137
AI Software Development – What Does The Data Say?
(codemanship.wordpress.com)
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 13 Aug 2026
137 points (100.0% liked)
Programming
28153 readers
204 users here now
Welcome to the main community in programming.dev! Feel free to post anything relating to programming here!
Cross posting is strongly encouraged in the instance. If you feel your post or another person's post makes sense in another community cross post into it.
Hope you enjoy the instance!
Rules
Rules
- Follow the programming.dev instance rules
- Keep content related to programming in some way
- If you're posting long videos try to add in some form of tldr for those who don't want to watch videos
Wormhole
Follow the wormhole through a path of communities !webdev@programming.dev
founded 3 years ago
MODERATORS
This aligns with my experience, largely. Of course it's still my job to maximize LLM effectiveness within my organization. Which is a delicate balancing act to protect my teams from overeager executive leadership looking for huge gains.
My own summary is that AI can be an accelerator, but the harder you lean into it, the worse outcomes will be. No matter how much code is written, you still need actual human minds to understand it and they can only handle so much volume before getting overwhelmed.
Also, if AI gives you 20% productivity gains, but that 20% goes into playing with AI trying to get more, you haven't really gained anything. Usage needs to be standardized rather than developers constantly negotiating with AI trying to coax out better outcomes.
One might even consider flourishing employees as opposed to not-burned-out ones.
This is a tale older than AI. Most of the AI productivity pushes I struggle to get adopted fail not because of AI bad or its too hard to do. They fail because of a broken CI/CD pipeline. They fail because some team thinks their process is sacred and unique.