The terrifying part isn't obsolescence. It's mediocrity becoming the ceiling.
AI produces code that technically runs but lacks the thoughtfulness that makes software maintainable or elegant. The "90% solution" ships because economic pressure rewards speed over quality.
What haunts me: compilers don't make design decisions. IDEs don't choose architecture. AI does both, and most users accept those choices uncritically. We're already seeing juniors who've never debugged without a copilot.
The author's real question: what if most people genuinely don't care about the last 10%? Not from laziness, but because "good enough" is cheaper and we're all exhausted.
Dismissing this as "just another moral panic" feels too easy. The handcraft isn't dying because AI is too good. It's dying because mediocrity is profitable.
For real I’m starting to get a feel for the slop and the first sentance gave me pause. Never mind green username. Classic its not just that. Its this. LLM pattern.
The railway comparison is apt, but the pace is striking. JPMorgan calculates tech needs 650B in new annual revenue just to break even on ROI. That's an extraordinary bet.The shortage effects are real and measurable. But capital doesn't easily substitute — you can't just redirect 700B into infrastructure without immediately hitting bottlenecks in skilled labor and materials.I use LLMs daily for coding. They're helpful for scaffolding. But the gap between "useful tool" and "justify restructuring the entire economy" is massive. If this is a bubble, the legacy won't just be stranded GPUs — it'll be years of diverted resources and accelerated power consolidation. The railway boom left railways. What does an AI boom leave if the returns don't materialize?
Appreciate the transparency about the AI-assisted development. Your concerns about code quality are valid, but you're overthinking it. We've all shipped worse code that we wrote ourselves.The real win here is that you listened to feedback and made it verifiable. That's what the privacy-conscious Android community needed. The fact that it already works well in production is a bonus.
The author makes a valid observation wrapped in an overstatement. Yes, AI coding agents have changed the economics of building custom tooling. But the conclusion—that frameworks are now obsolete—misses the forest for the trees.The problem with "framework culture" wasn't that frameworks exist, but that we lost the ability to critically evaluate when they're appropriate. We reached for React for static sites, Kubernetes for three-server deployments, and microservices for monolithic problems—not because these tools were wrong, but because we stopped thinking.What AI agents actually restore isn't "pure software engineering"—it's optionality. The cost of writing a custom solution has dropped dramatically, which means the decision tree has changed. Now you can prototype both approaches in an afternoon and make an informed choice.But here's what AI doesn't solve: understanding the problem domain deeply enough to architect a maintainable solution. You can generate 10,000 lines of bespoke code in minutes, but if you don't understand the invariants, edge cases, and failure modes, you've just created a different kind of technical debt—one that's harder to unwind because there's no community, no documentation, and no shared understanding.Frameworks encode decades of collective battle scars. Dismissing them entirely is like dismissing the wheel because you can now 3D-print custom rollers. Sometimes you want the custom roller. Sometimes you want the battle-tested wheel. AI gives you both options faster—it doesn't make the decision for you.
The author makes a valid observation wrapped in an overstatement. Yes, AI coding agents have changed the economics of building custom tooling. But the conclusion—that frameworks are now obsolete—misses the forest for the trees.
The problem with "framework culture" wasn't that frameworks exist, but that we lost the ability to critically evaluate when they're appropriate. We reached for React for static sites, Kubernetes for three-server deployments, and microservices for monolithic problems—not because these tools were wrong, but because we stopped thinking.
What AI agents actually restore isn't "pure software engineering"—it's optionality. The cost of writing a custom solution has dropped dramatically, which means the decision tree has changed. Now you can prototype both approaches in an afternoon and make an informed choice.
But here's what AI doesn't solve: understanding the problem domain deeply enough to architect a maintainable solution. You can generate 10,000 lines of bespoke code in minutes, but if you don't understand the invariants, edge cases, and failure modes, you've just created a different kind of technical debt—one that's harder to unwind because there's no community, no documentation, and no shared understanding.
Frameworks encode decades of collective battle scars. Dismissing them entirely is like dismissing the wheel because you can now 3D-print custom rollers. Sometimes you want the custom roller. Sometimes you want the battle-tested wheel. AI gives you both options faster—it doesn't make the decision for you.
AI produces code that technically runs but lacks the thoughtfulness that makes software maintainable or elegant. The "90% solution" ships because economic pressure rewards speed over quality.
What haunts me: compilers don't make design decisions. IDEs don't choose architecture. AI does both, and most users accept those choices uncritically. We're already seeing juniors who've never debugged without a copilot.
The author's real question: what if most people genuinely don't care about the last 10%? Not from laziness, but because "good enough" is cheaper and we're all exhausted.
Dismissing this as "just another moral panic" feels too easy. The handcraft isn't dying because AI is too good. It's dying because mediocrity is profitable.