Lenny's Podcast: Product | Career | Growth
00:12:29 · Benedict Evans
[00:12:29] S0: Well, one of the strands in my presentation, so I split the presentation into three sections. There's a section on capital, which is basically, where is all this CapEx going and are the model labs going to have differentiation? And then there's a section on deployment, which is basically, what does it mean for the software industry? And then the third section is, how does this change stuff? And one of the sort of strands I tried to pull together in the section on change is, what's the hard part of the job? Is the hard part of the job writing the code line by line? Is the hard part of the job giving you the SKU, or making the PowerPoint? Or is the hard part of the job something else? Is it the task or the job? And pulling that apart, sometimes the task is the job. The classic example is like an elevator attendant. If I live in a building that has an attended elevator, we have a manual elevator, so there's no button. There's a lever and the dormant drives you to your floor. It's a vertical speed cut. It's like one of those cramps in San Francisco. They drive you to the store, to your floor. And then those all got automated after the '50s, and now you press a button, and pressing the button is a job. So there were some things where the button, the job was a task and the task got automated. What happens much more, and this is why people talked about the Jevons paradox is this pricey elasticity, because the Jevons paradox is just price elasticity, applied price elasticity. If you make it cheaper to do something, what happens? Do you do the same for less money? Or do you do more for the same amount of money? Or do you do more for more money because you've got new ROI? And if you look at something like the history of accounting, or indeed professional services, this is a joke I made on Twitter back when it was Twitter, was like, young people won't believe this, but before Excel, junior investment bankers worked really long hours. And now thanks to Excel, Goldman's associates will work at lunchtime on Fridays. It's like, well, why is that not what happened? You could make the same point as software development. Before IDEs, and libraries, and operating systems, developers had to write all the code. Now if you write an iPhone app, 90% of the code is written for you by Apple. Apple wrote the modem driver, and the graphics drivers, and the file system.
00:14:37 · Benedict Evans
You don't need to write any of that. So, we've got intents as many engineers now. Well, no. And so then you kind of have to look at an industry and work out, well, which is it and what is the hard part? One of the analogies that occurred to me here is to look at the history of e-commerce, which is that what Amazon does is it gets you the SKU if you know what the SKU is. If you know what SKU you want, you want that microphone stand. This part number, you can go to Amazon and get it. If you don't know what microphone to get, probably shouldn't start on Amazon. Multiply that by many, many, many product categories. And so what Amazon does is get you the SKU, but knowing what SKU you want is another job. Claude Code can write you the code, but what code do you want? It can make you the features. Sure, but what features do you want? Who's your customer? What's the right product for that customer? How are you going to take it to market? And long way of answering a question, why do you hire McKinsey? Are you hiring them to get a 75-slide deck? Well, narrowly, Claude cowork will make a really, really crappy version of that. And you'll get all this kind of AI grifters on LinkedIn and Twitter and so on saying, "Hey, I made a McKinsey deck with Claude," and you look at it and you think, "Yeah, that's a bunch of doc crap." That's not what you'd get from McKinsey. But even if it was, that's not what you paid them for. What you actually pay Bain to do is to go and walk all over your company and work out. Yes, but why is it that you didn't do that? And how do the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually think, as opposed to what's on the first page of Google. It's all the other stuff. And the PowerPoint is just like the task, but that's not what you hired them for. The same with Amazon versus the retailer, the same with software development. So you've got that kind of split. The other analogy that occurred to me here is looking at the sort of class of industry that got steamrolled by the internet, because they had those two things and you could split the part. So you had the physical manufacturing or physical distribution, and then you had the other, the thing, what was the actual thing? Classic examples would be newspapers and recorded music.
00:16:44 · Benedict Evans, Lenny Rachitsky
So record companies do not think of themselves as being in the business of manufacturing small pieces of plastic, but that was what they actually did. And when that went away, they were screwed. Same thing with newspapers. Newspapers did not think of themselves as manufacturing. And trucking companies. When you decouple that, then that becomes a problem. But often you can't decouple that, or that wasn't really the problem; or you make that thing cheap and then all this other stuff happens as well. And so all of this is just vastly more complicated than saying, "Well, hey, we're just going to automate the accountants or we're going to automate the consultants." I mean, there's two charts in the presentation of the number of people employed as accountants, which went up right the way through the 20th century, and has gone up again since the beginning of the 21st century. So you have adding machines, and punch cards, and mainframes, and databases, and ERP, and cloud, with spreadsheets and PCs, and the number of accountants keep going up. And so why is that? Well, it's more complicated than automation. [00:17:45] S1: Even just looking at the most advanced AI companies, Anthropic, OpenAI, I just had Dan Shipper from Every on the podcast, everyone's just increasing headcount. The companies you would think would be least likely to add humans are adding many, many humans. And since your point, it's really complicated. What you're just kind just on the job, the coming job apocalypse, like Dario's talking about all the entry level people are no more jobs, just like...
00:18:09 · Benedict Evans
[00:18:09] S0: Yeah. I mean, there's a narrow point here, which is that I would place... I don't like argument from authority. And I don't think the fact that you run AI lab suddenly gives you... Or rather, if you're going to use argument from authority, then it should be relevant to the field. So, I'm interested in Dario's opinions on where models are going to go in the next six to 12 months. I'm not particularly interested in it opinions on series of labor and market value and comparative advantage. Yeah, maybe he had a course on that at university, so did I. So, I think one needs to be a little bit cautious on what Dario says. And that's setting aside the cynical view that he's just doing that department stock, which I don't believe at all. So this kind of comes back to my point about platform shifts. Every time we have a new technology, it automates away a bunch of jobs. And then that automation, whether it's price elasticity, and the enablement of the fact that they became automated, unlock so much of new jobs. And so you go back to 1800, 90% of us were peasants, and our major concern was like, are the crops going to fail? Because then we'll all go hungry. Or worse. And so ever since then, we've been automating jobs and creating new jobs, and you can always see the job that's going to go away, and you don't know the new job because it doesn't exist yet, and it's like something that sounds dumb anyway. Like railway engineer, what's a railway? Why would that be a thing? Who would want to go that fast? And so we've had that process over and over again. This is what any first-year economic student would tell you. We've had this process over and over again since 1800. And each time you go through it, you get a bunch of frictional pain and dislocation, and a bunch of people do their jobs, and a bunch of towns get hollowed out, and it all sucks. But when you come through on the other side, we're all richer, and we're not worried about the crops failing anymore. And this is the process of the last 200 years. So then the question is, is there some a primary reason why this would be different to those? Because the internet removed a bunch of jobs, PCs removed a bunch of jobs. There aren't many people working as typesetters anymore. Or telephone operators, or typists. The internet removed a bunch of jobs.
00:20:09 · Benedict Evans
And generally the jobs that go away are crap jobs, seen retrospectively, and the new job, the better, because GDP keeps going up. So, is AI different? And so then there's kind of a couple of answers to this. One theory is, well, this is going to be way quicker. And certainly the adoption of AI is quicker than previous technologies, but this is kind of because you're turning on the shoulders of giants. So you don't need to wait for everyone to buy a piece of expensive hardware, to buy a phone or a PC, or wait for the telco to deploy broadband. That's already there. So of course, ChatGPT can get 900 million chat users, because there's already 900 million people on the internet. When Mark Andreessen launched Netscape in what was it, '93, '94, there were like 50 to 100 million PCs on earth. So now you didn't have 900 million users then. But the point is then he didn't need to wait for phone networks or microchips. And before that, you didn't need to wait for electricity, and you didn't need to wait for mass production. So you're always standing on the shoulders of giants. There's always a compounding effect. So yeah, this is faster, but the internet was faster too. I think the other answer to this, and this kind of comes back to the professional services point is, you talk to these doomers on Twitter, and they would act like every big company is going to buy ChatGPT tomorrow, and then in two weeks time, they'll fire all their staff, and these people are mourners. This is one of many reasons why the doomers were mourns, but complete failure to understand the way the world works. And that was like the starting point why they then didn't understand anything else. Turn to a big company, enterprise software sales cycle, you'll know this better than me, enterprise software sales cycle is like 18 months, if you're lucky. This is always a problem. The enterprise sales cycle is shorter than the venture backed funding cycle. Longer rather. Longer. It takes you longer to get an enterprise deal than it takes you to go between raps. And this was always supporting the building, particularly for sectors like aerospace, or healthcare, or something. So, I know people aren't just going to tear out SAP and replace it with X, Y, Z.
00:22:06 · Benedict Evans, Lenny Rachitsky
Maybe in like three, five, 10 years, yes, that whole estate will look radically different, and all those jobs will have changed, but it will take two, three, four, five, 10 years, and it will take time sector by sector, and it will take time for people to work out, "You could do that thing with this." And one of the companies I always remember that we looked at when I was at Andreessen Horowitz is a company called Frame.io, which is video editing, video collaboration. And there's nothing there that you couldn't have done at least five years earlier, and maybe 10 years earlier. And actually, that's kind of a bad example because that relies on a bunch of stuff like cutting edge web technologies. But if you go out and pick 10 random SaaS companies that were started the day before ChatGPT launched, how many of them could have been founded at any point in the previous 15 years? The delay was somebody realizing, "Oh, that problem exists inside that industry, and this is the way that we would solve it." It didn't all happen the day after Google Docs. It took like 10, 15, 20 years for people to invent all that stuff, and work out that you could do that with this. And so all of that is like, the way of saying, well, yes, it is going to be quick, but actually, no, it will kind of take a while for people to work out how to completely change how their business works. [00:23:18] S1: Your view is so comforting, because basically it's like, okay, this is a huge deal, but we've been through many transformations before, and it's going to be okay.