Ben Thompson on Big Tech, China, and the AI Boom Running Out of Money - [Invest Like the Best, EP.487]

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1:28 And welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at Colossus.com. Mm-hmm. Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Some.

2:02 This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. Mm-hmm. So Ben, if you can believe it, how long it's been since we last did this. The world was very different. No AI at the time. We talked about aggregation theory mostly. I thought a fun place to begin since the world has changed so much. is to hear what you think it would mean for the US to win The AI race.

2:35 I think it would be very problematic for the US to win. Let's say we take the most sort of fantastical scenario where if you control AI You basically your military is better than anyone else. Somehow it fixes our manufacturing all these things that I don't think AI is necessarily going to do because they sort of deal with the real world.

2:53 But In this world, what is the game theory optimal response of China. The ball up to SMC. Game theory can get very sort of convoluted and complex. To me, this one actually isn't that complicated.

3:08 There is a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley. coming out of I think of one of the labs in particular. Where if we get to a place where We have a meaningful superiority. in terms of a military national security perspective.

3:23 I think that's very dangerous for the world. But in that state. How much does it extend beyond TSM C being blown up? In that state, I would assume we figured out how to build fabs here in the US. You know, to some degree and are less reliant on that one choke point.

3:38 I think there's a Little bit of magical thinking. Which I just invoked in terms of manufacturing and whether it be fabs, whether that be actuators. All these precursors, I think the degree to which

3:53 We are dependent on China is underappreciated. And is not something that is going to be fixed outside. of a conflict. Just because fixing so many of these things is going to be Dramatically.

4:06 Dumb. If your competitor is sourcing from China. And you're gonna start sourcing or eating things from the US. You're gonna be at such a disadvantage, relatively speaking, that you're just not gonna do it. So you do it when you have literally no choice. And

4:20 That works for very big headline items. Like you can browbeat Apple to move some of their iPhone manufacturing to India, for example. But even that is a good example because Apple is not truly moving out of China. They're diversifying to an extent. But It would just cost so much. It's like paying an insurance policy. That

4:39 If you don't have to pay it and it's astronomically expensive, you're just not going to pay it. It's one of those sort of Хіпотез that I just have a hard time even grocking because the only world I see Where we truly

4:53 Pull out. And have no dependency on China such that if they want to blow up Taiwan, who cares? There's no impact on us. Seems pretty fantastical to me. And I think there's a bit of facing reality in this regard.

5:06 That Is not present these conversations. Put yourself in their shoes. Like what do you think the motivations are? Everyone.

5:14 can use a good bogeyman. I think from the AI trade perspective. nothing works better than we have to be China. And I do think we need to be China. We need to be competitive. I despair at the extent to which over the last few years a particular

5:32 So many of our responses, particularly from a political perspective, has been to like try to be like China. I think we should be going the other direction, more openness, more innovation, less top down control, less restrictions on speech and things along those lines. America succeeds by being on the leading edge. And by leading into that. You said probably the US being purely dominant and AI is not the right end state for the world.

5:57 What is your ideal equilibrium for how this goes worldwide? There's a bit where AI right now is kinda like the Taiwan situation. In that The current status quo actually doesn't seem so bad.

6:09 The question is how sustainable is it? But maybe it's sustainable for longer than we think. The way I think about it right now is I think Open AI and Anthropic are clearly on the frontier. Who knows what's happening with Google and then Groc and Meta are

6:24 Chasing them. Meanwhile the Chinese are very capable, very smart, and also definitely distilling these models to sort of stay About six to nine months behind.

6:36 And It feels like a pretty good equilibrium that I think is generally favorable to the US. Now The question is how long could it stay this way? And there's lots of questions on there, like

6:46 Can the Chinese actually pull ahead? I'm still a little skeptical. for various reasons. Whether we from chips. Getting to the weeding edge and that lasts six to nine months is very difficult. It's gonna be instructive how Meta and Groc do in terms of actually

7:01 Catching up? Especially as we get into the world of AI improving itself, using AI to make the AI better, which I think is definitely a real thing. I think you see a real acceleration from both Open AI and Anthropic recently, which was sort of theorized and it seems to be coming true. And to the extent that's true. Can you actually catch up? And I think the b other question about this, by the way.

7:20 is to what extent does that apply to cost to serve, to marginal costs. If you can apply AI to optimizing your stack, to figuring things out, to analyze all the data. Easier cost to serve structurally lower than anyone else. This is the thing about the open source models. The talk about them being free is bizarre to me.

7:39 Because it's marginal costs. You still have to run inference like GLM. Or Kimmy. Kimi is very expensive to serve. The cost per answer is significantly higher. Everyone referring to these as free. It feels like in the narrative, it's in people's head that Free is free. Now I can use AI for free. No, you can't use that ad for free. You're not paying necessarily the R D to

7:59 Create the AI. But you're definitely paying the inference to sort of run it. So Right now I kinda like

8:05 Where we are. And the pushback would be That's right now. It's not gonna stay that way. Which I think is fair pushback, but I don't know. This way longer than we think. If you can know anything about the future of how this will go.

8:17 to be more confident in like where the equilibrium will end up. What is it? Is it like the length of the S curve, like how far up the S curve we are at some point these things presumably will level out, maybe not. What would be the thing you'd want to know that would give you a better sense of what the future might look like. I am concerned that with the scare around

8:36 People freaking out about Mythos and This hugging face incident. That the actual Implication of that is not that we reduce These dangers.

8:48 But we just stop releasing stuff. We on the outside. start to lose any sense of like where exactly the frontier what is actually the frontier and where it is. There becomes sort of a False sense of security.

9:02 Because right now everyone's basing their understanding of mythos on fable. But how good is Fable actually relative to Mythos? That's a gap. is only going to I think increase over time.

9:14 So I think that's a real question that I'm not sure about. This question of the recursiveness and AI sort of making itself better. Does that lead to some sort of takeoff? And At the end of the day there's

9:26 Timing questions in lots of different ways. The time you missed match in terms of The actual return on investment. Producing enough revenue to fuel investment. We're working our way down the capital curve. We started with free cash flow.

9:40 The speed with which the tech companies blew through the debt markets is kind of incredible. It took like a year. And now Google's issuing equity, NVIDIA's putting together these five hundred billion dollar thing. To tap into like pension funds and insurance floats and things like that. What's after that? Where's the money come after that? Well, ideally we actually flip back. to free cash flow.

10:01 Funding this. But If there's a gap there, if we don't get there soon enough. Then We could have a big blow up.

10:08 But at the same time Even if we have this blow up. The AI is not going away. It's not gonna stop improving. It's going to keep sort of progressing in a way that we look back on the dot com era, we look back on the railroad era, or we look back on whatever bubbles through history. ultimately immaterial in terms of the broad scope of humanity, even if they were

10:30 Very devastating. What can the railroads teach us to think? It's now the last bigger build out, right? In terms of percentage GDP. I think we might be bigger at this point, or it's like it was the biggest in the ballpark. The railroads had a real duration mismatch.

10:45 To build a railroad and make money off it was a decade or multiple decades long endeavor. Whereas you had to issue money

10:56 In the short term. the world ran out of money. Right. And I think that is Probably the aspect. I think that's why people reach for the railroads. Because Everyone talks about

11:05 Are we gonna have enough? Compute. Are we gonna have enough electricity? maybe the nearest term question is are we gonna have enough money? Which is kind of a bizarre thing to think about.

11:16 That's what happened in the eighteen seventies. The world just ran out of money. The funny thing is The railroads kept operating and they expanded the West.

11:24 their contributions to GDP was astronomical. They're still contributing to GDP. Railroad money is what's going into Google right now for Berkshire Hathaway. It's quite literal. Berkshire Hathaway has this problem To me, this NVIDIA deal is very much paired with the Google equity issuance, which I thought was shocking when it happened.

11:47 Why is it shocking? Because it's Google. They can't raise money. Like why are they issuing equity? Why are they reducing their upside if they believe so strongly in this? But the Brookshire comparison is interesting because To a rough approximation, they make

12:00 They have sees candies, famously, right? Tremendously high margin business. The problem with a lot of high margin businesses is the percentage profit you can make is very high, but the absolute profit you can make is no investment runway. That's right. Like you're just accumulating cash. The brilliance of the BNSF Railway.

12:18 thing was basically they took the C's candy profits. And said, here's another industry whose margins are way worse. But The absolute dollar amounts are so large that those way worse margins result in absolute profits that are much larger. B N S F in twenty twenty five or something, the amount of free cash they've threw off in one year.

12:39 was more than C scandy's had thrown off its entire lifetime. Even though you're talking about a low margin business compared to a very high margin business. I think there's an aspect from Berkshire Hathaway where Once your capital gets so large, you start operating in a world of like absolute numbers as opposed to percentage numbers. And the reason why I thought that was so interesting, that story, is

13:00 It seems to capture where Google itself might be going. And so there is very symbolic for them to invest in Google. Google has this unbelievable high margin business. of search. One of the most perfect, beautiful business models of all time. And the purest aggregator of them all. Like scales in every direction, doesn't have to invest any money to do it.

13:19 Everything's zero marginal cost. It's amazing. Meanwhile, there's this AI opportunity which requires just astronomical it's just Incinerating cash. But you can imagine If AI is

13:31 intelligence and it's TAM is basically all white collar work. And eventually with robotics everything potentially the absolute profits available here, even if the margins are lower. І сомач лардже. That Will we look back and Google search what sees candies?

13:50 It feels like that's what's happening in that world. You use all your free cash flow. They've done that. You tap. The debt markets to the tune of hundreds of billions of dollars. They've done that. You issue equity.

14:02 What does an equity issues do? It dilutes your interest in your interest in your shareholders. So you have a smaller percentage of the pie. Well you have a smaller percentage of an astronomically larger pie, at the end of the day, no one's going to be complaining. It was very symbolic. Berkshire Bing the symbol of that equity issuance. in that are they actually not just an investor in Google, but a model for Google and where they're going.

14:26 I'm curious, setting aside the commercial and competitive components of this like you're describing. How AI Pilled on the pure technology. Would you say you are relative to other people thinking about

14:38 The space. I have a view that is. Both super bullish and less bullish in some respects. Okay. So I am not fulvinced about the generalizable

14:50 argument. AI is clearly incredible at coding. It kind of blows my mind. That People were doing this a year ago. Like actually Writing out code.

15:01 Very good at math, obviously. But The obvious repos is that these are sort of verifiable domains. What is the evidence or where is the compelling evidence of being very good at verifiable domains.

15:15 cleanly translates to being very good at sort of unverifiable domains or domains that take have a very long sort of verification loops. I think that's still a little bit to be determined. And it's interesting because I raised this question and there are some people at the labs that were on a panel. And I was kind of annoyed at the answer because the answer

15:33 Took me for an AI bear. Oh well, people thought we couldn't solve chess, or we couldn't solve gold, we solved those easy enough. And I'm like I thought we could solve chess. I thought we could solve gold because they're knowable domains. Scale was the answer to both of those, but also both of those were bounded.

15:49 What is the go to example that's not just that's not go that is genuinely in a new space that's sort of an unknowable space where it's doing things that were not possible. That is sort of the I'm not fulvin since. However

16:06 A I trains. That a rough approximation trained on all the data of the internet. All the data of the internet. That's distillation. It distilled all of the end state of human thought. The actual typing on Reddit. It doesn't have the traces.

16:22 It doesn't actually have the thought. the emotion or whatever that w went into typing that comment or typing writing that essay. Say Neuralink, whatever. What if the actual payoff from Neuralink is actually capturing the traces of of human thought. That actually dramatically expands the capabilities of these models. In this world, my concerns about verifiability.

16:44 Is like well, we solve verifiability by getting more data. My sense is that A huge number of jobs, a huge amount of economic activity. does not exist in these domains that I'm not convinced that A is good at. Actually, there's a lot of people in the world. who are kind of like sentient AIs to a certain extent. They operate very well in verifiable domains. They're given jobs, they do them.

17:06 And It's almost like a somewhat pessimistic view of humanity. To a certain extent, but I think that market is so huge and so large that if The models did not improve at all from where they are right now. The economic opportunity is actually massive. I wrote an article a while ago.

17:22 There's the whole like accelerationist movement. What I call myself was a reluctant accelerationist. I think we need to push forward because We can't go back. And the worst thing we can do is get stuck where we are.

17:35 So I'm very AI peeled in terms of its impact on The economy. It's sort of upside in terms of monetization. I'm not sure about the timing. What would be like the gradient towards it? Imagine law or medicine where

17:51 I don't know whether or not you would consider those verifiable, like law is like a code. Of some sort medicine, we have a certain state understanding of things. I mean, I think medicine is By far one of the biggest opportunities. It's both one of the biggest opportunities and also one of the most challenging ones. because of all the regulations and all the access. Like if you could turn an AI

18:10 turn machine learning onto all the medical records. I think the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable. So that is a very optimistic view on the flip side. Like when is that gonna happen, right? I think the optimistic frame I put on humans is our capacity to create needs is sort of unlimited.

18:32 So I think we'll do a very good job of creating new opportunities and job sort of in the fullness of time. The sort of more pessimistic way to put it is our ability to create red tape and muck. is also fairly unlimited. How much of our economy is actually We've managed to create more and more jobs that is just make busy and

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20:06 Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, RidgeLine should be part of that conversation. You can request a demo at ridgeline.ai. If I go back to the early twenty tens. Maybe the

20:26 aggregation through stewing in your brain and then you published it in twenty fifteen. I think it's fair to say like that theory, that idea, maybe you could just quickly remind people what it is. defined the winners and losers of that era of technology. I'm really curious how you're thinking about what theory or principles will define this era of winners from like a financial perspective and market cap perspective.

20:48 I go back and forth even just on the question of Aeration theory itself. How much does that apply in a current? Yeah, yeah. Yeah, because I like w like a pushback that people have is One of key components of addition theory is zero marginal costs. And your margin costs.

21:04 shows in lots of ways. The one that I focus on the beginning was distribution. And people say, Oh, I don't want to distribution or pay Google Friends. Like, oh no, you have a website. Your problem Isn't that you have distribution. Your problem is you don't have demand. And you're paying for demand when you're paying for ads and things on those because the airators control demand. And they control demand because in a world of abundance.

21:23 The hard problem is not distribution, it's discovery. How do you actually find what you're interested in? So the companies that solve discovery. in their domain come to dominate that market. They get a virtuous feedback loop. That sort of aggregation theory in a nutshell. And the other thing is transaction costs. There's no transaction costs. Google can scale to the whole world. And it can scale to the whole world, not just on the user side, but also on the monetization side. the vast, vast, vast majority of advertisers on Google or Meta never talk to someone at Google or Meta. They just go up and they buy ads. It's all done by computers. The perfect business. Those computers from a business perspective Cost zero dollars.

21:56 AI obviously that Changes significantly. Inference costs are real. But then again, how real are they? They're real right now.

22:04 I don't know, are they depends on the company, but they're way more real than those prior examples. Well, like if you look at gross margins or for sure. But you have this incredible spread. So you have people, I think the vast majority of people who are using ad today are using it as basically a Google substitute. Or like a recipe maker or whatever it might be. And my suspicion serve those people Is

22:27 Extremely low. And low in the basically Similar to serving them a web page. I would imagine it's marginally higher. But not that much higher. Then you have on the other extreme people who are actually leveraging test time Scaling. It used to be we just scale by making the models bigger and bigger.

22:43 Now you can scale. As far as time, how long do you think about the answer? Well, you could think about the answer. For days or weeks or months. That is directly marginal costs. Every second longer you're thinking is costing more money, which speaks to like

22:58 We think about AI and inference as this one question. That's I was pushing back on you, but actually the marginal cost question for the different user. The user using free Chat G P T And the user trying to solve a math theorem. They're not even remotely in the same universe.

23:14 Yeah, I think you see this challenge. Actually in the enterprise in a very interesting way. Microsoft recently they are shifting their enterprise plan. So they come out with like an E seven plan. Hundred dollars per user per month.

23:27 That includes some amount of usage. But then they also are charging for usage on top of that. I think this is a kind of a fraught position for Microsoft to an extent. Because

23:39 The positive way to think about Microsoft. Is They do everything you need as a business. Every individual component might not be the best. But you get it all for one price and they all mostly work together.

23:52 And if you're particularly a small or medium sized business or even a large enterprise, There's real value in that. That's right. It makes life easy. The moment you start having to Think about how much you're paying.

24:05 It's not just that that's a new decision, number one, that is Untether from headcount. Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee and baked in the cost of that employee is$10 a month or fifty dollars a month for their license. It was kind of a thoughtless revenue stream from Microsoft.

24:25 Now if you think about usage, you have to Think. Every single month. How much do I want to spend? That introduces two problems. Number one, most companies aren't set up to do this.

24:36 They make budgets like once a year. This idea we're going to be thinking about through our budgetary allotment on like a monthly basis. Doesn't compute. There's an aspect where They're used to thinking about capex decisions or one time costs. And there's a bit where when I'm talking about this employee, like the loaded cost of employee.

24:55 It's not CapEx, but it's kinda like CapEx. It's like you make the decision up front and then you don't think about it anymore. The decision sort of already made. But if you're thinking about usage, you have to do it again. The final thing is if you're every month looking at your Microsoft bill and how much did I use You start thinking about what am I paying for? How good is each of these products? Should I actually just start thinking about and spraying this out?

25:16 And I think they had to do it because that extreme of user who uses a ton of tokens and is actually leveraging AI. costs way more to Microsoft than a hundred dollars a month. They can't support them. But they want to hold on to this set cost for the vast majority of boys who can fit in that.

25:34 Because They need to ask their customers to think A little bit. For those extreme employees, but they don't want them to think too much because that breaks the model in very surprising. Are you surprised at all that the recipe builder user that is very low cost to serve? that there hasn't been a great business model that's emerged around them.

25:52 Just yet. Google and Facebook are sort of business perfected in this prior era. They haven't seemed to figure this out at all. I am frustrated, but not surprised. This is obviously a market that should be supported by advertising. That is why advertising is always the consumer business model.

26:08 Consumers don't want to pay. There's two things to understand about consumers that Silicon Valley has to relearn. About every ten years. Number one. Consumers do not want to pay for software. And number two, consumers do not care about being productive.

26:21 We went through this in early SAS. The canonical company. For this in my mind is Dropbox. So Dropbox Unbelievable product. Like especially when it first came out.

26:31 In business school, I was one of the first people to use Dropbox. And that went off like crazy. I have so much storage still like my free Dropbox because I gave out my code to like so many people. So Drew Houston makes this amazing product so easy to use, just absolutely seamless. He was very Clear about this.

26:47 He wanted to build a consumer company. And there's that famous story of him meeting with Steve Jobs. Apple was interested in acquiring Dropbox and Like oh, we want to build a company and Steve's, you know, your feature not a company.

27:00 Which That plain Jane just file sync. Apple did make a feature as far as like sort of iCloud Drive. With Dropbox They grew very fast and then they had like a two year lull.

27:11 And in that two year lull, what they had to do was basically completely rebuild the app from the bottoms up. Because Not enough consumers are gonna pay for it. Enterprises could see the value they would pay. But if you want enterprise, you need permissions.

27:24 You need control. You need someone else to be able to set All these sorts of things. And their app wasn't even created to do that at all. So they had to rebuild the whole thing. And realize the only way we're gonna make money is by selling to companies.

27:36 Why do companies pay? Because companies are paying employees. To the extent they can make their employees more productive. they're getting a greater return on their investment. It's the complete inverse of

27:48 A consumer. A consumer's like I spent all day working. Why do I want to come home and be more productive? I want to sit on the couch and watch reels. But you see that with AI. And you also have this overarching just skepticism. Of advertising.

28:01 I've gotten so much traction on Shachechari by being an advertising appreciator. And I go back and read my early articles about advertising. Kind of directionally correct, but Also like

28:14 Or not very good at all. But I got so much traction doing it because I was the only person writing about advertising. In a world of everyone wanna have a blog and Twitter, no one wanna talk about advertising. But even now there's in Silicon Valley this sort of embarrassment about the fact that the valley is in many respects Monetized by advertising.

28:33 And particularly during the last sort of eight years, there was a Facebook's icky. The best engineers don't want to go work on this problem. And so you literally had open AI replaying the Dropbox story, but at like a hundred exercise. Being like no we're gonna sell Subscriptions to consumers. They did. They sold a lot.

28:53 But they didn't sell enough. If you're going to be in the consumer market. You have to be doing advertising. They're doing advertising now. It's a little weird they finally pivoted to doing advertising at the same time they're like, Oh crap, we need to go off for the enterprise because

29:06 And Tropic is kicking her in. So I'm not quite sure what they're doing there. They have been rolling out ad features. very rapidly. Things like Cappy and the connections with retailers, so you know if a purchase went through, so you can do all the tracking and things like that. I'm very interested to see How that goes.

29:23 There's a bit where Had they leaned into advertising immediately as soon as chat GPT was a hit. I think they would have a killer ab product right now. I think that Google would be in much bigger trouble. I think Meta would be in much bigger trouble. Because if you have this flywheel, the thing about advertising with consumers is your ability to monetize the consumer.

29:44 Goes up as the bond goes up. Yeah. Because the advertiser is bearing the price increase. So there's Zero elasticity issues. If you're charging consumers a price, if you want to raise the price like Netflix, this is their problem with the subscription plan. How much can they raise prices before consumers rebel? And Drop a tear or give up the service entirely.

30:02 Charging people money is hard. Giving people things for free. And it's very frustrating that Open AI did not pursue this sooner. I know you've been spending time with

30:14 Some of the big money firms and sources of capital. What is your sense of their appetite right now and how they're thinking about the future because I think this year it's gonna be eight hundred billion or something that we're gonna spend in CapEx. Next year's supposed to be one point three trillion, I think is the current estimate. It's gonna keep going up from there. We're burning through all the compute that gets installed. Basically immediately.

30:34 It's such a strange circumstance that we can use the capacity right away as soon as it's online. Well, that's the thing though. So there's a few timing mismatches that are happening right now. We can't use it right away. All the bulls on Twitter. is always like we don't have enough compute. We don't have enough compute. Well, we don't have enough compute because there was insufficient investment made in twenty twenty three and twenty twenty four. Which yes, absolutely. And by the way, if you think there's not enough compute, T SNC

31:00 decreased their rate of growth. in twenty twenty three and twenty twenty four and twenty twenty five. our shortage of compute is going to get Worse in the next few years. Because a fabric

31:11 the weed time is even greater than a data center. Today when we say there's not enough compute It's not like all the money that the companies are putting in today. Manifest in compute storage. No, it all manifests in compute in twenty twenty eight and twenty twenty nine. on the calls you have both Andy Jassy and Cyndadella

31:28 Are out there saying, look, we're just building data centers. Like these are the shells. We might not use them now. Maybe we'll use them in the future. And we only buy GPUs when we know there's demand for them. That is a great story to tell. I'm not sure. That I think is a lot of BS. The reality is if you've built the shell.

31:49 That money is sitting there. You're not gonna let it just sit there. If you invest at a fixed cost and this is the whole logic of commodity markets. I think tech in general doesn't understand commodity markets. Tech is by and large focused on

32:02 If I produce a highly differentiated product and that differentiation could be like software, it could be a network in terms of developers, it could be a social network sort of thing where peer to peer Where I'm highly differentiated than my ability to charge higher prices. Provide sort of my profit margin. So the Class example is like Apple.

32:19 They have their ecosystem and they have their software and they have third party and all those sorts of things. And so they can charge fifty percent margins on their iPhone. Everyone looks at Apple as like the ideal business model. That's how you run a business. But in a commodity market.

32:32 the price is set by the marginal supply of cost of service is all that matters that's right. I had a good friend in Taiwan Who's in shipping. Fascinating industry. It's kinda like the airlines too, another industry that I love to look at. You buy a ship. And The cost of that ship is depreciation.

32:48 Your marginal cost is actually quite low. It's the fuel to run the ship and the cost of the crew. And like your port fees. Not that much. What that means is you are going to run that ship. Full as he really costs. No, you're gonna run it no matter what. And you're gonna bring down the price of a container as low as it needs to be to cover your marginal costs. Now your paper losses.

33:09 in this situation might be very large because your accounting loss includes depreciation. But the depreciation is an accounting Sigmin, you already paid the money. You're going to run that ship. at whatever the market will bear and the container, the beauty of the container, it is a pure commodity.

33:25 The cost of the market is gonna be the marginal cost. Now If it gets low enough At some point people will exit because their marginal costs They're actually losing money.

33:35 Honest shipment. Not just paper money, but like actual real money. They will exit. But then the supplies diminished. So then the price will go back up? And you get this interplay of sort of coming in and off.

33:46 But then if they say the market's very high, like it was during Covid, it's like wow, we're making so much money right now because there's not enough supply. There wasn't enough supply of ships, so Containers went from usually being like three thousand, four thousand dollars to seventeen thousand, eighteen thousand dollars. The amount of money that these shipping companies of time was insane. What happens though? Well But more ships. The problem is

34:10 It takes two years to build a ship. If everyone makes this decision simultaneously You still may have a lot of ships. price plummets, et cetera. Where we see this is in components, in memory in particular. Memory, very famous for boom and bus cycles.

34:23 People entering the market late. But to what extent are data centers Going to be memory makers. Where right now everyone can see we don't have enough compute. So everyone's like we absolutely have to be investing Because there's so much money to be made. And look at our payback period.

34:41 The problem is you're measuring your payback period in a time of scarcity. Is that payback period gonna hold in a time of abundance? And the sort of the bold say there's never gonna be time abundance. AI short for time scaling, we're gonna be stored forever, which maybe we will be. My concern is even if that's right, we m could still have an air gap. In that.

34:59 There's so much money going into it right now. And not enough has come online to actually make sufficient revenues to handle the situation where we're out of capital. I believe in AI. I think it's a real thing. I think the economic impact is gonna be astronomical. I think all the concerns about societal impact are very real and are going to come to bear in a major way. You can believe all that and still be worried about.

35:21 Are we gonna make the bridge to this actually generating the level of returns necessary to continue to feel this sort of going forward. Can you zoom in on TSM C and the component makers where fabs are involved? And so far, at least my understanding is that they've been quite conservative in their Willingness to expand capacity, build new fabs.

35:41 meet the market's demand with similar growth. Which they have not done. If that just rate limits this whole thing and prevents us from getting one of these giant overbuilds. We can talk about a few different ones. Like we'll start with memory. Memory used to have tons and tons of

35:56 Memory makers. Every time there'd be a boom, Merrymakers would sort of re enter the market. New countries have come in, like Taiwan used to have like a memory market. But you would get these exact dynamics. If there's a shortage of memory, there's so much money to be made, you can't bring capacity on immediately. The same as shipping, it's the same as what we're seeing right now.

36:13 That would spur You get too much capacity, prices would plunge and people would just get blown out. Cause the issue is the upfront cost for these is so large. Just like buying a ship, like building a fab is even more so. And memory now, like do the weeding edges of memory are using things like EUV machine. So the costs are getting into The billions of dollars for these lines. What happens is every time with these boo and bus cycles

36:35 Some people would enter, more people get washed out. You go through these famous historical moments for these memory cycles. Companies just get blown out. One of the most interesting actually memory stories is how Samsung sort of took over memory. was they saw it as an opportunity and they had studied history.

36:51 And they realize that actually the way to take over the market is to invest into downturns. So that you're ready when the next cycle comes around, which requires a ton of guts and a ton of discipline and a ton of money. But they did that and basically wiped out the Japanese. That's when the South Koreans generally took over the market in a major way. But It got down to three.

37:09 And the problem is three, it's not a monopoly, but it's kind of an oligopoly. And They all got a lot more discipline. About Let's not make the mistakes of the past. And we're not colluding, but we all are on the same page about let's not do that.

37:25 And I think that dynamic sort of ran head on to the current moment. Where it just took a while for them to realize no there is a secular shift in Memory demand. That didn't exist for a very long time.

37:39 I think the memory solution will be solved eventually. The other risk they run is Apple's lobbying to get Chinese memory. What is the number one focus of like algorithmic changes? How can we use less memory? I think the memory makers probably screw themselves in the long run. by creating such a massive target on their back. I've analogized memory makers to Iran.

38:00 The issue with the Strait of Ormuz is it's very effective. It's more effective if you don't use it. Cause then it's always hanging out there as something you could do. Now they did it. Turns out it worked.

38:12 But The UAE. Saudi Arabia, they're gonna build pipelines, they're gonna build new ports. They're not gonna let this happen again. It's very painful right now, but

38:21 Say Iran wants to close the straight over moves in twenty thirty five. it's not gonna have any effect'cause it will have been built around. My concern for the Merry Makers is they might have done the same thing. No one's gonna let themselves get in this situation again as far as memory goes. T S M C is arguably worse'cause there's only one.

38:36 There is one company on the leading edge. Obviously Intel and Samsung are trying to get there. It's the same thing. All markets carry risk. And a lot of the question is

38:46 Who ends up holding the risk? What I think The while the tech companies didn't fully appreciate Ізтючтімсі одед риск онту. The big tech companies.

38:58 And the way they've done that is the risk that TSMC is worried about is overcapacity. If we build too much, it's not just that we built too much. And we have all these fixed costs that are not being fully utilized. But if we build a fab, we expect that fab to run for thirty years. We've like baked in too much capacity into the system for years and years and years. So they are very biased towards being much more conservative. There's a little bit of a culture component to this too. One of the most interesting TSMT stories, it's kind of analysis to that Samsung story, was Morris Chang.

39:30 retired in like the late two thousands. New leadership took over. There's the great recession. And so they pulled back their planned spending. He comes in.

39:39 Fires everyone. And he's like The iPhone just launched. This is the biggest opportunity we've ever seen. We need to be investing, not cutting. And they invested through the great recession and through that down chart. That's what laid the foundation for them taking over.

39:55 sort of weeding edge semi gunners in that time. Morse Shang is a one of one on the Mount Rushamore. In my mind of The greatest and most impactful tech execus of all time, the entire fabulous model. is so critical to what tech is and what it does.

40:08 And also just the guts to do that at that time. Particularly in Someone who live there, a culture that doesn't necessarily tend to make those sorts of bets. Cm C

40:19 They were Pretty conservative. To be totally honest. So what happens though? Where'd the risk go? T some C's like we don't wanna take the risk.

40:27 Risk doesn't disappear. It just moves. The risk is Right now. Where you have

40:34 Every single big tech company realizes if we had more compute, we could be making more money. So there's lots of foregone revenue and foregone profits that is the manifestation of the risk that TSMC handed off to them. Risk doesn't disappear, it just gets handed off. And sometimes that risk doesn't manifest in losing money, it manifests in not making money. And there is money not being made right now. Because

40:59 What happened was They were very excited about five G. They did a big wave of like investment. expanding their fabs and around twenty twenty, twenty twenty one, twenty two. And they're like, Oh yeah, we're good. Like I said, twenty twenty four Jashabika twenty twenty two.

41:12 Big thing in tech in twenty twenty three. In twenty twenty four. their growth rate went down. In twenty five their growth rate went down. In twenty twenty six, it's up now. It was very funny because I was writing about this a while ago.

41:24 And then I think it was like one or two earnings calls ago, suddenly CC Way, the CO and chairman. Is talking about like Use cases for AI, the whole earnings call in a way he never had before.

41:34 This is why the Marrymakers are scared. Usually there's like a bull whip. And they're worried about being at the end of the bull whip. where the demand happens and it works its way down the chain and they're at the end and then they double down. It's already too late. They're wasting all their money. And I think the thing with AI is If it's a bull whip, it's like the longest bull whip of all time. There's still so much to be built. And it just took a while for

41:56 Asia to get the message where these sort of companies are. I think they've by and large gotten it. But them getting the message. It then takes several years for that to actually materialize. Do you have a sense for how long you think it will take given the extreme shortage of

42:11 Compute. The interesting thing is what this means for Intel and Samsung's sort of logic business. I've been writing about the problem of this dependency on TSM C for years. One of my first articles in twenty thirteen was exhorting Intel.

42:26 Well, you say you have to build a fab business. You're not gonna be a desire anymore. There's a huge business in manufacturing chips. I thought I was late writing it then. Their stock goes to the moon throughout the twenty tens as they're riding the sort of cloud wave. It wasn't until

42:40 Twenty twenty, where they finally realize We fell behind. By the way, there's this huge opportunity. We're totally unprepared for it. We don't have a customer service. mindset or culture organization.

42:51 Or all the IP building blocks and all these things that TSM C has. And they need a customer. They need customers to help them actually build a real foundry business. So I would write about this as a problem. I write about the China issue. Like you're dependent on a company that is Sixty miles off shore of our greatest.

43:09 opponent who thinks it's theirs. These are big problems. That's where I came to appreciate this insurance issue. for a big tech company to go to Intel and say, Intel, you make our chip. And by the way. the biggest benefactor of this is going to be you because you're going to learn how to work with a partner.

43:26 And the biggest Pain is going to be us because we're going to have to figure out how to work with you. We could just go to TSMC. They are awesome. They are so great to work with. We know they're gonna do a good job. It just never made rational sense for anyone to go work with Intel. That was their fundamental problem.

43:44 And I Unchanging world. T SMC would just win forever. But this is where TSM C in summer stats made the same mistake as the money makers made the same mistake as Iran, if I can continue the analogy. Because they didn't invest the last few years.

43:56 The shortages are going to be so acute. Big Ten companies that were foregoing so much revenue and so many profits because we don't have enough compute. We will go through the pain of getting Intel up to speed, of getting Samsung's logic Up to speed. The scarcity

44:13 is what ultimately save Intel I expect at some point. that they're gonna announce some major partner for the first time. It's gonna be a big deal. But Ultimately T SM C brought it on themselves. It's the Care for High Prices is high prices thing. We're gonna route around.

44:28 There's all these things as like an analyst sitting on the side. You can write these things. And one of the things I sort of warned, no one's gonna pay insurance that they don't need to pay when that insurance expected value is Negative. The way to solve the gліtikal problem of dependents on T SMC.

44:44 is to come up with a compute use case that is so massive that everyone is economically incentivized to bring other people up to speed and then we get the sort of geopolitical insurance for free. If you think about the let's say top ten or fifteen technology companies Which ones do you think have the most interesting setups today for their business?

45:03 The answer's always Amazon. The reason Amazon is so compelling is the extent to which they build for them They are their first best customer. They provide the scale to get basically anything off the ground, which they then Sell to other people.

45:16 AWS is the most obvious example. AWS contrary to sort of popular thought was not spare Amazon capacity. Actually it took a long time to get Amazon.com onto AWS. What it drove was the understanding that we can't be having so many meetings. Like we need to have just compute that you can plug in, purely API surface. You don't need to talk to anyone, it's just there. And oh, by the way, if we do that for our internal retail teams, we could do that for anyone.

45:42 Turns out the retail is so big we have to start with everyone else. AWS actually started serving external customers before it served internal ones, but now it's serves them all. You got other products like say the logistics where It's the opposite. Right now we're using external providers for our logistics, UPS and FedEx and USPS. We need to build this up ourselves. And now

46:03 They're offering it to third parties. Other people can use their delivery services. You see this in market after market, they're talking about some of their AI products or their chip products. What's the beauty of the Graviton or the Trainium, particularly the earlier versions? The early versions were terrible. But if you're on Amazon

46:22 And you're using some of their managed services, like say the Redshift database service, they don't tell you what the processor is underneath that. You're just buying a managed service. So they can put all their crappy processors underneath the services they're selling. And that gives them the volume and the capacity to iterate them and get better. And they get to the point where they can actually sell them externally. Because they were the first best customer for Graviton, Graviton got better. Because they were the first best customer for Tranium, Tranium got better. And now Tranium is obviously running anthropic and AI products. We'll see if any of them take off.

46:53 They have call center software. They're call center. Or their customer experience is going through AI. By the way, it's pretty good. I haven't tried it. moving back to America, I've been buying lots of stuff. Every summer I'd buy lots of stuff in a very brief amount of time.

47:06 Sometime like the last year or so. You can go on and you're clearly talking to a chat bot, but the chat bot does a great job. And it actually does take care of the problem. So you could see that actually starting to work in that regard. But they're building up these АІ сервіси

47:19 for their own business that they're going to make broadly available. And some of them will work, some of them won't. It's such an elegant approach. given they have so many investments in the real world.

47:31 Their core business. feel so impervious to AI. for the model version of AI. It will benefit from AI, but their moat feels deeper than anyone as far as their core business. and their ability to just sort of generate new business lines organically.

47:48 Is very compelling. What about Apple? They've set this whole thing out, it seems. It feels like it might be a situation of better be lucky than good to a certain extent. Apple As their whole ecosystem. At the end of the day, they do own access to customers.

48:01 So they can get suppliers. This is the classic aerator plate. If you want access to customers. suppliers come to you, not the other way around it. So they can get suppliers for their AI. As needed. And by the way.

48:14 To the extent it's true that People don't want to be productive. They just wanted sort of a chat bot. Not only can they serve them a chatbot and finally getting a series that works.

48:25 But you can see a future where this absolutely can work on device and they actually don't even need to pay for inference costs either. Because they're using the customers' electricity. I don't think we're quite there. There's a reason they're using Google Cloud and N video chips, but you can certainly imagine a future where that's the case. And they're in physical goods. Actually making phones.

48:45 Is hard. Having retail, having distribution for physical goods. They're more insulated. The smartphone is per so perfect. It's small enough in your pocket. It's big enough to watch basically anything on it. You can run your whole life on it. All your entertainment is there.

48:57 when we talk about customers just want to be entertained, the T V is now an accessory. It's all on your phone. I don't see anyone taking over the phone. The question is, is the phone always gonna be the center? Or is there a bit where, particularly in the home, this is where open AI's efforts here are very interesting, where You want sort of an ambient AI where you just talk to the AI and it tells you what you need. Apple is the best position to provide that.

49:24 But Can they provide that? Without having Leading edge models. Can they provide that?

49:30 If they're so phone centric, or is it like a Microsoft situation? Microsoft Didn't miss Bobble. They were very early to mobile. The problem is their mobile was a small PC. They assumed the P C would always be the center and their phones were gonna be Something that was off that Apple realized no, we need to reset.

49:47 The phone is not gonna be accessory to the Mac. The phone is gonna be the phone. The iPod helped them realize that and going with Windows and all that, but will they fall into a Microsoft flight trap assuming The phone's so good. It's always gonna be the center. And then let's figure out around it. Or is this five the time when actually ambient, the cloud, just in general, AI being everywhere.

50:07 It can manifest through your phone, it can manifest through a device, can manifest on your computer. is actually better. And Is actually disruptive to them. I think it's possible. I also think it's totally valid for Apple to

50:23 double down on what they do. The other thing about the AI stuff is On what basis should we expect Apple to be good at this? At the most crude level. AI is this probabilistic endeavor.

50:35 Apple is the king of Deterministic. Products. A physical product, you ship that iPhone. You ship it once.

50:43 And it's gotta be Good. If it's bad, it costs you billions and billions and billions of dollars. Apple's never had a an iPhone recall. It's amazing. That care and decision making and diligence And

50:55 fierceness in terms of your supply chain and making hard decisions is very, very different than everything that goes into like making Great AI. I'm generally prefer companies to do what they're good at.

51:08 So from my perspective, I'm fine with Apple not doing it. I want them to keep making great devices. Of the five potential frontier AI winners, so OpenAI Anthropic, Gemini, SpaceX AI, Grok, and Meta. Which of those firms do you think has the most interesting setup? Open eye and anthropic obviously are the riskiest, but also have the biggest upside. Never discount. Number one, the power of belief.

51:31 They think they're creating God. the most impactful things in history. Have usually been fueled by religion. the two religious organizations in Silicon Valley are Open A is kinda like mainline.

51:42 They go to church every Sunday. There's sort of like evangelicals. That's anthropic. They're all in. It is core to their belief. That goes a long way. The fact you need to make a business work for you to survive.

51:53 Goes a very long way. Google just needs search to not die too quickly. Meta has the huge advertising business. In a world where Meta was run by anyone other than Mark Zuckerberg, they would not be on the weeding edge. That is one of the purest manifestations of founder.

52:09 Energy for better or for worse. Their business is so amazing. You see them just easily sort of doubling down on that. Google there's a bit where They had Google Cloud, they have TPUs. They've been doing research in this.

52:20 It makes sense why they're pursuing this. Meta being like actually we're gonna hire a completely new team or gonna start from scratch and this again is Pretty insane. credit to Mar Zuckerberg in that regard, again, you could decide Whether that's a good idea or not.

52:33 And then SpaceX AI Data centers of space. The theory is there. Do they have to own their own model though to do that? They get better margins if they do, than again if we actually run out

52:43 Whether through political opposition or power or whatever might be, if we run out of data centers on Earth. They can run whatever model they want, as we're seeing with selling their capacity to anthropic right now. They're all pretty interesting. Probably the case for space X AI.

52:58 Is probably the weakest. Because The data center and space play is so Highly differentiated. If that plays out, I'm not sure to what extent they need to even have their own model.

53:11 So why are you wasting billions and billions of dollars in the meantime? That's a fair question. From a tactical perspective, I love the cursor acquisition. That makes so much sense for both companies. And so I've been intrigued to see what they do. Meta's probably the most interesting. You've written a lot about this recently. I think there's a very good case to make that.

53:28 It is more reckless to not be on the frontier. If you're a digital company. The counter to meta is actually Microsoft. Microsoft is not on the frontier. The reason why Microsoft has

53:39 Forty million dollars of free cash flow last quarter. Microsoft paid a ten billion dollar dividend last quarter. There's some money, but their play is oh, we're gonna play all these off each other. We're gonna provide middleware, we're gonna provide the platform that enterprise will build on us and we're gonna sort of disintermediate the models. I think it's a rational play. It's The IBM play of the nineties.

53:58 History Echoes. Everyone talks about Google, Google like following in Microsoft, but falls IBM. And you can see that to an extent. What did IBM do? What's the analogy?

54:08 Yeah, this dominant we talked about it in the seventies. And then you fast forward to the nineties and IBM is this very distressed asset. And the thought was IBM needed to break up into all these different pieces they had. So Lou Gerstner comes in, he takes it over. Gersner's real key insight to IBM is we're pretty mediocre at everything. It's kinda like when I talk about Microsoft before. And that's the price of monopoly.

54:31 Once you've been a monopoly, you kinda lose your capacity to be good. Because you're Didn't need to compete anymore. And I think a lot of tech. Incoming companies have this problem.

54:41 It didn't matter what they did, they were gonna rake in money. And if you don't have the pressure If you don't have the incentive, if you don't have the fear of death. Or the fear of God. As we talk about these model companies. Then you don't do your best work. And the problem is that you once you lose that muscle, it's gone. You're just sort of fat and flabby.

54:59 So what Gersner realizes is actually the worst thing IBM could do would be to break it up into component pieces. 'Cause all those component pieces are actually not very good. Our biggest asset is that we're big. It's like what? No, what does it mean we're big?

55:13 It's the nineties, this internet thing's coming along. There's all these companies that kinda know they have to figure out the internet and they don't know what to do. They need someone who can come in. Understand their business and help them get online. That's basically what IBM did. So they built out and this is an echo of what's happening now. Huge consultant force.

55:31 And they put all their time into building basically middleware. Where they would go in and they'd put this layer between a company's old school mainframe. Which all these companies had. And then modern web services on the other hand, so they could have websites and e commerce sites and all this sorts of things. And it gave IBM a thirty year lease on life. Yes, in theory you could go get point solutions from all these hot Silicon Valley star ups, but you understand that. You don't know how to do that.

55:56 You know us, we'll come in. Will create all this middleware. build this big consulting force to help you implement it and you'll get online. And IBM basically brought all of corporate America online. That's Microsoft's playbook.

56:07 Microsoft will help you figure out AI. It will help you figure out in a way we are not giving away the crown jewels to these companies. We're gonna build this platform. This harness, this sort of middle layer. We're dependable, we're stable. You know us, we have backwards compatibility to the eighties. You can build on us. And then we'll manage all the changing models and what's updating and do all those sorts of things. And does that mean you'll get the absolute best experience?

56:32 No, middleware saws off the sharp edges. You sort of a lowest common denominator capacity. But If you value in this the oldest enterprise sales motion.

56:41 How did Oracle go to market? Oracle went to market in the nineteen eighties, Larry Allison with another technology taken from IBM or just IBM didn't want it, relational databases. And they're like, you don't want to be locked into IBM. relational database you could run anywhere. Come with us. The reason this is a joke is because Oracle wants to lock you more than anyone, right? But

57:00 All of enterprise sales Is companies whose long term goal is to lock you in. getting you on board by trying to make you scared of being locked into somebody else. All the cloud companies are like, oh, portability and whatever, you can be whatever.

57:14 They're like, Oh, just use our service that only runs in our cloud and now you're locked in. That's the Microsoft's playbook. It's a very rational playbook. And I think it makes sense. That's why they have extra money because they're not on the frontier. They are building massive data centers, but they're building data centers for inference. They're not building it for training. And their story about we're investing in time in response to customer demand. is more believable in that regard. They're not having to tell a fungibility story where we're building big data centers for training that will be used for inference down the road, maybe.

57:39 Go back to this notion that it's reckless to not be in the front. The reason why that's concerning though is At the end of the day. Why are we using Microsoft products again? Because we did before.

57:49 To what extent does it actually make sense to have All these artifacts, all these documents, all these email inboxes. Can't AI just do that? There's a real threat here. Where

58:02 to Microsoft software business. The whole systems of record thing is funny because One reason why says the records are so powerful is it's so hard to move them to somewhere else. 'Cause it's a very tedious, repetitive job. Oh, AI is actually surprisingly good at that. I'm not sure how good the systems of record Microsoft isn't so much systems of record. They do have some of the dynamics business.

58:22 It's user interface. It's like where you actually interact with the computer. That's the part. Веню сі кодекс. Quad co work or whatever. It is aimed

58:32 like an arrow to the heart of what Microsoft has. In the long run, all digital companies are, but Microsoft is very much. their strategy is sound. It's also Desperate in a Existential way.

58:43 And also in a They might pull it off because they're desperate sort of way. Meta is Not threatened immediately. But This is where my bullish view of AI comes in. I think

58:54 All digital. Companies are threatened. And meta is a digital company. They have software. Now one worry is AI takes up more and more time. Time ultimately is as bad as currency. We saw

59:05 I tried the sort of thing, didn't really take off. Social network's actually pretty hard. Also cost a lot of money. It's kind of really interesting. So this came up with the creator payment stuff. So you two

59:16 Very famous as as paid creators kind of from the beginning. And that's a much bigger drag on the business than people appreciate because YouTube has marginal costs to their content. Now unlike a Netflix. They don't have to pay that cost up front. They don't pay it after the fact.

59:31 So their sharing revenue is a better model than a Netflix model. Netflix has to pay up front for content and then ideally make more money. YouTube pays along the way. But Facebook They pay nothing. Instagram is this unbelievable product that generates all this money for which Facebook pays zero dollars for content. It's unbelievable.

59:51 It's funny because you could see a world where for YouTube AI generated content could theoretically be a positive because the inference cost of generating content could be less than what they're sharing with creators. For meta AI generated content to the extent they're the ones generating it is actually a worse margin profile than what they have today,'cause what they have today is free. So they have attention. There is a bullish world where meta

1:00:15 is actually very well placed because in a world where we interact with the AI all the time, the desire for a human connection becomes greater. And it's sort of like a meta going back to the roots. Meta One of their biggest mistakes actually, Meta was always a social network company. They killed Snapchat or stop Snapchat's growth by realizing Snapchat is a great product. Let's layer it onto our network.

1:00:35 They brought the network to bear to kill Snapchat. The reason why TikTok was just a blind spot for them. Is TikTok is classified as a social network? And it's not a social network at all. TikTok is an entertainment product.

1:00:46 It doesn't matter who you follow on TikTok. What you see on TikTok is a function of what you watched. And you're gonna get more of the same. It's a user generated content network. And the

1:00:57 Insight from tick tock was The way to get the best content. To limit it to your social network is an artificial constraint. We're gonna give you the best content from across the whole network. And the vast majority of content is gonna be crap. But this is like the absolute question before. You don't think about margins, you think about absolute numbers. The absolute amount of great content.

1:01:16 Even if the margin for great content is infinitimal, if we have a ton of content, the absolute amount of great content is gonna be very large. Like we're a social network. Meta's serving you content from your network of people you know. And TikTok serving you the best content from around the world.

1:01:32 That's why they took a huge chunk out of them. Meta had to shift. That's what's happened with Instagram with Reels. Yeah. It's not really a social network.

1:01:41 It is a entertainment product that pulls from the entire network. And social networking is like the group chat. It's possible in AI. Actually social network is important again. Because like we actually want humans. We want to have some sort of connection to them. That would be interesting to see how that plays out. Bye. The other thing with the models is

1:01:57 They're so impactful in advertising. biggest impact of the models, the biggest modernization right now is probably not enthropic opening eye, it's the incremental gain that is happening for Google and meta. Most of the stuff is pre LM. But We're getting to LMs, whether it be generating advertising content.

1:02:13 What do we want? We want verifiable domains. How do you verify if a generated image is good for an ad? Does the ad sell or not? They actually can validate their image creation. And they're Text creation.

1:02:25 in a way no one else can, and their validation is the ad marketplace. running a gazillion A B tests on all these different things, see what works, see what doesn't. Most ads are a throwaway. It's fine. The vast number of ads don't convert. They have this massive advantage, this huge liquid market that is a verification machine where the verifiers are humans deciding whether they click on that ad and make a purchase or not. but they're doing it at global scale, that can actually have a feedback loop to make their products better. You're also going to get a world where Ab matching is actually still fairly crude.

1:02:55 Here's the qualities of the person, here's the qualities of the ad. And it's like you create an embedding, like a vector calculation and see what numbers match. And then you sort of match an ad to the person. What do LMs do? LMs predict. We're gonna move to this world where Met is gonna look at people and say, this person probably wants to see this next. And they're gonna go find that thing and show it to them. the potential upside in terms of showing people better ads that are more relevant to them.

1:03:21 They only need to increase. A few percentage points for the returns to be billions and billions of dollars. This alone is worth them investing in being on the leading edge in having these amazing models. I think a big problem that it has Is they don't tell this story.

1:03:36 It's weird, but Mark Zuckerberg has the same problem Sam Altman does. He doesn't love ads. They have the best ad business in the world. They have an ab business that I think is a societal positive. You and I have set up these little content businesses.

1:03:49 That Make great money. Content is You get a right on social media. I grew up on Twitter, people sharing my links. It was amazing. If you're selling some product The beauty of the internet is

1:04:01 There is a niche out there that wants that product. The question is how do you find the niche? Facebook advertising. That's what it does. It helps products find the people who didn't even know they wanted that product, but when they get it, they're so happy they got it. That's a huge societal positive.

1:04:17 You have new business from a new entrepreneur making a new product. You have customers who are happy they got something that they didn't know they would get otherwise. Those customers, by the way, got lots of free entertainment and they didn't have to pay for it along the way. And men have made a bunch of money for themselves and their shareholders, which is basically everywhere in the world. This is why advertising is great and Meta's advertising in particular. is awesome. And I get frustrated. That Meta doesn't talk about that.

1:04:40 talked about the societal benefits of advertising, except in passing In twenty years. He's handed it off to other people to take care of and Maybe there's a bit where him not paying attention is why

1:04:52 There is A certain grit and grind that goes in building an advertising business. People get frustrated or have questions about as far as data and all those sorts of things. And maybe there was a bit where he didn't want to be involved in it and wipe his hands of it.

1:05:04 But you saw this when Apple passed ATT. App tracking transparency was One of the worst antitrust violations in the history of technology Apple unilaterally. obliterating all these business models while they're simultaneously building their own.

1:05:18 As far as advertising goes and doing all this tracking, why trust us. And meanwhile, they're running these advertisements. Like remember that advertisement of people on the bus, like overhearing everyone around them what they're saying. That was such a dishonest representation of how advertising works on the internet. You had Tim Cook in Congress talking about Companies selling data. Facebook's not selling your data. That's value to them. Why would they sell the day?

1:05:38 мета вознапред то респон I think you got this to Cheryl Sandberg back in the day. She wouldn't every call would talk about advertising how great it is and have a bunch of case studies. people who are benefiting from advertising and these new entrepreneurs. And then she left and it's kinda like that hole never got filled. It feels like it's a company that's kind of like embarrassed. We make a lot of money from ads, but we got glasses and we're doing AI. It's like

1:05:59 You have ads and ads are awesome. I think if they had communicate that more consistently. They We'd be in a better place generally.

1:06:07 from a PR perspective, they would have been a better place relative to Apple. And I think they would have an easier time right now convincing Wall Street that let us invest. The other problem is They've spent

1:06:18 Hundred Some billion dollars on Boculus. Which I dated all along. And so there's a bit where why should we let you spend money again?

1:06:26 The one major player and company that we haven't talked about much is Jensen and NVIDIA. And I'm curious how you would tie this back to the notion of not understanding commodity markets in Silicon Valley, whether or not you think compute ultimately is a commodity. I'm curious whether or not you think intelligence will ultimately be a commodity. It's interesting that Intelligence and compute. Which seem to be by far the most interesting and important topics in tech. Both might be

1:06:49 commodities and less differentiated than the most interesting thing about the internet is free distribution. Bandwidth is a commodity. The fact that I can pull out my phone right now and connect to any information source in the world for free. free on a marginal cost basis, it's because it's a commodity. It changed the world. Commodities change the world.

1:07:06 There is a aspect of differentiated products by definition have lower TAMs. Because there's a elasticity aspect to it. Not everyone can afford to pay for it. People's willingness to pay is going to differ. your market is gonna be constrained. Apple's never gonna serve the whole world.

1:07:20 By selling a device, whereas A Google can because it's free. That matters. You're paying for a commodity, but to the extent it is available to everyone is the extent it is impactful. The internet is a commodity.

1:07:33 It changed the world. So I don't think it'd be weird that intelligence ends up a commodity and changes the world. Commodities often are not thought of as as good of businesses as these differentiated hard merchant products. I'm curious for your thoughts on Jensen and NVIDIA specifically. The NVIDIA's position is I think definitely unnatural. You look at NVIDIA.

1:07:51 They've maintained all their margins. Isn't that amazing? It's twenty twenty six and everyone's coming for them and they're still charging. However much money for chip. But they're actually not maintaining their margins because This whole question of circular financing is people talk about Lucent and things like that, and you know, this whole deal and NV is providing a twenty five percent backstop.

1:08:09 But if you actually Ascribe a value to that. Two NVIDIA's Taking equity in the new clouds or whatever, the guarantee They're gonna buy all their compute to twenty thirty.

1:08:20 Why do they do that? So that the Entity in question can get a lower cost of capital, so they can buy more GPUs. Et cetera. But implicit in that, why do they get a lower cost of capital? They get a lower cost of capital because NVIDIA assumed risk.

1:08:35 This is my point before risk never disappears. It just appears somewhere else. Taking on risk has a price. There is a world where AI takes off, it never stops, and everything is fine. And NVIDIA captured all the upside of the risk.

1:08:50 But there's also a world where Say that it's this new car they backed up. A ton of compute comes to market. The hyper scholars have plenty of compute. They don't have no compute. NVIDIA's paying for a compute that no one wants. They w just lost a bunch of money. If you think about it, there's an expected value of that investment.

1:09:06 That expected value. It's not zero. It's not one hundred percent. It's somewhere in the middle. But that is a diminution of NVIDIA's profitability. If you actually look at their business holistically.

1:09:17 What that is is a price cut. Now the price cut didn't show up in margins, it didn't show up in what they're offering. But a lot of what NVIDIA is doing is how can we maintain our margins? Even if the Wide view, sort of discounted cash flow, expected value, holistic view of our company. People do discount cash flows, but are you actually considering all these pieces?

1:09:37 The reality is that moving stuff off the balance sheet, by and large, works. But they're doing all these deals. to maintain White. Feels somewhat unnatural. We have seen price cuts.

1:09:48 They're just manifesting in these very bizarre sort of ways. Now, in the long run, I think the challenge is Their ultimate competitors are the hyperscalers, particularly Google and Amazon. So Google and Amazon aren't just building their own chips. But they're also looking to sell those chips externally. Google already made a deal to sell like twenty percent of their TPUs to anthropic.

1:10:09 practically confirmed that they'll be selling Trenium threes. Or maybe training for or those on training chip sort of eventually externally. Which makes sense. That gives them a long term buy into these companies.

1:10:20 There's a huge amount of R D that goes into developing chips. They get more leverage on their spend. It all makes sense. And by the way, they're not selling their chips on differentiation. They're selling their chips as commodities. Nvidia's the one selling differentiation. People aren't going. to Amazon to use training. So they're not cannibalizing the attractiveness of their cloud by selling training outside. So they're NVIDIA's biggest problem.

1:10:40 Because what's the number one advantage that The hyperscalers have scale. Lower cost of capital. It's a capital fight. They have a lower cost of capital than the neo clouds do. The neo clouds are

1:10:52 They'll buy NVIDIA left, right, and center. And by the way, it also makes total sense that why SpaceX Silate E1 is out there. We will always buy NVIDIA because they're the best. No, you'll buy NVIDIA'cause they're the most fungible. I mean it is true. It is the most fungible. Kuda's moat is dramatically diminished.

1:11:06 Because the models don't care what they run on. And that's what actually matters was built on top of the models, but it still matters. It's still something of a mode. If you want to play the game space excess I is doing where we're gonna build a lot and rent it out. but reserve the right to pull it back. Of course you're gonna be on NVIDIA'cause the easiest way to rent it out is to be on NVIDIA. You saw this very early, by the way. You go back to twenty twenty four.

1:11:26 Twenty twenty three, NVIDIA starts talking about all these sovereign clouds. They start talking about they tried to call these neutron models. They have this thing in twenty twenty four, I remember. It was the first one where it's like the rock star GTC at San Jose and like the Hughes Coliseum and Just no one comes out. It was a very boring GTC. The old ones used to be NVIDIA demonstrating like fifty gazillion things as they're throwing stuff at the wall. They knew they had something to GPUs. They're trying to like Once LM showed up, it's like oh we have the use case. But they were coming up with all these enterprise offerings. I can't remember what they were called, but they're like these modules, basically.

1:11:57 That Of course, they were free, but they only ran on NVIDIA. And you could see what they were doing is they were trying to lock people in. Intel is a good example here. AMD clean them out. in hyperscaler sales. Cause the hyperscalers are put in the effort to get stuff working on AMD versus Intel. There are still small differences, even though they're X eighty six. because they're buying at such scale.

1:12:17 the investment to do it is worth it to get a better chip. Or lower price or whatever it might be. The part of Intel's business that never floundered. was selling to government and selling to enterprises. They don't have the resources of a hyperscaler. They're not buying at that scale. They're just gonna keep buying what they had before.

1:12:33 That's why NVIDIA talks about selling to sovereigns. That's why they talk about selling to enterprises, because they want to get in these markets where they're not going to be balancing this chip versus that chip. The hyper scalers have always been the threat to NVIDIA for that reason. They're actually bigger. So you have this issue where the hyper scalers are the threat. Cyber scars have a Better cost of capital than the other companies if you want to buy them. That's how you get this deal this week. I see this deal as a response. That's why it goes with the Google deal.

1:13:01 Google can just issue. The Sarros don't love it, but they're modernization capacity. is much higher than NVIDIA or NVIDIA's customers are. I think what NVIDIA is

1:13:11 hoping for. Maybe they wouldn't say this in so many words. But If we get to a world Where

1:13:17 We actually run out of power. That's probably good for NVIDIA. Because In a world where We're totally constrained on power. They're gonna want the best. We have to get the best efficiency. The best token efficiency. And I think NVIDIA is still the most token efficient. So that is a good world for them.

1:13:34 Probably the biggest problem for NVIDIA over the last couple of years. Is I think the US has actually brought a lot more power online. Then Expected. It surprised me. Whether it be what Eon did sort of behind the meter, which has been replicated, or West Texas and natural gas.

1:13:48 But even like restarting nuclear plants. We love how the US responds to these things. It's awesome. It's actually one of the biggest Encouraging signals about the US. Is I was writing early on assume this is a bubble.

1:14:02 You want there to be a long-term payout. The.com, we got fiber in the ground. And by the way, Google's played this game before. Google built its business by buying up dark fiber. They have the killer search engine, but so much of the power what they do is'cause they bought up all this dark fiber. That was basically free after the dot com era.

1:14:19 Our core internet still runs on worldcom fiber. That was a lasting benefit. The railroads, BNSF is throwing off money that's going to Google. From Northern Pacific and Jay Cook selling bonds to retail investors. You want a bubble that produces something that lasts. And very no, it's like what's gonna last from AI? The GPUs Don't last that long.

1:14:40 Data centers, yeah, okay, fine. But what is it gonna be? Power. It has to be power. If we're in a world where this all blows up and we have way too much power, that is an amazing world to be. We've always been energy constrained. Energy undergirds everything. What would it be like to live in a world of energy abundance? It's hard to even imagine because our minds are so constrained by the fact we've actually always been in energy scarcity. I think we've done an unbelievable job.

1:15:04 Power for sure is a constraint. It's going to be a constraint. But I think it has taken longer to become a constraint than anyone expected. And I won't be surprised if that includes Jensen Huang. I think he thought

1:15:18 Insufficient power. was going to be Nvidia's moat. Sooner than it happened. It turns out that

1:15:25 The longer we have enough power. The more time Amazon has to make trend better, the more time Google has to make TPUs competitive. From a inefficiency standpoint. And if we get in a world where You've in a world where those margins seem very hard to sustain.

1:15:39 I love hearing your takes on just everything going on. It's the most interesting time I've ever observed in this world that you love so much. So thank you so much for your time. Thank you very much. If you enjoyed this episode, visit Colossus.com. You'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most. Lear more at Colossus.com slash subscribe. You know how small advantages compound over time that's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default. Better data, better decisions, better economics over time. See how at ramp.com slash invest. As your business grows, Vanta scales with you. Automating compliance and giving you a single source of truth for security and risk.

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