AI-assisted coding didn't replace our engineers. It replaced what our engineers do.

A year ago, I spent hours writing code. Now I spend hours reviewing it. The AI writes. I architect, criticize, and catch what it misses.

This isn't a story about AI taking jobs. It's about jobs changing shape. If you're still thinking about AI as a threat or a toy, you're missing the shift happening right now.

Here's how we actually use AI-assisted coding at DevDash Labs — the tools, the workflow, and where we don't trust it at all.

The AI-Powered Coding Assistants We Actually Use

We tried a lot of AI code review tools and coding assistants over the past year. Most added noise. Three earned their place.

Cursor — our AI-powered coding assistant for autocomplete. It's the best at predicting what you're about to type. Fast, unobtrusive, rarely wrong. We use it as the always-on layer while writing code.

Claude Code— our AI-powered coding assistant for planning and building. When we need to scaffold a feature or work through a complex refactor, Claude Code handles the heavy lifting. It thinks before it codes. We use it for anything that requires multi-step reasoning.

Codex — our AI-powered coding assistant for criticism. It's faster than Claude, doesn't over-explain, and catches bugs we missed. We use it as a second set of eyes, not a first pair of hands.

That's the full stack. Three tools, three distinct jobs. Everything else we tried either overlapped with these or wasn't reliable enough for daily use.

AI Pair Programming: How We Actually Work With AI

The biggest mistake teams make with AI-assisted coding is treating it like autocomplete on steroids. They let AI write, hit accept, and move on. That's not AI pair programming — that's AI gambling.

Here's how we actually do it:

• We break work into phases. Big features get split into steps. AI handles one step at a time. This keeps context tight and mistakes traceable. If something goes wrong, you know exactly which step introduced it.

• We provide context upfront. Before AI touches code, we feed it: official docs, our internal guides, relevant blog posts, package documentation. "Learn this first" is a real prompt we use. The better the context, the better the output. This is the part most teams skip.

• We review every plan before execution. AI proposes, we approve. If the plan has issues, we fix them before a single line gets written. This is where most teams skip — and where compound errors start.

• We never accept all changes at once. Step by step. Review each diff. This takes longer upfront and saves hours later. Real AI pair programming means the human stays in the loop at every step, not just at the end.

  
  

Why AI-Powered Code Review Still Needs Humans

AI-powered code review catches syntax errors, spots bugs, and flags patterns faster than any human. But it misses things that matter more.

Three reasons why AI-powered code review still needs humans

• AI can't design systems.It doesn't know your codebase the way you do. It doesn't understand why you split things a certain way or why that pattern exists. Give it a component to build and it'll dump everything into one file — hundreds of lines, unscannable, unreusable. Or it'll do the opposite: refactor into twelve files when three would do. It optimizes for completion, not maintainability.

• AI can't write docs. We tried. The results were bloated with emojis, redundant explanations, and code blocks nobody asked for. The whole point of our docs is fast scanning for team leads. AI wrote novels instead.

• AI doesn't respect your standards. You can give it guidelines, patterns, specs. It'll follow them — mostly. Then it'll quietly drift. Small violations compound. By the time you notice, you're debugging decisions you never made.

This is why AI-powered code review works best as a layer, not a replacement. Let AI catch the obvious stuff. Humans catch the architectural stuff. Together, you cover more ground than either alone.

The Compound Error Problem

Here's what happens when you skip review in AI-assisted coding:

AI makes a small architectural choice you didn't catch. That choice shapes the next three files. Those files inform the next feature. By week two, you're maintaining a codebase designed by autocomplete.

We call this the compound error problem. Each individual mistake is small — sometimes invisible. But they stack. One quiet decision about file structure leads to three files that follow that structure, which leads to a feature that assumes that structure, which leads to a second feature built on top of the first.

By the time you notice, you're not fixing a bug. You're unwinding an architecture you never chose.

The fix isn't "don't use AI." The fix is review early, review often. Catch drift before it compounds. This is the single most important discipline in AI-assisted coding.

The New Job Description

The developers who thrive with AI-assisted coding won't be the best coders. They'll be the best critics.

Your job now:

• Architect systems before AI builds them.Define the structure, the boundaries, the patterns. Then let AI fill in the details.

• Set standards and enforce them relentlessly. AI will drift. Your job is to catch it.

• Review plans, not just code.The plan shapes everything downstream. Bad plan, bad code — no matter how clean it looks.

• Test what AI writes. It's confident but not careful. It will produce code that runs, passes basic checks, and silently does the wrong thing.

• Understand security. AI doesn't know what it costs when things break in production. You do.

You don't need to memorize syntax anymore. You don't need Stack Overflow or hours in documentation. Your AI-powered coding assistant handles that.

What you need is taste. Judgment. The ability to look at AI's work and say: this isn't good enough.

The Shift

AI replaced me as a coder. It made me an architect.

That's not a loss. It's a trade. Less typing, more thinking. Less syntax, more structure. Less doing, more deciding.

AI-assisted coding changed the job. AI pair programming is the new default. AI-powered code review is table stakes. The tools will only get better.

The developers who shifted with the paradigm are building faster than ever. The ones who didn't are still arguing about whether AI is coming for their jobs.

It already came. It just didn't take what they expected.

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Gopal Khadka is a Member of Technical Staff at DevDash Labs.