There is a strange dual opinion on language models right now. You either hear they are going to change everything, or change nothing at all. The recent data on mobile app releases shows both sides are wrong. The tool isn’t a monolith. On one hand, app submissions are skyrocketing because agents have made shipping code trivial. On the other hand, actual user traction is almost minimal. This chart is the literal data proof of what I discussed in this article. We are mistaking writing code for solving a problem. When you let an agent do the macro thinking just to get an app out the door, you end up with a system you have to read to make sense of, not one you already understand. They might look identical from the outside, but they are completely different beasts underneath. All those microscopic choices the model makes like the abstractions, the nomenclature, the structure are debt you inherit. If it’s a call you would have made, fine. But if not, the codebase is going to start violently resisting you the moment you try to pivot or ship a fast update based on user feedback. The code is there, but the understanding isn’t and you can’t easily put the comprehension back in once the lines are already written. That is why these thousands of new apps are flatlining. People used an agent to avoid the friction of thinking through the project. Now, they have an alien codebase that they can’t adapt when reality hits. Software development is not only about typing lines but a discipline of taking these fuzzy market problems and making them something you can test. The agent is fine with the tail end of that pipeline. But figuring out what the project actually needs to be? That is still entirely on you. If you don’t do that heavy lifting yourself, you just end up adding to the mountain of apps that nobody is opening. submitted by /u/sibraan_
Originally posted by u/sibraan_ on r/ArtificialInteligence
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