this post was submitted on 07 Dec 2025
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I Went All-In on AI. The MIT Study Is Right. (open.substack.com)
submitted 1 day ago* (last edited 1 day ago) by AutistoMephisto@lemmy.world to c/technology@lemmy.world
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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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[–] keegomatic@lemmy.world 6 points 12 hours ago (1 children)

You’re wrong, whether you figure that out now or later. Using an LLM where you gatekeep every write is something that good developers have started doing. The most senior engineers I work with are the ones who have adopted the most AI into their workflow, and with the most care. There’s a difference between vibe coding and responsible use.

[–] raspberriesareyummy@lemmy.world -3 points 9 hours ago (1 children)

There’s a difference between vibe coding and responsible use.

There's also a difference between the occasional evening getting drunk and alcoholism. That doesn't make an occasional event healthy, nor does it mean you are qualified to drive a car in that state.

People who use LLMs in production code are - by definition - not "good developers". Because:

  • a good developer has a clear grasp on every single instruction in the code - and critically reviewing code generated by someone else is more effort than writing it yourself
  • pushing code to production without critical review is grossly negligent and compromises data & security

This already means the net gain with use of LLMs is negative. Can you use it to quickly push out some production code & impress your manager? Possibly. Will it be efficient? It might be. Will it be bug-free and secure? You'll never know until shit hits the fan.

Also: using LLMs to generate code, a dev will likely be violating copyrights of open source left and right, effectively copy-pasting licensed code from other people without attributing authorship, i.e. they exhibit parasitic behavior & outright violate laws. Furthermore the stuff that applies to all users of LLMs applies:

  • they contribute to the hype, fucking up our planet, causing brain rot and skill loss on average, and pumping hardware prices to insane heights.
[–] keegomatic@lemmy.world 1 points 1 hour ago

We have substantially similar opinions, actually. I agree on your points of good developers having a clear grasp over all of their code, ethical issues around AI (not least of which are licensing issues), skill loss, hardware prices, etc.

However, what I have observed in practice is different from the way you describe LLM use. I have seen irresponsible use, and I have seen what I personally consider to be responsible use. Responsible use involves taking a measured and intentional approach to incorporating LLMs into your workflow. It’s a complex topic with a lot of nuance, like all engineering, but I would be happy to share some details.

Critical review is the key sticking point. Junior developers also write crappy code that requires intense scrutiny. It’s not impossible (or irresponsible) to use code written by a junior in production, for the same reason. For a “good developer,” many of the quality problems are mitigated by putting roadblocks in place to…

  1. force close attention to edits as they are being written,
  2. facilitate handholding and constant instruction while the model is making decisions, and
  3. ensure thorough review at the time of design/writing/conclusion of the change.

When it comes to making safe and correct changes via LLM, specifically, I have seen plenty of “good developers” in real life, now, who have engineered their workflows to use AI cautiously like this.

Again, though, I share many of your concerns. I just think there’s nuance here and it’s not black and white/all or nothing.