Benedict Evans: evaluation

Transcript context

…Yeah. Just to support this kind of general theme you have of it's like we don't know what is going to happen, this is unprecedented, if you're to zoom out a few years ago, maybe three years ago, four years ago, the last profession you think would be automated is engineering and coding. It's like, that feels like the hardest thing that's like, we're going to need people to build these things. Now it's like the most transformed role of any role. You went from writing all your codes to 0% of your code is AI. It's almost like you didn't realize it was boring manual labor that could be automated. You thought it was something else. It's funny. I mean, I was looking at this, this whole, there's this sort of US government called data set called ONET or something like that, which tries to analyze every single job and then people try and kind of score it, and they try and say, "Well, 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 diluted horseshit. And there's two reasons for this. The first reason is that this is, ironically, this is the logical systems problem, the expert system's problem. The problem with expert systems is, for anyone who doesn't know, you try to recognize a picture of a cat and so you start building up logical steps. So you make an edge detector, and then you make a third detector, and you make an eye detector and you make an ear detector, and 15 years later you've got 700 steps and it doesn't work. And this is what happens when you try and look at a profession and sort of break it down by which bits can be automated and which can't. You can't describe a profession like that. But anyway, we can't. You can't 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. I think the other side of the fallacy though is to talk about taxi drivers. So, if we'd been having this conversation in 1997, it's like the Uber test. 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. This is like a jake, but people said that. Newspaper, the internet will be great for newspapers. Their printing bills will get down. Well, yes, but no. But the other side is, well, obviously our 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, but like, no, that's not going to change anything. And of course, it completely changes the whole thing. And so the example I saw the other day was things that won't be affected by AI personal trainers. Okay. I take my iPhone and I balance it on the metal piece with the camera pointed at me, and I ask an AI to build me a training routine and watch me and tell me if I'm doing it right. Why do I need a personal trainer? Now, that might be complete nonsense, but that's how these things work. The stuff that you don't think is, you can't predict which things are going to be exposed necessarily. Or a lot of the big companies are things that didn't look like that would work and didn't look like I was exposed. The other side of this, of course, is this is one of the charts at the end of my presentation is comparing Uber and Airbnb, because this is like the cliche from Mark Andreessen that Uber doesn't sell software to taxi companies, Airbnb doesn't sell software to hotels. the end of my presentation is comparing Uber and Airbnb, because this is like the cliche from Mark Andreessen that Uber doesn't sell software to taxi companies, Airbnb doesn't sell software to hotels. Okay, now let's go and look at the market impact. Well, a whole bunch of cities were Uber demolished taxi business, and made it much bigger as well. The TAM became much bigger and everyone switched. Airbnb's impact hotels, if you actually go and look at the numbers, it's pretty marginal. They carved out this whole other business, and maybe they slowed down the growth of hotels a bit, but my wife flies to Milwaukee next week. She's going to land at eight o'clock at night. She wants to go to a hotel, she wants to have a room service, she needs a bath, she needs a gym at 6:00 in the morning, and then 7:00 in the morning, she's going to drive to the client's site. She's not going to stay in Airbnb. Absolutely zero chance she's going to stay in an Airbnb. And half of the hotel business is business travel. And as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said the problem with Benedict is his answer to everything is it depends. It's like, yeah, it does. It depends. So, it's back to my 1997 point. You can say some of this, but you have to have that humility.…

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