Speed is one of the most obvious advantages of AI.
We can write code faster, generate alternative solutions, prepare documentation, analyze data, or get to a first prototype more quickly.
But that speed has no direction. If the initial assumption is right, AI can accelerate the path to a good result. If the initial assumption is wrong, AI can just as efficiently accelerate us in the wrong direction. And that is an important distinction.
In a traditional process, slower execution sometimes gave the team enough time to notice that something did not make sense. When execution becomes ten times faster, a bad decision can much more quickly turn into ten files, twenty tasks, and several new dependencies.
At that point, the problem is no longer just the wrong idea. The problem is the amount of work already built around it.
That is why AI does not reduce the need for good decisions before the work begins. On the contrary, it increases their value.
As execution becomes cheaper and faster, it becomes more important to know clearly:
- what problem we are solving,
- who we are solving it for,
- what assumptions we are making,
- how we will verify whether we are right.
AI is an accelerator. But an accelerator does not decide where you are going.
If the direction is right, you will get there faster.
If it is not, you will simply reach the wrong place faster.
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