Lenny's Podcast: Product | Career | Growth
01:01:44 · Benedict Evans, Lenny Rachitsky
But that's, I think, the way to get at what this means, is you have to get past, we do the old stuff but more, and you have to get to what do you do that's different that's because of this. What has this change? What wasn't possible before? What gets unlocked as opposed to just doing the old thing but more of it? [01:02:02] S1: 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.
01:02:28 · Benedict Evans
[01:02:28] S0: 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.
01:04:42 · Benedict Evans
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. 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.
01:06:20 · Lenny Rachitsky
[01:06:20] S1: Coming back to this phrase you use, presume radical uncertainty is a nice core thesis here. So knowing all this, just it's hard to tell. We don't know exactly where it's going. Things are going to change a lot, but it'll probably be okay broadly. Just a lot of people listening are pretty worried about their jobs and their careers, and how much the world changes. What would be a couple things you recommend people do knowing what you know to be more successful in this future?
01:06:48 · Benedict Evans
[01:06:48] S0: Well, I should just wind back on what you just said. It's like, as Keynes tells us, in the long run, we're all dead. So on average, nobody died in World War I. Great. But if you're a 19-year-old in 1914, you've got a one in three chances of not coming back. So yes, clearly there's a bunch of professions where this is a major question and particularly if you're an associate, or would've been thinking about being an associate, this is a major question. And it's very unclear how those professions are going to play out. It's very unclear what happens to the pyramid structure of professional services. The only answer I think one can have is, don't stick your head in the sand and say, "I hate all of this stuff," because that gives you a great feeling of moral superiority. And you can go on Bluesky and shout at everybody, chat at each other about how evil AI is like. Great, I'm happy for you, but that's not going to help. What helps is you diving into this completely submerging yourself in it and coming at understanding what you can do with it, how this changes things, how you can be a great hire. And that may still not help. But if you're going to a law firm and they're like, well, we hired a hundred associates last year and this year we're only going to hire 50, go into the interview and say, "Well, I think AI is and I'm never going to use it," is probably not the right mood. So, that may not be particularly comforting, but I don't think there's an alternative, is you have to dive into this and absorb it, and internalize it and think about what it means, just as you and I did with mobile and with the internet.
01:08:33 · Lenny Rachitsky
[01:08:33] S1: I think that is actually very actionable and very consistent advice on the podcast, is just, do stuff, build it, don't just sit around and pontificate and be pissed at what's happening. To close this out, I'm going to take us to AI Corner, a recurring corner of the podcast. And the question to you is just, what's one way you've used AI and use AI in your work or life that is really interesting, something that other people might be inspired by?