英伟达供应商融资引市场折价 估值压缩现买入契机

违约担忧引发AI巨头估值重置 英伟达与美光跌入罕见折价区间 市场虽因供应商融资链条的潜在违约风险提前压制科技股倍数，但英伟达与美光凭借真实的利润爆发将估值压缩至历史极低位，反而形成坚实的安全边际与量化买入契机。 研讨会深入对比了当前的AI热潮与1998至2000年互联网泡沫时期的异同，指出巨头向缺乏造血能力的初创公司提供“供应商融资”已引发信贷市场的警惕与股价的估值压缩。然而与千禧年代无营收的“眼球经济”不同，当前的AI基础设施领头羊拥有爆炸性的真实利润与强劲现金流，且信贷违约尚未爆发。随着二级市场提前计价过度担忧，英伟达和美光科技的市盈率及PEG比率已回落至极具吸引力的历史折价区间，Seeking Alpha量化模型亦将英伟达上调为“强力买入”。主讲人建议投资者采取成长股与分红股并重的“杠铃策略”，以在消化行业软着陆波动的同时静待新一轮估值修复。 AI 基础设施巨头（如英伟达、博通、Alphabet）广泛采用“供应商融资”模式，为缺乏传统银行信贷支持的初创公司注资换取硬件订单，引发二级市场对其资产质量与代垫风险的严厉估值惩罚。 (4:09) 当前标普500基于2026年前瞻预期的PEG比率仅为0.81倍，相较1999年互联网泡沫时期的1.07倍折价达26%，显示当前AI行情伴随着远超当年的真实营收与利润支撑。 (33:10) 英伟达最新季度营收达962亿美元（其中数据中心贡献890亿美元），其前瞻市盈率已从15年历史均值的30倍压缩至20倍以下，前瞻PEG比率低至0.49，量化评级三年来首次转为“强力买入”。 (40:00) 美光科技前瞻市盈率降至5.4至5.61倍，相较行业均值折价超70%，华尔街近期有25位分析师上调其单季EPS预期，显现出极强的估值安全边际与预期修正动能。 (63:45)

Seeking Alpha量化分析师指出，当前AI基建热潮中存在类似互联网泡沫时期的“供应商融资”与资本过度扩张隐患，但与2000年概念炒作不同，头部芯片巨头正以真实的爆发性盈利推动估值大幅压缩，带来罕见的风险重估与估值折价。 暗藏危机的供应商融资闭环 (4:09) Steve Kress与Kevin Safford指出，由于缺乏稳定营收的AI初创公司难以获得传统商业银行信贷，英伟达、博通及Alphabet等行业巨头开始充当“影子银行”。巨头通过提供贷款、股权投资或算力长协锁定客户，而初创企业将几乎全部资金回流采购硬件与云服务。这种循环模式在短期内推高了账面营收，但也模糊了独立真实需求与供应商自造需求的界限。 这一财务网络正面临宏观环境的严峻考验。当前10年期美债收益率长期站稳5%以上，年内AI相关发债已突破2200亿美元，叠加电力与基建成本激增，大幅推高了项目的基准回报率（hurdle rate）。随着算力释放引发价格下行，一旦下游初创企业因盈利乏力违约，风险将顺着复杂的金融链条反噬硬件大厂，甲骨文五年期CDS利差飙升至2008年金融危机水平便反映了市场的这种恐慌。 泡沫历史镜像与盈利能力的本质差异 (10:00) 虽然当前英伟达约80%的市场份额与生态深度绑定让人联想到1998年路由器份额超70%的思科，以及因给客户兜底贷款最终崩盘的朗讯科技与北电网络，但两位分析师强调两代周期存在根本差异。互联网泡沫时期充斥着无盈利的“眼球概念”，而如今的AI头部企业具备充沛的自由现金流与真实商业需求，英伟达单季总营收已达962亿美元，其中数据中心贡献890亿美元。 从宏观估值来看，标普500指数过去十二个月EPS增速达32.8%（历史均值14%），市盈率仅为26倍；IT行业EPS增速更是高达106%，远超其17.1%的历史均值。对比1999年泡沫顶峰时1.07的PEG比率，当前至2026年预期的PEG比率约为0.81，折价约26%。分析师认为，AI基础设施仍处于建设早期，目前尚未出现营收失速、盈利溃败等违约前兆。 市场防御重估驱动英伟达估值重置 (35:30) 自6月以来，公开市场因担忧云厂商资本开支放缓、利润率潜在承压以及英伟达扮演“最后贷款人”的信用风险，对其实施了预防性估值惩罚。然而，在股价经历数月盘整滞胀的同时，英伟达的实际净利润持续呈指数级爆发，直接推动了市盈率倍数的压缩。 Seeking Alpha量化模型近期将英伟达评级上调为三年多来首次持续的“强力买入”（Strong Buy）。其前瞻市盈率从历史15年均值约30倍一度降至20倍左右，前瞻PEG比率降至0.49，较科技板块1.25的中位数折价61%，较自身五年估值均值折价40%至50%。量化评级显示，英伟达在增长、盈利能力指标上均斩获全A+，其极高的毛利率与净利率成为抵御周期波动的核心护城河。 美光科技呈现极端折价与软着陆预期 (47:00) 横向对比AI产业链估值，美光科技（Micron）呈现出分析师口中“独角兽级别”的极端偏离。美光的前瞻市盈率仅为5.4倍至5.61倍，较科技板块22倍的中位数折价76%，较其自身五年均值74.25倍折价92%；其企业价值倍数（EV/EBITDA）亦处于4.0倍的超低水准。尽管市场对存储周期存有疑虑，但在高算力需求的支撑下，华尔街共识预期美光前瞻营收增速达104%、EPS增速达191%，近90天内针对其全年的上调修正达29次（仅3次下调）。 分析师总结认为，当前市场环境更接近一场AI领域的“软着陆”：前期透支预期的估值倍数通过横盘与盈利增长被逐步消化，迫使高管层削减高风险的客户融资。这种缓慢的再平衡虽然伴随股价震荡，但能避免2000年那样毁灭性的系统性崩塌，反而为基本面坚实的高质量半导体资产提供了罕见的吸筹价格区间。 分析师指出，当前AI行情的核心悬念在于华尔街延伸至2030年的高增长预期能否兑现。未来伴随OpenAI、SpaceX等大型初创企业走向IPO，资本市场将从追逐算力基建概念转向极其严苛的净利润与自由现金流审查，依赖外部融资支撑的脆弱商业模式仍面临出清考验。

[12:30] Kevin Safford: the technology can be right while the investment can still be wrong [45:00] Steve Kress: NVIDIA's role as a supplier, investor, and sometimes customer may magnify risk if the returns are disappointing. [14:13] Kevin Safford: 对于AI基础设施而言，更大的担忧在于投资所产生的收入是否能够继续证明建设和融资这些设施日益增加的成本是合理的。 [12:30] Steve Kress: 我记得当年亚马逊根本不赚钱。因此，要持续把钱投入一家完全不产生任何利润的公司，绝对需要一个极其坚定的投资者。

[0:00] Thank you. Hello. Hello, I'm Steve Kress, head of quantitative strategy at Seeking Alpha, and welcome to the next stage of the AI boom financing, valuations, and financial. and risk. I'm joined by our quant strategist, Kevin Safford. Thank you very much for attending this webinar today. First, I have to get a little housekeeping out of the way. We are not advising you personally concerning the nature, potential value, or suitability of any particular security. You alone are solely responsible for determining whether any investment security strategy or any product service is appropriate or suitable for you based on your investment objectives and personal and financial situation. This presentation is for information purposes only. Content is presented as of the date published or indicated and may be superseded by future events. It represents my opinions, which may not reflect the views of CK Alpha as a whole. Past performance is no guarantee of future results. CK Alpha is not a licensed security dealer, broker, U.S. investment advisor, or investment bank. And without further ado, we will get into our presentation. By way of background, I am Steve Kress, Head of Quantitative Strategy at Seeking Alpha. I have been in the world of finance for about 35 years. The bulk of my career was spent at Morgan Stanley. I worked there for 13 years where I ran a proprietary trading desk in quantitative strategies. I was also at Northern Trust Global Investments where I headed up international development. And I was the founder of a hedge fund, which focused on quantitative strategies. As well as a fintech company that for more or less of a description was autonomous investment research. And that was purchased by Seeking Alpha in 2019. And it has been a great journey. We've developed a fantastic quant system for Seeking Alpha. And Kevin, why don't you tell the audience a little bit about yourself? Absolutely. Thank you, Steve. I am Kevin Sanford, the Quant Strategist for the Quant Growth and Income Portfolio here at Seeking Alpha. I bring over nine years of experience across banking, investment research and financial media with a specialized focus on macroeconomic analysis as well as portfolio strategy. Prior to joining Seeking Alpha, I worked at Stansberry Research and hold an MBA from the University of Maryland. Kevin, thank you very much. So today's presentation will cover the AI boom and the dot-com parallels, vendor financing, NVIDIA's valuation reset, and quantification. opportunities and risks that are at hand. First off, I want to show some numbers here. The AI boom is real, just as the Internet was real back in 1997, 98, 99. So there are definitely parallels, very revolutionary. And as we found in 1999 and 2000, there are definitely warning signs that we're seeing. that parallel that period. And that's why we want to bring it to your attention. the Current numbers that you see up here reflect NVIDIA and what we have seen generated by the company. So you're looking at the current quarter. It was $96.2 billion in revenues. That is a revenue growth rate of 106%, which is almost unheard of for a company of that size. And their data center alone was $10. Revenue alone was $89 billion. So you can see this is absolutely massive in terms of the revenues that they're generating, the growth that we're seeing. And that is real revenues and real growth. It's not what we saw often in 1999 and 1888, which was eyeball views, click-through rates. Many companies back then did not have the extensive revenue. Thank you. So this is a very nifty little AI vendor financing graph that we created. And it lays out the steps that we noticed in 1998, 1999, which are very similar to the AI ecosystem cycle that we are seeing today. So this is a feedback loop that can amplify both the boom and the boom. and the eventual potential reversal that can occur as well. So we're in the period of sort of phase one, and that's where vendor financing starts to come through. Now, typically, many of the startup companies, in normal periods, they would go to commercial banks, they might go to investment banks, maybe to private equity to get financing. And eventually, [5:00] - Actually what happens is these companies cannot go to the traditional lenders. The traditional lenders set out terms and parameters, that these startups cannot meet. So what many of these startups will do is they will actually go to basically the vendors and suppliers and ask for financing. And the larger companies like Broadcom and NVIDIA and Alphabet – Their view is, okay, we're going to provide financing to this firm. They can give us 100% of our business, so that will be revenue coming to us. So they provide the loan, they get interest on the loan, and then they're the benefactor of having orders for their products. So that gets us to the second stage where you have the AI infrastructure and spending accelerating, where normally, as I mentioned, if they went through conventional financing, they would not have that capital available. through alternative means. The debt markets absorb these larger AI commitments at stage three. And then you can actually see power demand. And we saw this back in 98 and 99. Power demand exceeds what the realistic buildout should be. You'll have data centers, and often you have utilities that are having to expand their facilities as well, or alternative energy sources will come about to have the energy and the grid in place to enable these companies to grow. We're seeing it also through GPUs and the clouds. That's level five. And then all of a sudden, when you get to level six, here we find AI computing price declines. So it actually becomes deflationary. earlier today, if you remove the energy component out of the current inflation picture, which is making inflation sort of sticky now and a lot of uncertainty. We both find that in the world of AI, it can actually be a very deflationary environment. And through that competition, prices would come down. However, as prices come down for these companies, it makes it harder for them to compete. So I won't bring you around the entire wheel, but you can see, as we're experiencing right now, you'll see data center projects disappearing, sort of that level eight, level nine. Financial relationships increasingly become interconnected. Many of these large vendors are reaching out to asset managers, such as BlackRock or private equity firms, to participate in these loans that they're making available. So it becomes very interconnected. And then you get to level 11 where you experience valuation compression. And we definitely have been experiencing valuation compression since June. And actually, many of these companies look like they're at a fairly attractive value given the growth rate, provided those growth rates are sustained. build out, period. I think this is a very interesting chart. This shows Oracle's five-year CD spreads, which has hit its widest level ever. You can see it absolutely is surging here to levels that you saw back in 2008, 2009. So, Oracle is one of the companies that is committing capital to the smaller companies. And you can see it is being reflected in those credit spreads. compression. that we have that really started in June is investors are saying, OK, if you are going to take the risk of supplying these loans to smaller companies that don't have revenues and don't have earnings, it's going to come out of your market cap. And I think probably a lot of people in the C-level suites weren't prepared for that. And there are many investors out there that are familiar with what happened in 1998 and 1999 and 2000 and the bubble bursting then. So they're bringing that to make sure that these companies don't get overextended. And that's why many of the share prices are selling off now. And it's sort of a natural revaluation that is occurring in the marketplace. And indeed, actually some of these companies are quite cheap. Kevin, I think I will hand it over to you and you could perhaps give a little bit more detail on the differences. Absolutely, Steve. So right here, you're going to see another chart that is organized to kind of compare and contrast a lot of the differences, as well as some areas that are showing similarities between that. 97, 98, 99 period and what we are seeing today. The reason that I find the late 1990s particularly relevant [10:00] compared to maybe other eras, which we saw market disruptions, is that the internet was genuinely transformative, right? And that's what many of us expect AI to be as well. Companies were building the infrastructure needed to support a new generation of technology, and the demand for networking equipment was growing rapidly. But the fact that the technology was transformative didn't mean every company supplying that infrastructure would be a successful investment. There are some with today's AI build out in that sense. Both periods involved enormous spending, intense competition to establish capacity, and suppliers making increasingly complex financial arrangements to support their customers and to actually secure that future demand. So, In the current cycle, NVIDIA and other major suppliers are operating in this environment where customers need access to this increasingly expensive computing infrastructure. Some suppliers are also investing in companies that purchase their products or providing other forms of financial support that Steve had previously mentioned. So, NVIDIA. That creates a question similar to the ones faced in investors faced in the late 90s, which is, are the companies. building capacity because they can generate attractive returns on that investment? Or are they getting caught up in the fear that being left behind in a rapidly expanding marketplace is the big driver? And that is a key question that investors continue to face today. And the financing environment becomes increasingly important as a subset of that question. Higher A business center that looks attractive when financing is cheap may look very different when construction costs rise, interest expense costs rise, and overall operating expenses become higher than anticipated. We need to also pivot here and focus on important differences between the two periods, because as Steve said, while we do see many similarities, we are going to touch on several key distinctions throughout the webinar today about how these periods are actually very different in a sense as well. So today's AI leaders have substantial revenue, earnings, cash flow, and AI demand supported by commercial activity. So that is a little bit of a deviation that we saw from the dot-com era, and the historic parallel is more centered on the risk of overinvestment and financial excess, not necessarily whether the infrastructure is real, whether the AI demand is real. as we look out into the 2030s is real, but continuing to distinguish that as an investor can become increasingly complicated. Thank you. All right, I'll take us to the next slide. Kevin, do you see the next slide? Yes, so we're talking about .com lesson. Uh, Yes. So as we are looking at the dotcom lesson and we look at Lucent Tech, which was absolutely huge back then in Nortel Networks as well, you could see the rise and fall of both of these companies. As Kevin said, the technology can be right while the investment can still be wrong. Obviously, the Internet continued to play out from 2000 and beyond. But early on, it was a very difficult period. finding the right companies to provide that financing to. And I remember back in the day, Amazon never made any money. So it definitely takes a very strong investor to keep putting money into a company that is not generating any earnings. Their revenues were going through the roof, but they had no profitability for years and years. So you can imagine if you're either at a C-suite or an individual investor, that is a long road to walk down. um kevin why don't you take this one here with higher interest rates raising the stakes Yes, and this is actually a chart and graphic that I think is extremely important when we kind of take the 10,000-foot view of the current situation to try to see the forest through the trees here. One of the important differences between the current AI investment cycle and some earlier tech booms is the cost of capital. cost to finance them and then how quickly they can generate their returns. So if you look at the chart here on the left, you will see that there is a very significant deviation here from the three decades leading up into the 20th. [15:00] B20 is on the left. and where we've been over the last five years. In the period of coming into 2020 from the '90s, we see interest rates continually trending lower, inflation continuing to trend lower. And right now, we have a 10-year Treasury yield that has sat above 5% for quite some time now, even approaching 5.3% at its peak. But we also have $220 billion plus in AI-related debt issuance this year. So, When you combine those two together, we really start to see how the risks start to amplify a little bit more. And as Steve had shown in the previous slide, when the credit default swaps start to rise, it's starting to take into account the percentage chances of defaulting on this borrowing. And the interest rates do play quite a big role in that. So. Why does that matter? When the interest rates rise, the borrowing does become expensive. But then the return required to justify not only existing investments, but to continue to get buy-in for future potential projects, it increases. So that bar and the hurdle rate is higher. A project that makes sense when financing is cheap may not make sense if it is going to be costlier when you account in electricity costs, construction costs and borrowing costs. So That is where we see the valuation effect. Investors can value companies partly on the earnings, cash flows they expect to receive in the future. But when those interest rates start to rise or they become stubborn, like we've seen, where they're just not going lower, regardless of what economic indicators we're seeing, that just continues to put pressure on the valuation. And it's just another kind of cog into that puzzle that we're having to filter through as investors. In my opinion, for AI infrastructure, the bigger concern is whether the revenue generated by investments will continue to justify the increasing costs of building and financing them. And now we're going to see this next slide. We're going to talk a little bit more into Nvidia itself as it is kind of the linchpin here in this AI revolution and how it's financing that AI revolution. And when we look at Nvidia's position today, it really reminds me and Steve a lot of Cisco's role during the Internet build out. If you remember... Cisco was a dominant supplier of networking equipment in the late 90s, and companies building internet infrastructure depended heavily on its products. Today, NVIDIA occupies a very similar central position in AI computing. And if you see on the right side, Cisco held over 70% of router market share in 1998. And right now we're seeing some estimates coming in even from major banks and research think tanks that... The NVIDIA's market share is right around 80% for some of their AI infrastructure and This is another indicator, you know, as we talked about interest rates, it's just another thing we have to kind of put into this pie of realization in what we're considering within risk, right? When we're making investments, what is that risk? - Yeah. Now, NVIDIA does have enormous cash-generating capacity, and that gives it the advantage when these customers and smaller infrastructure providers in the AI web need the capital or financial support. And it provides this through equity investments, cloud agreements, supply commitments and guarantees. And NVIDIA can help these other companies build it out. But it also... is demand driven to use its products down the road so from the video's perspective These arrangements make strategic sense today. They help expand the ecosystem, secure access to capacity, and potentially support future demand. The question is where that support begins to blur the line between independent customer demand versus supplier-supported demand. So imagine a company purchasing NVIDIA hardware while also receiving investments from NVIDIA, and then entering into agreements that bring NVIDIA back as a customer for cloud capacity. those do present legitimate commercial reasons for that structure, but also for concerns. So investors need to... as best they can understand the relationships and the economics behind them [20:00] And that's what we're going to continue to dive into here in this webinar. So here we see and we detail out some of the financials behind these AI companies investing into NVIDIA. And the headline figure here, $366 billion in future commitments. And these are based on the latest filing reports from NVIDIA. Now, it's important to explain what that number represents. It doesn't mean NVIDIA has borrowed or spent $366 billion in cash. these commitments, include agreements to secure supply and manufacturing capacity, cloud services, equity investments, data center leases, and other capital expenditures. So some contractual commitments are yet to be realized in the real world, especially with data center build out. And it's also important to note that these can be modified or canceled. And we've actually already seen a couple of arrangements specifically in Texas where we have seen data centers at least put on hold, if not canceled, because some regulators are trying to get a tighter grip on the capital outlay that is happening in the build out. Now, I'm not going to touch on every single one of these, but I do want to touch on the largest category. here in the supply and capacity of 279 billion. These agreements help NVIDIA secure the components and manufacturing capacity needed to produce its own systems as demand grows. Now, Steve touched a little bit on competition. So obviously, we can kind of take a little view inside NVIDIA's executive table here and what they view is important in building out how they view what potential competition could be. And that's why they're spending so much as they continue to build out their own internal systems here. So see, we can go to the next slide here. And this is where we can see the AI financing chain or web, as I had previously mentioned. It can be a little complicated, so don't be too overwhelmed here. But In large, I think the takeaway that I would say is that the diagram brings together this broader point that we've been discussing. There are numerous companies involved in the AI infrastructure, from chip designers, cloud providers, to AI developers and data center operators. Many of them have financial or commercial relationships with one another. And we need to consider how the economics work. lease that infrastructure to supply that capacity. Those providers need chips and equipment from companies like NVIDIA, while also raising capital and securing long-term customer commitments to finance their expansion. So that's why you see these relationships running in all different directions. NVIDIA may supply hardware to a cloud provider, invest in various companies across the ecosystem, and, And we also have AI companies signing large cloud spending agreements with providers that are then connected to NVIDIA. So we kind of tend to focus on a lot of the first degree relationships, but there's a lot of secondhand relationships here, which is really where you start to see some of the risk start to really manifest itself to at least pay attention to the, you know, independent demand that I'd mentioned versus the supply side driven demand. So. you know, I will say none of this automatically indicates wrongdoing or unsustainable demand. Long-term contracts and strategic initiatives are common ways to finance capital-intensive businesses. The concern is that the more interconnected these relationships become, the harder it can be to actually assess financials. each company's underlying financial strengths independently. You know, if one of the participants experiences weaker demand or difficulty obtaining finances, those effects may extend to suppliers, lenders, investors, customers, However, as the chain becomes more complicated, It's kind of like trying to find the needle in the haystack of where did it originate? And that's where, you know, we see the connections to 98, 99, where it did become very difficult for investors to really pinpoint it and it became obscured. Now we're just starting to see very... I would say rudimentary beginnings of these relationships. Obviously, nothing comes out to mind as super risky right now. Steve, did you have any, what do you take away from these relationships that just seem to be growing every day? Yeah, I think, you know, we're making a number of comparisons to sort of the 1999 period. [25:00] But as Kevin mentioned, These smaller companies were provided with financing back then. And what became apparent is by the time we got to 2000, A few of them started to default. And once those defaults started to take place, that's when you had the domino effect and everything began to fall apart. We're probably not even really close to that point. Here, companies are just getting the financing. They're just building out. So it is really early on in the days of AI still. I do believe we were further down the road when that Internet bubble came. However, where there is a parallel is the growth rates now are very similar to what we saw in 1999. The valuations are also very similar to what we saw back then. I will say, you know, in certain metrics, the market is still a bit of a discount compared to 1999. And even though the growth rates are similar, I think we are still earlier on here because many of these companies, as I said, are just getting their financing and they're just starting to build up where by the time we got to 2000. A few of them started to default. So, well, I think default is the only word you need to mention to get people's attention here. And we are probably far from that point right now. Kevin, next slide. Yes, so this next slide is kind of the sister slide to the original one that we had in the first few where Steve took us through kind of the circle wheel of the AI ecosystem and the warning signs that play into that. This takes more of the company view. So it takes really that 10-foot view when you're analyzing specific companies. And as we had mentioned, this does start to become more important as relationships continue to become more complex. So... I'm not going to go through all 10 of these right now, but I do want to point out some of the more important ones. really emphasized that We can't evaluate these companies today based on a single warning sign. It's actually quite rare to not at least see one of these flashing yellow, not necessarily red, but to at least have one of these kind of trip an analyst or investors as they continue to research and analyze these stocks. So for instance, a slowdown in revenue growth doesn't automatically mean a company is in long-term trouble. The risk becomes really more significant when we start to see several of these weaknesses begin to appear at the same time. So if we start to see revenue growth slowing, we're going to be along with earnings per share slowing, And then we see profitability numbers of previous quarters continue to follow suit. That's when we really start to raise the alarms because we're seeing the fundamentals that really drove anticipatory demand really start to deteriorate. Right. The four signals that really... are specific, I would say, to the AI era is when we see expensive valuations continue to skyrocket in the face of slowing growth and poor profitability within that company and increasing financing dependence. As Steve had mentioned, I wouldn't go as far as to say that I've seen this in any of the companies so far, but this is a really direct parallel in the path that several Internet and telecommunication companies did start to take as we got out of 99 and into the early years of the 2000s. Yeah, Kevin, what I would point out, even if you look at the first two boxes, box one and box two, where it shows revenue growth slowing, EPS growth slowing, we are not seeing that at all. Of anything, you know, most of these companies in the IT sector or semiconductor sector or anything that's sort of indirectly related to AI and infrastructure, right? And industrial companies, they are growing at leaps and bounds, and there's been no slowdown in their revenue. growth rates or their EPS growth rates. And, you know, we're coming upon earnings season here. It's been let off by the banks, which are contending with, you know, increasing inflation and yields, and they are interest rate sensitive. We are not expecting any of the IT stocks that are reporting to be impacted by that. And the expectation is most of them will probably be reporting better than expected for both top line and bottom line. So we're not even really close to let alone getting to box 10. So that's why... We are making comparisons to 1999 because the growth rates are similar, but the truth could be that the growth for this AI revolution could far exceed what we saw with the Internet. So we haven't even seen the first two warning signs. [30:00] And But we are seeing the risk in that companies are providing financing, and that definitely raises some warning flags. So, in regards to the current valuation structure, I highlighted that a few times. And instead of going back to 1999, I actually want to point where we are now. with the current growth rates of PE versus the 10-year average. So that would be the most recent 10-year average. And you can see we're using the trailing 12-month EPS growth rates and the trailing 12-month PE ratios. So you can see the current... EPS growth rate is 32.8%. The 10-year average is 14%. And you can see the PE currently is 26 times, and the 10-year average is actually 23.6 times. So, In essence, you're getting... EPS growth that is far, far greater. than the last 10 years, and you're only seeing a valuation that is slightly more. So relative to the last 10 years, it looks like we are having extraordinary growth, and we're actually not paying that much extra in terms of the valuation between 23 and 26 times. So 26 times could be perceived expensive, but given a growth rate of 32.8%, it's probably well worth many investors paying that multiple for that type of growth. So that is looking at the real growth into the S&P 500 for the current levels versus the last 10-year averages. Okay, so are we in a tech bubble? AI is creating a super cycle. So if we look at just the IT sector earnings, this is not the S&P 500 overall. This would just be the IT sector earnings. We could see that the current growth rate, the year-over-year EPS growth rate for the IT sector is 106%. The growth rate over the last 10 years on average was only 17.1%. 89 points higher. That is extraordinary. The multiple, meanwhile, we're looking at a trailing 12-month PE of 31 times versus 26 times. So, you know, You have to say to yourself, wow, for that PE difference just between 26 times and 31 times, you're getting significantly more growth than you have had over the last 10 years. And you might say to yourself, that's actually not that expensive for a market multiple, considering I am getting that type of growth. So this is all relative. Like, we want to look at the market price. versus where it was in the internet phase. But we also want to look at it compared to the last 10-year average as well. So this cover the last 10-year averages. If we go back one slide, Again, you could see where the current EPS growth rate is, the trailing growth rate at 32% versus 14% on that 10-year average. PE 26 times versus 23 times, really not that much more expensive for the growth that you're getting. And then when we look at the IT sector, that is amazing growth, 106% versus the 10-year average at 17%. Taking it to the next slide, the 2026 PEG ratio is at a 26% discount to where we were in 1999. This is where we go back to history. Peg ratio, if you're not familiar with it, it's a metric that I really like. It combines the PE of a company with its growth rate. So you're sort of blending two very important metrics together. Whenever you want to assess a company, you want to assess it on its growth and you want to assess it on its valuation. And you usually do it to the sector or historical average. that we had within the IT sector. It was the advent of the internet with mobile phones and telecom equipment, which all occurred during that '98 through 2000 period as it emerged, very similar to the AI revolution that we're seeing right now. So if we take a look at the trailing PE in 1999, It was 29.7 times. We actually have a trailing PE now that's lower. For 2026, it is 26.6 times. So you can see it's less by a multiple of three times. If you look at the EPS growth rate, the year-over-year growth rate, in 1999, it was 27.7%. Compared to now, in 2026, the EPS growth rate is 32%. So you're looking... In this comparison, the valuation is lower in this current environment and the growth rate is higher. That's exactly what investors look for. They want a valuation level that's more attractive and they want growth that's higher. [35:00] And when we combine those PEs and the growth rates, we actually have a peg ratio for 1999 that was 1.07 times. And the current peg ratio is 0.81 times. So that puts it at a 26% discount to where it was back in 1999. So, again, faster growth. Cheaper valuation actually looks more positive than it was in 1999. And without further ado, I think we should dive into NVIDIA at this point. Kevin, I'll hand it over to you again. Yeah, and I think... NVIDIA is pretty much the bell cow for why this time could be different. So right here, we see that the selection of the quant data and what it tells us about NVIDIA, it has continued to maintain that strong fundamental profile supported by the exceptional revenue growth, profitability, and its position at the center of AI spending. strong buy rating. And this is the first sustained period of a strong buy rating in over three years. And I think that that is pretty significant. This is fueled by an exceptional growth grade of A+. And, you know, It's evaluation grade. has actually come back down. We're going to see that visualized in a different way here on the next slide. But this is a really good overall look at how fundamentally strong NVIDIA is as a company. And we're going to see that that is important because a revolution can only be as strong as position as they continue to invest in real AI demand as we continue to see those commitments grow. So, Steve, if we go. I'll add in here just to provide a little bit of color. As you can see from this history, the stock has been a hold for quite a long time, and it's been a hold during a period where the stock has performed very well from 2024 all the way to this recent period. So you may be asking yourself, why did it just get a Well, there's a very good reason. We have a very good quant track record in terms of performance on our products. And it's important to know we only recommend a stock as a strong buy if it is collectively strong on value, growth, growth. profitability, momentum, and positive analyst revisions. We won't have that stock as a strong buy if we feel it's overvalued. So indeed, there could be instances where a company's stock does do well, but simultaneously, it could be overvalued, and that's not part of our model. Now, what's really attractive about this is if you're looking at those valuation grades, and these factor grades are all sector relative. So when you're looking at it, the valuation is relative to the IT sector. The growth is relative to the IT sector. So it gives you an instant characterization from our quant system where a company stands versus other companies in that sector. So you know right away when you look at the growth and profitability, this company, NVIDIA, is very attractive versus the sector. But what we also do here is we show where the metrics were three months ago and six months ago. And you could actually see NVIDIA just six months ago it had an F grade. It was a hold during that period. It was very expensive. But earnings have really climbed recently. The price of the stock has actually declined over the last couple months. So the valuation becomes a lot more attractive. You have rising earnings, falling stock price, That creates a more attractive PE. And you can even see the company's growth grade now is A plus versus six months ago was an A minus. So the growth has actually improved. And it's already far ahead of the rest of the IT sector, but it's actually gotten better while the valuation has gotten cheaper. Kevin mentioned profitability before. The company is extremely profitable. So it's for these reasons that the stock is a strong buy now. That's why it was a hold. Kevin, I'll take us to the next slide and hand it back to you. Yes, and since we were talking about that PE and NVIDIA's valuation, we can really visualize that here. And it has become one of the more interesting developments, specifically NVIDIA. As we have talked about this last three, six and even year to date in 2026 as well. Investors are not assigning the same premium to each dollar of expected earnings that they were willing to pay during those periods of peak enthusiasm that you can see here on the chart in 2022 and even at the tail. [40:00] end of 2023. NVIDIA's forward PE ratio was below 20 times expected earnings compared with the historical average of approximately 30 times over the last 15 years. So that is a significant change in how the market is valuing the company. And as Steve said, this is all primarily being driven by the increase in the denominator of that P.E. ratio as earnings has continued to exponentially increase as the price has at best kind of remained stagnated for much of this year. last two quarters. So see, we can go to the next slide. And we have here a view of our quant system detailing out its evaluation resets. And, you know, when we say NVIDIA may be on sale, that might be a little tongue in cheek. But, you know, it's not suggesting a lower multiple automatically makes it a bargain. Right. There are a lot of things that go into an attractive stock in the fundamentals that Steve has just mentioned with our five factor grades. the underlying business. The market has become more cautious about AI spending, competition, and the returns on the enormous capital being committed to the Dana centers. And I think we're starting to see some of those concerns at least a little bit materialize into the fact that the price has not kept up its pace with the earnings growth that we have seen. So this does A lower valuation multiple can obviously provide a more attractive entry point than investors had when expectations were considerably higher in comparison. So, you know, as we can see here on the chart, we have the P.E. ratios that are showing rise. upwards of 40 and 50% discounts compared to his five-year average. And as Steve had mentioned, It's not only beating its own sector competition here in terms of these multiples, it's beating itself. So even if it's competing against the number one peer in the industry, which is itself, it's not. It's crushing that as well. And that's something that is very hard to discount when you're continuing to look at the health of the company as a whole. Yeah, Kevin, I think, you know, if you took a look at this, table just six months ago, it was a sea of red. Really, the only thing that stood out as an attractive valuation was the peg ratio. And that continued to sort of be in the B plus to A territory. That's where you combine the PE and the growth. But many of these other metrics were in the red domain. And again, these academic letter grades here, they give you an instant characterization of where the company is on that metric versus other companies in the sector. So when you see of let's say the Peg on a forward basis, the very last one, you can see the multiple is 0.49 versus the sector at 1.25. So it's at a 61% discount to the sector on a peg, hence that green A grade there. And as Kevin pointed out, you're not only getting it at a discount to the sector, but you're getting it at a huge discount to its historical five-year average as well. So that really tells us the stock has been re-rated. Investors have spoken. that NVIDIA is providing funding. So the share price has come down. But the valuation actually is very attractive now compared to where it had been. Thank you. Kevin, next slide. Yes, and as we see that, we see that the growth... is continuing to outpace the stock appreciation. We see here the quarterly revenue has increased from about $57 billion in November of 2025 to $68 billion at the onset of this year. And in this latest quarter, it's all the way up to $96.2 billion as of August 2021. And what we can note here is that the latest quarter included approximately $89 billion in data center revenue alone. So we're really looking at a business that has more than demonstrated that demand for its products is real. And if you see on the right here, these figures, we see $89 billion. that in the face of revenue growth and the forecast even out, approaching 70%, the one-year share price has risen 30%. So this all kind of comes back to the fact that we are seeing – price appreciation when you expand that timeline out. However, when you start to go from year to date to six months to three months to one month, you're starting to see that share price growth start to be trimmed off a little bit. However, the earnings growth is kind of moving in that opposite direction. So, That is what we're really looking for here with revenue growth and how it combines [45:00] into that earnings growth along with margins that we're going to talk about here in a little bit. Kevin, I think I'll speed us up a little bit because we're at 446, and we definitely want to take some questions. This table, which you find on any of you are a premium subscriber or a pro subscriber, you simply put the ticker symbol of the stock in, and you can click on the growth tab. And this is, as it highlights here, it's just a straight A report card for NVIDIA's growth rate. Whether you're looking at forward reviews or EBITDA or EBIT or EPS, just unbelievable numbers, especially for a company of this size. And as you see, those A grades indicate that the growth is far stronger than the sector across the board. And, you know, even with some of the historical averages, the growth is superior as well, too. On our next slide, what the market is pricing in right now, it's pricing in slower industry spending going forward. That is one of the reasons why the stocks are being re which really started at the end of May Hyperscalary CapEx growth cannot accelerate indefinitely And the market investors and traders are reflecting that in the share prices even though in reality the companies are just generating an incredible amount of revenue and earnings. For number two, you see margin compression. Competition, in-house development, and infrastructure costs may pressure margins. We have seen the threat of that just barely with a couple stocks. But for the most part, I would not say that's wide scale yet because companies are making so much money. But, of course, they will hurt their margins if they continue to provide financing. And then we have number three, that transition to the financing dynamics. NVIDIA's role as a supplier, investor, and sometimes customer may magnify risk if the returns are disappointing. and that's what the marketplace is factoring in now and since June. And that's why we've had a re-rating in the valuation and the share price coming off. Kevin, I really like this one, but I got to give you credit for putting it together. Asked Kevin to sort of put together an AI valuation table where you see the PE compared to the Ford EPS I think he did a great job Go ahead Kevin I let you highlight this Thank you Steve So as he had mentioned the chart helps really frame NVIDIA's valuation in the context of the expected earnings growth along with its peers. The figures shown put NVIDIA at around 24 times forward earnings with the expected earnings per share growth at around 74%. And when we're looking at the chart here, we really want to see these companies on the left side of the x-axis here. And you'll see Micron here at the very top with just, quite frankly, unicorn numbers, exceptional numbers, whatever you want to call it. That's very unusual. However, we do see NVIDIA closer to its peers, but at a much more reasonable and healthy, really peg ratio as we kind of look at this relationship here compared to some other peers like AMD, which are starting to actually see some of its valuations become a little bit more stretched. Yeah, I really like this dot chart here. And again, Micron, really, it does stand out. It an outlier but it an outlier in terms of its attractiveness The forward P of the stock is 5 times That is an incredible discount to the sector and it has unbelievable growth So even if you felt the Ford EPS growth rate would decline, the stock is still dirt cheap. At minimum, it should probably have a multiple of at least 10 times compared to the 5.61 times. So really an outlier there. But right underneath it, as Kevin highlighted, we have NVIDIA. Gross profit margins, very important, especially when you get into a period where there's a lot of uncertainty in the markets and there's a sector rotation to safe havens. Investors typically like to focus on the quality of a company and the profitability. Growth and momentum become less attractive as factors, and margins and profitability become more attractive as factors during a period of uncertainty. And when you take a look at NVIDIA's profit, it is a straight-A report card. Whether you're looking at the gross profit margin, the EBIT, or the net income margin, it is well ahead of the rest of the group, hence the A-plus grades and just huge, huge margins. What if the market is wrong? [50:00] Kevin, I'll hand it back to you on this one. Yeah, so what if the market's wrong? And this really focuses on the compelling aspects of NVIDIA setup and the fact that analyst estimates continue to rise despite the repricing in the stocks valuation that we have seen. What you'll see here are the analyst earnings and revenue expectations and the revisions that we have seen going all the way out six years. you. When we see these revisions, the suggestion that analysts continue to raise expectations for future business, even as investors become a little bit less willing to pay that premium for those earnings. so We do get to a point, though, where if estimates can become too high, the market may not be pricing in all risks, even if it's pricing in what we believe to be a healthy amount of risks. We could start to see some of these numbers come down. I'll tell you what, if they even get close to these numbers, it'll be incredible, and it'll be far more sustained than we saw during the Internet and TMT era, where it started to come off sharply in the year 2000 as we hit those defaults. So it would be absolutely amazing if we saw... sustained. Yeah. Okay, so there are still some risks that remain. AI spending slows more sharply than expected. And we're, you know, seeing a number of different levels where that could be possible. There is a lot of political opposition to data centers, so that has slowed down data centers. Not in my backyard. It is having an impact. As you can see, the share prices of many stocks have fallen since June. A lot of that has to do with the financing structure that's put in place from alternative financiers, mostly the companies themselves instead of commercial banks and investment banks. that if they continue down the road providing this type of vendor financing, their stock prices will continue to suffer. And at some point, the C-level executives are going to say, okay, it's not worth the financing because we're paying the price at our own share price. So there could be a slowdown in financing on both of those levels. So there is still risk that there could be a slowdown. But the slowdown may not necessarily be a bad thing. valuations level with still strong growth, not necessarily pie-in-the-sky growth up. Kevin, I'll hand this next slide over to you. Yeah, absolutely. And, you know, as we come here to the conclusion of the webinar, this kind of encapsulates what we've been saying. What Steve's saying is that a slowdown may not be bad. And, you know, in my view... we see here that the AI landscape we're potentially going through, it could be a soft landing, right? And we hear that terminology a lot. with the economy and recessions. And You know, when a soft landing is maybe a little bit slower growth, but something that is digestible as a consumer or as an investor. But I think the takeaway here that a soft landing doesn't mean there won't be ups and downs or there might not be short term volatility or short term pain in markets. You know, when excess is built up, expectations have to reset. Capital gets slightly reallocated. and some of the weaker business models may become exposed. The difference is that these adjustments happen more gradually, giving the system time to adapt, rather than forcing everything to unwind at once. So in my view, This could be a healthier outcome here. Obviously, I think as Steve would point out, we're not seeing a decoalescing of these risks factors quite yet. However, That takeaway for me is that the adjustment doesn't necessarily have to be perfect for it to be healthy as we continue to navigate the coming years. Okay, so that takes us to the end of our presentation. I did highlight before that our quant system is very good at picking stocks, and we have two products that are very different that I do want to tell you about very briefly. One on the right-hand side, it's called AlphaPix. That's sort of a flagship quant product. It's been out for over four and a half years. And if you're looking for capital appreciation with our BX, [55:00] - That's QuantStrong by Ideas. That is a very good product to have. So twice a month on the trading date closest to the 1st of the 15th, We will send you an email and also post on the platform. the recommendation. That has worked out very well. So nice frequency based for long-term investors seeking capital appreciation, just two new picks a month. On the left-hand side, you'll see the Quant Growth and Income. Quant Growth and Income uses the same quant model that we use for Alpha Picks. The only difference is that the companies must have a dividend. So for investors that are well the current environment That's the product that you want. AlphaPix provides two ideas a month onward, and the Quant Growth and Income is actually a fixed portfolio of – 30 stocks and it rebalances twice a month. And actually what I want to do is take you into our platform and So you can actually see the product itself. This is the AlphaPix platform. And if we take a look, you can see on the right side the performance of AlphaPix up 349% versus the S&P 500 up 106%. And that's since inception from July of 22. So despite this difficult environment we've had this year with risk on and risk off, AlphaPix has provided really nice returns. And you'd see a couple of the recent reports. stocks that we recommended there. I'm also going to pull up the performance of some of our best performers. You can see Celestica, which we put in there on October of 2023, is up 1,000%. Powell Industries up 830%. Sterling Infrastructure up 625%. And Apple 11, 517%. Quite a few of our stocks are up triple digits. So AlphaPix is doing a great job presenting the best quant strong buys. Now I'm going to take us over to the quant growth and income. Now, this product actually just started in June of this year. So as the IT sector is going to heck in a handbasket, this product has actually performed quite well. You can see the portfolio return there. Again, the common thread, these are quant strong buys, but all of them pay dividends. We use the benchmark of the Vanguard High Yield Dividend Index. So since June 3rd, that index is up 0.04 compared to the Quant Growth and Income Quant product. It is up 11.47%. And you can actually see the chart on how it really just started to excel almost right away and continue to do well when the benchmark even went into the negative domain. today and you don't want to rely just on attending your webinar these are two products that are available one offering income generation and capital appreciation, and the other purely going for the best stocks and capital appreciation. I'm going to take us to our next slide. I believe that there is a bundle that is being offered now. We rarely offer these bundles. But if you want to sort of have what I call barbell approach, which is – a very attractive product offering during periods of uncertainty and volatility. It gives you the best of having stocks where we want to highlight that have excellent fundamentals, but may be coming off because of the current environment. So stocks like NVIDIA, which have a strong buy, have obviously been weak, but we think you should be opportunistic when looking at that. And then on the other side of the portfolio, we have the income generation with companies that Stocks that pay dividends sort of pay you to wait, so they don't typically come off quite as much, or they may be up, as we've seen with the Quant Growth and Income Portfolio, being up almost 12% from June to this period in time. And I think that brings us to our Q&A period. It's at 5 o'clock, so I do want to take in a couple of questions, if you're still on, leading off with, what would be the next stage of the AI boom? phase, I believe. Well, I think NVIDIA, based on its attractive valuation and growth rate, probably will have a few more stages. But of course, there are a couple of IPOs that'll be coming out with Anthropic and OpenAI. We've had the IPO occur this year. of SpaceX. So these are some of the newer, larger players that are now becoming publicly traded opportunities. And what the marketplace is going to be looking for at some point is real earnings that are coming through on these companies. They obviously have [60:00] very strong and big revenue basis at this time, but the market's going to be looking for real earnings. I believe SpaceX had a negative one penny for its last quarter, but I believe it's projected for 2027 for the full fiscal year to be at $1.99. So that's a very big movement from a negative one penny, and we'll have to see what happens with AdTropic. Another question that we have... So is let's say, have you done a comparison of NVIDIA's trajectory with Lucid and Nortel as they were rocketing higher and then had their downfall? Could you project any time frame of there was a dot com similar downfall that might occur? I believe we really did have a chart in here that sort of showed. Nortel and Lucent as well, which we threw in here. You can see where those stocks traded at one point. Here we go. Lucent Tech and Nortel really had quite a run-up in the 98-99 level going into 2000. Both those companies, and Nortel did have massive market share with routers. I believe Cisco had that market share as well. share prices being rewarded during the early time period. But as soon as companies started to default on their debt payments, both of these companies were very big vendors and financiers, and obviously the share prices were... really took it on the nose. And you could say there might be similarities. As Kevin pointed out in the presentation, NVIDIA has huge market share at this period, and their revenues and earnings have gone up tremendously. The real assessment will be here in the upcoming quarters. Will the earnings growth be sustained? Will it come off? And if it does come off, will it be because there potentially could be defaults from these smaller companies that have ride of the financing. So only time will tell. But yes, we definitely made the comparison here. to Nortel. Hopefully, NVIDIA will not suffer the same fate. Let's see. We're going to say, is this revenue growth already priced in and with broader market risk flattening the NVIDIA price? Well, certainly, I A lot of the growth had been factored in, and investors are beginning to factor in slower growth, even though that slower growth has not occurred yet. So the market is forward-looking, and since June, investors have been basically, Basically concerned about NVIDIA becoming a financier or a lender of last resort and projecting that at some point that could hurt their earnings. But as Kevin pointed out, a lot of the estimates that are out there now, they continue through beyond 2030. And consensus from Atlas is just for earnings and revenues to go substantially higher. So, again, only time will tell there. the risks have been discounted into the stock. Let's see. We will go for... One more question. Also, will the same plateauing apply to Micron, Intel, So Micron is super, super cheap now at five times. If we go to the CK Alpha platform, I will take you to Micron.com. Thank you. And what I want to highlight here, I'm going to go to the valuation page. And you can see the Ford PE on Micron is 5.4 times. The multiple for the sector is 22 times. So Micron is at a 76% discount to the sector. But if you look at the PE and the five-year average PE for Micron, It was 74.25 times. So it had been very, very expensive. And right now, at a multiple of five times compared to 74 times, it's at a 92% discount to its historical average. And we can even look at some different metrics. If we look at, say, EBITDA. It's currently at four times versus the sector of 14 times. So it's a 71% discount to the sector. And its five-year historical average is a multiple of 11 times. So at a current EBITDA multiple four times, that puts it at a 63% discount to the sector. When we look at growth, It is an A-plus report card. So forward growth, when we look at forward growth, it's actually the consensus of Wall Street analysts. We're not using our own growth model. We're bringing consensus. You can see the forward growth. [65:00] Review growth is 104%. And if you look at the EPS growth rate, it is 191%. So this stock was down 4% today. If we look at a chart, We could see its peak level around June was $1,200. A share is currently around $914. So the stock is definitely re-rated. And today was no exception with the shares being down 4%. A company is due to report their earnings. A company is due to report their earnings. Thank you. December 16th. And in the last 90 days, 29 adults have taken their earnings estimates up. For Micron, only three have revised it down for the fiscal year and for the upcoming quarter. Pretty much the same. 25 analysts have revised their earnings estimates up. And what that means is that there are 25 analysts out there that actually their earnings estimates were too low. So they increased their earnings estimate. So that's what it means when we say 25 analysts revised their earnings estimates up and only three analysts revised down. So, again, both of these companies showing incredible growth. with valuation frameworks that have gotten cheaper over the last couple of months. And of course, you know, they've gotten cheap to reflect the concerns of the marketplace. historical standards and by comparison to the sector, they seem very inexpensive at this time. I'd like to thank everyone for attending today. I know we went a little bit longer than expected. Hopefully, you found this very informative, a little bit of a history lesson, a little bit of an overview of the markets, where we were then and where we are now. And I hope you put this to get use in making investment decisions. On behalf of CK Alpha and Kevin, thank you so much for your attention today. We appreciate it.