this post was submitted on 28 Jun 2026
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I work as a glorified code monkey. It feels faster, sort of, in fits and starts.
When I start working on a task, the AI tooling almost feels like it's doing the job for me. It picks out the relevant part of the code base, makes changes in the right places, and even updates tests.
Assuming that part all went well, the waiting game begins.
Either run the "git" AI prompt or do it by hand. The prompt way has some nice bells and whistles in how it formats commit messages , but
It's
Just
So
Slow!
Then, either way, my commit goes. Now I wait for the auto build and test pipeline, the code scanner job, and copilot's review pass. Sometimes it brings up helpful things, sometimes it raises silly objections.
Then, ok, I think it looks good. I need approval from a busy coworker to actually click merge, so I send a link to the chat and hope someone isn't too busy.
Once I get that approval, it's wait again for a build agent to deploy my changes.
Now, hours later (sometimes the next day), I can load it up and see my change reflected in our test environment.
The wheels sure are spinning, but I can't say for sure that we're going any faster.
CI/CD is overflowing as the bottleneck for us, and AI isn't making running tests faster. It's making it slower, with more (truly good, useful, productive) unit tests and product features added every day than human teams could
But that adds a square increase on CI/CD time and load.. And what do you do while waiting for your test run? Make and submit more PRs of course!
I can assure I work much faster. Maybe it is a bit different since I work in research and that's indeed different from working on a large established codebase. Most of my projects are greenfield.
However, recently using Claude code I started many different projects I'd never have approached since I knew it would have taken me months to complete correctly. We are talking about porting file format readers and writers to new languages, then implementing novel algorithms to process such data and optimize it to work on different GPU architectures. Getting a working software takes about a week of work. A publishable cleaned up codebase 2/3 weeks. No way I could do this myself alone in such span of time.
On the other hand, I have students working with me, using AI is definitely not helping them to learn how to code and how to reason. In several occasions I had them showing me a novel huge equation which apparently worked, but then looking at it properly was just over fitting data and they had no way to explain why such an equation should be used.
For coding tasks I treat A.I. as a fresh intern that doesn't really know what they're doing but you can just ask them to do that horribly tedious task and just do a code review later. As you said, something like "rewrite this in Rust", or "create unit tests for this function". Then you fix all it's mistakes. I'd never let it touch an actual algorithm or anything important though.
To be fair, it is fairly good at selecting and optimizing algorithms; as long as you set up a set of correctness tests and performance benchmarks it can use to evaluate if it's doing well. The other day in about half an hour I got a 26,000% improvement in performance on a specific algorithm which I had implemented as it was published in the original paper. Same results under all conditions, but it decided to rearrange the formula to use a few matrix tricks and optimized for SIMD instructions. That is the kind of things I'd never do myself: I don't care if such calculation takes 3 weeks using 256 cores and 200 GB of memory, if I have to optimize for SIMD instructions that will likely take me one or two weeks and I don't have time for that. I'd rather burn compute. If it takes me a couple hours I'm very happy to bring down a computation which would take weeks to an hour or so.