In software development, we’ve long been taught to chase excellence. Clean architecture, elegant abstractions, efficient performance. But with the rise of AI coding assistants, the equation has changed. The speed and cost advantages of AI-assisted development now present a strong case for embracing code that is merely good enough.

The quality of code you get from an AI assistant depends heavily on how you guide it. Vague prompts like “Build a tax app” yield vague results. But if you give your AI a structured plan (e.g.“Here are 10 implementation steps, constraints, and preferred technologies”) you’ll get something functional and often surprisingly aligned with your goals.

Sure, code generated this way might be clunky, inefficient, or overprovisioned. It won’t match what a team of elite developers with decades of collective experience would write. But here’s the truth: that elite team will take weeks or months and cost a lot. The AI takes hours and costs very little.

Speed to market wins. Code that runs today and gets real user feedback beats perfect code that lives in feature branches for the next two sprints. That AI-generated backend, that clunky React front end …it’s done. It can sell. It can test a market. It can save internal teams hundreds of hours. That’s business value now, not technical purity later.

And let’s be clear; the current generation of AI assistants are the worst they’ll ever be. Every month, they get better at writing code, understanding edge cases, and handling complex instructions. The line between AI output and expert craftsmanship is fading fast. If the “mediocre” AI code of today is already powering real products, what happens when “mediocre” becomes indistinguishable from “senior engineer”?

Choosing AI-written code isn’t about cutting corners, it’s about aligning with reality. For the majority of software use cases the difference between good-enough and perfect doesn’t matter. What matters is cost, time, and results.

Most businesses don’t need the world’s best code. They need working code that solves a problem and gets out the door. AI delivers that now. And soon, it’ll deliver even more. Faster, cleaner, and smarter.

To be clear, this isn’t an argument to ignore code quality or best practices. It’s an argument to contextualize them. If you’re building avionics software, opt for rigorous engineering. But if you’re building a marketing dashboard, internal analytics tool, or MVP for a new idea, speed matters more than elegance.

This is the new tradeoff: higher infrastructure costs and messier code, in exchange for dramatically lower engineering costs and massively reduced timelines. In most cases, that’s a smart trade. And as AI tools improve, the gap between “mediocre AI code” and “expert-crafted code” is shrinking.