Recently, the focus of the global technology industry has undoubtedly fallen on the craze caused by DeepSeek. Almost overnight, the global market’s attitude towards China’s AI big models and related industries has changed 180 degrees – from the previous “overly pessimistic” to “extremely optimistic” in an instant, and 2025 seems to be the first year of the AI showdown between China and the United States.
International investment banks such as Deutsche Bank and Goldman Sachs have predicted that 2025 will be a key year for Chinese companies to rise in the global AI competition, not just DeepSeek. However, this is not the first time that foreign investment banks have been optimistic about Chinese companies. We need to stay sober and avoid being swayed by short-term market sentiment.
The reality is that our AI industrialization process is still in its infancy, and there is still a long way to go before it can be truly scaled up. Although the current market sentiment is enthusiastic, it is more important to explore how AI can truly promote industrial upgrading and create long-term value.
In this AI industrialization revolution, the Internet of Things (IoT) will become the core driving force, leading AI technology from the laboratory to practical applications in thousands of industries. According to IoT Analytics, the number of IoT connections worldwide will exceed 27 billion in 2025. The widespread IoT terminals can perceive massive amounts of data, providing 67%-72% of the data support for AI applications.
What is certain is that DeepSeek’s breakthrough will accelerate AIoT from 1.0 “Internet of Everything” to 2.0 “Intelligent Internet of Everything”, thereby promoting the in-depth application of AI in the industry and achieving a more thorough intelligent transformation.
The core value of AIoT 1.0 lies in “connection” – that is, allowing devices to communicate with each other and exchange data. The core value of AIoT 2.0 lies in “intelligence” – that is, allowing devices to not only perceive the world, but also make autonomous decisions, optimize operations, and continue to learn and evolve.
In this process, DeepSeek and the AI big model technology behind it will become key variables. Its impact is not only reflected in improving data processing capabilities, but also in promoting data-driven closed-loop intelligence – that is, collecting data from IoT devices, deeply mining the value of AI models, and ultimately feeding back to the optimization and decision-making of the physical world.
The realization of this closed loop will bring about a qualitative change in the industrial application of AI and accelerate the upgrading towards “artificial intelligence +” in thousands of industries.
So in today’s article, we’re going to explore:
How does DeepSeek accelerate the development of the AIoT industry?
Which specific scenarios of AIoT will be the first to achieve breakthroughs?
How can Chinese AIoT companies gain an advantage in global competition?
DeepSeek accelerates the “hardware awakening” and catalyzes the “intelligent connection of all things”
In recent years, small, low-cost, and high-performance open AI models are reshaping the innovation landscape of artificial intelligence. This trend not only lowers the entry threshold for AI, but also brings new possibilities for edge computing scenarios.
Compared with large models that rely on cloud computing, DeepSeek can run locally, which is a major breakthrough for industries that are sensitive to data privacy and AIoT devices that have extremely high requirements for low latency.
In order to truly embed AI into IoT devices with limited computing and storage resources, the basic model must be optimized. Although the AIoT industry has broad prospects, there are still three core challenges in the implementation of AI in edge computing environments:
1. Computing resources are limited: How to make AI run efficiently on edge devices?
IoT devices usually have limited computing power and cannot support the inference calculation of large AI models. The current mainstream model optimization methods include:
Pruning: Remove redundant parameters in the AI model to improve computing efficiency.
Distillation: Transfer the knowledge of large models to small models, so that they can still have powerful capabilities in low computing power environments.
Quantization: Reducing computational precision to reduce memory usage and energy consumption, enabling AI to run on embedded devices.
2. Data privacy and security: How to protect sensitive data at the edge?
Data security is critical in many critical infrastructures (such as power grids, medical devices, and smart factories). Traditional AI relies on cloud-based training and reasoning, but this means that data needs to be transmitted to the cloud, which may bring risks of privacy leakage.
3. Network efficiency and real-time performance: How to reduce the cost and delay of data transmission?
AIoT applications usually involve real-time processing of massive amounts of data. If all data needs to be uploaded to the cloud for analysis, latency and bandwidth costs will increase significantly. In some scenarios, such as autonomous driving, smart manufacturing, and smart cities, even millisecond-level delays can lead to serious consequences.
DeepSeek has initially verified that with the right optimization strategy, the basic model can be compressed and embedded into edge devices, thus breaking through the bottleneck of computing resources. Through edge AI reasoning, DeepSeek enables devices to process data and make decisions locally without relying on cloud computing, which brings the following advantages:
Real-time: Reduce the delay of data transmission back to the cloud and improve response speed. For example, in an autonomous driving system, DeepSeek enables AI to analyze camera data locally and make instant decisions.
Reduce network costs: Reduce data transmission volume, reduce bandwidth consumption, and enable AIoT devices to operate normally under low network conditions.
In the process of AIoT industrialization, DeepSeek’s key technological breakthroughs are mainly reflected in the following three aspects:
1. Localized AI models: Let AI run on edge devices
DeepSeek uses a model refinement approach to enable AI to run on devices with limited computing resources. For example, DeepSeek-R1 uses an efficient model architecture to enable AI to work in scenarios such as smart cameras, industrial sensors, and smart home devices without the need to connect to the cloud.
2. Distributed learning: realizing the self-evolution of AIoT devices
DeepSeek supports edge AI training, allowing devices to optimize themselves based on local data without having to upload data to the cloud. This is especially important for industries such as healthcare, finance, and industrial control that have extremely high requirements for data privacy.
3. Dedicated AIoT hardware: driving AI computing capabilities to the edge
Although DeepSeek has been optimized at the software level, AI still has high computing requirements. The dedicated AI chips and hardware accelerators that support it are also developing rapidly. As Moore’s Law continues to evolve, more powerful AI chips will continue to expand to the edge in the next few years, further promoting the development of AIoT.
As AI costs continue to decline and hardware performance continues to improve, the popularity of AIoT will grow exponentially. As a promoter of AI industrialization, DeepSeek is accelerating this process, enabling AIoT to release huge value in multiple industries such as smart manufacturing, smart cities, healthcare, and autonomous driving.
Priority benefits: Edge computing, AIoT chips and data management services
In the context of DeepSeek accelerating the development of the AIoT industry, which specific links and scenarios will be the first to achieve breakthroughs? Edge computing, AIoT chips and data management services are likely to become the three core driving forces for the industrialization of AIoT.
These three links will not only directly benefit from the advancement of AI technology, but will also play a core role in the AIoT ecosystem.
1. AIoT chips: Building AI computing infrastructure across industries
For AI to truly enter all walks of life, it must be deeply integrated with the devices in the industry scenarios, and these devices need to have local AI computing capabilities. Compared with traditional CPUs and GPUs, AIoT chips optimized for edge AI computing have the following advantages:
Lower power consumption: AIoT devices usually operate in low-power environments, such as smart cameras, industrial sensors, smart home devices, etc. Therefore, AIoT chips are more suitable for edge AI computing than high-power server GPUs.
More efficient AI reasoning: AIoT chips are optimized for AI computing and can efficiently run AI models in low-computing environments, improving reasoning speed and energy efficiency.
Lower AI deployment costs: With the popularization of low-cost, high-performance AI large models such as DeepSeek, the cost of edge AI reasoning is rapidly declining, further expanding the commercial prospects of AIoT chips.
In the article “Edge-side AI applications are accelerating, and AIoT chips are competing fiercely”, I analyzed that AIoT chips have already entered a global competition situation, and DeepSeek may make the competition between companies more intense.
DeepSeek’s open source strategy, coupled with its efficient reasoning capabilities and low computing power adaptability, will drive AIoT chip companies into a new round of growth. In addition, with the acceleration of the trend of local AI deployment, the demand for end-side AI computing will explode in 2025, and the growth potential of the AIoT chip market cannot be underestimated.
2. Edge computing: from “cloud center first” to “edge first”
Traditional AI computing relies on the cloud, but edge computing has natural advantages in terms of real-time performance, security, bandwidth cost, etc. DeepSeek has released the R1 model and its simplified version to enable AI computing to be more widely deployed on edge devices.
This not only lowers the computing threshold of AIoT devices, but also accelerates the transformation of enterprises to an “edge-first” computing architecture. As Microsoft CEO Satya Nadella pointed out in a financial report conference call, “artificial intelligence will become more ubiquitous” because more and more workloads will run locally, and DeepSeek’s development trend is highly consistent with this view.
3. Data management services: the “data hub” in the AIoT era
The essence of AI is data-driven. AIoT devices generate, transmit, store and analyze large amounts of data every day, which places higher demands on data management capabilities. Without efficient data management, no matter how powerful AI is, it will be difficult to function. However, AIoT data management generally faces many challenges, including scattered and complex data formats, high data security and compliance requirements, and diverse data-driven AI training needs.
Since AI requires a large amount of data for training and optimization, it is obvious that data management service providers will become key players in the AI industry chain. With the popularization of DeepSeek and similar open source AI models, more and more companies will use AI for data analysis, prediction and optimization, which will greatly promote the growth of the data management market.
In summary, in the process of AIoT industrialization, edge computing, AIoT chips and data management services may be the first to achieve breakthroughs. Next, we will explore how Chinese AIoT companies can gain advantages in the global market and the long-term development trend of the AIoT industry in the future.
Chinese AIoT companies have the first-mover advantage
Although the competition between China and the United States for large AI models is evenly matched, the outcome of the industrial application of “artificial intelligence +” has been decided, because in the global AIoT competition landscape, Chinese companies are in a unique and advantageous competitive position.
With a huge IoT device base, strong supply chain integration capabilities and government policy support, my country’s AIoT companies are expected to be the first to achieve large-scale commercial implementation in the global market and occupy a dominant position in the wave of AI industrialization.
1. A large IoT device base: data drives the industrialization of AIoT
China is one of the world’s largest IoT markets, and leads the world in IoT device shipments in the fields of smart home, smart manufacturing, smart city, and autonomous driving. This advantage brings two key resources to boost the development of the AIoT industry:
Rich application scenarios: The massive number of IoT devices means that AIoT has a natural landing environment in industries such as industrial manufacturing, smart healthcare, and smart transportation, and can be quickly commercialized.
Massive data resources: The core competitiveness of AIoT lies in data-driven intelligence. The large-scale deployment of equipment in the Chinese market enables companies to quickly accumulate data and optimize AI models, thereby forming a data closed loop and accelerating the maturity of AI industrial applications.
2. Strong supply chain integration capabilities: AIoT ecosystem with integrated hardware and software
Chinese companies have a complete industrial chain in hardware manufacturing, chip design, 5G communications and other fields, and can promote the development of AIoT through the integration of hardware and software, rather than relying solely on software algorithms.
There are many representative companies in this regard. For example, MeiG Smart is accelerating the development of DeepSeek-R1 applications on the terminal side, and plans to launch a 100TOPS-level AI module in 2025, with a long-term plan of over 200TOPS computing power to provide strong support for edge AI computing.
Fibocom ‘s high-computing AI module can fully support the DeepSeek-R1 small model and enhance the AI computing capabilities of terminal devices.
InHand has successfully deployed the DeepSeek-R1 distillation model on the EC5000 edge computer, providing efficient AI computing capabilities for scenarios such as industrial quality inspection, smart transportation, and telemedicine.
What can provide more help is that China’s 5G infrastructure is the world’s leading, enabling AIoT devices to interact with cloud/edge AI with lower latency and higher bandwidth, which is crucial for scenarios such as autonomous driving, smart manufacturing, and telemedicine. The popularization of 5G will further promote edge AI computing, reduce the dependence of devices on the cloud, and accelerate the implementation of the AIoT industry.
This integrated hardware and software ecosystem enables Chinese AIoT companies to make full-scale layout from underlying chips and device ends to AI computing platforms, forming a strong industrial synergy effect.
3. Policy support and market promotion: Deep integration of AI and the real economy
In recent years, the government has strongly supported the integration of AI and the real economy, and promoted the industrialization of AIoT through policy support, industrial funds, pilot projects, etc. Many cities have launched smart city pilots, such as Wuxi’s intelligent transportation system and Shanghai’s AIoT smart community. These projects provide real scenarios and policy support for the large-scale implementation of AIoT.
Therefore, in the wave of AIoT industrialization, Chinese companies are expected to be the first to achieve large-scale implementation in the global market and take a leading position in the AIoT competition by relying on their huge IoT equipment base, strong supply chain integration capabilities, and policy support. Despite the challenges of data compliance, standardization, and brand influence, Chinese AIoT companies are accelerating their global layout and will achieve global leadership in multiple industries such as smart manufacturing, smart healthcare, and autonomous driving in the future.
Last words
In the wave of AIoT industrialization, edge computing, AIoT chips and data management services have become the three key links that have taken the lead in breakthroughs. DeepSeek’s technological breakthrough enables AI to be deployed on end-side devices more efficiently and at a lower cost, pushing AIoT from “Internet of Everything” to “Intelligent Internet of Everything”.
Chinese companies have unique advantages in the AIoT race, relying on their world-leading IoT equipment foundation, integrated hardware and software supply chain integration capabilities, and government policy support. As the demand for edge AI reasoning grows, AIoT companies are accelerating the research and development of chips, modules, and computing platforms, and are implementing them on a large scale in the fields of smart manufacturing, smart healthcare, and smart transportation, defining a new landscape for the AIoT industry.
References:
1. Three Observations, by Sam Altman, from blog.samaltman.com
2. DeepSeek’s implications for edge AIoT, author: Andrew Brown, source: OMDIA
Author：彭昭
Source：“物联网智库”（ID：iot101）
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