Content | Bruce
Editor & Layout | Huanhuan
Design | Daisy
The "USB-C Moment" in AI Evolution: In November 2024, the MCP Protocol released by Anthropic is causing an earthquake in Silicon Valley. This open standard, dubbed the "USB-C of the AI world," not only restructures the connection between large models and the physical world but also harbors the code to break the AI monopoly and reconstruct digital civilization's production relations. While we are still debating the parameter scale of GPT-5, MCP has quietly paved the decentralized path to the AGI era...
Bruce: I've been researching the Model Context Protocol (MCP) recently. It's the second thing in the AI field that has excited me since ChatGPT, because it offers hope to solve three problems I've been thinking about for years:
- How can non-scientists and ordinary people participate in the AI industry and earn income?
- What are the win-win combinations between AI and Ethereum?
- How to achieve AI d/acc? Avoid centralized corporate monopolies, censorship, and AGI destroying humans?
01, What is MCP?
MCP is an open standard framework that can simplify the integration of LLM with external data sources and tools. If we compare LLM to the Windows operating system, and applications like Cursor to keyboards and hardware, then MCP is like the USB interface, supporting flexible insertion of external data and tools, which users can then read and use.
MCP provides three capabilities to extend LLM:
- Resources (knowledge expansion)
- Tools (execution functions, calling external systems)
- Prompts (pre-written prompt templates)
MCP can be developed and hosted by anyone, provided as a Server, and can be taken offline and stopped at any time.
02, Why do we need MCP
Currently, LLM uses as much data as possible for massive computations and generates numerous parameters, embedding knowledge into the model to generate corresponding knowledge in conversations. However, there are several significant problems:
- Large amounts of data and computations require extensive time and hardware, and the knowledge used for training is often outdated.
- Models with massive parameters are difficult to deploy and use on local devices, while users may not actually need all the information to complete their tasks.
- Some models use web crawling to read external information for timeliness, but due to crawler limitations and external data quality, they may produce more misleading content.
- Since AI has not effectively benefited creators, many websites and content are implementing anti-AI measures, generating massive garbage information that will gradually reduce LLM quality.
- LLM finds it challenging to extend to various external functions and operations, such as accurately calling GitHub interfaces to perform actions. It may generate code based on potentially outdated documentation but cannot ensure precise execution.
