We will provide our AI outlook. The AI boom has led to a physical build-out of chips, servers, data centers, and power infrastructure globally. The speed and scale of this hardware build-out is actively reshaping supply chains and logistics strategies. Join our analysts today as we assess the impacts of the AI boom to air and ocean freight markets, shipping lanes, and discuss the longevity of this build-out. Before we begin, we've seen a lot of interest in the application of AI to logistics operations, notably in shipment visibility and service improvements. However, in this webinar, we'll focus instead on the impact of the AI investment boom on freight markets and supply chains. Before we begin with the content, there are just a few administrative details to cover. We will have about 45 minutes of content to share, and we will save the last 15 minutes for the Q&A session. Please submit your questions in the Q&A box, and we will do our best to address your questions during our Q&A session. A copy of the presentation, notably the slides, will also be available later. To receive a copy of the presentation, please fill out the brief survey that will be emailed to you shortly after this webinar. Please visit our website as well and subscribe to receive information on Onyx's future webinar. We would also like to invite you to explore our latest insights on LinkedIn and the Vantage Point blog, which features a mix of short updates and in-depth articles. Please use the QR codes at the top to follow us either on LinkedIn or to subscribe to our Vantage Point blog. For those of you who are not familiar with Onyx, just to do a quick introduction. Onyx is a consulting division of Expeditors, and we help clients build more efficient and resilient supply chains. We are uniquely positioned to help our clients in identifying geopolitical, regulatory, economic, and operational disruptors, which can then be translated into a more forward-looking and resilient supply chain. All of these are done through advisory engagements and insights. Projects are also tailored to individual client needs, either as one-off projects or ongoing retainers. These are Onyx's services lines. In a nutshell, our service offerings cover various areas within the supply chain, like planning and strategy, trade and compliance, sourcing, and manufacturing. Please contact us if you have a project or a need where our advisory expertise can assist. Onto our speakers today. I'm excited to introduce the speakers who will be presenting today, myself, Adam Karson, and Suryo Nugroho. I'm a Senior Geopolitical Analyst in Onyx, and I work primarily on the Indo-Pacific. I hold honors degrees in history and international political economy, and my work has been featured in The Pacific Forum, The Asia Times, and other media outlets. Suryo Nugroho is a seasoned policy expert with 14 years of experience across geopolitics, policy analysis, and supply chain management. He currently serves as Onyx's Senior Geopolitical Analyst, leading the firm's Southeast Asia coverage. On to Adam Karson. Adam has more than 20 years of experience as an Economic Adviser to global leaders across a range of industries. He has extensive experience in the U.S., Europe, and Middle East. Adam most recently worked at Chevron as a Senior Economist and is responsible for Onyx's macroeconomic analysis and forecasting. So with that, I think we will cover one more slide on our content today in the webinar. What we will cover in the next 45 minutes or so, with 15 minutes for Q&A, is the current state where the AI build-out stands in terms of capital, supply, and delivery. We will cover hyperscaler CapEx plans through 2030. We will look at data center power demand as it relates to infrastructure, and then the supply chain and critical minerals for the AI supply chain. Adam will cover our possible future trajectories, bringing you through three scenarios of AI development moving forward: a fast build scenario, a slow build scenario, and a pullback. Lastly, he will touch on what it means for freight and what to watch. With that, I will hand it over to Suryo to kick us off. Thanks, Suryo. Thanks a lot, Olivia, for the kind introduction. Good morning, everyone. As Olivia has mentioned before, we will start by basically providing you with our analysis as well as insights on the current state of AI development. Where are we going with AI? What is the state of play of AI currently? There are four elements that we want to cover here. The first one is about capital. Capital is readily available around Big Five data. AI CapEx projected to exceed $1 trillion by 2029. The revenue is scaling as these AI companies have already started to also offer enterprise AI functions. The revenue for them is scaling up as well. In terms of economic impact, the economic impact is significant, especially for the U.S. economy. Now AI contributes around 1.5%-2% of the U.S. gross domestic products, as well as 50% of the growth net imports. Supply chains are heavily concentrated, and it benefits a handful of countries. We are talking about Taiwan, South Korea, China, and Mexico carry the value chain. There is one thing or one risk that you all need to monitor. We will discuss this in the second part, and Adam will talk about this later on in more in-depth. Delivery is lagging from two different sides. The first one is on the power supply or energy supply construction. Right now it is only about 5 GW. We are talking about components and equipment as well. There are a couple of bottlenecks emerging in the AI supply chain, namely high bandwidth memory, chips, bottlenecks as well. We are also seeing that packaging is also starting to experience a bottleneck as well. I will talk a bit more detail about the CapEx plans. The first one. Hyperscaler CapEx plans will grow by about 28% per annum from 2025 to 2030 after a tremendous growth of CapEx growth from 2020 to 2025, around 114%. Most of the CapEx will go to inferencing. Why inferencing is so important here? Because right now AI is at the phase of implementation, so now more and more people are using AI, and the cost of inferencing is really high. We are talking about because of the scale. Inferencing happens million of billions of time, depending on the use. Because of that, they need more data center. Hence, you can see the figure on the top, that CapEx on data center is also increasing tremendously. Because they need data center, more and more data center to do inferencing, right? To do a better inferencing, we are talking about speed as well. So instantaneous responses, which require advanced and basically power-hungry GPU. So more investments are needed to basically acquire a more advanced GPU for doing a better inferencing, right? Complexity, as the model becomes more advanced in terms of complex tasks, it requires more computational resources, which led to more dense data centers, as I said before, need to be built. With that comes with a rising cost of energy, as well as the wear and tear of the hardware, in this case, the GPU. So you see that the CapEx plans is really high, right? It is tremendous. For that, basically for the AI companies to justify the CapEx plans, they need to get high margins as well as sustained rapid growth, right? We anonymize the company here, but this company A, B, C, and so on and so forth, represents the top AI firms globally. When we talk about I think the most important graph here is the one on the right side, right? We are talking about the revenue growth required to break even NPV. There is one firms, more specifically company B, that relies on frugality to generate positive ROI, right? Company B, basically they need a smaller revenue, a lower revenue growth to basically make a break-even point, reach break-even point, right? Meaning that they spend more efficiently compared to the others. It is a different strategy, right? The other strategy is basically company A, C, and D, they rely more on the strong revenue streams. But they spend more on the infrastructure, they spend more on the data centers, and that is why they require a stronger revenue growth in order for them to justify their CapEx. Moving on to the AI, the supply chain mapping, right? As I have already said before, the AI supply chain is heavily concentrated in a handful of countries, more specifically here in five countries. Number one, the first one is Taiwan. We are talking about chips, the leading-edge logic in advanced packaging. So 90% of the sub-five nanometers of logic output is produced in Taiwan by one company, TSMC. Netherlands, because Netherlands have ASML. ASML is headquartered in Netherlands, and they are the only supplier of EUV lithography at the moment. Yeah, for the EUV lithography, it is pretty much concentrated in this one company. Right. In Japan, about 50% of global silicon wafers is produced in Japan. We are talking about photoresist and substrate film as well. In South Korea, we are talking about high bandwidth memory. There are only three companies that manufacture high bandwidth memory, right? One based in the U.S., Micron Technology, and the other two, SK hynix and Samsung, are based in Korea, or Korean companies. Clearly, the high bandwidth memory is pretty much concentrated in South Korea. I think we cannot leave China out of it because China basically dominates critical mineral refinery. About 99% of primary gallium refining is done in China. Also, a couple of different rare earths as well. We are talking about germanium, tungsten, and Olivia will talk about this, about the potential export control, because the Chinese government is actively basically developing measures to basically control the export of this refined critical minerals for gaining geopolitical advantage over the U.S. Absolutely. I think Suryo has taken us through, I think, a wonderful overview of the AI industry. In the next three couple of slides, I think as we finish up the context setting before we move on to the scenarios that Adam will take us through. Here we are really looking at infrastructure and specifically power demand. A headline for us today is that data centers power demand are projected to surpass heavy industry by around 2030, and that demand remains the highest in the U.S., China and Europe, while Southeast Asia more than doubles as well by this timeframe. When we think about it from an infrastructure perspective, for Southeast Asia in particular, that data center power demand will be driven by hubs in Singapore and southern Malaysia, which makes assessing, I think, the country capabilities of each of these regions to provide things like reliable power, affordable power, it becomes much more critical. Despite the fact that data center energy demand absolute growth is much smaller, they tend to cluster geographically, which makes grid integration than other sectors like industry, electric transport, or appliances. I think as we move on to critical minerals, what we have done for you here as well is to summarize a list of critical minerals most exposed to export controls. As we know, critical minerals are a pretty key upstream component of AI supply chains. When we think about the geopolitics of the longevity of the AI build-out, critical minerals are a big part of this. As Suryo mentioned, China is very dominant in refining, and it prefers to use these upstream inputs in trade confrontation. We have listed out here, I think, a couple of minerals that are most exposed. Something that I really would like to highlight for us here today is that, last year in 2025, in October, China kind of put in a series of export controls on critical minerals. That pause is expected to expire in the next two months, which makes the upcoming Trump-Xi summit very critical as we will see both governments try to reach, I think, an extension of that pause and to prevent some of these export controls from coming back into place. A key milestone, I would say, in the next two months. That being said, critical minerals, there are workarounds that exist for mining and price coordination. But diversifying China in refining is a long-term process. We've identified a few workarounds at the supplier and government level. We often, I think, tell our clients that you can work with suppliers to source pre-refined inputs from partner nations, conduct audits on geographic origin of your raw materials, coordinate on the minimum percentage of Western refined inputs, if that is a strategic requirement. Of course, license monitoring is a big part of your strategy here, really confirming the status and the speed of export license approvals. I think a lot of nations are also being quite active in mineral alliances and stockpiles. Specifically in the U.S., you have Project Vault strategic reserves, and members who essentially subsidize the difference from the preset minimum price if China tries to crash the price of germanium or another commodity to crowd out new miners. These alliances and stockpiles definitely exist as alternatives, but refining is still a chokehold that will take time to work past. A lot of that are currently underway, like the Canadian Ohio pipeline, Vietnam's processing plants, a project in France. All of these will only kind of kick in in the next couple of years and will take time to ramp up. Refining will still be a chokehold in the next few years. I think just to round off this section, I think we really want to maximize the amount of time available for the scenarios, which is really the key part here. For this context, we've kind of gone through an overview of the AI industry, its financial viability, and as well, some of the infrastructure and geopolitical constraints of the growth of this industry. With that, I'll pass it over to Adam, and he'll take us through the scenarios. All right. Thanks, Olivia. Thanks, Suryo. That is always a really good background level setting of where we are in the AI growth cycle and how the ecosystem is global and complex, which leads to some pretty interesting and equally complex scenarios that we need to think through and how they impact the logistics market, even the broad economy, but specifically how they impact air, ocean, and trucking. What I would like to start with is when we think about scenarios, I think it is really useful to think about who the main actors are, what are the decisions that need to be made, and how do those kind of factors drive which path we are on. If we take a step back then and just think about the environment, I think there are three groups that really determine the path forward here. It is their customers, investors, and suppliers. The first two customers and investors are really pushing the acceleration here, where you have, as Suryo mapped out, this rapid increase in the infrastructure build-out and the investor money flowing in to support those ambitions. You have customers on the downstream side of that, consuming a lot of AI bandwidth, consuming tokens, not just everything that we are doing individually, hacking away at LLMs all day, but also some enterprise solutions popping up, and that is starting to scale. If you look at Anthropic's revenue over the past year, it has gone up 10x or something in that order of magnitude. Those two actors are really full throttle, pursuing the opportunities here. Then you have the third actor, suppliers. This is everything from power to chips and equipment, everything that goes into the buildings, into the data center buildings, and supports that ecosystem. This is where things are struggling a little bit, as Suryo mentioned in the first couple slides, where we are not quite keeping up. Now, it is not doomsday. It is not that this is a major obstacle today or necessarily holding out the build-out today, but this is something that we really need to pay attention to because it is where probably the most immediate risks fall in terms of what pace the ecosystem can be built out. I just want to frame that. Keep those actors in mind as we talk about the scenarios and where the risks and opportunities may lie. We came up with three scenarios that really turn on revenue and delivery. I will explain what I mean by that. But those are the two things that really determine how those three actors evolve. The three scenarios we came up with are best case, which is a fast build. Even an acceleration from where we are today. This means that customers are realizing accelerated productivity gains, driving a lot more revenue growth. The 10x type growth we have seen over the past year, that continues, and that just becomes a flywheel. As that revenue grows, the hyperscalers build more, the investors put more money in there, achieving higher return on investment. A key here really is that in the best case scenario, it would rely on suppliers innovating past their bottlenecks, which I think is a pretty fair assumption, actually. With this much money and capital flowing into something, and if the prize is really as big as some people think it is, then the problems that need to be solved on the supply side probably are not that complicated. They just require some dedication and some capital. Really this best case is that everything comes together, you get that flywheel, as I said. One caveat here worth paying attention to in the best case is the full impact really depends on the labor market outcome. That's a whole other one-hour discussion we could have on how AI is going to impact the labor market. I'll maybe touch on that a little bit as we go on, but we just want to highlight that as a key caveat for that best case scenario. In terms of mechanisms for this best case, I think some key things worth highlighting are that this depends on things like behind the meter generation. If we're going to rely on public utilities building out massive power generation and grid, I mean, that would take a decade or more, right? This requires things like innovation behind the meter generation. It requires CapEx rates to remain very high but shift over time, and get a little more creative on how much can be prefabricated instead of build on site construction. Also things like the siting of data centers follows the power, not necessarily the demand. In the U.S. we're seeing certain states put some restrictions up around what data centers can be built and whether or not contingencies on whether they have impact on the grid. So I think you'll see more and more data centers move to geographies that have existing excess capacity on the power side. Moving across the screen here, that's the best case. Base case is a slower build and a plateau or even a bit of a slowdown from where we are. Thinking like 2026, 2027 is probably the peak rate at which we can build out the infrastructure. In this case, there are a couple conditions, right? You have customers are slower to achieve these scalable productivity gains. There's certainly some return on investment, but the capital comes at a higher cost and a lower ROI than the best case. Suppliers are more in a management mode. They're managing constraints, but don't really get ahead of them. Therefore, the build-out plateaus or slows in the next year or two. The key mechanisms here are that construction schedules are slipping. We're seeing these longer and longer lead times for some of the key infrastructure components, and that just becomes the norm. CapEx growth rates certainly accelerate from the super rapid growth we've seen over the past couple of years. Then back to the constraints. I think here we would see the constraints evolve, right? Right now you're seeing tightness for memory and the prices are shooting up then and probably now also for electrical equipment. Next, it might move to the grid. So you see this whack-a-mole approach to trying to deal with these constraints. Then you have the worst case scenario, which actually comes in two forms. You could have either a demand side shock or a supply side shock. This is just where the economics of the system breaks down. For example, on the demand side, you could see a situation where the economics for the consumer just don't make a lot of sense. Because they're not achieving productivity gains, so they pull back on some of their AI spending. Or vice versa, the economics for the big developers, the frontier kind of models don't work either. I think it's very plausible where there's a situation that in order to achieve scalable productivity gains at an enterprise level, you don't necessarily need the frontier models and pay that premium for those models. Actually, the models that are more of the fast followers that have a very clear business case and are solving very discrete problems, those are the ones that build up scale, but there's not a really strong revenue model behind those because they're more commoditized. In that kind of environment, you would see much lower return on investment, investors pulling funding or maybe even facing some losses on some of their bets. So that's the demand side. On the flip side, you could have a supply side problem. Olivia touched on some of the constraints, the policy-driven constraints, that we might see in the AI ecosystem going forward, specifically around critical minerals. Now, this is one where maybe the models are working well, productivity is scaling up, but there just isn't enough supply capacity to keep driving up the infrastructure investment. The cost of those materials becomes prohibitively expensive. The mechanisms here to pay attention to are that either revenue falters and/or there's some supply side issue that just finally gives way. Then the financing becomes tested and then when the tide rolls out, you have some very expensive assets with long lives that don't match the debt that was needed to finance it. Then also to watch out for the critical mineral export control, whether those return or not, could be a big turning point. So those are the three scenarios. Now, what do we make of them? How do we think about the impacts here? I've tried to break this down into two major buckets, the economic impacts and then the logistics and supply chain impacts. Let me preface this with saying, these are the direct first order impacts. Specifically related to the scale and pace of capital expenditures. Later on, I have a slide on how this multiplies the indirect impacts across the economy. For example, just to foreshadow that, in the worst case scenario, if you were to have a collapse in capital expenditures, you would also have other parts of the economy falling as well. You have these multiplier effects. Same with the best case scenario. You get that flywheel effect, you're going to have all kinds of impacts on the labor market, revenue growth, et cetera. So you have a lot of indirect impacts as well. But again, let's just focus here on first-order impacts. What happens to the economy and to the logistics markets under each scenario? I won't read everything on here, but just to highlight a few things. First, if we compare the economic impacts. In the best case scenario, here's where you get the productivity gains really accelerating, and you get a very material boost to U.S. GDP growth for the next decade. Upwards of, I think, a conservative estimate would be say half a percentage point or 0.6 percentage points per year above baseline growth for the next decade. So that may not sound like a lot, but if you compound that over 10 years, that's a pretty big jump in the size of the U.S. economy. Again, the catch here is what happens with the labor market, depending on what those indirect impacts are. You could have something above or below that 0.6. The base case is our baseline view. Here we are looking at GDP growth in the low- 2s over the next decade. I think one thing to pay attention to in the base case is that inflation increases before output. Go back to the mechanisms of this scenario where you have suppliers dealing, kind of triaging constraints as they come along. Which means you are going to have continued waves of inflationary pressure, like what we have seen over the past year or two. In the worst case scenario, whether you have a demand shock or supply shock, you kind of get different outcomes. The demand shock, I think is probably, I would argue, the more likely. Here is where you get, I think, much more negative impacts to the U.S. and global economy, where you are talking about potentially putting at risk the whole financial model that is backing this endeavor. If you get significant write-downs, equity repricing, here is where the direct impacts really multiply across the economy. You would probably get a recession at the end of the day. For the worst case scenario, this is really more of an inflationary scenario, in addition to marginally slower growth. If you look at that last row, what happens to logistics and supply chains? In the best case scenario, this is really where freight kind of takes off, especially heavy oversized ocean cargo becomes a real growth engine because we have to build out the really heavy physical infrastructure on the power generation and equipment side. I think in the base case, the air market is really the thing to watch that stays tight for the next year or two, but then may normalize. If we are essentially plateauing on how fast we can build out the infrastructure, then the air cargo market balance kind of mirrors that over the next couple of years. One thing to pay attention there is project cargo. There are long lead times there. That kind of cargo probably has a longer peak cycle over the next, maybe into 2028, 2029. In the worst case scenarios, here is where you get volumes falling. Again, tracking the investment cycle, volumes and rates fall together commensurately across both the chip and technology side, and also the power and infrastructure side. Both air and ocean are hit in that scenario. Air is hit disproportionately. Let me move on to this slide. Going into this in a little more detail, this kind of mirrors the last slide a little bit, but going into slicing it a couple different ways to think about what is improving or getting worse in each scenario. Again, I will not read everything here. I just want to highlight a couple things. Most importantly, I think power is the only input that basically gets worse in every scenario. In the short run, power, we are short on supply. The best case scenario assumes that that kind of catches up over the medium term, becomes more a comfortable supply-demand balance over the long term. But in the base case, that is something that really is a stress point, and creates those constraints that I have spoken about. I think another thing to pay attention to is how sourcing changes. By sourcing, I mean diversification, and whether or not we can de-bottleneck or de-risk some of the sourcing that is happening right now. In the best case scenario, we assume that kind of naturally, diversification kind of naturally happens as the ecosystem evolves, and gets a little more innovative and creative on where we are sourcing from. In the base case scenario, and certainly in the worst case scenarios, that diversification doesn't really happen much at all. So we are kind of living with some of the inherent risks in the AI supply chain that we have today. In particular, I would focus on Taiwan as being the main source of chips. What I really wanted to get to here is more direct impacts on the logistics market. I will just spend a minute on this slide. As I mentioned, I think air is hit the hardest, right? When we think about where the volumes are today, I think the numbers I have seen suggest that about, I think it was 7% or 8% of global air cargo is related to AI. That is on a volume basis. On a value basis, it is something like 40%, 50%, some crazy big number. On the ocean side, it is probably less than 1%. North America domestic trucking, probably 1%, but kind of concentrated in particular segments of trucking. So there are some areas of concentration there. When we think about how these scenarios impact logistics, air is definitely hit the hardest, just because of the scale, right? If you have a pullback in CapEx spending, you would have a commensurate decline in air freight, and that 7% of volume becomes some significantly lower number. These are just kind of directional, what to think about, what would happen to, on the demand side, growth for air freight, ocean container freight, project cargo, and heavy haul, and then trucking, specifically North American cross-border and last mile trucking. Obviously, when we look across the board, we are in a relatively kind of tight market, certainly in air, ocean, trucking. Not all of that is related to AI. We have held other webinars on the geopolitical factors driving that market tightness. In the best case scenario, while it might be the best case scenario for sort of anyone playing in the AI ecosystem, not a best case scenario for anyone outside trying to ship things, because it just means kind of tight market for the foreseeable future, upward pressure on rates. Base case, you kind of get a milder version of that, and potentially, in the long term, kind of a neutral impact. By long term, I mean, say, five years out. In the worst case scenario, that is where volumes kind of really take a big hit, and rates come down commensurately. One thing I want to kind of highlight. There we go. How we calibrate, those are obviously qualitative kind of measures, but we can calibrate this a little more precisely. I think the dotcom era provides a useful comparison to calibrate the downside scenario. If we look back to the telecom sector, 2000 to 2003, CapEx spending fell about 80% from its peak, and then took about a decade to soak up the fiber optic glut that we had built out. If we just kind of take that as a very rough example, I think a conservative estimate then would be, in a worst case scenario, AI CapEx falls by, say, by half, by 50%. AI related goods, as I said, were about 7% of global air cargo volume in 2025. So a 50% CapEx reduction means that it goes from 7% market share down to 3.5% market share. So all else equal, air cargo volumes fall 3.5%. In one year, that's a significant hit, right? That creates some looseness in the market. Certainly the transpacific lanes are much more exposed to U.S. high tech air imports. So you would see a lot more looseness on particular lanes. But just globally, a 3.5% decline is something that could be absorbed over a year or two. It's not necessarily an existential issue for air cargo. When you look at ocean and trucking, the volumes that we're seeing today are fairly negligible, probably less than 1% of global volumes are related to AI on ocean. So it is not enough to move the market, really. I'll get to the indirect impacts on the next slide, which probably would be big enough to move the market. But again, just the direct impacts, not a major shock to ocean. On cross-border trucking, you could see more of an impact there, and I think in particular on flatbed and heavy haul. So if you're using those services, that might be something that gets hit a little bit harder, and you could see some slackness in that segment of the market. I've talked about direct impacts, and now the indirect impact, I think is where the real money is here. This is where you get a compounding of both upside and downside. When we look at the best case, if you are getting this direct impact that you have some modest acceleration in CapEx from project cargo, air cargo, air freight, et cetera. But then you get this flywheel effect that spills out across the rest of the economy, and you have the U.S. economy just consistently growing at 3% or higher. That obviously has spillover effects into other sectors of the economy, in particular, consumer spending. Then you get even more rapid growth. So I think you get spillover effects that compound, not necessarily evenly, but you get much more kind of positive upside for ocean in that scenario as well. Base case is kind of what it is. There's not a whole lot there in terms of indirect impacts because it kind of looks like the world does today. So, you get some kind of positives and negatives. Positives on maybe the consumer side, some negatives because the AI CapEx plateaus, maybe even shrinks a little bit. So we basically end up where we are today. But then on the worst case scenario, and I'm highlighting a demand-side version of that worst case, is that if you get a, again, say you get that 50% reduction in AI CapEx. Right now, the current level and growth rate of AI CapEx is contributing somewhere between like a third and a half of U.S. GDP growth. Those are the best sort of third-party range of forecasts. My forecast is closer to 50%. If U.S. economy today is growing at 2%, a full percentage point of that is coming from AI. If you basically get rid of that, and the sector's not only flatlining but actually declining, the direct impacts alone might put us into a recession. Certainly, would probably flatline growth. If that happens, you have potential, you have equity, you have a bear market, a bear equity market. You have bonds getting defaulted on. You have ripple effects throughout the investment community, the consumer sector. This is almost definitely a recession. Probably worse than what we saw during the dot-com bubble. If that's the case, then you have these compounding effects and impact not only to air cargo, but then to ocean cargo as well because retail sales are probably going to decline for a solid two, three, four quarters. Just want to highlight here that these indirect impacts really compound, both on the bookends of these scenarios. Let me end up quickly with a couple slides, then we can get to Q&A. Just a couple of things to watch over the next year or so. First, always pay attention to memory prices and trans-Pacific air cargo and volume rates. I think those are the canary in the coal mines of how fast things are moving and is the build-out kind of keeping pace or even accelerating going forward. The next thing to really watch after that is what happens with Chinese export controls in November. Is that pause extended or not? Then we want to look at the power generation order books at year-end. This is a great forward-looking indicator of whether things are staying hot or cooling down. When we get into February, we want to look at the Q4 2026 plans, and then 2027 construction starts. What does that pace of growth look like? Finally, what I'll end you with here is two sets of conclusions depending on where you sit. Basically, are you in the AI value chain or not? If you are, things to pay attention to are the wait time for power equipment is not going to get shorter. We think for the time being, chips and packaging stay in Taiwan. I think medium to long term, that risk could we could de-risk that as we're building out North American capacity, but not quite yet. Mexico stays as a strong U.S. assembly hub, and that cross-border lanes are something to really pay attention to. There's a lot of talk about racks getting more dense, heavier, hotter. It requires more power and cooling freight per server. That's just more demand for power and cooling. If you're not in the AI value chain, the bottom line is you're competing with AI for the same capacity, right? Air and flatbed and cross-border trucking in particular, are going to remain hot in most scenarios. AI CapEx is really setting the rate. That's the marginal good being moved right now. That's what sets the rate and air spot rates are up 38%. Not all of that is AI. Obviously, there's the geopolitical stuff going on in the Middle East. But the fact demand just kind of keeps that upward pressure. Then the power and component costs, although you may not have direct exposure to that, you end up feeling that no matter what. So electricity prices are up, memory prices are up. So say you're in the auto sector looking for chips, the cost pressures are only pointing up to some of these key inputs. Then just pay attention if there's a pullback. So if we're in a downside scenario, that's really kind of the main driver of rates coming down. But the flip side of that is that, remember those indirect impacts. If we fall into that worst-case scenario, depending on where you sit in the economy, you may get kind of rolled up into those indirect impacts as well. Okay, so I went a few minutes over. We have about 10 minutes for Q&A. So I will stop sharing, and we can go to Q&A. Absolutely. We've got a long list of questions here. First of all, thanks, Adam. That was a wonderful set of scenarios. I'd like to kick off a question here in the Q&A box. I'm going to take a little bit of liberty here in rephrasing it, but what if we assume that the AI infrastructure build-out is a government-backed adventure, right, and it's not strictly based on market dynamics, but rather on strategic capability? How would this kind of affect the stability or the timeframe of the AI infrastructure build-out? I think either for Adam or for Suryo. I love that question, and I'll take a stab at it. This is something I've been thinking about a little bit as we prepared for this webinar. I think there's a strong case that you could make that parts of the AI ecosystem could be treated like a public utility. One hypothesis would be, and this is not a recommendation, it's just a hypothesis, is that frontier models could be treated as a public good, and have a completely different funding mechanism with a lot of taxpayer dollars. There's a ton of precedent for something like that. Think of how NASA operates, right? You go to the moon, all that's publicly financed, but all the benefits that spill out of that, all the technologies that are created as you embark on projects like that. I think you could make a very good argument that the frontier should be at least partially public financed, and that would also kind of open the door for some different tax models. How we prepare for labor market disruptions, kind of is like the corollary to that. Imagine that world where that kind of financing mechanism exists. I think that would add a ton of stability to the AI infrastructure build-outs, and alleviate some of that pressure on the margin, and allow more of a focus on really the enterprise scalability, fast follower models that are much less expensive to operate and consume. You kind of have a bifurcated view of the market that way. I think in my mind, it's easier for investors to have a clear ROI if they're not having to participate on the frontier all the time. I love that question. I think there's a kind of a whole field study that is kind of emerging right now. Suryo, I don't know if I made any sense at all in my answer to that. Go for it. Yeah, I think you made a really good point here, Adam, and a couple of countries have already started that. In fact, like the U.K., for example, they just earmarked about GBP 1.1 billion for developing a national AI supercomputer, right? This is to expand national computing capacity by 2030. That's a clear indication there that state has already started embarking on this journey, right? We are also seeing a couple of different countries like Canada, France, India, and the Gulf countries like the UAE, Saudi Arabia, they've already started embarking on AI infrastructure as well, right? A couple of them, they have already started also public-private funding. It's basically to reduce the risk of private investment in frontier sectors, as you said, Adam. Yeah, I think it's getting there. I think the traction is moving forward towards that direction. I can totally say that. Absolutely. We've got a lot of questions, which I think is a very positive sign. I'll take this one very quickly. What are the countermeasures companies can take regarding China's export controls regarding rare earths? What is the lead time to have a second country option? What may be helpful is a slide I think we presented earlier in a couple of workarounds. The first thing that I think we can really advise is to have visibility really into the upstream supply chain. Really having audits on the geographic origins of your raw materials. Alternatively, I think really staying on top on when those export licenses are passed, when they're being approved. Right now, I think the export licensing mechanism is still ongoing and MOFCOM is approving those. Just really staying on top on how long it takes for those licenses to be approved. Lead times, I think, is a very personal question to the company, very personal to the commodity and the product. But we will highlight that any kind of sustained, either at the government level or at the company level, efforts to diversify sourcing of these raw materials outside of China is really quite limited because of the long gestation period, because of China's dominance in refining. So it's absolutely possible to have alternative options. It's just that you may not get as much of that diversification as we would possibly like to. And I think with that, we've got one more question on. Sorry, excuse me. What U.S. ports will see the largest increase in incoming goods in 2027 due to the importing of goods in support of AI, either directly or indirectly? I'll take a stab at that. My understanding is that the ports that have seen the biggest volume so far in the U.S. are San Francisco, LA, Dallas, Fort Worth. What else? Chicago maybe. I'm not sure I would necessarily see a reason why those locations would change in the next 12 months. If you look out a bit longer, I think maybe there's a question that we can look into of where are data centers more likely to pop up over the medium term. I think the best outlook I've probably heard is that really pay attention to the Rust Belt, so kind of the middle of the country and then the Southeast. So if you think of that kind of an L-shaped corridor where data centers are most likely to be located, what ports, a lot of the tech stuff's coming in by air, so what airports make the most sense? And then the heavy kind of power infrastructure that's going to come in by ocean and then have to go on trains or trucks. I think certainly LA Long Beach, and then maybe if we're building out the southeast corridor, maybe it makes sense to land on the East Coast. But I think that's more of a two, three, four year kind of view. Perfect. And I think with that, we are at time. So thank you all today for your time. You will receive a survey shortly after this webinar. If you fill it out, you will get a link to a copy of the slides, and please stay tuned for our future webinars. Thank you very much. Thank you very much, everyone.