The State of AI with Marc & Ben

23:33And if I have a monopoly providing it, maybe the markup's infinite. But it's kind of this sort of technology forward kind of suppliers apply forward pricing model. There's a completely different pricing model for kind of business value backwards and sort of sort of so -called value -pricing, value -based pricing. And that's to your point, that's basically a pricing model that says, okay, what's the business value to the customer of the thing? And if the business value is a million dollars, then can I charge 10 % of that and get a hundred thousand dollars right or whatever? And then why is it cost a hundred thousand dollars as compared to five thousand dollars is because, well, because to the customer it's worth a million dollars, and so they'll pay 10 % for it.

24:12Yeah, actually, so a great example of that, we've got a company in our portfolio, Cresti, AI that does things like debt collection. That's right. Okay, so if I can collect way more debt with way fewer people with my, you know, it's a co -pilot type solution, then what's that worth? Well it's worth a heck of a lot more than just buying an open AI license because it open AI licenses not going to easily collect debts, not where it kind of enable your debt collectors to be massively more efficient or that kind of thing. So, it's bridging that gap between the value. And I think you had a really important point, the test for whether your idea is good is how much can you charge for it?

25:02Can you charge the value? Or are you just charging the amount of work, it's going to take the customer to put their own wrapper on top of open AI? Like that's the real testimony of how deep and how important is what you've done. And so, to your point of life, the kinds of businesses that technology investors have had a hard time with, kind of thinking about, maybe accurately, is sort of, it's the company that is a vendor that has built something where it is a specific solution to a business problem where it turns out the business problem is very valuable to the customer. and so therefore they will pay a percentage of the value provided back in the terms if for price for the software and that actually turns out you can have businesses that are not very technologically differentiated that are actually extremely lucrative.

25:52And then because that business is so lucrative they can actually afford to go think very deeply about how technology integrates into the business, what else they can do. This is like the story of a Salesforce .com for example, right? And by the way, there's a chance, a theory, that the models are getting really good. They're open source models. They're awesome. Lama, Mistral, these are great models. And so the actual layer where the value is going to crew is going to be tools or orchestration, that kind of thing, because you can just plug in whatever the best model is at the time, whereas the models are going to be competing in a death battle with each other and be commoditized down to the cheapest one wins and that kind of thing.

26:41So you could argue that the best thing to do is to kind of connect the power to the people. Right. Right. So that actually takes us to the next question, and this is a two -in -one question. So Michael asks, and these are, and I will say, these are diametrically opposed, which is why I appear to them. So Michael asks, why are VCs making huge investments in generative AI startups? When it's clear, these startups won't be profitable anytime soon, which is a loaded, loaded question, but we'll take it. And then Kaiser asks, if AI deflates the cost of building a startup, how will the structure of tech investment change?

27:14And of course, Ben, this goes to exactly what you just said. So it's basically the questions are diametrically opposed because if you squint out of your left eye, right, what you see is basically the amount of money being invested in a foundation model company is kind of going up to the right at a furious pace. these companies are raising hundreds of millions, billions, tens of billions of dollars. And it's just like, oh my god, look at these capital, I don't know, infernos, that hopefully will result in value at the end of the process. But my god, look at how much money is being invested in these things.

27:39If you squint through your right eye, you think, wow, now all of a sudden, it's much easier to build software. It's much easier to have a software company. It's much easier to have a small number of programmers writing complex software, because they've got all these AI co -pilots and all these automated software development capabilities that are coming online. And so on the other side, the cost of building an AI application startup might crash and it might just be that like the sales force. The AI sales force .com might cost a 10th or 100th or a 1000th amount of money that it took to build the old database driven sales force .com.

28:10And so what do we think of that dichotomy, which is you can actually look out of either I and see the cost to the moon as for startup funding or cost actually going to zero? Well, like so it is interesting. I mean, we actually have companies in both camps, right? Like I think probably the companies that have gotten to profitability the fastest, maybe in the history of the firm have been AI companies. There have been AI companies in the portfolio where the revenue grows so fast that it actually kind of runs out ahead of the cost. And then there are people who are in the foundation model race who are raising hundreds of millions, even billions of dollars to kind of keep pace and so forth.

28:55They also are kind of generating revenue at a fast rate. The head count in all of them is small. So I would say, you know, where AI money goes, and even, you know, like if you like it open AI, which is the big spender in startup world, which, you know, we are also investors, and is, head kind of wise they're pretty small against their revenue. It is not a big company head count. If you look at the revenue level and how fast they've gotten there, it's pretty small. Now, the total expenses are ginormous, but they're going into the model creation. It's an interesting thing. I mean, I'm not entirely sure how to think about it, but I think if you're not building a foundation model, it will make you more efficient and probably get the profitability like cracker.

29:48Right. So the counter, the counter, and this is a very bullish counter -argument, but the counter -argument to that would be basically that falling costs for like building new software companies are a mirage. And the reason for that is this thing in economics called the Jevons Paradox, which I'm going to read from Wikipedia. So the Jevons Paradox occurs when technological progress increases the efficiency with which a resource is used, right? Reduce in the amount of that resource necessary for any one use. But the falling cost induces increases in demand, right? Elasticity, enough that the resource use overall is increased rather than reduced.

30:21Yeah. And so that's certainly possible. Right. And so this is, you see versions of this, for example, you build in your freeway and it actually makes traffic jamps worse, right? Because basically what happens is, oh, it's great. Now there's more roads. Now we can have more people live here. We can have more people that, you know, we can make these companies bigger. And now there's more traffic than ever. And now the traffic's even worse. or you saw the classic examples during the Industrial Revolution coal consumption as the price of coal drops. People use so much more coal that actually the overall consumption actually increased.

30:49But people were getting a lot more power, but the result was the use of a lot more coal in the paradox. So the paradox here would be yes, the cost of developing any given piece of software falls, but the reaction to that is a massive surge of demand for software capabilities. And so the result of that actually is, although it looks like starting software companies, the price is going to fall. Actually, it's going to happen. It's going to rise for the high quality reason that you're going to be able to do so much more with software. The products are going to be so much better and the roadmap is going to be so amazing of the things you can do and the customers are going to be so happy with it that they're going to want more and more and more.

31:24So the result of it. And by the way, another example of Kevin Sparadak's playing out in another related industry is in Hollywood. CGI and theory should have reduced the price of making movies in reality has increased it because audience expectations went up. Yeah. And now you go to a Hollywood movie and it's wall to wall CGI. And so movies are more expensive to make than ever. And so the result of it, you know, but the result of Hollywood is at least much more, let's say visually elaborate, you know, movies, whether they're better or not is another question, but like much more visually elaborate, compelling, kind of visually stunning movies through CGI.

31:52The version here would be much better software, like radically better software to the end user, which causes end users to want a lot more software, which causes actually the price of development to rise. You know, if you just think about like a simple case like travel like okay Booking a trip through Expedia is like complicated. You're likely to get it wrong You're clicking on menus and this and that and the other and like you know did it In AI version of that would be like you know send me to Paris put me in a hotel I love at the best price, you know send me on the the best possible kind of airline the airline ticket and and then make it really special for me.

32:31And maybe you need a human to go, okay, we're going to, or maybe the AI gets far complicated and says, okay, well, we know the personal love chocolate and we're gonna like FedEx in the best chocolate in the world from Switzerland into this hotel in Paris and this and that and the other. And so the quality, the quality could get to levels that we can't even imagine today just because, you know, the software tools aren't what they're going to be. So that's right. Yeah, I kind of buy that actually. I think I'll argue. You're both nice. How about, yeah, or how about I'm in a land in whatever Boston at six o 'clock?

33:11I want to have dinner at seven with a table full of like super interesting people. Yeah, right, right, right, right. You know, right, right, like, yeah, you know, no no travel agent would do that for you today nor would you want them to. You can't. No. No. Right. Well, then you think about it. It's got to be integrated into my personal AI and like and miss it. You know, there's just like unlimited kind of ideas that you can do. And I think this is one of the kind of things that's always been underestimated about humans is like our ability to come up with new things we need. Like that has been unlimited.

33:50And there there's a very kind of famous case where John Maynard Keen's the kind of prominent economists in the kind of first half of last century had this thing that he predicted, which is like nobody because of automation, nobody would ever work a 40 -hour work week, you know, like good because once their needs were met, needs being like shelter and food and you know, I don't even know of transportation was in there. Like that was it. It was over and like you would never work past the need for a shelter in food. Like why would you? Like there's no reason to. But of course needs expanded. So then everybody needed a refrigerator.

34:29Everybody needed not just one car, but a car for everybody in the family. Everybody needed television set. Everybody needed like glorious vacations. Everybody, you know, so what are we gonna need next? I'm quite sure that I can't imagine it, but like some of these gonna imagine it and it's quickly gonna become a neat. Yeah, that's right. By the way, as Kane famously said, his essay, I think, was economic prospects for our grandchildren, which was basically that. Yeah. And what you just articulate is a Karl Marx said another version of that, just pulled up the quote. So that the society, when you know, when the Marxist utopia socialism is achieved, society regulates the general production, that makes the policy for me to do blah blah, to hunt in the next day is the quote, So to hunt in the morning, fish in the afternoon, rear cattle in the evening, criticize after dinner.

35:18What a glorious life. What a glorious life. Like if I could just list four things that I do not want to do, it's hot, fish, rear cattle and criticize. Yeah, yeah. Right? And by the way, it says a lot about Marx that those were his four things. Well, the criticizing being his favorite thing, I think, is basically communism and the nutshell. Yeah, exactly. I don't want to get to political, but yes. Yes, 100%. And so yeah, it's always this, yeah, do you, but what they have with Kain and Mark's at in common is just this incredibly constricted view of what people want to do. And then, and then correspondingly, you know, the other thing is just like, you know, people who want people who want to have a mission.

35:55I mean, probably some people just want a fish and hunt. Yeah. But, you know, a lot of people want to have a mission. They want to have a cause. They want to have a purpose. They want to be useful. This is actually a good thing in life that turns out, you know. It turns, it turns out. Yeah. In the startling turn of events. So yeah, so yeah, I think that I've long felt you know a little bit of the software. It's the world thing a decade ago I've always thought that I always thought that basically demand for software is sort of perfectly elastic Possibly doing finity and the theory there basically is if you just continuously bring down the cost of software You know, which has been happening over time then basically demand you know basically is like basically perfectly correlates Upward and the reason is because you know kind of as we've been discussing But it's kind of there's there's always something else to do in software There's always something else to automate.

36:36There's always something else to optimize. There's always something else to improve. There's always something to make better. And in the moment with the constraints that you have today, you may not think about that as, but the minute you don't have those constraints, you'll imagine what it is. Oh, just giving you an example. I mean, there's a good example of AI right now, right? So we have companies that do this. There have been companies that have made software systems for doing security cameras forever, right? And it's like, for a long time, it was like a big deal to have software that would do like, like, you know, we have different security camera feeds, a store them on a DVR and be able to replay them and have an interface that lets you do that.

37:09Well, it's like, you know, A .I. security cameras all of a sudden can have like actual like sematic knowledge of what's happening in the environment. And so they can say, you know, hey, that's Ben and then they can say, oh, hey, you know, that's Ben, but he's carrying a gun. Yeah. Right? Right? And by the way, that's Ben and he's carrying a gun, but that's because like he hunts on, you know, Thursdays and Fridays, as compared to that's Mary and she never cares a gun and like, you know, like something is wrong. And she's really mad. Right. She's got a really steamed expression on her face and we should probably be worried about it, right?

37:36And so there's like an entirely new set of capabilities you can do just as one example for security systems that were never possible pre -AI and a security system that actually has a semantic understanding of the world is obviously much more sophisticated than the one that does And it might actually be more expensive to make, right? Right. Well, and just imagine healthcare, right? Like like you could Wake up every morning and have a complete diagnostic like how am I doing today, like what are all my levels of everything? And how should I interpret them? Better than, this is one thing where AI is really good, is medical diagnosis, because it's a super high dimensional problem.

38:16But if you can get access to your continuous glucose reading, maybe sequins your blood now and again, this and that and the other, Yeah, you've got an incredible kind of view of things and who doesn't want to be healthier. You know, like now we have a scale that basically what we do, maybe check your heart rate or something, but like pretty primitive stuff compared to where we could go. Yeah, that's right. Okay, good. All right, so let's go to the next topic. So on the topic of data, so Major Tom asks, as these AI models allow for us to copy existing app functionality at minimal cost. Preparatory data seems to be the most important mode.

38:55How do you think that will affect proprietary data value? What other modes do you think companies can focus on building in this new environment? And then Jeff Weisshopped asks, how should companies protect sensitive data, trade secrets, proprietary data, individual privacy in the brave new world of AI? So let me start with a provocative statement. Ben, see if you agree with it, which is, you know, you sort of hear a lot the sort of statement and our cliche is like data is the new oil. And so it's like, okay, you know, data is the key input to training AI, making all this stuff work. And so, you know, therefore, you know, data is basically the new resource, it's the limiting resource, it's the super valuable thing.

39:29And so, you know, whoever has the best data is gonna win and you see that directly in how you train AI's. And then, you know, you also have like a lot of companies, of course, that are now trying to figure out what to do with AI. And a very common thing you'll hear from companies is well we have proprietary data, right? So I'm, you know, I'm a hospital chain or I'm a, you know, whatever, any kind of business insurance company or whatever, and I've got all this proprietary data that I can apply, that I'll be able to build things with my proprietary data with AI that won't just be something that anybody will be able to have.

39:56Let me argue that basically, let's see, I'm arguing like almost every case like that, it's not true. It's basically what the internet kids would call Coke. It's simply not true. And the reason it's just not true is because the amount of data available on the internet and just generally in the environment is just a million times greater. And so while it may not, you know, while it may not be true that I have your specific medical information, I have so much medical information off the internet for so many people and so many different scenarios that it just swaps the value of quote your data, you know, just it's just like overwhelming.

40:31And so your proprietary data as, you know, company acts will be a little bit useful on the margin, but it's not actually going to move the needle. And it's not really going to be a period entry in most cases. And then let me cite as proof for my belief that this is mostly co -op is there has never been, nor is there now, any sort of basically any level of sort of rich or sophisticated marketplace for data. Market for data. There's no there's no large marketplace for data. In fact, in fact, what there are is there are very small markets for data. So there are these businesses called data brokers that will sell you large numbers of information about users on the internet or something.

41:05And they're just small businesses. Like they're just not large. It just turns out like information on lots of people is just not very valuable. And so if the data actually had value, you know, it would have a market price and you would see it transacting and you actually very specifically don't see that, which is sort of a, you know, sort of quantitative proof that the data actually is not nearly as valuable as people think it is. Where I agree, so I agree that the data like just as here's a bunch of data and I can sell it without doing anything to the data is like massively overrated. Like, I definitely agree with that.

41:42And like, maybe I can imagine some exceptions like some, you know, special population genomic databases or something that are that were very hard to acquire that are useful in some way that's, you know, that's not just like living on the internet or something like that, I could imagine, where that's super highly structured, very general purpose and not widely available. But for most data in companies, it's not like that, and that it tends to not, it's either widely available or not general purpose, it's kind of specific. Having said that right, like companies have made great use of data, for example, a company that you're familiar with meta, uses its data to kind of great ends itself feeding it into its on AI systems, optimizing its products in incredible ways.

42:31And I think that, you know, us, Andrews, and Horowitz, actually, you know, so we just raised $7 .2 billion. And it's not a huge deal, but we took our data and we put it into an AI system. And our LPs were able, there's a million questions investors have about everything we've done, our track record, every company we've invested and so forth. And for any of those questions, they could just ask the AI. They could wake up at three o 'clock in the morning, go, do I really want to trust these guys? And go in and ask the AI a question. And boom, they'd get an answer back instantly. They'd have to wait for us and so forth.

43:07So we really kind of improved our investor relations product tremendously through use of our data. And I think that almost every company can improve its competitiveness through use of its own data. But the idea that it's collected some data that it can go like cell or that is oil or what have you. That's, yeah, that's probably not true, I would say. And you know, it's kind of interesting because a lot of the data that you would think would be the most valuable would be like your own code base. Right, your software that you've written so much of that lives in GitHub. Nobody is actually, I don't know of any company, and we do work with, you know, whatever, a thousand software companies.

43:56And do we know any that's like building their own programming model on their own code? Or, and would that be a good idea? Probably not just because there's so much code out there that the systems have been trained on. So like that's not so much of an advantage. So I think it's a very specific kind of data that would have value. Well, let's make it actionable then. If I'm running a big company, like if I'm running an insurance company or a banker of hospital chain or something like that, like how to, or you know, I'm going to consider a package goods company have to see or something. Like what, how should I validate?

44:31Like how should I validate that I actually have a valuable proprietary data asset that I should really be focusing on using versus maybe, versus in the alternate, by the way, maybe there's other things like, maybe I should be taking all the effort I was spent on trying to optimize use of that data and maybe I should use it entirely trying to build things using internet data instead. Yeah, so I think, I mean, look, if you're right, if you're in the insurance business, then like all your actual real data is both interesting and that I don't know that anybody publishes their actual real data. And so like I'm not sure how you would train the model on stuff off of the internet.

45:08Yes, similarly. That's a good, let me, can I challenge that one? So that would be a good thing. There would be a good test case. So I mean, insurance company, I've got records on 10 million people and the actual real tables and when they get second when they die. Okay, that's great. But like, there's lots and lots of actual real general actual data on the internet for large scale populations, you know, because governments collect the data and they process it and they publish reports. And there's lots of academic studies. And so like, is your large data set giving you any additional actual real information that the much larger data set on the internet isn't already providing you?

45:41like are your insurance clients actually, actually, really any different than just everybody? I think so because on intake, when you get insurance, they give you like a blood test, they've got all these things, we know if you're a smoker and so forth. And I think in the general data set, like, yeah, you know who dies, but you don't know what the fuck they did coming in. And so what you really are looking for is like, okay, for this profile person with these kinds of lab results, how long do they live? And that's where the value is. And I think that, you know, interesting, like, you know, I was thinking about like a company like Coinbase where they have incredibly valuable assets in terms of money.

46:27They have to stop people from breaking in. They've done a massive amount of work on that. They've seen all kinds of break -in types. I'm sure they have tons of data on that. It's probably like we're at least specific to people trying to break into crypto exchanges. I think it could be very useful for them. I don't think they could sell it to anybody. I think every company's got data that if fed into an intelligence system would help their business. I think like almost nobody has data that they could just go sell. And then there's this kind of in between question, which is, what data would you want to let Microsoft or Google or OpenAI or anybody get their grubby little fingers on?

47:13And that I'm not sure. That's a, but that I think is a question that enterprises are wrestling with more than it. It's not so much should we go like seller data, but it should we turn our own model just so we can maximize the value, or should we feed it into the big model? And if we feed it into the big model, do all of our competitors now have the thing that we just did? And, you know, or could we trust the big company to not do that to us? Which I kind of think the answer I'm trusting the big company not to F with your data is probably I won't do that. If your competitiveness depends on that, you probably shouldn't do that.

47:58Well, there are at least reports that certain big companies are using all kinds of data that they should be using to train their models already. So, yep. I think those reports are very likely true. Right. Or they have open data, right? Like this is, you know, we've talked about this before, but you have the same companies that are saying they're not stealing all the data from people. you're taking it an unauthorized way, refused to say open their data. Like why not tell us where you're going to came from? And then in fact, they're trying to shut down all open to snow open to earth, snow open to white, snow open data, and then open nothing, and go to the government and try and get to do that.

48:36You know, if you're not a thief, then why are you doing that? Right, right, right, what are you having? By the way, there's other twists and turns here. So for example, the insurance example, I kind of deliberately loaded it because you may know, It's actually illegal to use genetic data for insurance purposes. There's this thing called the GenoLaw. Genetic information, non -discrimination act of 2008. Basically, it basically bans health insurers in the US from actually using genetic data for the purpose of doing health assessment, actual assessment of, by the way, because now the genomics are getting really good.

49:08That data probably actually is among the most accurate data you could have if you were actually trying to predict what people are going to get sick and die. and they're literally not allowed to use it. Yeah, it is, I think that this is an interesting like we're misapplication of good intentions in a policy way that's probably going to kill more people than ever get saved by every kind of health, FDA, et cetera, a policy that we have, which is, In the world of AI, having access to data on all the humans, why they get sick, what their genetics were, et cetera, et cetera, et cetera, is the most that is.

49:53You know, you know, you talk about data being the new oil. Like that is the new oil. That's the healthcare oil is, you know, if you could match those up, then we'd never not know why we're sick. You know, you could make everybody much healthier, all these kinds of things. But, you know, to kind of stop the insurance company from kind of overcharging people who are more likely to die, we've kind of locked up all this data, a kind of better idea would be to just go, okay, for the people who are likely to, like we subsidize healthcare, like massively for individuals anyway, just like differential, differentially subsidized.

50:37And, you know, and then like you solve the problem and you don't lock up all the data. But yeah, it's typical of politics and policy. I mean, they must have more like that, I think. Well, there's this interesting question. It's like an insurance. Like basically, one of the questions people have asked about insurance is like, if you had perfectly predictive information on like individual outcomes, does the whole concept of insurance actually still work, right? Because the whole theory of insurance is risk pooling, right? It's precisely the fact that you don't know what's going to happen in the specific case that means you build these statistical models and then you risk pool and then you have variable payouts depending on exactly what happens.

51:11But if you literally knew what was going to happen in every case, because for example, you have all this predictive genomic data, then all of a sudden it wouldn't make sense to risk pool because you just say, well, no, this person is going to cost x, that person is going to cost y, there's no. Health insurance already doesn't make sense in that way, right? In fact, the idea of insurance is kind of like the, it's started with crop insurance where like, okay, you know, my crop fails. And so we all put money in a pool, in case like my crop fails, so that, you know, we can cover it. It's kind of designed for a risk pool for a catastrophic unlikely incident.

51:49Like everybody's got to go to the doctor all the fucking time. And some people get sicker than others and that kind of thing. But like where health insurance works is like all medical gets paid for through this insurance system, which is this layer of loss in bureaucracy and giant companies and all this stuff when like if we're gonna pay for people's healthcare, just pay for people's healthcare. Like what are we doing, right? Like if you wanna dissenset people from like going for nonsense reasons and just up the copag, like it's like what are we doing? Just get out. Well, then from a, yeah, from a justice standpoint, from a fairness standpoint, like, what it makes sense for me, you know, what it makes sense for me to pay more for your healthcare.

52:35If I knew that you were going to be more expensive than me, like, you know, I'm directly, you know, if you, if everybody knows what future healthcare costs is for person, yeah, there has a very good predictive model for it. You know, it's a societal willingness to all pool in the way that we do today. It might really diminish. Yeah. Yeah. Well, on them, like, like, you, you could also, if you knew, like, there's things that you do genetically and maybe we give everybody a pass on that. So like you can't control your genetics, but then like there's things you do behaviorally that like dramatically increases your chance of getting sick.

53:03And so maybe you know, we incentivize people to stay healthy instead of just like paying for them not to die. The there's a lot of systemic fixings we could do to the healthcare system. It couldn't be designed in that more ridiculous way, I think. Well, I could redesign a more ridiculous way. It's actually Murray Dickelson some other countries, but it's pretty crazy here. Nathan Odie asks, what are the strongest common themes between the current state of AI and web 1 .0? And so let me start there. Let me give you a theory of fantasy what you think. So I get this question, because of my role in Ben, you with me at Netscape, we get this question a lot because of our role early on with the internet.

53:43So the internet boom was like a major event in technology and it's still within a lot of people's memories. And so, you know, the sort of, you know, people like to reason from analogy. So it's like, okay, the AAD boom must be like the internet boom, starting an AI company must be like starting an internet company. And so, you know, what is this like? And we actually got a bunch of questions like that, you know, that are kind of an analogy questions like that. I actually think, and you know, and then Ben, you know, you and I were there for the internet boom. So we, you know, we live through that and the bust and the boom and the bust.

54:10So I actually think that the analogy doesn't really work for the most part. We're certain ways, but it doesn't really work for the most part. And the reason is because the the internet was a network, whereas AI is a computer. Yep. Okay, yeah. So people just out were saying, so they weren't like the PC boom. Or even I would say the microprocessor, like my best known as the external mic processor, or even the original computers, like back to the mainframe era. And the reason is because, yeah, look, what the internet did was the internet, you know, obviously was a network, but the network connected together, many existing computers and then of course people built many other new kinds of computers to connect to the internet but fundamentally the internet was a network and then and and that's important because most of most of the sort of industry dynamics competitive dynamics start up dynamics around the internet had to do with basically building either building networks or building applications that run on top of networks and this you know the internet the internet generation of startups was very consumed by network effects and you know all these positive so positive feedback loops that you get when you connect a lot of people together, things like Metcast Law, which is sort of the value of a network, the way it expands is you have more people to it.

55:21And then there were all these fights, all the social networks or whatever, fighting to try to get network effects and try to steal each other's users because of the network effects. And so it's dominated by network effects, which is what you expect from a network business. AI, there are some networks effects in AI that we can talk about, but it's more like a microprocessor. or it's more like a chip, it's more like a computer. In that, it's a system that basically, right, it, it, it, it, data comes in, data gets processed, data comes out, things happen. That's a computer. It's an information processing system.

55:51It's a computer. It's a new kind of computer. It's a, you know, we like to say the, the sort of computers up until now, I've been, what are called by Noim and Machines, which is to say they're deterministic computers, which is they're like, you know, hyper literal and they do exactly the same thing every time and if they make a mistake, it's, it's the programmer's fault. But they're very limited in their ability to interact with people and understand the world, we think of AI and large language models as a new kind of computer, a probabilistic computer, a neural network based computer that, by the way, is not very accurate and doesn't give you the same result every time.

56:22And in fact, might actually argue with you and tell you that it doesn't answer your question. Yeah, which makes it very different in nature than the old computers. And it makes it kind of compulsibility, you know, the ability to to build things, big things out of little things, more complex. Right, but the capabilities are new and different and valuable and important, because it can understand language and images and you know, all these, all these things that you see when you use. All of them means we can never solve with deterministic computers we can now go after, right? Yeah, exactly. So I think, Ben, I think the analogy, and I think the lessons learned are much more likely to be drawn from the early days, the computer industry or from the early days the microprocessor than the early days of the internet?

57:05Does that sound right? I think so. Yeah, I definitely think so. And that doesn't mean there's no like boom and bust and all that, because that's just the nature of technology. People get too excited and then they get too depressed. So there'll be some of that, I'm sure. There will be over buildouts, potentially eventually of chips and power and that kind of thing. You know, we start with this word, but I agree. I think networks are fundamentally different in the nature of how they evolved in computers. And the kind of just the adoption curve and all those kinds of things will be different. Yeah, so then this kind of goes to where I think the industry is going to unfold.

57:43And so this is kind of my best theory for what happens from here. This kind of this giant question of like, is the industry going to be a few god models or a very large number of models of different sizes and so forth. So the computer, You know, they're like famously, they're original computers, like the original IBM mainframes, you know, the big computers. You know, they were very, very large and expensive and there were only a few of them. And the prevailing view actually for a long time was that's all there would ever be. And there was this famous statement by Thomas Watson Sr., who was the creator of IBM, you know, which was the dominant company for the first like, you know, 50 years of the computer industry.

58:17And he said, he said, he said, I believe this is actually true. He said, I don't know that the world will ever need more than five computers. And I think the reason for that, it was literally, it was like the government's gonna have two and then there's like three big insurance companies and then that's it. Who else would need to do all that math? Exactly. Who else would need to, who else needs to keep track of huge amounts of numbers? Who else needs that level of calculation capability? It's just not a relevant concept. And by the way, they were big and expensive and so who else can afford them?

58:48right and who else can afford all the headcount required to manage them and maintain them. I mean, this is in the days, I mean, these things are big. These things are so big that you have an entire building that got built around a computer, right? And they'd have like, they'd famously have all these guys in white lab coats, literally like taking care of the computer because everything had to be kept super clean or the computer was stopped working. And so, you know, it was this thing where, you know, today we have the idea of an AI God model, which is like a big foundation model, then, you know, then we have the idea of like a God mainframe.

59:12Like, there would just be a few of these things. And by the way, if you watch old science fiction, and it almost always has this sort of conceit. It's like, okay, there's a big supercomputer and it either is like doing the right thing or doing the wrong thing. And if it's doing the wrong thing, that's off in the plot of the science section movies is you have to go in and try to figure out how to fix it or defeat it. So it's sort of this idea of like a single top down thing. Of course, and that held for a long time. Like that held for the first few decades. And then even when computers started to get smaller, so then you had so -called mini computers was the next phase.

59:42And so that was a computer that didn't cost $50 million $500 ,000, but even still $500 ,000 is a lot of money. People aren't putting many computers in their homes. And so it's like mid -sized companies can buy many computers, but certainly people can't. And then of course with the PC, they shrunk down to like $2 ,500. And then with the smartphone, they shrunk down to $500. And then sitting here today, obviously, you have computers of every shape, size, description, all the way down to computers that cost a penny. You've got a computer in your thermostat that basically controls the temperature in the room.

1:00:12And it probably cost a penny. and it's probably some embedded arm ship with firmware on it. And there's many billions of those all around the world. You buy a new car today. It has something new cars today. Have something on the order of 200 computers in them. And maybe more at this point. And so you just basically assume with the chip today sitting here today, you just kind of assume that everything has a chip in it. You assume that everything by the way draws electricity or has a battery because it needs to power the chip. And then increasingly you assume that everything's on the internet because basically all computers are assumed to be on the internet or that there they will be.

1:00:40And so as a consequence, what you have is the computer industry today is this massive pyramid. And you still have a small number of like these supercomputer clusters or these giant mainframes that are like the God model, you know, the God, the God mainframes. And then you've got, you know, a larger number of mini computers, you've got a larger number of PCs, you've got a much larger number of smartphones, then you've got a giant number of embedded systems. And it turns out like the computer industry is all of those things. And what is it, what size of computer do you want is based on what exactly are you trying to do and who are you and what do you need.

1:01:11And so if that analogy holds it basically means actually we are going to have AI models of every conceivable shape size description capability right based on trained on lots of different kinds of data running at very different kinds of scale very different privacy, different policies different security policies. is you're just gonna have like enormous variability and variety and it's gonna be an entire ecosystem and not just a couple of companies. Yeah, let me see what you think about. Well, I think that's right and I also think that the other thing that's interesting about this era of computing, if you look at prayers of computing from the mainframe to the smartphone, a huge source of lock in was basically the difficulty of using them.

1:01:53So nobody ever got fired for buying IBM because you had people trained on them. People knew how to use the operating system. It was just kind of like a safe choice due to the massive complexity of dealing with a computer. And then even with a smartphone, why is the Apple computer smartphone so dominant? what makes it so powerful as well, because like switching off of it is so expensive and complicated and so forth. It's an interesting question with AI because AI is the easiest computer to use by far. It speaks English. It's like talking to a person. And so like what is the lock in there? And so are you completely free to use the size, price, choice, speed that you need for your particular task?

1:02:47Or are you locked into the God model? and I think it's still a bit of an open question, but it's pretty interesting. And that thinks could be very different than prior generations. Yeah, that makes sense. And then just to complete the question, what would we say, so Ben, what would you say your lessons learned from the internet era that we live through that would apply that people should think about? I think a big one is probably just the the boom bus nature of it that like, you know, the demand, the interest in the internet, the recognition of what it could be was so high that money just kind of poured in and buckets and, you know, and then the underlying thing which in, in internet age was the telecom infrastructure and fiber and so forth got just unlimited funding and unlimited fiber was built out and that eventually we had a fiberglass and all the telecom companies went bankrupt and and that was great fun.

1:03:49But you know like we entered in a good place and I think that's something like that's probably pretty likely to happen in AI where like you know every company is going to get funded. We don't need that many AI companies so a lot of them are going to bust. There's going to be a huge you know huge investor losses. There will be an over build out of chips for sure at some point. And then, you know, we're going to have too many chips and, you know, some chip companies will go bankrupt for sure. And then, you know, and I think probably the same thing with data centers and so forth like, well, be behind behind behind and then we'll over build at some point.

1:04:25Wow. So that that that all be very interesting. I think that and that's kind of the that's every new technology. So Carlotta Perez has a great kind of has done, you know, amazing work on this where like that is just the nature of a new technology is such you overbilled, you underbilled, then you overbilled and you know, and there's a hype cycle that funds the build out and a lot of money is lost, but we get the infrastructure and that's awesome because that's when it really gets adopted and changes the world. I want to say, you know, with the internet, the other kind of big kind of thing is the internet went through a couple of phases, right?

1:05:04Like it went through a very open phase, which was unbelievably great. It was probably one of the greatest booms to the economy. It certainly created tremendous growth in power in America, both kind of economic power and soft cultural power and these kinds of things. And then it became closed with the next generation architecture with discovery on the internet being owned entirely by Google and other things being owned by other companies. And AI, I think, could go either way, so it could be very open, or with misguided regulation, we could actually force our way from something that is open source, open weights, anybody can build it.

1:05:49We'll have a plethora of this technology will be like use all of American and innovation to compete or will cut it all off, will force it into the hands of the companies that kind of own the internet today. And we'll put ourselves at a huge disadvantage, I think competitively against China in particular, but everybody in the world. So I think that's something that definitely, that we're involved with trying to make sure it doesn't happen, but it's a real possibility right now. The sort of irony is that networks used to be all proprietary and they opened up. Yeah, yeah, yeah. Landman, Apple Talk, NetBooey, NetBios.

1:06:34Yeah, exactly. And so these are all the early proprietary networks from all individual specific vendors and then the internet appeared and kind of TCPIP and everything opened up. Yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah, the other way, it started out as like open, just like basically just like the research. It came from source and AI, yeah. Right, right, right. another trying to, they're trying to lock it down. So it's a fairly nefarious turn of events. Yeah, very nefarious. It's remarkable to me. It is kind of the darkest side of capitalism and a company is so greedy.

1:07:07They're willing to destroy the country and maybe the world to just get a little extra profit. And they do it like the really nasty thing is they claim, I know it's for safety. We've created it in alien that we can't control, but we're not gonna stop working on it. We're gonna keep building it as fast as we can. We're gonna buy every frickin GPU on the planet, but we need the government to come in and stop it from being open. This is literally the current position of Google and Microsoft right now. It's crazy. And we're not gonna secure it. So we're gonna make sure that like Chinese buyers can just like steal our chip plans, take them out of the country.

1:07:44We won't even realize for six months. Yeah, it has nothing to do with security. It only has to do with monopoly. Yes. The other, you know, just been going back on your point of speculation. So there's this critique that we hear a lot, right, which is like, okay, you idiots, basically, it's like you idiots, you idiots, entrepreneurs, investors, you idiots, it's like there's a speculative bubble with every new technology, like basically like winner, winner, you people going to learn to not do that. Yeah. And there's no joke, there's no joke through this, which is the foremost dangerous words in investing are this time is different.

1:08:12the 12 most dangerous words in investing are the foremost dangerous words are investing are this time is different right like so like This history repeat does it not repeat the my sense of any reference car a lot of presses book Which I agree is good although I don't think it works as well anymore We can talk about some time but you know is it is a good at least background piece on this You know it is just like it's just in contravertively true basically every significant technology advance in history was greeted by some kind some kind of financial the bubble, basically since financial markets had existed.

1:08:41And this, by the way, this includes like everything from radio and television, the railroads, lots and lots of prior. By the way, there was actually a so -called, there was electronics boom bust in the 60s, called the, there's called the, Tronics, every company had the name Tronics. And so, there was that, so there was like a laser boom bust cycle. There were all these boom bust cycles. And so basically it's like any new technology, that's what economists call a general purpose technology, which is to say something that can be used in lots of different ways. Like, it inspires sort of a speculative mania.

1:09:10And, you know, and like, the critique is like, okay, why do you need to have a speculative mania? Why do you need to have a cycle? Because like, you know, people, some people invest in the things, they lose a lot of money. And then there's this bus cycle that, you know, causes everybody to depress, maybe it's the way it's the rollout. And it's like, two things. Number one is like, well, you just don't know. Like, if it's a general purpose technology, like AI, as in it's potentially useful in many ways, like, nobody actually knows upfront, like what the successful use cases are gonna be, or what successful companies are going to be.

1:09:36like you actually have to learn by doing. You're going to have to miss this. That's venture capital. Yeah. Exactly. Yeah. Exactly. So yeah, the true venture capital model kind of wires this in, right? We basically, in core venture capital, the kind that we do, we sort of assume that half the company's fail, half the project's fail. And if any of us, if we are in our tail, completely like lose money. Yeah. Like lose money, exactly. Yeah. And so like, and of course, if we or any of our competitors, you know, could figure out how to do the 50 % that work without doing the 50 % that don't work, we would do that.

1:10:05But here we sit 60 years into the field and nobody's figured that out. So there is that unpredictability to it. And then the other interesting way to think about this is like, what would it mean I have a society in which a new technology did not inspire speculation? And it would mean having a society that basically is just inherently like super pessimistic about both the prospects of the new technology but also the prospects of entrepreneurship and people inventing new things and doing new things. And of course, there are many societies like that on planet Earth. with just like fundamentally don't have the spirit of invention and adventure that a place like Silicon Valley does.

1:10:42And are they better off or worse off? And generally speaking, they're worse off. They're just less future oriented, less focused on building things, less focused on figuring out how to get growth. And so I think there's a, at least my sense, there's a comes with the territory thing. Like we would all prefer to avoid the downside of a speculative bus cycle, but it seems to come with the territory every single time. and I at least I have not, nobody, I'm a, no society, I'm aware of has ever figured out how to capture the good without also having the best. Yeah, and I'm like, why would you? I mean, it's kind of like, you know, the, the whole Western United States was built off the gold rush.

1:11:17And like every kind of treatment in like popular culture of the gold rush kind of focuses on the people who didn't make it any money. But there were people who made a lot of money, you know, and found gold. And you know, in the internet bubble, which you know, was completely ridiculed by, you know, kind of every every movie if you go back and watch any movie between like 2001 and 2004. They're all like how only morons did a dot com and this and that the other and there were all these funny documentaries and so forth. But like that's when Amazon got started. You know, that's when eBay got started.

1:11:58That's when Google got started. You know, these companies, you know, with that work started in the bubble in the kind of time of this great speculation there was golden those companies. And if you hit any one of those, like you funded, you know, probably the next set of companies, you know, which included things like, you know, Facebook and X and, you know, of snap and all these things. And so, yeah, I mean, that's just the nature of it. I mean, that's what makes it exciting. And it's just an amazing kind of thing that, look, the transfer of money from people who have excess money to people who are trying to do new things and make the world a better place, is the greatest thing in the world.

1:12:44And if we, some of the people with excess money lose some of that excess money and trying to make the world a better place. Like, why are you mad about that? Like, that's the thing that I could ever say. Like, why would you be mad at, you know, young, ambitious people trying to improve the world, getting funded, and some of that being misguided? Like, why is that bad? Right. Right. As compared to, yeah, as compared to, especially as compared to everything else in the world, and people are not trying to be. Yeah, you see you're rather like we just buy like you know lots of mansions and boats and jets.

1:13:19Right. Like what do you do? Exactly. We're donating money to ruin us. We're here. We're in his causes. Right. That's such a. What's the other news right now? Okay. So all right. We're at a minute 20. We made it all the way through four questions. We're doing good. We're doing great. So let's call it here. Thank you everybody for joining us. And I believe we should do a part two of this if not parts three through six because we a lot more questions to go, but thanks everybody for joining us today. All right, thank you.