AI Beats Zelda Puzzles—But Took 42 Tries to do it

AI reasoning models can now solve complex gaming puzzles that require planning six moves ahead. A color-changing puzzle from a Zelda game—where clicking an object flips its color and all adjacent objects—proved that current models like GPT-5.2-Thinking, Gemini 3 Pro, and Claude Opus 4.5 can crack riddles that traditionally required either strategic planning or mindless button-mashing.

The catch? Gemini 3 Pro sometimes needed 42 pages of trial-and-error reasoning to reach the correct solution. Claude Opus 4.5 failed entirely until given additional visual explanations and a mathematical equation. Only GPT-5.2-Thinking solved it correctly, reliably, and quickly every time.

This is your AI future: technically capable, occasionally correct, and generating enough computational waste to fill a small library in the process.

Planning Six Moves Ahead (After Trying 3,000 Wrong Moves First)

The puzzle follows simple rules: hit a blue or red orb, and all adjacent orbs flip to opposite colors. The goal is turning everything blue. Humans solve this through pattern recognition, spatial reasoning, or—more commonly—randomly clicking until something works.

AI models approached it differently. Gemini 3 Pro generated solutions through extended reasoning chains, sometimes producing correct answers immediately, other times documenting elaborate trial-and-error processes spanning dozens of pages. GPT-5.2-Thinking consistently found correct solutions efficiently. Claude Opus 4.5 initially misinterpreted the puzzle entirely, requiring additional prompting and mathematical frameworks to succeed.

The interesting part isn't that AI can solve Zelda puzzles—it's how inefficiently some models reach correct answers. Forty-two pages of computational reasoning for a six-move puzzle suggests these systems are brute-forcing solutions through exhaustive exploration rather than developing genuine strategic understanding.

That's fine for puzzles with clear win conditions and limited move spaces. It's less fine when you scale this approach to complex problems where exhaustive search becomes computationally prohibitive.

The GameFAQs Replacement Nobody Asked For

Researchers suggest combining puzzle-solving capabilities with agentic AI that plays games autonomously could eliminate human-written walkthroughs. Nvidia's NitroGen already demonstrates this: an AI agent plays through games, documents every action with screenshots, then feeds that information to a walkthrough writer that generates documentation.

If it works for games, the logic goes, it should work for any software needing documentation.

Except human-written walkthroughs aren't just action logs—they're curated experiences. Good walkthroughs anticipate player confusion, explain why solutions work, highlight missable content, and understand that documentation serves human comprehension, not just technical accuracy.

AI-generated walkthroughs will be comprehensive, exhaustive, and technically correct. They'll also be the literary equivalent of reading assembly language—every step documented, zero insight provided.

The GameFAQs community spent decades creating guides that balanced completeness with readability, personality with precision. They did it for free because they loved the games. Now we're proposing to replace that with agents that play games mechanically, document them exhaustively, and generate text that nobody particularly wants to read.

When "Can It Do This?" Becomes "Should We Make It?"

The Zelda puzzle proves current AI models possess non-trivial reasoning capabilities. Planning multiple moves ahead, understanding spatial relationships, and solving combinatorial problems are genuine achievements that demonstrate progress beyond simple pattern matching.

But we're watching the tech industry speedrun from "look what AI can do" to "let's replace human creators" without stopping to ask whether automation improves outcomes or just reduces costs.

AI solves the puzzle. It generates the walkthrough. It documents the software. And in the process, it eliminates the human expertise, curation, and insight that made those artifacts valuable in the first place.

The enthusiast who writes a Zelda walkthrough isn't just documenting solutions—they're sharing joy, explaining hidden mechanics, celebrating clever design. The AI agent produces technically complete documentation devoid of understanding why anyone would care.

We keep confusing capability with value. Yes, AI can generate game walkthroughs. No, that doesn't mean we should let it replace the community knowledge that gaming culture built over decades.

The Efficiency Trap

The real tell is Gemini needing 42 pages to solve a six-move puzzle. That's not reasoning—that's computational brute force with extra steps. It works, until it doesn't. It scales, until it hits problems where exhaustive search becomes impossible.

And we're already talking about deploying this approach to "any software that needs documentation," as if the Zelda puzzle success generalizes cleanly to enterprise systems with millions of edge cases and no clear win conditions.

The gap between "solved a constrained puzzle" and "can document complex software reliably" is massive. But we'll figure that out after deploying it, apparently.

Meanwhile, the human game guide writers who created value for communities because they loved doing it are being told their contributions can be automated by agents that play games without comprehension, document experiences without insight, and generate text without purpose beyond completeness.

Progress.

If you need content strategies that balance automation capabilities with human expertise, or documentation approaches that prioritize reader comprehension over technical completeness, Winsome Marketing helps companies deploy AI where it adds value without eliminating the value humans create.