E43: Benedict Evans on Automation Crypto, VR, and Predicting the Future of Tech

27:01And so there was this whole idea that open source was going to destroy Microsoft, that no one would ever pay for software, that buying software was evil, that selling software was immoral. It wasn't just like a bad model. It was immoral to do this. It was an ideological kind of religious objection. Of course, what happened is 25 years later, the Internet, everything runs on open source for consumer. And yet it kind of doesn't. Everything is open source under the hood, but Facebook isn't open. And Mac OS runs on open source, but it's not open. And yet it has millions and billions of app downloads.

27:38So what does it mean to say that it's open? Microsoft owns GitHub and people still pay for Office 365. So the reality when the religious thing kind of gets deployed can be a bit more complicated than that. And, you know, you kind of describe that model of, okay, you've got this Instagram running on this open source system and everyone can see how it all works. And so I'm an influencer. I post an ad. The advertiser puts the money into an escrow account on the blockchain. I can see it's there. When I post the ad, mechanistically, X percent of the money goes to me. Y percent goes to the early stakeholders in the system.

28:07Z percent goes to whoever it is that's running the infrastructure. And it's all in the code and you can see it all. Now, the problem with this, I think, is it's basically is to take everything that people in the late 18th and early 19th century said for why democracy won't work. Which is, you know, we asked, but, you know, how do you deal with the tragedy of the commons? What happens with the mob? What happens if people are tricked? And so you construct these elaborate constitutions and say, we'll have separation of powers and we'll have a court and we'll have this and we'll have that. And we'll have a rule that says the magistrate has to step down over this period.

28:41And if you look at the history of like the last, you know, Americans tend to not really understand this. But like, if you look at democracy in the 19th and 20th century, you can point to probably 10 or 50x more places where that didn't work. You know, look at all those African countries that got their nice model constitution the day of their independence. And where were they five years later? Look at all Latin America. You know, they all had these nice model constitutions. And like, what's the history of democracy in Latin America in the last 200 years? and you know or in europe you know look at germany you know look at italy they had these nice democratic constitutions and they kind of broke so you know it's very easy to say well we'll structure we'll write this constitution for how this instagrammy thing it's going to work what actually happens if it turns out that you need to change something and the people that have the votes don't want to change it how does that what happens then how does that work what happens next is it a good thing for everything to be securitized is it a good thing if every mechanic in a social network has money could have money attached to it is it a good thing if i'm being paid for every post is a good thing if i'm being paid to get them for for every penny of engagement every unit of engagement i guess because that creates um what's the phrase i always forget who it is but there's somebody who won the nobel prize for pointing out that if you use any economic metric to set economic policy then that metric will stop reflecting um the economy good heart's law yes exactly it's good heart's law yeah so good heart's law would apply here that if you suddenly, if all of the metrics and the engagement within a social network are now subject to money, that will change how it works.

30:14That won't just be kind of a neutral externality. That will change the mechanics of it. So you can kind of propose this kind of model, this ideal fantasy world, and then you say, well, yes, but what will actually happen? And you have these people who sort of say, well, there'll be no gatekeepers in this world. Well, really? I always thought there'll just be new kinds of gatekeepers. Big Cloud comes to mind as a model that tried to financialize Twitter in an extremely aggressive way. Yeah. There's a bunch of stuff wrong with that. One of them is doing the old thing, but on the new architecture almost never works.

30:46You have to do actually some new thing that takes advantage of the new architecture. It was also the wrong time because Elon hadn't bought Twitter and screwed it up at that point. Yeah. And it was also that it wasn't just that it was on the new architecture. The whole pitch was it's Twitter, but with Bitcoin, which reminds me an awful lot of open source projects 20 years ago where they would say, it's Microsoft Office, but open source. And guess what? But open source is not a selling point to anybody. It's a selling point to engineers. So if you are building engineering infrastructure and it's open source, that's actually a selling point.

31:24But if you just want to write letters, the fact that the thing you're using to write letters or do spreadsheets, he's open source. We don't care. It's not a feature. Yeah, it is interesting because, yeah, a lot of people are building Twitter clones in some capacity, especially as you mentioned, now's the time. And Farcaster is one that is effectively a Twitter clone, but with two things enabled by crypto. You know, one is that, you know, you can't censor someone. Two is, you know, building on top of the platform for developers, whereas Twitter shut down its API. Yeah, I mean, the censorship thing, again, I think is one of these kind of weird ideological back alleys.

32:01But if you go and ask, you know, 3 billion people who use social networks, what is the censorship problem with social networks, they will tell you the problem is they don't take down enough stuff. The problem is they don't get rid of enough Nazis. It's not that they take down too many Nazis. Yeah, and so then this question is the API, is the developer, you know, building on top of it a big enough thing? Yeah, well, this goes back to the democracy question, which is, who is it? You have to have somebody who's making decisions. You can't run a country entirely on referenda. You need to have some sort of executive power.

32:35And so there has to be somebody that can say, no, yes, we're shipping this. No, we're not shipping that. And I think you could argue this is one of the reasons why open source projects never managed to produce a breakout consumer product back, you know, the early 2000s. That could be a whole other podcast. But one of them is that open source projects were systemically extremely bad at user interface. They were very good at building engineering infrastructure. But, you know, we spent, you know, desktop Linux was a joke. This was the running joke. This is the year of desktop Linux. And here we are 20 years later.

33:04There is no desktop Linux for consumers. No consumer uses desktop Linux. it's only something that's used by engineers. And then you get the engineers saying, oh, but Android is running it. And yes, but people use Android because it's not, they got rid of all of the stuff, it abstracted away all of those UI issues and Google set and designed the UI. And so that again applies even more so I think with blockchain is how do you abstract away the UI? And this is like kind of one of the kind of elemental questions I think we're building something on a blockchain is how do you get to a point that the user doesn't need to know what architecture you've used?

33:42Or let me rephrase that. If you can do that relatively easily, but if you do that, then what's the point? Because if the point of this is that it's portable and composable and addressable and they have ownership, if you abstract that away so that it becomes easy to use, then what was the point in doing it? And so there's a kind of a weird, like there's an interesting product balance defined in that. How do you give people the benefits of ownership and portability without making them like print out their C phrases? What is the reason why you're not using, just using a database? Can you, those reasons survive if you are not requiring people to install MetaMask and audit smart contracts.

34:29Is there a path between those two or are they fundamentally in conflict? And I don't think there's much agreement on that at the moment. What you certainly can't do is say, well, everyone's going to have to learn how to use this because that just doesn't work. I mean, this was to say, well, everyone will have to learn how to compile their own code and how to install new kernel extensions because Linux is so great. No, no, fuck off. No one will do that. They just want to turn the computer on and have it work. And that was solvable in open source because Mac OS runs on open source, so the user doesn't need to know.

35:02But as I'm kind of repeating myself here, but a lot of the challenge for Web3 is that the reasons you're using it are things that the user would need to know about. And to your music example, why aren't you running that on a database. Now write down your answers and say, but do the users know about that? Because if the users don't know, then what's the point? Are you more bullish on re-imagining kind of internet applications or, you know, things like Instagram as the example we brought up then sort of, you know, but in a native web three way, as opposed to rebuilding the financial system or re-imagining the financial system.

35:39So again, so, so, so, so, so several observations here. One of them is I get back to my point about Netflix that an awful lot of these questions are basically financial services questions, not tech questions. Second, I think a lot of the discussions here are basically Americans wishing they had stuff that they don't know, maybe don't know that the rest of the developed world had like 20 years ago. Like, you know, I can do a free instant transfer. Yeah, I think I was doing that in 1998.

36:14We can get rid of checks. I moved to America, I had to pay my check for the rent. I had to phone my father and say, how does this work? What is this? Because I'd done two checks in my entire life. What does it mean? If they say they want me to endorse the check, I have literally no idea what that means. Because that's something my father hasn't done since the 80s. And so there is this whole chunk, and I think you can see this is kind of a PayPal as well, there's this whole chunk of leapfrog the last one 40 years of the financial services industry that America just never happened in America. So that's the piece.

36:47And so then I think, again, there's a strain of ideology. So I mentioned religion. There's an open-sourcing sort of religion of let's get rid of gatekeepers, let's get rid of Google and Facebook where previous open source people want to get rid of Microsoft. There's also this sort of gold bug, fiat currency, inflation, central banks are evil, the government might steal my money thing, which again, is very, it's very ideological. And, you know, look, if you live in Turkey or Argentina, then the government might either literally steal my money or deflate away half the value of it this year is a real thing.

37:21And keeping your money in gold under the bed is like a rational thing to do. Whereas if you live in, or, you know, Germany in the twenties or whatever it is, and, you know, if you were Jewish in the 20s, then having diamonds might be quite good, although sadly, it tended not to work. If you're living in Afghanistan, being able to put your money on a USB stick sounds like a great idea. If you live in Britain or France or America or Japan, this just sounds insane. Why the fuck would I want to do that? I'm not worried about the government stealing my money. And yes, you say this to people and they think you're an idiot.

37:57Most people are not worried about this. Most people do not own gold bars. That's not the use case for most people, which is again, being able to compile the source code is not something that makes people care about. And open source people could never accept that. Strip all of that away. Yes, there's some interesting things in there somewhere. Quite a lot of them have sort of speedrun the last hundred years of financial services innovation and financial services regulation or don't understand why we had that. Here we are. Useful book for anybody in DeFi. There we are. Reminiscences of a stock operation.

38:35Nice illustrated edition with the original artwork from the Saturday Evening Post. And this is a biography of what's his name? Jesse Livermore, who was a famous stock manipulator. And this is in the days when there basically weren't any laws about what you could do. I think an awful lot of people in the last couple of years were basically working their way through that by page by page. And they didn't then go and look up Jesse Livermore and find out what happened to him, which is that he went bankrupt three times and killed himself. There's a science fiction story about an alien race that humans encounter an alien race and sort of beam down libraries to them.

39:13And one of these sort of alien politicians reads Machiavelli and goes and sees his power and says, I should probably read Julius Caesar next.

39:24Yeah. And so there's a lot of speed running Wall Street here. Now within that, yes, there's some interesting ideas, but mostly these are Wall Street conversations. Can you do interesting new stuff? I don't know. Ask a commercial banker. It is interesting. Science fiction works both ways in terms of it inspires, but it also deflates. I used to be really excited about income share agreements. And then I read this book, The Unincorporated Man. And in the book, one corporation owns a stake in everybody that somewhat feels like they're slaves to the corporation. And that got me less excited about the idea of income share agreements for ownership and everyone's upside.

40:07A couple things before we get to the AI portion. There's 10 different ways of describing the metaverse. What is the most accurate way of describing it? Is it the idea that we're going to live more, especially as the headsets or tools get better, we're going to live more of our lives on the internet and thus a whole method of interaction and economy. So I would have agreed with you, except that now I encounter people who think that you'll be able to use the metaverse on your smartphone, at which point to me that I have literally no fucking idea what you're talking about. That's the internet. You don't need a new word to describe the internet.

40:41So there's two buckets to talk about here. One of them is relatively easy to understand is the idea that some combination of VR and or AR, meaning like these glasses are displays, becomes the next universal device after smartphones. And so VR gets much smaller, gets much lighter, gets much better. We work out the optics to have something that looks like this, maybe powered by something in my pocket, whatever, that can be a display that can place things into the world around me. That becomes a universal device. That's a pretty straightforward, easy to understand thesis. Now, the basic arguments against it is A, the optics will never work for AR, but for VR, the argument very simply is if I'd shown you a PlayStation 5 20 years ago, you would have said, oh my God, this is amazing.

41:26This is the future. And guess what? Games console install base is sort of 200 million units, maybe 250 million units. It's not a small market, but it's not a universal device. Most people see a games console game and go, that's pretty and walk away. And now there's a rabbit hole we can go down around, yes, there's three billion people playing games. Well, no, it's probably about one and a half and most of that's Candy Crush. Like what's the move like? How many people are actually playing games? And are they playing games on smartphones? But the core of it is that VR is by default this sort of deep, narrow experience.

41:59And maybe it will just remain a deep, narrow experience. Maybe it will remain a subset of games consoles. And most people will say that's very pretty, but won't use it and it become a universal device. AR, there's more basic science problems to solve, but we'll see. But that's like a narrow metaverse thesis in which when you say metaverse, you're basically saying it's like saying mobile internet. It's as though we come up with a cooler word, the cooler way of saying mobile internet. Well, this is VR internet. There's a much broader and much more hand wavy description, which is again, it's back to, you know, going back to talking about 18th century philosophers, it's sort of imagining this imaginary constitution.

42:34So this thing about 18th philosophers do is they describe some imaginary country and Plato does this with the Republic. Like it created, described an imaginary country which has an imaginary constitution where everything works incredibly well and all the stuff we don't like about our own society has been fixed. And so this is also the part of the problem with web three. It's like on web, in web three, there'll be no gatekeepers because of this, but that doesn't solve the reasons why there were gatekeepers. And, you know, in, you know, in, in my imaginary kingdom, the judges will all be just and the king will always be honest and like, yes, but why will they always do that?

43:10And the same thing with Metaverse is like, everything will be interoperable, everything will be portable, you'll be able to move assets between every different thing. Why? Does that make sense? I mean, I always reference that movie Wreck-It Ralph here, if you ever saw that. So like, video game characters move between games, basically. so but the kind of the point is okay so i'm so so all my assets and all of these different metaverse environments are all portable then i can take them from one place to another well why why is it that companies don't let you do that now why would that change why would they do that but more fundamentally you know i go into fortnight and i buy a 15 foot tall yellow banana character with a gun that shoots exploding tomatoes cool okay now i go and open up um my Top Gun game and I'm flying my F-14.

44:02So what am I, what am I sitting in the cockpit of the plane as a giant banal? Like, what does that mean? I go into Call of Duty and I'm sneaking through the forest, like crawling through the mud with the other VR, with my other VR special forces guys, except I'm a fuck off great yellow banana shooting. Like the reason the assets aren't portable between the games is kind of because the games are different. It's partly the business model, yes, and you can't move assets between Fortnite and PUBG, and maybe that would make sense, and they don't want you to. But it's also that they're designing the game, and the people designing the game want to be able to control how all the different components work so that it's a good game.

44:44But it's also that, as I said, I buy an AC-130 gunship that's got a bunch of 30-millimeter me to cannons and a couple of recoilless rifles on it. And I take that into call of duty. Like what happened? Does everybody else have to go and buy an F-14 and take that? Like, then it's like it's all over the game. So, I mean, a lot of that sort of portability interoperability stuff, it just seems to me like kind of vague hand waving that's completely detached from any discussion of what the actual product would be. We were talking a while ago about how long this stuff takes as well. And then maybe this is sort of a more fundamental point, But, you know, presume for the sake of argument that in between five and ten years' time, say in five years' time, VR really takes off or AR really takes off.

45:27Apple announces something this summer. Apple announced the iPhone in 2007. Sales didn't take off until 2010. So Apple announced something this summer. By 2025 to 2030, you've got hundreds of millions of people using this. Okay, now imagine it's 2000 and you're trying to describe what the smartphone internet's going to look like. You can't. Anything you say would be wrong. You wouldn't even have got the app store, although there were app stores. You wouldn't have got Instagram. You wouldn't have got Uber. You wouldn't have got TikTok. You wouldn't have got all the stuff that mattered about how it worked.

45:59And the same thing now, even if you believe that VR is going to be the universal device and 3 billion people will have one in 10 years' time, any prediction you make today about the specific market structure of that and who's going to be in charge and how it will work is going to be wrong. And it's kind of pointless to even attempt to do it. it's just this idea of, you know, gaming companies keep their virtual goods in game, because, you know, if you can make it portable, perhaps they'd be less likely to buy. There's this product question of anytime you provide more consumer surplus, does that just mean the new version of the business that had this sort of, you know, moat that is now, you know, less powerful because of the new technology?

46:39Is that new business just smaller? Or do they find this new business model that didn't exist before that now enables them to be bigger? Do you have a perspective on the idea of anytime there's more consumer surplus, are the businesses usually bigger or are they? It's difficult to answer. I mean, on the kind of, on the narrow game point, I think there's a business model point and there's a product point. I mean, in a different context, it's kind of like saying that Photoshop and Excel should be interoperable. What would that mean? You know, the interoperability layer is the file system, you know, and the copy paste, like for the idea that like Excel should be able to open Photoshop files, like, well, then it would be photos.

47:15Like what is, I don't even understand what that would mean. On this kind of surplus point, I mean, we've had this kind of long-term shift away from marginal cost, which is actually one of the things that's interesting about generative machine learning now. It's like, when is the last time it costs money every time a consumer clicks OK in your app? When is the last time, like, your consumer clicks OK and it, like, takes 30 seconds for something to happen? Like, dial-up maybe, but not even dial-up really. We're kind of in mainframe territory here. We're in 1970s time-sharing territory where it actually costs a material amount of money for a consumer to click on something.

47:59One of the kind of elements of questions about LLMs or whatever you want to call them, Generative Machine Learning, is the second question after the AGI question is what's the cost structure and the size of the model is going to look like in the medium term. Let's transition into the AI conversation. You've asked, is this the iPhone moment? Is this the internet moment? Is this something way bigger than both of those? Why don't you reflect on the different forks from here and why that's important? There's an urban legend that I think during the Cuban Missile Crisis, there's a rumour that the missiles have launched on the stock exchange.

48:36There's a rumour in the stock exchange that the missiles have launched. And everyone starts selling. And somebody, a veteran trader goes out and starts buying. And he says, well, look, this is binary. Either the rumour is true, in which case we're all dead, or it's not true, in which case the stocks are cheap. And that's kind of how I think about AGI in that no one knows. It seems deeply unlikely. There's a lot of very bad logic or illogical argument. But basically, the people who spent 20 years arguing about this stuff don't know. Well, they certainly don't agree. And I can't arbitrate between Jan Lekun and somebody else about who's quite right about what gradient descent is going to do because the only gradient descent I know about is going down the hill in second gear.

49:19So I don't fucking know. so it's always like take the AGI conversation and kind of park it because you go no, it seems very unlikely but right, so then the question is then you get into like 10 other questions and I think most people in Silicon Valley, everybody in Silicon Valley as I say is kind of walking around with a kind of hold on to the top of their head with both hands to stop it flying off because this is clearly like an enormously huge thing but then the question is okay but what is it exactly and then there's like 10 questions about, well, what is it? How does it work? What does this mean?

49:54I think the spread of opinions is that there are people who would say, okay, this is a second wave of AI, and that was a second wave of machine learning, which has been a very big deal for tech in the last 10 years. Or it's more generally, you could say this is a sort of equivalent to the iPhone, and it sort of completely resets a huge sway of how consumer internet experiences will work. That seems a relatively straightforward thing to say. You could push it a little bit further and say, this sort of looks a bit like the internet, I think Bill Gates said that it reminded him a lot of Xerox PARC and seeing a GUI.

50:25Because what a GUI meant was a step change in who could use computers because you didn't have to learn how to use a command line. You could just click on stuff and you could teach yourself much more. But somebody else had to write the program, still had to write the program, or you had to learn how to program it, or you had to buy the program or something. But the program had to be created for you to be able to use it. Whereas with an LLM in principle, you can kind of just ask the computer to make the thing for you. And so it's, again, a step change in how much software they can be, what software they can be, how accessible it could be.

50:55You know, it's a bit like Excel. You know, Excel is the world's biggest IDE. Like suddenly you can just like go out and create the program in it. It's like going from Excel to Bloomberg. You know, it's those kinds of step changes. Then there's people who I think are a bit more overexcited. You say, well, this is electricity. This is computers. This is fire. I'd again come back to that sense of how quickly this stuff gets deployed. Cloud is a relatively old and understood technology. And if you're in the tech industry now, it's like, it's boring, it's done, it's happened. It's only 20 % of enterprise workflows.

51:26Google just moved from Oracle to SAP to process their internal payments. I mean, this is like the joke, the single biggest thing protecting is from AGI is that everybody's using Discord that slows everything down. So you've got all of this kind of swirling mass of ideas. And I don't think anyone, we're kind of struggling to kind of pin down to even, okay, what are the half dozen or dozen questions? Nevermind, what are the answers to that? We were talking to a lot, Gil and Sarah Guo, from the investor perspective, you're a venture partner at Mosaic, and I'm sure they're asking you, How should we be thinking about that in terms of what are company opportunities that will be durable and defensible and not just taken up by the big companies?

52:16How do you think about what are the startup opportunities or how would you approach this as a venture investor? So some sort of a priori assumptions. One of them is you go and use Magene. company, and the first thing you think is, okay, imagine what this would be if it had some product around it. It's amazing. And then, I mean, obviously using it in Discord is just like ridiculous, but just the principle of just typing stuff into a prompt is not a great consumer UI or great UI for anyone. It's very powerful, but it's kind of, you know, it's not a good user interface. You know, there's clearly better user interface and better product to be built around that.

52:55And, you know, in general, like there isn't one database company, you know, you don't just go and buy Oracle. There's millions, hundreds of thousands of different database products that do different things. And when you do your expenses, you don't say like, I'm going to use some database now. It's, you know, it's an expensive product. And so you kind of need to build the product and go to market and the user experience and the customer success and the support around creating a tool to solve some problem within a company. And now that thing may all be running on top of OpenAI. There may be three foundation.

53:42This is sort of the phrase foundation model, which I hadn't heard until like two weeks ago. So a bit like metaverse, like what does foundation model mean? It may be that there's like three models and everything else plugs onto the top of those as APIs. So that may be one way it works, that you've got, there's like those three models. And then maybe there's the middleware. And then there's like the 10 ,000 companies, software companies in the world all plug onto that. That's one path. but even if that's the case even if there are only those three models you know when you do the we need to validate that we've done criminal records checks for people hiring getting hired as security guards in this shopping mall they're not just going to go to chat gpt and say hey do a criminal records check that's not how it works i mean you mess yes if we had agi maybe it would like if we actually had god level agi then yes you could just say hey hey god tell me does this person have a criminal record and i've done um but you'll you need actual specific product all around that so i think that's like a kind of a basic question like it's a kind of a basic sort of a priori assumption then everything becomes questions like why is it at what point does this how long does this stuff stay this expensive what does it mean if it does are there always only going to be two or three models or is it going to be more like machine learning where we got to a point that it's like databases there's millions of machine learning models um so that's sort of a second level question and then maybe a third the third question would be following on from those what happened with the last wave of machine learning was that you sort of say well you how do you have the data that's specific to this problem how do you get product and customers before how do you build the product before you've got the data how do you get the data before you for customers?

55:30So it's a kind of circular question. You need customers to get data, but you need data to have the product to get to customers. And does the product get better indefinitely if you get more data? Or does it reach a point where it kind of works? There's an S-curve. And how much data is that? And how many people could have that? Is that going to be like 10 people? Or could anybody have that much data? So those are just sort of very, very early sort of of how does this work kind of questions. I think stepping back, to me, the first is the AGI question, the second question is the cost, and the third is the error rate, which kind of is the AGI question.

56:06And if the error rate goes to zero, then you've kind of got an AGI. Maybe, maybe not. But right now, if I ask chat GPT-4, like the current one, write a biography of Benedict Evans, hit it reload four times, you'll get four different biographies of four different people. And they overlap quite a lot. But my first job was investment banking, yes, but at DKW, no. No, it was at McKinsey. No, I worked for The Guardian. And what's happening up here, of course, is it's pattern generation. It's not answering the question. It's not doing a database lookup. It's saying, what would an answer to that question look like?

56:41Or even what answers do questions like that look like? Which is why it matters how you ask the question. So if you ask the question and start by saying, imagine you're an expert, then you get a different answer because you get the kind of answer that an expert would get. By extension, you could kind of say, imagine you're an idiot and don't know anything about this. And then you'll get like an even more like, so you're like, there's this kind of what it's doing is it's matching the pattern of the question with the pattern of an answer. And at the moment, that's like pick a number 60, 70 percent accurate.

57:10At what point does that get to be 80, 90 percent accurate? In what domains does what accuracy do you need in what domains? and can you see the accuracy? We're still at the stage where it's happening so fast that there's always any questions and new ways of understanding it. As you were drawing that kind of stack diagram, it brought to mind a theory that I've heard you talk about before, which is that there's two ways to make money in software, the old bundling and unbundling. So my theory about what AI is starting to do right now that I wanted to bounce off you is I see it unbundling jobs into tasks and thus enabling kind of a re-architecting of how stuff gets done with the task as the fundamental unit of work.

57:57And then on the other side, the bundling side, I see things like ChatGPT, which is certainly first incarnation, starting to, with their plugin architecture, bundle all of the apps that we use in life into this kind of new super app where you talk first to a consistent interface and then it goes out and deals with all the APIs and browses the web for you. And at some point you may want to get into that experience, but to the degree that you don't, it can kind of handle it for you. So what do you think about that bumbling, unbumbling lens on AI? The metaphor that I always use is I always talk about Jack Lemmon in The Apartment.

58:42It's a Billy Wilder film from 1960, where he's a clerk in an insurance company. And you've got these long shots of this office floor in an office building in midtown Manhattan. And everybody has kind of a one-person desk with a typewriter and a Rolodex and an electromechanical adding machine and a stack of internal mail envelopes. And so what happens is a document arrives. You do the calculations on this adding machine. You type the results with this typewriter. you put an internal mail, you write somebody's name on it, you give it to a mailboy, he carries it off to another desk somewhere else in the building.

59:12And so the whole building is basically an Excel file. You know, Jack Lemmon is a cell and a spreadsheet. The whole building is a spreadsheet. Once a week, someone on the top floor presses F9 and the whole building recalculates from top to bottom and generates new insurance prices. And in, I mean, as you may know, computer originally was a job title. That was a person. He is a computer. That's what his job is. And in 1965 or 1970, they bought a mainframe. And all of that processing, information processing, got moved into a mainframe. If you go and look through all sentence reports, you'll see these job categories like data keers.

59:45A data key is somebody who is holding a piece of paper and is keying the data into a computer, which is not a job anymore, except for the IRS, obviously. And that's actually not a joke. That's literally what happens. If you kind of think about those cycles over the last 75 years, you know, what would you, what would we think is it, is it, is it that's happened is it's people used to do a data entry and input and calculation, and now they do more decision taking and they do maybe more routing. And there's a lot more sort of this problem has come in, who needs to send it to, who do I need to solve it to?

1:00:17And it goes to that person and they do these three things and then press this. So, you know, if you could, if you're sort of a more of a sort of a futurist kind of person than me, you might kind of come up with some phrasing here of people used to be tools and now they're routers or I don't even know what that would be. The way I tend to look at this is that you sort of automate more and more things and you free people up to do less repetitive tasks and more creative tasks or more tasks that require high-level brain functions in some way. We're going to automate another class of job. I mean, if you read or listen to Dan Brooklyn, who created the first spreadsheet software, his account, you know, how he has these stories, again, like we don't know now, but a spreadsheet was literally a sheet of paper with all the cells printed on it.

1:01:08And so a financial analyst would have like a past stack, like a printed pack of these sheets and use those to do the calculations with a pen and paper or maybe like a hand calculator. And so all he did was take that and put that onto a computer screen. And he would show this to people and they would like literally lose control of their bodily functions because this was like years of their life that was being done in seconds. And he has these stories of people who would like had been given a job, an accounting analysis job that was expected to take like three months. And they did it in an afternoon, like literally like from months to afternoons.

1:01:44And what happens as a result of Excel and spreadsheets and then Excel is that you have more analysts. It's not that you have pure analysts. We mentioned Goodhart's law. The other law here, I think, is Jevons paradox. So Jevons is a 19th century economist. And at this time, Britain is obviously has by far the largest Navy in the world, and it's converted to steamships. And Britain is basically made of coal. But people are saying, well, we're using all this coal really fast, and we might run out, and then we'll have like a serious geopolitical problem. And then a bunch of people say, yes, but the steam engines are getting much more efficient every year, so we'll use less coal.

1:02:20And Jeven says, no, no, no, no, no. If you make steam engines more efficient, we'll use more coal because the steam engines will be cheaper. And so when you make the use of the resource more efficient, you use more of the resource, not less of the resource. And so what I think this very obviously applies to computing, that when you make it cheaper and easier to do the analysis, you do more analysis. We don't do less. I mean, we can all just sort of think of examples of this in our lives. You go and spend three days in Excel doing analysis. If you'd had to do that by pen and paper, you just wouldn't have done it because it would take you like a month to do what you just did in 10 minutes in Excel.

1:03:03And so I think that's the sort of like building blocks that I have for thinking about what this does to employment. It's that it will be, I mean, it's the Steve Jobs line that computers are bicycle for your mind. It seems like it's both to me. I certainly see the bicycle for the mind paradigm. And it seems like that probably is the dominant benefit for a certain class of people. But then I look out at the world and I'm like, so many of these jobs really are largely routine. And I wonder if the right way to think about this is not so much bicycle for the mind, but like dominant technology. You can certainly find plenty of examples of technology changes in the past where it's like, we don't use more steam engines.

1:03:50We just have a better form factor that serves the vast majority of the use cases. I mean, there's this analysis that people did of, you know, motive power in England in the 19th century, of, you know, beasts of burden beginning in the 19th century, the aggregate horsepower of all of the steam engines at the end of the 19th century. And I'm sure if you ran that through to today, the number would have increased by many more orders of magnitude. Then sort of the economics textbook argument here would be that it's easy to see the jobs that any new wave of automation will remove because they're right there.

1:04:22You can go and look at them. You can see those jobs. it's more difficult to imagine the new jobs that will get created because you have to imagine them they don't exist and yet those jobs get get those jobs happen and we look over the last 200 years there's been this continuous process of jobs getting automated away and new jobs getting created that happened with the internet and it happened with pcs and it happened with guis and happen with computers. So the default presumption would be that this will just continue. And the fact that we can't imagine what these new jobs will be doesn't tell us there aren't any, because that's been the pattern for the last...

1:05:02Nobody else, nobody could have imagined our jobs 20 years ago. I think the counter argument to this is that what you've been doing is sort of moving up the scale of human capability. And so you start with human beings, a beast of burden. I mean, the other image I use, I use the image of Jack Lemmon in the apartment. I also use this image, which is a painting, 19th century Russian painting by Ilya Repin of four barge haulers on the Volga. And it is basically a dozen guys pulling a barge up the stream by hand, by leaning on a rope, pulling a barge up the stream. And just on the horizon, you can see a trail of black smoke from a steamer that tells you how this is all going to change.

1:05:41And nobody is employed to pull barges up the river anymore. And yet those people got other jobs. but what happens is you go from beast to burden to you go to you know people's you know to arms you go from legs to arms to hands so to speak and you're kind of moving up to the top and in principle you could kind of get to the top and there won't be anything else left that you can't automate the problem with that is again you could have said that 50 years ago you looked at all those people in that building and you said well all of those jobs will get automated away these are all these boring repetitive jobs so what will those people do guess what insurance companies probably employ just as many people as they do now certainly we haven't had a net change in employment and so there is this sort of we can't know what the new jobs are problem yeah i think it's always important and i always try to keep in mind too that it's like it's not going to be a binary where it's like all one of these effects or all the other right we'll definitely see both and kind of the the question is kind of like which one will predominate but i wonder if you would venture a prediction when it comes to, say, insurance companies.

1:06:45I would imagine that a lot of that headcount has gone into agent networks and effectively sales and broadening the market. And now I imagine a 2025 world where I go to allstate.com or whatever. And instead of a connect with your local agent, or maybe alongside of a connect with your local agent, there is a talk to your virtual agent and that virtual agent is certainly going to have some advantages but again this is what's been happening for 100 years 150 years i mean i was talking about this earlier like talking about travel agents there are no travel agents anymore near before the internet if you wanted to book a flight there were these stores that you went to and they had the flights they had books with the flights or maybe they had a terminal that connected to the airline system with the flights and they would make you the tickets gone those jobs are gone so what like yes it sucked if you were that was you and that was your business and you'd spent your life like building this business and there's always a frictional short-term pain of these jobs but like the process the cycle of jobs getting destroyed has been happening continuously since the industrial revolution and say all you've just done is just describe another job that will go well yeah those jobs will go away the question is whether there will be new whether there's some a priori reason to think why they won't be any new jobs now as opposed to all of the new jobs that got created over and over for the last 200 years.

1:08:07I think another question too, though, is like, how fast does the destruction happen? You know, because when you go back to the like industrial revolution, you are talking about a multi generation process from like, the first piston to like a refined steam engine. And here it looks like we go from GPT two in 2019 to GPT four in 2023. And that's the leap from like, basically babble to expert level human doctor. Like our next guest on the cognitive revolution is a guy who took GPT-4 on rounds. So if you're in Silicon Valley, kind of cloud is, if you're in the tech, like all of us in the tech industry, cloud is this kind of old boring thing and it's done.

1:08:45But cloud is only sort of 20 % of enterprise workflows. You know, Apple announced as the iPhone in 2007, it took until 2010 for the sales to take off. And it took until 2015 really for it to really take over the majority of the phone industry. I think we can sort of underestimate how long it takes to get the Scottish police force to swap out the system that they use for doing criminal records checks. They're not going to do that this year or next year. In fact, the UK sales tax system runs on DEC, literally runs on DEC computers, which DEC hasn't existed. As you had compact bought DEC in 99, because the company hasn't existed 25 years.

1:09:31so you know we can sort of i mean on the one hand like this whole thing is moving incredibly fast partly because there's a sort of standing on the shoulders of giants point that you don't have to wait for people to buy phones you know you don't have to wait for everybody in the world to buy a smartphone it's just everyone's already got we've already got cloud we've got the web we've got cloud computing we've got the gpus we've got you know stripe we've got everything everything's right there. So you can just add this one idea and that kind of, okay, now there's a thousand generative AI companies and there's like 10 new projects on product plant every hour.

1:10:06So that, yes, on the one side, you've got this very rapid speed of creation. On the other side, you want to go and get Allstate to deploy this system into their call center. Okay. It will take you three months to get a sales meeting. They will not deploy that this year. It will take them three years to deploy that, if that. So the entire world doesn't just change its infrastructure in six months, even if the product that they could buy is ready in six months, which actually won't be. It will take longer than that. So this stuff will take five, 10, 15 years to get deployed. I mean, there's another way of thinking about this maybe is we could have built TikTok in 2010.

1:10:50you know, you argue a little bit about what bandwidth looked like and how smartphones work. But it took 10 years for that idea to happen. There's a few better examples. We're using Riverside. One of the companies I used to like talking about is Frame.io, which is basically collaborative video, professional video, not actually editing, but like everybody who needs to see a piece of professional video, it's all the commenting and the version tracking and the sharing. So the editor can post the latest version and 50 people can see it and you can work on it and comment on it. Mark up that bit on that slide.

1:11:25You could have described that in 2005. The technology was probably ready by 2010. It was probably cheap enough by 2015. It got built in 2018. It got bought by Adobe. It takes time, both for this stuff actually to get deployed and for people to work out the right instantiation in the right product, in the right market to get that deployed. I think that's well put. This has been a fascinating overview on Web3, Metaverse, AI. Maybe in closing, as a closing question, we're almost at May 2023, so we're almost halfway through the year. We get back on a call February of 2024. or what can you predict are the questions that you'll be asking in your next presentation?

1:12:10Well, almost by definition, I can't. Particularly given what's happening at the moment, the kind of questions will change. I hope we'll have a better sense of what the questions are, and we'll have a better sense of some of the answers. I know you may disagree, but I don't think we have a consensus on what the models are going to cost and how that's going to evolve. I don't think we have a consensus really on what's going to happen to the error rate and whether we get stuck here and we're at the top of an S curve or whether it's going to go all the way up and improve radically. We don't have a good sense of whether we're in a world of three or four foundation models or 50 foundation models or everybody can have their own.

1:12:54And so those sorts of basic, how the hell does this, what does the structure of this work like at all? We may have a better sense of what those look like. but at the same time you know one two weeks ago no one was talking about auto gpt and you mentioned plugins which we kind of haven't talked about i feel like kind of ambivalent about chat gpt plugins because you know some people everyone looks at this and goes oh my god this is amazing now i could do anything i look at it and think well if it was so fucking good why does it need the plugins um and isn't this just basically you'd be using alexa to talk to um booking.com as built in 1998 like if this thing is absolutely fucking amazing and changes the world how is it that's using the Web 1.0 API to talk to a Web 1.0 company to answer this question.

1:13:36Well, I could just go to Booking.com and do that. And now all you're doing is giving me a natural language front end to a Web 1.0 company, which they could do. Now, AutoGPT, in a sense, isn't a way of solving that, which is, no, it's not one plugin. It can chain together 15 of them and actually solve the problem. Whether it can do that or not, I think, with anything, it seems more theoretical than real at the moment. One of the things I've talked about as well is a sense of UI. I think we will move on from command line. And I think we will have a better sense of what the right UI is for some of these things and the right way of presenting them will look like.

1:14:13You know, you have this kind of, you know, there's this phrase prompt engineering. So it's natural language, but prompt, which is it? If this is natural language, why do we have prompt engineers? Because of course it's not. You know, if I actually want to describe a piece of enterprise software, I don't think I could do that in 500 words. What are I actually going to do? I'm going to paste in a 500-page PDF into ChatGPT and press go and then say, great, okay, so what do you want to do? Like, I'm not sure how that's how it works. So there's a lot of hand-waving, which I'm guilty of at the moment as well.

1:14:48And there's a lot of sort of, how do we get from this breakthrough in principle to 10 ,000 products and the stack that supports that? It's like kind of look like, it's bad analogy, but it's like looking at multi-touch and going, okay, what is this? What's this for? How do you use this? I think radical uncertainty is a idea that we can all resonate with as we try to make sense of what's happening. But if you're listening to this, you're likely a student of technology and as a student of technology, Benedict Evans' newsletter and podcast is a must. So Benedict, we're grateful for you for spending some time sharing some of your learnings and ideas with us and look forward to continuing to follow along your writing and podcast and continue the conversation.

1:15:34Thanks so much for joining. Thanks for having me. Turpentine VC is a podcast from Turpentine, the network behind Moment of Zen and Econ 102. If you liked the episode, please leave a review in the Apple Store or rate us on Spotify. Thank you.