I had called this a while back, since the reasoning is simple: frameworks primarily exist to minimize boilerplate, but AI is very good at boilerplate, so the value of frameworks is diminished.

The larger underlying shift is that the economics of coding have been upended. Since its inception, our industry has been organized around one fundamental principle: code is expensive because coders are expensive. This created several complex dynamics, one which was frameworks -- which are massive, painful dependencies aimed at alleviating costs by reducing the repeated boilerplate written by expensive people. As TFA indicates, the costs of frameworks in terms of added complexity (e.g. abstractions from the dependency infecting the entire codebase) are significant compared to their benefits.

But now that the cost of code ---> 0, the need for frameworks (and reusability overall) will likely also --> 0.

I had predicted that this dynamic will play out widely and result in a lot more duplicative code overall, which is already being borne out by studies like https://www.gitclear.com/ai_assistant_code_quality_2025_rese...

Our first instinct is to recoil and view this as a bad thing, because it is considered "Tech Debt." But as the word "debt" indicates, Tech Debt is yet another economic concept and is also being redefined by these new economics!

For instance, all this duplicate code would have been terrible if only humans had to maintain it. But for LLMs, it is probably better because all the relevant logic is RIGHT THERE in the code, conveniently colocated with the rest of the functionality where it is used, and not obfuscated behind a dozen layers of abstraction whose (intended) functionality is described in natural language scattered across a dozen different pieces of documentation, each with varying amounts of sufficiency, fidelity and updated-ness. This keeps the context very focused on the relevant bits, which along with extensive testing (again, because code is cheap!) that enables instant self-checking, greatly amplifies the accuracy of the LLMs.

Now, I'm not claiming to say this will work out well long term -- it's too early to tell -- but it is a logical outcome of the shifting economics of code. I always say with AI, the future of coding will look very weird to us; this is another example of it.