Failing at Tower of Hanoi puzzles with 8 disks
Large reasoning models fail at Tower of Hanoi puzzles with 8 disks. That's not a technical detail, it's a current limitation.
Apple's research team tested leading AI models on controlled puzzle environments and found consistent accuracy collapse beyond basic complexity thresholds. Models including OpenAI's o3, Anthropic's Claude, and Google's Gemini were stumped by children's games, with less than 80% accuracy on 7-disk Tower of Hanoi puzzles and near-complete failure at 8 disks.
The core issue: these systems "fail to use explicit algorithms and reason inconsistently across puzzles". They're sophisticated pattern-matchers, not reasoning engines.
Why This Matters for Enterprise Strategy:
🎯 Deployment Reality: If your "reasoning" model can't handle 8-disk puzzles, what complex business problems can it actually solve reliably?
⚖️ Risk Recalibration: Pattern matching scales differently than logical reasoning. Your AI governance frameworks need to account for this distinction.
🔍 Value Alignment: Focus AI investments where statistical patterns create genuine value—content assistance, data analysis, workflow optimization—rather than complex logical reasoning.
So what?:
Companies building AI strategies around proven capabilities rather than promised reasoning will deliver more reliable outcomes. Apple's controlled testing reveals "fundamental barriers to generalizable reasoning" that marketing claims can't overcome.
This research provides clarity in a hype-driven market. Use it to build more grounded, effective AI implementations. đź’ˇ
Comment, connect and follow for more commentary on product counseling and emerging technologies. 👇
