The speaker argues that government datasets predicting what percentage of jobs AI will displace are fundamentally flawed. Breaking down jobs into automatable tasks mirrors the failure of early expert systems. In 1997, nobody predicted the internet would destroy newspapers and taxis the way it did, yet we keep trying to forecast AI's impact with simple percentages.
Chapters
there's a sort of US government dataset which tries to kind of analyze every single job and then people try and kind of score it and they try and say, well, you know, this profession is X or Y percent exposed to AI and AI can do Z percent of it today. I think this is just the most ridiculous bunch of deluded horseshit. There's two reasons for this. The first reason is that ironically, this is the expert system's problem. Like you try to recognize a picture of a cat and say you start building up logical steps. So you'd make an edge detector and then you make a fur detector and you make an eye detector and 15 years later you've got 700 steps and it doesn't work. This is what happened when you try and look at a profession and break it down by which bits can be automated and which can't. You can't describe a profession like that. You can't kind of look at a senior partner at a law firm and say, well, 17% of their work could be automated. This is horseshit. You can't do that. The other side of the fallacy though, is to talk about taxi drivers. Imagine we're in 1997. What will be crushed by the internet? Well, newspapers will be fine because they'll save money on the printing bills. Obviously, like taxi drivers, you couldn't automate that with the internet. It's got nothing to do with the internet. Maybe you'd have internet booking. No, that's not going to change anything. And of course it completely changes the whole thing. That's how these things work. You can't predict which things are going to be exposed necessarily.
