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We already see it; the combination of job-hopping & AI are a perfect storm really.

From launch to failure is definitely getting fast-tracked; few months ago we had yet another hospital system that just lost data; reading the code (no tests, no peer reviews; they don't use versioning) shows clear signs of LLMs; many files that do almost the same thing, many similar function names that do almost the same thing or actually the same thing, strange solutions to trivial problems, etc. The problem was that there was a script ran at startup which added an admin user, but if an admin user already exists, it truncates the table. No idea why, but it wasn't discovered earlier because after testing by the devs it was put live, devs left (contractors) and it just ran without issues until the ec2 instance needed maintenance by aws and was as such rebooted after which all users were gone. Good stuff. They paid around 150k for it; that is not a lot in our field but then to get this level of garbage is rather frightening. This was not life threatening, but I know if it was, it would not be better as you cannot make this crap up.



And let us be very clear: this happened REGULARLY pre-AI, with long-lived systems adding contractors 12, 6 or less months over 20 or more years. Now we have AI that allows even faster iterations of this with even less conceptual integrity.

The big problem: the decision makers(c-suite executives) never really understood what was happening before, so you can't expect them to see the root cause of the problem we're actively creating. This means it will not get the attention and resourcing needed - plus they'll be gone and on to the next one after taking a huge payday for slashing their R&D costs.


Agreed.




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