Some of these arguments are intellectually interesting, but I struggle to see strong real-world evidence behind them.
In practice, meaningful results with AI still seem to depend on people who understand data modeling, semantics, and system constraints. AI amplifies that knowledge; it doesn’t replace it.
I’d genuinely be curious to see concrete examples where abandoning modeling entirely has produced better, more reliable outcomes at scale.
It’s interesting that this xyz is dead message follows such a cause and effect path. “Because of this new tech xyz now must die”. It’s like the people crafting these arguments never learned that coexistence and compliments are a thing.
It’s now just clickbait for likes. Just don’t say excel is dead, every business leader director level and up will have a heart attack. “Where’s my PowerPoint of excels?”
"Schema-on-read 2.0" made me shudder. A friend of mine started using the phrase (to describe data I must clarify) "It should always hurt more going in than it does coming out"
Hello, here is a new Data Engineer apprentice. I recently read a passage from a book where the author or authors emphasize the need to have a solid data foundation even for later working with AI, ML, LLM models. Hoy can often see comments and posts trying to convince you that AI will take over a largo parte of your job responsibilities and that it will no longer be necessary yo learn skills that are fundamental for specific roles. The sad thing is that for someone like me who doesn't have much knowledge and experience un IT, it creates confusion and discouragement...
As usual a thought provoking post. I started philosophising in my head - if people give all their knowledge work to AI, what is the point of existing. It got deep, quickly.
While I think it’s obvious you don’t believe that data modeling or the value in learning things like SQL are dead, why is that the case Joe?
I’m going to assume it’s because one needs to understand what they’re doing in order to seperate “good” from “bad” or “right” from “wrong”. And in doing so, one gains some kind of efficiency or advantage (economic or otherwise)?
Some of these arguments are intellectually interesting, but I struggle to see strong real-world evidence behind them.
In practice, meaningful results with AI still seem to depend on people who understand data modeling, semantics, and system constraints. AI amplifies that knowledge; it doesn’t replace it.
I’d genuinely be curious to see concrete examples where abandoning modeling entirely has produced better, more reliable outcomes at scale.
Same
It’s interesting that this xyz is dead message follows such a cause and effect path. “Because of this new tech xyz now must die”. It’s like the people crafting these arguments never learned that coexistence and compliments are a thing.
It’s now just clickbait for likes. Just don’t say excel is dead, every business leader director level and up will have a heart attack. “Where’s my PowerPoint of excels?”
Bingo. It’s a weird zero sum argument
"Schema-on-read 2.0" made me shudder. A friend of mine started using the phrase (to describe data I must clarify) "It should always hurt more going in than it does coming out"
Hello, here is a new Data Engineer apprentice. I recently read a passage from a book where the author or authors emphasize the need to have a solid data foundation even for later working with AI, ML, LLM models. Hoy can often see comments and posts trying to convince you that AI will take over a largo parte of your job responsibilities and that it will no longer be necessary yo learn skills that are fundamental for specific roles. The sad thing is that for someone like me who doesn't have much knowledge and experience un IT, it creates confusion and discouragement...
Very understandable
As usual a thought provoking post. I started philosophising in my head - if people give all their knowledge work to AI, what is the point of existing. It got deep, quickly.
While I think it’s obvious you don’t believe that data modeling or the value in learning things like SQL are dead, why is that the case Joe?
I’m going to assume it’s because one needs to understand what they’re doing in order to seperate “good” from “bad” or “right” from “wrong”. And in doing so, one gains some kind of efficiency or advantage (economic or otherwise)?