Category: AI Stocks

AMD Reports in Line while AI Story to Improve from Here 

Posted on by io-fund

AMD reported in line, yet the market’s short attention span has likely forgotten the QoQ decline in the data center was expected. Per our last earnings report writeup: 

“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.” 

Therefore, what matters for the purpose of our position is if AMD can execute and drive much better data center segment results in Q3 and Q4. For example, in the opening remarks, it was stated while citing the Oracle deal which includes 130,000 MI355s: 

“We began volume production of the MI350 series ahead of schedule in June and expect a steep production ramp in the second half of the year to support large-scale production deployments with multiple customers.” 

We go through the obligatory financials below while noting from the earnings call additional comments about the one thing that really matters – that AMD executes in H2 and further executes in 2026.  

Revenue beat driven by Client and Gaming segments: 

AMD reported a slight beat on the top line at $7.685B in revenue compared to estimates of $7.43B. This represents growth of 31.6% compared to growth of 27.4% expected. Where the beat is somewhat problematic is that it was driven by Client and Gaming, rather than the data center. 

However, in terms of a bright spot in the report and a data center inflection point, Q3 estimates were at $8.3B going into the print and management is guiding for $8.7B at the midpoint for growth of 28% YoY and 13% growth QoQ. This will be driven by data center, as was alluded to on the earnings call: “Sequentially, we expect revenue to grow by approximately 13%, driven by strong double-digit growth in the Data Center segment with the ramp of our AMD Instinct MI350 series GPU products […]” with Client expecting to see only modest growth. 

In addition, AMD estimates have been steadily rising after a trough of sorts earlier this year. For example, the September quarter was expected to see as low as 17% growth per consensus in May, yet is now at 28% per management guidance.  

Revenue Segments: Data Center declines QoQ from China Loss 

Last quarter, management had stated, “we expect data center segment to decrease due to the exclusion of MI308 revenue.” Therefore, it was not a surprise when data center was down (11.8%) QoQ yet was up 14% YoY for revenue of $3.24B. This compares to DC revenue of $3.67B last quarter and $2.84B last year.  

Here is what it looks like on a YoY basis: 

Gaming and Client exceptionally strong in Q2 

While many consumer device companies are struggling right now, AMD is breezing past consumer demand concerns with incredibly strong Client and Gaming revenue. Whether this can sustain or if it was a pull forward remains to be seen, with management guiding for Q4 to be seasonally weaker than usual. 

  • Client revenue of $2.5B up 9% QoQ and up 68% YoY 
  • Gaming revenue of $1.1B up 73.4% QoQ and up 73% YoY 
  • Embedded revenue of $824M, flat QoQ and down 4.5% YoY 

According to the opening remarks, it was not a pull forward rather the popularity of its Ryzen processors and Radeon 9000 GPUs that drove the strong performance.  

Regarding Client CPUs, it was stated: 

“We delivered record desktop channel CPU sales as Ryzen processors consistently topped the best-selling CPU lists at major global e-tailers throughout the quarter [..] In mobile, demand for AMD-powered notebooks was strong with sellout growing by a large double-digit percentage year-over-year. We drove a richer mix of higher ASP mobile parts year-over-year as we expanded our share in the premium notebook segment where our Ryzen AI 300 CPUs deliver leadership performance and value for both general purpose and AI workloads. In commercial PCs, Ryzen adoption accelerated as OEM consumption increased more than 25% year-over-year.” 

Regarding the Radeon series which drove 74% QoQ growth in Gaming, there were partnerships with Microsoft/Xbox and Sony. The following was also stated: “In PC gaming, demand for our latest-generation Radeon 9000 series GPUs was very strong, with desktop GPU sell-through accelerating in the quarter as demand outpaced supply.” 

Despite Client being strong this quarter, management cautioned this is inventory building for the holiday season and this segment will be down in the fourth quarter “strong double digits.” 

Margins down due to China; Expected to rebound quickly  

EPS was in line with expectations at $0.48 yet was down (30%) from $0.69 in the year ago quarter. The company is expected to rebound quickly with EPS of $1.15 next quarter.  

Gross margin of 40% is significantly lower than previous quarters in the 50% range. This represents profit of $3.1B due to $800M in inventory changes from expert controls. Management pointed out that minus the $800M, gross margin would have been 54%: “Excluding the $800 million inventory write-down related to data center AI export controls, gross margin was 54%, marking our sixth consecutive quarter of year-over-year margin expansion led by a richer product mix.” 

 Adjusted gross margin of 43% represents adjusted gross profit of $3.33B. Notably, the margins were guided correctly and in line with expectations following the loss of China revenue discussed in the previous quarter.  

Operating margin of (2%) for operating profits of ($134M) also included the $800M in inventory changes. Adjusted operating margin of 12% was guided correctly and was in line with expectations. Adjusted operating profits of $897M beat expectations for $882M.  

Net margin of 11.3% was 600 bps higher than the previous year and 230 bps higher than last quarter. However, adjusted net margin was down significantly by 10 points to 10.2%.  

Cash Flow margins Improve QoQ, Debt profile improves 

AMD’s cash flow margins sustained well at 20% operating cash flow margin compared to 13% last quarter and 10% OCF margin last year. Free cash flow margin of 15% also expanded from a year ago at 8% margin and up from 10% FCF margin last quarter.  

AMD has cash of $5.9B on the balance sheet and debt of $3.2B, down from $4.2B last quarter.

Earnings Call Q&A: 

Key points on how AMD plans to execute from here: 

Sovereign AI to pick up in 2026: 

In the opening remarks, management discussed its “multibillion-dollar collaboration with HUMAIN to build AI infrastructure powered entirely on AMD CPUs, GPUs and software.” The HUMAIN deal refers to a $10 billion joint venture between Saudi Arabia and AMD to create an AI hyperscaler for the country’s sovereign AI initiatives.  

According to Lisa Su during the Q&A: “So look, we're really excited about the overall AI opportunity for us with MI355 and the MI400 series as we go through the back half of this year and into 2026 […] I think you heard from Tareq that was — he was at our event saying that, that would start with MI355, that we would expect that, that would continue on. I think what's attractive about our offering is our open ecosystem, and I think that really resonates with the sovereign community. But to your original question, I think it's an additive opportunity, and it's one that we believe will continue to be very important for us going forward with both MI355 as well as the MI400 series.” 

MI350s starting to ramp: 

When asked about the size of opportunity from the MI300s and MI350 Series this year (mainly the MI350s) and if this can get to $7B, management declined to be specific yet stated: “I think what we said in the prepared remarks is that we are seeing a strong ramp from Q2 into Q3. MI355, we actually started production in June. So we had some shipments sort of in the month of June, but it really is ramping as we go through this quarter and the third quarter. So in terms of guideposts, we said it would grow year-on-year from last year. And that, I think, is a strong ramp, and then we would expect it to grow into the fourth quarter as well.”

MI400s to ramp next year including Helios: 

The MI400 series will be the start of rack-scale systems for AMD, starting with Helios, which will connect up to 72 GPUs similar to Nvidia’s NVL72 systems. According to AMD, Helios will “deliver up to a 10x generational performance increase for the most advanced Frontier models, and we believe it will be the highest-performance AI system in the world when it launches.” The last part is doubtful yet the effort to close the gap with Nvidia will likely go a long way when coupled with lower pricing.  

AMD’s goal of reaching tens of billions in MI400 sales was also elaborated on: 

“Joseph Moore   Morgan Stanley: 

You used this language before, the kind of tens of billions opportunity around MI400. Can you talk about the time frame when that might occur and not to pin you down too much, but — and what would help you get to that level sooner rather than later? Should we think of that as a 2027 realistic outcome that you could be looking at $20 billion-plus? Just a little bit more color on that tens of billions comment. 

Lisa Su   Chair, President & CEO: 

Yes. I mean, maybe without being specific, Joe, I can give you sort of the way I look at it and back to this notion of are we incrementally more confident. I think we're seeing a lot of positive signs in our AI customer adoption, I think the strength of the MI350 series, the very positive feedback that we're getting on MI400 from customers, the work that we're doing in terms of ensuring that we are fully ready for large-scale deployments of not just inference but training. 

I think when we get to tens of billions of dollars, we're talking about significant gigawatt-scale type deployments. And those would be important for us to get there. And we're certainly, I think, engaged with all of the right customers that can enable that type of ramp. But I won't necessarily speculate on the exact time other than to say, certainly, that would be our set of aspirations.” 

It was later stated: “We would expect significant revenue contribution from Helios in 2026.” 

Conclusion: 

In my Top 15 stocks report the conclusion was the following: The risk to AMD is primarily in Q2’s data center growth decline, and how quickly can the company ramp its MI355s and subsequent MI400s while in the midst of Nvidia’s large shadow – will we see a solid surprise arrive in Q3, Q4 or even into next year? My best guess is the most meaningful AMD moment is not likely to occur during Blackwell’s NVL72s release – I think 2025 belongs to Nvidia and somewhere between 2026-2027 we switch it up. 

AMD has required some patience, but 40% returns YTD are not bad. We weren’t expecting much from this report given the concise management commentary from last quarter. However, we do foresee watching this stock very closely come 2026 with a placeholder in the portfolio should AMD surprise before then.

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. Beth Kindig and the I/O Fund own shares in “AMD” at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

AI Stocks in 2025: What Every Investor Should Know

Posted on by io-fund

The market evolves quickly, and nowhere is that more apparent than in AI stocks, which continue to lead in both innovation and returns. 

At the I/O Fund, our deep coverage of AI stocks, combined with active management of crypto positions, gives us a unique vantage point. As we move into the second half of the year, we want to highlight key insights every investor should understand about where AI stocks and crypto could go next. 

Back in February, we alerted our newsletter readers that a market pullback could create prime buying opportunities in select AI names. Between April 4th–7th, we issued 12 buy alerts across six AI stocks — some of which have since gained over 100% from those lows. 

Now, with the S&P 500 fully rebounding from its April 7th bottom — a 21% drop — and hitting new all-time highs, we are growing more cautious. Despite the strength in broader markets, we’re seeing early signs of another topping pattern, which could bring renewed volatility. 

Similar to February of this year, we forsee another excellent buying opportunity in the coming weeks. This is one of the areas where we excel at the I/O Fund – we don’t only provide unparalleled analysis on AI stocks, but we back this research up with buy alerts when the risk is low. 

Leading AI Stock Nvidia Will Lose Market Share – but it Won't Matter 

Two weeks ago, in the analysis “AMD vs Nvidia: The AI Stock That Could Win by 2028,” I covered how the AI training market is where Nvidia’s strengths are nearly insurmountable as the leader in combining parallel processing (CUDA) cores with matrix computations (Tensor Cores). Over the past few years, Nvidia has increased compute power by an order of magnitude to the point of defying Moore’s Law with architectural changes such as tensor cores and lower precision floating points.   

As a reminder, training is the process of a model learning patterns from labeled data through internal parameters (called weights). There is forward and backward pass or propagation for updating the parameters. This phase is computationally intensive, requiring significant memory and parallel processing power. 

You can read more about the history of Nvidia’s GPU architectures including Blackwell in the analysis: "Here’s Why Nvidia Stock Will Reach $10 Trillion Market Cap."Here’s Why Nvidia Stock Will Reach $10 Trillion Market Cap. 

There's no point in custom silicon or AMD trying to compete with Nvidia’s lead in training. Instead, Nvidia's monopoly in AI accelerators will see a loosening of its grip as a new market begins to take off — the AI inference market.  

As discussed in my recent analysis, inference takes batches of real-world data and quickly comes back with an answer or prediction — therefore, this stage needs low latency (or speed) over raw compute power. For example, inference will take a trained model and produce a probable match for new data in milliseconds. While it can be compute-intensive for large models like GPT-4, inference generally prioritizes low latency, higher efficiency, and lower cost.   

In many applications, it makes sense to run inference at the edge (closer to where data is generated). However, cloud inference is still widely used for models that are too large or resource-demanding to deploy on local devices. Compared to training, inference requires only the forward pass through the model, making it more efficient in terms of power and hardware demands.  

Nvidia will continue to be the leader, yet the 92% market share the GPU-leader commands today will erode over the next few years as inference is an easier market for a few select, strong competitors to rival Nvidia.  

However, this part is important: Nvidia does not need a monopoly at 92% on AI accelerators to extend its stock gains. The company has an outsized opportunity with AI software including autonomous vehicles. Last month, I was in New York and visited Charles Payne live in-studio to discuss why the most shocking moment for Nvidia is still ahead.

Why AI Stocks Could Soar: The $255 Billion Inference Opportunity Starts Now 

Token usage is exploding, which is a key metric that helps to illustrate the sudden, rapid growth of the inference market for stock investors. 

In the most recent earnings report, Microsoft reported 5X YoY growth to 100 trillion tokens whereas Alphabet reported 9X growth to 480 trillion tokens. OpenAI also announced in June they had crossed $10 billion in ARR, nearly doubling from $5.5 billion at the end of 2024. Anthropic’s ARR rose 200% in five months and 50% in 2 months to $3 billion. 

Last week, I spoke with Charles Payne about the $255 billion opportunity in this market and how it’s the sudden burst of activity from $0 to $255 billion that makes it especially attractive to investors.  

You can read more on why the inference market is heating up the Nvidia versus AMD stock debate, which I predict will have an ending few stock investors are prepared for.Nvidia versus AMD stock debate, which I predict will have an ending few stock investors are prepared for. 

Big Tech Operating Margins Will Offset Capex; But the Growth Story Will Lag 

Microsoft stood out this past earnings season due to Azure being the only cloud provider of the three platforms to see growth accelerate last quarter. Not only did Azure separate itself with this 4-point sequential growth acceleration, but it also grew at more than 2x the rate of AWS and 7 points faster than Google Cloud, reaffirming the company’s momentum in the Azure vs AWS vs Google Cloud battle.  

Despite lumpy Azure growth, our firm has been quite clear we foresaw Microsoft being the top winner in AI.Microsoft being the top winner in AI. 

Over the longer-term, Azure is expected to outperform both AWS and GCP through 2026, according to estimates from UBS. For 2025, Microsoft Azure growth is projected at 28.6% YoY to $83.3 billion, outpacing both AWS at 16.8% and Google Cloud at 25.3%, according to UBS. UBS also forecasts Azure to maintain a 28% growth rate in 2026 to $106.7 billion in revenue, whereas GCP is forecast to decelerate to 22% and AWS to >16% YoY.   

Microsoft Azure growth of 35% outpaced AWS growth of 17% and Google Cloud growth of 28%

Margins are likely to improve, however, even for those companies that are not seeing growth accelerate from AI just yet. Big Tech companies such as Microsoft announced an additional 9,000 layoffs this week for a total of 16,000 this year. Amazon announced in March plans to lay off 14,000 managerial roles for a total of 18,000 layoffsthis year with Alphabet at 12,000 layoffs this year.  

Although it’s common for Big Tech to have layoffs given the sheer size of their global workforces, the YTD layoffs amount to the yearly layoff numbers (roughly) with half of the year left to go. 

Additionally, Meta’s CEO has openly stated their goal is to replace developers with AI in 2025, stating in the last earnings report: “So I'd say it's basically still on track for something around a mid-level engineer kind of starting to become possible sometime this year, scaling into next year. So I'd expect that by the middle to end of next year, AI coding agents are going to be doing a substantial part of AI research and development. So we're focused on that.” 

I suspect Big Tech is already seeing massive productivity gains internally, which is why the bottom line continues to expand. For Big Tech, EPS growth is outpacing revenue growth. This can be achieved by using AI to replace engineers, sales and marketing, and HR departments, for example. The first companies to replace humans with AI will naturally be the Mag 7 as they are far ahead in the AI race compared to enterprise companies. 

Don’t Snooze; Nvidia’s Blackwell is Coming 

Nvidia has struggled to breakout and meaningfully hold all-time highs and the market is now snoozing on the stock. Our firm was early to warn investors that Nvidia was topping stating the I/O Fund was not buying Nvidia in October and offering additional analysis in early January that Nvidia’s stock was topping with a setup that pointed toward getting Nvidia at $101, $90 or $78.  

As a reminder, most analysts and research firms do not offer performance records alongside their analysis, whereas for five years the I/O Fund has outperformed other tech portfolios. This helps to illustrate why being a bull (or a bear) on any given stock is missing the point; we are here to make money and will gladly let our readers know if it’s time to sidestep a stock for 6 months or longer.  

We continue to own Nvidia – yet six months ago, we built bigger positions in other AI stocks. With that said, I believe Nvidia will return to lead the market in the second half of the year as Blackwell is (finally) arriving. 

Nvidia's premiere Blackwell SKU called the GB200 NVL72 delivers real-time trillion-parameter LLM inference, 4X LLM training, 25X energy efficiency, and 18X data processing. The GB200 also provides 4X faster training performance than the H100 HGX systems and includes a second-generation transformer engine with FP4/FP6 Tensor core. The 4nm process integrates two GPU dies connected with 10 TB/s NVLink with 208 billion transistors.

According to management commentary, the ramp is happening very quickly: “On average, major hyperscalers are each deploying nearly 1,000 NVL72 racks or 72,000 Blackwell GPUs per week and are on track to further ramp output this quarter.” The rough math here implies hyperscalers are deploying $3 billion every week right now since each rack goes for $3 million.

Nvidia will also be a leader in AI inference with the B200 helping startups to triple their token generation rate with Nvidia Dynamo on Blackwell NVL72s stated to “turbocharge inference throughput 30X for the new reasoning models” […] with the CEO later stating: “in the latest MLPerf Inference results, we submitted our first results using GB200 NVL72, delivering up to 30X higher inference throughput compared to our 8-GPU H200 submission on the challenging Llama 3.1 benchmark.” 

To put it plainly, you haven’t seen anything yet in terms of Nvidia’s hardware capabilities. The generation that is shipping now is by far Nvidia’s most ambitious and with a $3 million price tag for its largest systems, any Big Tech company that goes elsewhere will lag its peers, and that’s something Big Tech is not willing to chance. Look for not only Blackwell, but also the very rapid release of Blackwell Ultra in quick succession to be the moment when Nvidia defies the markets (yet again).

Lastly, What’s Next for Bitcoin: 

Since December of 2022, when Bitcoin was trading in the $16,000 region, we went against the crowd and called for a new bull cycle. Since that report, we released seven additional articles, confirming Bitcoin as a buy, and even sent out 13 buy alerts to our premium members at key spots between $25,000 and up to $60,000. 

While the narratives around Bitcoin support higher prices, history has shown that investing in Bitcoin without risk management can be painful. Bitcoin tends to do the opposite of what the narratives suggest at major turning points. To better prepare for the immense volatility in crypto, we lean into our process of analyzing sentiment through technical analysis and shifting our risk profile based on where we are in the uptrend. 

You can read more about our Bitcoin strategy in the analysis “Nvidia Q1/Q2 Guide: Blackwell is (Finally) Here”Nvidia Q1/Q2 Guide: Blackwell is (Finally) Here” 

There is still a scenario where Bitcoin can push toward the $200,000 region in a final swing higher. The video below outlines what we are looking for in order to position accordingly. 

Our AI Investment Strategy for 2025 and 2026:

Subscribe Below for Free to Access the Following:

  • AI investment strategy for 2025 and 2026 — including the single most important AI trend every investor should be positioning for, as well as the broader market setup that could define portfolio returns this year.
  • To help investors navigate what’s ahead, we’re also including two free videos that offer a high-level view of how we’re preparing for the next wave of market volatility — and how we plan to capitalize on the powerful tailwinds in AI heading into 2025 and 2026. 

Our AI Investment Strategy for 2025 and 2026: 

The #1 AI Trend to Position for …  

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

Beyond Blackwell, Nvidia’s future design lineup shows continual increases in power consumption. Its Rubin generation is expected to boost thermal design power (TDP) by 50% over Blackwell at up to 180 kW per rack, with the upgraded Vera Rubin then doubling this to 360 kW per rack by 2027. In its largest configuration, the Vera Rubin NVL576, dubbed the ‘Kyber’ rack, could draw as much as 600 kW (0.6 MW), or 5x that of the GB200 NVL72 in just a two-year design timeframe.  

Additionally, researchers from KAIST predict that the accelerator industry could see server racks as large as 1.54MW by 2032, or more than 12x growth from the GB200s in seven years.  

In the clip below, I outline ways to approach AI data energy stocks and the one thing that I believe will separate successful energy stocks from those that lag as we go into the next 1-2 years.

Broad Market: The levels we are watching to either sell/hedge or resume buying 

The bounce off the April low has traced a recognizable and fully formed pattern. Not only is the pattern complete, but it is also quite stretched, with all the waves in place. This is a typical signal we see around a market turn. Furthermore, this is also happening on decreasing momentum and volume, as the S&P 500 pushes higher. This is also a common signal we see around the end of a pattern, suggesting that buyers are not as abundant as the higher the market pushes.  

Until the S&P 500 breaks below 6100, we can continue to see the market drift higher. However, once we do break below 6100, we will likely be in the early stages of the expected correction. The nature of this drop will be very important in how we plan to position for the rest of 2025. There are two general scenarios that we are tracking are:  

  • Green – If the coming drop is a messy and overlapping correction that holds within the 5768 – 5345 region, it will be setting up another excellent buying opportunity. This scenario would suggest a move toward the 6500 – 7000 region into year-end. 
  • Red – If the coming drop is an aggressive and relatively direct drop that breaks through 5345, the odds will be quite high that we see a drop below the April lows.
Chart analysis showing potential market drop below April low with hedge strategy considerations by I/O Fund

Because we can outline a reasonable scenario across multiple markets where we do see a drop below the April low, once the market breaks 6100, we will likely add to our hedge. We prefer to lean into a defensive posture until the market tips its hat with one of the general scenarios outlined above.  

While tech portfolios are broadly outperforming in 2025, one factor remains under-discussed: as markets rise, so does risk. That’s where disciplined risk management becomes critical. Thanks to our tactical approach, our firm has delivered strong performance. Over the past five years, our cumulative returns would rank us #2 among hedge funds and #5 among ETFs. Get $100 off our Advanced tier by clicking here.2025, one factor remains under-discussed: as markets rise, so does risk. That’s where disciplined risk management becomes critical. Thanks to our tactical approach, our firm has delivered strong performance. Over the past five years, our cumulative returns would rank us #2 among hedge funds and #5 among ETFs. Get $100 off our Advanced tier by clicking here.

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

Recommended Reading:

Nuclear Power Emerging as a Clean AI Data Center Energy Source

Posted on by io-fund

Nuclear energy is emerging as a cleaner solution to power future AI data centers, which require constant, clean, and reliable baseload electricity to meet around-the-clock AI demand. Over the past year, interest in nuclear has accelerated, with Big Tech signing multiple nuclear power purchase agreements while US policy aims for accelerated development of the industry. 

Inference is expected to be a primary factor in surging power demand in AI data centers. Power demand for inference tasks is projected to increase at a 122% CAGR through 2028, as providers work to serve billions of requests and process hundreds of trillions of tokens. Big Tech is already showing signs of explosive inference growth with token generation up 5x to 9x YoY.  

As AI data centers push for scalable, clean energy sources, nuclear energy is receiving renewed attention despite having higher costs and some of the longest time to power in the industry.  

Below, we discuss nuclear energy’s potential to aid growth in AI data center power demand, its advantages and drawbacks, plus Big Tech’s increased interest in nuclear, including a record-setting deal, and more.  

GPU Power Consumption Continues to Soar 

One year ago, we first discussed how quickly power consumption was increasing with new GPUs in the analysis AI Power Consumption: Rapidly Becoming Mission-Critical. This trend is set to continue with Nvidia pushing towards an ultimate goal of super-sized 1MW server racks, or 8x more than GB200 racks.  

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

Beyond Blackwell, Nvidia’s future design lineup shows continual increases in power consumption. Its Rubin generation is expected to boost thermal design power (TDP) by 50% over Blackwell at up to 180 kW per rack, with the upgraded Vera Rubin then doubling this to 360 kW per rack by 2027. In its largest configuration, the Vera Rubin NVL576, dubbed the ‘Kyber’ rack, could draw as much as 600 kW (0.6 MW), or 5x that of the GB200 NVL72 in just a two-year design timeframe. 

This is paving the way for the path to 1 MW GPU server racks by the early 2030s. While not much is known about Nvidia’s Feynman generation, it is also likely to bring higher TDP versus Rubin, and a possible shift from direct-to-chip cooling to immersion cooling to handle immense thermal needs. Additionally, researchers from KAIST predict that the accelerator industry could see server racks as large as 1.54MW by 2032, or more than 12x growth from the GB200s in seven years. 

This continuous upgrade cycle to more powerful GPUs is likely to further boost data center electricity demand due to the sheer increase in TDP that each generation brings combined with a path to larger cluster sizes. However, moving to 1MW servers and beyond will likely require significant advancements in cooling tech and widescale commercialization of immersion cooling to handle these intense thermal needs.  

AI Data Center Electricity Demand Forecasts Show Major Growth

Driven by the explosion in AI demand over the past two years, this current acceleration in inference, and increasingly power hungry GPUs, AI data center electricity demand is forecasted to surge over the next few years. We have a handful of different viewpoints and projections from analysts and industry groups that, while differing slightly in timelines and scope, all point to the same conclusion.  

First, let’s put in perspective how much power data centers need. For example, OpenAI’s Stargate data center in Abilene, Texas is expected to have a 1.2 GW capacity with its second phase under construction, or enough power to supply approximately 1 million homes. When you factor in Nvidia saying that it has visibility into tens of gigawatts of projects, that would be the equivalent of tens of millions of homes that the power grid will soon need to account for.  

In a shorter-term view, Boston Consulting Group forecasts global data center power demand to rise at a 16% CAGR from 2023 to 2028, accelerating from a 12% CAGR. Hyperscalers are projected to account for 60% of this demand growth.  

Within BCG’s forecast, generative AI power demand is estimated to rise at a 65% CAGR, with AI training increasing at a 30% CAGR and inference rising at a rapid 122% CAGR. By 2028, BCG estimates gen AI will account for more than one-third of global data center power demand.  

Chart showing acceleration in global data center power demand from 71 GW in 2024 to 127 GW by 2028, driven by generative AI and inference.

Global data center power demand is expected to accelerate to a 16% CAGR through 2028, driven by generative AI and inference demand. Source: BCGBCG 

Deloitte similarly sees surging growth for power capacity in the US, forecasting 5x growth over the next decade. The firm estimated US data center power capacity to rise 24% from 33 GW in 2024 to 41 GW in 2025, before tripling to 120 GW by 2030 and rising further to 176 GW by 2035.  

Goldman Sachs estimated global data center power usage at 55 GW in early 2025, far below BCG’s 82 GW figure. However, GS projects power usage to reach 84 GW in 2027 and increase further to 122 GW by 2030. 

AI Data Center Electricity Share Could Reach Double-Digits 

In terms of electricity share, AI is expected to account for a much larger proportion of demand by 2030, especially in data-center heavy regions.  

The Electric Power Research Institute forecasts that data centers may see electricity consumption more than double by 2030, to account for 9% of the US’ total electricity demand. Globally, a report from SPhotonix estimates that data centers could account for 13% of total electricity demand by 2030. 

The Department of Energy projects that data center demand could nearly triple by 2028 in its high-end scenario, accounting for 12% of the US’ total demand, compared to just 4.4% in 2023. The agency’s low-end scenario projects data centers reaching 6.7% of total demand. Meeting this increase in demand in such a short time could require between 33 GW to 91 GW of new generation capacity.  

However, in more localized regions that have concentrated data center presence, such as Northern Virginia, data center electricity demand may be far higher and place further strain on the local grid. For example, Northern Virginia has more than 5.9 GW of data centers in operation, 1.8 GW under construction, and another 15.4 GW of planned projects. Per the EPRI, data centers already account for 25% of Virginia’s electricity demand, amplifying concerns that this demand will outstrip supply and cause rolling blackouts. This does not even account for the 3x growth in data centers based on the planned project backlog. 

Why Nuclear is Emerging to Serve AI Data Center Power Needs 

Given that time to power has been floated as a constraint by Big Tech executives recently, it’s important to touch on why nuclear is being named to address rising power demand considering other fuel sources can have much quicker time to power. Nuclear could add dozens of GW to the grid to serve data center needs, with up to 174 GW of capacity potentially able to be retrofitted at existing power plant sites. 

Compared to other fuel sources such as coal, solar and wind, nuclear provides a few key advantages for AI-focused data centers: 

Reliable baseload energy source: Unlike solar, wind and natural gas, nuclear provides data centers with access to highly efficient, reliable baseload power. Nuclear’s capacity factor (ratio of electrical output vs maximum capacity) can exceed 92.5%, far outpacing other renewable or preferred power sources, including wind at a 35% capacity factor (CF), solar at 25%, and natural gas at 56%. Nuclear is also not reliant on weather conditions and reduces interruptions that may be faced with wind or solar.  

High energy density and zero emissions: Nuclear is highly dense, with nuclear power plants producing around 1 GW on average, or enough for five 200MW data centers per plant. Nuclear is also virtually emission-free, aiding countries or providers in meeting rising electricity demand while aligning with net-zero commitments. As seen in the graphic below, based on the average use per person of 235,000 kWh/year, nuclear’s fuel requirements are <2% of other common fuel sources with far fewer emissions. 

Graph showing nuclear energy fuel requirements and corresponding emissions versus coal, oil and gas.

Nuclear requires far less fuel than coal, oil or natural gas to produce equivalent output, with minimal emissions. Source: IEAIEA 

Scalability: Due to its high density, nuclear’s high output per plant makes it a suitable choice for larger data centers, as a single reactor could meet the needs of a large hyperscale data center campus or power multiple smaller data centers if used solely for that purpose. 

Grid stability and on-site needs: Co-locating nuclear with AI data centers can reduce stress on the grid as nuclear’s high output could limit reliance on existing grid infrastructure, while excess power generated could be returned to the grid. Modular reactors also promise ease of providing on-site power generation either on or off grid. In the case of Northern Virginia, nuclear could ease pressure on the grid given the substantial backlog of projects planned in the region. 

Large existing footprint: A substantial amount of nuclear power could come from retrofitting existing sites, with analysts from Goldman Sachs estimating that between 60 GW to 95 GW of new capacity could use existing sites, reducing costs and construction timelines. It’s also estimated that anywhere between 128 to 174 GW of nuclear capacity could be retrofitted at operating or retired coal plants. 

Small modular reactor (SMR) tech: SMRs are emerging as they promise quicker time to power with shorter construction times and lower costs, while offering more flexibility in deployment versus a large-scale plant. SMRs could offer up to 300MW capacity, able to power larger data center campuses without supporting infrastructure. However, SMRs are far from full-scale commercialization, with the first reactors likely not coming online until around 2030. 

For additional reading, we have covered other data center power sources in these articles:  

A Note on Increased Policy Support 

While Big Tech’s quick turn to nuclear over the past year is supporting prospects of reigniting the industry at large, increased policy support from the current administration also serves as a tailwind.  

President Trump signed four executive orders targeting accelerated nuclear deployment and setting a goal of quadrupling US nuclear output by 2050. The orders call for increased uranium mining and enrichment capabilities to bolster the domestic supply chain, as well as accelerated testing of advanced reactor designs including SMRs and faster regulatory approval processes.  

Last week, the DOE announced a new program to help streamline the approval process and unlock private funding for advanced reactors and SMRs, aiming to have “at least three reactors achieve criticality by July 4, 2026.” Initial applications are due by July 21, 2025.  

Nuclear Energy has a Few Key Disadvantages  

Although nuclear has been gaining traction for AI data center needs, there are a few key downsides, most notable time to power and cost: 

High capex requirements: Capex for nuclear power plants is estimated to be 5x to 10x that of using natural gas, with nuclear costing between $6,417 to $12,681 per kW compared to $1,290 per kW for natural gas. Deloitte says restarting retired plants can significantly lower capex compared to new construction, with an estimated cost of approx. $6.2B for three plants with 2 GW capacity versus $37B for the same capacity in new construction.  

Long time to power: Nuclear faces long construction timelines, with large reactors (1 GW) taking between five years to nearly 11 years from breaking ground to connection to grid. Though slightly quicker, SMRs can still require nearly four to six year timelines. With power being a primary constraint and time-to-power at the forefront of discussions for Big Tech executives, nuclear’s prolonged construction may make it a story for 2030 and beyond, given that solar, fuel cells, and natural gas provide quicker alternative options.  

Cost and time overruns: Nuclear plants often see delays and higher costs than expected, and this is not isolated to the US. Per the IEA, nuclear projects in the US often see up to 2.6x overruns on cost and time in years, while France sees overruns greater than 3x. Break-even point for new builds tends to be ~30 years after breaking ground, with overruns potentially pushing this farther into the future.

Chart showing nuclear energy power plant cost and time overruns by country.

Nuclear power plant projects often face significant time and cost overruns, prolonging lengthy construction timelines and adding to high costs. Source: IEAIEA 

Low thermal efficiency: Despite having a high capacity factor, nuclear has a rather low thermal efficiency, meaning more of its power is lost to heat. Nuclear’s thermal efficiency is typically between 33% to 40% depending on reactor type, comparable to natural gas at 35% to 42% on a simple cycle gas turbine. However, when using combined cycle gas turbines, natural gas could see its thermal efficiency as high as 62%, making it more efficient and quicker to stand up than nuclear.  

Utilities Poised to Benefit from Big Tech Data Center Partnerships

Subscribe Below for Free to Access the Following: 

  • Info on new partnerships from Big Tech including a record-breaking multi-billion dollar nuclear deal 
  • An overview of nuclear stocks poised to benefit from increased AI data center power demand  
  • The one fuel source that is filling the gaps and meeting immediate power needs for Big Tech

Big Tech is showing an inclination to back nuclear, signing a handful of large contracts for multiple GW of capacity over the last year alone. However, a majority of these projects are unlikely to be ready by the end of the decade.  

Less than two weeks ago, Amazon and Talen Energy restructured and expanded their partnership into what would be the largest nuclear power purchase agreement (PPA) in history. The two finalized a 17-year, $18 billion deal scaling up to 1.92 GW of power from Talen’s Susquehanna plant. Amazon had previously acquired the collocated data center from Talen for $650 million in 2024, though regulatory headwinds had plagued the PPA. The new deal will see Talen provide energy to Amazon’s data center through 2042, while the two will also explore expanding output or developing SMRs in the future.  

Also earlier in June, Meta signed a 20-year PPA with Constellation Energy to purchase 1.1 GW of power from Constellation’s Illinois plant to meet growing AI power needs. The deal will come into effect in 2027, though it will not power Meta’s data centers directly but rather return power to the grid. This is also separate from Meta’s broader push to have 1-4GW of new nuclear capacity in the US beginning in the early 2030s. 

This follows a similar 20-year PPA deal between Constellation and Microsoft last year to restore the Three Mile Island Unit 1 nuclear by 2028, providing ~0.84 GW of power for Microsoft’s AI data centers. Constellation provided an update Wednesday morning, saying that TMI could restart by 2027, nearly a year ahead of schedule. 

Big Tech Also Exploring SMRs for AI Data Center Needs 

While the largest deals to date have been PPAs with power providers, Big Tech is also exploring SMRs with startups, though these are more focused on deployment timelines beyond 2030. 

In May, Google partnered with Elementl Power to provide capital for three project sites for advanced nuclear reactors, with each producing up to 0.6 GW. Google also partnered with Kairos Power last year to deploy a 0.5 GW fleet of SMRs by 2035. Both are a part of the search giant’s goal of bringing 10 GW of nuclear capacity online by 2035. 

Last year, Amazon announced partnerships with Energy Northwest, X-Energy and Dominion to explore SMR development through the 2030s. X-Energy is expected to deploy four reactors for 0.32 GW of power in the mid-2030s, with a goal of bringing five GW online by 2039.  

In late 2023, colocation and server management firm Standard Power selected NuScale Power’s SMR tech for two facilities it was planning to develop in Pennsylvania and Ohio to power data centers in the region. NuScale is expected to provide 24 units of 77 MWe SMR modules for combined capacity of 1.85 GW, though there is not a set timeline for delivery. The 77Mwe module just received NRC approval at the end of May.  

Natural Gas to Fill Gaps in Meeting Data Center Demand 

Though Big Tech is deploying more resources towards nuclear development, natural gas remains much more suitable and the preferred choice to meet immediate power needs. This is because natural gas is readily available and easily dispatchable over 3 million miles of pipelines, and more efficient than other renewable sources or coal. 

Utility providers are ramping up natural gas in the short term for AI data center needs, while industry executives have been outspoken about the importance of natural gas to fill gaps until nuclear is viable. 

For example, a report from Data Center Dynamics states that “utilities serving the Carolina, Georgia, and Virginia markets have announced plans to add 20GW of new natural gas generation capacity by 2040, with two-thirds of forecasted load growth tied to new data center capacity.” Duke Energy has also noted discussions with hyperscalers about accelerating projects to meet higher demand, with more than half of Duke’s project queue tied to data centers.  

Additionally, ConocoPhillips CEO Ryan Lance expects natural gas to play a much larger role in meeting demand over the next few years, while Talen Energy CEO Mac McFarland expects gas will have to fill the gap in the near-term until SMRs are ready. 

AI Data Center Energy Stocks 

Nuclear stocks that have a focus on AI data center energy needs are rather few and far between, though utility providers and companies in the uranium supply chain are also indirectly exposed to growing demand.  

On the utilities side, Constellation and Talen lead the way with strong engagements from Big Tech recently. Constellation boasts the largest nuclear fleet in the US with 21 reactors, producing 45,582 GWh of electricity in Q1. Dominion Energy plays a key role in Northern Virginia and is working with Amazon on exploring SMR development, while Duke Energy has a larger presence in the Southeast.  

In advanced reactors and SMRs, Oklo notably has one of the largest singular nuclear data center power agreements, a 12 GW Master Power Agreement with data center designer Switch. NuScale expects to receive its first firm customer order this year, and its 77 MWe reactor design recently received NRC approval. Nano Nuclear is developing four different microreactor designs, though it does not expect to commercialize its reactors until 2030 or beyond. GE Vernova also is developing SMRs, though its boiling-water reactors are currently a core part of the industry with 40 in operation.  

In the supply chain, companies involved in uranium development, mining and production include Cameco, BHP, Uranium Energy, NexGen Energy and Energy Fuels.  

Conclusion

Nuclear has seen a resurgence recently as it offers a solution to growing AI data center power demand needs as a clean, 24/7 baseload power source. Big Tech has signed numerous commitments to explore SMR tech and purchase multiple GWs of nuclear power to fuel AI data centers, bolstered by Amazon and Talen’s record-setting $18 billion deal. 

While nuclear offers a few key advantages from output to low emissions, its major drawbacks of high costs and lengthy construction timelines fail to solve the main bottleneck of the AI data center industry – time to power. Thus, the prospects of the industry are much more oriented towards 2030 and beyond, while gas may still be at the forefront of solving critical gaps in power demand and supply in the near-term. 

In July for our Premium members, we will dig deeper into a handful of nuclear-exposed stocks that may be poised to benefit from data center demand growth. Learn more about our Premium services here.Premium services here.

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

Recommended Reading:

Innodata: Early-Stage AI Data Engineering; Lumpy Growth 

Posted on by io-fund

Innodata is a company that has lumpy growth yet is also centered in the surging trend of AI data engineering, known as data-as-a-service (DaaS) which offers curated and synthetic data to augment large language models (LLMs). Notably, the company is a small cap, high risk stock. 

Complex reasoning models require an expanded data set, such as dozens of foreign languages or multi-step problems within math and chemistry, for example. This is in contrast to a static data set, which often produces too many hallucinations and can be inaccurate at times. For example, if a Big Tech company only used its proprietary social data to train LLMs, this may not be broad enough to prevent hallucinations since social data is limited in its context and scope. In many cases, additional data points are sought out to improve the accuracy of the model. 

In order to move toward general artificial intelligence (AGI), which is defined as AI models that think for themselves similar to a human, companies like Innodata are also tapped for their ability to augment accuracy through reinforcement learning and direct preference optimization, which utilizes subject matter experts to annotate data and to also stress-test the models for accuracy.  

Overview of Innodata’s Solutions: 

The problem Innodata aims to solve is to help generative AI improve its multimodal reasoning skills and to help the accuracy of agentic models. The definition of an agentic model is when the model is more proactive, has multi-layered memory for knowledge across sessions, and eventually will work across a multi-agent ecosystem with an orchestrator. Although very few enterprises use agentic models today, Big Tech and other enterprises rely on data solutions such as Innodata’s to build out the next level of complex problems that AI can solve. 

The CEO stated the following on the most recent earnings call in terms of the problem their solutions are aimed to solve: “As models grow more sophisticated, their performance hinges not just on raw computational power, but also on the breadth, depth and quality of the data they are trained on. Continuous data acquisition enables the models to better understand nuance, context, and intent across languages and domains.” 

Here is an overview of Innodata’s solutions and how they’re used: 

  • Fine tuning is using curated and synthetic data to expand the list of tasks and subtasks to where Innodata offers hundreds of capabilities through its data sets, such as programming tasks (coding), content creation (emails, papers, checklists), logic and semantics (sentiment analysis), multi-modal reasoning (input from audio, visual and text for more nuanced comprehension), etc. The list is quite long as to how synthetic data can augment proprietary data.
  • Model scoring, risk mitigation and red-teaming refers to stress-testing AI systems for vulnerabilities. It’s a common practice in cybersecurity that Innodata provides for generative AI models to help surface any biases or inaccuracies. Model scoring helps to rank a model compared to frontier models (i.e., your model is X% less accurate than Chat-GPT 4o).
  • Reinforcement learning from human feedback (RLHF): Generative AI requires human feedback to spot inaccuracies with expert annotators to help LLMs reflect the complexity of human interactions. The company advertises that it has 5,000 subject matter experts located globally who oversee a reward model.
  • Direct Preference Optimization (DPO) also uses feedback but is a more refined process due to optimizing models by assigning high probability or low probability to two outcomes. This offers a faster feedback loop as the model can more quickly learn from the higher probabilities to improve accuracy.  

Partnership with Nvidia’s NIM Microservices: 

Although very early stage and still in beta testing, Innodata announced a new platform at Nvidia’s GTC 2025 Conference. The company is partnering with Nvidia’s NIM microservices to help facilitate LLM development across enterprises. 

Nvidia’s NIM microservices is essentially an app store for LLMs, which offers foundation models, inference engines and APIs in out-of-the-box software containers for enterprises to easily build and deploy customized LLMs. Innodata helps by providing stress-testing and reinforcement learning/direct preference optimization to fine tune the models. 

Meta Invests $14.3 Billion into Scale AI 

Scale AI is a major competitor to Innodata that also annotates data with a global team of contractors. Scale AI was recently in the news following a $14.3 billion investment by Meta, which helps to underscore the importance of data engineering platforms and Data-as-a-Service (DaaS) for the purpose of fine-tuning large language models.  

Scale AI has a particular specialty in autonomous vehicles as the company helps companies like Waymo and Tesla label objects from lidar sensors and video frames. Reinforcement learning from human feedback (RLHF) — discussed above – is then used to improve the quality of the response. 

Following Meta’s investment, it was rumored that Google, OpenAI and Tesla are looking elsewhere to avoid strengthening Meta at the cost of their proprietary data. Although it’s speculative, the exodus of major players from Scale AI could become a tailwind for Innodata.  

While Innodata’s partnership with Nvidia is a boon, one reason that Innodata may struggle to capture the business is the company is vintage with an inception date in the 1980s. The other data labeling/tooling companies are native AI companies with API-first data pipelines. To contrast, Innodata has roots in legal, healthcare, publishing and PR content whereas these other companies were founded with natural language processing (NLP) in mind. 

For example, there are other private companies that stand to benefit as well, such as Labelbox, Appen (public company in Australia) and SuperAnnotate. From there, startups such as SnorkelAI also compete by relying on automated labeling, although it’s likely the workforce behind companies like Scale AI and Innodata is what's attractive to Big Tech given automation is an area where they lead. 

ScaleAI is valued at $29 billion compared to Innodata’s $1.5 billion market cap with last year’s reported revenue of $870 million last year. If we assume Scale AI is at $1 billion revenue now, that would be a 29X compared to Innodata’s 6X forward sales. 

Big Tech Seeking Data Quality as Differentiator  

If we read between the lines on the Meta $14B investment into Scale AI, then what we see is an emphasis on data quality as a key differentiator for frontier LLMs, such as Meta’s Llama, OpenAI’s Chat-GPT or even proprietary models for Waymo and Tesla’s autonomous vehicles. While we’ve heard companies like Palantir state LLMs will become commoditized, I will stick my neck out here to say I think Alex Karp is oversimplifying the quality of LLMs.  

Last month, I asked a question of Chat-GPT 4.1 about export licensing under the Trump Administration to help ascertain if a specific semiconductor was subject to export licensing due to manufacturing partners in Hong Kong and this was the response: 

Pictured above: Chat-GPT4o hallucination on simple, basic facts from a query dated May 20th, 2025 

Chat-GPT updates its training data about once per year with this example showing the limitations of lower quality data in terms of frequency of updates and/or limited resources for new data. 

As with all technologies, we are in the hype cycle for LLMs which precedes a period of mass consolidation. Meta knows it must be competitive on data quality, and clearly, its proprietary social data is not able to produce a broad level of intelligence in order to compete with a company like Google or OpenAI when comparing recent benchmarks  

Source: CapeStart 

Innodata’s Financials: Triple-Digit but Lumpy Growth; Anything Could Happen  

Innodata is a high beta stock with a $1.5B market cap and $241M estimated for fiscal year 2025 revenue. The company reported three consecutive quarters of triple-digit topline growth in Q1 with revenue rising 120.1% YoY to $58.3 million, marginally ahead of estimates for $57.6 million. Although revenue growth slowed by over 6 percentage points sequentially, it is expected to decline even more sharply in the coming quarters.  

For Q2, analyst estimates point to revenue growth decelerating nearly 50 points to the 73% range, before slowing to the low double-digits against peak growth comps. Management did not provide any quarterly guidance for Q2, though they maintained FY25 revenue growth guidance of 40% YoY, suggesting that with what management knew at the time of the earnings call, revenue growth is expected to follow this trajectory of a sharp back-half deceleration. 

However, it is important to keep in mind the fluid nature of Innodata’s business, and that any new contractual agreements or expansions could have a large and/or immediate impact on revenue. For example, in FY24 Innodata had originally guided for 20% YoY revenue growth, before raising that to >40% in Q1, then >60% in Q2 and ultimately to 88% to 92% YoY by Q3. Such a dynamic occurring again this year cannot be quickly written off, given that management is upfront about current engagements and prospective discussions with Big Tech customers. 

Customer Update: “Mag 7” and “Big Tech” Mentioned Repeatedly on ER Call 

Management provided a handful of updates on existing Big Tech customer expansions (which includes five of the Mag 7) and discussions with prospective customers in Q1. Keep in mind, the fiscal year revenue estimates right now are for $241 million yet discussions around SOWs present a strong case for Innodata exceeding this estimate as the year plays out: 

  • Innodata signed a second statement-of-work (SOW) with its largest customer, which as of Q4, was contributing revenue at a $135 million annualized rate, up more than 22% in two quarters on new expansions in Q4 and January.  
  • A Big Tech customer (noted to be one of the most valuable software companies in the world) was said to have a late-stage pipeline potentially valued up to “more than $25 million of bookings this year and continued growth over the next several years.” This customer began working with Innodata in Q2 ’24 and contributed just $0.4 million in revenue in FY24.  
  • Another Big Tech customer recently signed one expansion deal and is expected to soon sign a second expansion, worth a combined $1.3 million in potential revenue. Management said there is another opportunity with this customer worth up to $6 million, and for comparison, the customer generated just $0.2 million in FY24. 
  • Management said they signed a deal in Q1 with “one of the most highly regarded generative AI labs” worth $0.9 million, with expansion potential worth double that figure. 

To note, Innodata’s largest customer is by far its most important, as a $135 million annualized rate implies this customer is contributing nearly $34 million quarterly, or around 58% of Q1’s revenue. This is a rather significant customer concentration, in that any lost revenue from this customer would not easily be made up from others, as deal sizes touted by management in Q1 pale in comparison.  

With that said, the shakeup around Scale AI and the growing importance around data engineering, plus Innodata’s partnership with Nvidia would help level out the customer concentration by attracting more large customers.  

On the call, it was stated that Innodata is working on building 200 autonomous agents with its largest customer worth approximately $6 million at the onset: 

“With one of our smaller big tech relationships, one that I discussed a few minutes ago, we have begun a collaboration around both AI agent data set creation and AI agent building. The work we are hoping to kick off with them this quarter will involve creating approximately 200 conversational and autonomous agents across multiple domains.” 

Key Segments 

Innodata’s Digital Data Solutions (DDS) segment is the primary driver of this sharp growth acceleration and improvement in profitability in FY24 and FY25. The segment handles AI data preparation, labeling and annotation, AI training and related services.  

The Synodex segment transforms medical records into usable digital data for customers, while its Agility segment provides a platform for PR and communications professionals to target and distribute content to journalists and influencers globally. 

  • DDS revenue in Q1 rose 158% YoY to $50.8 million, accounting for more than 87% of revenue. This marked the third consecutive quarter with revenue growth above 150% YoY. However, given that Innodata’s revenue is expected to decelerate sharply by Q4, it’s likely DDS is behind this as the core growth driver, and could see growth return to Q3 23’s levels.  
  • Synodex revenue rose 7.6% YoY to $2.0 million, decelerating from 14.6% YoY growth in Q4. 
  • Agility revenue rose 11.6% YoY to $5.5 million, decelerating from 24.9% YoY growth in Q1.  

GAAP Profitable with Adjusted EBITDA Growth of 236% 

Considering Innodata has a mere $58.3 million in estimated quarterly revenue, plus $241B in estimated annual revenue, the margin profile is quite impressive since most companies operate at a loss until they reach scale. 

Margins weakened slightly sequentially in Q1, though the rapid ramp of DDS revenue that really accelerated in Q2 has driven margins down the line much higher on a YoY basis. 

  • Q1 GAAP gross margin was 39.9%, down 5.3 points sequentially but up 3.5 points YoY. Adjusted gross margin was 43.2%, up 1.8 points YoY. Innodata shared that it is targeting an adjusted gross margin of 40%, with this result being above expectations. 
  • GAAP operating margin was 14.4%, down 4.8 points sequentially but up more than 9 points YoY. 
  • GAAP net margin was 13.4%, down 4 points sequentially but up nearly 9.7 points YoY, benefiting from the increased operating leverage driven by improving DDS profitability. 

Innodata did not provide any clear guidance on Q2’s margins, though management noted that they plan to invest ~$2 million in Q2 to support the new SOW with its largest customer, which will occur ahead of associated revenue and thus impact margins.  

Turning to adjusted EBITDA, management forecast YoY growth for the metric, though it is not clear to which degree, given that there was no supporting commentary. Adjusted EBITDA for FY24 was $34.6 million for a 20.3% margin, with Q1’s 21.8% margin already positioning Innodata for growth. Adjusted EBITDA was up 236% YoY (although on small numbers). 

  • DDS adjusted EBITDA was $11.5 million for a 22.7% margin. This marks a substantial improvement from the 11.0% margin a year ago. 
  • Synodex adjusted EBITDA was $0.4 million for a 20.8% margin, down nearly 4 points from a 24.7% margin a year ago. 
  • Agility adjusted EBITDA was nearly $0.8 million for a 13.7% margin, down nearly 10 points from a 23.3% margin a year ago. 

EPS 

Despite Q1 starting off with triple-digit topline growth and a rather strong >40% guide for FY25, EPS growth is expected to be negative this year. This is primarily due to two factors: a $5.9 million income tax benefit in Q3 and strong outperformance in margins in Q4.  

In Q1, Innodata reported $0.22 in GAAP EPS, ahead of estimates for $0.17 and representing growth of 633.3% YoY.  

However, for Q2, analysts are currently expecting EPS of $0.11, down (50%) sequentially, before ticking higher to $0.17 in Q3. This would be a decline of nearly (67%) YoY versus $0.51 in Q3 2024, due to the income tax benefit. Q4 is not expected to bring any relief, with current estimates pointing to a (38.5%) YoY decline to $0.19.  

For the entire year, Innodata is expected to report a (22.0%) YoY decline to $0.69, before rebounding 46.3% in FY26 to $1.02. 

Cash and Balance Sheet 

Cash flows have improved significantly as revenue ramped, allowing Innodata to add significant cash to its balance sheet through 2024. As a result, Innodata has a relatively healthy balance sheet with no debt and an undrawn $30 million credit facility. 

  • Operating cash flow was $10.9 million for an 18.6% margin. This was lower than the 25.5% margin in the year ago quarter, with the strong print driven by a $3 million QoQ increase in deferred revenue. 
  • Free cash flow was $8.5 million for a 14.6% margin. This was lower than the 20.5% margin from the year ago quarter due to the relatively stronger OCF. 
  • Cash and equivalents on hand were $56.6 million, up from $46.9 million in Q4 and a substantial improvement from $19.0 million a year ago.  
  • Debt remained zero, with Innodata still having access to its undrawn $30 million credit line should it need extra funding. 
  • Deferred revenue was approximately flat QoQ at $8.03 million. 

Cash flow is a line item to watch as the company stated they plan to re-invest OCF and this could lead to debt or stock dilution: “Accordingly, we intend to reinvest a meaningful portion of our operating cash flow into product innovation, go-to-market expansion and talent acquisition, while still delivering adjusted EBITDA above our 2024 results.” 

Earnings Call:  

Largest Customer to be down 5% 

In the opening remarks, the CEO stated the largest customer would be down 5% going into the next quarter: “Inevitably, customer concentration can result in quarter-to-quarter volatility. For example, with our largest customer, we exited 2024 at an annualized revenue run rate of approximately $135 million. In Q1, we were running higher than this by about 5%, and in Q2, we anticipate that we could be lower by about 5%, but the customers' demand signals are updated continually and are highly dynamic.” 

An analyst asked about this in more detail during the Q&A when it was stated the new statement of work with the customer will provide “additional share of wallet that we can tap into.” Management is referring to 200 autonomous agents discussed above under the customer section, yet at the onset this is worth $6 million. 

Risks: 

There have been short reports on the company that led to a 30% drop in share price in one day. You can read the report from Wolfpack Research here   and a second short report from J Capital can be read here. These are worth a read for anyone seriously considering the stock. We utilize proper risk management in these cases, which includes a stop on the position – should we enter. We would also only buy on a breakout when technicals provide a green light.  

One of the primary risks to Innodata’s revenue acceleration and growth trajectory is We’ve already seen one large customer termination with Innodata, though that was attributed to Musk’s publicized take-over of xAI (Innodata said this customer “dramatically cut spending after a significant and highly publicized management change” in 2022). There is no guarantee that customer spend with Innodata will expand beyond the scope of the current deals, though the view that a majority of the Magnificent 7 are rapidly adopting generative AI products and will spend hundreds of millions on generative AI and LLM development over the next few years bodes well for future growth, both in terms of expanding the scope of deals and landing deals with new customers.  

Another risk presents itself in the volatile swings in share price that Innodata sees – as a small cap, it’s much more likely to see these substantial moves in such a brief period. For example, there have been multiple weeks and many days in which Innodata has seen moves in excess of +/- 10%. This level of volatility is not typically seen with large or mega-cap stocks and requires prudent risk management. Institutional ownership is also relatively low for a high-beta AI small cap at just 36%.

Conclusion: 

The takeaway is that as LLMs continue to fiercely compete, companies like Innodata will become force extenders in the race for more accurate and reliable output. Although Innodata has many competitors, consider that Meta’s investment into Scale AI is 14X larger than its acquisition of Instagram at $1 billon, which puts into perspective the importance of data quality for Big Tech companies. 

In the closing remarks, the CEO stated “we believe our business right now is on fire. The growth we're seeing year-over-year is just the beginning. What's happening now inside the Company is really like or unlike anything we've seen before.”  

Investors will have to get comfortable with early-stage tech given Innodata’s new product-market fit is very early stage. Scale AI provides a decent comp in terms of the value of a strong AI data engineering company. Innodata’s solutions will be put to the test now that Scale AI customers will be unwinding their partnerships. Anything could happen. If we were to enter, it would be with a tight stop, and we would raise our stop as the stock price increases. 

For stock setups including a potential entry price for Innodata, consider upgrading to our Advanced tier with real-time trade alerts and weekly webinars held on Thursdays at 4:30 p.m. EST. To receive $100 off our Advanced tier use ADVANCED100 or click here and email your request to upgrade.ADVANCED100 or click here and email your request to upgrade.

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

Recommended Reading:

Credo Reports 180% YoY Growth and 20% GAAP Operating Margin

Posted on by io-fund

Credo continues to report outstanding revenue growth, up 180% YoY in Q4 and guided to accelerate further in Q1 as management touted growing traction with hyperscalers, new design wins in qualification and strong customer forecasts driving sustained AEC growth.

GAAP margins have expanded significantly down the line with operating margin quickly approaching 20% as signs of operating leverage emerge. Cash flow margins were robust in Q4 on strong collections, while inventories surged over the past two quarters, indicating that Credo’s hypergrowth phase will likely continue for a few quarters.

Management hinted that a new DSP deal with a hyperscaler represents its largest revenue opportunity to date, with two new hyperscaler customers ramping up in FY26. Backed by these arising revenue streams, Credo guided for revenue growth of 85%+ next year, or over $800 million.

Brief Background on Credo:

Credo’s primary product line is active electric cables (AECs), while it also offers PAM4 digital signal processors (DSPs), optical transceivers, active optical cables (AOCs), and PCIe 6 retimers. Credo’s product portfolio is underpinned by its proprietary SerDes tech, which allows for comparable performance as its peers in data transmission but at a much lower cost.

AECs and active copper cables (ACCs) are challenging fiber optic networking in the two to seven meter space, as these solutions offer lower power, lower costs and at times, higher reliability over the shorter distance. AECs solve a critical issue of data loss that occurs with passive cables at longer lengths, especially in 800 Gbps/port environments with lengths longer than two to three meters. As data center network architectures look toward replacing fiber optic in some cases for short haul networking, both AEC and ACC are being evaluated.

AECs with retimers are a more expensive option compared to ACCs due to offering a cleaner signal, yet they have the additional benefit of being vendor agnostic, which is key for data center operators who are looking to upgrade as they add more racks.

Being copper-based, AECs are cheaper than fiber optics even with the cost of the retimer, and AECs consume less power due to having a small diameter. By allowing more air flow, there are fewer issues with thermal management. This is the primary catalyst for AEC growth within the data center.

In Credo’s case, for distances between two meters and seven meters, AECs are seeing heightened demand as servers scale up from eight GPUs to now 36 and 72 GPU per rack-scale AI systems, and also as clusters grow from 10,000 to 100,000 and soon million-GPU clusters.

For a deeper understanding of Credo’s products and market positioning, read more here: Credo: AI Networking Company Surging in Revenue from Active Electric Cables (AEC)Credo: AI Networking Company Surging in Revenue from Active Electric Cables (AEC)Credo: AI Networking Company Surging in Revenue from Active Electric Cables (AEC)

AEC Adoption Driven by Higher Reliability and Efficiency

For Credo, the strong growth trajectory of its AEC product line in Q4 and FY25 was driven by their higher reliability and energy efficiency, with management highlighting significant customer wins in Q4’s earnings call.

Credo expanded its AEC portfolio with the launch of its 800G HiWire ZeroFlap AECs for AI backend networks in October 2024, aiming to enable large AI clusters sized into the hundreds of thousands of GPUs. The new AECs were designed to reach seven meters with full host-to-switch connectivity, specifically for liquid cooled servers. Credo says the new AEC line saves up to 14 watts per link and up to $1,000 per GPU.

Credo says that ZeroFlap AECs now “are gaining traction as a robust rack-to-rack solution for distances up to 7 meters, offering over 100 times greater reliability than laser-based optical modules, virtually eliminating linked labs and significantly improving energy efficiency, which are both key enablers for best-in-class AI deployments.”

This increased reliability and focus on energy efficiency at the longer seven meter length have already driven a major customer win in xAI’s Colossus 100,000 GPU cluster. At that size, Credo’s ZeroFlap AECs could drive power and cost savings of up to 1400kw (~10 NVL72 racks) and $100 million.

Aside from xAI, Credo says it has a second customer ramping this year where the catalyst was “similar in the sense that their ability to move to these longer length AECs really opens the door for them to improve the reliability.” Credo is eyeing more deployment opportunities through FY27 as cluster sizes continue to increase, with AECs helping hyperscalers greatly improve density (more racks deployed for same amount of power) with a highly reliable, efficient solution.

Eyeing >100% YoY Optics Growth with 100G Optical DSPs and 800G Transceivers

While AECs take center stage for the role as the primary growth driver, Credo’s optics portfolio stands out as well. Management laid out robust triple-digit growth targets for FY26, alongside significant progress with industry-leading product deployment and major customer wins.

Credo recently announced a handful of industry-leading optics products that position it well for more customer wins and strong growth over the next two years. Credo unveiled its ultra-low power 5nm 100G optical DSP at OFC in May, which it says sets new industry-leading benchmarks for power efficiency with full DSP and linear receive optics (LRO) variants. Credo recently showcased its 3nm 200G per lane DSP, support 1.6T port speeds with leading power efficiency and signal integrity. Credo says this new solution positions it to drive the shift to 200G lane speeds over the next few years.

Credo also showcased its family of ultra-low power 800G optical modules with an industry-first power consumption of just 9W, powered by its Lark linear receive optic (LRO) tech. Credo said it “achieved error rates comparable to full DSP solutions” and attracted significant interest from hyperscalers who are prioritizing power efficiency. LRO solutions are gaining traction as they remove the DSP, reducing cost, latency and power consumption by 1-2W per module, which is significant at larger scales.

Highlighting the strength of its optics solutions, Credo secured a major full 800G DSP transceiver win with a US-based hyperscaler, with deployments commencing in fiscal 2026. Credo said that from a revenue standpoint, this win is “probably going to be the largest opportunity that we've had to-date.”

As a result, Credo CEO Bill Brennan is expecting the company to “double or even beyond double our optical revenue in fiscal '26” with accelerated growth in the years ahead. Most optical shipments currently are 50G per lane (400G) with several designs shipping, though Credo expects more traction and revenue growth from 100G per lane designs.

Brennan was also confident in Credo’s ability to drive market share gains in 100G DSPs. He explained that he feels Credo will “experience a lot of success in the 100G per lane market in the next 12 to 24 months” as full DSP and LRO variants launch simultaneously, with the expectation that Credo will be “really well positioned in that market as that develops.”

In terms of the timing for scale-up driven optics growth, Brennan said Credo has been consistent in saying designs wins will come this year with revenue ramp beginning in calendar 2026. Over the next two to five years, he believes optics and DSPs will grow “dramatically” to eventually become a >10% revenue business. Importantly, this comment suggests that optics remains <$40 million as of FY25, and the forecast for doubling or more than doubling in FY26 may only contribute approx. $40 million of an expected $370 million-plus in revenue growth.

PCIe 6, Scale-Up Seen as Growth Driver through 2027

Credo was highly positive about the transition from PCIe Gen5 to PCIe Gen6 driving growth for them in scale-up, with PCIe 6 expected to gain traction in FY26 and FY27.

Credo’s PCIe 6 AECs displayed at GTC promised the same reliability and energy benefits for scale-up networks and rack-scale architectures, while its PCIe 6 retimers showcased “superior performance and interoperability.” Management said that customer momentum for PCIe retimers is accelerating with design winds expected in 2025 and production revenue commencing in calendar 2026. On the AEC front, management said there were “new design wins in qualification” amidst growing traction amongst hyperscalers, positioning them for sustained strong AEC revenue growth.

For scale-up Ethernet, UALink or Nvidia’s new NVLink Fusion, Credo said that these networking standards create a large market for PCIe, shifting from Gen5 to Gen6. CEO Bill Brennan said that these will all be 224G series, with Credo aiming to “establish revenue and really increase that revenue base in the PCIe Gen5 and Gen6 timeframe. And then after that, we're going to be flexible in a sense of offering Gen7” where Credo’s AECs will be universal to Ethernet, UALink or NVLink Fusion.

Financials

Revenue Continues to Accelerate to 218% in Q1

Credo reported 179.7% YoY and 25.9% QoQ growth to $170.0 million in revenue in Q4, beating the consensus estimate for $159.6 million. Revenue growth has sharply accelerated throughout the fiscal year, up from the 60% to 70% level in 1H to high triple digits in 2H.

AEC maintained a “steep growth trajectory” with revenue reaching another record in the quarter, growing double-digits sequentially. Evidence of the rapid ramp of AEC and Credo’s other optic and retimer products, quarterly revenue has nearly tripled since the start of the fiscal year at $59.7 million.

For Q1, Credo guided to $185 million to $195 million in revenue, pointing to a nearly 40 point sequential acceleration to 218% YoY growth at midpoint. This was also 17% above consensus estimates for $162.4 million heading into the report.

Credo reported 180% YoY revenue growth in Q4 and guided for an acceleration to 218% in Q1.

Revenue growth estimates have moved sharply higher since February. Q1’s growth estimate just four months ago was 133.4%, and is now nearly 85 points higher, while Q2’s growth estimate has risen 74 points from 100.9%. 

For fiscal 2025, Credo reported a 122 point acceleration to 126.3% YoY growth, with revenue of $436.8 million. For fiscal 2026, Credo guided for revenue to exceed $800 million, for growth in excess of 85% YoY, while analysts are now expecting $804.1 million.

What’s important to note here is that analyst growth expectations are much lower than what Credo has been reporting. For fiscal 2026, analysts are expecting sequential growth of 3% to 4% each quarter to reach the $804 million estimate. In Q4, Credo had initially guided for QoQ growth of 19% and reported 26%, while for Q1, Credo has guided for 12% QoQ growth. Assuming Credo can maintain QoQ growth >7% through FY26 as new hyperscalers begin to ramp, these expectations will likely materialize as too low. However, it’s important to caution that Credo is coming up on difficult comps in Q3 and Q4 and those comps elevate risk as it can be a point where hypergrowth companies often fail to impress.

Key Segments – Product Revenue Growth Tops 300% YoY

Credo reported a significant 80 point sequential acceleration in product revenue growth to 303.3% YoY in Q4, with revenue of $164.5 million. Credo said AEC products are gaining traction in rack-to-rack distances up to 7 meters, with xAI being the most successful customer at that distance with a second customer ramping this year.

For optics, Credo noted that it reached its revenue targets and ended FY on strong momentum with an expanding customer base. As previously mentioned, Credo is targeting 100%+ optics revenue growth in FY26.

In retimers, Credo said growth was fueled by 50G and 100G per lane Ethernet products, with customer momentum accelerating. Credo added that for fiscal 2026, they anticipate strong growth in retimers driven by the shift to 100G per lane solutions.

Credo's product revenue growth accelerated sharply to 303% YoY in Q4.
  • Product Engineering Service revenue declined (60%) YoY and (50%) QoQ to $1.3 million.
  • IP License revenue declined (75%) YoY but rebounded 41% QoQ to $4.2 million.

Note on Customer Concentration

Moving forward, Credo expects to diversify its customer base, eyeing up to five >10% customers in FY26, up from three in FY25. Credo’s largest customer, rumored to be Microsoft, accounted for 61% of revenue in Q4.

Credo also has two new hyperscalers ramping in 2H 26, with the expectation that both could become >10% customers in the long-term, though management offered no timeline for that. CEO Bill Brennan said the first customer is expected to ramp in mid-year, sooner than expected, with the other looking to be later in the second half. Should Credo be able to ramp these two quickly, it could provide additional revenue and growth as tough comps roll around.

Margins Shine with 40% Adjusted Operating Margin in FY26

Credo has excelled on the margin front, driving strong expansion in margins in 2H and in fiscal 2025 despite being solidly in its hypergrowth phase, a difficult feat to accomplish.

For Q4:

  • GAAP gross margin was 67.2% for an expansion of 2.4 points YoY and 3.6 points QoQ. Adjusted gross margin was 67.4%. For Q1, Credo guided for GAAP gross margin of 63.4% to 65.4%, and adjusted gross margin of 64% to 66%.
  • GAAP operating margin was 19.9%, well ahead of guidance for 17.5%. This marked an exceptional ~33 point improvement from (13%) last year, and its second consecutive quarter above 19%. Adjusted operating margin was 36.8%, up more than 24 points YoY and more than 5 points QoQ. For Q1, Credo’s operating expense forecast implies a GAAP operating margin of 17.4%, and an adjusted operating margin of 36.1% at midpoint.
  • GAAP net margin was 21.5%, up more than 38 points YoY and down marginally QoQ. Adjusted net margin was 38.4%, up 19 points YoY and nearly 5 points QoQ.
Credo's operating margin was 19.9% in Q4, up nearly 33 points YoY.

For fiscal 2025:

  • GAAP gross margin expanded less than 3 points to 64.8%, while adjusted gross margin expanded 2.5 points to 65.0%.
  • However, Credo drove significant improvement to operating margins with prudent cost management. GAAP operating margin inflected to positive territory at 8.5%, up more than 27 points YoY. Adjusted operating margin expanded 25 points to 26.4%. For fiscal 2026, management shared that they are targeting adjusted operating margin of 40%, a 14 point YoY expansion.
  • GAAP net margin was 11.9%, up nearly 27 points YoY. Adjusted net margin was 29.7%, up 22 points YoY.

EPS Growth Expected to be Triple Digit in FY26

Credo has reported robust EPS growth driven by its margin strength, with fiscal 2025’s adjusted EPS of $0.70 increasing from just $0.08 in the prior year. Credo generated the bulk of this EPS in H2 as revenue and margins surged,

Adjusted EPS of $0.35 in Q4 beat estimates by 29.6%, representing growth of 400% YoY. Growth is forecast to accelerate to 782% in Q1 to $0.35 on a low comp, before slowing to 17% YoY by Q4 FY26 against a much tougher comp.

Credo's adjusted EPS growth is forecast to accelerate to 782% in Q1.

For FY26, Credo is expected to report nearly 111% YoY growth to $1.42 in adjusted EPS, driven by strong topline growth and a projected 14 point expansion in adjusted operating margin.

Free Cash Flow Margin of 32%, But Likely Will be Lower in FY26

Credo’s cash flow margins surged on strong collections, while its balance sheet remained robust with debt still at zero.

  • Operating cash flow was $57.8 million in Q4, up more than $53 million QoQ on higher “cash collection driven by the significant sequential product ramp.” OCF margin was 34% in the quarter, compared to 3.1% last quarter and 6.8% a year ago. For FY25, operating cash flow was $65.1 million, for a margin of 14.9%. This decreased from a 17% margin in FY24 as cash flow growth of 99% YoY lagged revenue growth by 27 points.
  • Free cash flow was $54.2 million in Q4, for a 31.9% margin. For FY25, free cash flow was $29 million, for a 6.6% margin, down from an 8.9% margin last year on higher capex. Credo mentioned that it expects capex to double YoY in FY26 on upcoming 3nm product tape-outs, which may pressure FCF through the year.
  • Cash and equivalents totaled $431.3 million, while debt remained zero.
  • Inventories were $90.0 million, up more than 69% QoQ and up 148% in two quarters. This implies Credo is preparing for its new products and new hyperscalers to ramp and hypergrowth to continue.

Tariff Impacts Downplayed

Importantly, despite its China exposure, management downplayed tariff impacts. China specifically accounted for 18.2% of revenue through Q3, but when including Hong Kong, China-related exposure is 75.4%, due to Hong Kong revenue nearly tripling YoY through Q3 to $152.7 million. Credo noted that geographic revenue represents shipment destination or location of contracting entity, which could be different from customers’ principal offices.

In the Q4 call, Needham’s Quinn Bolton pointed out that Credo’s AEC manufacturing partners BizLink and FoxLink are both located in China, questioning management about how tariffs could impact margins.

CFO Dan Fleming said Credo does not expect a “significant tariff risk” to gross margins and it is not the cause of Q1’s sequential guide down. CEO Bill Brennan was more vague on tariffs, saying Credo was “monitoring the situation closely and we're working very closely with our customers, and ultimately, we're trying to be as flexible as we can,” and in the worst case, Credo “could be out of one geographic location and into another” within months.

The more important question for Credo here is if it can accelerate and maintain strong revenue growth while potentially onshoring manufacturing over the next few years to mitigate future tariff risks.

Conclusion

It’s hard to nitpick much in Credo’s Q4 report aside from China revenue, which likely remained elevated given the geographic mix as of Q3. Management highlighted two additional hyperscalers ramping in mid and late-FY26, providing tailwinds to growth as these new projects ramp.

Analysts are only projecting 3% to 4% sequential growth through FY26, which seems low given that Credo guided for double-digit sequential growth in Q1 while highlighting those new customers ramping and more opportunities in optics as FY26 progresses.

The I/O Fund owns AI networking stocks that are linked to Nvidia and custom silicon projects such as Amazon’s $100B capex including Trainium. We share our portfolio with Pro and Advanced Members. Advanced Members also receive real-time trade alerts, entries, exits and trade plans in our weekly webinars. Take advantage of a limited-time offer for $75 off Pro or $100 off Advanced. Email us to upgrade

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

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

Recommended Reading:

Can AMD’s MI350X and MI355X GPUs Close the Gap with Nvidia?

Posted on by io-fund

This article is a continuation of our free newsletter from June 19, AMD vs Nvidia: The AI Stock That Could Win by 2028

Find out the following below: 

  • We compare AMD’s MI350X and MI355X with Nvidia’s B200s and GB200s to decipher if AMD has what it takes to close the gap with the AI leader 
  • Clear conclusions on the next 1-2 years that are tailored for stock investors and how we plan to position our portfolio  
  • The SKU that all investors should know about 

Last week, AMD introduced its Instinct MI350 series GPUs, including MI355X with up to 4X performance over the previous MI300X generation and up to 40% more tokens per dollar compared to Nvidia’s B200 accelerators. The company also previewed its Helios rack-scale server architecture featuring the MI400s for 2026 deployments. 

According to Tom's Hardware AMD is claiming the eight-GPU MI355X system is 1.3X faster than Nvidia’s DGX GB200s systems with Llama 3.1 and up to 1.2X faster than the B200 HGX systems in inference for DeepSeek R1 with equivalent performance as Llama 3.1 when tested at FP4. 

Here are a couple of key points in terms of how AMD is starting to close the gap with Nvidia for inference purposes: 

Floating point precision: 

AI accelerators are increasingly offering lower floating-point formats to help reduce memory consumption and bandwidth requirements, which in turn speeds up computation and lowers power consumption. For example, FP8 delivers better throughput and energy efficiency in LLM inference compared to FP16. The newer generations of GPUs will offer FP4 formats to further alleviate memory-bandwidth bottlenecks and improve performance for large matrix operations. 

I elaborated on the importance of floating-point precision in my analysis “Here’s Why Nvidia Will Reach a $10 Trillion Market Cap” when I stated: “The difference is that the smaller bit size allows for an economical way to achieve more speed when giving up a small amount of accuracy doesn’t make a critical difference. As discussed, this also helps in the face of a slowing Moore’s Law.” 

With the MI350X and MI355X, AMD is introducing FP4 along with the smaller formats of FP8, FP6 and FP4, which are especially helpful for inference. In the CDNA 4 architecture, the FP6 data rate shares the same peak PFLOP/s as FP4 — which for inference purposes means it will be comparable to or slightly exceed Nvidia’s B200s.  

ServetheHome states, “AMD is doing the higher performance (at a transistor cost) option of adding FP6 to the FP4 pipeline to give it a big boost.” 

Source: Tom’s Hardware, pictured above – FP6 performance is on par with FP4 performance.  

HBM3E and HBM4 Memory: 

AMD is attempting to compete on memory by slightly beating Nvidia with the MI355X having 1.6x more memory capacity than the B200s. This allows AMD to load full model weights into memory for fast inference and avoids having to share resources between multiple GPUs. The higher amount of memory also increases the batch size, which increases throughput while lowering latency.  

It’s important to keep in mind that Nvidia is preparing to send a shockwave through the AI market, once again, with its NVL72 and NVL36 systems. These systems combine 72 GPUs and 36 GPUs to think like one GPU, which I’ve covered recently here.covered recently here. 

Rather than AMD taking head-on Nvidia’s NVL72s and NVL36s right now – which are earth-shattering SKUs — the company is instead attempting to compete at the 8-GPU system level. Memory is a big part of that attempt. Inference craves low latency, thus having the model fit entirely in memory for inference purposes is a part of that strategy.  

What’s Important About the MI350X and MI355X: 

To put it plainly, on the AI accelerator front, this will be the first time that AMD will overlap Nvidia in terms of benchmarks on GPUs. Please do note, the amount of time that AMD’s current generation of GPUs and Nvidia’s GPUs overlap will be brief – and will only be at the single GPU and 8-GPU system level. AMD was originally expected to ship the MI350s at the end of this year yet are moving the shipments up – which fits with AMD’s tradition of underpromising and overdelivering.  

However, the accomplishment is noteworthy as it’s setting the tone as the inference market begins to ramp. In other words, AMD ceded the training market to Nvidia – but I do not expect that to be the case with the inference market. 

When Blackwell Ultra ships, the B300s will offer FP4 TFLOP/s that is 1.3X faster than AMD’s current MI350X and MI355X. With that said, because AMD has prioritized competing on memory — its bandwidth and capacity is expected to be on par with Blackwell Ultra. 

AMD’s CDNA 4 Architecture: 

The primary architectural changes of CDNA 4 were aimed at increasing memory capacity and bandwidth per compute unit. The lower precision compute capacity was also increased, favoring FP6 and FP4. 

AMD’s architecture is built on a chiplet design, and similar to the Zen-2 architecture discussed above, the chiplet design offers power efficiency improvements from monolithic designs by offering a dozen chiplets on a single processor.  

Although monolithic used to be preferable, to compare, Nvidia’s has evolved its architecture to utilize multi-die modules (MCM) which combines two reticle-limit dies. By utilizing high bandwidth connections, the two dies function as a single die to forego reticle-size constraints, helping to improve yields and results in higher performance. 

However, keep in mind that AMD was first to market with chiplets in the Zen architecture that helped stage the company’s comeback. Nvidia is the world’s best AI semiconductor design company, yet the point is that AMD is not necessarily a follower. In some design areas, AMD leads. 

A few more things to highlight from last week’s announcements: 

  • 3D packaging with CoWoS-S from the MI300s remains with XCDs, HBM3 memory, I/O Dies and the Infinity cache 
  • There are a total of 256 compute units with eight 32 CDNA per XCD. This is less than the last generation yet with the 3nm, each compute unit delivers more power 
  • There are two larger I/O Dies rather than four for better efficiency. The I/O Dies are built on a 6nm process. 
  • More memory at 288GB of HBM3E with 8TB/s 

MI400s “Helios” Will Close the Gap on Larger AI Clusters 

The market is forward-looking, which means investors should be too. AMD is closing the gap on single GPUs and 8-GPU systems, yet the MI400s will mark a pivotal moment as AMD will attempt to compete on rack-scale systems with Helios, its 72-GPU systems. If things go as planned, AMD will be competitive with Nvidia on GPU, memory and interconnect performance — while potentially taking the lead on memory capacity and bandwidth.  

By using UALink and potentially Broadcom’s scale-up ethernet, AMD will be able to deliver considerable bandwidth, with projections of 31 TB of HBM4 memory and 1.4PB/sec of bandwidth, which would beat Nvidia’s offerings by 50%. 

UALink, or Ultra Accelerator Link, is an open industry-standard interconnect that enables high-speed and low-latency communication for AI clusters. This is a joint venture between a consortium of Nvidia competitors, including AMD, Intel and Broadcom, to take-on Nvidia’s proprietary NVLink. The first generation of UALink supports 1.28 TB/s of bandwidth for systems of 4 to 8 accelerators while future generations will support racks of 72 accelerators and more. 

Conclusion: 

Judging by the poor stock performance over the past 1-2 years, the market thinks AMD is down for the count. I think it’s the nuances of AI training versus inference (and timing of those markets) that has made AMD appear to be inconsequential to AI hardware. Although I do not foresee AMD surpassing Nvidia in terms of market cap by a long shot, I believe it’s highly probable that AMD’s returns outpace the AI leader due to the sheer amount of revenue growth hidden within AI inference. Specifically, inference is expected to be a larger market than training, and AMD’s strengths will finally be on full display.  

My current prediction is that AMD does not need to even come close to overtaking Nvidia on revenue or market cap for the stock performance to exceed Nvidia's over the next few years. Rather, the unforeseen second wind from GPUs and AI systems will be enough to make second place the most rewarding era in AMD’s history.

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 and AMD at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

AMD vs Nvidia: The AI Stock That Could Win by 2028

Posted on by io-fund

Last week, AMD offered more details on the release of their groundbreaking GPUs with little fanfare in the markets – which is par for the course as AMD has a history of being forgotten about until the company can no longer be ignored. 

Five years ago, I dubbed AMD the “Dark Horse” for my premium research members as the company had a mere 4% share in the CPU-data center and was up against the near-monopoly of Intel. The term “Dark Horse” refers to a competitor that unexpectedly achieves victory as I was predicting AMD would eventually overtake Intel.  

Two quarters ago, AMD posted CPU server market share of 39.4% — officially surpassing Intel.  

In the technology industry, the probability of an underdog successfully taking on a first-place contender with a formidable lead is incredibly rare. Yet, there is an element of catching the market off guard that helps to compound the returns. The opposite of this is known as a crowded trade. 

Does AMD have what it takes to overtake Nvidia on stock performance in the next few years? Most investors assume Nvidia will continue to dominate — and AMD will remain a distant second. In this piece, I’ll walk you through why AMD’s positioning in the AI cycle could lead to an outcome few are prepared for. 

Background on what AMD Achieved  

When Lisa Su became CEO of AMD in 2014, the company was on the brink of bankruptcy, operating at a loss from 2012 to 2017. The huge bets the company made with the Zen architecture were bold, and saved the company from going under.

Chart showing how AMD’s 2017 Zen architecture helped boost margins and turn the company profitable

Pictured above: The Zen architecture released in 2017 helped AMD move from deep in the red to the black on margins. Source: MacroTrends 

Examining how AMD was able to stage the comeback through architectural changes in CPU architecture, process technology, and chiplets is key for investors as not only did it result in over 3,600% returns in 10 years, but the company is now setting up to become a strong contender in the GPU server market.

Line chart comparing Nvidia and AMD stock returns in 2022 before Nvidia’s breakout, questioning if AMD will catch up

Pictured Above: In 2022, Nvidia stock and AMD stock has seen returns in the same zip code before Nvidia’s meteoric rise. Will AMD catchup in the coming years? Source: YChartsYCharts 

AMD Released the Zen 2 Architecture in 2019: 

Five years after Lisa Su became CEO, AMD was preparing to not merely survive but rather to rival Intel. The Zen 2 architecture was an important release that allowed AMD to leapfrog Intel with a 7nm chip while Intel was still producing 14nm and 10nm chips. Because 7nm are twice as dense as 14nm, AMD was able to release a 64-core server chip and 128 threads rather than AMD’s previous 32-core server chip. Up until early 2019, Intel’s offering has been a 28-core server chip and 64 threads. The result of being first to the 7nm is that AMD was able to produce a more power efficient chip that allowed more cores. 

The Zen-2 architecture also introduced a multi-chip module that used the most advanced technology where it’s needed most by combining 7nm chiplets with a 14nm die. This was quite a competitive leap as Intel was still using a monolithic design. 

In this case, the 14nm was leveraged for memory controllers because the central hub runs input/output (I/O) and memory better. This helped AMD beat Intel on memory bandwidth. The design also greatly improved performance by putting the L2 cache on the core and the L3 cache across the core. Overall, these design improvements lower the power required while increasing the performance as it requires fewer NUMA hops, which in turn, increases instructions per clock, and this ultimately reduces latency. 

AMD’s second-generation EPYC server processors sparked the company’s comeback with 1.8 to 2 times the performance advantage of Intel’s Xeon processors, but perhaps most importantly, EPYC 2nd Gen was at half the cost as Intel in some instances. Undercutting Intel on price became a virtuous cycle as driving down costs means more chips will be bought from AMD.  

In a 2021 webinar on AMD’s stock that I held for Premium Members, I noted at the time that a third-party analyst named Michael Larabel benchmarked AMD as being 14% faster than Intel while costing about 30% less. The result is that for every $1.00 Rome chip sale, Intel lost $2.25 in Xeon SP sales. The savings can then be deployed to buy more Rome chips to further depress Intel’s revenue. 

Since the Rome Series, AMD has been able to take more market share with the Milan Series and Bergamo Series with improvements such as 3D stacking in Zen3, tripling the L3 cache size while only adding four clock cycles of latency, and further customizing CPUs for cloud native workloads with less cache and more performance per watt. Genoa was the 4th generation, and provided more cache for general purpose workloads.

AMD versus Nvidia: Why Memory Gives AMD an Inference Edge  

The word “inference” will come up a lot in the coming years for AI investors, and thus, it makes sense to have a brief discussion on how it differs from training.

  • Training: 

Training is the process of a model learning patterns from labeled data through internal parameters (called weights). There is forward and backward pass or propagation for updating the parameters. This phase is computationally intensive, requiring significant memory and parallel processing power.  

Training is where Nvidia’s strengths are nearly insurmountable as the leader in combining parallel processing (CUDA) cores with matrix computations (Tensor Cores). Over the past few years, Nvidia has increased compute power by an order of magnitude to the point of defying Moore’s Law with architectural changes such as tensor cores and lower precision floating points.  

For example, the H100 is able to switch from a 16-bit floating point to 8-bit floating point to significantly increase training speed by requiring less memory and speeding up data transfer operations. The transformer engine in the Hopper generation helps models to apply self-attention to detect how data elements in a series influence and depend on one another. 

The second-generation transformer engine in the Blackwell architecture offers FP4. This is helpful because AI models are moving toward neural nets that lean on the lowest precision and yet still yield an accurate result. In this case, 4 bits double the throughput of 8-bit units, compute faster and more efficiently, and require less memory and memory bandwidth. 

The premiere SKU shipping now is the GB200 NVL72, which delivers real-time trillion-parameter LLM inference, 4X LLM training, 25X energy efficiency, and 18X data processing. The GB200 also provides 4X faster training performance than the H100 HGX systems and includes a second-generation transformer engine with FP4/FP6 Tensor core. The 4nm process integrates two GPU dies connected with 10 TB/s NVLink with 208 billion transistors. 

The point is that taking on Nvidia’s lead in training is not AMD’s goal. You can, of course, use AMD’s GPUs for training, but this isn’t where AMD can feasibly compete – and thus, its stock has suffered during the LLM training boom. Since the launch of Nvidia’s Ampere in May of 2020, the stock is up 1700% compared to AMD’s 135%.  

You can read more about the history of Nvidia’s GPU architectures including Blackwell in the analysis: "Here’s Why Nvidia Stock Will Reach $10 Trillion Market Cap."

  • Inference: 

Inference takes batches of real-world data and quickly comes back with an answer or prediction — therefore, this stage needs low latency (or speed) over raw compute power. For example, inference will take a trained model and produce a probable match for new data in milliseconds. While it can be compute-intensive for large models like GPT-4, inference generally prioritizes low latency, higher efficiency, and lower cost.  

In many applications, it makes sense to run inference at the edge (closer to where data is generated). However, cloud inference is still widely used for models that are too large or resource-demanding to deploy on local devices. Compared to training, inference requires only the forward pass through the model, making it more efficient in terms of power and hardware demands. 

If we go back and look at how AMD was able to take on Intel — briefly, it was with an architecture that required less power at nearly half the cost. This helps illustrate that AMD’s strengths are a much better fit for inference rather than training.

Can AMD’s MI350X and MI355X GPUs Close the Gap with Nvidia? 

Last week, AMD introduced its Instinct MI350 series GPUs, including MI355X with up to 4X performance over the previous MI300X generation and up to 40% more tokens per dollar compared to Nvidia’s B200 accelerators …

Below, I tell you key things about AMD’s upcoming release and whether AMD has the chance to close the gap with Nvidia …

Find out the following below: 

  • We compare AMD’s MI350X and MI355X with Nvidia’s B200s and GB200s to decipher if AMD has what it takes to close the gap with the AI leader 
  • Clear conclusions on the next 1-2 years that are tailored for stock investors and how we plan to position our portfolio  
  • The SKU that all investors should know about

Subscribe now and save $100 off our Advanced plan or $75 off our Pro plan.Subscribe now and save $100 off our Advanced plan or $75 off our Pro plan.

Our five-year cumulative returns of 210% would place us as #2 if we were a hedge fund and #5 if we were an ETF.
Subscribe to continue reading

Paid subscribers, click here to view the full articleclick here to view the full article

Not ready to subscribe but want more thoughtful analysis from a top-performing team in tech? Every week, we publish free research. 👉 Sign up here.ere.ere.

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 and AMD at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

This AI Stock is Set to Surge from Inference Demand — Broadcom

Posted on by io-fund

This article is a continuation of our free newsletter from June 12, This AI Stock is Set to Surge from Inference Demand. 

For our Premium Members, we discuss the following:   

  • The one thing Broadcom CEO stated that all investors MUST hear to help position for 2025-2026.  
  • The clear catalyst within Broadcom’s product portfolio and timing for this product to help push forward the next leg up in AI revenue growth. 
  • The I/O Fund’s trade setup and buy zones we are eyeing for Broadcom given its immense demand yet stretched valuation.  

What Hock Tan Said that Every Investor Needs to Hear 

There was a subtle yet important change in commentary this past quarter around Broadcom’s hyperscale customer deployment expectations.  

  • In Q4 FY25, two quarters ago, Broadcom stated that they expected each of their three current hyperscale customers to deploy 1 million XPUs across a single fabric by 2027.  
  • However, in Q2, this commentary shifted – management now said they “eventually expect at least three customers to each deploy 1 million AI accelerator clusters in 2027.”  

This implies that one or more of their four prospective customers are also planning a significant accelerator deployment in short fashion, driving Broadcom’s total revenue opportunity higher. 

There were additional hints the current estimates are too low, such as when Hock Tan stated: “Turning to XPUs or custom accelerators. We continue to make excellent progress on the multiyear journey of enabling our 3 customers and 4 prospects to deploy custom AI accelerators. As we had articulated over 6 months ago, we eventually expect at least 3 customers to each deploy 1 million AI accelerated clusters in 2027, largely for training their frontier models. And we forecast and continue to do so a significant percentage of these deployments to be custom XPUs. These partners are still unwavering in their plan to invest despite the certain economic environment.  

In fact, what we've seen recently is that they are doubling down on inference in order to monetize their platforms. And reflecting this, we may actually see an acceleration of XPU demand into the back half of 2026 to meet urgent demand for inference on top of the demand we have indicated from training. And accordingly, we do anticipate now our fiscal 2025 growth rate of AI semiconductor revenue to sustain into fiscal 2026.” 

This circles back to Q4 2024’s serviceable addressable market (SAM) forecast, when management laid out a 60% CAGR through 2027 to a $60 billion to $90 billion SAM, which AI growth is now tracking. That SAM forecast was based on its view for three hyperscalers deploying 1 million accelerator clusters, or ~$20 to $30 billion per hyperscaler. Prospective customers were not included but it was noted they could “significantly” expand the SAM should they transition to revenue-generating customers. 

The subtle shift in deployment commentary hints that Broadcom’s SAM could expand to north of $100 billion on the high end should it be able to transition just one of its prospective customers to revenue-generating. With AI growth of 60% YoY this year and next tracking SAM growth, a possible SAM expansion and thus a higher SAM CAGR suggests AI revenue could remain stronger for longer, or expand above current forecasts as 2027 rolls around. Bank of America analysts seem to share this view, saying it is “only a matter of time” before the SAM forecast is raised, “especially as the FY27 sell-side AI revenue consensus estimate is still well below $45 billion.” 

Tomahawk 6 Enabling Path to 1 Million Accelerator Clusters  

Broadcom has been quite vocal about the industry’s path to 1-million-plus accelerator clusters, constantly reiterating how its three hyperscalers “each race towards 1 million XPU clusters by the end of 2027.” This would be multiples larger than current deployments, with xAI’s Colossus supercomputer recently expanding from 100K to 200K GPUs. Broadcom has continuously re-emphasized this forecast as it represents two major growth opportunities for the company: significant growth in accelerator deployments with inference tailwinds, and even more growth in networking deployments to support these clusters.  

The shift to Ethernet and away from Nvidia’s lock-in ecosystem of GPU + InfiniBand is benefiting Broadcom, with the industry pointing to rising Ethernet demand. Arista said that momentum for Ethernet “has really shifted in the last year” while Nvidia touted that its new Spectrum-X Ethernet is annualizing at $8 billion in revenue, or $2 billion quarterly. Broadcom noted that AI networking revenue rose 170% YoY in Q2 as demand remained above expectations.  

The company is committed to remaining on the leading edge of networking with its newest Tomahawk 6 switch, the industry’s first 102.4 Tbps Ethernet switch. The next-gen switch doubled the bandwidth of its predecessor, while offering flexible deployment ability with 1,024 100G or 512 200G SerDes options, reducing switch count.  

This raw performance upgrade paves the way for >100K to 1 million accelerator clusters by allowing larger leaf-spine fabrics to be constructed, while drawing less power and keeping latency low. Broadcom exec Ram Velaga said that demand for the new switch is “unprecedented” with multiple >100K accelerator deployments “using Tomahawk 6 for both the scale-out and scale-up interconnect.” 

When discussing Tomahawk 6, management points toward the flattening of the AI cluster as an important catalyst for this product, stating: “[…] Tomahawk 6 enables clusters of more than 100,000 AI accelerators to be deployed in just two tiers instead of three … this flattening of the AI cluster is huge because it enables much better performance in training next-generation frontier models through a lower latency, higher bandwidth and lower power.” 

Additional commentary the CEO shared in terms of the AI networking opportunity was that the opportunity for scale up is 5-10X more than scale out – setting up a nice trajectory as AI clusters grow: 

“In fact, the increased density in scale up is 5 to 10x more than in scale out. And that's the part that kind of pleasantly surprised us and which is why this past quarter, Q2, the AI networking portion continues at about 40% from what we reported a quarter ago for Q1. And at that time, I said I expect it to drop. It hasn't.” 

Quick Note on Margins 

The market loves this stock – and one of the primary reasons why is its earnings power. 

Broadcom reported adjusted operating income of $9.8 billion, up 37% YoY, outpacing revenue growth by a factor of 1.8x. Adjusted operating margin was 65.3%, expanding more than 8 points YoY. Adjusted EBITDA surpassed $10 billion for the first time, for a 67% margin.  

Margins are also rather strong in both of Broadcom’s segments: Semiconductor gross margin expanded 1.4 points YoY to 69%, while operating margin rose 2 points YoY to 57%. Infrastructure Software gross margin surged 5 points YoY to an astounding 93%, while excellent execution on integrating VMWare drove operating margin 16 points higher to 76%.  

However, VMWare’s expensive price tag means Broadcom’s debt is elevated, at $67.8 billion in gross principal debt versus $9.5 billion in cash. Given the structure of Broadcom’s debt with a majority at a fixed 3.8% rate, annual debt payments are currently close to $2.7 billion. 

Quick Note on VMWare Software: 

VMWare helped drive outperformance in Infrastructure Software, with revenue growing 25% YoY to $6.6 billion in Q2, ahead of management’s expectations for $6.5 billion on successful conversion of enterprise customers from perpetual vSphere to full VMWare Cloud Foundation (VCF) software stack subscriptions. Broadcom noted that strong VCF momentum has led to double-digit ARR growth in core Infrastructure Software. However, for Q3, Broadcom guided for a deceleration to 16% YoY growth to $6.7 billion. 

For a deeper dive on VMWare, read the analysis Broadcom: Networking/ASICs Giant and The Second Largest by AI Revenue.Broadcom: Networking/ASICs Giant and The Second Largest by AI Revenue. 

Broadcom Trade Setup: 

By Knox Ridley

Like many AI related tech stocks, Broadcom appears to be in a large-degree uptrend that is not finished. The pattern that this bull cycle is tacking is a diagonal pattern, which is a 5-wave pattern that is marked with strong swings in both directions.  

Based on the historic price action, there are two scenarios that we are tracking, both suggest higher levels from here, after we see an immanent period of volatility.  

  • Blue – This scenario suggests that the 3rd wave within the larger diagonal pattern ended in December of 2024. This would mean that we are in the 4th wave correction, and that the bounce off the April lows is a bounce within this larger correction. If this is playing out, the next drop will take the shape of an aggressive, and direct 5-wave pattern that ultimately breaks through $161.50. The final targets for this drop will be $139.50 – $102. We would then turn higher for another bull cycle to new highs. 
  • Green – This scenario suggests that the larger 3rd wave is not complete. When AVGO tops, the retrace will take the shape of a messy and overlapping 3-wave pattern, which will hold over $161.50. We will then turn higher toward the $400s in the coming months. This swing higher will complete the larger 3rd wave, as we set up for the larger 4th wave correction into 2026. 

We do believe that the broad market signals are suggesting a correction is immanent. Several warning signals are also flashing in AVGO’s chart. One of which can be seen in how the last swing to new all-time highs, just before their earnings report, was accompanied with decelerating volume and momentum. In other words, though the sellers have not stepped up, the number of buyers is fading the higher we go. This is a common pattern that we see just before reversals. 

In conclusion, how AVGO corrects from here is key. If we see a 3-wave retrace that holds over $161.50, it is setting up a great buying opportunity for a move to new highs. On the other hand, if we see a 5-wave pattern develop that breaks through $161.50, we will patiently wait for lower prices, which most investors believe is impossible based on how relentless this stock continues to advance.  

Conclusion 

The shift from AI training to AI inference is becoming increasingly visible as Big Tech and model providers highlight strong growth in tokens and revenue. Broadcom has already benefited from both increasing compute and networking needs – but we think the surge in inference demand will disproportionately (and positively) flow to Broadcom’s top line and bottom line. 

This is because custom silicon’s cost advantages and ability to drive lower inference serving costs at scale creates a strong value proposition for Big Tech. As more and larger clusters are deployed to serve exploding inference demand, there will be additional long-term tailwinds for networking for the Ethernet networking giant.  

Broadcom’s FY26 visibility is improving with management expecting near 60% YoY AI revenue growth to continue, while SAM could potentially expand past $100 billion as customer engagements remain strong.  

We have plans to add Broadcom to our portfolio – keep an eye on your trade alerts and join Knox in his weekly Thursday webinar at 4:30 p.m. EST for more information on buy levels.

Upgrade to the Advanced Annual Plan – Just $599Upgrade to the Advanced Annual Plan – Just $599

Email us to upgrade now or to claim this limited-time offer.

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

Recommended Reading:

This AI Stock is Set to Surge from Inference Demand

Posted on by io-fund

Broadcom stock joined Nvidia, Alphabet and Microsoft in calling out surging AI inference demand, noting that this rapid growth could drive increased demand for custom silicon in the second half of 2026, and with it, higher AI revenue. 

Despite an in-line print and guide, Broadcom’s AI revenue is tracking above Street estimates for next year towards the $30 billion mark, up nearly 150% in two years, with growing tailwinds from inference and networking as clusters increase in size. AI revenue growth is also tracking Broadcom’s addressable market forecast of a 60% CAGR.  

Broadcom is cementing itself as the clear second in AI with key ingredients for success as inference demand rises. However, its premium valuation to Nvidia looks to be pricing in above-expected AI revenue growth into 2027, likely closer to a 70%+ CAGR, as there exists a $160 billion gap in AI-driven revenue between the two. 

Inference Driving Possible Acceleration into 2H 26 

The AI ecosystem’s pivot from training to inference, now emerging as a strong revenue engine for hyperscalers, is a structural tailwind for Broadcom's custom silicon and networking products.  

We’ve seen quite a handful of signs over the last couple of months that inference demand (and revenues) are beginning to explode: 

  1. Microsoft reported 5x YoY growth in tokens processed to 100T in Q1, with AI contributing 16 points or nearly half of Azure’s 33% growth last quarter. Microsoft’s AI run rate at the end of January was $13 billion, up more than 175% YoY. 
  2. Alphabet reported 9x YoY growth to 480T tokens processed in April. 
  3. OpenAI this week announced that it had crossed $10 billion in ARR, nearly doubling from $5.5 billion at the end of 2024. 
  4. Anthropic’s ARR rose 200% in five months and 50% in 2 months to $3 billion. 

With hundreds of millions of users interacting frequently with AI assistants, inference becomes the focal point for providers such as OpenAI and Google. Meeting these levels of growing demand, without significant response delays or downtime, requires more and more accelerators, networking and interconnect products.  

Broadcom’s edge goes beyond the fact that custom accelerators are often multiples cheaper than Nvidia’s GPUs for inference tasks – it's that custom silicon is increasingly performant with each generation. By optimizing algorithms (software), Big Tech can drive higher performance from large language models (LLMs) — which helps to drive down costs while also increasing output for specific workloads. For example, a rough idea as to how much it costs Nvidia to make merchant GPUs is estimated around $3,000 to $5,000 whereas the company charges $25,000 to $30,000 – hence the AI leader’s excellent margins. Reducing Nvidia’s high pricing power is what Big Tech is after and this can be accomplished both in the hardware costs but also through optimizing the workloads for specific use cases. 

Big Tech is prominent in Broadcom’s custom silicon customer list, which includes Google and Meta. ByteDance reportedly emerged as the third customer last summer, though some reports surfaced earlier this year that this project could be cancelled. OpenAI and Apple are also heavily rumored to be prospective customers. 

Why Big Tech Is Chasing Cheaper Inference 

For the providers in the AI ecosystem, monetizing GPUs depends on inference, and thus revenue becomes a function of GPUs and tokens and profits become a function of cost. Nvidia’s Blackwell offers a massive leap in performance and can train models such as Meta’s Llama 3.1 405B in as little as 27 minutes, yet the cost advantages offered by custom silicon can translate into higher margins in the long run from lower inference serving costs.  

For example, Google recently announced that its upcoming seventh-gen TPU Ironwood is its “most performant and scalable custom AI accelerator to date, and the first designed specifically for inference.” Ironwood comes in two sizes, a 256 and a 9,216 chip configuration, with the larger size offering up to 42.5 exaflops of performance.  

Google adds that Ironwood offers 2x the performance per watt as last-year’s generation Trillium, with 6x more HBM and 4.5x the HBM bandwidth. This allows it to deliver more capacity per watt at a time when power is a primary constraint, and provide customers with more cost-effective AI workloads. 

This is exactly what Broadcom sees arising from this inference growth curve, as CEO Hock Tan asserted that the company has quite a bit of visibility into “increased deployment of XPUs next year, much more than we originally thought and hand-in-hand with it, of course, more and more networking.” The necessity of networking in larger clusters means demand is likely to remain robust even given custom silicon will not keep pace with Nvidia’s merchant sales into the hundreds of billions. 

Higher-than-expected deployments of custom silicon combined with strong demand for networking should provide robust tailwinds for AI revenue growth beyond 2026. Broadcom currently has enough visibility to place possible demand acceleration for 2H 2026 on the table, and this could easily persist through 2027 and beyond should inference demand flourish and as the path to 1 million accelerator clusters materializes.  

Assuming Broadcom can maintain another 60% YoY growth in FY27 on stronger demand and potential conversion of its 4 current prospects, AI revenue would close in on $50 billion, or up to 60% share of revenue. Even if growth then slows to 30% YoY in FY28, Broadcom would still be more than doubling its AI revenue to $65 billion in just three years. 

Broadcom Reports 170% YoY Growth in AI Networking 

Broadcom has cemented itself in second place in AI revenue as it closes in on $20 billion this fiscal year in AI revenue — with a line of sight toward $30 billion by the end of fiscal 2026. AI revenue accounted for more than 50% of Semiconductor revenue for two quarters in a row and nearly 32% of total revenue in Q2. 

AI semiconductor revenue rose 46% YoY to $4.4 billion, in line with management’s guidance. Although this was a deceleration from 77% YoY growth in Q1, Broadcom forecast $5.1 billion in AI revenue in Q3, pointing to a rebound to 60% YoY growth – marking ten consecutive quarters of growth.  

In the current quarter, the 46% AI semiconductor growth was driven by networking, which was up 170% YoY and represented 40% of AI revenue. In the opening remarks, the CEO stated the following regarding this outsized growth: “As a standard-based open protocol, Ethernet enables one single fabric for both scale out and scale up and remains the preferred choice by our hyperscale customers. Our networking portfolio of Tomahawk switches, Jericho routers and NICs is what's driving our success within AI clusters in hyperscalers.” 

Graph of Broadcom stock's quarterly AI revenue accelerating from $4.4 billion to $5.1 billion in Q3.

Broadcom’s AI revenue was forecast to reaccelerate in Q3 to 60% YoY to $5.1 billion. Source: I/O Fund 

Q3’s guidance was ahead of some analyst expectations for $4.9 billion in AI revenue in the quarter, ticking higher as Google’s TPU v7p (Ironwood) begins to ramp. Q3 would also mark the largest sequential growth in over a year on a dollar basis, at ~$700 million.  

Additionally, analysts look to already be penciling in further strength in Q4, with Bernstein’s Stacy Rasgon suggesting that Broadcom could be eyeing $5.8 billion in AI revenue in Q4 assuming it sustains 60% YoY growth. Given that Broadcom’s 1H revenue was up more than 57% YoY, this seems a reasonable assumption, especially considering management is eyeing near 60% growth in FY26. 

More importantly, AI’s strength is masking persisting softness in non-AI revenue, which could continue to be pressured due to Broadcom’s high consumer exposure. Broadcom noted that non-AI revenue “is close to the bottom” but it “has been relatively slow to recover” with revenue down (5%) YoY to $4 billion in Q2.  

A graph of Broadcom's AI versus non-AI revenue showing AI revenue share now exceeding 50% on strong growth.

Broadcom’s AI revenue accounts for more than 50% of Semiconductor revenue, masking persisting softness in non-AI revenue. Source: I/O Fund 

Despite this weakness extending into Q3 with revenue expected to be flat QoQ at $4 billion, semiconductor revenue is accelerating – growth accelerated from 11% to nearly 17% in Q2, with the $9.1 billion semiconductor revenue guide pointing to an acceleration to nearly 25% growth in Q3.  

Should non-AI revenue soon find the bottom and begin to recover, this will provide support for continued Semiconductor growth. However, any persisting weakness in non-AI stemming from this elevated consumer and Apple exposure that AI revenue must absorb presents a real risk that investors should keep in mind through the rest of the year. Broadcom is also one of the more exposed semiconductor companies to China with tariffs, with more than $10 billion in revenue from the nation in fiscal 2024.  

A graph of Broadcom stock's quarterly Semiconductor revenue growth showing acceleration from 11% in Q1 to 25% guided in Q3.

Broadcom’s AI revenue strength is evident as Semiconductor revenue was guided to accelerate 8 points to 25% YoY despite flat non-AI revenue. Source: I/O Fund 

Broadcom Stock to See Lift from AI Inference 

Broadcom is aiming to capture growing inference tailwinds, with management explaining that the recent surge in inference demand is driving increased confidence in their FY26 AI revenue growth rate.  

CEO Hock Tan said that Broadcom’s hyperscale clients are “doubling down on inference in order to monetize their platforms,” and as a result, he expects Broadcom could “actually see an acceleration of XPU demand into the back half of 2026 to meet urgent demand for inference on top of the demand we have indicated from training.” This new dynamic is what is driving Tan’s confidence in stronger growth in FY26, saying that he now anticipates the “fiscal 2025 growth rate of AI semiconductor revenue to sustain into fiscal 2026.” 

This commentary plus potential demand acceleration in 2H 26 suggests that Broadcom has visibility into $30 billion AI revenue potential next year. Broadcom has not provided a full FY25 AI revenue guide yet, but it is on track to deliver approximately $19 to $20 billion in AI revenue in FY25, up ~60% YoY assuming 60% growth to $5.9 billion in Q4.  

Graph of Broadcom stock's AI revenue projections showing 60% YoY growth in FY25 and FY26 to $19.5 billion and $30 billion.

Broadcom’s AI revenue is projected to grow approximately 60% YoY in FY25 and maintain that growth in FY26. Source: I/O Fund 

Maintaining 60% growth through FY26 would project AI revenue to $30 to $32 billion. This trajectory indicates Broadcom is likely driving AI revenue ahead of expectations over the next four to six quarters, with Morgan Stanley saying that $26 to $30 billion in AI revenue is “higher than what is in Street models.” Evercore is modeling 58% AI revenue growth in FY25 and 50% in FY26, implying $28.9 billion.  

Broadcom Passes Nvidia Stock's Valuation – First Time in 9 Years

There’s no denying that Nvidia is the outright leader in the AI accelerator market with an estimated $200 billion in revenue this year with roughly $180 billion of that from AI data center whereas Broadcom will report $20 billion this year.  

Who is in second place is no contest yet what is second place worth when there is nearly a $160 billion gap? Broadcom clearly has key ingredients to have earned this second-place position yet there is also exposure to China and exports via Apple and ByteDance, one of its rumored customers. 

Meanwhile, for the first time in nine years, Broadcom has a higher valuation than Nvidia. 

On the top-line, Broadcom trades at nearly 19x forward revenue, an almost 8% premium to Nvidia’s 17.6x multiple. AVGO stock was at a 14% premium heading into Q2’s earnings. This is also 65% higher than Broadcom’s 5-year average 11.4x forward revenue multiple.  

Graph of Broadcom stock versus Nvidia stock valuation on a forward price-to-sales basis, with Broadcom now trading at a premium valuation.

Broadcom is currently valued at an 8% premium to Nvidia on a forward price-to-sales basis. Source: YChartsYCharts 

On the bottom line, Broadcom trades at 38.2x forward earnings, a 13% premium to Nvidia and a more than 18% premium to the semiconductor industry at 32.3x. Broadcom has strong margins – 65% adjusted operating margin and 52% adjusted net margin – driving strong EPS growth, at a 25% expected CAGR through FY27; however, the custom silicon ramp presents some headwinds to gross margin as it grows its mix share.  

Graph of Broadcom stock versus Nvidia stock valuation on a forward price-to-sales basis, with Broadcom now trading at a premium valuation.

Broadcom trades at 38.2x forward earnings, a 13% premium to Nvidia and a more than 18% premium to the broader semiconductor index on a forward PE basis. Source: YChartsYCharts 

Broadcom’s competitiveness with Nvidia on margins and its ability to drive strong EPS growth via operating leverage, while capitalizing on growing accelerator and networking demand lend to its valuation, as it is a clear second to Nvidia and far ahead of smaller peers Marvell and AMD in AI revenue. However, this premium valuation looks to price in above-expected AI revenue growth through 2026, likely closer to a 70% or even 75% CAGR through 2026 as Broadcom is currently tracking its SAM CAGR at 60% through FY26. 

Is Broadcom Stock a Buy? 

Subscribe for Full Access to the Article  

Behind the Paywall is the Following information: 

  • It’s often subtle commentary – and not the headlines – that reveals the biggest opportunities. Find out the one thing Broadcom CEO stated that all investors MUST hear to help position for 2025-2026.  
  • The clear catalyst within Broadcom’s product portfolio and timing for this product to help push forward the next leg up in AI revenue growth. 
  • The I/O Fund’s trade setup – exclusive only to subscribers. We detail buy zones we are eyeing for the #2 stock in AI given its immense demand yet stretched valuation.
    Sign Up to Continue ReadingSign Up to Continue Reading

Paid subscribers, click here to view the full articleclick here to view the full article

Not ready to subscribe but want more thoughtful analysis from a top-performing team in tech? Every week, we publish free research. 👉 Sign up here.ere.

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 do not own shares in AVGO at the time of writing and may own stocks pictured in the charts.

Recommended Reading:

Palantir Stock: Strong Sequential Growth and Strong Underlying Key Metrics 

Posted on by io-fund

When thinking of strong earnings reports this past quarter, Astera Labs and AppLovin come to mind in terms of ticking critical boxes on fundamentals. However, one could argue that Palantir is tied for the top position on this list, although with Palantir, you pay for what you get with a valuation that is as outrageous as its CEO. 

The company continues to report accelerating growth on a QoQ and YoY basis. The cherry on top is that fiscal year guidance was raised, and key metrics support continued growth down the line.  

In Q1, the company reported $884 million in revenue for growth of 39%, up from growth of 36% last quarter and 21% last year. This represents QoQ growth of 7%. Perhaps most importantly, US commercial revenue drove the results, with 71% YoY growth and QoQ growth of 19% for the segment’s first-ever $1 billion annual run rate.  

Key metrics such as commercial total contract value (TCV), US commercial customer count, US commercial remaining deal value (RDV) and RPO are supportive of continued growth in future quarters.  

There are also robust cash flows and expanding margins to strengthen the story. What is this Perfect 10 worth? The market clearly loves this stock despite its 83 forward PS valuation; therefore, given Palantir’s ongoing invincibility, it’s truly anyone’s guess if the stock can sustain the valuation or not.  

Background on Palantir’s Platforms 

Palantir’s first platform was Gotham for government purposes before many of the integrated features were expanding to Foundry, which launched around 2021 for Commerical purposes (exact date is not available but generally understood to be around this time).  

Gotham and Foundry create a unified data set for actionable insights across industries such as manufacturing, product development, and customer experience. The data that Palantir gets is from the customer database although the company may use other data sets for government customers, such as scraping social media or other publicly available information on the web. The traditional deployment includes hosting Palantir’s servers in a customer’s data center. 

The difference between Palantir and other AI-enabled database competitors is that Palantir is able to answer questions a model cannot answer. Traditional business intelligence companies require a complete data set whereas Palantir is able to tackle situations where there is not a complete data set. You can think of the competitive advantage as being actionable depth, which Palantir has described as “the reasoning that goes into decision-making, not just data.”  

The core platforms were built for the “ability to construct a model of the real world from countless data points.” Unlike a SQL database, natural language is used to query data and return results in real-time rather than through strings.  

Gotham:  

Palantir Gotham was the company’s first platform, built for government operatives in defense and intelligence sectors. The platform enables users to identify patterns hidden deep within datasets using semantic, temporal, geospatial and full-text analysis.  

Here are some ways the platform is used: 

  • Graph application allows data objects to be seen as nodes and edges for the ability to visualize events, filter objects and plot characteristics 
  • Object explorer allows users to query billions of objects, somewhat similar to Apache Spark 
  • Browser allows perform search queries and surface information  

Pictured Above: Gotham uses AI detection models 

Pictured Above: Gotham uses ML models to detect objects and event matches acrsos varying sensor data types, satellite images, audio, text and video.  

Foundry: 

Palantir Foundry is the commercial offering and has four layers of tooling: Foundry Core, Data Foundation, Ontology and Workflows. This four-step process does the following, with the Ontology layer offering a distinct, competitive advantage: 

  • brings volumes of data into one place 
  • transforms the data into a format that analysts can work with and enables validation in any number of programming languages 
  • the “ontology layer” allows datasets to be turned into real-world concepts with the ability to accelerate on the company’s core ontology to reduce redundancy 
  • workflows is where it all comes together in an integrated environment for object exploration, point-and-click top-down analysis, code authoring, time series analysis, data science and application development. When a user has a question, it answers it using all layers and tools available

Pictured: Workflow builder on AIP platform 

Pictured Above: AI-powered Shipments and Supply Chains using AIP platform 

Apollo: 

The Apollo layer provides continuous delivery and an automated configuration layer that allows Foundry and Gotham to work across all cloud environments and also in places where there is little to no connectivity. On top of Palantir being able to form conclusions from incomplete data sets, the company can also deploy its platform and applications anywhere. 

Palantir’s marketing team says Apollo “goes where no SaaS has gone before” because it allows what is done on-premise to also run on multi-cloud SaaS with code that is deployed across all environments rather than written for a specific environment. The orchestration allows for on-hardware AI models to consume real-time data from sensors, radio, geo-data and time series data.  

Where bandwidth is not an issue, the company transmits all raw inputs and enriched metadata from models. Where there are constraints, the platform transmits meta-data only which can reduce bitrate by 20X. At times, a simulated environment can be created with Palantir’s Edge AI from historical data to help train AI models. The simulated environment is then deployed at the edge. With Apollo, Palantir’s centralized operations team is capable of 41,000 updates per week at no additional cost. 

Apollo Edge AI links together satellites to lower latency for the AI-enabled decision chain by orchestrating up to 237 satellites in what the company is calling a “meta-constellation.” This meta-constellation optimizes hundreds of orbital sensors and AI models to power Palantir’s models. One example is tracking submarines that pose a threat to the U.S. and its allies. In this case, submarines are being tracked on a granular level in areas where there is no bandwidth available. These are the kinds of obstacles that Palantir overcomes while being independent of one cloud environment, such as AWS or Azure. 

AIP: 

The Artificial Intelligence Platform has helped the stock surge in recent years as it integrates generative AI with operational data and workflows. When AIP is combined with Foundry and Apollo, it provides an AI service mesh that can run hundreds of microservices, scale compute through its Rubix engine and orchestrate updates through Apollo. Similar to Apollo, AIP Is independent from any one cloud environment.  

AIP Ontology is what Separates Palantir: 

The knowledge graph referred to as Ontology is a distinct advantage. The graph offers better context than a large language model would on its own – or as Palantir states, it’s “the reasoning that goes into decision-making.” 

You will often hear the management team state large language models will become commoditized, which is a way of saying the software that is on top of the LLM is where value creation comes from rather than the LLM alone. For this reason, AIP is designed to not only be cloud agnostic but to also be LLM-agnostic as it works with any large language model – for example, OpenAI, Anthropic, Meta’s Llama, etc. 

The platform also offers an AI agent workflows for building AI agents that are further optimized for specific use cases and customized through additional tools. Autonomous agents can be built and tested on the platform.  

When it comes to security and governance, Palantir’s roots in government contracts means the software company is exceptional compared to peers in this area.

Palantir Reports Accelerating Growth YoY and QoQ, Raises FY Guidance 

Palantir reported $883.9 million in revenue in Q1, beating estimates by more than $21 million. As stated above, this represents growth of 39%, up from growth of 36% last quarter and up from 21% last year. On a QoQ basis, Q1 accelerated 7% from Q4. This is an impressive performance given Q1 is typically one of the slowest quarters seasonally. For Q2, Palantir guided for $934 to $938 million in revenue, or 38% YoY growth. 

Over the past seven quarters, revenue growth has accelerated nearly 27 points, an exceptional feat driven by reaccelerating government growth, persisting AI momentum in US commercial, and strong execution.  

Driven by the strong Q1 report and upbeat Q2 guide, Palantir hiked its full-year revenue growth forecast by 5 points, a rather high-conviction move after just one quarter. Palantir now sees FY25 revenue of $3.89 to $3.902 billion for 35.9% YoY growth, a significant ~$150 million raise from its prior view for $3.741 to $3.757 billion for 30.9% YoY growth.  

With that said, the updated FY25 guidance also suggests that revenue growth may begin to moderate in the back half of the year, given 1H growth is in the mid-38% range. There was a hint in the call that government could be lumpy, thus it’s likely to be the cause for H2 being slightly lower than H1. The other possibility for H2 being forecast to report slower growth would be Europe or other global weakness, which was present in this report. 

Key Segments: US Commercial Revenue Growth Drives Results 

US Commercial drove the results this quarter although Global Commercial was still at a lower growth rate than Government due to weakness in Europe. It’s clear to see in the numbers below that Government contracts remain crucial for Palantir’s success. 

Government: 

  • Government revenue growth accelerated 5 points sequentially to 45% YoY to $487 million, accounting for 55% of revenue.  
  • US government revenue grew 45% YoY to $373 million, and international government revenue also rose 45% YoY to $114 million.  

Palantir said US growth was driven by new awards reflecting growing AI software demand, while international growth was driven by UK healthcare and defense sector work and the new NATO contract.  

In the call, the CEO used the word lumpiness when asked about government contracts, and notably, did not answer the question directly rather used it as an opportunity to talk about the overall business in both the quoted portion below and the lengthier response found here. 

“Dan Ives: 

Thanks. And, another amazing quarter. I mean, it's just — so my question is, given that what we're seeing in the government, isn't that another opportunity where you could actually gain more share of budgets as you go to more meritocracy? Like, Palantir should actually gain more dollars within the budgets of DoD and a lot of other agencies. 

Alex Karp: 

We're very optimistic about what we're going to do in the US, but the devil's in the details. And we're running this business for you with you as owners, which means it's like there's going to be maybe lumpiness, but we predict we're going to do very, very well […] “ 

There was mention on the call that they are seeing government demand globally minus Europe … although that could go against the trend toward sovereign AI.  

Per management: “I would say as an unknown variable, we're seeing very significant demand for our software, our government software around the world outside of Europe. And those are early days, but the demand — the signal there is very strong.” 

US Commercial Revenue Accelerates to 71%: 

Commercial revenue growth accelerated two points sequentially to 33% YoY to $397 million, as Palantir is growing rapidly in the United States, yet faces persisting headwinds in Europe.  

  • US commercial revenue accelerated from 64% last quarter to 71% YoY this quarter to $255 million, surpassing a $1 billion annualized run rate for the first time on elevated AI demand. However, the guide for next quarter does indicate Q1 could be the peak with fiscal year growth of 68% guided. 
  • International commercial revenue declined (5%) YoY to $141 million, weighed down by soft European demand and a one-time revenue catch-up in Q4. 

Not only did Palantir’s US commercial segment see revenue growth accelerate to the highest growth rate in nearly three years, but it also saw record growth in a handful of key metrics that support strong growth continuing through the year.  

  • US commercial accelerated 31 points YoY and 7 points QoQ to 71% in Q1, surpassing Q4 2023’s 70% level and the highest growth since Q2 2022. This strong growth means that Palantir’s US commercial segment is on track to rise more than 2.5x in two years.  
  • Palantir raised its FY25 US commercial growth guidance from 54% YoY to 68% YoY, projecting revenue of $1.178 billion, compared to $457 million in 2023. The raise represents about $100M more than previously expected. 

US commercial customer count rose 65% YoY and 13% QoQ to 432, with Palantir adding 50 net new customers in the quarter. Palantir has added 111 net new customers in Q4 and Q1 combined, its highest two-quarter total on record.  

The segment’s strong growth outlook is supported by robust key metrics: 

  • 2x YoY growth in US commercial deals closed above >$1M 
  • 127% YoY and 30% QoQ growth in US commercial remaining deal value to $2.32 billion 
  • 183% YoY growth in US commercial total contract value (TCV) booked of $810 million 

Key Metrics Support Continued Growth 

While US Commercial featured many strong key metrics yet NRR, RPO and Billings stood out with strong growth as well in Q1. 

  • Total remaining deal value (RDV) accelerated from 39.2% in Q4 to 45.6% in Q1 as it rose to $5.97 billion. 
  • RPO accelerated from 39.5% YoY in Q4 to 46.1% YoY in Q1 at $1.90 billion. 
  • Total contract value (TCV) booked increased 66% YoY to $1.5 billion. 
  • Billings rose 44.8% YoY to $905 million. 
  • Net retention rate (NRR) rose four points sequentially to 124%, its highest level in three years. Palantir pointed out that NRR should continue to expand in the coming quarters: “As net dollar retention does not include revenue from new customers that were acquired in the past 12 months, it has not yet fully captured the acceleration and velocity in our US business over the past year.” 

Margins 

Palantir’s margin profile is exceptionally strong, as the company continues to drive operating margin expansion while accelerating revenue growth. This helps the company’s Rule of 40 metric, which stands at 83 as it combines EBITDA margin with revenue – or more than double the ideal 40 that many SaaS companies set out to acheive yet cannot due to a lack of GAAP margins.  

  • GAAP gross margin was 80.4% in Q1, down 1.3 points YoY. Adjusted gross margin was 82.1%, down more than 1 point YoY. 
  • GAAP operating margin expanded to 19.9%, up more than 7 points YoY.  
  • Adjusted operating margin was 44.2%, up 8.5 points YoY. For Q2, Palantir guided its adjusted operating margin to 43.1%, which would represent a third consecutive quarter above 40% and up nearly 6 points YoY. 
  • GAAP net margin was 24.2%, up more than 7.5 points YoY.  
  • Adjusted net margin was 37.8%, up nearly 8 points YoY. 

Palantir also boosted its full-year adjusted operating income forecast from its prior view of $1.551-1.567B to $1.711-1.723B. FY25’s adjusted operating margin is now projected to be 44.1%, up from its prior view of 41.6%.  

EPS 

Despite the top-line beat, Palantir met adjusted EPS estimates in the quarter at $0.13, up 68% YoY. GAAP EPS was $0.08, up 100% YoY.  

Looking ahead through the rest of FY25, adjusted EPS growth is expected to decelerate, from Q1’s 68% YoY to 20% YoY by Q4. However, estimates have risen over the past three months – Q2’s growth rate has come up 11 points and Q3’s up by 9 points. 

For FY25, Palantir is expected to see adjusted EPS growth of nearly 43% YoY to $0.58, before decelerating to 25% growth to $0.73 in FY26. 

Cash Flows and Balance Sheet 

Palantir stands out for its ridiculously strong cash flows, though operating and free cash flow margins moderated quite substantially in Q1 relative to 2H 2024.  

  • Operating cash flow was $310.3 million in Q1 for a margin of 35%, down from 56% in Q4.  
  • Adjusted free cash flow was $370.4 million for a 42% margin, down from a 63% margin in Q4. Palantir raised its adjusted FCF guidance for FY25 from $1.5-1.7 billion to $1.6-1.8 billion, implying an FCF margin of 43.7%. 
  • Adjusted EBITDA margin was 45%. 
  • Cash and equivalents totaled $5.43 billion, while debt was zero. 

Conclusion: 

Part of our process is to highlight stellar earnings reports and Palantir certainly qualifies. It’s hard to find a blemish in the company’s current quarter as it’s perhaps the best report the company has reported yet – which is saying a lot. We are certainly seeing companies at the data layer doing well in AI with Oracle also reporting strong results, and this is likely to be a theme in the coming years.  

The valuation with Palantir is a gamble. The bulls believe they’ve speculated correctly, while there’s likely to be short sellers who do well with this stock too. PLTR is attempting to set a new bar for AI software with the 80 forward valuation, yet 39% revenue is a tricky spot to be as it barely qualifies as high-growth (yes, it’s US commercial segment does qualify, but you could say that for a few stocks trading a much lower valuations).  

Congrats to all the Palantir longs, it’s certainly paid off in spades. As for the IOF, this isn’t one I was able bite on at the high valuation and that remains my conclusion at this time. If we can get a more reasonable valuation, however, we’d love to have this one in the portfolio.

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

Recommended Reading: