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The working requirements of "when I click X, do Y" comes from PO's, sure. But the remaining implied requirements (what happens if there's an error in your app? How do you identify sensitive data that needs to be handled differently to "basic" data?) in my experience (working with teams of varying degrees of experience) are defined by engineering.

We're talking about being a couple of years away from ChatGPT being able to copy and paste from tutorials here, and with my understanding of AI (I've done a few ML projects in the last decade, but nothing that stuck), that's the "easy" part. The impressive part of things like Mindjourney is the synthesis, and we're not really seeing that or any signs that it's coming IMO.




> what happens if there's an error in your app? How do you identify sensitive data that needs to be handled differently to "basic" data?

Nothing happens, and you don't. That's what I meant by "bad software". I'm reminded of a story Clay Shirky told about AT&T analysts from the mid-90s trying to understand the web hosting business[0]:

> The ATT guys had correctly understood that the income from $20-a-month customers wouldn’t pay for good web hosting. What they hadn’t understood, were in fact professionally incapable of understanding, was that the industry solution, circa 1996, was to offer hosting that wasn’t very good. This, for the ATT guys, wasn’t depressing so much as confusing... For a century, ATT’s culture had prized - insisted on - quality of service; they ran their own power grid to keep the dial-tone humming during blackouts. ATT, like most organizations, could not be good at the thing it was good at and good at the opposite thing at the same time. The web hosting business, because it followed the “Simplicity first, quality later” model, didn’t just present a new market, it required new cultural imperatives.

0: https://gwern.net/doc/economics/2010-04-01-shirky-thecollaps... (shirky's old site has been down for some time)




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