AI in the software development flow: from generating snippet to orchestrating

Not long ago, using AI in development was a clumsy ritual. You described the problem in a chat window, received a snippet of code, copied it, pasted it into the editor, tweaked it and tweaked it. The AI lived outside your workflow, like a smart colleague you consulted via text.

This model is already outdated. The tools left the chat and entered the repository. And this changes the tool less than it changes its role.

This text is an overview of where the flow of development is going, and why the skill that matters is no longer writing but orchestrating.

The copy and paste phase

The first generation of AI in development was the generic conversational assistant. You opened ChatGPT, explained the context clearly and received a response that did not know its code, its conventions or its dependencies.

It worked, but with friction. The AI didn't see its design. Each question started from scratch. You were the conduit between the tool and the code, copying back and forth manually.

It was useful for answering questions and generating boilerplate. It was bad for anything that required context of the entire project. And pretty much every real job requires it.

There was also a hidden cost in this model: translation. You spent energy explaining to the AI a context that was already there in your repository. Every conversation was an exercise in describing the obvious to a tool that couldn't look at it. This friction limited use to small, self-contained tasks, precisely the least valuable.

AI enters the editor

The second generation brought AI into the environment where you work. GitHub Copilot popularized the online suggestion: you type, it completes, with awareness of the open file.

Cursor took this further by treating the entire editor as context. Instead of completing one line, it understands the project, edits multiple files from one instruction and talks about the code base without you having to paste anything.

The practical difference is big. AI started to see what you see. The friction of copying and pasting is gone. But the job was still, at its core, you driving, with the AI completing sentences.

AI acts on the repository

The current generation is the game changer. Tools like Claude Code and GitHub Copilot's agent mode no longer suggest: they execute.

You describe a task and the tool goes through several files, makes the changes, runs the tests, reads the output, fixes what's broken, and opens a pull request for you to review. It acts on the repository as a collaborator, not as an autocompleter.

This includes tasks that were previously too boring to automate on a case-by-case basis: writing the test suite that no one wrote, refactoring an entire module to a new standard, updating a dependency and adjusting all affected calls, documenting undocumented code. If you want to understand one of these tools in depth, I wrote about what Claude Code is and how it compares to Cursor and Copilot.

The nature of the work changes. You stop writing each line and start defining the task, observing the execution and judging the result.

The thesis: from typing to orchestrating and reviewing

Here is the central point. When AI generates code quickly and in volume, typing is no longer work. The work involves clearly defining what needs to be done and rigorously reviewing what has been done.

Orchestrating is breaking down a problem into tasks that the tool can perform, providing sufficient context, linking steps together and knowing when to intervene. Reviewing is reading what came back with a critical eye, because the AI makes mistakes with confidence and the error is wrapped in code that looks correct.

These two skills, decomposition and review, have always been hallmarks of good senior engineers. The difference is that now they are worth more than keyboard speed, which was what differentiated the productive junior.

It's an uncomfortable inversion of value for those who built their identity on the skill of writing code. But it's the direction of the flow.

There is a useful parallel here. A good engineering manager is no longer evaluated by how much code he writes, but by how well he directs, reviews and unlocks the team. AI agents are pushing the individual engineer into a smaller version of this same logic. You become the manager of a tireless, fast and too literal collaborator, who does exactly what you ask, including what you asked badly. Knowing how to ask well and demand the result becomes half the work.

Why trust does not accompany adoption

The data confirms that this transition is real and that it comes with caution. In the 2025 Stack Overflow Survey, with more than 49 thousand respondents, 51% of professional developers use AI daily. It's not a weekend experiment, it's routine.

And yet, trust does not follow. More developers distrust the accuracy of these tools than they trust. Daily use comes with skepticism, and that's healthy.

This is exactly the sign of someone who is orchestrating, not delegating blindly. You use it every day because it works, and you review it every day because you know you make mistakes. Blind trust would be the problem, not distrust.

What does this ask of a team

Adopting this flow is not distributing licenses and expecting magic. It's redesign practice.

It means investing in task definition: vague instructions produce vague results, and the quality of what comes back depends on the quality of what you ask for. It means strengthening code review, because the volume of things to review increases. And it means creating clear rules about what the tool can touch alone and what requires a human in control.

It also means rethinking seniority. In a flow where AI generates the trivial, the work left for humans is precisely what requires judgment. Very junior teams can make more per head with agents, but they need strong reviewers on hand, or they accumulate code that no one really understands. The composition of the team matters as much as the tool chosen.

A team that orchestrates well delivers more with the same people. A team that only speeds up typing delivers more bugs with the same people. The difference is in the process, not the tool.

If you still use AI as a separate chat in your code, try a tool that acts on the repository on a real, low-risk task, like writing tests for a stable module. The role change becomes obvious quickly. The natural next step is to understand the AI agents that perform end-to-end tasks.

Source: Stack Overflow Survey 2025.