>> The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding.
All code is temporary and should be treated as ephemeral. Even if it lives for a long time, at the end of the day what really matters is data. Data is what helps you develop the type of deep understanding and expertise of the domain that is needed to produce high quality software.
In most problem domains, if you understand the data and how it is modeled, the need to be on top of how every single line of code works and the nitty-gritty of how things are wired together largely disappears. This is the thought behind the idiom “Don’t tell me what the code says—show me the data, and I’ll tell you what the code does.”
It is therefore crucial to start every AI-driven development effort with data modeling, and have lots of long conversations with AI to make sure you learn the domain well and have all your questions answered. In most cases, the rest is mostly just busywork, and handing it off to AI is how people achieve the type of productivity gains you read about.
Of course, that's not to say you should blindly accept everything the AI generates. Reading the code and asking the AI questions is still important. But the idea that the only way to develop an understanding of the problem is to write the code yourself is no longer true. In fact, it was never true to begin with.
All code is temporary and should be treated as ephemeral. Even if it lives for a long time, at the end of the day what really matters is data. Data is what helps you develop the type of deep understanding and expertise of the domain that is needed to produce high quality software.
In most problem domains, if you understand the data and how it is modeled, the need to be on top of how every single line of code works and the nitty-gritty of how things are wired together largely disappears. This is the thought behind the idiom “Don’t tell me what the code says—show me the data, and I’ll tell you what the code does.”
It is therefore crucial to start every AI-driven development effort with data modeling, and have lots of long conversations with AI to make sure you learn the domain well and have all your questions answered. In most cases, the rest is mostly just busywork, and handing it off to AI is how people achieve the type of productivity gains you read about.
Of course, that's not to say you should blindly accept everything the AI generates. Reading the code and asking the AI questions is still important. But the idea that the only way to develop an understanding of the problem is to write the code yourself is no longer true. In fact, it was never true to begin with.