AI Eats the World: Benedict Evans on the Next Platform Shift

26:12If not, is it efficient to validate it with people? So, you know, the marketing use case, it's a lot more efficient to get a machine to make you 200 pictures and then have a person look at them and pick 10 that are good than to have people make 10 good images or even if you're going to make 500 images and pick 100 that are good. That's a lot more efficient than having a person make 100 images. But on the other hand, if you're doing something like data entry, and I wrote something about this, about OpenAI Launch Deep Research. OpenAI Launch Deep Research, their whole marketing case is it goes off and collects data about the mobile market.

26:45I used to be a mobile analyst. The numbers are all wrong. their use case of look how useful this is, their numbers are wrong. And in some cases they're wrong because they've literally transcribed the number incorrectly from the source. In other cases it's wrong because they've used a source that they shouldn't have used. But like if I'd asked an intern to do it for me, then an intern would probably have picked that. And to the point about verification, if you're going to do data entry, if I'm going to ask a machine to copy 200 numbers out of 200 PDFs and then I'm going to have to check all 200 of those numbers, I might as well just do it myself.

27:21So you've got like a whole swirling matrix of how do you map this against existing problems? But the other side of it is, how do you map this against new things that you couldn't have done before? And this comes back to my point about platform shifts because, you know, I see people looking at chat TVT or looking at generative AI and saying, well, this is useless because it makes mistakes. And I think that's kind of like looking at, like, an Apple II in the late 70s and saying, could you use these to run banks? To which your answer is no. But that's kind of the wrong question. Like, could you build professional video editing inside Netscape?

28:03No. But that's the wrong question. And later, yeah, 20 years later you can, but meanwhile it does a whole bunch of other stuff the same with mobile. Like, can you use mobile to replace, you know, your five-screen professional programming rig? No, therefore it can't replace PCs. Well, guess what? Five billion people have got a smartphone and seven or 800 million people have got a consumer PC. So it kind of did, but did a different thing. And the point of this is, like, the new thing, this is, you know, the disruption framing you mentioned earlier, the new thing is generally not very good or terrible at the stuff that was important to the old thing.

28:34But it does something else. Right. And a lot of the question is, okay, it may not be very good at doing, there's a class of old tasks that generative AI is good at. There's also many more old tasks that generative AI is maybe not very good at. But then there's a whole bunch of other things that you would never have done before that generative AI is really, really good at. And then how do you find those or think of those? And how much of that is the user thinking of it faced with a general purpose chatbot? How much of that is the entrepreneur saying, hey, I've just realized that there's this thing that I can do that you couldn't do before, and here you are.

29:12I've given you a product with a button that will do it for you. Right. And that's why there are software companies. Right. And on mobile, some of the new use cases, we're getting in strangers' cars. We mentioned Lyft and Uber, or dating people you met via an app, or lending your spare bedroom out, etc. et cetera. And those were net new companies that were built around those behaviors. And I think for AI, there's still the questions of what are those net new behaviors? We're starting to see some in terms of people engaging and talking with chatbots instead of humans or in addition. And then there's a question of, are these done by the model providers that currently exist, or are these done by net new companies, both on enterprise and consumer?

30:02Well, this is always a question, is how far up the stack does a new thing go? And, you know, I was talking about this with another former A16Z person who pointed out that like in the mid-90s, people kind of argued that, well, you know, the operating system does all of it. And Windows apps are basically just kind of thin Win32 wrappers. And, you know, Office is basically just, you know, a thin Win32 wrapper, like all the important stuff is being done by the OS, whether it's, you know, the document management and printing and storage and display, which was all stuff that used to be done by apps.

30:32Like on DOS, the apps had to do printing. The apps had to manage the display. We moved to Windows, like 90 % of the stuff that the app used to do is now being done by Windows. And so Office is just like a thin Win32 wrapper and all the hard stuff has been being done by the OS. And it turned out, well, that was, again, it's like frameworks are useful, but that's maybe not a useful way of thinking about what's going on. And the same thing now, like how much does this need single dedicated understanding of how that market works or what that market is and what you would do with that. I mean, I remember when we were at A16Z, there was an investment in a company called Everlaw, which is legal discovery in the cloud.

31:12And so machine learning happens and so now they can do translation. Are they worried that lawyers are going to say, well, we don't need you guys anymore. We're just going to go out and get a translate app and a sentiment analysis app from AWS. No, that's not how law firms work. Law firms want to buy a thing that Solstice doesn't want to buy legal discovery, software management. They don't want to go out and write their own API calls. I mean, very, very big law firms might, but a typical law firm isn't going to do that. People buy solutions, they don't buy technologies. And the same thing here, like how far up the stack do these models go?

31:45How much can you turn things into a widget? How much can you turn things into an LLM request? and how much, no, does it turn out that you need that dedicated UI? The funny thing is you can see this around Google because Google had this whole idea that everything would just be a Google query and Google would work out what the query was. And guess what? Now you want to use those Google flights. It's not a Google query. They use a certain point. And one of the interesting things about this, and I think it's interesting to think about what a GUI is doing, that some of what a GUI is doing, and the obvious thing that a GUI is doing, is that it enables Office to have 500 features and you can find them all.

32:26Or at least you don't have to memorize keyboard commands. You can now have effectively infinite features and you can just keep adding menus and dialog boxes and eventually you run out of screen space for dialog boxes. But you can have hundreds of features without people needing to memorize keyboard commands. But the other side of it is you're in that dialog box or you're in that screen in that workflow in Workday or Salesforce or whatever the enterprise software is, whatever any software, or the airline website or Airbnb or whatever it is. And there aren't 600 buttons on the screen. There's seven buttons on the screen because a bunch of people at that company have sat down and thought, what is it that the users should be asked here?

33:04What questions should we give them? What choices should there be at this point in the flow? And that reflects a lot of institutional knowledge and a lot of learning and a lot of testing and a lot of really careful thought about how this should work. And then you give somebody a raw prompt and you just say, okay, you just tell the thing how to do the thing. And you're like, but you've kind of got to shut your eyes, screw your eyes up and think from first principles, how does this, all of this work? It's kind of like, I always used to talk about machine learning as giving you infinite interns. You know, imagine you've got a task and you've got an intern and the intern doesn't know what venture capital is.

33:46How helpful are they going to be? And they don't know that companies publish quarterly reports and that we've got a Bloomberg account that lets us look up multiples and that then you should probably use PitchBook for this data rather than using Google. This is my point about deep research. Like, no, you should use this source and not that source. Do you want to have to work that out from scratch or do you want a bunch of people who know a lot about this stuff to have spent five years working out what the choices should be on the screen for you to click on it? I mean, it's the old user interface saying the computer should never ask you a question that you should have to work out that it should know by itself.

34:29You go to a blank, raw chatbot screen, it's asking you literally everything. It's not just asking you one question, it's asking you absolutely everything about what it is that you want and how you're going to work out how to do it. And so you're mentioning chat, you wrote about chat GPT isn't sort of a product as much as a chatbot is disguised as a product. I am curious, you know, when we sort of look back at this sort of, you know, platform shift, do you think that there will be another sort of iPhone sort of Excel-esque product that kind of defines the sort of platform shift in a way that chat GPT won't?

35:12or is it sort of that the world has to catch up to how to use ChatGPT or something like ChatGPT? So both of these can be true because there was a lot of like it took time to realize how you would use Google Maps and what you could do with Google and how you could use Instagram and all of these products have evolved a huge amount over time. So some of it is like you grow towards realizing what you could do with this. Like you realize that's just a Google query now. You realize that you could just do it like that and you realize I spent hours doing this and I just realized, oh, I could actually just make a pivot table.

35:45The other side of it is then, but you're still then expecting people to work it out themselves from first principles. And, you know, it's kind of useful to have somebody really, 100, 1 ,000, 10 ,000 really clever people sitting and trying to work out what those things are and then showing it to you as a product. I think another side of this is like, you know, there are always these precursors. So, like, there are lots of other things before Instagram. You know, YouTube didn't start as YouTube. It started as video dating, I think. There were lots of attempts to do online dating that all kind of worked until Tinder kind of pulled the whole thing inside out.

36:26And so there were always lots of things. What's the phrase? Local maxima. In fact, this is where we were, particularly with the iPhone. before because I was working in mobile for the previous decade it didn't feel like we were waiting for a thing it felt like it was kind of working like every year the networks got faster and the phones got better and you got a little bit better every year and we had apps and we had app stores and we had 3G and we had cameras and stuff seemed to be every year it was a bit better and then the iPhone arrives and it just blows the chart you've got this line doing this and then there's a line that does that.

37:04Although remember, also the iPhone took two years before it worked because the price was wrong and the feature set was wrong and the distribution model didn't quite work. And so, yeah, you can think everything's going well and then something comes along and you realize, no, oh, no, no, no, that's, which is the same for Google. Search was a thing before Google, it just wasn't very good. So there was lots of social stuff before Facebook and that was the thing that catalyzed it. so you know i just think deterministically this whole thing is so early that it feels like of course there are going to be you know dozens hundreds of new things otherwise hgc and z would just kind of shut down and give the money back to the lps because the foundation models will just do the whole thing and like i don't think you're going to do that at least i hope not no no if we have any regrets from the last few years it's it's it's not going bigger i think we didn't fully appreciate how much specialization there would be across uh sort of you know whether it's voice or image generation or take any sort of subsector that there would be, you know, net new companies created that would be better than the model providers that there would be even multiple model providers that are that in every category.

38:12You know, one thing we've always in the Web2 era, we always bet on the category winner, right? And the category winner would take most of the market. But these markets are so big and there's so much expertise and specialization that in One, there can be winners in every category. It's not just sort of the model providers taking everything, but that even in every category, including the model providers, there can be multiple winners and increasing specialization and the markets are just big enough to contain multiple winners. I think that's right. And I think the categories themselves aren't clear.

38:49And things you think this is a category and it turns out, no, it was actually that whole other thing. And the categories kind of get unbundled and bundled and recombined in different ways. I mean, I remember I was a student in 1995, and I think I had like four or five different web browsers on my PC, web servers on my PC. Because, I mean, Tim Berners-Lee's original web browser had a web editor in it, because he thought this was kind of like a network drive, and it was a sharing system, and not really a publishing system. So you would have your web pages on your PC, and you'd leave your PC turned on, and that would be how your colleagues would look at your Word documents, or your web pages.

39:25And so, again, we just don't know how, and I just kind of keep coming back to this point. I feel like most of the questions we're asking at the moment are probably the wrong question. And picking up on a strand within what you just said, though, the interesting, one of the things I'm sort of thinking about a lot is looking at OpenAI, because I'm sort of fascinated by disconnections. And we've got this interesting disconnect now, which is that, you know, if you look at the benchmark scores, so you've got these general purpose benchmarks where the models are basically all the same. And if you're spending hours a day and then you've got this opinion about, oh, I like Claude's tone of voice more than, I like GPT and I like GPT 5.1 more than GPT 4.9 or whatever the hell it's called.

40:04If you're using this once a week, you really don't notice this stuff. And the benchmark scores are all roughly the same. But the usage isn't. It's basically, Claude has basically no consumer usage, even though on the benchmark score, it's the same. And then it's ChatGPT. And then halfway down the chart, it's meta and Google. And the funny thing is, you know, you read all the AI newsletters, then meta's lost, they're out of the game, they're dead. Mark Zuckerberg is spending a billion dollars as a researcher to get back in the game. But from the consumer side, well, it's distribution. And the interesting thing here is that you've got, what I'm kind of circling around is, if the model for a casual consumer user certainly is a commodity, and there's no network effects or winner-takes-all effects yet.

40:54Those may emerge, but we don't have them yet. And things like memory aren't network effects, so stickiness, but they can be copied.

41:04How is it that you compete? Do you just compete on being the recognized brand and adding more features and services and capabilities and people just don't switch away? Which is kind of what happened with Chrome, for example. There's not a network effect for Chrome. and it's not actually any better maybe it's a bit better than Safari but you know you use Chrome because you use Chrome or is it that you get left behind on distribution or network effects that emerge somewhere else and meanwhile you don't have your own infrastructure so I suppose what I'm getting at is you've got these 8 or 900 million weekly active users but you don't have But that feels very fragile because all you've really got is the power of the default and the brand.

41:52You don't have a network effect. You don't really have feature lock-in. You don't have a broader ecosystem. You also don't have your own infrastructure, so you don't control your cost base. You don't have a cost advantage. You get a bill every month from Satya. So you've kind of got to scramble as fast as you can in both of those directions to on the one side build product and build stuff that on top of the model, which is our earlier conversation, is it just the model? You've got to build stuff on top of the model in every direction. It's a browser. It's a social video app. It's an app platform.

42:31It's this, it's that. It's like, you know, the meme of the guy with the map with all the strings on it. It's all of these things. We're going to build all of them yesterday. And then in parallel, it's infrastructure. pressure like and you know we do we've got to deal with open ai we saw a deal with with nvidia with with broadcom with amd with nvidia with oracle and well with petrodollars because you're kind of scrambling to get from this amazing technical breakthrough and these 800 900 million wows to something that has like really sticky defensible sustainable business value and product value Yeah.

43:10And so as you're evaluating the competitive landscape among the hyperscalers, what are the questions that you think are going to be most important in determining who's going to gain durable competitive advantages or how this competition is going to play out? Well this kind of comes back to your point about sustaining advantage and we talked about Google like if we think about the shift to particularly shift to mobile for meta this turned out to be transformative like it made the products way more useful for Google it turned out mobile search is just search

43:51and maps changed probably and YouTube changed a bit but basically for Google search Google search is search and the web search just means more people doing more search more of the time. And the default view now would seem to be well, Gemini is as good as anybody else. Next week, like the new model, I haven't looked at the benchmarks for GPT 5.1, which is out today. Is it better than Gemini? Probably. Will it still be better next month? No.

44:20So, that's a given. Like, you've got a frontier model, fine. What does that cost? It costs you, pick a number,$250 billion a year,$100 billion a year. What's our earlier conversation about CapEx? Okay, so Google can pay that because they've got the money. They've got the cash flow from everything else. And so you do that and your existing products, you optimize search, you optimize your ad business, you build new experiences. Maybe you invent the iPhone of AI. Maybe there is no iPhone of AI. Maybe someone else does it and you do an Android and just copy it. So fine, it's a new mobile. We'll just carry on.

44:58Search is search. AI is AI. We'll do the new thing. We'll make it a feature. We'll just carry on doing it. For Meta, it feels like there are bigger questions on what this means for search, or what it means for content and social and experience and recommendation, which makes it all that more imperative that they have their own models just as it is for Google. For Amazon, okay, well, on the one side, it's commodity infra, and we'll sell it as commodity infra. and on the other side, maybe stepping back, if you're not a hyperscaler, if you're a web publisher, a marketer, a brand, an advertiser, a media company, you could make a list of questions that you don't even know what the questions are right now.

45:43What happens if I ask a chatbot a thing instead of asking Google? Even if it's Google, from Google's point of view, well, I'll ask Google's chatbot, it's fine. But as a marketer, what does that mean? what happens if i ask for a recipe and the llm just gives me the answer what does that mean if my business is having recipes do you have a kind of split between and this is also an amazon question how does the purchasing decision happen how does this decision to buy a thing that i didn't know existed before happen what happens if i wave my phone at my living room and say what should i buy where does that take me in ways that it wouldn't have taken me in the past so there's a lot of questions further downstream, and that goes upstream to meta and to stomach stent for Google.

46:27It's a much bigger question in the long term for Amazon. Do LLMs mean that Amazon can finally do really good at-scale recommendation and discovery and suggestion in ways that it couldn't really do in the past because of this kind of pure commodity retailing model that it has? Apple, Apple's sort of off on one side. You know, interestingly, they produced this incredibly compelling vision of what Siri should be two years ago. It just turned out that they couldn't make it. Interestingly, nobody else could have made it either. You go back and watch the Siri demo that they gave and you think, okay, so we've got multimodal instantaneous on-device tool using agentic multi-platform e-commerce in real time with no prompt injection problems and zero error rates.

47:12Well, that sounds good. I mean, has anyone got that working? Like, no. OpenAI, Google and OpenAI, I don't have that working. I don't think Google or OpenAI could deliver the Siri demo that Apple gave two years ago. I mean, they could probably do the demo, but they couldn't consistently reliably make it work. I mean, that demo, that product isn't in Android today. And Apple, I mean, Apple to me has the most kind of intellectually interesting question, which is... So I saw Craig Federighi make this point, which is like, we don't have our own chatbot, fine. We also don't have YouTube or Uber.

47:50explain why that is different, which is a harder question to answer than it sounds like. And of course, the answer is, if this actually fundamentally changed the nature of computing, then it's a problem. If it's just a service that you use, like Google, then that's not a problem, which is kind of the point about, you know, where does Siri go? But the interesting candle example here would be to think about what happened to Microsoft in the 2000s, which is the entire dev environment gets away from them, and no one builds Windows apps after, like, 2001 or something. But you need to use the internet.

48:19To use the internet, you need a PC. And what PC are you going to buy? Well, Apple's not really a player at that time. And just getting back into the game, Linux is obviously not an option for any normal person. So you buy Windows PC. So basically, Microsoft loses the platform more and sells an order of magnitude more PCs. Well, not selling them, but there are an order of magnitude more Windows PCs as a result of this thing that Microsoft lost. And then it takes until mobile that then they lose the device as well as a development environment. So here's this kind of question, is if all the new stuff is built on AI and I'm accessing it in an app that I download from the App Store, to what extent is this a problem for Apple?

49:00And what would have to, you would need a much more fundamental shift in what it was that was happening for that to be a problem for Apple. And even if you take like the, you know, not the full, like the rapture arrives and we all just kind of go and live, sleep in pods like the guys in Up. not um yes what is it the one with the robot that's capturing the trash which one is that wally wally wally yeah you know the guys in the pods in that movie where we will be the people maybe we'll be like that in which case fine um but like there's a sort of a mid case which is like the whole nature of software changes and there are no apps anymore and you just go and ask the llm a thing fine what is the device on which you ask the llm a thing well it's probably going to have a nice big color screen and it's probably going to have like a one-day battery life probably just a microphone, probably a good camera.

49:47It kind of sounds like an iPhone. Am I going to buy the one that's a tenth of the price and just use the LLM on it? No, because I'll still want the good camera and the good screen and the good battery life. So it's not, there's a bunch of kind of interesting strategic questions when you start poking away. well, what does this mean for Amazon? Those are completely different questions to what does it mean for Google? Or what does it mean for Apple? What does it mean to Facebook? Or what does it mean to Salesforce? What does it mean to, you know, Uber? And then right back to what we were saying at the beginning of this conversation, you know, what does this mean for Uber?

50:26Well, their operations get X percent more efficient and now the fraud detection works. And, you know, okay, maybe they're autonomous cars, different conversation. But presume they're autonomous cars, that's a whole other conversation. and otherwise as Uber, what does this change? Well, not a huge amount. I want to sort of zoom out a little bit, this whole framing. So you've been doing these presentations for a while now. You've even bumped them up two times because so much is changing. And one of the things you do in each presentation is you're famous for asking really great questions and chronicling what are the important questions to be asking.

51:01I'm curious, as you reflect, you know, maybe post-GPT in 2022, or GPT-3 rather, the questions you were asking then and you reflect onto now, to what extent do we have some direction on some of those questions or to what extent are they the same questions or new and different questions or what is sort of your, if I woke up in a coma after reading your original presentation, let's say the one after GPT-3 launch came out and then seeing this one now, What were the sort of most surprising things or things that we learned that updated those questions? So I think we have a lot of new questions this year.

51:47So I feel like, you know, you could make a list of, as it might be, half a dozen questions in spring of 23. Like open source, China, NVIDIA, does scaling continue? What happens to images? is, how long does OpenAI's lead remain? And those questions didn't really change in 23 and 24. And most of those questions are kind of still there. Like the NVIDIA question hasn't really changed. The answer on how many models will there be, the answer is, okay, there's going to be, anybody who can spend a couple of billion dollars can have a frontier model. And that was pretty obvious in early 23. It took a while for everyone to understand that.

52:30And big models and small models, Will we have small models running on devices? No, because the small models, the capabilities keep moving too fast for the small models to shrink the small model onto the device. But those questions kind of didn't change for two, two and a half years. I think we now have, I think, a bunch of more product strategy questions as you see real consumer adoption and OpenAI and Google building stuff in different directions, Amazon going in different directions, Apple trying and obviously failing and then trying again to do stuff. there's some sense of like there is something more going on in the industry than just well let's just build another model and spend more money yeah there's more questions and more decisions now there's also more questions outside of tech in certainly on like the retail media side of um how do you start thinking about what you would do with this and again you know classic framing in my deck is like step one is you make it a feature and you absorb it and you do the obvious stuff step two is you do new stuff step three is maybe someone will come and pull the whole industry inside out and completely redefine the question and so you could kind of do like an imagine if here of like step one is um you know you're you're a manager at a walmart in the bay area or dc or whatever it is step one is find me that metric step two is build me a dashboard step three is it's black friday and i'm running managing a walmart outside of dc what should i be worried about like and that might be the wrong one but it's like you know step one for amazon is you bought light bulbs so here's so you bought bubble wrap so here's some packing tape but what amazon should actually be doing is saying hmm it's like this person's moving home we'll show them a home insurance ad which is something that amazon's correlation systems wouldn't get because they wouldn't have that in their purchasing data.

54:27And we're still very much at the like, we're still on the step one of that, but thinking much more, what would the step two, step three be? What would new revenue be for this other than just like simple dumb automation? What would new things that we would build with this be? Where would this actually, might actually kind of redefine or change what the market might look like? and that's obviously a big question for anyone in the content business. What does it mean if I can just go and ask an LLM this question? What kinds of content were predicated on Google rooting that question to you? And what kind of content isn't really that question?

55:12Like, do I want a Bolognese recipe or do I want to hear Stanley Tucci talking about cooking in Italy? Like, do I just want that SKU or do I want to work out which product I should buy? Which is, Amazon is great at getting you the SKU, terrible at telling you what SKU you want. Do I just want the slide deck or do I want to spend a week talking to a bunch of partners from Bain about how I could think about doing this? Do I just want money or do I want to work with A16Z's operating groups? like what is it that I'm doing here and I think the LLM is starting thing is starting to crystallize that question in lots of different ways like what am I actually trying to do here do I just want a thing that a computer can now answer for me or do I want something else that isn't because the LLMs can do a bunch of stuff that computers couldn't do before is that thing that the computer couldn't do before my business or am I actually doing something else we're about to figure out what is the, in a much more granular way, what is the true job to be done for many, many of these.

56:27Yeah. And, you know, going back to the internet, there was, you know, the sort of observation about newspapers is that newspapers looked on the internet and they talked about, you know, expertise and curation and journalism and everything else and didn't really say, well, we're a light manufacturing company and a local distribution and trucking company. And that was the bit that was the problem. And until the internet arrived, like, that wasn't a conversation you thought about. And then the internet suddenly makes that clear and suddenly creates an unbundling that didn't exist before. And so there will be those kinds of like, you didn't realize you were that before until an LLM comes along and points to, someone comes along with an LLM and says, oh, I can use this to do this thing that you didn't really realize was the basis of your defensibility or the basis of your profitability.

57:12I mean, it's like the joke about U.S. health insurance, that the basis of U.S. health insurance profitability is making it really, really boring and difficult and time-consuming. That's where the profits come from. Maybe it isn't. I don't know. I don't know. But for the sake of argument, say that's your defensibility. Well, an LLM removes boring, time-consuming, mind-numbing tasks. So what industries are protected by having that, and they didn't realize that? And these, you know, it's like you could have asked these questions about the internet in the mid-90s or about mobile a decade later. And generally, half of the questions you'd have asked would have been the wrong questions in hindsight.

57:51I remember as I was a baby analyzed in 2000, everyone kept saying, what's the killer use case for 3G? What's a good use case for 3G? And it turned out that having the internet in your pocket everywhere was the use case for 3G. But that wasn't the question that people were asking. And I'm sure that will be the thing now is there's so much that will happen and get built where you go and you realize, oh, that's how you would do this. You can turn it into that. And I'm sure you've had this experience seeing entrepreneurs. You know, you get every now and then they come in, they pitch the thing, you're like, oh, okay.

58:32You can turn it into that. I didn't realize it was that. Yeah, 100%. My last question that'll get you out of here is, if we're talking two or three years from now, or you're doing a presentation, you say, oh, this is actually bigger than the internet, or maybe this is like computing, what would need to happen? What would evolve our thinking? I mean, I kind of, you know, sort of come back to my point about, you know, Jews and Christians and the Messiah came. Nothing happened.

59:09We forget. I mean, there's maybe two ways, very brief ways to think about this. One of them is I think we forget how enormous the iPhone was and how enormous the internet was. And you can still find people in tech who claim that smartphones aren't a big deal. And this was the basis of people complaining about me, like this idiot. He thinks like generative AI is as big as those silly phone things. It's like, come on. I think another answer would be like, I don't want to get into the argument about, you know, what is the grace rating capability and benchmarks and all, you know, you can see lots of five-hour long podcasts of people talking about this stuff.

59:49But the stuff we have now is not a replacement for an actual person outside of some very narrow and very tightly constrained guardrails, which is why, you know, Demis' point, that it's absurd to say that we have PhD-level capabilities now.

1:00:12We would have to be seeing something that would really shift our perception of the capability of this stuff so that it's actually a person as opposed to it can kind of do these people like things really well sometimes but not other times. And it's a very tough conceptual kind of thing to think about because I'm conscious I'm not giving you a falsifiable answer. But I'm not sure what a falsifiable answer would be to that. When would you know whether this was AGI? It's the Larry Tesla line, AI is whatever doesn't work yet. As soon as people say it works, people say, well, that's just not AI, that's just software.

1:01:00And it becomes like a kind of a slightly drunk philosophy grad student kind of conversation as much as it is a technology conversation. Like what would it, have you ever considered, Eric, that maybe we're not others either? That's a thought.

1:01:19All I can say to give a tangible answer to this question is what we have right now isn't that. Will it grow to that? We don't know. You may believe it will. I can't tell you that you're wrong. We'll just have to find out. I think that's a good place to wrap. The presentation is AI Eats the World. We'll link to it. It's fantastic. Benedict, thanks so much for coming on the podcast to discuss it. Sure. Thanks a lot. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family.

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1:02:49Thank you.