AMD reported a double beat in Q1 with revenue of 36% and data center growth of 57%, with the beat filtering down to the bottom line with EPS growth of 55% — ahead of revenue.
However, the excitement from the beat soon faded after hours as the earnings call Q&A was decisively about the impacts of China export controls. AMD is the first to report among the larger AI accelerator design companies this quarter, and the information shared offers a glimpse at the harsh reality that AI semiconductors face as these companies adjust to global tensions.
For AMD, the impact of the MI308 being banned from China will be $700 million in Q2 and $1.5 billion in fiscal year 2025. This impact seems significant given AMD is $5B-ish in AI revenue, yet AMD assured analysts on the call their data center revenue would see “strong double-digit growth” this year despite declining sequentially in Q2.
On the Client side, analysts were also concerned that perhaps AMD saw a pull-in ahead of tariffs as PCs (Client) reported 68% YoY growth, to which management assured analysts many times on the call that it was due to higher average sales prices. However, keep in mind, the chances AMD remains completely unscathed from a weaker consumer due to the cumulative effects of tariffs is unlikely.
AMD’s Revenue Increases 36% YoY
AMD reported Q1 revenue of $7.44 billion, solidly ahead of the $7.12 billion estimate and above the upper range of its guidance for $7.1 billion, +/- $300 million. Revenue growth accelerated to 35.9% YoY, led by data center and client, though this is expected to be the peak growth quarter for the year.
For Q2, AMD guided revenue to be approximately flat QoQ at $7.4 billion, +/- $300 million. This represents YoY growth of 26.7% at midpoint, a more than 9 point sequential deceleration. Revenue growth is currently expected to decelerate further in 2H, with analysts estimating that AMD will exit 2025 with growth of just 12.3% YoY.
What’s important to note is that estimates for the back half of the year have been revised quite a bit lower over the past three months as tariffs and export controls have set in. In February, Q3 and Q4 revenue growth was estimated to be 6-7 points higher at 23.2% YoY and 19.2% YoY.
Export Controls Result in $700M Revenue Loss for Q2, $1.5B for FY2025
It was welcomed that management quantified the export control impact to understand better how the company can weather the loss of revenue.
Here is what the CFO stated in her opening remarks:
“As a reminder, in April, a new export license requirement was put in place for MI308 shipments to China, the impact of which is included in our guidance. We expect revenue to be approximately $7.4 billion plus or minus $300 million. This includes an estimated $700 million revenue reduction as a result of the new export license requirement. Despite this headwind, the midpoint of our guidance represents 27% year-over-year revenue growth.
For the full year 2025, we estimate the revenue impact due to the export license requirement to be approximately $1.5 billion.”
On one hand, it is quite impressive AMD can overcome this impact and meet consensus for next quarter. On the other hand, analysts have been lowering estimates as AMD was supposed to see revenue of $7.77 billion for growth of 33% as of last October rather than the 26.7% in the current quarter.
There were a few questions from analysts about the export license requirements, which are detailed below under the Q&A section.
Key Segments
Data Center to Decline Next Quarter
Data Center revenue grew 57% YoY but declined (5%) QoQ to $3.67 billion, driven by sales of EPYC CPUs and Instinct GPUs, and accounting for over 49% of AMD’s revenue in the quarter. While growth decelerated from 69% in Q4, it’s coming against a much tougher comp at 80% YoY whereas Q4 of last year offered a lower comp of 38% in Q4 2023.
AMD stated they gained market share again this quarter helped by EPYC 5th Gen Turin processors, which recently launched in October. EPYC-powered cloud instances doubled YoY among Forbes 2000 enterprises with on-prem growing by a “large double-digit percentage.” According to the opening remarks, there is “a clear path to continued share gains as customers ramp their 5th Gen EPYC offerings.”
Regarding GPUs, management stated their AI revenue increased by a “significant double-digit percentage year-over-year.” The MI325X is shipping in volume while the next-gen Instinct MI350-series chips are on track for “accelerated production by mid-2025.” We discussed last quarter that AMD was pushing up their delivery on the MI350s to mid-year for relative competitiveness. That’s a nice way of saying while Blackwell is delayed, AMD will attempt to nibble at their market share with a more aggressive timeline on their next generation. In the slide presentation, AMD stated they are partnering with Oracle to deploy a large cluster of MI355X GPUs and 5th Gen EPYC CPUs.
Data Center operating income was $932 million for a 25% margin, up from a 23% margin in the year ago quarter. However, it was 5 points lower sequentially and the lowest operating margin for the segment since last Q1.
For Q2, data center will decline due to the MI308 revenue being excluded. When asked about future quarters, the CEO Lisa Su stated the DC segment would resume growth after Q2: “in Q2, it's not going to grow year-over-year just given what we've said about the $700 million coming out of Q2 and how we had previously talked about the evolution. But we do believe that we'll grow year-over-year going forward, in Q3 and Q4 certainly, for us to do the full year with strong double-digit growth.”
Client & Gaming
Client and Gaming revenue rose 28% YoY, driven by 68% YoY growth in client revenue to $2.29 billion as gaming revenue declined (30%) YoY to $647 million. Sequentially, Client revenue was down less than (1%) from Q4 while Gaming revenue rebounded 15% QoQ. AMD said the segment’s growth was driven by strong demand for Zen 5 Ryzen processors.
Client revenue stood out in Q1, outpacing Data Center growth by more than 10 points YoY against its toughest comp in recent quarters at 85% YoY in Q1 2024.
Analysts poked around quite a bit on whether the high growth rate from the Client segment was a pull-in to get ahead of tariffs. Management pushed back on it being from tariffs and stated it was due to average sales prices: “And in particular, on your question of Client performance, we've certainly looked very carefully at the ordering patterns and what customers are telling us. We have not seen a lot of tariff-related activity in that business. I would say, though, what we have seen is a real stronger mix and strength in our overall ASPs. So the desktop channel, which is an area where we have a very strong gaming products right now, actually performed well above seasonality in Q1, and that is really the strength of the ASPs there. So that's what we saw in Q1.”
Combined operating income for the two was $496 million for a 17% margin, up from a 10% margin in the year ago quarter. However, operating income was flat QoQ.
Embedded
Embedded revenue declined (3%) YoY and (11%) QoQ to $823 million. While in line with guidance for a modest decline, AMD noted that the YoY decline was due to mixed end market demand.
Embedded operating income was $328 million for a 40% margin.
Margins Steady, but Q2 to See Sharp Decline Due to China Export Controls
While adjusted margins remained steady sequentially, AMD is taking a rather large hit in Q2 to their margins due to the MI308s. In mid-April, AMD flagged an $800 million hit from charges related to inventory, purchase commitments and related reserves, and as a result, guided adjusted gross margin to contract rather sharply. This will weigh heavily on adjusted operating margin in Q2.
Q1 GAAP gross margin was 50%, up 3 points YoY, while adjusted gross margin was 54%, up 2 points YoY.
GAAP operating margin was 11%, a strong expansion of 10 points YoY, and adjusted operating margin was 24%, up 3 points YoY.
GAAP net margin as 9%, up 7 points YoY, and adjusted net margin was 21%, up 2 points YoY.
For Q2, including the $800 million impact, AMD guided for a 43% adjusted gross margin (or 54% excluding it). With management forecasting operating expenses of ~$2.3 billion in Q2, adjusted operating income including the charge would be projected at $882 million for a 12% margin.
Excluding the impact (at that 54% adjusted gross margin), AMD’s expense guide would see adjusted operating margin at 23%, down 1 point QoQ.
EPS Posts Slight Beat in Q1
AMD reported adjusted EPS of $0.96 in Q1, slightly ahead of estimates for $0.93. This represented YoY growth of 54.8%, accelerating from nearly 42% growth last quarter.
Similar to revenue, Q1 is currently expected to be peak growth for EPS, with Q2 estimated to record 27.8% growth before slowing to the low 20% level by Q4. However, management commented that EPS growth is expected to grow much faster than revenue in Q2: “Looking at Q2, at the middle point of our guidance, revenue will be increasing 27%, and we do expect the earnings per share growing much faster than the top line revenue growth.”
Cash and Balance Sheet
AMD closed its acquisition of ZT Systems in Q1, which added more than $2 billion to both its cash and debt. Cash flow margins also expanded YoY with margins remaining in the double digits.
Operating cash flow was $939 million for a 13% margin, expanding from a 10% margin in the year ago quarter.
Free cash flow was $727 million for a 10% margin, expanding from a 7% margin a year ago.
Cash and short-term investments increased nearly $2.2 billion to $7.31 billion, while debt rose more than $2.4 billion to $4.16 billion.
Adjusted EBITDA was $1.95 billion for a 26% margin.
Inventories were $6.42 billion, up from $5.74 billion last quarter.
When asked about inventory, AMD stated it was due to preparing for the H2 ramp: “Well, on the inventory side, we built some inventory primarily to support very strong client and server ramp and also the second half Data Center GPU ramp. As you probably know, the lead time is really long to build. For the Q3, Q4 ramp, we really need to start the wafers right now. That's why the inventory has increased.”
Earnings Call Q&A:
MI350s and MI400s:
AMD is poised to become a stronger contender in the next two generations of Instinct GPUs. The MI350s will launch this quarter and the MI400s will launch in 2026. The MI350s feature the CDNA 4 architecture and will increase memory capacity and bandwidth by 1.5X with 288GB of HBM3e, and support for 35X higher throughput for better inference performance than the previous generation MI300Xs. Built on TSMC’s 3nm node, the MI350s also offer better efficiency and a 7X increase in AI compute capabilities.
The MI400s will offer a rack-scale architecture, assisted by AMD's acquisition of ZT Systems, a company that specializes in complex server designs. The MI400 will feature CDNA Next architecture with multiple chiplets and separate active interposers, with rumors the MI400s will be designed to increase data flow efficiency.
With the MI400s, AMD will be tasked with launching rack scale systems more smoothly than what we’ve seen from Nvidia these past two quarters. Here is what was said on the call in terms of the MI400 potentially closing the gap competitively with Nvidia – notably, AMD and all AI accelerators will remain in second place into the foreseeable future, yet the MI400 could be the moment when AMD becomes a firm second place winner.
“I think, look, we're excited about the MI350 Series launch that's coming up, but we are extremely excited as well about the MI400 Series and the road map there. I think we've been very active with customers on our road map. As you know, this is one of those areas where you absolutely have to be planning many quarters in advance for that. One of the primary reasons we acquired ZT Systems was exactly to address this rack-scale architecture.”
Export Controls and AI Diffusion Rules:
There was a question about the overall TAM of the AI market given China will no longer be a customer, and about the AI diffusion rules that have a deadline of May 15th to establish new rules. Lisa Su does not think China affects the $500B TAM she originally stated a few quarters ago, stating: “I think we always expected that there would be some amount of, let's call it, limitation on sort of leading-edge GPUs going into China. So that was factored in to our TAM expectation when we talked about $500 billion. So I don't think that dramatically changes the TAM.”
However, when it comes to AI diffusion rules, she was not as definitive as to the impact: “At the end of the day, when we look at sort of the U.S. AI companies, we have leading-edge technology. We want to ensure that the rest of the world can really use us as the primary platform. So I think it will be important to work through the AI diffusion rules and all of that as we think about longer-term TAM.”
Management Adamant Client Revenue is from Higher ASPs:
There were a few opportunities where management declined to connect higher Client growth to tariffs, and rather was adamant it’s from the strength of their product portfolio. AMD is being quite bold to state they are not seeing an impact from tariffs and do not expect to see a meaningful impact. Here is one of the exchanges in the Q&A:
CJ Muse:
I wanted to revisit your assumptions around Client. If you were to just flatline the Q1 actual, you would grow the business about 30%. You're obviously very bullish on taking share. You talked about huge tailwinds from ASPs. But curious, when you put it all together, how should we think about traditional seasonality into the second half, particularly with the potential of some pull-ins here in the first half?
Lisa Su:
Sure, C.J. It's a fair question. Look, we want to be very clear that our Client business performance is primarily driven by the strength of the product portfolio. And it's driven by some of the desktop channel products that traditionally are not so well tracked if you look at sort of the IDCs of the world. We are planning for, let's call it, second half sub-seasonal given that we're off to such a strong start in the first half of the year. And that is what we're putting into our sort of internal planning number. So you wouldn't see necessarily typical seasonality since the first half is better than seasonal.
That being the case, I think we feel strongly that, from a consumption basis standpoint, we see the data. So when we look at the Q1 performance, it was a very, very strong Q1 in terms of sell-out and consumption for our desktop business. And as we start Q2, we're now 4 weeks into it, we see those patterns continuing. So we're in an upgrade cycle right now. Gaming CPUs are usually repurchased when there are gaming GPUs that come out in new cycles. And I think we're benefiting from that on both the CPU and the GPU side, which is great. I mean, we're very happy with that, and we're ramping up production to ensure that we keep the channel full.
Conclusion:
AMD put up a solid report for Q1 on all accounts, yet the industry-wide headwinds that will take effect in Q2 introduce uncertainty for the semiconductor sector as a whole. Of the companies that will be affected, AMD is communicating they are ready to weather the storm. In data center CPUs, they have one of the most underrated products of all time – EPYC CPUs which continue to take substantial market share. In AI, they have a solid line up with the MI350s expected to launch this quarter to help offset the weight of losing China – in fact, by Q4, AMD is forecasting the loss of China revenue will be fully absorbed and there will be no impact by the time year closes out. In Client, they have some of the strongest AI PCs on the market and are confident average sales prices will remain above industry average for 2025.
However, AMD is a semiconductor stock in the center of a massive shift to supply chains and is in the crosshairs of a geopolitical war that will be defined by policies surrounding AI. It’s not a matter of if there will be rules that change how AI chips/components are exported, rather how severe those rules will be. Investors should be prepared for volatility as AI Diffusion rules will be set on or around May 15th.
I believe we will see near-term volatility but that ultimately AI companies in the United States will emerge stronger than they are today as we are all finally acknowledging that AI is not a fad, or a buzzword. Rather, the future of the country’s dominance relies on this technology succeeding, and conversely, relies on United States AI companies strategically denying AI systems to other countries. As an investor, you will never get a clearer signal as to the importance of a technology.
Overall, given volatility could be in our future as AI investors, this is a stock to watch closely should we get an even lower valuation, especially given where it's trading today is already quite cheap.
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 AMD at the time of writing and may own stocks pictured in the charts.
Astera Labs reported an impressive beat and raise in Q1, with GAAP margins strengthening as revenue continues to grow at a triple-digit rate. On top of this impressive beat, the growth story for Astera Labs is only beginning. The commentary regarding their product diversification and higher dollar content going into the second half of the year was quite clear as to the growing opportunity this company is poised to capture.
Primarily, Astera offers unique positioning that allows them to capture both the merchant GPU market and custom silicon market across its three products lines Astera, Taurus and Scorpio. This widens the TAM and allows for steady revenue growth despite hiccups or delays from a single AI system (which we’ve seen plenty of disruption recently across those with high customer concentration with Nvidia).
In addition to being a strong custom silicon vendor for hyperscalers, Astera will participate in Blackwell once it (finally) ships in volume as the company offers PCIe scale-out and Ethernet scale-up. Their new products Scorpio P-Series and Scorpio X-Series are fabric switches that are particularly well-suited for the immense demand that is expected for customization of racks as architectures scale-up in the second half of the year and beyond.
Notably, Aries PCIe retimers and Taurus Ethernet smart cable modules are driving the revenue today with the Scorpio P-Series beginning to ramp. However, there are many catalysts on the horizon for Astera which adds to the trifecta of a strong growth story:
Serving both ASICs and GPUs greatly increases TAM and diversifies revenue; rare in the AI systems ecosystem
Preparing to serve the scale-out demand with increasing higher dollar content; specifically on Scorpio but also on Aries
Offering strong cross-sell opportunities as it aims to be the first to solve unique challenges for both GPU and custom silicon utilization – and is solving these issues in a way that avoids vendor lock-in for the large hyperscalers who want a mix of both custom silicon and merchant GPUs (Nvidia or AMD).
Below, we look at Astera’s exceptional earnings report and provide notable commentary as to why Astera’s streak is likely to continue for some time.
Revenue Grows 144% in Q1
Astera Labs reported a blazing 144.3% YoY revenue growth in Q1 to $159.4 million, topping analyst estimates for $151.5 million in the quarter. Management said they witnessed strong demand for PCIe scale-up and Ethernet scale-out solutions for custom ASIC platforms, with initial shipments starting for its Scorpio P-Series and Aries 6 retimers in merchant GPUs.
For Q2, Astera delivered a solid raise at $170 to $175 million, more than 7% ahead of the $160 million estimate. This points to YoY growth of 124.5% at midpoint, ahead of estimates for just 108% YoY.
Astera has seen revenue growth decelerate over the past few quarters, with growth expected to continue decelerating as Astera laps its rapid ramp quarters. What’s impressive about this ramp is that Astera is guiding to deliver this 125% growth in Q2 against its 619% YoY comp (against a small base), for its seventh-straight triple-digit growth quarter.
For the full-year, Astera did not provide a guide, though estimates heading into Q1’s report were pointing to 70.4% YoY growth to $675.2 million in revenue. However, given that Q1 and Q2 have combined for a $20 million beat compared to current estimates, it’s likely that full-year revenue estimates will likely move closer to (or above) $700 million in the coming days. This would correspond to YoY growth of nearly 77%.
According to discussions on the call, management is being conservative in their guidance, stating they have more visibility than most as they are closer to their lead GPU customer (Nvidia) with hints that the larger systems will start shipping preproduction volumes at the end of this quarter. “I think the — so we will always continue to be conservative, just to underline what Jitendra said. But having said that, the revenue models and the guidance that we are providing or the outlook that we're sharing comprehends all this because we are so close to this customer that we see a lot of stuff, and we're able to consider and contemplate that when we provide guidance.”
Margins: Astera Becomes GAAP Profitable
Astera is making considerable progress on strengthening its GAAP operating margin, having guided for a (0.2%) margin in Q1 but reporting a 7.1% margin. This also marks a strong 15 point expansion in just 2 quarters. Elevated SBC at nearly 28% of revenue in Q1 is behind the wide disconnect between GAAP and adjusted margins, though it signals strong GAAP profitability potential in the coming quarters/years as revenue continues to scale.
Q1 gross margin was 74.9%, ahead of guidance for 74%.
GAAP operating margin was 7.1%, well ahead of guidance for (0.2%) and up from 0.1% in the prior quarter. Adjusted operating margin was 33.7%, up more than 14 points from 24.3% in the year ago quarter but down from 34.3% last quarter.
GAAP net margin was 20%, expanding nearly 30 points in three quarters. Adjusted net margin was 37.4%, up more than 15 points from 22% in the year ago quarter but down nearly 10 points sequentially.
Astera is guiding for margins to remain strong in Q2, with GAAP operating margin expanding. Gross margin was guided at 74% once again, while GAAP operating margin is forecast at 7.9%, up 0.8 points sequentially. Adjusted operating margin is forecast to contract 2.6 points QoQ to 31.1%.
The company foresees gross margin being stronger in the second half, yet was careful to temper the market expectations by stating gross margin goal is to remain above 70%. “So with that wider range of margins, we still expect our longer-term gross margin targets of 70% to be the direction we're heading, not this year, but over time. So I would still encourage people to think about the margins as we grow the company to trend towards 70%.”
EPS
Astera delivered an impressive 350% beat to GAAP EPS estimates in Q1, driven by its operating margin expansion, while forecasting EPS above estimates for Q2.
Adjusted EPS of $0.33 beat estimates by $0.05, representing YoY growth of 230%.
GAAP EPS of $0.18 beat estimates by $0.14, improving from $0.14 in Q4 and marking its second straight quarter of GAAP profitability on the bottom line.
For Q3, Astera guided for adjusted EPS between $0.32 and $0.33, approximately flat QoQ but up 150% YoY at midpoint. GAAP EPS was guided at $0.10 to $0.11, what would be a third consecutive quarter of GAAP profitability albeit down (42%) QoQ.
For the full-year, analysts currently expect Astera to report 50% YoY growth to $1.26 in adjusted EPS, with FY26 EPS growing 36% to $1.72. Heading into Q1’s report, GAAP EPS was expected to be $0.29 for the full-year, but considering Astera is guiding to deliver that figure in 1H, estimates are likely to move substantially higher.
Cash Flows and Balance Sheet
Cash flow margins expanded slightly YoY, though inventories rose and accounts receivable doubled sequentially.
Operating cash flow was $10.5 million for a 6.6% margin, expanding slightly from a 5.6% margin in the year ago quarter.
Free cash flow was $6.0 million, for a 3.7% margin, improving from a 0.3% margin in the year ago quarter.
Cash and equivalents increased $11.1 million QoQ to $925.4 million, while debt remained zero.
Inventories rose 18.2% QoQ to $51.1 million, likely driven by the ramp of Astera’s Aries 6 and Scorpio P-series products.
Accounts receivable surged 100.5% QoQ to $69.8 million, driven by Astera’s largest customers. Astera’s receivable balance from its top customer in the quarter rose 363% QoQ to $20.9 million, while its balances from its second and third largest customers rose 75% and 90% QoQ to $14.7 million each. Days sales outstanding also increased from 20-ish days in the past to 40 days this quarter. This is likely foreshadowing Astera is preparing for larger shipments in the next 1-2 quarters.
Customer Concentration and China Revenue
Astera is diversifying its customer base beyond its two largest customers, though it remains quite concentrated with its top four customers accounting for 80% of its Q1 revenue and its top two customers accounting for 49% of revenue. Although Astera does not disclose the exact customers, at one point, their largest customerwas AWS.
SEC filings show that China revenue has been growing as a percentage of revenue from under 15% in Q3, then surged to 35% in Q4, and remaining elevated at 28% in Q1. FY24 exposure was 18.3%, up from <5% in FY23
However, on the call, management stated it was less than 10% of revenue. Perhaps the difference being end market customer is less than 10% yet manufacturing in China represents a larger portion. Addressing this difference in the SEC filing on the call would have been ideal.
Thomas O'Malley Barclays Bank
First one is for you, Mike. You mentioned that there was a China impact on your sales. It's never been a significant portion of your model. But could you give us a feeling just how large that impact was and what that impact will be over the next couple of quarters?
Michael Tate CFO
Yes. So we ship into China with our retimers predominantly right now and they were attached to third-party merchant GPU systems, both were restricted hard stop during the quarter. So there was a modest impact that we have to overcome.
China revenues, when you look at end customer demand, is less than 10% of our revenues. So it's been manageable enough and given the strength of our business and other product lines to continue to grow through this challenge.
Earnings Call Q&A:
Higher Dollar Content from Scorpio-X and Aries PCIe6 Retimers
As growth investors, we are always looking for a catalyst that can sustain growth, or ideally, accelerate growth. For semiconductors and hardware components, there is no better catalyst than incoming higher dollar content for hardware companies.
Therefore, Astera Labs used these words many times on their call. Diving into the details of this, it’s primarily two products where they are forecasting higher dollar content and average sales prices (ASPs):
Aries PCIe6 retimers:
The transition from PCIe5 to PCIe6 will result in higher unit growth and higher ASPs including a gearbox that improves signal quality. PCIe 6 doubles the bandwidth from the 5th generation, with up to 256 GB/s of bandwidth per lane, which will require faster supporting components, such as the retimers that Astera Labs offers.
Here is what was stated on the call: “In fact, we have already started shipping preproduction volume for supporting some of the opportunities. And this would, again, not create an additional TAM to our Aries business because it's adding to the retimer TAM, but essentially bringing in a higher level of ASP simply because you're able to not only do retiming, but also do some of the speed matching that I noted.”
Scorpio X-Series:
The Scorpio P-Series is shipping this quarter and are qualified for Nvidia systems, yet the X-Series will ship in H2 with a bigger opportunity for custom silicon clusters. The Scorpio P-Series is a small chip that connects the CPU, GPU, NIC and NVMe storage. Rather than building a large switch, the company built a smaller device that is more efficient for high-speed signals to help feed GPUs with data. The fewer ports and smaller switch decrease complexity in a bid to compete against Broadcom with twice the lane count.
The X-Series is for back-end networking in GPU-to-GPU configurations (and custom silicon configurations), and will offer a higher port count. Astera is essentially building something similar to Nvidia’s NVSwitch with the X-Series, but for PCIe-enabled GPUs and ASICs. Per the last earnings call: “And this one, like Mike noted, it's a greenfield use case, meaning if you keep Nvidia and NV Switch aside, everyone else is starting to build configurations that are obviously going to need some kind of a switching functionality, which is what we are addressing with our X Series device.”
The X-Series improves efficiency for ever-increasing AI cluster sizes. The majority of AI clusters are in the tens of thousands GPUs, but are expected to go to the hundreds of thousands (already has with X and some other Big Tech companies), and will see AI clusters with millions of GPUs over the next couple of years.
In an effort to identify a catalyst that can sustain Astera’s exceptional growth, it would be this product that does so. The X-series is used to interconnect GPUs for higher GPU utilization, resulting in higher ASPs. Per the call: “So to that standpoint, X-Series does bring in a lot more value, and therefore, you can assume that the ASPs tend to be significantly higher. And that's — again, there are different — the X-Series is not one device, to be very clear, there are multiple part numbers. So there would be situations where maybe one part number is not at the same level as P-Series. But in general, you can just look at it from a per lane standpoint or per port standpoint, and look at the value delivered. And on that basis, the X-Series will always be a much more valuable, much more higher ASP product than a P-Series.
Notably, Astera maintains their largest opportunity for the X-Series is on the custom silicon side although they foresee hyperscalers wanting to customize their racks in a way that prevents vendor lock-in from both Nvidia and Broadcom.
“So these are fabric switches that are used to interconnect multiple accelerators together. So to that standpoint, a, it's not only a significant dollar opportunity because the ASP of this product tends to be high. But these are also products that are turning out to be anchor sockets for us. If you think of an AI rack being built, you have the accelerators and then you have the fabric that interconnects the accelerators.
So what we are transitioning and what we're excited about is that the Scorpio X device is now translating to be an anchor socket. Think of it as like a mothership around which we are able to now add a lot more products that go along with it, whether it's the silicon level products or module or other form factors that we're considering.
So overall, I want to say that from an opportunity space standpoint, for Astera, the custom ASIC-based implementation tends to offer a lot more opportunities.”
We’ve covered Astera’s products more in-depth in previous analysis here and here.here and here.
Scale-Up will Drive More Revenue
As we’ve discussed in great detail in previous analysis on Blackwell, scale-up architectures combine many GPUs or custom chips into one system with dozens of AI accelerators and soon hundreds of AI accelerators communicating in one cluster.
In line with Scorpio-X being a strong catalyst for the company, management double-downed on why scale-up is a massive opportunity for their products specifically, stating it will result in “hundreds of dollars per accelerator and serve as an anchor socket for integrating additional Astera Labs solutions.” They also stated “increasing accelerator cluster sizes, faster interconnect requirements and overall system complexity challenges are creating substantial dollar content opportunities.”
Given they provided an idea as to the dollar amount per accelerator, there was a question on the call relating to this. Again, I’m pulling out this exchange because a hypergrowth stock like Astera certainly needs additional forward-looking growth opportunities to justify it being in our portfolio – of which I believe scale-up opportunities satisfies this requirement.
Blayne Curtis Jefferies
I wanted to talk about scale up. You mentioned it several times. I think you even said a couple of hundred dollars per accelerator. Today, I think you're selling some retimers and then some PCIe cabling. Can you walk us through the progression of scale up in your participation and kind of can you maybe set some timing? Because I know UAL is probably later next year. So what's the scale-up opportunity for you in between now and then?
Jitendra Mohan Co-Founder, CEO & Executive Director
Blayne, this is Jitendra. Scale up presents a very good opportunity for us. As you know, so far, our revenues have been driven primarily by scale-out opportunities. But for the first half, as Sanjay laid out, we have a significant contribution from scale-up.
And the reason that's so important for us is scale up is really a very rich opportunity of high-speed interconnects that need to deliver low latency and high throughput. And that's where we play today with our Aries retimer products and starting shipments of Scorpio X family.
And we do expect this opportunity to continue to grow as cluster sizes grow and the data rates increase. So we have significant opportunities that we are working on for PCI Express based scale-up networks based on our current Scorpio X family.
But then it also dovetails very nicely into UAL, and we expect this to be a multibillion-dollar opportunity as we provide a full holistic portfolio of devices to address UAL infrastructure.
And as far as the UAL itself is concerned, the spec is not final. It's been released as the 1.0 spec. And so you can imagine that the products will start to be worked on now and start to see first samples in 2026 with the revenue contribution the following year. So that is a very big opportunity that we are very well positioned to take advantage of.
Commentary on Blackwell Delay
I’m certainly liking Astera’s commentary a lot better this quarter than last quarter in terms of us-Nvidia bulls being closer to the bigger Blackwell moment – although I will say it’s unclear right now if Nvidia will go more directly to Blackwell Ultra and skip the more problematic Blackwell NVL system SKUs (I will cover the scenarios as to how we get to H2 pre-earnings). To provide a preview, I have zero expectations that Nvidia’s Q1 will be a good report, instead, we are looking toward the August call and the October call as the stronger moments for AI this year.
Regardless, Astera is one to watch in terms of getting commentary on when we can expect Nvidia’s next catalyst – and we are getting a yellow light improved from a red light last quarter. I believe next quarter will be the green light: all systems go. But this requires patience as this puts us into late summer/early Fall but should be fully resolved by the Q4 time frame.
As noted in the past, the PCIe6 retimers are especially indicative of when Nvidia’s Blackwell systems are shipping. Per our previous analysis “PCIe 6.0 was expected to ramp with support initially offered in the GB200s. Back in March, Astera demo’ed PCIe 6.0 for a wide range of Blackwell products.
There was also indication back in the August call that Gen 6 was confirmed to be used in Blackwell’s GB200, and there were initial shipments: “We have started shipping initial quantities of preproduction orders of our PCIe Gen 6 solution, Aries 6. We ship and support our hyperscaler customers initial program developments that are based on Nvidia's Blackwell platform, including GB200.”
The Scorpio P-Series is also integrated into Nvidia’s Blackwell MGX systems per a recent announcement. Perhaps the strongest comment on the call regarding Nvidia timing was this: “Looking ahead to Q2, we anticipate accelerated shipments of Scorpio P-Series switches and Aries 6 retimers on customized rack scale AI platform based on market-leading GPUs. Additionally, we continue to identify further opportunities for Scorpio P-Series outside of rack scale systems with multiple engagements on modular topologies that support enhanced customization.”
In the opening remarks, it was also stated: “I'm excited to share that we will begin shipping preproduction volumes for Scorpio X-Series starting late this quarter” and later it was expanded on:
Sanjay Gajendra Co-Founder, President, COO & Director
“And then on the — Yes. On the customer on the business side, just to touch on that question that you asked. The great thing about our overall revenue profile is that there are multiple ways in which we are approaching the market, the diversity across both custom ASIC-based platforms versus merchant GPU-based platforms, scale up versus scale out and the multiple product lines that we have enables us to approach the market in many different ways.
And to that standpoint, for us, for first half, what we are expecting is that our revenue would be driven largely by the PCIe scale-up and the Ethernet scale-out opportunities along with the initial shipment of Scorpio P-Series and Aries 6 going into the customized rack.
And second half, of course, lays nicely on top with some of the production ramps that we're expecting with the customized racks, which again, for us, is the Scorpio switches, along with the PCIe 6 retimers.
These are now qualified. So we are starting to see that shipments start becoming significant. So that's part of the second half, and second half, of course, we have CXL initial shipments that we're expecting for production volumes and the Scorpio X switches for the scale up going into the custom ASICs.
Those are also expected to start hitting production — initial production volumes in the second half of this year, which essentially gives us multiple ways, if you will, and sets us up nicely for future revenue growth even beyond '25.”
Conclusion:
The market reaction on Astera may be muted, but I’m liking this report much better than last quarter. The commentary around H2 is becoming clearer and in terms of a holding period, 6-9 months is a brief period of time to wait if the stars are aligning across the suppliers.
I do not have high expectations (at all) for Nvidia’s Q1 but Astera is one piece of the puzzle pointing toward our AI portfolio leading again come August, and then October, and perhaps it’s a big enough splash that the streak continues into January and beyond as well. We will take this one quarter at a time, but I’m hearing what I want to hear, and that’s a sigh of relief. Keep in mind, the I/O Fund strives to be early so do not expect the market to agree with me immediately.
Of course, the path to Q2 earnings calls in July/August and then Q3 calls in Oct/Nov will be incredibly tricky as semiconductors are in the hot seat for global tensions. I’d expect near-term volatility in AI hardware stocks that eventually resolves in our favor. While many are likely nervous about how semiconductors fare, I’m excited as we are quite clear on what to buy and I will be happy to get these stocks on discount if the market is foolish enough to give it to us.
p.s. excuse the typos as our team is in a fast sprint covering many earnings reports this week
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.
Late last year, Oracle outlined an ambitious plan to nearly double its revenue by fiscal 2029, forecasting sales of $104 billion that year. Oracle is aiming to capture growing enterprise AI spending in the cloud, separating itself from its hyperscale competitors with its ability to offer lower-cost AI compute via lower latency, multi-cloud AI flexibility, and AI vector search capabilities.
Additionally, Oracle is a key player in Stargate alongside OpenAI, SoftBank, and other partners, with its selection proving that, despite heightened competition from much larger cloud providers, Oracle Cloud has the capability to meet the large data, scale and performance demands for future AI workloads. Assuming high-single digit to low-double digit share of database and enterprise software spending, Stargate could represent tens of billions in revenue to Oracle with the project seeing a maximum investment of up to $500 billion over the next four years. The first phase is already underway with Oracle and OpenAI working to deploy 64,000 Nvidia GB200 GPUs at a large-scale facility in Texas.
However, Oracle is already on track to miss Cloud revenue targets for this fiscal year ending in May. Management also provided an optimistic forecast that would require Cloud revenue to nearly triple by FY29, and grow at almost a 30% CAGR, setting a high bar that includes execution risk with strong competition in AWS, Azure, and Google Cloud.
Unique AI Offerings Support Oracle’s AI Growth
Oracle’s ability to drive lower latency and high performance is one of the main reasons enterprises use Oracle for AI, as it allows enterprise customers to run demanding AI workloads faster and at a lower cost.
RDMA (Remote Direct Memory Access) is helping to drive Oracle’s AI story by enabling direct memory access between servers without utilizing CPUs, resulting in low-latency, high-bandwidth performance. Bypassing the CPU greatly accelerates data transfer rates, a necessity for large AI workloads requiring massive compute.
RDMA is integral to Oracle Cloud Infrastructure as the backbone of Oracle’s Gen2 Cloud and increasingly large Superclusters for AI training and inference, allowing ultrafast, near real-time performance. Oracle says that it can offer less than 10 microseconds of latency between nodes, improving efficiency.
With less overhead and fewer CPU cycles, RDMA helps Oracle offer its AI clusters at a lower cost: Oracle says it “consistently charges less than Amazon Web Services (AWS) for the equivalent compute capacity.”
Oracle offers the widest range of bare metal GPU instances among major cloud providers, and scalability at any size up to 65,536 Hopper GPU clusters and 131,072 B200 GPU clusters, which are expected to come online in 2025. Oracle also offers very flexible VM instances, letting customers pay for only the capacity they need as they need it for any size workload, rather than offering fixed instance sizes.
Oracle provides a wide range of AI clusters for small, medium and large-scale AI training. Source: OracleOracle
Oracle’s AI vector capabilities also stand out given Oracle’s database roots, offering native AI vector search capabilities with seamless integration to leading AI models from OpenAI, xAI, Meta, Cohere and more. AI vector search lets enterprises search both structured and unstructured data in a variety of manners, enabling intelligent, relevant and accurate AI responses utilizing their data. Oracle noted in Q3 that its Oracle Database 23ai can convert data into any vector format to be understood by an AI model of choice, facilitating AI training and inference on private data in Oracle Database.
Oracle Bets Big on Future Revenue Growth; Yet Lacks History of Meeting Targets
Oracle’s executives exuded confidence in meeting ambitious long-term revenue targets, underscored by AI and multi-cloud momentum — yet analysts aren’t so sure, with consensus estimates that are considerably lower than guidance offered during the recent earnings call.
In fiscal Q3 2025 (Oracle’s most recent quarter), management reaffirmed that confidence in reaching the $66 billion target for FY 2026 was now “stronger than ever,” while they also guided for 20% YoY growth in revenue in FY 2027, faster than previously expected. This would imply revenue of approximately $79.2 billion.
This builds upon management’s comments from Q2 that they believe they “now have a clear light of sight to our future revenue growth,” with total cloud infrastructure (IaaS) revenue in FY25 expected to grow faster than the 50% reported in FY24 and accelerate further in FY26.
Putting this in perspective, Oracle is betting on a major revenue growth acceleration driven primarily by growth in AI and cloud.
Reaching management’s optimistic $104 billion target in FY29 would require revenue growth at a 14.45% CAGR from FY24’s $52.96 billion. This is more than double the 6.03% CAGR that Oracle reported from FY19’s $39.51 billion in revenue through FY24, relying on strong, consistent AI and cloud growth to achieve these targets.
Here’s an annual breakdown of the preliminary growth guided through FY27 and what level of growth would be needed to achieve FY29’s forecast:
Oracle is targeting double-digit revenue growth from FY26 to FY29 to reach its $104 billion revenue target. Source: I/O Fund
Revenue growth would need to hit both the 15% growth and 20% growth targets in the next two fiscal years, and remain in the mid-to-high teens for FY28 and FY29 – such as 16.5% growth in FY28 and 13.3% growth in FY29.
Compare this to the prior comparable period from FY19 to FY24: Oracle reported only one year with revenue growth in the double-digit range, and just 6% in FY24 with less than 8% growth expected in FY25.
It would represent a rather unprecedented acceleration for Oracle and a monumental feat to drive a 12 point revenue acceleration in two years and maintain double-digit growth thereafter at a larger scale.
Analysts Remian Dubious of Oracle’s Growth Potential
Despite management’s confidence in achieving its growth targets in FY26 and FY27, analysts remain dubious over Oracle’s ability to hit these numbers successfully, considering the company has missed revenue estimates in both the last two quarters and in six out of the last seven.
Analysts are currently projecting Oracle to fall short of targets in FY26, FY27 and FY29, implying that they do not share the same level of confidence in future growth as management.
Analysts are projecting Oracle’s revenue to fall short of management’s forecasts for FY26, FY27 and the long-term FY29 target. Source: I/O Fund
For FY26, analysts estimate Oracle will report 14.2% growth to $65.2 billion in revenue, falling short of both the stated 15% growth and $66 billion revenue target.
For FY27, analysts estimate Oracle will report 18.2% growth to $77.0 billion in revenue, which would be nearly 2 points and $2 billion shy of what management is forecasting.
By FY29, analysts estimate Oracle will report 13.3% growth to $100.5 billion in revenue, or about 3.4% shy of management’s long-term forecast.
There has been little change in FY26’s revenue estimate over the past six months, being revised just 0.4% higher. However, FY27’s revenue estimate has been revised nearly 4.3% higher to that $77.0 billion, yet it still remains more than $2 billion below where Oracle’s 20% guidance implies.
Analyst estimates for Oracle’s FY26 and FY27 revenue have been quite consistent over the past six months. Source: YChartsYCharts
This consistent shortfall for revenue growth and doubts over Oracle’s ability to reach its stated targets likely stems from Oracle’s inability to reach its near-term targets, including its $25 billion cloud revenue target for FY25.
The I/O Fund specializes in covering lesser-known AI stocks on our research site with trade alerts and weekly webinars. Learn more here.The I/O Fund specializes in covering lesser-known AI stocks on our research site with trade alerts and weekly webinars. Learn more here.
Oracle Falls Short of $25B Cloud Revenue Target for FY25
In fiscal Q2 2025, Oracle CEO Safra Catz said that Oracle was expecting its Cloud revenue to reach $25 billion for the full fiscal year. As of Q3, Oracle is firmly on track to miss that target.
Oracle reported $6.2 billion in Cloud revenue in Q3, up 25% YoY, bringing its YTD Cloud revenue up to approximately $17.7 billion. For Q4, Oracle guided for 26% to 28% YoY growth for Cloud revenue, implying revenue between $6.68 billion to $6.78 billion, or $6.73 billion at midpoint.
This would place FY25 Cloud revenue at $24.38 billion to $24.48 billion, or $520 million to $620 million short of its goal. Not only is Oracle at risk of missing this target, but it also is running close to falling short of its 50% growth goal for Cloud Infrastructure revenue.
However, Oracle is planning on having Nvidia GB200-based servers generally available in April 2025, and if there is high demand for the new upgraded system, it could serve as a tailwind for IaaS growth in fiscal Q4 as these were not available in Q3.
Oracle Aims for Cloud Infrastructure Growth Above 50%
Oracle’s Cloud Infrastructure (IaaS) business has been a strong driver for Cloud growth, outpacing Cloud revenue growth by at least 22 points for nine consecutive quarters, rapidly expanding its share of Cloud revenue to 44% from just 29% two years ago.
Cloud IaaS revenue also surpassed a $10 billion run rate in Q3, while growing at >50% the last two quarters. Cloud IaaS is an important element in Oracle’s $25 billion target, in that at least 40% of said revenue will be coming from Cloud IaaS.
Oracle’s Cloud IaaS revenue has grown consistently over the past 2.5 years to surpass a $10 billion run rate in Q3. Source: I/O Fund
IaaS’ performance has been driven by the strength of Oracle’s AI offerings. Growth reaccelerated 6 points sequentially in Q2 to 52% YoY, driven by AI, though it dipped slightly to 51% YoY in Q3.
In Q3, Oracle noted that demand was at “record levels”, that GPU consumption for AI training had increased 244% in the last 12 months, and that the company was seeing “enormous demand” for AI inference. Chairman Larry Ellison added that “AI training and multi-cloud database are experiencing hyper growth.” This follows Q2’s 336% increase in GPU consumption for AI training and “record level AI demand.”
Despite these triple-digit points and strong growth commentary, Cloud IaaS revenue’s growth has been quite small sequentially. Q3 saw the highest sequential growth at ~$300 million QoQ, or ~12.5%.
To meet management’s “faster than 50%” growth target for FY25, Q4 would need to match that QoQ revenue increase of at least $300 million; this would place IaaS revenue at $10.3 billion, or up 51.5% YoY from $6.8 billion in FY24. Should the segment return to recent historical trends of rising $200 million sequentially, Oracle would be running extremely close to reporting growth below 50% for the full year.
While demand remains strong and “continues to outstrip supply”, as management puts it, component delays are likely a factor in seeing limited QoQ growth in FY25 despite these extraordinary AI growth figures, as these delays are not expected to ease until Q1 FY26 (likely due to Nvidia’s Blackwell system timing).
Oracle has stated that component delays have “slowed cloud capacity expansion this year,” and expects these to resolve in FY26, easing a primary bottleneck to IaaS growth. The I/O Fund discussed how Nvidia suppliers were foreshadowing delays in the most recent earnings reports. With IaaS expected to accelerate further from >50% in FY25, the segment could grow to close to $16 billion, or at least $5.5 billion higher than FY25’s projected ~$10.3 billion.
Should IaaS account for ~50% of Cloud revenue for FY26, that would imply Cloud revenue of $32 billion, or an 8 point acceleration to 31% YoY growth from FY25’s Q4-guide-implied $24.4 billion. This is likely what Oracle would need to achieve its $66 billion target, assuming this growth means Cloud grows its revenue share by mid-single digits YoY.
63% Growth in Remaining Performance Obligations
While challenges remain for FY25 with Oracle at risk of missing its $25 billion Cloud revenue target, 2026 is more optimistic, with IaaS promising to add multiple billions in revenue and RPO suggesting the segment could see a strong acceleration.
RPO rose 63% YoY to $130 billion in Q3, compared to 50% YoY growth to $97.3 billion in Q3, a nearly $33 billion QoQ increase. This was driven by record deal activity with $48 billion in signed contracts. Chairman Larry Ellison chalked this up to Oracle’s ability to provide a more cost-effective AI solution to customers: “it really is a technology advantage we have over them. If you run faster and you pay by the hour, you cost less. So that technology advantage translates to an economic advantage, which allows us to win a lot of these huge deals.”
Additionally, Cloud RPO accelerated 10 points to 90% YoY, representing 80% of total RPO, up from 75% last quarter. This places Cloud RPO at around $104 billion, up from nearly $73 billion last quarter. Management said that while the growth in RPO is evidence that the AI training business continues to grow, AI inferencing and database demand also factored into the RPO growth.
RPO continuing to outpace Cloud and IaaS growth bodes well for Oracle’s forecasted revenue acceleration in FY26, and signals strong underlying demand trends with Cloud RPO nearly doubling. 31% of total RPO is expected to be recognized over the next twelve months, or ~$40.3 billion, up 7% from $37.6 billion at the start of FY25.
Oracle noted that this RPO figure did not include any contributions from Stargate yet, which promises a large potential opportunity given the initial commitment of $100 billion up to $500 billion total for the project. Management expects the “first large Stargate contract fairly soon,” though it’s hard to put an exact figure on what management defines as large.
Oracle Continues to Quickly Add Capacity
Oracle is quickly adding data center capacity due to this growth in RPO, with management commenting that they expect to double data center capacity this year to meet the high demand they see from this RPO and additional opportunities from Stargate. Capex is also on track to exit the year at a meaningfully higher rate than expected as Oracle accelerates build-outs.
Oracle noted in Q3 that they “expect fiscal year 2025 CapEx will be a little more than double what it was last year at around $16 billion.” A year ago in Q3 2024, management laid out preliminary capex of $10 billion, which at the time was expected to go towards 100 new data centers and expansions at 66 existing facilities.
This is quickly flowing to the data center, with CEO Safra Catz explaining that Oracle expects its “available power capacity will double this calendar year and triple by the end of next fiscal year” (by mid-2027). Catz added that Oracle’s “live data center count and power capacity is the leading indicator of the conversion of RPO to revenue,” suggesting that Oracle is working to significantly increase capex to quickly translate this surge in RPO to reported revenue, to meet growth acceleration targets.
To note, this rapid acceleration in capex is negatively impacting cash flows. Oracle generated $11.8 billion in free cash flow in fiscal 2024, but reported negative free cash flow of ($2.66 billion) in Q4 and just $70 million in Q3. On a TTM basis, free cash flow was just $5.82 billion as of Q3, down (53%) YoY, as accelerated capex is far outweighing limited growth in operating cash flow.
Oracle Cloud at Smaller Scale than Peers
Oracle seemed to have taken a slight dig at Microsoft, Amazon and Google in Q3, its hyperscale partners and competitors, stating that Oracle Cloud IaaS revenue grew at “a much higher growth rate than any of our hyperscaler competitors.”
However, this growth is coming off a much smaller scale, considering that Oracle is using IaaS revenue as the comparison – to recap, that’s at a $10.6 billion annualized rate, or ~$2.65 billion in Q3.
Google Cloud, the smallest of the hyperscalers’ clouds, reported 30% YoY growth in revenue to $12.0 billion in Q4. For the full year, Google Cloud revenue grew nearly 31% YoY to $43.2 billion.
Microsoft’s Intelligent Cloud revenue increased 19% to $25.5 billion in its fiscal Q2, with Azure revenue growing 31% YoY. Microsoft’s AI run rate reached $13 billion, up 175% YoY, or 30% higher than Oracle’s entire IaaS segment and half of its entire Cloud segment.
AWS reported 19% growth to $28.8 billion in revenue in Q4, and 19% growth to $107.6 billion for 2024. Put this way, AWS is generating more revenue in one quarter than Oracle’s Cloud is in one year. AWS also generated more than 1.6x Oracle’s Cloud revenue in operating income.
Oracle is by no means operating at the same level as its customers, with Microsoft’s AI run rate dwarfing Oracle’s IaaS business while growing well into the triple-digits. Cloud to Cloud, Oracle is lagging Google’s growth, the smallest of the three hyperscalers, by 5 points in the most recent quarter while at half the scale.
Outside of Cloud, Oracle’s other businesses – licensing, hardware, services – have seen minimal growth over the past few years, generating revenue of $31.5 billion in FY22 and $33.2 billion in FY24. Assuming growth at a 3% CAGR through FY29, ex-Cloud revenue would project to approximately $37 billion, meaning Oracle’s Cloud segment would need to reach $67 billion by FY29 to reach that $104 billion forecast.
This would equate to a nearly 29% CAGR from ~$24.4 billion in FY25 to reach $67 billion, or on an annual basis, 31% growth in FY26 and FY27, followed by 26% growth in both FY28 and FY29.
Here’s what this would look like:
Oracle’s Cloud segment would need to grow at a 29% CAGR from FY25 to hit $67 billion, a likely threshold for it to reach the $104 billion target. Source: I/O Fund
Conclusion
Projecting 30% growth for multiple years does not account for execution risk as AI is still quite early. While there are large competitors such as AWS, Azure and Google Cloud, there are also neocloud competitors, such as CoreWeave, that are shaking up the cloud IaaS market with GPU-as-a-service. These neoclouds are entirely optimized for AI, able to capture a higher rate FLOPs utilization (MFUs) — a metric that is quite important when considering time to market for AI models. My firm recently covered CoreWeave’s IPO for our premium members here.
Stargate will certainly provide a tailwind to Cloud revenue through FY29 should the maximum of $500 billion in investments materialize. Prior estimates from IDC place database and enterprise software spending at a high-single digit share of overall infrastructure spending. This could ultimately represent tens of billions in long-term revenue to Oracle that will complement existing growth in Cloud.
However, analysts do not share the same confidence in Oracle’s ability to meet its long-term targets, with consistent revenue misses in six of the past seven quarters; additionally, it is already falling behind its $25 billion Cloud revenue target for FY25 after just one quarter.
The I/O Fund is ultimately passing on Oracle and we are instead accumulating small and mid-cap positions that are better poised to benefit from the ongoing AI spending war. Premium and Advanced members receive real-time trade alerts and technical setups in our weekly webinars. Learn more here.
Disclaimer: 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.
NVIDIA’s groundbreaking hardware technologies and AI are unlocking unprecedented computational power. At the NVIDIA GTC 2025, NVIDIA unveiled its Blackwell Ultra GPU designed for the “Age of Reasoning” at its 2025 GPU Technology Conference (GTC). AI accelerators like GPUs are well suited for AI training and inference due to parallel processing, which allows for many calculations to be performed simultaneously. Only 30% of the top 500 supercomputers relied on accelerated computing; today, 80% do. The Green 500 ranking of supercomputers by energy efficiency shows an even more pronounced trend.
NVIDIA Blackwell Ultra GPU and GB300 NVL72 server key specifications included.
AI reasoning models emulate how the brain thinks to render a conclusion, popularized by OpenAI’s o1, Google’s Gemini 2.0 Flash Thinking and DeepSeek’s R1 A1 models. Reasoning models improve responses to queries and more powerful GPUs improve the performance of these models. Blackwell Ultra GPUs are the next generation of the evolution of the GB200 bolstered by more inference power horsepower, packing 50% FLOPS at 1.1 exaFLOPS of FP dense compute.
At the NVIDIA GTC 2025,NVIDIA GTC 2025, in his March 18 presentation titled, “The Next Frontier of AI Supercomputing: Efficiency With Unprecedented Capability”, NVIDIA’s Vice President of Hyperscale and HPC Computing, Ian Buck, stated, “Blackwell Ultra takes GB200’s 40x data center revenue opportunity to 50x”, citing faster token serving and higher throughput ideal for post-training for models like DeepSeek, which chomp through 100 trillion tokens.
NVIDIA GB300 NVL72 Unleashes Inference Horsepower
NVIDIA’s GB300 superchip combines two Blackwell Ultra GPUs with one Grace CPU. Blackwell Ultra GPUs can be used in the NVL72 rack server, which integrates 72 Blackwell Ultra GPUs and 36 Grace CPUs. The NVIDIA GB300 NVL72 has a fully liquid-cooled rack-scale design. AI factories achieve 50X higher output for reasoning model inference with the NVIDIA GB300 NVL72 compared to the NVIDIA Hopper platform when used with the NVIDIA Quantum-X800 InfiniBand or Spectrum-X Ethernet paired with ConnectX-8 SuperNICS.
Blackwell Ultra’s Silicon Photonics Slashes Power Consumption by Up to 77%
NVIDIA’s Blackwell Ultra GPUs use co-packaged optics with silicon photonics, which integrates optical and silicon components onto a single substrate. This reduces power consumption by eliminating the need for external lasers and pluggable transceivers to achieve a significant reduction in power from 39 watts to 9 watts. Buck said that silicon photonics "… gives you that benefit from going from 30 watts of power down to only 9 watts of power for the same number of ports, and that's huge. It doesn't sound like 39 sounds a lot. But if you get 400,000 GPUs in an AI supercomputer, there's like 24 megawatts of lasers like so that's a lot of laser light that could be optimized and made more efficient.”
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Beth Kindig, Lead Analyst at the IO Fund, pointed out in her “AI Power Consumption: Rapidly Becoming Mission-Critical”AI Power Consumption: Rapidly Becoming Mission-Critical” blog article that, "In my analysis last month on the Blackwell architecture, I made the argument these estimates are too low and that my firm expects we will see a $200 billion data center segment by end of CY2025 propelled forward by the B100, B200 and GB200, including the following points: “Taiwan Semi’s CoWos capacity, which is essential for Blackwell’s architecture, is estimated to rise to 40,000 units/month by the end of 2024, which is more than a 150% YoY increase from ~15,000 units/month at the end of 2023. Applied Materials has boosted its forecast for HBM packaging revenue from a prior view for 4X growth to 6X growth this year.””
The Next Generation CPU: Vera CPU: Grace’s Successor
NVIDIA’s next-generation CPU is Vera, a follow-on to Grace. With 88 cores (176 threads via spatial multithreading), Vera doubles Grace’s performance 2X, memory bandwidth by 5X per watt, and has a beefier chip-to-chip link for the upcoming Rubin GPU. “Every core talks to every other core,” Buck stressed, contrasting x86’s front-end focus. Vera’s 12-thread memory saturation trounces traditional CPUs, feeding GPUs for AI and HPC back-end tasks. Vera Rubin will launch in 2026. Vera Rubin NVL 144 will launch in the second half of 2026. FYI, Vera Rubin was an American astronomer who discovered dark matter. Rubin will mark the shift from HBM3/HBM3e to HBM4 and HBM4e for Rubin Ultra.
The Next Generation GPU Architecture: Rubin Ultra
NVIDIA will be launching Vera Rubin NVL 576 in the second half of 2027, which will have 14X the performance of GB300 NVL72. Rubin will have 1.2 ExaFLOPS of FP8 training compared to just 0.36 ExaFLOPS for B300, resulting in 3.3X compute performance. Bandwidth will improve from 8 TB/s to 13 TB/s. It will have 576 Rubin GPUs in a rack. Compute density is boosted by featuring four dies per package. Rubin Ultra NVL576 will have 365 TB of memory. The inference compute with FP4 rises to 15 ExaFLOPS with 5 ExaFLOPS of FP8 training compute. NVIDIA hinted the next-generation architecture after astronomer Vera Rubin will be named after theoretical physicist Richard Feynman.
The I/O Fund recently entered five new small and mid-cap positions that we believe will be beneficiaries of this AI spending war. We discuss entries, exits, and what to expect from the broad market every Thursday at 4:30 p.m. in our 1-hour webinar. For a limited time, get $110 off an Annual Pro plan with code PRO110OFF [Learn more here.]get $110 off an Annual Pro plan with code PRO110OFF [Learn more here.]
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.
Supercomputers and cutting-edge AI data centers are fueling the artificial intelligence (AI) revolution. Large-scale systems need comprehensive builds that are increasingly integrated to meet the evolving demands of complex workloads. As AI applications become more sophisticated, the need for infrastructure that's not only incredibly powerful but also energy-efficient is growing exponentially. Innovations like NVIDIA’s GB200 are designed to deliver the scalability needed for next-generation AI superclusters.
At the 2025 NVIDIA GPU Technology Conference2025 NVIDIA GPU Technology Conference (GTC), VP and Chief Architect of Systems, Mike Houston, and Senior Director of Applied Systems Engineering, Julie Bernauer, discussed large-scale systems design principles in their May 18 presentation, “Next-generation at Scale Compute in the Data Center.”
NVIDIA’s First Rack-Scale Product is the GB200 Superchip
The NVIDIA Grace Blackwell 200 (GB200) Superchip combines two Blackwell GPUs and one Grace CPU. It’s NVIDIA’s first rack-scale product. The NVIDIA GB200 NVL72 is a configuration and rack-scale, liquid-cooled AI computing platform, which is purpose-built for AI training and inferencing, handling up to 27 trillion parameters for generative AI models. The GB200 includes base components like Grace Hopper compute trays, NVLink switches (a connector in the middle of the rack linking all GPUs) and cable cartridges (literally miles of cables in the back to tie everything together). The design includes quantum switches for InfiniBand (a high-speed network for linking clusters) and spectrum switches for Ethernet.
AI 101: What are Clusters and Superclusters?
Clusters 101: are a network of independent computers (called nodes) connected by a high-speed network. A cluster serves as a unified resource, as they are separate machines configured to work together to act as a single powerful computing system. They are often used for parallel processing, which breaks down a large task into smaller parts distributed across the nodes, enabling faster processing than just a single computer could do. A key benefit of a node is high availability, meaning if one node (computer) fails, the other nodes can take over its workload, ensuring that the system remains operational. High-performance compute (HPC) clusters are used for tasks like research, scientific simulations and AI training.
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Superclusters 101: are very large clusters that may be comprised of hundreds to thousands of GPUs through many data centers. For example, Elon Musk’s xAI supercomputer Colossus, powered by 100,000 NVIDIA GPUs, is definitely a supercluster.
DGX started as single machines for AI but evolved into clusters for AI training. Pre-training can involve superclusters, but post-training can still involve 16,000 GPUs with smaller setups for fine-tuning and inference using trained AI to answer questions.
Optimizing the Benefits of Rack-Scale Architecture with GB200
NVIDIA’s GB200 NVL72 is a rack-scale system. Rack-scale designs a whole rack as one big, coordinated unit, not just random machines stuck together. Rack scale refers to integrating and compressing systems that may span across multiple servers, storage and networking devices onto a single server rack. GB200 can replace or consolidate a large number of GPU compute servers. This provides many benefits, including:
Improved GPU Density: The GB200 NVL72 contains 72 Blackwell GPUs, and 36 Grace CPUs interconnected with NVLink, NVIDIA’s proprietary high-speed (130 TB/s) signaling interconnect that enables all 72 GPUs and 36 CPUs to act as a single massive GPU. It's designed to offer exceptional performance in AI training and inference for large language models (LLMs).
Performance: The GB200 delivers up to 720 petaFLOPs for AI training and 1.4 exaFLOPs for inference. Since all components are within proximity in a single rack, communication between components has much lower latency, which is especially beneficial in data-intensive tasks, reducing bottlenecks and improving data throughput.
Increased Efficiency: Rack-scale architecture allows for better utilization of hardware by pooling resources to optimize performance. Consolidating resources within a single rack reduces the need for separate units, saving space and power in the data center.
Easier Management: Centralized management of the entire rack's resources simplifies setup and maintenance, also enabling automation tools for scaling, provisioning and monitoring to reduce manual interventions.
Cost Efficient: Fewer servers, storage, networking equipment, physical space, cooling, and energy usage save money. As IO Fund discussed in its article “AI Power Consumption: Rapidly Becoming Mission-CriticalAI Power Consumption: Rapidly Becoming Mission-Critical," the GB200 is “expected to consume 2,700W”, which can add dramatically to operating expenses, especially without rack-scale architecture.
Future Proofing: Rack-scale architecture enables the integration of evolving technologies as components can be switched out, repaired and upgraded, enabling more adaptability for future growth.
Unified Power and Cooling: Housing multiple components within a single rack reduces the complexity of cooling systems and improves energy efficiency to lower operational costs.
Scaling Up AI Factories with DGX SuperPOD, Reference Architecture and Fabric
At the 2025 NVIDIA GPU Technology Conference (GTC), NVIDIA unveiled its next-generation DGX SuperPOD AI infrastructure. In the “Next-generation at Scale Compute in the Data Center” presentation, VP and Chief Architect of Systems, Mike Houston, and Senior Director of Applied Systems Engineering, Julie Bernauer, spoke about
The SuperPOD is NVIDIA’s all-in-one HPC solution designed to handle the massive computational needs of AI models and simulations. Grace Blackwell nodes are the building blocks of the SuperPOD. When scaling up clusters and superclusters, there are three factors to consider. Reference architecture is comprised of pre-tested system designs that serve as a blueprint for new data center deployments to ensure optimal installation and performance, accelerating time to the first token.
Fabric refers to the data center’s network infrastructure that connects all the servers and devices enabling them to seamlessly communicate with each other to reduce latency between components, especially GPUs. Cooling is critical in large data centers. Liquid cooling is preferred to manage the heat produced by thousands of GPUs as it is much more efficient for high-density platforms. Future GPU architectures aim for higher density and more efficient connectivity to push the limits of AI computation.
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Core Scientific is a major Bitcoin miner leading the transition to high-performance compute (HPC) data centers with 1.3 GW of contracted powered infrastructure.
The Company signed 12-year hosting deals with AI Hyperscaler CoreWeave to provide 590 MW of HPC infrastructure valued at up to $10.2 billion.
CoreWeave will front the $750+ million in capex funds to modify Core Scientific’s data centers as an anchor client.
Core Scientific’s HPC hosting revenues could surge 21X per quarter when the full 590 MW of critical load go online in 2027.
ROTH MKM expects Core Scientific to reach a $1.58 billion run rate by 2027.
The I/O Fund will be holding a very tight stop on any position we initiate, and the stock would be for Advanced Market Signals only, indicating it qualifies for more advanced investors who are comfortable trading daily/weekly.
Core Scientific (NASDAQ: CORZ) is a digital infrastructure company that operates bitcoin mining and hosting services, and high-performance compute (HPC) hosting services through its nine purpose-built data centers. As one of the largest Bitcoin miners in North America, operating 171,000 mining rigs (164,000 owned), the Company is positioning itself for significant growth in the AI space.
Its strategic shift to high-performance computing (HPC) hosting is particularly compelling, allowing it to mitigate Bitcoin’s volatility while capitalizing on the surging demand for AI data centers. By securing high-margin HPC hosting contracts, the company is poised to tap into one of the most lucrative and rapidly growing markets in technology. While other Bitcoin miners are starting to catch on, attempting to transition to AI data centers, Core Scientific has a clear first mover advantage reinforced by lucrative multi-billion dollar 12-year contracts with upside revenue potential of $10.2 billion with AI hyperscaler CoreWeave with a trajectory aimed at generating 21X HPC hosting revenue growth in 2027.
Core Scientific’s Value Proposition for HPC Hosting Customers
Core Scientific provides many attractive value propositions to hyperscalers:
Specialized Power Infrastructure: HPC customers require more power than conventional data centers can offer. AI and HPC workloads require 6 to 10 times more electricity to operate. Traditional data centers are accustomed to single racks consuming 10 to 15 kW of power. Current AI racks push 80 KW as they will soon draw 120 kW to 150 kW in the next generations, with 200 kW within several years. Core Scientific has the existing infrastructure with nearly 900 MW of capacity for HPC hosting in addition to the 400 MW for Bitcoin mining capacity. , with 200 kW within several years. Core Scientific has the existing infrastructure with nearly 900 MW of capacity for HPC hosting in addition to the 400 MW for Bitcoin mining capacity.
Scalable HPC Infrastructure: They are actively transitioning their facilities to cater specifically to AI workloads with infrastructure optimized for machine learning and deep learning applications. Its Denton, Texas, facility is undergoing a $6.1 billion expansion, boosting its MW capacity by 97 MW to 394 MW with 47 more acres to 78 acres to host one of North America's largest GPU supercomputers for AI computing. It's being converted entirely for HPC hosting.
Location: Core Scientific operates 9 Application Specific Data Centers strategically located near major internet hubs in Georgia, Kentucky, North Dakota, North Carolina, Oklahoma, Alabama and three in Texas. Its new leased (with an option to buy) site in Alabama offers 11 MW of critical IT load and is scalable up to 66 MW of critical IT load, which Core Scientific is in discussions with potential new clients to contract for HPC hosting. They are developing a state-of-the-art 100 MW data center in Muskogee, Oklahoma.
Maintenance and Repair: Offering 24/7 around-the-clock monitoring, support and maintenance, Core Scientific is one of the largest application-specific integrated chips (ASIC) repair centers in North America, servicing their own and customer’s fleet of 171,000 bitcoin mining rigs. Parlaying from ASICs, they plan on using their expertise and manpower to replicate it and expand their offering on the GPU side.
CoreWeave: An Early Believer in the Core Logic’s HPC Transition
CoreWeave is an NVIDIA-backed AI Hyperscaler, providing AI cloud services by offering GPU clusters for HPC and AI workloads. CoreWeave is a specialized cloud provider offering an optimized platform for GPU-intensive tasks. They build and operate their own data centers equipped with a massive scale of over 300,000 Nvidia GPUs. They currently have 28 operational data centers and plan to open 10 new data centers in 2025, leasing a significant portion of their capacity with Core Scientific.
On March 6, 2024, Core Scientific announced an initial long-term deal with anchor customer CoreWeave to provide up to 16 MW of data center infrastructure for their HPC and AI workloads at their tier 3 data center in Austin, Texas. This helps to substantiate the pivot from Bitcoin mining to offering AI/HPC infrastructure, stating a “strategy shift” to AI may be good for a temporary stock price spike, but actually signing up customers is another story.
CoreWeave was already a GPU hosting client from 2019 to 2022, hosting thousands of GPUs. Despite the potential value of the March 6 deal being worth up to $100 million, it didn’t move the needle much for the stock price, which still traded under $4.00, selling off to $2.95 the following week.
CoreWeave Ups the Ante and Fronts the Capex Funds in $3.5 Billion Deal
Core Scientific was successful in delivering 16 MW of capacity more than 30 days ahead of schedule at its Austin, Texas, data center. This prompted more deals. On June 3, 2024, CoreWeave signed several 12-year contract deals securing 200 MW of infrastructure to host CoreWeave’s NVDA GPUs. Additionally, CoreWeave will fully fund (not pay for) the capital investments (capex), estimated around $300 million, required to modify Core Scientific’s “existing infrastructure into cutting-edge application-specific data centers customized for dense HPC." CoreWeave will put up the capital for the modifications and Core Scientific will credit them 50% of their hosting fees until it’s paid back fully.
Regarding CoreWeave paying for the capex, here is what was stated on the call:
“Yes. Thanks, Brett. I mean really, the difference in the CoreWeave deal is 100% funding of the CapEx. They were able to significantly buy down their rates. And I think as we look forward, what we're seeing for 2025 is frankly rather unique. If you're able to deliver capacity in 2025 and 2026 right now — we're definitely seeing those lease rates be much higher than we expected, especially given that many of these folks are willing to cover some portion of the CapEx of the build-out. So we're excited about where lease rates are going, and we believe we'll be able to extract a significant amount of value from the demand that we're currently seeing over the next few years.”
CoreWeave Contracts a Total of 502 MW Generating $8.7 Billion Over 12 Years
Once the 200 MW of HPC is operational Core Scientific estimates they’ll receive around $290 million annually or more than $3.5 billion during the initial 12-years terms of the contracts. CoreWeave exercised its options and signed for an additional 70 MW on June 25, 2024, and $105 million of capex funding, equating to an additional $1.23 billion for Core Scientific during its 12-year term. In August 2024, CoreWeave signed another 112 MW contract beginning in 2026.
In October 2024, CoreWeave exercised the rest of its options and signed another 120 MW hosting contract for 12 years for a total of a full 502 MW of critical IT load with a total revenue potential of $8.7 billion over the 12-year terms of its contracts. The average annual run rate is $725 million. HPC hosting revenues are expected to start flowing in 2025 with 200 MW delivered by the end of 1H 2025, up to 270 MW delivered by the end of 2H 2025, up to 382 MW by the end of 1H 2026 and up to 500 MW by the end of 2H 2026. CoreWeave is expected to fund capex costs of $750 million, which Core Scientific will credit 50% of the hosting fees until fully repaid.
CoreWeave: Providing 250,000+ NVIDIA GPU-powered AI Infrastructure For Lease
As Core Scientific’s largest anchor customer, it’s important to take a look into this client. Core Scientific is a direct benefactor of CoreWeave’s success. What’s good for CoreWeave is also good for Core Scientific.
CoreWeave is an AI hyperscaler that has evolved from a crypto miner that leased space and power (NVIDIA GPUs) from Core Scientific to an AI hyperscaler powerhouse with a roster of high profile clients including Microsoft, Meta Platforms, IBM, Cohere, NVIDIA and OpenAI. CoreWeave is a specialized cloud provider focused on offering scalable AI cloud infrastructure including access to over 250,000 NVIDIA GPUs, low-latency networking and high-bandwidth storage optimized for the massive computational workloads of AI training and inference and ML. The company stands out, as NVIDIA stated, “… CoreWeave has launched NVIDIA GB200 NVL72-based instances, becoming the first cloud service provider to make the NVIDIA Blackwell platform generally available.”
CoreWeave builds infrastructure that can scale at a moment’s notice that can go from zero GPUS to 10,000 GPU working on the same job within a minute.
In 2024, Microsoft accounted for nearly 62% of CoreWeave’s revenue (with Meta accounting for 15% according to H.C. Wainwright), which surged 737% YoY from $229 million to $1.9 billion. Customer concentration concerns were eased a bit with the signing of a $11.9 billion deal with OpenAI, who will also become an investor owning $350 million of stock. NVIDIA holds a 5% minority stake in CoreWeave, which will be going public in 2025.
Core Scientific Gets a Game Changer Deal with CoreWeave
The revenue potential of CoreWeave’s contracts is $8.7 billion over their 12-year terms, equivalent to $725 million annually once fully online. That equates to $181.25 million of quarterly HPC revenue, up from $8.5 million in Q4 or 21X potential, by early 2027. When compared to overall revenue, this is 2X growth on a quarterly basis.
The 21X growth in HPC and 2X growth in overall revenue is before the additional 70 MW $1.2 billion expansion deal announced at its Denton, Texas facility in its Q4 earnings release, resulting in the cumulative revenue potential of over $10 billion from CoreWeave, with 75 to 80% cash gross profit margins according to the Company. The full 590MW contracted to CoreWeave is expected to come online in 2027. This would be up from 250 MW expected in 2025.
CoreWeave Announces $1.2 Billion Expansion at Denton, Texas Facility
On Feb 26, 2025, coinciding with its Q4 2024 earnings release, Core Scientific announced a $1.2 billion 70 MW expansion at the Denton, TX site, for CoreWeave. The 70 MW of additional contracted power at the Denton site increases the full critical IT load to approximately 260 MW. The agreement increases CoreWeave's total contracted HPC infrastructure with Core Scientific to approximately 590 MW across six sites.
Under the terms of our Agreement with CoreWeave with respect to this additional 70MW, Core Scientific is responsible for funding $104 million of the additional required capex ($1.5M per MW), with CoreWeave responsible for the additional capex associated with the expansion. The company also retains the option for two additional five-year renewal terms.
“Looking ahead, we now expect to have delivered approximately 250 megawatts of HPC capacity to CoreWeave by the end of this year, with the full 590 megawatts coming online in early 2027. This represents a shift from our previous timeline and reflects both the size and complexity of the project, particularly the addition of an incremental 70 megawatts of critical IT load.“
Adam Sullivan also added this, “So from what we're seeing on CoreWeave's demand side is significantly stronger than what we saw in 2024. There's a lot of things going on in the market today that we're seeing that's actually driving continued demand and flow into CoreWeave. And so we're excited about continuing to expand with them at Denton. And Denton is going to be one of the largest supercomputers in the United States, and it's going to be a flagship asset for CoreWeave.“
One note of caution: Core Scientific has all the makings of a hypergrowth stock and this includes immense risk. The company is recently out of Chapter 11 Bankruptcy and has to raise cash to fund operations, which means taking on debt. The I/O Fund will be holding a very tight stop on any position we initiate, and the stock would be for Advanced Market Signals only, indicating it qualifies for more advanced investors who are comfortable trading daily/weekly.
Looking Beyond the Q4 2024 Headline Numbers
Core Scientific reported disastrous-looking Q4 2024 earnings results based on headline numbers, with an EPS loss of ($0.60), missing consensus estimates for a loss of ($0.09) by ($0.51). Revenues fell 33.1% YoY to $94.93 million, missing consensus estimates by ($2.14 million). Yet, the stock gapped over 10% following its release.
The reason is that just beneath the surface, Core Scientific is setting up to solve one of the biggest issues the United States and the AI market face: power supply. The company is going through a transition period as it moves into the AI/HPC data center markets supported by AI hyperscaler CoreWeave, who just signed a five-year $11.9 billion deal with OpenAI.
$224.7 Million Mark-to-Market Adjustment Shouldn’t Spook Investors
The initial sting of the reported ($265.5 million) GAAP loss in Q4 2024 may sound like bad news, but $224.7 million of it is a non-cash mark-to-market adjustment on warrants; just accounting noise. The “actual” Q4 net loss was ($31.8 million), not ($265.5 million).
Core Scientific issued warrants, which are considered liabilities under GAAP accounting rules since the Company has to deliver stock at the exercise price. If the stock rises in value, the Company has to post a larger liability, but it doesn’t mean they are taking any actual losses. The Company issued two tranches of warrants at $6.81 x 98.3M shares for Tranche 1 (CORZW) exp. January 23, 2027, and $0.01 x 81.9M shares for Tranche 2 (CORZZ) exp. January 23, 2029, as part of its plan to emerge from Chapter 11 bankruptcy in January 2024.
The Company still receives the funds when the warrants convert as they issue the required shares. There are no real losses. In fact, it’s relatively good news since the higher the stock price rises, the deeper the “paper losses” appear until the warrants are all exercised or expired and taken off the books. However, that presents a dilution issue of an additional 180.2 million additional common shares.
The 116 Million Shares from Warrants Remaining Might Spook Investors
During 2024, Core Scientific received $4.4 million in proceeds from 646,109 shares of Tranche 1 warrants being exercised. On December 24, 2024, 60.9 million Tranche 2 warrants were exercised for $600,000. This leaves 116 million warrant-related shares remaining of potential dilution on the remaining warrants. Core Scientific has 294 million shares outstanding as of February 20, 2025.
As of February 20, 2025, the pro forma diluted share count is 501 million shares. This includes the current 294 million shares outstanding along with 207 million additional unissued shares that include Tranche 1 and Tranche 2 warrants of 116 million remaining, convertible notes of 70 million shares and 21 million shares of restricted stock and reserve shares.
Revenues Sink as Company Converts Bitcoin Data Centers to HPC Data Centers
The company’s Q4 revenue fell by (33.1%) YoY and (0.44%) QoQ to $94.93 million, primarily due to the decline in self-mined Bitcoin to 974, down from 3,042 in the year ago period. The Company has been converting some of its Bitcoin mining data centers to HPC data centers and actively “sunsetting” Bitcoin hosting contracts as it transitions to HPC hosting. The Bitcoin halving event also occurred in April of Q2 2024, thereby causing Bitcoin revenue to shrink on a YoY basis further magnifying the deceleration. Revenue fell short of estimates by (2.2%).
Analyst expect revenue to fall (48.26%) YoY to $92.77 million in Q1 2025, and fall (30.03%) YoY to $98.73 million in Q2 2025.
Full-year 2024 revenues rose 1.6% to $510.7 million.
Analysts expect FY2025 revenue to fall (3.71%) YoY to $491.75 million.
Revenue Segments: Bitcoin Revenues Drop in Preparation for HPC Revenue Acceleration
As Core Scientific transitions from Bitcoin self-mining and hosting to HPC hosting, the revenue segments can be expected to drop in the Bitcoin segments and rise in the HPC hosting segment. The quarters may look predominantly worse until the Core Scientific HPC revenues start to ramp up as they go online. Based on analyst estimates, Q1 2025 may be the final “kitchen sink” quarter before revenues reaccelerate.
Margins Consistently Expand Through 2024
Q4 gross margin was 5%, compared to 27.7% in the same period last year.
Q4 operating margin was (41.9%), compared to 2.8% in the same period last year. However, EPS is showing a rebound on the horizon as higher margin HPC hosting revenues increase.
GAAP EPS Trending Towards Positive After Mark-to-Market Adjustments on Warrants
Q4 GAAP EPS was ($0.60) compared to ($0.11) in the same period last year. The EPS miss was primarily due to the $244.7 million non-cash market-to-market (MTM) adjusted on the warrants.
Analysts expect GAAP Q1 2025 EPS to improve to ($0.10).
Analysts expect GAAP Q2 2025 EPS to improve to ($0.07).
Analysts expect GAAP Q3 2025 EPS to improve to ($0.05).
Analysts expect GAAP Q4 2025 EPS to improve to $0.01 as CoreWeave's data centers come online.
Full year 2024 GAAP EPS was ($4.39) compared to ($0.65) last year.
Analysts expect full year 2025 GAAP EPS to improve to ($0.24).
Analysts expect full year 2026 GAAP EPS to improve to $0.40 as more of CoreWeave’s data centers come online.
Cash Grows as Core Scientific Issues $1.09 Billion in Convertible Senior Notes
Core Scientific closed Q4 with $836.2 million in cash and $1.09 billion in debt. The debt is comprised of two convertible notes. In August 2024, The Company issued $460 million in convertible notes due 2029, which enabled the Company to refinance its debt from a 12% interest rate to 3% while increasing its cash position and removing covenants to allow the Company to accumulate Bitcoin. The conversion price is $11.00 at a rate of 90.9256 shares per $1,000 in principal, which brings a total 41.82 million shares issued upon conversion.
In December 2024, Core Scientific priced an upsized $625 million convertible senior notes offering due 2031. The conversion price is $22.49 at a rate of 44.4587 shares per $1,000 in principal. This brings a total of 71.61 million additional common shares upon full conversion.
The Implications of Not Being Investment Grade
Its worth noting that there are implications of not being investment grade especially when needing to raise cash. Considering Core Scientific emerged from Chapter 11 bankruptcy in January 2024, this status alone shapes their cash-raising strategy. Being non-investment grade tends to mean higher borrowing costs, but Core Scientific was able to cut their interest rate from 12% to 3% by swapping out the debt with convertible notes.
It’s worth noting that Core Scientific’s 3% interest rate is impressive for a company just out of bankruptcy implying the institution(s) are very confident in Core Scientific’s strategy. However, that route also comes with its potential share of dilution (41.82 million new shares) if shares are converted at $11.00 per share. Core Scientific has the option to redeem early if the stock trades 130% above the conversion price for 20-30 trading days ($14.30) after the initial non-call period August 2027.
The additional $625 million convertible also comes with dilution (27.79 million new shares) but at a higher conversion price of $22.49 and no interest rate. However, Core Scientific achieved this funding with 0% interest implying very high confidence that Core Scientific will either redeem the notes at maturity or that the shares will surge above the conversion price enabling them to convert shares for a profit before then. Both convertibles are senior unsecured obligations, therefore in the event of bankruptcy or default, unsecured creditors rank below secured lenders.
Valuation
The Company trades at a forward P/E ratio of 12.27. The trailing twelve month (TTM) P/S ratio is 4.34 and forward P/S is 12.27. The five-year average P/S ratio is 5.08. The P/S ratio peaked at 9.3 in November 2024.
Management highlighted their strategic pivot from Bitcoin mining to HPC hosting. The Company delivered 500MW of capacity through 12-year agreements worth $8.7 billion, expanding its HPC infrastructure to over 1.3GW of contracted power. Its key initiatives include accelerating capacity expansion and targeting significant new site acquisitions, including projects in Auburn, Alabama, and Denton, Texas.
In Q4, the Company secured approval to expand its gross capacity at its site in Denton, Texas, by nearly 100MW, which equates to nearly 70MW of critical IT load. Denton is on track to host one of the largest GPU supercomputers in North America. The Auburn, Alabama, site currently has 11MW of critical IT load, and the Company is actively working with Alabama Power to secure a much larger power agreement. They are deferring significant capital deployment until they finalize negotiations with prospective customers.
CEO Adam Sullivan said this.
“Today, we announced a significant expansion of our relationship with CoreWeave at our Denton facility, which will bring that site to full capacity. This new agreement adds approximately 70 megawatts of critical IT load and represents approximately $1.2 billion in additional contracted revenue over a 12-year term. With this latest expansion, our total contracted value with CoreWeave now exceeds $10 billion, an amount that includes our Austin, Texas agreement, and covers roughly 590 megawatts of critical IT load once fully online. Of that total, just over 570 megawatts reflect the capacity we're converting at existing sites to HPC, where we expect 75% to 80% cash gross profit margins.”
However, Core Scientific will put up the CapEx to receive full HPC rental payments rather than 50% payments, with the other 50% being a CapEx credit for the upfront CapEx spent by CoreWeave. Sullivan said this.
“Under this newest agreement for the additional 70 megawatts, we will fund $1.5 million in capital expenditures per megawatt, whereas in prior agreements, CoreWeave covered those costs. In return, we will benefit from full rental payments during the first two years of the contract because there will be no CapEx credit associated with this new agreement.”
It’s worth noting that analysts may be considering 2027 full delivery as too ambitious considering as evidenced by the lowering of revisions. There are execution risks that may be out of their hands including grid delays and securing power with utilities (IE: working with Alabama Power to secure more power to the Auburn site), funding capex or negative developments with CoreWeave.
Prioritizing Customer Diversification and CoreWeave Timeline to Come Online Fully
Diversifying its customer base is a key priority as it aims to reduce CoreWeave's share of revenues to under 50% of critical IT load by 2028. The Company is in active discussions and remains confident in its ability to diversify its HPC customer base. The Company exited the year with 15MW of critical IT load. New block ASIC chips are expected in 2H 2025, which will refresh some of its Bitcoin mining fleet. Otherwise, there are no plans for any further CapEx spending in 2025 for its Bitcoin mining business.
CEO Adam Sullivan reiterated their top priority of diversifying new customers.
“We are in active discussions with dozens of new customers, including the vast majority of hyperscale providers in several large enterprise companies. Demand remains strong, but we're seeing considerably more due diligence compared to the first half of 2024. This heightened scrutiny reflects the influx of new market entrants who make ambitious capacity promises yet lack the tangible power agreements to back them up, much like the recent situation where a hyperscaler canceled contracts with companies that overstated their available power.”
The Company expects to have nearly 250MW of HPC capacity to CoreWeave delivered by the end of 2025. The full 590MW is coming online in early 2027. Core Scientific believes they can add another 300MW of capacity across existing sites by the end of 2027.
Earnings Call Q&A:
The Goal of Reducing CoreWeave’s Concentration of Revenue under 50%
Core Scientific is actively trying to diversify their concentration of revenue from CoreWeave.
Jeffries analyst John Peterson:
“Okay. And then I appreciate the goal of wanting to bring CoreWeave down to less than 50% of revenue by the end of 2028. I think that would require you to procure a lot more power this year in addition to signing on additional customers. So maybe just talk through the milestones that you need to hit throughout this year to be on track to do that.”
Adam Sullivan:
“We talk about the ability to continue to expand at existing sites. And that's a competitive process because we are getting direction in terms of how much additional power we're going to be able to achieve at some of our existing sites and then some of our new sites as well. Very attractive locations. Our focus today is on building blue-chip assets. And we want to have those blue-chip assets with blue-chip clients. And so that's where our focus is today. And we're going to continue to execute and acquire more sites to bring more capacity online to secure more contracts and achieve our goal of getting them below 50% by 2028.”
At 75% to 80% margins, 590 MW would yield $637.5 to $680 million, with a midpoint of $658.75 million after 2027 (assuming all 590 MW comes online). If CoreWeave is 50% of critical load by 2028, total HPC capacity needs to double to 1,180 MW from producing more power and acquiring more sites. It would require Core Scientific to assume more hyperscalers sign under similar 12-year terms to CoreWeave. Sullivan stated how diversifying its customer base was a top priority, “Starting with diversifying our customer base, this is the top priority for the company this year, and the goal is to sign enough contracts so that CoreWeave represents less than 50% of critical IT load by the end of 2028.” Sullivan mentioned 700 MW was available.
Nick Giles:
“So, appreciate your target that CoreWeave represents less than 50% of critical IT, but that implies that you sign at least the same amount with other customers, but you do have 700 megawatts that you've outlined between existing and new sites by 2027. So, should we assume that the delta would be new customers as well, or could that kind of 130 be split between a new customer and maybe one more tranche with CoreWeave?”
Adam Sullivan:
“Yes. So, we've outlined the 300 and the 400 number that's critical IT load megawatts, so about 700 megawatts. As we look forward if we have 590 of CoreWeave contracts the 700 available to us is really where our focus is going to be on executing new clients. So that's part of our goal to get them below 50%, to have enough capacity available and saleable for us to be able to bring them down to that level.”
How the Deep Seek News Only Made People Want to Move Faster
The Deep Seek news was a head fake as actual demand increased, and it only made people want to move faster.
Adam Sullivan:
“We've seen much more specific requests around locations in terms of developments and where they would like to build. But overall, the Deep Seek news for hitting the public markets rather hard from everything that we've seen on the actual demand side, demand continues to increase, and those conversations continue to progress very well.”
Diversifying the Customer Base Beyond Hyperscalers
While Core Scientific makes headlines when deals are made with name-brand hyperscalers, enterprise customers could also fill in pieces of the void to improve diversification.
Greg Lewis:
“Could you talk a little bit about you mentioned enterprise customers potentially. It's something that seems to be we're hearing more about beyond just the hyperscalers. As maybe you broaden out the customer base beyond just the hyperscalers, which it seems that latency is a big issue for them. Maybe scalability is a big issue for them. As you kind of look at potential enterprise customers, does that open up sites maybe in your portfolio and elsewhere that maybe under hyperscaler footprint wouldn’t work but through enterprise it might?”
Adam Sullivan:
“And so, we're looking at having hyperscale at the very least as anchor, potentially a single tenant. And if they're serving as an anchor, being able to fill out the rest of the capacity with enterprise clients as well. So, the demand, does it open up more sites with enterprise? Absolutely. But we're focused on blue chip assets with blue chip clients, which includes both of those groups.”
Delivery Times and Securing Power Agreements is a Competitive Advantage
Sullivan pointed out that many while demand remains strong, they are seeing considerably more due diligence compared to the first half of 2024 due to the influx of new market entrants that make “ambitious capacity promises” but actually lack the “tangible power agreements to back them up.” Sullivan referenced what may have been the rumored Microsoft cancellation of commitments with CoreWeave due to “delivery issues and missed deadlines.” Microsoft outright denied the cancellations.
Adam Sullivan:
“This heightened scrutiny reflects the influx of new market entrants who make ambitious capacity promises yet lack the tangible power agreements to back them up, much like the recent situation where a hyperscaler canceled contracts with companies that overstated their available power. Our proven track record and secured power agreements set us apart in this environment, and we won't be expanding our footprint unless we have a high degree of confidence in our ability to deliver for additional customers.”
When pressed about the rumor of Microsoft cancelling capacity with CoreWeave, Sullivan responded.
“I can't comment specifically on any relationship between CoreWeave and Microsoft other than what they've spoken about publicly. But, I mean, CoreWeave's continuing to expand. You're seeing it not only with Core Scientific, but really across the globe and internationally. So, from what we're seeing on CoreWeave's demand side is significantly stronger than what we saw in 2024. There's a lot of things going on in the market today that we're seeing that's actually driving continued demand and flow into CoreWeave. And so, we're excited about continuing to expand with them at Denton.”
Is Core Scientific in Discussions with Other Hyperscalers?
Needham analyst John Todaro inquired about discussions with other hyperscalers and CoreWeave. Sullivan noted they are in talks with a majority of hyperscalers in conversations with large enterprises. The customer conversations are continuing to evolve throughout the early part of 2025. Sullivan was asked if he saw any demand changes across inference and training workloads on the back of Deep Seek headlines.
Adam Sullivan:
“Denton was a site that we were really slating for CoreWeave. We did have conversations with some other hyperscalers and other clients on those megawatts. As we talked about the 300 megawatts potential at other existing sites, we're in conversations today with other potential customers around that. There's really no guarantee that anything like that would go to CoreWeave, because what we do want to do now is really focus on continuing to diversify our client base, and our existing sites are great campuses for us to do that.”
Elaboration on the Delays
Sullivan mentioned there were some delays from changing some of the designs to fit for the equipment further impacted by the constrained supply chains going out into 2026.
Adam Sullivan:
“And one of the things that we wanted to ensure that we achieved was that we had the right equipment on the right schedules for the site plans that we had. And so, that required us to change some of the designs to fit for the equipment that was available to deliver on the timelines that we set forward. And so, there was just some incremental delays there. But overall, we have high confidence in where the delivery schedules that we've put forward today. And we believe we're going to be able to hit those timelines.”
Management now expects critical IT load to be 250 MW, including the 16.5 MW, down from 270 MW plus 16.5 MW.
Brett Knoblauch:
“Thanks, guys. Really appreciate it. Maybe just quickly on the delays, if you will, or the pushback in timing. Just want to make sure I heard you right. You're now expecting critical IT load this year to be 250 megawatts. Does that include the 16.5? And before, you guys were expecting, I think, 270 plus the 16.5.”
Adam Sullivan:
“Yes, thanks, Brett. Yes, that's correct. That's really a push out of just one 40-megawatt building out into early 2026. And you're absolutely right. That number does include the 16.5 megawatts.”
Core Scientific Implements a Utility First Process For Evaluating Expansion Sites
For data center site selection, there is a shift away from large remote training sites towards locations that are closer to major metropolitan areas. This has been driven by demand and the need for proximity as the Company expands into new markets, which include the East Coast. However, prioritization is based on reliable utility partnerships.
Rosemarie Sison:
“Just to follow up on that comment that you made, Adam, about proximity to major metro areas. Would that mean that you're potentially looking at expanding out of the markets that you're in right now possibly into the East Coast or the West Coast as those opportunities present themselves?”
Adam Sullivan:
“Yes, absolutely. Thank you for the question, Rosemarie. I mean, we are building one of the larger data centers on the East Coast right now. And so, we have a lot of confidence in our ability to continue to expand in new markets. This is something where we're going to be one of the larger providers in the Dallas market. We believe something similar in the Atlanta market as well. So, we're definitely looking at continuing to enter into new cities. But albeit that looks a little bit different because we might have less familiarity with the utilities in that location. A point on that is we currently operate with seven utilities. We're continuing to expand our relationships across that base. And so, we're taking a very diligent process, a utility-first process, when we're evaluating entering new locations to ensure that we have a strong partnership and relationship with that utility so that we know that we have that firm power available when we go take them to a client.”
Conclusion: Solid Gameplan, Execution is the Key
Other Bitcoin mining companies are adopting Core Scientific's pivot to HPC hosting. However, Core Scientific's game-changing contracts amounting to over $10 billion in revenues over 12-year terms with CoreWeave give them a first-to-market advantage fortified by $10.2 billion in revenue potential from an AI disruptor.
CoreWeave, backed by NVIDIA as an investor and customer, is likely the leading hyperscaler in the market, positioning itself as a first mover in the AI data center space. Given NVIDIA’s potential preferential treatment toward CoreWeave, especially compared to competitors like Amazon who are building with custom silicon, CoreWeave is primed to lead the way in scaling AI infrastructure.
Hyperscalers will likely follow in CoreWeave's footsteps. This dynamic reinforces the notion that Core Scientific's strategic pivot to HPC hosting could be bolstered by CoreWeave's leadership in the hyperscaler space, further underscoring that what’s good for CoreWeave is also good for Core Scientific.
CoreWeave was initially interested in acquiring Core Scientific for $1.02 billion or $5.75 per share in June 2024, but was rejected and they decided to back them as they expanded their data center footprint. The downside to this relationship is the very limited customer concentration, as CoreWeave is their largest HPC hosting client. Core Scientific’s near-term future lies with CoreWeave. CoreWeave is expected to generate $10 billion from Microsoft as a client by the end of the decade.
As a potential lottery ticket element for investors, CoreWeave could revisit another acquisition attempt for Core Scientific after its IPO, where it would have additional cash and stock to use as currency. The initial acquisition attempt in June 2024 was for $1.02 billion in cash or $5.75 per share, which Core Scientific rejected stating that the offer “significantly undervalues the Company.” With an estimated $35 billion valuation, CoreWeave could make a much more attractive acquisition offer for less than it would be paying Core Scientific over its 12-year term leases.
Core Scientific has a solid game plan to accelerate its quarterly HPC hosting revenue by at least 21X in two years. As with any great game plan, the flaw always lies in the execution. Analyst estimates forecast one more kitchen sink quarter to go before revenues turn back up as HPC hosting revenues start to ramp up. The potential for more than doubling the outstanding shares to 501 million shares upon full conversion and vesting of restricted stock is concern down the road, but for now the game plan looks solid; the execution is the key.
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Jea Yu, Equity Analyst at the I/O Fund, contributed to this article.
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.
Alibaba is one of the hottest AI stocks in the market in 2025, with shares up more than 60% year-to-date. The surge in price is due to Q3 results showing Cloud revenue accelerated with AI revenue up triple-digits for a sixth quarter in a row. Notably, the triple digit growth is on much lower revenue than what United States Big Tech companies are reporting, with leader Microsoft at 13X higher AI revenue. AI is also not meaningfully contributing to revenue nor earnings despite six consecutive quarters of triple digit growth.
Despite the advancements that Alibaba is making in AI and tens of billions it is committing to invest in AI infrastructure, AI remains but a small portion of Cloud revenue, far below the scale of US-based peers. An ultra-competitive Chinese AI market engaging in a pricing war likely is hindering the country’s AI growth potential, as Alibaba is touting tens of thousands of users and millions of downloads with small dollar growth to show for it. I break this down and more below.
Alibaba has Made Visible AI Advancements
Alibaba remains committed to its dual-prong strategy of e-commerce and AI + cloud. The company has recently highlighted multiple advancements on the AI front, with a handful of its models showing “industry-leading” performance. Shares rose again on Tuesday as Alibaba struck a partnership with Manus AI to roll out a Chinese version of Manus’ rapidly popular AI agent.
China’s AI market is rapidly heating up with fierce competition from Alibaba, DeepSeek, Baidu, Tencent and Bytedance, among others. Alibaba is aiming to take a leading position in the AI market and announced recently that its Qwen2.5-Max mixture-of-experts model outperforms DeepSeek’s V3, Meta’s Llama 3.1 and OpenAI’s GPT-4o. In Q3’s earnings call, Alibaba teased the release of a new deep reasoning model built on Qwen 2.5 Max.
Alibaba says its Qwen model outperforms DeepSeek’s V3 and Llama 3.1-405B across major benchmarks.
Last week, Alibaba announced a new 32 billion parameter reasoning model, QwQ-32B, that it says outperforms DeepSeek’s R1 reasoning model, with 1/20th the parameters. QwQ-32B is the latest iteration of QwQ, which was first launched in November 2024 and “designed to enhance logical reasoning and planning by reviewing and refining its own responses during inference,” which a report from VentureBeat says allowed it to outperform OpenAI’s o1 on math benchmarks AIME and MATH, and scientific reasoning tasks.
Alibaba is committed to expanding access to its AI models and tools, with its Qwen-2.5 series of models available via APIs as well as its genAI development platform Model Studio. Developers also can access Alibaba’s family of multimodal models – its vision understanding model Qwen-VL, its visual generation model Wanx2.1, its audio language model Qwen-Audio, and its coding assistance model Qwen-2.5coder.
In Q3’s earnings call, Alibaba’s management highlighted that this approach is paying off, with more than 90,000 Qwen-based derivative models developed globally at the end of January, which made Qwen “the most popular among developers across the major model families.” Management added that more than 290,000 companies and developers accessed Qwen APIs through Alibaba Cloud's Bailian platform.
Alibaba Outlines Significant Capex Outlay to Support AI
To support its growth in AI and become a competitive global player, Alibaba outlined a plan to “aggressively invest in AI infrastructure,” with planned capex over the next three years exceeding what it has spent over the last decade. Management’s plan calls for spending of 380 billion yuan, or ~$52.4 billion, over the next three years predominantly for AI.
Management first discussed increasing capex in the June 2024 quarter, when expenditures nearly doubled YoY to ~12 billion yuan, or $1.6 billion. Management noted in the June quarter that confidence in this level of investment was driven by strong demand, and expected to stay around this level for the next few quarters.
However, capex quickly accelerated — in the September 2024 quarter, capex rose more than 310% YoY to 16.9 billion yuan, or $2.4 billion, and in the December 2024 quarter, capex rose 330% YoY to 31.4 billion yuan, or $4.3 billion. For the nine months of this fiscal year, capex totaled 60.3 billion yuan, or $8.3 billion, rising more than 246% YoY.
Capex Plans Well Below US Big Tech
This 30 billion yuan/quarter rate is a bare minimum of what’s needed to come close to management’s spending targets, signaling heightened capex will continue for the foreseeable future. However, at $52.4 billion over the next three years, Alibaba is spending only a fraction of what US Big Tech firms are spending, which could impact its competitive positioning.
For 2025, Amazon, Microsoft, Meta and Alphabet outlined plans for approximately $320 billion in capex, predominantly for AI, which is 6x more than what Alibaba is spending. Of the four, Meta has earmarked the least towards capex, at $60-65 billion, but even at the low end, Meta’s one-year spending is more than 15% higher than Alibaba’s 3-year plan.
In 2024 and 2025, Big Tech is on track to spend more than $550 billion in capex, or more than 10x higher than Alibaba’s plan in just two years as opposed to three. So while Alibaba’s AI spending commitment looks high compared to its historical investments, it’s spending just a mere fraction of what Big Tech is. The US is not likely to back down when it comes to AI dominance, which we covered in the article “DeepSeek Creates Buying Opportunity for Nvidia Stock.”
Alibaba Remaining Competitive on AI Pricing
China’s AI market is engaged in much fiercer competition than in the US, and this is evident within the pricing structures of AI models. Alibaba and rivals are pricing models at mere fractions of the cost of US-based competitors, in an effort to win over customers from each other and remain ahead in the broader global AI race.
China’s AI market has been in a pricing war since early 2024 – Alibaba had cut prices by up to 97% in May 2024. ByteDance further intensified the price war in December 2024, when it slashed prices for its new Doubao model with vision understanding capabilities to $0.00041 per 1,000 tokens, 85% lower than the industry average. Alibaba mirrored this move within two weeks, matching Doubao’s $0.00041 price for its Qwen-VL Max model.
Alibaba’s Qwen models are priced much cheaper than leading models from OpenAI and Anthropic as China engages in an AI pricing war.
Alibaba’s Qwen2.5 Max is priced at $0.0016 per 1,000 input tokens and $0.0064 per 1,000 output tokens, while its Qwen Plus is offered at $0.0004 per 1,000 input tokens and $0.0012 per 1,000 output tokens. Qwen Turbo is priced at $0.00005 per 1,000 input tokens and $0.0002 per 1,000 output tokens.
For comparison, ByteDance’s Doubao 1.5 Pro is priced at $0.00011 per 1,000 input tokens and $0.00028 per 1,000 output tokens. DeepSeek’s V3 model is priced $0.00014 per 1,000 input tokens and $0.00029 per 1,000 output tokens, while its R1 models is priced higher at $0.00057 per 1,000 input tokens and $0.00227 per 1,000 output tokens. Baidu recently announced that it is aiming to make Ernie free for all users by the start of April this year.
This pricing structure is allowing Alibaba to remain quite competitive in the Chinese AI market and more so on the global market, as China’s models are significantly cheaper than OpenAI, Anthropic and others, with Meta and Mistral two of the lower-cost competitors.
OpenAI’s GPT 4.5 is currently one of the most expensive models available, at $0.075 per 1,000 input tokens and $0.15 per 1,000 output tokens – that’s up to almost 50x more expensive that Qwen2.5 Max. OpenAI’s o1 is priced at $0.0015 per 1,000 input tokens and $0.06 per 1,000 output tokens, while the much smaller GPT 4o mini is priced comparatively to Chinese rivals.
Pricing is Alibaba Stock’s Achilles Heel for AI
However, it is this pricing structure and ongoing price war in China’s AI model market that is Alibaba’s Achilles heel. While the low-cost structure is enabling Alibaba to remain extremely competitive in the face of rising competition both domestically and globally, it’s serving as a bit of a hindrance to growth, with AI revenue likely still quite below the $1 billion mark, where US tech giants are touting multi-billion dollar AI revenue streams.
Alibaba first outlined the growing demand for AI model training and AI infrastructure services in its June 2023 quarter. The following quarter in September 2023, Alibaba laid out a two-pronged strategy for driving AI growth in the cloud.
Management said that they will aim to “build the most open cloud in the AI era, providing stable and efficient AI infrastructure for all industries and enabling all sectors to go intelligent,” and “build an open and prosperous AI ecosystem.” This was underscored by its Qwen family of models, its model application development platform Bailian, and its open-source platform ModelScope. The September quarter saw ModelScope’s cumulative downloads more than double sequentially, from 45 million in July 2023 to 100 million, attracting more than 2.8 million developers.
Discussing the March 2024 quarter results, CEO Eddie Wu explained that AI is serving as a primary driver for the revenue growth that is being seen in the broader Cloud segment:
“If you look at the overall revenue growth of the Cloud business today, most of that is already being driven, I would say by AI and AI-related new products. So going forward a lot of the incremental growth we can expect to see in the Cloud business will be related to investments the customers are making in AI. But also there is a complementary effect because the more that customers invest in and make use of AI the more demand they will also have for other of our various cloud offerings.”
For the December 2024 quarter, Cloud Intelligence revenue accelerated 6 points sequentially to 13% YoY to ¥31,742 million, or $4.35 billion, up from 7% growth in the September quarter. This marks the steepest sequential increase in what has been a rather gradual acceleration from 2% YoY growth in the September 2023 quarter.
Alibaba stock’s Cloud revenue growth accelerated to 13% YoY in the December quarter, aided by AI.
In its December 2024 quarter results, Alibaba noted that AI product revenue “maintained triple-digit year-over-year growth for the sixth consecutive quarter,” starting in the September 2023 quarter. AI helped drive an acceleration to the double-digits for Cloud Intelligence revenue growth.
There are a handful of stats that support increasing adoption of Alibaba’s AI products – more than 90,000 Qwen-based derivative models had been developed globally at the end of January, while more than 290,000 companies and developers have accessed Qwen APIs through Alibaba Cloud's Bailian platform. Qwen’s models have been downloaded more than 7 million times, and ModelScope has attracted more than 4,000 AI models and 5 million developers.
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Yet despite this and six quarters of triple-digit growth, Cloud revenue is only growing in the low-double digits, implying that AI’s contribution to revenue remains quite small. A rough estimate places AI’s contribution in the mid-single digit percentage of Cloud revenue, with revenue possibly around the $200-275 million range as of the December quarter. This would put Alibaba’s AI run rate between $800 million to $1 billion.
Compare this to Microsoft, where its AI run rate on Azure surpassed $13 billion last quarter, up 175% YoY. Microsoft is also showing rapid growth for AI platforms and tools – the number of Azure OpenAI apps running on Azure databases more than doubled YoY last quarter, while Azure AI Foundry reached 200,000 MAUs within two months. Microsoft’s Phi family of small language models have been downloaded more than 20 million times, nearly 3x more than Qwen. 160,000 organizations have used Microsoft’s Copilot Studio, creating 400,000 custom agents last quarter, up 2x QoQ.
AI’s Impact on Alibaba’s Cloud Margins
Alibaba noted last quarter that a shift to higher-margin cloud products, including AI, has aided EBITA growth in its Cloud segment. For the December quarter, adjusted EBITA rose 33% YoY to ¥3,138 million, or $430 million, decelerating dramatically from 89% YoY and 155% YoY growth in the prior two quarters.
Adjusted EBITA margin was 9.9% in the December quarter, up from 9% in the prior quarter. It’s hard to argue against the beneficial impact of AI on EBITA margin for Cloud, with margins beginning to expand significantly as AI embarked on its six-quarter stretch of triple digit growth in the September 2023 quarter, aside from a hiccup in the March 2024 quarter. Since that point, margin have risen nearly 5 points and are knocking on the double-digit range.
Alibaba stock’s Cloud adjusted EBITA margins have expanded as AI drives a shift to higher-margin products.
Despite the margin expansion and strong EBITA growth from a shift in product mix towards higher-margin cloud offerings and AI products, Cloud’s share of consolidated adjusted EBITA is still quite small. For the last four quarters, Cloud’s share has hovered between 5% to 6.6% of consolidated adjusted EBITA. While this was an improvement from 0.9% in the June 2023 quarter and 3.3% to 4.5% in the second half of 2023, segment adjusted EBITA growth has decelerated sharply to the lowest level in the past seven quarters, suggesting EBITA contribution may follow and plateau.
Alibaba’s Cloud Intelligence adjusted EBITA growth has decelerated more than 120 points in two quarters to 33% YoY.
What this means is that despite the six-quarter string of triple-digit growth for AI revenue, there’s minimal impact to the bottom line from this AI revenue surge at the moment. Earnings estimates for this fiscal year and next were relatively unchanged through much of the second half of 2024, within a 3% range, only rising in February following a 10% earnings beat in the December quarter.
Alibaba’s EPS estimates for this fiscal year and next were relatively unchanged through most of 2024 despite AI growth. Source: YCharts
AI revenue is not yet at the scale where it is meaningfully contributing to revenue or earnings, though Alibaba’s commitment to spend significantly on AI after witnessing six quarter of triple-digit growth is more positive for the long-term rather than the short term.
Valuation Reaching a Peak
Because AI is not driving the revenue scale or profits that we are seeing here with US Big Tech, Alibaba’s valuation is getting pricey, trading at peak levels from the past three years. Alibaba not only is facing tough competition from Big Tech but also from within domestic peers, with Tencent and Baidu both reporting strong AI growth.
Alibaba is trading at peak valuation levels from the past three years. Source: YCharts
Alibaba is currently trading at 15.1x forward earnings and 2.35x forward revenue, both at or just below peak valuations since early 2022. This rapid rerating has likely been driven predominantly by AI enthusiasm, given that a majority of the multiple expansion occurred following DeepSeek’s rout in late January.
For comparison, Baidu trades at a 40% discount to Alibaba on both metrics, at 9x forward earnings and 1.7x forward revenue, with its AI Cloud revenue rising 26% YoY, double the rate of Alibaba’s. Baidu’s Ernie handled 1.65 billion daily API calls in December 2024, with external API calls up 178% QoQ. Baidu’s Wenku platform reached 94 million MAUs, up 216% YoY and 83% QoQ.
There’s also risk that China remains behind the US when it comes to AI and monetization, with Tencent VP Martin Lau laying out three reasons why it lags behind US peers despite AI revenue reaching 10% of Cloud revenue. He explained that China does not have nearly as large as an enterprise market as the US, and within that, the SaaS ecosystem “is not really that vibrant in China.” He added that fewer AI startups in China are purchasing less compute, another reason the US leads. These three reasons are why Lau believes that AI revenue is starting to scale, but not exploding as it is in US.
Conclusion
Alibaba is one of AI’s top winners so far in 2025 with shares rising more than 60% YTD on AI enthusiasm as the giant has released highly-competitive Qwen models and struck partnerships with leading Chinese AI firms. However, the rally is likely front-running AI revenue to a significant degree, as Cloud’s low growth and management’s comments about AI revenue imply that AI is still growing off quite a small base.
Alibaba is quickly ramping AI investments to better compete on the global scale, but its AI run rate far lags that of US peers, with Microsoft recently reporting 175% YoY growth to a $13 billion AI run rate and Amazon and Google both reporting in the multi-billion dollars. Alibaba has a lot of ground to cover to get into the same realm as Big Tech on AI, as its AI run rate is still likely below the $1 billion mark.
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AI data centers are expected to consume up to 9% of all electricity generated in the United States by 2030.
AI GPUs have already tripled their power consumption, with Nvidia Blackwell raising the bar as the GB200 is expected to use up to 2,700 watts of power, up from 250 watts for the earlier A100.
Power consumption is the chokepoint for AI data centers that are desperate to lock in power purchase agreements (PPAs) to procure long-term power.
The artificial intelligence (AI) revolution is driving an intensely competitive race across various facets, from GPUs and ASICs to CPUs, storage, LLM models, and beyond. However, one critical component stands out as the key enabler for AI's future: power.
Simply put, AI cannot exist without the electricity that powers its applications, making energy the chokepoint for AI data centers. Electricity must be generated to keep these data centers running at full capacity, and there are five primary types of fuel sources: coal, natural gas, solar, nuclear and fuel cells.
AI and Data Centers Are Driving Up Power Consumption:
Power consumption is rising with each generation of GPUs. Nvidia’s A100 max power consumption was 250W. Its H100 GPU consumes 350 to 350W and up to 700W with SXM. IO Fund wrote, “Nvidia’s upcoming Blackwell generation boosts power consumption even further, with the B200 consuming up to 1,200W, and the GB200 (which combines two B200 GPUs and one Grace CPU) expected to consume 2,700W. This represents up to a 300% increase in power consumption across one generation of GPUs with AI systems increasing power consumption at a higher rate.”
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AI data centers need to meet this power demand, which is expected to surge with each generation of GPUs, which are now expected to roll out annually (rather than bi-annually). A single rack is used to consume 10 to 15 kilowatts (KW) of power, but current AI racks are now averaging 80 KW. The next generation is expected to draw 120 KW to 150 KW towards 200 KW within the next few years. This will drive data center consumption by 160% by 2030, where data centers could draw 9% of all the electricity produced in the United States.
Using Thermal Efficiency to Rank Fuel Sources
AI data centers are actively trying to secure long-term power purchase agreements (PPA) and are even exploring nuclear energy options, as evidenced by the 20-year PPA Microsoft signed with Constellation Energy. Hyperscalers are also co-locating data centers near power sources to ensure reliable electricity.
To ensure reliable power, hyperscalers are increasingly seeking long-term power purchase agreements (PPA) and exploring various energy options, including nuclear power. This was underscored by the 20-year PPA that Microsoft signed with Constellation Energy marking the largest-ever PPA in its history. Constellation Energy will launch the Crane Clean Energy Center, restoring the Three Mile Island Unit 1 nuclear reactor, which will add 835 MW of carbon-free energy to the electrical grid.
While electricity is generated from various energy fuels, not all fuels produce the same amount of energy. Thermal efficiency measures how effectively a fuel is converted into electricity. For example, a thermal efficiency of 25% means that 75% of the fuel is lost as heat. Higher thermal efficiency means more energy is converted into electricity, while lower efficiency results in greater energy loss. Let’s see how the energy fuel sources size up.
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Sizing Up the Five Types of Energy Fuel
These are the five energy fuel sources and their general thermal efficiency. These are general thermal efficiencies, which can vary based on location, technology and method:
Solar PV: While solar photovoltaic (PV) panels produce renewable clean energy, they are also the least efficient, averaging 15% to 20%, or 17.5% midpoint. They generate electricity directly from the sunlight. Of course, sunlight is not available 24/7, and efficiencies can drop during overcast and precipitation.
Coal: The world has been phasing out burning coal due to the high levels of carbon emissions. Coal-fired plants burn coal in a boiler, producing steam which flows into a turbine, spinning a generator to produce electricity. The U.S. Energy Information Administration (EIA) suggests 16% of the electricity in the United States is powered by coal. Coal has a thermal efficiency of 33%.
Nuclear: The thermal efficiency of nuclear power plants ranges between 33% to 37%, with a midpoint of 35%. Interestingly, nuclear is not much more efficient than coal as it also uses steam from nuclear fission to spin turbines that generate electricity. However, nuclear plants don’t produce any carbon emissions, making them the cleanest energy option.
Natural Gas: Gas plants produce electricity through two methods. The simple cycle gas turbine works through combustion by burning the natural gas in a combustion chamber to spin a turbine connected to a generator that generates electricity. This alone has a thermal efficiency between 35% to 42%, with a midpoint of 39%. Many gas plants use combined cycle gas turbines (CCGT), which use the exhaust heat up to 1,000 degrees Fahrenheit to boil water in a steam turbine, which adds an extra 20% to 25%, midpoint of 23% efficiency for a total of 62% thermal efficiency. There are carbon emissions generated from burning the gas.
Solid Oxide Fuel Cells (SOFCs): SOFCs can use natural gas, biogas, propane or methane to generate electricity. Natural gas is the preferred method, which produces an electrochemical reaction to produce electricity facilitated by a solid ceramic electrolyte. The thermal efficiency is up to 65%. However, using a combined heat and power (CHP) system will use the exhaust heat of up to 1,500 degrees Fahrenheit to be channeled to a steam turbine, adding an extra 20% to 25% for a total efficiency midpoint of around 87%.
This is the Clear Winner in Thermal Efficiency
As depicted in the chart, SOFC has the highest thermal efficiency when used with heat capture to add an extra 20% to 25%. SOFCs emit carbon when using natural gas as a fuel source, but not as much as gas turbines since there is no combustion, just an electrochemical reaction. For zero carbon, hydrogen can be used as a fuel source. However, it takes electricity to make the hydrogen that will produce electricity, which defeats the purpose for now. Also, while hydroelectric power can have efficiencies as high as 90%, the problem is the location (need to be near large masses of water) and cost (dams are expensive). They don't generate enough electricity (from KWs to hundreds of MWs) to be cost-effective.
As the cost of hydrogen falls and the infrastructure supports it, then it may become a more readily used energy fuel. Natural gas already has the infrastructure and continues to penetrate as a preferred energy fuel. SOFCs are a cleaner way to generate electricity with higher thermal efficiency to boot using natural gas. Investing in the right energy infrastructure is essential to powering the next wave of AI innovation.
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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.
TSMC released its monthly revenue for February on March 10th. Revenue grew by 43.1% YoY and down (-11.3%) MoM to NT$260.01 billion. February 2025 revenue was the new high for the month. In U.S. dollar terms revenue grew by 37.3% YoY to $7.93 billion using the average exchange rate of 1 US dollar to 32.78 NT dollars. This strong performance, coupled with Foxconn's impressive 56.4% year-over-year monthly revenue growth in February, suggests robust and sustained demand for AI.
A closer look at TSMC's monthly revenue reveals that month-over-month figures can be volatile. The February decline is likely attributable to seasonal factors, such as the Lunar New Year holidays and fewer working days. For context, February 2024 also saw a MoM revenue decrease of (-15.8%). This suggests that the recent month-over-month decline is not unusual and should be considered within the context of seasonal trends.
The management had provided an update last month that they expect the Q1 revenue to be near the lower end of the guidance range of $25 billion and $25.8 billion due to the Taiwan earthquake in January. Importantly, TSMC maintains its strong outlook for the full year 2025, anticipating revenue growth in the mid-20% range in US dollar terms, driven by robust AI demand.
Sustained sequential HPC growth
As the leading foundry for AI accelerators, TSMC is riding the enormous wave of demand from Big Tech. The chipmaker’s high-performance computing (HPC) revenues rose 19% QoQ to a record $14.25 billion and accounted for 53% of revenue in Q4, surpassing the 50% mark for the third time. The sequential HPC growth for the six consecutive quarters is a further testament that there is no AI demand slowdown and demonstrates sustained momentum in the AI sector. Management expects AI accelerators to be the strongest driver of the HPC platform growth in the next several years.
The above chart shows that TSMC’s HPC sequential revenue growth tells us that they're a few quarters ahead of Nvidia's bigger quarters like the 2022 sequential growth before the launch of Hopper Architecture.
TSMC is experiencing explosive growth in its AI segment, with revenue tripling in 2024. Management expects this remarkable growth to continue, projecting a further doubling of AI revenue in 2025.
Management expects AI accelerators to grow mid-40% CAGR for the next five years and expects AI accelerators to be the strongest driver of the HPC platform growth and the largest contributor in terms of the overall incremental revenue growth in the next several years.
The chart below further emphasizes the strength of TSMC's high-performance computing (HPC) segment, with revenue reaching a record $14.25 billion in the most recent quarter. This represents the largest sequential increase to date, surging by approximately $2.26 billion. This data underscores the significant growth trajectory of TSMC's HPC business, driven by robust demand for AI accelerators.
TSMC's Advanced Packaging in High Demand: NVIDIA Leads the Charge
TSMC also reported a surge in Advanced Packaging due to the strong demand for Nvidia’s Blackwell chips. NVIDIA has reportedly secured over 70% of TSMC's CoWoS-L capacity for 2025, with shipments expected to exceed 2 million units and grow by more than 20% each quarter, according to a report from Economic Daily News. The report also estimates that advanced packaging revenue accounted for approximately 8% of TSMC’s revenue in 2024 and is expected to exceed 10% in 2025.
TSMC, which is struggling to meet the strong demand for advanced packaging, plans to double production capacity this year to 75,000 wafers a month, according to a report from Taiwan Economic Daily. Furthermore, TSMC is projected to continue expanding CoWoS production in the coming year, reaching 90,000 wafers per month in 2026.
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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.
TSMC released its monthly revenue for February on March 10th. Revenue grew by 43.1% YoY and down (-11.3%) MoM to NT$260.01 billion. February 2025 revenue was the new high for the month. In U.S. dollar terms revenue grew by 37.3% YoY to $7.93 billion using the average exchange rate of 1 US dollar to 32.78 NT dollars. This strong performance, coupled with Foxconn's impressive 56.4% year-over-year monthly revenue growth in February, suggests robust and sustained demand for AI.
A closer look at TSMC's monthly revenue reveals that month-over-month figures can be volatile. The February decline is likely attributable to seasonal factors, such as the Lunar New Year holidays and fewer working days. For context, February 2024 also saw a MoM revenue decrease of (-15.8%). This suggests that the recent month-over-month decline is not unusual and should be considered within the context of seasonal trends.
The management had provided an update last month that they expect the Q1 revenue to be near the lower end of the guidance range of $25 billion and $25.8 billion due to the Taiwan earthquake in January. Importantly, TSMC maintains its strong outlook for the full year 2025, anticipating revenue growth in the mid-20% range in US dollar terms, driven by robust AI demand.
Sustained sequential HPC growth
As the leading foundry for AI accelerators, TSMC is riding the enormous wave of demand from Big Tech. The chipmaker’s high-performance computing (HPC) revenues rose 19% QoQ to a record $14.25 billion and accounted for 53% of revenue in Q4, surpassing the 50% mark for the third time. The sequential HPC growth for the six consecutive quarters is a further testament that there is no AI demand slowdown and demonstrates sustained momentum in the AI sector. Management expects AI accelerators to be the strongest driver of the HPC platform growth in the next several years.
The above chart shows that TSMC’s HPC sequential revenue growth tells us that they're a few quarters ahead of Nvidia's bigger quarters like the 2022 sequential growth before the launch of Hopper Architecture.
TSMC is experiencing explosive growth in its AI segment, with revenue tripling in 2024. Management expects this remarkable growth to continue, projecting a further doubling of AI revenue in 2025.
Management expects AI accelerators to grow mid-40% CAGR for the next five years and expects AI accelerators to be the strongest driver of the HPC platform growth and the largest contributor in terms of the overall incremental revenue growth in the next several years.
The chart below further emphasizes the strength of TSMC's high-performance computing (HPC) segment, with revenue reaching a record $14.25 billion in the most recent quarter. This represents the largest sequential increase to date, surging by approximately $2.26 billion. This data underscores the significant growth trajectory of TSMC's HPC business, driven by robust demand for AI accelerators.
TSMC's Advanced Packaging in High Demand: NVIDIA Leads the Charge
TSMC also reported a surge in Advanced Packaging due to the strong demand for Nvidia’s Blackwell chips. NVIDIA has reportedly secured over 70% of TSMC's CoWoS-L capacity for 2025, with shipments expected to exceed 2 million units and grow by more than 20% each quarter, according to a report from Economic Daily News. The report also estimates that advanced packaging revenue accounted for approximately 8% of TSMC’s revenue in 2024 and is expected to exceed 10% in 2025.
TSMC, which is struggling to meet the strong demand for advanced packaging, plans to double production capacity this year to 75,000 wafers a month, according to a report from Taiwan Economic Daily. Furthermore, TSMC is projected to continue expanding CoWoS production in the coming year, reaching 90,000 wafers per month in 2026.
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.