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AI race is moving away from the question of “which model is smartest” and toward a more practical one: who can control infrastructure, inference costs, safety, and distribution channels better. For Vietnamese marketers, this matters because it directly affects deployment costs, the speed of bringing AI into workflows, and the ability to turn AI into a competitive advantage rather than just a technology to experiment with.
- Key points:
- AI advantage is shifting from the quality of a single model to the ability to operate at scale, cheaply, and reliably.
- Safety, testing, and risk governance are becoming core parts of the game, no longer a “secondary” layer.
- Real value lies in integration: whoever connects AI to products, processes, and distribution better will have the edge.
- Vietnamese marketers should prepare for the era of “AI as infrastructure” instead of just buying tools because they are trendy.
What’s happening
Recent signals show that AI is entering a very rapid stage of maturity, but in a less flashy direction: model capability, serving infrastructure, and deployment safety are being evaluated at the same time. OpenAI and Anthropic both had to disclose incidents during external security evaluations, while UK AISI also recorded models capable of producing harmful behavior under intentionally relaxed guardrails in testing conditions. That shows the game is no longer about “launching a new model” but about proving that the model can be operated safely in the real world.
At the same time, providers and platforms are intensifying the performance race from an infrastructure angle. Artificial Analysis launched an endpoint accuracy index, emphasizing that each provider may trade some accuracy for speed or cost. Cursor has released the MoK megakernel for much faster MoE training than the public baseline. At the application layer, Not Diamond introduced a model router for coding agents to automatically choose the right model and reasoning level, while Base44 announced a dedicated model for app building with strong efficiency and better credit savings than competitors. Together, these developments point to one thing: AI is becoming a systems problem, not just a model problem.
At the product and commercial level, companies are also beginning to price AI as a business capability. SpaceX, in the broader picture, describes AI as one growth pillar alongside Starlink and Starship; it talks about expanding AI compute, improving Grok 4.5, and large service cloud contracts. Although each company tells the story differently, the common thread is that AI is being tied to revenue, distribution, and service costs, not just benchmark numbers.
Why AI advantage is shifting from models to infrastructure
The most notable takeaway from the sources is that a better model is no longer enough to create a durable advantage. Artificial Analysis shows that serverless endpoints can differ significantly in accuracy even when they rely on the same set of weights, because providers optimize for cost and speed in their own ways. In other words, businesses buying AI are not buying “a model” alone, but the way that model is served. For marketers, this is an important signal: the AI experience in the tools they use depends heavily on the infrastructure layer behind it, not just on the model name.
That view also matches the stories of Not Diamond Code and Base44. One automatically selects the model and reasoning level to reduce costs while maintaining quality; the other shows that a model specialized for app building can achieve near top-tier performance while saving significant credit. When cost-per-task becomes a critical variable, the advantage will tilt toward whoever optimizes the path from query to result, rather than whoever has the “prettiest” model on the leaderboard.
Cursor’s open-sourcing of the MoK megakernel also points to another layer of competition: kernel optimization, communication in MoE, and training efficiency are where the real differences are created. In other words, AI is becoming an industrial software race, where performance, operations, and ecosystem determine profit margins. Marketers do not need to go deep into the technical details, but they do need to understand that AI prices in products will increasingly be pushed down by infrastructure competition and pipeline optimization.
Safety and testing are becoming competitive advantages
Warnings from UK AISI, Anthropic, and OpenAI show a reality: the stronger the model, the more rigorous the testing it must undergo. Recent security evaluations were conducted under very permissive conditions, yet still found behavior that could be harmful or exceed expectations. This should not be read as “AI is dangerous, so slow down,” but rather as: the organizations that can prove better risk control will be easier to trust and adopt.
Alongside that is the rise of safety models and tools such as Mistral’s Shieldstral, as well as efforts by the Open Secure AI Alliance to share guidelines, reviews, and disclosures about incidents. When safety is standardized, it becomes part of the product, much like security in enterprise software. For brands, this is a major difference: B2B customers will not only ask “What can AI do?” but also “Is AI safe, does it control data, does it have an audit trail, and does it support permissions?”
This reality also creates a notable paradox. The side that is more open, tests more, and is more transparent may incur higher short-term costs, but it builds long-term trust. In the AI era, trust is not just a communications message; it is an operational capability. Marketing brands that want to use AI for creative work, customer care, or automation must also treat governance as part of strategy, not as something left to IT at the end.
What this means for the Vietnamese market
For Vietnamese businesses, the most important lesson is not to chase only the “strongest model” or the “newest tool.” What is more worth investing in is the ability to connect AI to internal data, customer service processes, content production, and performance measurement. In a context where many platforms are competing on price, accuracy, and smart routing, the businesses that organize clean data layers, clear use cases, and strong workflows will use AI far more effectively than those that simply buy accounts and leave them unused.
From a marketing perspective, the AI race is also changing how brands are built. Growth no longer comes only from advertising or content volume, but from the ability to personalize quickly, test cheaply, and respond in real time. As model routers, endpoint accuracy, and voice/text/image tools become cheaper and more flexible, the competitive edge for Vietnamese marketers will lie in how they combine strategy, data, and operations. In short: AI will not replace marketing teams, but it will replace teams that do not know how to reorganize the way they work.
What to do now
- Review all marketing workflows that use AI: which steps need a strong model, which only need a cheap model, and which can be automatically routed to optimize cost.
- Build a set of criteria for choosing an AI provider, including accuracy, cost, integration capability, logging/audit, and data policy.
- Prioritize use cases that can be measured immediately, such as content support, lead classification, customer replies, insight summarization, and ad variant generation.
- Set up an AI output review process: who is responsible, what the risk thresholds are, and how records are kept when deploying in brand communication.
The practical conclusion is that the AI race is shifting from “who has the best model” to “who turns AI into the most efficient, safe, and profitable system.” For Vietnamese marketers, now is the time to learn how to buy, operate, and measure AI as part of growth infrastructure, not as a piece of equipment for display.
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References
- OpenAI: We’re detailing two new incidents that occurred during external cyber evaluations conducted by independent evaluation partners.
- Anthropic: UK AISI cybersecurity evaluation of Claude and GPT-5.6 Sol
- Artificial Analysis: Endpoint Accuracy Index
- Cursor: Mixture-of-Kittens (MoK) open-sourcing
- Not Diamond: Not Diamond Code model router
- Base44 / Wix: Base1 benchmark performance
- Open Secure AI Alliance guidelines for incident learning
- Mistral AI: Shieldstral open-weights content safety model
