When you automate your way of work, you can always see the jobs that are going to go away because they're right there, and you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where, like, if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions. You're probably not even asking the right questions. That, I think, is where we are with this stuff today.
Welcome to Analyze Podcast, the premium podcast dedicated to dissecting the pulse of business, technology, and media globally. I'm Bernard Leung, and the AI cycle is no longer about just better models. It is becoming a capital markets test of infrastructure, distribution, and energy. Anthropic and OpenAI are moving towards public markets. SpaceX is preparing one of the most consequential listings in technology, and Anthropic's launch this morning of Claude 3.5 shows how frontier capability now comes bundled with safety, access, and governance questions. These headlines point to the same deeper story: massive capex, increasingly commoditized models, shallow but enormous user reach, and a new battle over who controls the curation layer over infinite intelligence. Today, we test whether the model layer becomes a utility, where durable value actually accrues, and how AI reshapes work and competition globally. With me today, recurring guest Benedict Evans, independent technology analyst behind AI is the World. Benedict, welcome back.
So, since our last conversation, what have you been up to recently?
Been on lots of airplanes. People want to know about this AI thing. You may have heard about it.
Yeah, I guess the way we talk about AI, right? I just want to go straight to the question. I mean, I guess early this year you have started updating your presentation every six months now, right? And AI is Eating the World probably now is in the 2026 edition. So, I think I want to start off first with the biggest question, which is OpenAI and Anthropic and the public market test. So, one article that I really enjoy reading, since I'm a subscriber of your newsletter, is how OpenAI competes, that the frontier models are pretty much near what we call commoditization with no network effects, and now everybody is trying to buy compute time, and this is not a moat. So, both OpenAI and Anthropic are now heading towards a public near-trillion-dollar capital market cap. So, what is the public investor actually buying if not a moat?
Well, so there's several different questions embedded in that. The first of them is like, at the moment, there's no apparent winner-takes-all effect in models. And this is a kind of subtle but important distinction. There's a difference: are there things that you're doing that no one else can do, no matter how hard they try, that they won't be able to do because of the nature of their business, that they won't be able to catch up no matter how much money you spend? Because that's where Google is in search. It doesn't matter how much money Microsoft spends, they can't catch up. And that's what happened with YouTube and with iOS and Windows and Instagram, that there were kind of inherent structural reasons why it's really difficult for them to lose their position. And we don't see equivalents of those in large language models yet. Now, the big challenge, of course, is we don't know how this market's going to evolve. And some people have a problem with that statement too, but you're at the very early stage of this market, you don't know how it's going to evolve. We also don't know how the science works, and we don't know how the science will change. So, stuff may happen that means there are network effects, but right now there aren't. And so, you're in the situation where you've got, pick a number, sort of three to six companies spending a lot of money, and then maybe anything up to a dozen companies that are willing to be three to six months behind. But there's not some mechanic that means that if one of them gets ahead, they'll get further ahead. And so, what we see now is, you know, Anthropic got Claude working, and we've got product-market fit, and you've got coding working, product-market fit. There's not some inherent reason why it's just impossible for Google and OpenAI to catch up with that and to get their own things working. They may do, they may fail to execute, but you can't kind of predict that. And so, that's a sort of a base observation. The models are all kind of the same, with the same kind of benchmark scores, the same kinds of evals, because they're using the same infrastructure and the same training data and the same algorithm. So, that's what you'd expect. So, then you get to a question, which is, firstly, would there be mechanisms, for example, use of capital, that would allow one of them to pull ahead? Secondly, how far up the stack do they go? Is the model the whole experience? Is the model what we use? Does the chatbot become the universal user interface, which is a question we've been asking for three years?
Or does mainstream mass-market adoption, and adoption that's not using it once a week or a couple of times a month, but using it all the time every day, does that need this to be wrapped in apps and use cases and tooling and go-to-market and data and everything else that we think of when we see software? Which is, you know, your phone has 50, you know, we all use 50 different databases every day.
But they will definitely run on SQL or whatever it is, but that's kind of not the point. That's not what the product is. And so, how far up the stack can the models go? Can an agent go up the stack? Or do the models have to be APIs that are used by stuff built by other people? And in the end, if there's 500 of these things, they can't all be built by Anthropic and OpenAI. Not because you can't get the model to write the code, but because you've got to build the business, and you've got to work out what it would be, and you've got to build a sales force, and you've got to do go-to-market, and you've got all the other stuff that happens after you've written the code. And so, I don't know why they would be able to do that any more than Microsoft could or Apple could. And so that kind of gets you to a preliminary thesis, which is, okay, models are kind of a crappy UX. Chats are crappy UX, and models are commodities. There needs to be loads of apps, the model company can't build all of those. And so the models kind of look like they're going to end up as commodity infrastructure. Now, then you get a bunch of questions.
So to your point about pricing, clearly right now we're in this moment of extreme pricing crunch. But we should just proceed. There's so many, like all the multipliers in that are going to get flung all over the place in the next year. So, you've got a trillion dollars of capex coming down the pipe. The models get 50 times, 100 times, 200 times more efficient per year. The chips keep getting better. You don't know what the next model will be. The next models may be way more efficient. The next models, on past history, I know they use way more tokens.
The only thing that's actually got product-market fit, like really, really got product-market fit, is coding. So imagine if we had something that had product-market fit that actually had lots of people who wanted to do it, rather than coding, which is in the end like tens of millions of people, not hundreds of millions of people. So all those levers are going to get swung all over the place.
And so where might it settle out in the end? And so I was thinking about this, that you can kind of throw analogies out there. So you can say, well, you know, what happened in telecoms is in mobile is that mobile has marginal cost. There's a bunch of people in tech that don't get this. And mobile networks have marginal cost. You have more users using it more, you have to build more network, and that costs money. And so what happened is that basically we went to paying 50 or 100 dollars a month every month for 20 years. Yes, not quite as simple as that, but we pay roughly the same amount of money. We now use 1,000 to 2,000 times more data. It's a trillion-dollar annual revenue industry, pays 200 billion dollars a year on capex, and the stocks have gone nowhere in 20 years.
So it's a big, big industry that spends a lot of money on capex and earns a lot of money, but there's not really any profit. There's not very much profit, and it's not a great investment as an investor, because all the value went up the stack.
All the cool stuff was built by other people. Now, there's other comparisons you can make. So you can also point to cloud, where there is a pretty good return and there is differentiation between Google and Microsoft and Amazon. But there again, there's no network effect.
And they are really there as well. All the businesses are using these type of services, right?
Run on AWS, but like AWS doesn't get a percentage of your Uber trip. So they don't actually get that much value or leverage further up the stack. They're just an essential component. You could point to chips. And the value of the chip comparison is that it gets more expensive with every generation. No, I don't mean Nvidia, I mean like TSMC and so on.
Semiconductor manufacturing, because of Rock's Law, gets more expensive. What Rock's Law observes, it gets more expensive every generation. And so now there's basically three chip companies.
Or two or one or however you want to count it, as opposed to 30 or 40 years ago when there were dozens.
Because with each generation it got progressively harder. So it might look like telecoms, it might look like cloud, it might look like semis. The fiber comparison is interesting because at first sight the fiber comparison is dumb because the fiber bubble 25 years ago was built out...
But nobody was using it then.
It was built out way ahead of demand.
Yeah. So there was all this empty fiber.
Whereas this, of course, is being built out behind demand, like you can sell every token you can make. However, the interesting part of the fiber comparison is with a massive disaggregation. So what happened with fiber was that you got split up. So you had ducts and dark fiber and light fiber and wavelengths and IP streams and probably some other stuff. And then you have people trading bandwidth on top.
And then there's always somebody who's got the slightly newer equipment with slightly more lower stuff. And so everyone is selling it at marginal cost or less.
Feels like tokens to fiber.
Well, so this is the question, is like the more the market gets disaggregated. So you've got Mark Zuckerberg saying, 'Well, you know, if I've got spare capacity, I'll sell that.'
And people trying to trade it, and people building like ETFs on top of it and stuff. Like the more the market gets fragmented. And then in parallel there's this, you know, this sort of other question of how long do you need the newest model? So I'm being slightly unsystematic in how I'm explaining this. But like, how long do you have to have the newest full-fat model? And at what point and for how many use cases can you use the six-month-old model or the three-month-old model or the open-source model or running it much more slowly or running it on your own equipment or whatever? How many use cases have to have the full intelligence, the full model running in the cloud at the massive price with using lots of tokens? So all of which is to say, like it's really, there's, you know, they're like the bell curve IQ meme.
Mhm. Yeah, yeah, yeah. That's right.
And it's like the dumb guy with the person with 50 IQ and the person with 150 IQ both say, 'Well, like commodities tend not to be high-margin products and compute tends to get cheaper every time.'
And then you can kind of sit in the middle trying to analyze, you know, how many TPUs does Google have this year?
Which is, you know, I'm being slightly unfair because that's a hard thing to do. But what does this actually tell you?
This is a story that occurred to me, and I'm sorry I'm monologuing, but I'll...
The story that occurred to me when I was a baby technology analyst back in 1999, there was a company that sold computer parts, computer components.
Yeah, yeah, components, yeah.
The early days, yeah. All this stuff. Sold all this online.
And it had been converted from originally it had been one of those companies that had 20 pages in the back of a computer magazine, if you remember that.
One of those companies. You know, the back of a computer magazine in the '90s, like it was like a 300-page magazine and the back 150 pages was one insert after another of 20, 30, 40-page from different resellers. And they were basically selling the same stuff and you'd kind of page back and forth and they'd say, 'Well, they've got the printer I want for $260 and they're selling it for $250.' So they make a website. I'm making this up. They're doing 100 million quid a year in revenue. We all go up to see the company.
On the way back, and I think another investment bank had got a mandate to float it at like 700 or 800 million.
And on the way back we're discussing it and this veteran banker says, 'It's a low-margin reseller, one-time sales.'
So nobody calls it obviously.
We spent all this time talking about Amazon and internet and consumer adoption and broadband and HTML and returns and pricing and the management team.
It's a low-margin reseller, one-time sales.
And that's... Now, that's a thesis. And you know, I suppose that the question I'm asking is like, explain to me why people who are basically using similar infrastructure, similar models, similar tokens to deliver a similar product, and there's going to be three to six of them, and it's going to get fragmented with better and faster and newer and older and cheaper and more expensive. Why is there pricing power here?
That's the point. Yeah, I really come to your point on that, and then I think you're also very clear about the fact that the only UI, even with agent AI happening this year, we are still using the chatbot interface.
It's not much change, right?
I mean, I think there's another strand I think that's interesting to pull on here, which is that if you think about the kind of classic S-curve framing of how technology evolves. So you think about the way that worked with mobile or the way that worked with the internet. There's a period at the beginning where it's kind of flat and it's not working and no one cares and it's dumb and stupid. And then there's a period where everything kind of starts working and the curve starts shooting up and it's really exciting.
And then there's a period where it's all kind of done and it flattens, kind of boring.
Which is where smartphones are now. It's where airliners are now.
It's where cars were until electric happened.
You know, there's not very much difference between this year and the model from five years ago. It's all the kind of stuff that you wanted. Everything's kind of been done and it's become a kind of mature, boring product. The reason I mention this is when you're at the beginning of that phase, it's very unclear how this is going to work and there are many potential paths because most of the stuff that people are going to... How can I put this? You got a car in 1985, like you need to change the spark plugs. And you drive along with a bundle of 50 tires on the back of the car because you get a puncture every three miles. And you generally have a mechanic with you, like if you're rich enough to own a car, you're also rich enough to have a mechanic on staff, and you need it because the car breaks down every like 20 minutes. And then nothing works. It's incredibly exciting, but nothing works. Same thing with PCs in the late '70s, early '80s. Incredibly exciting. And if you were using it then, you thought this is great. And what do you mean it doesn't work? Like no, like you know, the normal experience when we were kids was you'd be tapping away on your computer and you'd look up at the screen and it had frozen. And you'd crawl under the desk and unplug it and plug it in again and hope that you hadn't lost more than like 10 minutes of stuff. And so like everything's amazing and nothing works. And the stuff that has to happen for it to work doesn't exist. And you don't quite know how all that's going to evolve. And so that was where mobile was in like 2005.
But you feel that this cycle is going to be much faster.
My point is that yes, the cycle's faster. Yes. But that's where we are in the sense of, you know, are you completely confident that in 2005 you would have predicted how mobile was going to work?
Or even in 2010, there were all these people who were convinced that, like a lot of really clever people in tech were convinced that this was going to play out like Windows versus Apple. Apple was closed, Apple would get obliterated.
That didn't work out the same way.
That's not what happened, and we could explain why. And I didn't think that was going to happen, and I was right, but you know, I've been wrong about plenty of other things. I thought Windows Phone might have a chance, and it didn't, and I can explain why as well. The reason I'm saying this is like there's a stage in the evolution of the market where you can have strong, clear opinions about how this is going to work. And there's a stage in the market where like if you're still arguing about that, you're an idiot. Like if you're still arguing about like why would anyone use an iPhone or you know, you should be able to install any software you want on it. Like that was an argument from 10 years ago. Pay attention. But there's a stage at the beginning where you might have opinions about some of these questions. You're probably not even asking the right questions. And that I think is where we are with this stuff today. Where the PC was in the '80s, where the internet was in the mid-'90s. And we used like 1997 picking out that data at random.
We're where mobile was in either 2006 or 2010.
Where there's a lot where you just don't know how this is going to work and you don't know what the levers are going to be. The funny thing about mobile was like we thought it was working before the iPhone. It just wasn't growing very fast. And it seemed like it was kind of working.
And we thought Nokia is going to dominate the world and then...
Yeah, Nokia and Windows and BlackBerry.
And it wasn't clear that something was going to come along and just blow the roof off. And we all thought mobile was basically a PC accessory.
And it was, well, what do you do on the mobile internet? What are the mobile use cases? And of course, what actually happened is now you say desktop internet and the mobile made the PC a smartphone accessory. It's the smartphone. This is how it became the center of tech and the center of mobile usage. And no one in 2000 was saying that or 2005.
Because they couldn't imagine that the mobile phone is actually the personal computer that actually can grow to that large market size, right?
Almost literally. I mean, this is a great quote from one of the co-founders of BlackBerry. He said, you know, he looked at the iPhone and if this thing works, we're competing with a Mac.
Because this is the thing, it's still quite hard for if you're not in tech to understand that an iPhone is a Mac.
And it's a PC. PC in the general sense. And whereas it was competing with phones that had some compute added.
And then suddenly, no, this is just a whole PC. And it had like four or eight gig of storage, which was just a ludicrously large amount of storage. And it was running a PC operating system. So that was why, you know, BlackBerry and Nokia were completely screwed. The analogy I used to give was it was kind of like you were in the ocean liner business and then you look at jetliners, and you think that you just don't have any relevant skills at all for making that thing.
Let me point a question in another way. And I take this from my observation being a person living between the East and the West. I find that this batch of US companies, your Anthropic and OpenAI, are behaving more like Chinese companies. And I'll give you some features of Chinese companies. They work 996. According to Ravi's last two years, I think all the companies are expensing on their Saturdays only if people are working like crazy. I know some of the AI researchers are. In China, you see Alibaba is not just eBay. It is also the PayPal. It is also all the other services.
Much more horizontal competition.
Yeah. Horizontal, and they try to vertically integrate more.
You've got a much wider matrix with many rows and columns, and lots of companies have got lots of the cells filled in.
And you see this batch of AI companies, they are not just supplying to, like say, Claude Code, Anthropic supplies to Cursor. Now it's also coming in to compete with Cursor as well.
Yeah, I don't think that's so... I'm not sure about that. So I mean, I think...
So I think the 996 thing, I think that's... The contrast there is just, well, what was the Valley like in 2015 or 2020 when Google had won and had a fire hose of cash? And there were a lot of people there not working...
