(Note: this is a live document that could undergo revisions in the future)

I came up with a list of laws in relation to doing general Software Engineering work with reliance on AI, from my experience as a Master Software Engineer experimenting with AI and from observing other Software Developers who experiment with or use AI. 

Law #1: "The more Software Developers use AI, the more valuable Software Engineers who do not use AI become."

Software Engineers who are masters at delivering Software without using AI will actually have increased job security the more Software Developers in the worldwide Software Development community rely on AI to deliver Software without having true mastery over Software Engineering.

As more Software Developers become fully dependent on AI to build Software without truly understanding how AI gets work done, Software Engineers who do understand what is going on under the hood will dwindle and become more valuable than ever. In other words, they will have a competitive advantage over Software Developers who can only deliver Software features with AI as well as Software Developers who have not mastered Software Engineering.

Also, there will always be a need for Software Engineers who can maintain the Software of AI itself.

Law #2: "Software Developers benefit from AI in direct proportion to how weak they are in Software Engineering"

The weaker Software Developers are at Software Engineering the more they benefit from AI. After all, AI learns from Master Software Engineers and then applies its learnings in code generation done for lower-level Software Developers who lack mastery in Software Engineering. So, users of AI simply place themselves lower in the expertise hierarchy to be on the receiving end of what Master Software Engineers feed AI with their code. This explains why many experts like Linus Torvalds do not find AI very useful while devs who have zero degrees and qualifications feel like they get a lot from AI.

A beneficial thing to learn from this law is that it is more valuable for a Software Developer to hone in their Software Engineering skills (including the completion of university degrees) than to hone in their AI usage skills because if they achieve mastery over Software Engineering, they would cease to need AI to do their job well for customers. In other words, if a Software Developer feels like they benefit from AI even a little bit, then that means they have some unhandled weakness and are lacking some skills in Software Engineering they could be improving instead. Using AI after all takes away from the recommended 10,000 hours of Software Engineering practice to achieve mastery.

Law #3: "AI's speed is negated when it produces Software features faster than customers can test and learn."

Code generation is not the bottleneck when customers still have to test every Software feature at a human pace to provide real human feedback before iterating further for future improvement. So, given that customers still need to provide human feedback with non-AI testing, that negates the need for Software Engineers to produce code faster, rendering AI's speed benefits moot. Producing code with Lean Software Processes is fast enough for that need. 

Furthermore, Software has a limit to acceptable complexity by customers in relation to how many features it contains. So, if customers get too many features, they get overwhelmed, and that also invalidates the benefits of AI's speed. In other words, customers prefer quality over quantity.