Summary of Key Points
In recent years, new concepts in the AI industry (such as RAG, AI Agent, MCP, etc.) have seen explosive growth, far outpacing the speed at which ordinary people can update their knowledge. Behind this is a "conspiracy" involving academia, commercial companies, and the media: academia creates new terms for research papers; companies use these concepts to drive up prices; and the media amplifies public anxiety to attract traffic. As a result, traditional business leaders and others feel pressured to keep up, otherwise they risk appearing foolish. This has led to issues such as repeated investment and over-trust of unproven technologies. The article provides practical methods for determining whether new concepts are worth pursuing, emphasizing the importance of focusing on "solving real problems" rather than blindly chasing buzzwords.
Why Is the AI Community So Obsessed with Creating New Terms?
The trend of creating new terms in the AI community is not accidental but is driven by a collaboration among three groups:
- Academia: To publish research papers, they need "innovation," so old technologies are rebranded as "breakthroughs" (for example, "loop logic" is called Loop Engineering).
- Commercial Companies: To differentiate their products, they add new terms to ordinary tools and market them as "high-end solutions" (e.g., a platform described as "XX-native XX-driven"), significantly increasing prices.
- Media + Training Institutions: The media uses headlines like "Prompt Engineering is dead" to attract readers, while training institutions offer quick courses on the latest AI jargon to profit from eager learners.
The essence is that while technology matures and needs to become simpler, commercial success often requires it to appear more complex. The more obscure the term, the more likely customers are to pay for it.
What Are Those AI Jargons Really About? A Table to Help You Translate and Judge Their Value
Don’t worry if you don’t understand them; breaking down new terms into simpler language makes them easier to grasp:
| Jargon | Plain Language Explanation | Worth Pursuing? |
|---------------------------------|-----------------------------------|------------------------------|
| RAG | AI that looks up information before answering | ✅ Use when the technology is mature (e.g., customer service AI using product manuals) |
| AI Agent | AI capable of making decisions independently | ✅ Valuable, depending on use cases (e.g., corporate customer service, automated processes) |
| MCP | Provides a unified interface for AI to connect with external tools | △ Promising but requires stability (may not be essential for small companies) |
| Context Engineering | Ensures AI understands the relevant information at each step | ✅ Useful (just a new name for a common practice) |
| Harness Engineering | Creates a secure and reliable environment for AI | ✅ Critical for production (essential for using AI in businesses) |
| Fleet Engineering | Manages the division of labor and permissions among multiple AI systems | ○ Only needed by large companies (not essential for small firms) |
Key Takeaway: Most new terms are essentially rebranded old ideas. For example, Loop Engineering is just a concept from programming (known for decades), and Fleet Engineering refers to managing multiple AI systems (similar to how companies manage employees).
The Toll of Excessive Jargon: Repeated Spending and Over-Trusted Industries
The proliferation of jargon has serious consequences:
- Repeated Investment by Companies: Companies may spend millions on new technologies only to replace them with newer ones later (e.g., switching from RAG to Graph RAG, then to MCP).
- Anxiety and Burnout in Teams: Employees constantly have to learn new concepts, preventing the development of core skills.
- Over-Trusted Industries: Technology trends often peak and then decline rapidly (e.g., the four-layer Engineering Stack mentioned in 2026 may soon be forgotten). Excessive hype makes companies skeptical of new terms.
- Public Disinterest in Making Decisions: Faced with overwhelming information, many people simply give up trying to keep up.
Five Practical Ways to Avoid Being Misled by New Jargon
Don’t force yourself to understand every new term. Follow these five tips to avoid pitfalls:
1. Ask: “What specific problem does it solve?” When you encounter a new term, ask yourself if it can help you solve a current problem. If not, set it aside for three months; useful technologies won’t disappear.
2. Focus on Knowledge with Longer Shelf Life: Concepts like Prompt Engineering may be trendy for only a few months, but skills like "business understanding" and "problem definition" are more lasting.
3. Be a Late Adopter: Wait until the technology matures, tools become stable, and best practices are established before investing (e.g., use RAG when it’s fully developed).
4. **Don’t Believe in “One-and-Only Solutions”: Claims that a certain technology is the "only future solution” are often misleading; no technology is the only option.
5. Decompose Terms: Break down new terms into their components to understand their meaning and differences from existing technologies. For example, Loop Engineering simply means using AI to repeat tasks automatically.
In Conclusion: Don’t Let Concepts Overrule Your Judgment
What really matters is the ability to solve problems, not the terminology used to describe them. Next time you see an unfamiliar AI term, take a deep breath. You don’t need to understand it immediately. Ask yourself, "What problem does it solve?" If it’s truly important, it will still be relevant in six months. Concepts may become obsolete, but the value of solving problems remains timeless.
(End of Article)
(Note: The year 2026 mentioned in the text is a hypothetical context and not a real-time reference.)