The Apple AI Reasoning Study: Beyond The Provocative Headlines
An app to let you explore the idea yourself
Apple's recent "Illusion of Thought" paper has ignited fierce debate in the AI community, but the real story lies beyond its provocative title. While critics and supporters battle over Apple's motives, the research offers valuable insights into the limitations and potential of AI reasoning models.
What the Study Actually Found
The researchers identified three critical performance zones for reasoning models:
Simple tasks where reasoning may actually hurt performance
Medium complexity where reasoning provides clear benefits
Hard problems where even reasoning fails to help
They tested this framework using logic puzzles like Tower of Hanoi and river crossing problems, scaling up difficulty by adding more disks, participants, or moves. The results showed that models eventually hit a wall. This "reasoning collapse" occurs when increased complexity renders them unable to make progress.
The Backlash Was Predictable
Reactions split along predictable lines. AI skeptics seized on the findings as evidence that reasoning models are overhyped and that billions in AI investment represent misguided hubris. Meanwhile, supporters dismissed the work as Apple PR, claiming it was a cynical attempt by a company lagging in AI to score points against competitors.
A more measured response came from Anthropic researchers, who identified several methodological issues: inefficient prompts, token budget limitations that made some problems unsolvable, and puzzle setups that were inherently flawed for certain difficulty levels.
Missing the Point
Both extreme reactions miss what makes this research valuable. Yes, the experimental design has flaws. The prompts are needlessly complex, and asking for algorithmic code solutions would be more efficient. But that misses the point entirely.
The study wasn't trying to find optimal puzzle-solving methods. It was probing what happens when you systematically increase task complexity while keeping the reasoning approach constant. This is fundamentally different from cherry-picking the best solution for each specific problem.
What We Can Learn
For practitioners, the key takeaway isn't that reasoning models are useless; it's that they're not magic bullets. Just upgrading to a reasoning model won't automatically solve your hard problems. These models excel in certain contexts but fail in others, and they come with real costs: more tokens mean higher latency and expense.
The research also highlights the "overthinking" phenomenon, where models generate hundreds of tokens to solve simple problems. A one-disk Tower of Hanoi puzzle requires a single move, yet some models produce paragraph after paragraph of "reasoning" to reach that conclusion.
The Real Opportunity
Rather than dismissing this work, we should see it as opening new research directions. Can we use this framework to map tasks to appropriate model types? Can we develop better guardrails against overthinking? How do smaller, local models perform on these same benchmarks?
At Neurometric, we are exploring these questions. As part of our work, we’ve created a tool to help replicate the study on local Ollama-hosted models, which we are releasing today. It makes it easy to explore these puzzles and watch how different models “reason” about solving them at different complexity levels.