Hot take: AI coding agents aren't making senior developers faster

[u/Wild_Dependent4038]

I've started wondering whether AI coding agents are actually improving developer productivity at the senior level, or whether they're just moving the work to a different part of the process.

For smaller tasks, the productivity gain feels obvious. Generate some boilerplate, write tests, refactor something repetitive, investigate an unfamiliar API — agents are great at that.

But once the task involves an existing codebase with a lot of context, things get more interesting.

The agent has to understand the architecture, figure out which files actually matter, make changes without breaking unrelated behavior, and then explain why it made those changes.

At that point, I sometimes spend almost as much time reviewing, correcting, and steering the agent as I would have spent implementing the change myself.

And there's another problem: the better the agent gets at producing code that looks reasonable, the harder it can be to notice subtle architectural mistakes.

So I'm starting to think the real bottleneck isn't code generation anymore. It's context + verification + supervision.

Maybe the productivity curve looks something like this:

Junior developer + agent → huge boost
Senior developer + agent → depends heavily on the task
Complex production system + agent → supervision becomes the bottleneck

I'm curious what others are seeing in real projects.

Have AI coding agents genuinely made you faster overall, including review/debugging/cleanup, or are they mostly making the "first draft" of the code faster?

[u/shuozhe]

Doing a lot of manual code review for AI generate code.. sometimes it takes forever and many attemps to fix stuffs and I would be faster myself. What I can do is try 10 different solution and pick the best one at a similar timeline, and finalize it myself.. or 2-3 if it's urgent. In the past I had to commit before understanding the problem a lot of times :(

[u/Sunstorm84 → u/[deleted]]

The default answer to someone mentioning productivity isn’t really improved always seems to be “skill issue” and “use more tokens to run a metric fucktonne of passes to improve the output”.

I’m yet to see empirical proof of an improvement in overall productivity without an associated increase in the number of bugs produced, and significantly worse quality codebase.

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[u/dmigowski]

I am coding since 40 years, and AI makes me much much faster especially when I need to use techs I wasn't proficient in before.

[u/bodonkadonks]

Also, languages, frameworks, whatever that aren't massively popular get gimped heavily. I'm a data engineer and use dataform heavily, I basically can't touch any but the simplest pipelines with Claude without it making huge or very insidious mistakes. Adding insult to injury, codegraph or similar mcps are almost useless, initializing the context eats a fuck ton of tokens and it misses a lot of stuff.

The only way I can get it to do what I want is editing the .MD file to explain the model excruciatingly and specifying the schemas of all the tables even though you could infer them from the sqlx files (and it's not particularly complex, mostly CDC fed incremental pipelines), plus keeping the change scope very very small ( change these columns to take from these tables, add a js function that takes these parameters and outputs whatever.) any higher level tasks and Claude starts touching and breaking stuff it didn't need to touch.

Stuff like making an API that feeds on that data though? Claude really shines. Or making quick SQL queries

[u/forkmesh]

I think the trick is to have a good Harness. With Forkmesh we see about 40-80 merged PRs per day. Way more productive than before AI. And this includes human review on the branches before merge. 

[u/CrimsonBolt33]

There are tools to help with that....it's all about setup really.

I can say if you are just taking a single AI, directing it at your entire codebase and just telling it to do whatever it's less than optimal.

At the very least you can use a few agents to handle a single large task by delegating parts to its own subagents.

This helps keep context, and therefore error rate, down.

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