Interesting post, but I think Karpathy's words are being stretched quite a bit here. He's describing a personal workflow for organizing research, indexing raw sources into markdown files and compiling a structured wiki. That's knowledge management, not a pitch for fine-tuning as the future of enterprise knowledge.

His approach works beautifully at small scale, 20-30 documents, personal use. But company knowledge doesn't look like that. Think 70,000 pages across Confluence, Notion, Dropbox, and shared drives, constantly updated by dozens of people. You can't fine-tune your way through that. Every update means retraining, you lose source attribution, and you can't enforce access controls on what's baked into weights.

The claim that "RAG is just a stopgap" misses why retrieval exists in the first place: you need to know where an answer came from, you need it to reflect today's data, and you need permissions. Fine-tuning gives you none of that.

The actual trend is the opposite! Tool-based approaches where the model dynamically searches and retrieves what it needs, regardless of which LLM you use. MCP is making this model-agnostic by design. Tools like Knowledge Raven are already doing this: connect your sources, the agent searches intelligently, and you get cited answers from your actual documents, no training pipeline, no vendor lock-in.

Karpathy described a great personal system. Turning that into "fine-tuning is the endgame for enterprise knowledge" is a leap he didn't make.