Apple recently published a research paper titled “The Illusion of Thinking.”
It digs into how large language models (LLMs) don’t actually think—but often give the appearance of reasoning.
I found this fascinating, because it directly touches on one of the biggest open questions in AI:
Do LLMs really reason, or do they just mimic reasoning patterns?
When a model explains its steps, is it genuine reasoning, or a narrative built after the fact?
How do we, as developers, evaluate “thinking” in machines without falling into the trap of anthropomorphism?
I recently wrote a blog about this on Medium (published via Level Up Coding) where I broke down the paper, added examples, and shared what this means for us as engineers working with AI systems.
I’d love to hear your take:
- Do you think reasoning in LLMs is an illusion, or are we just at the early stages of genuine machine reasoning?
- How should we design with this limitation in mind?
Why this matters?
As builders, we often rely on LLM “reasoning” for coding help, decision-making, or even system design. If that reasoning is an illusion, then our guardrails and evaluation strategies matter more than ever.
Would love to spark a conversation on this
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