alt Hacker Newsthe (multi) billon dollar question is when that will happen, I think, case in point:
the OP is a kid in his 20s describing the history of the last 3 years or so of small scale AI Development (https://www.linkedin.com/in/silen-naihin/details/experience/)
How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and use cases etc.
When will AI be ready? Microsoft tried to push AI into big enterprise, Anthropic is doing a better job -but its all still in infancy
Personally for me I hope it won't be ready for another 10 years so I can retire before it takes over :)
I remember when folks on HN all called this AI stuff made up
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This is what people were saying about Rails 20 years ago: it wows the kids who use it to set up a CRUD website quickly but fails at anything larger-scale. They were kind of right in the sense that engineering a large complex system with Rails doesn't end up being particularly easier than with Plone or Mason or what have you. Maybe this will just be Yet Another Framework.
I'm currently in a strange position where I am being that developer with 15+ years of industry experience managing a project that's been taken over by a young AI/vibe-code team (against my advise) that plans to do complete rewrite in a low-code service.
Project was started in late 00s so it has substantial amount of business logic, rules and decisions. Maybe I'm being an old man shouting at the clouds, but I assume (or hope?) it would fail to deliver whatever they promised to the CEO.
So, I guess I'll see the result of this shift soon enough - hopefully at a different company by the time AI-people are done.
As a guy in his mid-forties, I sympathize with that sentiment.
I do think you're missing how this will likely go down in practice, though. Those giant codebases with years of domain rules are all legacy now. The question is how quickly a new AI codebase could catch up to that code base and overtake it, with all the AI-compatibility best practices baked in. Once that happens, there is no value in that legacy code.
Any prognostication is a fool's errand, but I wouldn't go long on those giant codebases.
How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and use cases etc.
At most of the companies I've worked at the development team is more like a cluster of individuals who all happen to be contributing to a shared codebase than anything resembling an actual team who collaborate on a shared goal. AI-assisted engineering would have helped massively because the AI would be looking outside of the myopic view any developer who is only focused on their tiny domain in the bigger whole cared about.
Admittedly though, on a genuinely good team it'll be less useful for a long time.
Thats the problem, the most “noise” regarding AI is made by juniors who are wowed by the ability to vibe code some fun “sideproject” React CRUD apps, like compound interest calculators or PDF converters.
No mention of the results when targeting bigger, more complex projects, that require maintainability, sound architectural decisions, etc… which is actually the bread and butter of SW engineering and where the big bucks get made.