Software engineering is based upon languages, operating systems and fundamental techniques getting on for 50 years old. Even AI systems are built on that. Sure, new paradigms have been introduced, but the is very little software wise today that couldn’t in theory have been built 50 years ago. All we have gained is ease of implementation and perhaps speed as tools have evolved, as well as improvements in the hardware we now use, but even that dates back to the 1980s in concept.. AI gives us a new paradigm for sure, we have now combined the ability to create programming applications with the ability to do general information research we used to have to do with search engines and human analysis. So now information systems development has merged with general information analysis. A program is now just a part of information retrieval. Whereby once writing an application was one thing and information retrieval was another. Google queries and search result analysis was one, writing an application was another. Now they are the essentially same thing. To write a prompt to solve any problem is likely to be more successful the more prior knowledge of the problem set that you have that I don’t deny, but for now. I think we all miss the revolution that has taken place with LLM development. We have rolled programming, web searching, problem analysis and problem solving into a single tool. It’s a tool potentially anyone can use regardless of their educational discipline. It’s not perfect yet and prior knowledge of the way things were done manually is still extremely helpful, but that may change given enough time. We see this in SW engineering today. How many Java or Python programmers know assembly language. How many know real computer architecture other than a perfunctory knowledge of the virtual machine? AI is not the be all and end all, of course not. But it is changing our way of thinking. Ten years from now how we obtain and process information will merge across discip...

If you use Opus or Astra on a $20 plan, that the same model as on a $100 plan. I personally don't see having to intervene as a huge issue. Reviewing AI code and telling it to correct stuff until it gets it up to my standards is far faster than writing the same code manually. Especially since just pointing an issue tends to be enough for it to fix it correctly, I usually don't have to be specific about the solution. AI is also very good at refactoring with little input. If the code works correctly but is organized like a disaster, a refactor pass with AI doesn't take long. Additionally, if your prompt is relatively large in scope like an entire new feature, plan mode helps a great deal. The model is more likely to ask clarifying questions, and it's easier to review and correct a plan than the implementation.