Apple cuts to the core of the AI reasoning illusion

Reasoning – rehearsed not real

To test how well today’s “reasoning” models actually think, Apple built controlled puzzle environments – like the Tower of Hanoi – with rising complexity. Rather than relying on benchmark questions the models might’ve already seen, they focused on unseen problems that let them observe not just whether a model got the answer right, but how it tried to get there.

The results were striking. On simple puzzles, reasoning-enabled models often made things worse. They’d find the correct answer quickly, then carry on “thinking” until they talked themselves into a wrong one. In contrast, standard models got it right by sticking with their first guess.

At moderate complexity, the reasoning models performed better. They broke problems into parts and used longer step-by-step outputs to reach accurate answers – this is where the so-called chain-of-thought approach (where models break down problems into sequential steps) made sense.

But that advantage didn’t last. Once puzzles became properly difficult, both standard and reasoning models hit a wall. Performance dropped to zero. The reasoning models generated longer answers, but not better ones. They didn’t adapt. They didn’t generalise. They just collapsed.

The models didn’t discover useful strategies or underlying algorithms. Despite having tools for reflection, they failed to build consistent approaches across similar problems. They weren’t reasoning – they were essentially mimicking structure without grasping what it meant.

As Apple put it, these systems “fail to use explicit algorithms and reason inconsistently across puzzles.” In other words, they imitate the appearance of logic, but not the logic itself.

Calling time on the reasoning hype

Apple’s paper challenges a growing narrative in AI – that new models don’t just produce text but can plan, solve, and “think” like humans. Firms like OpenAI, Google, and Anthropic are selling that story hard – Sam Altman has made repeated claims about GPT-4’s improved reasoning abilities.

But Apple’s findings throw doubt on all of that with a clear message: stacking more layers, throwing in more tokens, or asking a model to “think harder” doesn’t get you to true intelligence. If someone’s betting on AGI showing up next year, they may want to adjust their expectations. Critics like cognitive scientist Gary Marcus – long sceptic of large language models – were quick to applaud the paper. Gary has often said that today’s models are just “stochastic parrots,” repeating patterns they’ve seen without understanding them. Apple’s work offers evidence for that view. He even called it a “knockout blow” to the idea that scale alone will solve intelligence.

And he’s got a point. These systems can sound smart – even persuasive – while getting everything wrong. That’s fine if you’re summarising emails. But in medicine, law, or finance? The illusion becomes dangerous.

What this means for businesses

Apple’s findings are not merely academic; they deliver a crucial reality check for businesses grappling with AI adoption. In an era of burgeoning ‘AI co-pilots’ and decision-support tools, the pressure to delegate complex tasks to AI is immense, yet Apple’s research strongly suggests this is often premature and risky.

In high-stakes areas like finance, healthcare, or legal advice, overestimating AI’s abilities could be costly. An AI-generated strategy that looks convincing but rests on flawed logic might not just fail but could also cause real harm.

Keep it useful. Keep it grounded.

The gap between what current AI models appear to do and what they actually understand remains wide. For decision-makers, developers, and policy strategists, the lesson is simple: don’t confuse articulate output with actual intelligence. These models are still fragile, still shallow, and still prone to confident failure – presenting flawed logic with the same conviction as correct answers, which can lead down costly or even harmful paths.

That doesn’t mean progress isn’t happening. Instead, it highlights that scaling current approaches appears insufficient to bridge the fundamental gap between pattern recognition and genuine understanding. The road ahead likely requires truly novel architectures, perhaps blending symbolic AI or classical logic with modern learning techniques.

In the meantime, the best way to approach AI is clear-eyed and grounded. Let it help where it can. Don’t trust it where it can’t. And above all, remember: just because something sounds smart doesn’t mean it is.