"The AI is the framework now. Just generate vanilla JS."
I've seen this take more and more recently. While technically possible, this perspective lacks a fundamental understanding of why we use frameworks in the first place. Languages and frameworks aren't built for machines; they're built for humans.
A computer can run almost anything. But can a team maintain, scale, and collaborate on it? That's the real question.
Frameworks: A Shared Language for Humans
The true challenge of software development isn't making something work once. It's building a system that can be maintained, understood, and evolved over time by a team of collaborating people. This is the primary role of a framework.
It provides a common ground that enables effective teamwork:
- A Shared Language and Common Patterns: When a team agrees on a framework, they inherit a shared set of principles and terms. This drastically reduces the cognitive overhead for any developer working on the project.
- A Baseline of Quality and Best Practices: Good frameworks come with built-in solutions for common problems like state management, routing, and security, guiding developers toward robust and tested patterns.
- A Community for Tools and Troubleshooting: Frameworks are supported by vast ecosystems of libraries, developer tools, and community knowledge that save enormous amounts of time and effort when problems arise.
To build a system that can be maintained by an organization requires communicating how it works in a way that a team can quickly and easily understand. Frameworks are the medium for that communication.
The Risk of the "AI Framework"
Ignoring the collaborative benefits of frameworks for a black box of AI-generated code, no matter how functional it seems at first, is setting the stage for a maintenance nightmare.
Much like hiring that "10x" developer and letting them do whatever they want, you're trading short-term velocity for long-term chaos. The initial output might be impressive, but you're left with a bespoke, undocumented system that only one entity—the original creator (or in this case, the AI)—truly understands.
This approach invites significant risk:
- Poor Change Management: Without common patterns, every change requires deciphering a unique architecture.
- Slow and Tedious Maintenance: Fixing bugs or making updates becomes an exercise in reverse-engineering the AI's "thought process."
- Exponentially Longer Troubleshooting: When something goes wrong, there's no community forum, no official documentation, and no shared knowledge base to fall back on.
You risk creating an unmaintainable system that grinds future development to a halt.
The Future: Simpler, More Collaborative Frameworks
This isn't to say AI won't change how we build software. It absolutely will. But instead of eliminating frameworks, it's more likely that AI will accelerate their evolution.
My prediction? AI won't kill frameworks. It will force them to evolve. We'll likely see a shift toward simpler, more readable frameworks where the primary value is usability and maintainability, making it easier for both developers and AI to collaborate effectively within a shared, understandable context.