Apple Just Found a Major Flaw in “Reasoning” AI — But Does It Really Matter?
A recent study from Apple’s AI research team is generating buzz, and for good reason. Their paper dives into the performance of Large Reasoning Models (LRMs), a type of AI that’s supposed to simulate human-like logic and multi-step thinking. The headline? These models perform fine on simple logic puzzles but completely collapse on complex ones.
And not just a little. The researchers observed a total drop to zero accuracy on harder problems. Worse, these models often give up mid-task, reducing their reasoning effort as things get harder—even when they still have the compute to keep going. In some cases, they ignored explicit instructions on how to solve the task.
That’s a pretty damning indictment for anyone betting that today’s AI is on a smooth trajectory toward general intelligence.
But here’s the thing: Does it really matter?
AI Is Still Just a Tool
This research is important, but it doesn’t undermine the real-world utility of current AI. It simply reminds us that these systems are tools, not minds.
We don’t judge a hammer because it can’t turn a screw. And we shouldn’t dismiss a language model because it can’t solve every logic puzzle thrown at it. What matters is understanding where and how to use it effectively.
For content generation, data wrangling, and summarization? AI is transformative.
For nuanced reasoning under pressure, where ambiguity and judgment matter? Humans are still essential.
Why This Research Does Matter
To be fair, Apple’s findings aren’t trivial. They raise serious questions about:
The limits of “chain-of-thought” prompting, a popular technique for improving AI reasoning.
The assumption that larger models and more data will automatically yield smarter behavior.
The viability of current architectures for tackling problems that require actual logical consistency.
If you’re building tools that require autonomous multi-step problem solving (e.g., self-driving cars, autonomous agents in high-risk environments, or financial modeling systems), this research should make you pause.
The Counterargument: Progress Is Nonlinear
Some will argue that this is just a temporary hiccup. We’ve seen AI hit walls before, only to smash through them later. Today’s failures could inspire better architectures, training methods, or hybrid approaches (e.g., combining symbolic logic with neural networks).
There’s also the matter of benchmarks: are these logic puzzles really representative of the types of reasoning we care about in most day-to-day AI applications? Some researchers would say no, that these test cases, while academically valuable, are not where most users need AI to excel.
And to be fair, even humans are often bad at logic puzzles, especially under time pressure or incomplete information. So maybe we’re holding AI to an oddly high standard?
Final Thought: Use It With Eyes Open
Apple’s paper is a timely reminder: AI is powerful, but not magic. You can’t outsource critical thinking to a model that sometimes gives up halfway through a hard problem.
The smartest use of AI today isn’t trying to replace human reasoning - it’s designing workflows that amplify human strengths while respecting machine limits. At Storm King, we’re building tools and frameworks that do exactly that: pairing human foresight and contextual judgment with the speed and pattern recognition of AI.
Our current projects—including the Consilience in Action series—showcase how this human-machine partnership unlocks better outcomes than either could achieve alone. We’re not chasing artificial general intelligence—we’re engineering augmented intelligence that works with you, not instead of you.