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Month: March 2026

Western Digital: Visibility Extends Through 2028, Catching up to Seagate on Density 

Posted on March 27, 2026June 30, 2026 by io-fund

Data is the fuel for training and inference as it enables stronger models and better inference results. As more data is generated and the value of data increases, the demand for storing data is also increasing quickly.  

There are a few ways that Western Digital can awaken an age-old industry to meet the demands of hyperscalers. The first is to increase areal density from 32TB to eventually 100TB as we end the decade. By packing more capacity into the same footprint, Western Digital delivers improved economics to alleviate surging capex. The company’s UltraSMR-enabled JBOD platforms offer TB per drive, lower cost per TB and lower power (and space) per TB, delivering not only increased capacity but also lower total cost of ownership.  

The company seeks to work with a larger customer base beyond hyperscalers by not just saying “go figure out SMR” on your own to enterprises. SMR stands for shingled magnetic recording (more on this below). There are barriers to adoption for SMR as it’s complex to integrate and can have unpredictable performance at times. Western Digital is planning for when AI broadens beyond hyperscalers by offering a validated JBOD platform that tests and tunes UltraSMR, offers predictability through controlled reference architecture, and that is also quick to deploy.  

When translating UltraSMR’s benefits in investor terms, management states it offers a 20% capacity lift over CMR (conventional magnetic recording) and a 10% capacity uplift over industry standard SMR. The UltraSMR solution is software-based, thus it’s “very accretive” from a margin standpoint. Last quarter, there was a 50% mix on UltraSMR with expectations this will increase as the company’s top three customers are onboard with UltraSMR drives today and another two to three are moving toward adopting UltraSMR. 

Looking beyond the recording format, HAMR is an important catalyst for Western Digital as it’s a new write technology that uses a tiny laser to briefly heat the disk surface to write onto higher-stability media at much higher densities. This means WDC can pack more data onto the same drive size to deliver higher capacity. The step-function upgrade to increase capacity is a key element to Western Digital’s future product road map. Overall, HAMR is a primary capacity lever that will lead to 100TB hard drives along with combining higher bits-per-platter and higher platter count.  

The analysis below is dense at times, yet a necessary step to discussing a stock that has seen returns of 591% over the past year.  

Brief Product Overview  

Multimodal datasets are among the largest drivers of incremental storage demand. As inference and physical AI scale, the low-cost and high-density of HDD economics are hard to beat. Video is a data hog that and even modest growth here from multimodal AI can create what’s called “data exhaust.”  

As you'll see below, HDD is the lowest cost per TB for bulk storage. As inference requires more exabytes to be stored, HDDs offer an advantage in that storage tier. In fact, Western Digital’s management team foresees a CAGR of 25%+ over the next 5 years with HDD representing “80% of the storage media that deployed within a hyperscale environment.” 

UltraSMR and ePMR   

To answer this incoming demand, Western Digital’s ePMR tech (energy-assisted Perpendicular MR) tech adds electrical currents to the write head to improve density and write smaller bits. This increase in areal density allows more data to be stored in smaller spaces, helping drive down costs per TB and improve TCO for customers. ePMR is a tried-and-true tech, with WDC noting that it has been the ‘workhorse of the industry’ for the last decade.  

WDC is currently shipping its ePMR-based drives in 26TB CMR (conventional magnetic recording) and 32TB UltraSMR (ultra shingled magnetic recording) configurations. It is also scaling to the world’s first 40TB UltraSMR later this year, a new product addition to its portfolio and a capacity that was previously thought to be impossible to reach with ePMR.  

UltraSMR also features technological and read channel optimizations that boost HDD capacity by up to 18% versus traditional CMR drives. A key enabler is Western Digital’s OptiNAND architecture, which integrates embedded NAND devices, enabling multiple active write zones and increasing track density per disk. WDC says its UltraSMR drives offer among the highest capacities per drive for data-intensive workloads, and consume as little as 5.5 watts of power when ideal, driving TCO lower. The UltraSMR drivers are also available in WDC’s JBOD hybrid storage platforms, integrating 60 to 102 drivers to offer up to 3.26PB of storage capacity. 

HAMR 

WDC is also moving into heat-assisted magnetic recording (HAMR) to scale to higher capacity drives, as ePMR tech is reaching its physical limits in areal density. This refers to how many bits can be packed onto each square inch of disk platter (where data is stored).  

Thus, future capacity gains must come from shrinking bit sizes further, which can be achieved via HAMR. HAMR uses a laser diode to heat a microscopic spot on the disk, enabling polarity of a single bit to be flipped to allow data to be written. This allows for substantial areal density gains, moving from <3 TB per platter under the current UltraSMR drives to up to  ~10TB per platter by the end of the decade.  

WDC acknowledges that its ePMR roadmap can extend up to 60TB, but after that, the shift to HAMR is all but inevitable in order to progress towards 100TB capacities and beyond.

Source: WDC 

WDC is harnessing its patented laser technology to not only reach 100TB drives over the next few years, but scale even further by adding more platters to drives:  

“So for the last 6 years, we've been working on our own patented laser technology. It solves for those 3 problems. By emitting more light, harnessing more of that light into the recording technology, we will increase the aerial density of the HAMR platters from 4 terabytes all the way to 10 terabytes by 2028 per platter. We have 11 platters. It's one of the reasons I'm confident about 100 terabyte HAMR drives by 2029. This technology is not theoretical. It's actually already in the labs. 

We've watched it do in the recording. The other part of it, as you can see from the micrograph, they're shorter. So it allows us to add yet more capacity per drive by packing up to 14 platters into the same 3.5-inch form factor. 10 terabytes, 14 platters, that sounds like 140 terabytes.” 

Accelerating ePMR and HAMR Roadmap 

WDC is accelerating its ePMR and HAMR roadmaps, noting that ePMR shipments are growing double-digits sequentially next quarter. HAMR qualification timelines are also accelerating by six months. 

Management explained in fiscal Q2’s call that ePMR shipments reached more than 3.5 million units, while starting qualification of both its upcoming HAMR and next-gen ePMR drives at two different hyperscaler customers. ePMR shipments were guided to reach closer to 4 million in fiscal Q3, or growth of more than 14% QoQ.  

On HAMR, WDC stated that they pulled forward qualification and started in February with one hyperscaler, with another expected to be initiated relatively soon. Management emphasized at its Investor Day in February that they are “so confident in our road map for HAMR. From last year, we said back in the '27 for the ramp, we pulled it in by 6 months. So it's ramping in the first half of 2027.”  

Inference as an HDD Driver 

We recently covered discussions over the role of SSDs in ‘warm’ storage and Nvidia’s upcoming Inference Context Memory Storage platform in our SanDisk analysis in detail. Simply put, Nvidia is essentially proposing SSDs to take a more central role in the ‘warm’ tier where HDDs sits – what Western Digital and competitor Seagate consider ‘nearline’, where data does not have to be accessed instantaneously but must remain readily available.  

We covered Seagate’s response to this proposal in our analysis, with executives believing there ultimately will not be much change to storage architectures with HDDs remaining critical from a TCO perspective to handle the massive volume of data generated by AI applications. Western Digital mirrored this view, explaining that Nvidia’s new initiative will likely at its core accelerate the growth of volume generated, which means HDDs will remain critical for AI storage requirements – management expects HDDs to still account for 80% of storage solutions deployed by hyperscalers over the next five years. 

However, management also pushed back on the economics of utilizing quad-level cell (QLC) SSDs for AI storage workloads, emphasizing that QLCs offer 10% of performance of HDDs for 10X the cost: 

“This performance has led some customers to think about using QLC flash to provide that performance in addition to hard drives. It's very attractive because — but QLC has a problem when the data is constantly moving as it does in the AI workloads and in the cloud workloads. QLC wears out. And that means you have to make a lot of changes in your software, so it doesn't wear out.  

Otherwise, you end up with silent data corruption. And that is just as scary as it sounds. Hard drives, on the other hand, just don't. They don't wear out. They'll operate for years without wear out and customers like that and it simplifies their code. The other reason to consider QLC flash is that the headline performance of a QLC drive is 6 gigabytes per second. That's way more than the 200 to 250 megabytes per second of a hard drive. But that's a headline number. It's only true when that drive, that QLC drive is attached directly to the GPU with a great big bus. In the real world, in object stores deployed in massive scale, that's not how it's done. Hard drives and QLC drives are connected to the network via a thing called the SAE interface.  

It's a thin pipe that takes data from the drive to the network. It can only support 530 megabytes per second. So customers would get less than 10% of the performance of QLC for 10x the cost of a hard drive. Do you think that's a good deal? I don't now or as a customer.” 

WDC also commented on more specific inference-based HDD demand drivers, such as multi-modal models requiring significantly large data sets to store queries and prompts, video generation, as well as autonomous vehicles and robotics needing extensive data sets to function in real-world situations. All summed up, WDC believes that inference and these upcoming AI applications will drive storage exabyte demand at a >25% CAGR over the next five years, providing a solid foundation for long-term revenue growth as HAMR unlocks a path to higher capacity drives.  

There is one risk with this, in that there will still likely be use cases and applications where customers use a higher mix of SSDs. HDDs offer the lowest-cost per terabyte, ideal for “big data” storage, backups and large AI datasets. However, solid state drives (SSDs), which store data on flash memory chips, are far faster and lower-latency but at a higher cost per terabyte. This means leveraging a mix of HDDs and SSDs is a popular choice, and some AI workloads prioritizing latency may take a higher mix of SSDs.  

High Bandwidth HDD Design and Power-Optimized HDDs 

At its recent Innovation Day, Western Digital shared more information on its product roadmap and technological innovations driving its path to 100TB capacity. WDC is not seeking just capacity gains but also performance gains with high-bandwidth HDD design. This is especially important as throughput (data transfer speeds) must continue to scale alongside capacity to prevent performance bottlenecks. 

This has not been the case over the last several years. WDC explains that since 2017, CMR HDD capacities have increased by 116%, from its 12TB Ultrastar to 26TB, yet maximum sequential throughput only increased 18%, from 255MB/s to 302MB/s. This is critical as a lack of throughput gains will make HDDs less suitable for bandwidth-intensive AI workloads despite strong capacity gains.  

WDC is aiming to boost performance and throughput via two innovations, High Bandwidth Drive (HBDT) and Dual Pivot design technologies. Put simply, WDC is working towards delivering NAND flash-similar performance and throughput but with HDD cost and TCO economics.  

WDC says that its HBDT will enable simultaneous read and write from multiple heads on multiple tracks, which up until now has been done on a single track at a time. Currently, HBDT is able to access two tracks simultaneously, though WDC believes it can ultimately scale to four and eight tracks simultaneously. Under two tracks, WDC says sequential throughput will double and will scale further as HBDT innovation progresses towards eight tracks at once. Importantly, HBDT is already in validation with customers. 

Dual Pivot tech (DPT) unlocks further performance gains by adding a second independent actuator on a separate pivot. Similar to how read and write were previously done on a single track at a time, HDDs have been limited by having one single actuator containing the read-write heads. With DPT, a second actuator is added on the opposite end of the drive, allowing for independent seeking between the two heads.  

This is the main difference between DPT and previous dual actuator drives. Previous drives had to remove on disk to make space for the second actuator to boost performance, sacrificing capacity, whereas with DPT and the placement of the second actuator, WDC can reduce spacing between disks, and boost capacity and performance at the same time. DPT remains in the lab for testing and is expected to be available in 2028. 

Combining both technologies is expected to drive significant performance and throughput gains while on the path to >100TB capacity. WDC states that “two-track HBDT plus dual pivot is projected to increase throughput from today’s 300MB/s to approximately 1.2GB/s, a 4x increase.” This would mean that a 100TB drive would have similar throughput and performance as the currently-available 26TB drives, preserving HDD performance and cost economics. WDC adds that eight-track HBDT and DPT could theoretically increase throughput to 4.8GB/s, another 4x increase from two-track and 16x versus today.   

Here’s how management explained this increase in performance, and this quote provides some perspective as to why demand may be high for drives integrating these technologies, as it can seamlessly integrate into existing infrastructure: 

The thing [customers] really love about this is that we can go to 4, 6 and 8x the performance. It is scalable. By the time we get to 100 terabytes, we could be 8x the performance of today's drives. We already have the technology to do it, and we're developing it, so we're ready for when customers are ready for it. We'll introduce this capability at the 50-terabyte mark to meet the customers' demand so that they are ready for us to consume and take advantage of all this performance. … 

This design will fit in an existing customer chassis with that change. It can be made on the same manufacturing lines. And they just see more performance. So customers are really excited by this. double the transactions smoothly for customers. … Dual-pivot technology helps customers focus their software effort on improving more performance for AI versus having to deal with how the hard drives are working. And we'll introduce this at the 60-terabyte mark. … 

My biggest problem is finding them enough material so they can start testing. Dual pivot technology will be in their hands in late '27 and '28. So all the performance that the customer's hardware can support their existing boxes, their existing software, their existing networks without having — can be delivered from these drives with the capacity that we are building without having to use QLC. We deliver performance 10x cheaper than QLC can.” 

Management also shed more light on software optimizations and how this will also accelerate deployment timelines for upcoming tech. WDC explained that the “shortcut is a simple, open API that allows customers to integrate that API to their existing file system, their existing object store,” that will be available on flash and extending to all HDDs scaling to 100TB. This will provide faster qualification and faster time to production, with this becoming available in 2027. 

WDC is also working on power-optimized HDDs, aiming to lower power consumption by up to 20% and boost capacity with minimal trade-offs on performance. The lower power consumption not only will offer a lower TCO, but also make these HDDs more attractive from a hyperscaler perspective by saving more power that can be allocated towards more GPUs.  

WDC says that by spinning the drive slower, “we can reduce the power by 20%, but we only trade 5% to 10% of the sequential I/O [input output operations], not something that the customers have seen before or even thought was possible.” This also will add ~10% more capacity, or 10TB for a 100TB drive, or increasing capacity inside the same 3.5 inch form factor without changing the drive’s power profile. WDC adds that this lower power consumption is optimal for cold data, or data that needs to be accessed within seconds for AI inference, helping enable AI data storage at scale. 

Multiple Margin Levers to Pull 

WDC outlined multiple different margin levers at its disposal, from strong yields with its ePMR products to increasing mix of UltraSMR drivers and soon the HAMR ramp. WDC has seen strong margin expansion, with Q2 gross margins up nearly 8 points YoY and operating margin following suit with a nearly 7 point YoY expansion. Perhaps most importantly, WDC expects to drive further gross margin expansion over the next couple of quarters and beyond, due to these levers.  

Management explained that yields for ePMR products are in the low-90% range, noting that “as we get yields up, cost continues to decline. As the UltraSMR mix goes up within those new products as well, that's also going to be a driver of cost down as well.” For context, UltraSMR drives now account for 50% mix in its nearline portfolio, with WDC’s top three customers onboarded with two to three other major customers likely moving to adopt the drives soon. This is expected to drive an increase in UltraSMR mix over the coming quarters and provide solid gross margin tailwinds. HAMR will be another margin lever in play once these drives begin to ramp, as WDC expects its HAMR products to be neutral or accretive to gross margins. 

Touching on the cost front, WDC explained that costs per terabyte were down around (10%) YoY in the quarter, which, combined with a 2-3% increase in ASP, provides room for solid margin expansion. This also pertains to higher capacity drives, which management explained have a cost benefit as well, which likely see lower unit costs, similar to Seagate.  

Analysts questioned about incremental gross margins considering the combined levers WDC has, with management explaining that they are currently running at a 75% incremental margin: 

“On the gross margin line, the guidance that you're giving for 47% to 48%, I guess the back of the envelope math would suggest that you're maintaining what looks to be like a 70%, maybe 75% incremental margin flow-through. So, I guess, my question is, how do you think about the durability of that incremental margin? 

EVP and CFO, Kris SennesaelEVP and CFO, Kris Sennesael 

We delivered 46.1% gross margin, up 220 basis points quarter-over-quarter, up 770 basis points year-over-year. And we are guiding to 47%, 48%, so 47.5% at the midpoint, which is up 740 basis points on a year-over-year basis. And, Aaron, I think your math is working. The incremental gross margin is on or about 75%, depending on how you look at it on a year-over-year basis or a quarter-over-quarter basis. So I've stated before, I'm very comfortable with an incremental gross margin higher than 50% and definitely 75% is higher than 50%. 

I mean in gross margins, there's two sides to the equation. On one hand, you have pricing environment. On the other hand, you have the cost environment. In pricing, I've talked about that before. We see a stable pricing environment with prices on a price per terabyte, kind of, flattish to slightly up. Actually, last quarter, it was up 2%, 3% on an ASP per terabyte basis. So that clearly demonstrate the value that we continue to deliver to our customers. 

And on the cost front, the teams continue to execute really well. We continue to upshift our customers to higher capacity drives, which gives us a cost benefit. And then there is great execution as well on driving down the cost in our manufacturing assets as well as throughout the supply chain. And when you look at it last quarter, the cost per terabyte was coming down on or about 10% on a year-over-year basis. And so when you put this all together, we continue to drive further gross margin expansion. And we believe in the next couple of quarters and beyond, we will continue to be able to do that.” 

What the incremental margin means is that for every dollar of revenue that WDC adds compared to the prior quarter, 75% of that flows through to gross margin – WDC delivered a $199 million QoQ increase in revenue this quarter, with $153 million of that flowing to gross profit. Looking ahead, if WDC can maintain this and deliver >$200 million QoQ growth throughout CY26 (as current estimates suggest), it could exit the year with margins above 50%. 

Visibility through 2027-2028 at Top Customers, Pricing is Stable 

WDC has already signed firm orders through calendar 2026 with its top seven customers, though it has visibility into demand extending out into 2028. Management explained that they have ‘robust’ agreements with three of its top five customers, with two of these covering calendar 2028 and one extending through calendar 2028, with these LTAs including both volume and price conditions.  

Analysts asked questions about WDC’s long-term agreements and what the economics of these new orders would look like. WDC revealed that they do contain conditions for volume and pricing, but added quite an important caveat at its Investor Day – hyperscalers want ‘predictable’ pricing, in sharp contrast to rapidly rising (and fluctuating) SSD prices. This means that upcoming contracts will likely have set price escalators so hyperscalers do not get exposed to rapidly changing memory cost structures: 

Irving, just given the tightness of the HDD market and kind of the significant inflation that NAND is going through right now, can you maybe just talk about maybe your patience in being able to sign purchase orders further into calendar '27 to extract better economics just relative to maybe how you were approaching signing POs last year? Is that making any difference in the economics you're able to extract?  

CEO Irving Tan 

As we highlighted, we're pretty much sold out for calendar year '26. We have firm [purchase orders] with our top 7 customers. And we've also established LTAs with two of them for calendar year '27 and one of them for calendar year '28. Obviously, these LTAs have a combination of volume of exabytes and price. And in relation to pricing, I think first, it's important to recognize [that] there's actually a structural shift in the value that we deliver to them, especially in the impact that we have to their total cost of ownership as the business moves more and more towards inference where monetization is happening.  

So, in this case, the pricing that we've provided there reflects the value that we're delivering to them. And so as Kris mentioned, we continue to see going forward a stable pricing environment that gives us an opportunity to continue to extract more value as we deliver both better TCO value to our customers and to better support their supply-demand needs as well through higher capacity drives. 

This ties in to comments from Investor Day, where management confirmed that they want to ensure there is a fair value exchange between capacity/performance and pricing, noting that the goal is to deliver “predictable pricing” as hyperscalers are concerned about “high volatility of some tiers of the storage space,” referring to the strong QoQ growth in enterprise SSD prices. 

Management also offered some more insights onto pricing trends through 2026, noting that FQ2 had seen stable prices with ASP per terabyte up 2-3%, while projecting mid-to-high single digit YoY ASP growth through the year and remaining stable into 2027: 

“If I look at calendar year 2026, 4 quarters of calendar year '26, I do expect ASP per terabyte to go up mid- to high single digits year-over-year for all 4 quarters, mid- to high single digits year-over-year for '26. And then beyond '26, I expect us to continue to operate in what I call a stable pricing environment, of course, from the higher level that we established in '26.” 

Battling Head to Head with Seagate on HAMR, but Expects to Lead 

It’s safe to say that WDC faces fierce competition with Seagate in the HDD space on both exabyte shipments and on the tech front. WDC exhibits a fair lead in exabytes, shipping 192 nearline exabytes in the quarter versus Seagate’s 165 exabytes. However, Seagate current is ahead when it comes to ramping HAMR drives. 

For an apple-to-apple comparison to Seagate, WDC’s first HAMR products are expected to launch with capacities from 40-44TB, which aligns with Seagate’s Mozaic4+ platform also offering up to 44TB, as both feature 4TB capacity per platter. The key difference is that Seagate’s Mozaic4+ is now shipping to two hyperscalers and ramping through the rest of the year, representing a three quarter head start over WDC in the first half of 2027. 

However, Western Digital expects to quickly take the lead in HAMR-drive capacity, with management confident in achieving 100TB+ products as early as 2029. This goes back to our discussion on the HAMr under the product overview, with WDC aiming to reach 10 TB per platter by 2028 to enable shipping 100TB HAMR drives by 2029.  

One reason that WDC is able to move so fast at scaling capacity is that it begins qualification processes at hyperscalers when introducing new hardware into the labs, cutting ramp timelines by months: “Together with our customers, we started to introduce the hardware into our labs and the qualification of the hyperscaler software so that we can start the qualification process while we're still developing the drive.  

That cuts out months from a qualification process. So by the time we're ready for volume manufacturing, the drive is ready to ramp. As Irving said earlier, we've ramped up our latest generation of drives very quickly. That's one of the reasons. The other part with rapid generation of more capacity points, customers will have a lot of qualifications. So instead of qualifying every single capacity point, they qualify one set of capacity point, let's say, 36 to 41 terabytes, and we will just ship them more capacity as we make it available. One qualification, many capacity points.  

And the next one is going to be at 42 to 56 and so on. So that innovation, not just in the drive design, but also in the processes we do gets us faster time to capacity in customers' hands in their fleets where they need it the most. So putting it all together, our HAMR capacity goes from 40 terabytes to 100 terabytes by 2029.” 

On the flip side, Seagate has that capacity penciled in for the early part of next decade, noting in Q2’s call that its Mozaic3+ and Mozaic4+ “developments align with our long-term areal density road map that extends to 10 terabytes per disk, which we expect to deliver early in the next decade.” This shift will be key to watch, as it suggest that Seagate could soon lag WDC’s capacity roadmap by several years.  

In plain terms, WDC could have a significant capacity and TCO advantage by 2029 if it can realize these density targets, making it a more attractive solution for AI data storage needs 

Financials 

Revenue Growth Accelerating to 40%, Potentially Peak Growth 

WDC delivered fiscal Q2 revenue of $3.02 billion, up 25% YoY and 7% QoQ, although this marked a deceleration from 27% YoY and 8% QoQ in Q1. Management said this was driven by strong demand for its higher capacity nearline drives.  

Exabyte shipments rose 22% YoY and 5% QoQ to 215 EB, including 103 EB from its ePMR product line on 3.5 million shipments. Revenues are closely correlated with EB shipments with pricing contributing only a 2-3 points of upside.  

Looking ahead to FQ3, WDC guided for revenue of $3.2 billion at midpoint, representing an acceleration to 40% YoY growth, up 15 points, while QoQ growth would tick slightly higher to 6.1%. Current estimates suggest WDC will close out the fiscal year with $3.44 billion in revenue in Q4, up 32% YoY and another 7.5% QoQ. At present, this would suggest that fiscal Q3’s YoY acceleration to 40% would be the company’s peak growth quarter moving through calendar 2026. 

For FY26 ending in June, revenue is expected to increase 31% to $12.47 billion, while an initial look at FY27 points to 25.6% growth to $15.66 billion in revenue. This is above WDC’s long-term revenue growth model for >20% growth CAGR over the next three to five years.  

Key Segments 

Similar to Seagate, WDC sees the large majority of its revenue go to Cloud customers, with minimal exposure to Client and Consumer end markets. 

Cloud revenue was $2.67 billion in Q2, up 27.5% YoY and 6.5% QoQ, decelerating from 31.5% YoY and 7.8% QoQ in Q1. Management noted that they are seeing strong demand from hyperscaler customers. Cloud accounted for 89% of revenue in the quarter.  

Assuming similar Cloud mix at 89% in fiscal Q3, though there is potential for slight mix gains on seasonality in its other two end markets, Cloud revenue would be implied at roughly ~$2.85 billion, up 6.5% QoQ and 41.9% YoY. 

WDC’s Client segment represented 6% of revenue at $176 million, up 26% YoY, while Consumer accounted for 5% of revenue at $168 million, declining (3%) YoY. 

Strong Margin Expansion 

As discussed above, WDC is seeing strong gross margin expansion with multiple levers available, and this margin expansion is flowing down the line.  

GAAP gross margin was 45.7% in Q2, up 8 points YoY and 2.2 points QOQ, while adjusted gross margin was 46.1%, up 7.7 points YoY and 2.2 points QoQ. For Q3, adjusted gross margin is expected to be 47.5% at midpoint, up 7.4 points YoY and 1.4 points QoQ.  

GAAP operating margin was 30.1%, up 6.9 points YoY and 2 points QoQ, while adjusted operating margin was 33.8%, up 9.3 points YoY and 3.4 points QoQ. Adjusted operating margin is implied to be 35.5% in Q3, up 9.5 points YoY and 1.7 points QoQ, showing a slight degree of operating leverage.  

GAAP net margin was 59.7% as WDC benefited from a more than $1 billion gain on its 7.5 million share stake in SanDisk, which it has now sold for nearly $3.2 billion to help reduce debt. Adjusted net margin was 26.7%, up 9.3 points YoY and 3.5 points QoQ. 

For a quick view on how current margins stack up to WDC’s long-term model, it expects adjusted gross and operating margins to surpass 50%/40% over the next three to five years, or roughly three to five points of expansion.  

EPS Growth Robust but Decelerating 

Driven by the margin expansion, WDC is seeing strong EPS growth, though growth is expected to largely decelerate moving through the year. 

GAAP EPS was $4.73, up 54% QoQ and 272% YoY, again driven by the gain on its SanDisk stake. Adjusted EPS was $2.13, up 78% YoY and 20% QoQ, beating estimates for $1.98. 

For Q3, adjusted EPS was guided to be $2.30, +/- $0.15, up 69% YoY and 8% QoQ at midpoint. Adjusted EPS growth is expected to decelerate further to 61% YoY in FQ4, and slow to 50% by the end of FY27. 

For FY26, WDC is expected to report adjusted EPS growth of nearly 80% YoY to $8.85, with another 52% to $13.46; despite the deceleration on the quarterly view, it should be noted that this EPS growth rate is still quite strong compared to the base it is growing from. Under the long-term model, management expects EPS to surpass $20. 

Cash and Balance Sheet 

Cash flow margins also showed strong expansion, and while debt does look inflated, WDC’s sale of its SanDisk stake is meaningfully improving its debt profile. Management explained at Innovation Day that “if you look at the revenue growth that we've delivered over the last 12 months, the margin appreciation, ultimately, what is done is to translate into very strong free cash flow. And in the last 2 quarters, we returned 100% of the free cash flow that we've generated. And we also were able to bring our net leverage well below the 1 to 1.5x that we laid out last year.” 

Operating cash flow was $745 million in Q2 for a 24.7% margin, up from 16.9% a year ago and 23.8% in Q1. Free cash flow was $653 million for a 21.6% margin, up from 13.9% a year ago and 21.3% in Q3. 

As of Q2, WDC reported cash of $1.98 billion and debt of $4.65 billion, though it already has redeemed $1 billion in its 2029 and 2032 senior notes following the stake sale. 

Inventories were $1.35 billion, down marginally from $1.39 billion in Q1. 

Conclusion 

Western Digital has emerged as one of the top AI winners over the past year with a 591% return, with the company getting added to the NASDAQ-100 index in December of 2025.  

Looking ahead to 2026, Western Digital is already sold out of capacity with set price and volume commitments, with visibility from key hyperscaler customers extending into 2027 and 2028. WDC also has been excelling at driving strong margin expansion with a handful of key levers at its disposal.  

Over the next few years, WDC is leveraging its technological expertise and several new innovations such as HBDT and DPT to drive significant performance gains on the path to 100TB capacity. WDC is also expecting to quickly take the lead in higher-capacity HAMR drives versus key competitor Seagate, potentially giving it a key advantage by the turn of the decade in meeting AI-driven data storage demand.

Damien Robbins, Equity Analyst at I/O Fund contributed to this analysis.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

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Posted in AI Stocks, Data CenterLeave a Comment on Western Digital: Visibility Extends Through 2028, Catching up to Seagate on Density 

Nvidia Stock Prediction: The Path to a $20 Trillion Market Cap is Strengthening

Posted on March 27, 2026June 30, 2026 by io-fund
Nvidia Stock Prediction: The Path to a $20 Trillion Market Cap is Strengthening

Last week at GTC, Jensen Huang stated that Nvidia has a path to $1 trillion in cumulative sales across the Blackwell and Rubin generations from 2025 through 2027. If you follow Nvidia’s stock closely, this isn’t new information; rather it’s roughly aligned with what analyst forecasts already had baked in. 

The distinction is crucial for investors as separating what’s already priced in from what can make a meaningful difference in stock returns. The latter typically offers alpha, while the other potentially sets up an investor for losses (hence the saying: “buy the rumor, sell the news” “buy the rumor, sell the news”). 

The math for Nvidia to see $1 Trillion in Revenue was already there. 

We must go back to October to more fully understand why the statement that Nvidia has visibility to $1 trillion in revenue through 2027 is anti-climactic.  

Last October, Huang stated that the combined revenue from Blackwell and Rubin was an estimated $500 billion through the end of 2026. Our firm modeled something similar nearly two years earlier, when my original Nvidia $10 trillion market cap thesis was published, stating we would see a $320 billion data center segment in 2026 (FY2027).  

Beth Kindig of the I/O Fund first laid out the case for Nvidia reaching a $10 trillion market cap in June 2024 — a view Jensen Huang later expressed publicly in March 2026 nearly two years later. 

Blackwell revenue was $184 billion in 2025 when you combine compute and networking, along with the $320 billion expected in 2026, comes out to the $500 billion quoted at GTC in October. This means our model proved correct roughly 16 months before the CEO confirmed it. When I first made the $320 billion data center prediction in June 2024, it resulted in 56% upside, versus a 15% decline by the time the CEO effectively confirmed the thesis in October 2025.  

Being early can pay off at many points along a stock’s trajectory. Of course, this is modest compared to the I/O Fund getting ahead of the Street on Nvidia in 2018–2019, which led to returns of more than 4,000%. But each milestone still matters when you’re talking about one of the world’s most valuable companies, as generating outsized returns becomes far more difficult at this stage. 

Now consider the trajectory to $1 trillion laid out by Huang. Bridging from $500 billion (approximately $184 billion in 2025 and $320 billion in 2026) to $1 trillion implies about $500 billion in CY2027, or about 54% year-over-year growth for about $125B a quarter.  

The Street had largely modeled this in, with FY28 (CY27) quarters at $114B, $119B, $125.5B, and $132B for a total of $490.5B for the fiscal year ending in January.  

In other words, there was not much alpha in the comment, which helps explain why the stock didn’t move much from the “blockbuster” $1 trillion comment. 

Table of Nvidia’s projected revenue from FY2027 to FY2036 with YoY growth and 1‑, 3‑, and 6‑month analyst trend revisions, rising from $369B to $1.22T.

Chart showing Nvidia’s long‑term revenue forecast for FY2027–FY2031, including annual estimates, year‑over‑year growth, and multi‑period trend revisions that highlight consistently rising analyst expectations.

On the heels of the $1 trillion cumulative revenue comment, Huang publicly stated this week on a podcast that he sees a path to a $10 trillion market cap for Nvidia.  

This is the exact thesis I first published in June of 2024 in the article “Here’s Why Nvidia Stock Will Reach $10 Trillion Market Cap by 2030," 

“Nvidia has a market cap of $3 trillion today. We believe Nvidia will reach a $10 trillion market cap by 2030 or sooner through a rapid product road map, it’s impenetrable moat from the CUDA software platform, and due to being an AI systems company that provides components well beyond GPUs, including networking and software platforms.” 

Admittedly, 16 months later, that is no longer a contrarian call. Once the CEO goes on record with a number like that, it’s a sign the $10 trillion market cap narrative is transitioning from offering alpha to becoming increasingly priced in.

mid

Which is exactly why I pushed the thesis further out, publishing a new article last November that Nvidia has a path to end the decade with a $20 trillion market cap. 

How Nvidia Gets to $20 Trillion. 

The $20 trillion market cap will not come from GPU unit growth alone, though unit growth remains very important. Rather, the value proposition will increasingly focus on economic output. This marks a tremendous shift for how Nvidia is evaluated. 

As the AI market shifts toward inference, Nvidia’s product cycles will be optimized around token economics such as throughput, latency, power efficiency and cost per token. The goal is no longer to simply sell faster and more powerful chips, but to deliver superior economic value at the system level relative to custom silicon (in other words, let the battle begin).  

Leading up to this, Nvidia was competing on performance metrics, and MLPerf benchmarks still matter of course. But going forward, workload economics and system-level efficiency will play a much larger role in how their systems are evaluated. 

Historical Mispricing — How Analysts Missed Nvidia by 4X 

Nvidia’s stock price is highly dependent on upward revisions – more so than any company that I can recall. To put it plainly, Wall Street’s estimates have consistently underestimated this company, and this gap is critical for where investors can continue to find an edge. 

Three quarters after Nvidia’s breakout earnings report in May of 2023, analysts had eight months to price in the AI trade. Consensus for the fiscal year ending in 2028 was set at $138.3 billion. Today, that same estimate stands at $480 billion.  

Further out, the error widens with Jan 2031 estimates of $208 billion versus where they are today at $757.6 billion or nearly 4X higher. 

Line chart showing Nvidia stock long‑term revenue estimate revisions rising sharply from 2018 to 2026.

Chart showing Nvidia’s consensus revenue revision trend from 2018 to 2026, highlighting steady upward revisions for FY2027–2032. Key estimates—such as $369.42B for FY2027, $479.97B for FY2028, and $757.63B for FY2031—have climbed sharply, reflecting rising expectations for Nvidia’s AI, data center, and accelerator demand. The sustained increase in long‑term forecasts reinforces bullish sentiment around Nvidia stock and its multi‑trillion‑dollar market cap outlook. and its multi‑trillion‑dollar market cap outlook.

Only One Year Ago, Nvidia Revenue Estimates Were Far Too Low 

If we repeat this exercise and look back exactly a year ago, we will see that analysts continue to miss the target. Just one year ago, the fiscal year ending January 2028 was expected to see revenue of $294 billion. Today, consensus is at $480 billion. All else equal; those revisions could potentially represent alpha of 63%.  

If you look at FY2031, estimates were $343.5 billion compared to $757.6 billion – meaning analyst estimates were off by 2X from where they are today. 

Line chart showing Nvidia stock revenue estimate revisions rising from 2018 to 2026 for FY2027–2032.

Chart showing Nvidia stock revenue revision trends from 2018–2026, highlighting steadily rising analyst estimates for FY2027 through FY2032 as expectations for Nvidia’s AI and data center growth continue to increase.

How This Ties into Nvidia Reaching a $20 Trillion Market Cap by 2030 

My $20 trillion market cap thesis for Nvidia is grounded in certain assumptions, primarily that Nvidia reaches $930 billion in data center revenue in a single year prior to the close of the decade combined with a price-to-sales ratio of 22X – a few points below the stock’s 3-year median P/S of 28X.  

Right now, analyst estimates sit at $757 billion for the fiscal year ending January 2031. However, given the estimates have doubled in the past year alone, the 23% difference in estimates compared to my firm’s base case of $1 trillion seems achievable. 

As I recently emphasized in an interview with Bloomberg Asia, analysts revising estimates intra-quarter is one of the most important catalysts for this stock. 

What’s Pressuring Nvidia’s Valuation in 2026 

Despite odds favoring Nvidia ending the decade at $930 billion or more in annual revenue, the more pressing issue is valuation. The stock has been trading at a significant discount to its historical valuation, yet buyers are not stepping in. Meanwhile, Broadcom is trading right well above its 3-year median and AMD is two points above its 3-year median. 

Chart comparing P/S ratios and 3‑year median P/S ratios for Nvidia, AMD, and Broadcom from 2023 to 2026.

Chart comparing Nvidia, AMD, and Broadcom P/S ratios and 3‑year median P/S ratios from 2023–2026, showing Nvidia trading below its historical valuation while Broadcom and AMD remain closer to their 3‑year medians.

Source: YCharts 

I’ve heard some outlandish theories about this disconnect, but I believe the reason Nvidia is seeing a weaker valuation is fairly straightforward: the inference market offers immense opportunity for Nvidia yet is expected to lower Nvidia’s overall percentage of the AI accelerator market. Yes, this means Nvidia’s near-monopoly is set to end. 

I’ve covered this dynamic in detail in my Broadcom stock analysis here and and AMD stock analysis here.Broadcom stock analysis here and and AMD stock analysis here. 

According to TrendForce, custom silicon represents 20.9% of the market in 2025 yet is expected to expand to 27.8% of the market in 2026. Adding to the competitive pressure, AMD is expected to release Helios MI400s in the second half of 2026, which could further eat into Nvidia’s GPU market share, adding to the pressure of custom chips gaining 7 points with architectures like Google’s TPUs. 

Stacked bar chart showing global AI server shipment share by accelerator type—GPU, FPGA, and custom silicon—from 2023 to 2026.

Chart showing global AI server shipment share by accelerator type from 2023 to 2026, based on TrendForce data. GPUs remain dominant, while custom silicon grows from 20.9% of the market in 2025 to a projected 27.8% in 2026, highlighting accelerating adoption of ASIC‑based AI infrastructure.

Nvidia Versus Custom Silicon is Overly Simplistic 

The assumption that losing AI accelerator market share should result in a lower valuation is overly simplistic for four reasons. 

Capex Growth Expands the Entire AI Accelerator Market: The first reason is that capex is the primary multiplier. Because capex continues to grow the overall pie, the market will expand faster than any single architecture can absorb. Compute demand is compounding; a shrinking slice of a rapidly growing pie can still mean explosive revenue growth. 

GPUs Remain the Most Flexible Architecture for New Workloads: The second reason is that GPUs remain the default when workloads change. The versatility of GPUs is a competitive advantage as Big Tech does not always know what next quarter or next year will bring. Consider too that custom silicon is not only inflexible yet takes years to design. When a new model architecture emerges or workloads shift, GPUs step up in ways that custom silicon can’t. For example, during the reasoning model era, architectural breakthroughs such as OpenAI releasing o1 and DeepSeek releasing R1 required significantly more compute at inference. Custom silicon is a better choice when workloads are stable versus rapidly evolving like we saw with reasoning models and inference-time scaling.

Nvidia Monetizes at the System‑Level: Nvidia is monetizing the system rather than just the chips, evidenced by the explosive networking growth of 263% this past quarter. On that note, speaking of Broadcom, Nvidia’s management team stated that Nvidia is now the world’s largest Ethernet company, overtaking former Ethernet giant Broadcom, and this was accomplished in just a few years’ time.  

Fourth – and most importantly, the value of what Nvidia offers is rapidly shifting from raw compute to token economics. If Nvidia continues to lead in performance per watt and performance per rack, its premium valuation can persist. Big Tech will prioritize unit economics, which means if Nvidia’s systems cost 2-3X more, the goal will be to produce more tokens per watt than the alternative to offset the premium. 

This point is critical for how Nvidia plans to defend its positioning over the next few years.  When token volume scales 10X, 100X or even 1000X, Nvidia’s ability to sell more units increases, along with the platform of more networking, more software and more tooling. 

In that framework, it will be less about which systems cost $40,000 versus $15,000 and more about which platform delivers better economics at scale. 

The Market Is Not Pricing in Nvidia’s Inference Opportunity 

Last week, in the article “Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX, I argued that the $1 trillion revenue comment through 2027 wasn’t the headline. The real development was the new Groq 3 LPX racks delivering up to 35X higher throughput per megawatt. 

Why the Groq 3 LPX Integration Is a Major Catalyst 

The 256-chip LPX rack introduces Groq’s unique SRAM‑based architecture that allows Nvidia to offload decode‑phase workloads and massively increase token throughput. This primarily targets trillion‑parameter LLMs, million-token context, and multi‑agent systems, which are bottlenecked less by compute and more by how efficiently a system can move data and generate tokens. Paired with the new Vera Rubin GPUs, Nvidia claims this architecture can deliver up to 35X higher throughput per megawatt, with seamless integration into Vera Rubin deployments.   

The Groq acquisition is aimed to solve the limiter of inference throughput per watt, where memory bandwidth can become the gating factor to token output and cost. Nvidia is preparing to position its GPUs to be among the best inference options available, utilizing Groq’s unique SRAM-based architecture to significantly turbocharge token throughput and accelerate inference performance.   

Nvidia expects Groq will help drive up to a 15X increase in tokens per second, directly translating into higher tokens per megawatt, which is already scaling by a factor of 10X between Blackwell and Rubin. If these claims hold true, then cheaper inference will unlock more usage, and more usage should lead to higher revenue and higher profits as the AI monetization wave plays out. 

Nvidia is positioning its new Groq 3 LPX racks as a ‘token accelerator’ functioning in tandem with Vera Rubin GPUs to significantly boost token throughput and address the upcoming multi-agent future. The Groq LPUs are not meant to replace GPUs in inference workloads, but rather compliment them by optimizing for memory-intensive decode.   

Off the bat, Nvidia expects that combining Rubin GPUs and Groq racks will drive a substantial increase in token throughput, with Nvidia VP Ian Buck claiming the combination “moves us from a world where 100 tokens per second is a reasonable throughput to one of 1500 TPS or more for AI agent intercommunication.”   

To visualize this, anything over 100 TPS feels near-instantaneous, such as for chatbot users; in other terms, this would represent 1,500 words per second, or ~275X the average human reading speed. This distinction and shift from 100 TPS to 1,500+ TPS is more important than it might appear, as 100 TPS is optimized for human consumption, such as chatbot outputs, while 1,500 TPS is optimal for machine consumption, such as multi-agent communication, autonomous long-form reasoning and real-time AI systems that all require continuous, low-latency token generation.  

Rubin + Groq Racks and the $300B Annual Revenue Opportunity 

The introduction of the Groq LPUs as the seventh chip in Rubin’s co-design also represents a natural shift in Nvidia’s rack scale strategy that may help deepen its moat, where it disaggregates compute and bandwidth via different specialized architectures to optimize inference at the rack and system rather than chip level. Nvidia is moving quickly with the new combined infrastructure, with Groq chips in volume production at Samsung and CEO Jensen Huang saying they would be shipping around the Q3 timeframe.  

 Nvidia foresees a rather large opportunity from this new integration, with CEO Jensen Huang explaining at GTC that he believes the Groq racks could account for up to 25% of a data center footprint to extend the performance and value of Vera Rubin, as well as future chips. Overall, Huang added that combining Vera Rubin with the Groq LPX racks could unlock a $300 billion annual revenue opportunity for customers.   

While some analysts had cautioned that reaching the upper end of this would depend on buyer appetite and ‘ultra-premium’ tiers such as up to $150 per million tokens (nearly ~10X of GPT 5.4’s cost), the scale of the opportunity reflects Nvidia's belief that inference-optimized rack-level systems will become a key part of future AI infrastructure buildouts. 

Read more about the importance of the Groq 3 LPX racks and why the acquisition represents an important catalyst for the Nvidia’s stock. an important catalyst for the Nvidia’s stock.  

Nvidia Stock Broke Minor Support at $176 

Quick note on pricing: 

Nvidia topped in late October 2025, which was one of several occurrences that signaled growing weakness in the broader market. Since then, we have seen numerous large block trades hit Nvidia’s price while in this consolidation zone. This implies larger institutions are either accumulating a big move higher or distributing before another bout of volatility hits. 

Nvidia daily stock chart showing support levels, Fibonacci targets, and wave counts.

Chart showing Nvidia’s daily price action with key support near $170, Fibonacci extension targets, and Elliott Wave counts highlighting potential breakout and downside levels.

Nvidia is barely holding the lower end of this consolidation zone, and even broke minor support at $176. This is not ideal; however, final support is $170. Below this zone, and we would likely see Nvidia move toward the $155 – $135 region. If Nvidia can instead break over $200, then it would strongly suggest that it is setting up for the next leg higher.  

Conclusion: 

As discussed, analysts had difficulty pricing in the training market while it was in motion despite a clear product roadmap of the incoming GPU generations. Based on the history of analyst estimates being up to 4X too low 5-years out and 2X too low 1-year out for the training market opportunity, the chances that Nvidia’s inference opportunity is correctly priced in is fairly low in my opinion. This is especially true at this moment in time because inference has not made a dent yet on the return on capital for Big Tech companies. Furthermore, Groq is a recent acquisition and its impact on Nvidia’s revenue trajectory is not fully modeled yet.  

That is not to say that current estimates are 4X too low, but rather that the 23% gap between current estimates of $757 billion and the $930 billion gets closed is a reasonable assumption. It’s also reasonable that if Nvidia can prove strong execution in the inference market, the company will make a case for its premium valuation again. 

The I/O Fund’s portfolio is on fire this year with a lesser-known AI semiconductor stock up 170%+ YTD and another lesser-known AI semiconductor stock up 90%+ YTD. We are the team that predicted Nvidia would become the world’s most valuable company in 2019 – years before Street consensus, and we have dominated the AI trade in recent years.170%+ YTD and another lesser-known AI semiconductor stock up 90%+ YTD. We are the team that predicted Nvidia would become the world’s most valuable company in 2019 – years before Street consensus, and we have dominated the AI trade in recent years. 

In fact, our high-performing tech portfolio with cumulative returns of 326%, which would place us as #1 if we were a hedge fund and #3 if we were a tech ETF or mutual fund. To get a 60-page analysis on our Top 15 AI Stocks, sign up now.326%, which would place us as #1 if we were a hedge fund and #3 if we were a tech ETF or mutual fund. To get a 60-page analysis on our Top 15 AI Stocks, sign up now.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA at the time of writing and may own stocks pictured in the charts.

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Posted in AI StocksLeave a Comment on Nvidia Stock Prediction: The Path to a $20 Trillion Market Cap is Strengthening

Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX

Posted on March 20, 2026June 30, 2026 by io-fund
Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX

At GTC this week, Jensen Huang stated the revenue opportunity for Nvidia’s artificial intelligence chips may reach at least $1 trillion through 2027, up from a previous target of $500 billion. While that grabbed most of the headlines, there was another jaw-dropping statistic that will set the stage in the coming years – which was the ability to drive up to 35X higher throughput per megawatt with its new Groq 3 LPX racks.  

The 256-chip LPX rack introduces Groq’s unique SRAM‑based architecture that allows Nvidia to offload decode‑phase workloads and massively increase token throughput. This primarily targets trillion‑parameter LLMs, million-token context, and multi‑agent systems, which are bottlenecked less by compute and more by how efficiently a system can move data and generate tokens. Paired with the new Vera Rubin GPUs, Nvidia claims this architecture can deliver up to 35X higher throughput per megawatt, with seamless integration into Vera Rubin deployments.  

In some ways, this acquisition draws parallels to Nvidia’s $6.9 billion acquisition of Mellanox, which my firm covered for premium research members in 2020. Mellanox was a strategic purchase to clear the bottleneck at the time on GPU performance, which was scale-out networking. By combining Nvidia’s GPUs with the strength of Mellanox’s InfiniBand, smart NICs and switching, Nvidia was able to turn accelerators into clusters by removing the limiter at that time (scale-out networking). 

The Groq acquisition is aimed to solve a different limiter, which is inference throughput per watt, where memory bandwidth can become the gating factor to token output and cost. Nvidia is preparing to position its GPUs to be among the best inference options available, utilizing Groq’s unique SRAM-based architecture to significantly turbocharge token throughput and accelerate inference performance.  

Nvidia expects Groq will help drive up to a 15X increase in tokens per second, directly translating into higher tokens per megawatt, which is already scaling by a factor of 10X between Blackwell and Rubin. If these claims hold true, then cheaper inference will unlock more usage, and more usage should lead to higher revenue and higher profits as the AI monetization wave plays out. 

Below, we cover how Nvidia, the de facto leader in training, is now shifting its focus to inference architecture as the next catalyst. 

Why Nvidia is Rethinking AI Inference Architecture 

Year after year (and generation after generation), Nvidia has proven that it can consistently deliver massive efficiency gains on inference throughput and token processing speed. For example, Nvidia’s GB200 NVL72 boosts per-GPU throughput by up to 30X versus the HGX H100 platform, while the GB300 NVL72 boasts up to a 50x increase in AI factory output via a 10X increase in tokens per second per user and a 5X increase in throughput per MW.  

Versus the Blackwell NVL72 systems, Nvidia says Vera Rubin can deliver up to 10X more throughput per megawatt, rapidly compounding performance gains from its Hopper generation in just three years.  

Chart comparing Nvidia Vera Rubin NVL72 and Blackwell NVL72, showing up to 10X higher AI tokens per megawatt and rising TPS per user for inference workloads.

Source: Nvidia 

However, the more important piece of the puzzle is not just the rapid pace of these throughput gains, but how Nvidia can continue to deliver exponentially more throughput gains and how Nvidia will accelerate inference workloads further. The key answer to this is Groq, and ‘inference disaggregation’ at the rack level.  

Inference disaggregation refers to splitting up the two-step process of token generation, prefill and decode, instead of running both steps together. The prefill phase processes the entire input token sequence in parallel and stores information in the KV cache, relying heavily on GPU compute and not as much on memory (yet). The decode phase generates the output tokens one by one in a sequential manner, relying on the KV cache and previous tokens, making it extremely reliant on memory bandwidth and capacity to rapidly access cached tokens. When discussing how AI workloads are memory constrained, it comes from the decode phase.  

When both prefill and decode shared the same hardware (the GPUs), the two would interfere with each other and lead to delays, as a new prefill request would either force the system to pause decodes and prioritize the prefill, or run both again at the same time, elongating response times.

mid

With inference disaggregation, prefill and decode can be scaled and scheduled on different optimized hardware via Nvidia’s Dynamo; in this case the Rubin GPUs handle prefill and Groq LPUs handle decode. With disaggregation and the LPU’s massive memory bandwidth, Nvidia CEO Jensen Huang says the two combined can deliver up to 35X higher throughput per MW on trillion-parameter LLMs: 

“What if we disaggregated inference altogether with a piece of software called Dynamo? What if we rearchitected the way that inference is done in the pipeline, so that we could put the work that makes perfect sense on Vera Rubin and then offload the decode generation, the low latency, the bandwidth limited challenged part of the workload for Groq. And so we united, unified processors of extreme differences, one for high throughput, one for low latency. 

It still doesn't change the fact that we need a lot of memory. And so Groq, we're just going to add a whole bunch of Groq chips, which expands the amount of memory it has. And so if you could just imagine, out of 1 trillion parameter model, we have to store all of that in Groq chips. However, it sits next to NVIDIA Vera Rubin, where we could hold the massive amounts of KV cache that's necessary in processing all of these agentic AI systems. It's based upon this idea of disaggregated inference. We do the prefill, that's the easy part, but we also tightly integrate the decode. 

So the attention part of decode is done on NVIDIA's Vera Rubin, which needs a lot of math and the feed forward network part of it, the decode part is done — the token generation part is done — on the Groq chip. The 2 of them working tightly coupled together over today, Ethernet with a special mode to reduce its latency by about half. 

And so that capability allows us to integrate these 2 systems. We run Dynamo, this incredible operating system for AI factories on top of it, and you get 35x increase, not to mention additional new tiers of inference performance for token generation the world has never seen.” 

Inference disaggregation is not an entirely new concept, but rather it is the way Nvidia is approaching disaggregation that makes this move noteworthy. Instead of seeing disaggregation as a separate, service-layer optimization, such as what AWS is eyeing with its recent partnership with Cerebras, Nvidia is pushing to directly embed disaggregation into the rack to maximize throughput. 

Inside Groq’s SRAM Architecture and Its Massive Bandwidth Advantage 

Groq’s chips feature a completely different memory-based architecture than Nvidia’s GPUs, utilizing SRAM instead of HBM. This unique architecture gives Groq’s language-processing units (LPUs) a significant advantage in the decode phase and in low-latency, high-query inference workloads from extremely higher bandwidth.  

SRAM offers a major trade-off versus DRAM and HBM when it comes to memory storage capabilities within AI accelerators. Unlike typical DRAM, SRAM does not require capacitors and stores data without the need for periodic refreshing, as long as power is available. Because of this design, SRAM can offer the fastest memory access speeds with minimal latency, though at the cost of having a mere fraction of the capacity of HBM chips – the LPUs have just 500MB of capacity versus 288GB of HBM in its Rubin GPUs. 

Despite having just 500MB of capacity, each LPU delivers 150 TB/s of SRAM bandwidth — this is nearly 7X the 22 TB/s HBM bandwidth per Rubin GPU. In the rack-scale configuration, the Groq 3 LPX delivers an astounding ~2.5X increase in total scale-up bandwidth and a 25X increase in SRAM bandwidth versus HBM bandwidth. 

The Groq 3 LPX combines 256 individual LPUs for a total of 128GB of SRAM capacity, yet it offers 40 PB/s of SRAM bandwidth versus 1.6 PB/s of HBM bandwidth in the Vera Rubin NVL72. Total scale-up bandwidth reaches 640 TB/s versus 260 TB/s in the NVL72. This also dwarfs the upcoming NVL576 rack which offers just 4.6 PB/s of HBM bandwidth. 

This 25X increase in bandwidth is precisely the reason why Nvidia is aiming to offload decode and low-latency workloads to the LPX racks, as more bandwidth means more weight data can be processed per second, which, at its core, means more tokens can be generated per second.   

Nvidia Positioning Groq 3 LPX as a ‘Token Accelerator’ 

Nvidia is positioning its new Groq 3 LPX racks as a ‘token accelerator’ functioning in tandem with Vera Rubin GPUs to significantly boost token throughput and address the upcoming multi-agent future. The Groq LPUs are not meant to replace GPUs in inference workloads, but rather compliment them by optimizing for memory-intensive decode.  

Off the bat, Nvidia expects that combining Rubin GPUs and Groq racks will drive substantial increase in token throughput, with Nvidia VP Ian Buck claiming the combination “moves us from a world where 100 tokens per second is a reasonable throughput to one of 1500 TPS or more for AI agent intercommunication.”  

To visualize this, anything over 100 TPS feels near-instantaneous, such as for chatbot users; in other terms, this would represent 1,500 words per second, or ~275X the average human reading speed. This distinction and shift from 100 TPS to 1,500+ TPS is more important than it might appear, as 100 TPS is optimized for human consumption, such as chatbot outputs, while 1,500 TPS is optimal for machine consumption, such as multi-agent communication, autonomous long-form reasoning and real-time AI systems that all require continuous, low-latency token. 

The introduction of the Groq LPUs as the seventh chip in Rubin’s co-design also represents a natural shift in Nvidia’s rack scale strategy that may help deepen its moat, where it disaggregates compute and bandwidth via different specialized architectures to optimize inference at the rack and system rather than chip level. Nvidia is moving quickly with the new combined infrastructure, with Groq chips in volume production at Samsung and CEO Jensen Huang saying they would be shipping around the Q3 timeframe. 

 Nvidia foresees a rather large opportunity from this new integration, with CEO Jensen Huang explaining at GTC that he believes the Groq racks could account for up to 25% of a data center footprint to extend the performance and value of Vera Rubin, as well as future chips. Overall, Huang added that combining Vera Rubin with the Groq LPX racks could unlock a $300 billion annual revenue opportunity for customers.  

While some analysts had cautioned that reaching the upper end of this would depend on buyer appetite and ‘ultra-premium’ tiers such as up to $150 per million tokens (nearly ~10X of GPT 5.4’s cost), the scale of the opportunity reflects Nvidia's belief that inference-optimized rack-level systems will become a key part of future AI infrastructure buildouts. 

AI Monetization is Arriving, and Tokens are the Currency 

As we had covered in our Bloom Energy analysis, My Top 2026 Stock Pick for the AI Boom, the real risk to the AI economy lies in the physical constraints of scaling these AI ambitions — not in compute availability from companies like Nvidia or Broadcom, and certainly not in Big Tech’s software capabilities, but in power availability, thermal management, and infrastructure that were never designed for this magnitude of demand. 

This is the core challenge the AI industry now faces, and this means the most important equation for the upcoming inference-driven monetization wave is how many tokens can be generated, served, and monetized within a fixed power envelope. With Vera Rubin and the new Groq racks, Nvidia is increasingly orienting its GPU roadmap around that point, aiming to exponentially increase tokens per second per watt. It is about creating a platform that is not just faster, but able to deliver more of that monetizable output (tokens) per watt.  

Nvidia CEO Jensen Huang made this point extremely clear at GTC, explaining that “everybody is looking for land, power and shell. Once you build it, you are power limited. Within that power limited infrastructure, you better make for darn sure that your inference — because you know inference is your workload, and tokens is your new commodity, that compute is your revenues — that you want to make sure that the architecture is as optimized as you can.” With Vera Rubin, Huang emphasized that Nvidia is “going to take our token generation speed, token generation rate from 2 million to 700 million, a 350x increase” for GW-scale AI data centers. To roughly estimate what this could look like using a cost of $1 per million tokens, this would represent a step function from $2 in revenue to $700, before applying that to scale.  

While achieving the 350X increase in token generation may be reserved for hyperscalers operating at maximal scale and efficiency, this can be translated across the industry to emerging neoclouds and data center operators alike. Think of it this way — for a data center with a fixed 100MW power envelope, the amount of users and tokens that can be served with Vera Rubin and Groq racks are multiples higher than Blackwell and other generations.  

This means that driving TPS per MW higher is essentially a multiplier on revenue and margins, as more tokens under the same power footprint translates directly to higher revenue and lower costs per token processed. As Nvidia puts it, up to 10X more tokens per MW and up to 10X lower cost per million tokens with Rubin versus Blackwell – put differently, if it cost a cloud provider $10 to serve 1 million tokens that generated $15 in revenue, it would net $5 in profit. With Rubin, if it can generate 10 million at that same $10 cost, profit could reach as much as $140. 

The above scenario assumes high revenue for AI inference, which may compress as the AI inference market is built out. Yet, even with a ~67% compression in token costs (revenue) from $15 to $5, there will still be $40 in profit at the 10 million tokens, or an 8X increase. 

This does not mean that Nvidia is not immune to rising competition from custom silicon, as hyperscalers continue to turn towards custom chips to optimize for specific inference workloads and dramatically lower serving costs. For example, Alphabet lowered Gemini’s inference serving costs by 78% through 2025 via model optimizations, utilization and efficiency improvements, and its newest TPU generation is likely to drive further cost reductions through 2026. Meta also recently expanded its custom silicon roadmap with four new chips, focusing on ranking and recommendation model performance, genAI models, and inference via increasing HBM bandwidth and capacity each generation.  

Among the hyperscalers and startups with the deepest pockets, custom silicon will likely remain a key choice in AI deployments for its ability to offer much lower costs and high performance for optimized workloads. However, for neoclouds and companies without capital to build and deploy ASICs at scale, Nvidia is creating an extremely compelling value proposition by offering a platform optimized for token throughput at scale.   

Conclusion 

Nvidia is leveraging Groq’s SRAM-based LPUs and extreme memory bandwidth to significantly accelerate inference and token throughput by offloading the decode phase to the new chips. When paired with Vera Rubin, Nvidia claims this architecture can deliver up to 35X higher throughput per megawatt for trillion parameter LLMs. As the AI industry now faces power and infrastructure constraints rather than compute, the key differentiator in the upcoming AI inference monetization wave will be how to extract the highest number of tokens per megawatt to maximize revenue.  

For years, Nvidia’s dominance has been synonymous with training. Now, the company is making it clear it wants to own inference, which is the part of AI that actually scales into everyday usage and recurring revenue. The market latched onto Jensen Huang’s $1 trillion AI chip visibility through 2027, but the bigger tell may be what Nvidia is optimizing for next: tokens per megawatt. If Groq 3 LPX helps Rubin deliver anything close to the claimed throughput gains, Nvidia’s next growth leg won’t be about building bigger models—it will be about making inference cheap enough that demand explodes. 

The I/O Fund predicted Nvidia would become the world’s most valuable company in 2019 – years before Street consensus. Today, our team runs a high-performing tech portfolio with cumulative returns of 326%, which would place us as #1 if we were a hedge fund and #3 if we were a tech ETF or mutual fund. To get a 60-page analysis on our Top 15 AI Stocks, sign up now.Nvidia would become the world’s most valuable company in 2019 – years before Street consensus. Today, our team runs a high-performing tech portfolio with cumulative returns of 326%, which would place us as #1 if we were a hedge fund and #3 if we were a tech ETF or mutual fund. To get a 60-page analysis on our Top 15 AI Stocks, sign up now.

Damien Robbins, Equity Analyst at I/O Fund contributed to this analysis.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA at the time of writing and may own stocks pictured in the charts.

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Posted in AI StocksLeave a Comment on Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX

Ciena: Benefitting from Data Center Scale Across Demand 

Posted on March 19, 2026June 30, 2026 by io-fund

Ciena’s role is different than the many “inside-the-data-center" networking stocks the I/O Fund covers as instead of offering networking fabrics at the system level, Ciena offers optical transport across data centers and metro regions. While AI training is already pushing hyperscalers to connect clusters across data center sites, inference offers another leg up for metro/long-haul networking as workloads will be distributed across many data centers to scale significant application usage, especially as agents necessitate responsiveness and reducing latency. 

This quarter, Ciena proved it's a leader in data center interconnects with $1.43 billion in revenue and $1.35 in adjusted EPS – more than doubling year-over-year. Optical revenue grew 41% YoY and 10% QoQ with Waveserver and RLS up 80% YoY and backlog that grew by $2 billion to reach about $7 billion total. 

With 800G pluggables ramping, new platforms like RLS Hyper-Rail and Nubius products (Vesta Optical Engine and Nitro redriver) will help to expand Ciena’s footprint. Notably, Ciena benefits from two major customer pools: hyperscalers building and linking more AI data centers which drives demand for DCI/metro/long-haul optical networking, but also telecom carriers and service providers who are upgrading their networks in anticipation of increased AI traffic across the network.  

Below, we cover Ciena’s product positioning, its most recent earnings report and a few key catalysts to keep an eye on as we approach an inflection driven by the inference market. 

Product Overview: 

Ciena’s foundational business is to address connectivity needs in the wide area network (WAN), spanning long-haul, metro and data center interconnect (DCI). Ciena offers the optical backbone that connects AI data centers and regions and is poised to benefit as AI-driven traffic will lead to higher-capacity metro and long-haul upgrades. 

Below, we break down the main products that Ciena offers currently, such as RLS and Waveserver, and products that Ciena plans to expand into, such as Vesta higher-density optical engine and Nitro linear redriver to extend reach in active copper cabling. 

Optical Networking & DCI 

Ciena offers a range of optical networking solutions targeting scale across, campus and metro DCI (10-20km and 20-100km) to backbone and submarine (10,000+ km reach) optical systems.  

Ciena’s WaveLogic sends information over fiber using fewer lanes, which lowers power use and cost. Ciena uses WaveLogic inside its systems and offers compact pluggable modules that make it easier to add capacity without rebuilding a networking. Ciena’s Waveserver is the platform that connects data centers at scale by offering a packaged solution that can be deployed to expand bandwidth between sites quickly. The newer versions of Wavelogic detailed below allow Ciena to push higher speeds over the same fiber, which can reduce the number of components needed for a quicker and more simplified upgrade.  

  • Ciena’s WaveLogic 5 Extreme supports 200G to 800G line rates to enable 400GbE transport across any distance, including ultra-long haul and subsea, while its WaveLogic 5 Nano 100G to 400G ZR/ZR+ pluggable coherent optics are optimized for DCI applications with low power consumption of 15W with reach up to 140km. WaveLogic pluggables also seamlessly integrate into its Waveserver stackable interconnect platforms, scaling up to 12.8Tb/s per 2RU unit for cost-effective DCI solutions. 
  • Ciena’s WaveLogic 6 Extreme boosts speeds to an industry-leading 1.6Tb/s per wavelength, extending 800GbE transmission across the longest distances. Ciena says that WL6e delivers twice the capacity per wavelength in its existing 6500 and Waveserver platforms, helping reducing transceiver counts and lowering cost and complexity of systems. It also delivers double the capacity under the same power and space envelope versus WL5e. 
  • WaveLogic 6 Nano offers fit-for-purpose 400-800G ZR/LR/ZR+ pluggables and 1.6T coherent-lite pluggable transceivers to meet high-bandwidth AI data center applications. Ciena says that WL6n combines it vertically integrated DSP, electro-optics, and high-speed packaging to support high-performance data center fabric, or 2km, and data center campus, up to 20km, applications. 800ZR pluggables were implied to be ramping later in the year, while Ciena is also developing future 1600ZR/1600ZR+ solutions built on 2nm silicon. 

Reconfigurable Line Systems (RLS) 

RLS offers the infrastructure that routes wavelengths over fiber for metro and long-haul networks. COLORZ pluggables are part of the optical stack and are the endpoints that generate and receive the optical signal for DCI/metro links. 

RLS plays an important role in ‘scale-across’ applications, the third pillar of scaling AI infrastructure. We have detailed the first two pillars, scale up and scale out, in detail, yet the challenge is that both cannot extend infinitely, which is where scale across fits in.   

While scale across is much less discussed, it refers to linking together multiple geographically distributed AI data centers to act as one cohesive compute facility, theoretically facilitating a path to >1GW-scale clusters as it bypasses site/land and power constraints.  This can either be in close proximity such as OpenAI’s Stargate cluster in Abilene spanning multiple buildings, Amazon’s Project Rainier or Microsoft’s Fairwater data centers linking together via ‘AI WAN’ (wide area network). Coherent ZR/ZR+ modules excel in scale across as they maintain the signal integrity and reliability across long distances at the highest data rates.  

According to the management team, Ciena’s upcoming RLS hyper-rail solution is expected to be in high demand: “We are addressing this demand for scale across solutions with our RLS platform, the de facto industry line system standard for cloud providers as well as our 800ZR pluggable optics. To underscore this, we realized a second consecutive record quarter for RLS shipments and revenue. We expect to expand our role in scale across applications with the introduction of our new RLS hyper-rail solution. Hyper-rail delivers an order of magnitude increase in fiber density within existing rack footprints, helping customers scale traffic while reducing and, in some cases, avoiding costs and complexity associated with adding substantial numbers of amplifier huts.” 

  • Ciena offers reconfigurable line systems (RLS) in its 6500 RLS family, which addresses the highest-capacity bandwidth requirements in metro, long-haul and DCI applications. RLS can support 60Tb/s capacity on a single fiber pair, offering flexible configuration options while doubling fiber capacity without impacting existing C-band traffic. Ciena also says that its 6500 RLS can reduce physical footprint by as much as 70% versus traditional chassis-based systems from its modular form factor. 
  • Ciena is expanding its RLS portfolio with hyper-rail photonics, which feature new amplifier configurations to deliver 32x density of current solutions, scaling to 128 fiber pairs in a single rack. This is achieved by scaling from one rail per module to four, reducing power consumption by up to 75% and space requirements by up to 85% while integrating into existing rack footprints. Ciena explained that the RLS Hyper-Rail was co-developed with its hyperscaler customers specifically for multi-region, large scale AI connectivity, and will begin standardization at the end of 2026 and ramp in 2027. Ciena expects to be first to market in hyper-rail, allowing them to extend current RLS momentum into 2027 driven by share gains. 

RLS is an important cornerstone for Ciena’s Optical Networking portfolio, with management explaining that FQ1 marked a “second consecutive record quarter for RLS shipments and revenue”, with growth of over 80% YoY. Ciena is seeing robust momentum for scale across stemming from its RLS solutions and ZR/ZR+ modules this year, with management explaining that they “are already experiencing extraordinary demand with 3 hyperscalers choosing to use our optical solutions for their training applications across distance.” All three hyperscalers were said to be “significantly ramping including additional orders for multiple additional clusters from the first hyperscaler we announced in Q3 2025.” 

Scale Up, Scale Out and CPO 

Ciena’s $270 million acquisition of Nubis in September 2025 expanded its presence inside the data center, as the aforementioned products primarily offer networking across the data center. With this new acquisition, Ciena is moving into scale-up and scale-out networking and co-packaged optics (CPO).  

As a brief recap, CPO places optical transceivers directly on the chip package, rather than using separate optical modules, resulting in faster data transmission, reduced latency and higher bandwidth. This may be the best of both worlds: the performance of optical yet with reduced power consumption for increasingly power-hungry AI racks. 

Ciena recently unveiled its Vesta 200 6.4T CPX, which it says is the industry's highest-density and lowest-power pluggable CPO solution. Ciena says that the retimer-free linear-drive operation saves up to 70% power versus other retimed options. According to management, samples of the Vesta 200 6.4T optical engine will be available in Q2 2026. 

For scale-up inside the rack, Ciena is advancing Nubis’ Nitro Linear Redriver tech, which extends the distance that signals can be transmitted over copper cables and also reduce power by up to 80% versus AECs. Ciena will also sample the redriver in calendar Q2. As stated on the call, this is especially important for XPUs (such as from Broadcom): “For scale-up opportunities inside the rack, where XPUs are getting faster and driving heat and power concerns, we are advancing the Nitro Linear Redriver technology also from our Nubis acquisition.” 

Data Center Out-of-Band Management  

Out-of-band management is critical yet underdiscussed, as it provides a separate, independent network to control and recover data center infrastructure – for example, it allows hyperscalers to have visibility over servers and other components, and provide a pathway remotely recover systems without physical intervention. However, traditional out-of-band management faces increasing challenges as AI clusters scale up and out. This is because the maximization of GPU density in racks leaves minimal space for OOB connectivity, such as for switches, routers and console servers. It also is copper-heavy (and power hungry) by relying on extensive Ethernet cabling.  

Ciena designed its DCOM solution with Meta to meet hyperscaler configuration requirements, noting that its DCOM solution, leveraging its routers, switches and its passive optical network (PON) ecosystem to reduce rack space by up to 99%, reduce power, and streamline remote access. Ciena says it is continuing to work with Meta on DCOM and had expanded last quarter, with two other hyperscalers in discussion. Management believes that DCOM will be a significant opportunity in the data center and a defensible space for them due to its speed, vertical integration and software.  

Managed-Over-Fiber Networks (MOFNs) 

Advanced networks called managed-over-fiber-network (MOFNs) are projects that connect multiple data centers in a metro area and upgrade capacity and resiliency on existing metro networks. This drives demand for Ciena’s optical transport and DCI solutions, especially across Waveserver, coherent optics and RLS amplifier and routing gear.  

MOFNs are starting to show up as a growth driver because AI is creating significant traffic that has to be distributed between data centers. The customer base includes hyperscalers, neoclouds and service providers with 10% to 15% coming from telecom. 

Ciena is seeing strong momentum in MOFN, noting that its orders in India were up 40% YoY, specifically for these solutions. According to management, there were three greater than 10% customers including two global cloud providers and one Tier 1 North America service provider with strong MOFN activity and “order intake has been incredibly strong over the past 90 days, leading to a new record by a significant margin.” 

There was an interesting quote from management about MOFN and the neoclouds, as Ciena hinted that this is a preferred method of connectivity from its speed to market and less capex requirements from a lack of maintenance on the neoclouds’ part:  

“We're seeing obviously an emerging ramp here around a bunch of the loosely called sort of neo-scalers, which encompasses a fair range of different players. It's, I would say, largely right now MOFN orientated, given the capital expenditures, time to market for them, et cetera. But what is clear from it all is that the network is now a real priority for them. And I think that plays through to the hyperscalers, too. There's been such a maniacal focus and continues to be, obviously, on things like power, GPU accessibility, et cetera, et cetera. 

Now it's really about the network. The traffic is beginning to come out of the network, both for inference and for training. And the neo-scalers are obviously seeing that, too. So they're leaning in on the network. Now we're also beginning to see some of them wish to have control of some of that network as well and do their own builds. We're cautious about that approachment given the financial structure of some of those neo-scalers, not all of them.” 

Backlog Rises 40% QoQ to $7 Billion, Orders Fulfilled in FY27 

Ciena revealed strong backlog growth in fiscal Q1 and robust order activity tied to strong demand from hyperscaler customers. The backlog growth sparked multiple important discussions around conversion and pricing power, suggesting Ciena has strong visibility into next year with pricing emerging later this year as a growth lever.  

Backlog rose 40% QoQ to $7 billion, with management noting that Q1 demand was ‘unprecedented’ with a very strong order intake. Roughly 80% of the backlog is for products and software, or ~$5.6 billion, up from $3.8 billion last quarter. Management would not put a concrete number on RPO, but said that it would be roughly 60% of the orders Ciena took in during Q1.  

Ciena expects its backlog to continue to grow throughout the year yet noted that “nearly all” of the new orders it is currently receiving will be fulfilled in fiscal 2027, suggesting that supply remains extremely tight relative to demand with this providing strong visibility for next year’s growth.  

CEO Gary Smith provided some color on the demand drivers and strength of demand, explaining that Ciena is “seeing growth in cloud, general cloud, you're seeing inference, you're seeing this new market of training now emerge. As I said in my comments, we've now got 3 hyperscalers deploying us for training, and we're at the very early stages of that. 

So you put all of that together, and that yields the incredible demand that we saw in Q1. And as Marc said, despite the fact that we're ramping our capacity for delivery as seen in our results, demand is going to continue, we believe, to outstrip our ability to supply. And that's going to continue for — we believe, this year. And so we're going to end up with a larger backlog than we have right now as we turn the year despite the fact that we're ramping our capacity strongly throughout the year and obviously through '27 and '28.”  

Smith also later clarified that it was purely demand that was driving this order growth and backlog increase, explaining that hyperscalers are ramping significantly in optical technologies both across clusters and within racks.  

Pluggables to Triple to >$500M 

Also intertwined with the scale across momentum is Ciena’s projections for hypergrowth to continue within its ZR/ZR+ pluggables business, where it is expecting its pluggable revenue to triple after doubling last year.  

For context, Ciena stated in Q4 that pluggables had reached more than $168 million in FY25, so management’s guidance implies a ramp to >$500 million this year. This would represent a ramp from ~5% of Optical Networking revenue to likely low-double digit share in FY26 for pluggables, making them a key contributor to the segment and overall topline growth.  

Management offered some commentary on pluggables and the drivers behind this growth, pointing to 800G ramping through FY27 and their expectation to lead the market with the new data rate:  

Amit Daryanani, Evercore 

“How do you see the pluggables market, especially with 800G ramping up through fiscal '26 and '27? And if you just maybe compare and contrast a bit about your position in 400 versus 800, that will be helpful as you go into the next cycle. 

Scott McFeely, Executive Advisor 

So we've seen pluggable revenue increase sort of period-over-period, and we've talked in the past about our interconnect business and when we went from 2024 to '25, that doubling. That's sort of in the rearview mirror. And then we talked about it as a major portion of our inside and around the data center with our aspirations to triple that this year, and we're well on track for that. 

So we do see significant growth from a competitive perspective, as we've talked about in the past, through choices that we made to focus early introduction of the technology in the last generation more on our systems business and our pluggable business because that was the bigger opportunity. We weren't necessarily first movers in that market. So that probably cost us some share and it probably cost us actually, frankly, some margin dollars. That's not the case in the 800G. We're first to market there and 800G is moving quite along.” 

800G is expected to ramp through the first part of 2026 and persist through 2027, and while the tripling of revenue is certainly welcomed, the upcoming cycle is expected to see strengthening margin tailwinds through the course of the year. Ciena explained last quarter that they are expecting to lower 800G unit costs over time, and as the ramp progresses through Q2 to Q4, they expect significantly lower costs versus the start of 2026. This suggests that 800G margin headwinds in the initial early ramp will quickly pass through and potentially shift to margin tailwinds by year-end on a much larger revenue base. 

The primary challenge with pluggables was touched upon by management, in that the market is competitive, any first-mover advantages may be fleeting and pricing power is not a given. For example, Marvell recently unveiled its 1.6T ZR/ZR+ module, the first to come to market, with the new product expected to be initially available in the second half of the year, meaning Ciena’s lead in 800G may be short lived.  

Not Aggressive on Pricing, but Tailwinds Emerging in 2H 

Given the demand environment and supply tightness, analysts were curious about pricing power, noting that other component suppliers are being much more aggressive in raising prices and repricing their backlogs higher, and if Ciena would do the same. 

Ciena has already been upfront about demand outstripping supply for at least the next several quarters and how supply constraints have impacted revenue growth, meaning a more aggressive stance on pricing could drive revenue growth at a much faster pace.  

However, management downplayed the need or desire to reprice the backlog and be aggressive with pricing, explaining that they are already seeing market share gains and tangible impacts to growth and margins. This is likely because Ciena has already raised prices and expecting these to begin landing in 2H: “As we disclosed in Q4, right, the pricing increases that we talked about were really on the new orders. And because we had such a big backlog at the time, most of that was going to be seen in the second half. So you should expect those price increases to show up in Q3 and Q4.” 

Management was also upfront about why they are opting to not be as aggressive as other suppliers when it comes to pricing, rather using it as a lever to get better contractual terms, better cash conversion and longer-term purchasing commitments to reduce the risk associated with converting its larger backlog.  

George Notter, Wolfe ResearchGeorge Notter, Wolfe Research 

“Obviously, you're raising pricing. I know it's going to come through later in the year as you eat down the backlog. But just stepping back and thinking about the space, you've got higher memory costs, you've got component suppliers that are being really aggressive on price. They're repricing their own backlogs. It just seems like it's an environment where you guys could be more aggressive on price and even perhaps reprice your own backlog. So I'm just curious like why not be more aggressive here given the supply-demand dynamics and what's going on in the supply chain? 

Gary Smith, Ciena CEOGary Smith, Ciena CEO 
We've talked a lot about the good things that we're doing to manage our margins and the rest of it, including the value rebalancing. But it is a balance to it all. And that's what we're trying to strike as we go through this. I mean you're seeing it translate into improved financial performance in all dimensions, market share gains, revenue, gross margin improvement and operating leverage. We're seeing that. And it's a confluence of things….  

Marc Graff, Ciena CFOMarc Graff, Ciena CFO 

No, I think Gary said it well. Pricing is a lever, George, but we're also looking at can we improve cash conversion? Can we get better terms and conditions? Can we get longer-term purchasing commits with maybe some more noncancelable, less risky terms as we satisfy this quite large backlog.” 

Ciena is already showing signs of healthier payment activity within its days sales outstanding (DSO), which declined to 72 days in Q1, down from 77 days in Q4 and 90 days in the prior year quarter. Additionally, inventory turns reached 3.2X in Q1, up from 3.1X in Q4 and 2.3X in the prior year quarter, suggesting that demand is strengthening and payment terms are improving. Aiming for longer-term contracts and less risky terms would provide a higher degree of visibility into revenue through 2027 and will prevent Ciena from falling back into an inventory overhang like it had faced in early 2024.  

Financials 

Revenue Accelerates to 33.1% YoY in Q1  

Ciena reported fiscal Q1 revenue of $1.43 billion, representing 33.1% YoY growth, while sequential growth came in at 5.6% QoQ. Revenue also beat consensus estimates by 2.1%, reflecting stronger-than-expected demand across optical networking and cloud connectivity deployments.  

The strong YoY growth marks a notable improvement compared to the muted growth environment seen through much of FY24 and early FY25 as telecom customers worked through inventory digestion and moderated capital spending. The current acceleration suggests spending patterns across service providers and cloud operators are normalizing, particularly as network upgrades tied to AI infrastructure and high-capacity data center interconnect (DCI) deployments expand. 

Looking ahead, Ciena guided for Q2 revenue of approximately $1.50 billion, which would represent 33.5% YoY growth and a modest 4.9% QoQ growth.  

For the full year, Ciena guided for fiscal 2026 revenue of $5.9 to $6.3 billion, raising its growth forecast from 24% YoY to 28% YoY at midpoint. While the raise is certainly welcomed and indicative of the strengthening demand Ciena is seeing in RLS and pluggables, it indicates a rather soft 2H, after 40% and 34% growth in 1H. This suggests that the order momentum and backlog building are not translating over to 2H growth as of yet, but more so for 2027.  

Margins Expanded, Ahead of Guidance 

Profitability improved across all levels during the quarter, with margins expanding sequentially and outperforming management’s guidance in several areas. 

GAAP gross margin came in at 43.8%, generating $625.5 million in gross profit. Gross margins benefited from improved product mix and stronger volumes across higher-capacity optical platforms.  

On an adjusted basis, margins were slightly stronger, with adjusted gross margin of 44.7% on adjusted gross profit of $638.2 million. This result exceeded management’s midpoint guidance of 43.5%, suggesting better pricing discipline and favorable mix within Ciena’s optical portfolio. The company’s WaveLogic coherent optical platforms typically command higher margins, particularly as customers transition toward higher-capacity wavelengths such as 800G and beyond. Looking ahead, Ciena expects that its upcoming products such as RLS hyper-rail and more focused cost optimizations will help deliver improvement in gross margins. On the call, management explained that ” moving forward, you'll see even more aggressive cost reductions. And then the price increases that we talked about at the end of last year, those really haven't started to fully kick in until the second half of the year. So I think that creates additional tailwinds for us. So all in all, again, I think we're making really good progress towards that 45% way point, and you should see that throughout the year.” 

Operating leverage became more visible in Q2 as operating margins improved meaningfully in the quarter, reflecting the benefits of stronger revenue growth flowing through the cost structure. GAAP operating margin was 13.3%, improving 5.8 points YoY and 12.5 points QoQ, and marking Ciena’s first return to above a double-digit margin since the end of 2021. 

Adjusted operating margin was 17.9%, coming in well ahead of management’s 16% guidance. Because Ciena operates with a large fixed-cost R&D base tied to its optical platform development, revenue expansions typically translate into disproportionately stronger operating profit growth once demand improves. Operating leverage will remain a key driver of earnings growth if revenue continues expanding at a high double-digit pace over the coming quarters. 

This improving operating profitability flowed through to net income as well, with GAAP net margin of 10.5%, up 6.3 points YoY and 9.1 points QoQ. Adjusted net margin was 13.8%, with the difference reflecting stock-based compensation and other non-cash adjustments.  

EPS 

Ciena reported a strong 15.6% beat to adjusted EPS estimates in Q1, earning $1.35 in the quarter, up 110.9% YoY. GAAP EPS was thinner at $1.03, up 232.3% YoY on a softer comp. 

Looking ahead, Ciena is expected to see EPS growth accelerate in Q2 on a softer YoY comp, with growth decelerating through the back half of the year but remaining strong. Adjusted EPS is projected to rise 247% to $1.46 in Q2 before decelerating to 134.6% YoY to $1.57 in Q3. Growth is expected to reach 94.1% by Q4 to $1.77. 

For the full year, Ciena is currently projected to report 132.4% growth to $6.14, before decelerating to 34% growth in FY27 to $8.22. 

Cash Flow Generation Remains Healthy 

Cash flow generation remained solid in the quarter and improved alongside stronger profitability. Ciena’s balance sheet remains solid with a manageable leverage profile and ample liquidity. 

Operating cash flow was $227.7 million for a 16.0% margin, improving from a 9.7% margin a year ago. Free cash flow was also positive at $153.8 million, representing a 10.8% margin, up from 7.2% a year ago.  

These cash flow margins remain healthy for a hardware-driven networking company, particularly one with significant R&D investments in optical technologies and silicon photonics. Over time, improving operating leverage and scale could push free cash flow margins higher, particularly if higher-capacity optical products continue gaining share within the product mix. 

Cash and short-term investments totaled $1.30 billion, while total debt was $1.54 billion. 

Notably, capex was $74 million in Q1, which is 2X to 3X more than the company’s average capex over the past three years. The trend toward higher capex is likely to continue given a mix of strong visibility from customers and the need to secure critical components ahead of demand. Here is what was stated on the earnings call: “Second, we are deeply engaged with component vendors, which is where more of the industry challenges exist to secure and expand supply, including through responsible long-term purchase commitments. As shown by our Q1 results, we are navigating the supply environment well and are investing to expand capacity. However, we expect demand will continue to outstrip supply at least for the next several quarters.” 

Conclusion 

Ciena’s quarter reinforced the company is a direct beneficiary of AI-driven connectivity spend – not just inside the data center, but across metro and long-haul networks that connect AI clusters and regions. Ciena was also recently included in the S&P 500 – and for good reason as the $50B+ market cap company is set to report over $6 billion this year in revenue with a bottom-line that is doubling. 

Record revenue and expanding profitability, plus a backlog that pushes into fiscal 2027 all point to demand that is both strong and increasingly visible. The company’s 800G pluggables are ramping, and new platforms like RLS hyper-rail and Nubis products are set to expand Ciena’s footprint ahead of more long-haul/metro demand from distributed inference and multi-site training. 

Although our portfolio is loaded with market-leading AI networking winners, with some up as much as 100% YTD, Ciena holds its own by offering a differentiated way of participating in the next phase of AI infrastructure, where the focus shifts to scaling the bandwidth that connects data centers and regions.

Damien Robbins, Equity Analyst at I/O Fund contributed to this analysis.

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Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

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Posted in AI Stocks, Data CenterLeave a Comment on Ciena: Benefitting from Data Center Scale Across Demand 

Micron Fiscal Q2: Record-Breaking Fundamentals 

Posted on March 19, 2026June 30, 2026 by io-fund

As someone who looks at hundreds of earnings reports a year, there are times an earnings report shatters expectations like an Olympian breaking a record or an athlete leaving no doubt who is the best in the game. Micron did this by dropping an earnings report so strong on fundamentals that I cannot recollect seeing one quite like this. 

Micron blew the doors off with revenue growth of 196.3% YoY and up 75% QoQ for a beat of 22.3% on a massive revenue base of about $24 billion a quarter. The forward fiscal Q3 growth is eye-watering at 260.2% YoY and 40.4% QoQ. This is nearly 100 points higher than what analysts had slated for fiscal Q3 with consensus at 150.2% growth YoY. 

The $10.2 billion sequential increase is nearly unprecedented outside of Nvidia’s most recent quarter posting $11 billion QoQ growth – yet, let’s not forget that Nvidia is the world’s most valuable company.  

Here is what management stated about this record-breaking quarter:  

“Quarterly revenue nearly tripled versus one year ago, and revenue for DRAM, NAND, HBM (high-bandwidth memory) and each business unit reached new highs. Our fiscal Q3 single quarter revenue guidance exceeds the full year revenue for every year in our company’s history through fiscal 2024. For fiscal Q3, we anticipate exceptional records across revenue, gross margin, EPS and free cash flow.” 

To also help illustrate just how impressive this earnings report was, consider that Micron was not supposed to see $33 billion in a single quarter until FQ1 2028 (November of 2027) yet following a second quarter of $10B sequential growth, will now see this revenue in the quarter ending in May of 2026. 

As incredible as the revenue growth is; the margins are arguably even more incredible at an 81% gross margin and 76% operating margin guide for the next quarter.  

So, why would the market be selling the report after hours? Well, to be prudent, Micron was reporting a steep, negative gross margin of (9%) in FY2023 with one quarter as low as (32.7%) in FY23. Thus, the question of whether we are seeing a cyclical top or a structural shift in memory is a very valuable question to answer, and the importance of this broader question is only further reinforced by the results we saw this evening.

Supply Constraints and Product Road Map Combo Can Create Defensibility 

Although the market may be fatigued at hearing there are supply constraints, that dynamic is often what offers the highest level of defensibility for a supplier like Micron.  

Regarding AI memory, Micron stated they began volume shipments of HBM4 36GB (12-high) in 1H26 designed for Vera Rubin and they expect this to reach mature yields faster than HBM3e. Higher yields typically mean more sellable HBM per production run and typically support stronger margins. Additionally, Micron has sampled 48GB (16-high) HBM4 stack with HBM4e in development with a planned 2027 volume ramp, leveraging the 1y DRAM node in the product road map for sustained data center growth.  

On the earnings call, management emphasized that AI is trending toward more reasoning, longer context windows and agentic workflows – all of which require more DRAM capacity and bandwidth. As a result, GPUs and ASICs are expected to need increasing amounts of HBM plus DDR5/LPDDR to support training and inference.  

When it comes to HBM, management stated they expect to see robust growth at least through 2027: “And we continue to feel like we are in an extended space of robust industry demand that obviously, due to HBM being part of these numbers with its trade ratio is just stressing the entire industries and certainly our capabilities to be able to meet those demand numbers. So you're right. I mean, these numbers, at least in the foreseeable future are all supply limited numbers rather than the actual level — true level of demand. So yes, I mean, that's sort of the environment we are in. We do expect that next year, again, we will have a fairly robust level of growth in calendar '27. But yes, we are not providing a long-term number beyond that commentary.” 

On storage, Micron will benefit from rising SSD share from vector databases and KV-cache offload (we’ve covered this in the past here). It’s clear from management commentary that they see no end in sight to the supply shortages on SSD: 

“And we continue to see those shortages for the foreseeable future. That has been another driver. So when we put all of these together, the NAND market is significantly undersupplied to the demand in the data center, and that demand continues to escalate in part driven by KV cache, but also driven by just the insatiable appetite that these AI servers have to have fast storage capability available as these systems get deployed more and more. And so the outlook is really strong. And as we have mentioned earlier, our portfolio is incredibly well positioned to continue to gain share in that space, including our KV cache applications, by the way, yes.” 

The company is in high-volume production of G9 NAND PCIe Gen6 data center SSDs and cited strong adoption of its 122TB SSD that “delivers 16 times the sequential read throughput per watt of a capacity-matched HDD configuration.” 

In the most recent earnings report, Micron saw share gains in SSD for its fourth consecutive year in 2025 with management stating that NAND revenue more than doubled sequentially in fiscal Q2 to a record. 

It was also confirmed that NAND demand significantly exceeds supply, which is fairly evident in the following pricing strength: “Fiscal Q2 NAND revenue was a record $5.0 billion, up 169% year-over-year, and represented 21% of Micron’s total revenue. Sequentially, NAND revenue increased 82%. NAND bit shipments increased in the low-single-digit percentage range. Prices increased in the high-70s percentage range driven by tight NAND industry conditions and included favorable mix.” 

Counterpoints 

Memory can be stubbornly cyclical, and the market appears to be discounting the familiar pattern Micron has faced in past cycles where peak shipments were followed by a sharp reversal ahead of pricing and demand normalizing. 

Management commentary supports Micron being in a sustained uptrend as 2026 is supply-constrained and greatly limited by DRAM and NAND supply. However, despite the outsized demand and strong product road map, Micron will likely see peak sales before Nvidia’s Vera Rubin sees peak sales given HBM and data center DRAM sits earlier in the supply chain. Therefore, there can be air pockets tied to Nvidia’s GPUs shipping in volume even when the overall trend remains intact.  

Micron is also exposed to PC/consumer and traditional server revenue.  

Micron Capex Increases 

The CFO stated FY26 capex is raised to over $25B from previous estimates for $20B with some of the new fabs being greenfield sites. Although a glut of inventory is always on top-of-mind for a memory player that aggressively adds more capacity, there is likely more risk to Micron not seizing the opportunity to remain #2 with South Korea being particularly strong competitors. There are some facilities that are expansions yet many of these projects will not translate into meaningful revenue shipments until FY2028. 

In terms of if Micron could be adding more capacity at a peak, management felt confident this is not the case: “So we are very excited about that, and our supply is nowhere close to being able to meet the demand that we see for the foreseeable future.” 

The Shift to Strategic Customer Agreements (SCAs) 

Micron discussed shifting from long-term agreements to strategic customer agreements (SCAs): “We continue to work with customers on strategic customer agreements — or SCAs — that are different from prior LTAs (long-term agreements) and have specific commitments over a multi-year time horizon for improved visibility and stability in our business model. These SCAs also provide customers greater certainty to plan their businesses while reinforcing long‑term engagement across our broad product portfolio. We are excited to have signed our first five-year SCA.” 

If I had to point to one thing that could be causing the softer price action, it would be the read-through that SCAs could lead to a cap in pricing during a surge (like what we are seeing now in surging DRAM and NAND pricing).  

The upside to SCAs is they lock in supply and volume to smooth-out lumpiness in a cyclical industry yet could limit upside in exchange for that visibility. 

There were many questions on SCAs in the call – in fact, this topic dominated the Q&A session, here is one that encapsulates the concern around SCAs limiting upside in pricing yet also helping to remove the effects of a cyclical low, as well: 

“Sreekrishnan Sankarnarayanan 
TD Cowen 

Got it. Thanks for that, Mark. And then a quick question for Sanjay on the SCA. Congrats on your first 5-year SCA. How different is it from an LTA? Is this a multi-year volume and price commitment, or does the price get negotiated every year? And also, how to think about cancellation terms on the SCA in case the cycle slows down during the timeframe? 

Sanjay Mehrotra 
CEO, President & Chairman 

Thank you for recognizing us for the first SCA that we have completed here. And as you noted, SCA is multi-year agreement, and we noted that in our remarks as well. LTAs have tended to be typically 1-year agreement. And of course, in this environment of extremely tight supply outlook in the foreseeable timeframe as well, of course, our customers are very motivated in order for their own planning purposes and for their better predictability to have these structural strategic agreements with us. And of course, these agreements are really meant to bring stability and greater visibility into our business model as well. We have completed 1 SCA, so we are not going to be getting into the specifics here or these agreements. I'm sure you can appreciate that these SCAs are confidential in nature. But of course, these SCAs are meant to achieve the objectives for the customers in terms of their ability to plan and be able to count on supply commitments that are in the agreements, but also for us to be able to count on specific commitments that are there from the customers. And these are meant to go across the periods when the industry is very tight versus other parts of the industry environment as well. So that's why they're long-term agreements, and they have robust terms in them for us as well as for our customers.” 

The correct readthrough, if I had to guess, is that Micron is in the driver’s seat and is able to lengthen the LTA commitments to now 5-year terms for the company’s benefit.  

Financials 

By Royston Roche 

Revenue Growth of 196% 

Micron’s Q2 FY2026 ending February revenue grew by an impressive 196.3% YoY and 74.9% QoQ to a record $23.9 billion, beating estimates by a solid 22.3%. Revenue growth accelerated by nearly 140 percentage points from 56.7% YoY and 20.6% QoQ growth in the previous quarter. The $10.2 billion sequential increase was the largest in the company’s history and was primarily driven by strong AI memory demand. 

Management also provided a strong FQ3 revenue guidance of $33.5 billion, implying a YoY growth of 260.2% and 40.4% QoQ. The revenue guidance beat consensus estimates by a stellar 44%. Analysts expect strong revenue growth to continue and expect FQ4 revenue to grow by 233.8% YoY to $37.77 billion. 

DRAM Revenue Grew by 207% 

Micron’s FQ2 DRAM revenue grew by 207% YoY and 74% QoQ to a record $18.8 billion. Revenue growth accelerated by 138 percentage points from 69% YoY and 20% QoQ growth in the previous quarter. Bit shipments were up mid-single digits sequentially. Average selling prices increased in the mid-60s percentage range sequentially, driven by tight market conditions and also due to favorable mix.

NAND Revenue Grew by 169% 

FQ2 NAND revenue grew by 169% YoY and 82% QoQ to a record $5.0 billion. Revenue growth accelerated by 147 percentage points from 22% YoY growth in the previous quarter. NAND bit shipments increased in the low-single-digit percentage range sequentially. Average selling prices increased in the high-70s percentage range sequentially, driven by tight NAND market conditions and favorable mix. 

Revenue by Business Units 

CMBU Revenue Grew by 163% 

Micron’s Cloud Memory Business Unit (CMBU) FQ2 revenue grew by 163% YoY and 47% QoQ to a record $7.75 billion. Revenue growth accelerated by 63 percentage points from 100% YoY growth and 16% QoQ growth in the previous quarter. The strong sequential growth was primarily driven by an increase in prices and favorable mix. 

CDBU Revenue Grew by 211% 

Core Data Center Business Unit (CDBU) FQ2 revenue grew by 211% YoY and 139% QoQ to a record $5.69 billion. Revenue growth accelerated sharply from 4% YoY and 51% QoQ growth in the previous quarter. The strong sequential growth was primarily driven by higher pricing and growth in bit shipments. 

MCBU Revenue Grew by 245% 

Mobile and Client Business Unit (MCBU) FQ2 revenue grew by 245% YoY and 81% QoQ to a record of $7.71 billion. Revenue growth accelerated sharply from 63% YoY and 13% QoQ growth in the previous quarter. The strong sequential growth was driven by higher pricing, partially offset by lower bit shipments. 

AEBU Revenue Grew by 162% 

Automotive and Embedded Business Unit (AEBU) FQ2 revenue grew by 162% YoY and 57% QoQ to a record $2.71 billion. Revenue growth accelerated sharply from 49% YoY and 20% QoQ growth in the previous quarter. The strong sequential growth was driven by higher pricing, partially offset by lower bit shipments. 

Gravity-Defying Margins

Micron guided for a gross margin of 81% for FQ3. The record gross margin was primarily driven by higher memory prices, cost controls, and favorable revenue mix. The margins are so high that an analyst expressed concerns that perhaps Micron’s customers would be upset by it. Management replied that the demand supply imbalance is leading to higher memory prices. Also, customers are recognizing the higher value as high performance memory helps in driving down costs and improves the overall AI performance.  

Q: Vivek Arya (Analyst) 

“And for my follow-up, Mark, I wanted to revisit this 81% gross margin guidance. I appreciate you're not giving a specific forward view. But what do you — what has happened in kind of prior historical peaks where Micron's margins, I think, peaked in the low 60s, I believe, so what is the difference between the prior situations versus now?  

What have those kind of historical precedents indicated to you about how the trajectory of gross margins can be over the next several quarters? How do customers — do customers start to react differently when they see these level of gross margins and what is a very, very important input into their AI silicon?”  

A: Mark Murphy (CFO) 

“Vivek, I would say that keep in mind that the industry is supply constrained. So — and conditions will remain very tight, and that's beyond '26. So that certainly supports the near-term, medium-term pricing… We're investing in capacity, and we're also increasing R&D to continue to advance the technology and improve the value of memory. And we believe these will help with margins over time, and I think customers are recognizing that and entering into these agreements.” 

  • FQ2 gross profits grew by 499.2% YoY to $17.76 billion. Gross profit margin was 74.4%, an improvement of 37.6 percentage points YoY and up 18.4 percentage points sequentially. It beat the management guidance of 67%. The adjusted gross margin improved by 37 percentage points YoY and 18.1 percentage points sequentially to 74.9%. The strong gross margin was driven primarily by higher pricing, favorable mix, and cost controls. Management has guided further improvement of gross margin to 81% in the next quarter. 
  • FQ2 operating profits grew by 810% YoY to $16.14 billion. Operating margin came at 67.6%, an improvement of 45.6 percentage points YoY and 22.6 percentage points sequentially. It beat the management guidance of 58.7%. The adjusted operating margin improved by 44.1 percentage points YoY and 22 percentage points sequentially to 69% driven by operating leverage. Management has guided further improvement of operating margin to 76.2% and adjusted operating margin to 76.8% in the next quarter. 
  • FQ2 net income was $13.79 billion or 57.8% of revenue compared to $1.58 billion or 19.7% of revenue in the same period last year. Adjusted net income was $14.02 billion or 58.8% of revenue compared to $1.78 billion or 22.1% of revenue in the same period last year. 

Adjusted EPS Grew by 682% 

Micron’s FQ2 GAAP EPS grew by 756% YoY to $12.07, beating estimates by 36.3%. Adjusted EPS grew by 682.1% YoY to $12.20, beating estimates by 36%, primarily driven by higher memory prices, cost controls, favorable revenue mix, and operating leverage. 

Management also provided a strong guide for the next quarter. GAAP EPS guide is $18.90, implying a YoY growth of 1025%. While the adjusted EPS guide is $19.15, implying a YoY growth of 902.6% YoY, beating estimates by 77.8%.

Cash Flow and Balance Sheet 

Micron’s strong profits are leading to higher cash flows. 

  • FQ2 operating cash flows grew by 202% YoY to $11.9 billion with an operating cash flow margin of 49.9% compared to 49% in the same period last year. 
  • FQ2 adjusted free cash flows grew by 705% YoY to $6.9 billion with an adjusted free cash flow margin of 28.9% compared to 10.6% in the same period last year. Capex grew by 61.3% YoY to $5.0 billion. For FQ3, management has guided a capex of $7.0 billion and expects adjusted free cash flows to roughly double sequentially.  
  • Cash and investments were $16.6 billion and debt of $10.14 billion compared to $12.02 billion and $11.76 billion in the previous quarter. Micron repurchased shares worth $350 million and also reduced debt by $1.6 billion in the recent quarter. 
  • Inventory increased marginally by 0.6% sequentially to $8.27 billion. 

Conclusion: 

Micron’s record-breaking fundamentals are going far beyond what this company has reported before during previous peaks as the “normal” memory swings are being eclipsed by AI-era capacity scarcity.  

The difference between the AI cycle and the cyclical peaks in the past is that Micron is combining record fundamentals with improving visibility through multi-year customer agreements and a strengthening product roadmap (HBM4/HBM4E, 1γ DRAM, Gen6 SSDs). 

Management repeatedly stated the market is supply constrained beyond 2026, with new fabs coming online in 2028. Meanwhile, longer context windows, reasoning, and agentic workloads will keep HBM and DRAM demand elevated. NAND is proving it's no longer an afterthought with historic pricing surges that are causing heavyweight customers to seek more stability with SCA agreements.  

While I do believe SCAs could be the reason for softer price action, it’s my conclusion at this time that memory remains an important strategic asset that results in Micron being in the driver’s seat during a sustained upward trend, albeit with the occasional lumpiness inherent to supply chains. In that sense, I foresee Micron becoming more secular than the market has historically treated it.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in MU at the time of writing and may own stocks pictured in the charts.

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Posted in Data Center, Semiconductor StocksLeave a Comment on Micron Fiscal Q2: Record-Breaking Fundamentals 

Palantir Stock is Out of Favor, but is the Growth Engine Still Intact?

Posted on March 13, 2026June 30, 2026 by io-fund
Palantir Stock is Out of Favor, but is the Growth Engine Still Intact?

Palantir stock has softened alongside a broader software selloff, raising questions about whether its premium valuation is still justified. Beneath the muted price action, the company delivered sharp revenue re‑acceleration, triple‑digit U.S. commercial growth driven by AIP, expanding profitability, and record-leading metrics that suggest its long‑term growth engine could still be intact. 

Palantir is Underperforming AI Hardware Stocks 

Software stocks have been out of favor, with some down as much as 45%. Palantir’s stock is down about 10%, better than most software peers, yet it has still lagged AI hardware with stocks like Micron up as much as 50% year-to-date. Despite the softer price action, Palantir’s valuation still trades meaningfully above its 5-year and 3-year median. 

Line chart showing Palantir’s forward price‑to‑sales ratio compared with its 3‑year and 5‑year median PS ratios from early 2025 to March 2026, highlighting elevated valuation relative to historical averages.

Chart comparing Palantir’s forward price‑to‑sales (PS) ratio against its 3‑year and 5‑year median PS ratios. The forward PS line rises significantly above both long‑term medians, reaching 49.85 by March 2026, illustrating how Palantir’s valuation has expanded beyond historical averages. Source: YChartsYCharts

Palantir Stock Earnings: Revenue Re‑Acceleration and Margin Expansion     

Palantir’s underlying fundamentals continue to challenge investors who believe real value is found in cheap stocks. This past quarter, the company offered a rare re-acceleration in growth with revenue accelerating to 70%, an impressive 57-point acceleration over the last ten quarters, while guiding revenue to accelerate further to 73.6% in Q1. US commercial momentum remained unphased, with revenue accelerating 16 points sequentially to 137% YoY, surpassing the $500 million mark in the quarter.

Bar chart showing Palantir’s year‑over‑year revenue growth from Q2 2022 to Q1 2026, rising steadily from the mid‑20% range to 73.6% in the latest quarter.

Chart illustrating Palantir’s year‑over‑year revenue growth from Q2 2022 through Q1 2026G, highlighting a powerful acceleration. This underscores Palantir’s strong multi-year revenue momentum and reinforces the narrative of accelerating demand by its AI platform. 

I’m a growth investor through-and-through, yet it would be a mistake to think Palantir’s strength is found in the top line as I could point toward a dozen or more stocks with higher growth rates. Rather, it is Palantir’s ability to balance hypergrowth with profitability that sets it apart from SaaS peers. While revenue accelerated, profitability expanded alongside it: adjusted operating margin reached 57.4%, and adjusted EBITDA margin came in at 57%.  

Palantir’s Rule of 40 (revenue growth + adjusted operating margin) rose to 127%, up 46 points YoY and 13 points QoQ, putting the company in a class of its own. 

Bar chart showing Palantir’s Rule of 40 increasing from 54% in Q4 2023 to 127% in Q4 2025, highlighting consistent improvements in revenue growth and operating margins.

Palantir’s Rule of 40 came at 127% in Q4 2025, up 13 points QoQ and 46 points YoY. 

Cash generation remained equally strong with adjusted free cash flow of $791M for a 56% margin in the quarter—while management guided for FCF margin to expand further next year. 

US Commercial: The Key Driver of the Palantir Stock Bull Case 

Palantir’s AIP-driven US commercial segment remains the company’s core revenue driver. In a series of previous analyses, my firm has covered key elements as to why the artificial intelligence platform (AIP) is able to drive such strong growth. To briefly review, Palantir’s Artificial Intelligence Platform (AIP) integrates generative AI with operational data and workflows, and, when combined with Palantir’s other platforms, Foundry and Apollo, it provides an AI service mesh that can run hundreds of microservices, scale compute via its Rubix engine, and orchestrate updates through Apollo. 

The differences matter as unlike traditional AI-enabled database or business intelligence competitors, Palantir can operate effectively even when data sets are incomplete or fragmented—situations where most models struggle. In that regard, traditional business intelligence companies require a complete data set, whereas Palantir can handle situations where one isn't available. You can think of the competitive advantage as actionable depth, as Palantir has described it: “the reasoning that goes into decision-making, not just data.” 

mid

Additionally, Palantir’s knowledge graph, referred to as Ontology, is a distinct advantage. The graph offers better context than a large language model would on its own – or as Palantir states, it’s “the reasoning that goes into decision-making.” Palantir made key upgrades to AIP with the introduction of AI-forward-deployed engineers (FDEs) and the AI Hivemind, and brought Ontology to the edge, enabling deployment on mobile devices. 

Read more about Palantir’s products in our analysis “Palantir Stock 2026 Forecast: Is Its High Valuation Sustainable?” and Palantir Stock Forecast 2025Palantir Stock 2026 Forecast: Is Its High Valuation Sustainable?” and Palantir Stock Forecast 2025 

How AIP Is Powering Triple‑Digit Commercial Growth 

In the prior earnings report, US Commercial revenue grew 29% QoQ and 121% YoY to $397 million in Q3, accelerating sharply and supporting the view that AIP is driving existing expansions and new customer conversions.   

The acceleration continued this past quarter with US commercial revenue rising 137% YoY and 29% QoQ to $507 million in Q4. This led to a $2 billion annualized run rate in the quarter, up from a $1 billion run rate at the start of 2025. 

With that said, Palantir’s International commercial revenue is not nearly as strong as US commercial with revenue growth of just 8% YoY and 12% QoQ to $170 million in Q4.

Bar chart showing Palantir’s US Commercial year‑over‑year revenue growth from 2022 to 2025, rising from early volatility to a high of 137% in Q4 2025.

Chart displaying Palantir’s US Commercial year‑over‑year revenue growth from Q1 2022 through Q4 2025, highlighting the dramatic expansion of demand driven by the company’s AIP platform. 

Palantir Stock Forecast: What Key Metrics Signal for 2026 and Beyond 

Unlike 99% of other companies reporting this season, Palantir’s call offered little substance to sift through, with more clues on its growth opportunities hidden in the key metrics. 

Palantir’s NRR expanded 5 points to 139% in Q4; on a YoY basis, NRR has risen 19 points. In the earlier thesis, Palantir’s NRR had already been expanding (to 134% in Q3), with management emphasizing that the metric does not capture revenue from new customers acquired over the last twelve months—meaning upside can still be building beneath the surface.   

RPO then surged in Q4—up 62% QoQ and 143% YoY to $4.21 billion—while billings rose 91.1% YoY and 21.5% QoQ to $1.49 billion. Both of these key metrics witnessing this sharp step-up in tandem provides further confidence in Palantir’s 2026 accelerations panning out with the potential for upside to its initial guidance as each quarter progresses. 

Palantir also booked record TCV of $4.26 billion, up 138% YoY, with commercial TCV of $2.6 billion, up 161% YoY and 83% QoQ.

Bar chart showing Palantir’s remaining performance obligations (RPO) rising from under $1 billion in early 2023 to $4.21 billion in Q4 2025, with YoY growth accelerating to 143%.

Chart showing Palantir’s remaining performance obligations (RPO) from Q1 2023 through Q4 2025, highlighting a substantial increase from $0.94 billion to $4.21 billion over the period. This sharp expansion signals a rapidly strengthening multi‑year demand pipeline, reinforcing Palantir’s visibility into future revenue and supporting expectations for continued acceleration in 2026. 

The Other Side of Palantir's Story: Government Likely to Strengthen

Palantir recorded its best week since August as conflict in Iran broke out, as analysts were quick to point out that the conflict would help Palantir prove its value to the US military and continue to see strong government-driven deal momentum.  

Palantir’s exact role in the conflict is a bit unclear, as some reports suggest that Palantir’s Maven Smart Systems, powered by Anthropic’s Claude AI, was utilized to integrate fragmented data from multiple US agencies, from drone footage to satellite imagery and radar signals, to help map military movements, what other LLMs are not necessarily capable of doing. However, the Pentagon’s growing disputes with Anthropic, which saw the government deem Anthropic as a ‘supply chain risk’, could hinder its role, as defense contractors such as Lockheed Martin are expected to work to remove Anthropic from their supply chain per the Pentagon’s orders. 

Despite that uncertainty, Palantir’s platform does remain model agnostic and analysts believe there are ‘adequate alternatives’ to Claude that could be utilized, while the broader theme of conflict and the Defense Department saying they would award “bigger, longer contracts” for proven weapons systems support growth in Palantir’s government pipeline. As a reminder, Palantir had already won a $10 billion contract with the Army last summer, and any indication that increased government deal volumes for AI solutions could flow through to Palantir.  

Palantir’s government remained strong with 60% YoY and 15% QoQ growth in the fourth quarter to $730 million, and government accounted for nearly 52% of revenue in Q4. Palantir highlighted mission impact across the DoD and momentum in civil agencies, including an up to $448 million contract with the US Navy to modernize the shipbuilding supply chain and accelerate delivery of naval vessels. 

Margins and Cash Flow Are Reinforcing the Operating Model 

As stated earlier in the analysis, Palantir’s Rule of 40 score expanded 46 points YoY to 127%, which the company defines as revenue growth plus adjusted operating margin. Adjusted operating margin in Q4 was a record 57.4%, while Palantir guided for adjusted operating margin to remain strong next quarter at 56.8% at midpoint. 

Cash flows remain best-in-class with management guiding for adjusted free cash flow margin to expand in 2026 from an already strong 51% in 2025, projecting adjusted FCF up more than 77% YoY to $3.925–$4.125 billion. 

Why EV/EBITDA Shows Palantir Is Cheaper Than It Appears

When we revisit valuation, we see that Palantir’s strong bottom line has resulted in an EV/EBITDA that is as low as the April 2025 selloff – reinforcing the fact that Palantir is more of a bottom-line story than a top line story in terms of what truly sets the stock apart: 

Line chart showing Palantir’s EV to EBITDA ratio from early 2024 to March 2026, rising sharply through 2024–2025 before declining to 86.56 in early 2026.

Chart showing Palantir’s EV/EBITDA ratio that is as low as the April 2025 selloff. Source: YChartsYCharts

Will Palantir’s 2026 Setup for Beat/Raise Potential Mirror 2025? 

For 2026, Palantir initially guided for fiscal 2026 revenue to accelerate from 56.1% to nearly 61% YoY, driven by US commercial revenue accelerating six points to >115% YoY. Driving such an acceleration at these growth rates is undeniably difficult, yet there are hints that Palantir could go above and beyond these figures by this time next year. 

Looking back to 2025, Palantir’s beat/raise pattern offers a framework for how 2026 could play out if key metrics continue to strengthen. In 2025, management’s initial guide proved conservative as the year progressed; the open question now is whether Palantir can deliver similar upside from a higher revenue base.  

For example, in Q4 2024, Palantir had initially guided for roughly 30.8% YoY growth in FY25. This was then raised to 36% in Q1, then again to 44.7% in Q2. By Q3, Palantir’s FY25 guide was raised to 53.5%, before ending the year with growth of 56.2%, more than 25 points faster than originally anticipated four quarters prior. FY26 is already starting off at 60.6% projected growth, with all four quarters to go.  

Technical Analysis: Where Palantir Stock May be Headed Next 

Palantir has been tracing a large 5-wave pattern off the 2022 low. Regardless of how the internal waves are organized, the pattern remains incomplete to the upside — meaning the larger uptrend is still intact, and the current weakness is most likely just a correction. That said, given the magnitude of this 5-wave structure, investors should expect corrections of a larger degree than what they have experienced during the earlier stages of this advance. 

Price action currently supports two scenarios: 

Primary Count — A sustained break above $195 would invalidate the primary count and instead project a 5th wave advance toward the $240 region. This would complete the larger 3rd wave patern, as the final push higher will be met with lower volume and momentum as we push higher. Once completed, it will give way to a multi-month 4h wave decline. 

Alternative Count — We are in a significant 4th wave decline. The current bounce is expected to fail somewhere in the $186 — $195 range before rolling over into a final leg lower. Target support for the completion of this 4th wave sits between $119 and $87. The line in the sand for the entire bullish thesis is $66. A close below that level would cast serious doubt on the continuation of the larger uptrend.

Technical analysis chart showing Palantir’s long‑term Elliott Wave structure, corrective ABC pattern, Fibonacci extension levels, and projected price targets from 2023 to 2027.

Chart displaying a long‑term Elliott Wave analysis of Palantir (PLTR) from late 2023 through 2026, mapping out impulsive waves (1–5) followed by an ABC corrective structure. The price action shows a strong uptrend through waves (1), (2), (3), (4), and (5), with Fibonacci extension targets marked above current levels, including 236%, 250%, and 261.8% projections ranging roughly from the mid‑$200s to nearly $400. The chart also outlines potential corrective zones labeled (A), (B), and (C), with support areas between approximately $87 and $120 highlighted as key downside levels. Trendlines and moving averages show broader upward momentum, while future price projections extend into 2027.

Conclusion: 

Palantir’s Q4 report showed that the company’s AIP-driven momentum remains robust with no signs of slowing, further supported by most key metrics accelerating in unison. Palantir’s NRR expanded 5 points to 139%, its Rule of 40 score expanded 46 points YoY to 127%, and record TCV and RPO were the cherry on top of a strong quarter.    

Palantir also guided for revenue to accelerate to nearly 61% YoY in 2026, driven by US commercial revenue accelerating to >115% YoY. Driving an accelerate at multi-billion dollar scale is difficult, yet the company’s key metrics suggest growth rates may continue to move higher. 

Lastly, on valuation, the bottom-line ratio is more favorable than the sales multiple implies, as earnings power has improved due to the company’s margin expansion. Nobody has a crystal ball, but the company’s fundamentals suggest Palantir is positioned to trade higher than where it sits today if execution remains intact.  

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Posted in AI StocksLeave a Comment on Palantir Stock is Out of Favor, but is the Growth Engine Still Intact?

Seagate: Slow QoQ Data Center Growth, 2027 Capacity Under Discussions 

Posted on March 6, 2026June 30, 2026 by io-fund

Seagate has been a rather under-the-radar AI beneficiary with shares up more than 262% over the past year, keeping pace with heavyweight Micron and other strong memory sector performers. The company is working with cloud service providers (CSPs) to supply hard disk drives, stating they are qualified with 6 out of 8 CSPs on their new HAMR-based Mozaic products. As it pertains to data centers and AI, it’s expected that HDDs will handle the high-volume data storage requirements for agentic AI, which relies on persistent access to large volumes of historic data for planning, reasoning and decision-making. 

With that said, Seagate’s data center revenue growth is quite slow at just 5% QoQ in Q4, as the company is not exposed to the same swift price surges rippling across DRAM and SSD markets due to locked-in pricing for 2026.  

Looking ahead to 2027, Seagate could potentially raise prices with management stating that pricing is not locked-in yet (but notably, management was a bit evasive on the details when asked many times in the Q&A session): “But I would say we have — the vast, vast majority of the volume is already allocated. Calendar '27, we will start working on that fairly soon. Of course, we have very good indication and agreement on volumes, but we have not — we have not fixed the price yet.” 

Regardless of exact timing on when pricing can be renegotiated, Seagate’s fundamentals show exceptional margin growth as HAMR products offer stronger profitability and improved cash flows, leading to net leverage of 1.1x. It spells good things to come as HAMR products are only beginning to ramp now yet are making an impact on margins. 

Brief Product Overview: HDDs and HAMR Technology 

Hard disk drives (HDDs) offer the lowest-cost per terabyte, which makes HDDs ideal for “big data” storage, backups and large AI datasets. Compare this to solid state drives (SSDs) which store data on flash memory chips and are far faster and lower-latency. SSDs cost more per terabyte, thus leveraging a mix of HDDs and SDDs is a popular choice.  

As it stands today, SDDs have illustrated significant pricing power compared to HDDs. Therefore, because HDDs are considered more commoditized in the current market dynamics compared to SDDs, I’m quoting the management team in full as to why HDDs are likely to increase in importance as the AI economy plays out – as this is the predominant question Seagate must answer (when comparing to other memory sector stocks): 

“So if the concept is that drives aren't working hard, they're in the background, just storing data, that's not the way — a good way to think about it. That's not the way hard drives are being used right now. They're working 24/7. A lot of times, they're optimized for performance themselves, largely streaming performance, not random small block workloads. That's more of a memory thing. And so if you had an application that's random small block, it's probably memory. If you have big data, it's probably a little bit of memory on the front end and a lot of hard drive on the back end. And we think that — there are applications across the entire spectrum, of course, but we think that in the future when we start to talk about the concepts in their enormity, about checkpoints and physical AI and video and things like that, it's large, large data. So the architectural tier that stores the data will probably remain constant for the next decade.” 

Heat-assisted Magnetic Recording (HAMR) 

Seagate has developed heat-assisted magnetic recording (HAMR) tech for substantial areal density gains, which refers to how many bits can be packed onto each square inch of disk platter (where data is stored). HAMR uses a laser diode to heat a small spot on the disk, enabling polarity of a single bit to be flipped to allow data to be written.  

Other HDD tech such as energy-assisted magnetic recording (EAMR), like Western Digital’s ePMR (energy-assisted Perpendicular MR) tech, adds electrical currents to the write head to improve density and write smaller bits. While EAMR is simpler and builds on familiar PMR tech, offering faster time to market, it does not scale to the same capacity as HAMR. WDC acknowledges that EAMR is expanding to 30-40TB drive capacity with roadmaps to 60TB, while HAMR enables capacity to expand to >100TB by 2032. 

Seagate says HAMR allows data bits to become even smaller and more densely packed, while remaining thermally stable, and its HAMR-based drives can offer more exabytes of storage while taking up less space in the rack, offering substantial increases in TCO. To put this in number form, Seagate says a 1 exabyte deployment with HAMR would consume ~2 million kWh per year in 204 sq ft of space, versus 3.2 million kWh per year and 400 sq ft of space for a similar PMR deployment. 

Management believes that HDDs and higher-capacity storage systems will play a key role in meeting the storage needs for agentic AI, which need constant access to substantial volumes of data for reasoning and decision-making. Seagate projects AI workloads will drive “7.2 zettabytes of nearline storage demand over the next four years, exceeding the entire industry's consumption over the last decade.” 

Seagate’s Mozaic3+ HAMR, offering >3TB per disk, is qualified with all major US CSPs and the company says it remains on track to qualify with all global CSPs within the first half of CY26: “We ended the year shipping 3 terabyte per disk Mozaic-based HAMR products to our first CSP customer. And by year's end, quarterly HAMR shipments exceeded 1.5 million units and have continued to ramp. Mozaic 3 HAMR drives are now qualified with all of the major U.S. CSP customers and qualifications for our second-generation Mozaic 4 terabyte per disk products are tracking well to plan.” 

In terms of future catalysts, Mozaic4+ HAMR is currently qualifying, with the ramp expected to begin later this quarter and multiple CSPs expected to be qualified over the coming months. Mozaic is powering Seagate’s Exos family of products, which scale to multi-petabyte block storage systems, offering up to 2.5 PB (2,500 TB) in storage.  

In terms of volume, Seagate says that quarterly HAMR shipments have exceeded 1.5 million units and are continuing to ramp, or roughly 20% of its estimated ~7.2 million nearline unit shipments in Q2. To note, Seagate is ahead of Western Digital when it comes to HAMR, as WDC is not expecting to ramp its HAMR products until the start of CY27. 

HDD Role in ‘Warm-Storage’ 

We recently covered discussions over the role of SSDs in ‘warm’ storage and Nvidia’s upcoming Inference Context Memory Storage platform in our SanDisk analysis. We had stated: 

“Nvidia believes that “AI factories need a complementary, purposebuilt context layer that treats KV cache as its own AInative data class rather than forcing it into either scarce HBM or generalpurpose enterprise storage.” For example, the current inference context hierarchy begins with HBM (G1), providing near-instant access to latency-critical context in active generation, down to SSDs (G3) in the third tier to handle ‘warm’ data, or data that is used regularly but less frequently and still requiring efficient, cost-effective storage. Enterprise or shared storage sits at the bottom of the hierarchy (G4), handling ‘cold’ data, or data stored for long-term retention but much less frequently accessed.” 

The connection here to Seagate is that Nvidia is proposing SSDs to take a more central role in the ‘warm’ tier where HDDs sits – this is what Seagate and WDC consider ‘nearline’, where data does not have to be accessed instantaneously but must remain readily available.  

Analysts questioned about this possible shift to SSDs in warm storage: 

“Dave, while we have you, a little bit of a technical one, are you — what kind of activity are you seeing at sort of the so-called warm tier of storage? It's a question that comes up a bunch in our conversations. We've heard that it's obviously growing, it's growing both hard drive and flash storage is participating nicely, but would love to get your input on it. Because I think there's still — first of all, we love to know if what we're hearing is accurate. 

But secondarily, I think there's a lot of people that are assuming that that's really like it's becoming a NAND tier, largely a NAND tier in the GenAI world. And anyway, just love to get any context there that you have. 

CEO and Chairman William MosleyCEO and Chairman William Mosley 

“When you start talking about big data storage, if you will, in data centers, the tiering architecture is fairly well set and probably won't change based on economics and also architectures that are well known. People know how to play. So if the concept is that drives aren't working hard, they're in the background, just storing data, that's not a good way to think about it. That's not the way hard drives are being used right now. They're working 24/7.  

A lot of times, they're optimized for performance themselves, largely streaming performance, not random small block workloads. That's more of a memory thing. And so if you had an application that's random small block, it's probably memory. If you have big data, it's probably a little bit of memory on the front end and a lot of hard drive on the back end. … So the architectural tier that stores the data will probably remain constant for the next decade.” 

While there has been some chatter about this newfound role for SSDs to command a leading position in meeting inference-driven storage demand, Seagate does not believe that there will ultimately be much change to storage architectures, with HDDs remaining critical to handle the massive volumes of data generated by AI applications such as agents, video generation, world models and more.  

2026 Capacity Sold Out, Beginning to Contract for 2027 

One of the reasons for this shift to SSDs is that HDD capacity is already sold out through 2026, with Seagate already turning to signing long-term agreements (LTA) for calendar 2027. Seagate said its nearline capacity is already fully allocated through CY2026, and management expects to “begin accepting orders for the first half of calendar year 2027 in the coming months,” supported by strong demand visibility from some of its major cloud customers seeking supply assurance through 2027 and into 2028. 

Considering the supply tightness and elevated level of demand in the market for HDDs, analysts prodded about how pricing would look under new LTAs, and if new agreements would be closed at higher prices. Management noted that 2027 prices are still in discussion and have not been locked in, and implied that higher prices are possible due to demand: 

Q, Christopher Muse, Cantor FitzgeraldQ, Christopher Muse, Cantor Fitzgerald 

“And then, I guess, maybe bigger picture, as you think about overall average pricing per exabyte, we've gone from kind of down double digits to high single digits. And I think we just exited the quarter down 4% year-on-year. Do you see a world where pricing could flat or even move positive year-over-year?” 

CEO William MosleyCEO William Mosley 
“Pricing will be dictated by the demand. Right now, the demand is really strong. So I think as we roll through into '27 and '28, we look at how much capacity we're having. We're bringing online by virtue of the fact that we're making all these aggressive product transitions. We'll bring more exabytes to bear and then people go out there and renegotiate for those. I think flat to slightly up is certainly possible. And that's the way we're really managing it as we talk to our customers.” 

This ties into a later response where Seagate revealed that pricing has not been locked in for 2027, while 2026 volumes and prices are well defined – meaning any potential upside in 2026 would only come from excess capacity being sold in the open market.  

CFO Gianluca Romano explained that Seagate has “very good indication and agreement on volumes, but we have not fixed the price yet,” so if prices begin to move higher as higher-capacity next-gen HAMR devices ramp, considering how tight supply remains relative to demand, Seagate has tailwinds to both revenue growth and profitability.  

It should also be noted that in clear contrast to SSDs, HDD prices are not surging – analysts had implied HDD prices exited Q4 down (4%) YoY, while other reports placed prices up ~4% QoQ, a far cry from SSDs rising 40% to 100% QoQ – this is why Seagate’s QoQ Data Center revenue growth pales in comparison to competitors such as SanDisk. 

If Seagate can only realize a small low to mid-single digit price increase with 2027 contracts, this suggests growth will likely follow its projections for mid-20% exabyte growth with a few points' upside, rather than sharply accelerating on a YoY or QoQ basis. 

Financials:  

Revenue Growth

Seagate reported fiscal Q2 revenue of $2.83 billion, up 21.5% YoY and 7.5% QoQ, with sequential revenue growth across nearly all end markets. Growth was relatively steady on both a YoY and QoQ basis compared to fiscal Q1. 

Looking ahead, Seagate guided for FQ3 revenue to be $2.90 billion, +/- $100 million, pointing to YoY growth of 34.3%, accelerating nearly 13 points, though QoQ growth would be just 2.7% at midpoint. This softer QoQ read is due to typical March quarter seasonality from edge IoT markets, though management expects Data Center to more than offset that impact.  

Seagate also did not provide guidance for the full-year, but did state that its current outlook expects “sequential improvement to both the top and bottom line throughout calendar 2026,” meaning QoQ growth in each quarter through fiscal Q2 2027.  

Assuming low double-digit QoQ growth in FQ4 from the midpoint of Q3’s guide, FY26 revenue would roughly project to ~$11.64 billion, up 27.9% YoY, slightly above current estimates for $11.50 billion for 26.4% growth. 

Looking out to the first half of FY27, considering management’s commentary and similar QoQ growth rates as the past two years at ~7-8% QoQ, Seagate could exit calendar 2026 (FQ2 27) at ~$3.75 billion, or about 4% above current estimates for $3.61 billion. 

Key Segments – Data Center Growth Decelerates to 5% QoQ 

Seagate reports under two segments – Data Center and Edge IoT, with Data Center being the company’s primary revenue stream, accounting for 87% of its shipment volume in the quarter but only 79% of revenue. 

Data Center revenue increased 28% YoY and 5% QoQ to $2.22 billion in FQ2, decelerating from 34% YoY and 13% QoQ in Q1. Driving this was a deceleration in shipments – Data Center shipments were 165 exabytes in the quarter, up 4% QoQ and 31% YoY, slowing from 17% QoQ and 39% YoY in Q1.  

Seagate said it is seeing “sustained demand growth for our high capacity nearline drives across global cloud data centers as well as continued improvement from the enterprise edge,” expecting strong demand trends to continue for some time. In the enterprise OEM specifically, Seagate said it is “benefiting from slight improvement in traditional server units, along with increasing demand for storage servers, driven in large part by the adoption of AI applications and need to store data at the enterprise edge.” 

Outside of Data Center, Edge IoT revenue rose 2% YoY and 17% QoQ to $601 million, though it should be this sequential growth followed a soft FQ1 which saw an (11%) QoQ decline. Management said this was driven by anticipated seasonal improvement for consumer products.  

Looking ahead to FQ3, management expects Data Center to more than offset typical March quarter seasonality in edge IoT – assuming a similar (7%) QoQ decline in Edge IoT, this would project Data Center revenue to be up just over 5% QoQ, matching Q2’s pace.  

Gross and Operating Margin Reach Records 

Seagate is witnessing solid margin expansion, driven by pricing and operating leverage, with management pointing to high-capacity drives as a gross margin tailwind while opex continues to decline. Both gross and operating margin reached company records in Q2.  

Q2 GAAP gross margin was 41.6%, up 6.3 points YoY and 2.2 points QoQ, while adjusted gross margin was 42.2%, up 6.7 points YoY and 2.1 point QoQ. Management said this was driven by its pricing strategy and improving mix of high-capacity drives as HAMR shipments ramp. Seagate noted that the upcoming Mozaic4+ adds more content per unit, helping reduce costs and improve profitability. 

Q2 GAAP operating margin was 29.8%, up 8.8 points YoY and 3.4 points QoQ, while adjusted operating margin was 31.9%, up 8.8 points YoY and 2.9 points QoQ. Seagate continues to see a greater degree of operating leverage, as adjusted opex as a percent of revenue declined to 10.3%, from 12.4% in the year ago quarter and 11.1% in Q1. 

Q2 GAAP net margin was 21%, up 6.5 points YoY and just 0.1 points QoQ. Adjusted net margin was 24.8%, up 6.2 points YoY and 2.6 points QoQ. 

Looking ahead to FQ3, Seagate projected a notable uptick in both gross and operating margins. Adjusted operating margin was projected to be in the mid-30% range, up around 3 points QoQ assuming this would correspond to roughly 35%. Based on adjusted opex guidance to be ~10% of revenue, this would place adjusted gross margin at ~45% under this framework.  

Evercore’s Amit Daryanani questioned about the QoQ strength in margins, more specifically the gross margin – management would not offer much aside from pricing and mix, with a HAMR ramp at a recently qualified customer aiding this expansion: 

“Gianluca, I'm hoping you can talk a little bit about the March quarter guide because there seems to be a really sizable uptick in gross margins. I think it's up like 250 basis points or 100% plus incrementals. Could you just — is there anything you would call out in March quarter that's unique that's helping drive that kind of margin expansion? And is this really all coming from the core HDD business? Or is there a potential benefit from the old systems business helping you as well?” 

EVP and CFO Gianluca Romano 

“Amit, well, I would say, we expect to be a very good quarter. I don't think it's different than what we have done before. It's always based on the pricing strategy and the mix, as you know. We qualified another customer on HAMR, so we will ramp a little bit more volume on HAMR. This is helping us to get better margin. But fundamentally, is not really different in how we think we are going to execute the quarter and is good. I think the incremental margin looks very good.” 

Romano had also clarified that Seagate had previously presented a model with a 50% incremental margin above $2.6 billion of revenue, noting that this model covers the next two to three years, with the HAMR ramp helping drive further margin expansion.   

EPS 

Driven by the margin expansion, Seagate delivered strong sequential EPS growth, with adjusted EPS up 19% QoQ to $3.11, beating estimates by 9.6%. YoY growth for adjusted EPS was 53.2%, decelerating from 65.1% in Q1. GAAP EPS in Q2 was $2.60, up 67.7% YoY, decelerating from 72.3% in Q1.  

Looking ahead to Q3, Seagate guided for adjusted EPS to be $3.40, +/- $0.20, representing a more than 25 point acceleration to 78.9% YoY at midpoint. Growth is expected to decelerate back towards ~50% YoY in FQ4 to $3.87.    

For FY26, current adjusted EPS estimates sit at $13.02, up 60.8% YoY, while GAAP EPS is projected to be $11.71, up 73% YoY. 

Cash Flows and Balance Sheet 

Seagate noted that its free cash flow generation reached the highest level in the last eight years, while they also retired $500 million in debt to strengthen its balance sheet.  

Operating cash flow in Q2 was $723 million for a 25.6% margin, up 17.1 points YoY and 5.4 points QoQ.  

Free cash flow was $607 million for a 21.5% margin, up 15 points YoY and 5.3 points QoQ. Management guided for free cash flow to expand further in Q3, supported by strong demand trends and operational efficiency. 

Cash and equivalents totaled $1.05 billion with management noting that total liquidity was $2.3 billion including its undrawn revolving credit facility.  

Inventories remained flat QoQ at $1.5 billion.  

Debt was $4.5 billion exiting Q2, down from $5 billion at the end of Q1. Seagate said its net leverage ratio was 1.1x, improving 16% QoQ and 63% YoY, with the expectation that net leverage will continue to trend lower as profitability and cash flows increase.  

Valuation 

Seagate is trading close to peak multiples, a tougher pill to swallow considering the slow sequential growth the company is seeing in the Data Center. Seagate trades at 7.8x forward PS, well above its 5-year average of 3.0x, and just below its recent peak of 8.5x. Shares had traded as low as 1.5x in April. 

Looking at the bottom line, Seagate trades at 31.5x forward PE, below its 39.9x average, though it should be noted this is skewed higher due to margins falling negative in 2023. Over the past year, shares are trading nearly double its average of 17x. 

Conclusion 

Seagate’s data center growth is much slower than other memory peers at just 5% QoQ as the HDD space is witnessing soft pricing power relative to SSDs or DRAM. Data center growth is also unlikely to meaningfully inflect moving through CY26 as price and volume agreements have already been locked in. Seagate does see potential for some pricing upside in 2027 as contracts are open for discussions, yet for now, pricing tailwinds are muted compared to what is seen in enterprise SSDs and DRAM.

Damien Robbins, Equity Analyst at I/O Fund contributed to this analysis.

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Posted in AI Stocks, Data CenterLeave a Comment on Seagate: Slow QoQ Data Center Growth, 2027 Capacity Under Discussions 

“Tech Bubble” Warnings Cost Investors a 550% Nasdaq-100 Run

Posted on March 6, 2026June 30, 2026 by io-fund
“Tech Bubble” Warnings Cost Investors a 550% Nasdaq-100 Run

Investors have been hearing “tech bubble” warnings for more than a decade — but instead of collapsing, the Nasdaq‑100 has gained 550%. If we look back ten years ago to 2015, headlines such as “Sell everything! 2016 will be a cataclysmic year” confronted investors with calls for an imminent recession. The bears made repeated claims that a “tech bubble” was about to burst with some of the world’s most prominent venture capitalists drawing parallels to the dot-com era. 

What followed tells a very different story with not only the Nasdaq-100 up 550% over a 10-year period but also high-flying stocks like Shopify returned as much as 5200% and Nvidia returned 22,000% over the same period. 

It’s true that capturing those gains does not come easy. Investors had to hold through five drawdowns that were greater than 20%, including two declines greater than 30%, while tuning out a constant stream of bearish commentary – often from reputable sources – proclaiming the long-awaited tech bubble has finally “popped.” Despite these strong convictions, the long-term trend remained intact. 

Below, I examine periods when normal market resets were mischaracterized as a bubble. I then discuss why today’s AI cycle does not share those characteristics before concluding with the technical signals we are monitoring to confirm that AI remains in a sustained uptrend. 

Timeline of Tech Bubble Talk: 

The idea of a “tech bubble” — now rebranded as an “AI bubble” — is not a unique prediction.   

  • 2015: Headlines warned that tech valuations were unsustainable, with calls to “sell everything” ahead of a widely anticipated 2016 recession. Particularly, there were concerns due to high valuation of Chinese tech stocks. “Chinese technology stocks do resemble the dot-com bubble,” Vincent Chan, the Hong Kong-based head of China research at Credit Suisse Group AG, said in an interview on April 2. “Given stocks fell 50 to 70 percent when that bubble burst in 2000, these small-cap Chinese shares may face big corrections when this one deflates. On the other hand, the US was also preparing to raise interest rates for the first time since the financial crisis of 2008.
  • In 2015, despite the warnings, broader U.S. markets proved more resilient than feared, with the Nasdaq-100 rising 8.4% in 2015. Even though these narratives continued in 2016, the Nasdaq-100 managed to close in green with a gain of 5.9% in 2016.   
  • In 2017, investors were once again worried about high valuations and the unwinding of quantitative easing. Despite all the concerns, the Nasdaq-100 rose 31.5% in 2017.   
  • In 2020, despite rapid earnings growth, with companies like Zoom growing 326%, tech stocks were labeled a bubble amid pandemic-driven volatility and policy uncertainty. “Everybody loves a party … but, inevitably, after a big party there’s a hangover,” the billionaire investor Stanley Druckenmiller said in a Squawk Box interview. “Right now, we’re in an absolute raging mania.”" Although tech would later reset, the most explosive move followed these bubble warnings. 
  • In 2022, rising rates and tightening financial conditions reignited claims that the tech bubble had finally burst and the Nasdaq-100 was down (33%) in 2022. However, tech stocks recovered in 2023, and the Nasdaq-100 rose 53.8% in 2023.  
  • In 2024, the narrative re-emerged once again—this time framed as an “AI bubble”—despite strong balance sheets, accelerating earnings, and durable long-term demand drivers. However, Nasdaq-100 rose 24.9% with Nvidia rising 171.2% in 2024. 
Line chart showing the NASDAQ‑100 index rising more than 550% from 2016 to 2026, with several pullbacks marked at −19%, −24%, −30%, −37%, and −25%. The chart includes annotated media headlines predicting tech bubbles at various points along the upward trend.

Chart showing the long‑term performance of the NASDAQ‑100 from 2013 to 2026, highlighting more than a 550% gain since the 2016 low. It marks periods of major market corrections—ranging from (19%) to (37%)—alongside media headlines predicting a tech bubble or market crash, underscoring the gap between short‑term market fears and long‑term NASDAQ growth. 

Supply Constraints Make a Widespread AI Bubble Unlikely 

Bubbles are typically defined by oversupply, yet many pockets of today’s market are the opposite – they are supply constrained.  

During the dot-com era, the market was flooded with far more e-commerce and internet sites than demand could support, largely because barriers to entry were low. AI is quite the opposite as it’s an expensive technology that has a very high barrier to entry and lacks democratization. The buildout is constrained across critical inputs, such as compute, memory, networking, power, and advanced packaging, which further raises the hurdle for new entrants and slows supply growth. 

mid

TSMC, the world’s largest contract chipmaker and critical supplier for AI logic chips, has stated that its advanced-node capacity is roughly three times short of what AI demand requires, even amid ongoing expansion efforts. This reflects a real gap between what customers want and what TSMC can physically produce.  

AI data centers and accelerators consume an outsized share of DRAM and HBM, driving a global memory shortage that is now described as unprecedented and likely to persist beyond 2026. Companies like Tesla and Apple have also signaled a shortage of DRAM. The shortage has also led to the price hikes, and the cost of one type of DRAM soared by 75% from December to January. 

Energy is another gating factor that makes an “oversupply” outcome unlikely anytime soon. In 2024, we highlighted Wells Fargo’s projection that AI power demand could surge 550% by 2026, rising from 8 TWh in 2024 to 52 TWh, before accelerating another 1,150% to 652 TWh by 2030 — an 8,050% increase versus the 2024 baseline. Under that framework, training drives the bulk of demand earlier in the cycle, while inference becomes a larger driver later in the decade. 

There is more data from IEA, which projects global data center electricity demand will more than doube from ~415 TWh in 2024 to ~945 TWh by 2030 in its base-case scenario. Under the agency’s AI “lift-off” scenario, demand reaches 1,250 TWh — a trajectory that more closely aligns with Wells Fargo’s more aggressive outlook. 

The message from management teams is consistent with these forecasts: Google CEO Sundar Pichai noted that Google has been “supply constrained” even as it ramps capacity, citing longer supply-chain time horizons. Meta CFO Susan Li echoed the same point, saying Meta remains capacity constrained and will “likely still be constrained through much of 2026” until additional capacity from its own facilities comes online. 

AI is Driving Significant Revenue and Profits 

The late 1990s was defined by pre-revenue companies with many dot-com darlings reporting a mere $10 million to $20 million in revenue. This fact alone has been the reason other so-called tech bubbles were rather quick corrections such as mobile, social media, cloud infrastructure, and now AI are all trends that drove significant revenue and profits for public companies.  

Our firm was early to point out that Meta was now second to Nvidia on AI revenue with its AI ads automation platform reaching a $60 billion run rate in 2025; three-and-a-half years from launch. launch. Similarly, we can look at OpenAI’s trajectory from $1 billion in revenue in 2023 to an estimated $20 billion annualized revenue in 2025 – which represents the steepest rise in technology history.  

When you compare a successful dot-com company, the profile is very different from what we see in AI stocks today. Amazon rose 3,320% from January 1995 to March 2020 yet the bottom-line was deep in the red with a negative profit margin of (56%).  Meanwhile, a few AI companies like AppLovin, TSMC, and Reddit have reported net profit margins of 66%, 48%, and 35%, respectively in their recent Q4 results; a stark contrast to one of the dotcom bubble’s best stocks.  

Notably, prices across leading AI companies are not broadly untethered from fundamentals. In fact, a handful of companies are fundamentally cheaper now than they were at the first sight of this AI-driven boom in early 2023. For example, Nvidia currently trades at 21.7x forward earnings, and is lower than the multiple it traded the day prior to its May 2023 Hopper-driven blowout earnings report, despite shares rising 475% over the same period. 

Line chart showing NVIDIA’s forward price‑to‑earnings (P/E) ratio from mid‑2023 to early 2026, with values fluctuating between roughly 20 and 50.

Chart showing the forward price‑to‑earnings (P/E) ratio of NVIDIA (NVDA) from 2023 to 2026, illustrating how the valuation has fluctuated between approximately 20 and 50 over the period. Source: YChartsYCharts

Most importantly, those funding AI are highly profitable tech companies compared to venture capitalists who pushed unprofitable companies public for a quick exit during the dotcom bubble. Big tech is not looking for a quick exit and on track to spend $655 billion in 2026, up 60% YoY. 

AI Is Early Cycle; Not Late Cycle 

Cycle timing is arguably the most critical part of being a tech investor. Enter too early, with autonomous vehicles being a good example, and the opportunity cost can be very high as capital sits idle while adoption lags. Enter too late, like chasing the dot-com boom at the peak, and you risk buying the top with little hope for a near-term recovery. Equally as painful is closing a promising stock too early in the cycle, and seeing it rise sharply in the years that follow.  

Because cycle timing is emotionally taxing, investors often equate sharp downside volatility with “bubble” conditions—yet the two are not the same. 

The smartphone cycle was one of the most powerful product cycles in modern history. Apple launched its first iPhone in June 2007, just four months before the market topped in October. When the market top occurred, smartphone adoption was still in its nascent stages, leaving little opportunity for bubble dynamics to form; exiting the GFC, smartphone adoption proved to be robust, with TTM growth of 63% from August 2008 to August 2009, while also marking one of the fastest 10-quarter adoption curves in consumer tech history. Momentum on the app side was explosive with the App Store reaching 1 billion paid and unpaid downloads within nine months.   

Despite the rapid adoption post-iPhone launch, this did not insulate Apple from realizing several meaningful periods of volatility, including a 61% drawdown shortly after that launch in the 2008 bear market. Over the next decade, Apple again faced two major drawdowns of 45% and 34%; however, shares ended this decade more than 724% higher, highlighting that extreme volatility does not always mean it is a bubble. 

Line chart showing Apple’s stock performance from 2002 to 2026, highlighting a long‑term gain of more than 6,700%. The chart marks major drawdowns ranging from 32% to 61% and notes the release of the first iPhone during the 2007 period, illustrating the long-term mobile technology trend.

Chart illustrating Apple’s long-term stock performance from 2002 to 2026, showing a cumulative gain of more than 6,700% driven by the rise of mobile technology. Key drawdowns ranging from 32% to 61% are highlighted throughout the trend, including the significant decline around the time the first iPhone was released. Despite multiple large corrections, Apple’s overall trajectory reflects the strength and durability of the mobile technology mega‑trend.

Salesforce is another stocks that saw shares enter a multi-month drawdown of 73% in late 2008, though shares quickly returned to new highs in just 16 months and went on to rally 131% in the time it took the broader market to exit from the bear market. This is because Salesforce was still witnessing rapid revenue growth early in its adoption curve – revenues more than doubled to over $1 billion in the two years from 2007 to 2009, and by 2013, revenue had surpassed $3 billion. 

Dual-line chart comparing the S&P 500 and Salesforce from 2007 to 2014. The S&P 500 shows a 63% decline during the financial crisis and recovers over 4.1 years, while Salesforce drops 73% and later rises more than 131% from its low within 16 months.

Chart comparing the performance of the S&P 500 and Salesforce (CRM) during the 2007 market peak, the 2008–2009 financial crisis, and the recovery period through 2014. The S&P 500 experienced a 63% drawdown and required roughly 4.1 years to return to previous lows, while Salesforce declined 73% but rebounded far more quickly—surging more than 131%. The comparison highlights the significant difference between broad‑market recovery timelines and the faster rebound potential of high‑growth technology stocks.

Looking back to a similar time period, AWS is an excellent example of the build phase versus the yield phase to where it required extensive upfront capital that later became fast growing revenue. AWS revenue grew by 24% YoY in Q4, accelerating 4 percentage points from 20% growth in the previous quarter and was the fastest growth in the last 13 quarters.  

Cloud Software Nearing the End of its Cycle 

The cloud category has treated investors quite well with recurring revenue, resiliency during Covid, and some of the strongest examples of product-market fit available on the public markets over the past ten to fifteen years. However, I made the argument three years ago that cloud software was in the later innings of its cycle, as many best-of-breed companies saw growth fall off a cliff in 2022 and 2023.  

We had pointed out in our free analysis in late 2022, Slowing Growth In Cloud Stocks: When Will We Hit A Bottom, that nearly all cloud companies were reporting a notable, sequential slowdown between Q3 to Q4. These Q4 2022 guides marked a ‘historic slowdown’ for the once-resilient category, as Q4 is typically the strongest seasonal quarter for the industry. Snowflake was a prime example of this, as it had guided for 3% QoQ growth in Q4 2022, what would mark a 12 point decline from 15% QoQ the year prior. 

We followed up in March 2023, asserting in the analysis, Slowdown In Cloud Stocks On Thin Ice Following Q1 Guides, that hyperscalers were seeing growth rates plummet. AWS reported Q4 2022 growth of 20%, half of the 40% reported in Q4 2021 while guiding for Q1 2023 growth in the mid-teens; Azure saw a similar nearly 20 point deceleration from 49% in the March 2021 quarter to 30-31% guided for March 2022.  

These decelerations are clearly visible looking at some of the best-of-breed names over the last five years. Snowflake reported revenue growth north of 100% in Q1 2022, yet exited 2023 seventy points slower at 31% YoY; Twilio decelerated from 67% to the low single-digits.  

Line chart comparing quarterly year‑over‑year revenue growth for Snowflake, Twilio, MongoDB, SentinelOne, and CrowdStrike from 2021 to 2026. All companies show declining growth rates over time, with Snowflake at 30.12%, SentinelOne at 22.91%, CrowdStrike at 22.18%, MongoDB at 18.69%, and Twilio at 14.32% in early 2026.

Chart comparing quarterly year‑over‑year revenue growth for five major high‑growth software companies—Snowflake, Twilio, MongoDB, SentinelOne, and CrowdStrike—from 2021 through early 2026. The visualization shows a broad deceleration in revenue growth across the software sector, with each company gradually trending downward from higher peak growth rates recorded in 2021–2022. By 2026, Snowflake leads the group with 30.12% YoY growth, followed by SentinelOne at 22.91%, CrowdStrike at 22.18%, MongoDB at 18.69%, and Twilio at 14.32%. Source: YChartsYCharts

Simply put, the hypergrowth cloud era from 2015 through 2021 has passed, with sharp growth deceleration as rates rose leading to multiple compression. Those who think AI is disrupting cloud software are not accounting for the fact that cloud software was as the end of its cycle and ripe for both consolidation and disruption (a terrible place for investors to be positioned) – which is why I went to great lengths to make sure my premium research members knew to steer clear of cloud software three years ago.  

There is also more evidence that cloud is now late cycle, with the market being extremely saturated with more than 30,800 SaaS companies worldwide, each competing with one another for wallet share as production differentiation narrows. Market saturation preceded the disruptive fears from AI-based solutions automating workflows.

Today, the same narrative has resurfaced around AI. This analysis breaks down why the AI market is fundamentally different from past bubbles, why corrections have been misinterpreted, and what indicators we’re watching to confirm the trend remains intact. 

There Will Be a Correction; It Won’t Be a Bubble

While we do not believe AI is a classic bubble, that doesn’t mean we won’t see meaningful selloffs that create attractive buying opportunities. In fact, warning signs have been building since October 2025 that we may be approaching one of these pullbacks. One of the more concerning signals is that the Magnificent 7—what I consider the generals of this market—appear to have topped well before the S&P 500. 

Since November 2021, periods in which the equal-weight Mag 7 index fails to confirm new highs in the S&P 500 have been a reliable signal of a weakening market environment. A similar divergence is developing today, and until it resolves to the upside, it remains a meaningful warning for the durability of the broader uptrend. 

Chart comparing the S&P 500 (top, light blue line) with the Equal Weight Mega‑Cap 7 index (middle, dark line) from 2021 to 2026. Several shaded red regions mark periods of market weakness. A lower panel shows a small indicator line with green and red vertical markers.

This chart compares the performance of the S&P 500 with an equal‑weight index of the “Mega‑Cap 7” from 2021 through 2026. The upper panel shows the S&P 500 trending higher with periodic pullbacks highlighted by red‑shaded regions.

Looking under the hood, all 7 of the Mag-7 are currently making lower highs while the S&P 500 made higher highs. For reference, this is roughly 32% of the S&P 500’s weight that is preventing the broader market from moving higher.  

Multi‑panel chart showing the S&P 500 at the top and individual stock price movements for MSFT, META, NVDA, AMZN, AAPL, TSLA, and GOOGL beneath it from mid‑2025 to early 2026. Red arrows highlight notable reaction points for each stock on specific dates.

Chart comparing the performance of the S&P 500 with seven major mega‑cap technology stocks—Microsoft, Meta, Nvidia, Amazon, Apple, Tesla, and Alphabet—between mid‑2025 and early 2026. Each stock is displayed on its own horizontal price panel, while the S&P 500 appears at the top as the market benchmark. Red arrows highlight key reaction points, likely tied to earnings releases or major announcements, where individual stock prices either spike or decline.

Also, before volatility started picking up, we noted in prior reports, both retail and professional investor sentiment are elevated to historically concerning levels, suggesting an environment where risk is being discounted and investors behave as if there is no price too high. 

The AAII weekly survey (retail sentiment and positioning) and the NAAIM weekly survey (professional manager exposure) are at levels that exceed the average for most major tops. Since late October—around when several markets began topping out—NAAIM readings have ranged between the 78th and 96th percentile of all bullish readings, suggesting managers have been heavily allocated to equities for more than three months, and maintain this exposure.  

When compared to levels seen before prior market tops, these readings suggest sentiment and positioning are among the more extreme observations on record. 

Multi‑panel chart showing the S&P 500 at the top and individual stock price movements for MSFT, META, NVDA, AMZN, AAPL, TSLA, and GOOGL beneath it from mid‑2025 to early 2026. Red arrows highlight notable reaction points for each stock on specific dates.

S&P 500 Sentiment Comparison Table: Identifying NAAIM and AAII sentiment readings at major S&P 500 market tops, showing that current levels—high stock exposure, elevated bullish sentiment, low cash, and a strong bullbear spread—closely match historical conditions seen at previous peaks.  bear spread—closely match historical conditions seen at 

In other words, both retail and professional investors appear to expect higher prices and have expressed that view through high equity exposure. What is more concerning is that margin debt in the U.S. is at record highs, surpassing the 2021 peak.

Dual‑line chart showing the S&P 500 on the upper panel and broker‑dealer margin account levels on the lower panel from 1997 to 2025. Vertical red dashed lines mark prior peaks in margin debt that coincided with major market tops. The latest circle highlights a sharp rise in margin balances.

S&P 500 (SPX) margin debt chart highlighting how rising margin debt at brokerdealers has historically aligned with major S&P 500 peaks, with current margin levels approaching prior extremes that preceded significant market tops.  

These conditions often precede periods of volatility. However, late-cycle behavior does not automatically imply that a bubble is forming. Since 1980, only a small number of major market peaks coincided with the bursting of true systemic bubbles—most notably technology in 2000 and housing and credit in 2007. Many other peaks resolved into corrections and recoveries within longer secular bull markets. 

In terms of where this volatility could take us, the below scenarios are the most probable based on the current price action: 

  • Green – If we can continue to bounce through SPX 6869, 6901 and finally 6952.50, then we will likely push toward the 7200 range in the coming weeks. This will complete the final 5th wave in a very extended uptrend that started off the April low in 2025. If this happens, we will look for more stocks and markets to not make new highs with the S&P 500 to further confirm we are setting up for a period of volatility.
  • Red – In our last broad market article we outlined the importance of the 6780 – 7720 region in SPX. So far, these levels have held and it is where the market staged a bounce.

    These same levels remain of utmost importance for the bulls. If they break, then the period of volatility has already begun as we head toward 6500 – 6300 in the coming weeks. This will likely complete the first leg in a larger correction, as we mount a bounce that makes a lower high into later 2026. 

A line chart showing the NASDAQ‑100 index rising more than 550% from 2016 to 2026, with several pullbacks marked at −19%, −24%, −30%, −37%, and −25%. The chart includes annotated media headlines predicting tech bubbles at various points along the upward trend.

Chart showing the S&P 500 through detailed Elliott Wave analysis, highlighting major support and resistance zones, projected wave counts ((A), (B), (C), ①–④), and key Fibonacci‑based levels. Green bands mark overhead resistance and upside targets, while red bands outline a “danger zone” that could signal deeper downside if broken. Two shaded timing windows identify potential reversal periods. The chart emphasizes the market’s attempt to regain upper resistance after a corrective low, offering traders a clear view of breakout levels, downside risks, and the broader wave structure guiding the next move in the index.

Conclusion: 

AI will almost certainly deliver more volatility, and investors should expect meaningful selloffs that create buying opportunities. But volatility is not proof of a classic bubble. The dot-com era was defined by oversupply and fragile fundamentals; today’s AI buildout is being led by the world’s strongest operators, backed by real revenues and profits, and constrained by hard limits in compute, memory, networking, and power. 

The more important question isn’t whether we’ll see a pullback — it’s where we are in the cycle. AI is still transitioning from the training phase into the inference phase, where monetization will accelerate and the “capex with no revenue” narrative will begins to fade. In other words, the loudest bubble debates are arriving before the most important revenue engine fully turns on. 

We’ll continue to watch the same signals that matter in every tech cycle: whether fundamentals keep compounding, whether supply constraints remain binding, and whether the market’s leadership confirms a durable uptrend. If those conditions hold, then “it’s a bubble” may once again prove to be the most expensive words in tech for those sitting on the sidelines. 

Since our inception in May 2020, I/O Fund has delivered a cumulative return of 326%— if we were a hedge fund, we’d rank #1 and if we were a tech ETF or Mutual Fund, we’d rank #3 in the United States. 326%— if we were a hedge fund, we’d rank #1 and if we were a tech ETF or Mutual Fund, we’d rank #3 in the United States.   

Being early to many lesser-known AI winners helped us to achieve these results. To get our Top 15 AI stocks, real-time trade alerts, weekly webinars and deep-dive research from a proven team in AI and tech stocks, Sign up now.Top 15 AI stocks, real-time trade alerts, weekly webinars and deep-dive research from a proven team in AI and tech stocks, Sign up now.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

Recommended Reading:

  • My Top 2026 Stock Pick for the AI Boom
  • I/O Fund Jumps to 326% Cumulative Return, Ranking Among Wall Street’s Best
  • Bitcoin After the Cycle Peak: What Comes Next and How We’re Positioning
  • S&P 500 Outlook 2026: Rising Volatility Risk and Key Support Levels
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Broadcom Fiscal Q1: $100 Billion+ in AI Chip Revenue in 2027

Posted on March 5, 2026June 30, 2026 by io-fund

Broadcom guided to $22 billion in FQ2 revenue up 47% YoY and adjusted EBITDA at 68% of revenue. Within that, management guided semiconductor revenue to $14.8 billion, up 76% YoY, and AI revenue to $10.7 billion, up 140% YoY, indicating an acceleration from Q1.  

The most explosive comment was that: “Today, in fact, we have line of sight to achieve AI revenue from chips, just chips in excess of $100 billion in 2027. We have also secured the supply chain required to achieve this.”  

Management characterized this demand as being driven by a small number of hyperscalers  and frontier model builders, with both training and inference contributing as those customers will soon productize their LLM platforms. Within discussing the impressive customer list, the CEO of Broadcom hinted toward 2027 being significantly higher than $100 billion – plus another analyst did math that would show a sharp inflection in 2028 due to OpenAI’s incoming GWs. 

The majority of the Q&A was about the $100 billion comment, including surfacing why this estimate may prove to be far too low.  

$100 Billion for Chips Alone … and Counting 

AI revenue was at $8.4 billion this quarter and is guided to $10.7 billion next quarter for a run rate of about $43 billion. On the surface, the guide doesn’t look like much and would imply a deceleration given AI is growing at a rate of 140% YoY and 27% QoQ – whereas this is effectively saying Broadcom will double in 7-8 quarters.  

However, the easy-to-miss details on the guide is that the $100 billion is only for silicon and does not include networking. The words “significantly in excess” were also added later to the guide in the following statement during the Q&A portion: 

“Now to clarify your first part, Blayne. When I say we forecast, we have a line of sight that our revenue in '27 will be significantly in excess of $100 billion, I'm focusing on the fact that these are pretty much all based on chips, whether they are XPUs, whether they are switch chips DSPs, these are silicon content we're talking about.” 

According to the earnings call, networking is about 33% to 40% of AI revenue today: “AI networking revenue grew 60% year-on-year and represented 1/3 of total AI revenue. In Q2, we project AI networking to accelerate a lot more and grow to 40% of total AI revenue.” 

If we assume this mix continues on the low end for about 30% mix in AI networking of total AI revenue, then it’s reasonable to assume Broadcom’s AI revenue will be $143 billion with networking of $43 billion (or 30% of $143B). This represents a QoQ growth of 30% for 7-8 quarters – which is an excellent baseline to set. 

When you add the words “significantly in excess” of this amount, it spells a solid runway for Broadcom. 

The analysts in the Q&A session were not satisfied with this blanket comment and began poking around with one analyst doing the math on the customers plus what each GW is worth, and coming out closer to $180 billion to $200 billion in AI revenue. Here was the exchange – the CEO did not confirm the math but it still helps to know what the sell-side is thinking: 

“Stacy Rasgon Bernstein: 

I don't know if this is for Hock or Kirsten, but I wanted to dig in a little more to this substantially more than $100 billion next year. I'm trying to just count up the gigawatts. I counted, I don't know, 8 or 9, you have 3 from Anthropic, one from open AI, so that's 4. You said Meta was multiple, so east, that gets you to 6. Google, I figure should be bigger than Meta.so like at least 3, that's 9 and then you got a few others.  

I just thought that your content per gigawatt was sort of, call it, a $20 billion per gigawatt range. I guess what I'm asking, is my math around the gigawatts you plan to ship in 27 correct? And how do I think about your content per gigawatt as that ships — maybe we'll be "substantially" more than $100 billion. 

Hock Tan CEO: 

Stacy, you have a very interesting perspective, and I've got to [ remind ] you for that. But you're right. You can look at it a gigawatts, which is the right way to look at it instead of dollars because that's how we sell our chips. So you have to realize we — depending on our LLM customer, our 6 customers, sorry, not 56. The dollars per gigawatt is varies, sometimes quite dramatically. — it does vary. But you're right. It's so far from the dollars you are talking about. And if you look at it by gigawatt in '27, we are seeing getting close to 10 gigawatts.” 

OpenAI to Drive Strong Fiscal 2028  

During the opening remarks, the CEO stated that OpenAI represents 1 GW stating: “We expect OpenAI deploying in volume, their first-generation XPU in 2027 and over 1 gigawatt of compute capacity.” Later, an analyst drilled into the math to where OpenAI will eventually represent 10GWs, which could make for a strong inflection come 2028. 

“Joshua Buchalter, TD Cowen: 

Congrats on the results. I appreciate all the details on the expectations for deployment at specific customers. I was hoping you could just maybe reflect on how visibility has changed over the last 1 to 2 quarters that gave you the confidence to give us more details? And then on a specific one, you mentioned greater than 1 gigawatt for OpenAI in 2027, with that deal being for 10 gigawatts through 2029, that implied a pretty sharp inflection, I guess, in 2028. Is that the right way to think about it? And was that sort of always the plan? 

Hock Tan, CEO: 

Yes. Well, yes, this — as you've all seen, and you all know in this generative AI race that we are in now, and I shouldn't use the word race, let's call it, progression among the few players we see here, I mean, it's a competition. Each is trying to create an LLM better than the other and more tailored for specific purpose, be it enterprise, be it consumer, be it search. Each one is trying to create it more and more. And all of that requires not just training, which is important to keep improving your LLM models. But inference for productization and monetization of your LLMs.  

And we are going — and probably call it the fact that we've been engaged with some of them now for more than a couple of years. We're getting better and better visibility as they have more and more confidence that the XPUs they are working on with us is achieving what they are getting it as they get the sense that the XPUs they are working on with the software, with the algorithms they needed, they are having more confidence that this XPU silicon is what they need.  

And it gets better and better. And It's get better, we get more visibility as Charlie puts up perfectly because at the end of the day, we only have 6 guys to work on. And these 6 guys are all, as I said, look at XPUs and AI in a very strategic manner. They don't think 1 generation at a time. They think multiple generation multiple years.” 

Speaking of large customers, in the exchange above, Broadcom refutes the idea that Meta is facing significant hurdles for its MITA chips, stating in the opening remarks that: “Now contrary, the recent analyst reports Meta's custom accelerator MTIA roadmap is alive and well. We're shipping now. And in fact, for the next generation XPUs, we will scale to multiple [ gigawatts ] in '27 and beyond.” 

Financials 

By Royston Roche 

Revenue grew by 29.5%

Broadcom’s FQ1 ending January 2026 revenue grew by 29.5% YoY and 7.2% QoQ to $19.3 billion, beating estimates by 0.9%. Revenue growth accelerated by 1.3 percentage points from 28.2% growth in the previous quarter.  

Management provided a strong FQ2 guide of $22 billion, implying a YoY growth of 46.6% and 13.9% QoQ, beating estimates by 7.8%. The expected strong growth is primarily driven by AI revenue, which is expected to grow 140% YoY and 27% QoQ to $10.7 billion. Analysts expect strong growth to continue, with revenue expected to grow 71.7% YoY to $27.39 billion in FQ3 and 74% YoY to $31.35 billion in FQ4.

Key Segments 

Semiconductor Solutions 

FQ1 Semiconductor solutions revenue grew by 52% YoY and 13% QoQ to $12.5 billion and was better than the management guidance of $12.3 billion. Revenue growth accelerated by 17 percentage points from 35% growth in the previous quarter. Management expects semiconductor revenue to further accelerate to 76% YoY and 18% QoQ to $14.8 billion in the next quarter, driven by the surge in AI revenue.

FQ1 AI revenue grew by 106% YoY and 29% QoQ to $8.4 billion. Revenue growth accelerated from 74% YoY and 25% QoQ in the previous quarter. Management expects strong growth to continue in the next quarter and AI revenue is expected to grow 140% YoY and 27% QoQ to $10.7 billion.  

The company’s CEO, Hock Tan, said in the earnings call, “Now our custom accelerator business grew 140% year-on-year in Q1. This momentum continues in Q2. The ramp of custom AI accelerators across all our 5 customers is progressing very well. For Google, we continue our trajectory of growth in '26 with strong demand for the seventh-generation Ironwood TPU. In 2027 and beyond, we expect to see even stronger demand from next generations of TPU. For Anthropic, we are off to a very good start in 2026 for 1 gigawatt of TPU compute. And for '27, this demand is expected to surge in excess of 3 gigawatts of compute. Our XPU franchise, I should add, extends beyond TPUs.” 

Non-AI semiconductor FQ1 revenue was flat YoY at $4.1 billion, in line with guidance. Enterprise networking, broadband, server storage revenues were up YoY, offset by a seasonal decline in wireless. In FQ2, management expects non-AI semiconductor revenue to be $4.1 billion, up 4% YoY.

Infrastructure Software 

FQ1 Infrastructure Software revenue came at $6.8 billion, up 1% YoY and down (2%) QoQ and was in line with the guidance. VMware revenue grew 13% YoY. Management expects infrastructure software revenue to grow 9% YoY and 6% QoQ to $7.2 billion in the next quarter. 

Margins 

The company’s adjusted EBITDA margins beat management guidance in FQ1, primarily driven by operating leverage.  

  • FQ1 gross profits grew by 29.7% YoY to $13.16 billion. Gross profit margin improved by 10 basis points YoY and QoQ to 68.1%. Adjusted gross margin came at 77%, down 210 basis points YoY and 90 basis points QoQ and marginally beat the guidance by 10 basis points. Management has guided adjusted gross margin to be flat sequentially to 77% and down 240 basis points YoY. 
  • FQ1 operating income grew by 36.8% YoY to $8.56 billion. Operating margin improved by 230 basis points YoY and 260 basis points QoQ to 44.3% primarily driven by operating leverage. The adjusted operating margin was 66.4%, compared to 65.9% in the same period last year and 66.2% in the previous quarter. 
  • FQ1 net income grew by 33.5% YoY to $7.35 billion with a net profit margin of 38.1% compared to 36.9% in the same period last year. Adjusted net income grew by 30.2% YoY to $10.19 billion with an adjusted net profit margin of 52.7% compared to 52.4% in the same period last year.

FQ1 adjusted EBITDA grew by 30.2% YoY to $13.1 billion with an adjusted EBITDA margin of 68% and was better than the management guidance of 67%. Management has guided FQ2 adjusted EBITDA guidance to be flat sequentially and up 100 basis points YoY to 68%.

Adjusted EPS grew by 28% 

FQ1 GAAP EPS grew by 31.6% YoY to $1.50. Adjusted EPS grew by 28.1% YoY to $2.05, beating estimates by 1.3%, primarily driven by operating leverage. Analysts expect strong growth in the coming quarters and adjusted EPS is expected to grow 36.9% YoY to $2.16 in FQ2 and 69.5% YoY to $2.86 in FQ3.

Cash Flow and Balance Sheet 

Broadcom’s cash flows are improving, driven by higher profits. 

  • FQ1 operating cash flows grew by 35.1% YoY to $8.26 billion with an operating cash flow margin of 42.8% compared to 41% in the same period last year. 
  • FQ1 free cash flows grew by 33.2% YoY to $8.01 billion with a free cash flow margin of 41.5% compared to 40.3% in the same period last year. 
  • Cash was $14.2 billion at the end of FQ1 with debt of $66.1 billion compared to cash of $16.2 billion and debt of $65.1 billion at the end of FQ4. The company repurchased shares worth $7.85 billion and paid dividends of $3.1 billion in the recent quarter. Management authorized an additional $10 billion share repurchase program effective through the end of calendar year 2026. 
  • Inventory increased by 30.4% QoQ to $2.96 billion to support the strong AI demand. 

Conclusion: 

Broadcom’s most consequential message this quarter was not the near-term guide, but the long-range visibility: management stated it has line of sight to AI-related revenue in FY2027 “significantly in excess of $100 billion.” 

If that framework proves accurate, the market will be forced to shift from debating whether AI spend can persist to modeling how Broadcom scales into that demand across XPUs and networking as customers expand from early deployments into multi-gigawatt clusters.  

Notably, management’s comments imply substantial silicon content per gigawatt—for example, Anthropic’s trajectory has been discussed in terms that can translate into roughly $20 billion per GW—and they also referenced a back-half weighted OpenAI ramp that appears more likely to contribute in 2028–2029.  

And that’s only what we can quantify today …

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in AVGO at the time of writing and may own stocks pictured in the charts.

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Posted in AI Stocks, SemiconductorsLeave a Comment on Broadcom Fiscal Q1: $100 Billion+ in AI Chip Revenue in 2027

Monolithic Power: Strong AI Tailwinds to Drive 50% Enterprise Data Segment Growth in FY26 

Posted on March 4, 2026June 30, 2026 by io-fund

Monolithic Power Systems (MPS) is exiting 2025 with strong AI-driven momentum in its Enterprise Data segment, recording QoQ growth of 33% and 21.9% in the second half of the year. Management guided for the segment’s growth to ‘conservatively’ have a floor of 50% YoY in 2026, likely driven by the ramp of multiple ASICs and GPU platforms this year and the increasing need for voltage regulator modules and vertical power delivery in increasingly powerful AI accelerators.  

Brief Overview of What Monolithic Power Does 

  • Power management ICs, power ICs for networking 

The rapid proliferation of more powerful AI GPUs and increasing power consumption per generation necessitates more efficient power management ICs, which help to regulate, distribute and optimize power within the chip. PMICs also help regulate heat dissipation to prevent overheating an especially critical function as GPUs continue to push the thermal boundary higher.   

High-performance power ICs (and power over Ethernet ICs) are also critical in AI networking applications to help regulate power for networking switches and deliver power over Ethernet cables, enabling delivery of uninterrupted power and offering high reliability and interoperability for networking deployment. Monolithic participates here with PoE devices and modules.  

  • DC-DC converters 

DC-DC converters help enable precise voltage regulation from 400V DC power entering the rack to lower voltage rails that are required by AI accelerators. DC-DC converters can also handle higher power densities, reducing power consumption and optimizing performance of increasingly-power hungry GPUs such as Nvidia’s Blackwell generation. As upcoming chip generations continue to push thermal requirements higher, towards 2,000W per chip and beyond, managing heat via DC-DC converters is important to ensure maximal uptime, efficiency and performance. Monolithic offers a broad suite of DC-DC converters, including buck, boost and buck-boost converters to handle any step-up or step-down change in voltage. 

  • Multi-phase voltage regulator modules  

Multi-phase voltage regulator modules are increasing in content as GPUs push into higher power requirements. GPUs operate at very low voltage at roughly 1V — but draw extremely high current, which means power must be converted from 12V at the board level down to the GPU core. 

Rather than relying on one oversized regulator, engineers distribute the load across multiple phases to improve efficiency and thermal management. As power requirements rise, VRM designs typically expand to a higher number of phase counts, which results in increasing component content and system complexity, and ultimately more power management dollars per rack. 

Monolithic Power participates in this trend by supplying the multi-phase controllers and integrated power components that sit directly in that power delivery chain. Below, we also look at new packaging approaches that enable more compute density called vertical power delivery (VPD). 

  • Power modules  

Power modules combine DC-DC converters with built-in, integrated power MOSFETs, inductors, and other necessary components into a single module to lower BOM, reduce amount of external components required, and simplify AI chip design. Power modules such as Monolithic’s provide higher power density, offering more efficient power delivery even with a more compact design.  

Increasing Thermal Challenges, Shift to Vertical Power Delivery 

Nvidia’s Blackwell lineup brought a significant increase in power consumption, nearly double the H200’s 70 kW at 120 kW for the GB200 NVL72 and 140 kW for the upcoming GB300 racks.  

Beyond Blackwell, Nvidia’s future design lineup shows continual increases in power consumption. Its Vera Rubin generation is expected to boost thermal design power (TDP) by 50% over Blackwell at up to 180 kW to potentially 230kW per rack, with the Rubin Ultra boosting this to 600kW by late 2027.   

In its largest configuration, the Vera Rubin NVL576, dubbed the ‘Kyber’ rack, could draw as much as 600 kW (0.6 MW), or 5x that of the GB200 NVL72 in just a two-year design timeframe. These figures do not include networking, interconnects, cooling and other hardware, which will further boost power draw per rack.  

The rapid increase in power draw and demand for high current power supply places more emphasis on voltage regulator modules:  

“Even though these chips use increasingly small supply voltages — usually less than 1 V — they can require currents that are trending up to more than 2000 A. This pushes their continuous power demand, also called the thermal design power (TDP), to new highs. For instance, Nvidia’s Blackwell GPU burns through 1200 W, and the Blackwell Ultra consumes up to 1400 W. 

Simply put, the traditional placement of VRMs and lateral power delivery carried a major drawback that must be addressed – lateral routing increases power loss and drives heat generation higher, causing overheating (when next-gen GPUs are already getting hotter) and reduced performance. This is essentially forcing a shift to vertical power delivery (VPD), which places voltage regulators directly under the PCB and shortening delivery lengths. VPD helps enable higher power density, lets modules more efficiently power GPU, CPU and memory rails, while also freeing up space on the PCB for additional HBM stacks, signal routing or other components.  

With these key advantages and push to more powerful GPUs with each generation, MPS expects VPD to essentially become a non-negotiable in 2026: “This is just the direction of the market. It's the only energy-efficient solution you can put in place if you're going to operate in these high-voltage — high-current.” 

Enterprise Data Revenue Guided to Increase 50% in 2026 

MPS has a handful of AI-related or AI-driven growth opportunities across its different key segments, though the focus remains on its Enterprise Data segment, encompasses AI accelerators from GPUs, TPUs and other ASICs, as well as server CPUs. Enterprise Data is expected to be a core growth driver through 2026 with management laying out a target for a minimum of 50% YoY growth, raised from its previous view for 30-40%. This is likely due to GPU, TPU and other accelerator platforms ramping in unison, as well as vertical power delivery modules becoming a necessity. 

Looking at 2025, Enterprise Data exited the year with strong momentum with QoQ growth of 33% in Q3 and 21.9% QoQ in Q4, aligning with prior commentary from management regarding design wins ramping in the later part of the year, including an ASICs project that began ramping in Q2.  However, this strength was not enough to offset the soft 1H, which could potentially stem from allocation losses on with Nvidia’s Blackwell product family raised in late 2024. Due to the soft 1H, FY25 revenue declined (2%) YoY to $701.8 million, the only of MPS’s six segments to decline last year. 

All told, Enterprise Data exited the year at nearly a $1 billion run rate – putting the pieces together from management’s 50% growth guide would imply FY26 revenue of ~$1.05 billion (from $701.8 million in FY25), or potentially a scenario in which Enterprise Data sees more limited sequential growth from this level through 2026.  

Analysts picked up on this, questioning about the annualized run rate and growth, with CEO Michael Hsing emphasizing that 50% growth would be the floor:  

Christopher Caso, Wolfe ResearchChristopher Caso, Wolfe Research 

“You had made some comments on Enterprise Data for '26 on the last earnings call. And if I just annualize the Q4 numbers, you pretty much get to where that guidance was. So what are your thoughts on that in the year? And perhaps is there a seasonal element to Enterprise Data as we go through the year?” 

Bernie Blegen, Executive VP & CFOBernie Blegen, Executive VP & CFO 

“Sure. I'll start off on this one. As I said, in Q4, we saw some fairly pronounced changes in ordering patterns which has given us a fair amount more of confidence as far as what the outlook for Enterprise Data could be in '26. Now I think for those who worked with me for the last 10 years, you know that I like to stay pretty conservatively profiled when I make an estimate. So I'd probably say that whereas last quarter, I talked about a range of between 30% and 40%. Maybe I can increase that to a floor of 50% growth for 2025.” 

Michael R. Hsing, Founder, Chairman, President & CEOMichael R. Hsing, Founder, Chairman, President & CEO 

Well, 50%? I thought that we can do a lot more than that. 

Bernie Blegen, Executive VP & CFOBernie Blegen, Executive VP & CFO 

Conservatively. 

Michael R. Hsing, Founder, Chairman, President & CEOMichael R. Hsing, Founder, Chairman, President & CEO 

…And I don't see why not, it's not only 50%, we will be a lot more than that.” 

Supporting this 50% growth forecast is MPS’ engagement across the leading six to seven customers, design wins for current and future generations, and increasing visibility and backlog from strong order activity; backlog in particular was said to be extending into Q2 and Q3.  

Management explained that previously, they had been seeing very short lead times and limited backlog in the segment in prior quarters, yet now they are seeing longer lead times and rising backlog as customers are growing more concerned about capacity constraints across the industry. Management did offer some clarity later on that shed more light on the growth trajectory through the year, in a question related to that backlog/visibility:  

“If I talk to you guys 3 months ago, I think you were thinking maybe the 2026 year was going to be a little more second half weighted. Just given the stronger bookings that you've seen, the stronger order backlog as you see here today, would you say the shape of the year is a little more linear, less dependent on the second half?” 

Bernie Blegen, EVP & CFO: Bernie Blegen, EVP & CFO:  
“I'd say that the first half for enterprise data in particular, but for the company, it is more secure. I think there's still a lot of variables that need to be shaped before we really understand what the second half trajectory is going to look like. But obviously, the initial signs that we saw from the ordering pattern in Q4 and continuing into the year have been exceptionally positive. So now we have more of the high-level issue of trying to figure out what's real demand and what may be some double ordering on the part of our customers as they try to secure capacity.”  

Management has offered solid visibility into 1H, but is not willing to provide visibility into 2H at the moment. Some of this may tie in to the pace and timing of Nvidia’s upcoming Rubin platform, discussed below; yet, the key takeaway is that 2H will likely be the determining factor for where Enterprise Data growth lands. If orders continue to materialize, backlog remains high and can be converted to revenue, there is a chance that growth may indeed move beyond 50% as Hsing hints; however, if double ordering is indeed occurring, it could present a real risk to inventories and thus prices if demand and supply quickly realign.  

Circling back to Rubin, MPS could see some tailwinds in the back half of 2026, though growth from the upcoming GPU architecture may be much stronger come 2027. Analysts from KeyBanc are modeling MPS to capture roughly 70% market share in the VR200 NVL144 and R200 HGX platforms, estimating that it could add more than $100 million in revenue in 2H. For 2027, under the 70% share assumption, KeyBanc is modeling Rubin to contribute $420 million in revenue.  

Under KeyBanc’s framework, a $100 million contribution in 2H 2026 would add approximately 15 percentage points to Enterprise Data growth, potentially pushing total growth toward the mid-60% range if incremental. In 2027, a ~$420 million contribution could drive roughly 35–40 points of incremental growth, assuming the remainder of the business grows modestly.  

800V is a 2027-28 Story 

MPS was named as a key silicon provider and industry partner for Nvidia’s planned 800V DC architecture shift, which it believes will be needed to address rising power needs with next-gen rack architectures, from Rubin Ultra and beyond.  

While MPS has begun sampling its 800V solutions and was the first to do so, CEO Michael Hsing clarified that 800V revenue will not be “for this year, not for even for next year,” re-emphasizing the same point from Q3 that “some of these solutions for 800V as we discussed, those are like '27, '28 revenue ramps.” 

Notably, this would represent a major shift in where Monolithic Power sits as multi-phase controllers, ICs and PMICs are located on the accelerator board (or motherboard). 800V DC is about rack-level power distribution and this also shifts MPS from specializing in low voltage MOSFET-based devices to offering high voltage Sic/GaN devices in the future. Another significant difference is that MOSFET devices operate in the 100s of watts per rail power level compared to high-voltage SiC/GaN operating in the kilowatts per module power level.  

Therefore, this represents a strategic move up the power chain by entering high-voltage silicon with additional optionality. 

Needham’s Quinn Bolton asked about what role silicon carbide and gallium nitride-based solutions will play as some other participants are suggesting Nvidia may be seeking GaN solutions, whereas MPS is offering SiC-based ones. Management said that they have developed both SiC and GaN devices, and expect to be well positioned to capture demand regardless of which way the market shifts.  

Financials 

Q1 Revenue to Build on Q4’s YoY Acceleration 

MPS delivered a slight acceleration in YoY revenue growth in Q4, with revenue up 20.8% to $751.2 million, a 1.9 point acceleration from 18.9% growth in Q3. However, sequential growth stalled, with Q4 seeing revenue up just 1.9% QoQ versus 10.9% QoQ in Q3. 

Q1’s guidance points to this YoY acceleration continuing alongside a small step-up in QoQ growth, with management forecasting revenue to be $770 to $790 million, up 22.3% YoY and 3.8% QoQ at midpoint.  

Fiscal 2025 revenue increased 26.4% YoY to $2.79 billion, accelerating from 21.2% growth in 2024, with MPS noting it delivered record module revenue. MPS did not provide guidance for 2026 revenue, though consensus estimates currently sit at $3.39 billion, or a five point deceleration to 21.4% YoY growth. MPS said that it did achieve its milestone of securing >$4 billion in geographically balanced capacity, likely supporting demand needs and revenue through mid-2027. 

Key Segments 

MPS is a bit more complicated in that it reports revenue by six different end markets – Storage & Computing, Enterprise Data, Communications, Automotive, Consumer, and Industrial, with the first three being more AI-exposed – S&C with SSDs, DDR5 and HDD exposure, Enterprise Data with server CPU, GPU and TPU exposure, and Communications with optical transceiver and networking exposure.  

For a Q4 breakdown:  

Enterprise Data revenue increased 19.8% YoY and 21.9% QoQ to $233.5 million, driven by power management solutions for AI and server applications, and accounting for 31.1% of revenue. This accelerated from 3.8% YoY growth in Q3, though QoQ growth decelerated from 33%.  

Storage and Computing revenue increased 18.8% YoY but decreased (13.1%) QoQ to $162.1 million, accounting for 21.6% of revenue. Q4 revenue was driven by memory and storage solutions offsetting softer notebook-related revenue, although YoY growth decelerated from 26.9% in Q3. 

Automotive revenue increased 17.6% YoY but decreased (0.3%) QoQ to $151 million,  accounting for 20.1% of revenue. YoY growth saw a sharper deceleration than S&C, slowing from 36.1% in Q3.  

Communications revenue increased 31.2% YoY and 4.8% QoQ to $83.7 million, driven by routers and optical modules, and accounting for 11.1% of revenue. YoY growth saw a 20.1 point acceleration from 11.1% growth in Q3.  

Consumer revenue was $66.2 million, up 15.5% YoY and accounting for 8.8% of revenue, while Industrial revenue was $54.7 million, up 34.1% YoY and accounting for 7.3% of revenue.  

For 2025: 

Storage and Computing revenue was $732.5 million, up 46% YoY and accounting for 26.3% of revenue. Revenue was driven by growth in memory, storage, graphics cards and notebooks. For 2026, management said they expect Q1 to be down a bit in PCs, but largely wrote off fears about demand destruction from rising memory prices and expressed a difficulty in forecasting how the market pans out through year end. 

Enterprise Data revenue declined (2%) YoY to $701.8 million, accounting for 25.2% of revenue, down from 32.5% of revenue in 2024. Enterprise Data was MPS’ only segment to see revenue decline in FY25, as the other five all saw growth in excess of 25% YoY. 

Automotive revenue was $592.5 million, up 43.1% YoY, accounting for 21.2% of revenue. Considering the segment is still crucial for MPS’ revenue at more than one-fifth share, it cannot be overlooked; management was hesitant to put a number out for 2026 growth due to macro uncertainty, tariffs and EV subsidies, but noting strong engagement with OEMs and designs wins. Importantly, management said they are seeing more diversification outside ADAS, which saw a strong initial ramp in 2023 through 2025, though cautioned that growth will depend on how fast customers implement new products. Considering its larger contribution to revenue, if macro headwinds drag on growth, this could potentially offset some data center-driven revenue this year. 

Communications revenue was $309.1 million, up 36.8% YoY, accounting for 11.1% of revenue. Growth was driven by optical modules and routers, with one analyst implying that optical transceivers could account for ~5% of revenue, or potentially close to half of segment revenue. Management expects Communications to be an area of growth in 2026, driven by both optical modules as 1.6T ramps and by data center switches. CPO is a more long-term growth opportunity for Communications and not expected to move the needle much in 2026.  

Consumer revenue was $255.2 million, up 26.3% YoY and accounting for 9.1% of revenue, while Industrial revenue was $199.4 million, up 35.3% YoY and accounting for 7.1% of revenue.  

Margins 

MPS delivered marginal margin expansion in Q4, with gross margin sitting at the low end of its target range of 55-60%. Management explained that gross margin expansion will be driven by backlog growth, and this expansion could resume at some point in 2026.  

In Q4, GAAP gross margin was 55.2%, down 0.2 points YoY but expanding 0.1 points QoQ. Adjusted gross margin was 55.5%, down 0.3 points YoY and flat QoQ. For context, GAAP gross margin has been fairly consistent, remaining in the 55% range for ten consecutive quarters with Q4’s print.  

GAAP operating margin was 26.6%, up 0.3 points YoY and 0.1 points QoQ, while adjusted operating margin expanded a bit more sequentially, up 0.3 points YoY and 0.4 points QoQ to 35.8%.  

GAAP net margin was 22.6% in Q4, down 1.6 pts QoQ, with the YoY figure not being comparable due to a $1.29 billion tax benefit gain from a foreign subsidiary recognized in Q4 2024. Adjusted net margin was 31.3%, down 0.6 points YoY but up 0.5 points QoQ.  

For Q1, MPS guided GAAP gross margin to be 54.9% to 55.5%, and adjusted gross margin to be 55.2% to 55.8%, both flat QoQ and down 0.2 points YoY at midpoint. Management offered some insight into factors for gross margin expansion, with this primarily being tied to having a longer time horizon to manage backlog – however, any expansion will likely be minimal at best:  

“In order for us to show improvement, we really need to have a little longer time horizon as far as backlog to be able to manage in it. So we are starting to see a backlog developing, which I don't want to make too much out of with just 1 quarter's of experience but we should be able to resume at some time during the year. The cadence that we've historically shown of incremental sequential improvements at maybe 10 to 20 basis points quarter-over-quarter.” 

Despite the flat QoQ gross margin guide, management is forecasting GAAP operating margin to increase sequentially, due to leverage from opex guided to decline (2%) QoQ. Q1 GAAP operating margin was guided to be 28.3% at the midpoint of revenue and opex guidance, up 1.7 pts QoQ and 1.8 points YoY. However, adjusted operating margin was guided to be 35.2%, down 0.6 points QoQ but up 0.5 points YoY.  

EPS 

MPS’ earnings were mixed in Q4, with GAAP EPS missing estimates by (1.9%) yet adjusted EPS beat by 1.1%.  

GAAP EPS was $3.46, below the $3.53 estimate, with YoY growth not comparable vs the $29.88 print from Q4 2024 from the tax benefit. Adjusted EPS rose just 17.1% YoY to $4.79, slightly ahead of estimates for $4.74; this marked a slight acceleration from 16.5% growth in Q3. 

Looking ahead to Q1, GAAP EPS is projected to be $3.86, up 38.4% YoY, while adjusted EPS is projected to be $4.89, accelerating slightly to 21.1% YoY. Adjusted EPS growth is expected to closely match revenue growth rates each quarter in 2026, likely due to minimal margin expansion. 

For 2026, GAAP EPS is estimated to be $17.07, up 33.9% YoY, while adjusted EPS is estimated to be $21.52, up 21.1% YoY, nearly matching FY26’s revenue growth estimate of 21.4%.  

Cash Flows and Balance Sheet 

Cash flow was a soft spot in Q4, with operating cash flow margins contracting pretty substantially on both a YoY and QoQ basis. Operating cash flow was $104.9 million for a 14% margin in Q2, down from a 32.5% margin in Q3 and a 27% margin in the year ago quarter. FY25 operating cash flow was $838.2 million for a 30% margin, down 5.7 points YoY.   

Cash and equivalents totaled $1.27 billion, while debt remained at zero. 

Inventories totaled $564.6 million at the end of Q4, up 11.6% QoQ, likely due to the comments about backlog building and order strength and indicating revenue growth can remain strong in 1H. 

Risks 

There are still a few puts and takes to MPS’ story, in that the Enterprise Data guide of 50% may imply limited sequential growth beyond Q4’s run rate, while gross margins remain at the low end of management’s targeted range with limited opportunity for expansion. Additionally, despite increased attention around Nvidia’s shift to 800V DC power delivery architecture, this is not expected to become a revenue contributor until 2027, at the earliest. 

Valuation 

Monolithic Power is trading just over 10% above its average multiples on the topline, and nearly 20% below its peak levels from late 2024 – shares are currently trading at 16.6x forward revenue, above its average 14.9x multiple but below its peaks around 21x. 

The bottom line shows a similar picture, with shares trading at a 53.7x forward PE multiple, above its five-year average of 48.2x and below peaks of around 67x in late 2024. 

Conclusion 

Monolithic Power is forecasting strong AI-driven momentum in its GPU and ASICs-focused Enterprise Data segment, with management raising the segment’s growth forecast for 2026 to a floor of 50% YoY from 30-40% previously. MPS will remain on our watchlist as it relates to the 800V DC shift with Rubin Ultra in late 2027, though this potential growth opportunity remains many quarters away.

Damien Robbins, Equity Analyst at I/O Fund contributed to this analysis.

Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

Recommended Reading:

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Posted in Green Energy, SemiconductorsLeave a Comment on Monolithic Power: Strong AI Tailwinds to Drive 50% Enterprise Data Segment Growth in FY26 

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