If you go forward 12 months the AI bubble will have burst. If not sooner.
Most companies who bought into the hype are now (or will be soon) realizing it’s nowhere near the ROI they hoped for, that the projects they’ve been financing are not working out, that forcing their people to use Copilot did not bring significant efficiency gains, and more and more are realizing they’ve been exchanging private and/or confidential data with Microsoft and boy there’s a shitstorm gathering on that front.
The most successful ML in-house projects I’ve seen took at least 3 times as long than initially projected to become usable, and the results were underwhelming.
You have to keep in mind that most of the corporate ML undertakings are fundamentally flawed because they don’t use ML specialists. They use eager beavers who are enthusiastic about ML and entirely self-taught and will move on in 1 year and want to have “AI” on their resume when they leave.
Meanwhile, any software architect worth their salt will diplomatically avoid to give you any clear estimate for anything having to do with ML – because it’s basically a black box full of hopes and dreams. They’ll happily give you estimates and build infrastructure around the box but refuse to touch the actual thing with a ten foot pole.
There aren’t enough AI specialists. More are being created by picking up these projects.
The problem is that AI is too hyped and people are trying to solve things it probably can’t solve. The projects I have seen work are basically fancy data ingress/parsing/summarisation apps. That’s where the current AI tech can really shine.
If you go forward 12 months the AI bubble will have burst. If not sooner.
Most companies who bought into the hype are now (or will be soon) realizing it’s nowhere near the ROI they hoped for, that the projects they’ve been financing are not working out, that forcing their people to use Copilot did not bring significant efficiency gains, and more and more are realizing they’ve been exchanging private and/or confidential data with Microsoft and boy there’s a shitstorm gathering on that front.
If you have the ability to build an AI app in house - holy shit shit that can improve productivity. Copilot itself for office use… Meh so far.
The most successful ML in-house projects I’ve seen took at least 3 times as long than initially projected to become usable, and the results were underwhelming.
You have to keep in mind that most of the corporate ML undertakings are fundamentally flawed because they don’t use ML specialists. They use eager beavers who are enthusiastic about ML and entirely self-taught and will move on in 1 year and want to have “AI” on their resume when they leave.
Meanwhile, any software architect worth their salt will diplomatically avoid to give you any clear estimate for anything having to do with ML – because it’s basically a black box full of hopes and dreams. They’ll happily give you estimates and build infrastructure around the box but refuse to touch the actual thing with a ten foot pole.
There aren’t enough AI specialists. More are being created by picking up these projects.
The problem is that AI is too hyped and people are trying to solve things it probably can’t solve. The projects I have seen work are basically fancy data ingress/parsing/summarisation apps. That’s where the current AI tech can really shine.