Nvidia: A Leader in AI Hardware and AI Software

Posted on July 15, 2022June 30, 2026 by io-fund

If you were to guess, when do you think we wrote the following paragraph?

“When a thesis is not reflected in the revenue segments yet, there are typically lower entry points and ongoing volatility. You’ll see in the technical analysis that although I could not be more bullish on this stock long-term, there is weakness in the semiconductor sector and we hope this translates to a lower entry point for our readers.

The market is also in a fierce debate between AMD, Intel, and Nvidia and is also distracted by other chips, such as Micron and NXP. In my analysis, I look for growth. How big is the market relative to how big the company is now?

You can ignore Nvidia’s gaming revenue and other segments for the main trajectory that we are focused on. Gaming is great for stability and earnings reports, but the growth will not be from gaming (a market where Nvidia is already a mature, market leader). I’m also not focused on PC sales or the CPU-powered cloud, as the first is not a growth market and the second is not the piece in the cloud stack that will accelerate future technologies.”

That was written in 2019 yet the far majority of those concerns could be stated verbatim right now. Do we care about PC sales or gaming consoles? No, although our stance is that we have to expect these concerns will affect our semiconductor positions at times. The good news for Nvidia and AMD investors is that as time goes on, the less consumer-related hardware will have an impact. The 2022 Nvidia Investors Presentation provided numbers which show in detail how consumer exposure will become less of a concern in the future for these AI heavyweights.

When do you think we wrote this analysis?

“Over the past few weeks, I have read many lagging explanations on the chip shortage – too many fabless semiconductor companies, too few foundries, automobile manufacturers paused ordering in March and didn’t prepare for the sharp rebound, tensions with China, and even a fire at the Asahi Kasei plant that specifically manufactures sensing devices for the automobile industry.

While all of these are true, the overarching issue is that the role of semiconductors has changed from a commodity to the primary accelerant of future technologies. This is because connectivity, automation, and ultimately AI, will disrupt every corner of every industry.

We saw this happen with data and cloud but now we must accelerate this to the next level for AI/ML and the common denominator is semiconductors. Automotive is only the beginning. We can add renewables to the list and even e-commerce as AR/VR and AI/ML attempt to prop up the leaders who are competitive enough to add these features first.

As a tech stock analyst, I don’t have the luxury of lagging analysis of any kind. My subscribers require (and deserve) forward-looking, and with my intense focus on semiconductor chips, I don’t think my readers are surprised that semis are under pressure due to an increasingly important role.

I have repeated (perhaps too many times) that there is no way forward without the semis. We are seeing this manifest in automotive right now, but as investors, we should get used to hearing about semiconductor shortages.

You and I can debate Palantir, Snowflake or C3.AI, for example, and the valuations or the right angle for AI/ML-driven software, but the common denominator to these companies is the need for semiconductors to drive forward AI and 5G.

Now, we add the enormous push for auto manufacturers to compete with Tesla, Apple, Lucid Motors and what we have is a bottle neck where the automotive industry filters into semiconductors.

My guess is the demand won’t be letting up for many years as we are no longer in the cyclical pattern that semis are notorious for. Instead, demand will outpace supply for years to come.

Is this a bad thing or a good thing for our stocks? As investors, we can either listen to the news or listen to management. In this case, they are not aligned. Machines trade off news and natural language processing (NLP) but as human investors, we have the advantage of looking deeper into the issues.

I have written volumes of analysis leading up to the triple-digit growth we are seeing now in the data center from AI accelerator chips. Most of this was written when data center growth was negative. For instance, my Nvidia thesis was set end of 2018 — and in 2019 Nvidia reported negative data center revenue year-over-year for four quarters in a row.reported negative data center revenue year-over-year for four quarters in a row.

I mention this because following a trend’s trajectory is more important than immediate gratification from the market. The trend will always win out over time.

I have maintained that chips will eventually lead the AI market and are the best angle for investing in edge computing. I have also defended our stocks against custom silicon. Now we have the first of what I predict will be many semiconductor shortages and bullish to me.

The shortage is that there are hundreds (thousands really) of companies that rely on semiconductors. This will come to a head with AI and 5G as those who go-to-market soon with these features will have an enormous competitive advantage.”

That was written at the height of the bull market in February of 2021. My goal is to illustrate there has always been headlines to worry about for the semiconductors. We’ve firmly held these stocks and bought during dips. In the past, from 2018-2019, I focused on the GPU-powered cloud and the CUDA moat here and here. Our 2020 coverage centered on the A100 GPU which we discussed at time of launch for premium here and continued coverage on the A100 about a year later on the free side.

Here is background on the A100:

“Nvidia released the Ampere architecture and A100 GPU as an upgrade from the Volta architecture. The A100 GPUs are able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. This allows Nvidia a competitive advantage by offering both training and inferencing. 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.”

Nvidia's AI Dominance Will be Propelled Forward by Software:

I wanted to go back through a bit of Nvidia’s history – what was the thesis and how did the thesis evolve? – before I go into how Nvidia will continue to dominate. In my opinion, I believe this is the most important analysis I have ever written on Nvidia because the company is changing rapidly into a software company.

The shift that Nvidia is going through has gone unnoticed and that’s to our benefit. Because we have been hell bent on finding what companies will dominate AI hardware, I’ve been asked frequently who do I think will dominate AI software (Palantir? Snowflake? Google?)

I’m prepared to give you that answer today: I believe Nvidia will be one of the biggest or perhaps the biggest AI software stack company in the world.the biggest AI software stack company in the world. The analysis below kickstarts our in-depth coverage on this new thesis — and I fully believe I will be quoting this analysis in five years from now when we check back on how the AI software thesis played out.

Before I go into semiconductor jargon where I risk losing your attention, I want to make sure our Members are fully aware that the segment where Nvidia will dominate with AI software is automotive. I am not talking about a few OEMs that trickle into a little bump in revenue. I am saying that Automotive is scheduled to become Nvidia’s number one segment – even over data centers – and to the tune of it being 3X larger than its gaming segment.

Don’t take my word for it because the CFO said exactly that (more on this below) and there is ample evidence that this is happening, which I also detail for you. Wall Street won’t be giving this the credit it deserves until 2023 at the earliest but you will hear non-stop “Nvidia Automotive” coverage by 2024-2026 as this segment ramps. I go over why those are the target dates below.

But first, let’s talk about the H100 and how this new GPU architecture will also help Nvidia lead on AI software at the enterprise level. There is plenty going on outside of Automotive that we need to cover so I kept automotive for last.

GTC Highlights: The Hopper H100 GPU

In March at GTC 2022, Nvidia announced the Hopper H100 GPU with 80 billion transistors and will be released in Q3 of this year. For reference, the A100 has 54 billion transistors. This is Nvidia’s solid attempt to keep their stake in the ground in leading high-performance computing over AMD’s Instinct MI250/250X and the newly announced MI210.

It’s easy to focus on hardware with Nvidia (and AMD) yet these companies are becoming more software-driven each year. By owning the majority of the market for AI accelerators, these two companies are afforded an opportunity to also own the software layer as a means to lower the barrier to entry for training models, deploying inference across various frameworks, and other workloads related to deep learning, conversational AI, video conferencing algorithms, and more. By supplying the hardware, these companies have natural inroads to machine learning operations (MLOps).

The H100 is the New Artificial Intelligence Infrastructure

DGX, DGX Pods and DGX SuperPods:

The H100 will power all AI and high-performance computing systems including the PCI express accelerator for mainstream servers and many H100 GPUs can be combined to power advanced AI through the following systems: DGX, DGX Pod and DGX SuperPod.

The difference between the A100 and H100 is the performance will be two to three times faster. The H100 GPUs and the DGX H100 server pods and super pods offer Nvidia the next leg-up as the company has solved an important bandwidth issue.

Hopper tackles some of the bigger issues around previous generations like speeding up algorithms by offering dynamic programming on GPUs to break down problems to simpler subproblems, boosting bandwidth by 3X with SHARP in-networking computing and Infiniband Switches, and the H100 can leverage NVLink to connect eight H100s into one giant GPU for 640 billion transistors, 32 petaflops, 640GB of HBM3, and 24 terabytes per second of memory bandwidth.

The chip is custom built by Taiwan Semiconductors with a 4nm design making it the world’s fastest 4nm GPU. The H100 has about 50% more memory and interface bandwidth than the A100. That’s 1.5X more bandwidth with the NVLink connection and PCIe 5.0 doubling the bandwidth of PCIe 4.0. The H100 will ship with support for 80GB of HBM3 memory at 3 TB/s speed.

The NVLink is now able to link together server nodes to build a data center-sized GPU. NVLink was originally designed to bypass the PCIe slot and has become an important tool for chip-to-chip connectivity, especially for high-speed operations. There is a dedicated chip called the NVSwitch which has increased the NVLink’s bandwidth. The ultimate goal is to run 32 servers with their own operating systems to run a single job. NVLink will complement the InfiniBand networking for high-performance computing and NVLink will be default for all of Nvidia’s chips, including GPUs, CPUs, DPUs and SoCs.

Where the H100 really stands apart is the leap in performance with about 3X more performance than the A100 and the H100 is up to 6X faster. The leap in performance is measured by H100’s ability to deliver up to 4,000 TFLOPS of FP8 compute, 2,000 TFLOPS of FP16 compute and 1,000 TFLOPS of TF32 compute and 60 TLOPS of general purpose FP64 compute. The A100 lacked support for FP8 compute at default whereas the H100 will leverage a transformer engine to switch between FP8 and FP16, depending on the workload.

According to Nvidia, 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 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 H100 DGX Pod is a 32-node, 256-GPU system. The H100 DGX Pod connects 32 DGX systems using the NVLink Switch System to 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 AI computing.

From there, multiple H100 DGX Pods can connect through the Infiniband Switch to scale DGX Superpods with thousands of H100 GPUs. DGX SuperPods are turnkey systems that power enterprise AI. DGX SuperPods were also available with the A100 yet the H100 will have 6X better performance with 1 exaflop of FP8 AI performance to run trillions of parameters (more on this below).

Spectrum-4 Ethernet Platform

Perhaps one of Nvidia’s most important advancements for the H100 is the ability to attach the network directly to the GPU to avoid bottlenecks at the CPU. This is accomplished by sending data with direct memory access at 50 gigabytes per second. Hopper HGX and DGX are networking and interconnects that facilitate moving data with an advanced networking processor called the CX7. The result is the H100 CNX that avoids bandwidth bottlenecks and frees the CPU and system memory to process other parts of the application.

The Spectrum Ethernet platform, which consists of a Spectrum-4 Switch, CX7 SmartNIC and Bluefield-3 DPU will be used for several of Nvidia’s AI platforms, such as Riva, Merlin and Omniverse. These workloads include natural language processing, recommenders, and digital twins and will be supported by a networking system that helps exchange large databases between nodes. 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. Quantum-2 allows for in-network computing to do data reductions in the network. It’s also more likely that Ethernet is used for 5G and sensors.

Eos: The First Hopper AI Factory

Nvidia is building AI factories to compete with AI supercomputers, which are blueprints for AI infrastructure that can be adopted by cloud partners and enterprises.

Eos is built with 18 H100 SuperPods, with 576 DGX H100 systems and 360 NVLink Switches. Nvidia states EOS is 1.4X faster than the fastest supercomputer and offers 4X the AI processing of the world’s fastest supercomputer. This will deliver 18 EFLOPS of FP8 AI compute or 9 EFLOPS of FP16 compute.

Previously, FP16 was the standard for AI whereas FP8 is gaining more support to become the industry standard. Depending on what AI compute you use, benchmarks will not be apples-to-apples if FP8 is compared to FP64 performance. Right now, AMD’s Frontier supercomputer is #1 with 1.1 exaflops of FP64 performance compared to the upcoming Venado supercomputer’s 10 exaflops of FP8 performance.

The difference is that the smaller bit size allows for an economical way to achieve more speed when giving up a small amount of accuracy doesn’t make a critical difference. This also helps in the face of a slowing Moore’s Law. FP8 is most commonly used for inference yet may be used for training in the future due to boosting throughput. Following the release of the Hopper H100, Intel released Gaudi2 which supports FP8. Chip makers Graphcore, AMD and Qualcomm have recently pushed for an industry-standard for the low precision floating point format FP8 rather than integer formats.

Here is what Nvidia said in the GTC keynote:

But the trend in AI computing has been toward developing neural nets that lean on the lowest precision that will still yield an accurate result. The smaller formats compute faster and more efficiently, and they require less memory and memory bandwidth. The addition of 8-bit floating-point units in the H100 leads to a significant speedup—double the throughput compared to its 16-bit units”

DPX Instructions (ISA):

The H100 improves dynamic programming with DPX Instructions that will help specific AI Algorithms to perform up to 7X faster than previous GPUs and 40X faster than CPU-based algorithms. As algorithms require more complexity, the new set of DPX instructions will help break the complex problems down into simpler subproblems using GPUs instead of CPUs or FPGAs.

The DPX ISA are expected to be broadly available with the CUDA 12.0 release. Examples of where this will be useful include disease research and drug discovery where the process can be sped up 35X for real-time processing to match the rate of DNA sequencing. Route optimization and finding the shortest distance between destinations for use in factories and autonomous driving systems, or Floyd-Warshall acceleration, is boosted up to 40X compared to CPU-only servers. These instructions will also be used for quantum computing and SQL queries as dynamic programming can help find the optimal order for joining a set of tables.

GPU Confidential Computing:

Data is encrypted at-rest and in-transit yet is often unprotected during use. Meanwhile, the data used to train AI models is worth millions in investments and is trained from domain knowledge and company-proprietary data. The new H100 offers confidential computing whereas previously only CPUs offered the protection of both data and applications during use.

Nvidia is Becoming a Leading AI Software Company

It would be easy to read the information above and to assume Nvidia is improving its hardware. However, the company’s future resides in software which will remove some of the cyclicality of hardware revenue. I believe once Nvidia’s software revenue begins to reveal itself in earnings reports, the market will finally piece together the true potential of this AI powerhouse.

It’s both the hardware and the software stack that led me to say Nvidia will surpass Apple in 5 years. You know this story well: the relationship between a hardware company leveraging their position to capture the lion’s share of the software — because that’s exactly what Apple did.

There are four layers to Nvidia’s full-stack accelerated computing: hardware, system software, platform software and applications. Below, I discuss a few ways that Nvidia is capturing more of the software stack due to vendor lock-in effects from their dominance in hardware.

As stated, in the past, our focus was the GPU-powered data center. This was a four-year thesis from 2018 and we doubled up on the thesis in June of 2020 for the A100 release. I want to make sure and emphasize that Nvidia’s lesser-known catalyst is actually the software.

The H100 is helpful in maintaining a lead in GPUs, which is critical turf to protect with GPUs being the most popular AI accelerator, however — the AI/ML catalyst will be further fueled by the Nvidia’s lead in software. This is why the majority of who will remain the AI leader will be up to developers and not the C-suite partnerships on hardware that characterized Intel’s lead over the past few decades. The developers choose the frameworks, the SDKs, libraries and the other parts of the software stack, and because of this, they also choose the GPUs they build on rather than IT departments.

Transformers

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.

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. Transformers are partial to the parallel processing that GPUs offer.

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.

Nvidia and Microsoft recently worked on a Mega transformer model with 530 billion parameters and the future for AI engineers is trillion-parameter transformers and applications. The H100 is already prepping for this. According to Nvidia, the training needs for transformer models will increase 275-fold every two years compared to 8-fold for other models. The H100 GPU with its Transformer Engine supports the FP8 format to speed up training to support trillion-parameter models. This leads to transformer models that go from taking 5 days to train to becoming 6X faster to only taking 19 hours to train.

The transformer engine is software combined with the new hardware in the H100’s tensor cores. As discussed, the A100 was designed for floating-point numbers to 16 bits while the H100 is designed for 8 bits. This is helpful because AI models are moving toward neural nets that lean on the lowest precision and yet still yields an accurate result. In this case, 8 bits double the throughput of 16-bit units, compute faster and more efficiently, and they require less memory and memory bandwidth.

The main feature from the Transformer Engine is the ability to choose what precision is needed for each layer in the neural network at each step, transitioning between 8-bits, 16-bits, 32-bits, plus the H100 is able to do matrix math with two forms of 8-bit numbers with either 5-bits as the exponent or 4-bits as the exponent: E5M2 and E4M3. This is important because the E4M3 may be favored for back propagation while E5M2 may be favored for inferencing.

Pictured above: Nvidia is prepped to support model sizes growing up to 275X every two years

Triton Inference Server:

Nvidia offers AI frameworks to reduce time for developers throughout the AI workflow from data processing and ETL to deep learning model training and large-scale inferencing. These libraries include Dali, Rapids, Triton and Magnum I/O. The library supports all popular frameworks and offers pre-trained models and data pipelines.

Triton is open-source inference software that helps developers deploy models across GPUs and CPUs, it supports Tensor Flow and PyTorch, any query type and any model – such as Transformers or CNNs (used for image recognition) and RNNs (used in speech recognition). The inference engine helps developers take AI development from experimentation to production by removing the need for multiple inference servers and simplifying machine learning infrastructure on the backend.

MLOps (machine learning operations) helps developers with less ML expertise to train and deploy models yet there were limited use cases with little help in deploying custom models. Triton offers high performance inference and scalability with Dockers and Kubernetes while serving up to hundreds of models with the model control API. By supporting all popular frameworks, Triton helps developers avoid framework lock-in due to the consistent interface regardless of training framework or hardware.

Nvidia will Power the Lion’s Share of Automotive – and that means software licensing

Nvidia’s lead in automotive across dozens of OEMs requires its own deep dive. The reason I haven’t prioritized this is because Hyperion 8 is shipping in 2024 and Hyperion 9 will ship in 2026. However, as long-term investors, we should touch base now on the long-term vision for yet another large and sweeping revenue segment. In fact, automotive promises to be Nvidia’s largest segment by 2030 – so on that alone, imagine what Nvidia investors have in front of us.

Nvidia’s Orin SoC (system-on-a-chip) is designed for the neural networks that run robots and AVs at the edge. This is the central computer for the car. The Orin SoC is capable of 254 trillion operations per second by combining Nvidia GPUs with Arm CPU cores and TensorRT APIs. The goal is to help OEMs move from Level 2 autonomous systems to the elusive Level 5 and it stiffens the competition with Tesla’s FSD. Notably, at the release two years ago, Tesla pointed towards Orin’s power consumption as a potential issue for EV batteries but this has not stopped many competing EVs from adopting Nvidia’s in-vehicle hardware and DRIVE software stack.

The EV manufacturers that have already moved forward with Nvidia DRIVE Orin include: Nio, Xpeng, BYD, Lucid Group, Mercedes and Land Rover, GM Cruise — you name it, it’s probably in production with Nvidia at this moment. The company’s current automotive pipeline exceeds $11 billion over the next six years – expect this small blip of pipeline to grow exponentially.

Nvidia recently announced an upgrade to Orin called Atlan with 1,000 TOPS on one chip, or more than then Level 5 compute in AVs today. This chip will catapult forward the computing performance of AVs and is expected to be released in 2023.

Nvidia DRIVE is the operating system and software stack for vehicles that offers an execution environment and includes both security and over-the-air updates. DriveWorks is an SDK that enables self-driving applications. Drive AV offers key ingredients to an autonomous system, such as perception, mapping and planning modules. Regarding mapping, Nvidia DRIVE Map is a multi-modal drive engine that can map independently and has two map engines. Drive IX is open-source software that offers vision, voice and graphics for the user experience. (I will do a separate deep dive on Nvidia Automotive in 2023).

The entire autonomous platform is called Hyperion, which includes the compute and sensor toolkit. This includes the hardware plus a 360-degree camera, radar, lidar and ultrasonic sensor suite. As stated, Hyperion 8 ships in 2024 with Hyperion 9 shipping in 2026, which will double the processing speed and offer an increase in sensors. Nvidia offers open-source developer kits to help increase its compatibility across various projects.

Rather than train the vehicles on the road, Nvidia trains in simulation and can create virtual world obstacles for the vehicles to learn from. This is a different approach from companies like Tesla who have millions of cars on the road collecting data which they then augment for unusual events with a photorealistic simulator.

Tesla has the most data of any car manufacturer which helps the company competitively as more data equals better performing models especially in terms of object detection. More data from millions of cars on the roads also helps with prediction as Tesla collects data from incorrect predictions that can be added to the training set. By leveraging a prediction neural network, Tesla does not need to use human labeling or annotation and can instead use what’s called a temporal sequence of events — in other words, Tesla rewinds events and labels objects automatically with the use of a supercomputer.

The advantage here is that training neural networks correlates with the miles (which again, are substantial due to size of fleet on the road compared to competitors) rather than correlating with the need for human labeling. The result of automatic labeling is that Tesla is able to predict rare situations with more accuracy.

Where Nvidia delivers a strong advantage is the company has decades of history with graphics and simulation due to its gaming roots. As stated, Tesla also uses imitation learning and has a photorealistic simulator which uses vector space for labels and functions like a game engine. However, Nvidia has been quietly working on their simulation platform for many years internally despite only recently marketing Omniverse to the public. In this case, Nvidia has such a high-level of confidence in their simulation skills that they forego the real-life fleet to primarily train virtual 3D models. The company is also packaging the simulation platform for many other uses cases, such as AI factories, 5G networks, power plants and climate research. Developers can work with 3D tools through Python-based development.

Here’s a 10-minute demonstration with the simulation platform here around minute 7:00.

To keep it simple, Tesla’s primary advantage is the data they have collected as no other EV/AV has collected this level of data from real drivers. To contrast, Nvidia has arguably the best simulation platform due to decades of graphics work. These digital twins are only now being widely marketed despite being in development for over 5 years. The license costs $9,000 and Nvidia has estimated its current addressable market is 20 million engineers. Notably, Nvidia’s Hyperion will also be deployed in millions of vehicles over time, offering similar levels of data as Tesla’s fleet.

The Tesla VS Nvidia debates have not formally begun but they are certainly in our future … so brace yourself. Ultimately, the way Nvidia stands apart is the company does not directly compete on manufacturing vehicles. This is something anyone can agree on. That means many OEMs will use Nvidia’s DRIVE system whereas Tesla is less likely to commercialize their software as they’re viewed as a main competitor.

As long as Nvidia continues to innovate and maintain a lead, the popularity of its DRIVE system is likely to remain due to the company’s strategic advantages in AI and supercomputing. The company did an excellent job of tackling the edge computing use case of autonomous vehicles first.

Hardware is only part of the equation. The long-term plan is for Nvidia to license software for autonomous vehicles, which will create a recurring revenue stream. The licensing fees will go well beyond Omniverse to include the actual owner of the vehicle paying a subscription fee to Nvidia for its software. Tesla does this with their AutoPilot software which has grown from $5,000 to $12,000 per vehicle.

The breakdown according to the 2022 Investor Presentation looks like this:

$100 billion from gaming
$300 billion from chips and systems
$150 billion from AI Enterprise software
$150 billion from Omniverse software – fees are charged to both users and robots/digital twins
$300 billion from Automotive – primarily software

What Nvidia is communicating is that software revenue will surpass hardware revenue long-term.

Here is what Kress stated: "Our software content per vehicle can be in the thousands of dollars over the lifetime of the vehicle compared to the hundreds of dollars for the hardware. And second, software scales with the installed base of vehicles, not annual production.”

Note on CUDA:

The software discussion on Nvidia is not complete without a mention of CUDA. We called this Nvidia’s moat back in 2018 and we continue to believe it provides an important moat. The CUDA-related libraries include frameworks that span quantum computing, robotics, 5G networks, cybersecurity and drug discovery. The universal skills taught around CUDA and Nvidia’s SDKs help to drive more business for Nvidia’s GPUs.

Note: I’ve covered Omniverse in-depth here.

Risk: Valuation

The primary risk right now is valuation as Nvidia trades 2X higher than its peers on both the top line sales valuations and on the bottom line with earnings and cash-based valuations. There’s probably equal risk in waiting for Nvidia to drop another 50% as there is in buying Nvidia at the 2X valuation. One reason Nvidia may be valued here is because it’s slowly becoming a software company. Regardless, Knox’s technicals help immensely in determining if the market will continue to award Nvidia it’s gold medal valuation or if the market will discount Nvidia based on sentiment-driven headlines. This is a position we plan to keep on building so you can keep an eye out for those trade alerts over the next few years.

Conclusion:

Finding great companies is only half the battle, fighting negative sentiment is the other half – and semis have no shortage of this in any market – hence our beginning quotes from 2019 and also 2021.

Nvidia is the strongest company in terms of product on the market today. That doesn’t mean semis won’t be a roller coaster – we should fully expect that semis will undulate in sentiment and price while we hold our stocks over many years. We can’t change the way Wall Street works — which is a pendulum that swings between value stocks and growth stocks — but we can describe in great detail why concerns around gaming and consumer electronics slowing down is not going to change our position. We do not care to perfectly time entries or to find a perfect bottom – you’ll be hard pressed to find any legendary investor recommend that this be an investor’s goal. What we care about is finding quality companies and building those positions over time. Nvidia fits this description.

Nvidia: A Leader in AI Hardware and AI Software

Posted on July 15, 2022June 30, 2026 by io-fund

If you were to guess, when do you think we wrote the following paragraph?

When do you think we wrote this analysis?

Now, we add the enormous push for auto manufacturers to compete with Tesla, Apple, Lucid Motors and what we have is a bottle neck where the automotive industry filters into semiconductors.

My guess is the demand won’t be letting up for many years as we are no longer in the cyclical pattern that semis are notorious for. Instead, demand will outpace supply for years to come.

I mention this because following a trend’s trajectory is more important than immediate gratification from the market. The trend will always win out over time.

Here is background on the A100:

Nvidia's AI Dominance Will be Propelled Forward by Software:

GTC Highlights: The Hopper H100 GPU

The H100 is the New Artificial Intelligence Infrastructure

DGX, DGX Pods and DGX SuperPods:

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.

Spectrum-4 Ethernet Platform

Eos: The First Hopper AI Factory

Nvidia is building AI factories to compete with AI supercomputers, which are blueprints for AI infrastructure that can be adopted by cloud partners and enterprises.

Here is what Nvidia said in the GTC keynote:

DPX Instructions (ISA):

GPU Confidential Computing:

Nvidia is Becoming a Leading AI Software Company

Transformers

Pictured above: Nvidia is prepped to support model sizes growing up to 275X every two years

Triton Inference Server:

Nvidia will Power the Lion’s Share of Automotive – and that means software licensing

Here’s a 10-minute demonstration with the simulation platform here around minute 7:00.

The breakdown according to the 2022 Investor Presentation looks like this:

$100 billion from gaming
$300 billion from chips and systems
$150 billion from AI Enterprise software
$150 billion from Omniverse software – fees are charged to both users and robots/digital twins
$300 billion from Automotive – primarily software

What Nvidia is communicating is that software revenue will surpass hardware revenue long-term.

Note on CUDA:

Note: I’ve covered Omniverse in-depth here.

Risk: Valuation

Conclusion:

Nvidia: A Leader in AI Hardware and AI Software

Posted on July 15, 2022June 30, 2026 by io-fund

If you were to guess, when do you think we wrote the following paragraph?

When do you think we wrote this analysis?

Now, we add the enormous push for auto manufacturers to compete with Tesla, Apple, Lucid Motors and what we have is a bottle neck where the automotive industry filters into semiconductors.

My guess is the demand won’t be letting up for many years as we are no longer in the cyclical pattern that semis are notorious for. Instead, demand will outpace supply for years to come.

I mention this because following a trend’s trajectory is more important than immediate gratification from the market. The trend will always win out over time.

Here is background on the A100:

Nvidia's AI Dominance Will be Propelled Forward by Software:

GTC Highlights: The Hopper H100 GPU

The H100 is the New Artificial Intelligence Infrastructure

DGX, DGX Pods and DGX SuperPods:

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.

Spectrum-4 Ethernet Platform

Eos: The First Hopper AI Factory

Nvidia is building AI factories to compete with AI supercomputers, which are blueprints for AI infrastructure that can be adopted by cloud partners and enterprises.

Here is what Nvidia said in the GTC keynote:

DPX Instructions (ISA):

GPU Confidential Computing:

Nvidia is Becoming a Leading AI Software Company

Transformers

Pictured above: Nvidia is prepped to support model sizes growing up to 275X every two years

Triton Inference Server:

Nvidia will Power the Lion’s Share of Automotive – and that means software licensing

Here’s a 10-minute demonstration with the simulation platform here around minute 7:00.

The breakdown according to the 2022 Investor Presentation looks like this:

$100 billion from gaming
$300 billion from chips and systems
$150 billion from AI Enterprise software
$150 billion from Omniverse software – fees are charged to both users and robots/digital twins
$300 billion from Automotive – primarily software

What Nvidia is communicating is that software revenue will surpass hardware revenue long-term.

Note on CUDA:

Note: I’ve covered Omniverse in-depth here.

Risk: Valuation

Conclusion:

Confluent Update and Q4 Earnings

Posted on February 24, 2022June 30, 2026 by io-fund

Below, we do another overview of Confluent’s product and an update following the Q4 earnings report. Here are two resources we recommend reading from our premium site for more information on the company.

Confluent Product Overview and Q3 Earnings

Big Data, Analytics and the Importance of ML

We believe open source with enterprise-grade features will become a key market moving forward as it solves for the downside of open source such as a lack of technical support. In Kafka’s case, the downside are things like a lack of data verification and having to manually connect to various data warehouses and other platforms to import/export data. Confluent also makes the argument that multi-cloud and hybrid cloud architectures are best served with a supported enterprise version for multi-tenancy security and data residency.

Notably, from my perspective, we are not betting on Confluent being used over the open-source version of Kafka in a direct competition, rather we are betting that Kafka will increase in importance. In this case, if Kafka continues to grow, Confluent will take a percentage of this market share should more enterprises prefer a managed version of Kafka. 70% of the Fortune 500 use Kafka and 80% of the Fortune 100. According to this site it has a 12.5% market share.

Kafka is popular because of its high-performance real-time data streaming capabilities for mission critical applications. It is distributed and fault-tolerant, which means if one component fails, the system will still work. It can also scale to hundreds of clusters and billions of messages.

As discussed in our original write-up, Kafka was developed at LinkedIN to process the large number of messages per second the social media company handles. The framework enables event streaming, which helps messaging and data integration. There is high scalability with a publish/subscribe model that allows applications to share and create data in a serverless and microservices architecture. What Kafka solves for is the ingestion of events data in real-time with low latency with continuous read/write. If data remains at rest and/or in a mainframe environment, then companies cannot be truly data-driven. Kafka on the other hand can scale from a billion messages per day to a trillion messages per day.

Machine Learning and Kafka

Confluent opens up the amount of data that can integrated. The thesis is the increase in the number of companies that will need real-time data processing and real-time data analytics due to the increase in software driven architectures. The idea is that “data in motion” will replace data at rest, or batch data processing from traditional databases. This is also important for the real-time data streams that machine learning requires.

Kafka is more than a messaging system as discussed in this article and is used for business applications, streaming ETL middleware, real-time analytics and edge/hybrid use cases for the framework.

Here are some examples of how Kafka can be used outside of messaging systems:

Fraud detection through a machine learning pipeline for Paypal’s billions of messages
Data correlation in real-time for Lyft for matching maps, estimated time of arrival and cost calculations
Unity uses Confluent to be internally data-driven across R&D and cloud-services, plus to help drive the monetization network by rewarding players for watching ads and incorporating banner ads
Continuous calculations for betting platforms
Drug discovery that is automated and scalable

Machine learning requires model training from historic data and also model deployment for scoring and predictions. Training can be done with batch yet scoring is partial towards real-time data. ML-powered applications run inferences on large volumes of data to return predictions very quickly (milliseconds). Rather than use Remote Procedure Calls (RPC) and frameworks like gRPC, some companies use a Kafka streaming model.

Here is how the company states the problem that Confluent seeks to solve:

“By becoming more software driven, more businesses will rely on real-time data. Confluent believes that data in rest is not able to meet the current and future demands of software-driven businesses. Daily batch processing and static real-time queries or “point-in-time” queries with stored data lead to an unnecessarily large and tangled architecture that is not capable of data flow between applications.”

Enterprise-grade Features

As with Spark and other open-source projects, there is a marketplace for making the frameworks easier to use. Confluent Kafka opens up the amount of data that can be integrated, for example, to combine transactional data (orders, inventory) with sentiment-driven data (likes, page clicks). This helps with predictive analytics and also machine learning because the “data flow” allows for algorithms to work as they are intended to.

In order for data to be in motion, Confluent’s platform connects data from many different sources. The company has over 50 fully managed connecters with Big Data and Analytics from Azure, Amazon/AWS, Google and Databricks. Without these connectors offered by Confluent, integrations between systems on an open-source framework can take months and also require intensive resources to manage.

Confluent is attempting to stave off competitors through “completeness of product” which touches on our multi-cloud and hybrid cloud discussion. We’ve discussed hybrid for a few years, yet our most recent write-up was here and here on Datadog. The recent write-up is worth a read if you want to know exactly why agnostic, best-of-breed products are sometimes outpacing Big Tech when it comes to cloud services and products. Datadog is the best example of a product where customers are avoiding vendor lock-in.

The completeness of product goes beyond multi-cloud and hybrid as Confluent is attempting to hold off competitors through data security and data governance, as well. Because data is often an organization’s most prized asset, it often has internal processes for compliance. There is often external, geographic compliance required by governments and industry agencies, as well, for global companies.

In order for completeness of product to work, Confluent needs to have a large geographic footprint. The company has added eight more regions for Confluent Cloud with an emphasis on APAC. There is also a new partnership with Alibaba Cloud. This can help offer differentiation for multinationals who have operations in China.

Competitors:

Regarding direct competitors, one example is Amazon MSK which offers a competing managed streaming service. This competitor is a good option for developers provisioning a Kafka cluster and a new streaming platform may not be needed in this case.

Rather than re-architect Kafka to be cloud-native, Amazon MSK cloud-enabled it as provisioned infrastructure. This means Confluent is stronger than MSK with scaling elastically by offering elastic quotas, which eliminates the need to size clusters for spikes. It’s also stronger on multi-tenancy security. Amazon MSK also does not offer Kafka Connect or Kafka Streams.

For more enterprise uses where Kafka Connect or Kafka Streams is required, then Confluent is more likely to be used to save development time and learning curve in writing Kafka Connects sinks and source.

Blockchain and Metaverse Potential

We’ve written at length about Confluent’s core use. However, there is a blockchain potential with Confluent with one case study right now with Dapper Labs.

“These are steps that attracted Dapper Labs. They're one of the most innovative NFT companies delivering fun and games on the blockchain. They have a number of decentralized apps, but one that's risen dramatically in popularity is called NBA Top Shot. To date, there have been over 10 million digital collectible transactions and Confluent is at the center of their data streaming architecture to facilitate these purchases. Dapper chose us to run their mission critical workloads because of the scalability and security of our cloud solution.”

There’s also a case for 5G networks needing data in motion. Here’s what was said about Dish on the call:

“A significant customer for both AWS and us is DISH Network. With their new 5G smart network, DISH is transforming how people and enterprises leverage data. They deployed Confluent Cloud over AWS to connect their network systems and customers with real-time data. This means that Confluent is a key part of their network's data backbone, starting with fault management and network resiliency functions to ensure network availability, and our enhanced collaboration with AWS is making it easier for customers like DISH to unlock data in motion everywhere.”

Confluent Q4 Overview

Confluent has been accelerating in revenue for four consecutive quarters and also across other key metrics.

The company reported fiscal year 2020 revenue growth of 58% year-over-year and fiscal year 2021 revenue growth of 64% year-over-year. Confluent Cloud revenue growth for fiscal year 2020 was 117% compared to FY2021 revenue growth of 200% year-over-year.

If we look at Q4, total revenue is outpacing the fiscal year growth for 2021 and also outpaced Q3. Revenue growth for Q4 was at 71% — the highest growth rate from publicly available information which dates back two years to Q1 2020.

Cloud revenue did decelerate on a sequential basis, however, the company stated Q4 is often seasonal due to engineers being out of the office and on vacations. We will see if this picks back up in Q1. Regardless, on an annual basis there was a significant improvement. Notably, if we look at 2020 cloud revenue, we can see it’s lumpy at times with Q3 2020 being the weakest and Q2 2020 being the strongest.

In regards to “sandbagging” which is essentially the company guiding low and blowing out the guidance, which has happened a few times now, the company has a lot of moving pieces in terms of business model and likely wants to win trust with institutions. We are not opposed to this even if it means the price action was somewhat severe after the earnings report due to the guidance. What we are more concerned with is that Confluent continues to raise and beat, and that the underlying key metrics help us to substantiate the company’s longer-term strength.

Bradley stated the following in our last write-up and got pretty close to the revenue growth that Confluent actually reported:

Looking forward, management guided that Q4 revenue will rise 55% YoY $109 million, which would mark a deacceleration from the most recent growth rate of 67% YoY growth. However, this estimate is likely conservative, as management guided that Q3 sales would grow 46% YoY to $90 million and actual Q3 sales grew 67% YoY to $103 million. If we assume that Confluent beats it guide by a similar amount in Q4 as it did in Q3 ($13 million), then Q4 sales growth will accelerate to 73% YoY (this is merely an observation – no guarantees).If we assume that Confluent beats it guide by a similar amount in Q4 as it did in Q3 ($13 million), then Q4 sales growth will accelerate to 73% YoY (this is merely an observation – no guarantees).

Most notably, the company is reporting high remaining performance obligations growth of 91% year-over-year. This is higher than the 75% year-over-year we saw in Q3.

Bradley discussed this in our last write-up:

Confluent also states that RPO is an important metric to monitor in order to measure the health of the sales pipeline. In Confluent’s first conference call as a public company (Q2), CFO Steffan Tomlinson explained that:

“Given the various revenue components and billing terms in our model, remaining performance obligations or RPO and current RPO rather than billings, are important metrics to measure the health of the business. RPO provides insight into the organic momentum of our business as it represents contractually committed revenue to be recognized in the future regardless of billing terms and variability in cloud consumption pattern”. RPO provides insight into the organic momentum of our business as it represents contractually committed revenue to be recognized in the future regardless of billing terms and variability in cloud consumption pattern”

Financials Deep Dive

By Bradley Cipriano

A slight blemish during the quarter was Confluent’s customer growth, which lagged the growth in sales. Customers increased 65% YoY to 3,470, which lagged the 71% YoY growth in total sales. This drove subscription revenue per customer up 4% YoY to $31,000/customer, implying the recent acceleration in sales was driven by higher spending rather than customer growth.

Generally, growth from new customers is more sustainable and higher quality relative to growth from increased spending. However, DBNRR remained robust at over 130%, signaling that customers are increasing their spend over time.

It is odd that customer spending increased but cloud growth deaccelerated during the quarter. Since cloud is a usage-based revenue model, increased spending should have driven cloud outperformance. However, cloud spending slowed from 245% YoY growth in Q3 to 211% in Q4. On the Q4 call, management explained that cloud was impacted by seasonality due to relatively lower spending over the holidays which lead to slightly slower rates of usage. While this may be true, it doesn’t explain the YoY deacceleration, as this trend would have existed in the year-ago quarter. Nevertheless, there is inherent variability in a usage-based model so investors should not expect an acceleration in sales every quarter.

Given the slowdown in customer growth and slight deceleration in cloud sales, the Street may be concerned that Confluent’s growth may be somewhat cannibalistic. This would explain the sell-off in its stock following otherwise strong results which reported a beat and raise. Investors may be wondering if cloud growth is coming at the expense of platform growth, or vice versa?

CEO-Founder Jay Kreps discussed this concern on the call and stated that the company is growing both in the cloud and in hybrid environments. He said that “we don't really view this as kind of a transition where we're just shifting from platform to cloud and just kind of swapping out customers from one product to the other. Effectively, we have to have kind of an outpost in each environment a customer is in. So, we expect to continue to see growth in Confluent Platform throughout this, and we think that's not a bad thing. That's a good thing.” CFO Steffan Tomlinson added that “what our customers are telling us is, by and large, they're running hybrid environments”.

A common issue with ramping cloud sales is that sales in other parts of the business stagnant, but we do not believe this is the case. For example, Confluent’s financial results remain high quality which suggests that cloud/platform sales are not cannibalistic.

For example, net deferred revenue (deferred revenue less accounts receivables) increased 105% YoY to $109 million, or 31% of TTM subscription sales. This was an improvement from the 26% and 23% level in Q4 2020 and Q4 2019, respectively. The rise in net deferred revenue relative to subscription sales signals that the company is receiving relatively more cash upfront, improving the quality of topline growth. If sales were cannibalistic, we would have likely seen a reduction in cash receipts and/or a deacceleration in growth. Instead, cash improved and sales accelerated.

Furthermore, RPO also increased 91% YoY to $501 million, an acceleration from the 75% and 72% YoY growth rates in Q3 and Q2, respectively. While we need the 10K to fully assess the quality of RPO, total RPO represents 92% of management’s NTM guide, up from 81% in Q3. This improves the quality of forward sales and suggests that there is conservatism in management’s forward guide.

However, we do note that cash support for RPO declined slightly during the quarter. Total deferred revenue-to-RPO fell from 52% in Q3 to 49% in Q4. This trend is likely driven by the rise of cloud bookings, since cloud is a usage-based model and new cloud customers are typically on pay-as-you-go plans, which are billed in arrears. On the Q4 call, CEO-Founder Jay Kreps explained that cloud accounted for 50% of ACV bookings in Q4, highlighting how cloud will be the majority of revenues going forward. As customers become more familiar with Confluent’s products, they will likely increase their commitments and convert from pay-as-you-go customers to larger customers that pay upfront. As a result, we view the slight decline in upfront cash receipts as a natural progression for the firm and not a major concern at this time.

Cash Levels and Stock Based Compensation

Confluent recently raised nearly $1 billion in cash following a convertible debt offering in December. Following this raise, the company has over $2 billion in cash, which is well above its current cash burn of ~$108 million (based on TTM free cash flow). The company is focused on growth, so investors should be prepared for continued losses and cash outflows. On the Q4 call, management highlighted that their near-term priorities are to continue to invest in innovation and to expand its geographic footprint, signaling that growth is being prioritized over near-term profitability.

Nevertheless, given Confluent’s relatively large cash balance, we likely should not expect an equity raise in the near term. However, the company will still be dependent on capital markets until it is sustainably cash flow positive. Looking forward, the Street expects EBITDA (a proxy for cash flows) to remain negative through at least FY2023, suggesting that Confluent will remain reliant on capital markets for the next few years. Importantly, there are signs of improvement, as free cash flow margin improved from -30% in the prior year to -22% in the current quarter.

Furthermore, Confluent has relatively high levels of stock-based compensation (SBC), which subsidizes cash used for working capital but dilutes shareholders. Stock-based compensation has trended near 48% of quarterly sales for the last two quarters and was 40% of TTM sales. This is relatively high and ranks in the top 10 for cloud (shown below), but is a function of Confluent recently going public (which frontloads SBC). We expect SBC to decline as a percentage of sale going forward as it laps the IPO and topline growth outpaces expenses.

UiPath Fiscal Q2 Update: Decelerating Revenue Doesn’t Tell the Whole Story

Posted on September 17, 2021June 30, 2026 by io-fund

UiPath’s core product is UI-based automation and they’ve recently acquired Cloud Elements to add API-based automation. The software robots are able to work across programs in the background, build applications, send emails and interact with chatbots. Although making AI/ML actionable is UiPath’s sweet spot, the trend towards low code/no code is also a tailwind for UiPath. We know the AI-bellwether Nvidia has led the market this year, which helps us gauge where we are in the AI cycle as semis must move first. Therefore, Nvidia helps provide evidence that we are early to UiPath (unlikely automation moves exactly in sync with GPUs) and the goal will be to remain invested through the ups/downs as the story unfolds.

I had posted on the forum my thoughts on the decelerating revenue and why the unique business model and change in billing terms is causing the top line to look weaker than the company actually is. Management emphasizes to focus on ARR and we break down why that’s important. Although a change in billing terms can be seen as a weakness, we don’t think this is the case as the change in terms could open up the company to more customers who aren’t willing or able to pay up front. We had outlined in our original analysis that UiPath is an expensive product with a customer concentration at the enterprise level. Customer growth above $1 million was up 100%, therefore, UiPath is showing strength in its core customer base. However, UiPath is now ready to invest at the citizen developer level in the Studio X product.

Earnings Overview

This section posted on the forum Thursday, September 16th:This section posted on the forum Thursday, September 16th:posted on the forum Thursday, September 16th:

UiPath beat and raised guidance, so there was no issue here. Rather the issue is the company’s growth is decelerating and this is raising some question marks as to how long the deceleration will continue. The market is forward looking, and in this case, the market is pricing in lower growth. The guidance does not fully illustrate the deceleration in licensing and deferred revenue. We break this down for you below.

First, I want to talk about UiPath’s unique business model which is to license software robots and then charge for support and maintenance. Here is the difference as outlined in the S-1 filing:

Licenses:

We sell term licenses which provide customers the right to use software for a specified period of time. From time to time, we also sell perpetual licenses that provide customers the right to use software for an indefinite period of time. For each respective type of license, revenue is recognized at the point-in-time when the customer is able to use and benefit from the software, which is generally upon delivery to the customer or upon the commencement of the renewal term.

Maintenance and Support:

We generate maintenance and support revenue through technical support and the provision of unspecified updates and upgrades on a when-and-if-available basis for both term and perpetual license arrangements. Maintenance and support for perpetual licenses is renewable, generally on an annual basis, at the option of the customer. Maintenance and support represents stand-ready obligations for which revenue is recognized ratably over the term of the arrangement.

The reason the company emphasizes ARR as the key metric to focus on is because it accounts for UiPath’s upgrade-heavy business model and it shows the strength from retention. Expansion revenue is essentially what drives UiPath rather than yearly subscriptions alone (revenue). Once a company licenses software robots from UiPath, they are more likely to license more software robots over time and to need more maintenance and support. This upgrade cycle is unique from other cloud companies that have only specific cohorts they can upgrade and are more focused on yearly subscriptions (i.e., Pro Plan to Enterprise Plan, etc.).

Management at UiPath is communicating that the ARR forecast is more important than the revenue forecast as it accounts for the upgrades they are expecting. The dollar based net retention rate for the company is very high at 144%. Evidence of the expansion revenue model is also seen in the company’s lifetime value which is 233X for the top 25 customers and 62X for the top 100 customers. UiPath’s customers are enterprise customers with large budgets, which is why we saw the $1 million+ ARR cohort up 100% this quarter and those accounting for $100,000+ ARR up 59% this quarter.

Why UiPath Sold Off Despite a Beat:

Given what analysts know about Q3 guidance, the current projections for fiscal year 2022 is at 44%, which is down from 81% in the last fiscal year. We’ve included the analyst projections for the following year, which right now are at 34% growth to $1.17 billion.

However, the ARR hints towards stronger revenue growth in the future. For fiscal year 2021, revenue of $608,000 exceeded ARR of $580,400. This year, we are seeing ARR slightly outpace revenue if we based revenue projections on analyst consensus. While revenue is forecast to grow 44% this fiscal year, ARR is forecast to grow at 51%. Historically, UiPath’s revenue exceeded ARR.

Another explanation for why we are seeing the lapse between ARR and revenue is that the CFO mentioned a change in terms of how the company bills. In the past, the company billed multi-year deals all at once, and instead, they are shifting towards billing annually. The management stated the reason is that it allows more upgrades as their customers’ needs change and it results in less up front from their customers. In the example provided, instead of buying 10,000 robots at once, they would buy 1,000 and then 5,000 and then 10,000 with the new billing structure with the revenue realized across three years rather than realized in one year.

Here is the quote about the difference between revenue guidance and ARR: “So when I talk about an annual ramping contract, one of the things that is really positive for us is digital transformation is a long-term trend. And what has happened with the strength of UiPath’s platform is automation is a staple for the long-term requirements for customers to transform the way they work in digital transformation. So what that means is instead of buying simple annual contracts, what we see a larger demand for is getting larger term commitments from some of our customers. But the way they look at that is instead of buying 10,000 robots today, they may buy 1,000 robots today, 5,000 next year, 10,000 in year three. And those – the license deliveries would happen into those years as we go down.
And so, one of the things that we look at is that we like that because that is better ROI for our customers. And I – when we think about the impact on financial metrics, two things. One is remember, we – and I repeat this, we really drive our company to ARR. From an ARR perspective, there is no impact that is there. Based on the way the contracts are structured, if license delivery happens in the out years, then that does have – that creates variability in revenue because we only can recognize the revenue upon delivery of the license. And so that is kind of the way that I think about it from a modeling perspective on revenue. But again, I stress ARR really, there is no impact, and that’s how we drive the business.”Based on the way the contracts are structured, if license delivery happens in the out years, then that does have – that creates variability in revenue because we only can recognize the revenue upon delivery of the license. And so that is kind of the way that I think about it from a modeling perspective on revenue. But again, I stress ARR really, there is no impact, and that’s how we drive the business.”

This change in how customers are billed likely led to lower licensing revenue in the current quarter of 20% growth compared to 72% growth from the fiscal year (not apples-to-apples comparing Quarterly to Fiscal Year but helps provide color). This is down from 57% last fiscal quarter ending in April. We see evidence that those licenses will be recognized in future quarters not only in ARR but also in support and maintenance growth, which is recognized ratably and grew 74% year-over-year compared to 79% in the quarter ending in April. In other words, customers aren’t falling off or downgrading rather they are paying for licenses across many years rather than pre-paying.

Expanding on Earnings:

UiPath reported revenue of $195.5 million compared to the consensus for $184.3 million. This represents 40% growth year-over-year compared to 65% growth in the prior quarter. EPS also beat at $0.01 compared to a consensus of ($0.05) EPS. UiPath has a total of 9,100 customers with 600 added in the recent quarter. The company also has 4,700 partners after adding 400 in the most recent quarter. The Partner Network is part of our thesis on UiPath and we think this number carries significance in terms of a defensible position. This quarter, the company highlighted its partnerships with Alteryx and Smartsheet.

The company’s adjusted gross margins are at 86% in the most recent quarter with adjusted free cash flow at a loss of $3.5 million. As stated, licensing revenue slowed down with the revenue mix being $95.5 million in licenses compared to $79.5 million in the year-ago quarter. Maintenance and other Support was at $90.3 million compared to $51.9 million in the year-ago quarter.

We believe the management is correct in focusing on ARR. As stated above, revenue typically exceeds ARR. Therefore, if we draw conclusions based on the historic performance of the company, the revenue growth would be above 60% in this quarter and above 51% in future quarters. In addition, net new ARR was up 33%. We will need to see how long it takes before the company absorbs the change in billing terms, but due to where we are in the AI cycle, we prefer to be patient. If this was cloud software, which is moving towards consolidation, we would be more concerned. Hopefully, the above section explains why we are not concerned at this time.

Guidance came in higher than expected at revenue of $208 million at the midpoint compared to consensus of $206 million. The guide in ARR was at 54% next quarter and at 51% growth for the fiscal year. When asked if the raised guidance was from a demand signal, the company pointed towards their Net Retention Rate, which remained robust at 144%, indicating that demand for their products has remained strong.

From my perspective, UiPath has very few comparables on the market but we can lump the company in with cloud software with the understanding that cloud software’s growth reflects a mature market while UiPath’s does not. Forward fiscal year P/S is at 32 based on $874 million consensus estimates for this year and 1-year forward is at 24 if based on consensus estimates of 1.17 billion. We think this is a reasonable valuation for a company that is at the forefront of automation, which is one of the least-hyped yet most practical commercial uses for AI and ML. However, the full lockup is next month and we had stated in our initial coverage on UiPath and reiterated across other analysis that even the most quality companies come under pressure from insiders and early investors needing an exit.

Of the companies pictured above, ZS, TEAM and NET have similar forward revenue growth and we can see they are fetching much higher valuations. It will be interesting to see how this unfolds if UiPath’s revenue does indeed catch up to ARR and enterprise customer growth.

Product Catalysts:

The focus of this update has been primarily on the financials as we’ve written a deep dive on the product, which you can find here.

UiPath has a few catalysts this fall, including releasing attended robots for the Linux operating system and also for Mac users. Mac OS users make up roughly 17% compared to Windows, there is a higher concentration of Mac users among citizen developers and also in enterprises in the tech industry. Linux makes up a very tiny portion of overall operating system market share at less than 2% yet dominates cloud infrastructure at 90% penetration. In 2021, 100% of the world’s top 500 supercomputers ran Linux. Here are the releases the company has planned including a web-based version of StudioX:

Now, we continue to invest in StudioX as a major tool to foster the community of citizen developers. We are going to extend it also to be available multi-platform. So, you will expect quite a bit of investment from us in the coming few quarters. It’s – right now, we are doing really a major advance into multi-cloud and multi-platform. And we are launching Linux-based robots. We just announced yesterday the public review available. We are launching Mac support. We are going to launch early next year, the web-based StudioX that will make it even easier to adopt. But overall, we really believe that it’s important to have a suite of tools that cater to a large array of options from professional developers to citizen developers and to all business users.

Conclusions:

The lockup for UiPath happened in tranches after the first fiscal quarter results and also following the second fiscal quarter results, per the S-1 filing, with some restrictions. The full lockup with no restrictions occurs on October 18th. This is likely weighing on shares more than anything in the current ER as some shares were up for lockup expiration at the time of fiscal Q2. Participating in IPOs rarely works out in the first few months of the listing unless you’re actively trading, which we stated clearly in the original write-up on UiPath. However, we decided to add this to LTBH to show our seriousness in building AI positions.

With that said, we will likely hold off on Confluent until post-lockup. You’ll get this analysis soon to set the stage for why we are building MDB, ESTC and eventually CFLT as a package as we think there’s an important trend bubbling beneath the surface. Keep an eye for this deep dive end of next week.

Here’s Why Nvidia Will Surpass Apple’s Valuation In 5 Years

Posted on September 2, 2021June 30, 2026 by io-fund

This article was originally published on Forbes on Aug 27, 2021,12:24am EDTForbes on Aug 27, 2021,12:24am EDT

Nvidia has a market cap of roughly $550 billion compared to Apple’s nearly $2.5 trillion. We believe Nvidia can surpass Apple by capitalizing on the artificial intelligence economy, which will add an estimated $15 trillion to GDP. This is compared to the mobile economy that brought us the majority of the gains in Apple, Google and Facebook, and contributes $4.4 trillion to GDP. For comparison purposes, AI contributes $2 trillion to GDP as of 2018.

While mobile was primarily consumer, and some enterprise with bring-your-own-device, artificial intelligence will touch every aspect of both industry and commerce, including consumer, enterprise, and small-to-medium sized businesses, and will do so by disrupting every vertical similar to cloud. To be more specific, AI will be similar to cloud by blazing a path that is defined by lowering costs and increasing productivity.

I have an impeccable record on Nvidia including when I stated the sell-off in 2018 was overblown and missing the bigger picture as Nvidia has two impenetrable moats: developer adoption and the GPU-powered cloud. This was when headlines were focused exclusively on Nvidia’s gaming segment and GPU sales for crypto mining.

Although Nvidia’s stock is doing very well this year, this has been a fairly contrarian stance in the past. Not only was Nvidia wearing the dunce hat in 2018, but in August of 2019, the GPU data center revenue was flat to declining sequentially for three quarters, and in fiscal Q3 2020, also declined YoY (calendar Q4 2019). We established and defended our thesis on the data center as Nvidia clawed its way back in price through China tensions, supply shortages, threats of custom silicon from Big Tech, cyclical capex spending, and on whether the Arm acquisition will be approved.

Suffice to say, three years later and Nvidia is no longer a contrarian stock as it once was during the crypto bust. Yet, the long-term durability is still being debated —- it’s a semiconductor company after all —- best to stick with software, right? Right? Not to mention, some institutions are still holding out for Intel. Imagine being the tech analyst at those funds (if they’re still employed!).

Before we review what will drive Nvidia’s revenue in the near-term, it bears repeating the thesis we published in November of 2018:

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 FGPA 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.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.”

Notably, the stock is up 335% since my thesis was first published – a notable amount for a mega cap stock and nearly 2-3X more returns than any FAAMG in the same period. This is important because I expect this to trend to continue until Nvidia has surpassed all FAAMG valuations.

I/O Fund

Below, we discuss the Ampere architecture and A100 GPUs, the Enterprise AI Suite and an update on the Arm acquisition. These are some of the near-term stepping stones that will help sustain Nvidia’s price in the coming year. We are also bullish on the Metaverse with Nvidia specifically but will leave that for a separate analysis in the coming month.

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Nvidia Not Standing Still with Ampere Architecture and A100 GPU

“Nvidia’s acceleration may happen one or two years earlier as they are the core piece in the stack that is required for the computing power for the front-runners referenced in the graph above. There is a chance Nvidia reflects data center growth as soon as 2020-2021.” -published August 2019, Premium I/O Fund

Last year, Nvidia released the Ampere architecture and A100 GPU as an upgrade from the Volta architecture. The A100 GPUs are able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. This allows Nvidia a competitive advantage by offering both training and inferencing. 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.

At the onset, the A100 was deployed by the world’s leading cloud service providers and system builders, including Alibaba cloud, Amazon Web Services, Baidu Cloud, Dell Technologies, Google Cloud platform, HPE and Microsoft Azure, among others. It is also getting adopted by several supercomputing centers, including the National Energy Research Scientific Computing Center and the Jülich Supercomputing Centre in Germany and Argonne National Laboratory.

One year later and the Ampere architecture is becoming one of the best-selling GPU architectures in the company’s history. This quarter, Microsoft Azure recently announced the availability of Azure ND A100 v4 Cloud GPU which is powered by NVIDIA A100 Tensor Core GPUs. The company claims it to be the fastest public cloud supercomputer. The news follows the launch by Amazon Web Services and Google Cloud general availability in prior quarters. The company has been extending its leadership in supercomputing. The latest top 500 list shows that Nvidia power 342 of the world’s top 500 supercomputers, including 70 percent of all new systems and eight of the top 10. This is a remarkable update from the company.

Ampere architecture-powered laptop demand has also been solid as OEM’s adopted Ampere Architecture GPUs in a record number of designs. It also features the third-generation Max-Q power optimization technology enabling ultrathin designs. The Ampere architecture product cycle for gaming has also been robust, driven by RTX’s real-time ray tracing.

In the area of GPU acceleration, Nvidia is working with Apache Spark to release Spark 3.0 run on Databricks. Apache Spark is the industry’s largest open source data analytics platform. The results are a 7x performance improvement and 90 percent cost savings in an initial test. Databricks and Google Cloud Dataproc are the first to offer Spark with GPU acceleration, which also opens up Nvidia for data analytics.

The demand has been strong for the company’s products which have exceeded supply. In the earnings call, Jensen Huang mentioned “And so I would expect that we will see a supply-constrained environment for the vast majority of next year is my guess at the moment.” However, he assured that they have secured enough supplies to meet the growth plans for the second half of this year when he said, “We expect to be able to achieve our Company's growth plans for next year.”

Virtual Machines for AI Workloads

Virtualization allows companies to use software to expand the capabilities of physical servers onto a virtual system. VMWare is popular with IT departments as the platform allows companies to run many virtual machines on one server and networks can be virtualized to allow applications to function independently from hardware or to share data between computers. The storage, network and compute offered through full-scale virtual machines and Kubernetes instances for cloud-hosted applications comes with third-party support, making VMWare an unbeatable solution for enterprises.

Therefore, it makes sense Nvidia would choose VMWare’s VSphere as a partner on the Enterprise AI Suite, which is a cloud native suite that plugs into VMWare’s existing footprint to help scale AI applications and workloads. As pointed out by the write-up by IDC, many IT organizations struggle to support AI workloads as they do not scale as deep learning training and AI inferencing is very data hungry and requires more memory bandwidth than what standard infrastructures are capable of. CPUs are also not as efficient as GPUs, which have parallel processing. Although developers and data scientists can leverage the public cloud for the more performance demanding instances, there are latency issues with where the data repositories are stored (typically on-premise).

The result is that IT organizations and developers can deploy virtual machines with accelerated AI computing where previously this was only done with bare metal servers. This allows for departments to scale and pay only for workloads that are accelerated with Nvidia capitalizing on licensing and support costs. Nvidia’s AI Enterprise targets customers who are starting out with new enterprise applications or deploying more enterprise applications and require a GPU. As enterprise customers of the Enterprise AI Suite mature and require larger training workloads, it’s likely they will upgrade to the GPU-powered cloud.

Subscription licenses start at $2,000 per CPU socket for one year and it includes standard business support five days a week. The software will also be supported with a perpetual license of $3,595, but support is extra. You also have the option to have get 24×7 support with additional charges. According to IDC, companies are on track to spend a combined nearly $342 billion on AI software, hardware, and services like AI Enterprise in 2021. So, the market is huge and Nvidia is expecting a significant business.

Nvidia also announced Base Command, which is a development hub to move AI projects from prototype to production. Fleet Command is a managed edge AI software SaaS offering that allows companies to deploy AI applications from a central location with real-time processing at the edge. Companies like Everseen use these products to help retailers manage inventory and for supply chain automation.

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Fiscal Q2 Earnings and More on the Arm Acquisition:

Over the past year, there have been some quarters where data center revenue exceeded gaming, while in the most recent quarter, the two segments are inching closer with gaming revenue at $3.06 billion, up 85 percent year-over-year, and data center revenue at $2.37 billion, up 35 percent year-over-year.

It was good timing for Jensen Huang to appear in a fully rendered kitchen for the GTC keynote as professional visualization segment was up 156% year-over-year and 40% quarter-over-quarter. Not surprisingly, automotive was down 1% sequentially although up 37% year-over-year.

Gross margins were 64.8% when compared to 58.8% for the same period last year, which per management “reflected the absence of certain Mellanox acquisition-related costs.” Adjusted gross margins were 66.7%, up 70 basis points, and net income increased 282% YoY to $2.4 billion or $0.94 per share compared to $0.25 for the same period last year.

Adjusted net income increased by 92% YoY to $2.6 billion or $1.04 per share compared to $0.55 for the same period last year.

The company had a record cash flow from operation of $2.7 billion and ended the quarter with cash and marketable securities of $19.7 billion and $12 billion in debt. It returned $100 million to the shareholders in the form of dividends. It also completed the announced four-for-one split of its common stock.

The company is guiding for third quarter fiscal revenue of $6.8 billion with adjusted margins of 67%. This represents growth of 44% and with the “lion’s share” of sequential growth driven by the data center.

We’ve covered the Arm acquisition extensively with in a full-length analysis you can find here on Why the Nvidia-Arm acquisition Should Be Approved. In the analysis, we point towards why we are positive on the deal, as despite Arm’s extremely valuable IP, the company makes very little revenue for powering 90% of the world’s mobile processors/smartphones (therefore, it needs to be a strategic target). We also argue that the idea of Arm being neutral in a competitive industry is idealistic, and to block innovation at its most crucial point would be counterproductive for the governments reviewing the deal. We also discuss how the Arm acquisition will help facilitate Nvidia’s move towards edge devices.

In the recent earnings call, CFO Colette Kress reiterated that the Arm deal is a positive for both the companies and its customers as Nvidia can help expand Arm’s IP into new markets like the Data Center and IoT. Specifically, the CFO stated, “We are confident in the deal and that regulators should recognize the benefits of the acquisition to Arm, its licensees, and the industry.”

Conclusion:

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.

UiPath: Robotics Process Automation

Posted on May 27, 2021June 30, 2026 by io-fund

c15d441a-5b32-4147-8bb9-d78d991e3263_UiPath+Robotics+Process+Automation+Premium+Analysis.pdf

I’m genuinely curious as to whether UiPath will be the first in the tech universe to hold its opening IPO price. There’s a chance it does and I will attempt to communicate why this company could be one of the strongest performers the public markets have seen from my industry.

UiPath is becoming a darling within tech circles. That’s maybe the most critical thing to understand as the Partner Network for UiPath could potentially form a moat. The high switching costs most certainly help the company to become defensible. UiPath makes the choice very clear on what company to choose for RPA, no matter what your organization’s needs are, and from there they give enterprises no reason to switch by continually iterating a cutting-edge approach to RPA.

The last time I was this excited about an IPO was two years ago with Zoom Video. We held off to buy the stock post-lockup. That decision will be yours to make. What distinguishes UiPath for me is that AI stocks have a way of sneaking upward in price. We saw this with Nvidia during the tech rout as Nvidia (the AI bellwether) has performed well this year compared to other leading tech stocks with strong earnings.

Pictured above: Nvidia, the AI bellwether, has outperformed other leading tech companies this year. Little does the market know, that AMD is also becoming a force in AI with the Xilinx acquisition.

What is RPA – Macro Overview

Robotics process automation has many supporting macro statistics because its essentially machines replacing humans. The ROI is astounding when you have an error-free employee who works 24/7 and does not tire or need bathroom breaks. To illustrate, a few automations can save 20 minutes of work per person daily and enabling 10K employees with a software robot could save more than $30 million a year (based on an average salary of $35/hour).

There are many fears that RPA will eliminate jobs to the detriment of society. Proponents say this isn’t exactly true, rather RPA will eliminate menial and low satisfactory tasks. According to analysts like Forrester, 14.9 million jobs will be created by 2027 to work alongside robots. It’s not clear though how many jobs robots will replace and if the 15 million is actually a deficit.

According to McKinsey, $3.6 trillion of work can be automated. The piece of the pie that UiPath is after is the automation of applications for enterprises. The number of applications deployed by enterprises has increased by “approximately 70% over the past four years,” according to Wall Street Journal.

The 10,000-foot view of what RPA solves is that interoperability of applications is cumbersome with “a compounding effect on the complexity of business processes” and work done by IT departments.

According to McAfee, the average enterprise has deployed 464 custom applications and deploys an additional 37 new applications in a 12-month time span. Companies with fewer than 1,000 employees run 22 custom applications while companies with over 500,000 run 788 custom applications, on average. The majority of these applications (58%) are used internally while 36.2% are used by customers, partners and suppliers. These larger enterprises – with the 788 applications on average — are the companies that UiPath is targeting.

Source: McAfee, 2017

In the United States, real output per hour grew 31% during the decade ending December 2009 while it grew 13% in the subsequent decade through 2019. UiPath believes this decline in output is due to the overwhelming number of applications and software within enterprises.

This is partly because applications are specialized and are not able to address the end-to-end processes that enterprises require. The concept that UiPath proposes is to automate those steps and have a human review the exceptions rather than every detail of every order.

Product Overview:

It’s important to start with product for a company like UiPath – and most companies in tech, really – because without knowing what the special sauce is, companies can get lost in the noise. UiPath is a platform that allows companies to run software bots that process automations. What separates UiPath is the use of AI computer vision to read information, hence having the acronym “UI” in the name, which stands for user interface. The company also leverages machine learning to think and process the information and robotics process automation (RPA) to interact with applications.

The combination of computer vision and machine learning is UiPath’s special sauce. The AI-based computer vision increases the reliability of automation. The AI-based computer vision is able to adapt and interpret varied document types and user interfaces. This is the missing piece in automation that other forms of orchestration or choreography do not have.

The key sentence in the S-1 filing is this: “Our platform enables the reusability and reliability of UI elements by capturing them as objects in a repository.” This means that the AI computer vision is able to dynamically recognize and interact with variables and dynamic objects or applications. In plain terms, it means UiPath can emulate a human by responding to variables the way that humans can. (Anyone working on autonomous vehicles will tell you, the issue with full autonomy is the variables, not the mechanics of driving).

UiPath’s architecture is UI-based orchestration. This increases the reliability of the automation as it’s able to adapt and interpret varied document types and UIs. As stated, the platform captures UI elements as objects in a repository.

To compare, here are other ways automation and/or integrations are handled:

· Integration orchestration: When on-prem wants to integrate with SaaS platforms, companies like MuleSoft and Webmethods offer third-party connectors. This is called Integration Platform as a Service (iPaaS)

· Business process orchestration: Offers business processes a central process, yet requires human intervention.

· API based orchestration: Lacks a central component and is event driven

· Event driven architecture: Event driven to where the events are autonomous through choreography rather than orchestration (the difference being that orchestration requires a composer while choreography establishes a pattern that does not require supervision).

Source: Solace, Microservices

UiPath recently acquired Cloud Elements to add API-based automation to its core offering of UI-based automation. This is the first time the combination will be offered in a single platform. This places UiPath on the same playing field as the orchestration methods listed above, yet with the combination of computer vision/UI-based orchestration. This acquisition takes aim at that market share by providing the best of both worlds. The acquisition brings 200 new native integrations to UiPath

Why is UiPath Better?

The next question to answer is why is computer vision/UI method better? The first is that UI-based automation is not confined to specific APIs. The result of using computer vision (and the other components that I review below – but let’s keep the focus on computer vision for now) is that UiPath is an end-to-end solution rather than a point solution. The goal is to automate the process, not the API, and other orchestrations lack the ability to automate across many applications and link AI capabilities to execute. Without computer vision, the end result will not be human emulation.

The company points out in the S-1 filing that the typical AI/ML environments are developed by data scientists yet need to be used by other departments that carry the processes out (billing or customer service, for example). My takeaway on this is that the other methods for integration and automation do not necessarily cut down on the number of people required and/or does not reduce the technical abilities required to work with the automations. By requiring data scientists to be the central and only hub, end-to-end automation is not possible.

The modular setup is also an advantage. Solutions can be integrated into new, third-party technologies for future development.

When we talk about robots, we are talking about software robots that are on a desktop computer, can work across programs in the background, are able to build applications, send emails, and interact with chatbots. This is achieved with this build:

· AI computer vision can dynamically recognize and interact with variables and dynamic objects and applications

· AI-enabled platform helps identify which processes should be automated including interoperability with 75 AI technology partners

· Document Understanding leverages optical character recognition (OCR) and natural language processing (NLP) and ML to handle processes with humans handling only the exceptions

· Low-code Development drag-and-drop tools to serve a range of technical skills

· Governance and Security ensures compliance

Product Specifics:

UiPath is an expensive product and this is reflected in its customer concentration at the enterprise level. There is a Community Edition that is free, which is a smart way to onboard more developers at the student level.

Studio is UiPath’s integrated development environment (IDE) that allows access to the Automation Cloud. There are three variations: Studio, Studio Pro and StudioX. The difference is what technical level the user has with StudioX requiring very little skills (i.e. “low code”) with drag-and-drop while StudioPro requires Advanced skills.

Automation Hub:

Automation hub allows for central management of the automation pipeline. It’s a command center to see and control the end-to-end system. It also allows the administrator to visualize automation complexity and understand the impact and ROI.

Process Mining:

Process mining taps into a data source from enterprise applications and makes use of this event data. The goal is to streamline processes to become more efficient. For instance, if your goal is to improve customer retention rate, then you can track how long customer service responses take, delivery rates, and what is causing delivery problems so you can address the situation. Process mining also helps you identify bottlenecks that can benefit from automation.

Process mining changes all this by tapping into a data source that already exists. This is done through the ETL “extract, transform, load” process. Most of your enterprise applications (like SAP and Salesforce) record every activity and transaction that happens within each stage of a process. This is called event data.

Business processes suitable for process mining include accounts receivable, claims and accounts payable. In financial services, it can be used for loan approval, risk and investment management or fraud. In health care, process mining can be used to reduce paperwork and streamline processes like the spike in demand for testing we saw during Covid.

Task Mining and Task Capture:

Task capture allows for the mapping of business workflows. Employees can record the process they want to automate and Task Capture will gather data for each step. The software generates a process map into a file for the development team to use to create automations.

Task mining will have its public launch in 2021 and will allow enterprises to record work performed by users across a list of applications.

Business Model and Automation Flywheel:

UiPath benefits from a flywheel effect. The reason that a flywheel effect occurs is because when companies use UiPath to add robots, they see a substantial return on investment, and then deploy more robots.

The company is built and ready to scale with flywheel effects as UiPath can be customized for every need of the enterprise. The robots are designed to work in any environment (cloud, hybrid, on-premise), for any level of technical ability (low code to advanced code), is licensed through subscriptions annually or multi-year, and can work alongside a human or be fully automated, is additive, and can be used as a unified solution or individually (that’s a mouthful).

The point is that UiPath is prepared to offer a solution for any customer need and to scale as the needs of the enterprise changes.

If a picture is worth a thousand words, then perhaps this helps illustrate the flywheel effect:

The graph above shows that the 2016 cohort of customers have increased their ARR from $395,368 to $22.7 million in a five-year time span. This is a multiple of 57X. The company’s top 50 customers have grown ARR by 81X. This is measured by the ARR generated in each customer’s first month as a customer.

There are some examples in the S-1 filing that show up to 69X increase in customer ARR within one year. That’s an outlier with others increasing 32X, 6X and 4X in one year. Even the lowest number here is impressive, and is driven by cross-department sales, increase of robots per employee, increased adoption across products, and expanded use cases.

Partner Program and Developer Ecosystem:

Partner programs are especially important for a company like UiPath as it can help to extend business models and also help to scale a product very quickly. It’s also important for global growth across various regions. UiPath is the automation back bone to many other products that you’re familiar with: Microsoft, Google, Amazon Web Services are integrated with UiPath so their cloud computing customers can utilize cloud-based AI capabilities. Other integrations include Adobe, Alteryx, Oracle, Salesforce, SAP, ServiceNow and Workday.

UiPath is unique in that it’s tailored to citizen developers who prefer low code and also advanced developers. Right now, the company counts 750,000 registered developer accounts. There is a marketplace with 1,200 vetted and pre-built automation activities that can be used for enterprise workflows. UiPath says there are 10,000 downloads per month in the Marketplace.

Market Size and Valuation:

I expect that UiPath will trade at a high valuation into the foreseeable future. This is because of the specific trend the company is capturing. I think individual investors can get lost in the noise of popular stocks, but I don’t think institutions fall prey to this as easily. You’ll notice the companies that are favorites among retailers are not favorites among institutions. That’s one reason we run this site, is to bring to your attention to companies that you won’t want to miss out on.

What about sector rotations, like we just had? Even still, companies like Snowflake retained their position of highest valuation, comparatively speaking. It went from a jaw-dropping 80 forward P/S in February to about 50 forward P/S – yet this company still led the pack of cloud software valuations.

We typically don’t buy above 40 forward P/S (you can reference when I discussed this during heightened exuberance in this Motley Fool video). We don’t know UiPath’s forward valuation until we see more analyst consensus numbers come out. The earnings report on June 8th will help quite a bit. However, if we adjust the revenue growth to a reasonable 65% forward, then we see UiPath trading at 41X.

Please note, this is calculated on 65% revenue growth next year, which is an educated guess. We will know more about UiPath’s forward estimates in the coming weeks.

If we take the LTM valuations, we see UiPath stretching the upper limits again. LTM is important here since UiPath’s forward is not available yet.

The I/O Fund does not give financial advice and highly valued tech companies require an appetite for risk. We simply tell you what we are doing with our own money. We bought UiPath starting last week after the blog notification went out and we will be watching it closely near the lock-up expiration as to whether we need to exit and enter again. This extra work is worth the long-term trajectory that robotics offers us as tech investors.

The reason UiPath is unique from the others on this list is because the opportunity for RPA is fully in front of the company, whereas many of the others are centered in a trend that is in motion.

I also think UiPath will continue to take more market share relative to the RPA market, and the key metrics around increased ARR help prove this as they are a bit mind-blowing. Other than ARR, I can’t quantify exactly why I think UiPath will take more market share other than it has a solid reputation and there is a buzz around the company that is hard to communicate. People are pleased with UiPath, they’re upgrading and buying more, and it’s becoming the company that everyone (who is anyone) needs to partner with.

Market Size:

There are a range of estimates on the size of the Robotics Process Automation market.

According to the S-1 filing, IDC places a $17 billion value for 2020 to reach $30 billion by 2024. Meanwhile, UiPath states in the S-1 that the “fully automated” enterprise is a market of $60 billion. There is also a reference in the S-1 to an estimate from Bain & Company placing the market for automation software at $65 billion.

Forrester states there are 1.69 billion knowledge workers globally. So, that’s helpful to picture TAM.

Third-party analysts are more conservative – no surprise there as S-1 filings usually publish the highest numbers available. According to Statista, the market will be worth $10 billion by 2023.

Global Market Insights places the market at $23 billion by 2026. Grand View Research states the market will reach $13.74 billion by 2028 at a CAGR of 32.8%.

That’s quite the range and is tough to extract a real market size from these numbers. However, there is agreement across the third-party sources that the robotics process automation market (specifically, RPA only) was $2 billion in 2020. This means UiPath owned about 30% of this specific market at $600 million in revenue. If we take the $10 billion Statista is putting out there, then UiPath can see a path to $2 to $3 billion in revenue by 2023 in RPA specifically. I always give room in these market growth estimates, by the way, so 2023 should be considered 2024 by Statista. It’s hard to nail down market growth with adoption in tech products.

Financials:

Please note, UiPath’s fiscal year ends January 31st.

We covered the financials here in a short write-up.

The good news about UiPath is that as the top line improves, the bottom line is also improving. Revenue growth from $336 million to $608 million, represents 81% growth for fiscal 2021. As mentioned in the valuation section, we won’t have a complete picture on forward growth until the upcoming earnings report on June 8th. Right now, I’m assuming we will see growth this year in the 60-percentile range. This is a guess so I will update you when we get real numbers from the earnings and analyst consensus reports.

In the past, operating margins have been an issue for the company with an operating margin that was triple digits in the red (154%) in 2019. The company’s current operating margin is (18%). Adjusted operating margins were (113%) and (4%), respectively.

The net losses have also improved from ($520) million in 2020 to ($92) million in the current year. The fiscal year 2020 losses were at (155%) of revenue compared to (15%) of revenue in the current fiscal year.

The bottom-line losses were partly driven by sales and marketing costs which were at 144% of revenue in fiscal year 2020. Surprisingly, R&D is low at 39% of revenue in 2020 and 18% of revenue in 2021.

The current gross margins of UiPath are at 89% which is among the highest in the software industry. This has been consistent with 82% margins in fiscal 2020. Free cash flow is at 4% of revenue, or $30 million in fiscal 2021.

The company is an enterprise-focused company and is well utilized. As of last year, the company counted 80% of the Fortune 10 and 61% of the Fortune Global 500 as customers. This grew to 63% of the Fortune Global 500.

As stated, the main business model is to increase spend per customer as enterprises will deploy more robots across more departments, increase number of products used, and expand use cases for automation. That’s important to repeat because typically this high of penetration could actually be a headwind for growth.

The dollar-based net retention rate for the company was 153% in 2020 and 145% in 2021. After subtracting churn, the gross retention rate is 96% and 97%. With that said, this metric is becoming less meaningful the more cloud and subscription-based companies come on the market. We have many in the 130 to 160 range and it’s similar to checking vitals – we want to know the company is healthy but it doesn’t tell us much about the nuances of longevity.

The company has a NPS rating of 71, helping to illustrate a high level of customer satisfaction. Despite serving large enterprises, including 8 of the Fortune 10, UiPath does not have any customer making up more than 10% of revenue.

Conclusion:

We finally have the AI pureplay we’ve been waiting for in the software category. This company is likely to be valued high into the foreseeable future due to the attractiveness of the trend (robotics). We feel institutions will want to participate in this trend with this risk/reward ratio that UiPath offers, which to reiterate, is lower risk on execution due to the excellent end-to-end platform, strong partner program, leading market share and ability to scale quickly across enterprises, with the differentiation of computer vision.

Please look for Knox’s trade notifications as he patiently builds this to become a core position of ours. If you made me choose, (and we do have to choose as we disclose allocations to you), I would place UiPath above Snowflake on conviction. Post-IPO lockup, and pending Knox’s excellent skills in finding the right entry, you can expect to see UiPath in our top 10 by year-end or shortly thereafter. Note that we aren’t rushing into a position at this valuation, rather keeping our toe in the water, and navigating as we see what the market does.

UiPath: LTBH Position – Report Coming Soon

Posted on May 16, 2021June 30, 2026 by io-fund

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UiPath: Robotics Process Automation (RPA)

When I think of artificial intelligence, one of the first things that comes to mind is robotics process automation (RPA). To be clear, these two are not the same – but together, advanced AI skills can be integrated into robots to understand documents including structured and semi-structured data, visualizing screens, and comprehending speech. You can think of RPA as the last-mile delivery for artificial intelligence. In other words, what do you do with AI, ML and NLP – what’s the outcome? A popular choice will be to automate processes with robotics.

I’ll go into greater detail in the upcoming PDF report but the main takeaways from the S-1 Filing are:

· Revenue growth of 81% to $608 million

· Dollar-based net retention rate of 145%, ranking it in the top 5 among public SaaS stocks who disclose this metric

· Gross margins of 89% which are among the highest in the software industry

As the top line increases, the bottom line is improving.

Over its last 12 months, UiPath has a free cash flow margin of 4% to go along with a -18% operating margin. The -18% operating margin is a significant improvement from the -154% operating margin the company recorded in 2019.

UiPath logged a $92M net loss in its last fiscal year, an improvement from the $520M net loss the company announced the year prior.

At the time of its S-1, UiPath had a total of 7,968 customers, exceeding a 70% CAGR in customer growth over the last 2 years. Customers with over $100K+ ARR totaled 1,002. UiPath automates millions of repetitive tasks for an impressive list of customers that includes 63% of the Fortune 500 and 8 of the Fortune 10.

UiPath has an EV/NTM Revenue valuation of 35.9x using its 81% YoY growth run rate. Below is a comparison of UiPath to some other high growth software stocks. PATH currently ranks 3rd among the highest valuations in the software industry.

Caution: IPO lockup periods usually see a decline in price

We’ve stated many times in the past that IPOs are tricky and we tend to not participate. We patiently waited for Snowflake, for example. We did the same on Zoom as we were prepared to find our lowest entry post-IPO.

The reason a lock-up period is followed by a lower stock price, even when the company is fundamentally strong and will go on to make bigger gains, is because some investors need to exit and go find their next big win. These are seed round and Series A investors who made plenty in the public offering and prefer to go find new portfolio companies.

Therefore, if we enter UiPath, we could exit again prior to the lock-up period expiring. This isn’t because we don’t want a position in the company, rather it’s that we need to compete on performance, and to also be transparent so our subscribers are fully aware of how we navigate volatile tech growth.

Maybe UiPath will be the first to hold its opening price after lock-up. In the meantime, I want to give you a heads up in case the I/O Fund initiates before we release the deep dive research.

5 Soon-to-Be Trends in Artificial Intelligence And Deep Learning

Posted on February 5, 2020June 30, 2026 by io-fund

This article was originally published on Forbes on Jan 31, 2020,08:06pm ESTForbes on Jan 31, 2020,08:06pm EST

Artificial intelligence is frequently discussed yet it’s too early to show real gains. AI’s major headwind is the cost of the investment, which will skew returns in the short-term. When the turnaround occurs, however, companies who are making the investment can expect to be rewarded disproportionately with a wide performance gap. In a recent report, McKinsey predicts AI leaders will see up to double the cash flow.

We can see some evidence of this in Alphabet’s revenue segment, Other Bets, which includes many AI projects with a loss of $3.35 billion in 2018. Of this, Deep Mind is responsible for $571 million in losses and owes its parent company $1.4 billion. The autonomous driving project, Waymo, had its valuation cut by 40% due to delays last September.

We see other companies taking on massive and expensive AI projects, such as Baidu, Facebook, Tesla, Alibaba, Microsoft and Amazon. Except for Tesla, these companies are flush with cash and can afford the transition costs and capital expenditures required for artificial intelligence.

Sign up for I/O Fund's free newsletter with gains of up to 403% – Click hereSign up for I/O Fund's free newsletter with gains of up to 403% – Click hereClick here

Despite tech giants pouring cash into AI investments, most of the industries that stand to benefit are not in the tech industry, per se. This week, I attended Re-Work’s Deep Learning and AI Summit, where AI engineers and executives gathered for presentations and discussions about the projects they’re spearheading.

Here are a few ways that AI is slated to make an impact sooner rather than later:

1. Training AI to Know What it Does Not Know

The next decade will determine if humans or machines are better are making a medical diagnosis as more health care companies turn to AI for accuracy. One problem that Curai is working on, is how to train a model to know when it doesn’t know, so a human can intervene to avoid the misclassification of unknown diseases. This approach is known as physician-in-the-loop.

Daphe Koller of Insitro presents on Machine Learning and Drug Discovery at Re:Work Deep Learning and AI Summit

BETH KINDIG / RE:WORK DEEP LEARNING AND AI SUMMIT

2. Reducing Call Center Burden

United Health received 36 million calls in 2017 with 7.6 million calls transferred to a representative. The AI platform solution involves deep learning for a pre-check portal and claim queue, Automatic Speech Recognition (ASR) to translate audio to text, and Natural Language Processing (NLP) for unsupervised clustering, to generate new call variables and automate transfer calls.

3. Retail Giants making Big AI Investments

Retail had a large presence at the conference with Wal-Mart Labs, Proctor and Gamble and Target presenting on ways they plan to make the retail experience more optimized. Perhaps these companies are being more careful to embrace technology and AI after the last decade ushered in many competitors who stole critical turf (i.e. Amazon).

Imagine a shopping experience where the carts are plentiful, cashiers are always open, and inventory is fully stocked. Rather than focus on replacing cashiers, Wal-Mart is more focused on inventory control. This is a different approach than competitor Amazon Go, designed to be cashier-less.

4. AI Could Be the Answer to Restoring Privacy

Privacy has been in the headlines lately as regulators and social media users begin to question what is a fair exchange for a free service. While the battle is nearing two years since Cambridge Analytica, other companies are creating AI recommendation engines so powerful that little information is needed about the person making the choice; their preference is enough to determine what to recommend next.

Netflix is a leader here with its recommendation engine for content. Pinterest also employs a complex recommendation engine to surface the best image for an individual out of the billions of images on Pinterest’s platform. This is done through the process of query understanding to candidate generation to ranking to blending to the final result. In layman’s terms, this is how a discovery engine narrows down choices from billions to hundreds.

5. Prepare to Be Blown Away by AI-Assistants

Over the next few years, we will become hands-free and will have better posture and fewer car accidents. Once AI-assistants are fully built out, our interaction with mobile devices may become the brunt of criticism from future generations. Many companies are working to own this space as the ecosystem lock-in and data produced by AI-assistants will be incredibly valuable – expect a full-fledged battle between Amazon, Google, Facebook and Apple in this space.

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.

Dynatrace Premium Research

Posted on January 21, 2020June 30, 2026 by io-fund

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Dynatrace Premium Research

Dynatrace

Dynatrace is a premium application management software company that ranks high on product evaluations by Gartner and Forrester. The company claimed 8.8% of the APM market in 2018, placing the company in third place behind New Relic at 10.3% and Cisco’s AppDynamics at 11.2%. The company is ahead of IBM at 8% and Broadcom at 7.6% of the APM market.

The all-in-one cloud platform is priced higher than other APM products and is sold as a package rather than as separate modules. The offerings include real-time topology and AI algorithms to monitor applications, infrastructure and business operations.

In 2016, Dynatrace launched a full-stack cloud monitoring platform. The platform is now the main driver of growth at 80% of annual recurring revenue, up from 75% of total annual recurring revenue last quarter, and up from 39% a year ago. Subscriptions and services combined make up 98% of revenue compared to licensing at 2%.

Dynatrace is seeing the positive effects of transitioning to the subscription-based model in its operating margin. The non-GAAP operating margin grew from 13% in fiscal Q1 2019 to 22% in fiscal Q1 2020, ending in August. The most current non-GAAP operating margin in fiscal Q2 2020 was 23%.

Dynatrace is focused on large, enterprise accounts with greater than $750 million in annual revenue. This is reflected in the company’s average account totaling over $200,000 ARR per customer.

Product

Please refer to the Datadog Premium Research report for more information on the APM market including information on competitors New Relic and AppDynamics. t for more information on the APM market including information on competitors New Relic and AppDynamics.

Dynatrace’s product road map is geared towards exceeding Cisco’s AppDynamics and New Relic in AI-powered analytics, such as self-learning AI, real-time discovery, automated problem remediation and the use of AI chatbots.

Full-stack observability is another area where Dynatrace stands out. Rather than offer infrastructure monitoring or application monitoring separately, the company has developed a more comprehensive approach to business observability. In a recent earnings call, Dynatrace stated the company was four years ahead of the competition in full-stack observability, which helps return business value to its customers.

In a sponsored case study, Dynatrace returns up to 311% ROI over three years to its customers with the investment paid back in six months.

Hybrid cloud

Dynatrace’s product roadmap includes expanding into multi-cloud and hybrid cloud and using purpose-built AI to perform root cause analysis faster.

Hybrid cloud is a technology that enables companies to store some of their data on their own servers while simultaneously sending other data to the private and public cloud. Companies prefer hybrid cloud because it is cost-efficient, transparent, and safe. Hybrid essentially helps to push many companies off the fence in deciding between cloud and on-premise.

According to a recent study, 76% of companies are committed to hybrid cloud. This is the main catalyst for why Microsoft Azure has gained in popularity against the heavyweight Amazon’s AWS as Microsoft set out to specialize in hybrid cloud in 2016. (I’ve hammered this point home a few times on Microsoft). It’s important to pay close attention to this trend as hybrid will be a driver for the remaining growth in cloud.

Fundamentals

As stated, Dynatrace is reporting 23% non-GAAP operating margins since moving to an all-in-one cloud monitoring platform. The company is profitable on a non-GAAP basis at $0.06 in the most recent quarter, yet has reported negative GAAP EPS of -$1.58.

Total revenue increased 27% year-over-year in the most recent quarter with subscriptions and services growth exceeding this at 37% YoY and annual recurring revenue increasing 44%.

The question that is worthy of speculation, is if the subscription growth of 37-47% is going to pick up the overall revenue growth in the forward year as the Dynatrace platform eclipses the classic products at 80% versus 20%. Subscription and services also far exceed licensing at 98% versus 2%.

The company is projecting full year fiscal 2020 revenue to be between $533 million and $535 million. This is up from $431 million in fiscal 2019, or 24% growth.

As of now, the company is not projecting the kind of rampant growth that the more popular cloud software stock report although revenue growth has been steadily increasing since the 2016 product pivot. Revenue growth was negative from 2017-2018 as the company absorbed the transition and was at 8% year-over-year growth from 2018-2019.

The CEO believes the company is in the sixth quarter of a 10-12 quarter transition from the licensing model to the subscription model. The dollar-based net expansion rate of 140% is well above the cloud software benchmark, which is a very good sign for future revenue growth. This is higher than any cloud software subscription company that reported net retention in 2018 with the previous leaders being Smartsheet at 130% and Alteryx at 131%. Netdollar expansion rates measure whether the growth from the existing customer base offsets any losses. Typically, these numbers will decline over time. With Dynatrace, the number has increased due to the pivot to cloud platform.

Non-GAAP operating income is expected to be in the range of $119 million to $121 million. This will put non-GAAP EPS at $0.23 to $0.24 for the fiscal year ending in March.

There was nearly $1 billion in debt on the balance sheet, but this has steadily improved over the last year with the help of the IPO. Following the public offering, which produced $590 million in net proceeds, the current debt balance is $540 million. Cash flow for fiscal Q2 was $27.2 million, and $174 million on trailing 12-month basis.

According to Dynatrace’s S-1 Filing, the addressable market is $18 billion. Gartner places the addressable market for global IT operations software at $29 billion with compound annual growth rate of 6.7% to $37.5 million in 2023.

Notable Price Volatility & Upcoming Lockup

Expiration

Dynatrace launched in 2006 and raised $22 million before Compuware bought the company in 2011. The private equity firm, Thoma Bravo, bought Compuware for $2.5 billion in 2014 and spun Dynatrace off as a private company after merging Dynatrace with Keynote, another APM company in Thoma Bravo’s portfolio.

After the company went public in August, Thoma Bravo reduced its stake from 71% at the IPO to 61% over the course of a week in December. The stock price fell 12% during this time with the offering from Thoma Bravo of

27.5 million shares.

Most importantly, the company’s lock-up period will expire on January 28th. The company reports quarterly earnings the following day on January 29th. One of the current trends in this IPO market is for lock-up expirations to result in a short-term drawdown in stock price.

Technical Analysis

By Knox Ridley

Just under 6 months ago, Dynatrace (DT) listed on the NYSE at $16. Following the recent, hot IPO trends, DT closed on the first day of trading 49% above its IPO price at $23.85. Notably, shares opened at $25.50.

However, unlike the broad market that continued to rally in the back half of 2019, DT then began a 36% drawdown that bottomed just above its IPO listing price at $17.13.

Basic Technical Analysis

Using basic Technical Analysis, we can follow the initial downtrend with the price and MACD using the downward sloping, blue-dashed lines in the chart above. The MACD signaled long before the bottom that the momentum was fading.

Notice the green arrow sloping up on the chart. As the MACD was making higher lows, price was making lower lows. This is the type of positive divergence that we see before a bottom. The trend reversed when both price and the MACD broke through the blue downward sloping lines shown on the chart, as DT began to make higher highs and higher lows for the first time since going public.

Since then, Dynatrace has been in a standard uptrend. It’s worth noting that DT broke above the all-time high at $26.90 this year. This is a sign of strength that we want to see prior to initiating a long position.

Just like on the way down, we can use the same trendline tools to gauge the health of the current uptrend. The MACD signaled weakness prior to the actual recent top. As the price was making new highs, the MACD was making lower highs, which is a sign of fading momentum. The RSI, MACD and price all followed their own internal trendlines in unison, highlighted in blue. Recently, all three have broken these trends, which is a sign that a possible reversal is underway.

Elliott Wave Analysis

Using Elliott Wave, we can get a clearer idea of what the structure of Dynatrace is telling us. The initial downtrend followed a standard 3-wave structure, where the C wave unfolded in a final 5-wave impulse before bottoming. Since a reversal at $17.13, the uptrend broke out to new highs, which takes a larger degree downtrend off the table for now.

We have a clear 5-waves up off the bottom, which is highlighted by the green roman numerals. These waves coincided with the standard Fibonacci levels that make up a 5-wave uptrend – e.g., 3rd waves typically break at the 161.8%extension of waves 1 and 2, and the 5th wave terminates around the 200% extension.

This would now put us in a larger degree 2nd wave, which will be confirmed if Dynatrace breaks through $26.50 level. Above this region, and DT could press higher, extending for its final 5th wave towards the $30 region before we see a larger degree pullback.

If Dynatrace breaks support to confirm the 2nd wave scenario, I’ll look between the 38.2% retrace level, around $25, and the 61.8% retrace level, around $22, to initiate a position.

If this is the structure we are dealing with, expect the 3rd wave up to take us to new highs and beyond. However, if DT closes below $19.25, I will consider this a failed impulse and stop out of any long position.