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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[–] SocialMediaRefugee@lemmy.world 19 points 1 week ago (2 children)

Just sell it to AI customers for AI cash.

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[–] kreskin@lemmy.world 13 points 1 week ago* (last edited 1 week ago) (6 children)

I work in an company who is all-in on selling AI and we are trying desperately to use this AI ourselves. We've concluded internally that AI can only be trusted with small use cases that are easily validated by humans, or for fast prototyping work.. hack day stuff to validate a possibility but not an actual high quality safe and scalable implementation, or in writing tests of existing code, to increase test coverage. yes, I know thats a bad idea but QA blessed the result.... so um .. cool.

The use case we zeroed in on is writing well schema'd configs in yaml or json. Even then, a good percentage of the time the AI will miss very significant mandatory sections, or add hallucinations that are unrelated to the task at hand. We then can use AI to test AI's work, several times using several AIs. And to a degree, it'll catch a lot of the issues, but not all. So we then code review and lint with code we wrote that AI never touched, and send all the erroring configs to a human. It does work, but cant be used for mission critical applications. And nothing about the AI or the process of using it is free. Its also disturbingly not idempotent. Did it fail? Run it again a few times and it'll pass. We think it still saves money when done at scale, but not as much as we promise external AI consumers. The Senior leadership know its currently overhyped trash and pressure us to use it anyway on expectations it'll improve in the future, so we give the mandatory crisp salute of alignment and we're off.

I will say its great for writing yearly personnel reviews. It adds nonsense and doesnt get the whole review correct, but it writes very flowery stuff so managers dont have to. So we use it for first drafts and then remove a lot of the true BS out of it. If it gets stuff wrong, oh well, human perception is flawed.

This is our shared future. One of the biggest use cases identified for the industry is health care. Because its hard to assign blame on errors when AI gets it wrong, and AI will do whatever the insurance middle men tell it to do.

I think we desperately need a law saying no AI use in health care decisions, before its too late. This half-assed tech is 100% going to kill a lot of sick people.

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[–] just_another_person@lemmy.world 13 points 1 week ago (1 children)
[–] AutistoMephisto@lemmy.world 9 points 1 week ago (2 children)

What's interesting is what he found out. From the article:

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.

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[–] Rhoeri@lemmy.world 12 points 1 week ago (2 children)

AI is hot garbage and anyone using it is a skillless hack. This will never not be true.

[–] nullroot@lemmy.world 16 points 1 week ago (6 children)

Wait so I should just be manually folding all these proteins?

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[–] jbloggs777@discuss.tchncs.de 10 points 1 week ago (2 children)

While this is a popular sentiment, it is not true, nor will it ever be true.

AI (LLMs & agents in the coding context, in this case) can serve as both a tool and a crutch. Those who learn to master the tools will gain benefit from them, without detracting from their own skill. Those who use them as a crutch will lose (or never gain) their own skills.

Some skills will in turn become irrelevent in day-to-day life (as is always the case with new tech), and we will adapt in turn.

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[–] lepinkainen@lemmy.world 11 points 1 week ago* (last edited 1 week ago) (11 children)

Same thing would happen if they were a non-coder project manager or designer for a team of actual human programmers.

Stuff done, shipped and working.

“But I can’t understand the code 😭”, yes. You were the project manager why should you?

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[–] DupaCycki@lemmy.world 9 points 1 week ago

Personally I tried using LLMs for reading error logs and summarizing what's going on. I can say that even with somewhat complex errors, they were almost always right and very helpful. So basically the general consensus of using them as assistants within a narrow scope.

Though it should also be noted that I only did this at work. While it seems to work well, I think I'd still limit such use in personal projects, since I want to keep learning more, and private projects are generally much more enjoyable to work on.

Another interesting use case I can highlight is using a chatbot as documentation when the actual documentation is horrible. However, this only works within the same ecosystem, so for instance Copilot with MS software. Microsoft definitely trained Copilot on its own stuff and it's often considerably more helpful than the docs.

[–] m3t00@piefed.world 8 points 1 week ago

ask your ai pal for help

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