This reminds me of the exploration-exploration trade-off in reinforcement learning: you want to maximise your long term profits but, since your knowledge is incomplete, you must acquire new knowledge, which companies do by trying stuff. Prematurely dismissing GenAI could mean missing out on new efficiencies, which take time to be identified.
Well, if only 95% of our ideas don't work, with a little hard work and sacrifice, we are livin' in an optimish paradise.
Incidentally, "everyone" is wrong a lot of the time.
And depending on how you look at it, science itself is experimentation, but at least it mostly results in publications in the end, that may or may not be read, but at least serve as records of areas explored.
Scientists and mathematicians often burn barrels of time and unpublished ideas, not to mention following their curiosities into random pursuits, that give their subconscious the free space to crystalize slippery insights.
With their publishable work somehow gelling out of all that.
Yes, of course. Incompetent leaders do incompetent things.
No argument or surprise.
The point I made was less obvious. Competent leaders can also/often appear to throw money away, but for solid reasons.
I've never heard of such a thing
Bonfires of money.
Predictably. Because all three of those concerns require highly speculative action to properly address.
That doesn't make those reasons invalid. Failures are expected, especially in early days. And are not a sign they are making spurious bets, or starry eyed about industry upheavals. The minimal return is still experience gained and a ramped up institutional focus.
How many of us here speed up our overall development by coding early on new projects before we have complete clarity? Writing code we will often throw away?