Why AI Hates Modern Frameworks (and Loves Web Standards)

There's a paradox nobody wants to say out loud: the same frameworks companies pick because they're "enterprise-ready," "scalable," and "industry standard" are, for an LLM writing code, a minefield. Angular , React with its whole ecosystem, Nx with its monorepos: these are powerful tools, built by humans to coordinate teams of humans on massive codebases. And for that purpose, they're often the right choice — if your primary constraint is coordinating hundreds of engineers over a decade, the conventions and tooling of an established framework earn their keep. But there's a second actor in the room now. When the one writing the code is an AI, the very traits that make these frameworks "robust" turn into pure friction. The argument I'm making isn't "Angular and React are obsolete." It's narrower: we've historically optimized software architecture for human cognition, and LLMs introduce a different cost model that may favor simpler, more deterministic architectures — at least in some domains. Let's break down why, in three points. 1. The Token Tax (and the Cognitive Bottleneck) An LLM doesn't "understand" code the way we do — it processes it token by token, and every token costs something: money, latency, and context window that could otherwise be spent reasoning about the actual problem. Try asking an AI to generate a simple input form in a typical Angular/Nx context. To do it "properly" it has to: create the component (separate .ts , .html , .css files) declare the @Component with all its metadata import and wire up the right modules possibly touch an NgModule or a standalone-components config navigate 4-5 folder levels inside a typical Nx structure ( apps/ , libs/ , feature-x/ , data-access/ , ui/ ...) All of this before writing a single line of actual logic. That's architectural complexity that, for a human, pays for itself over time thanks to tooling, autocomplete, and internalized conventions. For an LLM generating text sequentially, it's a tax paid on every singl