The first batch of government and enterprises to use DeepSeek are even more anxious

Once rang the bell

·February 27, 2025 19:25

DeepSeek's' cold thinking 'after the fire: How difficult is the last mile for AI to land in government and enterprises

Today, in the AI craze sparked by DeepSeek, everyone is excited and enthusiastic about the powerful and intelligent AI experience. There has also been a wave of integration on the enterprise side, with many exploring the application of DeepSeek.

However, most people may overlook that despite DeepSeek's high cost-effectiveness and open source free nature, it has greatly lowered the threshold for AI usage and reduced the cost burden on enterprise organizations. But this path of application is actually not so easy to take.

When DeepSeek is deeply applied to the core production systems of enterprises and various aspects within the organization, a large number of questions will erupt: is one DeepSeek enough? Will there be more powerful models in the future? How will it be replaced or integrated at that time? How can existing data be fed into DeepSeek, or even the next stronger model? wait.

These issues are troubling CTOs, especially after corporate organizations have started integrating with DeepSeek, they are even more anxious.

The common pain of enterprise AI application from laboratory to production workshop

At present, several leading enterprises in various industries such as government affairs, finance, electricity, coal mining, and manufacturing have publicly announced the introduction of DeepSeek to carry out intelligent applications. According to statistics, currently over 40 out of 98 state-owned enterprises have launched DeepSeek applications.

Looking at specific application scenarios, most of them are still focused on general scenarios such as customer service, office, and research and development, and some top enterprises have begun to expand into core scenarios. How to fully empower the entire production process of enterprises with DeepSeek and enable big models to truly "go to work" is the most concerning issue for everyone next.

1、 Multi model collaboration is inevitable, and more innovative models will emerge in the future.

The human world is a complex and diverse world, with complex and ever-changing scenarios and problems. A single model cannot meet the needs of all scenarios. For example, a typical manufacturing enterprise has both intelligent Q&A and knowledge base scenarios in the sales and service process, as well as scenarios for factory intrusion, accurate warning of abnormal events, and optimization of production processes in the production process. All of these require natural language models, machine vision models, and predictive models to complete. DeepSeek performs well in natural language, scientific computing, and other fields, but it is not omnipotent and cannot cover all areas of AI applications.

While most government and enterprises are integrating with DeepSeek, the models previously deployed and used will still be used in parallel, especially machine vision and prediction, which are widely used in the industrial field, will still play an important role. In the long run, the iterative updates of model technology will become faster and faster, which means that there will definitely be new models or training paradigms emerging in the future.

Therefore, the coexistence and collaborative development of multiple models is an inevitable choice for the AI industry to meet diversified needs, and will not be replaced by the emergence of a specific model.

2、 The surge in computing power: The demand for computing power is increasing instead of decreasing, and the evolution of technology requires strong infrastructure.

The development of AI technology, especially the continuous evolution of deep learning models, has led to an exponential increase in the demand for computing power. From early simple neural networks to today's complex large-scale pre trained models, every technological breakthrough relies on powerful computing power support.

Although DeepSeek has reduced training costs by 90% through algorithm optimization, it has also lowered the threshold for computing power, stimulating more industries to embrace AI. Based on long-term development, both the popularization of AI applications and the iterative updating of models cannot be separated from the continuous improvement of computing power.

Therefore, the growth trend of computing power demand is unstoppable, which is an important material foundation for promoting the development of the AI industry and will not change with the emergence of individual technologies or products.

3、 Application is king: delve into industry scenarios with systematic thinking.

Every industry has its unique knowledge system and operational logic, and AI can only truly unleash its enormous potential by continuously delving into it, optimizing and customizing it. Therefore, in the implementation of AI applications in government and enterprises, it is necessary to deeply integrate general models and industry knowledge to maximize the value of AI technology. For example, in the financial field, while leading AI technology is important, it is also necessary to combine analysis of customer transaction data, credit records, social networks, and other multi-source data to build risk assessment models, in order to accurately determine customer credit risk, decide whether to approve loans, loan amounts, interest rates, etc., which are currently beyond the capabilities of general models.

In this process, a scientific methodology is required, which is a systematic engineering. DeepSeek provides enterprises with a new option for large-scale models, but the key steps and principles in the implementation process will not change. This includes multiple stages such as precise requirement sorting, efficient data preparation, model selection and customization, deployment, operation and maintenance, and integration with existing business processes and systems of the enterprise.

Firstly, enterprises need to clarify their business objectives and identify which business processes can be optimized or innovated through the use of large-scale models. Secondly, when deploying the model, it is necessary to consider the enterprise's IT infrastructure, data security, and privacy protection requirements, and choose an appropriate deployment method, such as private cloud, hybrid cloud, or edge deployment. Furthermore, high-quality industry data is needed to train and fine tune large models to match the actual scenarios of the enterprise. After the model is put into use, its performance needs to be continuously monitored, and timely adjustments and optimizations should be made based on business changes and user feedback. This entire methodology is a guarantee to ensure that big models can play a role in enterprises and achieve sustainable development, and will not be overturned by the emergence of new models.

Overall, the difficulties and fundamentals of AI application in government and enterprises have not changed, which determines the direction of AI application in government and enterprises. We must firmly grasp the basic situation, actively layout according to our own development needs and industry characteristics, and achieve stable and sustainable development.

Connecting the Last Mile: A Sustainable Evolution Platform is the Core

In fact, the hotspots of big models have been constantly changing. From ChatGPT to DeepSeek, in just 2 years, the market's C-position has already been replaced. In the development path of technology, change is inevitable, and uncertainty is also inevitable. For large government and enterprises, building an AI architecture with sustainable evolution capabilities is a necessary condition for enterprises to achieve stable and sustainable development in the field of AI as a major foundation for coping with future changes.

1、 A stable AI development platform. The AI platform is a bridge connecting the lower level software and hardware infrastructure with the upper level large model, and its technical architecture needs to be convergent, simple, and unified. The downward trend of AI platforms requires the integration of cloud platforms to encapsulate the complexity of underlying software and hardware, and to solve computational efficiency through elastic resource scheduling, ensuring the scheduling and operation of computing power, models, and various resources; Upward, it is necessary to support rapid adaptation of models from diverse sources, and provide a series of toolchains to support one-stop development and deployment of models, data, and applications. Through fine-tuning, evaluation, compression, and end-to-end standardization of deployment, models can be launched faster.

In addition, AI platforms also need to flexibly choose various deployment modes such as public cloud, private cloud, or a combination of both according to the needs of enterprises in different periods and scenarios. Meanwhile, for scenarios with edge business requirements, it is necessary to plan the architecture of cloud edge collaboration in advance.

Hybrid cloud is a more suitable solution. Taking Huawei Cloud Stack, a hybrid cloud solution provided by Huawei Cloud, as an example, various deployment modes such as Ascend Cloud Services, Full Stack Hybrid Cloud, and Edge Lightweight can be provided for government and enterprise customers to flexibly choose from based on their different AI needs at different stages. In the early stages of exploring applications, government and enterprise customers can quickly pilot and try out applications using Huawei Cloud Ascend Cloud Services with just one click. In the deep application stage, government and enterprise customers can also choose to push the models trained on Ascend Cloud to the local central cloud. Based on the full stack cloud service provided by Huawei Cloud Stack, combined with customers' local private data, the models can be fine tuned and trained to train specialized models that better match their own business needs.

In this way, government and enterprise clients do not need to migrate platforms or restructure architectures, and can smoothly complete AI application deployment and experimentation at different stages, efficiently achieving the digital transformation and upgrading of enterprises.

2、 Standardized implementation paradigm. The evolution of architecture also comes from its understanding and implementation of AI landing paradigms. Often, a platform with standardized landing paradigms can solve various problems related to the landing of government and enterprise AI applications in an orderly manner, which often requires a series of development tools to support implementation.

For example, for data development, a powerful set of data development tools with efficient data collection, cleaning, and preprocessing functions can effectively improve data quality. Based on these high-quality data, DeepSeek models can be trained and fine tuned to adapt to enterprise scenarios.

In the model development process, a series of advanced tools are used to support the design, training, and optimization of the model. Including model training, fine-tuning, edge deployment, and conducting quantitative compression and model evaluation to help developers understand the strengths and weaknesses of the model and make targeted optimizations.

There is also an application development process where efficient and user-friendly application development frameworks can help users quickly integrate trained models into various application systems, including Prompt templates, pre built plugins, RAG, etc., achieving minute level AI application creation. At the same time, the visual interface design function can lower the development threshold, improve development efficiency, and enable non professional developers to quickly build fully functional AI applications.

3、 Accumulation of talent and experience. The evolution of architecture not only relies on technical capabilities, but also on the accumulation of talent and experience. This is a seemingly abstract ability, but in reality it still has traceable features. With the acceleration of AI applications in government and enterprises, these capabilities are becoming an irreplaceable asset of the platform.

By establishing a comprehensive experience accumulation mechanism, these experiences can be organized, summarized, and shared. For example, by establishing an internal knowledge base, regularly organizing experience sharing meetings and technical exchange activities, structured management of internal experience and knowledge can enable enterprises to quickly learn from past successful experiences and avoid detours when facing new large-scale model goals.

At the same time, develop a comprehensive AI talent training plan to attract and cultivate a group of high-quality AI professionals. Clarify the recruitment standards, training plans, growth channels, etc. for AI talents. At the same time, enterprises should create a good innovation atmosphere, encourage employees to try new technologies and methods, provide practical and innovative platforms for employees, stimulate their creativity and potential, and build a strong competitive AI talent team.

conclusion

Today, the AI craze sparked by DeepSeek is redefining the opportunities and challenges for government and enterprise organizations in the field of AI. Faced with the constantly growing demand for computing power and complex and ever-changing business scenarios, enterprises not only need to keep up with the pace of the times and actively embrace new models and technologies, but also need to take a long-term perspective, carefully select the correct AI architecture and evolution path based on their own actual situation and development needs.

This is not just a process of technology selection, but also a test of the company's strategic vision and execution ability. Only by selecting a stable, reliable, and flexible AI platform can we ensure the success of government and enterprise AI in key areas such as large-scale deployment, application development, data engineering, model training, and fine-tuning, and respond to the constantly changing future with a long-term stable attitude.

*The images in this article are all sourced from the internet

This article is from the WeChat official account "Ringing Talk". The author is Zeng Ringing, 36 Krypton has been authorized to release it.

The viewpoint of this article only represents the author himself, and the 36Kr platform only provides information storage space services.

**批用上DeepSeek的政企，更“焦虑”了