I work as a professional app developer. And I find this take to be naive.
Most of the time when I review code from AI, there is always something to improve.
It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.
The maintenance is not only the burden on the human and LLM. With too many moving parts, it becomes harder and harder to build and verify the correctness of future features. Yes you can write test for this and that, but it didn’t need to exist in the first place.
The second problem is correctness issues. Especially the edge cases. You cannot just manually test out a race condition on a phone! Sometimes it happens! Sometimes it doesn’t! If it leads to a visible signal like a crash, then yes, you can try to reproduce it. But there are a lot of these that are “silent” and would just lead to bad experiences.
We already had a software quality crisis! And I think such views only exacerbate the situation! Quality matters!
And this is not an anti-AI stance. I vibe code personal projects where I don’t even look at the code. But when I use AI as a professional engineer, I act like a professional. Because these products do have an impact on people’s lives.
All true (and thanks for posting a concrete example rather than "LLMS suck"). But my take is that none of this is much different than before times when I had teams of developers creating applications. They would often make similar mistakes which I would either need to catch or which would flush out in the field. Where it seems that LLMs are not excellent is where the person driving it is also the senior domain expert so can immediately spot pitfalls. But typically using humans to develop software this was really not often the case. Those people get promoted so they're no longer cutting the code. Under that scenario (replacing subordinate humans) I find the current models are either on-par or somewhat better (specifically because the models can also act like a peer senior dev, discussing approach options etc).
I agree with you! And I’m not trying to romanticize the past! Humans/me wrote slop too.
I think we are over-indexing on speed of delivery. I think this is a mistake. The alpha is in speed and quality.
Currently, my experience is that human + AI can write software faster and with better quality than either party can do alone.
> It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.
I understand this, but I just can't bring myself to care. I've been doing professional software work for almost two decades. These sorts of improvements/time savers are great without AI. With AI? Whatever. It's fine.
When the underlying lib has an issue, it'll be quicker to debug with the whole thing in context.
So exactly what value are you adding, then?
I take the specifications from the customer and type them into the AI
One hopes you are better at that than the many others who are doing the same.
You do realize that letting the LLM produce more output means that maintenance will be more expensive? I can easily see a world where claude and gpt are producing more tokens to sell you more tokens.
This is not as obvious as many naively believe.
It depends on how hard to maintain the code added is, how likely it needs to change in the future, and most importantly on the cost.
If reviewing and manually improving the code takes hours, the cost may already be in the thousands.
That buys you a lot of AI usage, roughly a few months of continuous work.
You have to balance this with the chance that the suboptimal code the AI generated is actually fine and maintainable enough, and also the chance that during further work on that code a model might implement the same optimization on its own.
Just the other day I burned through my 5h quota twice in a row because I had opus spawn a review session on a medium sized PR and I don't know what happened but I told it to summarize to me and it said it spent 100M tokens throughout 50 subagent sessions.
And more recently its been recommending that i install this new browser called Aside. I did, and it almost felt like I was installing malware so I Uninstalled it fairly quickly (it also was not a great browser)
I feel like theres collusion somewhere.
yea man they are trying to nickle and dime you
The models will only get better and inference cost will go down.
I don’t see any reason to think the same thing that happens with all tech won’t happen here.
Enshittification and profit maximalization is around the corner looking for you.
So you don't use agentic AI to develop a large mobile app and you think my take is naive?
I also used to work full time as a Android developer for five years, and I'm pretty sure I know better than you about the quality of my app that I work on everyday.
No where did I say we don’t do agentic dev!
Years of experience doesn’t mean much! I will challenge you on ideas. And the idea you are sharing is dangerous and unprofessional.
Especially at scale. e.g., we process more than 3.5 billion orders annually! This is serious business. Edge cases are common.