this post was submitted on 07 Dec 2025
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Just want to clarify, this is not my Substack, I'm just sharing this because I found it insightful.

The author describes himself as a "fractional CTO"(no clue what that means, don't ask me) and advisor. His clients asked him how they could leverage AI. He decided to experience it for himself. From the author(emphasis mine):

I forced myself to use Claude Code exclusively to build a product. Three months. Not a single line of code written by me. I wanted to experience what my clients were considering—100% AI adoption. I needed to know firsthand why that 95% failure rate exists.

I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.

Now when clients ask me about AI adoption, I can tell them exactly what 100% looks like: it looks like failure. Not immediate failure—that’s the trap. Initial metrics look great. You ship faster. You feel productive. Then three months later, you realize nobody actually understands what you’ve built.

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[–] theneverfox@pawb.social 21 points 2 days ago (3 children)

AI isn't good at changing code, or really even understanding it... It's good at writing it, ideally 50-250 lines at a time

[–] Evotech@lemmy.world 6 points 2 days ago* (last edited 2 days ago) (1 children)

I'm just not following the mindset of "get ai to code your whole program" and then have real people maintain it? Sounds counter productive

I think you need to make your code for an Ai to maintain. Use Static code analysers like SonarQube to ensure that the code is maintainable (cognitive complexity)!and that functions are small and well defined as you write it.

[–] theneverfox@pawb.social 8 points 1 day ago (1 children)

I don't think we should be having the AI write the program in the first place. I think we're barreling towards a place where remotely complicated software becomes a lost technology

I don't mind if AI helps here and there, I certainly use it. But it's not good at custom fit solutions, and the world currently runs on custom fit solutions

AI is like no code solutions. Yeah, it's powerful, easier to learn and you can do a lot with it... But eventually you will hit a limit. You'll need to do something the system can't do, or something you can't make the system do because no one properly understands what you've built

At the end of the day, coding is a skill. If no one is building the required experience to work with complex systems, we're going to be swimming in a world of endless ocean of vibe coded legacy apps in a decade

I just don't buy that AI will be able to take something like a set of State regulations and build a complaint outcome. Most of our base digital infrastructure is like that, or it uses obscure ancient systems that LLMs are basically allergic to working with

To me, we're risking everything on achieving AGI (and using it responsibly) before we run out of skilled workers, and we're several game changing breakthroughs from achieving that

[–] MangoCats@feddit.it 0 points 1 day ago (1 children)

I think we’re barreling towards a place where remotely complicated software becomes a lost technology

I think complicated software has been an art more than a science, for the past 30 years we have been developing formal processes to make it more of a procedural pursuit but the art is still very much in there.

I think if AI authored software is going to reach any level of valuable complexity, it's going to get there with the best of our current formal processes plus some more that are being (rapidly) developed specifically for LLM based tools.

But eventually you will hit a limit. You’ll need to do something...

And how do we surpass those limits? Generally: research. And for the past 20+ years where do we do most of that research? On the internet. And where were the LLMs trained, and what are they relatively good at doing quickly? Internet research.

At the end of the day, coding is a skill. If no one is building the required experience to work with complex systems

So is semiconductor design, application of transistors to implement logic gates, etc. We still have people who can do that, not very many, but enough. Not many people work in assembly language anymore, either...

[–] theneverfox@pawb.social 1 points 18 hours ago

So is semiconductor design, application of transistors to implement logic gates, etc. We still have people who can do that, not very many, but enough. Not many people work in assembly language anymore, either...

Yeah, that's a lost tech. We still use the same decades, even century old, frameworks

They're not perfect. But they are unchangeable. We no longer have the skills to adapt them to modern technology. Improvements are incremental, despite decades of effort you still can't reliably run a system on something like RISK.

[–] MangoCats@feddit.it 0 points 1 day ago (1 children)

It’s good at writing it, ideally 50-250 lines at a time

I find Claude Sonnet 4.5 to be good up to 800 lines at a chunk. If you structure your project into 800ish line chunks with well defined interfaces you can get 8 to 10 chunks working cooperatively pretty easily. Beyond about 2000 lines in a chunk, if it's not well defined, yeah - the hallucinations start to become seriously problematic.

The new Opus 4.5 may have a higher complexity limit, I haven't really worked with it enough to characterize... I do find Opus 4.5 to get much slower than Sonnet 4.5 was for similar problems.

[–] theneverfox@pawb.social 1 points 20 hours ago

Okay, but if it's writing 800 lines at once, it's making design choices. Which is all well and good for a one off, but it will make those choices, make them a different way each time, and it will name everything in a very generic or very eccentric way

The AI can't remember how it did it, or how it does things. You can do a lot... Even stuff that hasn't entered commercial products like vectorized data stores to catalog and remind the LLM of key details when appropriate

2000 lines is nothing. My main project is well over a million lines, and the original author and I have to meet up to discuss how things flow through the system before changing it to meet the latest needs

But we can and do it to meet the needs of the customer, with high stakes, because we wrote it. These days we use AI to do grunt work, we have junior devs who do smaller tweaks.

If an AI is writing code a thousand lines at a time, no one knows how it works. The AI sure as hell doesn't. If it's 200 lines at a time, maybe we don't know details, but the decisions and the flow were decided by a person who understands the full picture