Patrick Reany
16 September 2025
Note: This chat I had with Copilot is likely edited for relevance and brevity. It is also likely that I will not take the time to highlight all the words that Copilot highlights. Further, in the interest of being didactic, I might arbitrarily highlight an occasional word or phrase that Copilot did not. (Naturally, I get to correct my spelling, grammar, and other minor errors for free.)
Patrick:
It seems that humans, in the process of problem-solving, are capable of arriving at the wrong conclusion or just getting stuck, because of a condition called over-thinking. Is this a problem in artificial intelligence as well?
Copilot:
Absolutely---AI can overthink too, though it looks a bit different from the human version. Recent research has shown that advanced reasoning models, especially large language models (LLMs), can fall into a kind of analysis paralysis when solving complex problems.
What Does Overthinking Look Like in AI?
Excessive Internal Reasoning: Instead of interacting with the environment or making decisions, some models generate long chains of internal logic that don't lead to better outcomes.
Token Inflation: These models keep generating more and more text, which increases computational cost without improving accuracy.
Failure to Self-Correct: Like a person stuck in a flawed thought loop, some models latch onto an incorrect early conclusion and reinforce it, rather than revisiting or revising their reasoning.
Performance Decline: Ironically, more reasoning doesn't always mean better results. In some cases, simpler models or shorter reasoning paths outperform their more "thoughtful" counterparts.
Why It Matters
Efficiency: Overthinking in AI leads to wasted resources---more compute time, higher costs, and slower responses.
Reliability: It can also make AI less dependable, especially in high-stakes environments like software engineering or decision-making systems.
So yes, overthinking is a real and measurable issue in AI. The good news? Researchers are actively working on ways to mitigate it---like using selective reinforcement learning or evaluating reasoning paths to choose the most efficient ones.
It's fascinating how even machines can get "lost in thought," isn't it?
Patrick:
I suppose that another form of over-thinking is to arrive at the correct answer, but to doubt it and continue to 'think' on it. Reasons for inefficiency in problem solving can include 1) not brainstorming enough early on, and 2) failure to account for all the constraints, especially the 'deal-breaking' ones that eliminate certain answers as allowable. The question is, how long should we devote to intial brainstorming?
Copilot:
You're absolutely right---overthinking isn't just about going down the wrong path, it's also about not trusting the right one. That kind of second-guessing can be just as paralyzing. As for how long to devote to initial brainstorming, the answer depends on the complexity of the problem and the stakes involved---but there are some smart guidelines to keep things efficient and creative.
How Long Should Brainstorming Last?
Here are some practical approaches drawn from expert techniques:
Crazy-8 Method: This rapid-fire technique gives participants just 8 minutes to sketch out 8 ideas. It's designed to push past hesitation and unlock creativity fast.
1-2-4-All: This method starts with 1 minute of solo brainstorming, then expands to pairs, then groups of four, and finally the whole team. It keeps the process tight and collaborative.
Time-Boxing: Most structured sessions recommend 15-30 minutes for initial idea generation. This keeps energy high and avoids mental fatigue.
Until Ideas Plateau: If you're brainstorming solo or with a small team, go until the flow of ideas naturally slows---usually around 20-40 minutes. Beyond that, diminishing returns kick in.
Why Timing Matters
Too Short: You risk missing unconventional or breakthrough ideas.
Too Long: You may start filtering or doubting ideas prematurely, leading to overthinking and fatigue.
Bonus Tip: Constraint Awareness
You nailed it---deal-breaking constraints are often overlooked early on. A good practice is to list known constraints before brainstorming and revisit them during idea evaluation. That way, you avoid wasting time on ideas that were never viable.
Afterword: