Microsoft – AI Will Help Drive $100 Billion In Revenue By 2027

Posted on June 20, 2023June 30, 2026 by io-fund

This article was originally published on Forbes on Jun 15, 2023,11:18pm EDTForbes Forbes on Jun 15, 2023,11:18pm EDT

Given the runup in AI-related valuations, separating the real deal from companies that are merely AI wannabes is critical. The first few things to consider are, will this company see revenue from AI and, if so, how soon.

Source: YCHARTS

Although many AI stocks will not report enough AI revenue to survive the fierce, competitive battle the tech industry faces due to AI/ML, Wall Street investors can reasonably assume that Microsoft will be a leader in this space. Microsoft’s AI platform is rather insulated from widespread competition outside of Google Cloud and AWS, and the company’s software assets are particularly well suited for AI advancements, such as Office 365.

In April of 2022, our firm re-entered Microsoft with a note to our premium research members about the company’s dominance in AI before Chat-GPT3 was released. We repeated this in October of 2022 when we called Microsoft a “sleeping AI giant”:

“Microsoft is a sleeping AI/ML giant. Google gets a lot of attention here yet I think they are equally prepared to serve this market […] To help Microsoft rival Google, the company has been investing in OpenAI, which is a large R&D operation that is breaking ground with AI algorithms that help computers to create images from text, reduce the amount of code that developers need to write, and to also help robotics think and act like humans, among other things […] DALL-E is a “12-billion parameter” version of GPT-3 that creates images from text. The partnership with Microsoft will bring DALL-E to apps and services, including the Designer app and Image Creator tool in Bing and Microsoft Edge – this was announced earlier this month at Ignite.”

Analysts have been raising their price targets to the high $300s with an Evercore analyst raising his price target to $400 stating: “the infusion of AI across Microsoft’s product portfolio represents a potential $100 billion incremental revenue uplift in 2027.”

To provide some context, Azure and Office 365 helped Microsoft add almost $100 billion in revenue over the past four years. It increased from $110 billion to $198 billion in revenue. The stock appreciated 180% over that time frame. At the time, the market did not comprehend the revenue potential in these two businesses. We believe that history will repeat itself and the market is underestimating the impact AI will have on MSFT’s future sales growth across its business lines.

However, valuation poses a risk to Microsoft’s current stock price, and as outlined below, our firm prefers to wait before we add again to our position.

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6 Ways Microsoft Can Drive Another $100 Billion with AI:

Open AI APIs:

The OpenAI opportunity extends beyond Microsoft’s installed base, which is an important change to Microsoft’s market position. This is because OpenAI APIs run on Azure even if the customer isn’t directly an Azure customer. Management commented on this in the earnings call:

“Second, even Azure OpenAI API customers are all new, and the workload conversations, whether it's B2C conversations in financial services or drug discovery on another side, these are all new workloads that we really were not in the game in the past, whereas we now are.”

Generative AI for Government:

One market that gets overlooked in terms of its AI impact is the Federal Government. It is currently undergoing a major shift into the cloud. In a blog post, the company CTO Bill Chappell wrote: "Microsoft continues to develop and advance cloud services to meet the full spectrum of government needs while complying with United States regulatory standards for classification and security. The latest of these tools, generative AI capabilities through Microsoft Azure OpenAI Service, can help government agencies improve efficiency, enhance productivity, and unlock new insights from their data. Many agencies require a higher level of security given the sensitivity of government data. Microsoft Azure Government provides the stringent security and compliance standards they need to meet government requirements for sensitive data."

Many years ago, I wrote about the Pentagon contract and why Microsoft would be a front runner when it was widely reported AWS was the sole Big 3 contender for the contract. This analysis pointed toward the long-standing history Microsoft has in being favored by government entities.

Microsoft CoPilot:

The company introduced Microsoft 365 Copilot last month. It is the productivity tool that combines large language models (LLMs) with the data in Microsoft Graph and Microsoft 365 apps. The use cases of Copilot in Word include giving the users the first draft while saving the time on sourcing, writing, and editing the content. Similarly, Copilot in PowerPoint will help to create presentations based on previous content. Copilot in Excel can analyze trends from the data, create charts, and helps to make informative decisions.

To have a suite of productivity products that can see an immediate impact from AI-related R&D is a large part of the $100 billion that Microsoft can potentially add to the top line by 2027.

Edge/Telecom Partnerships:

Another important driver is Microsoft’s close partnerships with many of the telecom and data centers around world which will further cement its strong position in edge computing.

In February, Microsoft announced it had previewed two AI-powered services that are designed to manage telecom networks. Jason Zander, executive vice president of strategic missions and technologies at Microsoft said, “What we’re doing is taking our native cloud work and making it specific to this telecom operator network space. I think a really great example of that is all the AI ops work that we are introducing into the system."

Microsoft Bing:

In the most recent quarter, Microsoft announced that the new AI-powered Bing and Edge has seen a positive response. The company crossed 100 million daily active users of Bing. This is how Microsoft described the early impact of ChatGPT.

“Of the millions of active users of the new Bing preview, it’s great to see that roughly one third are new to Bing. We see this appeal of the new Bing as a validation of our view that search is due for a reinvention and of the unique value proposition of combining Search + Answers + Chat + Creation in one experience.”

Notably, Microsoft Bing has 3% market share and for every additional 1%, Microsoft will make an additional $2 billion.

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Microsoft is one of the largest cybersecurity companies

Microsoft’s cybersecurity segment reports more than $15 billion in revenue. The company was also the only Big 3 cloud vendor to not only build a multi-cloud product but also multi-cloud security. Today Microsoft’s cybersecurity sales dwarf the revenue of many cybersecurity best-of-breed products combined.

Source: I/O FUND

Installed customer base provides cross selling opportunities for new AI/ML based products and functionality

In the spring of 2022, I wrote about how reducing cloud costs was going to be a key trend in 2022 and beyond. We believed that Microsoft was uniquely positioned to benefit from this trend as it aggregates cloud services to help drive down costs. This is especially attractive for the Fortune 500 whereas startups, SMBs and mid-sized enterprises are likely to seek out and manage a larger portfolio of cloud services from various vendors.

Among the Big 3, Microsoft dominates the Fortune 500 with 95% running on Azure. Retaining the Fortune 500 in the migration to the cloud was accomplished through hybrid computing where Microsoft was first-to-market on serving a mix of on-premise, private and public clouds for their large enterprise customers. As the leader in on-premise systems, Microsoft was perfectly positioned to win with hybrid architectures. The company took this a step further and undercut other services on prices across its suite of software and platforms to win aggregate, long-term contracts.

Microsoft’s Risk is Valuation

Microsoft business model is low risk compared to many other AI stocks. However, there is certainly risk in the company’s valuation. The risk is compounded when market exuberance front runs a trend and overshoots the mark of what a company can realistically report in the coming years. Microsoft’s valuation is high relative to its 5-year median. If you look at the 5-year median prior to the current runup, the stock has a historic valuation of 9 PS Ratio and is currently trading at a 12 PS Ratio. Similarly, the 5-year median PE Ratio at the start of the year was 25 and the stock is currently trading at 36.

Source: YCHARTS

Conclusion:

AI will be a constantly evolving space and while many investors are rushing in at overstretched valuations, we prefer to be patient. Over time, we agree with the analyst that Microsoft’s competitive moat has positioned it to monetize the AI opportunity, much like with Azure and Microsoft 360, across its business lines so that its revenue will increase by $100B in the medium-term.

Microsoft is a real-deal AI stock and the increase in valuation has clearly factored in some of this. However, our updated sum-of-the parts analysis indicates there is still upside. Our current bull case price target is $440. As the story unfolds over the next few quarters, we see additional upside. However, in light of the strong rally from the Jan 2023 lows, we believe incorporating technical analysis to attempt to get the stock lower is important in determining optimal entry levels. In other words, the risk the stock sells off is much higher than usual right now. Sure, the stock price could continue to climb higher, but the world’s best investors favor being patient and buying when the market is in a state of fear rather than a state of greed. When we do add to our key positions, we issue real-time trade alerts. Learn more 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.

Nvidia Will “Still” Surpass Apple’s Valuation

Posted on May 29, 2023June 30, 2026 by io-fund

This article was originally published on Forbes on May 24, 2023, 07:45 am EDTForbes on May 24, 2023, 07:45 am EDT

My coverage on Nvidia as an AI leader began in 2018 (yes, really – five years ago). Since then, I’ve covered the AI microtrend for this specific stock 27 times on my research site, which is the equivalent of a novel.

I’ve also gone on record to say that Nvidia will surpass the valuation of Apple. That particular analysis compared the impact that AI will have to mobile, with AI adding $15 trillion to GDP compared to mobile’s $4.4 trillion. Mobile brought us three FAANGs: Apple, Google and Facebook. It has been my stance for years that AI will bring us a new set of FAANGs, one of which will be Nvidia.

However, now is not the best time to buy the stock. Rather than flatly tell you that while offering no way forward, I want to continue providing value to my readers by discussing when my firm plans to buy the stock again.

But also, we should discuss why the market is rallying on this company specifically. Good investors must do both – understand what makes a company stand out while being patient on price.

AI is Not a Buzzword for Nvidia

Short sellers mistakenly believe that AI is a buzzword for Nvidia. This is true for many stocks, but not for the leader in parallel processing.

Here is what I wrote five years ago on the topic:

Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FPGA chips used by Microsoft [from Xilinx]. Meanwhile, Intel’s CPU chips will struggle to compete as artificial intelligence applications and machine learning inferencing move to the cloud. Intel is trying to catch-up but Nvidia continues to release more powerful GPUs – and cloud providers such as Amazon, Microsoft and Google cannot risk losing the competitive advantage that comes with Nvidia’s technology.Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FPGA chips used by Microsoft [from Xilinx]. Meanwhile, Intel’s CPU chips will struggle to compete as artificial intelligence applications and machine learning inferencing move to the cloud. Intel is trying to catch-up but Nvidia continues to release more powerful GPUs – and cloud providers such as Amazon, Microsoft and Google cannot risk losing the competitive advantage that comes with Nvidia’s technology.

The Turing T4 GPU from Nvidia should start to show up in earnings soon, and the real-time ray-tracing RTX chips will keep gaming revenue strong when there is more adoption in 6-12 months. Nvidia is a company that has reported big earnings beats, with average upside potential of 33.35 percent to estimates in the last four quarters. Data center revenue stands at 24% and is rapidly growing. When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.”When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.”

-Free Newsletter and Seeking Alpha, November 2018 with AI Thesis repeated again in April of 2019Free Newsletter and Seeking Alpha, November 2018 with AI Thesis repeated again in April of 2019

When I wrote that, Nvidia was considered a gaming stock with high-risk exposure to crypto. What is astonishing is that the company was still considered a gaming stock with high-risk exposure to crypto a mere seven months ago.

You may recall, the stock was down 60% last year after a $2.5 billion miss on gaming, and the market was pricing in a long recovery due to Ethereum’s merge to Proof of Stake (PoS). The bears believed the Merge would flood the market with mining GPUs and Nvidia would be unable to overcome this setback.

At the time, there was no mention of Nvidia’s AI lead despite the H100 GPU being released the very next month! despite the H100 GPU being released the very next month! Instead, the market had investors believing that Ethereum, with only 200 million users, which is a smaller user base than Snap or Pinterest, could tank the GPU-juggernaut on the eve of the company’s largest release to-date: the H100 GPU.

The reason I’m emphasizing this is because my firm has worked hard to be a quality resource on tech stocks. Often times there is a major disconnect between the market’s pricing and a tech company’s positioning. There is no greater evidence of this than when Nvidia was down 60% seven months ago yet is the top performing stock in the S&P 500 today.

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Why the Market is Bulled Up on Nvidia

I want to take the opportunity to explain why Nvidia has the ability to arrive at a valuation that is 3X higher than its peers and in some cases 12X higher.

Source: YCHARTS

I’m not defending this valuation, rather I want to explain how it’s possible that smart money continues to buy up here.

The H100 is a Turning Point from Hardware to Software

“The Hopper architecture is ramping and it’s yet again going to disrupt the GPU and AI accelerator market. I’ve written quite a bit about Nvidia […] however, I will keep it simple by saying the A100 GPU is what led the company’s gains since Q2 2020 and the Hopper H100 GPU is what will lead the company’s gains for the next two years.” -Premium Site August 2022, following Nvidia’s $2.5 billion revenue miss.Premium Site August 2022, following Nvidia’s $2.5 billion revenue miss.

Note: the information below is a bit technical, so I’ve bolded the key points for a quick read.

For context, the A100 GPU was a monumental release for Nvidia as the Ampere architecture unified training and inference onto a single chip, whereas in the past Nvidia’s GPUs were mainly used for training.

The result is a 20x performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offered the largest leap in performance to date over the past 8 generations. One year later, the Ampere architecture had become the best-selling GPU architecture in the company’s history.

The A100 was special but it’s the H100 that is Nvidia’s iPhone moment. The reason is quite simple – it’s the release that will help Nvidia breakout from hardware and put the company firmly on the map for AI software.

Hardware has allowed Nvidia to become a $700B market cap company, but it is the recurring revenue from AI software that will propel Nvidia into a market cap worth trillions.

You know this story well: the relationship between a hardware company leveraging their position to capture the lion’s share of software —- because that’s exactly what Apple did. My contention is that the iPhone was successful because of the moat iOS developers created, and the additional flywheel from the App Store. I discussed this more in a webinar “The New Kings of Tech”

The H100 delivers 9X more throughput in AI training, and 16X to 30X more inference performance. The company also states in HPC application-specific workloads, the H100 is 7X faster. The goal of the H100 was not only to add more transistors and make the H100 faster, but to also offer function-specific optimizations. This is achieved through the transformer engine.

The transformer engine is one of the key aspects of the H100. Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Prior to transformer models, labeled datasets had to be used to train neural networks. Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly. Transformers are partial to the parallel processing that GPUs offer.

The Hopper architecture aims to answer one of the bigger challenges facing superfast compute, which is that moving data into traditional servers overloads the CPU and system memory and becomes bottlenecked by PCI-Express.

By improving the bandwidth issue, Nvidia’s goal is to create more demand for their DGX Pod and SuperPod Systems, which in turn, will create more demand for their software.

The DGX SuperPods scale into a super-GPU capable of 768 terabytes per second. To compare, the entire internet requires 100 terabytes per second. This results in 1 exaflop of FP8 AI performance that runs trillions of parameters. FP8 is most commonly used for inference yet may be used for training in the future due to boosting throughput.

Whereas traditional workloads required many connections exchanging small amounts of data, the workloads of the future will require data to be shared quickly between GPUs and storage. This is accomplished by bypassing the CPU and sending data directly to the GPU while using the network hardware to move the data.

This is ideal for enterprise use cases where people are more likely to use Ethernet while AI and HPC workloads continue to use the Quantum-2 based off Mellanox’s InfiniBand.

Not only will Nvidia begin to monetize through software on the DGX systems but accessibility will improve through CSPs, or cloud service providers. This is an attempt to democratize AI development while driving software sales. On a trailing 4-quarter basis, cloud service providers drove 40% of data center revenue. This is important as cloud service providers will help move DGX Cloud along and AI-as-a-service.

Nvidia’s TAM of $600 Billion is Easily and Quickly Achievable

“The conclusion to my analysis is the same as the introduction, which is that I believe Nvidia is capable of out-performing all five FAAMG stocks and will surpass even Apple’s valuation in the next five years.” – Forbes and Free Newsletter, August 2021Forbes and Free Newsletter, August 2021

Last year, CEO Jensen Huang provided a total addressable market of $300 billion in hardware and $300 billion in software. Meanwhile, Elon Musk is deploying 10,000 GPUs in the cloud and there will likely be tens of thousands more to inference a widely deployed model for a social media generative AI project.

Per the analyst on the call: “So it seems like the incremental TAM is easily in the several hundred thousands of GPUs and easily in the tens of billions of dollars. But I'm kind of wondering what this does to the TAM numbers you gave last year. I think you said $300 billion hardware TAM and $300 billion software TAM. So how do you kind of think about what the new TAM would be?”

Huang aptly answered: “I think those numbers are really a good anchor still. The difference is because of the, if you will, incredible capabilities and versatility of generative AI and all of the converging breakthroughs that happened towards the middle and the end of last year, we're probably going to arrive at that TAM sooner than later.”

Today, Nvidia trades at 1.5X this TAM at $773 billion compared to an achievable TAM of $600 billion. This would suggest the stock price does not yet fully reflect the future market opportunity. Also, compare this TAM of $600 billion to Apple’s revenue of $394 billion, which helps illustrate why I said in the past that Nvidia Will Surpass Apple’s Valuation.

What to Expect in the Upcoming Earnings:

If we set aside the AI thesis for a moment, you can see below why Nvidia has rallied as the revenue is expected to rebound from (-21.4%) for the upcoming quarter to as much as +32% growth by fiscal Q3 ending in September. The popularity of the H100 could lead to a beat somewhere across these next few quarters. In addition, the RTX40 Series lower-end model will be released today for $299 and up, and this may further help the gaming revenue for Q2 and beyond.

Nvidia is unique in that the healthy growth is expected to continue into the foreseeable future — long after the company laps the quarters of the crazy gaming miss. What we don’t want is to invest in companies propped up temporarily by low comps. This is not the case with Nvidia.

Here is Nvidia’s revenue growth over the past five quarters, which shows the effects of the gaming segment:

Source: I/O FUND

As stated in our previous earnings coverage on Nvidia, all segments are expected to grow sequentially. I believe this is a major contributor for the rally we are seeing. The market saw what we saw, which was a sharp rebound in the fundamentals and that is critical to understanding why Nvidia is the top stock in the market right now.

Compare that picture to this one:

Source: I/O FUND

It’s not only the top line that is rebounding but also the bottom line too (which makes sense but is important to point out):

Source: I/O FUND

Moving along, data center revenue was $3.75B in Q1 FY23. The projected mid-point above is $4.075B, representing 8.7% YoY growth and 12.7% growth sequentially. Here is what was said on the call:

“Thanks for the question. First, talking about our data center guidance that we provided for Q1. We do expect a sequential growth in terms of our data center, strong sequential growth. And we are also expecting a growth year-over-year for our data center. We actually expect a great year with our year-over-year growth in data center probably accelerating past Q1.”

Source: I/O FUND

So, what we don’t see in the graph above is what the “accelerating past Q1” will be and this is the one data point that can get the stock to move AH.

Where Nvidia’s Price Will Go Next:

With the cash we raised throughout 2022, NVDA was the primary target of deploying some of this cash once our analysis signaled a bottom was in place. The below is a real-time trade notification we sent to our members on the October 13th.

Source: I/O FUND

Since February of 2022, we have been systematically taking gains at key levels. Even with logging sizable wins in this position in 2023, it remains our top position while still having enough cash to buy at lower levels.

It is our belief that NVDA is setting up for a sizable pullback, which we believe will open the door for better long-term entries. The reasons for this are below:

The structure/pattern of NVDA’s bounce signals caution. If we look at the pattern off the October low, it may feel like a straight line up; however, you can clearly see 3 swings (marked a, b, c). The first swing up off the low (a), a bearish retrace that makes a higher low (b), and the current swing that we are still in (c).

Source: I/O FUND

When we see a 3 wave pattern off of a major low, more times than not, it is a corrective bounce in a larger downtrend. While it may feel impossible at such heights, please keep in mind how sentiment can and does work against us as investors. It felt like tech could never go down in late 2020, and then it felt like it would never go up in Q4 of 2022.

Nvidia is no different, and what we have is a pattern that suggests a larger pullback than most expect is likely, at minimum. So, until this 3 wave pattern can morph into a 5 wave pattern, the odds favor a sizable pullback soon.

Further evidence of this can be seen in how NVDA is now at a significant supply region that marked the top in late 2023. We are now in the region that would constitute a double top playing, and note how price keeps trending higher with less momentum.

We are approaching a double top in conjunction with one of my favorite “sell signals” – when you have price making 3 higher highs, while the momentum indicator being used is making 3 lower highs. This is clearly happening on multiple time frames, which we believe warrants caution.

Similar patterns can be seen on the weekly chart of NVDA below. As price pushes higher, it is doing so on less momentum and less volume. When we see the same pattern on multiple time frames, it further builds the case for caution.

Source: I/O FUND

Regarding the targets we are tracking for entries, there are two general paths I see playing out from the price data in the above charts.

The Blue Count suggests that we completed the large degree uptrend that started in 2018. This would put is in a very large corrective rally with the final leg lower coming later this year/early next year. This would have us retest the October lows, and possibly slightly lower. The big tell for this count playing out will be if the coming pullback is a 5 wave pattern pointing down. If we see a large 5 wave drop from the highs, it is signaling that NVDA will likely go lower than most are anticipating.

The Red Count suggests that the October low was THE low. This will still set us up for a sizable pullback into the $220 – $167 region before setting up to make a run to new highs. This count implies that the large uptrend that started in 2018 is not complete and will be targeting fresh highs in the coming year. The tell for this scenario will be if the coming pullback is a 3 wave pattern. If we see a 3 wave pullback, we will look to be heavy buyers in the general target box just outlined.

Conclusion:

My firm tracks Nvidia very closely due to its leading allocation in our portfolio. We saw evidence of a gaming bottom in November, which we published about here. We also felt Nvidia had masterfully timed it’s RTX40 Series with the Ada Lovelace architecture plus the H100 release to drop exactly when the crypto mining selloff would be most felt. We discussed this here in September. These points were entirely overlooked by Nvidia critics.

Yes, a $2.5 billion revenue miss is crazy – but what was lying beneath the surface for chances of a quick recovery? The devil is in the details and not a lot of investors or analysts care to look into Nvidia’s complex hardware products.

For my readers, it has worked out in their favor that talking heads prefer to discuss stocks after they are up triple digits in price, and that the masses are collected around hindsight narratives. My firm is carefully and patiently building an AI portfolio that we believe will outperform institutions and hedge funds. Taking our sweet time to enter Nvidia at the lows – as we have done for the past five years and will continue to do so for the next five years — is part of that strategy.

Our firm issues real-time trade alerts when we buy, sell or trim stocks. You can learn more and view our other notable wins here.

Highlights from Google I/O 2023

Posted on May 18, 2023June 30, 2026 by io-fund

Google recently held its annual developer conference Google I/O 2023. Google is a large real estate owner with arguably more data than any other tech company in the world. This advantage cannot be overstated when it comes to training large language models (LLMs). In addition to having a strategic advantage for future development of LLMs with data, Google can offer advertisers instant ROI.

The primary announcements from the event were:

Google drops the waitlist for Bard and announces new features.
Google launches new Large Language Model, PaLM2
Unveils its new AI-powered Search.
Google Cloud announces new A3 supercomputer VMs built to power LLMs.

Google drops the waitlist for Bard and announces new features

Among the more exciting announcements at Google I/O, the company dropped the waitlist for Bard and the chatbot is now available in 180 countries and territories. Bard supports English, Japanese, & Korean languages, and will soon support more than 40 languages. Google is also rolling out features such as better source citations, the ability to export content generated in Gmail and Google Docs, support for more visuals and an upcoming Google Lens integration to analyze pictures and write captions.

Background on Google’s Bard:

Earlier this year, Google’s stock (Alphabet) tumbled 7% when chatbot Bard was unable to complete a search with 100% accuracy. During the demonstration, Bard returned incorrect information about which telescope was the first to take pictures of a planet outside the Earth’s solar system. This was a minor mistake given how far large language models and generative AI has come, rather it was the timing that was a bit flawed as OpenAI’s ChatGPT, the chatbot powering competitor Microsoft Bing, had been dominating headlines since its November 30th launch.

Microsoft, being an opportunist, took it a step further and announced Bing would now be powered by a faster and more accurate version of GPT-3.5 one day after Bard’s failed demonstration: “We’re excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 – and it is even faster, more accurate and more capable.”

Both companies have been preparing for this moment for many years. Microsoft invested $1 billion into OpenAI a few years ago with a new $10 billion round announced last month. Meanwhile, Google acquired DeepMind in 2014. Google also previously developed conversational neural language models such as LaMDA, which is used by Google’s Bard for its conversational AI technology.

Despite the mishap with Bard, it would be a human-generated mistake to think Alphabet does not command a place of leadership right now in generative AI. Alphabet was one of the first tech companies to focus and invest on AI and natural language processing (NLP). We pointed out to our premium research members in July of 2022 that ChatGPT is based on transformer architecture that Google initially introduced in 2017 when we said:

“Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Sequential text, images and video data are used for self-supervised learning and pattern recognition, which results in more data being used to create better models. Prior to transformer models, labeled datasets had to be used to train neural networks.

Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly.

Google first introduced transformer models in 2017 and transformers are used in Google and Bing Search. Transformers also led to BERT models, which stands for Bidirectional Encoder Representations from Transformers, and is commonly used for text sequences. Transformers are also used in GPT-3 (it’s the T in GPT) which improved from 1.5 billion parameters to 175 billion parameters. GPT-3 has the ability to report on queries it has not been specifically trained on.”

Earlier this month, Google’s CEO, Sundar Pichai, gently reminded the AI community of how cutting edge Google’s research is when he stated, “Transformer research project and our field-defining paper in 2017, as well as our important advances in diffusion models, are now the basis of many of the generative AI applications you're starting to see today.”

BERT was designed to help Google better understand search intent, as despite billions of searches every day, about 15% of those searches are for brand new terms. This prompted Google engineers to develop a model that could self-learn.

The result is that searches results are more accurate by taking into consideration the nuances of language.

Google launches new Large Language Model, PaLM2

Google launched a new Large Language Model, PaLM2, that will power the updated Bard AI chat tool and more than 25 other new products & features including productivity software (Gmail, Google Docs), Healthcare and Security.

PaLM 2 has the following capabilities:

Multilingual: The LLM is trained on more than 100 languages, which increases language proficiency
Reasoning: The LLM’s dataset has improved logic, common sense reasoning and mathematics
Coding: The LLM can generate code including programing languages such as Python, JavaScript and specialized languages such as Prolog, Fortran and Verilog.

Google Unveils its new AI-powered Search

The company has unveiled its new generative AI-powered search that will be subject to a waitlist. Google cites the example of the following search “what's better for a family with kids under 3 and a dog, bryce canyon or arches.” Previously, you had to break the question down into smaller ones, sort through the vast information available, and then put things together yourself. Now with generative AI, search will be able to better understand the question.

Generative AI will also provide a better experience for online shopping by instantly getting relevant information like reviews, images, and ratings. The new shopping experience is based on Google’s Shopping Graph, which has more than 35 billion product listings.

The company announced the ‘About this image’ feature, allowing users to identify fake images. It mentioned in its press release, “When the image and similar images were first indexed by Google, Where it may have first appeared, and Where else it’s been seen online (like on news, social, or fact checking sites)”.

Google launches new Large Language Model, PaLM2

The company launched the new Large Language Model, PaLM2, that will power the updated Bard AI chat tool and more than 25 other new products & features announced during the Google I/O 2023.

Its predecessor PaLM, launched in April 2022, was a 540 billion based parameter, and the company did not provide this detail for PaLM2. PaLM stands for Pathways Language Model. “What we found in our work is that it’s not really the sort of size of model — that the larger is not always better,” DeepMind VP Zoubin Ghahramani said in a press briefing ahead of the announcement. “That’s why we’ve provided a family of models of different sizes. We think that actually parameter count is not really a useful way of thinking about the capabilities of models and capabilities are really to be judged by people using the models and finding out whether they’re useful in the tests that they try to achieve with these models.”

PaLM2 is faster and more efficient than previous models. Some of the improvements highlighted by the company are that PaLM2 is trained for improved multilingual text, spanning over 100 languages, reasoning, and coding, including popular languages like Python & JavaScript. For example, due to the multilingual capabilities of PaLM2, it has helped Bard to expand to new languages. PaLM2 is available in four sizes: Gecko, the smallest, followed by Otter, Bison, and Unicorn. Other use cases include improved Workspace features while working in Gmail, Google Docs, and Google Sheets. PaLM2 can also be used for enterprise use cases like Med-PaLM2 in medical research and Sec-PaLM in cybersecurity.

The company also said that it’s working on a more powerful model called Gemini and it will also be available in various sizes so that it can be easily deployed to various products.

Google Cloud announces new A3 supercomputer VMs built to power LLMs

Google Cloud announced the A3 GPU supercomputer that can be used to train and run Artificial Intelligence and Machine Learning models. While the A3 GPU supercomputer is on a private preview waitlist, the previously announced G2 VMs are now in general availability. The G2 VMs are powered by the new Nvidia L4 Tensor Core GPUs. The company said that it is the first cloud provider to offer these new GPUs for serving generative AI workloads.

The A3 GPU VMs are made of eight Nvidia H100 Hopper architecture GPUs, 3.6 TB/s bisectional bandwidth between A3’s 8 GPUs via the Nvidia NVSwitch and NVLink 4.0, 4th Gen Intel Xeon Scalable processors, and 2TB of host memory.

The A3 supercomputer can deliver up to 26 exaFlops of AI performance, thereby improving the time and cost of training large machine learning models. The A3 workloads will be run on Google’s Jupiter data center networking fabric which the company states “scales to tens of thousands of highly interconnected GPUs and allows for full-bandwidth reconfigurable optical links that can adjust the topology on demand.”

Conclusion:

I would not be surprised if we exit 2023 with a reimagined way to use Search Engines. The iteration cycle here is likely to move quickly compared to AVs or the Metaverse, as there are real-world applications where AI can be applied without safety issues (AVs) or friction in terms of user adoption (Metaverse/VR headsets). Instead, the scale has already been built with Search being a viral, daily activity used by nearly every human on earth. AI advancements will simply improve what is already in place.

Cutting-edge chatbots can be quickly deployed on the search engines that already exist, and this is a substantial difference from other overhyped, early-stage technologies. Their accuracy may still need time, but they're probably not too far off from being deemed “reliable enough.”

Investors should expect that AI will become a winner(s)-take-all market. In time, the difference in how search and other applications operate in terms of user experience plus ROI for advertisers will help carve a larger lead.

Premium Members should check the forum for updates on our timing for an entry into the stock.

Highlights from Google I/O 2023

Posted on May 18, 2023June 30, 2026 by io-fund

The primary announcements from the event were:

Google drops the waitlist for Bard and announces new features.
Google launches new Large Language Model, PaLM2
Unveils its new AI-powered Search.
Google Cloud announces new A3 supercomputer VMs built to power LLMs.

Google drops the waitlist for Bard and announces new features

Background on Google’s Bard:

“Transformers are becoming one of the most popular neural-network models by applying self-attention to detect how data elements in a series influence and depend on one another.

Transformer models eliminate this need by finding patterns between elements mathematically, which substantially opens up what datasets can be used and how quickly.

The result is that searches results are more accurate by taking into consideration the nuances of language.

Google launches new Large Language Model, PaLM2

PaLM 2 has the following capabilities:

Multilingual: The LLM is trained on more than 100 languages, which increases language proficiency
Reasoning: The LLM’s dataset has improved logic, common sense reasoning and mathematics
Coding: The LLM can generate code including programing languages such as Python, JavaScript and specialized languages such as Prolog, Fortran and Verilog.

Google Unveils its new AI-powered Search

Google launches new Large Language Model, PaLM2

The company launched the new Large Language Model, PaLM2, that will power the updated Bard AI chat tool and more than 25 other new products & features announced during the Google I/O 2023.

The company also said that it’s working on a more powerful model called Gemini and it will also be available in various sizes so that it can be easily deployed to various products.

Google Cloud announces new A3 supercomputer VMs built to power LLMs

Conclusion:

Premium Members should check the forum for updates on our timing for an entry into the stock.

Super Micro: Sandwiched In The AI Trend

Posted on May 18, 2023June 30, 2026 by io-fund

Super Micro, also known as Supermicro, is sandwiched in the AI trend between hyperscalers and major chip design companies. The company is a server maker that started off by making motherboards and other components before it began making complete systems. The company is unique in that it sits between being an equipment manufacturer (Dell, HP) and being a design manufacturer (Foxconn).

The company competes with Dell, IBM, Hewlett Packard, and China’s Inspur. The chart below gives you a general idea of the landscape although Supermicro has doubled its revenue since 2021. The server maker is now a $6.8 billion company and ended 2021 as a $3.5B company (Counterpoint estimates were about $1B off) but the chart below is useful in visualizing the competitors.

The word “competitor” is being used loosely here as many of these companies will not necessarily be able to compete on liquid cooling for AI development platforms, or with Supermicro’s Building Block design. The companies pictured above have stagnated and this has worked out for Supermicro, a company that could have stagnated but continued to innovate instead.

According to IDC, the worldwide server market forecast is expected to deceleratedecelerate from 20% to 0%. Supermicro declined in revenue going into 2023, however, according to management, this is due to supply chain issues. This is important to distinguish as the 2023 bull case for Supermicro rests on the high demand the company is seeing, that due to supply chain issues, the company is unable to fill. The prevailing bull thesis is that Supermicro’s supply chain issues will ease in the near term.

According to the most recent Investor’s Presentation, Supermicro grew 5X faster compared to the industry average for subsystems and server systems. While there is pressure for tech management teams to cut costs, Supermicro may be more insulated by working closely on making AI systems with the most cutting-edge chips. This includes AMD 4th Generation Zen Epyc processors, Intel Xeon and soon Sapphire Rapids, Nvidia Grace CPUs and Ampere Arm-based third-gen CPUs, Nvidia’s A100 and H100s GPUs, AMD’s MI250 and MI300 GPUs and Intel’s Ponte Vecchio GPUs.

Another piece to the bull case for Supermicro is the near-term goal to reach $10 billion, which will put the company behind Dell and Hewlett Packard. Should the company reach its $20 billion long-term goal, then it could very well be the leader or at least a strong rival to Dell and HP. If/When this happens, it’ll be due to AI systems. It was stated on the call that AI/GPU and rack-scale solutions represented 29% of our total revenue and the company expects “significant future growth.”

Supermicro’s revenue quickly accelerated last year due to one large customer in Q3 2022 to Q1 2023. This one large customer, which was later identified as Meta, accounted for upward of 20% of revenue in June and September of 2022. By December, Meta had accounted for 10% of revenue. This was the subject of a short report. However, if you invest in a small cap or low mid-cap semiconductor company, there is going to be high customer concentration.

There was also a hint on the call that another customer may be ramping: “an existing Cloud Service Provider customer represented more than 10% of revenues for the first time.”

Liquid Cooling

As the performance of CPUs and GPUs increase, the heat these systems generate increases. Liquid cooling is becoming a popular alternative to air cooling to sustain maximum performance with the added benefit of driving down costs for supercomputers. According to a press release in 2021, liquid cooling can improve data center power usage effectiveness (PUE) and total cost of ownership (TCO) “by over 40% on power costs.”

Here's a quote from the CEO on the importance of this competitive advantage:

“The power consumption and thermal challenges of these new technologies have risen dramatically and 40KW or even 80KW rack solution demands are getting stronger and popular for computing hungry DC and industries. Having high power efficiency and air/liquid thermal expertise has become one of our key differentiators of success.”Having high power efficiency and air/liquid thermal expertise has become one of our key differentiators of success.”

In 2022, Supermicro stated that liquid cooling is being used in 10% of supercomputers but will grow to be used in the “vast majority” in order to offset the heat generated by power-consuming components. The company offers Direct to Chip cooling, Immersion cooling and Rear-door Heat Exchanger cooling. This design works better than air cooling, which needs air conditioning and server fans to run constantly.

Direct to Chip Cooling: Running a cold liquid over the top of a running chip by using a pump to circulate liquid. This is a closed loop system, or also known as a self-contained cooling system.
Immersion Cooling: The system is immersed in liquid for cooling.
Rear Door Heat Exchanger: Uses a specialized rear door to the rack where coolant absorbs the heat.

Water removes heat better than air. Liquid molecules are closer together than air molecules, which results in higher heat transfer. Artificial intelligence/Machine Learning and Big Data require massive amounts of data processing, and as future generations of CPUs and GPUs are released, these systems will exceed air cooling capacity.

Liquid cooling also solves CPU throttling, which occurs when CPUs and GPUs overheat and are throttled back to avoid damage to the chip.

AI Development Platforms

AI development platforms remove the need for disparate hardware and software by offering an end-to-end platform. Supermicro has partnered with Nvidia to offer Certified systems with the new H100 GPUs for the Nvidia AI Enterprise Platform.

These systems come with 3-year AI enterprise software subscriptions, and include workflows, frameworks, pretrained models and infrastructure optimization, in the cloud, in the data center and at the edge.

Supermicro is closely partnered with Nvidia on the H100 GPU rollout with air flow designs that reduce fan speeds, lower power consumption, lower noise levels and lower the total cost of ownership.

The most recent system announced in March enables AI workloads to be run in offices and the system can be rack-mounted, as well, for data center environments. The self-contained cooling system reduces operating expenses and helps the machine to run quietly for AI, deep learning, machine learning and high-performance computing (HPC) applications.

Supermicro is able to deliver workload optimized products quickly because of its building block design. The AI market is moving quickly, and SMCI can build and validate systems partly due to a modular design that allows systems to be updated from new products, such as when Nvidia, AMD or Intel have new design releases.

Financials:

Supermicro saw strong price action due to strong guidance for next quarter and due to strong guidance of 20% revenue growth for fiscal year 2024, which begins in July.

The current quarter revenue and EPS missed management guidance and analyst estimates. SMCI reported $1.28 billion for fiscal Q3 growth of (5%), which missed guidance of $1.48 billion, at the midpoint. GAAP EPS was $1.53 and Adjusted EPS was $1.71. This compares to management guidance of: “GAAP diluted net income per share of $1.75 to $2.02 and non-GAAP diluted net income per share of $1.88 to $2.14.”

This was Supermicro’s first miss dating back to 2019. Management said the following about the miss: “The shortfall was primarily due to key new component shortages for Supermicro’s new generation server platforms which have been mostly resolved to-date.”

The fiscal Q4 revenue guide was for $1.7B to $1.9B, which is above the $1.64B that analysts were expecting. The EPS forecast form management is for $2.21 to $2.71, compared to analyst expectations of $1.76.

Per the earnings call: “If supply conditions improve sooner, we expect to be above that range, despite some economic headwinds ahead. In other words, I continue to expect our fiscal year 2024 revenue to be at least 20% year-over-year growth and we are accelerating to reach our mid- to-long-term growth objectives of $20 billion per year.”

According to a previous earnings call, the CFO stated: “GPU prices and CPU prices are going up, especially with the new refreshes that are coming out. So we anticipate that [average selling prices] will continue to go up.”

At 20% growth, the company will surpass $8 billion in revenue next fiscal year, ending in June. The FY2025 analyst estimates are for growth of 11%.

Margins:

Server solutions and systems come with thin gross margins. Despite thin margins, Supermicro is a company with strong earnings with EPS of $10.73 for FY2023. Please see below for questions from analysts on the call regarding gross margin.

Gross Margin of 17.6% compares to 15.5% in the year ago quarter. The company stated GM was lower due to product mix and new platform ramps.
GAAP operating margin of 7.7% compares to 6.6% in the year ago quarter. This is lower compared to previous quarters this year in the 9% to 10% range. The company stated it was lower due to lower revenue.
GAAP net margin of 6.7% is up from 5.7% a year ago. This is lower compared to previous quarters this year in the 9% to 10% range.

Cash Flow:

The company reported strong cash flow in the current quarter with a margin of 15.5% for operating cash flow and 14.8% margin on free cash flow. This equals $198 million and $190 million, respectively. There is $362 million on the balance sheet and $187 million in debt.

Notably, the company has lumpy free cash flow with FY2022 and FY2019 ending negative. Here’s a snapshot of the most recent quarters:

Source: Investor Presentation

Supply Chain Issues

According to management, Supermicro’s decline in revenue growth is due to supply chain issues. Per the CFO’s opening remarks:

“Fiscal Q3, 2023 revenues were $1.28 billion, down 5% year-over-year and down 29% quarter-over-quarter, which was below our initial guidance range of $1.42 billion to $1.52 billion. The shortfall was primarily due to key new component shortages for Supermicro’s new generation server platforms which have been mostly resolved to-date.

We note that our shipments against a record backlog may be constrained by supply chain bottlenecks due to high demand for our advanced AI server platforms.”

Supermicro builds complete systems, and the supply chain issues can extend beyond CPUs, GPUs and memory to also include difficulty obtaining metal-oxide-semiconductor field-effect transistors (MOSFETs), diodes and capacitors. If there is low availability with these components, the systems won’t be complete in order to ship. According to The Register, lead times were at 26 weeks back in October compared to the 10-14 weeks that is the target for a healthy supply chain. This is an improvement from 40 weeks a year ago.

Here was another comment on the earnings call:

“These new AI product demands from top-tier companies have led us to challenges in terms of new key components availability.

Compounded with the economic headwind, our Q3 results were reflective of these difficult yet opportune conditions. The good news is that we have already started to address these component shortage pressures over the past few months and we are in a much-improved situation going forward. We have started to produce and ship some back orders since April.”

Risks:

Investors risk entering a frothy AI market. Most tech investors have mastered the extreme exuberance followed by extreme fear that tech seems to oscillate between. We are nearing extreme exuberance on AI with social media exploding over Chat-GPT and Bard. It’s rarely a good sign, and I’m saying that as someone who is exposed to AI-related stocks and benefits from this exuberance. Entering AI stocks right now should be done with a stop in mind.

This company had a short report out earlier this year that caused the stock to selloff. You can read the concerns here.

Regarding fundamentals, the gross margin is the primary concern raised in the earnings calls.

Here was one question from an analyst:

Nehal Chokshi

And what about with respect to gross margin?

David Weigand

Yeah. So, Nehal, we — yeah. Back two years ago, we gave a 17% to 21% — 23% topline growth. Obviously, we’re in there at a minimum of 20%. And for the gross margins, we continue to, like I said, to wrestle with taking market share and also balancing that against gross margins.

But we’re confident with our new manufacturing facilities coming online that we will be able to improve our gross margins. And we also, as we come out of this quarter and we begin to ramp our new product offerings that we will be able to improve margins as well.

Here was another question about the gross margin:

Ananda Baruah:

But I would love to get a better understanding of how you guys are thinking about sort of the gross margin manifestation if we think about the continued layering in of larger footprint, which may come at a slightly lower margin. Is it really that over time, we just expect a greater presence of that lower margin business with some efficiency gains or is it just in the beginning here, the margin will be lower for the new business, but then collectively, the P&L gross margin expands over time?

David Weigand:

Yeah. So we’re looking at it and on — in the — your latter alternative, Ananda, and here’s why. So right now, there’s three things that we’ve been facing. We’re having to face more air transportation costs in order to make our deliveries. So that impacts our margin. And also, we’re having to pay other expedite fees. That impacts our margin.

Number two, we ran a lot less through our factories than in Q3 than we did in Q2. So your margin efficiency, your ability to spread your fixed costs, it’s tremendously impacted on a smaller scale. So as we scale up, we improve our margins.

Thirdly, the — as we ramped our new product offerings, there is an efficiency on these new — on the production of these new products. So we are going to improve the efficiency of these products, which will improve the margin. And so those three things alone speak to margin improvements.

Charles Liang

I can add some color. I mean, as I shared, I mean, we are building a $20 billion of revenue, hopefully in midterm and that’s why a grow our capacity and support a large customer is very important to us. Once our volume becomes higher, our costs will be improved and then business operation efficiency will be higher.

So we are doing better great way to grow our revenue. And so, I mean, once we start to reach that number under $10 billion to $20 billion, I guess, our gross margin will start to grow, because we won’t always invest for big growth after that.

Valuation:

Supermicro has an old school semiconductor top line valuation that reflects its roots as a server maker. Interesting enough, it’s trading at its previous 2015 high. The 5-year median is 0.46 but there’s been too big of a product pivot to rely on this for the future valuation.

On the bottom line, SMCI trades in line with Intel. The bottom line is probably a better gauge for this stock than top line. I took a screenshot with July 2022 marked so you could see where AMD typically trades when it’s not going through a major cyclical event with PCs. It also shows where Intel and SMCI were back in July versus now – about 50% higher valuations.

The 5-year median for SMCI is 15 and has been up to a 20 5-year median in the past.

Conclusion:

The AI market is frothy but we may take a shot at entering. If we stop out, then no big deal. We’d like exposure to Supermicro for its ambitions to overtake the incumbents, plus the clear path the company will take to do so.

February Stock Tip: The Best Way to play Big Tech AI in 2023

Posted on February 11, 2023June 30, 2026 by io-fund

Recently, we wrote about the 2023 outlook and trends for overall IT spending and Big Tech capex. In summary, both are expected to be flat to slightly down. Here is what we said on the premium site:

“Overall, Big Tech has forecasted capex to be flat to slightly down y/y. However, an important theme was a shift toward higher ROI capex such as technical infrastructure and reduction in lower ROI capex, such as office facilities. After embarking on an aggressive capex program in 2021 and 2022, Big Tech has taken a pause to reassess their cost base and to reprioritize capex in light of the current macro environment.

Put another way, the size of the capex pie isn’t expected to grow in 2023 compared to 2022, but the slice spent on technical infrastructure (i.e. Cloud and AI), will grow at the expense of labor, office facilities etc. A change in capex mix that we believe is supportive in the medium-term of NVDA and AMD.”

There is a collective shift from higher return capex at the expense of lower return capex. From an investing perspective, the key takeaway is to identify markets where demand continues to be driven by secular demand and avoid those facing cyclical demand headwinds. For example, there is continued demand for Hyperscale Data Centers and AI related investments while the memory sector is grappling with weaker consumer related demand exacerbated by excess inventory.

The key theme from Big Tech Q422 commentary was the strategic importance and focus on AI investments to enhance their competitive positioning. Here at I/O Fund, we have continually looked for opportunities to invest in this secular theme and identify companies with strong market positions and competitive product offerings led by focused management teams with an identifiable investment catalyst.

With that in mind, we thought it would be worthwhile for our readers to revisit our positive investment thesis on Nvidia. It’s one of our largest core positions.

Simply put, as Big Tech continues to build out hyperscale scale data centers and AI based technology, they will require specialized semiconductor chips – AI accelerator chips – to provide the necessary computing power required. At the moment, the AI chip market is a duopoly with Nvidia and AMD. However, Nvidia’s position is much larger than AMD and a “better” GPU. So as Big Tech continues these AI related investments, Nvidia is the first place Big Tech will go to buy them – namely Nvidia’s H100 GPU chip. (Note: Later in the year, AMD will release a GPU to rival Nvidia and we will cover this for you including correct timing as the I/O Fund has predicted every twist and turn AMD has taken in its enormous comeback against Intel – for now, Nvidia has a near monopoly on GPUs for AI acceleration.)

Given the market dynamics outlined above, here is how Nvidia’s CEO Jensen Huang described the AI market opportunity in response to a question by Vivek Arya around the overall capex outlook. Huang’s comments focused on Nvidia driving growth from AI acceleration, rather than general purpose computing. This implies that capex can be flat while Nvidia will be serving the most valuable piece in the stack. AI acceleration, according to the CEO, will not be flat or down. A similarly positive tone echoed by Big Tech.

“And then, Jensen, the question for you. A lot of concerns about large hyperscalers cutting their spending and pointing to a slowdown. So if, let’s say, U.S. cloud capex is flat or slightly down next year, do you think your business can still grow in the data center and why?”

“Vivek, our data center business is indexed to two fundamental dynamics. The first has to do with general purpose computing no longer scaling. And so, acceleration is necessary to achieve the necessary level of cost efficiency scale and energy efficiency scale, so that we can continue to increase workloads while saving money and saving power. Accelerated computing is recognized generally as the path forward as general purpose computing slows. The second dynamic is AI. And we’re seeing surging demand in some very important sectors of AIs and important breakthroughs in AI.”

“And so, you could see that our company is indexed to two things, both of which are more important than ever, which is power efficiency, cost efficiency and then, of course, productivity. And these things are more important than ever. And my expectation is that we’re seeing all the strong demand and surging demand for AI and for these reasons.”

In light of Big Tech’s focus on higher return capex, Jenson’s comment was very informative on how Nvidia stands to benefit from Big Tech’s change in capex mix. As Big Tech continues to invest in AI infrastructure, they will need chips that provide the highest computing power and productivity with the most efficiency. At the moment, Nvidia’s H100 is the best AI chip to fulfill these requirements.

How will Nvidia benefit?

The key investment catalyst for Nvidia is the adoption and implementation of the H100 GPU by its customers.

So without getting too technical, here is an outline of the medium and long term investment thesis.

Nvidia’s March 2022 introduction of the Hopper H100 GPU with 80bn transistors – 48% more than Nvidia’s A100 with 54 billion – is a game-changer. Simply put, more transistors means faster speeds and increased computing power
H100 is 6x faster and its performance is 2-3x better than Nvidia’s prior A100 GPU. H100 has 50% more memory and interface bandwidths. Higher bandwidth will create more demand for their software in the future. The ability for the GPU to connect directly to the network will avoid CPU bottlenecks
The A100 has led company gains since Q22020, now the H100 will lead the next leg of growth. In the most recent Q322 investor call, management indicated H100 will quickly overtake A100
H100 will power AI based and high performance computing systems. There are four layers to Nvidia’s full stack accelerated computing: hardware (AI accelerators), system software, platform software and applications. Overtime, this position will enable Nvidia to monetize more of the software stack due to vendor lock-in effects. In the Q322 call, management indicated this is effectively starting “now” at the enterprise level
Over the long term, Nvidia will combine its hardware offering with software component primarily targeting the auto industry
Nvidia is taking a play out of Apple’s playbook that helped it’S market cap grow to 2 trillion. Nvidia’s goal is to leverage their dominate position in hardware to capture the lion’s share of the software. That’s exactly what Apple did with mobile devices and software related apps and services.
Most importantly, and not covered at the level it deserves (or at all by the media), Nvidia is going to be an AI software leader. This marks a monumental shift for a company that is traditionally hardware-only. We have written about this long-term opportunity for our premium subscribers here.
This transformation has not yet been appreciated by Wall Street nor reflected in the stock price. Nvidia’s 2022 Investors presentation identified a $300B Market opportunity.

To use a baseball analogy, Nvidia has just begun the first inning of this transformative process.

Upcoming catalysts

Nvidia is up about 52% ytd and is due to report earnings on 2/22/23. We will be looking for continued signs that gaming has bottomed, adoption trends of H100 and whether management expects a 2H23 bounce similar to what their peers guided for. We’ll touch upon these topics after the company report earnings.

It is important to note that Gaming is still an important business for Nvidia for its earnings contribution. Gaming’s exposure to consumer-related hardware products like PCs and gaming consoles has historically been the source of cyclical growth concerns and stock volatility around earnings releases. Future growth will not come from gaming, where Nvidia is already a mature, market leader. Nvidia’s 2022 Investor’s Presentation provided future estimates which detail how consumer exposure should become less of a concern to investors. Overtime, Nvidia will transform from a gaming to an AI software focused company.

There were signs that gaming weakness had bottomed in Q322 and the market may still be focused on that in Q422. Our main focus will be on H100. If the nascent signs of H100 adoption seen in Q3 continue to grow, this will increase our conviction on Nvidia and it will begin to get attention from Wall Street it deserves as 2023 unfolds.

Why 2023 May be a Strong Year for Nvidia:

Big Tech is not immune to the weaker macroeconomy nor consumer. This has been evident in their earnings releases. For Big Tech’s next capex act, their commentary focused on shifting capex to higher ROI investments with a focus on cost efficiency. These comments have increased our conviction that investments in AI are a key strategic priority and will continue.

From an investing perspective, it supports our investment thesis in Nvidia and AMD. Nvidia’s new H100 GPU chip has positioned it to benefit from the buildout in AI related and hyperscale data center infrastructure. Critically, given their dominant market position in AI chips, this will enable Nvidia to then monetize and gain a greater share in the software stack. In addition, AMD plans to commercially release its MI300 GPU this year.

Per the most recent AMD earnings call:

“MI300 will be the industry's first data center chip that combines a CPU, GPU and memory into a single integrated design, delivering 8x more performance and 5x better efficiency for HPC and AI workloads, compared to our MI250 accelerator currently powering the world's fastest supercomputer. MI300 is on track to begin sampling to lead customers later this quarter and launch in the second half of 2023.”

In the most recent earnings report, Nvidia management commented that the H100 adoption rate and software monetization at the enterprise level is happening faster than expected.

This month, keep an eye out for technical analysis from Knox Ridley, where he will go over how he plans to manage the Nvidia position in the portfolio. On a side note, he nailed Nvidia’s bottom with an entry of $108.51 on October 13th with a real-time trade alert. You will get his very best technical analysis on a leading position in the portfolio that the analyst team believes will fundamentally stand apart this year. Stay tuned for this!

In addition, Essentials Members will receive an earnings update on Nvidia following the earnings report to better gauge 2023 timing and entries.

We can’t urge you enough to take your time with each stock as too many research services pump out content for content’s sake. We are a real, live portfolio that is audited, and we show you the exact process we follow to make smart investing decisions. For the February stock pick, we want to drill down deep so our readers get top notch coverage of one of our highest conviction holdings. Don’t be surprised if you get more Nvidia coverage this month rather than moving on quickly to another name. Institutions take months to research a stock, and this level of depth is exactly what we bring to retail investors.

Have a wonderful weekend and we will see you next week!

Big Tech Capex, the Next Act – AI Take a Bow

Posted on February 10, 2023June 30, 2026 by io-fund

In the past, we have written about the importance of Big Tech’s capex programs and its impact on demand for semiconductors. Particularly in 2021 and 2022, where there was a significant increase in data center and cloud computing related capex. It has been our position that Big Tech capex – which includes Google, Meta, Amazon and Microsoft – is a leading indicator for AI semiconductor companies and has been a secular tailwind for our holdings such as Nvidia and AMD. Now that Big Tech have reported their fiscal 2022 earnings, we thought it’d be a good time to review the 2023 capital expenditure outlook for the IT market and Big Tech.

2023 IT Market Spending Forecasts

In January 2023, Gartner released their 2023 forecasts for overall IT spending. Gartner forecasts growth of $4.5 trillion, an increase of 2.2% from 2022. Looking at the breakdown, Software and IT services continue to see meaningful y/y growth. Meanwhile, after exhibiting healthy 12% growth in 2022, Data Centers is forecasted to be almost flat at 0.7% in 2023. Devices continues to be negatively impacted by inflationary pressures impacting consumer demand.

In contrast to Gartner’s 2023 forecast of flat growth in overall Data Center spending. The growth in Hyperscale Data Centers is forecasted to grow at levels that vastly outpaces Data Centers. Hyperscale Data Centers are large data centers operated by Amazon, Microsoft and Google.

According to Precedence Research, The global hyperscale data center market size was estimated at USD 62 billion in 2021 and is expected to hit around USD 593 billion by 2030, a forecasted growth rate (CAGR) of 28.52% during the forecast period 2022 to 2030.

This growth is also reflected in forecasts for the Artificial Intelligence Chip market. In December 2022, Allied Market Research forecasts that the global artificial intelligence chip market will grow from $11.2 billion in 2021 to reach $263.6 billion by 2031, growing at a CAGR of 37.1% from 2022 to 2031. AI chips – supplied by Nvidia and AMD – will provide the computing power necessary to drive these hyperscale data centers.

Big Tech FY2023 Earnings Commentary

How did the recent Big Tech commentary on 2023 capex align with these market forecasts? Overall, Big Tech has forecasted capex to be flat to slightly down y/y. However, an important theme was a shift toward higher ROI capex such as technical infrastructure and reduction in lower ROI capex, such as office facilities. After embarking on an aggressive capex program in 2021 and 2022, Big Tech has taken a pause to reassess their cost base and to reprioritize capex in light of the current macro environment.

In 2016, Big Tech in total spent $30b in capex, in 2022 they spent $150b, a five-fold increase. Big Tech commentary indicates 2023 capex will be flat to slightly lower than 2022.

What did FAAMG say about 2023?

Alphabet:

Google spent $31.5b on capex in 2022 compared to $24.6b in 2021 and forecasted 2023 to be at a similar level to 2022. Although the forecasted growth rate in capex is lower than historical levels. Management commentary around capex was very telling on where the priorities lay. On the Q422 call, management referenced AI a total of 56 times in relation to its importance to the future growth of the company. Here are a few snippets that stood out with an emphasis on AI being Google’s #1 priority.

Sundar Pichai, CEO

I'll focus on two major things today in a bit more detail, and then I'll give a shorter-than-usual quarterly snapshot from across our business. First, how we unlock the incredible opportunities AI enables for consumers, our partners and for our business; and second, how we focus our investments and make necessary decisions as a company to get there … the AI opportunity ahead. AI is the most profound technology we are working on today. Our talented researchers, infrastructure and technology make us extremely well positioned, as AI reaches an inflection point.
Our AI is a powerful enabler for businesses and organizations of all sizes and we have much more to come here. There's a few flavors of this. Google Cloud is making our technological leadership in AI available to customers via our Cloud AI platform, including infrastructure and tools for developers and data scientists like Vertex AI.
AI also continues to improve Google's other products dramatically
On the AI side, it is a really exciting time. I think we've been investing for a while, and it's clear that the market is ready. Consumers are interested in trying out new experiences. I think I feel comfortable with all the investments we have made in making sure we can develop AI responsibly.

Philip Schindler, CMO

Going forward, we are focused on growing revenues on top of this higher base through AI-driven innovation. Sundar highlighted the incredible opportunities underway with AI and the transformative impact it will have on businesses. Already, breakthroughs in everything from natural language understanding to generative AI are fueling our ability to deliver results that drive meaningful performance for advertisers and are useful to users.

Ruth Porat, CFO

And as I indicated in opening comments, when we look at capex for 2023, we do expect it's going to be generally in line with 2022 with an important mix shift. We're increasing our investments in technical infrastructure. And that's not just for AI. That's to support investments across Alphabet, in particular in Cloud as well. And at the same time, we're meaningfully decreasing our capex for office facilities.
With AI, this is obviously an Alphabet strategic priority, and we see huge opportunity ahead

Meta:

For Meta, capital expenditures, including principal payments on finance leases, was $32b billion for 2022 compared to $19.3b in 2021. 2022 capex was driven by investments in servers, data centers and network infrastructure. Meta forecasted 2023 capex to be between $30-33b down from their prior guidance of $34-37b. Similar to Google, management commentary around AI and capex was very telling on where the priorities lay.

Mark Zuckerberg, CEO

Now before getting into our product priorities, I want to discuss my management theme for 2023, which is the Year of Efficiency. We closed last year with some difficult layoffs and restructuring some teams. And when we did this, I said clearly that this was the beginning of our focus on efficiency and not the end. And since then, we have taken some additional steps, like working with our infrastructure team on how to deliver our roadmap while spending less on capex
And next, I want to give some updates on our priority areas. Our priorities haven’t changed since last year. The two major technological waves driving our roadmap are AI today and over the longer term, the metaverse.
AI, it’s the foundation of our discovery engine and our ads business. And we also think that it’s going to enable many new products and additional transformations in our apps. Generative AI is an extremely exciting new area with so many different applications. And one of my goals for Meta is to build on our research to become a leader in generative AI in addition to our leading work in recommendation AI.
Yes, I can start with generative AI. Yes, I think this is a really exciting area. And I mean, I’d say the two biggest themes that focused on for this year and one is efficiency and then the kind of the new product area is going to be the generative AI work.
A lot of the trends that we are seeing here is, we are using larger models, which require more computation. We have shifted the models from being more CPU-based to being GPU-based

There is a positive readthrough on Zuckerberg’s comment on the shift from CPU to GPU models. This could potentially benefit Nvidia and their H100 GPU.

Susan Li, CFO

Turning now to the capex outlook for 2023, we expect capital expenditures to be in the range of $30 billion to $33 billion, lowered from our prior estimate of $34 billion to $37 billion. The reduced outlook reflects our updated plans for lower data center construction spend in 2023 as we shift to a new data center architecture that is more cost efficient and can support both AI and non-AI workloads
So we’re shifting our data centers to a new architecture that can more efficiently support both AI and non-AI workloads. And that’s going to give us more optionality as we better understand our demand for AI over time. Additionally, we’re expecting that the new design will be cheaper and faster to build than previous data center architecture. Along with the new data center architecture, we’re going to optimize our approach to building data centers. So we have a new phased approach that allows us to build base plans with less initial capacity and less initial capital outlay, but then flex up future capacity quickly if needed. We’re still planning to grow AI capacity significantly, and that connects
The current surge in capex is really due to the building out of AI infrastructure, which we really began last year and are continuing into this year. We will be measuring the ROI of these AI investments, and their returns will continue to inform our future spend. Our intention is still to bring capex as a percent of revenue down, but capital intensity in the nearest term is really going to depend, in part, on the revenue outlook and our needs to further build AI capacity for future demand

Javier Olivan, COO

I think if you look at the strategy on ads, we really have two parts, which is continue investing in AI and that’s where we are seeing a lot of the improvement in ads relevance.

Microsoft:

For Microsoft FY 2022 capex, including assets acquired under financial leases, was $29.2 and compared $24.2 to FY 2021. For FY 2023, Microsoft has stated “… we expect a sequential decrease on a dollar basis with normal quarterly spend variability in the timing of our cloud infrastructure buildout.”

Satya Nadella – Chairman and Chief Executive Officer

The age of AI is upon us and Microsoft is powering it. We are witnessing non-linear improvements in capability of foundation models, which we are making available as platforms. And as customers select their cloud providers and invest in new workloads, we are well positioned to capture that opportunity as a leader in AI. We have the most powerful AI supercomputing infrastructure in the cloud. It’s being used by customers and partners like OpenAI to train state-of-the-art models and services, including ChatGPT.

Amazon:

For Amazon, capex including equipment financial leases, was $58.3b in 2022 compared to $55b in 2021. These expenditures primarily reflect investments in technology infrastructure. In the past, management has indicated that about 50% of total capex has gone toward infrastructure. Management gave no guidance for 2023 other that these investments will continue.

Conclusions

Per the most recent AMD earnings call:

In the most recent earnings report, Nvidia management commented that the H100 adoption rate and software monetization at the enterprise level is happening faster than expected. We will further outline how Nvidia is well positioned to benefit from this spending in AI and what to look for in Nvidia’s upcoming earnings report. We’ve recently covered AMD here.

Keep a look out for future posts.

Interview with Real Vision: Nvidia is the #1 AI Stock and Why Cloud Looks Weak

Posted on January 13, 2023June 30, 2026 by io-fund

Last week, I joined Samuel Burke from Real Vision to discuss “3 Ideas.” We discussed why I see Nvidia as the #1 AI stock and also why cloud is weaker than it appears.

View the Clip on Twitter hereView the Clip on Twitter here

The full 30-minute video is available here with a Real Vision subscription or 7-day free trial.

For more information on our Nvidia thesis, you can access previous research here:

For more information on why Cloud is Weak, you can access our previous research here:

Slowing Growth in Cloud Stocks: When Will We Hit a Bottom

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CrowdStrike Stock: Cloud Darling Reports Weak Sequential Key Metrics

Posted on January 4, 2023June 30, 2026 by io-fund

This article was originally published on Forbes on Dec 29, 2022,09:41pm ESTForbes on Dec 29, 2022,09:41pm EST

CrowdStrike has one of the better fundamental profiles out of the cloud category. This is due to its 50%+ revenue growth rate, GAAP operating margin of (7%) and free cash flow margin of 31%. The company also has one of the best Rule of 40 numbers in the cloud category at 89%. The companies that have higher growth rates or higher Rule of 40 numbers tend to be IPOs, which are designed to be strong out the gate and then fade over time. Meanwhile, CrowdStrike has consistently offered best-of-breed performance for over three years.

Therefore, it’s important to look into what caused CrowdStrike’s weak price action following its earnings report particularly because the stock is widely recognized as one of the strongest cloud stocks on the market. CrowdStrike’s steep selloff of (27%) over the past 30 days isn’t fully satisfied by the $10 million miss on forward revenue and ARR in the last earnings report. Forward Q4 revenue was expected to be $634M and the company guided $619M to $628M for a miss of about $10 million, if we take a midpoint of $624 million (about 1.5% miss). ARR was $2.34 billion compared to analyst expectations of $2.35 billion, for a $10 million miss (less than 1% miss).

Although this likely contributed, I believe the analyst we quoted in our Pre-ER write-up for premium members may be providing a missing link. An analyst from Barclays was modeling for net new ARR of $224M to $230M-plus for this key metric compared to actual results of $198 million.

At the midpoint, this would be more of a miss of 14.6%.

Here is what was said in the Pre-ER write-up for our premium members:

“An analyst note from Barclays’ Saket Kalia is modeling ARR net addition of $224 million “but thinks upside could be $230M-plus given strong pipeline commentary.” At $230M, it would represent 5% sequential growth and 35% YoY growth. This would be down from 15% sequential growth in the previous quarter and 45% YoY.”

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The reason we flagged this prior to earnings is because the net new ARR at a high point of $230M would still mark a strong deceleration to 5% sequential growth down from 15% sequential growth last quarter. This means the company would have to meet the number the Barclays analyst modeled or we would be nearing flat to negative sequential growth on net new ARR. Therefore, we emphasized the importance of this number prior to the earnings report as it was truly a “line in the sand” moment for CrowdStrike’s earnings performance.

With the actual of $198 million reported, this dropped the net new ARR to negative sequential decline of (9%) down from $218 million last quarter. This marks a change compared to the comp of +13% sequential growth from Q2 2022 to Q3 2022.

In August/September time frame, during the Q2 reports, we also emphasized that the market is nervous that cloud will become the other shoe to drop by stating: “I also want to be a messenger and say that another reason we are seeing strong price activity [with cloud stocks] is that analysts are concerned that enterprise spend will be the next shoe to drop. This concern was expressed across quite a few cloud companies’ [Q2] earnings calls. The thinking is that enterprise spend will follow consumer spend, (eventually), yet is slower because budgets are cut more slowly and added back more slowly.”

Because enterprise and cloud budgets are slower to be cut than ad or marketing budgets, there is outsized pressure being placed on sequential growth. The market does not care about YoY because it’s assuming enterprise spending wasn’t affected yet this time last year. We cautioned in a previous analysis two weeks ago “Slowing Growth in Cloud Stocks: When Will We Hit a Bottom” to be careful of YoY guidance as QoQ growth in cloud saw a remarkable slowdown.

CrowdStrike Q3 Financials:

CrowdStrike beat both top line and bottom line for Q3. In fact, an area where CrowdStrike continues to stand out from its peers is the health of the bottom line and both Q3 actual and Q4 guide was no exception in this regard.

For example, the free cash flow margin of 30% is exceptional for the cloud category. The company reported revenue of $581 million for growth of 53% compared to revenue of $574 million expected for growth of 51%. This is a slight deceleration from 58% last quarter.

For Q4, the company guided for revenue of $619 million to $628 million compared to expectations of $634 million. At the midpoint of $623.5million, this is a $10.5 million miss. This represents growth of 44.7%.

Adjusted EPS for Q3 came in at $0.40 compared to $0.32 expected. Adjusted EPS guide for Q4 also beat at $0.42 to $0.45 compared to $0.34 EPS expected.

GAAP operating margin of (9.70%) compares to (9%) last quarter and (10.5%) in the year ago quarter. This resulted in GAAP operating loss of ($56.4) million which is a tad higher than the $48 million losses last quarter and the $40 million losses in the year ago quarter.

The adjusted operating margin was a beat in Q3 and Q4. This was a bright spot in the report with adjusted OM of 15.4% compared to 13% estimated. This compares to 16% Adj OM last quarter and Adj OM of 13% last year. This was essentially flat and it’s important it did not contract. The guide on adjusted operating income of $87.2M to $93.7M implies an adjusted operating margin of 14.5%.

CrowdStrike is very strong on cash flow and is one of the top-ranking cloud stocks in this regard. This quarter the company reported a free cash flow margin of 30% for FCF of $174 million. The company is guiding for a FCF margin of 28% to 30% next quarter. The operating cash flow was $242.9 million for a margin of 41.8%.

There is $2.47 billion in cash on the balance sheet. The company paid $140 million in stock-based compensation for a margin of 23.7%.

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The I/O Fund has launched a new$99/year Premium Newsletter called "Essentials" — this newsletter delivers premium samples for our readers who want more actionable analysis for their tech portfolios. This month, we released a stock pick that we believe will be a leader in 2023 plus a video with the buy plan.$99/year Premium Newsletter $99/year Premium Newsletter called "Essentials" — this newsletter delivers premium samples for our readers who want more actionable analysis for their tech portfolios. This month, we released a stock pick that we believe will be a leader in 2023 plus a video with the buy planbuy plan.

Key Metrics:

To recap, CrowdStrike reported a quarter with 52% growth and forward growth in Q1 of 44.7%. The company leads popular cloud stocks on free cash flow with a 30% margin and has a healthy adjusted operating margin of 15%. Although stock based compensation weighs on GAAP operating margin, it still ranks high compared to peers with a GAAP operating margin of (9.7%) —- so why did the stock selloff after hours and is down (27%) over the last 30 days?

The answer is found in the key metrics.

RPO was up 44% year-over-year for $2.797 billion and was up 11.6% sequentially. However, management reminded analysts that ARR is the leading key metric for their business.

Ending ARR grew 54% year-over-year to $2.34 billion and grew 9.3% sequentially. Therefore, because ending ARR was strong, the net new ARR could be easily underestimated in terms of impact. The net new ARR at $198 million in fiscal Q3 compared to $218 million net new ARR in fiscal Q2 indicates a 9% sequential decline.

The market has the jitters right now so the sequential decline is important to pay attention to especially because management said to expect further weakness in the upcoming Q4 quarter. Here is what the CFO said:

“Even though we entered Q3 with a record pipeline, we are expecting the elongated sales cycles due to macro concerns to continue, and we are not expecting to see the typical Q4 budget flush given the increased scrutiny on budgets. While we do not provide net new ARR guidance given the current macro uncertainty, we believe it is prudent to assume that Q4 net new ARR will be below Q3 by up to 10%.”While we do not provide net new ARR guidance given the current macro uncertainty, we believe it is prudent to assume that Q4 net new ARR will be below Q3 by up to 10%.”

This implies a net new ARR of $178.3 million for Q4 (10% lower than the current quarter at $198.1M) compared to net new ARR of $216 million in the year ago quarter. This is important because it’ll mark not only a sequential decline but a year-over-year decline in net new ARR. The market had already sold off for what I presume was a sequential decline in CrowdStrike’s leading key metric, and management then stated the decline would be steeper for Q4 on the call. Once the comment above was made, we were certainly not going to see a reversal in the stock price from the earnings call.

Customer count was strong at 44% growth. The mix of domestic versus international was slightly lower than usual for North America at 69% with EMEA being slightly higher at 15%. Deferred revenue grew 56.4% year-over-year and backlog grew 19%.

Additional Commentary:

CrowdStrike was transparent about the importance of ARR even in the face of net new ARR being lower than expected.

Here is what was said by the CFO:

“And then finally, just to comment on ARR. You pointed out that's how we run our business. ARR, though, is really an X-ray into the contracts themselves. And as we view that as the most important — or most transparent metric into the outlook for our business, that's the one where we're focused on. So, hopefully, that gives some more clarity on how we think about cRPO and ARR.

Later on, an analyst did zero-in on the (9%) decline.

“Andrew Nowinski

Great. Thank you for taking the question this afternoon. So total ARR of $2.3 billion, growing 54% is still absolutely amazing, I was – and it's at scale. But I was wondering, were you surprised that the net new logos that you added were down 9% this quarter?

Burt Podbere

Thanks, Andy. So when we think of the net new logos, it really corresponds to what we talked about in terms of what we saw in that SMB space. The SMB space is the one that drives the velocity of our net new logos. And as we talked about, we saw an 11% increase in our sales cycle in the SMB space. And that actually equated into $15 million in terms of deals in that space that could push out. And so when you think about 15 million in that space and what it means in terms of logos, where you can do the math, it's a pretty big number.

So that's how we think about net new logos corresponding to what we saw in net new ARR from the SMB space. So from that perspective, we weren't surprised at the end of the day when we saw that what happened with respect to the increased sales cycles and the amount of money that got pushed out in the SMB space.

“Push out” refers to a delayed sales cycle for an impact of $15 million. The CFO did reiterate the 10% further sequential decline in net new ARR between Q3 and Q4 when he said:

“When we do talk about net new ARR, I did talk about in the prepared remarks about how we think about up to 10% headwinds going into Q4 from Q3, and that's just to coincide with some of the headwind activity that we saw accelerated at the end of this quarter. So that's how we think about that.”

Conclusion:

The market is cooling off from previously popular cloud stocks. The reason is that QoQ likely hints at what is to come for enterprise budgets that are typically determined in January of the new year. There will certainly be some cloud stocks that are stronger than others, comparatively. Attempting to guess which ones these will be carries outsized risk if the QoQ trends we saw in Q4 continue into Q1.

The quarter from CrowdStrike sounded very familiar, in my opinion.

Here is a brief overview from our Microsoft’s post-earnings report:

“Microsoft is guiding down for next quarter with analyst expectations for the December quarter at $56.04 billion compared to management guidance on the call for revenue of $52.75 billion, at the midpoint. This represents 2% growth. […] That’s a 11% deceleration over the next few months. Some of this is coming from Azure as the company is expected Azure to decline 5% next quarter for its current growth rate. This will be 37% growth on a constant currency basis, down from 42% this quarter.”

While some investors believe this is a stock picker’s market – we disagree with this thinking. In May, we pivoted to hedging up to 100% of the I/O Fund portfolio as macro will eventually affect even the strongest companies. We are seeing that now with Tesla – a strong consumer company that is following its consumer peers into a material slowdown that is entirely macro based. Our macro coverage, such as Divergences Point Toward the Market Moving Higher, which called the October low, is published bi-monthly for our free readers and published daily for our premium readers along with real-time trade alerts. The hedging strategy has proven successful since we pivoted 8 months ago, primarily it has removed the pressure of the market’s intense selloff while allowing us to build key positions at valuations that are extremely low.

Ultimately, we started to move toward a neutral stance with cloud after Q2 reports after we saw initial signs of weakness and continued to trim/cut following some Q3 reports. We continue to hold one cloud name at a high allocation and we hold three more at medium sized allocations. We call this a neutral stance to where we are participating but not overweight. If we get additional signs that cloud is too weak to withstand macro pressure, we have a short candidate in mind. If we get signs that cloud will be resilient in 2023, we will buy into those with underlying strength.

Notably, the I/O Fund portfolio manager sees a relief rally of sorts coming in the early part of this year. That will be the time that we determine what to do with our remaining cloud positions — whether we sell into strength or buy into weakness.

Note: This analysis was originally published on November 30th 2022 and accompanies our previous free analysis: Slowing Growth on Cloud Stocks: When Will We Hit a Bottom.Note: This analysis was originally published on November 30th 2022 and accompanies our previous free analysis: Slowing Growth on Cloud Stocks: When Will We Hit a Bottom.Slowing Growth on Cloud Stocks: When Will We Hit a Bottom.

SentinelOne Q3 Earnings: FCF Positive by Next Year

Posted on December 7, 2022June 30, 2026 by io-fund

SentinelOne had an excellent report minus the fiscal year guide of 50%. The market is likely digesting this, as are we.

The price action on SentinelOne was muted although there were some notable positives from the report. Key items discussed included the company becoming free cash flow positive next year (calendar year 2023) and profitable on an adjusted basis by the following year CY2024. The company beat sizably on adjusted margins.

With that said, the market is digesting lower cloud growth rates across the board, and although SentinelOne has maintained excellent growth this quarter and for next quarter’s guide, the next fiscal year guide is likely what’s causing the flat price action.

The guide at this time is for a baseline of 50% growth, marking a deceleration from 105% growth this year. This technically is a miss from the FY2024 analyst expectations of 64% growth, although “baseline” is vague and the company could meet the original expectations in time.

Other than this, the company beat across the board.

Q3 Financials & Key Metrics

SentinelOne beat on revenue with $115 million reported for growth of 106%. This was a 7% beat.

For next quarter, the company did not budge on guidance, which is likely weighing on the report.

The guide of $125 million met analyst expectations of $124.5 million, yet the market typically wants to see a stronger guide if the current quarter provides a beat.

This shows you how picky the market is becoming as fiscal Q4 guide is 92% which puts SentinelOne at the top of the cloud category as most cloud stocks are guiding for a 30% to 50% deceleration in growth rate, while SentinelOne is guiding for a 10% deceleration.

The company slightly raised full year guidance to $420.5 million for growth of 105%, up from 103.3%.

SentinelOne reported in line with EPS at ($0.35) and beat on adjusted EPS at ($0.16) versus ($0.22).

Gross margins came in as expected with 64% GAAP GM and 71% adjusted GM.

The positive surprise was the adjusted operating margin of (43%) compared to (57%) expected. This compares to (69%) in the year ago quarter. GAAP operating margin of (90%) reflects the high stock based compensation at 40% of revenue. This results in GAAP operating losses of ($104M) and adjusted GAAP operating losses of ($49.5M).

The company reported free cash flow of ($64.7M) and has $1.2 billion on the balance sheet.

Key Metrics:

Net new ARR was in the spotlight because of Crowdstrike which we covered here. SentinelOne reported net new ARR of $49 million compared to the guide for “mid-$50 million.” The company stated the miss was largely due to deals closing in Q4 that normally would have closed in Q3. To back this up, the company is guiding for 20% sequential growth. Crowdstrike guided for a YoY and QoQ decline.

“To be clear, we expect Q4 net new ARR to increase by at least 20% sequentially compared to the third quarter. We believe this is a prudent view and reflects a continuation of the macro headwinds we experienced in Q3, yet we are in a position to deliver a seasonally strong end of the year.”

ARR growth slowed to 106% down from 122% last quarter for $487.4 million. Customers over $100K grew 100% to 827 total, down from 117% last quarter. Total customers grew 55% down from 60% last quarter for 9,250 total customers.

As you’ve likely noticed, ARR tracks very closely to revenue for this company. Management provided a 50% growth rate for ARR next year, which translates to 50% revenue growth.

“Based on a prudent view of the current economic environment and expectations of further macro deceleration, we believe we will deliver at least 50% total ARR growth in fiscal year 2024.”

As noted above, 50% is lower than what analysts had for fiscal year 2024. This is one comment an analyst made:

“Alex Henderson

Great. Thank you so much. You gave a guide — preliminary guide, I guess, is the right way to say it for FY 2024, 50% ARR growth. The question I have for you is really without giving a forecast, can you give us some sense of the way you are thinking about the OpEx spend in that environment, will you still produce at a 50% type growth rate, the same or a similar degree of leverage or do you think the leverage becomes a little bit more muted as a result of the slower growth before the reacceleration?

Dave Bernhardt

We think that the ARR, let’s call it, tentative guidance for next year is really a floor. When I think about it, we believe it’s conservative. We are looking at it as something we can build from. In terms of our OpEx spend, we have always said and you have definitely seen this over the past couple of quarters where we beat by 17% and 14% in terms of operating margins. A lot of our spend is highly elective and we will invest when it makes sense and we will pull back when it doesn’t.”

Additional Notes:

The company provided bold comments regarding profitability and free cash flow, and essentially moved the target up by a year to become FCF positive by the end of next calendar year and adjusted profitability the following year after that (CY2024).

“We are on track to exit fiscal year 2023 with two quarters of about 25 percentage points at the year-over-year operating margin improvement. Continuing this progress forward, we expect another 25 points of operating margin improvement in fiscal year 2024 and our goal is to achieve profitability in fiscal year 2025.”

Here was the question on FCF:

Hamza Fodderwala:

“And then secondly, for Dave, you mentioned, operating profitability in fiscal 2024. I just want to be clear, is that for the full year of fiscal 2024 and would you expect free cash flow breakeven to proceed that by about four quarters? Thank you very much.”

Dave Bernhardt:

“And Hamza, to answer your second question, we have talked about timing of free cash flow, in creating free positive cash flow. We are still expecting that to happen at the end of next fiscal year and then what we are hoping for and really working to achieve is how to get breakeven in fiscal year 2025. So the following year. So we do expect free cash flow to hit before profitability and then those two will be much more mapped together.”

Conclusion:

You’ve probably seen by now that our cloud holdings are being reduced. The thought process around reducing exposure has been outlined going into Q3 when we’ve said a few times the market is nervous that enterprise spend/budgets will be the other shoe to drop. If this is true (I’m a messenger here), then we are at the beginning and not the end of a softer cloud market as Q3 marks the beginning of this new phase of economic slowdown (with phase one being the consumer).

For example, I said here:

“I also want to be a messenger and say that another reason we are seeing strong price activity is that analysts are concerned that enterprise spend will be the next shoe to drop. This concern was expressed across quite a few cloud companies’ earnings calls. The thinking is that enterprise spend will follow consumer spend, (eventually), yet is slower because budgets are cut more slowly and added back more slowly.”

Most of this will become evident when next year’s budgets are transparently disclosed with cloud’s full year guidance. Right now, if we are being real with ourselves, the Q4 guides are shockingly low. What the cloud category is guiding down on growth rates between Q3 and Q4 used to take a year or two (for example, a growth rate decel of 67% to 40% for SNOW or 47% to 26% for MDB — or choose any others, it’s rampant). This level of decel used to take a year or longer and we are now getting a 30% to 50% decel sequentially.

The market is probably due for a bounce (not my department) so we will likely reduce our exposure carefully. Despite what the market does in the near term, the predominant growth trend in cloud — from what I’m seeing – is down. As a category, cloud is providing the biggest decel it’s ever gone through. So, that’s important to not lose sight of.

What does this mean for next fiscal year and will there be a further decel given what we’ve seen from Q4 enterprise budgets?

Of course, we believe companies like SentinelOne, MongoDB, Snowflake, etc, will be around for the next 10 years. But if the trend is down and the growth rates are being slashed, a real recovery in this category will not be on the table until this is reversed.

A Member said on the forum the other day, sometimes it’s better to leave the 20% off the bottom on the table to improve timing on returns. I agree with this because cloud could need the better part of next year to recover and we can easily get back in (knowing that we will be leaving some money on the table).

This discussion is separate from how we go about this as the market has been deep in the red last three days so there may be a better opportunity to reduce exposure than right now.

6 Ways Microsoft Can Drive Another $100 Billion with AI:

Open AI APIs:

Generative AI for Government:

Microsoft CoPilot:

Edge/Telecom Partnerships:

Microsoft Bing:

Microsoft is one of the largest cybersecurity companies

Installed customer base provides cross selling opportunities for new AI/ML based products and functionality

Microsoft’s Risk is Valuation

Conclusion:

Recommended Reading:

AI is Not a Buzzword for Nvidia

Why the Market is Bulled Up on Nvidia

The H100 is a Turning Point from Hardware to Software

Nvidia’s TAM of $600 Billion is Easily and Quickly Achievable

What to Expect in the Upcoming Earnings:

Where Nvidia’s Price Will Go Next:

Conclusion:

Recommended Reading:

Google drops the waitlist for Bard and announces new features

Background on Google’s Bard:

Google launches new Large Language Model, PaLM2

Google Unveils its new AI-powered Search

Google launches new Large Language Model, PaLM2

Google Cloud announces new A3 supercomputer VMs built to power LLMs

Conclusion:

Recommended Reading:

Google drops the waitlist for Bard and announces new features

Background on Google’s Bard:

Google launches new Large Language Model, PaLM2

Google Unveils its new AI-powered Search

Google launches new Large Language Model, PaLM2

Google Cloud announces new A3 supercomputer VMs built to power LLMs

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Supply Chain Issues

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How will Nvidia benefit?

Why 2023 May be a Strong Year for Nvidia:

Big Tech FY2023 Earnings Commentary

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Q3 Financials & Key Metrics