AsianFin -- Edge AI will boost global GDP by 0.3–0.8 percentage points annually, which is not just a technological advancement, but a critical step in transitioning towards an intelligent society, said Jany Hejuan Zhao, the founder and CEO of TMTPost Group.  

Zhao made the remarks in a forum named “AI Computing Power Development” hosted by the World Internet Conference on Tuesday. The forum, with a theme of "Building an Integrated, Inclusive, and Green AI Computing Power Ecosystem," was part of the Mobile World Congress 2025 held in Barcelona, Spain from Monday through Thursday.

The forum featured speeches from prominent figures, including Zhao Houlin, former Secretary-General of the International Telecommunication Union; Sihan Bo Chen, Head of Greater China at GSMA; Li Zixue, Chairman of China’s telecom equipment giant ZTE Corporation; Zhang Dong, Vice President of telecom giant China Mobile Communications Group; Liu Jun, Executive Vice President of electronics giant Lenovo Group; Besik Bughanishvili, Chairman of Georgian Development Fund; Jiang Tao, Co-Founder of tech company iFlytek; Luigi Gambardella, the President of non-profit international organization ChinaEU; and Jany Hejuan Zhao, the founder of TMTPost Group.  

“We are at a pivotal moment in the Fourth Industrial Revolution,” said Zhao, adding that China accounts for more than 35% of the global edge AI market, and it is expected to exceed 150 billion US dollars by 2030.

Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center. According to the latest data, the global edge AI-powered device market size has exceeded $60 billion, with a compound annual growth rate (CAGR) of 22%, far surpassing the growth rate of cloud-based AI services. This signals a fundamental shift—AI is moving from centralized cloud computing to real-time edge processing. Gartner projects that 75% of enterprise data will be processed at the edge by 2025, marking a historic transition from “centralized intelligence” to “distributed intelligence,” Zhao noted.

However, to fully realize this transformation, three major challenges must be addressed:

First, the lightweight solution of AI models is crucial. According to Stanford’s AI Index Report, model parameters increase by 230% annually. Yet, edge AI requires lightweight solution to ensure compatibility with edge deployment. Second, edge AI must possess the ability to continuously evolve, based on real-world data, to maintain its performance and adaptability. Finally, edge AI needs to achieve deep penetration into industrial scenarios, enabling precise adaptation across different industries to enhance efficiency in local operations.  

In addition, to drive the empowerment of edge AI, Zhao proposed three supporting systems. First, it is essential to establish a "data flywheel" ecosystem to achieve efficient data utilization and cyclical value creation. Second, promoting the deep integration of AI with 5G and the Internet of Things (IoT) will provide stronger technical support for edge AI. Lastly, forming an open and collaborative industrial community will facilitate the integration of resources and collaborative innovation among stakeholders.  

In this significant historical transformation, Zhao believes that China holds unique advantages in the field of edge AI and will occupy a leading position: 37% of global edge AI patents originate from China; the deployment rate of edge AI devices in smart cities exceeds 60%; and 45% of edge AI applications are used in industrial quality inspection scenarios. By 2025, China's edge computing market is projected to reach 200 billion yuan.

The transcript of Zhao’s speech is as follows:

Distinguished leaders, industry pioneers, ladies and gentlemen,

Good morning!

I am Zhao Hejuan, Founder & CEO of TMTPost Group. It is my great honor to join the AI Computing Power Development Forum in MWC.

As a longtime researcher, analyst, and entrepreneur in AI applications, I would like to share some of my observations on how the edge AI model or on-device AI model is reshaping industrial intelligence, which will have three parts: The Rise of Edge AI,the Key Challenges for Edge AI and China’s Unique Edge AI Advantages.

Firstly,about the Rise of Edge AI

We are at a pivotal moment in the Fourth Industrial Revolution. According to the latest data, the global edge AI device market size has exceeded $60 billion, with a compound annual growth rate (CAGR) of 22%, far surpassing the growth rate of cloud-based AI services. 

Among them, China accounts for more than 35%, and it is expected to exceed 150 billion US dollars by 2030.

This signals a fundamental shift—AI is moving from centralized cloud computing to real-time edge processing. Gartner projects that by 2025, 75% of enterprise data will be processed at the edge, marking a historic transition from “centralized intelligence” to “distributed intelligence.”

Secondly, what will be the key Challenges for Edge AI?

To fully realize this transformation, three major challenges must be addressed:

1. Model Optimization for Edge Deployment.

AI models are growing exponentially—Stanford’s AI Index Report states that model parameters increase by 230% annually. Yet, edge AI requires lightweight solutions.

For example:

 • Carnegie Mellon University developed a blind navigation ring that compresses environmental recognition models to just 52KB.

 • Dutch startup Epitel created an epilepsy warning system in 0.5MB, providing 90-second early alerts while reducing false alarms by 40%.

These breakthroughs prove that smaller AI models can be just as powerful in real-world applications.

2. Continuous Learning and Evolution

AI must continuously improve based on real-world data.

Google's DeepMind lab has unveiled a new AI diagnostic system, "Med-PaLM Oncology," which can identify early signs of 13 types of cancer within 3 seconds. The system has achieved a clinical validation accuracy rate of 96.7%, surpassing that of human doctors.

This aligns with IDC’s Edge Intelligence Evolution Theory—when edge devices gain continuous learning capabilities, their efficiency improves exponentially.

3. Breaking Industry Barriers

Edge AI is revolutionizing industrial sectors.

 • In Tesla's Shanghai factory, an edge AI vision system has reduced the false alarm rate to 0.5%, increased the detection accuracy rate to 99.98%, and improved the efficiency by five times.

 • In Shouguang, eastern China’s Shandong province, an edge AI-powered agricultural drone improved pest detection accuracy by 40% and reduced pesticide consumption by 35%.

Gartner predicts that by 2025, the efficiency of local links in the manufacturing industry will increase by 20% -50%.

However，to maximize Edge AI’s potential, we must build three essential pillars:

1. A “Data Flywheel” Ecosystem

IDC predicts Every day, the world generates 14.849 billion TB of edge data, but less than 15% is utilized.

 • In the latest AI smartphone improved local data processing 6x, reducing latency to 8 milliseconds.

 • Smart excavators cut energy consumption by 22% using edge decision-making.

2. AI-5G-IoT Integration

According to Boston Consulting Group, integrating AI with 5G and IoT is unlocking new efficiencies:

 • At Qingdao Port, a 5G + Edge AI system improved container scheduling efficiency by 40%.

 • At Ant Group, Blockchain + Edge AI reduced cross-border payment processing time from hours to seconds.

3. An Open and Collaborative Industry Community

Today, over 200 global open-source edge AI projects exist, with Chinese enterprises contributing 22%.

The Linux Foundation’s 2024 Edge Computing White Paper states that open collaboration can reduce edge AI deployment costs by 60%.

A great example is the Huawei Ascend + SenseTime partnership, which developed a lightweight AI model toolchain, tripling development efficiency.

The last part, I would like to talk about some China’s Unique Edge AI Advantages.

China is in a strong position in the global Edge AI revolution:

 • 37% of global edge AI patents originate from China.

 • The deployment rate of edge AI devices on the smart city side exceeds 60%.

• 45% of edge AI applications in industrial quality inspection scenarios.

 • By 2025, China's edge computing market is expected to reach 200 billion yuan.

Looking ahead, the future of edge AI isbased on comprehensive forecasts from multiple institutions:

• By 2026, 50% of enterprise edge AI systems will adopt dynamic task allocation strategies.

• By 2027, 90% of edge AI devices will support multimodal interaction.

• By 2030, 30% of industrial edge devices will be equipped with self-learning capabilities.

• Edge AI will boost global GDP by 0.3–0.8 percentage points annually.

This is not just about technological advancement—it is a critical step in transitioning towards an intelligent society.

To conclude, let me share a real-world case from TMTPost’s research—the AI-powered transformation of an automotive factory.

After edge AI was integrated into 287 production steps:

 • Per capita output increased by 4.6 times.

 • Defect rates dropped to just 3 PPM (parts per million).

This confirms today’s core message—when AI computing power reaches the industrial frontline, we unlock not just an efficiency revolution but a fundamental upgrade in human productivity.

Let’s work together to drive this silent yet transformative revolution forward.

Thank you!

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