Reality Check: AI Isn't Replacing Engineers in 2025

AI isn’t a developer replacement. It’s scaffolding. We’ve built production systems across nearly every major language. And while AI is rewriting the way code gets produced, here’s the bottom line: AI isn’t replacing engineers. It amplifies velocity and makes progress look effortless - sometimes even magical - but it doesn’t know which shortcuts will collapse later. It doesn’t weigh tradeoffs, prune complexity, secure before scale, or own the data model that everything else rests on. It doesn’t know when to refactor, when to componentize, or when a “fix” today will become debt tomorrow. Only engineers with experience and judgment can make those calls - the hundreds of daily nudges and tradeoffs, big and small - that turn scaffolding into a system built to last. That’s the line between a demo that dazzles and a product that endures. Baseball, Not Catch AI gives everyone a glove and a ball. Anyone can play catch now. That’s powerful; you can vibe an idea into existence in hours, even from your phone. But shipping production systems isn’t catch. It’s the major leagues. In the majors, the game is all about tradeoffs: Pitch selection: Do you throw heat now, or set up for later innings? (Speed vs. scalability decisions.) Bullpen management: Burn your relievers too early, and you’re exposed in extra innings. (Burn dev time on features vs. saving capacity for stability.) Defensive shifts: Positioning for what’s most likely to come, not just reacting. (Architecture decisions anticipating scale, not just fixing today’s bug.) Batting order: Lineup changes ripple through the whole game. (Refactors that unlock future velocity but cost cycles today.) AI can play catch, but it doesn’t call games. It doesn’t see the whole field, or know when to bunt, when to steal, or when to pull the starter. That’s engineering judgment. Agents as Teammates, Not Tools Think of AI agents like tireless junior engineers. They’ll happily scaffold APIs, generate tests, and grind all night. But they don’t know when they’re wrong. Left unsupervised, they’ll ship broken products, duplicate logic, or bury you in inline CSS. Agents are not malicious; just naive - rookies who can hustle but don’t know how to close a ninth inning. The leverage is real, but only if paired with engineers who can review, prune, and keep the codebase clean. Otherwise, today’s velocity is tomorrow’s tech debt. Where AI Shines Prototypes: days become hours API scaffolding: weeks become days Test coverage: from spotty to near-complete Documentation: generated alongside code We’ve rebuilt legacy systems in days instead of quarters. Agents generate scaffolding; engineers fill in the critical 30% with experience and judgment. The Mirage Risk The danger is that early results can feel magical. A vibe coder (or even a seasoned engineer leaning too hard on agents) can ship something that looks impressive overnight. But without tradeoff decisions, refactors, and discipline, that shine doesn’t last. What seems like a working product today can become unmanageable tomorrow; brittle, bloated, and fragile under real traffic. AI hides complexity instead of managing it. Experienced engineers do the opposite: they expose, confront, and resolve it before it becomes a liability. Where AI Fails AI cannot: Make security-critical decisions Handle compliance or regulatory nuance Design architectures that last for years Judge trade-offs and incentives And it creates new risks: Security blind spots: default code with unsafe patterns Overgrowth: monolithic files instead of components Cruft: abandoned versions, dead imports, ghost code Inline everything: CSS, markup, logic mashed together Even some experienced engineers can get punch-drunk on the acceleration, caught up in the thrill of “instant progress” and abandoning the discipline that actually ships. The engineering truth remains: slower is faster. Reviewing code properly. Stopping to refactor and componentize. Adding critical comments (including agent directives to prevent future mistakes). Testing deployments. Running regression tests on affected areas. Getting fresh eyes on the code, not tired developers or reward-seeking bots. These methodical steps aren’t delays; they’re what separates a demo from a production system. Meanwhile, AI is rewarded for task completion, not correctness; it will happily shim, mock, or simulate critical flows, only for reality to surface later. That’s when engineers step in to mop the slop. A Pragmatic AI Workflow (the boring reality) Here’s how we combine AI leverage with engineering discipline when building UI-first, user-facing web apps: Step 1: PRD Before CodeStart with a Product Requirements Document (PRD). Not just a feature list, but context, clarifications, tradeoffs, and what matters. We ask what’s missing, anticipate agent pitfalls, and tighten scope. Optional Step: Figma MocksClearer specifications make UI agents more eff