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submitted 4 days ago* (last edited 4 days ago) by AutistoMephisto@lemmy.world to c/technology@lemmy.world

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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[-] KazuyaDarklight@lemmy.world 20 points 4 days ago

My big fear with this stuff is security. It just seems so "easy", without knowledgeable people, for AI to write a product that functions from a user perspective but is wide open to attack.

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[-] kreskin@lemmy.world 12 points 4 days ago* (last edited 4 days ago)

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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this post was submitted on 07 Dec 2025
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