AI coding has gone through several major stages in recent years.

The first stage:

AI helps you write code.

You ask:

“Build me a login API.”

The AI returns a piece of code.

It felt impressive, but the real value was limited.

Because software development is not only about writing code.

The second stage:

AI starts understanding projects.

AI tools can read codebases, understand file structures, modify multiple files, help with refactoring, debugging, and feature development.

Tools like Cursor, Claude Code, Codex, and GitHub Copilot have accelerated this shift.

Now we are entering the third stage:

AI is becoming part of the development process itself.

After spending significant time with Codex recently, the biggest change is not simply that AI can write more code.

The bigger change is the relationship between developers and AI.

Before:

Humans define requirements.

AI generates code.

Now:

Humans define goals.

AI analyzes tasks.

AI modifies projects.

Humans review results.

AI continues iteration.

This is becoming a new collaboration model.

After using Codex for a full day of development, one thing became obvious:

When AI usage limits become less restrictive, the biggest change is not just saving time.

The entire development rhythm changes.

Previously, many ideas were abandoned because the cost of experimenting was too high.

Now:

A new page can be tested quickly.

A technical approach can be validated quickly.

A complex refactor can be analyzed by AI before deciding whether to execute it.

Development is moving from “careful modification” toward “rapid experimentation.”

However, AI Agents are not replacement developers.

Today, they are very good at:

  • executing clearly defined tasks
  • writing and improving code
  • refactoring modules
  • generating tests
  • handling repetitive work

But they still need human guidance for:

  • discovering the real problem
  • understanding product goals
  • judging user experience
  • making architectural decisions

This was also my biggest takeaway after experimenting with GitHub Agency Agents.

They do not feel like product owners.

They feel more like highly capable engineers who execute well once the goal is clear.

You still need to tell them:

What is the objective?

What problem are we solving?

What does success look like?

The future competition in AI coding may not only be about model intelligence.

It will be about connecting:

Models.

Code.

Tools.

Context.

Project management.

Cost.

This is why AI Workspaces are becoming increasingly important.

A chat window is only the entry point.

The real value is the workflow behind it.

This is also the direction Toket AI is exploring:

Not helping users find a single “best model.”

But helping them understand tasks, choose models, control token costs, and build AI workflows that can run continuously.

The AI Coding Agent era has just started.

Developers may not simply be replaced by AI.

But developers who cannot collaborate effectively with AI may eventually be replaced by developers who can.

Estimate task cost in the AI Cost Estimator or refine prompts in the Prompt Optimizer.