The information asymmetry framing is the most useful part of this. But I'd push back on where you locate the problem. It's not really a paradox, it's a design failure. The prompt-as-interface forces users to encode what they know into natural language on the fly, which is lossy and inconsistent. The solution isn't better prompting or bigger context windows. It's structured context gathering before inference. Interview the user systematically, build a high-resolution data model upfront, then let the model work against that. The quality difference is significant. I've spent the last year building AI systems that do exactly this. Not chat, but structured intake flows that front-load the information problem. The outputs are categorically different from what you get in a back-and-forth chat session. Not because the model is smarter, but because it's better informed. The NBA coach analogy holds: give the coach a 30-minute intake session before the first lesson and suddenly the advice stops being generic. On the agent systems critique, fair, but I'd separate two things. Token cost is an engineering problem, solvable with better retrieval, caching, compression. The "just a big system prompt" criticism is more interesting. Some of what gets marketed as agentic is basically dressed-up RAG. The genuinely agentic stuff, dynamic tool use, multi-step reasoning with actual state, is meaningfully different. The problem is the category is polluted by hype. The game interface idea is interesting but I suspect the real bottleneck isn't visual metaphor. It's persistent memory and reliable state management. Fix those and the interface question mostly solves itself.