DeepSeek distraction: Why does the AI local infrastructure rather than the model define the success of the enterprise
Imagine driving Ferrari on the road of collapse. No matter how fast the car is, if there is no solid foundation to support it, its entire potential will be wasted. This ratio summarizes today’s corporate AI landscape. Enterprises are often obsessed with shiny new models such as DeepSeek-R1 or Openai O1, while ignoring the importance of value from infrastructure. Enterprises must not only focus on who builds the most advanced model, but also needs to start investing in strong, flexible and secure infrastructure, so that they can work effectively with any AI model, adapt to technological progress, and protect their data.
With the release of DeepSeek, this is a highly complex large language model (LLM). It has controversial origin. The industry is currently plagued by two issues:
- Is DeepSeek real or just smoking and mirror?
- Do we invest in companies such as Openai and NVIDIA?
The Twitter comments on the tongue suggest that Deepseek will do what Chinese technology is best: “It’s almost the same, but cheap.” Others seem to be so good. The NVIDIA market fell nearly 600 billion US dollars a month after its release. Axios believes that this may be an extinct activity of venture capital companies. The main voice questioned the $ 500 billion commitment to the STARGATE project in just 7 days after the announcement of the announcement.
Today, Alibaba has just announced a model claiming to exceed DeepSeek!
The AI model is only part of the equation. This is a shiny new object, not the entire packaging of the enterprise. The lack of AI local infrastructure.
The basic model is just a technology. It requires the ability and can transform it into a tool for strong business assets. As AI develops at the speed of lightning, the model you adopted today may be out of date. Enterprises really need not only the “best” or “latest” AI model, but also seamlessly adapt to new models and effectively use their tools and infrastructure.
Deepseek represents destructive innovation or exaggerated hype is not a real problem. Instead, the organization should put doubt theory aside and ask if you have the correct AI infrastructure architecture, which can maintain flexibility as the model improves and changes. Can they easily switch between models to achieve their business goals without re -designing everything?
Model and infrastructure and application
In order to better understand the role of infrastructure, please consider using the three components of AI:
- Model: These are your AI engines-LARGE language models (LLM) such as CEMINI, Gemini and Deepseek. They perform tasks such as language understanding, data classification, prediction and other tasks.
- Infrastructure: This is the basis of the AI model operation. It includes the tools, technology and hosting services required for integration, management and extended models, and at the same time keep them consistent with business demand. This usually includes technology that focuses on calculation, data, arrangement and integration. Amazon and Google and other companies provide the infrastructure of running models and integrate them into the technology stack of the enterprise.
- Application/use case: These are applications that end users see, and it uses the AI model to achieve business results. Hundreds of products are fixed from existing companies on AI to existing applications (that is, Adobe, Microsoft Office and Copilot.
Although models and applications often attract people’s attention, the infrastructure has quietly enabled everything to go smoothly and lays the foundation for future models and applications. It ensures that the organization can switch between the models and unlock the actual value of AI without damaging the bank or destroying operations.
Why is AI local infrastructure is the key task
Each LLM is good at different tasks. For example, ChatGPT is very suitable for dialogue AI, and MED-PALM aims to answer medical questions. AI’s landscape is very fierce, so that the best models today may be cheaper and better competitors tomorrow.
If there is no flexible infrastructure, the company may find that it is locked in a model without being able to switch without a complete reconstruction technology stack. This is an expensive and inefficient position. By the outdated infrastructure of the investment model, companies can integrate the best tools into their needs-whether it is from Chatgpt to DeepSeek, or a new model launched next month.
Today’s cutting -edge AI models may be outdated within a few weeks. Consider using hardware progress such as GPU-companies will not be able to replace the entire calculation system of its latest GPU; on the contrary, they will ensure that their systems can seamlessly adapt to the new GPU. The AI model requires the same adaptability. The correct infrastructure can ensure that enterprises can be upgraded or switching models, without re -designing the entire workflow.
Most of the current corporate tools do not consider AI construction. Most data tools (e.g., part of the traditional analysis stack) are designed for heavier manual data operations. Transforming AI into these existing tools usually generates low efficiency and limits the potential of advanced models.
On the other hand, AI local tools are specially built and can be seamlessly interacting with the AI model. They simplify processes, reduce dependence on technical users, and use AI not only processing data, but also to extract the ability to operate insights. AI local solutions can abstract and complex data and make them be used to query or visualize the purpose.
The core pillar of the success of AI infrastructure
In order to make the future business determine the priority of these basic elements of the AI infrastructure:
Data abstract layer
AI is regarded as a “super -powered child.” It has high ability, but requires clear boundaries and guidance access data. AI local data abstract layer acts as a controlled gateway to ensure that your LLMS only accesss relevant information and follows appropriate security agreements. No matter which model you use, it can also access metadata and context consistently.
Explanation and trust
Artificial intelligence output usually feels like a black box-useful, but it is difficult to trust. For example, if your model summarizes customer complaints for six months, you not only need to understand how the conclusion is obtained, but also what specific data points are needed to provide information for this summary.
The local infrastructure of AI must include tools that provide interpretability and reasoning, so that people can go back to the model output to return to its source and understand the reason for the output. This can enhance trust and ensure repeated results.
Semantic layer
Semantic tissue data so that human and artificial intelligence can intuitively interact with them. It abstracts the technical complexity of the original data, and uses meaningful business information as the context of LLM while answering business questions. A malnourished semantic layer can significantly reduce LLM hallucinations. Essence
For example, LLM applications with a powerful semantic layer can not only analyze your customer loss rate, but also explain why customers leave according to the mark emotions in customer reviews.
Flexibility and agility
Your infrastructure needs to be agile-enables organizations to switch models or tools according to the development needs. A platform with a modular architecture or pipeline can provide this agility. Such tools allow companies to test and deploy multiple models at the same time, and then expand solutions to the best ROI.
Governance of AI accountability system
Artificial intelligence governance is the backbone used by the person in charge of AI. Enterprises need a strong level of governance to ensure morality in regulatory guidelines and use models safely. Three things of AI governance management.
- Access control: Who can use this model? What data can it access?
- transparency: How to generate output, can you review AI suggestions?
- Reducing risk: Prevent AI from making unauthorized decision -making or incorrect use of sensitive data.
Imagine a scene, that is, open source models such as Deepseek can access the SharePoint document library. Without governance, DeepSeek can answer questions that may include sensitive company data, which may lead to misleading analysis of disaster violations or damage to business. The governance layer reduces this risk to ensure that AI conducts strategic and security deployment throughout the organization.
Why is the infrastructure is particularly critical now
Let’s re -examine DeepSeek. Although its long -term impact is still uncertain, it is obvious that global AI competition is heating. Companies operating in this area can no longer rely on a country, suppliers or technology will always maintain their dominant position.
There is no strong infrastructure:
- Enterprises are facing greater risks of outdated or inefficient models.
- The transition between tools has become a time -consuming, expensive process.
- The team lacks review, trust and understanding the ability of AI system output.
The infrastructure not only makes the adoption of AI easier, but also unlock the entire potential of AI.
Build a road instead of buying engines
Models such as DeepSeek, Chatgpt or Gemini may grab headline news, but they are just part of a larger AI problem. In this era, the success of real enterprises depends on the strong and enjoined AI infrastructure in the future.
Don’t be distracted by the “Ferrari” of the AI model. Focus on the establishment of “roads” (infrastructure) to ensure that your company is booming now and in the future.
In order to start the flexibility tailored for your business, the scalable infrastructure AI is time to take action. Maintain a leading position and make sure your organization is prepared for the next AI landscape.
