If Oracle can become the next $1 trillion AI stock – or if Stargate is a one-time deal that will leave the stock flat from exorbitant GPU costs
Bull, base and bear case scenarios, with detailed revenue and segment projections showing where and when revenue upside lies
An invitation to our next webinar – exclusive only to subscribers. In our upcoming 1-hour webinar, we will detail our buy, trim and sell zones for Oracle and other leading AI stocks.
Oracle Cloud May Grow Much Faster than Big 3
Though Oracle is growing off a much smaller cloud base than say Azure, robust IaaS momentum could drive its Cloud growth at a much faster rate than the Big 3 – defined as Microsoft, Amazon and Alphabet — over the next few years.
As stated above, consensus currently models in $46 billion in IaaS revenue in FY28. For the IaaS segment to increase 4.5x from FY25’s $10.2 billion in revenue, this requires growth at a 65.2% CAGR, or a slight deceleration from >70% YoY in FY26 to >60% YoY in both FY27 and FY28.
This rapid IaaS growth could fuel a 40% CAGR for Oracle’s total Cloud growth by FY28, taking its Cloud segment from $24.4 billion to $66 billion. This 40% CAGR will far outpace AWS’ growth in the high-teens, and Google Cloud and Azure in the high-20% to low-30% range.
While this represents a significant reshapement of Oracle’s business model towards high-growth cloud, it also implies that Oracle is aiming to be much more competitive in AI and firmly establish itself as the fourth AI hyperscaler. Despite coming off a much smaller revenue base, Oracle’s forecasted 40% cloud CAGR suggests that it will start to take market share from the Big 3.
Scenario Analysis
Quick Look into FY26, Long-term Targets
Before we present the scenario analysis, it’s prudent to briefly touch upon Oracle’s FY26 guidance and long-term targets, as these pertain directly to the three scenarios.
For FY26, Oracle slightly raised its revenue guidance, now expecting revenue to be $67 billion, for YoY growth of 16.7%, up $1 billion from its prior view for $66 billion. Management remains confident in exceeding its 20% growth target for FY27, or $80.4 billion.
Oracle has maintained its FY29 revenue target of $104 billion, which would require growth at a 14.5% CAGR since FY24. Given FY26 was raised, if the company maintains that CAGR then it will see 4% higher revenue in FY29 versus that target. For comparison, the consensus estimate for FY29 has risen $6 billion over the last three months, from $100.5 billion to $106.5 billion.
Bull Case Projects 55% Upside
Based on the model below through FY29, Oracle could see IaaS maintain a robust 59% 4-year CAGR to nearly $65 billion in revenue, with a deceleration to 40% YoY in FY29. This could drive Cloud revenue at nearly a 39% CAGR to more than $90 billion, or 85% of revenue, doubling its share from 43% in FY25.
This also assumes an (8%) CAGR in licensing, support, hardware and other revenue, driven by increased cannibalization of on-prem licensing beyond FY27 as Oracle’s cloud shift accelerates.
Overall, this bull case projects revenue slightly below FY27’s target, but sees much greater upside versus consensus by FY29 as the $30 billion deal begins to kick in and as Oracle benefits from increasing synergies with OpenAI.
To note, OpenAI is optimistically forecasting $125 billion in revenue by 2029, which could translate into tens of billions to Oracle from AI infrastructure costs. This forecast also likely hinges on limited hardware constraints to allow Oracle to meet such high demand.
However, high capex requirements to build out multiple GWs of capacity are likely to keep free cash flow limited – this model projects FCF margins in the mid-single digits by FY29. On a cumulative basis from FY26 through FY29, free cash flow is expected to under $7 billion.
The bull case scenario also assumes adjusted operating margins are slightly softer in FY26 and FY27 as amortization of intangible assets decreases, before expanding into FY29 as cloud and IaaS mix expands substantially.
Adjusted net margin is expected to increase slightly towards 32%, on the possibility that high capex requirements lead to debt raises and increased interest expenses.
Under this scenario, topline growth is expected to remain rather robust at an 18.8% CAGR from FY25, more than four points ahead of Oracle’s guided 14.5% CAGR. Peak revenue growth is forecast for FY28 at almost 22%, while EPS growth is expected to be >20% YoY for FY27 through FY29. As a result, Oracle is assigned a 10x P/S multiple and a 31x P/E multiple.
This is approximately in line with Oracle’s peak top-line valuations over the past five years and a 15% discount on the bottom line, accounting for a slight YoY deceleration in FY29. However, this is around a 40% premium to Oracle’s five-year average P/S multiple and a 30% premium to its five-year average P/E.
This valuation is also approximately in line with Microsoft’s current five-year average forward P/S and P/E multiples of 10.7x and 30.5x. To note, Oracle is projected to have a much higher cloud mix at 85% of revenue, growing at a 39% CAGR, versus Microsoft’s Intelligent Cloud at 66% share by 2029 on a 16% CAGR.
These assumptions return a price target of $386, or a 55% return, propelling Oracle into the $1 trillion club.
Base Case Sees Potential 27% Upside
The base case scenario assumes revenue remains roughly in line with consensus estimates through FY27, with low-single digit upside in FY28 and FY29 as Oracle begins to recognize growth from the recent mega-deal.
Under this scenario, IaaS growth will decelerate a bit quicker into FY29, with revenue at 36% YoY versus 40% YoY in the bull case scenario. This still results in a 57% IaaS CAGR to $62.3 billion in revenue, driving a 37% cloud revenue CAGR, nearly 2 points below the bull case.
In the base case, Oracle is assumed to maintain operating margin in the 43% level moving forward, with some slight net margin expansion to the 31% range. This would project FY29 EPS of $11.69, or almost 2% ahead of consensus.
This scenario sees Oracle maintaining solid topline and bottom-line growth rates, at almost 16% YoY for revenue and 18% YoY for adjusted EPS in FY29. In this, Oracle is assigned an 8.5x P/S multiple and a 27.1x P/E multiple, a 15% premium to its current five-year averages of 6.9x and 23.7x, respectively, as the company exceeds its long-term targets on strong cloud momentum.
These assumptions return a $316 price target, or 27% upside from current levels.
Bear Case Projects Flat Return
The bear case scenario assumes revenue again remains roughly in line with consensus estimates through FY27, before lagging consensus in FY28 and FY29. However, this scenario still assumes Oracle remains ahead of its FY29 target due to the mega-deal, as even a delayed ramp to 2029 still likely derisks that $104 billion revenue target.
IaaS revenue is projected at a 56% CAGR through FY29, with growth decelerating rather sharply from 70% in FY27 to 34% by FY29. SaaS growth is estimated at a slower 11% CAGR, with mid-single digit growth in FY29. This combines for a 35% cloud revenue CAGR, nearly 4 points slower than the bull case and 2 points slower than the base case. Even with these assumptions, FY29 revenue is still projected more than 1% ahead of Oracle’s target at $105.2 billion.
In this scenario, margins are projected to decline marginally through FY27 on account of that decreasing amortization expense, with further softness into FY29 from an aggressive pursuit of capacity expansion and high capex in an effort to support stronger IaaS growth.
This also assumes that Oracle will turn to the debt markets to fuel this capacity expansion, adding hundreds of millions to interest expenses and creating up to a ($0.20) EPS headwind by FY29.
As such, topline growth is projected to peak just below 20% in FY27, before decelerating to the low 14% range by FY29, resulting in a four-year CAGR of 16.8%. Similarly, EPS growth would peak at 21% in FY27 before decelerating 9 points to 12% YoY by FY29.
As such, Oracle is assigned a 7x P/S multiple and 23.3x P/E multiple, approximately in line with its current five-year averages, accounting for this two-year deceleration in growth rates. It’s also possible under this scenario that cumulative FCF generation through FY29 remains negative to barely positive, assuming capex growth comes in above >15% YoY in both FY28 and FY29.
This scenario would return a price target of $249, or essentially flat with its current share price.
Conclusion
Oracle laid out rapid growth targets for fiscal 2026 driven by strong AI momentum, forecasting cloud infrastructure growth to accelerate 20 points and total cloud revenue to accelerate 16 points. Coupled with r >100% RPO growth guidance, its expanded arrangement with OpenAI and its mega $30 billion/year cloud deal, confidence is increasing in Oracle’s long term cloud trajectory.
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Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.
The FANG acronym rose to popularity in 2013 and was extended in 2017 to FAANG to include Apple. If history is any indication, the world’s most valuable companies over the next ten years will not look like the previous ten years. Being early to identify which companies can take over this coveted status is how generational wealth is built.
As a tech industry analyst who has seen what one generous winner can do for an entire portfolio, I want to pause and acknowledge that an investor needs to only identify one company that can hold a top 5 position in order to see life-changing gains. To choose all five would be to defy incredible odds. This analysis is aimed at identifying what companies we believe will hold “world’s most valuable” by 2030.
Not only is tech the most valuable industry today, but what the tech industry is setting up to do over the next ten years will provide exponential gains compared to the 2020-2030 era. With that said, tech is going through a period of consolidation and this means the stakes are high in identifying the winners. To complicate matters, the market is not efficient with tech stocks as each product is quite nuanced and impossible to efficiently price without manual deep dives. Instead, the market will indiscriminately penalize all tech and indiscriminately reward all tech — and each time the liquidity tide rolls in and then out again, it becomes sink or swim. At times like this, we are very flexible as we know we only need to identify a handful of winners.
Our portfolio has twenty positions at any given time, yet we believe it will be 5-10 positions total that create 90% of our wealth by 2030. We are long-term buy and hold investors yet we acknowledge and accept this means we exit those who show weaker-than-expected results for more than one quarter — or we trim to 1% to hold a place for the stock in our coverage. Because we are flexible, we can always revisit a stock when the story resumes and the earnings match the thesis again.
The equity market is driven by sentiment and macro factors, which we expand on herewe expand on here, yet the underlying strength of tech fundamentals is hard to deny. The best way to predict what will happen next is to look closely at what happened over the past decade.
In 2007, following Steve Jobs famous iPhone keynote, a burgeoning app economy was driven forth by iOS and Android developers. Google’s search engine was already a success yet mobile catapulted it’s use by putting the mobile device into far more hands over three years’ time than personal computers did over two decades.
There were many ways to capitalize on this massive addressable market — the iPhone and iOS apps dominated the highest spend on mobile, Facebook proliferated to 2 billion people and Google expanded to acquire YouTube. In this way, mobile drove gains for three FAANGs.
The adage is that history rhymes but it does not repeat. I believe a large addressable market is certainly required to produce the new wave of FAANGs – however, rather than consumer driving the gains, I believe it will be enterprises.
Below, I discuss the enterprise-level market that will be four times larger than mobile and two stocks that will directly participate. Imagine participating in 4X the FAANGs by 2030. That’s what I believe will happen due to one key trend and I discuss exactly why this will be achieved below.
Later in the analysis, we look at cloud and the trends that will drive cloud over the next ten years. This will catch investors who are complacent off guard as cloud is already going through a period of consolidation and we are seeing new business models emerge, such as the consumption model whereas the SaaS model with annual recurring revenue has dominated the past decade. Microsoft helps prove that cloud is certainly capable of FAANG-status.
We will also look at blockchain and crypto as I have been covering this space since 2013, which predates the Ethereum network. I was trained in Silicon Valley and my role is to introduce public investors to the next wave of innovation in as safe a manner as possible. I agree that 90% of cryptos will go to $0 yet I/O Fund has been firm for years that Bitcoin would reach $1 trillion in market cap and we have two Layer 1 networks to discuss with you plus a middleware company. Whether you’re ready for it or not, Web3 will replace the internet by 2028-2030 and we are fully prepared to participate in the substantial gains the blockchain will produce.
Lastly, FAANG is not entirely dead and consumer will have its moments too. We discuss the two FAANGs we own and what major catalysts these companies have in their future. We also briefly touch on some consumer-facing stocks we own and the large addressable markets they have the potential to capture.
For those of you who are new to the I/O Fund, we are prolific in our analysis. I began writing analysis on products, startups and enterprise-technologies in 2011 and moved over to the public markets seven years later. There is a library of analysis available to you that dates back to our launch in 2019 and additional free analysis in 2018. Due to the sheer number of products I have analyzed, we are able to hold an all-tech portfolio across semiconductors, cloud, ad-tech, blockchain and more.
We also encourage you to sign up for trade alerts as Knox’s active tradesKnox’s active trades help frame the market and whether we are risk-on or risk-off. We also have an automated hedge signal and are audited annually. You can learn more about how we manage an all-tech growth portfolio here.
Yet, the investors on our site need to do their part, which I can summarize as the following:
Speak with your financial advisor about your risk level. We are not financial advisors. Instead, we simply show you the trades we are making with our own money.
Use our pie chart to view our allocations. The larger the allocation, the higher the conviction – and vice versa, the lower the allocation, the lower the conviction.
As stated, we are flexible as we expect a handful of companies to drive the majority of our gains. If we receive new information, we will manage risk accordingly by lowering allocation or exiting the position.
We firmly believe all tech investors need a long-term time frame for tech. The best tech investors in the world are venture capitalists and they seek an exit 5-7 years after they’ve funded a round. The reason to have a long holding period is that it’s nearly impossible to time an entry, therefore professionals instead time their exit. By having a long-term horizon, you can be patient until the market conditions are in your favor to take your exit.
Accept that tech is volatile. For example, high beta tech has sold off around 40%/year since 2018. I have been down this much and more so on positions that became 1,000% and 2,000% winners. This is not value investing, it’s an entirely different sport.
We have a proprietary hedge that we developed. The hedge went live in April and is designed to help us remain comfortably invested during drawdowns. You can learn more about the hedge here and watch the webinarhedge here and watch the webinar.
Without further ado, let’s talk about who will be the world’s most valuable companies in 2030
Artificial Intelligence and Machine Learning will Exceed the Mobile Economy
Smartphones had a 10-year cycle of maturation with the iPhone distribution beginning in 2008 and the app economy had a similar 10-year maturation for digital advertising. We know mobile is reaching saturation as the iPhone often has flat quarters and Facebook’s DAUs are also flat. Following a decade-long run:
The smartphone market was valued at $720 billion in 2019 and the global mobile application size was $155 billion.
The mobile advertising market was valued at $60 billion — Facebook
The market is roughly $2 trillion for mobile yet the market cap of these companies combined is $4 trillion. Meanwhile, Pricewatershousecooper is predicting the AI market to reach $15.7 trillion with some believing AI will be the next electricity. Semiconductors will not comprise the entire $15.7 trillion but according to McKinsey, they will “capture 40 to 50 percent of the total value from the technology stack.”
“Many AI applications have already gained a wide following, including virtual assistants that manage our homes and facial-recognition programs that track criminals. These diverse solutions, as well as other emerging AI applications, share one common feature: a reliance on hardware as a core enabler of innovation, especially for logic and memory functions.”These diverse solutions, as well as other emerging AI applications, share one common feature: a reliance on hardware as a core enabler of innovation, especially for logic and memory functions.”
The artificial intelligence economy will be four times larger than the mobile economy. Put differently, mobile gave us companies with up to $2 trillion market caps and AI will give us companies with $8-$10 trillion market caps. Let’s break this down.
The size of total addressable market is critical to produce the world’s most valuable companies. FAANG companies have illustrated this well and that the private markets base nearly every investing decision on TAM.
Pictured above: Google’s search engine revenue growth from 2008-2022
Google’s search engine is used by over 4 billion people
Android is used by 2.5 billion people and YouTube 2 billion people.
Facebook is at 2.9 billion users
Apple has 1.8 billion active devices (about 1 billion iPhone)
Microsoft Windows has 1.4 billion users and MS Office has 1.2 billion users = Microsoft coming from the dot-com era shows us that mobile produced much larger addressable markets across three companies compared to the previous decade.
However, not only will AI semiconductors power the accelerated computing and the training and inference that reaches every person on earth, it will ultimately power the automobiles, the streetlights, vehicles, refrigerators, factories, cities and spaceships. This will extend the addressable market beyond the 7 billion global population to reach 100 billion connections. Now, imagine this – it will be writing the software too and running the machine-to-machine automations.
View my Bloomberg appearance here where I discuss the AI market being 4 times larger than mobile.Bloomberg appearance here where I discuss the AI market being 4 times larger than mobile.
Here is a glimpse of what AI will do for GDP in each country:
AI is expected to nearly double GDP in the United States by 2035 and across Europe and Japan. The same study shows the American worker will increase productivity by 35% due to AI.
Accelerated Computing Required for Artificial Intelligence and Machine Learning will Produce Two (New) FAANGs: Nvidia and AMD
Accelerated computing is a term first used by the gaming industry when graphic accelerators were put into use to accelerate the work of a central processing unit (CPU). Nvidia created GPUs to offload tasks from CPUs for rendering 3D images. The CPUs provide the instructions while GPUs perform multiple calculations from streams of data. Parallel processing became a natural fit for the data center including training artificial intelligence and deep learning models due to processing multiple computations simultaneously.
Please reference the additional resources below where we have done previous deep dives on every company mentioned in this summary.Please reference the additional resources below where we have done previous deep dives on every company mentioned in this summary.
Nvidia’s Moat is Called Cuda:
Nvidia has a parallel computing platform and programming model called CUDA that is universally known due to the company’s first mover advantage in GPUs. The GPUs themselves do not create the moat. The compute platform creates the moat. Universities teach CUDA, which helps strengthen the universal platform for building GPU-accelerated applications as students graduate with this universal skill.
Hardware does not offer a moat. The iPhone was not the moat. Instead, it was the strength of the developer ecosystem that propelled Apple to become a $2 trillion company. The moat that Apple has enjoyed was created by the third-party developers who created iPhone applications in C and C++ with XCode, which made the device more attractive due to the mobile app ecosystem.
Android then became the second operating system in the mobile duopoly. Due to the friction of learning too many languages, the mobile ecosystem did not entertain any further competitors. This is despite there being 4 to 5 billion smartphones globally (i.e. it’s certainly feasible from a consumer supply/demand view point to entertain more operating systems and app stores), yet the limitation came from the number of languages developers are willing to learn. Microsoft Windows failed because it launched too late, and developers had already chosen the two languages they were willing to work with.
Developers create a moat because they don’t want to learn new systems – the cost of time, especially when bringing products to market is very valuable. For instance, AI startups are not going to shop a new software layer to program GPUs right now as it’ll slow down their time to market and it’s critical to launch products quickly. If there’s a competitor to Nvidia and the startup is already developing on the CUDA platform, then it better be a heck of a value proposition.
Nvidia’s Game Changer Was the A100 GPU:
In 2019, I had already stated to our premium research customers that Nvidia would become one of the world’s most valuable companies. However, the path became clearer in 2020 when the company released the A100 GPU which combines both training and inference onto one chip. The result is a 20X performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offers the largest leap in performance to date over the past 8 generations.
Note: Reference the resources below for more information on the A100 GPU including our coverage in 2020.
Fast forward, and nearly two years later after the A100 GPU launched, Nvidia had this to say in the most recent earnings report:
“[Data center revenue] doubled year-over-year. and we're seeing really strong adoption of A100. A100 is really quite special and unique in the world of accelerators. And this is one of the really, really great innovations as we extended our GPU from graphics to CUDA to Tensor Core GPUs. It's now a universal accelerator […] And so from database queries to data processing, to extraction, and transform and loading of data before you do training and inference and whatever image processing or other algorithmic processing you need to do can be fully accelerated on A100.”
Buried a bit deeper into the previous earnings call, management stated this: “The flagship NVIDIA A100 GPU continue to drive strong growth. Inference-focused revenue more than tripled year-on-year.”Inference-focused revenue more than tripled year-on-year.”
These are the kinds of critical moves we try to get in front of by covering the A100 GPU at its launch. Two years later, and we see management saying inference revenue has tripled and data center revenue doubled due to this specific product.
View my interview on TDAmertrade where I discuss Nvidia’s data center segment and why automotive will be strong in the second half of 2022.my interview on TDAmertrade where I discuss Nvidia’s data center segment and why automotive will be strong in the second half of 2022.
AMD: The Dark Horse of AI Chips
The Dark Horse refers to an underdog or an overlooked competitor that emerges seemingly from nowhere to succeed. We believe AMD is a force of nature that the market often underestimates due Intel’s overhyped public relations strategy. Meanwhile, the competitive prowess of Lisa Su has led to the second biggest comeback in the history of the tech industry after she took over AMD in 2014 on the brink of bankruptcy.
Note: We’ve done a 1-hour webinar exclusively on AMD. Reference the resources below.
AMD’s Strength Came from the Zen Architecture
The Zen architecture was introduced under Lisa Su in 2017. These processors are chipset free and fully integrated. Communication between CPUs is done through Infinity Fabric protocols. The result of the new architecture was more energy efficiency and the ability to execute more instructions per cycle.
The company released the second generation of its Zen architecture and this is when AMD started to clearly outpace Intel in terms of computing power, memory and energy use – all at a lower cost. This was due to multi-chip modules that combine a 7nm with a 14nm to use the most advanced technology when and where it’s needed most by leveraging the more mature process node. The L1 cache and L2 cache locations in the core and across the core also helped the company beat Intel on memory bandwidth.
Intel was still producing a 14nm chip with a 10nm supposedly on the way. Essentially, AMD leapfrogged the incumbent with a product that is more power efficient and allows for more cores per chip.
Note: read more about the Zen architecture in the resources listed below.
If technical jargon around chips isn’t your thing, then this is probably the most important line from our original analysis in terms of AMD’s competitive prowess: “It’s estimated that for every $1.00 in Rome chip sales, Intel loses $2.25 on average in Intel Xeon SP sales. The savings are then deployed to buy more Rome chips, which can further depress Intel’s revenue.”
We can clearly see AMD taking market share in server CPUs although losing ground in desktops and laptops (our thesis is server market share so that’s less important to us). Notably, overall CPU market share for AMD is up.
Most importantly, look at where AMD was when it launched the second generation of Zen (roughly 2%) to today (roughly 11%) market share – or nearly 6X from this major design win. Moving forward, Intel will need to deliver a 7nm chip – but by then Lisa Su will already be releasing a 5nm design.
As the analysis below points out, Intel needs to make up for lost time, meanwhile, Lisa Su is unlikely to allow that now that AMD has clawed its way back from near-zero. Our site was early to AMD overtaking Intel and this was the analysis we chose to publish during the Covid selloff in March of 2020.
Tech investors should pay attention to AMD’s ability to stave off the competitor and the new inroads AMD will have following the Xilinx acquisition. Xilinx’s FPGAs are particularly well suited for accelerated computing yet require an easier software development platform – which I suspect AMD and Lisa Su will fully deliver in the next year.
So far, Lisa Su has simply set the foundation for her company to see substantial AI tailwinds.
AI Acceleration Goes Far Beyond Data Centers
In the months to come, I will detail for I/O Fund members the additional revenue segments that will cause Nvidia and AMD to catapult their current market caps. The data center does not even scratch the surface.
Primarily, these companies will participate in the lion’s share of AI acceleration in the automotive industry, edge computing and edge devices, 3D virtual worlds and robotics simulation, industrial automation, software automation — and probably most crucially, why the leading hardware companies of today are moving into licensing software and why that will cause an eruption in revenue for these particular hardware companies. Look for this special report before next earnings season.
Before I move onto cloud, I’d like to mention that we hold two more semiconductor positions – Marvell and Lam Research. We foresee holding these companies for the long haul and linked resources below spell out why we’ve chosen these two names out of the hundreds of semiconductors on the market.
2030 Cloud Companies Won’t Look like 2020 Cloud Companies
Tech is synonymous with innovation, and consequently, innovation is synonymous with the word change. This is why winning cloud investments from the past ten years will not look like the next ten years. Cloud has treated investors well, yet cloud is going through a transformation that will shake up the previous paradigm. The previous paradigm was one where most cloud stocks were successful, and was distinguished by easy cash. Now that cash is tighter, there will be fewer winners in this category. We covered the fundamental change to cloud’s bottom line here in: Compartmentalizing Cloud Stocks.
Cloud: Only the Strong will Survive
In 2010-2021, the public markets saw hundreds of cloud companies go public. Yet anyone with a decade or more experience in tech will tell you that consolidation eventually will come knocking.
Consolidation is a natural part of the tech industry where competitors become acquired or they merge with stronger companies to avoid failing entirely. This helps a small minority to emerge as the defacto leaders. I believe that cloud companies will survive either through consolidation or standardization, which means cloud companies that have evolved to serve more than one market, which in turn helps drive down costs.
Let me illustrate:
Recently, a report came out that repatriation, or moving some workloads back to on-premise, has resulted in quite a bit of cost savings for companies like Dropbox, Crowdstrike and Zscaler, who use hybrid approaches. The report is quite surprising as the conclusion is that $100 billion to $500 billion in market value is lost on cloud deployments in terms of margins. One use case that is detailed is Dropbox, a company that reported savings of $75 million in two years after repatriation, which in turn, helped the company’s gross margins increase from 33% to 67%.
The problem with cloud is that it’s not uncommon for companies to spend about 60% of their revenue towards committed cloud spend. The solution is aggregating services and products to drive down costs.
Two companies we own that offer standardization are Datadog and SentinelOne. Standardizing means interoperability between various cloud environments and integrated interfaces. This is especially important with multi-cloud or hybrid cloud where companies have more than one environment. This is becoming the new normal to prevent vendor lock-in.
If corporations continue to standardize on Datadog’s platform, then the company will continue to capture market share. Since dealing with multiple cloud vendors quickly becomes cumbersome, there is a natural tendency to standardize in tech, especially with software. Moreover, cloud applications need to communicate, so having everything on one platform can make detecting and resolving issues less complex and costly. The cloud industry is on the cusp of this standardization trend with cloud software vendors, with Datadog leading the way. In this way, Datadog is best positioned to benefit from both the rise in cloud usage and the standardization of cloud software.
SentinelOne is a security position we own. Although the company has many competitors in the EDR space, they extend the acronym to XDR to not only include devices and workstations, but to also include other data points on the network, such as containers and cloud-native applications, and also across the entire stack, such as email, the network, and identity.
SentinelOne uses many data sources to create a data lake. The single pool of raw data is built across a wide range of sources, including other vendors or internal data sources. What matters to customers is that every threat is detected very quickly, and SentinelOne proposes a solution that is able to do both because automation and AI is best done at the data level rather than managing thousands of user endpoints to mitigate attacks.
According to SentinelOne, using their products can produce cost savings can be up to 353% – granted this number is a marketing department, however, the point is that any company increasing ROI in cybersecurity has a real chance of taking market share if their product improves the results. The savings quoted is achieved by reducing the amount of cybersecurity tools a company needs by standardizing endpoint security across more data types. The consolidation in this case saves up to $3 million over a three-year period and the enhanced threat detection saves $671K over three years. Due to automation, $1.2 million can be saved over three years by reducing time and employee hours across the IT team.
Big Data and Analytics/ML – Consumption Model is Here to Stay
There is an oft-quoted statistic that 90% of the world’s data was created in the last two years – and this stat is from 2018. The world produces 44 zetabytes of data across the digital universe as of 2020 and there is expected to be 200+ zetabytes of data in cloud storage by 2025. Each zettabyte has 21 zeroes or is 1,000 bytes to the 7th power. By these estimates, we can expect to see up to 5X growth specifically in data centers. Statista places the number at 181 zetabytes by 2025 up from 64.2 zettabytes in 2020.
In regards to data integration in the cloud, this spans from data lakes, to ETL pipelines, cloud data warehouses and object storage. Data fabrics and data virtualization is key to both hybrid and multi-cloud strategies.
The key thing to know about Big Data, Analytics/ML is that these companies will test financial analysts as they do not bill according to subscriptions like many software companies do. Instead, companies like Snowflake, MongoDB and Confluent bill customers based on consumption. This is a relatively new approach to software billing, which makes it harder to model and forecast near term sales.
As data creation, ingestion and storage soar in the cloud environment, cloud software providers are starting to migrate away from subscription agreements, which are fixed, to a consumption-based pricing model, which are uncapped.
Consumption-based pricing has a few drawbacks. For example, its less predictable than subscription revenue and there isn’t a ‘floor’ on revenue, because if consumption declines then so will sales. However, the flip side is also true, consumption billing does not have a ‘ceiling’ on revenue, so if customer consumption rises, so does sales. This uncapped revenue potential is key to why growth could be quite substantial in this category compared to cloud SaaS peers.
Here is a disclosure from Snowflake in the 10Q:
“Consumption for most customers accelerates from the beginning of their usage to the end of their contract terms and often exceeds their initial capacity commitment amounts. When this occurs, our customers have the option to amend their existing agreement with us to purchase additional capacity or request early renewals”
We want CAGRs that are larger than mobile’s CAGR for 5-10 years. According to industry analysts, the CAGR for machine learning is at 38% between 2022-2029, growing from $15 billion in 2021 to over $200 billion in 2029. Some of this is being driven by Big Tech yet as more companies seek a vendor agnostic approach and multi-cloud workloads, there is ample room for agnostic companies to do well.
Compare this to the smartphone market which grew at 24.9% CAGR with some years in the 12% CAGR range. Here’s an example of a reference for CAGR during Apple’s high-growth years: “The Asia-Pacific smartphones market shipment stood at 87.8 million in 2010 which is expected to reach 294.1 million in 2015 growing at a CAGR of 27.3% during 2010 – 2015.”
Here's how Datadog’s CEO describes what is going on in terms of big data in the Q2 earnings call: “it's almost a given that there will need to be a different way of charging for capturing some of the value provided to customers that can't just be attached to the straight volumes of data that are being exchanged because those volume of data are exploding exponentially while our customers' revenues are not going to explode exponentially.”
To capitalize on the Big Data, Analytics and ML trend – which we fully believe has the potential to produce a FAANG – we hold long-term positions in MongoDB and Snowflake. We are comfortable with the fluctuations of the consumption model, which means some volatility at times, as the consumption model will be tied to higher revenues in the long-term.
Note: We hold a 1% position in Confluent which translates to a lower conviction than MongoDB and Snowflake for this trend. We have recently trimmed 2.5% from Snowflake with the goal of building a bigger position in MDB. Please reference Knox’s trade alerts for more information on these positions and others in real-time.
We encourage tech investors to look at cryptocurrencies with a level head. It’s easy to dismiss the blockchain as a fad and it’s also easy to gamble on crypto for a quick gain. We think both approaches are wrong. Instead, our approach is to fully embrace the blockchain and it’s volatility by being willing to trim when the uptrend hits our targets close to the top and layer back in around the bottom.
Knox has a strong track record in navigating Bitcoin’s volatility and we fully expect to continue to trim at the top and layer-in at subsequent bottoms for the next five years – with real-time trade alerts sent to our premium members.
We own the following cryptocurrencies in a longer-term fashion: Bitcoin, Ethereum, Chainlink and Avalanche. The first three come with a higher conviction simply due to the size of their ecosystems yet we think Avalanche stands-out as a secure, decentralized protocol that can scale.
We also own very small, token positions in what we call a YO/LO portfolio (You Only Live Once) where we are a bit more liberated to take higher risks than we would with our core portfolio. Reference the resources below for more information.
Bitcoin:
We covered Bitcoin within a month of launching our premium site in 2019 and it’s in my top 5 for FAANG status. Notably, we had predicted Bitcoin would reach $1 trillion market cap when it was selling off from the $7-$10,000 range to $3,000 range.
The primary reason we are proponents of Bitcoin is that it is the world’s most secure financial network with a higher level of security than the 10,000 global banks combined. This solves a genuine need for the financial system as payments and transfers cannot be automated without a decentralized blockchain solution.
Crypto transfers eliminate processing fees and also hedges against inflation. There are transaction fees charged by crypto exchanges but these fees are not inherent to Bitcoin and will lower in time with more competition.
Apple, Google, Microsoft, and Amazon crossed market caps of $1 trillion because their products scale to global populations and are required on a daily basis. Bitcoin not only scales to global populations, but it also protects their livelihood – a necessity rather than a convenience. This is why we see populations that are not necessarily tech-savvy most enthusiastic about Bitcoin. In 2019, I argued that Bitcoin will reach a $1 trillion market cap as solving a real financial need for global populations should be worth as much as a search engine, enterprise software, social media network, warehouse fulfillment (AMZN), or iPhone hardware company.
In our original report we used the example of Venezuela, where during a period of hyperinflation, the price of a cup of coffee rose to 2,800 bolivars up from 0.75 bolivars within one year, representing an increase of 373,233%, according to Bloomberg data. Essential goods such as toilet paper and medicine were also very costly.
Bitcoin was immediately available to Venezuelans as a store of value and offered them an option to cross the border and escape an autocratic regime. Since then, El Salvador has adopted Bitcoin as their country’s currency.
Currently, the United States is at debt levels of about 133 percent of gross domestic product (GDP). There has been a steady rise in the level of national debt to GDP due to decreased tax revenue and increased spending, especially on health care.
The United States is unlikely to see hyperinflation to the level of Venezuela (at least, let’s hope not). However, trust in fiat currencies will erode as debt continues to climb.
Japan is an excellent case study for an economy that is struggling due to quantitative easing. The Japanese debt-to-GDP ratio is at an all-time high at 254% due to its quantitative easing. Government debt to GDP in Japan averaged 137.4% from 1980 to 2017.
Easy money policies from Japan’s central bank harmed domestic asset returns by suppressing local interest rates. Ranking as the world’s third largest economy, Japan resorted to negative interest rates in 2016. In April 2016, it was reported that a “Japanese bank buying 5-Year U.S. Treasuries with perfectly hedged currency and duration risk would (lose) 0.9% a year.”
Consequently, Japan is a thriving bitcoin market and has seen increased crypto deposits. According to the Japan Virtual and Crypto assets Exchange Association (JVCEA) Japan’s virtual currency deposits recorded 1.41 trillion yen or about US$13 billion in March last year, the volume was about seven times more than in March 2020.
During the recent Ukraine-Russian war the use of crypto has once again taken prominence. The Ukrainian government has also accepted crypto donations during this crisis. In the words of Alex Bornyakov, Deputy Minister of Ukraine’s Ministry of Digital Transformation, “In times like these, response time is crucial. Crypto is playing a role to give us flexibility to respond really quickly to deliver the army’s required supplies.” The lack of financial access might also increase the use of crypto in both the countries. The Ukraine central bank had suspending electronic transfers and reduced cash withdrawals and there were reports that Ukrainians were turning to cryptocurrency.
According to Alex Gladstein, Chief Strategy Officer at the Human Rights Foundation, “The fact that it can’t be frozen, the fact that it can’t be censored, and the fact that it can be used without ID is very, very important,” He further added, “And they are why bitcoin is such an important humanitarian tool.”
We’ve written extensively on Bitcoin and we encourage you to read more about the importance of the Lightning Network in our resources below, which is a payment protocol that operates on top of cryptocurrency blockchains and enables fast transactions.
The Lightening Network will be used for small transactions that don’t require the security of the bitcoin network. Large transfers that require decentralized security will continue to take place on the original layer.
The final iteration for the Lightning Network will be the cross-chain atomic swaps, which will exchange crypto tokens between different blockchains without the need for a crypto currency exchange.
Benefits of the Lightning Network:
Transactions will take place on the Lightning Network channels and outside of the blockchain:
Fees will be minimal to non-existent for small payments like coffee, dinner, and local stores.
Quick transactions no matter how busy the network is. The transactions will be instantaneous and able to keep pace with Visa, MasterCard and Paypal.
Cross-chain atomic swaps will eliminate the need for separate crypto exchanges.
The Lightning Network can reach 1 million transactions per second.
If you want a perfect parallel to the mobile duopoly of Android and iOS, then it will be Web3. We began with artificial intelligence because by increasing GDP, AI/ML promises to be the technology that delivers the most gains in the public market’s history – far exceeding mobile. Yet, the blockchain offers a parallel to mobile as what layer one networks set out to achieve is very similar to what Apple’s app store achieved.
The primary difference between Ethereum and Bitcoin is that Ethereum is not trying to compete as a currency. The focus of Ethereum is on its network, not the coin. Butkin’s vision is to create an open network for decentralized applications (dapps) and smart contracts based on the Turing complete programming language Solidity. The takeaway is that just like Apple hosted apps on its operating system, Ethereum hosts d’apps on its network.
These three benefits are: decentralization, security and scalability. The issue is that most decentralized networks cannot offer all three without some compromise.
Ethereum faces constraints in transactions per second (TPS) and how to overcome the high energy costs of mining that comes with decentralized security. The network simply can’t scale without the upcoming release of Ethereum 2.0.
In our premium analysis last year on Ethereum here, we discussed the difference between Proof of Work (PoW) and Proof of Stake (PoS). In addition to the Proof of Stake merge that Ethereum must complete, the network must also launch shards. Nodes in the previous network must download a transaction, calculate it, archive it and read every transaction in Ethereum’s history, which is terribly inefficient. Shards create a subset of the network where nodes are dispersed for more efficient processing. Ethereum 2.0 must also replace Plasma with ZK Rollups, which allow for hundreds of transfers to be rolled into a single transaction.
In November, we wrote another update on crypto and Ethereum, stating that the expectation was for Proof of Stake to merge in late 2021 with Shards and Rollups expected by late 2022 or early 2023. The timeline for PoS is delayed yet again until Q3 2022, which puts Sharding and Rollups out another year potentially to Q3 2023. (Read more in the resources below).
The takeaway: Ethereum has a wide lead in terms of number of d’apps and developers (remember that developers adopting CUDA created Nvidia’s moat). However, the Merge to Proof of Stake is an unknown which leaves the Layer One network market wide open for now.
Avalanche
Layer One Networks are considered early-stage tech investing which carries higher risk. Ethereum clearly has a head start, and after the proof of stake merge, we could see the network take off in a meaningful way.
However, there are other Layer One networks to consider. We hold an allocation in Avalanche due to it’s Ethereum Bridge, application-specific subnets, and the launch of a consumer-facing app over the next quarter. Avalanche also has a high Nakamoto Coefficient, which is a number that designates the number of nodes that would need to be corrupted to slow or prevent a chain from functioning properly.
Avalanche launched with three chains. Per our YO/LO write-up: The X-Chain is for creating and exchanging assets including NFTs, the P-Chain validates and creates subnets, and the C-Chain is for executing Ethereum Virtual Machine (EVM) contracts. The C-Chain offers interoperability with Ethereum, which is why the Avalanche bridge is the most popular ETH Bridge currently. The P-Chain is what is used to create and manage subnets. The coordination of Avalanche validators occurs on the P-Chain and it can support thousands of subnets and millions of validators.
As we stated in the AVAX write-up: “Ethereum is running into issues with 500,000 to 1 million daily active users. Meanwhile, a single mobile application sees hundreds of millions of users, such a Twitter or Spotify. What Layer 1 can handle this level of adoption? That is a platinum-level question for investors to answer. To be clear, it could be Ethereum in 2023 if the developers and users prefer to not migrate. However, if the ecosystem runs out of patience and seriously looks for an alternative, then Avalanche is a candidate.”
Warren Buffet famously said: “The stock market is a device for transferring money from the impatient to the patient.” I believe Chainlink could be our best performing asset in our portfolio by 2030 as the middleware that enables smart contract through decentralized oracles.
Smart contracts are a more advanced use of blockchain where an exchange between two parties is automated based on conditional provisions. These self-executing contracts are written into lines of code, and the agreements contained exist across a distributed, decentralized blockchain network.
Smart contracts offer a more complete use for blockchain. First discussed in 1996 by Nick Szabo, some claim that smart contracts are the real use case for blockchain as they aim to automate financial transactions, and in the future, can automate machines.
We have written extensive deep dives and webinars on what the company does but for those who would rather get the elevator pitch, it’s this: Chainlink is the most likely candidate to become the Google of Web3. In fact, ex-Google executives are joining Chainlink as strategic advisor, former CEO Eric Schmidt, new Chainlink Chief Product Officer, Tensorflow’s Kemal El Moujahid.
We are very bullish on Chainlink and it was our first one-hour deep dive webinar for this reason. However, it requires a longer-term mindset, which we certainly have at the I/O Fund. Our goal for our position is sizable gains by 2025 with an exit in 2028-2030.
“Winners keep winning” is a reliable and true statement. We began this analysis by showing you a chart of how the world’s most valuable companies change every 10 years. However, there is an important caveat: tech overtook oil to become the world’s most valuable industry in 2010 and we have yet to see the pattern that tech sets in terms of how often the top 5 will rotate now that tech is in the driver’s seat.
Microsoft:
We were one of the first analysts to cover Microsoft Azure’s hybrid computing strategy and why that could narrow AWS’ lead in the cloud IaaS market. At the time, tech was selling off in Q4 2018 yet we were firm that Microsoft would emerge as a significant leader in this space by specializing in a mix of on-premise and cloud deployments.
Hybrid cloud allows for scenarios where customers can keep their most sensitive data on their own servers while sending workloads to the private or public cloud that gain an advantage from mining data more efficiently and requires improved accuracy and productivity.
Azure’s strength in offering both on-premise and cloud in a hybrid solution has prompted Amazon to chase Microsoft with recent efforts to improve its hybrid strategy. Today, Azure claims more than 95% of the Fortune 500 as customers because of its hybrid flexibility.
Azure growth of 46% is performing quite well given the tough comps it has overcome, and Microsoft’s best financial metric during this tech selloff is that commercial bookings increased 28% this quarter following 32% increase last quarter. I would look for Azure to remain elevated against AWS and Google Cloud for those two reasons – hybrid cloud leader which attracts large enterprises and its ability to reduce costs with its tech stack.
In our latest Q2 webinar, I discussed why reducing cloud costs is a key trend for 2022 and beyond. To put it simply, Sayta Nadella said in this quarter’s earnings call: “More value for less price means you win.” In the same breath, he also said: “Most businesses are not looking to their IT budgets or to digital transformation for budget cuts.” These two statements echo my first point in the Q2 2022 webinar which is that both are true: increase in cloud spending will continue and companies will want to lower costs associated with cloud.
That’s going to be the trick moving forward – which companies assist cloud migrations while lowering costs. Microsoft is the leader here as the company aggregates many cloud services and products under one umbrella which creates substantial leverage to undercut on price.
Microsoft is increasingly becoming a cybersecurity company, as well, with $15 billion in revenue and growing at a rate of 45%. Microsoft was careful to build a multi-cloud product and is the only Big 3 cloud vendor to be multi-cloud on security right now. This also helps to drive down costs for Microsoft’s customers.
There are additional catalysts for Microsoft beyond Azure’s winning streak, its large security footprint, and the ability to lower costs. The first catalyst is that Microsoft is setting up to own the edge through its telecom partnerships. Another catalyst is that when more enterprises adopt AI/ML, whether it’s automation, super computers and/or other use cases for training and inference, it will a natural decision to use Microsoft if they’re already optimized for Azure. As discussed, enterprises will drive forward the next major market in tech (AI/ML) and Tier 1/Fortune 500 will be the largest customers for AI/ML. Power Automate was up 72% year-over-year, surpassing $2 billion in revenue, and this is only the beginning.
Third, the company ranks with Nvidia and Unity for inroads to the Metaverse as it owns many gaming publishers now and is the most widely used VR headset (HoloLens). The company also has Teams to introduce Metaverse-like qualities to business meetings. It will be industrial that drives forward 3D worlds (not consumer) and Microsoft is auspiciously positioned.
We have been meticulously building our portfolio for the next FAANGs of 2030 since we launched the site in 2019 with the understanding that even getting 1 or 2 correct can create generational wealth. Our goal from the beginning has been to stick with our winners and to cut our losers and we have compiled quite a bit of research along the way.
Alphabet is a new addition to our portfolio and one we’ve been circling for some time. If the 2014-2022 era in digital advertising was known as the walled garden era primarily fueled by third-party data then 2022-2030 will be known as the brick walls of first-party data – meaning, publishers controlling their data become the trend that drives forward digital advertising as we move into more AI/ML-driven ads.
In fact, we are in the midst of what is the biggest shift in digital advertising since advertising went digital. The shift is due to Apple and other real estate owners shutting off how data is collected across mobile and desktop. In the mobile era, third-party data was rampant but all of that changed with the release of iOS 15.
Note: We were one of the top authors on this topic with coverage dating back two years before the change occurred on both Forbes and MarketWatch here.
Google will be following in Apple’s footsteps by changing how third-party data is collected on Android and Chrome. This will greatly strengthen the company as not only is Google an equal or greater real estate owner compared to Apple but the company is also a publisher for the purpose of ads with Search and YouTube. This means long-term ads placed on Google will be more effective and produce higher ROI than those with less signals to work with.
These data collection changes are coming just in time for the advantage from first-party data to be realized across AI/ML (with digital ads) and a myriad of other uses cases.
We’ve focused quite a bit on enterprise for the purpose of this article yet we want to acknowledge that consumer-facing tech carries strength in most macro environments.
We hold the following stocks to capture consumer-driven gains. Notably, we’ve covered in the past how supply chains are contributing to consumer spending and inflation. We are watching this closely for when growth in this area rebounds. You can access our research on this here.
Roku: Netflix made it into FAANG and CTV ads will be a bigger market than subscriptions – primarily CTV ads will do well globally. Roku must prove its hardware strategy will pay off in global markets starting with LatAm.
Snap:: This company has had a brutal month – yet audience metrics have been strong post-Covid and the Gen-Z/Millennial concentration is important to take note of.
Shopify: This company is spending an unknown amount on the fulfillment center yet can rival Amazon simply through unlimited distribution channels. Social commerce will eventually take off despite the setback from Apple’s iOS changes.
Twilio The Twilio management team is known to be visionaries and they are pivoting into an API-forward marketing platform with strong PII data – they are early to API-driven automation taking over marketing and advertising.
Conclusion:
Thank you for taking the time to read this report. Despite tech being one of the most volatile sectors in the market, it is also the most rewarding. Had you entered Apple between 2008-2010, it would have blown away all other positions in your portfolio across all other industries. The same goes for a Facebook or a Google position. Half the battle is finding them, which we think we are particularly well suited for, and the other half of the battle is knowing which stocks to sell and which ones to hold when the tide rolls out. We show you how we do this with real-time trade notifications plus a hedge for ample insurance during drawdowns. There are many positions we own today that we will own in 2030 with the goal of perfectly timed exit. We are patient and thorough in our research as we acknowledge and accept that approximately 5 companies will lead to 90% of our wealth in 10 years.
SentinelOne continues to be the strongest cloud stock on the top line. This earnings report did not disappoint on the top line with 109% revenue year-over-year of $78.3 million compared to consensus of $74.64 million. ARR was up 110% year-over-year to $339 million. Customer count with ARR over $100K also outpaced revenue growth at 131% increase. Net retention rate grew to 131% which is above the 130 line.
The company is expecting growth of 109% at the midpoint for next quarter with $8 million coming from the Attivo acquisition. My notes have analysts expecting $84.83 million so if we add the $8 million for $92.83 million, the company is beating those estimates (consensus would have been for 103% growth including Attivo). Organic is expected to be in “the low to mid 90%” range, which reflects a raise from the 85% consensus for Q2.
The company raised full year organic revenue to “the midpoint of 80% range” up from the previous guidance of 80%. The full year guidance was raised to 98% including Attivo for revenue of $405 million. The company stated Attivo would contribute $30 million to full year revenue (although one analyst felt the math was off and has Attivo contributing $35 million). Previously, organic revenue growth was guided at $368 million for FY2022 with analyst consensus slightly higher at $370 million.
To SentinelOne’s credit, the company offers clear communication about margins. It’s one of the few companies where the CEO will discuss this at the onset of the opening remarks.
Per the analysis on compartmentalizing cloud stocks here, we went into the earnings report wanting to see an improvement in operating margin. The company was expected to report (86%) to (84%) Non-GAAP operating margin and provided a beat at (73%). This is up from (127%) last year for an expansion of 54%.
The GAAP operating margin remains an eyesore due to stock-based compensation at (115%) of revenue, up from (165%) in the year-ago quarter.
SentinelOne demonstrated strong improvement in gross margin from 51% in the year-ago quarter to 65% in this quarter. It’s up 2 basis points sequentially and up 15% YoY and is the highest GM for the preceding four quarters.
The one thing that could have weighed on the AH price action was the guide on operating margin for Q2 being (75%) to (73%) – as the critical point here for SentinelOne is that the full year guide management has provided for two quarters is for Non-GAAP op margin for FY2023 to be (60%) to (55%). Even though Q1 was a beat on Non-GAAP margin, the path to delivering on the guidance becomes a bit obscure the longer we remain above this level.
The reason we’ve accepted the weaker (albeit improving) margins is that the company is working towards being FCF positive by 2025 and is not likely to raise cash before this occurs. Any change to this would cause us to look at our thesis again.
The company expects to improve adjusted gross margin to 69%-70% and if we assume similar GAAP percentage as this quarter, it would be about 66% GAAP GM for FY2023.
Singularity Cloud was the company’s fastest growing module, growing over 50% sequentially.
Management focused on the strength of their MITRE Attack results with 100% protection, 100% detection, 100% real-time protection, 99% visibility, 99% analytic coverage. I’m sure we will hear Crowdstrike’s response to this on Thursday 🙂
One analyst asked about the European segment and management stated there is a wholesale movement away from Kaspersky either by choice or by mandate and this is a tailwind for them. Secondly, the Broadcom-VMWare acquisition is favorable for them as they are now capturing CarbonBlack business. In terms of taking business from these two vendors: “We expect that to accelerate in the quarters to come.” I’ll expand more on this when I get the transcript.
“Will SentinelOne be able to provide a meet/beat on operating margin in the upcoming quarter and a meet/beat for the full year guide? This must happen and we also need revenue to remain strong.”
SentinelOne’s operating expenses were front-end weighted last year with Q1 being the steepest operating loss and the year getting progressively better (nearly 100% improvement on weak numbers).
If last year is any guide, then SentinelOne is capable of meeting their full year guidance of (60%) to (55%). The company did beat its operating margin guidance this quarter and revenue was strong including key metrics.
I continue to believe the key to this stock is the ongoing revenue strength and its ability to prove to analysts and institutions that it’ll generate cash by 2025. Due to Crowdstrike being a close comparable, it’s likely SentinelOne can (and must) follow in Crowdstrike's cash efficient footsteps, which is what the market will want to see. The product continues to prove itself as exceptional and there was evidence of this in terms of high ARR customer growth, beat/raise on revenue, strong growth on cloud product, and we are likely to see nice movement in the identity product soon. We are keen on SentinelOne's ability to standardize multiple areas of cybersecurity and to do so at a high MITRE ranking.
Despite the red AH on the stock, I see no notable issues with this ER from my perspective.
