🚀 The evolution of LLM architecture will inevitably lead to MCP (Modularized-Composable-Parameters). Current limitations demand Web3-native solutions:

1. Outdated Knowledge: Massive training cycles trap models in the past. Web3 needs decentralized, real-time knowledge updates (think on-chain data streams). [4]

2. Overparameterization: Bloated models ≠ better UX. Modular design could enable task-specific parameter clusters (e.g. deploy only code-gen modules locally). [1]

3. Toxic Data Crawling: 74% of web content could become AI-hostile by 2026. Web3 needs:
- Tokenized creator incentives
- Verified data marketplaces
- Anti-spam consensus mechanisms

4. Functional Limitations: Static APIs ≠ dynamic real-world ops. Imagine:
- DAO-curated plugin registry
- On-chain version control for APIs
- Community-verified execution proofs

The future lies in decomposing monolithic LLMs into interoperable modules with blockchain-based coordination. True progress isn't bigger models - it's creating incentive-aligned systems where humans and AI co-evolve.