Software Engineering

If models write more of the code and humans increasingly review, constrain, and verify it, then popularity in the human-coded era is not the same thing as fitness for the next one. The harder problem is no longer just producing code. It is building stacks that can survive cheap generation without collapsing under ambiguity, review burden, and correctness debt.

Code production is no longer our bottleneck. The newfound velocity of AI coding agents hasn't solved our problems; it has simply moved the bottleneck further down the pipeline, creating massive SDLC backpressure. The human "Looks Good To Me" on a PR is now the single biggest liability in deployment. It’s time to stop acting like typists and start acting like architects.

AI dramatically accelerated individual coding and local execution. End-to-end delivery barely moved. We optimized leaf-node execution but left the tree structure completely manual. Business intent (“add fraud detection”) decomposes into tasks across teams, each carrying implicit assumptions that only conflict during integration. Performance budgets, data freshness requirements, and retry semantics remain undiscovered until week 8 of 10. AI can breeze through well-defined tasks with deterministic verification. But getting from business requirement to that well-defined task? That’s where projects die.

Mobile apps ruled the last decade, but AI is quietly dismantling the foundations they were built on. From declining iOS and Android job postings to LLM interfaces replacing entire app flows, this piece argues the golden age of mobile development is over and what comes next might not even run on iOS or Android.