Top AI Analyst Unpacks Today's AI Hype Cycle

You can wave your hands and say, 'No, this is like electricity.' Okay, fine. It's like electricity. Well, what happened with electricity? And there was a time before electricity. And people before electricity weren't dumb either.

We get deep into logical fallacies here. So, we've got Jeff Hinton was 10 years ago saying, 'Stop training radiologists.' And the problem here is he doesn't actually understand what radiologists do.

I have a lot of sympathy for Sam Altman.

I'm not sure how analytic you can get about saying, well, is this as big as mobile or as big as the internet or as big as computers? Was mobile bigger than PCs? Like I struggle to kind of how you can kind of quantify those things. I think the place that I come from is just to say, look, the internet was kind of a big deal and did absolutely change everything and so was mobile and so was PCs. And yes, I think you certainly kind of can say, you know, that the web was bigger than PCs cuz PCs was like 50 or 100 million devices. You know, when Marc Andreessen launched Netscape, there were like 75 million, 100 million PCs in the world and now there's 5 billion smartphones. So clearly, it's got bigger over time. I suppose the thing I sort of struggle with is that nothing like this has ever happened before and we've not had technology wiping out great swathes of jobs before and we're automating stuff in a way that this has never been done before and I kind of I was talking to somebody the other day at a big company and they were, you know, very smart guy, PhD and all sorts of computer sciency stuff and they said, you know, big companies have never had to deal with people building tools and systems for themselves before and I thought, 'So, have you heard of shadow IT? Have you heard of Excel?' Yes, this is different, but it's always different. And we do go through these big changes, and they're always different, and they're bigger, but that doesn't mean you shouldn't kind of go back and look and say, well, what is it that happened the last five times we had a world changing piece of technology? I don't think there's much percentage in arguing about which one is bigger or smaller. I think what's interesting is to say, well, what happened the last five times we had something like this? And yes, you can wave your hands and say, 'No, this is like electricity.' I'm like, 'Okay, fine. It's like electricity.' Well, what happened with electricity? And there was a time before electricity. And people before electricity weren't dumb either. And people were kind of scratching their heads and trying to work out what to do with this. And so, it's just useful not to imagine that nothing ever has happened, never happened before. And to kind of sit and scratch your heads and think, well, what are the patterns that you can see here? I wrote something at the end of last week talking about token pricing. And one of the sort of blocks of the essay was well how did mobile work? How did semiconductors work? How did fiber work? How did operating systems work? And none of those have predictive power. Like you can't prove that this is going to work in a certain way by arguing about how close it is to mobile. But you should go back and look at what happened at mobile and think okay so what does this tell us about what might happen? So you can point to semiconductors and say you had this escalating cost structure, Moore's law that said that, you know, the cost of a cutting edge fab doubles every four years.

And that's and foundation models look a little bit like that. You can look at mobile and say well mobile had marginal cost like adding more a lot of people in tech don't understand this. So mobile networks have marginal cost. You add have you have if you double the number of traffic you have to build a bunch bunch more base stations and put a bunch more equipment on the base stations and so you have marginal cost and that looks a lot like the supply crunch we've had in the last 6 months where everyone got an iPhone and started watching YouTube and the networks fell down and they had to change pricing schemes. But the interesting part of that comparison is that mobile data traffic in the last 15 years has gone up by like one or 2,000 times and it's a trillion dollar industry and they spend $200 billion a year on capex and they don't make any money. I mean they make a bit of money but like all the value is other people you know they didn't get to do Uber they don't run your banking they don't do YouTube that's all other people and all the value went up stack and obviously that's one of the questions about about about about AI you can point to cloud you can point to operating systems Sam Altman said we're going to be like Windows but like mobile LLMs don't have network effects so it doesn't look like they're going to turn out like Windows the point is to say look there's a bunch of different competitive dynamics industry dynamics what happens at different layers as a stack. Can you compete up? Can you compete down? And you can learn from all of those.

Well, so I think, you know, you could make a very obvious kind of counterargument, which is just to point out that each of these compound. Part of that is that's why it's happening quicker. But like mobile internet didn't need to wait for the internet, right? And the internet didn't need to wait for PCs and PCs didn't need to wait for semiconductors and semiconductors didn't need to wait for, I don't know, specialty chemicals or, you know, whatever you think the enabling technology is, photolithography. And that didn't need to wait for electricity. And electricity had a whole manufacturing industry that already existed and so on. So there's always this kind of compounding effect and you know I mean another way of saying this is big as the internet is to say well the internet was 10 times bigger than PCs.

And so maybe this is 10 times bigger than the internet. The big like difference like the actual tangible difference to previous platform shifts is that we don't know the physical limits of this. So we do not have a good scientific understanding of why the models work so well and what will happen next which was whereas with like you go back to 2010 you didn't know what the next iPhone would be but you knew it wouldn't have like a retinal implant and a one year battery life and flight and cost $5 you know in the internet like 1995 you didn't know how you had no idea how the internet was going to work you probably thought you did but you didn't but you knew that PCs are like $2 and $3,000 and most people don't have one and telcos aren't going to give every household on earth fiber to the next month like you kind of knew the basic kind of physical parameters and so that's clearly a kind of a conceptual difference. And but I think then there's then you can kind of say, okay, we we've got this this stuff we don't know. Fine. Then you can kind of break that apart. So there's there's maybe there's one kind of binary question which is, you know, there's there's like a there's an urban legend may or may not be true that during the Cuban missile crisis, there's a rumor starts that the missiles have launched and everyone on the stock exchange starts selling and one veteran trader goes out and starts buying and he says, 'Well, it's binary.' You know, either the rumor is true, in which case we're all dead anyway, or it's not, in which case the stocks are cheap and you know, if this stuff like if you know, if if if AGI really does, you know, get a happen and ASR, whatever, pick your acronym, your thought concept. If all of that happens, then we've got bigger problems than worrying about middle-class unemployment like we're all pets, fine. Otherwise, then okay, let's worry about let's try and work out what this means for enterprise software.

Exactly. It's like Yeah, exactly. You know, you either you can worry about that. A lot of these conversations end up in Hunt for Metaphors. It's like, you know, it's like nuclear weapons. It's like this, it's like that. A lot of these conversations also end up sounding like this the stuff you say at 2 o'clock in the morning if you're a slightly drunk philosophy grad student like hey man have you thought that like maybe we don't have structural understanding either like well great thank you I can't do anything with that I can't analyze that this is like the dart fallacy dart thought that you could like deduce the nature of god from first principles like no you can't we don't know we'll find out meanwhile let's build some stuff and work out what works I mean you get back to there's a little bit of an echo of this. I you go back to you ever read the the John Perry Barlow Declaration of Independence of Cyberspace?

God, you're so young.

So he wrote this beautifully poetic thing and it's like you giants of steel and iron and stone. You have no sovereignty on the internet. And people kind of thought this was going to end war.

Because everyone would understand each other now. And so you have to remember there was an awful lot of this kind of wild-eyed millenarian utopianism around the internet too. Some of which was true, some of which wasn't. Meanwhile, let's build some software and work out what people do with it. And that gets you like then so you set that whole argument aside right OpenAI kind of looks like Netscape. Chatbots are this is this a web browser like and again you don't want to be too rigid on the metaphor but you have this stuff that's amazing and it doesn't work. And I think this is kind of a more interesting way of thinking about where we are now that go back to like trying to use the internet in the mid '90s or trying to do mobile internet, never mind 2010, 2002. And it kind of sort of works and it's obviously really cool and amazing and it's going to change everything. And I have this very vivid memory of of sitting in a meeting with a mobile software company in like 1999 and I had my Palm 5 and I had a Nokia phone that had GPRS and an infrared port. And so we were trying to connect my Palm 5 to the internet over GPRS over infrared to this Nokia and we're sitting there going, 'Oh my god, this is so cool.' And like but nobody that didn't actually work and no one is actually going to do that for another 10 years. Obviously, this isn't another 10 years, but it is that sort of moment of like what are the building blocks here? Where's the value capture? What are the points of leverage? What are the experiences that people are actually going to use to get billions of people to use this every day as opposed to 10 million people using it all day and a billion people using it once or twice a month, which is kind of where we are now. I'm exaggerating a bit. We've got like kind of like 10 15% of people are daily active users right now. And even daily active is you're still you're using this once or twice a day. You're not using it all the time. And then there's another 20 or 30% 40% of people who are like weekly active users or monthly active users. And you remember from social like weekly active user was [ __ ] So like if you're using this once or twice a week, you're clearly not in the dream of Silicon Valley that this changes computing.

It's a useful new tool. sometimes and how do we bridge that and I think this is kind of a more interesting split like you've got software development where this has product market fit unquestionably yeah proof works changes everything in software development fine and then we can talk about that then you've got like a bunch of people who are kind of like us like they're knowledge workers who work for themselves are flexible free phone got lots of stuff going on and you're using this a lot and I think this reminds me quite a lot of people who use Notion and then you've got all these other people who are like, 'Yeah, it's kind of useful. Yeah, that was cool, but aren't using it all the time.' And how does that change?

The analogy I always use here is imagine you're an accountant seeing the first software spreadsheets in the late '70s. This is life-changing. Now, imagine you're a lawyer seeing it. Okay, that's cool. Yeah, I could use that for my timesheet next week, but that's not what I do every day. And I think that's kind of the split and that's we're talking before we started recording about like being in the Silicon Valley bubble. If you're in the valley bubble and you've got five Mac minis in a cluster and you know you're leaving you're walking around all day holding your laptop open so you know Claude Code can carry on running you're not really you're not the normal user here. the normal user you're you're the guy using Telnet to connect to the internet connecting to TNET over using TNET on the internet in 1992 or you're using dial up on Netscape one in 1994 was it Mosaic even before that in 1994 you're not like getting the real experience.

So I think one of the questions here and this is like a UX interface deployment kind of question is how many of the problems that we might want to automate would you struggle to explain to an actual person? Let me put this another way like presuming the model capability presuming this here's this thing you want to do with Claude, ChatGPT and it kind of can't do it or you can't work out how to tell it to do it. Would that work if you had a person at the other end? How hard would it be for you to explain what you want? What would that be like? Would that be the right way of doing it? I mean, I'm like, you know, I'm not neither the lawyer nor the accountant. I'm like a weird outlier in that I work for myself and I don't have anybody working for me. And partly because of that, I don't have lots of repetitive tasks because I've structured what I do so that I don't have lots of repetitive tasks cuz I can't do them because I don't have a bunch of interns. So now I get Claude and Copilot and ChatGPT and now I've got a bunch of now I've got this thing but I don't actually have the tasks to give to it. What I'm kind of getting at is is there is the model capability and then there's there is your ability to isolate and describe the things that you could give to it.

And that's a different problem. This is I think is particularly a problem with voice and chat. But it's quite hard to work out that you have that problem and to work out that you could give that problem to the system. With every new technology, it's harder and it takes longer to make the new things that you could only do with the new thing as opposed to automate doing more of the old thing. Like with every new technology, you start by taking the thing you're already doing and doing it more and faster with the old tech with a new technology and forcing the new technology to fit the old way of doing it. And it takes longer to think of the completely new thing that's only possible because of this. And generally the completely new thing isn't automating a whole bunch of stuff you already doing. It's something else. One of the sort of analyses I did in the presentation I published in May now, which is feels like a lifetime ago. Dogs. Yeah, dog years. Exactly. AI years. Was you know number of people working as accountants in the USA in the course of the 20th century is a straight line up and to the right. Even as the tech industry progressively automates over and over again like punch cards and adding machines and mainframes and all the stuff that the tech industry's done, number of accountants keeps going up. And there's two things you can say about this. One of them is is now this this this cliche, the Jevons paradox that everyone rushed to look up on Wikipedia last year. Which basically is price elasticity. Like if you make it cheaper to do stuff, do you do the same work for less money or do you do more work for the same money or more work for more money because you might have a different ROI. But I don't think that's what that is. Like I don't think that if you are like you know 5 years in at PwC now you're doing exactly what you'd have been doing if you had been 5 years in at PwC in 1970 but more. It wasn't called PwC then there anyway. Price Waterhouse something else. Anyway, you're not doing what you were doing then but more. You're doing the stuff that you were doing then is now 10 seconds once a week.

What does that mean? How would what do you where does that fit? What do you do with that? How does that fit into an organization? And it's not as necessarily as obvious and easy to answer that as it seems. If you take like accounting or consulting or something, you know, and you think about like

Yeah. So the the the way to to frame this is like we've been automating higher and higher level human functions. So we started in the 19th century with human beings as beasts of burden. We automate legs.

And then we automate arms and we automate fingers and all we have left is our brains.

I don't think I think that's kind of an unfalsifiable statement and I think the problem is you could have said that in 1950 because you kind of didn't there all this other stuff happened that you couldn't see then that it turned out you couldn't automate and of course we don't know where this stuff is going to go and what it will be able to automate and won't automate. So as I said you know there's a there's like an unfalsifiable statement that says like you know the only way you can falsify it by coming back and kind of waiting for 5 years and seeing what happens. Like it may be yes that we get a thing that actually can do anything that people can do by definition. Well clearly then everything changes but right now we don't have that. We have something that's very jagged.

And we don't know whether it would go all the way to that. And we certainly can't just presume well I mean this is the thing that drives me crazy about this chart of like you know the AI can do something that takes people 17 hours.

That's not it's not linear like that. Intelligence isn't linear like that. So, you know, it may be that yes, this goes all the way to do stuff that everything that anybody could possibly do, but at the moment it can't. We don't know when or if it will be able to do that. Go back and look at all the other times that you automated absolutely everything that your 25-year-old was doing and what is it they're doing they're doing now and how did that change? I mean, this is like, you know, the going back to the 19th century. This is the lump of labor fallacy that you can always see the jobs that will go away and you don't know the new jobs. And the new jobs will be doing things with completely different kind of skills that it didn't occur to you existed or that you needed or wanted. These are all unfalsifiable statements.

Yeah. I mean, we we get deep into logical fallacies here. So you've got Jeff Hinton, whatever it was 10 years ago, saying stop training radiologists. And the problem here is he doesn't actually understand what radiologists do. So there's a narrow problem is actually machine learning couldn't do what he thought it could do. And the broader problem is but that isn't what radiologists did anyway.

The PowerPoint might be the tangible expression of the project, but that isn't what they're paying you for. They're buying all sorts of other stuff and the same thing as an accountant or the same thing as a lawyer. The the thing I wonder here is about consumer surplus. So, or whatever the enterprise version of consumer surplus is. So for the sake of argument like in 1980 you know your lawyer goes and sends paralegal to the basement into the library.

And they come back after a day with two precedents. Great. Today 5 years ago your accountant your your your associate would have gone to the database and they had found 20 precedents and so did the opposition. Right? And so the client gets billed the same amount of money for the same number of hours. You deliver the same number of pages in the filing. Your filing has exactly the same likelihood of winning that it did then and you get kind of the same result you would have got then consumer surplus like you you've done kind of a bunch more stuff for the same price to deliver the same result which is you know this is the extreme this is the iPhone you know the iPhone is $10,000 $100,000 of consumer electronics disappeared so there will certainly be some cases where that's what's happened I think there's another way of of looking at this here maybe which is just to think about how massively variable this will be so there's a chart I've used for a while. You know the Marc Andreessen loves software is eating the world thing that Uber doesn't sell software to taxi companies and Airbnb doesn't sell software to hotels. Really important observation. Fine. What did Uber do to taxis and compare that to what Airbnb did to hotels? Turns out you go and look in the like say look in New York. It's a bit fuzzy because it depends by the city but in New York taxi market is down by I forget the number three quarters. Uber is gone like this. And the result is there's a bigger market with more people and I mean the markets looks different. The average number of rides per driver is different and so on. But the point is it unlocked all this new demand and demolish the yellow car business. Fine. Now look at Airbnb maybe slowed down the growth of hotels a bit.

It's maybe 10 5 10 15% the size of the hotel market. And the reasons why are well hotels and cabs are different.

Half of hotel business is business. There's a conference business. As long as the models have jagged capabilities like this where they work some other the models are jagged and then the use cases are jagged and some of the use cases are physical and not just about processing information. So I wrote an essay a couple of weeks ago about like trying to predict job impact because like clearly this will have impact on employment but trying to do these like star charts of radar charts of which ones where you say ah well you know Opus 8.6 can do 93% of what a first year law firm associate is doing. This is a delusional statement because you can't measure what the law firm student associate is doing like that. You also can't measure whether the model can do it. This is actually the expert in the system fallacy where you people kind of thought that you could measure how you recognize a cat from a dog and you're saying it's the same mistake. You can't measure what a law associate does. Point I was making was like first of all like the the accountancy job changed which is the comment we made earlier. Secondly, there are industries that your analysis would say are completely unaffected that will get demolished by something else like journalism, right? For the sake of argument, the internet didn't change what it was to be a journalist at all, but it demolished the local advertising business, which wasn't in your analysis at all, right? And the same thing for Uber. Like, you wouldn't have looked at the smartphone. Nobody was I mean, I was in the mobile business. We were talking about location all the time. Nobody saw that opportunity, right? And so you kind of go and back test these analysis where you say, 'Well, this job is exposed this much and this job is exposed this much.' It's horseshit. You know, it's like the joke about the physicists who are predicting which horse is going to win a race. And they say, 'First, we're going to presume that the horse is a perfect sphere.' You know, it's like great. If if you presume that if you define this into something that you can understand, then you can understand it. But it isn't that. It's something else.

Yeah. So the you know the do have you do you ever come across Anselm's proof? Anselm is this medieval theologian and Anselm says okay first proposition God by definition is that by which nothing could possibly be greater than God. Okay that seems reasonable. Second proposition something that exists is greater than something that doesn't exist. Okay. Therefore God exists. And 30 seconds later everyone in the room says and some dude that's [ __ ] [ __ ] cuz like you could prove anything like that. Like, well, a horse that exists is better than a horse that doesn't exist. Like, it would be better if a unicorn existed than not exist. Like, you can't prove stuff like that. But it took like a thousand years of people arguing about this to work out why that was wrong.

And the reason I mention it is like if you define AGI as necessarily inevitable and necessarily going to kill us all, then AGI is necessarily going to kill us all. Great. But you haven't proved anything.

I think there's that like kind of pulling apart like what are the sort of interesting sets of questions? Firstly there is like a capital conversation. So, what's going on in chips? What goes on in data centers? When do chips catch up with supply and demand? Like, what's his CEO of SK Hynix the other day said he reckons, you know, supply and demand is out of whack until 2030. Well, I was like, well, he would say that, wouldn't he?

Yeah, he's got a good business. So there's all of that stuff, data centers, time to power, grid deployment, etc. Secondly, there is the sort of fairly what seems to me really incontestable fact that right now there's something between three and six companies making models and they're all kind of the same. And you know, someone's ahead this week, someone's ahead next month, you know, but the fact that xAI is like a paradoxical statement like the fact that xAI managed to jump straight back up to almost at the top of the leaderboards after completely flunking out is a really negative signal for xAI.

Capital, you know, I mean at this point like there's only so many companies that can can bear the cost to train.

Well, so this was my semiconductor comparison that we made earlier. What happened with semiconductors is it got harder and more expensive with each generation and so we shrank from dozens of companies at the cutting edge just to just one and then two one step behind. That may be how this evolves. We're not there yet.

Yes, compute is like the major moat of these business. As long as scaling continues then you should sort of expect the number of cutting edge companies to shrink down. Then there's a then there's a question of well what what where do you need to be on what is it the Pareto curve?

The other extreme, the end of the curve is like dictation works for free on your phone. And what's in the middle? Like does Amazon where on that curve does Amazon need to do be to do how much to get what ROI on recommendations or review summarization or whatever. And so there's there will be people all the way along that curve. And presumably as long as you the further the cheaper you get the more commoditized it gets and you get to like you know you've got dynamic real-time bidding across 30 neoclassicals. And so how much stuff is at the head of the curve that needs that? How much value how competitive is the head of the curve remain? And of course you know there may be a paper that says hey guess what you can have a model that's 10x the size for the price. So it may be that you get some price collapse even as everything scales. We don't know. But then like how much of the value can the model capture? And this is kind of what we were talking about earlier about the the sort of how do you explain it to the model question. And the thing I was thinking about I do a weekly newsletter and I wrote a column about this last night so it's sort of fresh in my mind is like most people are three things. Most people aren't tool builders. Most people don't see the problem that the tool would be solving. And most people aren't in a position to build the tool even if the other two weren't true. And so I kind of explain what I mean by that. Like if you think about like most enterprise soft half of enterprise software you look at it and you don't get it. The customer looks at it and they don't get it. You don't see why that's a problem. You don't see why we need that. We don't understand why we would do that. Think about how many tools we all use every day where the first time you saw it you thought what's the point of that.

Very often you don't see that you've got that problem. And then even if you see it, most people the skill of being like a really great salesperson is completely different to the skill of being really good at designing a new piece of sales enablement software. The skill of being a really good video editor is completely different to the skill of being really good at designing a completely different way to edit video. I mean the person who is who does do that probably is does know a lot about video editing but most people aren't that person. So the skill of actually working out what the software should be, what the tools should be, how it should be done, what the right structures and workflows, and how the network should work. Think about every great software pitch you've ever seen. They're like, 'Oh, wow. That's a really clever way of doing it.'

I would never have thought of that. And yet you're expecting like random middle managers in the back office of big companies to dream that up like this afternoon if you give them Claude. And then like the third piece is very often it's regulated data and even if it's not it's you know there's 1500 people who are touching this data and it needs to go into a system of record and there's money attached to it. So you can't like have random people just like building tools and like replacing SAP or replacing these systems and so everything gets more fuzzy. So like there's a sort of there's like an improvised space of CSVs and Excel and Tableau and Perl and so on and then you've got your big iron horizontal systems and then you've got 4 or 500 SaaS apps inside every big company and AI kind of shuffles all of those around. And so like if you're PwC and you hire a thousand graduates a year, 10,000 graduates a year, you use software. If you're Redpoint and you maybe hire a couple of people every year, you've got Google Sheets. And then there's something in between. And then there's a point where you say, 'Hey, maybe we should use Notion or maybe we should build this software. Maybe we should get this thing.' And then this horrible moment where you see, hey, maybe we should use Workday. Which is like the ultimate test for AGI. If it can navigate Workday successfully and that none of that is binary like and so then okay, there will be this new SaaS app that somebody built using AI and maybe or maybe not it does stuff using AI that you couldn't do before, but also you'll be able to do that in SAP now.

Well, how did you choose whether to buy software or use Excel or use SAP before?

Yeah.

So I mean, you know, again, without arguing by analogy, I will point to analogy. TSMC has a monopoly on the cutting edge of semis. You don't write apps for TSMC.

Yes. But their net income last year was half of Apple's. It's a great company. Very profitable company. Uber does not write apps for TSMC. You have a multiple layers in the stack, different abstraction layers, different ways of building that. And the right people to build an enterprise go to market for creativity software are different people to the right people to build. You know, this is why we have a stack. This is why AWS doesn't own the entire tech industry. It's why every app on your iPhone isn't made by Apple.