Modern intelligence, whether silicon or biological, is often tripped up not by ignorance but by abundance. When a system has the bandwidth to entertain countless possibilities, it will often do so even when the problem demands only one. This is the problem of overthinking. It is not confined to anxious students before an exam or committees drafting endless reports. It now appears in machine intelligence too.
The pattern is the same. A large language model with vast parameters, trained on oceans of data, receives a simple task: write an email, solve an arithmetic puzzle, summarize a paragraph. It could apply the shortest path to the solution, but the surplus capacity tempts it into elaboration: building scaffolding for a hut, stacking analysis upon analysis until the original goal is obscured. The human version is familiar: the writer who takes three days to craft a birthday card, or the engineer who designs a spacecraft to carry groceries.
It was not supposed to be this way. The promise of AI “triage” was to select the right model for the right problem. A trivial query would go to a lightweight system, while a dense legal contract would be parsed by the heavyweight. In theory, this mirrors the human brain’s ability to recruit different mental resources depending on the task. In practice, if the triage itself is handled by a highly capable model, we are back where we started. A system too clever for its own good can overcomplicate the act of deciding how not to overcomplicate.
Before the release of the most advanced models, there was a certain blunt efficiency in older systems. They could not afford to waste cycles on ornate reasoning, so they didn’t. Just as a village carpenter without power tools cuts wood cleanly with a single saw, a smaller model works directly from inputs to outputs. The risk of convolution was minimal because convolution was beyond its means.
This limitation hints at a broader truth about intelligence: the ability to simplify is not a crude by-product of ignorance but a hallmark of mastery. Seeing the simple in the complex requires recognizing which details can be safely ignored without damaging the structure of the answer. It is a skill that mathematics prizes and that politics often lacks, where simple slogans are prized but seldom accurate.
Not all humans excel at this. Some are chronic complicators, capable of turning a dinner plan into a logistical nightmare. Others, whether through temperament or training, can cut to the core of a problem in minutes. This talent is partly instinctive, but it can be cultivated. It demands the discipline to resist showing off all that one knows, and the humility to accept that the shortest path might be the best one.
In education, this principle is often inverted. Students are rewarded for showing all their working, which is fine for demonstrating understanding but can entrench the habit of exhaustive thinking even when unnecessary. In technology, the same bias exists: “more features” is often sold as progress, even when each extra layer increases the chance of failure. The smartphone with twenty overlapping settings menus is no more “intelligent” than one with a handful of intuitive buttons.
The challenge for AI design is to embed this selective simplicity without crippling capacity. One approach is genuinely multi-tiered systems, where the triage mechanism is not a miniature genius in its own right but a deliberately constrained judge. Another is to train large models not only to produce accurate answers but also to value resource economy, much as humans learn to answer a question in an exam within the time limit.
For individuals, the lesson is parallel. High mental horsepower can be as much a liability as an asset if it is allowed to run without restraint. Some of the most effective thinkers are those who know when to stop thinking. They can hold complexity in reserve, deploying it only when the problem justifies the cost. The rest of the time they rely on heuristics, rules of thumb, and the confidence that a rough answer now may be better than a perfect answer too late.
We live in an era that celebrates maximalism: bigger models, bigger data, bigger ideas. But as both humans and machines show, sometimes the smarter move is to shrink the frame. To stop not because you cannot go further, but because you have already gone far enough.
