The Knife and the Chef — How AI Tools Amplify (or Limit) Developer Quality

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The Knife and the Chef — How AI Tools Amplify (or Limit) Developer Quality
The Knife and the Chef — How AI Tools Amplify (or Limit) Developer Quality

Introduction

Hello, my name is Spas Cholakov with 20 years of experience as a software developer.

I've been writing software for twenty years, and the last few have changed how I think about getting work done. This piece is about what I've seen lately: how we can boost our performance as developers, which tools actually help, and where the gains really come from. I'll talk about what makes us faster and cheaper to run, and — just as important — what still determines quality. Speed and cheaper cost increasingly come from the tools we use; quality still comes from us. If you care about shipping more without dropping the bar, this is the framing I use and the practices I rely on.

Client Expectations

Clients and stakeholders always want the same thing: pay less, get the product sooner, and still have it built to a high standard. Cheaper, faster, and top quality — all three at once. The real question is how we can meet those expectations. The answer isn't working longer hours; it's how we work and what we use. In the next sections I'll share what I've seen: where the speed and cost gains actually come from, and where quality really comes from, so we can deliver on all three.

AI or Manual

Whether you use AI tools or not changes where your time goes and where you get stuck. Both paths can be productive; the difference is what you optimize for and what you trade off.

Using AI. With AI, you get speed on first drafts, boilerplate, and lookups: less typing and less time in docs. The downside is wrong or generic answers you have to fix, and the risk of leaning on the tool so much that your own judgment and fundamentals get weaker. You perform better with AI when you use it for the right tasks and always review and adapt the output.

Not using AI. Without AI, you keep full control and a clearer mental model of the code, and there's no noise from bad suggestions. The cost is more time on repetition, exploration, and lookup — you are the only bottleneck. On top of that, developers working without AI not always, but often ship lower-quality, lower-performance solutions anyway (that depends on the developer's level), in both greenfield and legacy code.

With AI you gain speed, and noise exists either way (in both greenfield and legacy code) — so that's a wash. The real split: with AI you're faster and generally better; without AI you're slower and "better" only if your level is high enough. So AI is the default win for speed and outcome; without it, quality depends entirely on the developer.

One real cost of leaning on AI is that you forget the code. The codebase and the reasoning slip from memory. When questions come later — why is this here, how does it work? — you can't answer immediately. You have to go back, read the code, and summarize it again to remember what you did and why. So AI gives you speed and usually better output, but overuse trades away that immediate, in-your-head understanding.

Clients want cheaper, faster, and high quality. AI gets you cheaper and faster: more code in less time. Quality still comes from the developer. If the developer is strong, knowledgeable, and experienced, the output will be good; if not, it won't. So: use AI for speed and cost, and invest in developer level for quality.

Which AI Tool Should We Use: GPT Website or IDE + AI?

GPT (website) Pros: Good for open-ended questions, design choices, "how does X work," learning. You copy-paste code in and get answers. No tie to a specific editor.

GPT (website) Cons: No direct access to your project. You must paste files and paths yourself. No refactors or multi-file edits in the app. Context is manual and easy to lose.

IDE + AI Pros: Sees your repo, open files, and errors. Can edit across files, refactor, and run in your project. Short feedback loop: ask → apply in place.

IDE + AI Cons: Tied to IDE. Subscription. Model and behavior depend on setup.

What IDE + AI (Cursor, Codex, Copilot, Codeium, Windsurf, etc.) Offers

  • 🔥Multiple agents / parallel proposals: Run several agents or prompts in parallel and get different solutions to the same task, then pick or merge the best one.
  • 🔥Shared rules and prompts for teams: Use project rules (e.g. .cursor/rules) and shared prompts so everyone uses the same instructions. Keeps style and patterns consistent.
  • 🔥Voice input: Audio/voice typing lets you describe or dictate without leaving the editor.
  • 🔥Codebase-aware chat: @-mentions (@codebase, @docs, @web) give the AI direct context from your repo and docs.
  • 🔥Composer / multi-file edits: The AI can propose and apply changes across several files in one go.
  • 🔥Inline completions and tab-accept: Suggestions as you type and one-key accept for boilerplate.
  • 🔥Inline Edit (Ctrl+K / Cmd+K): Select a few lines, trigger the shortcut, describe the change — the IDE edits only that snippet in place.
  • 🔥Different modes: Agent for multi-step work; Plan for breaking a task into steps; Debug for analyzing errors and suggesting fixes; Ask for explanations without changing code.
  • 🔥Frequent updates: New models and features often, so you get better suggestions without switching tools.

If we take Cursor as an example: it stands out with multi-agent workflows, shared team rules and prompts, and voice input. Codebase context, multi-file Composer edits, and inline completions reduce context-switching and manual editing. Because Cursor updates often, you keep getting improvements in speed and behavior over time.

Other tools (Copilot, Codeium, Windsurf): Some are free or cheaper; similar "code in editor" idea. Quality and understanding of your codebase differ. Fewer have full project context and chat that can edit; many are mostly autocomplete.

Trade-offs: Need answers + learning, no project context → GPT website is enough. Need to ship code fast inside your codebase → Cursor (or similar IDE-native AI) wins. Budget-first → try free tiers of Copilot/Codeium/Windsurf; if you miss project-wide edits and chat, Cursor is usually the upgrade.

Summary: Use the GPT website for reasoning and one-off code snippets; use Cursor (or another IDE-integrated tool) when the goal is changing and navigating your real codebase. Best combo for many: GPT for "why" and design, Cursor for "do it in my project."

Where Quality Comes From

Speed and cost come from the tool, quality comes from the developer. AI makes output cheaper and faster, but how good that output is still depends on who's using it. A junior or mid-level developer will ask weaker questions, give vaguer context, and accept or refine suggestions less well — so results stay average. A senior developer with deep experience will prompt more precisely, spot wrong or generic answers, and shape the result so it fits the system and stays maintainable. The tool is like a knife: in skilled hands it can produce something fine — a clean, well-made salad. In average hands you get something passable. In the worst case, you hurt yourself. Same tool — the outcome depends on who's using it. So quality and performance still come from the level of the developer; the tool amplifies that level instead of replacing it.

How to Improve Code Quality & Architecture

Developer quality comes from:

A familiar pattern

We've seen this before. When we invented the calculator, we didn't stop doing math—we did more of it, and faster. When we invented the assembly line, we didn't stop making things—we made more, and built them faster. Each time, people worried that machines would take our jobs and that there would be nothing left for humans to do. In the end, those tools didn't replace us; they boosted our productivity. They amplified what we could do instead of making human work obsolete. AI fits that same pattern: another tool that extends our capacity rather than replacing the need for human judgment and responsibility.

Conclusion

I believe top quality work will come from human plus AI. The human brings judgment, context, and the ability to say what "good" means; the AI brings speed and the ability to generate and explore options. Use the tool for what it's good at, and keep the human responsible for the outcome. That's the combination that satisfies "cheaper, faster, and high quality."

From the Author

One of the motivations to write this article was that 2026 I notice that some companies are not progressing and support old thinking not to use AI tools extensivly, write code manually and etc - thinking that in this way the quality is better. Well, it was.. when the tools were less good (in the beginning), but not anymore. So in result the code that was delivered actually wasn't with good quality when I analyze it. Best practices weren't followed, you can see ANYscript on a lot of places, slow pages loaded over 5 secs, and etc. And Visual Studio was used with copying code from GPT website. And they say that using AI is not such a good idea.
I personally think that the IT sector is one of the fastest-evolving areas and people working there should read, learn and evolve non-stop with every single day, otherwise you will fall behind.