When software is no longer the limiting factor, the fundamental question changes. It is no longer about who can build, but about who can think clearly enough to describe what should be built. It may sound technological, but in reality it is about people, decisions, and power.
By: Per Imer, CEO, Homerunner
Contains: 920 words
A little over a decade ago, I almost always gave the same advice to companies facing technology decisions. Choose standard software. Avoid custom development. Keep complexity down.
The advice was not conservative. It was practical. Custom development was expensive, slow, and risky. Organizations became dependent on individuals, solutions were difficult to maintain, and every change required new projects. Standard software created stability and predictability, while custom solutions often created technical debt and organizational lock-in.
That advice was correct in a world where software production was scarce. Development capacity was limited, and prioritization was unavoidable.
That world no longer exists.
The decisive change is not simply artificial intelligence as a tool. It is the production model behind software itself.
Tasks that previously required months, teams, and budget approvals can now be completed in hours. What once required specialists can now be created by a single person capable of clearly formulating the problem.
Software is no longer the hard part ... Thinking is.
Where software once followed a linear path from code to compilation and execution, the model now looks different. An intention is described, an AI generates the solution, and the result can be used almost immediately. The distance between idea and implementation has dramatically collapsed.
And when that distance collapses, organizational structures begin to shift.
In many organizations, development capacity has functioned as the primary bottleneck. Changes had to be prioritized, estimated, and scheduled. Ideas could be postponed by referring to limited resources.
The backlog was more than a planning tool. It became a legitimate response to almost any ambition. We would like to do it, but we do not have the capacity.
That argument loses its strength when solutions can be built the same day a problem is identified.
When technology is no longer the constraint, the real question becomes whether the organization has enough clarity to decide what should actually be built. The discussion moves from capacity to prioritization, from technology to thinking.
And with that shift, power begins to move.
Historically, those who controlled development resources also controlled pace. And pace is one of the most underestimated forms of power inside organizations.
When employees can now describe a solution and see it realized almost immediately, decision-making authority moves closer to where problems actually exist. Hierarchies do not necessarily disappear, but their original justification begins to change.
The bottleneck moves from technology to understanding.
The new limitation is no longer whether something can be built, but whether someone can formulate the problem clearly enough that it should be built.
aThe concept of Vibe Code can easily be misunderstood as another technical discipline. In reality, it represents something entirely different.
It is not about everyone learning to program. It is about the ability to express a thought with such precision that a machine can translate it into functionality.
- Can you define the need clearly?
- Can you describe the desired output precisely?
- Can you explain the rules behind a decision?
If the answer is yes, you can build solutions. If the answer is no, AI will not help you. The machine produces exactly what is described, not what was intended.
AI therefore acts as an amplifier of thinking quality. Clear thinking creates better solutions faster. Unclear thinking creates confusion faster.ut is still real. Closed data reduces the world to make it manageable. At that point, intelligence begins to resemble efficiency without understanding. Open data is not chaos. It is an acknowledgment that the world cannot be reduced without loss, that decisions always have ripple effects, and that truth is rarely local.
Open data forces systems to see the whole, not just the event. It does not make decisions perfect, but it makes them meaningful.
Genuine thinking is rarer than we often assume. Many professional roles have long been shaped by execution, coordination, and optimization within predefined frameworks. The need for deep reflection has been limited because complexity lived in the systems rather than in problem formulation.
Thinking requires something different. It demands the ability to conduct an internal dialogue in which one both challenges and defends one’s own assumptions. It requires holding opposing perspectives long enough to understand them before choosing direction.
This capability now becomes essential because machines do not interpret intentions. They respond to formulations. If the formulation is unclear, the outcome will be unclear.
When software can be created almost instantly, the quality of human reflection becomes the true differentiator.
The greatest risk is not that organizations will build too much software. The risk is that they attempt to preserve structures designed for a world defined by technological scarcity.
If leadership tries to maintain old control mechanisms. If IT continues acting as a gatekeeper in a world without technical scarcity. If middle management protects processes instead of improving them. Stagnation emerges.
Not because technology is missing, but because organizational structures prevent movement.
Vibe Code will not remain a niche skill for technologists. It will become a foundational organizational capability.
- The ability to think structurally.
- The ability to formulate clearly.
- The ability to transform ideas into functioning solutions without friction.
Machines can build almost anything. But they cannot choose direction. They cannot decide what is worth creating or why it matters. When software is no longer scarce, the real limitation becomes the quality of our thinking and our willingness to allow organizations to evolve alongside reality.
If we fail to do that, it will not be AI that holds us back, it will be ourselves.
