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TMTPOST -- I, as a longtime researcher, analyst, and entrepreneur in AI applications, delivered a speech and shared my views on edge AI at 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.

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 advantages in 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.

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."

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

• 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.

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.

• 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:

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.

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.

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.

In the last part, I would like to talk about China’s unique advantages in edge AI.

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

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.

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.

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