|
|
|
|
|
by al_borland
67 days ago
|
|
When the narrative around AI is that people should rely on it all the time, people will be judged by your token use (it better be high), the AI is smarter than everyone and will take all the jobs, the AI is the best programmer, and more… When things fail repeatedly, it highlights that the emperor has no clothes. If it’s as good as they say, why can’t it figure out how to not go down every day? How can people rely on it for their job if it goes down everyday? Maybe they shouldn’t rely on it. If it’s supposed to be such a good engineer, why should it have the same scaling issues as Twitter did 20 years ago with 20 years of lessons learned and 20 years of development for more modern and scalable infrastructures? Shouldn’t it know all the tricks to scale and have redundancy to keep availability high? Does it not know the demands? When expectations are out of line with reality, there will be snark when things fail. Those expectations have been force fed to us by these AI companies for years now, so I don’t have much sympathy or patience to offer them. They created these expectations of their platforms and if they can’t live up to them, then maybe it’s time for recalibrate the public image of what AI really is and what it can do… and what its limitations are. |
|
There seems to be a mass anxiety around the job market even. I‘ve seen a lot of social media content, including videos of people giving advice, especially to younger tech workers.
The most dangerous (psychologically, socially, economically) are people in important positions, who understand just enough to see some of its usefulness, but not enough to assess where its assumptions and guarantees actually are.
Even moreso if they see workers as a mere cost center instead of an asset.
But here is my perhaps naive, hopefully brave prediction: the real winners of this shake up are not decided yet, and neither bean counting nor superficial engagement with the topic will be sufficient or even useful.