Lenny's Podcast: Product | Career | Growth
00:58:28 · Lenny Rachitsky, Benedict Evans
[00:58:28] S1: So maybe following that the right and coming back to the kids thing and the jobs thing, is there a job you are steering your kid away from? And is there a job you kind of think you want to steer them towards? [00:58:39] S0: I don't know about that. It's probably a little bit early yet. He's not quite at the like, "I want to be a fireman stage," but... [00:58:47] S1: That might be a great job. [00:58:49] S0: Yeah. And certainly if I look at my career, I started as an equity analyst, and then I went and worked in an industry, and then I was a consultant. The days when you kind of knew what your career was going to be over, there were sickly some people were like, you want to be an architect, you want to be a software engineer, you want to be X or Y. I don't know. I think the only kind of thinking I have here is that you slowly work out, there's a bunch of skills that you have, and there's a bunch of jobs that makes you good at, and then there's a bunch of stuff that people will pay you for. And you want to get at least two of those, and preferably all three. [00:59:22] S1: Okay. So zooming out a little bit, let me ask you a Meta question. What's a question about AI that you think nobody's asking yet, or not enough people are asking that we should be asking ourselves?
00:59:34 · Benedict Evans
[00:59:34] S0: Sure. I mean, we talked about value capture. Obviously this is a whole, everyone is asking like, I'm not sure how many people are asking whether model labs have pricing power. I think a lot of people are just presuming that the situation today will continue, or that of course they will. So I think that's maybe a question that not enough people ask. I think the question I pose towards the end of my presentation, which we talked about earlier, is like, what's the task and what's job? Or what is just the thing that becomes a button or make SKU versus what are people actually hiring you for? Is that kind of a useful way of thinking about this? And clearly there are going to be some jobs where no, that is just a task, and that job gets automated away. But there's a bunch where that kind of isn't the question. The way I actually pulled that together at the end of the deck was a chart of a global recorded music revenue, which as you may know is kind of a U-shaped curve more or less. And so it's dropped by about half from 2000 to 2015 or so, and since then has come back to about 75% of the peak, adjusted for inflation. And the way that I look at this is to say, and that's driven by streaming. And I kind of looked at this and said, well, the first half of this chart is saying, what happens if I don't have to pay $15 to get a CD to get that track? And the second half of the chart is saying, what happens if $15 a month gets you all the music that there is? So it's kind of a completely different sort of question. And that's a way that you could look at Google, or the way you could look at Airbnb or all these kinds of companies. Is it to begin with you do the old thing but more whether you need technology, you do the old thing, but more of it on the new place. So you put Flickable mobile, you print out Googles, and then you make new things that are only possible with a new thing. And then maybe you go a bit further and you kind of completely redefine the question and you make something that isn't that at all. Spotify is not an online music store. It's something else. And right now, those questions, you only even know what the question is after it's been asked and you've built a billion dollar thing that lots of people use because obviously Spotify look crazy, and people look crazy, and Airbnb look crazy.
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?