Crypto X AI will not just stay in the Memecoin track in the next round . The bear market is more suitable for in-depth research. Only by understanding more feasible narratives can it reach the top of the wave in the future.
When I was sorting out various reports on the AI track these days, I recalled the Crypto + AI stack released by Coinbase Ventures @cbventures .
JK @jonathankingvc Ideal scenarios for combining Crypto and AI are: AI Agents interact on various Crypto infras. Software codes (smart contracts) created by AI lead to a surge in the number of Dapps and enhanced user experience. Users can own and govern their own AI big models and profit from them. And divide them into
1. The computing power layer is dominated by decentralized computing power providers such as Aethir
2. Expand the data layer of AI big models with training data sets as the core
3. Middleware layer composed of various new AI-based Infra (training/privacy reasoning/Agnet platform)
4. Application layer
Nowadays, it seems that there are very few Crypto+AI application layer products that retail investors can directly experience, and the experience is not good . The most direct reason is that the foundation of the stack layers below the application layer has not been laid.
Recently, FLock.io @flock_io , which has been promoting decentralized AI and model training through on-chain incentives starting from the model itself, has realized that training large models specifically for the Crypto track can prove itself. Although it is a narrative of larger decentralized AI model training, the feasibility of the route can be verified by outstanding products in niche areas in the early stage of the project to accumulate more early supporters for FLock.io , so FLock.io 's Web3 Agent Model came into being.
If the application-layer Crypto AI Agent is your intelligent assistant, then its big model is equivalent to the brain of your assistant. Only with deep experience and knowledge can you make every interaction and execute every instruction correctly for you.
The most outstanding indicator of FLock.io 's Web3 AI Agent big model is 75.93% FC precise matching accuracy, which simply means that it understands Web3's AI big model better. For instructions that other models cannot recognize or are nonsense, the Web3 AI Agent big model cooperates with industry track partners such as IO.net , based on the AI Arena Task 1 collaboration framework, and reduces the deviation of a single data source through decentralized training, making the AI Agent called based on the Web3 AI Agent Model more practical and accurate.
At this point we have to mention FLock.io as an ecological growth flywheel derived from the natural incentive properties of Crypto products.
Through the basic $FLock, more high-quality model trainers are encouraged to join , bringing higher quality data and training skills.
1. These contributions help build better AI big models;
2. More powerful AI models can attract more feature-rich and practical Agents and Dapps to call them;
3. Practical DApps and AI Agents have strong market competitiveness and generate more revenue with the support of the Web3 model;
4. More Agents and Dapps calling large models will generate more model incentives for trainers;
5. These new data and usage feedback can further optimize and improve large AI models . At the same time, the prosperity of the ecosystem will attract more high-quality model trainers to join through rewards and recognition.
Through this continuous cycle of positive feedback, the FLock.io ecosystem can achieve continuous growth and self-enhancement. AI Infra is still in its early stages, not to mention the Crypto AI field. We look forward to seeing more infrastructure BUIDLers like FLock.io in the industry. Only by laying a solid foundation for the model can we see a more stable growth and a flourishing application layer.
