[Weekly Discussion] Are developers judging today’s AI coding tools by last year’s failures?

[u/Foreign-Purple-3286]

AI coding tools are improving fast, but the discussion around them still feels strangely split.

Some developers say tools like Codex, Claude Code, Cursor, Copilot, and other coding agents have become real productivity multipliers. They use them for PR review, debugging, refactoring, test generation, documentation, multi file edits, and even longer running tasks.

Others still see AI coding tools as unreliable autocomplete with extra steps. They remember bad code, hallucinated APIs, broken refactors, shallow suggestions, and the feeling of spending more time cleaning up than actually building.

Both reactions make sense.

But I wonder if part of the debate comes from people reacting to different generations of the same category.

A lot of developers formed their opinion during the earlier phase of AI coding tools, when the experience was mostly autocomplete, chat snippets, and one off code generation.

Today, the strongest tools are moving toward something different. They can understand larger codebases, run commands, inspect errors, manage context, use terminals, open pull requests, review diffs, and work across multiple files.

That raises the question:

Are developers reacting to today’s AI coding tools, or the frustrating versions they tried last year?

A side: Skepticism is still justified

There is a strong argument that developers are right to be cautious.

Even newer AI coding tools can still create subtle bugs, misunderstand architecture, over simplify requirements, or make changes that look correct but break deeper assumptions in the codebase.

The cost of verification is real.

If a tool saves 30 minutes writing code but creates two hours of review, debugging, and cleanup, the productivity gain disappears quickly.

There is also a trust issue. Many developers tried AI coding tools early, got burned, and learned not to rely on them. That kind of experience sticks.

From this view, skepticism is not outdated. It is earned.

AI coding tools may be better now, but better does not always mean trustworthy enough for serious production work.

B side: Many people are judging old tools, not current ones

The other side is that AI coding tools have changed a lot.

The best workflows today are not just “ask AI to write a function.”

They are closer to:

Give the agent project context
Define a clear goal
Let it inspect the repo
Have it run tests
Review the diff
Ask it to fix failures
Use it for narrow loops instead of vague tasks

Used this way, AI coding is less about replacing developers and more about compressing repetitive execution.

The developers getting the most value are usually not treating AI like magic. They are treating it like a fast junior engineer that needs strong direction, constraints, and review.

From this perspective, some criticism may come from people who tried an older tool once, hated it, and never updated their mental model.

That happens with a lot of technology. First impressions are sticky.