The current discussion around Apple’s insightful paper, often cited as “The Illusion of Thinking: Understanding the Strengths and Weaknesses of Large Reasoning Models” (or with slightly varying subtitles), is making waves. Many are interpreting it as a damper on overly optimistic AGI promises or even as a harbinger of a bursting AI bubble.
But amidst all the excitement – is this finding really that surprising? And more importantly: what does it mean for the practical application of AI in businesses?
My perspective on this:
- Not entirely unexpected: For many who delve deeper into the subject, the paper rather confirms an existing assumption: LLMs are, at their core, highly sophisticated pattern recognizers based on probabilities and the relationships learned from training data. The idea of “true thinking” in a human sense has always been an ambitious attribution.
- Impact is what counts! The philosophical debate about whether models truly “think” or “understand” is fascinating, but perhaps secondary to the current business impact. The crucial question is: do they deliver value? And here, I see a clear YES.
- New solutions, new business models: Regardless of whether it’s “true intelligence” – LLMs, including the critically examined “thinking models,” are already enabling the solution of problems that previously seemed unattainable. They create efficiency, drive innovation, and open up entirely new business models. This is precisely where the immediate, tangible value lies.
Thus, the Apple paper isn’t a nail in AI’s coffin but a valuable reality check. It helps us better understand the strengths and (current) limitations of this impressive technology, enabling us to use it more strategically and effectively instead of blindly following overblown hype. It’s an invitation for precision, not a call for a standstill.
What are your thoughts? Is “The Illusion of Thinking” a necessary curb on exaggerated expectations, or an opportunity to focus even moreSharply on real-world benefits? I look forward to the discussion!
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