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Chapter 1: What has changed in AI over the past year?
Mobile didn't need to wait for the internet. The internet didn't need to wait for PCs and PCs didn't need to wait for consumer electronics and semiconductors and so on. So you always got this accelerating adoption.
Benedict Evans is a tech analyst known for his presentation, AI Eats the World. He sees AI differently than the world, spotting patterns others miss and dives into how people really use AI.
They built this amazing piece, incredibly sophisticated, very expensive global infrastructure with enormous growth in use all the time, and it changed all of our lives, and we all pay for it, and they didn't make any money from it because all the value moved up the stack.
The place that's got product market fit right now is coding, and so it's gone from whatever it was, $9 billion run rate at the end of last year to $47 billion run rate now. That's all software, isn't it? So what happens when someone else in some other field gets something worked?
One of the characteristics of tech is that the moment that you understand something and you know what's going to happen is the moment you should move on to something else.
Google said that the risk of under-investing is riskier than over-investing. Investors are kind of looking at all these companies and saying... Every major technology platform shift creates the same challenge. Separating what we know from what we're guessing. AI is already changing software development, reshaping infrastructure spending, and forcing companies to rethink products and workflows.
But many of the biggest questions remain open. Who captures value? What becomes a product? What gets automated? and what entirely new categories emerge. Benedict Evans has spent years studying how previous technology waves unfolded, from PCs and the internet to smartphones and cloud computing.
In this conversation, we discuss what AI has already changed, what remains uncertain, and how to think about the next phase of the AI transition. Benedict, welcome back to the AsyncZ podcast. Thank you. Last time you were here, we were discussing the first iteration of your presentation, AI Eats the World. You wrote it almost a year and a half ago.
At this point, you always begin your presentation with, what are the big questions? But I'm curious this time, before getting into the questions going forward, I want you to reflect on what have we learned since you originally made the presentation? What's played out? And let's reflect back.
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Chapter 2: Why has coding become AI's breakout use case?
And of course that comes with the supply crunch around capacity and price imbalance, imbalance of supply, demand, capacity, capex, pricing that we see at the moment. So that's kind of the big shift. We had a moment of this is kind of sort of working and kind of exciting but we're not quite sure what we're going to do with it until it works for coding. We'll work for anything else.
Yes, almost certainly. But that's what's working right now. And so that's become, we've got this kind of much narrower focus. Otherwise, the chartman numbers keep coming up. The models keep getting bigger. The cap X keeps growing. The usage keeps growing. People are using this more. But most of the sort of fundamental questions you might have had two or three years ago don't really have answers.
Like we don't know if there'll be a winner. in the models. We don't know if they can capture value up the stack. We don't know how much the models can do. We don't see a way that consumers will use this daily rather than weekly with the technology we have right now. So all of those questions are still open.
Yeah. And on the coding, could we have foreseen that that would have been the use case that really would have taken off? Or what's the reflection on that?
But deterministically, you could have said, well, look, who's messing about with this stuff? Software developers. What are software developers going to try and make work? Software development. So at a very kind of simplistic, naive level, well, yeah, the stuff that should work is software development. I often compare this moment to the internet in 97, 98.
But it's also like the PCs in the early 80s or the late 70s. It's incredibly exciting, but it's not quite quite clear what it's for and it doesn't quite work yet. And clearly the first thing that people did with PCs was make computers. And the first thing that people are doing with LLMs, and in a sense LLMs are computers, is to make more compute. And so that's not terribly surprising.
I think the shift has been at the beginning of this year, clearly, that agentic coding went from being kind of useful to really changing everything.
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Chapter 3: How is AI reshaping software development and infrastructure?
Clearly there were people who would say, well, this is going to be able to do absolutely anything. So they will say, well, yes, look, I told you. But I don't think anyone could have deterministically predicted exactly when that was going to happen and that it was going to be coding that would work first.
And what have we learned? Say more about what this means for engineers, junior engineers, senior engineers, the jobs discussion, how teams are organized, et cetera. What have we learned so far?
I don't think we've learned anything. I mean, this didn't work six months ago. And everyone is scrambling around trying to work out what it means. And you can get very, very into the noise and the detail and what did somebody say at a party yesterday. So, oh my God, that's how it's all going to work.
Chapter 4: What unresolved questions remain about AI's impact?
No, it's going to take a couple of years for this all to settle down. If nothing else, because of the pricing, this enormous crunch between the demand and supply and hence the pricing. So we don't know what our team's going to look like. I think people are asking new questions around the sort of the obvious one of do you hire junior people? And if so, what are they doing?
And why were you hiring junior people in the past? And were you actually hiring to do the thing that they did? Or were you hiring them to do something else?
Chapter 5: How do previous technology shifts inform our understanding of AI?
And so if you automate away a class of stuff that used to get done by people, then what will happen? And that sort of becomes much more real now in software development because you actually are automating a bunch of stuff that used to be done by people. So those questions are kind of now rather than theoretical.
But I don't think anybody can possibly say they kind of know what the market structure is going to look like or what the career of a software engineer is going to be in three years' time. I think you'd be insane to think that you could know that yet.
Yeah. Talk about OpenAI. Talk about what's most surprised you. How have you kind of made sense of their sort of strategy development and the questions that they have going forward?
Chapter 6: What does the future hold for enterprise adoption of AI?
Well, it's always been such a tranquil, drama-free environment. Obviously, they've had the issue with Fujisume having to take a medical leave, which kind of shuffled things up a bit. Look, clearly the second half, last quarter of last year, their question was, right, well, the models are the models, but what else? And how do we get people to do other stuff with this?
So we'll do ads, we'll do e-commerce, we'll do shopping carts, we'll do payments, we'll do a browser, we'll do a social video app, you know, everything. Ask ChatGPT for 15 ideas for what we could do to build value on top of infrastructure, and then we'll do all of them. It's almost literally what it looked like.
And then Anthropic, with having less capital raised, said, no, we're going to focus on coding. And they got coding working. Whether that was like a deliberate strategy or kind of they stumbled into it is for other people to say. But clearly that worked. And then so opening, I kind of swing around it. Okay, well, clearly that's the thing.
But the question kind of still remains, like the stuff that's working right now is software development and some things in some other fields. And then there's a lot of people who are kind of excited about using this around the edges and using this for some things. But it's very unclear how it is that this is instantiated as product and taken to the other 90% of people.
We still see in the data that sort of 10% of people are daily active users and 30, 40% of people are weekly active users. And if you're only using this once a week, then you haven't achieved nirvana yet.
And there's clearly this kind of very widespread between people in the valley who bought a cluster of Mac studios and are running OpenCRAW all day versus those other 40% of people who say, yeah, it's kind of useful. I used it last week for something. And I'm like, how do you bridge that? Software is a place where that's really, really bridge, jumped over that bridge.
And I don't think, and then there's a lot of other places where people are kind of scratching their heads and using it up to a point. And then there's a lot of places where corporations are using it to automate some specific back office process where you're not asking the user to work out what they do with a new tool. Instead, you're saying, okay, here's a problem that we can solve.
And I go and talk to companies outside America and outside of tech and talk to consultants and investors. They're looking at those one-at-a-time point solutions.
So like I speak a couple of days ago to a commodities company and they want to use LLMs to get better predictions on their cash flow because they deal with all sorts of small producers and they don't necessarily know when their invoices are going to get paid and it's a very low margin business so that's a big deal. And so they want to use LLMs to get better cash flow forecasting.
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Chapter 7: How will AI change consumer behavior and software economics?
And all you seem to do is say, well, we don't know. And there's kind of two problems with that. One of them is there's a class of places where I actually do say, I don't think this is going to work. I think it's going to work like that. I don't think foundation models are a product. I don't think a chatbot is a product. I think the value will be further up.
But the other side of this is when you're at this stage in the cycle, there's many paths And you don't know which of those paths it's going to be. And to try and say, well, I think it's going to be that one is, you know, you might be right. But you do have to kind of be conscious of like how uncertain this is and how many different paths it could take.
That's the nature of this part of the cycle is all bets are open.
you know we get to the point where the s curve kind of curves up and it narrows in and you know there was a moment when you know windows phone might have worked in hindsight no it probably wasn't going to work but you know there was a moment when it wasn't clear how mobile was going to work and there was a moment where it was clear right this is what's happening now we move on to next quest the next question and i think the kind of the the
I'm sorry, I'm kind of monologuing a bit, but like one of the characteristics of tech is that the moment that you understand something and you know how it works and what's going to happen is the moment you should move on to something else. You should always be looking for the places where we don't know what the answers are.
Because, you know, I haven't updated my Apple spreadsheet in like five years because we know what happened. They won't. I don't care what this year's iPhone looks like. I don't pay attention to their market share in China like it happened. Next question.
Just to flesh it out, you mentioned the prediction of you don't think foundation models are the product you think will move up. Explain the reasoning there a bit and what it could look like.
So I think there's like three or four like building blocks you can put on the table. One of them is that it's not clear how you could build a model that was fundamentally better than everybody else's model in some sort of sustainable differentiated way. There doesn't seem to be a network effect. There doesn't seem to be sort of levers you can pull and a position you can get into where
where Instagram is or YouTube is or Google search is. And we don't see an equivalent of that for LLMs. Now you have different emphasis. Maybe this one's better than that. Maybe you like this one more than that. But there doesn't seem to be a sort of fundamental differentiation, fundamental competitive difference between the models, except your willingness to spend money.
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Chapter 8: What are the implications of AI for various industries?
But we don't know. So where is this going to settle down? You're going to have, as it might be, half a dozen companies that are all competing to sell this stuff. And so where is the price discipline going to come from? Particularly when some of them have got whole other business models as well, like Google selling ads. So they've got a different attitude to pricing to open AI.
Yeah.
And so, like, I think the challenge here is, like, there's a difference between where we are right now and where this should end up, which is kind of a first-year economics student kind of conversation. Like, right now we're in this period of extreme disequilibrium of supply and demand and price and capacity.
But just because demand for tokens is infinite, that doesn't mean that you can't get to a different price equilibrium. Because, of course, that's what happened with mobile data. Like, demand for bits is infinite. It's grown 1,500, 2,000x in the last 15 years.
But you've still got your supply and price equilibrium, and you've still got a murderous price war between telcos in most parts of the world. Because fundamentally, you're selling kind of a commodity to people who will swap back and forth. And of course, developers will also swap back and forth. Now, this is, you know, I'm happy to say that this might be completely wrong.
It may be that we get to a world in which there's only two companies that can make an LLM and they have pricing power. Or we get to a world in which most of what we do gets subsumed into the model or they have leverage further up the stack. And You know, it's kind of my point about iOS versus Android.
Just because you can say, well, it worked like that the last three times, that doesn't prove what's going to happen this time. But it doesn't mean at least you should sort of ask the questions, and you should certainly, I'll just say, as a sort of primary observation, like, this situation right now is transitory.
You know, we're in this extreme scarcity, and then we have a pricing system, and we have a free market, and we have a surge of CapEx, and like a trillion dollars of CapEx. So, like, those multiples are going to move around. And then what?
Yeah. I mean, going back to your, it's a good segue to your point you made earlier of like, hey, you know, we know, you know, Apple's, Apple won. Next question. As a segue, what are some of the next questions that you're most focused on or that, you know, we should be paying most attention to?
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