Introduction

Is AI really thinking, or is it just very good at pretending to think?

This was the central question in my recent fireside chat on AI with Alec, where we dug into Apple’s new research paper, “The Illusion of Thinking.”

The paper explores the limitations of reasoning in modern AI systems and highlights how even today’s most advanced models can fail when faced with truly novel problems

In this post, I’ll share some key ideas from the conversation, why this paper matters for business leaders and builders, and how it informs the way we at Specialized Data Company approach designing intelligent systems.

(👉 You can watch the full episode here.)

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What Is “The Illusion of Thinking”?

Apple’s research takes aim at the hype around large language models (LLMs) by stress-testing their ability to reason. While LLMs excel at pattern recognition (predicting the next word in a sequence), they often struggle when asked to break down a complex, unfamiliar problem.

This echoes what Meta’s Chief AI Scientist recently said: scaling up LLMs alone won’t get us to AGI (Artificial General Intelligence). To cross that chasm, we’ll need fundamentally different architectures and approaches.

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🧠 LLMs vs LRMs: A Useful Analogy

During the podcast, I used two quick games to illustrate the difference between LLMs and LRMs:

✅ LLMs (Large Language Models) are like playing fill-in-the-blank with familiar idioms:

Actions speak louder than... → Words

It takes two to... → Tango

They excel at completing patterns they’ve seen before.

✅ LRMs (Large Reasoning Models) are more like solving a math problem step by step. Foe example, what’s 9 × 19?

You might calculate 9 × 20 = 180 and subtract 9 to get 171. That process of decomposing a problem is similar to what LRMs attempt to do with chain-of-thought reasoning.

But Apple’s paper argues that even LRMs aren’t really “thinking.” Instead, they mimic how humans reason, and when they hit a complexity ceiling, they tend to fail catastrophically.

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📉 The Business Risk: When AI Fakes Confidence

One key takeaway from the paper is LRM’s inability to solve generalizable problems. This is what CNBC’s Deirdre Bosa labeled as “memorization at scale” in this video: https://www.youtube.com/watch?v=-5lviu6ZDXo 

For businesses, this matters because AI systems can produce plausible-looking outputs that are completely wrong. Without proper auditing and monitoring, this “drift” can have high-stakes consequences.

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🛠️ The Path Forward: Tightly Scoped AI Agents

So how should business leaders think about AI today?

In the near-term, the biggest wins will come from tightly scoped, purpose-built AI agents designed for specific use cases. I shared this example on the podcast: a recruitment firm struggling to match 100,000+ résumés with job descriptions.

Instead of fine-tuning a massive LLM (time-consuming, expensive, and unscalable), we designed a retrieval-augmented generation (RAG) based system which, put simply, functions as such:

1. Uses vector search to intelligently narrow down the résumés.
2. Feeds this smaller dataset into an LLM to refine matches further.
   

This system dramatically improved accuracy and efficiency, helping the firm surface highly-qualified candidates they’d otherwise miss.

It’s a reminder that AI is most powerful when paired with domain-specific data and well-designed workflows, not as a one-size-fits-all magic wand.

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🚨 Mind the Gap: Marketing vs Reality

The paper also highlights a growing gap between AI marketing hype and actual capabilities.

On one side, companies talk about “superintelligence.” On the other, Apple shows these models can’t solve even moderately complex problems unless they’ve seen them before.

Leaders need to cut through this noise to make smart, pragmatic decisions about where and how to deploy AI.

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Final Thoughts

AI isn’t magic. It’s a set of tools that are powerful yet have their own imperfect idiosyncrasies.

To unlock its full potential, businesses must combine clear-eyed strategy with deep technical understanding, ensuring systems are robust, auditable, and built for the right problems.

At Specialized Data Company, we believe the future lies in creating intelligent systems where humans and machines collaborate seamlessly, each doing what they do best.

If you’re exploring how AI can create value in your business, and how to avoid its pitfalls, let’s talk.