ChatGPT has got 800 or 900 million weekly active users. And if you're the kind of person who is using this for hours every day, ask yourself why five times more people look at it, get it, know what it is, have an account, know how to use it, and can't think of anything to do with it this week or next week. The term AI is a little bit like the term technology. When something's been around for a while, it's not AI anymore. Is machine learning still AI? I don't know. In actual general usage, AI seems to mean new stuff, and AGI seems like new scary stuff.
AGI seems to be a little bit like this. Like either it's already here and it's just small software, or it's five years away and will always be five years away. We don't know the physical limits of this technology, and so we don't know how much better it can get. You've got Sam Altman saying we've got PhD-level researchers right now, and Demis says, no, we don't, shut up. Very new, very, very big, very, very exciting world-changing things tend to lead to bubbles. So yeah, if we're not in a bubble now, we will be.
Benedict, welcome back to the Acquired podcast.
We're here to discuss your latest presentation, "AI Eats the World." So for those who haven't read it yet, maybe we can go share the high-level thesis and maybe contextualize it in light of recent AI presentations. I'm curious how your thinking has evolved.
Yeah, it's funny. One of the slides in the deck references a conversation I had with a big company CMO who said, we've all had lots of AI presentations now. Like we've had the Google one and we've had the Microsoft one. We've had the Bain one and the BCG one. We've had the one from Accenture and the one from our ad agency. So now what? So it's sort of 90-odd slides. So there's a bunch of different things I'm trying to get at. One of them is I think just to say, well, if this is a platform shift or more than a platform shift, how do platform shifts tend to work? What are the things that we tend to see in it? And how many of those patterns can we see being repeated now? And of course, some of the patterns that come out of that are things like bubbles, but others are that lots of stuff changes inside the tech industry, and you know, there are winners and losers, and people who were dominant end up becoming irrelevant, and then there are new billion, trillion-dollar companies created. But then there's also what does this mean outside the tech industry? Because if we think back over the last waves of platform shifts, there were some industries where this changed everything and created and uncreated industries, and others where this was just kind of a useful tool. Like, so you know, if you're in the newspaper business, that had a very different impact. The last 30 years look very different to if you were in the cement business, where you know the internet was just kind of useful but didn't really change the nature of your industry very much. And so what I tried to do is give people a sense of, well, what is it that's going on in tech? How much money are we spending? What are we trying to do? What are the unanswered questions? What might or might not happen within the tech industry?
But then outside technology, how does this tend to play out? What seems to be happening at the moment? How is this manifesting into tools and deployment and new use cases and new behaviors? And then as we kind of step back from all of this, how many times have we gone through all of this before? You know, it's funny. I went on a podcast this summer and I sort of opened the line, I said something like, well, you know, I'm a centrist. I think this is as big a deal as the internet or smartphones, but only as big a deal as the internet or smartphones. And there's like 200 YouTube commenters underneath saying, you know, this is more and he doesn't understand how big this is. And I think, well, it was kind of a big deal. It was kind of a big deal.
And you know, I sort of finished the day by looking at elevators because I live in an apartment building in Manhattan and we have an attended elevator, which means there's no buttons, there's an accelerator and a brake, and the doorman gets in and drives you to your floor. And in the '50s, Otis deployed automatic elevators. And then you get in and you press a button. And they marketed it by saying, ah, it's got electronic politeness, which means the infrared beam. And today when you get into an elevator, you don't say, ah, I'm using an electronic elevator. It's automatic. It's just a lift, which is what happened with databases and with the web and with smartphones. And I kind of think now, it's funny, I've done a couple of polls on this on LinkedIn and Threads of like, is machine learning still AI? Is AI kind of in AI? The term AI is a little bit like the term technology or automation. It only kind of applies when something's new. Once something's been around for a while, it's not AI anymore. So like databases certainly aren't AI. Is machine learning still AI? Hey, I don't know. I mean, and there's obviously an academic definition where people say, this guy's an idiot. No, of course I'm going to explain the definition of AI. But then in actual general usage, AI seems to mean new stuff.
Yeah. And AGI seems, you know, like new scary stuff.
Yeah. It's funny. I was thinking about this. There's an old theologian's joke that the problem for Jews is that you wait and wait and wait for the Messiah and he never comes. And the problem for Christians is that he came and nothing happened. Like, you know, the world didn't change. Like there is still sin. You know, like for all practical purposes, nothing happened. And AGI seems to be a little bit like this. Like either it's already here and so you've got Sam Altman saying we've got PhD-level researchers right now and Demis says, what? No, we don't, shut up. And so either it's already here and it's just more software, or it's five years away and will always be five years away.
Yeah. Yeah. It's interesting. Let's compare back to previous platform shifts because some people, you know, look at something like the internet and say, hey, there were net new trillion-dollar companies, you know, Facebook and Google, that were created from it and just sort of all sorts of new emerging winners. Whereas they look at something like mobile and say, hey, you know, there were big companies like Uber and Snap and Instagram and WhatsApp, but these were billion-dollar outcomes or tens of billion-dollar outcomes, but really the big winners were in fact Facebook and Google. And so in some sense, mobile perhaps was sustaining. You feel free to quibble with the definition of sustaining, disruptive, but sustaining in the sense that maybe more of the value went to incumbents or companies that existed prior to the shift. I'm curious how you think about AI in light of that in terms of is it enabling, is more of the gains coming from net new going to come to net new companies like OpenAI and Anthropic and others that follow, or are more of the gains going to be captured by Microsoft and Google and Facebook and Meta and companies that existed prior.
So I think there's several answers to this. One of them is like you kind of have to be careful about framings and structures and things because you end up arguing about the framing and the definition rather than arguing about what's going to happen. And you know, they're all useful but they've all got holes in them. And you know, what mobile did was it kind of shifted us. You know, there's a bunch of things that it changed fundamentally. It shifted us from the web to apps, for example, and it gave everybody in the world a phone. It gave everybody in the world a pocket computer. So even today there's less than a billion consumer PCs on Earth and there's something between five and six billion smartphones. And it made possible things that would not have been possible without it, whether that's TikTok or arguably I think things like online dating. And you know, you can map those against dollar value. You can also map those against kind of structural change in consumer behavior and access to information and things. And I think you could certainly argue that Meta would be a much smaller company if it wasn't for mobile, for example. So you know, you can kind of argue the puts and calls on this stuff a lot. There certainly, you know, not all platform shifts are the same. And you know, you can do the sort of standard sort of teleology of say, well, there were mainframes and then PCs and then the web and then smartphones, but you kind of want to put SaaS in there somewhere and you kind of want to put open source in there and maybe you want to put databases. And so you know, these are kind of useful framings but like they're not predictive. They don't tell you what's going to happen. They just kind of give you one way of understanding some of the patterns that we have here. And of course the big debate around generative AI is this is just another platform shift or is it something more than that? And of course the problem is we don't know and we don't have any way of knowing other than waiting to see. So this may be as big as PCs or the web or SaaS or open source or something, or it may be as big as computing. And then you've got the very overexcited people living in group houses in Berkeley who think, you know, this is as big as fire or something. Well, great. But does this create new companies? I mean, you go back to mobile, you know, there was a time when people thought that blogs were going to be different to the web, which seems weird now. Like Google needed like a separate blog search. This was seriously, this was a thing. There was a time when it was really not clear, and I think you kind of generalize his point.
You go back to the internet in the mid-'90s, you know, we kind of knew this was going to be a big thing. We didn't really know it was going to be the web. So before that, we didn't know it was going to be the internet. We knew there were going to be networks. We didn't know, it wasn't clear it was going to be the internet. Then it wasn't clear it was going to be the web. Then it wasn't really clear how the web was going to work. And you know, when Netscape launched, like Mark Zuckerberg was in junior high or elementary school or something, and Larry and Sergey were students, and like Amazon was a bookstore. So you can know it but not know it. And you could make the same point about smartphones. Like we knew everyone was going to have an internet-connected thing in their pocket, but it was not clear it was basically going to be a PC from a PC company from the '80s and a search engine company. It was not clear it wasn't going to be Microsoft. So I think you have to be super careful in making kind of deterministic predictions about this. What you can do is say, well, when this stuff happens, everything changes. And that's happened five or ten times before.
I'm curious how you got conviction in this idea or got the prediction that, hey, AI is going to be as big as the internet, which of course is pretty big, but I'm not yet, Benedict, I'm not yet at the conviction that it's going to be any bigger. I'm curious what sort of inspires that sort of statement. And then also, what might change your mind either way? You know, that it might not be as big as the internet because of course the internet was obviously very big, but also that perhaps it might be bigger.
Well, so I think, you know, I don't want to... I remember I made a diagram of kind of S-curves going up slightly. Someone said, well, what's the axis on this diagram? I don't want to kind of get into, you know, is this 5% bigger than the internet or is it 20% bigger? I think the question is more like, is it another of these industry cycles or is it a much more fundamental change in what technology can be? Is it more like computing or electricity as a sort of structural change rather than here's a whole bunch more stuff we can do with computers? I think that's sort of the question. And there's a funny sort of disconnect, I think, in looking at debates about this within tech because, you know, I watched one of the OpenAI live streams a couple of weeks ago and they spend the first 20 minutes talking about how they're going to have like human-level, PhD-level AI researchers like next year, and then the second half of the stream is, oh, and here's our API stack that's going to enable hundreds and thousands of new software developers just like Windows, and in fact literally quote Bill Gates. And you think, well, those can't both be true. Like either I've got a thing which is a PhD-level AI researcher, which by implication is like a PhD-level CPA, or I've got a new piece of software that does my taxes for me. And well, which is it? Either this thing is going to be like human-level, and that's a very, very challenging, problematic, complicated statement, or this is going to let us make more software that can do more things that software couldn't before. And I think there's a real schizophrenia in conversations around this because like scaling laws and it's going to scale all the way, and meanwhile I'm going here, look how good it is at writing code. And again, like, well, is it writing code or do we not need software anymore? Because in principle, if the models keep scaling, nobody's going to write code anymore. You'll just say to the model like, hey, can you do this thing for me?
Yeah. Is it a little bit of a hedge or like a sequencing thing or?
Well, some of it's a sequencing thing, but you know, in principle, if you think this stuff is going to keep scaling, like why are you investing in a software company?
Like, you know, we'll just have this like god in a box that can do everything. And I think this is the kind of funny challenge, and this is I think the fundamental way that this is different from previous platform shifts is that with the internet or with mobile, or being deemed with mobile, mainframe, is like you didn't know what was going to happen in the next couple of years. You didn't know what Amazon would become, and you didn't know how Netscape was going to work out, and you didn't know what next year's iPhone was going to be. And ten years ago when we cared about that, you kind of knew the physical limits. Like you knew in 1995 that telcos were not going to give everybody gigabit fiber next year, and you knew that the iPhone wasn't going to have a year's battery life and unroll and have a projector and fly or whatever. But we don't know the physical limits of this technology because we don't really have a good theoretical understanding of why it works so well. Nor indeed do we have a good theoretical understanding of what human intelligence is, and so we don't know how much better it can get. So you could do a chart and you could say, well, you know, this is the roadmap for modems and this is the roadmap for DSL and this is how fast DSL will be, and then you can make some guesses about how quickly telcos will deploy DSL, and then you can say, well, clearly we're not going to be able to replace broadcast TV with streaming in 1998. But we don't have an equivalent way of modeling this stuff to know what the fundamental capability of it is going to look like in three years, which gets you to these kind of slightly vibes-based forecasting where no one really knows. So, you know, Jeff Dean says, well, I feel like, and Demis says, well, I feel like, but no one knows.
And then Karpathy goes on our podcast and says, I feel like, you know, it's a decade out.
Yeah, I know. Well, I saw this meme of what's-his-name, but like he says, like, the answer will reveal itself. And somebody, I'm going to say photoshopped, but of course he wouldn't have been photoshopped, turned him into a Buddhist monk wearing like an orange outfit, like the future will reveal itself. Well, but this is the problem. We don't know. We don't have a way of modeling this.
Yeah. And so let's connect this to sort of the upfront investment that some of these companies are making. Because we don't know, is there a risk of overinvestment leading to some potential bubble-like mechanics? Or how do you think about that question?
Well, deterministically, very new, very, very big, very, very exciting world-changing things tend to lead to bubbles.
And I don't think anybody would dispute that you can see some bubbly behavior now. And you know, you can argue about what kind of bubble, but again, that doesn't have very much predictive power. And you know, one of the features of bubbles is that when everything's going, everything goes up all at once and everyone looks like a genius and everyone leverages and cross-leverages and does circular revenue, and that's great until it's not. And then you get a kind of ratchet effect as it goes back down again. So yeah, if we're not in a bubble now, we will be. I remember Marc Andreessen saying, you know, 1997 was not a bubble. '98 was not a bubble. '99 was a bubble. Are we in '97 now or '98 or '99? If we could predict that, we'd live in a parallel universe. I think there's I suppose maybe kind of two more specific, more tangible answers to this. The first of them is we don't really know what the compute requirements of this stuff are going to be, and forecasting that feels a lot like trying to forecast bandwidth use in the late '90s. Imagine if you were trying to do the algebra on that. You say, well, this many users, how much bandwidth does a web page use? How will that change as bandwidth gets faster? What happens with video? What kind of video? What bit rate of video? How long do people watch a video? How much video? And then you could build the spreadsheet and it would tell you what global bandwidth consumption would be in ten years. And then you could try and use that to back-calculate how many routers is this going to sell. And you could get a number, but it wouldn't be the number. There'd be a hundredfold range of possible outcomes from that, and you could make the same point about the algebra of consumption now. So right now we have a bunch of rational actors saying, well, this stuff is transformative and a huge threat, and we can't keep up with demand for it now, and as far as we know the demand is going to keep going up. And we've had a variety of quotes from all of the hyperscalers basically saying the downside of not investing is bigger than the downside of overinvesting. That kind of thing always works well until it doesn't.
And I saw a slightly strange quote from Mark Zuckerberg saying, well, if it turns out that we've overinvested, we can just resell the capacity. And I thought, let me just stop you there, Mark, because if it turns out that you can't use your capacity, everybody else is going to have loads of spare capacity as well.
All these people now who are desperate for more capacity, if it turns out we can get the same results for a hundredth of the compute.
That will be true for everyone else too, not just you.
Yeah. So in an investment cycle like this, you tend to get overinvestment, but then after that, there's very limited predictions you can make about what's going to happen. I think the more useful kind of way to look at this is to think, well, you've got these kind of transformative capabilities that are already increasing the value of your existing products if you're Google or Meta or Amazon, and you're going to be able to use them to build a bunch more stuff. And why would you want to let somebody else do that rather than you doing it, as long as you're able to keep funding and selling what you're building.
And it may well turn out that we have an evolution of models in the next year that means you can get the same result for a hundredth of the compute that you're using today. Bearing in mind that it's already going down, pick your numbers, twenty, thirty, forty times a year.
But then the usage is going up. So you're in this very, as I said, it's like trying to predict bandwidth consumption in the late '90s, early 2000s. You can throw all the parameters out, but it doesn't get you to something useful. You just kind of need to step back and say, yeah, but is this internet thing any good?
Well, yeah, because I'm curious if the bottlenecks are, if you see them as more on the supply side or the demand side, you know, more technical constraints or is it just, is AI any good? Are there enough use cases to justify the type of spend? What are you seeing and what are you predicting?
So maybe two answers to this question. The first of them is I think we've had this sort of bifurcation of what all the questions are. So there are now very, very detailed conversations about chips, and then very, very detailed conversations about data centers and about funding for data centers, and then about what does a new enterprise SaaS company built on AI, what margins will it have and how much money does it need to raise. And so there are venture capital conversations, and so there are many different conversations within which, like, I don't know anything about chips. You know, I can spell ultraviolet, but I don't know what an ultraviolet process is. It's like, it's more violets, I don't know. And so you've got this, you know, it's like the Milton Friedman line, no one knows how to build a pencil. We've got this, it's turned into... I think a second answer might be, I think there's two kinds of AI deployment, generative AI deployment. One of them is there are places where it's very easy and obvious right now to see what you would do with this, which is basically software development, marketing, point solutions for many very boring, very specific enterprise use cases. And also basically people like us, which are people who have kind of very open, very free-form, very flexible jobs with many different things, and people who are always looking for ways to optimize that.
And so you get people in Silicon Valley who are like, you know, I spend all my time in Perplexity. I don't use Google anymore. You know, I've replaced my CRM with this. And then obviously people who write, if you're writing code, this works really well. Well, if you're in marketing, you know, all these stories of big companies where they're making 300 assets where they would have made 30. And then Accenture and Bain and McKinsey and Infosys and so on sitting and solving very specific problems inside big companies. Then there's a whole bunch of other people who look at it and they're like, it's okay. And you go and look at the usage data and you see, okay, ChatGPT has got 800 or 900 million weekly active users, 5% of people are paying. And then you go and look at all the survey data and it's very fragmented and inconsistent, but it all sort of points to something like 10 or 15% of people in the developed world are using this every day, another 20 or 30% of people are using it every week. And if you're the kind of person who is using this for hours every day, ask yourself why five times more people look at it, get it, know what it is, have an account, know how to use it, and can't think of anything to do with it this week or next week.
Yeah.
Is it because it's early? And it's not like a young people thing either, incidentally. And so, is that just because it's early? Is it because of the error rates? Is it because you have to map it against what you do every day? And one of the analogies I always used to use, which isn't in the current presentation, I've used in previous presentations, is imagine you're an accountant and you see software spreadsheets for the first time. This thing can do a month of work in 10 minutes, almost literally.
