The "Game Changer" in the second half of the AI era has foreigners exclaiming "Amazing!"

Another Chinese AI technology has gone viral overseas!

Recently, many AI - focused bloggers on the overseas social media platform X have engaged in a heated discussion about a new technology from China.

Some people said, "China isn't just playing around. This is a game - changer!"

Some even exclaimed, "China is truly breaking boundaries!"

Others said, "China isn't just 'playing chess'; they're redefining the entire 'game'!"

What kind of technology could possibly earn such high praise from foreign people?

They even exclaimed "Amazing", "Superb", and "Exciting" (it's like the author is doing an advanced vocabulary substitution exercise for the IELTS exam).

The top AI technology blogger Jaynit Makwana posted, "......It's called AI Flow - a system where models adapt, collaborate, and deploy......"

The technology blogger Rishabh tweeted, "......(It) may reshape the way generative AI operates at the edge... It's faster, more cost - effective, and smarter than any technology we've seen before..."

Rasel Hosen replied in the comments, "......Embracing a future where AI seamlessly integrates with our lives could truly revolutionize collaboration models. I can't wait to see how it develops!"

Muhammad Ayan said, "This is exactly the kind of architecture we need for real - time AI deployment."

VibeEdge even described it as "Game Changer".

The author immediately searched and found the definition of AI Flow, and it also has a Chinese name - Zhi Chuan Wang.

Zhi Chuan Wang (AI Flow) is a key technology in the cross - field of artificial intelligence and communication networks, that is, through a network hierarchical architecture, based on the connections between agents and the interactions between agents and humans, to achieve the transfer and emergence of intelligence.

Through Zhi Chuan Wang (AI Flow), intelligence can break through the limitations of devices and platforms, freely flow between different layers of the network, from the cloud computing center to terminal devices, achieving on - demand response anywhere.

What the author didn't expect is that this technology comes from a central state - owned enterprise in China - China Telecom.

According to the AI technology blogger EyeingAI, "AI Flow by Professor Xuelong Li (CTO at China Telecom and Director of TeleAI) and the team explores how AI can actually work better in the real world."

It turns out that Zhi Chuan Wang (AI Flow) is a technology that China Telecom's Artificial Intelligence Research Institute (TeleAI) is focusing on, developed by its dean, Professor Xuelong Li, leading the team.

Professor Xuelong Li is the CTO and Chief Scientist of China Telecom Group. He is one of the few experts globally in both optoelectronics and artificial intelligence. He has been elected as a Fellow in OSA (Optical Society of America), SPIE (International Society for Optics and Photonics) in the optoelectronics field, and AAAI, AAAS, ACM in the artificial intelligence field, as well as IEEE.

The reason why these overseas bloggers noticed Zhi Chuan Wang (AI Flow) is due to a cutting - edge technology report published by the TeleAI team on arXiv in mid - June:

AI Flow: Perspectives, Scenarios, and Approaches

Report address: https://arxiv.org/abs/2506.12479

After this technology report was published, it quickly caught the attention of the global technology market research and consulting firm Omdia, which also released a short industry review report. When analyzing the trends and directions of the implementation of generative AI technology, it recommended that all parties in the industry put TeleAI's Zhi Chuan Wang (AI Flow) technology "On the Radar".

Lian Jye Su, the Chief AI Analyst at Omdia, also posted a tweet on the social media platform:

"By bridging the gap between information technology and communication technology, Zhi Chuan Wang (AI Flow) provides strong support for resource - intensive applications such as autonomous vehicles, drones, and humanoid robots without compromising on latency, privacy, or performance. The future of distributed intelligence is here - a future in which advanced applications can break through device limitations while maintaining real - time response capabilities and data security."

What exactly is AI Flow? And why do we need it?

Opening the technology report, it starts by mentioning two well - known figures: Claude Shannon and Alan Turing. One is the founder of information theory, and the other is known as the father of computer science. They laid the foundations for information technology (IT) and communication technology (CT) respectively.

The report points out that the development of IT and CT has shown a parallel trend. On the one hand, it continuously improves the performance of individual machines, and on the other hand, it builds networks to achieve more efficient interconnection between multiple machines. This synergy has triggered a technological revolution, which has now reached its peak driven by large - scale AI models.

The boundaries of AI's capabilities are expanding at a speed beyond people's imagination. It can write poems, draw pictures, and write code, and also drive robots, drones, and autonomous vehicles. Some even believe that we are entering the so - called "second half of the AI era". However, the high resource consumption and high communication bandwidth requirements of large - scale models are facing huge challenges in achieving ubiquitous intelligence.

The real reality is that apart from chatting with AI in the chat box, our mobile phones, wearable devices, and cars still seem far from the so - called "ubiquitous intelligence".

Thus, a huge paradox emerges: Since AI is so powerful, why hasn't it seamlessly integrated into all aspects of our daily lives?

The answer actually lies beneath AI's powerful appearance. A harsh reality is that almost all top - notch AIs cannot run directly on the terminal devices around us. They are veritable "cloud giants", heavily relying on data centers with huge computing power thousands of miles away.

For example, to run the DeepSeek - R1 model with 671B parameters (full - blooded BF16 version), theoretically, at least 1342 GB of memory is required, and the computing power needed to ensure the Token output speed is astonishing. Obviously, these requirements far exceed the carrying capacity of most mobile phones, cars, and other edge - side devices.

This absolute dependence on the cloud has brought the most fatal shackle to the popularization of AI applications: latency.

As former Intel CEO Pat Gelsinger said, "If I have to send data to the cloud and back, it will never respond as fast as if I process it locally." - This is an unbreakable "physical law".

For autonomous vehicles where every millisecond counts and surgical robots that require real - time response, this latency is unacceptable and even life - threatening.

This is the "last - mile" dilemma in the popularization of AI: the scenarios that most need instant intelligence are often far from the cloud; while the most powerful intelligence is trapped in the cloud and cannot come down.

How to break this deadlock? In the past, the industry's approach was to build faster chips and larger data centers, but this is becoming more and more like an "arms race" with a sharply decreasing input - output ratio.

While everyone is obsessed with building higher walls of computing power, the answer to breaking the deadlock may come from a long - neglected field that is more related to the essence of everything interconnected - communication.

Zhi Chuan Wang (AI Flow) is exactly this disruptive answer!

It is an innovative architecture that integrates communication networks and AI models, aiming to build a bridge that allows intelligence itself to break through platform limitations, freely flow like data between the hierarchical architectures of "edge, edge - side, cloud", and arrive on demand, achieving Ubiquitous AI Applications (making AI applications everywhere).

Just like its Chinese name, "Zhi" represents artificial intelligence, "Chuan" represents communication, and "Wang" represents the network. It is a network that enables the "transmission" of "intelligence".

After carefully reading TeleAI's technology report, it is found that Zhi Chuan Wang (AI Flow) is a combination of punches, including three core technology directions.

• Device - Edge - Cloud Collaboration: It provides the hardware foundation for the distributed operation of intelligence.
• Familial Model: It can flexibly scale to adapt to different devices and achieve efficient collaboration by reusing calculation results.
• Connectivity - and Interaction - based Intelligence Emergence: Through the connection and interaction between models, it gives rise to the emergence of intelligence that exceeds the capabilities of any single entity, achieving the effect of 1 + 1>2.

Device - Edge - Cloud Collaborative Distributed Inference

To achieve enhanced intelligence and timely response of AI services, Zhi Chuan Wang (AI Flow) adopts a hierarchical device - edge - cloud collaborative architecture. This three - layer network architecture can provide flexible distributed inference workflows for various downstream tasks and is the foundation of model collaboration, which is one of the cornerstones of Zhi Chuan Wang (AI Flow).

First, let's look at the three - layer network architecture commonly used in today's communication networks, namely the device layer (edge), edge layer (edge - side), and cloud layer (cloud).

Among them, edge - side devices have the shortest communication delay but very low computing power; servers deployed at edge nodes such as base stations (BS) and roadside units (RSU) have slightly stronger computing power but slightly longer communication delay, while cloud servers have very strong computing power but the highest communication delay due to network routing.

Since edge nodes are close to terminal devices, they can provide medium computing