alt Hacker News> The key question is: Do we want (desire or require) humans to understand the implementation?
Understanding the implementation is not the root cause here. The actual issue is that all software are built for a particular needs which is human related. In most software, the cost mostly decays to a constant value unless that need has changed or an external event has occurred.
Embracing AI fully is keeping the software in constant flux as it can’t guarantee it will actually follow the requirements and the platform specs. Any prompt has a non zero chance to introduce regressions and breaking changes, disrupting the workflow of the users.
The tools to write the code does not matter to the user just like the hammer does not matter to the person that needs a home. But if your tools results in a badly constructed house that always needs repair and what not, expect complaints.
> Embracing AI fully is keeping the software in constant flux as it can’t guarantee it will actually follow the requirements and the platform specs. Any prompt has a non zero chance to introduce regressions and breaking changes, disrupting the workflow of the users.
You can pin down existing expected behavior pretty well with automated test cases and even bringing formal verification into the mix. Using LLMs to create those, it's not even a major cost factor. Then you are safe from such regressions. Just like when humans make changes. So, I don't see how this is introducing a difference.
> The tools to write the code does not matter to the user just like the hammer does not matter to the person that needs a home. But if your tools results in a badly constructed house that always needs repair and what not, expect complaints.
Indeed. Though I don't see the connection to my point.