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You.com Founders Predict an AI Winter Is Coming in 2026 (Sponsored)
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Richard Socher and Bryan McCann are among the most-cited AI researchers in the world. They just released 35 predictions for 2026. Three that stand out:
The LLM revolution has been “mined out” and capital floods back to fundamental research
“Reward engineering” becomes a job; prompts can’t handle what’s coming next
Traditional coding will be gone by December; AI writes the code and humans manage it
This week’s system design refresher:
MCP vs RAG vs AI Agents
How ChatGPT Routes Prompts and Handles Modes
Agent Skills, Clearly Explained
12 Architectural Concepts Developers Should Know
How to Deploy Services
MCP vs RAG vs AI Agents
Everyone is talking about MCP, RAG, and AI Agents. Most people are still mixing them up. They’re not competing ideas. They solve very different problems at different layers of the stack.
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MCP (Model Context Protocol) is about how LLMs use tools. Think of it as a standard interface between an LLM and external systems. Databases, file systems, GitHub, Slack, internal APIs.
Instead of every app inventing its own glue code, MCP defines a consistent way for models to discover tools, invoke them, and get structured results back. MCP doesn’t decide what to do. It standardizes how tools are exposed.
RAG (Retrieval-Augmented Generation) is about what the model knows at runtime. The model stays frozen. No retraining. When a user asks a question, a retriever fetches relevant documents (PDFs, code, vector DBs), and those are injected into the prompt.
RAG is great for:
Internal knowledge bases
Fresh or private data
Reducing hallucinations
But RAG doesn’t take actions. It only improves answers.
AI Agents are about doing things. An agent observes, reasons, decides, acts, and repeats. It can call tools, write code, browse the internet, store memory, delegate tasks, and operate with different levels of autonomy.
How ChatGPT Routes Prompts and Handles Modes
GPT-5 is not one model.
It is a unified system with multiple models, safeguards, and a real-time router.
This post and diagram are based on our understanding of the GPT 5 system card.
When you send a query, the mode determines which model to use and how much work the system does.
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Instant mode sends the query directly to a fast, non-reasoning model named GPT-5-main. It optimizes for latency and is used for simple or low-risk tasks like short explanations or rewrites.
Thinking mode uses a reasoning model named GPT-5-thinking that runs multiple internal steps before producing the final answer. This improves correctness on complex tasks like math or planning.
Auto mode adds a real-time router. A lightweight classifier looks at the query and decides whether to use GPT-5-main or GPT-5-thinking when deeper reasoning is needed.
Pro mode does not use a different model. It uses GPT-5-thinking but samples multiple reasoning attempts and selects the best one using a reward model.
Across all modes, safeguards run in parallel at various stages. A fast topic classifier determines whether the topic is high-risk, followed by a reasoning monitor that applies stricter checks to ensure unsafe responses are blocked.
Over to you: What's your favorite AI chat bot?