge helps marketing teams set better guardrails. In the same campaign, someone who understands how the model handles context will spend less time fixing prompts, be less likely to blame the wrong team, and be less likely to buy the wrong tool just because the demo looks good. This is the practical value of understanding AI at the operational level, not at the slogan level.

Grok Imagine Image 2.0: faster image editing, but higher moderation responsibility

xAI has brought Imagine Image 2.0 into Grok with clearer editing functions, such as background removal, smart resizing, and combining up to 5 images in a single generation. For marketing, this is the kind of feature that immediately increases visual production speed, especially for social, ads and e-commerce campaigns that need many variations.

But the more automation there is, the clearer the moderation loop must be. If design and content teams do not define brand image standards in advance, AI images can produce drafts very quickly but remain inconsistent in style, messaging and legal safety. Reference: https://x.com/moneyacademyKE/status/2086316544162939101

Pi App Studio and vibe coding: marketing will buy speed, not just tools

Pi App Studio is pushing a very clear direction: build apps with AI-assisted development, using familiar AI tools such as ChatGPT, Claude, Gemini and Copilot. This approach matters for marketing because many teams do not need a large software product right away; they need a landing page, a mini app, an internal form, or a content-checking tool that can run quickly.

What changes here is the buying mindset. Instead of waiting for IT to deliver a long project, marketing can test a small workflow, measure the result, and then scale it. What is being bought is not just software, but the speed of iterating ideas. Reference: https://x.com/santosinakatomo/status/2086294550151152122

What mechanism is pushing the value of the new AI model toward control, safety and workflow?

This is the core of the story. Three mechanisms are simultaneously reshaping AI’s value: control over outputs, safety levels for sensitive tasks, and the ability to plug into real processes.

Output control: a powerful model is not automatically trustworthy

Sam Altman says Astra is a powerful model, but making it safe enough for broad use needs more time because of its cyber capabilities. That is a direct reminder that a strong model is not necessarily a business tool that can be used hands-off right away. For marketing, the risk is not only a wrong answer; it is also content that drifts from brand voice, touches sensitive data, or creates something that cannot pass internal review.

So the real value of the new AI model lies in the control layer around it: who can use it, what it can be used for, how logs are handled, and which parts require human approval. Companies that do not set rules early will pay for it in correction time. Reference: https://x.com/haider1/status/2086354861738647949

Infrastructure and access: whoever controls the AI runtime environment gains an edge

The fact that modders were able to open a Windows desktop through GeForce NOW and then run LM Studio on a cloud machine is a very clear example of the value of access. When users can touch the real runtime environment, AI is no longer just a concept floating in the cloud; it becomes part of the working infrastructure. Reference: https://x.com/Pirat_Nation/status/2086361843254071466

User sitting in front of a cloud-connected TV, with cables and a controller beside them

For marketing, the lesson is elsewhere: the tool that allows better connection to workflows, files, dashboards, storage and user control will be preferred over a model that is only strong on benchmarks. In other words, the cost is not only in the license; it is in how much control you have over data, outputs and the runtime environment.

Workflow fit: the real value of AI is moving beyond the prompt

When Pi App Studio lets users build apps with AI tools, or when Imagine Image 2.0 supports image editing directly in Grok, AI’s value is not only in the final answer or image. It lies in being able to plug directly into the steps before and after: receiving the brief, creating a draft, editing, publishing, and measuring performance.

This forces marketing to see AI as one link in a process, not a toy to experiment with. If the workflow is already well designed, AI shortens repeated steps. If the workflow is not standardized, AI only makes the mess faster. Reference: https://x.com/moneyacademyKE/status/2086316544162939101; https://x.com/santosinakatomo/status/2086294550151152122

In Vietnam, the new AI model will be chosen by how easy it is to test and control

The Vietnamese market usually does not buy AI because of one impressive demo. Many businesses buy because they can test it quickly in a small team, avoid locking themselves into one vendor, and handle internal data properly. In that context, the new AI model that supports a clear workflow, allows strong output control, and does not require overly complex infrastructure investment will have the advantage.

Vietnamese corporate meeting room reviewing an AI buying checklist beside a locked filing cabinet

What Vietnamese marketers need to remember is that the risk is not only in licensing costs. It also lies in adaptation costs: retraining the team, adjusting workflows, setting guardrails, and keeping content aligned with the brand voice. Under tight budgets, a “very powerful” tool that is hard to deploy often loses to a good-enough tool that can be used immediately.

Content, performance and CRM teams should pay special attention to three things: the ability to use internal data, the ability to review before publishing, and the ability to switch tools if the model changes policy. This is a more practical way to buy AI for the Vietnamese market.

What Vietnamese marketers should do with the new AI model

Evaluate models by task: content, images, internal chatbots, sales support, rather than giving them a generic score.
Set output moderation rules before bringing AI into email, ads, social and sales materials.
Prioritize tools that allow small tests, quick changes, and do not force data to be locked into a single platform.
Assign one person to own the AI process in the team to monitor data risk, copyright and brand voice.

See more marketing analysis and guides at https://marketing365.vn.

Follow more analysis from Marketing365 to stay updated on the latest marketing trends.

Read more articles in the same category AI Developments.