A rational conversation on where AI is actually going | Benedict Evans

23:04It took like 10, 15, 20 years for people to invent all that stuff and work out that you could do that with this. And so all of that is like the way of saying, well, yes, it is going to be quick, but actually, no, it will kind of take a while for people to work out how to completely change how their business works.

23:18Lenny Rachitsky:Your view is so comforting because it's, you know, basically it's like, okay, this is a huge deal, but we've been through many transformations before and it's going to be okay. Well, I have a slide towards the end of the presentation, which the title is something like, this is going to be completely different from everything else, just like everything else. And then the next slide is an IBM ad from the 50s, which has got this sea of white men holding up in white shirts and ties, all holding up, fly doors. And the slogan of the title of the ad is, it's an IBM ad. It says an IBM electronic calculator.

23:55This is before it was called a computer. it's an electronic calculator it's the size of a fridge it's like having 150 extra engineers like how many people listening to this comfort list like their company slogan is basically will give you 150 extra engineers i mean isn't that like the whole picture claude goad 150 extra engineers for free or not free that's like a lot of money um so and yeah that's what it gave you and so yes we keep going through this over and over and over again just to kind of make that tangible. I mean, obviously we couldn't be doing this without the internet. So there's a slide in my presentation, which is, we could maybe talk about, but it's a slide or chart showing how many products are stocked in supermarkets in America since the 50s.

24:36And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it. But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how many schools there were in supermarkets every year. And then I had to realize they'd been around since the 50s. And if I dug long enough, I might be able to bake a whole time series and I could make a whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. You really need to go and find a library where they publish that number.

25:12And then the number's in that report. You'd have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone and spend like$50 on long-distance phone calls to find a library that has these. Or maybe you call the Food Marketing Institute and they say, yeah, sure, if you buy a, you know, we'll sell them to you for$500 each. So then, you know, you're going to get on a trip. Maybe you live in New York or somewhere that has this. And two weeks later, you've got the chart and you look at it. And then the other side of this is the life of an analyst is you spend all day making a chart and you look at it and go, oh, that's not really interesting.

25:41So you spend two weeks to make the chart and then you look at it and go, yeah, I'm not going to use that. And for me, this was like two hours in Google. And so we forget how big a deal the internet was. That's a long way of saying it. But we forget we've had these absolutely enormous changes. And then we don't see it. Because it's like, that's the world the world's always been.

26:03Lenny Rachitsky:What's different potentially this time, just to admit what your quote is, it's different. Everything's going to change just like last time. The big difference, obviously, is AGI might emerge and superintelligence, where that is, could it, you know, does the work of humans can do a lot of this stuff for us, can actually replace jobs? Just like thoughts on that element of this transformation we're going through. I don't know. This is one of the ways I've struggled to write about AI is like certainly like 2023, early 24, like all the questions were questions you could have asked in like December 2022.

26:39Like the questions didn't really change and the strategies didn't really change. And I think the AGI question is kind of the same. I mean, the thing that the observation one can make, like, you know, we have no theory of what human intelligence is. We have no theory of why these models work so well. We have no theory of how much better they will get. So we're all just kind of vibes forecasting as to what will happen. And then you can have like the 2 a.m., you know, doped out philosophy students talking about, hey, man, like, is this consciousness? Maybe we aren't conscious either. We just think we are.

27:09Yeah, great. Thank you. I think the one thing one can observe today is, so we have no idea. We don't know. We can guess, but we don't really know where this is going to end up. What I think you can say today is that there's a lot of kind of redefinition of terms. So I think a quote I used to in my presentation late last year was an AI scientist called Larry Tesla who said AI is whatever machines can't do yet. Because once machines can do it, people say, well, that's just software. And so certainly, I mean, I did do a poll on social media every now and then asking, is machine learning still AI?

27:42Because I've certainly heard people say, oh, that's not AI, that's just image recognition. That's not AI, that's just sentiment analysis. So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in the 60s, airliners, jet airliners were technology. Now a jet airliner isn't tech. And so there's a sort of sense of AI as like a moving target, as whatever just started working. And I think the point here is now clearly you can see people redefining AGI to mean the stuff that works now. So is AEI, what's the definition now? It's like it can do a certain percentage of economically valuable work.

28:16Well, that's a very different thing to it has a soul and it's fucking alive. Because a database can do that. Like, you know, an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people. and it turned out there was a whole bunch of other stuff that it couldn't do that we didn't do then we didn't know existed so there's a lot of like kind of creative redefinition here super intelligence i'm not sure is super intelligence more than agi or less than agi because last year i thought super intelligence was like really good but not as good not actual agi and now it's like oh no no we've already got agi but super intelligence that's really hard it's like all these terms like what are you i didn't even what even it's funny i was having an argument on hacking news this morning we remember the idea you remember the argument which is never never a good use of time.

28:58But you remember the argument of like, you know, people would argue about whether crypto is blockchain or whether blockchain is crypto. There isn't a right answer to that. Let's just be sure. You know, it's important to understand what you mean when you say that, but there isn't like a correct answer to this. Are we going to get to something that has human level intelligence? I don't know. I don't think we have any way of answering that question. Maybe, maybe not. You can make arguments either way. Meantime, does it mean in the meanwhile, we've got this thing that's clearly kind of a completely transformative technology.

29:29And maybe the serious point here is you don't have to believe, even if the models start getting better tomorrow, if this is it and we hit a brick wall tomorrow, this is an incredibly useful technology that's going to change the world and get rolled out over the next 10 years. So you don't have to believe in any of that stuff to believe that this is a giant deal.

29:45Lenny Rachitsky:Something that's definitely changed, I had a former boss, Mark Andreessen, on the podcast, and we didn't actually talk about this during the conversation, and he brought it up before we started recording and I never got to it, is he had this insight that the opportunity set for companies now is so much larger. We used to have no trillion dollar companies. Now we're going to have dozens of trillion dollar companies. Just like the size companies can grow to is going up so much. Valuations also go up along with that. And his point is just people haven't really grokked just how large companies can get now.

30:17Lenny Rachitsky:Like everyone's hitting 100 million AR in like five months, six months. Just thoughts on that. yeah i mean this was his whole software's eating the world thesis from you know 15 years ago whenever it was yeah you know the tam it gets progressively bigger because you can address larger and larger parts of the economy and so you know if you think about the kind of the classic platform ship framing that you know mainframes are i think peak mainframe install base was something like 70 80 000 units i mean slightly fuzzy term what exactly is a mainframe and what's the difference at what point does it become two mainframes as one but something like that that order of magnitude and then when the internet kicks off there are as i said 50 to 100 million pcs on earth maybe today there are something over a billion one to one and a half billion but obviously a lot of those are corporate like seven eight hundred million consumer pcs in the world there's about five and a half six billion mobile smartphones in the world which is and which is why you can have 900 million weekly i-t users on gtpc and so there There was this narrative like five years ago, like, well, we've run out of people.

31:19So, like, the next thing can't be in order of magnitude bigger, which was true up to a point. But that was, like, the wrong model because clearly what's happening now is you're moving in another direction. You're just, you know, branching out and automating big, big news ways of the economy. Now, you know, back to your job point, you know, you could argue, well, we're just going to replace all the people with AI and, like, all the money will go to Sam Altman. And, you know, Mark can buy himself another gold stream. I think the, add to the fleet, I think the kind of the other answer is, you know, it's back to the lump of labor fallacy.

31:51And, you know, the last 200 years that, you know, each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, unlocks prosperity for all of us. And that's painful as you go through it, but it always creates more value. And so here you could certainly make an analog to, you know, the useful analog to the electricity industry. is just saying how that electricity became part of absolutely everything. And software has been kind of slowly working its way out. You know, the analog here would be electricity in factories, and then electricity sort of slowly spreads out.

32:25And so that would be the point, again, that, you know, it slowly spreads out to do more and more things. And so, you know, more and more value and a bigger and bigger contribution to the economy. It also, of course, disappears inside things. And, you know, the other side of the point of my capital section in the presentation is, you know, there's this quote from Sam Altman where he said, you know, we're going to be selling electricity. We're going to be selling AI intelligence on a meter like water or electricity. And you look at this and think, you know, my dear sweet child, you need me to explain the margin structure of the utility industry to you.

32:59Because guess what? When you watch television, the TV company isn't paying a percentage of your monthly bill to the electricity company. You know, when you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. And, you know, clearly this is like the much more kind of specific tactical question at the moment is, do we even end up with three giant models or does it become hundreds of models and open models and local models and so on? And even if we do end up with, you know, say, pick a number, three to six to ten giant foundation models that cost hundreds of billions of dollars a year, fine, do they get all the value from that?

33:39Now, I started my career as a telecoms analyst, and so, you know, still pay attention to it a bit. Global mobile industry has a revenue of about a trillion dollars a year, maybe a bit more now. And it spends about$200 billion a year on CapEx every year. Total telecoms is about 300. Mobile is about 200. About 15 to 20 % of revenue every year. And if you look at a chart of mobile data consumption, it's an exponential curve, like perfect curve going straight up. And the number now, I think it's about 1 ,500 to 2 ,000 times what it was in 2010 globally. And the stocks have gone nowhere in 25 years because it's an X-growth, low-margin commodity utility where they're selling this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication, but all the cool stuff is made by you.

34:33It's made by the people listening to this podcast. It's made by somebody else. This was that kind of pivotal moment where the telcos thought that they would do all the stuff that you did on your iPhone. And not only do they not do it, but Apple doesn't do it either. It's all further up stack. And so this is, you know, the kind of the elemental question right now around foundation models is, does the model do the whole thing? Can you just go to the chatbot and get the chatbot to do the whole thing? Can the model companies keep building these like clawed for X, clawed for Y things, which to me look very much like what you see if you hit file new in Excel.

35:06It's like the templates, but like all of those are actually billion dollar companies as well. And if not, no, does it all have to be apps, quote unquote, whatever app means? And if it all has to be apps, who builds those? Well, they can't all get built by the model labs, just as they didn't all get built by Microsoft. And so if they're all built by other companies, does the models, their notion models have leverage up the stack the way Windows did? Or is this more like AWS, where if you're, I don't know, an engineering company or a law firm buying a piece of software, you don't care which cloud it runs on.

35:36And you don't have to standardize on AWS because that's where all the software is. And the developers all standardize on AWS because all the customers use AWS. That's not how it works. That's how Windows or iOS works, but that's not how cloud works. And so it does sort of seem to me that if the chatbot isn't the UX and it needs to be apps and the model companies aren't going to build that, and the models themselves are basically commodities, at least as you can see them as users, then why would the model companies have pricing power? And wouldn't all the value be further up the stack? Aren't you basically, have you got like three to six companies selling a commodity at marginal cost?

36:12Now, obviously, the semi-analyst guys are like, no, no, no, no, no, there's going to be infinite pricing power forever. I'm sorry, I'm exaggerating. But I think you have to, really important to kind of draw a distinction between where Where are we now where you have radical price disequilibrium and you've got these, what's the guy, the open-claw guy spent$1.5 million on tokens last month? But that's like somebody getting like a 50 grand mobile data bill in 2010. That's temporary. What is the steady-state equilibrium point where all of these lines, the lines on the chart kind of get lined up and we don't have this kind of weird, crazy stuff going on?

36:50And then will you have pricing power or have you got like three or four or five companies kind of all selling the same thing? And so then you should have a pricing price. You should have lower pricing and lower margins and the value should go up stack.

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38:24Lenny Rachitsky:And the bigger opportunity is in the application layer, the people building on the models, the wrappers. Yeah, I mean, this is a very sort of deterministic thesis, which is the models companies, crucially, what I said is the models don't seem to have network effects. So there doesn't seem to be a winner-takes-all effect where one of these will run away ahead of the other. So you should have competition indefinitely. If you have competition indefinitely, you don't have primary, like, really radical differentiation of what the product is, then why would you have pricing power? And meanwhile, if you need to have thousands of applications that are all different, built by different people, those can't all be built by the model people.

39:03So it should end up looking more like cloud than it looks like Windows. Now, that may be completely wrong. And one of the points I make in the presentation is imagine having this conversation about the internet in 1997. What would you have got right? Or indeed having it about mobile in 2000. You would have missed almost all of it. You certainly would have said that a has-been PC company from Crippuccino would win the whole thing. No one would have said that. And a search company with a weird Lego. Like, search? What's I got to do with mobile like no forget it you're an idiot so I we should presume we don't know but they're all you know these sort of basic building blocks like well but why would they have pricing power um I don't know I had a when I was a baby analyst in like 99 we went to see a dot-com company in the UK that was trying to do online selling computer crafts components online like and um like they had this whole model and this whole story and the brand and by the whole thing and we went up to see them and we're on the train back from Birmingham and this just sort of who's a senior banker called David Tate.

40:07We're all sitting talking about it. And Tatey says it's a low-margin reseller, one-time sales. You can say.com all you like, it's a low-margin reseller. And I think that's kind of the crux of this is they're undifferentiated commodity infrastructure providers. There's a lot of science to it, but there's a lot of science in mobile. I mean, what do you pay for flat panel screen? There's Nobel Prizes in flat panel screens. They're still a low-margin commodity. I look forward to be proven wrong, but like, hey, that's what it looks like now.

40:39Lenny Rachitsky:This is great. So I know you're not an investor. I know you didn't actually do investing at A16Z, even though you work for A16Z. Partner. Partner. Just sit around and pontificate. Partner. Are there companies you would invest in? Like if there are a couple of companies you'd invest in now, is there some on that list or categories even? You know, I mentioned briefly that I was an analyst. I was a sell-side equity analyst. I partly because I was not interested in talking to clients, partly because I was not interested in share prices, which would seem to be like a disqualification to be an equity analyst.

41:12And, you know, I don't, you know, there's, there's, there's like a huge difference between being right and being early. And there's a huge difference between the right company and the right price. Now, you know, deterministically, you can look across the market and say, well, You know, it's like, you know, like the bell curve IQ meme and, you know, the guy with 50 and the guy with 200 are both saying, Jeff Bezos, smart guy, I buy stock. And, you know, you can certainly like overthink all of this. And, you know, you can look at, you know, Google, Apple, Facebook, Amazon and say hard to see a problem for them really with all of this.

41:52You can certainly see questions for all of them. And one of them may drop the ball. but it's worth kind of remembering what happened in mobile the internet was just like a big obvious platform shift the funny thing about mobile is that some companies missed it completely and for some of them it really didn't change anything for Google it didn't change anything for Meta this was great this is a way better way to do social than on PC because you've got a camera and notifications and it's on your phone all the time with you Amazon, what does this change? It doesn't change anything I mean, I'm massively oversimplifying here.

42:28But the point is now, meanwhile, Yahoo Mail fails to make the jump. There are companies that were already kind of dying that fail to make the jump. Maybe eBay, you can argue about individual names. The point is that, like, we went through that shift and it didn't change anything for half the industry, half the internet industry. And so I think, you know, that you could kind of propose a little bit of that here. It's what Steven Sinowski at A16Z, who used to run Windows, would always say, you know, incumbents always try and make the new thing feature. And sometimes they're right. Sometimes it's a feature.

42:56Lenny Rachitsky:Actually, along those lines, something I wanted to get your take on. There's this thread that's been happening across a bunch of guests, which is around distribution becoming a bigger and bigger moat. Because as software is easier to build, everyone's launching products. Everyone's trying to compete for attention. It's getting harder and harder. It's always been hard to get people's attention, but it's just like the noise in the market is just going up like crazy. And to me, that tells me distribution is becoming a more and more valuable skill and asset. And it also tells me incumbents are going to be a lot more successful because they already have distribution versus a startup that's trying to break through.

43:35Yeah. I mean, there's like a version of, you know, the Drake meme of like, he says, I don't like that. I do like this. It's like, you know, I don't like CIN GPT wrappers. I do like harnesses. so yeah I did spend some time talking about this in the presentation I did at the end of last year that if the product is a commodity then the distribution is what matters and I wrote a thing about SAP GPT earlier this year how do they compete well there's an obvious comparison a lot of people made is with web browsers that fundamentally web browser and there's a distinction here I think between the web browser as product and web browser rendering engine in that the rendering engine can be better or worse.

44:16But the browser product is just like a really thin wrapper for a rendering engine. There's an input box and an output box. And what else? Which is like, what's the last innovation in browser design? Tab browsing, which is 20 years ago, 25 years ago. And every now and then somebody tries to innovate in browser design and it never works because you found the platonic ideal. It's like trying to innovate in smartphone design. It's a glass rectangle. There's nothing you can do there. And so what happened, of course, is that Microsoft uses distribution to break in. Then, of course, what also happens is, setting aside the lawsuit, is that it turns out that winning browsers doesn't matter anyway because the value is further up stack.

44:52And so Microsoft will use browsers for like five, six years, and it doesn't matter. It doesn't get them anything. And so clearly what's happening now is that Google is using distribution to drive Gemini. And what's the difference between Gemini and Trott? And if you're using this stuff all day, then you know. But like normal person, there's no difference. and the same thing with Meta. Like, you look at survey data on which LLM's people use, even before, like, the NUS thing, like, the LLAMA thing, like, Meta was, like, behind, it was up there between ChatGPT and Gemini, which if you're in tech, people have completely written it off, but it was like they'd sprayed it on every surface.

45:29It wasn't that bad. It was fine. So distribution of an adequate product, when the field is basically commodity distribution on brand, become a big deal. You can see that in the open AI strategy late last year. People called it everything everywhere yesterday. And so they were just trying everything to work out how they would get that. How can we get a flywheel? How can we get distribution? How can we get something that sticks? How can we get something that people use this before Google and Meta and Amazon? Spray it everywhere and get everybody using that one. And then you've got the inertia and the power of the default.

46:04And why would you switch? which obviously met Apple is kind of the last penny to drop here. That was this sort of slightly weird opening idea. And now there's even weirder story that open air want to sue Apple. Good luck with that. The funny thing about the Apple deal, I think, is just not to go off on a tangent, but like if you go back and watch the WWDC from 2024, the whole second half of it is Apple intelligence. That was like the most compelling vision of a personal AI assistant. Still the most compelling vision I've seen. They then couldn't ship it, but then neither was anybody else. And you watch it again and you're like, okay, so you want tool using a Gentic on-device AI with no prompt injection and no hallucinations and a completely standardized API system across 10 ,000 apps with a tensor all work perfectly.

46:50And like, well, that sounds good to me, but like, I'm not surprised they couldn't ship it. But nobody else has shipped that. But like that vision was great. You know, I really want to see what happens at WWDC in a month. Like, do they actually ship that now?

47:02Lenny Rachitsky:Powered by Gemini. But that's also another point. is like, okay, there's going to be the AI intelligence, whatever we call it, Gemini intelligence on Android, and then there's going to be Apple intelligence on iOS, which is powered by Gemini, but it's not going to be the same set of products. The model is just like the dumb thing underneath, the funny way of putting it, the dumb thing underneath that powers the feature. The model is the commodity that powers different decisions about what the feature should be and what different distribution. And in that situation, of course, Apple's got like a billion devices that can run this on edge.

47:31And Google has this wonderful marketing slogan, coming soon to our most powerful devices, meaning it won't work on most Androids. So again, distribution questions.

47:42Lenny Rachitsky:Interesting. Google IOS next week, so we'll see what they launch. Oh, no, they launched the Android. It just shows how modern is it today. Well, no, they launched it last week, which just illustrates how much we stopped paying attention to Android and iPhone. Google did a whole big thing last week. They're replacing Chromebooks with Google Books, and they've got a new Android intelligence powered by Gemini that will roll out to like the five people who bought a Pixel phone. It's a huge amount of work for Google. I want to go in a slightly different direction. Something that I'm curious if you're following is just the anti-AI sentiment that feels like is growing.

48:21Lenny Rachitsky:Feels like if you've seen these surveys, AI is less popular than ICE. People are trying to stop data centers from being built. I think Eric Schmidt just did a commencement speech and people were booing him every time he mentioned AI. Just like, where do you think, what do you think is going on? Where do you think this goes over time? It's interesting. And it's a big sort of fuzzy, massive difference stuff, I think. There is like tangible, like my electricity bill went up, which applies actually in a very small number of places, objectively. But it did, and this is a question. The water thing is weird because it's just like completely fake.

49:02And I should explain what I mean here. Data centers use water for cooling. It's mostly closed loop. But the number of data centers relative to the total amount of water use in the USA is tiny. I actually went and dug into this at the Livermore Lab. Did a study at the end of 2024 where they estimated U.S. data center water consumption. And it came out at about 0.017 % of U.S. water consumption. Now, obviously, if you live in a small town and you've got one well and they capped the well and gave all the water to the data center, then you're really pissed off. But that's a planning problem. That's not a data center problem.

49:37In generality, yes, data centers are what, like 5 % of US energy and might grow 1 % a year for the next five years, 1 % a point a year. But the water stuff is just nonsense. And then you get into more tangible, like, well, what is happening with this? Is it taking jobs away? where you can watch a bunch of three-hour podcasts of economists talking to each other, and the main answer is we really don't know yet. There's a bunch of charts that kind of say yes and a bunch of charts that kind of say no. And clearly there's a slowdown in employment of 18 to 24-year-olds, but that seems to be the same for people who do and don't have degrees and the same for people in fields that look exposed to AI and fields that don't look exposed to AI.

50:25So there's a lot of econometric argument about this. I mean, there's a broader point here. In fact, which is a different point here, that we have very little data on what's going on in AI from anyone. The model labs don't tell us anything. They don't give us any meaningful usage information. They give us these weird studies of how many people use this for this and that. They don't give us a daily active use number. We do not have a daily active user number for chat TPT. It's crazy. and all the data comes from academic economists trying to back stuff out of BLS surveys or consultancies and marketing agencies like spending a whole bunch of money to survey 20 ,000 people and saying, what are you doing with this stuff?

51:05We don't have good data on what's going on and how many people are really using this. But to the employment question, hence there's a lot of people looking through all the stuff that the US Census collects and trying to work out, well, where can we see this? Can we see productivity? What can we see? And the answer right now, I think, is there's no clear consensus that we're seeing an impact on jobs. But of course, politically, that doesn't matter. If you're a student and you can't get a job, and that clearly is an issue, whether it's because of AI or whether it's because of Trump and terrorists, it's a different question.

51:32Then you get niche things like people who draw book covers for young adult romance novels are very upset that now you can get a picture of a naked woman on the back of a dragon flying through a volcano without paying them. So there's, I'm sorry, I'm being deliberately unkind, but there's a little, you know there's a there's any you know people particularly like novelists people who write ebooks uh there's a huge culture war over whether it's okay to use ai this is whole sort of ai slot question and you know if you saw the number that like 30 40 percent of new podcasts generated by ai so there's a lot of there's a big fuzzy massive questions some of this i think it's a little bit like the backlash we had around social but much more compressed and like social some of the backlash around social was true and some of it was sort of true and some of it wasn't.

52:18You know, always like exemplified in the whole like Facebook sells your data thing, which is just A, not true. And B, the people who believe it are absolutely adamant that of course it's true and you're obviously a lunatic for suggesting otherwise. You know, it's like the line from Jonathan Swift that you can't reason somebody out of an idea that went reasonably to. So you get this kind of wide, it was a long way out of answering questions, but you got this kind of wide kind of spread of ideas, just as you kind of did with social. There's like 20 different things, some of which are really real and some of which are really not real.

52:50And a lot of which are kind of a fuzzy mess in the middle. All of which means that meanwhile, you've got Trump saying he wants a new executive order on dangerous models, which I actually don't think is the thing that drives the backlash. You know, they're worrying about myth or cyber. I don't feel like that's, you know, a main street America conversation. But that's the thing that got Trump interested in this stuff again.

53:12Lenny Rachitsky:Let me go kind of in a tangential direction. Something that I like to ask folks that have kids that come on the podcast, especially people that are thinking so deeply about where things are going, knowing what you know about just where the world is heading, what AI is going to do to the future. How are you changing the way you raise your kids? Just what are you teaching them differently, potentially, that might help them in the future? I don't know. I think there's a curve here in that if you've got kids who are going onto the job market in the next year or two, then everything is up in the air and no one knows how this is going to work.

53:43If you've got kids that are going onto the job market in like five years, then who knows? But stuff will have settled down a lot by then in probably unpredictable ways. So I could be a lot more worried if I had a 21-year-old. You know, I don't. I've got a kid in his early teens. So those questions vary. then you've got a lot of the questions that were the same before chat tvd around you know the collapse of gatekeepers the you know no should you really believe what that influencer on tiktok says and you know where exactly you're getting your understanding of what's going on in israel and all of those kinds of social media internet media consumption kinds of questions I don't know there are people who are like super super intentional about you know every minute of their child's life I'm not I'm kind of recall you know the George Carlin line you know that anyone who drives faster than you is a maniac and anyone who drives slow is an idiot and that certainly applies to parenting and so I'm you know like everybody thinks they're somewhere in the middle But, you know, I don't have, you know, a deeply systematic and widespread and coherent like plan for this is what my child is going to be doing in three, six, 12, 18 months time.

55:06I'd settle for him not breaking his Chromebook again.

55:09Lenny Rachitsky:I like that your general vibe is it's going to be OK, guys. It's going to be OK. Yeah, I don't know if you I think if you, you know, maybe this is because I'm British and we haven't had political violence in 500 years. And I think, you know, maybe if I came from Iran, I'd have a different attitude to being calm about the future. I think there's a layer of like, yes, this will change a bunch of stuff and we'll need to worry about it. But that's kind of a constant. We've always had that. Remember in the whole wave of the panic around social media, I dug up. There were a whole bunch of books in the late 70s about databases.

55:48There was a whole panic about databases. and again half of it was true like um you know if everybody's like police records and arrest or if all police records and all government records are online then that's different if you think about for example the deep nudes deep fake nudes issue for example there's like a dumb reaction to this which is to say um haven't you heard of photoshop which is true but a 15 year old kid couldn't use photoshop to make hardcore pornographic nudes of every girl in their high school and send them to the whole school in one afternoon and turn them into video exactly even well yeah even more and now they can so like that is different it's kind of like you know the challenge of social you know the thing people would say in the 90s is it's great you can be you know the only gay kid in your village and you can find other gay people and you can find your tribe.

56:45And guess what? It turned out you could also be the only Nazi in your village or the only pedophile in your village or the only somebody who wanted to look at child porn. And I'm like, yeah, now you can find the other people who like looking at child porn and they'll tell you it's great. So, oops. We connected everybody. And unfortunately, that meant we connected all the bad people and all of our own worst instincts and every problem in society. And so that will happen again with AI. You know, we can deep fake news like the obvious thing we can see now. There will be a whole bunch more of this stuff.

57:11But there's also, and you know, something a kind of technical audience should know about. Do you know about the post office scandal in the UK? No. Okay. So sidebar here. So in the UK, post offices are mostly franchises run by small business people. So they're run by like pharmacies, classically. Very often Indian immigrants, second generation Indian people. And so the post office, like 15 years ago, rolled out a new point-to-sell computer system. So they have a separate counter in the back. That's the post office. And so the post office rolled out this new computer system and built by Fujitsu that had a bunch of bugs in it that showed shortfalls in cash.

57:46The post office looks at this and says, aha, we knew these people were stealing from us. Hundreds of people get to prison. Bunch of suicides. Bunch of bankruptcies. People lose their homes. Meanwhile, people from the post office and people from Fujitsu are going to court and swearing there's no bugs in the system and nobody else has had this problem. This is 1970s technology. That's really the point, that every wave of technology comes with ways that you can ruin people's lives, either deliberately or by accident. This is the whole thing of Chinese mass surveillance is deliberate. This is maybe people should go to prison, maybe not.

58:20But like we have this with every technology. We have a bunch of ways that you can ruin people's lives and you have to be conscious of that and also kind of not panic about it.

58:28Lenny Rachitsky:So maybe following that thread and coming back to the kids thing and the jobs thing, is there like a job you are steering your kid away from? and is there a job you kind of think you want to steer them towards? I don't know about that. This is probably a little bit early yet. He's not quite at the like I want to be a fireman stage. That might be a great job. Yeah, and certainly if I look at my career, I started as an equity analyst and then I went and worked in industry and then I was a consultant. The days when you kind of knew what your career was going to be. However, there were simply some people where you want to be an architect, you want to be a software engineer, you want to be X or Y.

59:03I don't know. I think, you know, the only kind of thinking I have here is that you have, like, you slowly work out there's a bunch of skills that you have. And there's a bunch of, like, jobs that makes you good at. And then there's a bunch of stuff that people will pay you for. And you want to get at least two of those and preferably all three.

59:21Lenny Rachitsky:Okay. So zooming out a little bit, let me ask you a meta question. What's a question about AI that you think nobody's asking yet or not enough people are asking that we should be asking ourselves? Sure. I mean, we talked about value capture. Obviously, this is a whole, everyone is asking. I'm not sure how many people are asking whether model labs have pricing power. I think a lot of people are just presuming that the situation today will continue or that, of course, they will. So I think that's maybe a question that not enough people ask. I think the question I posed towards the end of my presentation, which we talked about earlier is like what's the task and what's the job but what is just the thing that becomes a button or makes goo versus what are people actually hiring you for is that kind of a useful way of thinking about this and clearly there are going to be some jobs where no that is just a task and that job gets sort of made it away but there's a bunch where that kind of isn't the question the way i actually pulled that together at the end of the deck was a chart of global recorded music revenue which as you may know is kind of a u-shaped curve more or less and so it's dropped by about half from 2000 to 2015 or so.

1:00:29And since then has come back to about 75 % of the peak adjusted for inflation. And the way that I look at this is, and that's driven by streaming, and I kind of looked at this and said, well, the first half of this chart is saying what happens if I don't have to pay$15 to get a CD to get that track. And the second half of the chart is saying what happens if$15 a month gives you all the music that there is. So it's kind of a completely

1:00:54Lenny Rachitsky:different sort of question. And you could, you know, that's a way that you could look at Uber or the way you could look at Airbnb, all these kinds of companies. Is it to begin with, you do the old thing, but more. With any new technology, you do the old thing, but more of it on the new place. So, you know, you put Flickr on mobile, you print out your emails, and then you make new things that are only possible with a new thing. And then maybe you go a bit further and you kind of completely redefine the question and you make something that isn't that at all. You know, Spotify is not an online music store.

1:01:26It's something else. And right now, you know, those questions, you only even know what the question is after it's been asked and you've built a billion dollar thing that lots of people use. Because obviously Spotify looked crazy and Uber looked crazy and Airbnb looked crazy. But that's the sort of, I think, the way to get at what this means is you have to get past we do the old stuff but more. and you have to get to what do you do that's different that's because of this what is this change what wasn't possible before what gets unlocked as opposed to just doing the old thing

1:02:00Lenny Rachitsky:but more of it yeah just to support this kind of general theme you have of it's like we don't know what is going to happen like this is unprecedented if you if you were to zoom out like a few years ago maybe three years ago four years ago the last profession you think would be automated is engineering and coding. It's like, that feels like the hardest thing. That's like, we're going to need people to build these things. Now it's like the most transformed role of any role. Like you went from writing all your code to 0 % of your code is AI. It's almost like you didn't realize it was boring manual labor that could be automated.

1:02:32You thought it was something else. It's funny. I mean, I was looking at this, there's a sort of US government called data set called O-Net or something like that. We try to kind of analyze every single job and then people try and kind of score it. and they try and say, well, you know, this profession is X or Y percent exposed to AI and AI can do Z percent of it today. I think this is just the most ridiculous bunch of deluded horse shit. And there's two reasons for this. The first reason is that this is like, ironically, this is the logical systems problem, the expert systems problem. The problem of expert systems is like for anyone who doesn't know, you try to recognize a picture of a cat and so you start building up logical steps.

1:03:14So you'd make an age detector, and then you make a third detector, and you make an eye detector, and you make an ear detector. And 15 years later, you've got 700 steps and it doesn't work. And this is what happens when you try and look at a profession and sort of break it down by which bits can be automated and which can't. You can't describe a profession like that. Or at any rate, we can't. You can't kind of look at a senior partner at a law firm and say, well, 17 % of their work could be automated. This is horseshit. You can't do that. I think the other side of the fallacy, though, is to talk about taxi drivers.

1:03:51So, you know, if we'd been having this conversation in 1997, it's like the Uber test. Imagine we're in 1997. What will be crushed by the Internet? Well, newspapers will be fine because they'll save money on the printing bills. This is like a joke that people said that. Newspaper, the Internet will be great for newspapers. Their printing bills will go down. Well, yes, but no. But the other side is, well, obviously, like taxi drivers, you couldn't automate that with the internet. It's got nothing to do with the internet. Maybe you'd have internet booking, but like, no, that's not going to change anything.

1:04:18And of course, it completely changes the whole thing. And so, like, the example I saw the other day was like things that won't be affected by AI personal trainers. Okay. So I take my iPhone and I balance it on the metal piece with the camera pointed at me. it and I ask an AI to build me a training routine and watch me and tell me if I'm doing it right, why do I need a personal trainer? Now, that might be complete bonsons. But that's how these things work. Like the stuff that you don't think is, you can't predict which things are going to be exposed necessarily. Or, you know, a lot of the big companies are things that didn't look like that would work and didn't look like that was exposed.

1:05:02The other side of this, of course, is this is one of the charts at the end of my presentation is comparing Uber and Airbnb, because this is like the cliche from Mark and recent, that like Uber doesn't sell software to taxi companies, Airbnb doesn't sell software to hotels. Okay, now let's go and look at the market impact. Well, the whole bunch of cities were Uber demolished a taxi business and made it much bigger as well. The TAM became much bigger and everyone switched. Airbnb's impact on hotels, if you actually go and look at the numbers, is pretty marginal. They carved out this whole other business and maybe they slowed down the growth of hotels a bit, but you know my wife flies to milwaukee next week she's going to land at eight o 'clock at night she wants to go to a hotel she wants to have room service she needs a bathroom bath she needs you know she needs a gym at six in the morning and then she gets seven in the morning she's going to drive to the client site she's not going to stay in airbnb like absolutely zero chance she's going to stay in airbnb and half of the hotel business is travel is business travel and you As soon as you actually get into anything, then it gets complicated.

1:06:01I remember somebody on social media said a problem with Benedic is his answer to everything is it depends. It's like, yeah, it does. It depends. So it's back to my 1997 point. You can say some of this, but you have to have that humility.

1:06:20Lenny Rachitsky:Yeah. Coming back to this phrase you used, presume radical uncertainty is a nice core thesis here. So knowing all this, just it's hard to tell. We don't know exactly where it's going. Things are going to change a lot, but it'll probably be OK broadly. Just a lot of people listening are pretty worried about their jobs and their careers and how much the world changes. What would be a couple of things you recommend people do, knowing what you know, to be more successful in this future? Well, I should just kind of wind back on what you just said. It's like, as Keene's tells us, in the long run, we're all dead.

1:06:53so you know it's all you know like on average um you know on average nobody died in world war one great but if you know if you're if you're a 19 year old in 1914 you you've got a you know one in three chances of not coming back so um yes you know clearly there's a bunch of professions where this is a major question and particularly if you're an associate or one would have been thinking about being an associate this is a major question and it's very unclear how those professions are going to play out. It's very unclear what happens to the pyramid structure of professional services. The only answer I think one can have is, don't stick your head in the sand and say, I hate all of this stuff, because that gives you a great feeling of moral superiority.

1:07:39And you can go on Blue Sky and shout at everybody, shout at each other about how evil AI is. Like, great, I'm happy for you. But that's not going to help. What helps is you diving into this completely submerging yourself in it and coming out, understanding what you can do with it, how this changes things, how you can be a great hire. And that may still not help. But, you know, if you're going to a law firm and they're like, well, we hired 100 associates last year and this year we're only going to hire 50, going to the interview and say, well, I think AI is bullshit and I'm never going to use it is probably not the right move.

1:08:17so you know you can that that that may not be particularly comforting but i don't think there's there's an alternative is you know you have to dive into this and absorb it and internalize it and think about what it means just as you know you and i did with mobile and with with the internet

1:08:33Lenny Rachitsky:i think that is actually very actionable and and very consistent advice on the podcast is just just do stuff build it don't just sit around and pontificate and be be pissed at what's happening to close this out. I'm going to take us to AI Corner, a recurring corner of the podcast. And the question to you is just what's one way you've used AI and use AI in your work or life that is really interesting, something that other people might be inspired by? I don't know. I struggle with this question because I'm sort of the lawyer looking at chat TVT. So, you know, the stuff that I would do that I would automate are sort of precise information retrieval task, which is precisely the thing that this is kind of worst at.

1:09:17And, you know, that's not a criticism. It's just an observation. The kind of stuff that I would want a machine to do for me is the stuff that AI kind of can't do for me very, very, very well at the moment. I use it for proofreading. I use it, you know, for images. I used it redecorating my apartment. That worked fantastic. You well at that. Here's a picture of this room repainted at this light and this table and this rug. No change of color of the rug. There's a kind of plaster stuff where it works. But a couple of years ago, somebody said AI is good at stuff that computers are bad at and bad at stuff that computers are good at.

1:09:50And that's, I struggle to find many examples of those where I need it. But then, you know, I'm a kind of a unique, weird job. You know, I sit at my desk all day, you know, trying to synthesize a whole bunch of other stuff into a whole bunch of new ideas. That's not particularly common for people to spend their time. I struggle to find AI use cases I am the accountant looking at the spreadsheet and thinking well that's very clever and this is clearly going to completely transform everything but I actually don't make spreadsheets every day

1:10:22Lenny Rachitsky:I went to a stand-up comedy show of Pete Holmes, I don't know if you know him and he made this joke that we want AI to do like clean the poop off the street and do all these like hard things that nobody wants to do but instead it's like oh let me help you write, let me help you create imagery, it's like this bohemian It's like, no, I don't want to do all these ugly things. I want to be creative, make art. Yeah, well, I mean, there's variations of all of this. It's like I don't want the AI to do the stuff I do for fun. I want to do the boring stuff that I don't do for fun. And finding that mesh.

1:10:55I mean, joking apart, this is going to come back to my chatbot point, that the chatbot is a blank screen in a jagged edge. What am I supposed to do and what will work? And that's a big problem. and the solution to that problem is to wrap it in use cases. Part of it is also like AI just disappears. So most of what I write now I dictate. I dictate as a voice memo and that's automatically transcribed. Is that still AI or is that just voice recognition? Probably an LLM in there. Okay, so maybe that's AI. Well, okay, so what? At a certain point, it's just automation.

1:11:28Lenny Rachitsky:What do you use for that, for voice transcription? So I actually find Apple Notes, the Apple, the one built into the iPhone, well it's fine I mean I'm conscious of the people want others but like I mean I dictate it there it is it worked so I'm happy with that all right final question before we get to our very exciting lightning round is there anything else that you wanted to share anything else you want to leave listeners with no I think you know I've monologued plenty and I've gone through a bunch of stuff in the deck go read the deck and sign up to my newsletter and then you will get many more mags of brilliant Benedict Evans wisdom, some of which may even be useful.

1:12:06Someone unsubscribed from my newsletter and they said, you didn't give me any actionable stock ideas. And I'm like, well, on one level, that's completely true. On the other level, maybe not.

1:12:17Lenny Rachitsky:Well, with that, Benedict, we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Sure. First question, what are two or three books that you find yourself recommending most to other people? The tough one for me, because I just read an enormous amount of books and then I can't remember which ones I've read. I sometimes often joke that there's a classic British comedy from the late 19th century called Three Men in a Boat, which is like my I Ching. Like we're having trouble hanging a picture. Well, there's a section about that. You know, we're having trouble doing this.

1:12:48Ah, well, there's a story about that, all of which are hilarious. So Three Men in a Boat is my I Ching. There's a book by, I think, William Cronin about the economic history of Chicago, which is fascinating and actually very relevant to technology because it's talking basically about standardization and packetization and logistics and channel conflict and network dynamics and network neutrality. So like when the meat packers of Chicago reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it, and then ship it back to New York than to kill it in New York. and the pricing of refrigerator cars.

1:13:21And it's exactly like reading about broadband. It's all the same kind of business issues, which is fascinating. What else have I read? I don't know. Read books. Read different books. Generally read books for grown-ups. Please read something other than Lord of the Rings if you're going to name another company. I saw this and what was the latest, like, Peter Thiel company? I was like, read another book. Everything is named after a character from this one book. there's more than one book in the world there is more than one book then all about science fiction read read about different things read about things you don't know about kind of along those lines

1:13:54Lenny Rachitsky:you have a favorite recent movie or tv show and you've really enjoyed i don't know i've dropped so badly off the the current media treadmill and i just spend most of my time watching classics which are like always the ones that you're supposed to have seen and that all seem intimidating and then you watch them and you're like oh that was actually really good um i watched to Seventh Seal recently, which is like one of those Jake Woody Allen, terrifying, boring movies. And it was brilliant. It was really interesting. And it's like, it's only like an hour. So go watch one of those movies that you are supposed to have seen or hadn't seen.

1:14:26Lenny Rachitsky:Favorite recent product you recently discovered that you really love? It could be a gadget, it could be an app. I was speaking at a partner meeting for a company earlier this week, what's today, Monday, no, last week, and met the founder of the company who has a very famous network the CEO of the company has a very famous name and admired his shoes and didn't say anything but then went and googled like half an hour later yeah okay I'll buy a pair of this you want to share the brand or you want to keep it keep it secret okay we'll keep it secret I don't know I think one comes it one comes in waves of new products and you know you get into waves of new things and um like when's the last time there was a cool app like iPhone apps that was you know all that white space went I mean it's partly a a function of product shift, a platform shift.

1:15:11All the white space went for cool new apps, and now we haven't quite got, actually this is to the earlier point, we don't have breakout a consumer AI app yet, because I think because of marginal cost more than anything else, you can't make it free and get 50 million users and then have a revenue model. But we don't have those breakout things yet. For consumer. For consumer, no. I keep getting these ads for voice recorders. Like somebody's selling like a business card size, like hardware voice recorder. on my bit. But I didn't get it. I've got a voice recorder on my phone.

1:15:43Lenny Rachitsky:Yeah. All kinds of cool stuff coming. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to often in Worker in Life? I suppose I've mentioned earlier, apparently, I mostly say it depends. That's going to be the title. It'll probably be okay. Yeah. Okay. That's the vibe I get. I like that. I like that it's probably going to be okay. Not for sure. Okay, final question. I saw somewhere that you own a lot of old phones. Is that true? It is, yes. I kept, I mean, I was a telecoms analyst and a mobile analyst, and I kept all my phones up to a point. Now they're kind of uninteresting.

1:16:21But as you may remember, like before the iPhone, particularly outside the USA, there was this huge creativity and expansion in what phones look like because everyone was basically innovating around a little teeny tiny gray square. So everyone was trying to differentiate from everything else. before it kind of results. It's kind of like cars, actually. It's like cars before street, before like wind tunnels, cars all look different and everyone's trying to innovate around because you've got the same four wheels and the same engine and everyone's trying to like differentiate based on like the shape and then everything converges on one shape and it's kind of the same with phones.

1:16:52Like everyone, everything converged on one shape. Before that, there was all this innovation. So yeah, like I have like a whole bunch of PDAs and smartphones.

1:16:59Lenny Rachitsky:How many phones are we talking about? I don't know, like 20 or 30. Okay, okay, okay. It's not so crazy. What's like the oldest one? What's the oldest one you got? So I have one of those, I should have told me I'd have got the box down. I have one of those Ericsson Sharkfin flip phones from my 98 or something, which is, again, like hardware design, visual design, trying to differentiate. shapes i've got an imade phone from 2001 and a j phone phone from 2001 that has a camera so i came back from japan in 2001 and my phone had a color screen and a camera and like i just had like endless client meetings and people just wanted to see the phone with a color screen like it's a mind-blowing didn't work outside japan it's actually i plunked it in the other day it still charges up i mean clearly i can't do anything with it um and like i mean there's a little bit of an analogy in there as well and like we thought there'd be all these different shapes and sizes and before the iPhone people kind of imagined like well some people will have like a little pocket pc and some people have a keyboard and you have like folding or all these different ideas for what it would look like and it all we didn't realize it was all going to converge on one device.

1:18:04Lenny Rachitsky:Benedict this was amazing I learned a ton I feel better after this conversation. Two final questions where can folks find you online where they find this presentation and how can listeners be useful to you? If you can google me as I always say my parents had good SEO. So Google Benedict Evans. And so there's a website where I publish all the presentations that I've done and sign up for my newsletter, which comes out every week. Otherwise, how can they be useful to me? I'm always trying to understand stuff and I'm always trying to ask different questions. The worst thing in tech is to carry on talking about the same stuff.

1:18:37It's like, the moment you really understand something is the moment you have to push onto something else. And so I'm always trying to think like, no, am I just talking about the same thing over and over again. Like last year, I just spent probably too much time saying, but these models still hallucinate. Stop telling me they don't hallucinate. And they do. They still hallucinate. You know, you push them a little bit further, any question, and you'll still get like, no, that's not true. But that doesn't mean they're not useful. So you have to kind of keep pushing myself. So that's always the challenge for me is how do I push?

1:19:10And then, yes, if you want me to come and present to your board in the Caribbean, then let me know.

1:19:15Lenny Rachitsky:And by the way, the domain is ben-evans.com if folks want to check you out. And evans.com. Ben and Dick, thank you so much for being here. Thanks a lot. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.