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AI generates code fast, but validating it drains you. Here’s why and how to fix it.

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You just asked your AI coding assistant to add authentication to your API. Thirty seconds later, you’re staring at 500 lines of generated code spread across 12 files. You feel productive: code appeared instantly!

But now you’re reviewing. First error spotted. Then another. Wait, how many more are buried in here? Is this even worth fixing, or should I just scrap it and start over? Two hours later, your brain is fried, you’re pissed off at the tool, and you still aren’t sure if the implementation is correct.

A recent study tracked experienced developers working on their own codebases (projects averaging over a million lines of code). When these developers used AI tools, they took 19% longer to complete their tasks. Yet they believed the AI had made them 20% faster.

The problem is workflow architecture, and it’s exhausting developers in ways we’re only beginning to understand.

Your Brain Wasn’t Built for This

When you write code yourself, your working memory tracks what you just wrote. The context is fresh. You’re in flow state, building line by line, each decision naturally following from the last. This is creation, and your brain handles it well.

AI coding changed this. Code appears in large batches. Now you must reconstruct the entire mental model from scratch to validate it. This is evaluation, and it’s cognitively expensive.

When you wrote 50 lines, you lived through every decision. When AI generates 500 lines, you’re reading someone else’s novel and trying to spot the plot holes, while the author is a system that has no concept of plot.

The review burden is massive. Most people focus on how fast AI generates code or how good the output looks. But review time is where AI tools actually cost you. You feel faster because code appears instantly. But you measure slower because validation drains you. The bottleneck moved from generation to review, and review AI code is harder.

Ever approved a 500-line PR at 5 PM because your brain was too fried to properly review it? That’s not laziness. That’s your working memory hitting its limits, and the workflow demanding more than you can give.

Work in Small Loops

The solution is to work in discrete loops. Each loop should be small enough to fully validate before you proceed. This isn’t a nice-to-have — it’s how you prevent errors from compounding. Validation happens while context is fresh in your working memory.

Errors compound geometrically, not linearly. The AI chooses the wrong abstraction in step one. By step five, you’ve got an entire architecture built on a faulty foundation. AI excels at generating code that looks fine in isolation but creates incoherent systems when combined. Small mistakes caught early stay small. Architectural mistakes discovered late metastasize into ten files that all depend on the wrong decision.

Fast feedback catches errors while they’re fresh. Slow feedback discovers them after they’ve spread. We already learned to keep PRs under 400 lines, make small frequent commits, and use continuous integration to catch problems early.

AI tools broke these practices by generating massive changesets. Small loops restore them. But small loops only work if your tools are designed around them.

The Right Division of Labor

AI and engineers have fundamentally different strengths. You excel at creative problem-solving, understanding business context, making architectural tradeoffs, and judging what actually matters to users. You can’t keep an entire codebase in your head — no one can. AI can’t make creative judgments or understand business priorities, but it can track every file, every dependency, every pattern across millions of lines of code simultaneously.

Your working memory holds about seven items. AI’s context window holds 200,000 tokens. At 5 PM, you’ve exhausted your seven slots reviewing code. AI is still processing 200,000 tokens.

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OpenAI Codex

Some AI coding tools were designed backwards. The web version of OpenAI Codex connected to your GitHub, generated entire implementations, then presented you with a code review interface. Generate first, validate later, when your working memory has no context and the blast radius is already huge.

Claude Code works differently. You plan the change together. AI generates one increment. AI checks it against your codebase. You approve or adjust. Repeat.

**AI generates batch → Human reviews (cognitive overload) **vs Plan with AI → Generates increment → Validates against context → Human spot-checks → Iterate

This explains why some tools feel different. Copilot’s inline suggestions work because you validate line by line. Cursor’s diff-first approach shows changes in reviewable chunks. Tools that generate entire features in one shot create cognitive overload.

You change a function signature, and AI finds every call site that needs updating. You establish a new pattern, and AI applies it consistently across the codebase. The division is clear: you make architectural decisions while AI handles the exhausting consistency checking.

When each increment is small enough to review immediately, mistakes stay contained. AI can move aggressively without breaking everything, and you can approve changes confidently because the blast radius is manageable. You maintain control over the architecture while AI maintains the mental model of how everything fits together.

The bottleneck is workflow architecture that doesn’t respect human cognitive limits. When you evaluate AI coding tools: does it let you validate in small loops, or does it dump massive changes on you after the fact?

At Ducky.ai, we built the retrieval infrastructure that lets AI agents remember everything, this persistent memory is the foundation for the small-loop workflow this article describes.