Just wanted to apologize for the shallow dismissal and the snark. This is going to generate cash flows so the associated activities are totally legit and not parasitic and a waste of oxygen. My gut reaction was not civilized.
Our company has been doing emergency fixes (system that are down, costing companies significant money and they cannot fix) for decades. We have been seeing a significant uptick in occurrences the past few years.
I wonder if vibe coding is a bit like DIY plumbing. You can do it yourself a bit and then later when water starts gushing all over your bathroom you hire an emergency plumber at a high fee.
That raises the question if LLM-generated code is going out of fashion in general. The article seems to assume it will always exist and always need clean-up. But what if it's not worth it and instead you should (mainly) return to the world where humans write code? Simply because salary < LLM-credits + cleanup costs.
I have been taking on "rescue" projects for a while through my business. Previously, the barely-functioning code was usually being generated via outsourcing agencies but it seems the new source is now going to be LLMs.
I imagine it will be the same set of issues really. Just a different way of cost cutting measures. There can be good reasons to take shortcuts but, in my experience, the problems start when you're not mindful that there's a price to pay for taking these shortcuts. Whether it comes from managers, employees or outsourced personnel, it's the same result.
I haven't thought about advertising it as a separate type of service(for vibe coded platforms) yet but maybe I should. The Australian software market is small so haven't been hearing much about the results of those experiments.
I've done a fair amount of vibe coding cleanup, ironically using a fair about of LLMs, a lot of leadership are under the false impression that more code means better product, their ignorance is my gain.
I wonder how many of these vibe coding build apps will grow to massive apps/be really popular (I imagine a lot of them will)... and what kind of security vulns we'll see everywhere because of how it was initally built...
i can only imagine that services like described in this article will become a very common part of getting proof of concepts built with AI into production.....
Vibe Coding is accelerating the death of documentation and architectural clarity.
Companies are measuring success by tokens generated and time-to-prototype, ignoring the massive, hidden cost of cleanup/maintenance.
> Startups save weeks getting to MVP with Vibe Coding, then spend comparable time and budget on cleanup. But that’s still faster than traditional development.
That's the core of situation as described in the article. I wonder how true that is, that it's faster overall than having developers build the MVP.
From what I've seen, I think developers can build just as fast, especially with AI assistance. They may not want to though, knowing full well the MVP/prototype will go directly into production.
Better to take some time to have a decent architecture early on. Product and management probably see that as a waste of time.
On the other hand, vibe coding allows the product team to make exactly what they want without having to explain it to developers. That's the real draw to this, basically a much better figma.
Perhaps there is a market for a product oriented vibe coding tool, that doesn't pretend to make code, but gives developers much better specifications while allowing the product and business side better input in the process.
Janitor Engineers [0] are already a thing? Damn. Also, all links in this article starting from the "Why AI code fails at scale" section are dead for some reason, even though it was written only 5 days ago. That raises some questions...
EDIT: Not trying to offend anyone with this [0], I've actually had the same half-joking retirement plan since the dawn of vibe coding, to become an "all-organic-code" consultant who untangles and cleans up AI-generated mess.
Would be interesting to see an in depth breakdown on a project that has went through the vibe code to cleanup pipeline in full. Or even just a 'heavy LLM usage' to 'cleanup needed' process. So, if the commits were tagged as LLM vs human written similar to how it's done for Aider[0]: At which point does the LLM capability start to drop off a cliff, which parts of the code needed the most drastic refactors shortly after?
In my experience vibe coding is useful at the very beginning of a project (especially if the tooling selected does not have a boiler late generation) and at the very end when a lot of the work is refactoring.
YMMV but I’d rather build the MVP without much AI but then clean it up using it.
A while ago, I was having an intense and heated argument with a good friend in the Finance sector about whether AI would replace jobs. I informed him that AI can only do something repeatable, "already solved problems", or what I call "shit kicking work". His response was something to the effect that I was underestimating how many people's jobs were _entirely_ shit kicking work.
To be fair to him, my partner who works in Healthcare has the same concern, and for quite precisely the same reason: if the kind of work normally done by juniors who are training/building their skills is done by machines instead, where do the next generation of seniors come from?
My response to both was that I was confident the market would fix this problem itself - people will not pay for garbage. There is a reason books are still printed by established publishers. Why buy a book when you can just print a book on printer paper yourself? Because reading a book on printer paper sucks.
I cannot imagine that machines will ever replace any work where there is any meaningful threshold for "correct". I am so intrigued to see how this plays out across the broader economy.
This mirrors exactly what we learned from outsourcing over the past two decades. The successful teams weren’t those with the best offshore developers - they were the ones who mastered writing unambiguous specifications.
AI coding has the same bottleneck: specification quality. The difference is that with outsourcing, poor specs meant waiting weeks for the wrong thing. With AI, poor specs mean iterating indefinitely on the wrong thing.
The irony is that AI is excellent at helping refine specifications - identifying ambiguities, expanding requirements, removing assumptions. The specification effectively IS the code, just in human language instead of syntax.
Teams that struggled with distributed development are repeating the same mistakes with AI. Those who learned specification discipline are thriving because they understand that clear requirements determine quality output, regardless of the implementer.
I'm pretty good at getting LLMs to write well-tested, production-ready code. I think it's a matter of knowing the right tools and techniques to use and steering the project in the right direction. Type-checking, linting, and a solid test suite go a very long way. It would be a nightmare to work on any project without these.
It could be interesting to take on a consulting project. I haven't done any contract work for many years but I'm curious to see if I could provide some value. Let me know if you need help with making a project more maintainable: refactoring, linters, writing tests, setting up CI/CD pipelines, etc.
Agreed, seeing similar good results with those guardrails. Also seeing good results when you actually spend energy and think through the high-level architecture (and document it). Makes it a lot easier for the LLM to snap into place and pattern match.
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[ 5.1 ms ] story [ 58.4 ms ] threadYou learn a little more for next time.
I imagine it will be the same set of issues really. Just a different way of cost cutting measures. There can be good reasons to take shortcuts but, in my experience, the problems start when you're not mindful that there's a price to pay for taking these shortcuts. Whether it comes from managers, employees or outsourced personnel, it's the same result.
I haven't thought about advertising it as a separate type of service(for vibe coded platforms) yet but maybe I should. The Australian software market is small so haven't been hearing much about the results of those experiments.
Low confidence to change it, low internal and external quality.
Also some differences: low age-to-quantity ratio, schedule pressure, inflated expectations.
It's most cost-effective to shift errors from runtime to compile time and from compile time to design time.
Unfortunately, AI rushes people to runtime as fast as possible.
i can only imagine that services like described in this article will become a very common part of getting proof of concepts built with AI into production.....
The real skill is now cleanup, not generation.
That's the core of situation as described in the article. I wonder how true that is, that it's faster overall than having developers build the MVP.
From what I've seen, I think developers can build just as fast, especially with AI assistance. They may not want to though, knowing full well the MVP/prototype will go directly into production.
Better to take some time to have a decent architecture early on. Product and management probably see that as a waste of time.
On the other hand, vibe coding allows the product team to make exactly what they want without having to explain it to developers. That's the real draw to this, basically a much better figma.
Perhaps there is a market for a product oriented vibe coding tool, that doesn't pretend to make code, but gives developers much better specifications while allowing the product and business side better input in the process.
EDIT: Not trying to offend anyone with this [0], I've actually had the same half-joking retirement plan since the dawn of vibe coding, to become an "all-organic-code" consultant who untangles and cleans up AI-generated mess.
[0]: https://aider.chat/HISTORY.html
YMMV but I’d rather build the MVP without much AI but then clean it up using it.
To be fair to him, my partner who works in Healthcare has the same concern, and for quite precisely the same reason: if the kind of work normally done by juniors who are training/building their skills is done by machines instead, where do the next generation of seniors come from?
My response to both was that I was confident the market would fix this problem itself - people will not pay for garbage. There is a reason books are still printed by established publishers. Why buy a book when you can just print a book on printer paper yourself? Because reading a book on printer paper sucks.
I cannot imagine that machines will ever replace any work where there is any meaningful threshold for "correct". I am so intrigued to see how this plays out across the broader economy.
AI coding has the same bottleneck: specification quality. The difference is that with outsourcing, poor specs meant waiting weeks for the wrong thing. With AI, poor specs mean iterating indefinitely on the wrong thing.
The irony is that AI is excellent at helping refine specifications - identifying ambiguities, expanding requirements, removing assumptions. The specification effectively IS the code, just in human language instead of syntax.
Teams that struggled with distributed development are repeating the same mistakes with AI. Those who learned specification discipline are thriving because they understand that clear requirements determine quality output, regardless of the implementer.
> How Senior Engineers Clean Up Code and Scale Products with AI
> Partnering with us means investing in quality, not just speed. We leverage AI to accelerate development and testing,
https://donado.co/en/articles/2025-08-02-prototype-to-mvp/
It could be interesting to take on a consulting project. I haven't done any contract work for many years but I'm curious to see if I could provide some value. Let me know if you need help with making a project more maintainable: refactoring, linters, writing tests, setting up CI/CD pipelines, etc.