You’ve probably seen the champagne-popping headlines. Fable 5 and GPT-5.6 Sol have officially “solved” Baba Is You, the notoriously mind-bending puzzle game that requires deep logic and creative rule-bending. The AI industry is calling it a triumph of reasoning. But while everyone is busy celebrating the milestone, they’re ignoring a creeping, uncomfortable reality: we are paying a massive price for a parlor trick.
Passing a test by brute-forcing every possible answer isn’t intelligence; it’s just expensive mimicry.
Baba Is You is supposed to be the ultimate test of adaptability. You push blocks around to rewrite the rules of the game itself. To beat it, you need actual comprehension. But here’s the dirty secret of Fable 5 and GPT-5.6 Sol: they didn’t “understand” the game. They conquered it through sheer, unadulterated computational scale. They threw enough parameters and compute at the problem until the puzzle cracked, not through logic, but through statistical weight.
This isn’t a breakthrough in artificial reasoning. It’s a warning sign. When you solve a benchmark that demands creativity by simply scaling up the model size, you aren’t proving the AI is smart. You’re proving the benchmark is brittle.
We aren’t building minds; we’re building massive, energy-guzzling encyclopedias that crack the moment you change one variable.
Change the rules of Baba Is You slightly, introduce a novel variation, and watch these “solved” models collapse. They fail spectacularly because they don’t possess generalized reasoning. They possess a massive, energy-hungry memory of patterns. The awe we feel at their capability should be undercut by a deep unease. How much power, data, and capital was burned just to pretend a machine understands a puzzle?
If you’re investing in, building, or just cheering on AI progress, you need to wake up. We have to stop treating benchmark mastery as a proxy for actual intelligence. True generalization means taking a learned rule and applying it to a scenario you’ve never seen before. Brute-forcing a benchmark until it breaks doesn’t move us closer to AGI; it just makes the math more expensive.
The real intelligence test isn’t whether an AI can solve the puzzle. It’s whether we can stop fooling ourselves into thinking it actually did.
FAQ
Q: Why does it matter how the AI solved it, as long as it got the right answer?
A: Because in the real world, you can't brute-force a novel problem. If a model only works because it memorized a specific test, it's useless—and dangerous—when deployed in dynamic, unpredictable environments.
Q: Should we stop investing in large models altogether?
A: No, but we need to stop treating scale as a magic bullet. The industry needs to pivot focus toward benchmarks that actually test generalization and novel reasoning, rather than just rewarding pattern matching at massive scale.
Q: Is the AI benchmark industry fundamentally broken?
A: Exactly. Benchmarks have become PR tools rather than scientific measures. Passing them often means almost nothing about a model's actual cognitive capabilities, only how much compute was thrown at the wall.