There’s a pattern I keep seeing with AI adoption that doesn’t get talked about enough. A lot of companies are rushing to plug AI into everything. Especially development. The assumption seems to be that if you can generate code faster, you can move faster as a team. But that hasn’t really matched what I’ve seen in practice. Most developers aren’t spending their day just writing code. A lot of the work is thinking through problems, designing systems, debugging weird issues, and making sure everything actually holds together long term. When AI is used in the right places, it helps. Repetitive tasks, quick drafts, getting unstuck. It can save real time there. But when it gets pushed into more complex parts of the workflow, it can actually create more work. Things look fine at first, then you end up spending extra time fixing or untangling what was generated. It reminds me a bit of past outsourcing waves. Short term efficiency, but sometimes at the cost of long term clarity and maintainability. I ended up writing out a more complete breakdown of where AI actually helps, where it tends to cause problems, and how to use it without making your systems harder to manage. https://open.substack.com/pub/altifytecharticles/p/the-truth-about-agentic-ai-that-no?r=7zxoqp&showWelcomeOnShare=true Curious how others here are handling this right now. Are you seeing real gains, or just shifting the workload around? submitted by /u/Key_Database155
Originally posted by u/Key_Database155 on r/ArtificialInteligence
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