Ep8. AI Models, Data Scaling, Enterprise & Personal AI | BG2 with Bill Gurley & Brad Gerstner

27:13Yes. You can't use it up. You're saying, well, you can use it up at the amount of gathering and work you have to do to get a million to, you know, think about it. It's like several books. Yeah. And so it, you know, people talk about it like it's this wonderful thing, but it's not, it's not, you know, overly usable. And then the other one, I thought the most interesting thing out of the, the, the Sam podcast was he, he talked about whether or not developers were kind of going wholesale on top of, open AI or whether they were just using it in a lightweight way and then doing a bunch of stuff externally.

27:50And he implied that most people are doing the ladder. But then he said, if you do that, we're going to steamroll you and you need to bet on us being successful. Which would mean dumping your data and trusting open AI more fully. I don't know. What was your interpretation of what he was trying to do? It was exactly that plus the following. If you, if you take his, you know, taken for what he was saying, which is the models are going to get better. Yes. Well, what room does it leave for anything else? Because if you shouldn't be taking a model and wrapping it with your own, you know, your own code or your own technology or your framework, and then you're going to assume the model gets better.

28:28Well, why do you need, why do you need whatever I'm building if the model can just do everything? I actually, I actually thought Sam and Brad were really articulate on this point, whether you believe them or not. And I think it was consistent with the tweet that Aaron Levy sent out yesterday, which is people are not thinking ambitiously enough as to where these things are going. And, you know, today we're really in the land of answers, right? We're running some rag over some, you know, HR data that we have in our company and building a little chatbot so it can answer questions more efficiently than my HR group can can answer questions.

29:04But they're saying it really needs to think about agentic thinking like what is that multi step reasoning that can be done in the business. And, you know, I know how big your HR group. Well, here's pretty easy to do. Here's pretty easy to do. But, you know, so my sense is that I'm kind of in this Aaron Levy camp that when you look out two or three years, I mean, listen, every week we're blown away by, you know, how these models are progressing. It's hard for me to think in three years at the rate of progress and the amount of investment that's going into this that we're not going to be a lot further down the path in terms of this in terms of this reasoning.

29:44And when we get there, I think people are going to want that to be more proprietary because I think the advantages that are going to ignore to the enterprise are are even more. Let me throw one other thing in here. You know, I'm a city with my team this week and we're trying to figure out who are the winners and losers, not of the providers of the arms, but the buyers of the arms. Okay. So if every fortune 500 company is buying AI, one of the things that Bill often reminds me is fine. It will give a little improvement to an airline that starts using AI, but airlines are a competitive industry and they're just going to compete away all the profits.

30:22And so that's a defensive move, right? You don't actually improve the business model because all the earnings get competed away. So what you want to find is a market leader, somebody who has 70 or 80 % of a given market who gets to hang on, right, to all of these. Or compound their lead. Right. Or compound their lead. And so, you know, there's a company coming, coming public in a few weeks called lineage, which is in the cold storage business. So they basically are in a grow part of the food supply chain. You know, any refrigerated storage of, you know, a food. And I think they have a huge percentage of the market.

30:58And I think they have 50 data analysts and scientists now in San Francisco. Because if they can turn this group a quarter of an inch on spoilage, a quarter of an inch on energy consumption to keep this food. Oh, bottom line. And so it's all to their bottom line. And by the way, it doesn't get competed away. Right. And so they're looking at leveraging, you know, I happen to know because they were a snowflake customer and they were using some snowflake AI, you know, to improve these use cases. And so I think they're going to be a whole host of businesses bill, industrial businesses that capture some of these profits and get to hold on to them.

31:35I'll be interested in Sonny's reaction to your question and maybe to my answer, which is I, when I meet a company and see them using AI in a way that feels like ultra compelling from us improvement of their own strategic business position. It's almost always a more traditional AI model that's running a very particular optimization problem. It's not a LLM application. And this stuff's all happening simultaneously. You know, I think that I think that's true. I don't think it particularly matters because what generative AI has done. What the chat GPT moment has done is it's caused every enterprise to get off their ass to get all their data organized because that's a condition required to benefit from any of this stuff.

32:26But then I think what they do figure out along the way is some basic, you know, machine learning around time series or forecasting or things that have been around for quite a while, Bill, is where they get the most bang for the buck, maybe not from the generative AI. But they might get there because they got into the pool because they were motivated by the may maybe I certainly think it's an accelerant based on everything we're seeing. Yeah, you know, I'll disagree with you. I think, you know, what this technology really enables is, you know, we get spoiled in Silicon Valley because we can get the best engineers to build like the most difficult things.

33:04But I think for the average business to do most problems, whether it was, you know, pre generative AI was very, very difficult. Now you can basically take a generative AI model and have it do one of the most advanced things in the world. And, you know, we've shared an example in our chat where you can take a picture of a plate of food and tell it to return to you what's in that food and how many calories might it be and what's a portion size. Right, that's done sort of again with one prompt. And so now you've given that ability to every business, every small business, right. So like this business you're talking about, they can do a lot of improvements without having to have 50 people in San Francisco.

33:42So I think that's where the improvements are really going to come. Although I could push back on you and use your own statement about open AI and if they achieve everything, like that they may be commoditized just because. Well, someone said to take an apply to that business, right. And it may just be the one, you know, the one tech person in that business. It's a, I was on a walk last weekend with a great economist over at Stanford and we were talking about whether or not about the amount of productivity improvement that would be unleashed into the economy because of AI. And what was interesting is, you know, productivity has actually been under assault in this country because we've limited immigration, which was a huge source of productivity because de globalization is actually hurting productivity because we're not moving the productions of goods and services to the lowest cost places.

34:37You know, anything that's causing friction. So he's like, all the goodness to come out of AI, we need it just to replace the headwinds that we have productivity and other places. But I digress. Let's move on to the, what can I add to that, which is, I think just building on the point that you said like Aaron Levy was talking about, we're not thinking about it big enough. And, you know, where I, where I, you know, someone shared this on Twitter and I can't find the original author, but if we can, some point we'll share it, which is in the industrial revolution, you saw, you know, car making go from something bespoke one car per day to a factory making a thousand same for clothing, same for farms.

35:16And, you know, we've looked at technology as this huge accelerant, but we really haven't had the industrial revolution for technology. It's still pretty bespoke, you know, one developer writing code and now you have this idea where, you know, go back to a place where you spend a lot of time travel search, right? You could have one agent do a thousand certain, an agent and a thousand, you know, instances of it do a thousand searches for you and find what you're looking for. We haven't seen that in technology yet. And I think that's, that's the era we're really about to go into, which ties back to, you know, the point that you said you were having on your walk around efficiency for society.

35:52Yeah, I mean, I think about it in the context of what we've called business intelligence, right? We've been investors in companies like Tableau, you know, obviously Snowflake, etc. over the years. And, you know, it's not really business intelligence, right? Issuing me a report that tells me how many black t -shirts I sold yesterday, right? It's nice, but it's not all that informative. What you would like is an agent to scour all of your data, compare it to all the data of other companies and say, here's something that is anomalous, or we can predict something or suggest something based upon patterns we're seeing in other businesses.

36:30That's all, you know, we've been talking about that for a decade, right? I actually think we're getting a lot closer to that moment where now we're going to be able to have these resources because what do these things do really well, Bill? They devour data, they spot patterns, and they predict. Yes, right? Take what you said times a thousand. Shouldn't be a single agent, it could be a thousand of them doing it on your data. We beat up what it's going to do in the enterprise, but you know, one of the areas that I'm even more excited about as these models get smaller, is what it's going to mean for personal search and personal AI.

37:05So when we think about that, you know, Google reported tonight, they had billions of what they call their SGE searches, so these are, you know, their AI searches. They talked about dramatically driving down the cost of inference of those searches that you can probably tell us a little about. So you know, Meta's rolled out search across all of their apps. There's a search bar on Facebook on IG on WhatsApp. And you can search any top right you can go there and say, hey, show me the recipe for fried chicken or show me how to, you know, play a guitar or show me where I should stay at a hotel, you know, when I'm visiting Milan.

37:43And Zach did say in his announcement, kind of as a shot across the bow at at Claude and at chat GPT that they had the most cap the most capable free personal assistant, right, you know, that you could get out there. You know, we had Apple announced open ELM, which were these models from 270 million parameters to three three billion parameters. You know, it seems like the next step that everybody's looking at is really the smaller models that can get us to, you know, a personal assistant on device, whether it's on phone, whether it's on glasses, et cetera. So when you looked at the announcements this week, you can go to either of you.

38:28It felt to me like the disruption caused by Lama 3 was almost more impactful to what we're going to see along the lines of consumer AI and search than it was in the enterprise. Any thoughts about that. Yeah, I think it, you know, it ties back to a point we touched on earlier, right, the as we make smaller models more capable and we make even smaller and smaller models that can maybe reference those larger models. We're onto a place where it becomes more affordable, right, what we don't really think about, you know, if you think about the larger models is even so crazy, it's like like a year ago.

39:07Right, you know, all the way back a year ago, you're using a 30 ,000 dollar plus unit of compute to run this thing with hundreds of gigabytes of memory. Now, whether you look at the Apple stuff or five that came out of Microsoft, you can run that on your phone. People already running it on their phone. I saw a demo of some folks running it in the Apple Vision Pro. Right. No specialized hardware. Right. And the key is, you know, if we're going to run it on the phone, we got to compress all of that intelligence into a smaller and smaller model that's less power consumptive. Right. If you put one of these larger models on it, burns up the battery, burns up the phone too much, you know, generated by that bill, you referenced a quote, you know, zuck from door cash where he said, I don't think in the future we're going to be primarily shoving all these things in the context window to ask more complicated questions.

40:00There will be a different stores of memory or different custom models that are more personalized to people. One of the things that I was most intrigued by in that interview was his focus on the personalization to people. He went so far as to say, understanding the content around emotions is a different modality unto itself, which got me thinking, you know, not only are they producing smaller models, but they probably have the largest store of human emotions. What reactions to one another, emoticons to one another, you know, biggest certainly social graph on the planet, which seems to put them in a really good position when it comes to this personal assistant that we all talk about.

40:45I know you your view is we're not going to get anywhere close until we get memory and we haven't solved memory. Well, I mean, he hinted at it, but everyone hints at it. It comes up a lot and there's a, I mean, I push it to sunny, but it's unclear whether you can accomplish what people hope to be achieved in a personal assistant with rag with fine tuning or if you really need a model to be actually, you know, trained on my data. And that latter part, no one knows how to do a fact cost effectively. Yeah. So I don't know what pieces have to fall in place for us to get to that place. Yeah. And there's no secret like everyone seems to be aware that that's the end goal, but I don't.

41:32I think there are a few breadcrumbs that were, I don't know if you say, you know, I'll suggest some of them. Maybe you can. So a few breadcrumbs that were dropped out there, both by Apple and by, by Zuckerberg in this regard. I mean, I'll just kick it off by saying what he said in that podcast is like, listen, in the first instance, what we do is we build software around the model that kind of hacks this stuff together. And we see kind of what works. And so yes, in the first instance, it may in fact be you have a really small model. You do some rag on it, maybe in certain instances, it communicates with a more sophisticated model.

42:09But in that, you know, in that rag can be a lot of personal information. I think Apple has said the same thing. But then what he importantly said is if that works, then on the next go around, we figure out how to build that into the model itself. Yeah. I think building on that, if you look at the breadcrumbs from, you know, all the major folks. And I think there was like a wired article that came out where Sam said, you know, the next model necessarily won't be bigger. I think you did say that. I thought that was interesting. Yeah. And the reason is, and you know, you had a thing Brad last year at the barn where you had Brad Light caps being great.

42:48And the general message that keeps coming out of the open AI contingent is that customization and memory. And so my, and I don't have anything beyond this. But I would say my guesses. That's what they focused on with GPT. And by the way, that's an important point. Like I think in GPGP T5, it's not going to be the final state. But I think you're going to see the beginning to memory and the beginning to have actions. Right. And this is, you know, months away. And you, you and I have a bet on this. I know. Well, yeah, but that could be a major another, you know, tremor. But one interpretation of the statement that the models aren't going to get bigger.

43:27It won't it could be a me a culpo to the exact thing. Like, okay, like I don't want to play this game anymore. But to it could mean that the, the LLM training has kind of just run its course. And you got to go do this next thing. But the next thing is not a necessarily an exponential leap. It may, it may be like a early alpha or beta. And it may be a little more stumbling as you bring. I don't think I see little evidence that the scaling has run its course. I mean, like the smartest people on the planet who are putting their own money, real money up against this. Elon is building a much, much bigger cluster to train a much, much bigger model.

44:11As is open AI, as is Zuckerberg. I mean, what Sam just said, the bigger models aren't the world. Well, I mean, he may be doing the same game that everybody else is doing. We're trying to throw everybody off the scent of building a bigger model. Why is he trying to build his own chips, nuclear power plants and everything else if he's not going to build big models. I mean, you only don't take him in his word. Well, I'm just saying that I think the world, as I said earlier, is bifurcating into two, like a world of specialized models. We are going to have very large frontier models. There will be a point at which you hit the, you hit diminishing returns.

44:47Jan Lekun has said we're going to need a different architecture to get to AGI. He's speculated that it's probably two or three generations more of scaling before we get to that point where it no longer makes economic sense to continue to scale it. But we're going to, I mean, that's a lot of, if it continues a pace, that's a lot of developments over the course of the next two, three generations before we hit the upper limits of that. And by the way, I think we're already seeing some creative things like the data scaling that we saw. You know, past the chinchilla point, those are really creative innovations to get around or to augment kind of the compute problem.

45:26So to me, I come back to this and I, you know, it makes me really excited again about where we are in this state of consumer search. You know, and personal assistance. Google's probably innovating better than they ever have because they're pushed out of their monopoly position by everybody else now. It sounds like, you know, they're seeing some great results come out of that. You know, I thought it was really interesting, you know, when you see, you know, Dali and David Woodland, who's the product lead on on metaglasses, talk about what they announced this week. Now it has met a AI with vision.

46:05It's, you know, now available to everybody, you know, not only can you use these things to call and to message using what's up, but as all these integrations and these overlays. So, I mean, we haven't seen this kind of shake up in the world of search and in the world of consumer products in a while. And now it's, you know, there was this, all this noise this week about humane. Right, the startup up in San Francisco and, you know, it got panned in a consumer review. You know, and one of the biggest challenges with that product because I use the product as well, right, is the models weren't small enough.

46:43It doesn't have, it can't run the inference on device. So it has to go out to the cloud to do it. And the second you have to go out to the cloud, it ruins the experience because now you have latency. We're a year away, probably max from that thing being able to have a billion parameter or 500 million parameter model that basically has all the capability you need it to have. Totally agree. And we're also compressing the amount of time that it takes to go out to the cloud because we'll get those models to start running faster. So we're going to see a convergence there on two fronts, the local and then the ability for that model to reach out in the cloud and get a faster response out of the cloud.

47:23That's what I think is being underestimated, you know, just swinging back around to model size, right, the smaller models run faster just naturally. And so that gets you to faster responses and we know the internet's been on a huge push for lower latency across whether it's loading web pages or search results or whatever it is. And so I think we're starting to see a push in that direction. We all got kind of comfortable with the pace of chat GPT. But if you kind of go away for a second and try one of these smaller models somewhere else and go back to chat GPT, you'll really have that like we all had that moment for a bit between dial up and high speed internet.

48:01And we're maybe a dial up at home, so in high speed at work, that's the feeling that you get when you switch between those two things. One of the debates I know, Sonny, you've been having, you know, with our team and I'm firmly, you know, in your camp on is, you know, this idea that dating back 20 years when it comes to consumer products, even, you know, speed improvements that are barely perceptible at Google have pretty important implications for their revenue. And so I think what we're seeing with these smaller models and all of these other developments and you guys are helping, you know, certainly to lead the way a grock, you're just seeing massive improvements in token per second.

48:41And I think, you know, when you start having agents talk to agents, you take humans out of the loop, right. Now computers can talk really fast to one another, but we have to have low cost fast inference, you know, that's able to support that. We do and we think of the use cases that we all like and I think we all love perplexity, but you think about, you know, what happens behind the scenes when you type like a small request, it shoots off something into a couple different places, including a search results pictures and all those kinds of things that all that has to be processed by the LLM like really quickly.

49:13Did you play with the Met AI picture generator? I did. Where you just added another little word or attitude. That speed is insane. Yeah. And compared to like a year ago when you're doing it, when you'd wait, you know, 15 seconds. Right for the next. Well, this gets back to Sonny's point as well. When you're doing that, just think if the cost of inference was really high, there's no way he could roll that out to three billion people. Right. Because all the sudden people would start playing with it and his op -ex on, you know, on the business would blow up in his face. Part of the reason he's I think pushing toward these smaller models, opening these models, you know, and he said in that podcast interview, they help me lower the cost of inference.

49:56You know, we the cost of training. So, you know, we can we can lower the cost of inference. Well, maybe to, you know, just a wrap, we can I wanted to hit on a few topics. Sonny that we've covered over the course of last few weeks. Of course, Bill and I did have been doing a couple of deep dives on full self driving at Tesla, as well as, you know, their rideshare project that's now move front and center because of the breakthroughs they've had on FSD. And on the Tesla earnings call this week, they bill answered some of our questions. So a couple of the questions Bill and I had is, is this going to occur within the Tesla app?

50:33Well, you can see here. You know, this beautiful depiction, you know, of a ride share within the Tesla app. There are a lot of Tesla app holders. We had a question whether or not it was going to be owned and operated or whether it was going to leverage the millions and millions of cars that are out there in the fleet. And Elon, I thought elegantly put this, you know, we're going to be both Uber and Airbnb. You know, we're going to, you know, own some of the fleet. We're also going to let those people who buy cars from us, you know, put their cars into the fleet. My own hunch is that it will also be distributed both 1p and 3p, although he didn't go so far as saying that.

51:11And what I mean by that is not only distributed in the Tesla app, my hunch is that as this scales, it'll make sense to do a partnership with Uber. And frankly, I wouldn't be surprised to see some of the people who operate on the Uber platform become fleet operators of Tesla's for Tesla. And so I think there's a really interesting opportunity for an integration there. But I thought that was pretty consistent. We weren't too far off in terms of our estimation there. And there's, I mean, this is kind of, they obviously have already made it clear they're going to be talking about this for a long time.

51:45But this is the kind of first draft, if you will. I think there's a lot to see as this stuff rolls out. You know, Waymo's had to apply for these licenses to get these cars on the street. Yes. We don't have Tesla's, one they haven't even applied for those things. But we don't have them driverless on the road. Right. Right. Which would be a step that would need to take place before this was rolled out. But it intersects with that really big purchase of, you know, each one hundreds that they talked about as well. We now have a lot of companies that have reported it and there's not one of them yet that has not raised their CapEx guidance to buy more GPUs.

52:32I mean, Elon himself is going to let his in the past four weeks, the incremental purchases they've signaled or in not just Tesla, but there's data on the internet today that X .ai, AI is raised six billion, presumably most of that's going into infrastructure as well. And you know, you and I have a couple bets going, but you know, when it comes to whether or not GPUs or under supplier over supplied you know what I've stipulated is every supply shortage does ultimately result in a glut. But people have been calling for this glut now for, you know, 12 months anyway. And they're calling for it again this year.

53:16We're not going to see it again this year. There's, you know, and you see what to do. We we we own planning. And you know, and you just see it, you know, in fact, you know, meta was down 15 or I think it ended up down 10 or 11%. And one of the major reasons it was down is Zuckerberg said I'm going to put the, you know, the sellerator to the floor, he increased the midpoint of his CapEx guide by three or four billion dollars, you know, which I said, you know, I had a lot of people inbound to me and say, hey, you know, there no longer be an efficient or they're no longer being fit. Which you know to which I responded by saying in two years that company has gone from 22 billion in net income to 55 billion in net income.

53:58They've reduced their head count from 85 ,000 people to 69 ,000 people. What they are demonstrating is what you can do when you're efficient. You can redeploy all of that incremental profitability into investing, not in some 10 year project that we don't know what the payback is, but directly into GPUs and AI where you can see the payback in a pretty short period of time leveraging it in their core business. And so, you know, it was, you know, while we're on it, you know, he went and bought gym equipment. I did see that too. I didn't see that too. Bill, you and I talked about IPOs, you know, Dan Primerick, you know, came out with this article.

54:40That was pretty controversial. I think among VCs, I saw a lot of people responding to it. It was entitled VCs, you're blowing it. And there was one line in there that caught my attention, you know, where he said, VCs, let startups stay private too long, often well passed their hyper growth phase that justified sky high valuations. You and I debated this last week. How much revenue do you have to have to go to go public? I think you and I are both in the camp that if you have 100 million of trailing revenue, you're growing well, you have great unity, can I mean, you can certainly go public. If you price it right, I'm, you know, I've said it on Twitter.

55:21I say it in boardrooms. I think being in the public markets is a great place for companies to be. I think it, you know, it puts them in the big league. It makes them, you know, there's plenty of room to innovate there. However, what I would say to Dan is, you know, when we sit on the board, we can advise. But ultimately, we're not the decision maker. Right. It's got to be a collaboration with the founder of the company. And ultimately, I think the company should go public when it's the right time for the company to go public. And for some companies, that is at that earlier phase. I think a lot more could go public at that earlier phase.

55:57But I also think there are certain situations, you know, take SpaceX, for example, where I think it's behooved them to stay private longer. Right. And they've had plenty of access to the private markets to raise capital. So I didn't know if you had any reactions to the Dan. Well, I mean, I think in a lot of ways, I agree with what are you saying? And I disagree with maybe with the way it was positioned. But keep in mind, Dan's one of the very few analysts and writers that focuses on LPs, like most of these writers focus on BC's or the founders themselves or whatever. And he's constantly talking to LPs.

56:36And I think there is a very real situation, especially where we came out of ZERP, where there's a vast amount of paper marks that are sitting on these LP books that are aging out that are exposed to dilution, you know, on an annual basis. And I suspect they're very nervous. And I suspect they're talking to him and that that's where he's building the ceases. And I think that's probably right. I also think that a number of people, you know, that invest in late stage and people that we know have have built a business model where they kind of like company staying private longer. They are the ones that everyone talks about Amazon with public at this price and then the public captured it.

57:21They kind of view their game as capturing that growth instead of the public markets. And the third thing I was just say is our business has gotten nothing but more competitive from the minute I entered it to today. And I think that's going to keep happening. And that competition forces people to be very founder friendly to say what they want to hear to support secondary, so it's we've talked about that when you support massive secondary, as you're taking the number one pressure out of the system that used to lead founders to want to go public, because their employees are like, I need liquidity, I need liquidity.

58:01So you do a release valve and you take that away. And and I do think there will be a lot of situ and then they actually one last thing to mention just because of where we came from evaluation perspective, we know a lot of people are sitting there afraid that they can't meet their last mark. For sure. And so then you're kind of never never land and how do you get out of this and where the odds are going to grow back to that. And there's kind of a lack of so anyway, those dimensions, I think he's sitting at a right I agree with you. And this is where I think you got it wrong is that like no single VCs going to stand up and make a company go public.

58:39Like that's not going to happen. I mean, I do see the market of all the listen, you got to get you're in you raise 10 year funds. You need to get liquidity. You're in the business to provide returns and liquidity to your partners. If I look at the private equity business, right, they evolved in such a way where they didn't have to take the company's public. They would just sell to another private equity company. And it may very well be in the VC landscape bill and I see this more and more. You know, I know a big company right now raising it over 10 billion and I know a lot of early stage VCs who are selling into that round.

59:16Right. And so that I thought there was one and I didn't rippling say 600 million was going to people that are actually early. So that's not the one I was referencing, but rippling may in fact be one of those. And so the market may be responding to some of these imperatives, you know, by LPs to get liquidity. But what I would say to Dan is certainly with the venture capitalist sitting around this table, you have two people who think that the public markets are a great place for companies to innovate companies to thrive companies to raise capital companies to recruit build build brands, etc. And we know we're in the business of liquidity.

59:53Seeking as an entrepreneur though, can I add one thing to that, which is I think we're going to see like a bifurcation builds on our conversation from earlier. There's going to be companies that are cheaper and cheaper to build and run. And you know, that has one impact, but there's companies that are going to need a lot more capital to build and run. And so that may force folks into the public markets, sort of like the way it was in the late 90s with a lot of those business because they needed a lot more capital. What do you guys think about that? There was a interesting article that Tim O 'Reilly wrote about a month ago, I could put the link in everybody.

1:00:27He implied that he also attacked the VCs and said they were going about it all wrong, but he implied that the AI was now in its uber phase where the, you know, talk about Uber and lift and doordas all raising billions and it's spilling out on the floor. And I personally think part of it is simply a recognition by the investment community writ large, the network effects exist and increasing return exists. And so when they think this is the next big thing and they see open AI take a lead, their gut response is, well, if I had invested early in Amazon or Google or whatever, I'd get paid almost no matter what the price.

1:01:11And so it's the institutionalization of a belief in network effects that's leading to the money pouring and then and then it's a competitive dynamic. Like once your company raises 200 million a billion, if you're in that market, you raise it too. And it does create chaos. Like I do think it creates chaos. Maybe we'll just end with just kind of the volatile week it's been in markets. You know, we talked to you know, you asked me what I thought was going to happen this week. You told me to give me a scorecard, but, you know, the reason it's so volatile this week is not just because we've had some mixed earnings reports or at least mixed reactions.

1:01:56But I think the economic backdrop is, you know, is unsettling. GDP came in a lot weaker than expected this morning. At the same time, the PC for Q1 came in a little bit higher. Now we got the monthly PC report coming out tomorrow. We'll see where that shakes out, but this idea that we could have a slowing economy at the same time that we have inflation continuing to go up. Right. This is this very fearful place called stagflation that nobody wants to be in. Now the market is now pushed out the rate cut forecast to December of this year. Right. So this higher for longer is now in place. Remember when we started the year, we thought we're going to have six rate cuts very accommodating Goldilocks environment.

1:02:38What's surprising to me to be perfectly honest is how well technology stocks have performed outside probably software, but how well they perform not with standing this fact and the only reason they've been able to do that is the re acceleration caused by AI. So if you look at what happened tonight, Google beat the issue to dividend for the first time they announced a buyback. Right. So they're really their margins are expanding. So they're finding efficiencies in that business. They're listening to the markets. But you know, I think impressively, you know, what they're doing in that core business, the things they announced around SG, you got to give that management team a lot of credit.

1:03:17We talked about the stock. The stock was up, I think 10 or 15 % after hours. So I think it's at an all time high and doing incredibly well. Meta, you know, missed is down 10 % but still up 30 % on the year. So I imagine those those two companies are about in the same area, you know, year today we talked about why they why they got hammered is because, you know, they're going to invest even more aggressively in AI, which is a long term investor. I'm pretty thrilled about. And then if you look at Azure or Microsoft's quarter, it was pretty blockbuster. And we'll show this chart by jamming, but Azure AI contributed 7 % of growth this quarter.

1:03:57So it's now translates into about a 4 billion run rate business, you know, that didn't even get broken out until five quarters ago. So, you know, so again, I think if you look at technology generally, it's performing, it's performing really well despite this kind of volatile economic backdrop. And we'll see what we're we're PC rolls in on Friday, we'll see where the rest of technology comes in my hunch is that the largest companies and technology back to your network effects and your scale advantages. I'm not sure that smaller tech technology companies are seeing the benefits that the largest technology companies.

1:04:39So we'll see that as it reports, but I think the largest data platforms and hyperscalers continue to benefit. Boys has been fun. Yeah, great. Let's do it again. Thanks for being on sunny until next time.

1:05:05Thank you.