Crypto X AI's next round will not only stay in the Memecoin track.

The bear market is more suitable for in-depth research, and understanding more feasible narratives will enable us to ride the wave to the top in the future.

Crypto X AI's next round will not only stay in the Memecoin track. The bear market is more suitable for in-depth research, and understanding more feasible narratives will enable us to ride the wave to the top in the future.

While organizing various reports on the AI track these days, I reflected on the stack released by Coinbase Ventures @cbventures regarding Crypto + AI.

JK @jonathankingvc envisions an ideal scenario for the combination of Crypto and AI: AI Agents interacting across various Crypto infrastructures. Software code (smart contracts) created by AI leads to a surge in Dapp numbers and an enhanced user experience, allowing users to own and govern their own AI large models and profit from them. This can be divided into:

A computing layer primarily based on decentralized computing providers like Aethir.

A data layer centered around training datasets to expand AI large models.

A middleware layer composed of various new AI-based infrastructures (training/privacy inference/Agent platforms).

An application layer.

As it stands, there are very few Crypto + AI application layer products that allow retail investors to feel the impact directly, and the experience is poor. The most direct reason is that the foundational layers beneath the application layer have not yet been well established.

Recently, starting from the model itself, FLock.io @flock_io has realized that training large models specifically for the Crypto track can validate its path through on-chain incentives to promote decentralized AI and model training. Although it is working on a larger narrative of decentralized AI model training, validating the feasibility of a standout product in a segmented field during the early stages can help FLock.io accumulate more early supporters, leading to the emergence of FLock.io's Web3 Agent Model.

If the application layer's Crypto AI Agent is your smart assistant, then its large model is equivalent to your assistant's brain. Only with deep experience and knowledge can it handle every interaction and execute each instruction correctly.

The most prominent metric of FLock.io's Web3 AI Agent large model is 75.93% FC precision matching accuracy, which simply means it understands Web3 better. Instructions that other models cannot recognize or that produce nonsensical outputs are addressed by the Web3 AI Agent large model through collaboration with industry partners like IO.net, based on the AI Arena Task 1 collaboration framework. By reducing single data source bias through decentralized training, AI Agents calling upon the Web3 AI Agent Model will be more practical and accurate.

At this point, we must mention the ecological growth flywheel derived from FLock.io's natural incentive properties as a Crypto product.

By leveraging the basic $FLock incentives, more quality model trainers are encouraged to join, bringing higher quality data and training skills.

These contributions help build better AI large models;

More powerful AI large models can attract more feature-rich and practical Agents and Dapps to call upon;

Practical DApps and AI Agents, supported by the Web3 model, possess strong market competitiveness to generate more revenue;

More calls to the large model by Agents and Dapps will generate more model incentives for trainers;

This new data and usage feedback can further optimize and enhance the AI large model, while the prosperity of the ecosystem will also attract more quality model trainers to join through rewards and recognition.

Through this continuously looping positive feedback, the FLock.io ecosystem can achieve sustained growth and self-enhancement. AI Infra is still in its early stages, let alone the Crypto AI field. We look forward to seeing more foundational BUIDLers like FLock.io in the industry. Only by laying a solid foundation for the model can we witness a more stable and flourishing application layer explosion.