The Illusion of Thinking

Current AI doesn’t reason — it simulates reasoning. AGI — the point where machine thinking equals human thinking — remains an open frontier. Apple’s work reminds us to approach AI with both optimism and humility.

Mark Saymen

6/8/20251 min read

Apple Drops a Reality Bomb: Today's "Reasoning" AI Isn’t Really Reasoning

📢 Apple’s new paper, "The Illusion of Thinking", delivers a wake-up call: leading “reasoning” models like OpenAI’s o3‑mini, DeepSeek‑R1, Claude 3.7 Sonnet‑Thinking, and Gemini don’t actually reason — they merely excel at memorizing patterns and mimic thought processes.

🔍 Key Findings from Controlled Puzzle Tests:

Apple used classic logic puzzles (e.g., Tower of Hanoi, River Crossing, Blocks World), unseen during model training, to stress-test “reasoning” AIs.

  1. 💥 Accuracy Collapse
    All models performed well on easy puzzles, struggled at medium difficulty, and hit 0% accuracy on hard puzzles, showing a sharp cliff in capability.

  2. 🧠 Thinking Backfires
    Rather than trying harder, models actually used fewer tokens — effectively giving up as puzzles got tougher.

  3. Three Performance Zones

    • Easy: Standard LLMs outperform “reasoning” models.

    • Medium: Reasoning models show some advantage.

    • Hard: All models fail, even with long chain-of-thought.

  4. Algorithm Blindness
    Even when provided the correct algorithm, models still collapsed at the same complexity level — no strategic execution happening.

  5. Inconsistency Across Tasks
    A model could solve Tower of Hanoi with 100+ steps but fail on a simpler 5-step River Crossing — exposing fragile, dataset-based pattern matching.

🤯 Implications for AI and AGI

  • Reasoning = Pattern Recall, not general logic or planning.

  • Chain-of-thought prompts don’t guarantee deeper cognition — they may just allocate fluff tokens .

  • Benchmarks must evolve: puzzles with controllable complexity revealed limitations that math/coding tests may miss.

💡 What This Means for Practitioners & Builders:

  • Use LLMs for idea generation, drafting, or scalable computations, not for critical planning or novel problem-solving.

  • Treat chain-of-thought as a tool, not proof of cognition— under the hood, it's predictive mimicry

  • Human oversight remains essential — don’t mistake confident AI outputs for true understanding.

🏁 The Final Word:

Current AI doesn’t reason — it simulates reasoning. AGI — the point where machine thinking equals human thinking — remains an open frontier. Apple’s work reminds us to approach AI with both optimism and humility.

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