In the past turning a product idea into something tangible took one to two weeks. Now it can take just an afternoon, using "vibe coding," where AI helps write code based on natural language instructions.

Within just a few years of its invention, AI is bringing enormous convenience. From writing to programming, people can produce work faster, more smoothly and at a higher baseline quality. But that leads to an oft-repeated question: Is AI killing creativity?

The question sounds reasonable, but it points at the wrong target. No machine is stealing human imagination. If something is being eroded, it does not come from hostile technology; it comes from how people embrace convenience.

AI does not kill creativity by thinking for us. It does something subtler. It takes people straight to a near-best version and, in doing so, removes the evolution of thought, the process where ideas take shape, get tested, go off track, and get rebuilt.

A programmer is coding with the help of AI. Photo by Pexels

In writing, this happens almost immediately. AI can produce fluent, grammatical and logically sound paragraphs. Writers now start from a higher baseline than ever. But when they no longer struggle with sentences, something else fades: A sense of the weight of words.

In the past, each draft and revision helped writers refine precision and push an idea to the sharpness they wanted. When that process disappears, the skill weakens. They can still produce good text. They lose the ability to state exactly what they mean, and even the ability to ask AI to do so. As expressive ability declines, the starting point of every request becomes vague.

The rise of vibe coding shows a similar pattern in software. After the early excitement about speed, engineers face a flood of "best" versions from AI. Each one works. Each looks optimal within the given request. But the limit does not lie in AI's ability to solve. It lies in the request itself. When engineers no longer build logic from the ground up, a process that once forced them to understand systems at a basic level, their ability to define problems precisely begins to weaken.

AI can expand the space of answers, but not the space of questions. An engineer can generate many "correct" versions, yet all are bounded by the quality of the prompt, which is bounded by the engineer's own ability. Without training through intermediate steps, that boundary does not move. It only repeats faster, in more forms. When systems face new conditions, the ability to diagnose and rebuild also declines.

People often measure creativity by the final product. That misses its nature as a process. An idea rarely appears fully formed. It begins as fragments, gets questioned and is revised again and again, sometimes getting worse before it improves. That cycle of error and correction is where thinking is reorganized.

AI does the opposite. It presents an optimized version almost instantly: coherent, logical and aligned with past data. There are no steps backward, no in-between states, no initial instability. The result is better and faster. The internal movement of thought disappears.

A similar pattern appears in agriculture, when farmers move away from manual tools. Machines raise productivity, standardize processes and stabilize results. But another kind of knowledge fades: the repeated, daily contact with the soil.

Without working the soil each day, farmers lose sensitivity to small changes, shifts in moisture, soil structure and unusual crop behavior. They can still farm effectively as long as conditions remain stable. When the environment changes, the issue is not the tools. The issue is that they no longer recognize that the soil has changed.

In every case, the problem lies in the mechanism, not the tool. When a system automates well enough to replace intermediate steps, it does not just optimize outcomes but also cuts off the process through which people continuously adjust their understanding based on reality.

Creativity is not just about finding the best answer within a fixed system, but is the ability to see when the system itself no longer holds. Every optimized system assumes stable conditions. When that assumption breaks, what becomes outdated is not the result, but how people understand the problem.

AI only helps people find answers faster and not to see when the question needs to change.

Overusing AI can create the feeling of reaching a peak, but it is not the peak of individual ability; it is merely the peak of averaged data. Every optimized baseline is also a limit.

Creativity is not about being placed at the highest point. It is the process of climbing, slipping, falling, and climbing again. When that motion is replaced by an optimized launchpad, people may reach better results in the short term at the cost of their ability to push limits in the long term.

The risk is not that AI produces good text. The risk is that users get used to starting from an optimal point. When the starting point is always high, the ability to climb weakens. When everything arrives nearly finished, the muscles of revision atrophy.

When people stop training their ability to express, they lose control not only over the product, but over the question itself.

The answer is not to turn away from AI; it is to deliberately preserve the parts of the process that technology removes. It is not to compete with machines, but to retain the ability to sense, express and detect deviations.

Keep writing and revising instead of accepting ready-made output.

Keep building and understanding systems instead of only steering them through prompts.

And, like in farming, do not lose direct contact with the "soil" of your own field.