It seems to me right now you could do like a double blind test of the same prompt given to Grock, Claude, Gemini, Mistral, Deepseek, and bet most people would be able to tell which is which. Benedict Evans is a technology analyst known for his insightful takes on platform shifts in the tech industry. He sees AI differently than others. He's spent decades spotting patterns others miss and dives into how people really use AI. Why is it that somebody looks at this and gets it and goes back every week?
But only every week. The very high level threat to Google is that you have this moment of discontinuity in which everybody resets their prize and we consider set of faults. And so it's no longer just the default that you go and use Google. There's this sort of question for Apple around. Does this now actually change the experience of what the smartphone is?
What the ecosystem is? Does it end up kind of getting Microsoft it in the sense that I want to start with your most controversial take on AI? That's funny. My I suppose my take on AI, controversial take on AI rather like my controversial take on crypto is being a centrist in that seems to me very clear. This is like the biggest thing since the iPhone, but I also think it's only the biggest thing since the iPhone and there's a bunch of people who think no, it's much more than that.
It's a minimum. It's more like computing. And then you've got people going around saying, no, this is more like, you know, the electricity or the industrial revolution or, you know, transhuman, it's something. My sort of base case is to say this is kind of another platform shift and all the new stuff will be built around this for the next 10 or 15 years. And then there'll be something else.
And so the impact on employment will be kind of like the impact on employment from the other platform shifts and the impact on the economy and productivity and intellectual property and there'll be a whole bunch of different weird new questions. Just like there were a bunch of different weird new questions before and then in 10 years time it'll be software. Put this in historical context for us with other platformships. Everybody's saying this time is different, which everybody does at each platform, shift on imagine what's the same? Well, that's the, there's a famous book about financial bubbles called this time is different because people always say this time is different.
It always is like the dot com bubble was different to like the late 80s and the Japanese financial bubble was different to, you know, pick any other bubble. You want, they're always different. But that doesn't mean they're a bubble. And the same thing here, I have a diagram I use a lot from 1995, this research firm made a diagram of something called they called cyberspace because it wasn't clear. It was just going to be the internet.
It was clear that everyone was going to have some kind of computer thing connected to some kind of network. But remember the phrase information superhighway? Which sort of conveys that it would be centralized and controlled by cable companies and phone companies and media companies, which is sort of how everything was previously worked. It wasn't going to be the internet. It wasn't going to be kind of radically decentralized information.
That's an anything to do what they wanted. It wasn't going to be the web and only the web because there were all these other things going on. If you look at Mary Mika's first big public internet report from 1995, she has a set for forecasts for web users and email users and she thought email users would be way bigger. It wasn't clear like that was all one thing. And then it wasn't clear that it was about the browser.
It wasn't clear that the browser wasn't where the value capture was because Microsoft cravalled its way into dog and it's in browsers but that turned out to matter. And then all the value was insight advertising and social which were five years later. And so you can be very, very clear that this is the thing and then still be completely unclear how it's going to work. The same thing with mobile internet. Just like mobile internet now, it's kind of like saying black and white television on television.
Just stop internet mobile internet, black and white TV, color TV. It only says mobile internet anymore. It's like talking about e-commerce. You're starting to have people talk about physical retail and retail. And but it wasn't clear.
You know, I was a telecom analyst in 2000 and it was very clear mobile internet was going to be a thing. It was not clear that there would be basically small PCs. Like that was the fundamental shift to the iPhone is it's a small Mac. It's not a phone with better UI. It's a small Mac.
And it wasn't clear that the telcos would get no value. It wasn't clear Microsoft and Nokia would get no value. It wasn't clear what take 10 years before it took off. And it wasn't clear it would replace the PC as a center of the tech industry. I mean, everyone was talking about what's a mobile use case.
What would you do? You'll do some things on your mobile phone, but what? But obviously your PC will be how you use the internet. Of course, that's not how it worked. And so we kind of forget because now we don't see it because now it's kind of became part of the air.
We breathe and we're in strange and different. All these things are. There's something I love talking about, which is the rise of automatic elevators. So until the 50s elevators were manually operated, they were basically vertical sweet cards. They were tramps, they were pranks and you have a driver who has a lever with an acceleration on a break.
If you've been into a New York co-op, you may have seen one of these. They call it an attended elevator. There's a lever you push it that way to go down middle to stop that way to go up. And then in the 50s, it is creates the autocronic. I think it's called the autocronic elevator, which had electronic politeness, which basically meant the infrared thing.
That's lots of door closing. But if you get into an elevator, now you don't say, I'm going to use an automatic elevator with electronic politeness. It's just slipped. We kind of forget how weird and different all the other things were. And yes, this is new and weird and different in a bunch of kind of strange, confusing, confounding ways we can probably talk about.
But we sort of forget that other things are weird and strange and different too. Is this the first major platform shift where the incumbents have an advantage? Because they have the data. I'm pretty sure people saw Microsoft had an advantage on the internet and Google and Meta had an advantage on mobile. And everyone thought IBM was going to win PCs.
Like once IBM made a PC, that was it. It's all over now. And we've kind of forget that there were PCs before and then IBM made one. And that kind of became the standard, but then IBM lost it. So what happens with the incumbents?
Do they grab on to using the technology instead of adopting it because adopting it would mean killing the golden goose? Like what happens in a platform shift with incumbent? The master of my college at Cambridge said that history teaches nothing except that something will happen. And you know, there's always the example on the count, for example. So with any new kind of any platform shift and a platform, the term platform shift itself is, you know, it's a useful term, but you have to be careful not to be trapped by your terminology and get into this sort of arguments about, well, is it a platform shift or with not a platform shift and how do you define platform?
Shut up. The thing is when any with any of these sort of fundamental technology changes, the incumbents always try and make this feature and they try and absorb it. And the same thing outside of technology, existing companies try and absorb it and they use it to automate the stuff that we're doing. And then over time, you get new stuff, you unbundle both the incumbent's intake and you unbundle existing companies because something that's possible because of this new technology. So you can also kind of jump into the new thing.
And sometimes the new thing kind of really is a feature. And sometimes it's no use of fundamental change in how everything works. And sometimes that sort of contingent, you know, this whole sort of parlogueame like drinking game that historians play about, kind of historical and ability, you know, what would have happened if that battle had been lost or if that politician had been assassinated or not assassinated. And it depends. Sometimes the answer is, well, no, nothing then then everything would have been completely different.
Sometimes the answer is, well, no, then, you know, what if Napoleon had won at Waterloo? Well, then he'd have lost another battle six months later. Like nothing would have changed. The whole environment had changed. It was if, you know, the revolution hadn't happened in spring in 1917, then you would have happened in the summer, all the orders.
Sometimes it's like really clear. I mean, I always think the Kodak example here is kind of interesting. Tell me a bit. Because, you know, like, it's like the cliche that people say, okay, they had digital cameras and they didn't get it or they ignored it or they didn't want to do it. And I was like, well, that was 1975.
And the thing they had was the size of a refrigerator. I know that was not a consumer product. And it took until the late 90s, before the technology was actually viable as a consumer product. So of course, like, they didn't do it in the 70s because you couldn't. What actually happens is once it starts happening, Kodak go all in digital cameras.
At one point, they were the best selling digital camera vendor in the USA. And if you look at their annual ports at the time, they think this is going to be great because they're going to sell way more photo printers. So they're selling these inkjet photo printers. People are going to produce them way more photos. So they're going to take way more printable.
The printable is a screw cut up. One of them is smartphones. And you've got to argue that what actually screws Kodak is not the camera. It's a social media and it's not printing anymore. That's what killed that's one side.
The other side of it is that film was this high margin product where they had a bunch of unique intellectual property and digital cameras are a low margin commodity where they were competing with the entire consumer electronics industry with no differentiation. And so even if they go even at the point being even if you go all in into that market, it's still a crappy market where you've got no differentiation. So you can kind of, you know, you can kind of put all of these things on the table and shuffle them around and say, well, in hindsight, obviously, back we were screwed. And in hindsight, obviously Google is going to be able to make the jump. And in hindsight, you know, in hindsight.
And yeah, maybe. Is there a parallel between the second point you made about Kodak and Google today where, you know, they have a high margin in search business and a low margin AI business? So I think it's, I'd be nervous about knowing what the margins are in AI because we've had, you know, depending on who you are, like the price to get a given result, it's probably gone by two orders of magnitude. But then that was the state of the art two years ago. And now there's a new thing which is more expensive.
And so there's a little awful lot of kind of shifting planes and shifting, you know, there's a lot of algebra and all the variables in the algebra will change at the moment. So it's kind of hard to quite know what that is. I think the obvious Google threat right now is that Google shows you a bunch of links and results and ideas. And those could now be solved in a different way. Well, we can kind of go back a second.
The very high level threat to Google is that you have this moment of discontinuity in which everybody resets surprise and recently we consider set of faults. And so it's no longer just the default that you go and use Google. And for this, actually, that's like maybe being a 10% better on that. In fact, as we saw from the micro the Google GSC trial last year, actually, Google is still the best search engine by quite a long margin relatively other traditional search engines. But what we have now is like a reset of the playing field and Google has a whole bunch of advantages as to why they might win in that playing field.
But there's a reset base of what the product is and how you set it and your structure around selling it. And do you have the right politics and the right all structure to build that and the right incentives and internal conflicts and then the consumer behavior kind of gets reset as well. My understanding of AI is that so much is data driven. Like you have proprietary data sources, you have better data as you can train, better model. That gives you one of the key inputs.
So I think it's actually the opposite, which is that everyone's kind of using the same data, which is you need such an enormous amount of generalized text that the amount that Google has all that meta has is not actually enough to move to be a kind of a fundamental difference in what you can train with. So you don't think like YouTube as a repository is an advantage for like a significant. So it depends push back. Yeah. So it depends.
So the models that we're training now, we're training on text. So that's not really being trained on YouTube. We saw this lawsuit around book copyright with meta that they downloaded a torrent of pirated books. Because guess what? That's they don't have enough start text and it's not the right kind of text.
They don't have lots of pros. They've got lots of short snippets of text. So I think the generality of our lens is you just need such an enormous amount of data that everyone kind of needs all the text that there is and all the text that there is is kind of equally available to anyone. So we've kind of like the data is a level playing field effectively. Yes, because you need so much more.
And it's also not necessarily the kind of data that you have. So obviously Google has, you know, an enormous repository of scrape data because they read the web all the time. But anyone else with a billion dollars can go and do that. Right. Or you can go and download the Common Rule from AWS.
How far away do you think we are from autonomous sort of AI making AI better? So no human intervention, but AI sort of going out in the real world, getting feedback, adapting itself, ending itself better. There is a sort of like people, power to like the form like you put like suddenly magically this thing grows and becomes amazing and knows everything. I don't think we're at that stage now. I don't think anyone really knows when it would happen.
So it's very sort of impressionistic. I think another answer might be you kind of have to be very careful looking at headlines and thinking like, what exactly is that telling me? So in Swropic has done a bunch of things where they say like the AI was threatening to blackmail me. Yeah, I saw that. And you read the story and you think, OK, you asked what's basically a story generating machine.
Please tell me a story of what you would do if in this situation where most people will probably say X and the machine says probably X and you say, my God, it's that it would do X. Like, well, yeah, it would black me based on human behavior. It would blackmail you. OK, how would it do that? What do you mean it would blackmail you?
It's kind of like the reduction of this is you want this point. Somebody else made that like you want murder is good on a piece of paper and you put it in a photocopy and you press go and you say, Oh, my God, the machine says murder is good. Well, no, you told the machine to say that. And that's what these answer topics are. They basically you tell the machine to say a thing and then it says it.
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Where do you stand on regulation of AI? So I think regulation of AI is thought of the wrong level of abstraction. Talking about regulating AI as AI is the wrong level of abstraction. It's like saying we're going to regulate databases or regulate spreadsheets or regulate cars. Well, we do, but not like that.
When you regulate stuff, there were trade-offs. You learn about this in your first year in economics class, like regulation has costs and consequences. And it's not necessarily, you know, there's always a trade-off. And often you're making product decisions or engineering decisions that do actually have trade-offs. It's like a three-way trade-off of like, what's good for the product?
What's good for the consumer? What's good for the competition? What's good for the company? What's good for the consumer? I think the regulatory stuff is interesting in the framework of multiple countries sort of competing for super intelligence.
How would you advise a country to prepare for AI? I'm the president of the United States and I call you and I say Benedict, you have five minutes. I need, what do I need to prepare for? What can I do to put our country in the best position possible? Well, yeah.
What's your objective? Is your objective to have a nice press release? No, it's to dominate AI. A long time ago, I used to get these questions about like, how can we replicate Silicon Valley? And I always feel like the answer to those questions as much as possible is, you can't.
I mean, occasionally, there are things you can do, like you can create funding structures, you can make it easy. You can try and jump-stop, because systems you can try and jump-stop funding availability. But most of the answer is things like getting out of the way. I think the idea of trying to create national champions is very hard. Now, that almost kind of becomes an economist's question rather than a technology question.
How do you create national champions? Where is that word? Where is that not word? I'm sure there's a bunch of books and papers about, you know, where does industrial policy work work? Where does it not work?
From a technology analyst perspective, I think of this in terms of, A, what are you doing that would make this harder? And B, think of this as just more startups. What are the things that we're doing that make it harder to develop that? Because without picking a winner, it's not about picking a company and lacking them. It's like, if you do, like, this ridiculous law that California had a year or two ago, if you treat this as like nuclear weapons and you say, this is incredibly dangerous and we need to have it under extremely tight control so that nobody does anything bad with it, which is basically the EU approach.
Go back to economics class, policies have trade-offs to governies to choose. You're making a choice when you do that and you're choosing that as costs. Personally, like most people in tech, I think the idea that this is all going to kind of produce bioweapons and take over the law and get us all this just idiotic. I think it's just a bunch of kind of child's logical policies within that. But you have to be conscious. You say, if we don't, you know, the kind of the Biden approach to Genech of AI,
very explicitly was to say, this is sort of social media 2.0. Like, social media 2.1 was terrible and destructive and bad. And I think there's a, I don't agree with that. I think there's a huge dose of moral panic within that. But be that as it may, if you make a decision that says we are deliberately and explicitly going to make it really hard to build models and really hard to start a company that builds models and really hard to do anything with any of this stuff, then guess what? It's kind of like, you know, the mayoral election in New York today.
Like, if you make it really hard and expensive to build houses, houses will be more expensive. You make that choice. If you do that, you cannot then complain that houses are more expensive. You can choose that, but you can't complain. Why do you think as a society we don't understand that?
Part of this is that like in the most non-emotive fields, we kind of do. You understand people understand that, you know, if you make, you know, more employment regulation tends to produce lower growth but more protection for employment than you can't be choosing a trade-off. You know, people kind of, I think everybody on both sides of that equation understands that that that's the trade-off and you're choosing one versus the other. The point is you can have a fully functioning free market and you can regulate some of the negative externalities of free markets that anybody in any part of the economic spectrum understands their negative externalities to free markets. You can also have like a government provided alternative. You can have the government do the part-apartant.
Where you have some of the kind of most obvious gaps between the US and Europe, it seems to me, sometimes, in places where you kind of have neither. So the US neither has a government controlled health care system nor a free market health care system. You have neither a government controlled housing which you have in like in weird places like Singapore nor a free market in housing. So you kind of break the free market. So you stop the prices like this. It's like the great insight of higher care. It's the pricing as a signal.
Pricing as an information system. It's telling people what's wanted. It's not just a signal of worth. It's a signal of demand. There's a fascinating book I read a while ago called Red Plenty, which is about Soviet Central planning in the 60s, 70s, 80s.
And it's about sort of what happens when you have a central planning that just cannot cope with the level of complexity of a sophisticated economy in the 60s and 70s as opposed to let's make grain and tractors and locomatives in the 20s and 30s and steel, which already kind of works. But once you actually have a sophisticated industrial economy, central planning can't handle the complexity. And so you try and create incentives and structures around that while not having pricing. And that just doesn't work. I suppose that's sort of a generalized point, which is like a market economy is a system.
And if you pull a lever here, something will move there. And you can't just pull a lever here and say, well, I don't want that to move because it's a democracy. It will move anyway. Because you have to understand how the system works and understand what consequences you want from that and what your parameters are within this. One of the things that I admire about you is that you're sort of known for spotting patterns.
I have a theory on how to learn pattern-notching. I'd love to hear your pushback on this. My theory on how we learn is I call it the learning loop. We have an experience. We reflect on that experience and we create a compression and that compression becomes our takeaway.
So we can watch a movie, read a book, and you come away with a compression of it. But you can work backwards from that compression to the experience. But what we consume most of the time is other people's compressions. So when people read your newsletter, they're consuming a compression of the work that you've done, but not the actual raw work. So in a way, it's an illusion of knowledge of if you haven't done the work in that area.
It's funny. I have a draft thinking about what I learned to do to web search and publishing and discovering commerce and big foot, hand-weighty fuzzy, all that stuff. And I was thinking about this and there's a book written by French Academic, sort of a year ago or something called How to Talk About Books You Haven't Read, which sounds very kind of snide. But his point is that the book you read when you were 17 and you really didn't get it, and if you read it now, you get it. And there's a book that he's got this list of, there's a book that everybody else has read, so it says you've read them.
There's a book that you've read three other books by that writer, so you don't really need to read this one too. You get it. You need to read another Malcolm Gladwell book. You've got the Malcolm Gladwell experience. So this is a generalised sense of pattern and accumulation of what you've seen, what you've half seen, what you half remember.
There's also, I think, what your viewers, listeners might notice is I kind of have two modes, two or three modes. I have a mode that's sort of discursive and slightly rambling and free associating, and I'll kind of spiral off in different directions and hopefully come back to the point. And then there's a mode where I want to try and pin the thing down and break it apart and say, what are the two, three, four things that are happening here, which is what you see in the slides? No, like, what is it? It's this and then this and then that.
And as that captures that, that's a way of trying to understand what this is. I try and work out what I think about this, how I understand it, how you can break it apart by kind of pinning it down. The thing about data is, and the thing about slides in the analysis is like, I'm always asking who cares. And I'm always asking, yes, but what actually matters here? Why are you showing me this slide? Why am I showing you this chart?
And so you kind of have to ask, like, well, what are the actual questions? What are the questions we're not asking on AI that we should be asking? I mean, we're asking, OK, well, there are some people who are saying all about capture is going to be in their models. This is kind of funny split between people who are just talking about the models getting better and everybody else is saying, well, all the value is going to be in the application layer and, you know, one of the companies and let's fund Cusser and let's fund all this stuff. And why isn't there a consumer breakout yet?
And other people are saying, well, you mean there was a consumer breakout? Everyone's using chat TPT, which is, well, not exactly, which is my data point. Some people using chat TPT, most people don't get it and don't get it. Still just fascinating. There's this kind of core where's the value capture question.
Then there's like a bunch of questions we could have asked two years ago where we don't have an answer. Will the error rate ever be controllable or manageable? Will you ever get from all the nodes when it's wrong, which to me seems like given the statistical systems, seems like a contradiction in terms. But maybe you can make a list of a dozen questions we could have asked in early 23. We didn't really have answers to any of those.
I mean, there were some people who were asking these things commodities were trying to catch up to a tree answer, even then was obviously, yes, of course, which is what happened, which deep-sea kind of demonstrated. But we don't have that many new questions since then. The thing that I puzzle about right now is, first of all, this whole nexus, as I said, of like, what is LMA, what is SEO for LMA? You know, we have infinite product, infinite retail, infinite media. How will you choose what to buy?
What happens if I go to an LMA and say, what matrix should I buy? What life insurance should I get? How does that... Well, that posies doesn't suggest you, as we don't know yet. Then there's a question around, like, the differentiation in the LLMs as product.
Like, it seems to me right now you could do like a double-blind test of the same prompt given to Grok, Claude, Gemini, Mistral, Deep-Seek, do a double-blind test. I bet most people wouldn't be able to tell which is which. That question of like, is there product differentiation? Can there be product differentiation around the LLM as consumer product? Because right now, the model is the commodities, but the chat TBT is way, way, way, way, way, way usage.
So chat TBT is at the top of the app store, ranked Gemini, bubbles between like 50 and 100, none of the others from the top 100, same in Google Trends, same in the usage numbers, same in the revenue fair corporate API, corporate is the whole of the story. But as a consumer thing, it's like chat TBT is no like the brand, it's the default, it's the Google, you use it because you've heard of it, and none of the others have broken it. Is that where we are now? Is there... But then if you look at the products, the products are not any of the models all the same, the underlying models are the same, the products all the same.
It's really hard to tell the difference except they've got different policies and different icons, different branding, different branding. But the i-port is all the same. And it's reminded me of looking at browsers. In the browsers are all the same. The rendering engine underneath might be different, just as the other might be different.
But you've got an input box, an output box. And the output box wonders what we're going to give you. And the only innovation in browsers in the last 25 years is basically tabs and merging search in the address box. And there's like the browser, the people trying to do it now, but it hasn't worked, it hasn't worked, it hasn't worked, it hasn't worked. And is that sort of how our lens will work?
In that it's about the distribution and the brand is not actually about the product or the model. Or is it maybe more like social in the photo show, in your commodity? But there's a big difference between Instagram and Flickr and all the other people that try to do photo showing. And so you have to... That would almost be an argument that it's sort of one or take off, right?
It's very hard for... Like, use code as an example, it'd be very hard for code to compete if they don't have enough usage to continuously make the investment. Well, that's a slightly different thing. So the winner there doesn't appear to be a sort of self-reinforcing cycle in which more people use it because more people use it. The product gets better because more people use it.
So more people use it, which is what you have with operating systems because you have more apps, several more users, several more apps. So what you have with Google Search, that Google has all the feedback from how people use it. The makes are such an embedded. You have a network of features in social media that you're there because you're there, because your friends are there because you're there. There's no apparent equivalent in LLMs right now.
There's no reason why the LLMs get better because more people use it. Now that may come. You have that... The open AI and people have been doing memory where it remembers what else you've asked, but that seems more like a switching cost than a network effect. And also it might be easier for you to just ask it what it knows about you and then tell Claude or vice versa.
There's not clear if that... But we are at that sort of stage where you're looking at the browser and saying, is there a way that you can create stickiness yet? Or that you can create a network effect on the browser? Or is it just that the browser itself is a commodity? Now, capital is not a winner-takes-all effect in that convention.
So it would always anyway, it's a different kind of winner-takes-all effect. I mean, I wouldn't conventionally think of capital as a network effect. Or is it not a product? It's not something that's inherent in the product. It's something else.
In AB that, yes, chatgp. The open AI has more money so they can make them all better. There's like six rabbit holes and one of them. I'm going to move on to something new. If open AI can...
I like your point about sort of the point where AI gets better because people are using it, then there's a huge advantage to being open AI. Because we don't have any... We don't have visibility on what that would be yet. At that point though, whoever's in the lead would sort of... If it did, then you could get kind of a run away.
But we should kind of go back and think about my space. Because you have like... In the early phases of these things, you can see the same thing with the only PC industry. You've got to dozen of them. And there's often an early leader that falls away later.
And so my space was the early leader that fell away later. Then you get a late stage where the S-curves kind of flammed out, where all the network effects were kind of solidified and the product quality is solidified. It was very easy actually to get people to switch back and forth between my space and Facebook and Bebo and Friends, Green Knight and whatever the other things in the early days. Then you kind of get this separation out. But then of course then Instagram comes along and then TikTok comes along.
And so as soon as you have something that's a different proposition, that tend to be extremely easy to pull away, you know, Google lost YouTube. They had to buy YouTube, Facebook lost to Instagram and WhatsApp and they had to buy some bows. So those are quite fragile and quite narrow when it takes all effects or at least they're there PFB. We don't know what that would be or what the modalities would look like. Modalities are great meaningless words like saying societal.
We don't know what that would look like. And therefore we don't know how we did it or how it would work because we don't have it yet. You can't, as I'm sure you know, you're not retraining the models all the time with the data. So you don't have that kind of run away effect as continuous flow and more queries produces better results. So it's kind of tricky to do that yet.
I want to come back to something you said. You said some people look at chat TPT and don't get it. Yeah, I think this is really important. There's a whole bunch of survey data on how many people are using this stuff. You've got the numbers from, so, so, so chat open AI, I could say, well, we've got this many weekly active users. Funny thing about social is people,
when social happened, people would talk about registered users. You know, in the early days of the internet, people would talk about it. It hits. And then we realized that if your web page has seven items in the menu bar, that's seven gifts. So that's seven hits. So it's meaning less than you have to switch
to page impressions. And then there's registered users. And then there's monthly active users. And on social people, so hang on a few using Instagram ones, you're not using it. In state, you have to use it or nothing. And weekly active users, we don't like either. Now, open AI is doing
weekly active users. And Sam Walton was a social media software fan, he knows this is a bullshit number. You look at survey data, and I had to do the slide in the last presentation, I did about five different surveys from the US from late last year earlier this year. And it's all roughly the same. It's like something around 10%, give or take three or four percent of people depending on survey. They're
using this, say they're using this every day. Another sort of 15 to 20% of people say they're using it every week. And then the, so you've got like say 10% of people using it every day, say 15 to 20% of people another 20 or 30% of people who say I use it every month or two. And another 20 or 30 people have a percent of people who say I had a look, I didn't get it. And then you have this survey where people say 70% of people are using AI. And like, wait, what do you mean? The whole other rabbit hole,
which is, you know, people say, well, did you use Snapchat? I'll say you're using AI. What do we mean by AI? So which is let's be specific. Let's talk about you are using a consumer facing LLM chatbot. You're going to chat, you people call and ask questions like that. And I'll look at it.
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letters, Notion.com slash knowledge project to get Notion Mail for free right now. When you use our link, you're supporting our show to Notion.com slash knowledge project. And to me, there's a bunch of you could make tricks this. So some of this is it's early. There's a catch point here, which people do with the chart and they say, Oh my god, it's so fast. It's like, it's fast as a small phone. Yes,
because you didn't need to buy a thousand dollars on a phone. Right. It's faster than PC. Yes, because you know what PC is cost in the 80s adjusted translation? It's like five grand.
That is free for a lot of them. It's free. It's a website. You just go there. Of course, it's got fast adoption and there's way more people online as well. So even the absolute numbers are
faster than they were for Facebook 20 years ago, 15 years ago, because there's way more people online now. Yes. So that's again an example of my unfair, but relevant comparison. You'll send numbers to all those joins. So of course, you can get to way more people quicker, but do you have to keep asking? Yes, but why do so many more people look at this and not get it or even worse?
They're not getting it. I can kind of see because people look at everything and don't get it. Why is it that somebody looks at this and gets it and goes back every week, but only every week. Why is it they can only think of something to do with this once a week? I worry about this people.
I mean, I'm just thinking if these numbers are accurate, the 10% of the 15, 90% of the people that I spend the most time with are within that 10%. Well, I'm not interesting. Tell me more about what here actually I'll preface this conversation with. My kids don't use Google anymore. They use it to find phone numbers or local businesses or places distance. Everything else, they basically
have defaulted to chat. GPT now. Again, I'm going to all AI conversations seem to be analogies. So it's like nuclear weapons. No, it's not. The comparison, I think, is interesting
here. It was not perfect, but it's interesting is to look at early spreadsheets for software spreadsheets, paper down directly in creates visit. And the other guy's name, he creates this account in the late 70s. And I think to get an Apple to do one with a screen and everything costs like 15 grand, just in translation. And you show this from accountant, and it's like, you can change the interest rate here. And all the other numbers change. And we see that now and
yes, 1978, that was a week of work. That was like amazing. Yeah, they would do a week of work in half now. Yeah, all bless. And he has all these stories about a cajons who would, you know, they would be given a one month project and they'd get it done in a week and then they'd like to play golf for three weeks because they probably didn't actually want to tell the client I needed it in a week because the client was saying they hadn't done it properly. So I look at chat GPT,
and I think, right, I don't write code. I have zero use of something like that for me. I don't really do brainstorming. Okay. I don't do summarization of things. I don't do
the things where it's sort of out of the box, easy and obvious. And then there's a sort of mental load, okay, I've got to kind of find think of what things am I doing that it could do for me. And that most people don't think like that. So there's a sort of I said, I remember there's like a matrix, there's a matrix of like, who has the kinds of tasks that it's good at, obviously, who is good at has the kind of tasks that it's good at, not obviously, who is good at thinking about new tools for the things that they're doing, who isn't? If you want to kind of blown away, you don't reflexively use a using sales force.
And you had a button that said, draw me an email to reply to this client, then that gets massive adoption. Well, that's the way we talked about it. That's a different thing. Yeah. Is it is it the chatbot as product where you get this blank screen and you kind of look at it and you scratch your head and you have to think, well, what is it that I would do with this? And then you
have to form new habits around it. Or is it that it's wrapped in product and UI where somebody else has said it would be really useful for this, wouldn't it? And then you look at it and go, Oh, yeah, that would I could do that. Do you think it's better with qualitative or quantitative? I think it is presently, and I'm going to get a binary statement, I think today it has zero value for quantitative analysis. Oh, interesting. Because if, well, let me qualify that,
do the numbers need to be right or roughly right? Because what all of these things do is they give you something that's roughly right and roughly is a spectrum, but it's always high to be 3.1. Depends how big the circle you're measuring is. You know, this is a line about pie that we can calculate it. The however many digits we have is enough to calculate the diameter of the universe or something, but people still adding numbers. So, you know, there's a little bit of
paradox in here. You know, you get infinitely close. A certain point doesn't matter. And this is a high level. This is some of the argument that if the thing gets infinitely close to reasoning without ever actually reasoning, does it matter? Like a certain point, the thing is always right.
The thing is only once in a billion years, does it matter that it's not always right. The problem today is it's not once in a billion years. If it doesn't times a page. You don't want to spit that out and give it to somebody. And I don't know. So, I had a very early example of this. And I was going to speak at an event at
the beginning of 2023, and the conference people had asked me for a long vote with yourself, and I didn't have to win that one. And so they made one, and they used it with actual TVT and not told me, and they just sent it to me to check and I looked at them and said, what the fuck is this bullshit? That's 2020, but generation ago, I was in the point. That's not relevant to the point I'm going to make. The point I'm going to make is, A, it was always the right kind of degree, the right kind of university, the right kind of experience, the right kind of jobs. It just wasn't actually right things.
But B, I could take that and fix it. So for them, it was useless. For me, it was completely very useful. I was like, I spent 30 seconds fixing it instead of spending hours going ahead, which is what I say, right or wrong depends. Which is a very kind of French force of a kind of question.
Is the answer is the prompt? Does the prompt have errors? It kind of depends why you wanted it. Okay, well, that I don't have use cases where I want something that's roughly right. I don't have use cases where I want a list of 10 ideas or I wanted to brainstorm, I wanted to draw from email, or I wanted to write code, or I wanted to generate some images.
You know, my friend do works at a consultancy and they want pencil sketches or concepts and now they can just use mid-journey to make those. That's great. Does that sketch, like does that person at the back have three legs, not anymore? No. And if they did it, it wouldn't matter. You could Photoshop that out.
I don't do that. That's, I don't create images. So I don't have a good mapping of the stuff. This is good for early against the stuff that I do. And the stuff that it maybe would be useful for is the stuff where it's actually not yet very good. And the things where you would mitigate that
by saying, well, I would fix it. I don't do those things. Okay, so this is a good thing, because I wanted to come back to something you said you said you think about writing, and in a world where you're taking something generated by AI and editing it, that's different than writing. Talk to me about thinking by writing. I, why don't you, my chat, my particular use case, which is more mental model than a practical thing is I write something and I think it's such a, what I was asking the past is kind of your point about pattern recognition is I look at something and say, am I adding value here? Am I saying something useful? Am I saying something different?
Am I asking the key question? Am I pushing further? Am I pushing the next question rather than just answering your questions? Now I can just say, is this what chat GPT would have said? And if the answer is, this is what chat GPT would have said that I didn't publish it. Not because people can
get it from chat GPT, but because anyone would have said that. That's a perfect analysis in the sense that it raises the baseline of what qualifies as insight. The difference is the slope of the insights. And so you wouldn't say it if chat GPT is going to say and push back on the spell means, but the chat GPT level of insight to use an example, it could be clod, it could be grok could be any of them, is increasing at a faster pace than most people. And eventually those slopes intersected already with maybe up to intern level. And next year it might be masters level or it might even far surpass
that and it hasn't some domains in terms of math. The year after it might, and so maybe it's like five years before it passes Benedict. And maybe it's four years before it passes somebody else. Maybe it's like past me a way long time ago. So I think this, this shows me the direction is we could take that. One of them is, that's an interesting theoretical philosophical question is originality,
which is to say AlphaGo could do original moves because it could do all the moves and do moves that no one had done before, not knowing what people had done before. But it had an external scoring system and neither of them was good. They could not have feedback. Yeah, they could have feedback because they move as a score. They can evaluate the score every move. The classic parable of the
monkeys and typewriters or you know the Borgias in front library is the Sniffy Bat Loop. Yes, there's always inflow brick and dings and you must be generated at random. The monkeys were typewriters but there's no way of knowing. You can see there's the music now. You can generate stuff that you wouldn't know for. And our land variance is bad. Originality is a lower score.
So what's the feedback loop for original but good? Now it might be that that's the same sort of false question as saying is it really reasoning or is it just right 99 times, you know, nine followed by many zeroes? Does it actually understand or is it just always right without understanding? Does it actually know that's original or is it not? And that's a kind of a puzzling, I didn't know the answer to that.
Maybe the one more question but it's kind of a puzzle as to how would these, how far can these things make things that are both different from the training data and good? And is knowing that this is different, good? Is it really different or is it just matching the pattern on a longer frequency? And how much could you actually have predicted that given enough data that is not actually outside the pattern? It just kind of looks like it is if you're zoomed in more and if you zoom out more, then that is matching the pattern. How would you know that people will, it's like, you know, think about
music. Like, how would you know that people would like punk? You could get, you can very easily imagine gender AI system that can make you more stuff that sounds like, yes, or more stuff that sounds like Pink Floyd. It might not sound like good Pink Floyd for Floyd but you could imagine it would make more stuff that sounds like the Grateful Dead. You know, a Grateful Dead fan say when they run out of drugs. It's music terrible. You can imagine that you can imagine the challenges knowing that now
people have really fed up of 70s prog rock and they would really like something else and that something else would be punk and that would work. It's knowing that people in the 40s were really fed up of the war and would want luxury and that Christian Dior's new luck would work and would express that. Could an LLM do that thing? How much variance do you need? I don't know, it's an interesting experiment to ask that question. There's a completely different place to take this which is to say
this is an appeal for boutiques and in-person events and the unique and the curated and the individual. There's a shock I always used to always talk about, I'm not sure if it's actually still there, but the shock intake you know that just sells one book and they change what it is once a month. They may have placed any other book I've been talking about it for years, but the point is you don't go into the shock and have to work out what book do I. You have to know the shock exists or you can be Amazon and you've got 500 million SKUs and they know they've got everything, like actually the stuff they don't have because they want to be individual. They don't have any major. But to take
argument, Amazon has everything but you can't go to Amazon and say what's good book or what's good lamp? They have all lamps. You can't just go to it and say what lamp should I buy? All of retailing and merchandising and advertising is about where are you on that spectrum and what else do you do? Do you spend the money on rent or advertising or shipping or what and how does that work?
There's a sort of a polarization between well if I know I want the thing I can get it within 12 hours but how do I know I want the thing and I alluded to earlier an LLM is one like paradoxically an LLM could suggest you the unique individual thing would the LLM also create the unique individual thing that's a high one and a second that's a different question. It's questioned further down the pipe but the more that the LLM can do what everybody would probably do or say what everyone would probably say then the more you push to other places. That makes a lot of sense. I mean there's always going to be a market for insight whether it comes from LLMs or people and you have to be providing insight. You know our world as quote unquote content creators is a very wide spectrum of people who do very different stuff and there's you know there's people doing AI slop and there's people doing you know the what is it called the passive income thing but there's people who do very different kinds of content coming from different places for different reasons. You know Scott Galloway does very different
kind of stuff to me. Mary Mika does very different kind of stuff and they usually do different kind of stuff to me it's just part of that is about who you are and your story and your authenticity of it and some of it is about no one cares who you are but you're saying interesting stuff and some of it's a recommendation algorithm or something else. There's a book by Zola about the creation of department stores called Bono de D'Amour which means happiness of women and it's basically about an 19th century Jeff Bezos calling an apartment store and department store went to existence out of thin air through fossil will and lucky invents fixed prices so that you can have discounts and lost leaders and mail order and advertising and you know he puts the slow moving expense of stuff at the top of the store when he puts food and makeup on the bottom of the store there's nothing new under the sun and meanwhile the shopkeepers on the other side of the street are saying like you're seeing what I mean he's doing now he's selling hats and gloves in the same shop he's got no morals he'll be selling fish next to of course he spoke with him like there's like other whole plot point is about loss leaders so there's like you can you can again you can understand back and think well people have freaked out about industrialised mass produce product before people have freaked out about their being too much content there's a line that Erasmus was the last person to have read every book right there's too much AI content slot on the internet now like yeah how many books do you think
will be published in 1980 do you think everyone was reading all the books then yeah same thing just different scales I guess what advice would you give students to well when I was a student we were also based we learning Japanese I think that was just the tail end of that you know I was sort of lucky to have a sort of very expensive and old fashioned and hand-for-profit education as it was all about learning how to learn and learning how to think I think there are skills that people use to sneer at that for me shouldn't have been sneered at and certainly shouldn't know I mean I'm old enough to remember when people would just sort of smugly say well I'm not computer literate to say that was like there's like being a calm mechanic or something I don't know how to do that that's not my problem somebody else's problem and I don't think anyone now partly this is because of mobile I don't think anyone thinks like that anymore should you learn to code no I think you should find out if you want to learn how to code I think this is like saying should you learn an instrument or should you you know go take theater classes that may or may not be what you should be doing because what does learn to code need in 10 years time is a different question but should you I don't think you should presume you will or won't be a software engineer I think you should presume that you will need to be
curious and that you'll have many careers and different kinds of jobs I think you should be focusing on learning how to think but I know I think you should be presuming that everything will change everybody says something like learning how to think I feel like you would have a really good what does that mean like break that down for me because it probably means different things to different every now and then I'm slightly perplexed to get any miles key for career advice because I think you looked at my LinkedIn like sort of company shutdown company shutdown company shutdown like last year there that didn't work coming from the UK and seeing the US system I never really liked the US idea that like if you want a good job you should be doing mass and business and engineering now that may be how people hire students here but I never liked the idea that you're like studying philosophy or studying history or studying literature is useless because you're learning about history that's not what I learned yes you know I wrote a lot of analogies about history none of which you think I studied university what I learned studying history at Cambridge was how to ask what the next question is how to break this apart how to read a hundred books or 50 books in a week and find the bits that you need how to synthesize lots of information how to ask what does that
actually mean as opposed to what it looks like it means do you believe this is incredible or should you just get us an idea how do you put this together and think about how you explain something and that's what my friends who studied English did or my friends who studied philosophy engineering did that was what you were being taught how to do you wouldn't be what being taught to be any which I get to be a historian or to and I hesitate to think that you know you can only build a company if you've had it or only work for Goldman's or McKinsey or a big law firm if you had a particular kind of education a particular kind of degree I think you should be looking for what's going to challenge you and push you and give you the ability to learn and think in different ways but again this is me you know what are the skills that you have how does your brain work how do you think about things and it's really got 20 years to work out what I was good at so I'm not sure that you can know that as a student so you have to try and find what you're good at as well as you know learning to think maybe learning things like I do maybe that's not what you should be doing you should be learning what is it that you should be learning to do what are the things that you're good at try all the different things I don't know it sounds
like a university commencement speech now I didn't fucking know but you don't know what you're going to be good at it so you kind of want to try and like create options for yourself what did you learn about investing working at age 16c so there's a bunch of like maxins or sayings that's probably I wouldn't want to dignify them as like theses or anything else but there's a whole bunch of maxins and sayings adventure which you know we could have a podcast talking about they were better people to give you a podcast talking about the mechanics adventure but like you have you're understanding what startups are and how they work and how the machine works and startups are an industry and Silicon Valley is like a machine for creating startups and still too many people kind of look and say well that was a dumb idea and say well that's the one question the question is if you look at a startup and you think could it work and if it did work what would it be and could those people make it work and then you understand more you're like the mechanics of well how does social media work and how do people look up and what is it like to create a startup which is a whole other conversation I think something else that I learned was calibration there's a sort of again another metaphor I always think of which is that if you go to like a really great art gallery like you go to the moment or
you know the metal or something everything else must be if you go to a smaller weirder art gallery like London's gallery in London called the Wallace Collection or I was in Rome a couple of weeks ago and I went to one of the old housequarety palaces and this palace is like it's like 10 or 15 rooms of pictures and they've got like a quite good Tintoretto and a maybe Tishin and a Raphael you see it glowing across the room and like oh that's why he's Raphael and it's the same when you see lots and lots of startups like oh that's why he's a nice etchit oh no that's why this is what looks like we get 10 minutes in and I've got another 45 minutes I've got to pretend to be interested in polite to the finance and good experience it's seeing that contrast and texture and seeing what good looks like seeing what worked what didn't work what people tend to say how this tend to work as a pattern recognition as much as anything else you also get there's a whole lot of other cultural context you get around it you know Silicon Valley is going to be very high school you know I was used to say it's an industry town and I was used to say it was like being a college town where there's one
subject so everybody you meet is doing the same thing and so in some ways that's very powerful you know everybody around you's doing it you want to do a PhD everyone around you's doing a PhD here's the world expert in subject of course you are like being a little class kid of course you're going to university what do you mean everyone's going to university of course you're going to do great work of course you're going to do great work of course you're going to start a company and you're surrounded by the people who've done it you want to get CTO who's done it five times you want to get a great C who's done it five times like they're all there the other side of that is you'll never meet anybody who isn't working on exactly the same stuff and isn't interested in what you're working on so you have no external context you have no external perspective the nearest theater is in LA you know you know you can go I think I think I said it's an LA you want to go to an art gallery you got to get a wallet who's the best position right now so from the outside looking in you know Sock is is going all in on AI Elon seems to be going all in on AI which of sort of the leading companies do you think like a Y why are they all of a sudden shifting and really you know they were dabbling in it before but now they're they're really committing you know tens and hundreds of billions of dollars and then who's the best positioned in this sort of space if you had to pick one and a master entire net worth in it who would it be well that's that's several different questions let's pick them over like this was a technology they've been kind of floating around before chat tp 3.5 and everyone kind of saw that didn't work very well and then chat tp 2.0 and then actually works well enough and then there's this explosion of interest in sin and so I think last year the Google Google Microsoft AWS and meta spent about $220 billion
of CapEx last year and we'll probably spend something over 300 a year and so that's basically more than doubled almost triple I think from a couple of years ago so there's enormous urgent CapEx investment in this and we've got these stories about like more meta boards scale half 49 percent of scale.ai for $15 billion apparently looked at both of the other recent open house they've seen super intelligence and what's the other one's thinking machines which are basically pre-revenue labs with somebody from open AI at multiple tens of bit in situations and apparently like some of them complained that Marcus offering people a hundred million dollars to join so Marcus has this kind of weird situation in that his own models aren't actually very good but it's not this very kind of weird relationship with open AI open AI Sam Altman is I was going to say potarizing figure but actually opinion is about him tend to be fairly unanimous and tend to be fairly negative like everybody's ever worked with him quit opening out itself still kind of sets the agenda but much less than it was two years ago and I wouldn't want to do like a detailed like calling the scores on who's models God and who's labs are good but you know technically Google is clearly like farming and also and is doing is making great models llama seems to fail at long forward because it seems to have been an embarrassment and so metrics kind of scrambling to catch up apples are slightly different position in that they always have a position that they don't want to be first they want to do it right and that they don't need to be to be doing whatever the latest because you mentioned it's like they didn't have a YouTube I think Craig
very he said well you need to be off to a PC like we don't have a YouTube you just call it YouTube but we don't have a YouTube we don't have a college service we don't even go through the worry we also don't have a chapel okay that was was quite a question the question for Apple is how much does it would integrating an LLM into the operating system change the experience of what it is potentially shifting competitive balance with with pixel which at the moment has basically only gets more popular people who work for the tech press and literally no else buys pixels while the kids Google doesn't want to compete with something if you get a home to get a whole smartphone industry rabbit hole there's this sort of question for Apple around does this now actually change the experience of what the smartphone is what the ecosystem is does it end up kind of getting Microsoft it in the sense that you're going to still get for the time being you're still going to buy a smartphone it's not at all apparent there's going to be another device and if there is it's a long way and it might be an Apple device as well you're still going to buy a smartphone if you're going to buy the nice one with a good battery and the fast chip to run the air models and the good screen and the best camera which will still be a microphone because I will still have the best chip to even hold on to other odd roadages but everything you do when it will be from someone else and it will be someone else in the sensor it's an app from the app store it will be someone else in the sensor it's a model running in the cloud which is what happened to Microsoft in the 2000s which was everyone had to get on the internet to get on the internet you needed a computer you weren't going to buy a computer you weren't going to buy a smartphone but you're still going to buy a
Linux computer you probably wouldn't buy Mac either so everyone bought a Windows PC but they were using it to do web stuff not Microsoft stuff so Microsoft kind of lost that and say that would be the concern for Apple would be you're still buying your iPhone and you'll buy the iPhone at this auction because it will be so unlike when it will be a lot of the phone and you'll use it to do chat GPT but the counter argument would be to say yeah you'll do is it to do chat GPT and DoorDash and Uber and Instagram and that cool new game and the other new cool game that's enabled by our lams and to do TikTok and to do and and and and and and and and it will be kind of the same except selves so there's a sort of you sort of mean this is sort of slight fuzzy in the around what the bear case for Apple actually is does it sort of end up like Microsoft did how bad is that exactly what does that mean yeah if we all go to wearing something like this that's lit up by AI then that's a bigger shift but it's very unclear how close that really is just for the optics there's another access here which is what happens Google search where someone go how do you map the search activity that goes to an L of them that and how do you map that against whether revenue is and also from the other side how do you map that against where the publishers are how do you think about whether you just shift your habit and you're actually using chat GPT is Google it's basic
you do what Google does but you've shifted the brand and you're going to that search box instead of the other search box I don't think you can count them out in absorbing that in so who would you out of the public companies you have to put your money in one of the top like seven well then there's a valuation question and a long time ago I was a public markets analyst and I was bad at being a public markets analyst for a bunch of reasons why not which was I was never interested in share prices well we've given the standard disclaimer you're forced to what would you it's hard to see iPhone sales slipping from what we see now even half the cool sexy Google stuff is in the Google app on the iPhone I think meta and Google though is this sort of big question around where the ad revenue goes and how much your ad revenue gets pulled away to different places I think Instagram is probably in a very good place in terms of changing what advertising looks like and how that works I mean I had a slide in my presentation which was like what meta and Amazon wants to do is to make LLM's commodity in for the salt cost net this is one that I can source because I want to make a commodity and for the salt cost and they differentiate on top with meta stuff with Facebook so it will
be just infrastructure and also like it to be commodity infrastructure that's what everybody was is they sell to a lot of the infrastructure across and they do a bit of anybody else and they got a lot of money from doing that go to Amazon's financials and basically all the money comes from AWS and the ads people complain about AWS don't come realize the ads make it Amazon 50 billion 60 billion dollars better than you lost it. So Amazon seems to be fine but there's a bunch of stuff to navigate around how does this change how people buy stuff on Amazon who does that lead Microsoft was aligned from Bismarck that the great man is somebody who hears called footsteps through history and grabs onto his coat of his class. And like sat here is like try to first of all try to grab onto the R&A with whole other things that we don't talk about anymore.
Now it's AI their own models are not really ranking I mean they're hiding stuff but like they're still struggling they've got this weird contentious relationship with Sam Altman and open AI and it's basically not their models. On the other hand like they're going to sell an awful lot of Azure to run all this stuff which again is this tension is it is it that everybody's used just use it to do the thing or is it that someone is going to come to you with a great accounting product to run on Street and it runs on Azure and it uses some other some other who cares which one it is it just does better you know you can edit your bank and it does the cool stuff and you can it does the cool stuff you know my use case for an LMM is do my fucking invoicing for me.
It's not even that it's work out why exactly it is that that clients ERP doesn't like my bank account and not have me spend the next three months bouncing emails back and forth with somebody in India about one second the second on getting this done that would be a great use case that and I'm going to do that yet if they could that would be great but that we're not there yet so Microsoft and Google are in this sort of position of being the incumbent. I can't remember this. Give you more system. I'm thinking my way to the answer to the question for Google and Microsoft there in it they have an incumbent business that is potentially disrupted pretty profoundly by this but they also have a cloud business that sells all the new stuff for this Amazon has an incumbent business that doesn't get disrupted by this at least much less obviously and a cloud business that will be very happy selling all of this stuff. Meta doesn't have a cloud business selling this stuff and has a bunch of new ways to make money from all of this new stuff except they've got to have some better balls.
Apple, is this a competitive threat to the iOS ecosystem? A lot of stuff would have to happen first and they'd have to drop a lot more balls before that was to happen and meanwhile they're still going to sell you the nicest living rectangle to do all of this stuff and then we're talking about glasses and VR which is a whole other two hour conversation about when does that happen. There's still people poking around in crypto like Web 3, Web 3, remember maybe that someone said that people still working on crypto like those Japanese soldiers on Island's in the Pacific, you don't know the wars over but there's still people working on crypto so that's like another disruption thing coming down the pipe. Who are the other incumbents? Netflix is a TV company, even the car company, Elon Musk. There's a whole Tesla conversation fascinates me because Tesla bears balls think it's a software company and Tesla bears think it's a car company and at the moment it's a car company.
I mean yes they launched autonomous driving but what did they launch? They launched half a dozen existing model cars with test drivers doing a GFX drive that everyone else was doing 10 years ago. Is that going to scale? Is they finally going to get the flywheel of having all the camera data meaning they will work with just cameras? We've been, I think there's a conversation you could have asked 10 years ago, I'm great stuff, 10 years ago.
Are they going to take all the effects in autonomous cars? Will Tesla get it working with cameras before everybody else gets it working with LiDAR? Well, Waymo's got it working with Vision Grande of LiDAR or whatever that's that cost, it's 10 to thousands of dollars of extra stuff on the car. So they got it working with all of that stuff. Tesla does not have it working with cameras. Will Tesla get it working with cameras before Waymo can get rid of LiDAR?
We could have had that conversation. I literally was on podcasts 7, 8 years ago having those conversations. We don't know the answer. Maybe we don't know. But the interesting Tesla point is people always look at it and say, it's the iPhone of cars. No, it's not.
What's happening is that cars are becoming Android with no iPhone and Tesla is just selling and is in that metaphor, Tesla is just another Android phone maker and they're competing with the whole Chinese industrial policy to make more and there's going to be a flood outside. The US is ever detected by terrorists. Overall, it's very clear what's happening is just a flood of EVs with just as good as Tesla's. We always end on the same question, which is what is success for you? We live in the luckiest time. We are not worried about rocket landing on our heads. We're not worried about our children dying from diseases. We're not worried that the bank might be closed tomorrow and all your money is gone.
We're doing something interesting that we enjoy and that pays the rent that we want to be able to pay. I get paid to fly around the world and give supplies for money. So I think I'm doing okay. I could always be doing more. But I'm always looking for the next question. I'm always trying to be curious.
This was a great conversation. Thanks for taking the time to do it. Right. Thank you. Thanks for listening and learning with us. Be sure to sign up for my free weekly newsletter at FS.blogslashnewsletter.