I think we'll see it first in internal reporting tools, where the stakeholder tries to explain something very specific they want to see (logical or not) and when it's visibly wrong they can work around it privately.
When I read things like this I really wonder if half of the people on HackerNews have ever held a job in software development (or a job at all to be fair).
None of what you describe is even remotely close to reality.
Stuff that half works gets you fully fired by the client.
Do you perhaps have any friends at companies which hired overseas contractors? Or system-admins working at smaller companies or nonprofits? They're more-likely to have fun stories. I myself remember a university department with a master all_students.xls file on a shared drive (with way too many columns and macros in it) that had to be periodically restored from snapshots every time it got corrupted...
I have seen the above story play out literally dozens of times in my career.
How does that make any sense? How is AI, and especially GenAI, something that is by definition fallible, better in ANY way than current frameworks that allow you to write CRUD applications deterministically with basically one line of code per endpoint (if that)?
As a direct example from myself, I now acquire and run small e-commerce brands. When I decided to move my inventory management from Google Sheets into an actual application, I looked at vendors but ultimately just decided to build my own. My coding skills are pretty minimal, but sufficient that I was able to produce what I needed with the help of LLMs. It has the advantages of being cheaper than buying and also purpose-built to my needs.
So yeah, basically the tl;dr is that for internal tools, I believe that LLMs giving non-developers sufficient coding skills will shift the build vs. buy calculus squarely in the direction of build, with the logical follow-on effects to companies trying to sell internal tools software.
As you visualize whole swaths of human workers getting automated away, also visualize the nitty gritty of day-to-day work with AI. If it gets something wrong, it will say "I apologize" until you, dear user, are blue in the face. If an actual person tried to do the same, the blueness would instead be on their, not your, face. Therein lies the value of a human worker. The big question, I think, is going to be: is that value commensurate to what we're making on our paycheck right now?
The value of a human worker is in a more meaningful apology? I think the relevant question here is who's going to make more mistakes, not who's going to be responsible when they happen. A good human is better than AI today, but that's not going to last long.
there is going to be so much money to make as a consultant fixing these setups, I can't wait!
I mostly agree with this for now, but obviously LLMs will continue to improve and be able to handle greater and greater complexity without issue.
> Especially if the person driving it doesn't know what they don't know about the domain.
Sure, but if the person driving it doesn't know what they're doing, they're also likely to do a poor job buying a solution (getting something that doesn't have all the features they need, selecting something needlessly complex, overpaying, etc.). Whether you're building or buying a piece of enterprise software, you want the person doing so to have plenty of domain expertise.
What systems do you think are going to start disappearing? I'm unclear how LLMs are contributing to systems becoming redundant.