Lenny's Podcast: Product | Career | Growth
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.
00:23:28 · Benedict Evans
[00:23:28] S0: Well, I have a slide towards the end of the presentation, which I know the title is something like, "This is going to be completely different from everything else, just like everything else." And then the next slide is an IBM ad from the '50s, which has got this sea of white men holding up in white shirts and ties, all holding up flood rules. And the slogan of the title of the ad is, it's an IBM ad, it says, "An IBM electronic calculator..." This before it was called a computer, "... it's an electronic calculator it's the size of a fridge." Is like having 150 extra engineers. How many people listening to this company list, like their company slogan is basically will give you 150 extra engineers? I mean, isn't that like the whole pitch of Claude Code? 150 expert engineers for free. Or not free, that's like a lot of money. And yeah, that's what I gave you. And so yes, we keep going through this over and over and over again, just to kind of make that tangible. I mean, obviously we couldn't be doing this without the internet. So there's a slide in my presentation, which is, we could maybe talk about, but it's a slide of chart showing how many products are stocked in supermarkets in America since the '50s. And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it. But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how many scoots there were in supermarkets every year. And then I had to realize they've been around since the '50s. And if I could dug long enough, I might be able to make a whole time series, and I could make whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. You really need to go and find a library where they publish, and they publish that number. And that the numbers in that report, you'd have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone, and spend like $50 on long distance phone calls to find a library that has these. Or maybe you call the Food Marketing Institute and they say, "We'll sell them to you for $500 each." So then you're going to get on, maybe you live in New York, or someone that has this.
00:25:31 · Benedict Evans, Lenny Rachitsky
And two weeks later you've got the chart and you look at it. And then the other side of this is the life of an analyst is you spend all day making a chart and you look at it and go, "Oh, that's not really interesting." So you spend two weeks to make the chart and then you look at it and go, "Yeah, I'm not going to use that." And for me, this was like two hours in Google. And so we forget how big a deal the internet was. That's a long way of saying it. But we forget we've had these absolutely enormous changes, and then we don't see it, because it's like that's the world the world's always been. [00:26:03] S1: What's different potentially this time, just even what your quote is, it's different, everything's going to change just like last time, the big difference obviously is AGI might emerge, and super intelligence. Does the work of humans can do a lot of this stuff for us, can actually replace jobs? Just like thoughts on that element of this transformation we're going through.
00:26:27 · Benedict Evans
[00:26:27] S0: I don't know. This is one of the ways I've struggled to write about AI is like certainly in 2023, early '24, all the questions were questions you could have asked in December 2022. And the questions didn't really change. And the strategies didn't really change. And I think the AGI question is kind of the same. I mean, the thing that the observation one can make, we have no theory of what human intelligence is, we have no theory of why these models work so well, we have no theory of how much better they will get. So we're all just kind of vibes forecasting as to what will happen. And then you can have like the 20 AM doped out philosophy students talking about, "Hey man, is this consciousness?" "Maybe we aren't conscious either. We just think we are." "Yeah, great. Thank you." I think the one thing one can observe today is we have no idea. We don't know. We can guess, but we don't really know where this is going to end up. What I think you can say today is that there's a lot of kind of redefinition of terms. So I think a quote I used in my presentation late last year was an AI scientist called Larry Tesler who said AI is whatever machines can't do yet, because once machines can do it, people say, "Well, that's your software." And so certainly, I mean, I did do a poll on social media every now and then asking, "Is machine learning still AI?" Because I've certainly heard people say, "Well, that's not AI. That's just image recognition" "That's not AI. That's just sentiment analysis." So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in the '60s airline, jet airlines is the technology. Now a jet airline isn't tech. And so there's a sort of sense of AI is like a moving target, is whatever just started working. And I think the point here is now clearly you can see people redefining AGI to mean the stuff that works now. So is AEI, what's the definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very different thing too. It has a soul and it's fucking alive. Because a database could do that. Like an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people. And it turned out there was a whole bunch of other stuff that it couldn't do that we didn't do then, we didn't know existed.