[MUSIC] >> Hello, everyone. Welcome back to Seminole's Weekly. I'm here today with the guys from the chipbook team. We're going to talk all about what is chipbook, what's open source data, some of the use cases that people are using it for, and some of the nice viral tweets that the guys have put out in the last couple of weeks. Guys, welcome to the show. Great to have you on. >> Good to be here, Jordan. Please wrap up. >> Thank you, Jordan, for having us. >> All right, let's jump in. So, chipbook, chips and wafers, can you give us a little bit of background about what you guys do with Seminole's, is what it is? >> Yeah, sure. Yeah, so first, Jordan, great to be here. Long time listener, first time caller. >> All three. >> Let me give you a little bit of a background of what chips and wafers is and what the chipbook is. Essentially, Hi, and I both came from the buy side. We worked at a hedge fund where we covered semis. Not exclusively semis, but one thing that's true of any sector you cover on the investment side of things is that you're constantly looking for data. Data is valuable to generate investment ideas. You're looking to validate ideas. Let's say you have some idea and you want to know there's an extensor not. You're looking for some sort of all data platform or some information you could use to validate an idea. And sometimes you want to track an idea. Let's say you have a great thesis. It seems to check out, but how do you time that position? How long do you hold it? When do you size it? When do you get out? So you have to track the idea. And everybody in the hedge fund world is looking for alternative data. In fact, just a funny anecdote. A few weeks ago, we attended a conference for all data, which was all data providers came to this one conference center and they had dozens of hedge funds. And they were all looking to buy some sort of alternative data platform. So I think maybe 60 vendors at the platform. We were the only guys who were selling semiconductor related data. And the reason I'm pointing that out is just to say that we recognize it on the buy side as well as that there is a source of data of information that's out there. All open source, it's all public, but it's messy and it's distributed all over the world. It's in different languages. It's coded. Oftentimes, most of the time, you don't even know what you're looking for and how to use it. So there's all this data out there. And what we do is we say that data is interesting information. But information is interesting. What we want to do is turn that interesting information into actionable intelligence. So what we do that is we kind of learned how to gather all of this data, put it together in a package that can help inform your idea generation, you're tracking and monitoring your investment thesis validation. So this chips and wafer's chip platform is a way for the investment community. And for semiconductor companies themselves to have insight into big picture where the industry is going. And on a more granular level, how that impacts individual companies, trends, themes, and flexions within the space. Just to give sort of an idea of what the data is, most of it is open source, import data, export data, production statistics, inventory, all that stuff is out there. But it's a pain in the neck to find and we try to bring that to our to our chip customers. And if I can interject here, maybe just add a little bit on what Simi is saying. So you know, Simi used Simi's the word intelligence and like a helpful parable that I like to think about. And Simi mentioned that we would both came from the buy side before the buy side. I was in the military for a few years, like an interesting parable to think about in this context is this concept of what people like to call open source intelligence or OCEAN for short. So you know, for years that any respectable military operation, you know, had obviously their entire intelligence director working across organizations. And they have their intelligence signals, right? And they have signals intelligence and the visual intelligence and human intelligence. And then about like the beginning of the 21st century, like this whole new theme of open source intelligence comes out. And at first, it's kind of like only reserved for like the nerds on the internet that are scraping IP addresses and random YouTube videos. And like within the intelligence community was totally disregarded at first because like, like, bro, what's interesting about open source intelligence, right? If it's open source and anyone can access it, why is that valuable? Why do I need that? And all the intelligence agencies just say, like, I don't need this. If everyone can access it, what what used to I have for it? But what everyone has come to understand over time is that one thing that open source until has a lot of times over all these other sources of intel is that it's real time. Yes, it's messy, but it provides super valuable signals super early and a lot of times in places that it's really hard to get to with all the other forms of intelligence, right? If obviously if you could have a human asset inside some of that super valuable, you want to get that, but it's not easy to do that. However, if there's a guy on the street corner with recording a video that you've been up to YouTube and then you're able to see that video. So like, yes, it's open source, but you actually have, you know, you have access to that where in the past, that would have been super hard. So again, like just to bring this full circle, like when people say, like, wait, like open source data, like, is that valuable? So like the like the obvious answer is that like, yes, it's super valuable, but you need to know how to find that you need to know where to find it. You need to know how to sift through it. You need to be able to distinguish between what's noise and what's a true signal. So that's like a like a helpful parable how to think about, you know, this whole, this whole idea and this whole concept of open source, data to help inform decision making, especially in this end industry. Yeah, and I think this can, so this is obviously about trends. If you look back at historical data and then you try and inform the current day, you need to stay up to date needs to be current. So more so it's like establishing a process to get access to this data and then actually making sure it's current up to date. Yeah, you know, monthly or quarterly updates. Can you give me some examples of like real data in the chips and wafers context? You know, like we've seen you guys put out some interesting teasers on the semi-analysis account or the chips and wafers account. Maybe we can run through some of those as examples. Yeah, let me, let me sort of take one step back and sort of give a sort of the value proposition so I can explain like where the examples come in. Everybody knows there's available open source data. People are aware it's out there and you have a lot of banks that put out, you know, a generic statistic. As an indication that, you know, waf is up or waf is down, for example, you know, people are looking at a lot of the waf equipment type imports, exports, things like that. I think where the secret to using it properly is to have the ability to get as granular as possible. So like just looking at the waf side, for example, wf e as a category and I've seen statistics waf e is up 10%. So let's assume extra on is going to go up or may not going up or whatever equipment manufacturer you're invested in. The problem is that w at e is like the broadest category in the world. It includes wafer manufacturing equipment like tools to make bulls and to slice wafers. It includes deposition tools, etch tools, lithography tools, iron and planters. It even includes, it even includes packaging equipment like flip chip tools and wire bonders. Oftentimes it even includes inspection tools and metrology tools. So if you're tracking like KLA metrology and you're looking broadly at wf e as a metric to monitor your investment, you're looking at a metric which is far too broad to at all be meaningful for your investment. And you can make mistakes like I one thing I like to say a lot is the only thing worse than having no map at all is having the wrong. Investing based on the wrong bit of information is very, very dangerous. So the value comes in trying to be as granular and targeted as possible. And that comes out in waf e that comes out in the AI supply chain using the AI supply chain. Supporting companies as a tracker, both for the big guys, but also as investment opportunities on their own. When you look at ship shipments and production and inventory levels around the world, being able to distinguish between logic memory within memory, being able to look at flash DRAM, HBM from different countries and knowing who's making what for what customers. So I think the granularity is the way that we try as best as possible to have our information targeted. Maybe I'm trying to think maybe you want to do one example or talk a little bit about, you know, some use cases from the data. We could do some use cases, though, the only thing I would just add on what you were saying up till now that like I think it's important to expand on this really this idea that. At like a lot of the investors that we talk to like everyone knows semiconductors is huge especially over the past three years since AI has entered everybody's life. Like everyone knows that semiconductors is a is a super hot, you know, market right now right really most people do not understand just how massive the supply chain is like they do not understand where it starts and they like really can't even understand where it is right where like you're thinking about a token output on the chat about that you're using, but you know it's a you know open AI or call it doesn't like that that's what the consumer seeing like at the end right now they are like that started 4,000 steps earlier. When some random Japanese chemicals company, you know was making a polysilicon ball, you know that from the light and they like that everything.
close from there. So it's this massive supply chain. It's like super duper hard to comprehend. And like a really like another good value proposition. Again, we do open source data and we try to entire supply chain. We start all the way, like I said, from that chap, which Japanese company and try to go as far as we can to where the where the data allows us to go. And so like the ability to see that entire value chain to see what's moving up, what's moving down is super valuable because, you know, obviously, they also impact each other, right? Like, you know, Cindy mentioned WFV, like if WFV is an input, then obviously the output of that is chips that the thousands of founders are doing after that benefits chips that goes to the server ODA as opposed to the server ODA is going to the hyperscaler data centers. But the data centers that's token outputs. So just to be able to like to see that, you know, that whole wave of that whole supply chain move and like noticing where the different signals hit to understand how, you know, where that's happening upstream, then how that how that impacts the downstream is something super valuable. Yeah, let me let me give you an example. Let me give one example of memory and then we can talk more about, you know, early signals. But one example is like memory. So obviously we're in this memory super cycle and everybody in the world wants to know about that memory. It was probably a year ago, we called out the memory cycle. How did we see it early? So obviously you can look at, you know, memory, memory sales numbers, once Samsung reports and high-nature ports. But then you're already like looking backwards. What we were looking at, for example, was Taiwanese DRAM inventory levels. And we had seen that inventory levels of Taiwanese DRAM were rising for months. They were building, building, building. And all of a sudden, a conventional DRAM here, by the way, not not HBM, like just like conventional DRAM, not HBM, not A I related, at least at the time, not A I related DRAM, sorry, so we go. Yeah, and we were watching it, did Taiwan DRAM levels going up and they were hitting like historic highs. And then all of a sudden, I must have been about a year ago, probably it was the summer of 2025, we see the first month, for the first time in a year, the inventory levels drop. And then we call that out, we see something's happening. But let's wait to see, it's only been one month. The next month, we see the inventory levels drop again. And then a third month. And then we realized we're starting to trend. And sure enough, I think we've now been at 11 or 12 months in a row, where Taiwanese DRAM inventory levels have dropped. And what that told us as the inventory level started to drop is, well, beginning to see a demand supply and balance, where the demand is now on stripping the supply. That was a very, very early call we were able to make by tracking a relatively obscure specific data point that was available. It was open source, but you had to know how to find it. And you have to know more importantly, what is it telling you about the memory cycle? And then we start to track, okay, everyone's going to be looking at like Korean memory exports. But what are we seeing when it comes to Chinese memory exports or Taiwanese memory exports? Because that gives you a better sense of the broader market demand beyond the specific HBM chips, which were also tracking as well. But those were all ways that we were able to have earlier that identification of a trend within memory. And number two, to continue to foster that tracker going forward. Now, what do we do today? So now everybody's made huge investments in memory companies and you're sitting on huge positions. So if you're a hedge fund right now sitting on a huge position on the memory company, what you're really nervous about is when does this cycle ends? And I need to know before hi-next reports on a company call. Oh, by the way, the demand is now is falling off and supply outstrips demand or somebody comes online with huge capacity that's no longer, when no longer in a supply constrained environment. So what now what we're doing as a tracker is not to generate the investment idea because that we did a year ago. Now we're tracking and monitoring your investments for you by making sure we're following memory shipments globally, both from Korea, Taiwan, China, we're following DRAM, we're following Flash, we're following HBM. All of those are invaluable trackers because the size of the position you're sitting on is huge and the the risk of not getting out of those positions or maybe or or selling too early. But those positions are worth it's tremendous amount of money to our hedge funds. So staying on top of that data on a monthly basis for our customers, I think is a huge value proposition. Yeah, it makes perfect sense. I mean, we had Sraman on a few weeks ago, he was talking about the allocation of TSMC 3 nanometer between Nvidia, Apple and then some of the smaller smartphone players. And I think just this morning, we saw Xiaomi smartphones from China, you know, they're 35% down on shipments or something like that. So it can I mean, some of these calls can happen in weeks, but they can also happen in like months of you know, it can take months or even like a year plus for for some of this stuff to play out when it comes to inventories or stuff shipping and moving around. Do you have any stuff currently that you're tracking? Like what what is this? Because I mean, to some extent, looking at historical data is is different than forecasting the future, but historical data on something so far back in the supply chain is like predicting the future on things that are downstream of it in the supply chain. And maybe if I'm going to talk about WNFE because WFE is a great example of that because you know, WNFE equipment ships six, 12, 18 months before production, you can start. Yeah, totally. And I'll speak to that in a minute, but just to address kind of like what's said, we just talked about beforehand, just like with the examples that we were giving about memory, is like, you know, the odds are in investing in general. Like it's it's going to be rare that you're going to have that like silver bullet piece of information, just one thing that gives you everything you need to know, right? This is the time to go long as companies should like your like the odds of that happening with one piece of information is, you know, highly unlikely to basically zero. And so like going on the examples that said it was giving like for example, like that we were able to call out if we were seeing in the Taiwan D-RM inventory or, you know, memory exports coming from non-HBM geographies like South Korea. Like those were those were signals. Again, go back to what I was saying beforehand, like you get these signals and you just need to be paying attention to these signals and you need to be aware of them before they kind of hit the market because you know, if it's already on the print of the company when you know results come out, it's too late, right? So this this idea to be able to track those signals. And again, you know, sometimes you're like going back to the intelligence parable that I gave you does sometimes it's no way, sometimes it's signal, but you need to be aware of the signal so that you could then have that on your radar to understand, wait a second, like does this translate into actual intelligence? And suddenly it gives you, you know, even heightened awareness of the questions you need to be asking or the areas that you need to be focusing on. So that's just to to speak to what somebody was talking about, but you know, then you were talking about what are certain examples of uptrying things that you could look at that then impact the downstream we talked about wof so again, like wfe is massively important because again, go back to what I said beforehand, wfe moves from the equipment manufacturer into the fab once it's installed in the fab, that's what allows you to output wafers and tap it chips, which ultimately ends up in the data centers, which you know, adds up in the memory modules that go, you know, into the into the packages. So like and again, you have lead times on the dot so like if you order it today, it's only showing up your fab six to 12 months from now, then you have an installation period, and then you have time until it ramps. So like being able to track the wfb movements again, that's that's giving you like a good 12 to 24 month, you know, preview into what wait for capacity is going to look like down the road. And again, once the company says it, once you are know that it's coming, it's already too late. And so like I think like a really good example to discipline, like something that we track super duper closely because we understand just what an impact it has on the market is specifically on China wfb, not the Chinese manufacturing of wfb, although I'll touch on that and it's like also, but primarily the amount of wfb flowing into China, right? Because first of all, when AI and memory was down, like everyone's, you know, everyone's talking right now about know how much South Korea, you know, the revenue composition of South Korea, they of the AIS and L-Print earlier today, kind of what that was, but like memory was like really muted for two years. And you know, you had to SMC, but everyone else kind of like wasn't putting in noirgas and like really what was like, you know, propping up all of these wfb companies is just the China demand. Like, you know, companies were getting to 25, 35, 45, even 50% accordingly revenue is coming from China. All right, this is companies like SMECR, YMTC importing equipment. So that's the example that I'm giving right now, that's one of the things that we trapped you closely. So like we track wfb imports into China at the provincial level, right? Because you have, you know, you have like DC, you have SMEC, you have WAHONG, you have CXMT, and they're all operating different, you know, in different provinces. And we're tracking the wfb inflow into all those. Because this has like two massive consequences, right? The number one is what does that mean to the wfb company revenues, right? If we're thinking about KLA, if we're thinking about Tokyo Electron, if we're thinking about ASML, like obviously it impacts them because the, they're China revenue. And you know, how that fits in is like obviously super material to how people are going to be looking at, are going to be looking at their results. But then also, right, once Chinese capacity comes online, at all these companies, right? Everyone wants to know, like when is there going to be this massive adoption of Chinese memory, right? When are you going to be, when are you going to be SMECR being adopted by a ton of people? When does this capacity going to become, you know, when is that, when is the memory capacity going to come online? And then suddenly we'll start seeing more of a supply demand balance, like, you know, unlike what we're going to right now. So like that's like a really good example where we're tracking this like super upstream thing because again, it's like also informing you how you need to be thinking about about what wfb revenue is going to look like for these companies that are selling into China. But then also what this is going to mean for Chinese capacity moving forward because that, like, you know, that's something else is just to keep in mind for the Chinese companies, but also that it was impact, right? If you know, if people are adopting more, you know, SMEC than like maybe that's coming out.
at UMC, right? Maybe that's counting out of, you know, Texas Instruments. So it's just another thing to keep in mind. And like just-- Yeah. Maybe can you look backwards and give an example there of like the impact of tariffs or the impact of some of the regulations or stuff that the US has explored in terms of trying to restrict companies actually selling equipment into China? Yeah. So that was the example I was going to give. So like we had observed-- now again, like this is a while ago. But like anyone who looked at the China WFU revenue, like the massive massive brand happened in 2024. Because that's what we're still talking about in that time. And people were talking about all these restrictions that were going to come online. So like you basically saw this massive, massive, massive lithography built up happening inside of China. Like people were like, oh my god. I just saw photography. We saw-- I mean, get positioned at everything. We saw everything. There was definitely lithography was definitely more-- I think that they were probably thinking that lithography-- like maybe that there's going to be a lot more focus but on SML. And like they'll probably be able to get like, you know, deposition and even with some restrictions in place. So like there was a lot more focus on lithography. But again, yes, we saw this happening in that life. So like 2024 was a ridiculous year in terms of Chinese WFE demand. And then if you look back at all of the WFE companies transcripts at the end of 2024, when they were guiding it to 2025, they were all like, we realized that that was just, you know, this-- that was the stockpiling ahead of tariffs, ahead of regulation, ahead of-- you know, whatever the administration was going to do, we're guiding that 2025 is going to be somewhere between 20 and 25% down, everyone was saying that. And we were tracking the state super closely. And like, month after month, it is on par at the same levels with what 2021 was like. Now again, like, you know, and this is already when you're starting to see, you know, TSMC is ramping a little bit. And I have all these other orders. And at the same time, Chinese demand is like literally remaining the exact same thing. And like, we're going month after month, we're, you know, we're tracking all of this. And like, at your time, if you look at China revenue from the WFE players, they were not down 25% year over year. It was even-- it was slightly-- I can remember it was slightly up-per-slite. The down-to-down, it was basically flat year over year. And like, 2024 was massive, like, absolutely massive. And now, when you look at all the WFE company commentary, going to 2026, what are they all saying? China's going to be down 20%, 20% in 2026, right? And so that's what they're guiding. And like, maybe it's true. But like, for example, AS&I was saying that, you know, they're actually seeing China go up in 2026 a little bit. So like, now, like, something that's on our radar, is like, wait a second. OK, so we already saw this play out in 2025, where people said it was going to be down. And we tracked it. It was actually the same. And that obviously impacts what's 2026 going to look like. And that's always something that we're tracking super differently. But that sort of speaks to the importance of having like a granular tracker. So, you know, I do think that some degree we're going to see a slowdown in China WFE imports. But the degree of slowdown may not be consistent across the supply chain. So certain tools you may see a slowdown, certain tools you may not. I don't know exactly how this is going to play out, but we did see that the inspection equipment in China was holding on a little bit longer than some of the front-end tools. I don't know if it's holding on anymore. We're going to find out more in the next week or so. When more data comes out, and that will be included in our chip coming out next week. But our chip comes out once a month. So next week we're going to put out the new one. I think there's going to be some really important data in there about that split, which tools are slowing in China, which ones are not. Because that also creates a huge opportunity. Because if everybody is assuming something about WFE writ large, but there are exceptions within that, being able to identify those exceptions that are going to be hit by the macro noise. But ultimately, we'll, on an earnings level, continue to perform as a huge buying opportunity. So that's something we'll probably have more insight into next week. But to Khayyam's point, there's a lot of value and tracking each one of those things on a granular level. Yeah. In the back of my mind, I'm thinking about how it all connects from high level. Like when you were walking through that, it's the diffusion regulation that Biden was exploring. That people were saying Trump was going to repeal. And then he did how that actually flows through to shipments and to earnings. And a lot of it is kind of also trying to figure out what's the normal run rate. Meaning I think Biden comes out with these rules so that everybody in the world rushes to pull forward orders. Because people want to get things in before the restrictions kick in. And so you see a huge like Khayyam was saying. You see a huge ramp in imports. Let's say China WFE equipment. Well, looking forward, is there now an elevated capacity expansion in China, which will be at elevated level, albeit maybe not as high as 2425? But is there a new norm? Or do we have to look back at historic run rate levels to get a sense of how far can it drop? I think people sometimes have a hard time with visualizing how far something can drop after it's already down. So you'll say, oh, well, listen, shipments are already down 10%. So I guess we've bottomed out. And companies love to say that. Every single company call we'll talk about now. We've hit the bottom. We've always hit the bottom. We're constantly hitting the bottom. So what is the bottom? Well, look historically at norms. I think 180% historic high is the bottom or something like that. I remember when wire bonders, like a very obscure packaging tool, I'm not obscure, but unexciting, unsexy packaging tool, wire bonder orders were flying into China during COVID. And it was just so out of whack with the historic norm, which we've now kind of returned to. And the order levels dropped off 10, 15, 20%. So you see kiloconsophers like, OK, now we've kind of hit the bottom again. But if you looked at it at historical data, it was clear that we were far from the bottom. And having that perspective, even though it's like outdated data can certainly inform your investment decision going forward. And I love what I am said about that is it's like the mosaic theory. Like sophisticated hedge fund investors are very, very smart. They're not looking for somebody to like feed them the answer. They need pieces to the puzzle. They're going to put the puzzle together. But every single puzzle piece you offer them, they will incorporate that into their mental model when they develop their thought about the business. So every one of these data points, not the answer, but it's a puzzle piece. And when you know how to put those together, I'll give you an example. Yeah, I mentioned for more puzzle pieces here. What? I mentioned for more puzzle pieces here, man. Give me some-- Yeah, I'll give you this. This is like an obscure one. But it's interesting. One of the things we were looking at was photo mask writers going into China. Two years ago, there was a big build up. China was building up their own mask shops. And we were watching photo masks go into China. And all of a sudden, they started to slow down, which kind of made sense because they had built up more capacity than they needed. But watching the slow down of photo mask writer important to China made us start asking questions. Why aren't they going into China anymore? What's happening? And what we found was actually counterintuitive. First, I thought, OK, they're not going into China because they already are not utilizing the capacity that they have available. But then it's like, why aren't they utilizing that capacity? If they're able to create photo masks at a cheaper price than the Western suppliers, why don't they just do it? And what we discovered is that a lot of the chip makers weren't comfortable buying masks from China because in order to get a mask made for you, in China, you need to share your chip designs with the Chinese companies. And they didn't want to do that. So that made us realize that photo masks were a huge, huge, onshore motivation. Of all things, you don't want your photo mask to be made in China. And that's why even though China had built up huge capacity and mask writers, the customers weren't comfortable buying the photo masks from China. And so we said, OK, well, who are the photo mask makers in the Western world? Well, Fotronics, P-Lab is an American company, even with the Chinese subsidiary, which is like a win-win of both sides. And then we realized, like, P-Lab is a huge long and short enough. Like, once they came out with that story, the stock like doubled. So that's like a nobody was handing you that story. But by tracking the data, you begin to know what question is to ask. And then it leads you down the road of discovery. And you figure out winners and losers that way. Makes sense. Yeah, great story. Can you run me through how that actually gets incorporated in a chip book release? What does it actually look like when people subscribe? What do they actually get access to? How clearly are you spelling out these long and short decisions that you're recommending to people or just providing data? What formats of that data come in? OK, that's a great question. I'm jumping if I'm missing anything. But basically, the chip book looks like this. It's basically-- I like to make it very simple. It's basically a PDF with 35 pages of charts. The first 10 charts are the same charts every single month. It comes out in a monthly basis. The first 10 are like fundamental building blocks of the semiconductor industry. Things that every semiconductor analyst, every investor, every company needs to keep their eye on. Basic things. One of the hyper-staylor spending. What do the main silicon content product shipments look like? Whether that's PCs, smartphones, auto, wafer shipments, PCBs? We're not just looking like N market. We're also looking very early in the supply chain. But basic-- [BLANK_AUDIO]
building blocks. Those are 10 slides that appear every single month. The next 25 slides rotate on a monthly basis. We track probably two to 300 different data sets, but not every month are those 200 data sets. Interesting, actionable, are there any inflection? Sometimes they're just boring and nothing happens that month. So as opposed to sending our customers a 250 page chipbook that would just like completely overwhelmed any analyst, nobody would even look at it. What we do is we say let's identify. We don't have a specific number. It could be 15. It could be 25. Whatever we think is actually interesting. We pick out another 25 or so slides and we amend those. We append, amend, append those to the first 10. So now we have 35 pages. We have the 10 and then we have the 25 that vary every month. Every single page of the chipbook has the chart and it has eight on the bottom three things. It says number one, what is this data? Number two, it says what are the stocks, not all of them, but what public companies are connected to this data? And number three, it gives an update every month of what we're seeing in the data. So you can scroll through the chipbook. You could flip through it and you could say here are the companies that are connected. Here's what happened this month. Here's my update. That's how you read it. What we now add to the chipbook as well, which I think is a super valuable tool is we write an executive summary at the beginning of every chipbook. So the front page of the chipbook is the executive summary. In the executive summary, Chiam and I tell you the two, three, four, five most important implications of the chipbook. These are the trends that we're seeing this month. These are actionable ideas. These are either investment ideas. They're ways of tracking very important components. They're ways of tracking the overall industry. And we call out in the executive summary what page in the chipbook you can find that data. So we'll say, you know, this is what we're seeing in PCs page seven, which relates to our view on China exports page 13, which has implications for silicon content from this company page 15. So you could just read that one page executive summary and it's a very, very valuable piece of research that allows the customers to then go flip through in depth the 35 pages of the chipbook. That's kind of the overview of what it looks like. Yeah, super, super good summary. It begs the question for me of where people go from there. Let's say they've got some chart, some theme that they really are interested in that they got kind of focused on from that review of the chipbook. You guys have been doing this for a long time before you were part of semi-analysis, right? Where's the connection to the rest of the semi-analysis organization? Maybe you could tell me a little bit about what it's like working with, you know, other teams at semi-analysis, other data, other research that we do beyond the 35 pages that a lot of people treat as maybe the entry point to this industry, even if they end up wanting to go bigger, deeper, whatever it is. I'll start off here and I'll say, I'm rave our memory analyst, Ray Wang. I think it was here, how many weeks do you see here, Jordan? I think it did the podcast few weeks ago, right? Yeah, yeah. It's like three, four weeks, so he put out a tweet, I can't remember if it was earlier this week or last week. And I'm going to botch up exactly what he said, but it was something along these lines. He's like every single day I'm amazed at the quality of people and research that are at semi-analysis. And I just got to echo that because, you know, those who are fortunate enough to be inside our, you know, chaotic Slack channels, know just like, and chaotic is like a massive understatement. But anyone who's inside that knows just like these are all people who are in it for love at the game. And like, you know, everyone's always, you know, sharing ideas, sharing data, sharing what they're seeing. And it's obviously, you know, massively encouraged, you know, to be to talk about what it is that you're, you know, what is that you're seeing. And so, like, and again, semi-analysis, you know, we talk about the entire supply chain, like the semi-analysis product portfolio spans that, you know, the entire semi-value chain, right? You have enough fuels, you have energy, you have data center, you have accelerator, you have, you know, the core research team doing a great job. Everyone's doing a great job. And, you know, we in our, you know, small part of the chip team, like, have had like the great fortune of being able to feed that into, like, the different people that we talked to, like, like we talked a lot, you know, we talked a lot about WFB in this conversation, like, yeah, obviously, you know, we're talking to, you know, Jeff, the great WFB team about like what we're seeing in the, you know, the different WFB arenas to help inform them in, you know, their research process. And if we see something interesting that we think is like, you know, a good output to put into, you know, a core research piece, like, we'll, we'll put them there, like, I'll give an example of that, you know, we, like one of the data sets that we were tracking was this whole idea of A, B, F, Substrait, which I was like, extolled over the past two weeks, like, we put a piece out through, like, we put it in the chip, like, in addition, we also put that to core research piece because we thought that was a good platform to put it out, talking about, like, really these massive tailwinds that were coming to the A, B, F, Substrait space you basically had, you had BPUs that were increasing in size and a layer count and like that was driving demand, like, you know, suddenly the CPU shortages come up and I was talking about CPU Substrait also. And like, this was all leading towards the fact that like, we knew and we were seeing really interesting data on like what was happening to, you know, an A, B, F production and, like, you know, for both production value and production volume, I don't like, you know, Japan and Taiwan, the Charlotte F2 biggest A, B, F supplier, or so. And it's just like, you know, we put that out there. So it's just like, you know, having our hands and all these different data sets, you know, across the value chain has been really fun in the sense that we've been able to, you know, to contribute and to cross pollinate, across other teams. Yeah, I mean, the integration looks like. Yeah, I mean, like, I said, this is like the smartest group of semi-analysts anywhere assembled in the world. Like, we worked on the buy side, covered semis, we thought we were really smart. You show up here and you're just surrounded by people who get it on a very deep level. I think what we maybe add to the team, and what we all add, like I'm said, we're contributing to core research. We're talking to Ray. We're talking to Stravon. Like, all of us are sharing information that helps build up what we're able to provide our customers. But I think very broadly or very quickly is ours is a objective quantitative set of data, which complements other qualitative research. So like, for example, to me, it's like a no brainer. You're subscribing to core research and chip. That's like the basic building blocks. That's table stakes in order to understand what's going on in the industry. You have core research, which is a brilliant research tool that every hedge fund should have their hands on. And then you want to have the complementary data set that helps inform, to help depth, gives more depth, more granularity, more color, more whatever finance word you want to use. It makes it more meaningful. So you have a qualitative and the quantitative before you even get to the models, right? Then you're in a different world entirely. But I think that we complement each other on that qualitative, quantitative basis. I'm going to jump back now a few topics, because I think that we wanted to say something about this and then forgot because we got into something else. But you asked about like no, about the diffusion rules and where you kind of see geopolitics play into the supply chain like how we were able to catch up on that. So like one of our tweets that we put out earlier this week that that kind of would viral. Let's talk about this idea. One of the things that we're tracking is smartphone imports into the US. And since the Trump nomination in November, or pressure of November, November 25, you we saw this like, yeah, thanks for putting it up. We saw this massive drop and like not me import side in the US imports of smartphone a massive drop of smartphones that were coming in that were coming in from China. It was like, that's interesting. And it was, you know, it's interesting because like again, we talked about supply chain like have supply chains moved. How do they react when there's geopolitical instability? Right. Like what what is changing? What's happening? How could you track that? How could you know? Right. So like obviously, you know, it would be nice if you had, you know, a guy who went to the Foxconn assembly and test facility in China, who would then report back and be like, yep, you know, it's still here or nope, they moved it out of here, but the realities you don't have all the time. So, you know, we try to supplement that vacuum information with the sources that we tap into. Like this is something interesting. Now again, like it's it's important to just like highlight specifically for this data set. So this exploded. I can't it's yeah, it's almost at 600,000 views. It's like important for me to highlight that like this specifically like obviously the smart lens supply chain is is also massive, right? Like obviously every single processor that goes into an Apple iPhone is coming is coming from TSMC in Taiwan and then it's going to then it's going to China. It's like when you when you look at a smartphone that was important to the US and the country where it came from like obviously that's not representing the entire phone and everything that's there, you know, there's there's different rules about the percentages of components that need to be in there, but like the fact is and and maybe if you want to bring back the tweet, you could see in the second chart that in the second tweet that we put there, you could see if the chart that actually talks about this, like it's unmistakable that you know it used to always be coming through China, even though there were the different components that came from the different countries, but then like that basically dropped to you know, 25% and suddenly it's coming from other countries like Vietnam and India. So like clearly the supply chain made a shift and realized that even if it's the last you know step of you know FATP, you know final assembly test and package needs to move out of China so that we're able to respond to whatever's going to be happening in the you know in a political landscape in the US, there was a very very clear response to that and that was something that we tracked here. And then in this simmy maybe maybe you want to talk about because there was another tweet that we put out earlier this week on Katari Helian right everyone's talking about the war in the Middle East and how it in fact the same supply chain we put out something about that. Yeah there it is maybe you maybe you want to respond about you know what what this is why it's important and why like this didn't exist until we put it out and that's why it blew up. Yeah, let me add one more thing just.
time was saying about the smartphones because the shift that time was describing is actually even more pronounced by PCs. But what I think I liked best about the tweet was in addition to the, you know, hundreds of thousands of people, there was like almost a lively debate that we started because of that tweet. Basically, is it real or not? Some people are like, you know, it's not real. It's just final assembly. It's just putting a sticker made in Vietnam. So it won't be made in China. Some people are like, this shows that we're moving in a direction once the ball gets rolling. Who knows going to happen next? The point is, and maybe the whole thing is a shenanigan. Like maybe it's not even real. They're just like, you know, making a look that way. So it's not coming from China. I don't know for sure. I don't think any of us really know for sure right now. But I think that the data opens up a conversation that forces the world to look at this and say, what's happening? Like is this real or not? Can a supply chain shift that quickly? And I wish we don't have it in front of us. But I wish I could show you like the PC one is just like, opposite directions. You know, it was like 90% coming out of China. Now it's like 6% in terms of PC imports the US. So the question is like, not only does this data allow us to give answers, but it provides us with a direction in terms of asking questions. And that's also what you're saying, Jordan, is like, that's how we work in semi-analysis. Like we're going to put that data out there and they're going to say, Doug, Saravan, Ray, Dylan, Dan, like, what do you guys think? And then as a result, the conversation is a lot more meaningful within the company and within the entire ecosystem. So I just want to add that. I'm set about the guitar. Helium. Oh, that was an interesting one. So as soon as the Iran war started, so one of the implications that was recognized by the industry is that guitar, who was getting bombed by Iran, provides a large portion of the semiconductor industry's helium, which is using chip manufacturing. So like what happens when the guitarries, either the facilities are blown up or they're shut down or shipping lanes are closed, what happens to all the helium that's coming from guitar? So there was one camp that was like, who cares, don't worry, the supply chain will be replaced elsewhere. And there was another camp that was like, oh, we're in big trouble because a lot of the helium comes to guitar and we need that. And I think what bothered us is like, where's the data? What is the actual number? How much of the helium for semiconductor manufacturing comes from guitar? And what first thing we did is we figured out, okay, when it comes to Korea and and Taiwan and China, what's the answer? What percentage comes from guitar? And what we found is that well over 50% in all three countries, meaning the major manufacturing facilities, over 50% of their helium is coming from guitar. So it's a problem. But then the question becomes, how quickly can that supply chain reinvent itself and start getting helium from the US or from Russia? And what the chart over here shows is that very quickly, the supply chain was able to make a about face. And whereas really the Taiwanese had stopped importing helium from the US and had relied almost entirely on guitar, all of a sudden now, they were able to shift their supply chains over to the US, which is a good sign. Now, can they go all the way to 100% in the US? I think so, but that's something we're definitely going to want to track. And the other thing, the next question I would ask is, what does the pricing look like? Meaning there was some reason why the Taiwanese decided to stop importing their helium from the US and start importing it from guitar. Is that a pricing question? And if they have to now shift back to the US facilities, what does that do to the pricing? There could be of the total bomb helium is so low that it won't have a huge impact. But that's definitely something to ask. So I thought this is a great example. And like I said, for some reason, nobody else in the world had gone through the trouble to actually look at the data. Everybody was talking vaguely. They have six months of supply. They have eight months of, they have two months of inventory. Does it really come from guitar? Does it come from Russia? Our question always is, show us the data. Let's just look at the facts. And then we at least know what we're talking about to have an intelligent conversation. And then we can begin to ask the next questions. So that's what I'm trying to do. In some ways, in some ways, what's jumping in mind is the fact that this is a process. You guys have a system. You have the access to the data, you know, where to look. You have to software built to be able to build these charts on effectively a moment's notice when something happens in the world. And so it's jumping to me that like this guitar helium chart for those, maybe just listening is from April 12th, three days before we're recording this conversation. And the tweets about the Foxconn China assembly network stuff was from April 13th. So you guys had a big week of viral tweets talking about this stuff. But I think it goes to show again, like the fact that we don't know what's coming. We don't know what geopolitical trends are coming. We don't know what kind of macro big picture stuff is coming in terms of like demand of tokens or constraints in the supply chain. And having the process established to be able to build a chart and get some insight from that data is is in some ways more valuable than actually having one individual data point. You need to be able to like adopt to whatever area of the market is in focus. I don't think a lot of people forecast helium being a big focus three months ago. The six months ago, right? If you told us we'd go viral on the helium post. Yeah. Last week. I don't think we would have called that one. Yes. Yeah. There's the. I think I'm thinking of that guy. I care. Like there's the mean template of the guy who's like, wait, you're talking about this. Like, you know, like that like today came out because of this, uh, this like all birds thing and their transition to now being an AI company. So he's like, you know, it's like the guy in the pocket. It's like, wait, is he all birds? Like, you know, the AI company. So like when I hear he or he laid for the first time, like helium, you know, like the inflatable balloon. It's like gas. But like, no, apparently it's for a person. Like, Dr. Zanda and like to go back to like what Cindy started with this whole thing. Like we were from the by side. Like the reason that like you were talking about the process. Like how we do this. Like when you're on the by side, you're looking for data. Like you're looking for things in order to inform you. Right. Like how much of an impact is this actually? Right. Like the reason I love that that helium tweet was because I don't know if this is true. But like your Twitter timeline, my Twitter timeline was dominated by helium. It was like, uh, it like, and it was literally it was like, it was that. It was that chaotic in the sense because there were those who are saying like, guys calm down. This is totally totally negligible. Like stop getting worked up on it. And then there was the other side that was like, you know, TSMC is going to zero tomorrow. Like it's over. Like it's so over. And then like, Cindy and I like at each other were just like, like where did it like can someone please give me an intelligent answer as to like what is it been up till now? Are there other suppliers you get in there? And then of course, like Cindy said, there's there's the questions after that. Like how does this impact pricing, you know, um, who are those other suppliers you could benefit from? But like let's answer the question like, is this a big deal or not? And that and, you know, and we've developed, you know, this process of like, you know, okay, let's answer the question like, who's impacted from this? You know, where are these different sources coming from? And can we get an answer to this? That's like in the data and again, the data is objective. Like we don't we don't manipulate the data. We don't change the data. Like the data is what the data is. So that's that's why I particularly like that. Um, that's why I particularly like that chart because like there was so much noise like about helium as like, let's cut through the noise and just find numbers that could either back this up or not back this up. And again, it would be nice if you got the procurement manager at TSMC on the phone to just tell you like, oh, like this is the amount that we get in. We are not able to get it from other suppliers. But like you don't have access to that. Like you can't like it'd be nice to do good, but you don't. So like where can you find alternatives to that in order to inform your decision-making process, your investment process? And you know, and that's where that's where Cindy and I and the Chippewa product try to be, try to be handy. Awesome guys. Well, I think this is a great place to wrap. Semianals is in pursuit of truth. You know, well, I got I got I got I got I got I got to add one more thing though because I need because right now right now it would be in armchair geopolitical strategist because I think that you know, Cindy talked about the war in the Middle East right now. And I think that there's one thing that we need to be talking about that people aren't thinking about. And that is that I think it's important to realize. And again, like there's obviously a lot in the air right now with with this war and what it means. But I think that one thing that people are not realizing is that the largest winner or loser of this war, funnily enough, I think in my from my perspective is actually TSMC. And nobody's talking about that. I'm going to give I'm going to give my like wacko perspective as to why I think that this is. Yeah, you're going to be like, well, what's he talking about? But like let's let's try to frame this war right now. Then I'll say how this ties into TSMC. If you think about the war right now, you have the US fighting with a partner nation in this case, Israel, who is a technological ally to the US and you know, provides a lot of tech that goes into the US who are fighting for what the US is an adversary and what for Israel is an existential threat. And they're they're managing a campaign together in order to try to take out to take out the enemy. And how this war is going to play out is going to massively impact a new theater, the next theater, because you always need to be thinking from the US perspective, there's obviously a very big reason why Israel want to be in this war. But the US need to be thinking about what the US perspective is. And I think that the US perspective has to always will be what's happening on the Pacific front in China. Now how does this relate to TSMC? Now, we framed what the situation is like right now. And then at least now let's move that to the Pacific theater. So you have China, which is an adversary to the US who isn't in a way an existential threat to a small island there, Taiwan, which is a technological ally to the US because they're providing basically a lot of the backbone to the largest US.
companies, like we put out the sending analysis, put out that table that like eight out of the largest 10 companies by market cap all rely on TSMC. So again, like putting this into the framing. So you have China, you have the technological partner, which is Taiwan and the US, right? And if there were to ever be a future campaign in the Pacific theater, right? If the US is able to effectively execute this campaign and show that they were actually able to fight with a partner nation in the theater against an adversary, that could be seen as a massive deterrent in other theaters also, right? In this case, moving to the Pacific theater, if really the US is able to come out of this war as the proclaimed winner. And I think that that's, you know, that's still a lot of people still don't know. But if they basically are able to create that effective deterrent against the Chinese adversary, that obviously means that, you know, that there's a big deterrent there from the Chinese making a move on Taiwan, remember, and talk about 2027. And the biggest winner from that is obviously TSMC. Now again, it could also be the biggest loser because if the deterrent is not effective and it doesn't end up propelling, you know, the big China. So then that's going to be an issue. But I think that like when you think about what the outcome of this war in the Middle East is going to be, like yes, obviously there's massive implications for what the Middle East is going to look like. But if you're thinking two steps ahead, one of the big companies that are going to have probably the biggest impact on this is going to be TSMC. And that's something that, you know, people need to be thinking about. And when you see the outcome of this war, because again, this entire conversation has been about signals, right? How this outcome of the war is going to be is going to have some downturn effect on what's going to happen to TSMC. So that's going to be taking off my semi-analysis nerdy semi-analyst hat. And suddenly I'm a, you know, geopolitical analyst, but whatever, just a random tangent that came to mind. The focus from, yeah, geopolitical analyst moving the focus from the straight-of-hor movies to the straight-of-alaka going forward or something like that. Yeah. Simpson Wafers in War. Okay. I don't know if we want to coin that one on this podcast, but all right guys. Well, look, I learned a lot. I appreciate the overview of Chips and Wafers. I definitely appreciate the walkthrough of some of these examples. And we got to, yeah, we got to, we got to do this again soon because I think some of some people need to need to listen to this. Check out some of the data and then watch to see how things play out in the next few months to see if we, we have the same track record while they're paying attention that we claim to do as we look back at some of the previous tweets or the previous calls that we've made. Anyway, thanks so much for taking. Thank you, Jordan. I appreciate it. And people are interested. You can find the Chips and the semi-analysis website. You can even download a sample there and they go with the show notes. But it's there. It exists. And yeah, thanks for the time, Jordan. This was also show notes. We have a transcript. Yeah, but we can definitely put links to semi-analysis.com, email address,
[email protected]. Seminoleysis.com/chipbook or slash institutional/chipbook. That's probably the place. Last chipbook. Yeah, we'll put the links in the show notes and the description on YouTube and Spotify and wherever everybody else has listened to this. So, thanks guys. [BLANK_AUDIO]
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