[woah]

> Why would a carpentry shop buy hundreds of thousands of dollars of power tools without consulting with their employees to see what they actually need to get their job done more effectively?

Are you suggesting that changes to new production technologies are always driven bottom up by line workers? I'm guessing that historically that's rare.

[oblio]

Historically that rarely happens because industrial equipment is/was generally too expensive for the average worker to purchase on their own, plus workers usually have a budget of roughly 0 to buy extra tools, especially expensive ones.

But to give you an example, also roughly 0 companies made developers use Linux and still many developers choose it, so bottom up improvements happen in a decent chunk of cases. Nobody paid for PostgreSQL promotion. Or Python, etc.

[TeMPOraL]

> so bottom up improvements happen in a decent chunk of cases. Nobody paid for PostgreSQL promotion. Or Python, etc.

It does, but for better or worse, it's an anomaly. Even now, maybe nobody was paid for PostgreSQL or Python promotion, but modern OSS tools and programming languages usually have a business backing it. Linux, too, wasn't commercially promoted until it was; RedHat isn't exactly a charity after all.

Conversely, no one paid for initial AI promotion either - ChatGPT exploded organically after release, and for the first year or two, companies had a problem because a good chunk of their staff, including especially non-engineers, discovered just how useful it was and wanted to use it at work, casually violating every internal policy, bylaw and even regulatory policies about data sharing. The massive spend on promotion - including first-party spend - came later, but at that point it was already obvious ~everyone is going to be buying it.

I suppose bottom-up vs. top-down may be in part about how mature a technology and industry is.

[WhiteOwlLion]

Then those same organizations that "give in" end up providing inferior AI tools, and you face the dilemma of either using poor models and accepting poor results or sharing data with better models that may not be governed correctly.