Crypto X AI Next Round Will Not Be Limited to Memecoin Track, Bear Market is More Suitable for In-depth Research, Understanding More Feasible Narratives to Reach the Peak of Future Waves.
While organizing AI track reports these days, I recalled the stack about Crypto + AI published by Coinbase Ventures @cbventures.
JK @jonathankingvc Ideal Scenarios of Crypto and AI Combination: AI Agent Interacting on Various Crypto Infrastructures. Software Code (Smart Contracts) Created by AI Leads to Dapp Proliferation and Enhanced User Experience, Where Users Can Own, Govern, and Profit from Their AI Large Models. And Divided into
1. Decentralized Computing Power Provider Layer Represented by Aethir
2. Data Layer Focusing on Training Datasets for Expanding AI Large Models
3. Middleware Layer Composed of Various New AI-based Infrastructures (Training/Privacy Inference/Agent Platforms)
4. Application Layer
Now, Crypto+AI Application Layer Products Directly Perceptible to Retail Investors Are Extremely Rare and Poorly Experienced. The Most Direct Reason is That the Infrastructure Layers Below the Application Layer Are Not Yet Well-established.
Recently, Starting from the Model Itself and Promoting Decentralized AI and Model Training through On-chain Incentives, FLock.io @flock_io Realized That Training Large Models Specifically for the Crypto Track Can Prove Itself. Although Focusing on a More Massive Decentralized AI Model Training Narrative, Validating the Feasibility through Outstanding Products in细分 Domains in the Early Stage Can Accumulate More Early Supporters for FLock.io, Thus the Web3 Agent Model Emerged.
If the Crypto AI Agent at the Application Layer is Your Intelligent Assistant, Then Its Large Model is Like the Brain of Your Assistant. Only by Possessing Deep Experience and Knowledge Can It Perform Well in Each Interaction and Execute Every Instruction Correctly.
FLock.io's Web3 AI Agent Large Model's Most Outstanding Indicator - 75.93% FC Precise Matching Accuracy Rate, Simply Put, It's an AI Large Model That Better Understands Web3. Instructions That Other Models Cannot Identify or Are Nonsensical, the Web3 AI Agent Large Model, Through Collaboration with Industry Track Partners Like IO.net, Based on AI Arena Task 1 Collaborative Framework, Reduces Single Data Source Bias Through Decentralized Training, Making AI Agents Calling the Web3 AI Agent Model More Practical and Accurate.
At This Point, We Must Mention the Ecological Growth Flywheel Derived from FLock.io's Natural Incentive Attributes as a Crypto Product.
Through Basic $FLock Incentives, More High-Quality Model Trainers Are Attracted, Bringing Higher Quality Data and Training Skills.
1. These Contributions Help Construct Better AI Large Models;
2. More Powerful AI Large Models Can Attract More Functional and Practical Agents and Dapps to Call;
3. Practical Dapps and AI Agents Supported by Web3 Models Gain Strong Market Competitiveness and Generate More Revenue;
4. More Agent and Dapp Calls to Large Models Will Produce More Model Incentives for Trainers;
5. These New Data and Usage Feedback Can Further Optimize and Improve AI Large Models, While the Ecosystem's Prosperity Will Attract More High-Quality Model Trainers Through Rewards and Recognition.
Through This Continuously Cycling Positive Feedback, the FLock.io Ecosystem Can Achieve Continuous Growth and Self-Enhancement. AI Infrastructure is Still in Its Early Stages, Let Alone the Crypto AI Field. Looking Forward to Seeing More Infrastructure BUIDLers Like FLock.io. Only by Laying a Solid Foundation for Models Can We Witness a More Stable and Flourishing Application Layer Explosion.
