Hey folks!

Sorry this one took so long, it has been a lot harder than I expected it would be to write while also watching the World Cup.

Before getting into it, I just wanted to say thank you (for the hundredth time) for the engagement and support I’ve received on Substack since joining back in March— we’ve gone from 40 subscribers in April to over 1,300 as I write this.

In a post on 5/14, I announced that the first 1,000 subscribers would get 2 months of paid subscription for free whenever I decide to launch that tier. I was at 380 subscribers at the time. Just two weeks later, much quicker than I expected, I reached 1,000. While I’m, of course, very grateful and excited about this momentum, it made me feel like I should keep this promo going to continue to show my appreciation for all those who have opted to tune in to what I have to say.

So in that vein, I will also be giving subscribers 1,001 to 2,000 1 month of paid subscription for free. I’m still working out the particulars of how this is going to look, but I hope to have more details to share very soon.

With that, let’s get into it!

The Memory Money Printer

In this piece, I’d like to dive back into data center memory as a continuation of my previous post on Sandisk (SNDK), discussing why I think it could be the unlikely winner of the RAMpocalypse:

Note: SNDK is now up 65% just five weeks after the publication of this post as AI NAND SSD demand has continued to rise.

In that article, we traveled through a brief history of computer memory and detailed the relevant types in the modern data center, including hard disk drives (HDDs), NAND flash solid-state drives (SSDs), and dynamic random access memory (DRAM).

I’ll provide some brief recap here, but if you want some additional context, I recommend reading the above article before continuing. And, per usual, I’ll be focusing on AI data centers specifically in my analysis.

So to sum it up:

HDDs are the archives of AI training— they offer an extremely low cost-per-bit, which makes them the only viable choice for storing the immense amounts of data that frontier models use for training and development.

NAND flash SSDs are the intermediate storage layer for AI workloads. Since HDD read-write speeds can be more than 100 times slower than SSDs, the latter is absolutely necessary when the objective is speed, which is most often the case in AI processes. These use cases include checkpointing during model training, handling key-value cache (context) and parameter overflow during inference, and maximizing GPU utilization by keeping nodes fed with whatever datasets they require as fast as possible.

Last but not least, DRAM is the McLaren that actually runs the mind-bogglingly complex math that AI uses to weigh inputs and compute outputs. This category includes high-bandwidth memory (HBM) on the GPUs, which does the brunt of the calculations, and DDR5, which handles the “agentic loop” (e.g. calling external APIs, running databases queries, etc.) and also handling the DRAM-dependent operations that are too large for HBM to handle.

This is the memory hierarchy that makes the AI data center tick. And it is absolutely printing money. For a few examples, over the last 12 months, the main HDD makers, Western Digital (WDC) and Seagate (STX), are up 800%+ and 600%+, respectively, NAND maker Sandisk is up 4300% (!!), and South Korean NAND/DRAM giant Samsung is up nearly 500%.

These meteoric returns are being driven by acute supply shortages across the board as hyperscalers have been buying up any and all memory modules for data center build-outs, which are happening at a breakneck pace:

AI capex from just these four companies alone is expected to surpass $600 billion in 2026, and, in fact, recent earnings reports indicate that the above chart is likely underestimating the true number. Add in Oracle (ORCL), neoclouds, and other data center operators, and this number is probably close to $1 trillion. With the pace of AI’s progress, we are almost certain to eclipse this number in 2027.

This is the demand environment, but the supply environment is equally as important— when AI demand began picking up steam, DRAM and NAND were still barely clear of a pretty severe downcycle during which the above companies were weathering negative gross margins (i.e. they were losing money on each unit shipped). So when data center demand came knocking, memory capacity was cold, utilization rates were low, and supply was scarce. In a nutshell, the mother of all demand booms happened when the memory suppliers were least ready to handle it.

At the crossroads of the RAMpocalypse sits a company that isn’t the king of any market, but maintains a modest piece of the pie in both DRAM and NAND— Micron Technology (MU).

I wrote my initial coverage of MU back in September 2024 and initiated the stock at a Strong Buy @ $85 per share on my expectations for an upcoming memory supercycle (gift Seeking Alpha article can be read here). I also bought a sizable position at that price that I have added to on dips on the way up. This call has returned more than 1300% over that time.

In this article, I’d like to take a close look at Micron and the DRAM market as a whole to answer the question on every investor’s mind: how high can this baby fly?

Just a quick note before we move on that this piece will cover a lot more than just Micron. I think that to truly understand these markets and make sound investing decisions, it’s vital to understand the technology before we do any stock picking.

DRAM vs. SRAM: Need For Speed

If you’ve been following the AI revolution long enough, you’ve likely seen the terms DRAM, SRAM, DDR5, HBM, etc; understanding what these terms mean and how they all fit into the data center is vital to forecasting how the supply-demand dynamics of the memory market will progress in the years ahead. Let’s begin with DRAM, specifically what DRAM is not.

First and foremost, DRAM is not storage— it is a form of volatile memory, which essentially means that data lives only as long as the system maintains power. If power cuts out, everything is wiped. In contrast, SSDs and HDDs are forms of non-volatile memory, which retain data integrity even when they are unconnected from a power source. The 1 transistor, 1 capacitor (1T1C) design of a DRAM cell has remained unchanged for more than 50 years because it is cheap, performant, simple (relatively), and blazing fast.

DRAM (which recall stands for dynamic random access memory) is “dynamic” because the capacitors that retain charge and represent bits—charge means 1, no charge means 0—will leak electrons over time and need to be refilled lest data be lost; the memory controller continuously reads every capacitor and tops up the charge if it’s running low. While this sounds complex, and it is, the economics of DRAM are extremely favorable.

Because each cell is just 1T1C, DRAM is significantly denser than competing memory frameworks, namely static random access memory (SRAM). SRAM was actually invented before DRAM (both by IBM’s Robert Dennard) but each cell requires 6 transistors for each bit of data, making it vastly more expensive per bit. However, as its name implies, SRAM does not need to constantly restore charge because it doesn’t utilizes capacitors in its design:

You can see just from the diagram above why DRAM is denser— the cell is simpler and takes up significantly less space; but what SRAM lacks in density, it makes up for with raw speed, achieving 10x to 100x faster read-write speeds than DRAM.

Because of its costliness, SRAM has historically been mostly relegated to applications that require speed but not a lot of capacity, like CPU and GPU cache, and, as such, represents a tiny fraction of total memory bit shipments (<1%). However, SRAM has been making waves in AI inference as a way to max out bandwidth between GPUs and, in so doing, provide lightning-fast output.

Cerebras (CBRS) and Groq are the two main players trying to use SRAM to fill an AI inference niche, but with very different approaches— while both achieve bandwidth upwards of 10-20 petabytes per second, Cerebras achieves these results by fabbing all chip components onto a wafer scale engine (WSE) that boasts 44 GB of SRAM and Groq takes a divide-and-conquer approach that utilizes high-speed networking to connect hundreds of chips that each have around 500 MB of SRAM.

The idea in both examples is to store as many model parameters and as much of KV cache on SRAM as possible to speed up inferencing. However, this comes with some major drawbacks:

First, as mentioned, SRAM is much more expensive per bit so Cerebras and Groq racks have a lot less memory than other AI accelerators in general; AMD’s MI450 series boasts 432 GB of HBM4 per GPU, roughly equivalent to 864 of Groq’s LPUs in raw capacity terms. To repeat, one AMD GPU, has the memory capacity of 864 Groq LPUs. Groq is magnitudes faster, of course, but for any decently-sized model or context window, the math on using a SRAM-based array math gets expensive very quickly.

Second, SRAM is hitting a physics wall— while new nodes continue to give DRAM chips a substantial reduction in form factor and a boost in performance and power efficiency, SRAM advances have been much more muted. To illustrate, current-gen DRAM (DDR5) is about 30% denser, uses 20% less energy, and is 30% faster than previous-gen DDR4 (ballpark estimates). In contrast, current-gen SRAM runs on TSMC’s (TSMC) N3E node and has the same SRAM cell density as the previous generation, which means it’s ~0% denser, uses ~0% less power, and is ~15% faster due to other optimizations.

TSMC’s upcoming N2 node bucks this trend with the inclusion of gate-all-around (GAA), but this is likely to be a one-time performance boost as the SRAM wall continues to loom. Part of the reason for this divergence, is that DRAM’s simplicity and density allows it to scale vertically—HBM4 can have 12 or 16 layers stacked on top of each other—while SRAM is currently locked into the 2D plane (i.e. the former has more runway to continue improving and expanding and the latter could be destined to lag behind).

Bottom line is that DRAM is currently dominating the AI data center because it’s fast enough for most applications and the cost-per-bit is so much lower than SRAM that it’s not even much of a competition. This race has gotten closer as DRAM prices have surged, but SRAM capacity was already so low on account of the niche applicability that it’s not like SRAM manufacturers can scale up to take market share anyway. No, DRAM is the king of AI build-out and will continue to be.

Okay, okay but why am I telling you all this? This post is supposed to be about Micron! Patience, dear reader, trust me that this is all very relevant. Kind of. To be honest, I just like writing about this stuff— a deep dive of the technical differences between SRAM and DRAM probably aren’t all that important to understanding the Micron bull thesis. But aren’t you glad we went over it anyway in the name of intellectual curiosity? Oh, you’re not? Okay, oops my bad, let’s get to the good stuff, then.

AI’s Memory Workhorse

Within the wide world of DRAM in 2026, there are three kingdoms: DDR5, LPDDR5, and HBM4. The numbers don’t really mean anything and are just marketing for denoting when a new generation of memory chips hits the shelves (or the racks in this case).

DDR, which stands for double date rate (not Dance Dance Revolution), was the first of these products to be released and it gained traction in the late 1990s as extremely performant CPU memory. Fast-forward to the present day and we arrive at DDR5, which is the current industry standard and launched in 2020— this represents an overwhelming share of total DRAM shipments today and is used as the high-velocity memory store for CPUs in everything from consumer PCs to AI data centers. In a sentence, DDR5 is the highest-performing general-purpose DRAM available today.

However, not every application requires the best performance, typically because that also comes with the highest power draw. So, back in the mid-2000s, memory manufacturers started to tweak the DDR design and came up with LPDDR or low-power DDR. Historically, LPDDR featured smaller components and better energy efficiency at the cost of needing to be soldered onto the base silicon. This meant it was performant but not modular, which made it great for mobile devices but was a dealbreaker in a data center context where components are constantly replaced and upgraded.

High-bandwidth memory, or HBM, is another beast entirely— while DDR and LPDDR serves as memory pools for CPUs, HBM is specifically designed to maximize throughput to parallel processors like GPUs or other AI processors. HBM1 came out back in 2015 consumer and enterprise graphics cards, but has seen a surge in demand as AI accelerators scale to hundreds of GB of DRAM per chip.

If you’re looking for a primary driver of the RAMpocalpyse, you found it in HBM. In response to the blistering demand from hyperscalers and lucrative gross margins, the Big 3 of Micron, SK Hynix, and Samsung have been feverishly building out new HBM capacity, converting existing DRAM capacity to HBM, and even eschewing NAND production for HBM in many of their upcoming fabs. HBM also uses 3-4 times more silicon wafers than run-of-the-mill DDR DRAM, all of which has contributed to the supply shortages we see throughout the memory market today.

But before we get into all that, let’s discuss what these DRAM products bring to the table when it comes to the big picture of an AI data center, starting with DDR and LPDDR since they essentially serve the same function: CPU memory cache.

Remember how I said that LPDDR wasn’t viable in the data center because of its lack of modularity? Well, Nvidia (NVDA) decided to accept that as a challenge. Back when it was selling x86-based Hopper compute trays (H100 and H200), Nvidia shipped all of its racks with DDR5, which worked fine enough but was always just a stop-gap to something much more ambitious: the superchip.

In May 2023, Nvidia unveiled the Grace Hopper 200 (GH200) superchip, which had its own ARM-based Grace CPU tied together with 8 Hopper GPUs by its NVLink-C2C fabric, offering massive bandwidth and performance. However, there was something different about this server rack compared to its predecessors— instead of DDR5 as the CPU-side memory, the GH200 used LPDDR5X.

What the heck? Why did Nvidia decide to use memory originally designed for smartphones in its AI data center servers? Two reasons: bandwidth and heat.

DDR5 is typically connected to a CPU using dual-inline memory module (DIMM) slots with copper traces. But to achieve the bandwidth that Nvidia wanted for the GH200 would have required a huge number of memory channels, all with copper traces that run to separate DIMMs. This would use a lot of power and generate substantial thermal output, both no-nos in AI servers.

Instead, Nvidia turned a historical weakness of LPDDR in the data center—that it needs to be soldered onto the substrate—into a strength. Instead of designing a tangled web of copper traces to reach DIMMs with DDR5, Nvidia engineers plopped LPDDR5X right next to the Grace CPU and utilized its high bandwidth to feed the chip-to-chip complex and its low power to avoid overheating the rack. A truly elegant solution.

However, while this solution worked to avoid heat build-up and achieve higher bandwidth, it still didn’t solve the modularity issue. That is, if the LPDDR5X in a GH200 superchip dies, you can’t just replace it with a new one on account of the whole “soldered to the motherboard” thing, which meant that the Grace CPU and the Hopper GPU would be bricked too. Hyperscalers didn’t love that but they accepted the trade-off since LPDDR-based servers provided the other aforementioned benefits.

The Grace Blackwell superchip (GB200) had the same pros and cons, but the modularity issue became even worse because, while a GH200 had 1 Grace CPU and 1 Hopper GPU on the same board, the GB200 had 2 Grace CPUs and 4 Blackwell GPUs. So every time the LPDDR5X died in a GB200, you lost all of that silicon. Not cheap.

LPDDR had become an essential driver of the data center build-out and there were no alternatives on the horizon, so Nvidia decided to take on another challenge— what if LPDDR could be made modular? And so, SOCAMM was born. The acronym here is long and unhelpful so, suffice to say, SOCAMM enables LPDDR memory to be slotted into a board in the same way that DDR can be slotted into DIMMs.

This has the benefit of keeping expensive hardware running even if a memory component decides to short-circuit with the minor downsides of using 5-10% more power, generating a bit more heat, and adding a small amount of latency to the system. All worthy sacrifices for the cost and operational benefits. Nvidia plans to incorporate SOCAMM2 into its Vera Rubin racks coming in 2H2026.

Nvidia's adoption of this technology is expected to push LPDDR bit shipments for AI servers close to smartphone levels by 2027 and likely beyond in the years that follow:

However, LPDDR is far from the most popular choice in AI data centers. In fact, for current build-outs, it’s fair to say that Nvidia is really the only player that has eschewed traditional DDR memory. The reasons for this bifurcation are plentiful but it mostly comes down to differing design objectives.

As we discussed, LPDDR is very good for power efficiency and bandwidth; in AI workloads, these are important benefits. But what about a custom tensor processing unit (TPU) deployment at a Google (GOOG) data center? One of the ways hyperscalers save money on costly data centers is by mixing and matching components from different suppliers— DDR memory has been used in this arena for years and it “just works” with all these different moving parts.

Another factor here is the sheer pace of development. Nvidia is designing a new server rack every year with updated CPU and GPU configurations, new memory designs, and overhauled specs, meaning it can react extremely quickly to changes in market dynamics. Meanwhile the custom silicon initiatives at hyperscalers can take upwards of 5 years from planning to mass production, so it’s a lot easier to plan for widely available technologies like DDR rather than count on LPDDR, which was unproven in AI workloads (until now).

It’ll be interesting to see where the industry goes for the next generation since Nvidia is usually a pioneer for new AI technologies— will LPDDR be the future of AI data center deployments? AMD is using traditional DDR5 for its Helios rack-scale deployment, sporting its MI400 and MI450 series accelerators, releasing in 2H2026, but is aiming to support LPDDR5X for certain products by 2027 as power efficiency and heat dissipation increasingly become top priorities.

From my perspective, it looks like LPDDR is the future of AI data centers, specifically in inference and agentic workloads where CPU memory bandwidth becomes more of a factor. I expect the power savings to push hyperscalers into LPDDR designs for their in-house silicon and for memory manufacturers to scale up LPDDR production significantly.

Recall that this has only been a discussion of DRAM for CPUs— AI workloads by their very nature are astronomically more dependent on GPU DRAM, or HBM. Let’s take a peek inside:

If you saw this diagram and thought “hmm they just put more memory on top like a DRAM skyscraper,” well, yeah, that’s exactly what they did. These DRAM slices are connected to each other and the substrate by through-silicon vias (TSVs) and operate as a single memory reservoir. This density is not only beneficial for AI workloads— it’s necessary.

HBM is the top of the pyramid in the AI data center memory hierarchy:

Every operation that can be done on HBM, is. Only when a workload requires more memory than this pool does it spill to the lower tiers of the pyramid like CPU DRAM or SSD. In a Vera Rubin rack, each Rubin GPU has 288 GB of HBM4 for a total of around ~20 TB per rack. In a Helios rack from AMD, that number is even higher— an MI455X GPU has 432 GB of HBM4, good for a whopping 30 TB per rack (both have 72 GPUs per rack).

And these modules are not cheap. HBM is the second most expensive component in an AI server rack behind the GPU itself and can cost a magnitude or two more per GB than DDR or LPDDR: the chips require TSMC’s (TSM) chip-on-wafer-on-substrate (CoWoS) advanced packaging tech to stack the HBM and the logic die on the same substrate, have low yields due to their complexity, require more wafers to produce than typical DRAM, and are in extremely short supply.

I could go on, but this is the current lay of the land in data center DRAM. Let’s bring this back to Micron shall we?

But first…

Jack Of All Trades

For those who are unfamiliar, Micron is the American foothold in memory. In any given quarter, the company controls about 20-25% market share in DRAM and 15% market share in NAND. It’s two main rivals are South Korean megacorps Samsung and SK Hynix, which together control around 80% of the DRAM market and 55% of the NAND market.

Everyone is talking about Micron now because the stock has risen 800%+ in the last 12 months, but back in late 2024, before the RAMpocalypse took hold, the company was valued at just around $100 billion. Then operating results began to explode. If guidance for the upcoming quarter (expected this Wednesday) holds, we’re talking about the company earnings decades worth of profits in just a few quarters:

You might also notice from this chart that Micron’s recent run is a prime example of my favorite two words in stock analysis (operating. leverage.) as margins scale along with top line results.

But no one is doubting that Micron is now earning money hand over fist— the question is how much further can this rally continue, which naturally leads into a discussion of structural vs. cyclical demand and whether AI can buoy memory for the foreseeable future. While this is an interesting topic, I think it misses the point a bit.

The fact of the matter is, every secular trend becomes cyclical eventually. Smartphones and PCs used to be all the rage, straining memory supply and boosting prices, until they too became commoditized. AI will not be an exception to this rule— while the natural rate of DRAM demand might be elevated going forward, cyclicality isn’t going anywhere anytime soon. So, when the downcycle hits—and it will hit—Micron is going to get hit as well.

With that in mind, I prefer to analyze Micron through the lens of historical valuation and current upcycle momentum, rather than debating long-term trends in the memory market. Let’s start with the latter. To evaluate how long the RAMpocalpyse might last, we need to look at what’s driving it.

Contrary to popular belief, HBM is NOT what is directly driving Micron’s record profits; in it’s Q2 2026 earnings report, the company reported $23.8 billion in revenue, but, by most estimates and using management’s previous commentary on revenue run rate, HBM likely only represents about 10-15% of that total.

The vast majority of Micron’s current revenue and profits are being driven by high-volume products like DDR5 and LPDDR5/X, which are highly commoditized and can be manufactured for very cheap. In fact, on the company’s Q2 earnings call, CEO Sanjay Mehrotra said:

So, yes, it is correct that the margins for non-HBM today are higher than HBM margins.

I mentioned earlier that HBM uses 3-4 times more wafers than traditional DRAM: that’s called the “production trade ratio” and that, in addition to the fabrication and packing complexity, is why HBM is more expensive per bit for buyers and for manufacturers.

However, even though margins are currently higher for traditional memory products, the Big 3 operate on much longer timelines than just a year, five years, or even ten years; of every memory product on the market, HBM is by far the least commoditized, scarcest, and the most desirable to high-end customers with deep pockets. So, when the RAMpocalypse fades or ends and supply catches up to demand, DDR5/6 and LPDDR5/6 will come back down to Earth, but HBM will remain a premium product with premium margins.

This expectation is apparent from the technological focus of upcoming memory fabs:

• SK Hynix recently pulled forward the opening of its M15X fab in Cheongju, South Korea, which was originally intended for NAND production but has been re-purposed to service DRAM demand instead. It has also fast-tracked its $90 billion Yongin fab to an early 2027 opening, focusing nearly exclusively on HBM.

• Samsung is ramping its P4 fab, which has minimal NAND capacity and has shifted much of its cleanroom space to DRAM. Its P5 will similarly be dedicated to HBM and DDR6.

• Micron is expected to ramp its new Boise facility, Fab 15, to produce DRAM and HBM and a planned megafab in Clay, New York which will also produce leading-edge DRAM.

HBM is sold out through 2026 and much of 2027 and nearly all greenfield capex is going towards building out this capacity because Samsung, SK Hynix, and Micron are all racing to establish a beachhead in this new, lucrative market. Their success or failure in this area could have tens of billions of dollars worth of consequences.

And judging by Nvidia’s and AMD’s plans, HBM bit growth is not going to slow down anytime soon. Nvidia is planning its Rubin Ultra platform for 2027, which will have an eye-watering 1 TB of HBM per GPU, to regain the HBM lead AMD has established, but AMD is planning for the MI500 series to have similar HBM per GPU as well. So HBM capacity is doubling nearly every generation.

The difference between HBM and other memory technologies is that, while DDR is likely to take about 7 years from DDR5 to DDR6, HBM generations are coming and going significantly faster— HBM3 entered volume production in 2024 and HBM4 has already entered volume production this year (a 2-year gap)!

Older generation aren’t going to just go away either, which means Micron and co. will need production lines to support HBM3, HBM3E, HBM4, etc. and this number will keep multiplying in 2-3 year increments; I think it’s obvious why these companies are spending so much to build out new fabs that focus on this nascent industry and why it will continue to drive operating results and supply shortages for years.

What’s remarkable to me is that, even as HBM pulls away production lines and the price of traditional memory skyrockets, the market continues to pay without much of a fuss:

We expect industry DRAM bit shipments in calendar 2026 to grow in the low-20s percentage range, slightly above our prior outlook. In DRAM, cleanroom constraints and long construction lead times, higher HBM trade ratio, higher HBM growth rates and declining bits per wafer growth from node migrations constrain bit supply growth.

Demand is so strong that Micron expects to ship 20% more DRAM (in bits) in 2026 than it did last year, even as prices rise to unprecedented levels and supply is tighter than ever.

I’ve seen some buzz that the non-HBM memory crunch might be ending because Nvidia downgraded its specs on Vera Rubin to use 28 TB of LPDDR5X per rack instead of the original planned 54 TB. The logic goes: AI doesn’t need as much server DRAM as expected, therefore the supply-demand imbalance will ease and prices will come back down, hammering Micron’s share price.

My read on this move is the complete opposite: the supply shortages are so bad that Nvidia has decided to make do with less than its desired amount of memory to keep prices manageable. It’s a signal that the RAMpocalypse is continuing full steam ahead, not that it’s alleviating. I think it’s also likely that the supply for LPDDR5X, previously used in mostly mobile applications, is just not ready for a full-scale AI roll-out from the most prolific company in the industry.

To compensate, data center operators will likely need to lean a bit heavier on NAND flash SSDs as the KV cache and model weight overflow, and guess who makes those too? (It’s Micron btw). Those who’ve read my previous article on Sandisk (SNDK) will know that I expect the NAND shortage to persist even longer as DRAM hoovers up all the memory capex, further providing a boost to Micron’s results.

So, in sum, every type of memory is now in a supply shortage and Micron is one of the only companies set to continue benefiting. Data centers are already sprouting like weeds in the US, but China just announced a nearly $300 billion commitment to AI infrastructure and other countries are following suit. Like I said earlier, a downcycle is all but inevitable because that’s how secular trends always go, but it seems to me that we are a long ways away from the next one.

Investor Takeaway

That last line is relevant because, as I’ve covered in many articles on MU in the past, the stock tends to inflect around TTM revenue peaks and troughs, which is to say, that upcycles tend to end right before revenue begins to slow and downcycles end right before revenue beings to pick up. Due to the supply shortages, exploding AI demand, and lagging new fab capacity, this inflection is unlikely to occur before 2028 at the earliest, in my opinion.

Now, perhaps you could make the argument “well this time is different because of how much the stock has moved up in such a short amount of time”. The counter there is that the stock’s forward P/E is actually pretty much in line with Micron’s multiple through a typical upcycle, indicating this cycle isn’t even out of the ordinary for the company, it’s the much higher nominal amount of revenue and profit that’s abnormal.

To me, this means MU still has more room to run and will continue to run until supply begins to close the gap to demand. Another reservation I’ve heard so many times is “but how can I buy here? It’s already gone up so far!”

Well, if I may be so uncouth as to quote myself, here’s an excerpt on that fallacy from one of my previous articles:

Yes, Micron shares have gone parabolic. Yes, volatility will be higher. Yes, there are risks here if the upcycle ends earlier for some reason (recession, capex reduction, etc). But the operating results driving this run are real and the demand that is causing shortages throughout the supply chain are persistent. Memory is the hardest bottleneck to fix, in my opinion, and Micron will be benefiting all the while.

The company is slated to report earnings this Wednesday 6/24 and all eyes in the market will be on management’s commentary about the next few quarters. I anticipate bit shipments will surge yet again and both DRAM and NAND will remain tight, leading to another quarter of record-high guidance for Q3 2026.

As with previous earnings, there is likely to be some profit-taking barring a truly outrageous print. I will be holding onto my MU position ($85 cost basis) and buying more if a dip presents itself. I am also considering selling a bull call spread or two depending on where premiums end up. But regardless of the short-term price movements, I still see MU as an easy hold for the next two years at least as data center build-outs continue and memory needs increase.

Fin

First of all, thank you all for reading! This is my longest piece yet and it took hours upon hours to put together (especially while trying to multi-task watching the World Cup). I truly appreciate you choosing to spend your time reading my thoughts and opinions and I encourage you to share yours in the comments or in direct message.

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Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

Before getting into this piece, I just wanted to give a deep and heartfelt thank you to everyone who has subscribed since I launched my Substack a couple months back— our community has gone from 40 at the end of April to over 1,000 as I write this! Those first 1,000 of you will get 2 months of paid subscription for free when I figure out how I want that to look in the coming weeks.

I will say though that I did not expect to hit 1,000 subscribers this fast so I am going to keep (half) the promo going and my next 1,000 subscribers (i.e. until 2,000) will get 1 month of paid subscription for free!

Thank you to all :)

AI Panic?

We’re here to analyze an earnings report, but I’d be remiss if I didn’t also discuss the ongoing market panic, which took hold on Friday as solid jobs numbers raised the odds of a 2026 rate hike by the Federal Reserve and led to a massive risk-off trading day. Naturally, the most speculative stocks got hit the hardest, which in today’s market means the AI sector.

I know most publications just assume that you already understand the market mechanics at play behind everything I just said in the previous paragraph, but I’m going to explain it anyway because I think your typical Wall Street and finance types tend to obfuscate and hide behind acronyms in order to make it seem like only they can do what they do. So, here’s what happened on Friday with context.

Inflation in the US has been rising due to a number of factors including the war in Iran (fuel prices going up), tariffs, and resilient consumer spending; when inflation goes up, the Federal Reserve usually wants to raise interest rates because higher rates discourage spending and reign in inflation. However, higher rates also raise the risk of recession because they dampen consumer spending and make corporations hesitant to binge on hiring new employees. In sum, the Fed wants to keep rates high when inflation is high, but they have to make sure doing so doesn’t kill the labor market.

On Friday, the nonfarm payrolls report showed an increase of 172,000 jobs in May, blowing projections out of the water and indicating a recovering labor market after declines throughout much of 2026. This sounds like good news for the economy, no? Why did the market sell-off on more people getting hired? Well, remember in the previous paragraph that the Fed wants rates to stay high to combat inflation but only if the labor market won’t be too negatively impacted— the strong jobs report gives the Fed a reason to keep rates high or even raise them further to counter inflation.

Many were still holding out hope that the Fed would actually cut rates this year to stimulate economic growth—lower interest rates means consumers and companies can take out debt for cheap—but the if the labor market is strong, this is pretty unlikely. As such, higher rates will increase borrowing costs for funding capital expenditures and could slow progress on construction for projects like AI data centers.

In sum, what we saw on Friday was the market adjusting its expectations of how much will be spent on AI infrastructure over the next few years— a rate hike means borrowing gets more expensive, which means companies will spend less and suppliers will earn less. This is the simple calculus that led to the AI bloodbath:

I’ve seen many a headless chicken running around this weekend wondering if the bubble pop is finally upon us and whether its time to sell. Answering that question in full is out of the scope of this post, but, no, I do not think this is the Big One and I do think that hyperscalers are almost certainly going to be building out AI capacity whether the federal funds rate is 3.5% or 4%; on a multi-year time horizon, this is a blip in the cycle and I expect most the affected names to recover quite quickly, as they have a dozen times before.

With that, let’s move on to the main subject of this article.

Credo Earnings: Turning Weaknesses Into Strengths

My headline is, of course, referring to Credo Technology (CRDO), which reported earnings on Monday of last week, 6/1. For those who have read my Substack, you’ll recognize this as my top stock pick of 2026, which I originally initiated here:

The stock has since risen by more than 120% in just two months as the market has begun re-pricing AI networking stocks in response to increasing demand and supply shortages for components. I will recap a bit here, but I’d highly recommend reading the above article for a full break-down of the company, its various product offerings, and its market opportunity.

For this piece, we’ll be taking a look at Credo’s Q4 2026 earnings and what it could mean for the stock going forward.

As a quick refresher, Credo is a networking company that develops and sells a multitude of different products that help connect components within data centers with an emphasis on AI deployments. These include:

• Active electrical cables (AECs), which use digital signal processors (DSPs) to increase the effective range of copper. This is the flagship product.

• Pluggable optical transceivers that connect server racks together.

• Discrete DSPs for both copper and optical that other manufacturers include in their AECs and transceivers.

• Photonic integrated circuits (PICs) and other co-packaged optics (CPO) technology (courtesy of the recent acquisition of DustPhotonics).

This is the lay of the land going into earnings, with the market wondering where the company fits into the supply chain for 800G, 1.6T, and, eventually, 3.2T.

Let’s take a look at the actual operating results:

Credo reported revenue of $437 million, non-GAAP gross margin of 68.3%, and non-GAAP EPS of $1.16, all up substantially YoY as networking has become the latest bottleneck to hit the AI build-out. You’ll also notice that the company has maintained superb (my two favorite words in stock analysis are…) operating leverage as margins have remained high as revenue has ballooned, indicating a scalability to the business that will be essential to expanding its valuation multiple.

Credo also provided the following guidance for Q1 2027:

Revenue is expected to be $470 million at the midpoint and non-GAAP gross margin is expected at 68%, which represents a continuation of the growth narrative and affirms that demand is still rolling in for AI networking components. The gross margin guide implies a bit of contraction, but Credo management has a habit of being quite conservative and I expect this will come in flat to slightly up.

Besides the headline numbers, I think this report solidified an important aspect of Credo’s value proposition: the company is perfectly positioned to straddle the copper to optical transition and benefit from both sides in the process.

As we discussed in prior CRDO articles, as data transmission speeds increase, signal integrity over copper drops off rapidly— AECs can reach around 7 meters at 800G, 5 meters at 1.6T, 2.5 meters at 3.2T, and so on. Data center operators will almost always use copper wherever possible because of how cheap and ubiquitous it is, but, as time goes on, more and more configurations will require optical solutions.

At 800G, a solid percentage of inter-rack (scale-out) connections are copper and virtually all intra-rack (scale-up) connections are copper as well. At 1.6T, scale-up will likely remain copper-dominated, while for scale-out, the calculus starts to shift towards optical, especially with CPO, which comes with significant power savings. At 3.2T, scale-out will likely be nearly completely optical while scale-up will become more of a competition between copper and intra-rack CPO.

While this progression has the obvious downside of degrading Credo’s AEC cash cow, the company made a masterful move that turns this weakness into a strength. By acquiring DustPhotonics and setting up its product portfolio for the optical revolution, Credo has positioned itself as a vertically integrated supplier of AI data center connectivity; instead of being a legacy copper shop that will lose share as time goes on, the company can now service scale-up and scale-out for any deployment depending on customer need.

Are you a hyperscaler that’s upgrading a legacy 400G deployment to 800G? Credo can provide the AECs for inter-rack and intra-rack connectivity. Are you a neocloud spinning up a brand new 1.6T data center? Credo has AECs for scale-up wiring and pluggable optical transceivers or CPO/NPO (near-port optics) integrations for scale-out. Customers love a one-stop shop and, with its latest M&A, Credo aims to do it all.

The copper wall seems a lot less worrying than it once was. On the earnings call, CEO Bill Brennan stated:

In fiscal 2027, we expect our optical DSPs, SiPho PICs, and ZeroFlap Optics will each contribute more than $100 million of revenue. In total, more than $600 million of revenue, with this expected ramp accelerating in the second half of the year.

$600 million in optical revenue would represent nearly 50% of the company’s 2026 revenue for the completed fiscal year, demonstrating just how quickly this transition is ramping. I expect this trend to accelerate as the industry moves to 1.6T over the coming quarters and Credo’s AECs continue to dominate the current generation.

Revenue concentration is always brought up as a risk for Credo, but compared to some other suppliers, the company is actually fairly diversified and getting more so. Here were the numbers in the most recent quarter:

The three largest customers in Q4 were the same customers that were the largest in Q3 as well. It's 34% for the largest, 27% for the second largest, 16% for the third, and notably the fourth customer, which was at 10%, they were not previously a 10% customer during fiscal 2026.

Earlier in the call, Brennan additionally stated:

We think that the neo clouds are going to represent a growing and more meaningful opportunity for Credo over the coming years. I do think you're right that if we looked at that group of customers collectively, I definitely think it could be on the order of 20%.

The largest end customers in the data center networking market are hyperscalers, which inherently lends itself to a concentrated customer portfolio, so having four different companies represent more than 10% of revenue is about as good as it gets in that context.

I have some more to say about ZeroFlap Optics and how the software angle enhances Credo’s offerings, but I’ll save that for a later date.

Investor Takeaway

CRDO had reached around $230 per share during last Monday’s trading, then fell sharply (~12%) after hours following this report. I see this initial reaction as a combination of profit-taking after a meteoric run-up plus investor fear over margin contraction considering the slight 30 bps QoQ decline in Q4 and the projected 30 bps decline at the midpoint in Q1 2027 (I think this is just a speed bump related to the DustPhotonics acquisition and should dissipate over the next couple quarters).

Despite the initial knee-jerk reaction downwards, the stock recovered throughout Tuesday and ended nearly unchanged, likely getting a boost from a massive sector rally following Nvidia CEO Jensen Huang’s comments calling Marvell (MRVL) the “next trillion dollar company”. Nvidia took a $2 billion stake in MRVL back in March (worth much more now), so I’m not sure I’d necessarily trust him to be unbiased, but regardless, AI networking names surged throughout the week.

So now we’re going into a new week of trading with CRDO still hovering above $200 per share— what should investors do now? I’ll first note that the stock held up impressively well during the Friday sell-off, actually trading in the green most of the day before dropping around 4.5%, a smaller decline than the Nasdaq (QQQ) and significantly outperforming smaller AI suppliers. The stock is now up 4.5% on Monday as I write this, reclaiming those losses.

I added to my personal position in the Monday after hours chaos and will continue to add on dips as volatility remains elevated in this sector; Credo is well positioned for the next era of the AI revolution and my holding period is likely to be at least five years. I expect the interest rate question to push the market into even larger swings, but these should be viewed as opportunities rather than risks, in my opinion.

I have lots more to say, but this article is already getting a bit late in the game considering earnings was a week ago, but life got in the way unfortunately— I’ll try to be more timely in the future!

Fin

Thank you, dear reader, for getting all the way to the end, I truly cannot overstate how much I appreciate and enjoy being able to share my thoughts with you and receive your feedback and insights in return.

If you liked this post please share it with your friends and subscribe so we can grow our community and be better, smart investors together!

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As always, thanks for reading and best of luck!

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

Hey folks! Before getting into the content of this piece, I just wanted to quickly mention that, as I unveiled in my previous post, I am offering my first 1,000 subscribers 2 months of paid subscription for free. While I haven’t yet figured out the details on when this paid tier will go live or how it will function, I hope to have it all sorted shortly and I want to thank everyone who made building a community here possible.

Alright, now on to the analysis, hope you like it :)

Previous Coverage

I’ll start by saying that I have covered the CPU market for quite a while— I’ve been writing bullish articles on AMD since it was trading for $2 per share back in 2015 (gift article here for the curious), which at the current share price of $518 is closing in on a 25,000% gain, and I was bearish on Intel (INTC) from 2015 until around 2024 when I turned bullish as the stock plummeted into the $20 range after its restructuring and near-collapse. With INTC now trading around $120, that call is up 500% in less than two years.

I bring this up not to boast (okay, maybe a little bit to boast), but to establish my history with the sector to my new audience on Substack (you!) and provide my track record in this particular arena. I’ve covered the AMD vs. Intel battle probably more than any other topic in my writing career because I just find it so damn compelling and interesting, and we are entering a brave new world of CPUs that I can’t wait to dive into with y’all.

Just a note before moving on, this piece is largely historical background, context, and setup for a larger discussion on AI data center CPUs. There won’t be much in the way of stock analysis for this part, but I think it contains vital information for understanding the market as a whole. If that doesn’t sound interesting to you, feel free to jump back in for Part 2!

CPUs And The Birth of Backwards Compatibility

In my last piece on Sandisk (SNDK), which you can read here, we went all the way back to the 1950s for historical context on the birth of computer memory; for this article, we won’t go back quite that far, just to 1978— the Bee Gees were topping the charts with Saturday Night Fever, the Great Blizzard of said year was dropping 4+ feet of snow across the Midwest, and (surprise, surprise) more drama in the Middle East as protests and unrest set the stage for the Iranian Revolution the following year. It was also the year that Intel (INTC) would change the world forever.

In June of 1978, the company released the Intel 8086 CPU for around $350-$400 in today’s dollars, sporting 29,000 transistors, and made on its 3200nm node. For reference, and as a brain-melting example of the exponential progression of technology, Intel’s current lineup of CPUs contains tens of billions of transistors, is manufactured on a 2nm node, and still goes for around the same price (depending on model, of course).

However, the 8086 also featured something that remains the dominant force in computing to this day: the x86 instruction set architecture (ISA) (taken from the last two digits of the product name). In a nutshell, an ISA is just a standardized set of instructions that determine how software code translates to actual operations on hardware.

While previous ISAs were custom-built for each processor and required developers to change or completely overhaul their software for new hardware releases, x86 was designed with a software-first approach, emphasizing backwards compatibility and seamless integration with new product launches.

But like with many new technologies, the 8086 was too early— it was a 16-bit CPU in an 8-bit PC world and was too expensive for most manufacturers to include in their lineup. So Intel had a bright idea to take their cutting-edge processor and dumb it down, throttling it down to 8-bit block communication (but keeping it on x86) in an attempt to attract suitors and releasing it as the Intel 8088. And it worked.

Before long, IBM included the 8088 in its budget line of PCs, which soared to monopoly-esque market share, locking x86 into the mainstream; it carried this momentum into an unbreakable partnership with Microsoft’s Windows throughout the 1990s and 2000s and has been a mainstay ever since.

However, despite this win, IBM was notorious at the time for its aversion to supply chain risk and would not commit to a continued agreement with Intel if it was the sole supplier of chips, forcing Intel to sign second-source agreements with a multitude of companies to manufacture exactly the same CPU. One of these companies was Advanced Micro Devices (AMD), which signed a 10-year cross-licensing agreement with Intel in 1982.

AMD and Intel were well acquainted from a previous dust-up— the former reverse-engineered Intel’s 8080 chip and proceeded to sell their own clone, leading to a dispute, which resulted in an earlier cross-licensing agreement in 1976. These second-source relationships remained intact until 1985, when Intel announced its 80386 processor and refused to share the designs with its partners, aiming to monopolize the CPU market. So what happened to IBM’s second-source rule?

Well, in the years between Intel’s original second-source commitment to IBM and this move, companies like Compaq and Dell had begun manufacturing IBM PC clones, which significantly reduced IBM’s market share and leverage, emboldening Intel opt for the “Red Wedding” strategy. During this time, Intel had also committed massive investment towards building out an in-house fab footprint, allowing it to service global demand without help from copy shops.

While many second-source manufacturers waved the white flag and exited the market, AMD decided to take Intel to court for breach of contract. After years of court drama, which I’ll skip for this post, Intel ended up being hit with substantial anti-trust fines and AMD won the right to continue manufacturing its own x86 chips, cementing a legal duopoly that has persisted to the present day.

But x86 isn’t the only instruction set and there were many folks who were unsatisfied allowing one standard to dominate the industry.

Enter, RISC.

While its software compatibility lock-in was nigh unbreakable, x86 did have a weakness that others tried to exploit: complexity. The full breadth of comparing x86 to RISC is out of the scope of this piece, but, suffice to say that x86 was not built from the ground up to be a foundational, multi-decade architecture— 40+ years on, it is a Frankenstein of a design paradigm that is as innovative as it is grotesque.

This naturally led to the birth of a competitor framework known as reduced instruction set computer (RISC) architecture, which simplified the hardware and instructions, emphasized power efficiency, and was future-proofed from the very beginning. These began to spawn in the mid 1980s, the most popular of which you might have heard of: ARM.

While RISC standards were superior to x86 in many ways, the latter had more than a five-year head start to establish a customer moat, hook software developers on the backwards compatibility of the hardware, and build up a manufacturing footprint large enough and advanced enough to service industry demand. As such, by the time RISC really hit the market, Intel was too entrenched and had a supply chain that could brute-force superior performance out of its chips despite x86’s weak spots.

To shorten this story just a bit, a lot happened in the 1990s and 2000s eventually resulting in ARM becoming the dominant RISC standard and main competitor to x86. Fast forward some more to the present day and the CPU market is basically a triopoly in terms of who owns ISA IP with Intel and AMD butting heads on the x86 side and ARM having a virtual monopoly on the RISC side.

Okay, now that we’ve covered some of the history and context of this lucrative industry, let’s get into the dynamics of the modern CPU space with a focus on AI data centers.

AMD vs. Intel

It’s probably easiest to start from the manufacturer level and work our way downstream because nearly every relevant CPU on Earth is made by the same entity: Taiwan Semiconductor Manufacturing Company (TSMC).

The hottest real estate in the world today cannot be found in New York City, Hong Kong, or even Monaco, where apartments regularly go for more than $5,000 per square foot. No, the most valuable real estate on the planet are wafers on TSMC production lines, which crank out millions of the most advanced chips that humanity is capable of conceiving and creating.

Virtually every AI accelerator (GPU, XPU, etc.) that isn’t made in China and nearly every bleeding-edge CPU, x86 or ARM, is made by TSMC. The silicon brains for network switches? TSMC. Photonic integrated circuits for co-packaged optics (CPO)? TSMC. Advanced modems? Believe it or not, TSMC. This is but a tiny subset of the products they ship.

The company’s only real competition is Samsung and Intel, which operate their own foundries but have found it immensely difficult to match TSMC’s performance and value proposition. As such, TSMC commands a near monopoly on high-end silicon while the other two mostly only produce in-house chips for their own processors.

This wasn’t always the case. Back around 2015, Intel was far-and-away the foundry top dog— it’s processors were faster, more efficient, and more widespread than anything TSMC could hope to create. Intel used this performance advantage to keep AMD on life support and maintain a margin stranglehold on the desktop and server CPU markets. But just when it looked like Intel’s vice-grip would be impossible to break, disaster struck.

In what has been extensively chronicled as one of the most significant debacles in semiconductor history, Intel’s 10nm process node was announced as an aggressive jump in transistor density, even attempting to surpass Moore’s Law by pushing for a 2.7x density increase rather than the usual 2x. There were many factors that led to its downfall including replacing copper interconnects with cobalt, deigning not to wait for next-gen extreme ultraviolet (EUV) lithography machines from ASML and instead trying to squeeze out performance from older direct ultraviolet (DUV) methods, and more.

In sum, Intel flew too close to the sun and ended up with a manufacturing node that only achieved the yields necessary for high-volume production years after its initial launch window, leaving time for TSMC to catch up and then leapfrog, which is where the industry stands today.

This probably sounds like meandering history— where’s the investing relevance, Kumquat Research?? Patience, dear reader! This context is key to why Intel has fallen behind in the CPU race and how it aims to bounce back.

At risk of stating the obvious, because Intel operates its own foundries in competition with TSMC, it can’t simply go to the best shop in town to make its chips— that would result in massive losses and write-downs/write-offs that could very well be existential; AMD has no such albatross— it became completely fabless (designing chips but not manufacturing them) in 2018 (after its supply agreement with GlobalFoundries (GFS) expired) and jumped at the chance to manufacture its chips with TSMC. And so it went that TSMC’s victory over Intel became AMD’s victory as well.

Over the course of a few years, AMD’s EPYC line of server CPUs, aided by TSMC’s fabrication prowess, gradually began to outperform Intel Xeon server chips on both performance and power efficiency, eroding the the latter’s long-held monopoly in the data center:

The chart above vividly illustrates AMD’s methodical dismantling of Intel’s server moat as total market share has converged over the last decade, inching ever closer to parity. The performance gap between the two has only been magnified by the AI arms race, which requires the absolute bleeding-edge to service compute and power-intensive workloads and has led to renewed momentum for AMD.

This has been especially true on the high-end, where AMD has been dominating in recent quarters as its EPYC server CPUs have cemented themselves as the go-to x86 processor with deployments increasing by 50% YoY according to Lisa Su. EPYC Turin has been a major success and sixth-generation EPYC Venice, primed for launch in 2H2026, appears likely to keep up the pace.

And even with AMD’s market share gains, I don’t consider that to be the most important metric where the company is winning— that would be revenue share. If market share is an overall accounting of volume shipped and average selling price, revenue share is the holy grail because it tracks who owns the high-margin, profit-generating segment of the market. Here is where AMD has really made its mark.

Below is the most recent lay of the land according to Mercury Research:

Focusing on server because that’s is where the AI revenue is bucketed, AMD saw 33.2% unit share and a whopping 46.2% revenue share, +670 bps YoY and +490 bps QoQ. In plain terms, the company is selling 33% of total server CPUs but is capturing 46% of total profits, indicating that the top-end is massively shifting away from Intel and towards AMD.

This should come as no surprise— Intel has been in process node limbo for years as it has continued to try and catch TSMC. Former CEO Pat Gelsinger, who took the job in 2021, embarked on an ambitious 5 nodes in 4 years (5N4Y) plan to supercharge foundry progress, which was, in my opinion, simultaneously both necessary and risky. Five years on, the plan has been, I would qualify, a mild success as Intel now has a node that is at least somewhat of a challenger to TSMC, though yields have a ways to go before they can support both internal Intel chips and external customer orders (although I’d note that the 5N4Y plan didn’t work out so well for Gelsinger, who was unceremoniously replaced by Lip-Bu Tan last year).

So now Intel is working diligently to boost yields for its latest 18A process node so it can ramp up and provide the server CPUs for which the AI market is now clamoring, while also planning to achieve high-volume production on its 14A process node by 2029, which the company hopes will finally put it back on even ground with TSMC and, therefore, AMD.

Remember earlier when I said that “nearly every” relevant CPU is made by TSMC? Intel’s server Clearwater Forest and mobile Panther Lake processors are the only real exception worth mentioning. And even then, they are shipping well below the desired output because of the aforementioned yield issues, so despite Intel’s progress, it evidently has a long way to go to actually re-establish itself in the foundry upper echelons.

Okay, we’ve now covered that x86 landscape in a decent amount of depth, but we haven’t even mentioned the other side of the ISA equation since the first section of this article. ARM processors are increasingly gaining relevance in the data center for a multitude of reasons, so next, let’s discuss who is making and using these CPUs and why they are gaining share against x86.

ARM’s Resurgence

There is comparatively little nuance in ARM relative to the x86 market— all major ARM processors are made by TSMC, full stop. Amazon’s (AMZN) Graviton, Nvidia’s (NVDA), Grace and Vera, Google’s (GOOG) TPUs, etc. are either designed in-house or by a third-party vendor like Broadcom and Marvell, but all are physically produced in TSMC fabs.

If x86 is a stable duopoly, ARM is the wild west: every hyperscaler is currently building out their own ARM processor designs to fill out their data centers and cut out the middle men.

But how is this possible? AMD and Intel don’t allow other companies to make x86 processors, so why is “ARM” allowing other companies to use its blueprint ISA for new CPUs? This is the crux of the x86 vs ARM battle in the modern data center.

AMD and Intel have opted for a “walled garden” approach wherein they own the intellectual property and use it to hold a monopoly on the bare metal produced and sold on the instruction set. This keeps margins fat and prices high, but starts to falter when customers get fed up and when competition comes a-knocking; Hyperscalers have wanted an alternative to x86 for quite some time mainly in order to get more flexibility in chip design and to reduce Intel’s and AMD’s pricing leverage.

Enter, ARM.

Through the RISC crucible of the 1990s, ARM emerged as the leader of the pack and was used in many low-power applications throughout the decade and into the 2000s when off-the-shelf Samsung ARM cores were used in the first iPhone.

ARM realized that, while it couldn’t hope to compete with x86 by producing and selling its own chips, it could offer an enticing proposition: in exchange for an upfront fee and a small royalty fee per chip sold, anyone could use the ARM license to create their own CPUs. With this business model, ARM covers R&D for upgrading the ISA and the cores then reaps the rewards from licensee sales. A lean, cash-generating strategy.

Note: When I say “ARM” is doing these things, I’m referring to the entity that now exists as Arm Holdings (ARMH) which holds the ARM IP and does the aforementioned ISA and core R&D.

ARM has multiple tiers of licensing but the most relevant for this article is the one that allows the licensee to basically create whatever type of silicon they want using the ARM ISA. Apple, Nvidia, Qualcomm (QCOM), and all other tier 1 players pay tens of millions of dollars for this license in addition to the per-chip royalty. ARM’s usage has skyrocketed from smartphones and microcontrollers all the way up to powering AI workloads at scale; to understand why this shift is happening we have to analyze hyperscaler considerations when adding new compute capacity.

Hyperscalers have three main concerns when it comes to purchasing components for new data centers or “AI factories” as Nvidia CEO Jensen Huang has taken to calling them (I’m not a fan of the term, I pictured AI robots building things and got excited but, nope, still just a data center). These probably won’t surprise you since they’ve been the primary semiconductor sticking points for as long as they’ve been around: cost, performance, and power efficiency.

A full technical breakdown comparing x86 and ARM on the ISA level is probably overkill, but the important bits are that ARM has typically been known for offering power efficiency and better price-to-performance, especially at the hyperscaler-level where companies like Amazon and Google can mass produce millions, whereas x86 can work in nearly any configuration and offers better raw performance and higher clock speeds albeit at higher power draw.

But this status quo is shifting. For example, Nvidia’s upcoming Vera ARM CPU is aiming for a thermal design power (TDP) of around 500 watts all-in, which is around the same TDP as AMD’s current generation of EPYC Turin x86 CPUs. In its race to create ever-more powerful AI-focused CPUs, Nvidia has done away with the old conception of ARM and is ushering in a new era of performance.

On the other side of the equation, companies like Broadcom are designing ARM CPUs for hyperscalers that are more in line with traditional ARM notions like power efficiency and maximizing performance-per-dollar. These deployments often cost a fraction of what it takes to deploy x86 or Nvidia CPUs and use significantly less energy, which is crucial as heat dissipation becomes an imperative and the power generation bottleneck grows.

In sum, the ecosystem is getting quite crowded and it’s becoming a bit difficult to sift through who will come out winners and who will come out losers. It might initially appear like x86 is being phased out in favor of ARM in high-margin hyperscale deployments and that it is approaching legacy status, which might have been true had it not been for the next evolution of the AI revolution: agents.

Fin

I hate to turn on the house lights and play “Closing Time” just when the show is getting good, but this article is already getting quite long and a Part 2 would probably be in everyone’s best interest. In the next post in this series, I’ll cover the current CPU landscape in much more detail, including how agentic AI is turning the market upside-down, and pick my winners and losers, including analysis of Nvidia, Arm Holdings, AMD, Intel, Broadcom, and other players.

I truly appreciate each and every piece of feedback so if you liked the article, or even if you didn’t, let me know in the comments or in my chat.

But if you did like the post share it with your friends and don’t forget to subscribe for more! Let’s build a community of better, smarter investors together.

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As always, thanks for reading and best of luck!

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

Hey folks! Before getting into the content of this piece, I just wanted to quickly mention that, as I unveiled in my previous post, I am offering my first 1,000 subscribers 2 months of paid subscription for free. While I haven’t yet figured out the details on when this paid tier will go live or how it will function, I hope to have it all sorted shortly and I want to thank everyone who made building a community here possible.

Alright, now on to the analysis!

Nvidia Earnings: The Crowd Goes Mild

As some of you who have followed me on Seeking Alpha might know, I’m not Nvidia’s (NVDA) biggest fan at the moment. I was bullish as recently as September 2024 on AI optimism, but I have soured on the stock in recent years and months as its market cap has expanded and the law of large numbers and competition threaten the company’s monopoly. This culminated in an article in November 2025, where I downgraded NVDA to a Sell and have been bearish ever since.

You can read that article here (and read the comments section to see some fun reproachment from some very angry bulls)!

But before we get into all that, let’s first walk through the Q1 2027 earnings report:

Revenue came in at $81.6 billion (+85% YoY/+20% QoQ), non-GAAP gross margin was 75% (+1420 bps YoY/-10 bps QoQ) and non-GAAP EPS ballooned to $1.87 (+140% YoY/+18% QoQ) as demand for the company’s Blackwell GPUs remained strong and drove record results.

The slight margin contraction is likely because we are entering a slight lull between Blackwell’s peak and the ramp of Rubin sometime in 2H2026, but will likely recover and potentially reach new heights.

The company also announced an $80 billion share repurchase plan and increased the quarterly dividend from $0.01 per share to $0.25 per share (or about a 0.4% yield).

Looking forward, Nvidia provided the following guidance:

Revenue is projected at $91 billion at the midpoint (+95% YoY/+11.5% QoQ), non-GAAP gross margin at 75%, and non-GAAP EPS probably around $2.10 (+100% YoY/+12.2% QoQ). These numbers came in above consensus estimates, but the stock is muted after hours, literally flat as I write this. Evidently, the market is reading these numbers beyond the surface level, as am I.

To clarify before we move into my thoughts, my thesis isn’t and has never been that Nvidia is set for collapse or that the stock will significantly under-perform the broader market; in fact, I think the company is in a great long-term position to continue capitalizing on AI and whatever comes after. However, my bear thesis is three-fold and these results did nothing to assuage my concerns:

1. Valuation: NVDA is priced like the company will continue to maintain its monopoly, but I’m skeptical.

2. Competition: It’s only a matter of time, in my opinion, until ASICs and open standards like Ethernet wrestle back control of the market from Nvidia.

3. The Agentic Era: AI beyond the chatbot paradigm is more constrained by CPUs than GPUs, which will erode Nvidia’s leverage.

I’ll dig into these individually in a later, more in-depth article, but taken in sum, I see Nvidia as simply too large to continue to return the outsized gains investors have come to expect and instead see the stock as more akin to a blue-chip cash cow, maintaining momentum with the AI trade and making for some interesting covered call writing opportunities, but ultimately lagging in the growth category. The company’s decision to raise the dividend is evidence of this evolution, in my opinion.

We can already see this playing out as the AI sector has surged in the last two months with names like Credo (CRDO), AMD, and even Broadcom (AVGO) outpacing NVDA’s advances, multiple times over in the case of the first two. As most of these companies continue growing and expanding their operating results, Nvidia is approaching a ceiling for one very simple reason: it was the first mover.

GPUs were the original AI bottleneck, which Nvidia capitalized upon and then leveraged to create a compute and networking empire. It won. But to do that, it squeezed and continues to squeeze absolutely heinous margins out of hyperscalers that were and are in build-at-all-costs mode in order to win the AI arms race. If there’s one thing Big Tech hates, it’s supplier monopolies.

Which segues into my next topic— competition. Not only is Nvidia’s valuation buckling under its own weight in raw terms, but the monopoly it has enjoyed and that has built that valuation is coming under threat from all sides.

On the compute side, nearly every major hyperscaler has committed to producing their own ASICs, at least for inference, because of how much more affordable and power-efficient they are at scale than Nvidia’s GPUs; the who’s who of this upper echelon either build ASICs in-house (Amazon), or enlist companies like Broadcom and Marvell (MRVL) to design them. These ASICs are in response to Nvidia’s pricing monopoly as well as a means to alleviate the overall supply shortage (though these two pain points go hand in hand, of course).

On the networking side, Nvidia’s InfiniBand and NVLink are in the crosshairs of, essentially, the entire tech industry as companies, including every Big Tech hyperscaler, AMD, Broadcom, Marvell, etc., all want to break up Nvidia’s vertically-integrated vendor lock-in ecosystem. Ethernet and UALink are two concerted efforts to open the standard in the inter-rack and intra-rack domains, respectively.

This is already bearing fruit as the market share is shifting hard towards Ethernet, with deployments expected to dwarf Infiniband within a few years:

Nvidia is counteracting this by launching its own Ethernet switches, but they are still trailing from a technological standpoint for now, and even so, this shift represents a significant margin erosion.

I can talk about this a while longer, but let’s save that discussion for a later date and move on to my last point— the Agentic Era. You have likely heard the term “agentic AI” before but in case you haven’t, it essentially refers to an AI workflow that can operate independently and in the background in contrast with traditional AI, which is the back-and-forth chatbot implementation. This is significant because agentic AI moves the bottleneck from GPU compute to CPU compute.

Whereas traditional AI typically has a CPU-to-GPU ratio of about 1:8, agentic AI deployments could be closer to 1:1 or even 2:1:

AMD CEO Lisa Su acknowledged this change on the company’s recent earnings call:

At our Financial Analyst Day in November, we outlined the server CPU market growing at approximately 18% annually over the next 3 to 5 years. Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by Agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030.

Agentic AI build-outs are expected to nearly double AMD’s server CPU growth projections over the next few years!

The reason why is quite simple— when you interact with an LLM in the traditional mode, you are essentially doing the thinking, error-checking, monitoring and other processes before you ask another question. But an AI agent must do those things on its own, which means constantly running operations, checking for failures or success, iterating on previous work, checking context over and over again, and waiting for responses from external software.

A hyperscaler’s greatest fear is idle GPU time— GPU utilization is what keeps a data center humming and profitable so workloads that are spending a lot of time waiting are extremely dangerous. The solution? Buy more CPUs silly! If a data center includes more CPUs, it can still maximize GPU time by increasing the total number of workloads that need compute at any given time. GPUs will still be important, but they will lose some of their leverage as the only bottleneck of AI infrastructure.

Nvidia also makes CPUs, of course, but these are more geared towards maximizing margins and the ability to ship rack-scale products; the GPU is the killer app for the company’s servers. In addition, the company’s competitive moat is a lot more pronounced in GPUs than it is in CPUs, where AMD is releasing monstrously powerful chips every year, which is why, overall, fewer GPUs and more CPUs will likely have a significant net-negative effect on Nvidia’s margins.

Investor Takeaway

For all the reasons stated above, I remain bearish on NVDA going forward. Not bearish like it’s gonna drop like a rock, but bearish like, if you want to benefit from the AI trade, there are better options out there for growth. Optical stocks still have a lot of room to run, in my opinion, memory is still in short supply, power is a major bottleneck, and the list goes on. To me, Nvidia’s AI story is now one of quiet victory.

I suppose if you want to buy-and-hold for the long haul to benefit from eventual dividends and to diversify your portfolio, I think NVDA is a fine option. Writing covered calls would be my preference in such a situation. But in the short-term and long-term, I see the company’s monopoly coming under fire and margins taking a hit as competitors catch up in AI inference and networking, and hyperscalers diversify away from Nvidia’s walled-garden ecosystem. As such, I am initiating NVDA at a Sell.

Fin

This is my first earnings reaction piece on Substack, let me know if you enjoyed if you’d rather I stick to other post types like long form! And in general, I love to hear from y’all so if you think my thesis is smart, silly, or both, please let me know in the comments or you can message in the chat.

Lastly, if you like my content please subscribe! I truly appreciate being able to engage with the Substack community and share my writing and receive insight and feedback in return.

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As always, thanks for reading and best of luck!

Disclosure: I held a beneficial long position in CRDO, AMD, and AVGO at the time of this writing.

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

I figured I’d start this piece with a rundown of my performance to date since joining Substack because at the end of the day that’s why you’re here, right? To read some analysis, get some picks, make some money, and be entertained while doing it?

I wouldn't say I’m quite the most entertaining writer on this platform, but I have been quite good on the money making front so far if I do say so myself, and my subscribers have reaped the rewards!

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Of course, if my picks had gone down out of the gate, I would have told you that my holding period is multi-year and not to worry about the short-term; but because they went up I can brag and say that, actually, the short-term is very important.

So here’s the short of it:

1. Credo Technology (CRDO): +90% (Strong Buy on 3/23 @ $100)

2. Lumentum (LITE): +15% (Strong Buy on 5/8 @ $900)

Okay, so I’m definitely giving myself too much credit here— I think my theses were sound but timing has been more than fortunate. I mean, the Monday after my post on LITE, the company was added to the Nasdaq-100 index, triggering the aforementioned 15% move. I certainly could not have predicted that, but it’s always better to be lucky than good, and I mentioned as much on a recent note. To which one of my subscribers, replied “luck is also a part of skill,” which I’m going to choose to believe because it makes me feel smarter. You can read that LITE piece here:

But I’ve been investing for 15 years or thereabouts and I know that timing giveth and, oh, does it taketh— all we can do is keep evaluating and roll with the punches.

I want to change gears a bit for this post, away from data center networking and instead towards data center memory. In the title I referred to a “memory supercycle,” which is the term that has come to describe the increase in demand across all memory types due to AI build-outs and the corresponding rapid increase in average selling prices (ASPs) of these chips.

I called this trend back in September 2024 when I rated MU a Strong Buy (gift Seeking Alpha article can be read here) and have continued to pen bullish coverage (and to hold + add to my position) throughout the run. When I published that first article, MU was trading at $85 per share— it is now at $800 per share (+840%) as I write this. The question on every investor’s mind is whether the run is over or if there’s more gas left in the tank.

You’ve probably heard acronyms like HBM, DRAM, and NAND and that they’re important to AI build-outs. But do you know why? Or the differences between them?? Or what they even are??? Oh, you do? Okay then you can skip to the section entitled “The Memory Landscape”. Or if you just wanna know the damn stock pick, you can skip there too.

Everyone else, read on for a brief trip down memory lane!

A Brief Trip Down Memory Lane

Before we discuss the current memory landscape, I think it’d be a good/fun idea to provide a brief history of how far we’ve come. We won’t go all the way back, 70 years should do nicely.

The year is 1956. Rock n’ roll is taking the country and the world by storm as Elvis releases hits like Hound Dog and Heartbreak Hotel, Egypt shuts the Suez Canal, triggering a war with Britain, France, and Israel, and somewhere in a lab in California, IBM researchers are designing and building what will become the first computer with a hard disk drive, or HDD.

It was called the IBM 305 RAMAC and it looked like this:

The section on the left where the man is pointing is the HDD, which was about the size of two refrigerators and boasted 50 spinning magnetic disks that could store around 5 MB of data. That’s equivalent to about two copies of the original DOOM game (released in 1993), though the 305’s processor would theoretically take a few hours to render a single frame.

HDDs are an example of what’s called “non-volatile memory,” a term that essentially means that even when the device is not connected to power, it retains previously stored data.

In the decades that followed, HDDs became the preferred method of data storage as they shrunk, gained capacity, and dropped in cost; it would take about 30 years before a true non-volatile competitor would arise and another 20 years after that for said challenger to become viable in the mainstream. I’m referring to flash memory, which today is synonymous with NAND, and is used to manufacture solid-state drives (SSDs).

Japenese engineer Fujio Masuoka invented NAND flash while working at Toshiba in the 1980s, but it wasn’t until the early 90s that a company called SanDisk brought one of the first commercial SSDs to market. At first glance, SSDs were a no-brainer— flash memory was magnitudes faster, more reliable, smaller, and used significantly less power.

But even so, while SSDs were in many ways superior to HDDs, they couldn’t compete on the most important metric of all— cost. It wasn’t until the early 2000s that advances in bit density and economies of scale made NAND flash SSDs a true option in the enterprise space and eventually in the consumer market a few years later.

While HDDs are a mechanical marvel constructed with pretty common materials, SSDs are fabbed directly onto silicon using CMOS manufacturing, which offers scalability and miniaturization benefits that the former couldn’t hope to match. As such, before AI came around, SSDs were fast becoming the non-volatile storage medium of choice while HDDs were relegated to more niche applications where latency and read-write speeds were not bottlenecks.

And finally we come to the last of the Big 3 memory types in the modern ecosystem: dynamic random access memory, or DRAM. Unlike HDDs and SSDs, DRAM is a type of “volatile memory,” which loses all data once power is no longer running through the system. This sounds like a downside, but the very thing that makes it volatile is also what makes it blazingly fast. The DRAM used today follows a one-transistor, one-capacitor (1T1C) memory cell design, first invented by Dr. Robert Dennard in 1966 while working at IBM, as an alternative to the existing six-transistor SRAM design.

Reading and writing to DRAM is 1,000x faster than SSDs and 100,000x faster than HDDs, making it the only possible solution for modern day applications like smartphones, computers, and especially AI; DRAM has a much higher cost per bit than non-volatile options, but programs and software would be unusably sluggish without it.

Okay, that was a very high-level overview, but there’s the gist. What you need to know as an investor is that all three of these memory types are in extremely short supply right now in what is being called the RAMpocalypse (okay, fine no one is really calling it that but I’m trying to make it happen). Memory demand for AI infrastructure is skyrocketing and production capacity is woefully inadequate.

While HDDs, NAND, and DRAM are all needed in massive quantities for data centers, they each serve a very different purpose:

HDDs: These are the archives behind the AI machine— hyperscalers have been wrangling and storing as much raw data as possible to train their models and HDDs are the only storage device with a cost per bit low enough to accomplish this task.

NAND/SSDs: Because the training data in HDDs is accessed infrequently due to glacial read-write speeds and latency NAND SSDs are the staging layer of AI training and inference. They are used to checkpoint models during training, hold and stream high-velocity datasets to running GPUs, store model weight and key-value cache overflow for inference, and more.

HBM/DRAM: HBM and general purpose DRAM like DDR5 have very different functions in the data center, but suffice it to say, these are the workhorses of the entire ecosystem— GPU nodes each have hundreds of GB of HBM to do matrix math at blinding speeds and CPUs and other data center equipment rely on oodles of DDR5 to enable this dance.

As more and more compute deals have been agreed upon and signed, orders have streamed in across this hierarchy and memory makers have struggled to keep up. Exacerbating the issue has been the deep memory downcycle from which the industry has only recently recovered.

Let’s next do a quick breakdown of the competitive lay of the land.

The Memory Landscape

I’ll be glossing over HDDs because they aren’t the focus of this article, but the market is basically split 40-40-20-ish between Western Digital (WDC), Seagate (STX), and Toshiba, respectively.

The DRAM market is similarly split around 40-40-20 between SK Hynix, Samsung, and Micron (MU), respectively, and includes multiple different products, most important of which are DDR5 and HBM.

The NAND oligopoly is a bit more complex— Samsung owns about 30% share, SK Hynix around 20%, Micron 15%, Kioxia and Sandisk (SNDK) about 15% each, and then China’s YMTC <10%.

Because of the low variability between products, memory is an oft-cited example of a commodity and is subject to cyclicality— when demand outstrips supply, prices are high and these companies make money, and when supply outstrips demand, they lose money. We are currently experiencing one of the most severe supply shortages in the history of memory, which has created a record upcycle. The market has certainly taken notice:

Sandisk is leading the pack with a 3400% gain over the last 12 months, but every player has seen their stock soar. This parabolic move has led investors to ask two primary questions:

1. Is memory cyclicality dead?

2. If memory cyclicality is not dead, when will this upcycle end?

I have been continuously probing and answering these questions for years now since my initial coverage of MU back in September 2024, and I have held my shares throughout the run, despite folks calling the top every step of the way, for a very simple reason— supply is nowhere close to catching up with demand and therefore the memory upcycle isn’t close to over. (I can talk about whether memory cyclicality is dead until the cows come home but we can save that discussion for another time).

I could get further into the weeds of memory market dynamics, but for once in my life, I’m actually going to focus on one aspect of my thesis for this post. I remain bullish on MU due to the tailwinds of AI demand and continued supply shortages, but I’m coming around to being perhaps even more bullish on a company that I didn’t expect to catch my eye— Sandisk (SNDK).

Now I know what you might be thinking: “but Kumquat Research, SNDK is already up 3000%+ over the last 12 months, surely this is the peak and buying in now would be folly!” I understand your concern, dear reader, though I would refer you to the discussion on fallacies in my previous Substack article. In short, just because a stock has gone up, that doesn’t mean it won’t continue to go up. In fact, as momentum has permeated markets, this is truer than ever before.

So let’s take a look at Sandisk and see why this stock could continue to be the unlikely winner of the RAMpocalypse.

But first, break time!

I always like to take a bit of a breather when I’m reading a long piece to process what I’ve read so far and make sure I understand the setup so I can best appreciate the climax.

2 Free months of Paid for First 1,000 subscribers

I’d also like to take this moment to let you know that I’ll be doing a little promotion wherein my first 1,000 subscribers will get 2 months of paid subscription for free. I know what you might be thinking: “but, Kumquat Research, you don’t have a paid subscription!" And, yes, dear reader, that is currently true. However, I do eventually plan to offer a paid tier on my Substack and wanted to give back and say thank you to everyone who made building a community here possible!

So thank you :)

Alright, without further ado, whenever you’re ready…

Memory’s Dark Horse

If you noticed from the market share breakdown above, there’s only two relevant players in DRAM and NAND that don’t manufacture both memory types: Kioxia and Sandisk. These are the NAND pure plays. Here’s where it gets a bit complicated. Sandisk and Kioxia have a joint venture that actually owns and operates NAND fabs in Japan; Kioxia handles most of the staffing and operations for the fabs and the two split operating expenses, R&D on node development, and capex.

New memory factories are notoriously expensive to build so this division of responsibilities allows these two smaller companies to compete with giants like Samsung and SK Hynix. As part of this arrangement, Sandisk and Kioxia each get 50% of the NAND chips that come off the production lines, which they then package with firmware, controllers, and other components to sell their own SSDs to their own customers.

When all is said and done, Sandisk commands around 15% of the total NAND market, which, before the AI craze, honestly wasn’t worth all that much. The stock’s market cap in June 2025 was just $6 billion. In fact, NAND was just coming out of a pretty tough downcycle at the time and the company had actually lost $0.30 per share (non-GAAP), or $43 million, in Q3 2025. Consumer smartphones were still the biggest revenue driver for flash memory at the time.

Then new data center construction began and demand for SSDs spread like wildfire. Profits soared and have continued to soar. A couple months back, TrendForce provided the following estimated price increases for Q1 2026 and projections for Q2:

NAND flash prices jumped 85-90% in the first quarter and are tracking for an additional 70-75% increase in Q2, which leads to some crazy compounding. In that vein, in the company’s most recent earnings report (Q3 2026), it reported revenue of $5.95 billion (+251% YoY/+97% QoQ), gross margin of 78.4% (+5507 bps YoY/+2730 bps QoQ), and non-GAAP EPS of $23.41, or $3.6 billion in net income. Recall that this is up from a loss of $43 million just 12 months ago. Guidance was even crazier.

Sandisk expects EPS of $31.50 EPS at the midpoint in Q4 2026, which would come out to close to $5 billion in net income, or more than 80% of the stock’s June 2025 market cap. If you had any doubts regarding why SNDK has been on such a tear, let these results put those doubts to rest.

Why Sandisk?

Okay, but Sandisk isn’t the only one that’s seeing crazy operating results, so why will it continue to outperform the other players in the sector? I believe the answer to that lies in the past. There have been some major memory busts in the last 30 years, but the dot com crash was particularly bad— DRAM prices dropped 80%, SK Hynix (then known as Hynix Semiconductor) was on the verge of bankruptcy.

In fact, Hynix was taken over by creditors in a last ditch attempt to survive the downturn and Micron actually made a bold attempt to acquire the South Korean company for over $3 billion (rejected by the Hynix board at the 11th hour). Not long after, Micron ended up on the cusp of its own bankruptcy scare, which it narrowly avoided.

Point is, every company that ends up in the memory business long enough eventually experiences one of these busts and learns two very important tenets:

1. No matter how robust demand appears to be, don’t ramp supply too fast.

2. High-margin products are oversupply insurance.

The nice thing about an oligopolic market (for the sellers anyway) is that the small number of participants means that the group doesn’t need to collude in order to do what’s the best for the collective. So, when NAND prices crashed last year, all the major players significantly scaled back capacity in unison due to a common, implicit understanding that the supply glut needed to be cleared in order to restore favorable pricing.

This understanding carries over to the current undersupply environment— every memory player wants to scale to capture more of the market but nobody wants a repeat of the most recent post-COVID downcycle. Instead, Samsung, SK Hynix, Micron, and Sandisk are all continuing with existing fabs and moving up timelines where possible, but the pace is measured. These companies, more than most, work on extremely long timelines and make decisions on what’s going to happen five, ten, even twenty years from now. These actions correspond to tenet 1 above.

Tenet 2 is also in the interest of reducing the impact when a demand bust occurs and dictates that high-end, high-margin products are more resilient to downturns. We can see this playing out as possibly the most obvious culprit as the root cause of the RAMpocalypse— all the major DRAM players have been shifting existing production to HBM, which uses 3-4 times as many wafers, to meet exploding demand for the lucrative chips needed to power AI data centers.

This has led to a knock-on effect of surging prices for DDR5 and even legacy DDR4 memory that even Chinese manufacturers can produce.

This makes complete sense. HBM is the highest-margin, least commoditized, and most future-proofed memory chip on the market today so capturing and cementing market share there is substantially more important than capturing the short-term boom in DDR5, etc. This effect is being felt in NAND as well— demand is strong and capacity can only scale so fast, but even more than that, the Big 3 memory makers are actively eschewing NAND to capitalize on the DRAM upcycle.

This also makes sense— in the hierarchy of profitability, NAND is squarely at the bottom of the pyramid. It is the least diversified and easiest to manufacture. It might sound like I’m making the case against investing in Sandisk as a pure play NAND manufacturer, but it is exactly this long-term un-attractiveness that makes the short-term play so compelling.

To show you what I mean, let’s take a look at the expected fab openings for the Big 3 over the next few years:

• SK Hynix recently pulled forward the opening of its M15X fab in Cheongju, South Korea, which was originally intended for NAND production but has been re-purposed to service DRAM demand instead. It has also fast-tracked its $90 billion Yongin fab to an early 2027 opening, focusing nearly exclusively on HBM.

• Samsung is ramping its P4 fab, which has minimal NAND capacity and has shifted much of its cleanroom space to DRAM. Its P5 will similarly be dedicated to HBM and DDR6.

• Micron is expected to ramp its new Boise facility, Fab 15, to produce DRAM and HBM and a planned megafab in Clay, New York which will also produce leading-edge DRAM.

Notice the trend? Nearly all greenfield memory capex is going towards HBM and cutting-edge DRAM while NAND has been left to languish. The only real capex being contributed to NAND is Sandisk and Kioxia re-tooling their fabs for higher density NAND SSDs and Micron expanding its Singapore NAND facility, but wafer output isn’t expected until 2H2028.

So while Samsung, SK Hynix, Micron are dominating headlines with HBM investments, NAND is hurtling towards a supply-demand imbalance that could last years longer than the HBM/DRAM shortage. And the DRAM upcycle is likely to last until at least 2028, and likely longer, based on the demand backlog that still needs to be cleared plus the expected additional AI capex in the years to come. In sum, for as long as DRAM is in short supply, NAND will be even more so.

Just last week, Reuters reported that SK Hynix has received unprecedented offers from Big Tech hyperscalers to lock in multi-year memory chip purchase agreements and even to help finance expensive EUV lithography machines necessary for bleeding-edge DRAM manufacturing. Evidently, DRAM demand is still aggressively chugging along, which means the runway for NAND could be longer still.

Investor Takeaway

Many are citing P/E ratios as evidence of undervaluation (or overvaluation) but the truth is that we are so far removed from valuation metrics because of the speed and magnitude of this trend that we need to rely on other indicators. An analysis of past cycles, which I’ve done in previous articles on MU, demonstrates that memory stocks tend to inflect downward in the months before TTM revenue peaks. In plain English, 1) following momentum in operating results is profitable and 2) trying to bail out of an uptrend early leaves money on the table.

Perhaps this cycle will be different because of the sheer enormity of this supply shortage, but historical evidence points towards years of additional gains as NAND remains scarce and new investments are funneled elsewhere. SNDK is essentially the only pure play NAND maker and I think the stock will continue to benefit from market tailwinds.

I should note, this is far from a risk-less venture— any indication that NAND demand is easing or that the AI trend buckling is likely to crush the stock in an epic draw-down that could exceed 50%. This play is not for those with a low risk tolerance. I nibbled on a position at $1400 and will likely continue to add over the next few weeks and especially if we see any large pullbacks.

I would also note that volatility in this sector, and memory specifically, has been extremely high— SNDK’s beta is close to 2.5, meaning it’s approximately 150% more volatile the broader market. If this sounds like a scary prospect to you, you might be better off waiting for a better entry point, though with the caveat that the stock could run significantly higher in the meantime.

In the wake of the ongoing NAND shortage and how little capex is flowing into alleviating said shortage, I am rating SNDK a Strong Buy.

Fin

If you made it this far, thank you so much for reading! These long-form pieces take a lot of research and effort to put together and I truly appreciate y’all for taking the time to peruse them. If you enjoyed the read and know anyone that might be interested, please share it so we can grow this community and become better, smarter investors together!

And don’t forget to subscribe for more :)

As always, thanks for reading and best of luck!

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Disclosure: I held a beneficial long position in MU and SNDK at the time of this writing.

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

The year is 2023. Well, it’s actually New Year’s Eve 2023 and you’re reflecting on what a great year it has been for your portfolio. Your top winner? Why, it’s Nvidia (NVDA), of course, which closed the last trading day of 2023 at around $50 per share, up 240% for the year. Magnificent.

You’re at a party with your friend—let’s call him Jensen—and you’re discussing investing ideas for the upcoming year. Jensen’s portfolio didn’t do so hot and he’s looking to spruce up his returns. You give him the hard sell on NVDA— the company has dominant market share and demand for GPUs is only going to keep going up! He seems convinced but also hesitant. “It already went up 200%+ this year though, how much higher can it go?”

There it is. A simple sentiment that everyone has felt at one point or another in their investing endeavors that seems so right despite making no logical sense. Why is it easier to continue to hold a stock that has gone up 200% rather than buy a stock that has done the same? A gambler might call it “playing with house money,” which is a silly concept implying that money you win from your gambling activities is somehow not yours so it’s okay to lose it? Hmm, makes even less sense when I explain it.

I bring this up to emphasize the following— becoming a successful investor is, first and foremost, about neutralizing your emotions and your biases to make the optimal decision at any given point in time.

If a stock has gone up 200%, that doesn’t necessarily mean it’s too late to buy. Conversely, if you’re holding a stock that has gone up 200%, that doesn’t mean you shouldn’t take profits and sell. We must continue to evaluate our positions from a place of objectivity. By avoiding common trappings and fallacies— that is how we win.

Sector Recap

I bring this up because we are currently witnessing this fallacy on a broad scale. It started with NVDA, which I used in the example above, and has now trickled down to memory, AI networking, and elsewhere. Micron (MU), which I have been long and bullish on since September 2024 (Seeking Alpha gift article can be read here), is up 650% since my initial call and all throughout the run, I have seen folks saying it’s too late to jump in. It already went up 200%, how much could possibly be left? 300%, 400%, etc.

We are now seeing the same pattern play out in networking stocks. For those of you who read my inaugural Substack stock analysis, you know that I chose Credo (CRDO) as my top stock pick of 2026 on March 23rd, when it was trading around $100 per share. It has since gone up around 85% in a month and change:

Exhibit A why you should subscribe to my Substack! (segue, nailed it✅)

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I received messages and comments asking if it’s still a buy— surely an 85% run means there can’t be much left in the tank, right? So I went back and took another look, trying my best to eliminate my biases, suppress emotions, and just look at the facts. And what I found re-confirmed my thesis that CRDO is a buy-and-hold for the next five years at least.

But CRDO is not the only AI networking stock that has been running, and, in this article, I’d like to focus on a company that is at the forefront of data center communication technology and that I think has the chance to become a powerhouse in the industry for years to come… but I’m going to keep up the suspense a little longer :)

My preference is always to provide industry context before I get into the analysis of individual companies because I think seeing the big picture is how we truly understand what we’re buying and how we identify trends, current and future.

NOTE: I went over some of this in my CRDO article, so if you feel quite confident you understand AI data center networking, you can skip to the section called “The Future Is Light“

The Path Of A Prompt

Let’s begin as we usually do with an example. Say you’re an engineer at Anthropic and you’ve been given the task of debugging a failing Python workflow. The error logs are a bit dense and you’d rather just have a summary of the issue than read through the trace; you boot up Claude Code (or Gemini if you’re feeling a bit spicy) and paste the error message in with a prompt at the end in the vein of “pls help”. You hit “Enter”.

The prompt is packaged up nice and snug and sent over the Internet using TCP to the nearest available data center that services whichever LLM you end up using. Here, it is greeted by a series of load balancers that determine where your request should go and is then forwarded on to a hierarchical series of network switches until it reaches what is called a top-of-rack (ToR) switch. It looks kinda like this:

The ToR switch is the brains of the operation and its main job is to relay instructions to the processors in the server rack, to receive the resulting computations, and to route the data back up the switch hierarchy. Miraculously, this process only takes a few seconds. How crazy is that?

Within the data center, there are three primary domains of communication: intra-rack, inter-rack, and external; in this article, I want to explore how these domains are changing in the modern data center and who stands to benefit.

External is the easiest starting point because not much has changed in this regard, even today— data center operators have used fiber optics, or light, for long-haul since way back in the 80s, mainly because photons over fiber can maintain signal integrity over much longer distances than electrons over copper. In contrast, within the data center (i.e. short-haul) nearly all connections were copper because it was cheap and simple, especially relative to optical solutions. This remained more or less the status quo for 30+ years.

That is, until AI came about and kicked off the greatest capex race in the history of the world— we’ve gone from OpenAI releasing ChatGPT in late 2022 to, by some estimates, more than $800 billion in expected AI capex in 2026. As models get better, data centers need processors and SSDs with more memory, they need more computational power to crunch numbers, and, critically, more bandwidth for all the different components (GPUs, network switches, etc.) to talk to each other. We’ll be focusing on that last part.

Data centers are moving to speeds of 1.6T and 3.2T (terabits per seconds) because higher bandwidth means more compute utilization— less idle time means faster calculations, faster responses, and a potential competitive edge. Here’s where things get interesting from a networking perspective.

As I discussed at length in my post on Credo, the intra-rack and inter-rack copper connections, which have been the industry standard since forever, are increasingly losing relevance for two main reasons: signal degradation and heat. At 1.6T speeds, sending a signal over copper uses a lot of heat and the signal can only travel about 0.5 meters before it degrades into indiscernible noise, making it unusable for inter-rack links and even in most intra-rack links (between GPU nodes and the ToR switch).

To address this issue, Credo invented active electrical cables (AECs), which increase the range of copper wire by adding a digital signal processors (DSP) to either end of the link that cleans, amplifies, and re-generates signals, stretching copper’s viable range to around 3-5 meters at 1.6T. This has kept optical at bay by reinforcing copper’s cost edge, but as transmission speeds race higher, copper’s domain is getting shorter and shorter and interconnects are getting too hot. This is the “copper wall”.

By the time 3.2T launches, probably sometime in 2028, inter-rack links within cutting-edge data center deployments are likely to be mostly optical and a good percentage of intra-rack links will be as well. Now, you might be thinking: what the heck is an optical link? Great question!

In a nutshell, an optical link, commonly known as a transceiver, converts electricity to light, sends the signal over a medium, usually fiber glass, and then converts the light back into electricity. Easy right? Well, not quite. A transceiver has a bunch of quite complex components including but not limited to the laser (emits light), modulator (turns light into data), photodetector (detects signal and converts into electricity), and two DSPs to maintain signal integrity.

Optical transceivers are more expensive than copper but are fast becoming a necessity as signal degradation and heat push the latter to the limit. At 1.6T, AECs still dominate intra-rack connections and optical transceivers handle most inter-rack connections, but this is changing rapidly. Credo is the foremost AEC player and even it has seen the writing on the copper wall, evidenced by its acquisition of DustPhotonics.

Before moving on, we have to make an important distinction between pluggable optics and co-packaged optics (CPO) / near-port optics (NPO).

Pluggable optics have been the standard for decades and are pretty much exactly what they sounds like— transceivers that literally plug into the network switch on one end and into the GPU node on the other. In a pluggable optics setup, a signal is sent by a GPU, travels over tiny copper links to the edge of the printed circuit board (PCB) where it reaches the pluggable transceiver, is amplified and re-generated by a DSP, converted to light, and sent on to the switch where it is converted back into electricity.

The problem with this approach is (I sound like a broken record), signal integrity and heat. At higher transmission speeds, even just getting electrons from the GPU to the transceiver plug generates heat and causes attenuation, requiring a power-hungry DSP to re-construct the signal. So optics makers had an idea: just cut out the copper links entirely! This is where a technology called silicon photonics (SiPho) comes in.

I explained this is in more detail in my most recent Credo article, but essentially, silicon photonics replaces the high-speed copper traces on a PCB with a photonic integrated circuit (PIC), which contains all the necessary components to guide photons from the GPU to the fiber medium and then on to the switch. The key here is that the PIC immediately converts the electrical signal that comes out of the GPU into light, which maintains signal integrity and enables the transceiver to utilize a less powerful DSP to reconstruct the data.

But what really makes SiPho necessary for the next generation of data centers is that PICs are fabbed on silicon, meaning they can be included directly on the processor substrate (co-packaged with the optics, or CPO) or on a module that sits right next to the processor (near the optics, or NPO). Broadcom (AVGO) estimates that CPO reduces power consumption by around 40%, making it invaluable as heat dissipation becomes a data center imperative.

Another major difference between pluggable optics and silicon photonics is that each process requires a different type of laser in its implementation:

1. Modern pluggable optical transceivers typically use electro-absorption modulated lasers (EMLs).

2. SiPho uses continuous wave (CW) lasers.

More on this later.

Now that we have the requisite context, let’s explore why hyperscalers have been transitioning to SiPho so aggressively.

The industry is shifting hard to SiPho in 1.6T and 3.2T deployments for three main reasons:

1. Because CW lasers can be made using widespread CMOS manufacturing they are cheaper and can be built in higher volumes than EMLs, which require complex and expensive indium phosphide (InP) fabs.

2. At 1.6T, transceivers actually have eight “lanes” that each work at 200G speeds. With pluggable optics, a transceiver requires eight separate EMLs for each lane whereas SiPho can split the light from 2-4 CW lasers to service all eight lanes, reducing the number of lasers in the system.

3. Because SiPho requires fewer lasers, cuts copper links from the system, and eliminates the need for a powerful DSP, the energy savings are significant and heat generation is substantially reduced.

Because of these benefits, SiPho is the future of inter-rack and intra-rack interconnects and will likely take a larger share of the market as transmission speeds increase.

Okay that was a lot of words and acronyms, let’s take a quick break:

Or if you’re hungry for more, feel free to continue, of course! The next section will discuss one my top stocks to own over the next five years…

The Future Is Light

In the shift towards optical in the data center, the winners will be the companies that sell components that are both 1) needed for cutting-edge deployments and 2) not easy to manufacture. One such company is Lumentum Holdings (LITE).

Lumentum makes the most cutting-edge optical transceivers around and the lasers that make them tick. This has not gone unnoticed by the market as the stock has been on an absolute tear over the last 12 months:

“But Kumquat Research, you’re telling me to invest in a company that has already gone up 1200%? How much higher could it possibly go!” Well, dear reader, recall our discussion at the very beginning of this post on fallacies and acting without emotion. Yes, LITE has already blown up— but I think there’s more fruit left on this tree.

In the section above, I detailed the infrastructure of your average data center and where the industry is headed (i.e. 1.6T and 3.2T); Lumentum is one of the companies enabling this shift— it manufactures the EMLs, CW lasers, and PIC modules necessary for this transition. Let’s start with EMLs.

EMLs have been around for a while as the high-end laser of a choice for long-haul connections, but it has only been with the advent of AI and an increased appetite for transmission speeds that they have made their way into the data center proper. EMLs are reliable, maintain great signal integrity, and can function over nearly any distance, with the downside that they are quite expensive and generate a lot of heat. These downsides have kept them relegated to use cases that are too far for copper/AECs.

However, as 800G data centers have proliferated rapidly and 1.6T deployments are just getting started, even though EMLs aren’t viable in the intra-rack domain, demand has soared well past capacity, resulting in a severe shortage that has seen prices skyrocket. On the company’s most recent earnings call (Q3 2026), CEO Michael Hurleston stated the following in response to an analyst question about supply-demand dynamics:

Look, I think we’re still chasing behind relative to demand. We are steadily increasing supply. . . The supply-demand imbalance is probably even higher than we reported in our last call, somewhere greater than 30%. I think last time we gave a metric of 25% to 30%. We still seem to be behind significantly.

We had conversations today with customers, significant customers looking to really up their demand and get output from us, and we simply can’t service that. So we are stepping up.

This shortage, and its affects on boosting Lumentum’s margins, have been partially responsible for the stock’s run this year:

The bulk of Lumentum’s revenue is from selling 100G EMLs (for 800G deployments), and the company is really the only player making 200G EMLs for 1.6T at scale, which is becoming a larger part of the mix; management also said they expect to sell 50% more EMLs in FQ2 2027 (December quarter) YoY as capacity ramps up. But the market and customers don’t usually tolerate such conditions for long, so in steps the up-and-comer: CW lasers.

Partly because EMLs are so expensive and in such short supply and partly because of the power savings, the adoption of CPO with silicon photonics and CW lasers has picked up significantly. As we discussed earlier, the cost-benefit analysis for SiPho starts to inflect towards the benefit side at 1.6T and Lumentum stated as much on its Q2 2026 earnings call:

We would expect silicon photonics to be the majority of the transceiver shipments in -- at the 1.6T node. We think the numbers are so large based on what we're seeing in terms of the demand from our customers, that our EML shipments even in the face of a mix shift toward silicon photonics. The absolute number of EMLs will go up for us and rather appreciably.

As data center operators race to outrun heat build-up, SiPho will become more and more commonplace, but EMLs will remain in use for a while to support deployments that can’t easily replace pluggable optics and for applications where SiPho isn’t quite up to snuff yet. This puts Lumentum in the Goldilocks zone— it is simultaneously reaping the benefits from both sides as the industry transitions from EMLs to CWs on the leading-edge.

Now, you might be wondering: if CWs are easier to make, lower margin, and fewer of them are required in a transceiver, doesn’t this hurt the company’s value proposition long term? Lumentum is way ahead of you.

EMLs are certainly the more valuable product per unit, but, because of the manufacturing complexity, scaling them to proper volumes in the age of AI isn’t realistic. On the other hand, CW lasers, while more commoditized, can be sold in much higher volumes, resulting in higher revenue and profits despite lower overall margins. Further, CW lasers are still manufactured using indium phosphide, which isn’t easy to grow and refine.

Even more important than the shift from EMLs to CW lasers is Lumentum’s shift a couple years back to producing the whole optical transceiver rather than just selling the lasers themselves. The company had actually sold its transceiver business in 2019, well before AI came on the scene, and re-entered the market with its purchase of Cloud Light in 2023 for $750 million.

By selling the entire transceiver along with its cutting-edge lasers, Lumentum has positioned itself to hyperscalers as the all-in-one solution to optics—whether intra-rack, inter-rack, or external—at a time when integration is accelerating exponentially. Naturally, this requires tight partnerships with the drivers of AI capex and, accordingly, the company counts many of the world’s top companies as its customers, including one especially beneficial partner— Nvidia.

In March, Nvidia announced a $2 billion investment in LITE and a separate multi-billion purchase agreement for EMLs and CW lasers, including committing to using Lumentum components in CPO designs for its upcoming Rubin and Feynman architectures. Nvidia has been making similar strategic investments throughout the AI supply chain, including with Lumentum’s rival, Coherent (COHR), and its buy-in represents a seal of approval regarding the company’s technology as well as an indicator that cutting-edge AI infrastructure will include Lumentum products for the foreseeable future.

This is borne out by operating results, which the company reported just this week in a Tuesday earnings release:

Revenue came in at $808 million (+78% YoY/+21% QoQ), non-GAAP gross margin was 47.9% (+1270 bps YoY/+540 bps QoQ), and non-GAAP EPS was $2.37 (+315% YoY/+42% QoQ) as AI demand continue to push sales and margins higher. For those of you have followed my articles in the past, you’ll immediately recognize this as a prime example of my favorite two words in stock analysis: operating leverage. The ultimate green flag for a growth company is when sales and margins are both rising in tandem, indicating that the business can scale sustainably.

The company also has a stellar balance sheet, boasting $3.1 billion in cash & equivalents & short-term investments relative to virtually zero debt (I love un-levered businesses).

Lastly, Lumentum provided the following guidance for Q4 2026:

At the midpoint, revenue is expected to be $985 million (+105% YoY/+21.8% QoQ), operating margin is projected at 35.5% (+2050 bps YoY/+330 bps QoQ), and EPS is targeted at $2.95 (+235% YoY/+24.5% QoQ). My main takeaway from this guidance is that growth is expected to not only remain robust, but to accelerate. Margins will likely only increase as 1.6T modules ramp and become a larger part of the sales mix.

What makes Lumentum such an interesting play is that it currently sells high-end products with okay, not great, overall market penetration (external and some inter-rack), but with a massive market opportunity ahead of it as inter-rack goes almost completely optical and even intra-rack moves away from copper. This means that, not only is Lumentum benefiting from the increasing number of data center deployments, but is also gaining share within each deployment.

On that note, I would also be remiss if I didn’t at least mention one of Lumentum’s smaller but growing segments in optical circuit switching (OCS) since it could be key to future data center content share. The goal behind OCS is, basically, to replace spine network switches, which route traffic to and from ToR, or “leaf,” switches, with a series of mirrors that can bounce light around instead of converting it into electricity and then back to light before routing it to its destination.

Converting between electrons and photons makes up an overwhelming majority of the heat generated in a data center and cutting that process out of the system saves energy, time, and money. Lumentum manufactures the micro-electro-mechanical Systems (MEMS) mirrors that power these OCS complexes, which are being explored for possible GPU-to-GPU communication as well. If that movement gains steam, the windfall for Lumentum could be massive.

Google currently uses OCS made with Lumentum MEMS in their TPU deployments and other hyperscalers are sampling the technology as well, but it still has a few downsides that will likely keep it niche-bound for now. It is nevertheless an interesting project of which investors should be aware.

Investor Takeaway

The day Lumentum was set to report earnings, the stock reached an all-time high of $1021 per share before selling off around 12% over the next two trading days and is down again after hours on Thursday as I write this following some poor prints from elsewhere in the tech world. This is indicative of the kind of volatility that lays ahead for LITE and the AI networking sector as a whole— huge swings up and down of 20% in a week are not rare occasions.

Which is to say, this stock is not for the faint of heart. The AI build-out is happening rapidly and capex is massive and real, but the cart is certainly being put before the horse a bit and valuations bear that out. LITE is trading at a forward P/E of around 115, which is quite lofty, but the PEG ratio of 0.86 demonstrates just how much the company is expected to grow.

Over the next five years, I think we will see Lumentum continue to benefit from an increased share of data center capex and its bleeding-edge tech will be the backbone for AI infrastructure deployments from 1.6T to 3.2T to whatever comes after. The company has gone from a component supplier to a vertically-integrated photonics powerhouse and, despite the run-up over the last 12 months, I will be putting aside my biases and emotions and have bought into the updraft.

As I said, I expect volatility to remain elevated for the foreseeable future, which can be a bit frightening, but also adds opportunities to add to positions on short-term dips and market panic. I initiated my coverage on LITE over on Seeking Alpha (gift article link here) at a Strong Buy and I am carrying that rating over to Substack. I also opened a position at a cost basis around $800 per share and, per my previous sentence, I will be adding to that position within the next couple days to take advantage of the ongoing sell-off.

Another important note is that this is not a short-term play for me and I fully intend to hold LITE for at least five years, depending on how the AI build-out shakes out. The catalysts I mentioned might take a bit to materialize and anyone looking for a short-term win might want to look elsewhere.

Fin

Thank you, dear reader, for sticking around for another long one, I truly put in hours of research and care and learned quite a bit myself; I trust that you got something out of it as well! These long-form pieces require a good deal more concentration and effort than I’m used to, but they are also a good deal more fulfilling and I really appreciate the responses and feedback I get from you all in return!

If you did enjoy this piece and my other content, I’d encourage you to please share it so we can grow our community and become better, smarter investors together. And don’t forget to subscribe for more!

As always, thanks for reading and best of luck!

Disclosure: I held a beneficial long position in LITE at the time of this writing.

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

Hey folks :)

Before getting into this post, I just wanted to shout out each and every one of you for helping me to build this Substack from basically square zero. My previous article on CRDO just broke 1,000 views and my subscriber count has jumped from ~20 to over 90 in such a short amount of time and I’m so grateful to all of you for making that happen! I’m always happy to read and respond to comments or chat with the community so feel free to share your thoughts and I’ll share mine.

Without further ado, let’s get into it!

A Quick Performance Recap

First impressions are important, wouldn’t you say? I have a pretty sizable following on Seeking Alpha (you can check out my profile here), where I’ve been publishing stock analyses for over a decade, but my Substack is brand new so, naturally, I was a bit nervy making my inaugural stock pick on the platform last month— when trying to build a reputation as an individual that understands markets, it can certainly derail your momentum if your first rec crashes and burns. But with a ton of research, a ceasefire, and a solid helping of luck, it looks like the Kumquat Research project is off to a decent start here on Substack!

For the full performance recap, on March 23rd, I published an article entitled “My Top Stock Pick for 2026” in which I detailed the inner workings of Credo Technology Group (CRDO) and recommended the stock as a Strong Buy. At the time, the stock was trading at around $100 per share. Fast forward a bit and on April 24th, less than one month later, CRDO closed at $195, good for a 95% gain. You can read that post here:

I would of course be lying if I told you that I expected this much of a jump in such a short period of time, but as the old adage goes, it’s better to be lucky than good, right? While the magnitude of the move was unexpected, it was driven by all of the catalysts that I laid out in the article above— Credo’s persistent progress in procuring data center business, the company’s ongoing pivot to optical, and market fears creating a favorable risk-reward profile for investors. More on this later.

The primary purpose of this article is to update my thesis in the wake of Credo’s purchase of DustPhotonics for around $1.3 billion in cash, stock, and milestone awards, and to discuss how the acquisition fits into the company’s long-term strategy in the data center.

And just one final note before we get into it, I’ll do be doing a bit of recap throughout, but I’d highly recommend you at least skim through my previous Credo article before reading the rest of this post in order to get a deeper understanding of the context and takeaway here.

Going All-In On Optical

It has certainly been quite a run for CRDO, but it’s not the only network connectivity stock that has been getting a boost lately. In just the last month:

• Marvell (MRVL): +77%

• Broadcom (AVGO): +33%

• Celestica (CLS): +37%

• POET Technologies (POET): +165%

And the list goes on. This trend has been picking up steam for weeks as investors have begun to realize the scale of the data center networking bottleneck, and the rally was further validated on Thursday, April 23rd when MaxLinear (MXL), reported Q1 earnings and outlook that blew past estimates on the back of strength in its optical data center business— MXL ended Friday up a whopping 80%!

This has been, in my opinion, a long time coming. AI demand has rippled through the data center supply chain like a wave starting with compute then to memory, on to CPUs, and now to network equipment like Ethernet switches and optical transceivers.

1. The first beneficiary was Nvidia (NVDA), which had a monopoly on AI accelerators for a few years and benefited handsomely (shares are +1270%+ in five years) as a result.

2. Next was Micron (MU), which has 5X’d in less than a year on booming demand for HBM, a major component in AI accelerators.

3. Flying under the radar a bit until today was the server CPU shortage, which has led AMD and Intel (INTC) to run up by 70% and 85%, respectively, over the past month including a blowout on Friday as Intel reported earnings and subsequently jumped by more than 20%+.

4. Lastly, as I detailed above, networking stocks, like CRDO, have been running up lately in anticipation of a data center bottleneck on connectivity that could last at least the next few years and possibly longer.

During my deep dive on Credo last month, I provided a breakdown of the current AI landscape and claimed that:

1. The company’s active electrical cables (AECs) still have a significant role to play in the data center despite an approaching so-called “copper wall”.

2. Credo’s optical pivot has real momentum and it is well positioned to benefit from both copper and optical during the industry’s transition.

I don’t want to re-hash too much so I’ll be focusing this piece on the second point above, but, suffice to say, I think active copper still has some good years left before being phased out and will continue to be a high-margin winner for the company during that time.

Let’s instead turn to Credo’s inroads into the optical market, specifically its acquisition of DustPhotonics. Prior to this purchase, the company’s optical strategy consisted of (this is the last list, I promise!):

1. Selling digital signal processors (DSPs) to original equipment manufacturers (OEMs) to build optical transceivers. Credo’s next-generation 1.6T DSPs are called Bluebird and Cardinal.

2. ZeroFlap, which monitors telemetry through a link using the DSPs and phones home to a software called PILOT, in order to prevent costly network flaps.

3. Launched (in March 2026) its own in-house design for an optical transceiver using its 800G Robin DSP to compete on the entire interconnect.

That last item is particularly relevant because it’s the perfect example of Credo’s long-term strategy— as the industry moves towards vertical integration and components get closer together, survival means being able to offer as much of the overall product package as possible.

The reason for this, as it is with most new innovations in the data center, is physics. The first physics problem is heat build-up— As AI accelerators, optical interconnects, Ethernet switches, etc. become more powerful, they generate substantially more heat. If this heat is not dissipated, the entire server will literally melt.

The second physics problem is signal integrity. This is also the reason for the “copper wall”— As transmission speeds increase, the distance over which a signal remains usable and readable decreases.

Solving these issues necessitates moving components as close together as possible, onto the same substrate ideally. Now, you might be wondering, “but Kumquat Research wouldn’t convergence increase heat? Wouldn’t that be like all the components just giving each other a big hug or something?” It’s a good question and the full answer is probably out of the scope of this article, but essentially, the bridges between components are the most heat-intensive links in these systems, which means it’s extremely expensive anytime a signal has to be converted, travel somewhere, and be converted back.

Let’s imagine a data center running 3.2T components where AECs have been pretty much phased out and most connections are optical: a GPU node generates a signal that travels along the copper in the PCB to the transceiver where the DSP converts the signal to light, which then travels the rest of the way to the Ethernet switch. When it gets to the switch, the signal is either converted from light to electrons with a second DSP or by the switch itself (Linear Receive Optics or LRO configuration).

Since, as we discussed, moving and converting signals is the most expensive part of the system from a power consumption standpoint, driving an electrical signal through the copper links on the PCB and converting it to light with a DSP so that it can make the trip to the networking switch are considerable sources of power and heat; as cutting-edge data centers approach 1.6T and 3.2T speeds, eliminating these costly steps is fast becoming less of a luxury and more of an imperative.

This is where DustPhotonics comes in. The acquisition gives Credo access to technology that has been around for a while but is only now really securing major design wins in data center deployments: silicon photonics (SiPho).

A New (Ph)rontier

Optical companies like Lumentum (LITE) have been developing and pushing silicon photonics for years, but hyperscalers have almost always preferred to continue using cheap and effective copper instead of making the switch to more complex and expensive photonic solutions. But with copper rapidly becoming ineffective for the most cutting-edge AI data centers, the technology has suddenly jumped to the forefront.

But before we get into its application in the data center, let’s quickly discuss what silicon photonics actually means— in a nutshell, SiPho aims to replace sending data as electrons over copper interconnects to instead sending photons over silicon “waveguides”. While the former is cheaper, simpler, and less error-prone, the latter generates significantly less heat and can carry signals further (both of which are necessary in the age of AI).

These waveguides, so named because the silicon guides the light waves from one place to another, are the physical pipes in what are known as photonic integrated circuits (PICs). PICs include the waveguide, lasers, modulators (data → light), photodetectors (light → data), and any other components that are involved in a SiPho system. Returning to the data center, what does this actually look like in practice?

To answer that question, we come to the concept of co-packaged optics (CPO), which has been getting major buzz as we approach 1.6T and 3.2T and is the biggest driver, in my opinion, of Credo’s decision to acquire DustPhotonics. As I mentioned earlier, heat and signal degradation are forcing components closer together within the server rack, which has led to the idea of replacing pluggable transceivers with optics that are already part of, or co-packaged with, the chip. As a visual learner, I know this can be a bit hard to picture so let’s take a peek at a Broadcom Tomahawk 6 Ethernet switch without co-packaged optics as a visual aid:

These switches are the bi-directional brains of the data center— on the rack level, a switch sends instructions to the GPU nodes on what computations perform and the GPU nodes send the results of these computations back to the switch.

The first thing that stands out in this picture is the sheer number of ports— 64 in total— each of which can fit a pluggable optical transceiver. As you can imagine, this panel generates a ton of heat. Moving the optics within the switch, can, by some estimates, lower power consumption by 30-50%, significantly reducing the amount of heat produced by the system. We now have the context to answer how silicon photonics enables CPO and solves the heat and signal degradation issue.

To recap it one last time since this can all be pretty tough to grok right away, in most data centers around the world today that use optical interconnects instead of AECs, electrons from a GPU, representing bits of data, are sent over copper built into the PCB to the edge of the substrate, are converted to light by a DSP within an optical transceiver, and travel to the switch where they are converted back into electrons. Simple but hot.

CPO allows you to replace the power-hungry copper links and DSPs with a transceiver that’s built right onto the chip die. So rather than bits traveling over copper to the edge of the PCB then to a DSP, instead a silicon photonics PIC is used to immediately convert electrons into photons within the chip substrate, which are then sent straight to their destination. This works because, as the name suggests, silicon photonics PICs can be fabbed directly onto the same silicon substrate used for the processors.

Another similar application is near port optics (NPO), which entails building a PIC on separate silicon that can be easily connected to the primary processor silicon rather than producing both on the same substrate as is the case for CPO.

These approaches solve two major issues that I’ve repeated throughout this article (oops I lied, here’s one more list):

1. Signal degradation: In a typical pluggable optical transceiver, the transmitter DSP is usually needed to clean, amplify, re-generate, and convert the electrical signal to light before sending it on, but in CPO, the electrical signal is converted to light immediately. This maintains better signal integrity and allows lower-power DSPs to be used or can even eliminate the need for DSPs altogether (Linear CPO).

2. Heat: Building off the first point here, much of the power savings and heat reduction from CPO comes from removing the DSPs and cutting out the need to send signals from chips over copper links to transceivers.

The move to CPO means that, not only will AECs begin losing ground as transmission speeds go up, but pluggable optics/cables in general seem poised to fall out of favor within 5-7 years, at least at the bleeding edge. Or, put another way, the advent of CPO basically means you get on the die or you die (🥁). Credo’s optical shift should signal to investors that the company is very aware of this reality and is pushing hard to put itself in the best position to benefit from the next big trend in AI infrastructure.

Investor Takeaway

To bring it all home, I think Credo’s purchase of DustPhotonics is the missing piece of the puzzle, which is why the stock saw such a positive reaction to the news:

The stock had already been on a run, up 34% in two weeks, when on April 13th, it announced the acquisition and jumped by an additional 20% the following day, kickstarting the current run to nearly $200 per share. As I mentioned in my previous article, the market has been pricing Credo like a one-trick copper pony even as operating results improved and as it has made promising investments into widening its optical footprint. The DustPhotonics move is the strongest sign yet that this notion is incorrect.

In its press release announcing the acquisition, the company stated the following:

These SiPho PICs are deployed in transceivers at leading hyperscale AI clusters and are also in design for leading Near Port Optics (NPO) and Co-Packaged Optics (CPO) applications.

While the company continues to make progress selling individual components in the optical space, and DustPhotonics continues this progress, creating the whole optical enchilada is where the industry is heading.

As such, the next step for Credo will likely be to create a 1.6T pluggable optical transceiver using the Bluebird and Cardinal DSPs it already sells to other manufacturers since NPO and CPO isn’t likely to reach an inflection point until 3.2T at the earliest (expected sometime in 2028). But once 3.2T and later arrives, the DustPhotonics IP will be integral to creating Credo-designed optical transceivers that include the PICs fabbed directly onto processor dies, the fiber that carries the photons into and out of the nodes, and whatever DSPs are necessary, if any, to facilitate the process.

Despite the move upward, I think that the market is still underestimating the windfall to Credo and that there’s still a risk premium on the stock due to the copper association. It’s quite common after sharp jumps like the one we’re seeing with CRDO and across the sector that volatility will follow and that some of the gains may be given back. Personally, I will be holding my CRDO position through any potential chaos and, depending on the magnitude of dips, will consider adding on the way up.

While I don’t think the stock is necessarily a long-term hold due to my concerns regarding the stability of the AI trend beyond a few years, I do feel assured in following the capex, which indicates that infrastructure spending will remain strong for at least the next few years. This will benefit CRDO and, barring any unforeseen upheaval, I anticipate holding throughout that timeframe.

Fin

Alright, not quite as long as the first one but still a pretty sizable read. A very special ‘thank you’ to all of you who read the whole way through and a less-special ‘thank you’ to all those who skimmed (but you too are appreciated nonetheless)! Like I said in the beginning of this post, I’m so grateful to all of you for helping me grow this community so we can all become better, smarter investors together.

And if you think anyone you know would be interested in what I wrote here, please share this post— I always appreciate the insight that new potential members bring to our little community.

And, of course, if you liked this post don’t forget to subscribe for more! I think my next topic of discussion will be the CPU market craziness that is causing the stocks of Intel and AMD to soar.

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Here’s a gift link to my recent Seeking Alpha article on AMD to whet your appetite :)

And lastly, as always, thanks for reading and best of luck!

Disclosure: I held a beneficial long position in CRDO at the time of this writing.

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

I figured I’d add this section as a prologue considering the stock that is the subject of this article is now up more than 80% since publication a few weeks ago and readers might have questions regarding whether they missed out on all the fun. While it happened faster than I expected, all the catalysts that I discuss in this piece are becoming ever more apparent to the market, which kicked off this recent run, and which I expect to kick off more runs in the second half of 2026, albeit with likely elevated volatility.

So to answer the question of whether there are more gains to be had: the bad news is you missed out on 80%+ by not subscribing to my Substack (hehe) but the good news is that I think this run has more legs in the short-term. So with that context in mind, please enjoy!

A Quick Preamble

I know it’s been a while since my inaugural post and you’ve no doubt been wondering “where, oh where, has Kumquat Research been”? How can I navigate this geopolitical crisis without his sagely advice?? Well, I was actually busy taking a much-needed vacation, which was then followed by having my flight to Israel (for a wedding) canceled due to that little kerfuffle going on the Middle East, leaving me out of commission for what is probably the most striking sell-off in years, if not since COVID.

But do not fear, for I am with you. I have some macroeconomic thoughts on the current happenings, but I’ll save those for another post. With this post, I’d like to remedy the lack of actual stock recommendations on this Substack and add some content beyond my self-aggrandizing introductory post. So let’s get into it!

Note: If you don’t care about my creative writing exercise and just want to know what the damn stock pick is, you can skip to the “Networking Is Sexy” section.

AI: From Birth to Maturity

Picture this: The year is 2022. You’re sitting down in the dining room to eat Thanksgiving dinner (or on the couch watching football if you’re my family). The turkey is juicy, the gravy delicious, and the stuffing to die for. Things are simple. The world makes sense. Nvidia (NVDA) is trading at around $15 a share and “AI” is still just a term most people associate with movies like 2001: A Space Odyssey and Blade Runner. Just a week later, on November 30, 2022, OpenAI would release ChatGPT and kick off one of the most rapid, capital-intensive arms races in history. It occurs to you that this ChatGPT thing might have some legs so you open up your portfolio and use all your available cash to load up on NVDA, Amazon (AMZN), Microsoft (MSFT), and Google (GOOG).

Now fast forward three years: It’s Thanksgiving 2025. Your mom has Gemini open on her phone and is asking it (way too loudly) the best way to cook a turkey but with a twist. Your uncle, who usually spends the time arguing politics with anyone who’ll listen, is instead glued to his laptop, where AI agents running on Claude and custom-trained on his particular flavor of ideology are arguing with strangers on Reddit more efficiently than he could ever hope to achieve in person. And you? You’re admiring your sweet gains from the bets you made back in 2022, of course.

NVDA is the big winner with 1250% return, but the others are nothing to scoff at— GOOG, AMZN, and MSFT are up 260%, 181%, 105%, respectively, adding trillions, with a ‘t,’ to their valuations. It looks like the so-called “AI bubble” might be a myth after all.

But then, after years of stock market gains driven by optimistic investors enamored by AI’s prospects and who expected investments in the technology to supercharge operating results, something changes in late 2025.

Return on investment, or ROI, which had been the proverbial can, kicked unceremoniously down the road for years, suddenly rushes to the forefront— how exactly is all this massive capital expenditure helping the bottom line, now or in the future? The breaking point ends up being all the hyperscalers announcing absolutely massive increases in planned capex for 2026 in their earnings reports:

Just Microsoft, Meta, Google, and Amazon alone are projecting $600 billion in capex in a single year. This is unprecedented and it appears the market was spooked by the sheer scale of it all. Amazon, which announced $200 billion in capex, is down 15% from its pre-earnings price, Microsoft (~$110-120 billion) is still down more than 20%, and Google ($175-185 billion) remains 15% lower as well. Evidently, the market is getting antsy about whether this will end up being cash well spent.

I’m inclined to agree with that sentiment— $200 billion is a lot of money! That amount could be used to acquire all the publicly-traded neocloud companies (Nebius, CoreWeave, IREN, etc.) AND a good chunk of the top AI data center connectivity players (e.g. Credo, Celestica, and Astera Labs). That sum could pay a 2% dividend to AMZN shareholders for close to 5 years!

So is the AI trade past its peak? Or even dead? The Iran war is certainly doing nothing to help provide clarity on these questions. But one thing is abundantly clear— this capex is happening whether investors are souring on AI spending or not. Hyperscalers appear convinced that these investments will pay off and are determined not to get left behind. History would tell us the obvious move here is the “picks and shovels” play, Nvidia. That would have been correct up until recently, but I think Nvidia has saturated the market and its valuation ($4.2 trillion) is therefore too large to provide the kind of returns folks have come to expect.

For more details on my NVDA thesis, you can read my article on Seeking Alpha discussing the upcoming headwinds facing the company here (NOTE: Seeking Alpha has a paywall, but feel free to direct message me or ask in my Substack chat and I can provide a gift link)!

Don’t forget to read the comments for some extra entertainment courtesy of bulls who were very upset at me for my opinions.

So if the hyperscalers are out and the ultimate picks and shovels play is out, where does that lead us? Well, it leads us right to the primary topic of this post! (segue ✅) The question we now have to ask is— what stock benefits from the current trend?

Networking Is Sexy

As AI ROI comes into focus, big tech pushes ahead with huge spending commitments, and Nvidia tops out, I see the next phase of this sector as one that will reward the vital, yet unsung, players in the data center supply chain— the companies that will benefit from AI spending regardless of whether investors are impressed by the ROI of said spending.

Alright, I could probably tease for a couple more paragraphs, but just to eliminate the suspense, my top stock pick of 2026 is Credo Technology Group (CRDO).

CRDO has followed the same trajectory as other AI powerhouses over the last few years, up by more than 750% since 2022 as data center infrastructure build-outs have exploded to accommodate increasing demand. Despite this impressive run-up, the stock is still well off 52-week highs ($213 per share back in December) as overall AI sentiment has tapered off in the intervening months.

Backtracking a bit, you might be wondering— what the heck does this company even do? A great question, dear reader. The quick and dirty explanation is probably best illustrated by an example.

Let’s say you’re Microsoft and you have customers that want to run AI workloads on your cloud platform. Looks like you need a data center! You find a suitable location with ample power infrastructure and begin procurement. The first item on your list is, of course, compute so you buy a few thousand Nvidia GPUs to run the actual workloads your customers need. But your customers don’t want access to a single GPU at once— they want to be able to easily scale up to as many chips as they need. Alright, let’s get these GPUs connected and available to be used like a true cluster.

The full technical diagram is a bit out of the scope of this post, but picture you have a group of GPUs bundled together called a “node” and you have a group of nodes bundled together called a “rack”. You need your GPU nodes to talk to each other (intra-rack), you need your racks to talk to each other (inter-rack), and, finally, you need your racks to talk to the Internet (external connectivity). The intra-rack layer is where Credo comes in.

With previous generations, a node typically held 8 GPUs, but with Nvidia’s new Blackwell family of processors, a node now has 4 GPUs (these GPUs are connected to each other by proprietary Nvidia stuff that’s not relevant to us right now). Now we need to connect this node of GPUs to an Ethernet switch that routes instructions into and results out of the node.

A common setup you’ll see in many data centers is to have what are called active electric cables (AECs) connect each node within a rack to an Ethernet switch at the top of the rack, allowing it to act as a single unit. The primary function of these AECs is to transmit signals from the node to the Ethernet switch and vice versa. In the past, these links would be made out of regular copper and would transmit signals over short distances.

However, for physics reasons, signals degrade more quickly (i.e over shorter distances) at higher transmission speeds, meaning, as the rate of data communication has risen dramatically, regular copper cables increasingly can’t be used for most data center purposes. In fact, at 1.6T (terabits per second), the next-generation standard for data speed, copper can only hold a stable signal for 1 meter, which doesn’t leave a lot of wiggle room.

AECs get around this issue by including a chip on either end of the cable that can boost the signal. At 1.6T transmission speeds, AECs increase the effective range of otherwise “passive” copper cables from a max length of around 1 meter to between 2.5 meters and 7 meters depending on the use case. Anything outside that range necessitates using optical solutions (i.e. lasers) which are more error prone, more expensive, and more power-hungry.

The King Of Copper

Credo is the market leader in developing these AECs (~90% market share) and this business represents a vast majority of the company’s revenue. And business is booming:

This chart illustrates what I often refer to as my favorite two words in stock analysis: operating leverage. The ultimate green flag is when a business demonstrates scalability by generating rapid revenue growth while simultaneously expanding margins in tandem. Credo has this in spades.

In it’s recent Q3 2026 earnings report, the company reported revenue of $407 million, up 200% YoY and 50% QoQ, while non-GAAP gross margin ballooned to a record 68.6%. This improvement translated down the income statement, resulting in non-GAAP EPS of $1.07, up 325% YoY and up 60% QoQ, which is extremely impressive and evidence that the company is a prime beneficiary of the ramp in data center capex.

For Q4 2026, Credo guided for $430 million in revenue at the midpoint, up 152% YoY and 6% QoQ, and a non-GAAP gross margin of 65% at the midpoint, all of which beat consensus estimates. However, in response, the stock dropped by as much as 20%. That didn’t make much sense to me, but it did provide an opportunity to buy more shares sub $100, which I obliged.

The drop lasted just a single day as, 24 hours after Credo’s earnings, competitor Broadcom (AVGO) reported blowout earnings and its CEO, Hock Tan, said the following in the company’s conference call:

We next step up to 400G SerDes in 2028, our XPU customers will likely continue to stay on Direct Attach Copper. This is a huge advantage as the alternative of going to optical. It's more expensive and requires significantly more power.

CRDO popped almost immediately in the after hours session, indicating that much of the market’s current pessimism surrounding the stock seems to be coming from fears that copper is living on borrowed time in the data center as optical solutions take over. Broadcom, as a major supplier in all facets of the data center, has excellent visibility into these build-outs and Tan’s endorsement of copper until at least 2028 seems to have eased concerns over Credo’s near-term prospects.

On the surface, these concerns appear to be well founded— as transmission speeds increase, the max length of copper AECs will continue to shrink; it’s no coincidence that Tan mentioned 2028 as the next milestone for copper since that also happens to be when 3.2T is expected to become the industry standard. At those speeds, the max length drops to around 0.5 meters for passive copper cables and around 2.5 meters for AECs, though those numbers can likely be stretched to 1.5 meters and 5 meters, respectively, if expensive, high-end AECs are used:

While this seeming expiration date on copper isn’t ideal for Credo, I think the market is being much too pessimistic about the company’s ability to adapt to this reality:

1. While max AEC lengths are undeniably shrinking, even at 3.2T, Credo’s offerings can still accomplish what they currently accomplish at 800G and 1.6T— intra-rack signal transmission. They lose some flexibility in other applications, like inter-rack communication, which is possible at current cable lengths in certain data center orientations, but the core intra-rack value proposition remains intact.

2. The first casualty of shorter cable lengths will be the passive copper interconnects that have even less wiggle room than AECs and are still present in tons of data centers around the world. Replacing these with AECs has been and will continue to be an additional revenue stream, replacing some lost sales from shrinking active copper cable shipments.

Another important aspect to this story is that, while the market worries about the seeming copper wind-down, the next few years will be extremely lucrative regardless. Remember from the chart above that just Microsoft, Meta, Amazon, and Google are expected to double their AI capex to $600 billion in 2026— the first three of those are confirmed Credo customers and the company stated in its last earnings report that it has ramped two additional hyperscale customers to full volume. The amount that these customers alone will be spending on data center infrastructure and AECs will drive record revenue and earnings for Credo in 2026, and it seems exceedingly unlikely that 2027 will be any different.

The company has also secured its first neocloud customer, which is essentially any data center operator that’s not one of the hyperscalers, and is in talks with additional ones. This is an exciting, new frontier for Credo and represents a potential long-term growth opportunity, even though it will remain dwarfed by larger customers for the time being.

But, more importantly than everything I’ve mentioned so far, is that Credo has not simply been waiting to accept its fate in copper— the company has seen the optical future and is pushing into the market aggressively.

The Optical Pivot

The reason active copper has remained so prevalent in the data center despite optical’s push is because it’s simple, cheap, energy-efficient, less failure-prone, and, over short distances, actually faster. The only real drawback, as we’ve covered extensively, is the limited length of the cable. Because of the innate benefits, data center operators will continue to use it for as long as possible; where exactly the so-called “copper wall” ends up being is anyone’s best guess, but as speeds rise, lasers will increasingly become the go-to choice.

Seeing the writing on the wall, Credo has undertaken a comprehensive approach to establish itself in the optical market and is already selling in volume to many of its hyperscale customers.

Going back in time a bit, Credo actually sold optical components for close to a decade before it started selling AECs. Its first foray into the optical market was to license out its designs for what are called Digital Signal Processors (DSPs), which are essentially chips that facilitate signal transmission. Companies paid royalties for this design and used them in their optical transceivers. Credo eventually realized that there was an opportunity for copper cable equipped with DSPs to fill the short-haul niche in data centers at a much cheaper price than existing optical solutions, giving birth to AECs.

The company started manufacturing AECs, which have two DSPs, one on each end, which clean, amplify, and re-generate the signal as it comes out of the GPU node and repeats the process before it enters the Ethernet switch, significantly increasing the signal range beyond what a “dumb” copper cable can provide. But while AECs brought the company to prominence, it never stopped selling DSPs to optical customers and that has remained a foothold in the market even as the Credo has grown to become the king of copper.

There are two main products that the company has developed for the next generation of 1.6T optical transceivers: Bluebird and Cardinal. The former is a fully-integrated DSP that works in many different architectures and can be used in a multitude of different configurations within the data center. The latter is tailor-made specifically for hyperscale-level deployments— it’s extremely low latency, low power, and more streamlined.

To re-iterate in case of confusion, Bluebird and Cardinal are just the DSPs (signal re-generators) that sit at either end of the optical transceivers. Other manufacturers use these DSPs in their own custom transceiver designs. If this sounds like a missed opportunity for Credo to make its own transceivers, apparently management thought so too because the company just launched (literally this month) an 800G optical transceiver built using its Robin DSP. This is an all-in-one replacement for an AEC and does the job of preparing Credo for a post-copper world. A 1.6T transceiver is likely just down the road, setting up a smooth transition between active copper and optical over the long-term.

Beyond just selling its own typical optical transceivers, Credo has also pioneered a new class of transceiver that only has a DSP at one end of the interconnect— even beating Broadcom to market with a September 2025 release. The concept is called Linear Receive Optics (LRO) and it essentially keeps the transmitter DSP and cuts out the receiver DSP, offloading the work of signal re-generation to the Ethernet switch. This saves energy otherwise consumed by the receiver DSP and reduces latency previously needed to clean and re-generate the signal.

Bluebird and Cardinal come in both the original two-DSP configuration and LRO configuration as Credo takes aim at high-performance data centers that can utilize each in different applications.

And last but not least, all of Credo AECs and optical transceivers are equipped with a technology that it calls ZeroFlap. In networking parlance, a “flap” occurs when a link disconnects and reconnects. No big deal right? Well, even just a microsecond flap can cause an entire workload using thousands of GPUs to fail, wasting time, energy, and potentially massive amounts of compute. To solve this pervasive issue, Credo developed ZeroFlap as an integrated monitoring service that uses the DSPs to measure telemetry through the fiber or cable, which includes things like error rates and temperature, and reports back to a software called PILOT that can alert a network administrator to potential flaps before they occur, saving energy, compute, and time.

Competitors have technology that aims to accomplish the same thing, but Nvidia and Broadcom have these features built into their switches directly, meaning anyone that wants to customize their racks, which many hyperscalers do, would require a separate transceiver that has the same capability. Credo’s ZeroFlap fills that niche. Which is sort of the overall proposition here— Credo excels at identifying gaps in the data center supply chain and filling them with efficient, performant, and affordable solutions.

In sum, while AEC will continue to be the company’s bread and butter for the next few years, this shift into optical with a real value proposition and differentiated product will keep the company future-proofed in the event copper falls out of favor.

Break Time

Alright well that was a lot of words and a lot of technical details and wayyy too many acronyms. I do hope you found some of it interesting and entertaining to read because I certainly had a fun time researching it. You’re almost to the end, just a couple more short (I promise) sections discussing some risks and how investors might want to play the stock going forward. Whenever you’re ready…

Risks

Of course, no investment thesis would be complete without a risk assessment! I think there are two clear and present dangers when considering the Credo’s future: concentration risk and competition risk.

As of its last quarter, 88% of Credo’s revenue comes from just three customers, including 39% from just the largest among those. The concentration risk here is apparent. If any of these customers chose to diversify away from Credo or opted to slow AI spending in general, the company would likely lose a significant portion of its revenue, which could lead to inventory build-up, contracting margins, and of course reduced profits.

I’m not too concerned about this risk at the moment because securing hyperscalers as customers and then increasing share is sort of the name of the data center game. Yes, Credo is heavily reliant on a few large customers, but that’s who the customer base is— whales. The company did say in its last earnings report that it expects an additional 10%+ customer in addition to the current three by the end of the FY2026, so diversification is gradually increasing, but concentration risk will always be a factor.

On the competition side, this risk is more pressing but similarly hard to dispel. There will always be companies trying to take the same real estate, especially when one of those competitors is a product and patent giant like Broadcom, but one of the benefits of a highly concentrated customer base is that there is de facto vendor lock-in. For example, Amazon and Microsoft, two of Credo’s largest customers, are familiar with the company’s transceivers and cables and how they fit into the data center ecosystem. Changing to another provider would incur switching costs that usually represent a significant hurdle to all but the most determined companies. Additionally, companies at the top of the market like Broadcom offer a highly-integrated approach, leaving niches in the market for customizable networking solutions, which Credo has maneuvered deftly.

Investor Takeaway

Okay, we’ve reached the part where I tell you what to do. Except this isn’t financial advice so I can’t tell you that. What I can say is that, through ongoing strength in active copper combined with the company’s recent breakthrough selling its own optical transceivers, I think Credo is looking like an attractive short-term and medium-term play. AECs will continue to be vital in the data center gold rush and discrete DSPs + ZeroFlap transceivers appear poised to secure a beachhead in the optical space.

As AI capex commitments come through and infrastructure is built out all around the world, the market for components, which has been consistently supply-constrained for years, looks likely to remain that way. This favorable market environment is driving revenue growth and strong margins for Credo, which is on track to report record revenues in FY2027.

On the valuation side, CRDO appears quite cheap. The stock currently trades at a PEG ratio of around 0.4 (anything below 1 is considered potentially undervalued), which likely indicates that the market is still pricing in a good amount of fear regarding copper’s prospects, but as I’ve argued throughout this post, I think these concerns are overblown and we’ll see shares rise throughout the year as operating results continue to outperform despite negative sentiment.

I already have a fairly large position in CRDO and will likely add more if the stock drops below $100 again. I think LEAPS with a mid-2026 or early-2027 expiration date would also be a smart move and I plan on opening a small long-term options position within the next month depending on what entry points present themselves.

Fin

I love interacting with y’all so please leave a comment if you have any feedback. It could be “Thanks for the article, good read!” or it could be “Wow that was terrible and a disgrace to the art of stock analysis!” I appreciate all messages equally!

And subscribe for more! More what? Who knows! Could be more long-form content, maybe a short macro analysis, or a movie review (seeing Project Hail Mary as soon as I can). So I guess, subscribe for any of that?

Anyway, as always, thanks for reading and best of luck!

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Disclosure: I held a beneficial long position in CRDO at the time of this writing.

Disclaimer: This post is for informational purposes only and does not constitute financial, investment, or legal advice. I am not a financial advisor. All investments involve risk. I may hold positions in the companies discussed. Please do your own research or consult a professional before making any investment decisions.

Apologies for the potentially confusing title, this is my first post on Substack and I was going for something pithy yet enigmatic.

Did it work? Did I catch your attention?

To be honest, I don’t quite understand how this website markets people’s writings, if it does so at all, so I could be asking the above question to exactly zero other human souls. Sounds a bit lonely, but I suppose sometimes writing just to write feels good too. Freeing. Meditative. Arrogant? Maybe a little.

On Writing (For Money)

Okay, making money, right. While this is my first time writing on Substack, I have written for other publications hundreds of times. Well, it’s really just the one publication actually, a site called Seeking Alpha that some of the more tuned-in stock market aficionados might recognize as a sort of free market hub for financial analysis. Anyone can become a contributor, you just need to get past the sometimes-simple, sometimes-not editorial process and et voilà your article will go out to thousands! And best of all? Pseudonyms are fair game.

This was an extremely appealing system to my high school senior self for obvious reasons, chief among them being you can be judged by your words and predictions rather than your (lack of) education and qualifications. And so began my journey of making (other people) money. Of course, the journey was intended from the jump to make ME money, but I found myself feeling a responsibility for and connection to my calls— a certain pride.

I would type the days away arguing with randoms on the Internet, relishing in the chaos and obsessively checking my watchlist. I’d be lying if I said this behavior wasn’t rooted in a somewhat toxic mixture of narcissism and competitiveness, but that same urge also pushed me to spend hours on researching and writing articles, even ones that barely anyone would read.

Back in 2014, I wrote an article on a little-known stock called Allied Motion Technologies that went by the ticker AMOT (now ALNT) and had a valuation of just $120 million. I spent days on this piece poring over every SEC filing on the company’s investor relations website, looking into comparable companies, and assessing valuation, resulting in an article that, in hindsight, was honestly pretty mid and not all that interesting to read.

For the curious among you, here’s a gift link (and before getting judgy, do remember this was 12 years ago).

Upon publication in October 2014, ALNT was trading for around $9.50 per share. The stock began rising almost immediately and six months later it peaked around $28 per share. An investment of $1,000 at the original price would have yielded close to $2,000 in profit.

I felt like a god. Who said that this whole “predicting the stock market” thing was hard? This was my first real taste of the rush of making (other people) money. Of course, this was mostly metaphorical money considering the article has received a total of 527 views in 12 years, for a grand total annual viewership of not-quite 44 people. And at least 50 of those are probably from me, reminiscing about the good old days before I had ever had the misfortune of experiencing a 9 to 5 or knowing what Reddit was.

And in addition to not making (other people) money, I also didn’t make myself any money on my successful call because, well, I was a high school student, so I didn’t have any money either. And yet, it felt good. Really good. To be right and to have it on my proverbial resumé that I was right. But as my following grew, I actually started to make good on making (other people) money. And yes, this is the part where I shamelessly plug my big wins in hopes that you’ll Subscribe for my next picks™!

• I wrote my first article on Advanced Micro Devices (AMD) back in July 2015 when it was trading for $2 a share, recommending a Buy rating. I’ve been riding it up ever since as it now trades around $210, good for a 10,000%+ gain.

• I recommended a Buy rating on Micron (MU) in September 2024 when it was $88 per share. Now it’s at $375 (and at $400 just last week), a 320% gain.

• A few other of my more recent greatest hits:

  
  Rocket Lab (RKLB) in October 2024: $9.6 → $72 = 600%+
  Intel (INTC) in October 2024: $23 → $47 = 100%+
  Power Solutions (PSIX) in December 2024: $29 → $83 = 180%+

Am I just listing these to brag? Of course I am, I trust you didn’t skip the part about my aforementioned narcissism.

Do I have a few losers? Definitely, who doesn’t. Am I gonna mention them here and tarnish my prophetic aura? Hell no. So yeah, there was some light cherry-picking, but I also score pretty well on objective measures. For example, I’m ranked in the top 5% of analysts on TipRanks, which is a site that, if it made me look bad, I’d say was bullshit (and honestly, it is) but since it makes me look good I’m a big fan.

Okay, if you came away from the previous couple paragraphs thinking they were a confusing hodgepodge of contradictory indications that didn’t make you all that confident that I’m actually decent at stock analysis, good— because the lesson here is don’t trust anyone that tells you they’re good at stock analysis, including me.

But on second thought, this feels like the opposite of the “call to action” these sorts of posts are usually meant to end with. So how about this: you shouldn’t blindly trust me, but I do have a pretty decent track record of making (other people) money so maybe you should listen to my musings and decide for yourself. Subscribe! ⁿᵃⁱˡᵉᵈ ⁱᵗ

Why My Substack

So why am I writing on Substack and what can you expect from me in the future? Seeking Alpha is a more professional and rigid service that has fairly strict guidelines about what can and cannot be included in articles published to their site. In a sentence, sometimes I just like to get a little silly with it and I felt like Substack would be a more fitting place for such a style and such a voice.

As for the content you can expect, well, if wasn’t obvious, I’m going to be discussing stocks. The ones I like, the ones I hate, the ones I find amusing, the ones I find boring. And who knows, I might also throw in the occasional book review just to keep things interesti— no wait come back, this isn’t a Trojan horse for my contemporary romance book club I swear!

Above all else, my least favorite thing about finance and stocks is how much obfuscation and jargon “insiders” use to make themselves seem smarter and to convince you that what they do is so unknowable and mystical that you should just give them your money and they’ll handle the big, scary stock market for you. I will do no such thing. I want this stack (?) to be a forum for education, constructive discussion, and making (other people) money.

If that sounds good to you, then subscribe for more :)

Thanks for reading!