How to view the collaboration between Flock and Qwen

Author: Haotian

Yesterday, the DeAi training platform Flock.io in the Web3AI field officially announced a partnership with Alibaba Cloud's Qwen large language model. If I remember correctly, this should be considered the first integration collaboration initiated by web2 AI towards web3 AI. Not only did it allow Flock to truly break out of its circle, but it also revitalized the morale of the web3 AI track, which has been under pressure and sluggish. Let me elaborate on this:

1. As I explained in my pinned tweet, web3 AI Agent has been trying to stimulate the implementation of agent applications through Tokenomics, and also engaged in rapid deployment of that set of competitive paradigms, but after the Fomo boom of asset issuance, everyone found that web3 AI has almost no chance of winning compared with web2AI in terms of practicality and innovation.

The emergence of Web2 innovative AI technologies such as Manus, MCP, and A2A has directly or indirectly burst the bubble of the Web3 AI Agent market, leading to a situation where the secondary market was once in turmoil.

2. How to break the deadlock? The path is actually quite clear. Web3 AI urgently needs to find an ecological niche that complements Web2 AI, to solve the high cost of computing power, data privacy issues, and the problem of fine-tuning vertical scene models that centralized AI in Web2 cannot address.

The reasons are nothing more than that pure centralized AI models will inevitably face concentrated problems in terms of computing power resource acquisition channels and costs, as well as data resource privacy issues, while the distributed architecture attempted by web3 AI can utilize idle computing power resources to reduce costs, and will also protect privacy based on technologies such as zero-knowledge proofs and TEE. At the same time, it promotes the development and fine-tuning of models in vertical scenarios through data ownership and incentive contribution mechanisms. Regardless of the criticisms, the decentralized architecture and flexible incentive mechanisms of web3 AI can have an immediate effect on solving some of the problems existing in web2 AI.

3. Speaking of the collaboration between Flock and Qwen. Qwen is an open-source large language model developed by Alibaba Cloud, which has become a popular choice for some developers and research teams due to its outstanding performance in benchmark tests and the flexibility that allows developers to locally deploy and fine-tune the model.

Flock is a decentralized AI training platform that integrates AI federated learning and AI distributed technology architecture. Its most significant feature is to protect user privacy through distributed training while keeping "data local," allowing for transparent and traceable data contributions, thereby addressing the fine-tuning and application issues of AI models in vertical fields such as education and healthcare. Specifically, Flock has three key components:

1. AI Arena, a competitive model training platform where users can submit their models to compete against others for optimization results and rewards. Its main goal is to motivate users to continuously fine-tune and improve their local large models through a "gamified" design, thereby selecting better benchmark models.

2. FL Alliance, in order to address the cross-organizational collaboration issues present in traditional sensitive vertical scenarios such as healthcare, education, and finance, has achieved enhanced model performance through localized model training and a distributed collaboration framework, allowing multiple parties to work together without sharing raw data.

3. Moonbase, which is the neural hub of the Flock ecosystem, serves as a decentralized model management and optimization platform, providing various fine-tuning tools and computing power support (computing power providers, data annotators). It not only offers a distributed model repository but also integrates fine-tuning tools, computing resources, and data annotation support, enabling users to efficiently optimize local models.

4. So, how should we view the collaboration between Qwen and Flock? Personally, I believe that the extended significance of their collaboration is even greater than the current substance of the cooperation.

On one hand, against the backdrop where web3 AI is continuously being overwhelmed by web2 AI technology, Qwen, representing the tech giant Alibaba, has already established a certain level of authority and influence within the AI community. Qwen's proactive choice to collaborate with a web3 AI platform fully demonstrates the potential of web2.

The recognition of the Flock technology team by AI, along with the subsequent series of research and development by the Flock team and the Qwen team, will deepen the interaction between web3AI and web2AI.

On the other hand, the previous web3 AI was once just a shell of Tokenomics, performing poorly in actual Utility implementation. Although various directions such as AI Agent, AI Platform, and even AI Framework were tried, they could not produce truly effective solutions when it came to DeFi, GameFi, and other areas. The unveiling by web2 tech giants has, to some extent, set the tone for the future development path and focus points of web3 AI.

The key point is that after experiencing a pure "asset issuance" Fomo frenzy, web3 AI needs to regroup and focus on a goal that can deliver real results. In fact, web3 AI has never just been a channel for easier and more efficient deployment of AI agents to issue assets, nor is it a game of issuing assets to raise money. It needs to strive for collaboration with web2 AI, complementing each other's ecological niches, and truly demonstrating the indispensability of web3 AI in this wave of AI trends.

I am pleased to see more cross-border collaborations like web2AI and web3AI being achieved.