FLock.io has reached a strategic cooperation with Qwen under Alibaba Cloud, Web3 AI needs to find a complementary ecological niche with Web2 AI.

web3 AI urgently needs to find a complementary ecological niche with web2 AI to address the high cost of Computing Power, data privacy issues, vertical scene model fine-tuning problems, and so on.

Written by: Haotian

Yesterday, the DeAI training platform in the Web3AI field @flock_io partnered with @Alibaba_Qwen under Alibaba Cloud.

The large language model officially announces a partnership. If I remember correctly, this should be the first integration cooperation initiated by web2 AI towards web3 AI. Not only does it allow Flock to truly break out of its circle, but it also revitalizes the morale of the web3 AI track under the pressure of a sluggish market. Let me elaborate:

1. I have explained in the pinned tweet that the previous web3 AI Agent has been trying to stimulate the landing of Agent applications through Tokenomics, and also tried the fast deployment competitive paradigm. However, after a wave of Fomo from asset issuance, everyone realized that when it comes to practicality and innovation, web3 AI has almost no chance of winning compared to web2 AI.

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 significant bloodbath in the secondary market.

2. How to break the deadlock? The path is actually very clear; web3 AI urgently needs to find a complementary ecological niche with web2 AI to solve issues that centralized AI in web2 cannot resolve, such as the high cost of Computing Power, data privacy issues, and the fine-tuning of vertical scenario models, among others.

The reasons are mainly that pure centralized AI models will inevitably face concentrated problems in the channels and costs of obtaining computing power resources, as well as issues related to data resource privacy. In contrast, the distributed architecture attempted by web3 AI can utilize idle computing power resources to lower costs, and will also protect privacy based on hardware and software 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.

No matter the criticism, the decentralized architecture and flexible incentive mechanisms of web3 AI can have an immediate effect on solving some of the problems that exist with web2 AI.

3. Speaking of the collaboration between Flock and Qwen. Qwen is an open-source large language model developed by Alibaba Cloud, and its outstanding performance in benchmark tests and the flexible freedom it allows developers for local deployment and fine-tuning have made it a common choice for some developers and research teams.

Flock is a decentralized AI training platform that integrates AI federated learning and AI distributed technology architecture. Its main feature is that it allows "data to stay local" while protecting user privacy through distributed training, providing transparent and traceable data contributions, thereby solving the fine-tuning and application issues of AI models in vertical fields such as education and healthcare.

Specifically, Flock has three key components, which I will briefly share here:

1. AI Arena, a competitive model training platform where users can submit their own models to compete with other participants for optimization results and rewards. Its main purpose is to motivate users to continuously fine-tune and improve their local large models through a "gamification" mechanism design, thereby selecting better benchmark models.

2. FL Alliance, to solve the cross-organizational collaboration issues existing in traditional sensitive vertical scenarios such as healthcare, education, and finance, has achieved collaborative model training through local model training + distributed collaboration framework, enabling multiple parties to enhance model performance together without sharing raw data.

3. Moonbase, which is considered the nerve center of the Flock ecosystem, acts 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 Power resources, and data annotation support, empowering users to efficiently optimize local models.

4. So, how should we view the cooperation between Qwen and Flock? In my personal opinion, the extended significance of their collaboration is even greater than the actual substance of the cooperation at present.

On one hand, against the backdrop of web3 AI being continuously overshadowed by web2 AI's technological advancements, Qwen, representing the tech giant Alibaba, has already gained a certain level of authority and influence within the AI community. Qwen's proactive choice to collaborate with a web3 AI platform fully demonstrates web2 AI's recognition of the Flock technology team. Meanwhile, the subsequent series of research and development between the Flock team and the Qwen team will deepen the interaction between web3 AI and web2 AI.

On the other hand, the previous web3 AI was once merely a shell of Tokenomics, performing poorly in terms of actual utility implementation. Although various directions such as AI Agent, AI Platform, and even AI Framework were attempted, there was no real solution that could address problems when it came to areas like DeFi or GameFi. The unveiling by web2 tech giants this time 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 more accessible and efficient deployment of AI agents to issue assets, nor is it a game for raising money by issuing assets. It is necessary to strive for collaboration with web2 AI to complement each other's ecological niches. Only by doing so can we truly demonstrate the irreplaceability of web3 AI in this wave of AI trends.

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