Did Apple Just Burst the AI Reasoning Bubble? I don't think so
A shallow dive into Apple's latest research that challenges everything we think we know about reasoning models
Just when I was getting excited about reasoning models, Apple researchers decided to play party pooper and published a paper that's making waves across the AI community. "The Illusion of Thinking" - even the title is provocative!
As someone who's been very bullish on reasoning models(and honestly, still am), this paper hit me somewhat. But here's the thing - we really needed this kind of study to understand where we really stand with AI reasoning.
What Apple Actually Did
So, instead of just throwing another math benchmark at these models, Apple's team got a bit creative. They built controllable puzzle environments like Tower of Hanoi, but with a scientific twist. Why did they use puzzles? Because:
Complexity Control: Want to make a problem 2x harder? Just add more disks.
No data contamination: Fresh problems that models haven't memorized.
Easy to evaluate reasoning: They could peek inside the "thinking" process, not just final answers.
Objective truth: Puzzle simulators don't lie about correctness.
The Three Regimes of Testing
So Apple researchers discovered that reasoning models don't just "get better" - they operate in three distinct regimes:
1. The Embarrassing Zone (Low Complexity)
This is where regular LLMs actually outperform reasoning models. And I somehow know about it based on my experience with reasoning models. For simple problems, actually giving models more "thinking time" actually made them worse where they would happen to have the right answer but they would overthink and just go into some planning loop. It's like watching a chess grandmaster overthink a basic move and blunder.
2. The Sweet Spot (Medium Complexity)
This is the sweet spot for reasoning models. This is where we see the reasoning models do great. Models genuinely benefit from that internal monologue, working through problems step by step.
3. The Cliff (High Complexity)
This is where everything fell apart. Both reasoning and regular models just... stop working. Complete collapse as per the paper.
But there is an interesting Twist
Till now all was good and what I was expecting. But here's what really baffled me: Apple discovered that as problems get harder, reasoning models actually think LESS, not MORE.
You'd expect models to use more computational juice when facing tougher problems, right? Nope! They hit some internal complexity threshold and basically give up, reducing their reasoning effort despite having plenty of tokens left in the budget.
It's like watching a student encounter a hard math problem and immediately writing "I don't know" instead of trying harder. Except these are supposed to be our most advanced AI systems.
Why I'm Still Bullish (Despite Everything)
Now, you might think this paper is enough to crush my faith in reasoning models. Quite the opposite! Here's why I think it is still the next thing in GenAI:
1. We're Still in the Stone Age
The reasoning models tested (o1, o3-mini, DeepSeek-R1, Claude 3.7 Sonnet) are literally first-generation attempts. Remember GPT-1 and the hype around it? Yeah, we're at that stage for reasoning.
2. The Sweet Spot is Real
That medium complexity regime isn't a fluke - it's a proof of concept. When reasoning works, it REALLY works. We just need to expand that sweet spot. You really cannot expect researchers to come up with a fully baked solution so fast.
3. We Now Know What to Fix
While Apple did identify problems it also provided a roadmap for researchers:
Fix the overthinking issue in simple cases
Make thinking effort scale properly with complexity
The Real Lessons for Practitioners
But what about applied ML folks? If you're working with reasoning models right now, here are my takeaways:
A. Know Your Complexity Zone
Simple tasks? Maybe stick with regular models for efficiency. Don’t click that reasoning button in Claude until you need to.
Medium complexity? Reasoning models are your friend
Ultra-complex? Manage expectations and push back(for now)
B. Don't Just Scale Compute Throwing more tokens at reasoning won't magically solve hard problems. We need architectural breakthroughs which will come.
C. Test Beyond Accuracy Apple's approach of analyzing intermediate reasoning steps should be standard practice. Final accuracy is just the tip of the iceberg.
D. Build Proper Benchmarks If you're evaluating reasoning models, create controlled environments like Apple did. Math benchmarks are contaminated anyway.
The Bigger Picture: What This Means for AGI
This research touches on something fundamental: the difference between pattern matching and genuine reasoning.
Current reasoning models might be sophisticated pattern matchers that happen to work well in a specific complexity range. True reasoning would scale consistently with problem difficulty - something we clearly haven't achieved yet.
But here's my contrarian take: this might be exactly how human reasoning works too. We all have complexity limits where our thinking breaks down. Maybe the path to AGI isn't about eliminating these limitations but understanding and working with them.
The Road Ahead
The next generation of reasoning models will most likely have:
Dynamic thinking allocation: More effort for harder problems
Better calibration: Knowing when to think vs. when to answer quickly
Architectural improvements: Maybe transformer variants specifically designed for reasoning
Hybrid approaches: Combining different reasoning strategies
My Take
This paper is the best thing that could have happened to the reasoning model field. It's forcing us to be honest about current limitations while providing a clear path forward.
Yes, current reasoning models are "illusions" in some sense. But so was GPT-1 compared to modern language models. The question isn't whether current models are perfect - it's whether we're on the right track.
And based on that medium-complexity sweet spot? We absolutely are.
What do you think? Are you still bullish on reasoning models, or has Apple's research changed your perspective? Drop your thoughts in the comments - I'm genuinely curious about the community's reaction to this research.