That's Not What I Meant by 'Using AI' | Mudlej

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Listen to two developers debate the use of AI in software development and you will often hear something strange: both are making reasonable claims, both have evidence, and neither can understand how the other believes what they believe.

One says AI-generated code is unreviewable slop that will rot your codebase. The other says he shipped a working product in a week. Both are telling the truth. They are just not describing the same activity.

Developers can have vastly different experiences using AI to develop software, depending on the approach they use. This article is my attempt to map1 these different approaches, explain each one, and give each a distinct name.

Whether you like AI or dislike it, we first need to know what we are talking about. A shared vocabulary comes before any meaningful discussion.

Note

I’ve put most of my personal opinions in callouts like this one, collapsed by default, so you can mostly read the classification if you prefer.

Architecture, Detailed Design, and Implementation

To avoid relying on terms that may mean different things to different readers, I want to use a simple example to show what I mean by them.

Architecture

What programming language to choose and why?

What framework or library to use, and why?

What are the system’s components? How will they communicate?

How is the data modeled? Where is it stored?

Detailed Design :

How does the component do the computation / business logic?

What are the public functions for each component?

How are we going to pass data around?

How are errors handled?

Implementation :

The actual code that implements the detailed design.

0. Traditional Development#

The developer owns architecture, detailed design, and implementation, working mostly as developers have for decades: writing code in the IDE, using compilers, static analysis, documentation, and conventional tooling. AI is not a meaningful part of the process.

Trade-offs

Pros: The developer has deep understanding and maximum control over the codebase.

Cons: Bottlenecked by the developer’s speed, especially for exploration, debugging, and boilerplate.

1. Organic Development (OD)#

The developer is still inside the code editor doing nearly everything manually , including coding. But he might occasionally use AI to research the domain or tools, refine a design, debug an issue, explain unfamiliar code, or review a change.

The developer might even use AI to draft small code snippets, such as asking an AI chatbot or using AI inline autocomplete to help with a code snippet, then manually integrating it into the codebase, usually after some tweaks and edits. This is close to how the developer would have used Stack Overflow in the past.

AI here acts like an on-demand teammate , but the human remains at the center of all development tasks.

Trade-offs

Pros: Improves traditional development with better speed, feedback, and accuracy while preserving full human ownership.

Cons: Productivity gains might be limited compared to other approaches because the developer is still doing most of the work directly.

Critical systems should probably keep using this approach, at least for the time being.

Example

This article was written organically. I spent a few days writing it fully by hand. After I felt I had said everything I wanted to say, I reached out to AI as a researcher to see what other people had said about the topic, and as a reviewer to challenge what I wrote. That said, the feedback I got from my fellow engineers was much more insightful than any AI feedback I got.

Agentic Development (AD)#

There are at least three approaches to agentic development, and they can be quite different. What they have in common is that the code is generated by AI, not written directly by the developer.

AD is usually done from the terminal using CLI tools such as Claude Code, Codex, OpenCode, and similar agents. Developers doing AD spend a lot of time inside these tools.

When it started

Agentic development started to become a serious option after the November Inflection in late 2025, around the release of Opus 4.5 and GPT-5.2.

Learning Cost

All types of agentic development have a learning curve. AD introduces many concepts that developers were not taught in school or bootcamps: context windows, agents, subagents, teams, agent SDKs, skills, commands, memory, harness engineering, and more.

2. Reviewed Agentic Development#

In Reviewed AD, the developer:

Defines the architecture.

Defines the detailed design.

Prompts an AI agent to generate the code .

Reviews the generated code line by line .

The human still owns the architecture and detailed design, but delegates implementation to AI while still reviewing the generated code meticulously, often pushing it back toward his preferred shape. This is usually done through a terminal or an IDE chat.

AI here acts like a tireless junior developer whose output needs careful review.

In...