A recent article by Geoffrey Huntley argues that software development is becoming extremely fast and cheap because of AI. That observation is correct. Code that once took days can now be produced in minutes. Entire features can be scaffolded almost instantly.
But this is only half of the story.
What the article highlights about speed, it only hints at when it comes to understanding. And that is where the real challenge now sits.
The bottleneck has moved
AI removes much of the effort from implementation. It does not remove the effort required to understand a problem.
Before any code is written, someone still needs to figure out what the system should actually do. This includes understanding the domain, clarifying rules, resolving ambiguities, and aligning different stakeholders who often have conflicting expectations. These activities are inherently human. They require time, discussion, and careful thinking.
No model can shortcut this completely. It can assist, but it cannot replace responsibility.
As a result, the bottleneck has not disappeared. It has simply moved. What used to be a technical constraint is now a cognitive one.
Speed without clarity creates waste
There is a common assumption that faster coding automatically leads to faster delivery. In practice, this rarely holds.
Organizations are not limited by typing speed. They are limited by decision-making, coordination, and clarity. Even if implementation becomes ten times faster, unclear requirements will still slow everything down. In some cases, they even make things worse.
With AI, it becomes very easy to produce working software quickly. But if the underlying understanding is wrong, you only get one outcome: incorrect systems, delivered faster.
The result is not acceleration. It is amplified rework.
The illusion of progress
AI gives a strong feeling of progress. Code appears quickly. Interfaces look complete. Systems seem to function.
But this can be deceptive.
If the behavior is not clearly defined, the system may look finished while still being fundamentally wrong. The gap between appearance and correctness grows. This is dangerous, especially in complex business systems where edge cases and rules matter more than the happy path.
In such an environment, speed without clarity is not an advantage. It is a liability.
From coding to specifying
We are entering a phase where coding is no longer the hardest part of software development. Describing behavior precisely is.
The central question shifts from “How do we implement this?” to “What exactly should be implemented?”
This changes the skill profile of teams. Writing code becomes less of a differentiator. Understanding problems and expressing them clearly becomes the core capability.
Clarity becomes the scarce resource.
Why this makes requirements critical again
For a long time, requirements engineering was often treated as secondary. Many teams preferred to “figure things out in code.”
That approach does not scale in an AI-driven world.
If the input to an AI system is vague, the output will be unreliable. AI does not resolve ambiguity. It amplifies it. This makes well-defined requirements essential again, not as heavy documentation, but as precise descriptions of behavior that can be shared, reviewed, and trusted.
From prompts to structured understanding
Some teams try to address this by improving their prompts. While this works for small tasks, it breaks down in larger systems.
Prompts are transient. They are hard to review, difficult to version, and almost impossible to maintain as a shared source of truth. They do not provide stability.
What is needed instead is a set of structured, persistent artifacts that describe the system clearly. This is where the AI Unified Process provides a useful perspective.
It shifts the focus away from ad-hoc prompting towards stable specifications. Behavior is defined explicitly through artifacts such as system use cases, linked to requirements and domain models. These artifacts are not documentation for its own sake. They are the foundation that guides both humans and AI.
In this approach, implementation follows specification. Changes start with behavior, not with code. This ensures that speed does not come at the cost of correctness.
A different way of building software
The real transformation is not from manual coding to AI-assisted coding. It is from coding-first to understanding-first.
AI is extremely effective when the problem is well defined. It becomes unreliable when the problem is vague. This means that the quality of the outcome depends directly on the quality of the specification.
Teams that recognize this shift adapt their process. They invest more in clarity, reduce ambiguity early, and keep their systems small enough to remain understandable. They do not rely on AI to figure things out. They use AI to execute clearly defined intent.
Final thought
AI did not remove complexity. It relocated it.
The effort is no longer in writing code. It is in understanding what should be built.
This leads to a simple conclusion:
The teams that succeed will not be those who generate code the fastest. They will be those who understand their domain best and can express that understanding clearly.
In a world where code is cheap, clarity becomes the ultimate competitive advantage.
