The Great AI Overthink

Apple confirms what we’ve all suspected at Voiceflip (the ones yelling "I told you so" at their screens) in their latest article, The Illusions of Thinking.

Let's be honest. We've all watched a "smart" AI model try to solve a simple logic problem and slowly spiral into a verbose meltdown. It starts strong. A few solid steps. Then it takes a weird turn, starts mumbling about edge cases, and confidently lands on an answer so wrong it should come with a warning label.

At Voiceflip, we've always suspected something was off with these so-called "thinking" models. The ones that generate long, elaborate reasoning traces before giving you an answer. You know the type. They use Chain-of-Thought. They reflect. They double-check. They sound like a philosophy major trying to do your taxes.

Turns out, we were right to be skeptical. Apple’s study puts real science behind the suspicion. They ran experiments using puzzle environments instead of traditional math benchmarks and came to a clear conclusion:

Large Reasoning Models are impressive,

but they fall apart exactly where it matters most.

The Three-Act Drama of Reasoning Models

Apple tested models using controlled puzzles like Tower of Hanoi, Checker Jumping, River Crossing, and Blocks World. These puzzles allowed them to vary complexity in a way that math tests cannot. The results revealed a familiar story, broken into three distinct phases:

Phase One: Low Complexity

Standard language models actually outperformed their more verbose "thinking" counterparts. They were faster, used fewer tokens, and got the answer right more often.

Phase Two: Medium Complexity

This is where the reasoning models earned their keep. With enough breathing room, their extra processing helped them explore more options and land on the correct solution more often.

Phase Three: High Complexity

Everything collapsed. Both types of models failed completely. Accuracy dropped to zero. The reasoning models started thinking less as the problems got harder, which makes no sense but tracks with what we have seen in practice.

This last part is the kicker. As complexity increases, the models literally reduce their reasoning effort. They use fewer tokens. They give up early. And they still have plenty of compute budget left. It's like watching a student put their pencil down halfway through the exam because the questions looked too hard.

But What If You Just Tell the Model What to Do?

Apple tried that too. They gave the model the algorithm. Step-by-step instructions. And still, the models fell apart at the same complexity thresholds. Execution, not discovery, is the problem. These models cannot reliably follow instructions at scale. That is not a data issue. It is a reasoning limit.

We have seen the same thing. You can give a model a perfect template, and it will still decide to go off-script because some probability somewhere told it that a bold guess was better than a correct one.

Not All Thought is Useful

What this study really shows is that today's AI models are very good at sounding smart. They narrate their thinking. They use all the right language. But once the problem gets hard enough, all that thinking turns into noise. They overthink. They second-guess. Or worse, they fixate early and just keep talking.

Apple calls out this behavior directly. In easy tasks, the models often find the right answer early, then waste tokens wandering around. In harder tasks, they never get there at all. In both cases, the reasoning trace does not help. It just burns through your compute.

Our Take at Voiceflip

We are not here to dunk on Chain-of-Thought. We use it too. But we use it with structure, fallback logic, and validation. Because we have learned that "sounding smart" is not the same as being helpful. Especially when the end user does not care how elegant the process was, just whether the answer was right.

This is why we built our platform around real-world messiness. Documents with inconsistencies. Questions with no clear format. People who want clear answers without sitting through a 40-step logic dump. We optimize for clarity, not drama. And one thing the study doesn’t cover, but we’ve seen repeatedly in development, is that models often fail before the problem becomes difficult. A messy prompt, a conflicting source, or an ambiguous question with no grounding is enough to derail them. That’s why Voiceflip doesn’t rely on language models alone. We combine them with additional layers that help keep answers grounded, reliable, and focused. It’s not about making the model think more, it’s about making the whole system respond more usefully, with output that works clearly and controlled.

So thank you, Apple. Your research just gave us a well-written, graph-filled version of what we have been explaining to clients for the last year. Long thinking traces are not magic. They are often just longer ways of being wrong.

And no, adding more Chain-of-Thought will not fix it.