Lenny's Podcast: Product | Career | Growth
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.
00:28:30 · Benedict Evans
So there's a lot of creative redefinition here. Superintelligence, I'm not sure, is superintelligence more than AGI or less than AGI? Because last year I thought superintelligence was like really good, but not as good, not actual AGI. And now it's like, "Oh no, no, we've already got AGI, but superintelligence, that's really hard." It's funny, I was having an argument on Hacker News this morning, you remember the argument, which is never a good use of time, but you remember the argument of like people would argue about whether crypto is blockchain, or whether blockchain is crypto. There isn't a right answer to that. Let's just be sure. It's important to understand what you mean when you say that, but there isn't like a correct answer to this. Are we going to get to something that has human level intelligence? We don't know. I don't think we have any way of answering that question. Maybe, maybe not. You can make arguments either way. Meantime, in the meanwhile, we've got this thing that's clearly kind of a completely transformative technology. And maybe the serious point here is like you don't have to believe, even if the model stopped it getting better tomorrow. If this is it and we hit a brick wall tomorrow, this is an incredibly useful technology that's going to change your world and get rolled out over the next 10 years. So you don't have to believe in any of that stuff to believe that this is a giant deal.
00:29:46 · Lenny Rachitsky
[00:29:46] S1: Something that's definitely changed, I had your former boss, Mark Andreessen on the podcast, and we didn't actually talk about this during the conversation, and he brought it up before we started recording and I never got to it, is he had this insight that the opportunity set for companies now is so much larger. We used to have no trillion-dollar companies, now we're going to have dozens of trillion-dollar companies. Just like the size companies can grow to are going up so much, and valuations also go up along with that. And his point is just people haven't really grokked just how large companies can get now. Everyone's hitting a hundred million AR in five months, six months. Just thoughts on that.
00:30:23 · Benedict Evans
[00:30:23] S0: Yeah. I mean, this was his whole software's eating the world thesis from 15 years ago, whenever it was. Yeah. The TAM, it gets progressively bigger, because you can address larger and larger parts of the economy. And so if you think about the classic platform ship framing that mainframes are, I think peak mainframe install base was something like 70, 80,000 units. I mean, slightly fuzzy term, what exactly is a mainframe, and at what point does it become two mainframes as one. But something like that, that order of magnitude. And then when the internet kicks off, there are, as I said, 50 to 100 million PCs on earth, maybe. Today there are something over a billion, one to one and a half billion, but obviously a lot of those are corporate. Like seven, 800 million consumer PCs in the world. There's about five and a half, six billion smartphones in the world, which is why you can have 900 million weekly active users on ChatGPT. And so there was this narrative five years ago of like, "Well, we've run out of people. So the next thing can't be in order of magnitude bigger." Which was true up to a point, but that was the wrong model, because clearly what's happening now as you're moving in another direction, is you're just branching out and automating big news weights of the economy. Now, back to your job point, you could argue, well, we're just going to replace all the people with AI and all the money will go to Sam Altman. And Mark can buy himself another Gulfstream, add to the fleet. I think the other answer, it's back to the lump of labor fallacy, and the last 200 years, that each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, unlocks prosperity for all of us, and that's painful as you go through it, but it always creates more value. And so here you could certainly make an analog to... The useful analog to the electricity industry is just saying how that electricity became part of absolutely everything. And software has been kind of slowly working its way out. Another would be electricity in factories, and then electricity sort of slowly spreads out. And so that would be the point again, that it slowly spreads out to do more and more things. And so more and more value and a bigger and bigger contribution to the economy. Also, of course, disappears inside things.
00:32:38 · Benedict Evans
And the other side, the point of my capital section in the presentation is there's this quote from Sam Altman where he said, "We're going to be selling AI intelligence on a meter like water or electricity." And you look at this and think, "My dear sweet child, you need me to explain the margin structure of the utility industry to you." Because guess what? When you watch television, the TV company isn't paying a percentage of your monthly bill to the electricity company. When you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. And clearly, this is like the much more specific tactical question at the moment, is, do we even end up with three giant models or does it become hundreds of models and open models and local models and so on? And even if we do end up with, say, pick a number, three to six to 10 giant foundation models that cost hundreds of billions of dollars a year, fine, do they get all the value from that? Now, I started my career as a telecom analyst, and so still pay attention to it a bit. Global mobile industry has revenue of about a trillion dollars a year, maybe a bit more now. And it spends about $200 billion a year on CapEx every year. Total telecoms is about 300. Mobile is about 200. It's about 15, 20% of revenue every year. And if you look at a chart of mobile data consumption, it's an exponential curve, like perfect curve going spread up. And the number now, I think it's about 1,500 to 2,000 times what it was in 2010 globally. And the stocks have gone nowhere in 25 years, because it's an X gross low margin commodity utility, where they're selling this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication, but all the cool stuff is made by you. It's made by the people listening to this podcast. It's made by somebody else. This was that kind of pivotal moment where the telcos thought that they would do all the stuff that you did on your iPhone. And not only do they not do it, but Apple doesn't do it either. It's all further up stack. And so this is the elemental question right now around foundation models is, does the model do the whole thing? Do you just go to the chatbot and get the chatbot to do the whole thing?