SHANGHAI, July 11, 2025 /PRNewswire/ — AI Flow, the innovative framework developed by TeleAI, the Institute of Artificial Intelligence of China Telecom, has been recognized as a key role in the intelligent transformation of telecom infrastructure and services in the latest report by Omdia, a premier technology research and advisory firm. The report highlights AI Flow’s exceptional capabilities in addressing the edge GenAI implementation challenges, showcasing its device-edge-cloud computing architecture that optimizes both performance and efficiency as well as its groundbreaking combination of information and communication technologies.

According to the report, AI Flow facilitates seamless intelligence flow, allowing device-level agents to overcome the limitations of a single device and achieve enhanced functionality. The same communication network can connect advanced LLMs, VLMs, and diffusion models across heterogeneous nodes. By facilitating real-time, synergistic integration and dynamic interaction among these models, the approach achieves emergent intelligence that exceeds the capabilities of any individual model.

Lian Jye Su, Chief Analyst at Omdia, remarked that AI Flow has demonstrated sophisticated approaches to facilitate efficient collaboration across device-edge-cloud tiers and to achieve emergent intelligence through connective and interactive model operations.

The unveiling of AI Flow has also drawn great attention from the AI community on global social media. AI industry observer EyeingAI said on X “It’s a grounded, realistic take on where AI could be headed. ” AI tech influencer Parul Gautam said on X that AI Flow is pushing AI boundaries and ready to shape the future of intelligent connectivity.

Fulfill the Vision of Ubiquitous Intelligence in Future Communication Networks

AI Flow, under the leadership of Professer Xuelong Li, the CTO and Chief Scientist of China Telecom and Director of TeleAI, is introduced to address the significant challenges of the deployment of emerging AI applications posed by hardware resource limitations and communication network constraints, enhancing the scalability, responsiveness, and sustainability of real world AI systems. It is a multidisciplinary framework designed to enable seamless transmission and emergence of intelligence across hierarchical network architectures by leveraging inter-agent connections and human-agent interactions. At its core, AI Flow emphasizes three key points:

Device-Edge-Cloud Collaboration: AI Flow leverages a unified device-edge-cloud architecture, integrating end devices, edge servers, and cloud clusters, to dynamically optimize scalability and enable low-latency inference of AI models. By developing efficient collaboration paradigms tailored for the hierarchical network architecture, the system minimizes communication bottlenecks and streamlines inference execution.

Familial Models: Familial models refer to a set of multi-scale architectures designed to address diverse tasks and resource constraints within the AI Flow framework. These models facilitate seamless knowledge transfer and collaborative intelligence across the system through their interconnected capabilities. Notably, the familial models are feature-aligned, which allows efficient information sharing without the need for additional middleware. Furthermore, through well-structured collaborative design, deploying familial models over the hierarchical network can achieve enhanced inference efficiency under constrained communication bandwidth and computational resources.

Connectivity- and Interaction-based Intelligence Emergence: AI Flow introduces a paradigm shift to facilitate collaborations among advanced AI models, e.g., LLMs, vision-language models (VLMs), and diffusion models, thereby stimulating emergent intelligence surpassing the capability of any single model. In this framework, the synergistic integration of efficient collaboration and dynamic interaction among models becomes a key boost to the capabilities of AI models.

See AI Flow’s tech articles here:

https://www.arxiv.org/abs/2506.12479

https://ieeexplore.ieee.org/document/10884554 

AI Flow’s First Move: AI-Flow-Ruyi Familial Model

Notably, TeleAI has just open-sourced the first version of AI Flow’s familial model: AI-Flow-Ruyi-7B-Preview last week on GitHhub.

The model is designed for the next-generation device-edge-cloud model service architecture. Its core innovation lies in the shared intermediate features across models of varying scales, enabling the system to generate response with a subset of parameters based on problem complexity through an early-exit mechanism. Each branch can operate independently while leveraging their shared stem network for computation reduction and seamless switching. Combined with distributed device-edge-cloud deployment, it achieves collaborative inference among large and small models within the family, enhancing the efficiency of distributed model inference.

Open-source address：

https://github.com/TeleAI-AI-Flow/AI-Flow-Ruyi 

About TeleAI

TeleAI, the Institute of Artificial Intelligence of China Telecom, is a pioneering team of AI scientists and enthusiasts, working to create breakthrough AI technologies that could build up  the next generation of ubiquitous intelligence and improve people’s wellbeing. Under the leadership of Professor Xuelong Li, the CTO and Chief Scientist of China Telecom, TeleAI aims to continuously expand the limits of human cognition and activities, by expediting research on AI governance, AI Flow, Intelligent Optoelectronics (with an emphasis on embodied AI), and AI Agents.

For more information:

https://www.teleai.com.cn/product/AboutTeleAI

Photo – https://mma.prnewswire.com/media/2729356/AI_Flow.jpg

Welcome back to This Week in Stratechery!

As a reminder, each week, every Friday, we’re sending out this overview of content in the Stratechery bundle; highlighted links are free for everyone. Additionally, you have complete control over what we send to you. If you don’t want to receive This Week in Stratechery emails (there is no podcast), please uncheck the box in your delivery settings.

On that note, here were a few of our favorites this week.

1. Who Invests and Why? As Mark Zuckerberg and Meta inflame the already raging talent wars, I wanted to explore if there was a way to understand who was willing to invest to win, and who was not. I came up with two scales: how big is the business opportunity for a given company, and whether or not that company’s philosophy is about helping users, or doing things for them. Not only does this intersection of Tech Philosophy and AI Opportunity explain the actions of Meta and Apple, it also helped me fully rectify some of my long-standing confusion about Google. — Ben Thompson
2. Apple Searches for an AI Partner. If Apple isn’t going to pay for AI talent, then they need a partner, which is why Apple is considering a partnership with either Anthropic or OpenAI to power a new version of Siri. For one, thinking about what OpenAI and Anthropic would want from a deal with Apple provides a window into the goals distinguishing two of the leading AI labs in the world. As for Apple, the news highlights the corner that they’ve backed themselves into after several years of failed AI efforts internally and one prolonged and very public failure with last year’s Apple Intelligence rollout. The choices now? Either surrender control and branding to OpenAI, or pay big money to Anthropic (a far cry from collecting $20 billion a year from Google for default search placement). In either case, Apple management will have to leave its comfort zone, and looking at the past few years, perhaps that comfort zone was the problem. — Andrew Sharp
3. Is Xi Jinping on His Way Out? Every week I survey the news to prep for Sharp China, and for about two months now, there’s been a steady thrum of rumors concerning the political fate of Xi Jinping. Connecting the dots between Xi’s unexplained absences from public view, a spate of dismissals of powerful generals from the People’s Liberation Army, and a surprise absence at the BRICS summit in Brazil a few weeks ago, various internet sleuths and commentators are wondering whether Xi’s long-unshakeable hold on power may be waning. For the second half of this week’s episode, Sinocism’s Bill Bishop, who’s been studying the CCP for 30 years, explained why he finds the public evidence unconvincing and the rumor ecosystem increasingly frustrating. It was a rollicking conversation, and one that I caveated with my own note: what’s most remarkable to me about this rumor cycle is that because of the CCP’s unbelievable opacity, there is a hard limit on what any expert can conclusively say about the future of anyone in power—even the big man, himself. — AS

Stratechery Articles and Updates

Dithering with Ben Thompson and Daring Fireball’s John Gruber

Asianometry with Jon Yu

Sharp China with Andrew Sharp and Sinocism’s Bill Bishop

Greatest of All Talk with Andrew Sharp and WaPo’s Ben Golliver

Sharp Tech with Andrew Sharp and Ben Thompson

This week’s Stratechery video is on Checking In on AI and the Big Five.