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.

Special Report: The New Kings Of Tech

Posted on June 6, 2022June 30, 2026 by io-fund

The FANG acronym rose to popularity in 2013 and was extended in 2017 to FAANG to include Apple. If history is any indication, the world’s most valuable companies over the next ten years will not look like the previous ten years. Being early to identify which companies can take over this coveted status is how generational wealth is built.

As a tech industry analyst who has seen what one generous winner can do for an entire portfolio, I want to pause and acknowledge that an investor needs to only identify one company that can hold a top 5 position in order to see life-changing gains. To choose all five would be to defy incredible odds. This analysis is aimed at identifying what companies we believe will hold “world’s most valuable” by 2030.

Not only is tech the most valuable industry today, but what the tech industry is setting up to do over the next ten years will provide exponential gains compared to the 2020-2030 era. With that said, tech is going through a period of consolidation and this means the stakes are high in identifying the winners. To complicate matters, the market is not efficient with tech stocks as each product is quite nuanced and impossible to efficiently price without manual deep dives. Instead, the market will indiscriminately penalize all tech and indiscriminately reward all tech — and each time the liquidity tide rolls in and then out again, it becomes sink or swim. At times like this, we are very flexible as we know we only need to identify a handful of winners.

Our portfolio has twenty positions at any given time, yet we believe it will be 5-10 positions total that create 90% of our wealth by 2030. We are long-term buy and hold investors yet we acknowledge and accept this means we exit those who show weaker-than-expected results for more than one quarter — or we trim to 1% to hold a place for the stock in our coverage. Because we are flexible, we can always revisit a stock when the story resumes and the earnings match the thesis again.

The equity market is driven by sentiment and macro factors, which we expand on herewe expand on here, yet the underlying strength of tech fundamentals is hard to deny. The best way to predict what will happen next is to look closely at what happened over the past decade.

In 2007, following Steve Jobs famous iPhone keynote, a burgeoning app economy was driven forth by iOS and Android developers. Google’s search engine was already a success yet mobile catapulted it’s use by putting the mobile device into far more hands over three years’ time than personal computers did over two decades.

There were many ways to capitalize on this massive addressable market — the iPhone and iOS apps dominated the highest spend on mobile, Facebook proliferated to 2 billion people and Google expanded to acquire YouTube. In this way, mobile drove gains for three FAANGs.

The adage is that history rhymes but it does not repeat. I believe a large addressable market is certainly required to produce the new wave of FAANGs – however, rather than consumer driving the gains, I believe it will be enterprises.

Below, I discuss the enterprise-level market that will be four times larger than mobile and two stocks that will directly participate. Imagine participating in 4X the FAANGs by 2030. That’s what I believe will happen due to one key trend and I discuss exactly why this will be achieved below.

Later in the analysis, we look at cloud and the trends that will drive cloud over the next ten years. This will catch investors who are complacent off guard as cloud is already going through a period of consolidation and we are seeing new business models emerge, such as the consumption model whereas the SaaS model with annual recurring revenue has dominated the past decade. Microsoft helps prove that cloud is certainly capable of FAANG-status.

We will also look at blockchain and crypto as I have been covering this space since 2013, which predates the Ethereum network. I was trained in Silicon Valley and my role is to introduce public investors to the next wave of innovation in as safe a manner as possible. I agree that 90% of cryptos will go to $0 yet I/O Fund has been firm for years that Bitcoin would reach $1 trillion in market cap and we have two Layer 1 networks to discuss with you plus a middleware company. Whether you’re ready for it or not, Web3 will replace the internet by 2028-2030 and we are fully prepared to participate in the substantial gains the blockchain will produce.

Lastly, FAANG is not entirely dead and consumer will have its moments too. We discuss the two FAANGs we own and what major catalysts these companies have in their future. We also briefly touch on some consumer-facing stocks we own and the large addressable markets they have the potential to capture.

For those of you who are new to the I/O Fund, we are prolific in our analysis. I began writing analysis on products, startups and enterprise-technologies in 2011 and moved over to the public markets seven years later. There is a library of analysis available to you that dates back to our launch in 2019 and additional free analysis in 2018. Due to the sheer number of products I have analyzed, we are able to hold an all-tech portfolio across semiconductors, cloud, ad-tech, blockchain and more.

We also encourage you to sign up for trade alerts as Knox’s active tradesKnox’s active trades help frame the market and whether we are risk-on or risk-off. We also have an automated hedge signal and are audited annually. You can learn more about how we manage an all-tech growth portfolio here.

Yet, the investors on our site need to do their part, which I can summarize as the following:

Speak with your financial advisor about your risk level. We are not financial advisors. Instead, we simply show you the trades we are making with our own money.
Use our pie chart to view our allocations. The larger the allocation, the higher the conviction – and vice versa, the lower the allocation, the lower the conviction.
As stated, we are flexible as we expect a handful of companies to drive the majority of our gains. If we receive new information, we will manage risk accordingly by lowering allocation or exiting the position.
We firmly believe all tech investors need a long-term time frame for tech. The best tech investors in the world are venture capitalists and they seek an exit 5-7 years after they’ve funded a round. The reason to have a long holding period is that it’s nearly impossible to time an entry, therefore professionals instead time their exit. By having a long-term horizon, you can be patient until the market conditions are in your favor to take your exit.
Accept that tech is volatile. For example, high beta tech has sold off around 40%/year since 2018. I have been down this much and more so on positions that became 1,000% and 2,000% winners. This is not value investing, it’s an entirely different sport.
We have a proprietary hedge that we developed. The hedge went live in April and is designed to help us remain comfortably invested during drawdowns. You can learn more about the hedge here and watch the webinarhedge here and watch the webinar.

Without further ado, let’s talk about who will be the world’s most valuable companies in 2030

Artificial Intelligence and Machine Learning will Exceed the Mobile Economy

Smartphones had a 10-year cycle of maturation with the iPhone distribution beginning in 2008 and the app economy had a similar 10-year maturation for digital advertising. We know mobile is reaching saturation as the iPhone often has flat quarters and Facebook’s DAUs are also flat. Following a decade-long run:

The smartphone market was valued at $720 billion in 2019 and the global mobile application size was $155 billion.
The mobile advertising market was valued at $60 billion — Facebook
The total global ad spend worldwide is valued at $560 billion — Google

The market is roughly $2 trillion for mobile yet the market cap of these companies combined is $4 trillion. Meanwhile, Pricewatershousecooper is predicting the AI market to reach $15.7 trillion with some believing AI will be the next electricity. Semiconductors will not comprise the entire $15.7 trillion but according to McKinsey, they will “capture 40 to 50 percent of the total value from the technology stack.”

“Many AI applications have already gained a wide following, including virtual assistants that manage our homes and facial-recognition programs that track criminals. These diverse solutions, as well as other emerging AI applications, share one common feature: a reliance on hardware as a core enabler of innovation, especially for logic and memory functions.”These diverse solutions, as well as other emerging AI applications, share one common feature: a reliance on hardware as a core enabler of innovation, especially for logic and memory functions.”

The artificial intelligence economy will be four times larger than the mobile economy. Put differently, mobile gave us companies with up to $2 trillion market caps and AI will give us companies with $8-$10 trillion market caps. Let’s break this down.

The size of total addressable market is critical to produce the world’s most valuable companies. FAANG companies have illustrated this well and that the private markets base nearly every investing decision on TAM.

Pictured above: Google’s search engine revenue growth from 2008-2022

Google’s search engine is used by over 4 billion people
Android is used by 2.5 billion people and YouTube 2 billion people.
Facebook is at 2.9 billion users
Apple has 1.8 billion active devices (about 1 billion iPhone)
Microsoft Windows has 1.4 billion users and MS Office has 1.2 billion users = Microsoft coming from the dot-com era shows us that mobile produced much larger addressable markets across three companies compared to the previous decade.

However, not only will AI semiconductors power the accelerated computing and the training and inference that reaches every person on earth, it will ultimately power the automobiles, the streetlights, vehicles, refrigerators, factories, cities and spaceships. This will extend the addressable market beyond the 7 billion global population to reach 100 billion connections. Now, imagine this – it will be writing the software too and running the machine-to-machine automations.

View my Bloomberg appearance here where I discuss the AI market being 4 times larger than mobile.Bloomberg appearance here where I discuss the AI market being 4 times larger than mobile.

Here is a glimpse of what AI will do for GDP in each country:

Source: AccentureAccenture

AI is expected to nearly double GDP in the United States by 2035 and across Europe and Japan. The same study shows the American worker will increase productivity by 35% due to AI.

Accelerated Computing Required for Artificial Intelligence and Machine Learning will Produce Two (New) FAANGs: Nvidia and AMD

Accelerated computing is a term first used by the gaming industry when graphic accelerators were put into use to accelerate the work of a central processing unit (CPU). Nvidia created GPUs to offload tasks from CPUs for rendering 3D images. The CPUs provide the instructions while GPUs perform multiple calculations from streams of data. Parallel processing became a natural fit for the data center including training artificial intelligence and deep learning models due to processing multiple computations simultaneously.

Please reference the additional resources below where we have done previous deep dives on every company mentioned in this summary.Please reference the additional resources below where we have done previous deep dives on every company mentioned in this summary.

Nvidia’s Moat is Called Cuda:

Nvidia has a parallel computing platform and programming model called CUDA that is universally known due to the company’s first mover advantage in GPUs. The GPUs themselves do not create the moat. The compute platform creates the moat. Universities teach CUDA, which helps strengthen the universal platform for building GPU-accelerated applications as students graduate with this universal skill.

Hardware does not offer a moat. The iPhone was not the moat. Instead, it was the strength of the developer ecosystem that propelled Apple to become a $2 trillion company. The moat that Apple has enjoyed was created by the third-party developers who created iPhone applications in C and C++ with XCode, which made the device more attractive due to the mobile app ecosystem.

Android then became the second operating system in the mobile duopoly. Due to the friction of learning too many languages, the mobile ecosystem did not entertain any further competitors. This is despite there being 4 to 5 billion smartphones globally (i.e. it’s certainly feasible from a consumer supply/demand view point to entertain more operating systems and app stores), yet the limitation came from the number of languages developers are willing to learn. Microsoft Windows failed because it launched too late, and developers had already chosen the two languages they were willing to work with.

Developers create a moat because they don’t want to learn new systems – the cost of time, especially when bringing products to market is very valuable. For instance, AI startups are not going to shop a new software layer to program GPUs right now as it’ll slow down their time to market and it’s critical to launch products quickly. If there’s a competitor to Nvidia and the startup is already developing on the CUDA platform, then it better be a heck of a value proposition.

Nvidia’s Game Changer Was the A100 GPU:

In 2019, I had already stated to our premium research customers that Nvidia would become one of the world’s most valuable companies. However, the path became clearer in 2020 when the company released the A100 GPU which combines both training and inference onto one chip. The result is a 20X performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offers the largest leap in performance to date over the past 8 generations.

Note: Reference the resources below for more information on the A100 GPU including our coverage in 2020.

Fast forward, and nearly two years later after the A100 GPU launched, Nvidia had this to say in the most recent earnings report:

“[Data center revenue] doubled year-over-year. and we're seeing really strong adoption of A100. A100 is really quite special and unique in the world of accelerators. And this is one of the really, really great innovations as we extended our GPU from graphics to CUDA to Tensor Core GPUs. It's now a universal accelerator […] And so from database queries to data processing, to extraction, and transform and loading of data before you do training and inference and whatever image processing or other algorithmic processing you need to do can be fully accelerated on A100.”

Buried a bit deeper into the previous earnings call, management stated this: “The flagship NVIDIA A100 GPU continue to drive strong growth. Inference-focused revenue more than tripled year-on-year.”Inference-focused revenue more than tripled year-on-year.”

These are the kinds of critical moves we try to get in front of by covering the A100 GPU at its launch. Two years later, and we see management saying inference revenue has tripled and data center revenue doubled due to this specific product.

View my interview on TDAmertrade where I discuss Nvidia’s data center segment and why automotive will be strong in the second half of 2022.my interview on TDAmertrade where I discuss Nvidia’s data center segment and why automotive will be strong in the second half of 2022.

AMD: The Dark Horse of AI Chips

The Dark Horse refers to an underdog or an overlooked competitor that emerges seemingly from nowhere to succeed. We believe AMD is a force of nature that the market often underestimates due Intel’s overhyped public relations strategy. Meanwhile, the competitive prowess of Lisa Su has led to the second biggest comeback in the history of the tech industry after she took over AMD in 2014 on the brink of bankruptcy.

Note: We’ve done a 1-hour webinar exclusively on AMD. Reference the resources below.

AMD’s Strength Came from the Zen Architecture

The Zen architecture was introduced under Lisa Su in 2017. These processors are chipset free and fully integrated. Communication between CPUs is done through Infinity Fabric protocols. The result of the new architecture was more energy efficiency and the ability to execute more instructions per cycle.

The company released the second generation of its Zen architecture and this is when AMD started to clearly outpace Intel in terms of computing power, memory and energy use – all at a lower cost. This was due to multi-chip modules that combine a 7nm with a 14nm to use the most advanced technology when and where it’s needed most by leveraging the more mature process node. The L1 cache and L2 cache locations in the core and across the core also helped the company beat Intel on memory bandwidth.

Intel was still producing a 14nm chip with a 10nm supposedly on the way. Essentially, AMD leapfrogged the incumbent with a product that is more power efficient and allows for more cores per chip.

Note: read more about the Zen architecture in the resources listed below.

If technical jargon around chips isn’t your thing, then this is probably the most important line from our original analysis in terms of AMD’s competitive prowess: “It’s estimated that for every $1.00 in Rome chip sales, Intel loses $2.25 on average in Intel Xeon SP sales. The savings are then deployed to buy more Rome chips, which can further depress Intel’s revenue.”

We can clearly see AMD taking market share in server CPUs although losing ground in desktops and laptops (our thesis is server market share so that’s less important to us). Notably, overall CPU market share for AMD is up.

Most importantly, look at where AMD was when it launched the second generation of Zen (roughly 2%) to today (roughly 11%) market share – or nearly 6X from this major design win. Moving forward, Intel will need to deliver a 7nm chip – but by then Lisa Su will already be releasing a 5nm design.

As the analysis below points out, Intel needs to make up for lost time, meanwhile, Lisa Su is unlikely to allow that now that AMD has clawed its way back from near-zero. Our site was early to AMD overtaking Intel and this was the analysis we chose to publish during the Covid selloff in March of 2020.

Tech investors should pay attention to AMD’s ability to stave off the competitor and the new inroads AMD will have following the Xilinx acquisition. Xilinx’s FPGAs are particularly well suited for accelerated computing yet require an easier software development platform – which I suspect AMD and Lisa Su will fully deliver in the next year.

So far, Lisa Su has simply set the foundation for her company to see substantial AI tailwinds.

AI Acceleration Goes Far Beyond Data Centers

In the months to come, I will detail for I/O Fund members the additional revenue segments that will cause Nvidia and AMD to catapult their current market caps. The data center does not even scratch the surface.

Primarily, these companies will participate in the lion’s share of AI acceleration in the automotive industry, edge computing and edge devices, 3D virtual worlds and robotics simulation, industrial automation, software automation — and probably most crucially, why the leading hardware companies of today are moving into licensing software and why that will cause an eruption in revenue for these particular hardware companies. Look for this special report before next earnings season.

Before I move onto cloud, I’d like to mention that we hold two more semiconductor positions – Marvell and Lam Research. We foresee holding these companies for the long haul and linked resources below spell out why we’ve chosen these two names out of the hundreds of semiconductors on the market.

Nvidia Resources:

AMD Resources:

Marvell Resources:

Lam Resources:

Lam Research Analysis: 2021/2022 Update

2030 Cloud Companies Won’t Look like 2020 Cloud Companies

Tech is synonymous with innovation, and consequently, innovation is synonymous with the word change. This is why winning cloud investments from the past ten years will not look like the next ten years. Cloud has treated investors well, yet cloud is going through a transformation that will shake up the previous paradigm. The previous paradigm was one where most cloud stocks were successful, and was distinguished by easy cash. Now that cash is tighter, there will be fewer winners in this category. We covered the fundamental change to cloud’s bottom line here in: Compartmentalizing Cloud Stocks.

Cloud: Only the Strong will Survive

In 2010-2021, the public markets saw hundreds of cloud companies go public. Yet anyone with a decade or more experience in tech will tell you that consolidation eventually will come knocking.

Consolidation is a natural part of the tech industry where competitors become acquired or they merge with stronger companies to avoid failing entirely. This helps a small minority to emerge as the defacto leaders. I believe that cloud companies will survive either through consolidation or standardization, which means cloud companies that have evolved to serve more than one market, which in turn helps drive down costs.

Let me illustrate:

Recently, a report came out that repatriation, or moving some workloads back to on-premise, has resulted in quite a bit of cost savings for companies like Dropbox, Crowdstrike and Zscaler, who use hybrid approaches. The report is quite surprising as the conclusion is that $100 billion to $500 billion in market value is lost on cloud deployments in terms of margins. One use case that is detailed is Dropbox, a company that reported savings of $75 million in two years after repatriation, which in turn, helped the company’s gross margins increase from 33% to 67%.

The problem with cloud is that it’s not uncommon for companies to spend about 60% of their revenue towards committed cloud spend. The solution is aggregating services and products to drive down costs.

Two companies we own that offer standardization are Datadog and SentinelOne. Standardizing means interoperability between various cloud environments and integrated interfaces. This is especially important with multi-cloud or hybrid cloud where companies have more than one environment. This is becoming the new normal to prevent vendor lock-in.

If corporations continue to standardize on Datadog’s platform, then the company will continue to capture market share. Since dealing with multiple cloud vendors quickly becomes cumbersome, there is a natural tendency to standardize in tech, especially with software. Moreover, cloud applications need to communicate, so having everything on one platform can make detecting and resolving issues less complex and costly. The cloud industry is on the cusp of this standardization trend with cloud software vendors, with Datadog leading the way. In this way, Datadog is best positioned to benefit from both the rise in cloud usage and the standardization of cloud software.

SentinelOne is a security position we own. Although the company has many competitors in the EDR space, they extend the acronym to XDR to not only include devices and workstations, but to also include other data points on the network, such as containers and cloud-native applications, and also across the entire stack, such as email, the network, and identity.

SentinelOne uses many data sources to create a data lake. The single pool of raw data is built across a wide range of sources, including other vendors or internal data sources. What matters to customers is that every threat is detected very quickly, and SentinelOne proposes a solution that is able to do both because automation and AI is best done at the data level rather than managing thousands of user endpoints to mitigate attacks.

According to SentinelOne, using their products can produce cost savings can be up to 353% – granted this number is a marketing department, however, the point is that any company increasing ROI in cybersecurity has a real chance of taking market share if their product improves the results. The savings quoted is achieved by reducing the amount of cybersecurity tools a company needs by standardizing endpoint security across more data types. The consolidation in this case saves up to $3 million over a three-year period and the enhanced threat detection saves $671K over three years. Due to automation, $1.2 million can be saved over three years by reducing time and employee hours across the IT team.

Big Data and Analytics/ML – Consumption Model is Here to Stay

There is an oft-quoted statistic that 90% of the world’s data was created in the last two years – and this stat is from 2018. The world produces 44 zetabytes of data across the digital universe as of 2020 and there is expected to be 200+ zetabytes of data in cloud storage by 2025. Each zettabyte has 21 zeroes or is 1,000 bytes to the 7th power. By these estimates, we can expect to see up to 5X growth specifically in data centers. Statista places the number at 181 zetabytes by 2025 up from 64.2 zettabytes in 2020.

In regards to data integration in the cloud, this spans from data lakes, to ETL pipelines, cloud data warehouses and object storage. Data fabrics and data virtualization is key to both hybrid and multi-cloud strategies.

The key thing to know about Big Data, Analytics/ML is that these companies will test financial analysts as they do not bill according to subscriptions like many software companies do. Instead, companies like Snowflake, MongoDB and Confluent bill customers based on consumption. This is a relatively new approach to software billing, which makes it harder to model and forecast near term sales.

As data creation, ingestion and storage soar in the cloud environment, cloud software providers are starting to migrate away from subscription agreements, which are fixed, to a consumption-based pricing model, which are uncapped.

Consumption-based pricing has a few drawbacks. For example, its less predictable than subscription revenue and there isn’t a ‘floor’ on revenue, because if consumption declines then so will sales. However, the flip side is also true, consumption billing does not have a ‘ceiling’ on revenue, so if customer consumption rises, so does sales. This uncapped revenue potential is key to why growth could be quite substantial in this category compared to cloud SaaS peers.

Here is a disclosure from Snowflake in the 10Q:

“Consumption for most customers accelerates from the beginning of their usage to the end of their contract terms and often exceeds their initial capacity commitment amounts. When this occurs, our customers have the option to amend their existing agreement with us to purchase additional capacity or request early renewals”

We want CAGRs that are larger than mobile’s CAGR for 5-10 years. According to industry analysts, the CAGR for machine learning is at 38% between 2022-2029, growing from $15 billion in 2021 to over $200 billion in 2029. Some of this is being driven by Big Tech yet as more companies seek a vendor agnostic approach and multi-cloud workloads, there is ample room for agnostic companies to do well.

Compare this to the smartphone market which grew at 24.9% CAGR with some years in the 12% CAGR range. Here’s an example of a reference for CAGR during Apple’s high-growth years: “The Asia-Pacific smartphones market shipment stood at 87.8 million in 2010 which is expected to reach 294.1 million in 2015 growing at a CAGR of 27.3% during 2010 – 2015.”

Here's how Datadog’s CEO describes what is going on in terms of big data in the Q2 earnings call: “it's almost a given that there will need to be a different way of charging for capturing some of the value provided to customers that can't just be attached to the straight volumes of data that are being exchanged because those volume of data are exploding exponentially while our customers' revenues are not going to explode exponentially.”

To capitalize on the Big Data, Analytics and ML trend – which we fully believe has the potential to produce a FAANG – we hold long-term positions in MongoDB and Snowflake. We are comfortable with the fluctuations of the consumption model, which means some volatility at times, as the consumption model will be tied to higher revenues in the long-term.

Note: We hold a 1% position in Confluent which translates to a lower conviction than MongoDB and Snowflake for this trend. We have recently trimmed 2.5% from Snowflake with the goal of building a bigger position in MDB. Please reference Knox’s trade alerts for more information on these positions and others in real-time.

Snowflake Resources:

MongoDB Resources:

SentinelOne Resources:

Compartmentalizing Cloud

Big Data and Analytics/ML

Confluent

The Blockchain is Eating the Internet

We encourage tech investors to look at cryptocurrencies with a level head. It’s easy to dismiss the blockchain as a fad and it’s also easy to gamble on crypto for a quick gain. We think both approaches are wrong. Instead, our approach is to fully embrace the blockchain and it’s volatility by being willing to trim when the uptrend hits our targets close to the top and layer back in around the bottom.

Knox has a strong track record in navigating Bitcoin’s volatility and we fully expect to continue to trim at the top and layer-in at subsequent bottoms for the next five years – with real-time trade alerts sent to our premium members.

We own the following cryptocurrencies in a longer-term fashion: Bitcoin, Ethereum, Chainlink and Avalanche. The first three come with a higher conviction simply due to the size of their ecosystems yet we think Avalanche stands-out as a secure, decentralized protocol that can scale.

We also own very small, token positions in what we call a YO/LO portfolio (You Only Live Once) where we are a bit more liberated to take higher risks than we would with our core portfolio. Reference the resources below for more information.

Bitcoin:

We covered Bitcoin within a month of launching our premium site in 2019 and it’s in my top 5 for FAANG status. Notably, we had predicted Bitcoin would reach $1 trillion market cap when it was selling off from the $7-$10,000 range to $3,000 range.

The primary reason we are proponents of Bitcoin is that it is the world’s most secure financial network with a higher level of security than the 10,000 global banks combined. This solves a genuine need for the financial system as payments and transfers cannot be automated without a decentralized blockchain solution.

Crypto transfers eliminate processing fees and also hedges against inflation. There are transaction fees charged by crypto exchanges but these fees are not inherent to Bitcoin and will lower in time with more competition.

Apple, Google, Microsoft, and Amazon crossed market caps of $1 trillion because their products scale to global populations and are required on a daily basis. Bitcoin not only scales to global populations, but it also protects their livelihood – a necessity rather than a convenience. This is why we see populations that are not necessarily tech-savvy most enthusiastic about Bitcoin. In 2019, I argued that Bitcoin will reach a $1 trillion market cap as solving a real financial need for global populations should be worth as much as a search engine, enterprise software, social media network, warehouse fulfillment (AMZN), or iPhone hardware company.

In our original report we used the example of Venezuela, where during a period of hyperinflation, the price of a cup of coffee rose to 2,800 bolivars up from 0.75 bolivars within one year, representing an increase of 373,233%, according to Bloomberg data. Essential goods such as toilet paper and medicine were also very costly.

Bitcoin was immediately available to Venezuelans as a store of value and offered them an option to cross the border and escape an autocratic regime. Since then, El Salvador has adopted Bitcoin as their country’s currency.

Currently, the United States is at debt levels of about 133 percent of gross domestic product (GDP). There has been a steady rise in the level of national debt to GDP due to decreased tax revenue and increased spending, especially on health care.

The United States is unlikely to see hyperinflation to the level of Venezuela (at least, let’s hope not). However, trust in fiat currencies will erode as debt continues to climb.

Japan is an excellent case study for an economy that is struggling due to quantitative easing. The Japanese debt-to-GDP ratio is at an all-time high at 254% due to its quantitative easing. Government debt to GDP in Japan averaged 137.4% from 1980 to 2017.

Easy money policies from Japan’s central bank harmed domestic asset returns by suppressing local interest rates. Ranking as the world’s third largest economy, Japan resorted to negative interest rates in 2016. In April 2016, it was reported that a “Japanese bank buying 5-Year U.S. Treasuries with perfectly hedged currency and duration risk would (lose) 0.9% a year.”

Consequently, Japan is a thriving bitcoin market and has seen increased crypto deposits. According to the Japan Virtual and Crypto assets Exchange Association (JVCEA) Japan’s virtual currency deposits recorded 1.41 trillion yen or about US$13 billion in March last year, the volume was about seven times more than in March 2020.

During the recent Ukraine-Russian war the use of crypto has once again taken prominence. The Ukrainian government has also accepted crypto donations during this crisis. In the words of Alex Bornyakov, Deputy Minister of Ukraine’s Ministry of Digital Transformation, “In times like these, response time is crucial. Crypto is playing a role to give us flexibility to respond really quickly to deliver the army’s required supplies.” The lack of financial access might also increase the use of crypto in both the countries. The Ukraine central bank had suspending electronic transfers and reduced cash withdrawals and there were reports that Ukrainians were turning to cryptocurrency.

According to Alex Gladstein, Chief Strategy Officer at the Human Rights Foundation, “The fact that it can’t be frozen, the fact that it can’t be censored, and the fact that it can be used without ID is very, very important,” He further added, “And they are why bitcoin is such an important humanitarian tool.”

We’ve written extensively on Bitcoin and we encourage you to read more about the importance of the Lightning Network in our resources below, which is a payment protocol that operates on top of cryptocurrency blockchains and enables fast transactions.

The Lightening Network will be used for small transactions that don’t require the security of the bitcoin network. Large transfers that require decentralized security will continue to take place on the original layer.

The final iteration for the Lightning Network will be the cross-chain atomic swaps, which will exchange crypto tokens between different blockchains without the need for a crypto currency exchange.

Benefits of the Lightning Network:

Transactions will take place on the Lightning Network channels and outside of the blockchain:
Fees will be minimal to non-existent for small payments like coffee, dinner, and local stores.
Quick transactions no matter how busy the network is. The transactions will be instantaneous and able to keep pace with Visa, MasterCard and Paypal.
Cross-chain atomic swaps will eliminate the need for separate crypto exchanges.
The Lightning Network can reach 1 million transactions per second.

Bitcoin Resources:

Layer One Networks

If you want a perfect parallel to the mobile duopoly of Android and iOS, then it will be Web3. We began with artificial intelligence because by increasing GDP, AI/ML promises to be the technology that delivers the most gains in the public market’s history – far exceeding mobile. Yet, the blockchain offers a parallel to mobile as what layer one networks set out to achieve is very similar to what Apple’s app store achieved.

The primary difference between Ethereum and Bitcoin is that Ethereum is not trying to compete as a currency. The focus of Ethereum is on its network, not the coin. Butkin’s vision is to create an open network for decentralized applications (dapps) and smart contracts based on the Turing complete programming language Solidity. The takeaway is that just like Apple hosted apps on its operating system, Ethereum hosts d’apps on its network.

These three benefits are: decentralization, security and scalability. The issue is that most decentralized networks cannot offer all three without some compromise.

Ethereum faces constraints in transactions per second (TPS) and how to overcome the high energy costs of mining that comes with decentralized security. The network simply can’t scale without the upcoming release of Ethereum 2.0.

In our premium analysis last year on Ethereum here, we discussed the difference between Proof of Work (PoW) and Proof of Stake (PoS). In addition to the Proof of Stake merge that Ethereum must complete, the network must also launch shards. Nodes in the previous network must download a transaction, calculate it, archive it and read every transaction in Ethereum’s history, which is terribly inefficient. Shards create a subset of the network where nodes are dispersed for more efficient processing. Ethereum 2.0 must also replace Plasma with ZK Rollups, which allow for hundreds of transfers to be rolled into a single transaction.

In November, we wrote another update on crypto and Ethereum, stating that the expectation was for Proof of Stake to merge in late 2021 with Shards and Rollups expected by late 2022 or early 2023. The timeline for PoS is delayed yet again until Q3 2022, which puts Sharding and Rollups out another year potentially to Q3 2023. (Read more in the resources below).

The takeaway: Ethereum has a wide lead in terms of number of d’apps and developers (remember that developers adopting CUDA created Nvidia’s moat). However, the Merge to Proof of Stake is an unknown which leaves the Layer One network market wide open for now.

Avalanche

Layer One Networks are considered early-stage tech investing which carries higher risk. Ethereum clearly has a head start, and after the proof of stake merge, we could see the network take off in a meaningful way.

However, there are other Layer One networks to consider. We hold an allocation in Avalanche due to it’s Ethereum Bridge, application-specific subnets, and the launch of a consumer-facing app over the next quarter. Avalanche also has a high Nakamoto Coefficient, which is a number that designates the number of nodes that would need to be corrupted to slow or prevent a chain from functioning properly.

Avalanche launched with three chains. Per our YO/LO write-up: The X-Chain is for creating and exchanging assets including NFTs, the P-Chain validates and creates subnets, and the C-Chain is for executing Ethereum Virtual Machine (EVM) contracts. The C-Chain offers interoperability with Ethereum, which is why the Avalanche bridge is the most popular ETH Bridge currently. The P-Chain is what is used to create and manage subnets. The coordination of Avalanche validators occurs on the P-Chain and it can support thousands of subnets and millions of validators.

As we stated in the AVAX write-up: “Ethereum is running into issues with 500,000 to 1 million daily active users. Meanwhile, a single mobile application sees hundreds of millions of users, such a Twitter or Spotify. What Layer 1 can handle this level of adoption? That is a platinum-level question for investors to answer. To be clear, it could be Ethereum in 2023 if the developers and users prefer to not migrate. However, if the ecosystem runs out of patience and seriously looks for an alternative, then Avalanche is a candidate.”

Ethereum Resources Here:

Avalanche Resources Here

Chainlink:

Warren Buffet famously said: “The stock market is a device for transferring money from the impatient to the patient.” I believe Chainlink could be our best performing asset in our portfolio by 2030 as the middleware that enables smart contract through decentralized oracles.

Smart contracts are a more advanced use of blockchain where an exchange between two parties is automated based on conditional provisions. These self-executing contracts are written into lines of code, and the agreements contained exist across a distributed, decentralized blockchain network.

Smart contracts offer a more complete use for blockchain. First discussed in 1996 by Nick Szabo, some claim that smart contracts are the real use case for blockchain as they aim to automate financial transactions, and in the future, can automate machines.

We have written extensive deep dives and webinars on what the company does but for those who would rather get the elevator pitch, it’s this: Chainlink is the most likely candidate to become the Google of Web3. In fact, ex-Google executives are joining Chainlink as strategic advisor, former CEO Eric Schmidt, new Chainlink Chief Product Officer, Tensorflow’s Kemal El Moujahid.

We are very bullish on Chainlink and it was our first one-hour deep dive webinar for this reason. However, it requires a longer-term mindset, which we certainly have at the I/O Fund. Our goal for our position is sizable gains by 2025 with an exit in 2028-2030.

Chainlink Resources Here

FAANG Isn’t Dead

“Winners keep winning” is a reliable and true statement. We began this analysis by showing you a chart of how the world’s most valuable companies change every 10 years. However, there is an important caveat: tech overtook oil to become the world’s most valuable industry in 2010 and we have yet to see the pattern that tech sets in terms of how often the top 5 will rotate now that tech is in the driver’s seat.

Microsoft:

We were one of the first analysts to cover Microsoft Azure’s hybrid computing strategy and why that could narrow AWS’ lead in the cloud IaaS market. At the time, tech was selling off in Q4 2018 yet we were firm that Microsoft would emerge as a significant leader in this space by specializing in a mix of on-premise and cloud deployments.

Hybrid cloud allows for scenarios where customers can keep their most sensitive data on their own servers while sending workloads to the private or public cloud that gain an advantage from mining data more efficiently and requires improved accuracy and productivity.

Azure’s strength in offering both on-premise and cloud in a hybrid solution has prompted Amazon to chase Microsoft with recent efforts to improve its hybrid strategy. Today, Azure claims more than 95% of the Fortune 500 as customers because of its hybrid flexibility.

Azure growth of 46% is performing quite well given the tough comps it has overcome, and Microsoft’s best financial metric during this tech selloff is that commercial bookings increased 28% this quarter following 32% increase last quarter. I would look for Azure to remain elevated against AWS and Google Cloud for those two reasons – hybrid cloud leader which attracts large enterprises and its ability to reduce costs with its tech stack.

In our latest Q2 webinar, I discussed why reducing cloud costs is a key trend for 2022 and beyond. To put it simply, Sayta Nadella said in this quarter’s earnings call: “More value for less price means you win.” In the same breath, he also said: “Most businesses are not looking to their IT budgets or to digital transformation for budget cuts.” These two statements echo my first point in the Q2 2022 webinar which is that both are true: increase in cloud spending will continue and companies will want to lower costs associated with cloud.

That’s going to be the trick moving forward – which companies assist cloud migrations while lowering costs. Microsoft is the leader here as the company aggregates many cloud services and products under one umbrella which creates substantial leverage to undercut on price.

Microsoft is increasingly becoming a cybersecurity company, as well, with $15 billion in revenue and growing at a rate of 45%. Microsoft was careful to build a multi-cloud product and is the only Big 3 cloud vendor to be multi-cloud on security right now. This also helps to drive down costs for Microsoft’s customers.

There are additional catalysts for Microsoft beyond Azure’s winning streak, its large security footprint, and the ability to lower costs. The first catalyst is that Microsoft is setting up to own the edge through its telecom partnerships. Another catalyst is that when more enterprises adopt AI/ML, whether it’s automation, super computers and/or other use cases for training and inference, it will a natural decision to use Microsoft if they’re already optimized for Azure. As discussed, enterprises will drive forward the next major market in tech (AI/ML) and Tier 1/Fortune 500 will be the largest customers for AI/ML. Power Automate was up 72% year-over-year, surpassing $2 billion in revenue, and this is only the beginning.

Third, the company ranks with Nvidia and Unity for inroads to the Metaverse as it owns many gaming publishers now and is the most widely used VR headset (HoloLens). The company also has Teams to introduce Metaverse-like qualities to business meetings. It will be industrial that drives forward 3D worlds (not consumer) and Microsoft is auspiciously positioned.

Microsoft Resources Here:

Alphabet:

We have been meticulously building our portfolio for the next FAANGs of 2030 since we launched the site in 2019 with the understanding that even getting 1 or 2 correct can create generational wealth. Our goal from the beginning has been to stick with our winners and to cut our losers and we have compiled quite a bit of research along the way.

Alphabet is a new addition to our portfolio and one we’ve been circling for some time. If the 2014-2022 era in digital advertising was known as the walled garden era primarily fueled by third-party data then 2022-2030 will be known as the brick walls of first-party data – meaning, publishers controlling their data become the trend that drives forward digital advertising as we move into more AI/ML-driven ads.

In fact, we are in the midst of what is the biggest shift in digital advertising since advertising went digital. The shift is due to Apple and other real estate owners shutting off how data is collected across mobile and desktop. In the mobile era, third-party data was rampant but all of that changed with the release of iOS 15.

Note: We were one of the top authors on this topic with coverage dating back two years before the change occurred on both Forbes and MarketWatch here.

Google will be following in Apple’s footsteps by changing how third-party data is collected on Android and Chrome. This will greatly strengthen the company as not only is Google an equal or greater real estate owner compared to Apple but the company is also a publisher for the purpose of ads with Search and YouTube. This means long-term ads placed on Google will be more effective and produce higher ROI than those with less signals to work with.

These data collection changes are coming just in time for the advantage from first-party data to be realized across AI/ML (with digital ads) and a myriad of other uses cases.

Google Resources Here:

Consumer Isn’t Dead

We’ve focused quite a bit on enterprise for the purpose of this article yet we want to acknowledge that consumer-facing tech carries strength in most macro environments.

We hold the following stocks to capture consumer-driven gains. Notably, we’ve covered in the past how supply chains are contributing to consumer spending and inflation. We are watching this closely for when growth in this area rebounds. You can access our research on this here.

Roku: Netflix made it into FAANG and CTV ads will be a bigger market than subscriptions – primarily CTV ads will do well globally. Roku must prove its hardware strategy will pay off in global markets starting with LatAm.
Snap:: This company has had a brutal month – yet audience metrics have been strong post-Covid and the Gen-Z/Millennial concentration is important to take note of.
Shopify: This company is spending an unknown amount on the fulfillment center yet can rival Amazon simply through unlimited distribution channels. Social commerce will eventually take off despite the setback from Apple’s iOS changes.
Twilio The Twilio management team is known to be visionaries and they are pivoting into an API-forward marketing platform with strong PII data – they are early to API-driven automation taking over marketing and advertising.

Conclusion:

Thank you for taking the time to read this report. Despite tech being one of the most volatile sectors in the market, it is also the most rewarding. Had you entered Apple between 2008-2010, it would have blown away all other positions in your portfolio across all other industries. The same goes for a Facebook or a Google position. Half the battle is finding them, which we think we are particularly well suited for, and the other half of the battle is knowing which stocks to sell and which ones to hold when the tide rolls out. We show you how we do this with real-time trade notifications plus a hedge for ample insurance during drawdowns. There are many positions we own today that we will own in 2030 with the goal of perfectly timed exit. We are patient and thorough in our research as we acknowledge and accept that approximately 5 companies will lead to 90% of our wealth in 10 years.

Semiconductor Stocks: Q2 2022 Overview

Posted on June 3, 2022June 30, 2026 by io-fund

This article was originally published on Forbes on May 28, 2022,11:54pm EDTForbes on May 28, 2022,11:54pm EDT

Semiconductor stocks have gained prominence due to growth drivers such as artificial intelligence, high-performance computing, 5G, robotics, machine learning, and electric vehicles. Despite supply constraints and the challenging macro environment, semiconductor stocks have withstood the tech sell-off better than other sectors. This is due to many semiconductor companies being profitable with strong free cash flows.

We reviewed the stocks in the sector to find out which companies stand out in terms of revenue growth, profits, cash flows, and earnings surprise.

Top 20 semiconductor stocks with highest growth rates for the current fiscal year.

Source: YCharts

In the above chart, Indie Semiconductor leads with the expected growth of 130% year-over-year in the current fiscal year. The company is riding the growth trend in advanced-driver assistance systems (ADAS) and electric vehicles. It has a Serviceable Addressable Market (SAM) of $40 billion by 2026. The company supplies chips and software to the automobile sector. It’s chips power sensor capabilities like LiDAR and Radar, and vehicle electrification.

The company’s revenues accelerated by 171% YoY to $22 million in the recent quarter. The management expects revenue to grow 178% at the mid-point in the next quarter. While the company is not profitable at the moment. The management expects it to be profitable in the second half of next year.

AMD is expected to grow 60% this year due to the Xilinx acquisition. The company had initially guided for organic growth of 31% during Q4 results. The Xilinx acquisition was completed in February this year, and partly by better demand from end markets. In the recent quarter, the company’s revenue grew by 71% YoY to $5.9 billion, with organic revenue growth of 55%. Even if we exclude Xilinx, the company is a leading growth stock among the semis due to data center growth and gaming.

Top 20 semiconductor stocks with highest growth rates for the next fiscal year.

Source: YCharts

Navitas Semiconductor has the highest growth rate in the above chart. The company is a leading player in the Gallium Nitride (GaN) chips. The benefits of GaN include fast charging and better power efficiency. Currently used in mobile phones & laptops, EVs are the future opportunity. Ambarella is another interesting company to watch. The company’s chips which were previously popular for using in drones and cameras have recently found a niche in the automobile sector. The company’s AI computer vision chips benefit from the Internet of Things, ADAS, and autonomous driving. The company’s revenue in the 4Q FY2022 grew by 45% YoY to $62.1 million. The computer vision revenue accounted for more than 25% of the FY2022 revenue and is expected to be 45% of FY2023 revenues.

Semiconductors with Top Forward P/S Sales multiples

Source: YCharts

In the above chart, SiTime Corporation has the highest forward P/S ratio. The company is a leading provider of Silicon Timing Solutions. In the recent quarter results, the company’s revenue grew by 98% YoY to $70.3 million. The revenue is expected to grow 50% this year and 23% in the next year. The strong growth rates are reflected in the company’s share price, which has doubled in the past year.

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Wolfspeed is another leading company that has a premium valuation due to the company’s expertise in Silicon Carbide chips. The company’s revenue is expected to grow 38% this year and 44% in the next year. According to MarketsandMarkets, the Silicon Carbide market is expected to grow at a compound annual growth rate of 19% from 2021 to 2026. Hybrid and electric cars are the future growth drivers for Silicon Carbide.

The company recently entered a deal with Lucid Motors to supply Silicon Carbide devices from the newly opened Mohawk Valley Fab. According to Gregg Lowe, CEO of Wolfspeed, “As the world advances towards an all-electric future for transportation, Silicon Carbide technology is at the forefront of the industry’s transition to EVs, enabling superior performance, range and charge time. Our investment in the Mohawk Valley Fab ensures our customers, including Lucid, have access to the advanced products they need to deliver innovative solutions to the market.”

Nvidia has seen some weakness recently due to the broader tech sell-off. However, the company deserves a premium valuation due to the company’s growth prospects in the AI data center and solid long-term prospects in the automotive chip industry.

Quarterly Revenue Surprise

Source: YCharts

Cirrus Logic crushed analysts’ consensus revenue estimates by 17%. The company’s Q4 FY2022 revenue grew by 67% YoY to $490 million. The company’s guidance for the next quarter is between $350 million to $390 million, representing a YoY growth of 26% at the mid-point of the guidance. It was higher than the analysts’ consensus estimates of $295 million. John Forsyth, CEO of the company, said, “We delivered strong financial results in FY22 as revenue increased 30 percent year over year driven by high-performance mixed-signal content gains.”

Top 5 ranked semiconductor stocks based on Free Cash Flow Margin

Source: YCharts

Cirrus Logic not only beat analysts’ revenue estimates it also ranked the highest among the semiconductor companies with the highest free cash flow margins. This is an important financial metric in the current environment as we have noticed in the current earnings season that many companies that fell short in this metric the shares got sold off.

Top 5 ranked semiconductor stocks based on Net Profit Margin

Source: YCharts

In the above chart, Indie Semiconductor, which we discussed earlier in our article, also ranked the highest among the companies with the highest net profit margins. Intel ranks third in the category. However, the company faces significant competition from AMD, which can be seen in the lower valuation the company is trading.

I/O Fund’s Semiconductor Q4 2021 Earnings Preview

Posted on January 21, 2022June 30, 2026 by io-fund

TSMC broke off Q4 semiconductor earnings after it reported on 01/13/22. Sales at the massive foundry pure play grew 21% YoY in Q4, and net income increased 16%, setting the stage for a strong earnings season for the semiconductor industry. In the analysis that follows, I give a brief overview of the semiconductor industry and discuss key metrics that investors should be aware of heading in Q4 earnings.

Semiconductors: Top 10 EV/FWD Revenue Multiples

Below is a table of semiconductor stocks ranked by their EV/1-year forward sales multiples, along with their most recent YoY growth rate, gross and free cashflow (FCF) margins. Semiconductors experienced strong demand in 2021 and the market has rewarded the outperformers with premium multiples. Nvidia (NVDA) sports the highest multiple of the group at 21x, likely due to Nvidia’s dominate position with GPUs and its strong topline growth rate.

There are a cluster of other top performing semiconductor firms valued around 11x to 14x EV/Fwd revenues, such as ASML, a leading semiconductor equipment provider that has seen strong demand as capacity in the sector ramps up to address supply issues. Another standout is WOLF, which has seen strong demand for silicon carbide solutions, a relatively new technology that is being adopted by the automotive market. WOLF claims that it is the sole vertically integrated supplier of silicon carbide for high power and RF application, which likely contributes to its premium multiple.

Semiconductors: Top 10 Three-month Forward YoY Growth Rates

Below is a chart of forward sales growth expectations for the semiconductor industry. Looking forward, AEHR is forecasted to grow the strongest from our universe of semiconductor stocks (n=74). The company is benefiting from tailwinds in EV and datacenter exposures, which are expected to ramp in the near term. INDI is also expected to grow strongly in the upcoming quarter as the company guided for more than 50% sequential growth heading into Q4, driven by demand for its solutions in the automotive sector and a recent acquisition. RMBS is also expected to grow nearly 100% next quarter, as demand for its memory interface chips remains strong in the current environment. Our research suggests that demand for automotive and memory solutions in the semiconductor industry are strong tailwinds heading into Q4 earnings.

Top 10 Weekly Share Price Movements

Below is a table of the weekly change in share price for our universe of semiconductor stocks (week ended 01/14). TSM has already reported Q4 results, and the foundry pure play’s results came in strong as the company guided Q1 2022 sales to increase 8% sequentially, up from its most recent 6% QoQ growth rate. The strong guide likely led to the strong price action in TSM’s shares. Many other top performing semiconductor stocks are equipment providers, such as VECO, UCTT, ICHR, AMAT and LRCX. The market is likely pricing in increased demand for semiconductor equipment, as fab expansions lead to more equipment purchases going forward.

Top 10 Changes in sales growth estimates – last 90 days

The table below ranks the semiconductor companies by their topline revisions over the last 90 days. An increase in topline revisions signals that the Street believes that the company will grow faster than initially believed. INDI has had the largest topline revision in the semiconductor industry, as the company guided for 50% sequential growth, which includes benefits from its recent acquisition of TeraXion. SYNA has also had its topline estimates revised up by 13% over the last 90 days, driven in part by its recent acquisition of DSP Group, which the company explained would help it expand its ability to cross-sell AI solutions at the edge of the network. The market likely agrees with management and its share are up 40% over the last 90 days.

Update on EV/Fwd revenue multiples:

Overall stats:

Overall Semiconductor forward median: 5x
Top 5 Semiconductor forward median: 14x
Overall Semiconductor forward average: 6x

EV/FWD SALES:

As shown below, the median and average semiconductor EV/1-year forward sales multiple has trended up since April 2020. Semiconductor valuations have increased as demand for semis has remained robust, driven by a global chip shortage. The world may be entering a new normal where semiconductors are used in everything, such as datacenters, automotive, and IoT devices. This trend reduces their cyclical nature and has likely led the market to reward the industry with a premium multiple. This will likely be a multi-year trend and if the semiconductors cycles continue to shorten, then multiples may continue to rise.

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Top 5 EV/FWD SALES:

In the chart below, we can more clearly see the large dispersion in semiconductor valuations, as the top 5 premium valued semiconductor stocks have had their EV/Fwd sales multiples expand since 2020. The median multiple has also expanded, but at a slower pace. However, the delta between the top 5 and the median semiconductor stock has started to narrow in 2022, as the median valuation remained relatively static while the top 5 has had their valuations compress. If Q4 earnings come in strong, then the market may push valuations back up to their historic highs.

EV TO FWD Sales Growth Buckets:

We can further dissect the change in semiconductor valuations by breaking up the group into high growth (>30%), mid growth (>15% and <30%) and low growth (<15%). The below chart shows the historical valuations for stocks in various growth buckets. High growth semiconductor stocks have had their valuations compress recently relative to mid-growth semiconductors. Q4 earnings will be pivotal for the high growth semiconductors, and the market will likely reward the group with higher multiples if growth remains strong going forward.

Top EV TO FWD SALES:

The below chart provides a more holistic view of the semiconductor industry ahead of Q4 earnings, sorted by their EV to Fwd revenue multiples. NVDA has the richest valuation and is valued well above the peer median of 5x. Nvidia is benefitting from multiple tailwinds, such as data center growth, gaming, cryptocurrency, and automotive. Lead tech analyst Beth Kindig outlined why she believes that Nvidia will be worth more than Apple in the future, stating 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”.

Growth adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth)

The last chart is based on EV to FWD sales but also takes into account forward growth expectations. By scaling valuation relative to forward growth, we can more clearly see which companies are cheapest relative to forward growth. A low value in the below chart means that a company is cheap relative to growth. Note that some names may be skewed due to acquisitions. It is interesting to note that NVDA falls from being the richest valued semiconductor stock to closer to the median once you take into account its strong growth rate. TXN and MTSI are some of the most expensive semiconductor stocks based on this metric and others such as AEHR and KLIC are cheapest (not shown).

Finally, the last table we will be discussing includes aggregate semiconductor operating metrics. The below table illustrates the median topline growth, margins and FCF generation for the semiconductor industry. The median growth rate was 32%, and the market expects the median semiconductor stock to grow 20% in Q4. Gross margin remains robust at nearly 50% and cashflows are also healthy at 16% of three-month sales for the median semiconductor. Strong growth, margins and cashflows highlight the strong health of the semiconductor industry, which makes sense considering the outsized demand for chips in the current market.

Strong growth and positive cashflows signal that the semiconductor industry is healthy and performing well. The I/O Fund expects this strength to continue going forward. Find out which semiconductor stocks the I/O Fund will be watching heading into Q4 earnings in our I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q4 Earnings.

The I/O Fund is a team of analysts that share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here.by clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q4 Earnings

Posted on January 21, 2022June 30, 2026 by io-fund

It is the first of the series of earnings previews for Q4. We chose Lam Research, AMD, Teradyne, Nvidia, Texas Instruments, Broadcom, and Qualcomm for the Semiconductor sector. To understand valuations across semis and how the sector is positioned moving into earnings, please reference our analysis, “I/O Fund’s Semiconductor Q4 2021 Earnings Preview.”

Lam Research – Earnings on January 26th

Source: YCharts and Earnings reportsYCharts and Earnings reports

According to the analysts’ consensus estimates, revenue is expected to grow 28% YoY for the next quarter. In the last earnings call, the management mentioned that early indication suggests that Wafer Fab Equipment will show another year of growth in the calendar year 2022. It will be interesting to hear their comments on WFE in the next earnings call, particularly since the analysts forecast its revenue growth to slow down for the next two fiscal years. Revenue is expected to grow 4% in the FY23 and FY24, down from 21% for FY22.

Wells Fargo in its recent report says that the chip-equipment makers will remain volatile in the upcoming earnings season. Analyst Joe Quatrochi said that trading in semiconductor capital equipment stocks, "could remain relatively volatile" over the near term as share prices are likely to be affected by any changes that companies make to their quarterly earnings and sales outlook.

With regard to Lam Research, he says that the ongoing supply chain issues and the company ramping up production at a new facility in Malaysia are having a negative impact on the company’s gross margins.

Barclays analyst Blayne Curtis raised the firm's price target to $750 from $625 and keeps an Overweight rating on the shares. The analyst sees "positive outlooks providing some relief" for the semiconductor group but still struggles with "just how much upside is left as cyclicality still looms for many names."

Mizuho analyst Vijay Rakesh raised the firm's price target to $770 from $700 and keeps a Buy rating on the shares. The analyst gave his outlook across semis and automotive technologies and his top sectors in 2022 are memory, wafer fab equipment, data center, 5G and electric vehicles.

Please note, the I/O Fund may or may not agree with the financial analysts mentioned above yet we objectively report what the Street is saying. You may view our previous analysis on the company below:

I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q3 Earnings

Advanced Micro Devices Inc – Earnings on February 1st

Source: YCharts and Earnings reportsYCharts and Earnings reports

AMD’s stock rose around 570% in the past three years. The company’s strong revenue growth is due to its superior products have also boosted its share price.

The company’s revenue grew more than 50% YoY in the last five quarters. The analysts’ consensus estimates suggest revenue to grow 40% YoY in the next quarter to $4.53 billion. The company’s data center revenue more than doubled in the last quarter and it will be interesting to hear the management’s comments on this in the next earnings call.

Source: YCharts

Wells Fargo analyst Aaron Rakers has an overweight rating and a $180 price target. He believes that the company will likely continue to take market share over the next five years while growing its total addressable market, which could boost earnings to $6 per share by 2025. “With an expectation that the PC CPU market will sustain a structurally higher post-COVID TAM (est. a ~$40B TAM), an estimated mid/high-single digit CAGR in AMD's data center TAM [CPU + GPU], and with the inclusion of a ~$8.5B incremental TAM via Xilinx, we estimate that AMD now addresses a $100B-110B+ TAM (vs. $79B TAM outlined at March '20 Analyst Day)".

KeyBanc analyst John Vinh has an overweight rating and price target of $155. In his words, "One of the most compelling data center growth stories, given its exposure to cloud and continued market share gains,". He further added, "We view AMD as one of the most compelling server growth stories in the semiconductor industry, given its outsized exposure to CSPs vs. enterprise. Additionally, we expect AMD to significantly outpace cloud industry growth in 2022 of high teens, as we expect continued market share gains."

AMD Stock is Approaching a 20 Year Roadblock – Will History Repeat?

Teradyne Inc – Earnings on January 26th

Source: YCharts and Earnings ReportsYCharts and Earnings Reports

Teradyne’s revenue grew 16% YoY in Q3 and the consensus estimates suggest revenue to grow 14% YoY in the next quarter. Industrial automation currently constitutes roughly 10% of the total revenue. According to the management this could be one of the growth areas for the company’s future as the penetration is low.

The company’s outlook for 2022 and the medium-term earnings outlook update is expected in the next earnings call. This is particularly important as analysts expect slower revenue growth of 9% for 2022 and 2023.

Source: Investor PresentationInvestor Presentation

Deutsche Bank analyst Sidney Ho has raised the price target to $170 from $150. The analyst is more optimistic about long term demand drivers, rising capital intensity, and the regional push for semiconductor manufacturing capabilities entering 2022. This should lead to wafer fab equipment spending sustaining at a high level. He believes semiconductor capital equipment stocks can justify trading at multiples above historical averages.

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Piper Sandler analyst Weston Twigg has a price target of $173. The analyst has a positive view on the company's fundamentals given its Arm test opportunities, memory market share gains, and continued robotics growth. He expects Teradyne's annual robotics revenue to exceed $1B by 2025 on "strong post-pandemic automation trends." Twigg views the company as a "compelling robotics automation play, coupled with good multi-year core test market tailwinds."

NVIDIA Corporation – Tentative Earnings date is February 24th

Source: YCharts and Earnings ReportsYCharts and Earnings Reports

Nvidia’s revenue grew over 50% YoY for the past six quarters. Analysts expect revenue to grow 48% YoY in the next quarter to $7.43 billion. Growth has been particularly strong in the data center, which grew at a compounded annual growth rate of 82% from FY17 to FY21.

Source: Investor PresentationInvestor Presentation

Citi analyst Atif Malik opened a "Positive Catalyst Watch" on shares of Nvidia post the Consumer Electronics Show. Management commented on the "strong" holiday gaming season, "solid" data center demand trends and gaming/networking foundry supply improvements in the second half of the year. The analyst views Nvidia's January quarter earnings, launch of a new data center and potentially gaming 5nm products at a conference in March as positive catalysts for the stock.

BofA analyst Vivek Arya reiterates a Buy rating on Nvidia with a $375 price target after hosting an investor call with the company's CFO, Colette Kress. The analyst "heard confidence around momentum" heading into 2022 across gaming, data center and "nascent omniverse/autos opportunities." Capacity remains a bottleneck, with demand outpacing supply throughout 2021, especially in gaming, though management noted they are working hard on securing supply and they expect constraints to ease in the second half of 2022. The analyst calls Nvidia a "top compute pick" and continues to believe the company is best positioned to "address several of the most important, multi-decade secular growth opportunities."

Please note, the I/O Fund may or may not agree with the financial analysts mentioned above yet we objectively report what the Street is saying. You may view our previous analysis on Nvidia below:

Throwback: Nvidia will Surpass Apple’s Valuation in 4.5 Years

The Key To Unlocking The Metaverse Is Nvidia’s Omniverse

Holding Nvidia Stock Will Pay Off Due to Two Impenetrable Moats

Texas Instruments Inc – Earnings on January 25th

Source: YCharts and Earnings ReportsYCharts and Earnings Reports

Texas Instruments revenue is expected to slow down drastically from 22% YoY growth in Q3 to 9% growth in the next quarter. The company has been steadily increasing its dividend payments over the years and has a decent dividend yield of 2.31%. At the time of writing, the stock has returned about 8% in the past year.

Source: YCharts

Barclays analyst Blayne Curtis raised the firm's price target to $180 from $170 and keeps an Underweight rating on the shares. The analyst sees "positive outlooks providing some relief" for the semiconductor group but still struggles with "just how much upside is left as cyclicality still looms for many names."

The company was downgraded by Citi. Analyst Christopher Danely lowered his rating to neutral and cut his price target to $187 from $220. In his words, "We estimate the new fab and higher depreciation will negatively impact gross margins by roughly 1%-3% in 2022 and our C22 EPS estimate is 6% below consensus.”

Broadcom Inc – Tentative Earnings date is March 4th

Source: YCharts and Earnings ReportsYCharts and Earnings Reports

Broadcom’s revenue grew 15% YoY in Q4 FY21 and the consensus analysts estimate suggest revenue to grow 14% in the next quarter. The company has good free cash flows and for the fiscal year 2021, it constituted 49% of the total revenue. The current dividend yield is 2.58% and the quarterly dividend was increased by 14% to $4.10. One risk to watch is Apple planning to make chip components in-house. The company’s shares rose about 30% in the past year.

Source: Investor PresentationInvestor Presentation

Piper Sandler analyst Harsh Kumar raised the price target to $750 from $680 and keeps an Overweight rating on the shares. For 2022, the analyst favors "larger, more profitable, cash generating names that have a clear growth path ahead of them based on end-markets." Kumar sees cloud, enterprise, 5G infrastructure, electric vehicles, and connectivity as the primary areas of focus. He's cautious on the automotive end-market more broadly and PCs.

Bank of America analyst Vivek Arya, who rates Broadcom buy with a $750 price target and Skyworks neutral with a $190 price target, notes that both companies have significant exposure to Apple (AAPL), with 20% for Broadcom and 59% for Skyworks, but industry checks suggest the impact is "overblown in the near to medium term." Apple's hiring could be for its plans to develop its own 5G modem, which would hurt Qualcomm and not Broadcom or Skyworks.

Read our past coverage on the stock below:

I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q3 Earnings

Will Dividend Stocks Become the Inflation Trade?

Qualcomm Inc – Tentative earnings date is February 3rd

Source: YCharts and Earnings ReportsYCharts and Earnings Reports

The company’s adjusted revenue grew 43% YoY in the Q4 FY21 and the analysts’ consensus estimate suggests revenue to grow 27% in the next quarter. However, growth will slow down to around 8% in the FY23 and FY24, down from about 19% revenue growth in the FY22.

The management expects future growth from automotive and the Internet of Things as the company looks for opportunities beyond the smartphone business. The company expects its addressable market to grow from the current $100 billion to $700 billion in the next decade.

KeyBanc analyst John Vinh raised the price target to $210 from $185 and keeps an Overweight rating. The analyst notes that at its analyst event, Qualcomm derisked concerns about Apple (AAPL), indicating its share of the 2023 iPhone would decline to 20% and would exit fiscal 2024 at a low-single digit percentage of QCT revenues, yet expects handsets to still grow at the industry three-year CAGR of 12%, as Android is expected to grow faster and offset. Qualcomm pointed out it has secured multiyear chip agreements over the next two years with all handset OEMs, Vinh adds. The analyst also highlights that auto revenues are expected to grow to $3.5B, anchored by key wins at General Motors and BMW.

Deutsche Bank analyst Ross Seymore raised the firm's price target to $210 from $190 and keeps a Buy rating. “It laid out an impressive roadmap of accelerated and diversified growth, further bolstered by a wide array of customer testimonials," The company's "impressive" financial targets are "significantly de-risking" by removing 80% of Apple in fiscal 2024, albeit with no certainty that the business will indeed be lost by that much that soon, according to the analyst.

The I/O Fund is a team of analysts that share their research publicly as they build a portfolio of 20 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here.by clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Throwback: Nvidia will Surpass Apple’s Valuation in 4.5 Years

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

Throwback: Nvidia will Surpass Apple’s Valuation in 4.5 Years

Last August, I predicted that Nvidia could surpass Apple on market cap. Here is what I said in my Forbes article: “I believe Nvidia is capable of out-performing all five FAAMG stocks and will surpass even Apple’s valuation in the next five years” and I expanded on this by stating, “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.”

Source: YCharts

As strong as Nvidia has been on price action, Apple will not allow my prediction to be an easy slam dunk as the heavyweight briefly claimed a $3 trillion market cap.

Source: YCharts

Currently, Nvidia has a market cap of $690 billion and Apple has a market cap of around $2.9 trillion. Nvidia’s market cap rose about 22% compared to Apple’s 17% since my publication of the article. I made this prediction in August of 2021, and during the month of November, we were beginning to make headway with a diversion between semiconductors and big tech.

Source: YCharts

One of the main reasons for me to make the bold statement that Nvidia will surpass Apple’s valuation is that the market opportunity for Nvidia is vast when compared to the mobile economy, which benefitted Apple.

“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.”

When we began covering Nvidia, we were stating the company would become a leader on AI while most analysts were stuck on the gaming storyline as this was Nvidia’s core product for many decades. This caused many investors to miss out on the top supplier for AI accelerator chips in the data center. We had predicted this three years ago when we wrote: Nvidia has two impenetrable moats – which are developer adoption and the GPU-powered cloud. Notice, we did not mention gaming or crypto mining despite this being the only two narratives on this company at the time.

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We published this again in 2019 for premium members when we stated:

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.

Since the original 2018 publication on the two impenetrable moats, Nvidia has greatly outperformed FAAMG. We believe the same will be true over the next five years.

Source: YCharts

One reason for this is that 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.

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.

There are many other catalysts that will help Nvidia become the world’s most valuable company to prove my prediction true, including the metaverse, automotive, data analytics such as Spark with GPU acceleration, virtual machines for AI workloads and perhaps edge devices by licensing (or acquiring) Arm architecture.

We only have to wait until August of 2026 to see if Nvidia did indeed pass up Apple’s market cap. However, the wait should be an easy one if Nvidia continues to treat investors to the smooth gains (like butter) we’ve seen as of late.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Lam Research Analysis: 2021/2022 Update

Posted on December 30, 2021June 30, 2026 by io-fund

Equipment for semiconductor manufacturing usually falls into one of the three categories: wafer fabrication equipment, assembly, or testing equipment. Water fabrication equipment (WFE) is a primary segment for Lam Research, specifically for memory and storage chips. The deposition process creates layers of insulating and conducting materials with techniques like chemical vapor deposition or atomic layer deposition, which allows for thin films of atomic layers to be coated onto surfaces.

The excess material is then etched away. Deposition and etch are processes that require complex machines for wafers to be built into integrated circuits. Lam Research has done well by specializing in memory chips. In order for Lam to do well, the WFE market must be growing and Lam must create new equipment and processes to maintain or grow market share. Our goal is to participate in memory with less cyclical risk through Lam’s specialization, especially with 3D NAND, which is an emerging market where Lam leads.

We pointed this out that Lam lets us participate in the memory market with reduced risk in our original analysis when we stated:

“Analysts covering Lam Research like to point out that the company is protected from supply and demand as memory manufacturers will continue to buy from Lam Research even during a low point in the cycle. This was proven during 2015 when Lam Research did not feel the effects of the memory trough.”

The price action below since we covered Lam helps to illustrate what we mean:

Lam has significant business in supplying equipment for leading edge nodes and this is the leading growth market for Lam. Two years ago, we discussed how Qualcomm will sell up to 50% more dollar chip content per device versus 4G generations, which refers to the dollar value of chips the device holds. Something similar is happening at the equipment level as complexity increases.

Management said the following in the most recent earnings call,

“At the leading-edge, semiconductor content growth, large die, and rising capital intensity are fueling increased wafer starts and strong WFE spending. In Foundry/Logic for instance, the next-generation processor chip for a top smartphone maker is more than 20% larger than its prior iteration. In DRAM, higher capital intensity is being driven by the increasing need to correct single bit errors through the addition of an extra on-chip bit. In 3D NAND, increasing device layer counts and the resulting higher degree of manufacturing difficulty is requiring the addition of new deposition and etch processes to address stress management, defect control, and multi-stack integration challenges.”semiconductor content growth, large die, and rising capital intensity are fueling increased wafer starts and strong WFE spending. In Foundry/Logic for instance, the next-generation processor chip for a top smartphone maker is more than 20% larger than its prior iteration. In DRAM, higher capital intensity is being driven by the increasing need to correct single bit errors through the addition of an extra on-chip bit. In 3D NAND, increasing device layer counts and the resulting higher degree of manufacturing difficulty is requiring the addition of new deposition and etch processes to address stress management, defect control, and multi-stack integration challenges.”

Lam’s Reliant equipment has also posted 11 quarters of record revenue. This equipment provides a lower cost of ownership for non-traditional chip markets, such as micro electromechnical systems (MEMS), power chips, radio frequency (RF) filters and CMOS image sensors for better connectivity and more powerful imaging. This particular segment refers to trailing edge nodes, which means larger nodes, such as 24nm, 28nm or 90nm processes. Although we’ve covered leading edge nodes, such as 5nm in the past when discussing AMD, Lam has also found success in supplying equipment for larger nodes as these are used in automotive and medical equipment. In fact, the emphasis on leading edge nodes may be why Lam has found success in the overlooked trailing edge market. Management stated that demand is exceeding WFE equipment.

As Bradley points out below, the following statement was quite encouraging in regards to Lam’s business overall: “As a result, we see the WFE investment required to achieve the same bit growth percentage over the next 5 years to be notably higherbe notably higher than the 5-year period just completed.” Lam has grown sales at a CAGR of 28% over the past five years. Consequently, the stock has seen over 550% gains in five years from a share price of $105 to $724.

While these comments are encouraging, we need to watch Lam Research closely into 2022 as Gartner is predicting a slowdown in WFE equipment in 2023-2024 as integrated circuit manufacturers and foundries “pause to digest the new capacity.”

There are other forecasts predicting a slowdown to occur sooner with 6% growth in equipment for 2022 following an estimated 34% in 2021. The foundry and logic segments, which are more than half the WFE sales, are forecast to grow 8% in 2022 following 39% in 2021. Similar forecasts are provided for NAND and DRAM equipment with a marked slowdown in 2022.

On the earnings call, however, the idea a slowdown would occur in 2022 was negated when an analyst asked about the potential for a slowdown:

Good afternoon and great job on the quarterly execution, guys. You know, the market is concerned that we're heading into a multi-quarter downturn in Memory, kind of similar to the 2018/2019 Memory downturn, which is a pretty severe 6-quarter downturn, but the one thing I clearly remember was that ahead of that downturn, your memory customers proactively cut their CapEx very, very rapidly.

Now, if I look at it this time around, there's some near-term pricing weakness in memory. But the overall memory demand environment remains pretty strong, and I think most memory companies seem optimistic right under the baton outlook for next year.

So I guess the question is, has the Lam team seen any signs similar to the 2018 downturn of customers either getting concerned or canceling or slight pushing out of shipments due to a concern on our projected memory downturn next year? -Harlan Sur, JP MorganHarlan Sur, JP Morgan

Here was management’s response:

“Yeah. Harlan, let me take that first. I think the simple answer is no. When the vast majority of our conversations with customers today is still about delivering equipment that they feel they badly need to meet their near-term requirements. And as Doug mentioned in his prepared remarks, I would say lead times have stretched out to the point where our visibility into demand in '22 is better than usual.. When the vast majority of our conversations with customers today is still about delivering equipment that they feel they badly need to meet their near-term requirements. And as Doug mentioned in his prepared remarks, I would say lead times have stretched out to the point where our visibility into demand in '22 is better than usual.

So I don't think that the hypes of initial indicators that you're talking about are things we're seeing right now. We feel much more constrained by supply chain challenges and ability to meet shipments and an over shipping situation.” -Tim Archer, CEO

Regarding the comment on supply chain challenges, Lam stated on the call that this is the biggest challenge the company faces right now. There are hundreds of parts for WFE and many are now supporting new process flows and 3D architectures. It only takes a delay on one of those parts to slow Lam’s delivery: “We're beginning to see constraints in the supply chain. So we have to work our way back up through some of those things. And that's the biggest thing we're dealing with right now.”

Lam’s Product & Growth Opportunities

Lam’s main competitors are ASML, Applied Materials, Tokyo Electron and KLA with Lam tied for third place. There are a few key products that Lam is developing and bringing to market that could help increase the company’s market share. Certainly, the manufacturing expansion in the United States, Korea, Taiwan and Malaysia hints towards Lam expecting it will need more capacity.

The first growth opportunity for Lam is 3D NAND. We covered 3D NAND in detail in our Micron analysis with the 176-layer release that is 40% higher than the nearest competitor, Samsung. The new NAND device is also 10 times denser than previous 3D NAND devices with increased power efficiency and capacity limitations removed. The data transfer rate is also very fast at 1,600 MT/s while maintaining the same height as the 64-layer device.

Here's an excerpt from the Micron analysis that will help frame Lam’s new etch solution:

“According to Micron, “current 3D NAND design has begun to reach the limits of its monolithic die-level maximum capacity. It will continue to fall short of the immense system-level storage capacities demanded by future data-driven applications. Cell-to-cell capacitive coupling complications and smaller etch requirements account for many of these limitations.” If Micron is correct, then this could be an opportunity for the company to see more market share on 3D NAND.”smaller etch requirements account for many of these limitations.” If Micron is correct, then this could be an opportunity for the company to see more market share on 3D NAND.”

Micron is trying to move very quickly with their new replacement-gate design which replaces the traditional floating-gate design before Samsung or others catchup while Lam is busy solving the issue from the front-end WFE perspective with a high-productivity cryogenic etch solution. The etch removes the material in devices at cold temperatures below 100 degrees Celsius for high-aspect ratios with 200+ layers. The cold temperatures are achieved with liquefied nitrogen gas. You can click here for an article that describes this process.

According to management on the call, the cryogenic etch solution has already gone through QA and is being shipped this year. If the company is successful with this solution, it could extend to leading edge 3D DRAM and foundry/logic (3D DRAM is not on the product road map right now but management hinted that it will be put into production in the future).

“As one example, Lam has developed a new high-productivity cryo etch solution, which increases etch rates in high-aspect ratio features required for NAND devices with greater than 200 layers. We have installed this new capability at every major 3D NAND manufacturer for qualification with additional systems now shipping to support planned ramps to high-volume production next year.”We have installed this new capability at every major 3D NAND manufacturer for qualification with additional systems now shipping to support planned ramps to high-volume production next year.”

In the Micron report, we also discussed EUV or Extreme Ultraviolet Lithography where we stated the following:

“This manufacturing method uses smaller 13.5nm wavelengths of ultraviolet light to etch wafers as opposed to lasers from Deep Ultraviolet Lithography (DUV). You could argue that EUV is a point of weakness for Micron as Samsung is using this manufacturing method while Micron is delayed until 2024.”

EUV photomasks reflect light with alternating layers of molybdenum and silicon as opposed to conventional photomasks that block light with a quartz substrate or chromium layer. TSMC and Samsung are leaders with EUV for 5nm production. This process adds capital intensity, and this is good for Lam.

“In patterning, we're using the learning we have acquired over many years of multi-patterning etch leadership to win new applications as the industry adoption of EUV progresses. EUV requires use of special photoresist materials which, given the material composition, can amplify existing challenges with pattern roughness, and defectivity.

Unaddressed, these will lead to performance in yield loss, especially at smaller device dimensions. Lam has developed critical etch and deposition technologies to help solve these EUV implementation issues. In etch, we introduced earlier this year a new pulse plasma etch capability that has demonstrated an order of magnitude reduction in EUV-related pattern defectivity.” -Tim Archer, CEO opening remarksLam has developed critical etch and deposition technologies to help solve these EUV implementation issues. In etch, we introduced earlier this year a new pulse plasma etch capability that has demonstrated an order of magnitude reduction in EUV-related pattern defectivity.” -Tim Archer, CEO opening remarks

Conclusion:

Lam’s management stated they feel confident that they have visibility into next year and that the company has “significant unmet demand.” The company also stated there are “tailwinds relative to the business” for 2022.

Does that mean every quarter will meet or exceed guidance? No, it could be lumpy and that’s the nature of semiconductor stocks. The analyst on the call mentioned a 6-quarter slowdown. If this doesn’t happen in 2022, it could happen in 2023, etcetera. Semis are especially challenging right now because they’re expected to be cyclical but are transitioning into a more secular trend. Therefore, a slowdown could actually be much further out due to the drivers we discussed in the Micron report.

However, the key reason we think Lam could fare better than its peers is because as 3D layers increase, capital intensity also increases. The process does not scale linearly, instead it’s non-linear because it takes longer than 2X to etch a stack that is 2X high and requires more complex etch and deposition equipment.

Here’s one of the more important statements the company said on the call in regards to our thesis and stock position: “In 3D NAND, increasing device layer counts and the resulting higher degree of manufacturing difficulty is requiring the addition of new deposition and etch processes to address stress management, defect control, and multi-stack integration challenges.” This in turn, leads to increased investments in WFE to maintain percentages in bit growth.

As Micron, Samsung and others continue to compete on 3D NAND, and maybe even 3D DRAM in the future, we think Lam will become a clear winner. Cryo etch is leaving R&D for the first time and this is because Lam is pushing the envelope to serve this emerging trend. Lam is the leading equipment provider on 3D NAND and being a first mover here is key in serving the memory market moving forward. EUV patterning is another area where Lam leads and is seeing demand as the company aims to solve EUV implementation issues and the pattern defects that occur with equipment from its competitors.

Lam Q1 FY2022 Results

By Bradley Cipriano

Lam’s sales, earnings and cashflows all increased over 30% in the most recent quarter, signaling the unique position the company is in. We believe that Lam’s growth and earnings will continue to be robust going forward as the memory market nears an inflection point. Furthermore, capex from key customers signals that sales will continue to be strong in the near term.

In the latest quarter ending in September, Lam’s Q1 FY22 sales grew 36% YoY to $4.3 billion, which met the Street’s estimate. System revenue, which includes Lam’s leading edge equipment in deposition, etch and clean markets, increased 36% YoY to $2.9 billion while customer support and other increased 34% YoY to $1.4 billion. Management guided for Q2 sales to grow 39% YoY to $4.4 billion, which would mark the ninth consecutive quarter of YoY topline growth.

Gross margin declined 150 bps YoY to 45.9% as supply chain issues impacted margins. GAAP earnings increased 48% YoY to $8.27 per share, while non-GAAP earnings were $8.16/share, which beat estimates by 2%. TTM free cashflow increased 51% YoY to $3.2 billion and cash on hand remained high at $4.9 billion, but declined QoQ due to $1 billion in share repurchases during the quarter.

Demand was driven by memory, as 64% of equipment sales were for memory, up from 58% in the year-ago quarter. Lam described the key drivers for its products in its 10Q as “3D device scaling, multiple patterning, process flow, and advanced packaging chip integration, [which] will lead to an increase in the served addressable market for our products and services in the deposition, etch, and clean businesses.”

Lam is benefitting from a secular tailwind in semiconductor demand, and there are signals that memory is becoming less cyclical as the boom and bust cycles of years past are smoothing out due to rising demand from AI, 5G, IoT and edge computing.

Since Lam provides equipment that is used by its customers to manufacture semiconductors, we can measure their capex levels to get an understanding of where the market is moving. As shown below, quarterly capex trends from our sample group of semiconductors (n = 72) have accelerated during 2021. Capex grew 10% QoQ both in Q1 and Q2 and then increased 9% QoQ in Q3 to ~$78 billion.

Aggregate capex is up 32% YTD in 2021, well above the prior five-year Q3 YTD average of 13%.

The above capex trends add support to CEO Tim Archer’s comments on the Q1 call that “[Lam is] exiting this year with significant unmet demand… on the supply side, rising capital intensity, different architectures, new processes that need to be inserted into process flows to deal with increased manufacturing complexity. And those will be drivers for WFE structurally for a very long time. So I think there are a lot of things that will be positives for WFE in 2022, from an equipment perspective.”

The strong capex outlined above and demand for WFE provides more visibility into Lam’s 2022 sales. However, the caveat of the strong capex is that some of the capex should turn into output next year, which could lead to overcapacity. However, there are signs that the memory market is becoming less cyclical as technological innovations drive structural demand. I discuss these innovations in more detail next.

Lam and 3D NAND

Looking forward, Lam is also preparing for new technologies in the memory market. One of the new technologies is 3D NAND – which Beth had previously discussed in Lam’s premium analysis here.

3D NAND moves memory from a 2D plane to a 3D plane, which dramatically improves the storage capacity. It also requires a lot more equipment to manufacturer, which Lam provides.

CEO Archer added that “we see the WFE investment required to achieve the same bit growth percentage over the next 5 years to be notably higher than the 5-year period just completed. However, as the leading equipment supplier to the 3D NAND market, we are investing in new and differentiated capabilities to ensure scaling remains cost effective”

As 3D NAND nears an inflection point, there will be a structural increase in demand for WFE investments, benefitting Lam’s sales, earnings and cashflows. The equipment required to manufacturer 3D NAND is significantly higher. So, if the market increasingly adopts 3D NAND, then demand for Lam’s WFE should also increase at a relatively faster rate than prior years.

A risk to our thesis going forward is that Lam does not innovate fast enough to keep pace with changes to the memory market. While Lam’s research and development expense has increased YoY for sixth consecutive quarters, it fell to just 9% of sales in the most recent quarter. This is below the five-year seasonal average of 12% of sales.

On a TTM basis, R&D expense declined to 10% of TTM sales, which was also below the five-year average of 12% of TTM sales. However, R&D may seem low relative to sales due to the company’s rapid increase in sales recently. Nonetheless, this is a trend we will need to monitor going forward

It is likely that Lam’s prior R&D investments are now starting to pay dividends. Lam is well positioned to benefit from the rise in capital intensity from key customers. We had previously discussed in our Micron analysis that Micron was expected to continue to ramp capex, driven by investments in 176-NAND. Micron disclosed on its most recent conference call (12/20/21) that it expects capex to be around $11 to $12 billion in FY2022. This would represent a 15% increase in spending, which followed a 22% rise in FY2021. In the most recent quarter (q1 FY22), Micron’s quarterly net capex increased 22% to $3.3 billion, a record high. The growth in Micron’s capex is a forward looking metric that translates into demand for Lam’s WFE, driving topline growth at Lam.

Conclusion

Lam’s sales, earnings and cashflows all grew over 30% in the most recent quarter. Management guided for continued growth next quarter and stated that visibility into CY2022 was clear due to strong demand for semiconductors and memory. The company has tailwinds with new technological innovations such as 3D NAND, but needs to keep investing into R&D to ensure it can compete in the future. The company should continue to do well in the near term but we will need to monitor its investments in innovation going forward to make sure growth is sustainable.

I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q3 Earnings

Posted on October 20, 2021June 30, 2026 by io-fund

This quarter we chose Nvidia, Lam Research, Ambarella, Advanced Micro Devices, Broadcom, Marvell Technology, and Qorvo for an earnings preview. We also tried to understand what analysts are expecting from these companies. The list includes popular names in the semiconductor sector and companies like Qorvo, which has been out of favor recently in the eyes of investors.

To understand valuations across semis and how the sector is positioned moving into earnings, please reference our analysis “I/O Fund’s Semiconductor Q3 2021 Earnings Preview.”

NVIDIA Corporation – Earnings on November 17th

Nvidia’s revenue growth is expected to continue to be strong. Management mentioned in the last earnings call that the QoQ growth will be led by strong demand in the data center along with other segments that are also growing steadily. They also indicated that the gross margins will be around 65.2% plus or minus 0.5%. We also expect some new comments on the Arm acquisition in the earnings call. You can read our analysis of Why the Arm Deal Should be Approved here.

KeyBanc analyst John Vinh has an overweight rating on the stock and increased the price target from $245 to $260. He mentions that “The firm's quarterly supply chain findings are mixed, as demand remains healthy, but a multitude of supply disruptions, including COVID lockdowns in Southeast Asia, power restrictions in China, and kitting issues, could result in near-term uncertainty and limit upside.”

Jefferies increased the price target to $260 from $233. They are positive on the company due to the strong performance of the company’s data center and software segments. Their report also suggests that Nvidia’s share in accelerator instances increased 1% in July to 79% on new deployments of A100 and V100 chips.

Goldman Sachs analyst Toshiya Hari said after the company’s strong previous quarter earnings report, “We increase our go-forward estimates on the back of today’s print, and importantly, continue to view Nvidia as the best positioned company to address and monetize the proliferation of accelerating computing”.

Please note, the I/O Fund may or may not agree with the financial analysts mentioned above yet we objectively report what the Street is saying. You may view our previous analysis on Nvidia below:

The Key To Unlocking The Metaverse Is Nvidia’s Omniverse

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

Making Sense of The Nvidia-Arm Acquisition

Our premium members have been updated frequently on the company with ongoing entries at $31.50, $51.20, $97.40, $105.30, $120.60, $124.50, $123.50, $138.90, $206.60, $208.90

Lam Research – Earnings on October 20th

Lam research continues to have steady revenue growth. The wafer fabric equipment demand has been strong as the semiconductor industry is benefitting from the leading technologies like 5G, IoT, AI, and edge computing.

UBS analyst Timothy Arcuri who has a buy rating on the stock lowered the company’s price target to $715 from $780. The analyst sees some “potential moderation" in DRAM and NAND WFE spending moving through 2022.

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Susquehanna analyst Mehdi Hosseini downgraded the stock to neutral and lowered its price target to $690 from $750. The analyst says "the beat-and-raise cycle for the company is already behind us with all the good news already dialed. With quarterly wafer fab equipment peaking in the second half of 2021, there is enough of a deceleration in spend growth rate into 2022 that cannot be offset by services and/or share gains.”

Ambarella Inc – Earnings on November 23rd

Ambarella has seen rapid revenue growth in the last quarter and consensus estimates suggests 61% revenue growth in the next quarter. The company has successfully tapped the computer vision technology market with the increasing demand for its chips for drones, VR cameras, security cameras, and automotive cameras. However, it would be prudent to note that the growth was partly due to lower comps due to Covid-19.

KeyBanc analyst John Vinh upgraded Ambarella to Overweight from Sector Weight with a $185 price target. The analyst sees "multiple favorable tailwinds" related to the adoption of computer vision within the security and automotive end markets amid limited competition with the HiSilicon ban. He also has increasing confidence in front-facing advanced driver assistance systems adoption. Further, with its "highly differentiated CV/AI assets" and adoption in auto tech, Ambarella represents one of the most attractive takeover targets in semiconductors.

Roth Capital analyst Suji Desilva believes “Ambarella represents a differentiated investment opportunity in computer vision and low-power video processors.” He has a buy rating on the stock and has raised the company’s price target to $170 from $130.

Cowen analyst Matthew Ramsay has an outperform rating with a price target of $150. The analyst said “that the business is really inflecting in terms of growth and operating leverage with upside that is no longer a leveraged story to GoPro and drones.”

Advanced Micro Devices Inc – Earnings on October 27th

The company had a blowout last quarter. It also raised the revenue guidance for the full-year 2021 and the 5nm roadmap is on track for 2022. The company has been able to capture market share from Intel and reports suggests that Intel might cut CPU server prices.

Source: Earnings Presentation

Piper Sandler analyst Harsh Kumar has a price target of $120 and an overweight rating. The analyst says AMD is looking very solid into year-end and is very well positioned as enterprise spending returns, as the company should benefit within both the PC and server markets.

BMO Capital analyst Ambrish Srivastava has a price target of $110. He says " Considering the company's continued execution and the expansion in estimates, the shares look relatively more reasonably valued than they did earlier in the year,” argues Srivastava, who also believes there is continued upward bias to estimates through the year as AMD starts to ramp designs it has already won on the data center side.

Goldman Sachs analyst Toshiya Hari has a buy rating and a price target of $130. In his words “We believe AMD’s recent CPU/GPU wins in supercomputing have important and positive implications for the company’s forward trajectory in the data center, as design wins in supercomputing are awarded based primarily on performance with good insight into future technology/product roadmaps”.

Broadcom Inc – Earnings on December 10th

Broadcom delivered record revenues in the third quarter with good growth in cloud, 5G infrastructure, broadband, and wireless. The management expects the trend to continue in the next quarter. The free cash flows were 51% of total revenue and they expect the cash flows to be strong in the next quarter.

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Barclays’ analyst Blayne Curtis has an overweight rating and a price target of $540. In his words “AVGO has one of the highest exposures to the Enterprise end market (50% of semi revenue), which should continue to improve into next year. AVGO guided Wireless up 33% QoQ (in line with our model) and we see some upside into Jan given likely contents gains at AAPL (WiFi 6E, touch, wireless charging, DCM) with another potential step up in 2023 with the AAPL modem. AVGO remains one of our preferred names for 2022 given its cheap relative valuation, more resilient end markets into next year (Ent, DC, Wireless), further content gains at AAPL, and likely accretive Software M&A”.

Truist analyst William Stein has a buy rating and a $564 price target. In his words “Investors should continue to buy the stock for its 3% dividend yield and the double-digit dividend growth over the long term”.

JPMorgan analyst Harlan Sur has an overweight rating and a $600 price target. He believes that the “stock offers a solid setup for 2022 based on order visibility and product cycles”.

We’ve discussed in the past how Broadcom could potentially be a good choice for an inflation trade:

Will Dividend Stocks Become the Inflation Trade?

Marvell Technology – Earnings on December 3rd

The company’s revenue growth has been strong due to the growth in the data center revenues (40% of 2Q FY22 revenues). The inclusion of Inphi also provided an earnings bump. The management believes that the data center will further drive the third quarter growth along with 5G business. During the recent Investor Day the company increased the long-term growth rate to 15%-20%.

Needham analyst Quinn Bolton has a buy rating and a $75 price target. "Marvell's management highlighted the significant growth opportunities associated with cloud-optimized silicon. These growth drivers are accelerating the company's SAM, which is now forecast to grow from $20bn in 2021 to $30bn in 2024, or at a 13% CAGR, with the 5G/data center/automotive portion of this SAM growing at a 20% CAGR over this period.”

Barclays’ analyst Blayne Curtis has an overweight rating and a price target of $70. In his words “Marvell raised its growth target of 15-20% with drivers across every segment. This has been a multi-year transition but the message was very clear that the company has re-shaped its portfolio to take advantage of growth in the Cloud and Infrastructure markets with the broadest set of IP and process technologies.”

KeyBanc analyst increased the price target to $80 from $70. “Marvell increased its long-term revenue growth target to 15%-20% from 10%-15% previously. The increased growth rate is being driven by Increased focus on optimizing solutions for cloud data center growth, emphasis on auto, cloud and 5G, and expansion into autonomous compute processors, which represents a $5.3B TAM.”

Qorvo Inc – Earnings on November 04th

The company’s 1Q FY 22 results have been good. However, the stock has failed to meet market expectations. The management’s revenue guidance for the next quarter is $1.235B to $1.265B, the midpoint $1.25B suggests a 18% YoY growth and a growth of about 27% YoY when adjusted for last year’s 14-week quarter. They also increased the top line forecast for the fiscal year 2022 to 15%-20% from the earlier of approximately 15%.

Oppenheimer analyst Rick Schafer raised the company’s price target to $250 from $220 after the company earnings beat and forecast upgrade. He is positive on the company increasing the outlook in spite of the supply constraints.

Benchmark analyst David Williams has a buy rating and $225 price target. He is also positive after the company’s strong previous quarter results. “The demand environment remains strong and he continues to appreciate incremental content gains in 5G with increasing complexity”.

Craig-Hallum analyst Anthony Stoss also raised the company’s price target to $225 from $220. The analyst cites “big bottom-line beat, with Qorvo posting a 52.5% gross margin, and operating margin of 33.1% near record levels.”

The I/O Fund is a team of analysts that share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here. by clicking here or sign up for our free newsletter here.

Our premium members have been updated frequently on Nvidia with ongoing entries at $31.50, $51.20, $97.40, $105.30, $120.60, $124.50, $123.50, $138.90, $206.60, $208.90

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

I/O Fund’s Semiconductor Q3 2021 Earnings Preview

Posted on October 20, 2021June 30, 2026 by io-fund

Taiwan Semiconductor officially opened the sector’s earnings with a bang as it beat consensus estimates and also guided strong revenue in the fourth quarter. Specifically, the company’s Q3 YoY sales growth of 16% beat estimates by 0.5% while guidance for Q4 sales growth represents an acceleration to 23% YoY growth, which came in ahead of the consensus estimate by 1%.

The results are positive considering some of the recent bearish Wall Street reports on semiconductors that suggested lower revenue due to the chip shortage, China worries, and Covid-19 negative impact, mainly in Asia. There were similar worries in the last quarter and most companies were able to beat estimates. In this earnings preview, we review key semiconductor companies to get a pulse on what to expect.

In the analysis that follows, we give a brief overview of the semi-conductor sector and discuss key metrics that investors should be aware of heading into Q3 earnings.

Semiconductor Stocks: Top 10 EV/FWD Revenue Multiples

Below is a table of semiconductor stocks ranked by their EV/FWD sales multiples, along with their most recent YoY growth rate, gross and free cashflow (FCF) margins. Semiconductors have experienced strong demand in 2021 and the market has rewarded the outperformers with premium multiples.

Nvidia (NVDA) sports the highest multiple of the group at 20x, likely due to its dominate position with GPUs. SiTime (SITM) isn’t far behind NVDA at 18x. SITM’s premium multiple of 18x is likely a function of the firm’s strong 107% YoY growth rate as demand for its silicon timing solutions has increased in multiple different industries.

The I/O Fund recently covered Nvidia in a full length analysis on why Beth believes Nvidia will surpass Apple’s valuation over the next 5 years. In the analysis, we discuss how the A100 Ampere architecture is able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. The A100 is becoming the best-selling GPU of all time and these leaps in performance is partly why Nvidia has done so well against competitors.

Semiconductor Stocks: Top 10 Three-month Forward YoY Growth Rates

Looking forward, semi-conductors are expected to continue to grow strongly. Kulicke & Soffa (KLIC) is expected to grow over 100%, driven by increased demand for its solutions in the semiconductor, automotive and advanced display markets. Many other names are expected to grow over 50%+ in the upcoming quarter. Marvell (MRVL) is expected to grow 50%+ next quarter, but this growth is skewed by the firm’s acquisition of Inphi. Adjusting for the Inphi acquisition, MRVL’s organic growth next quarter is expected to grow 20%, which would represent an acceleration from the prior quarter organic rate of 17%.

Top 10 Weekly Share Price Movement

In the table below, we ranked the semiconductor stocks that saw the largest one week increase in their share price. Ambarella’s (AMBA) stock is one of the top performers this past week, as analyst anticipate 61% growth next quarter. AMD is also up 11% over the last week, and analyst expect the company to continue to capture market share going forward. We explore what analysts are saying about these stocks and a few others in more detail in our I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q3 Earnings.

Top 10 Changes in sales growth estimates – last 90 days

The table below ranks the companies that have had the largest revisions to their growth outlook over the last 90 days. ADI completed its acquisition of MXIM, which largely explains the rise in forward growth expectations, and absent the acquisition management had initially guided for 17% YoY growth. AMD revenue estimates for Q3 have increased 10% over the last 90 as the company’s guide for Q3 sales came in above expectations.

Update on EV/Fwd revenue multiples:

Overall stats:

Overall Semi-conductor forward median 5x
Top 5 Semi-conductor forward median 18x
Overall Semi-conductor forward average 6x

EV/FWD SALES:

Semiconductor valuations have trended up during the year as demand for semis has remained robust driven by a global chip shortage. The world may be entering a new normal where semiconductors are used in everything, reducing their cyclical nature and leading to a premium multiple being awarded to the group.

TOP 5 EV/FWD SALES:

The chart above highlights the large dispersion in valuations in the semi-conductor space, as market leaders such as NVDA and AMD have been awarded much higher multiples than the peer median. While median valuations have been mostly static the last two years, the top 5 group has seen its valuation drop from 20x in Q4 2020 down to 12x in Q1 2021 but has since recovered to 18x ahead of Q3 earnings season. If Q3 earnings come in strong, then the market may push valuations back up to their historic highs.

EV TO FWD SALES Semiconductor Universe:

We can further dissect the change in semi-conductor valuations by breaking up the group into high growth (>30%), mid growth (>15% and <30%) and low growth (<15%). The above chart shows that the high and mid-growth semiconductor stocks used to be valued much lower, while mid-growth semis have seen their valuations remain fairly static.

EV TO FWD SALES Semi-conductor UNIVERSE:

The above chart provides a more holistic view of the top 30 valued semiconductor stocks based on EV to FWD sales estimates. NVDA has the richest valuation and is valued nearly 400% higher than the peer median. As mentioned above, this is likely due to the firm’s dominate position in the GPU market.

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Growth adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth)

The last chart is based on EV to FWD sales but also takes into account forward growth expectations. By scaling valuation relative to forward growth, we can more clearly see which companies are cheapest relative to forward growth. A low value in the chart below means that a company is cheap relative to growth. Note that some names may be skewed due to acquisitions. DQ looks to be the cheapest based on this metric, however the company is based in China so the market may be discounting it due to political risks.

Finally, the last table we will be discussing includes aggregate semiconductor operating metrics. The above table shows that the group as a whole is performing well, as the average median growth rate in the most recent quarter was a robust 41% YoY rate. The group’s median FCF margin of 19% also highlights the strong position the group is in, as many semiconductor peers are cashflow positive and are also growing rapidly.

Strong growth and cashflows highlight the good health of the semiconductor sector, which makes sense considering the strong demand for chips in the current market. Find out which semiconductor stocks the I/O Fund will be watching heading into Q3 earnings in our I/O Fund’s Preview of 7 Semiconductor Stocks Ahead of Q3 Earnings.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Aehr Test Systems Deep Dive 2021

Posted on October 5, 2021June 30, 2026 by io-fund

Aehr Test Systems is a small cap semiconductor company that is nearing an inflection point. The company has developed a unique technology that provides tangible benefits for testing emerging semiconductor components, such as silicon carbide and silicon photonics. Silicon carbide is increasingly being used in EVs while silicon photonics is being integrated into edge computing data centers. I explain the tailwinds driving adoption of these materials in greater detail below.

Aehr’s key markets: Silicon Carbide and Silicon Photonics

Aehr’s wafer level burn-in testing systems are specifically built for testing silicon carbide and silicon photonics. Silicon carbide has recently been adopted by the automotive industry, which is driving demand for Aehr’s testing systems. Aehr disclosed in its 10K that “silicon carbide (SiC) semiconductors have emerged as the preferred technology for battery electric vehicle power conversion in on-board and off-board electric vehicle battery chargers, and the electric power conversion and control of the electric engines.

These devices reduce power loss by as much as greater than 75% over power silicon alternatives like IGBT (Insulated-Gate Bipolar Transistor) devices, which has essentially changed the entire market dynamic. With this development, the Company sees most, if not every automotive company that is working on electric vehicles, moving to silicon carbide-based powertrain and charging systems in the near future.”

Tesla was the first to start using SiC in its vehicles with its Model 3, and more EV manufacturers are quickly following suit, due to SiC’s ability to withstand hostile conditions, improve efficiencies and lower failure rates. CEO Gayne Erickson explained on the Q1 call that while the SiC market had been around for years, “it really started to take off when Tesla introduced their Model 3 with a silicon carbide traction inverter in it, and then quickly shifted all of their products to it.” The adoption by Tesla was a ‘jolt’ that has spurred further adoption by others. For example, German chipmaker Infineon Technologies launched an SiC inverter for electric vehicles in May 2021. According to Yole Research, “the silicon carbide power semiconductor device market is expected to increase over 500% between 2020 and 2026, growing at a compound average growth rate (“CAGR”) of 36% to $4.5 billion”.

All of these silicon carbide chips need to be tested, and Aehr’s wafer level burn-in testing systems are the most cost-effective way to test these chips at scale.

Source: Asia Nikkei

On top of the robust growth expected in the automotive SiC market, the company also has favorable tailwinds from the rise of silicon photonics, which is also starting to ramp. In its 10K, Aehr disclosed that its lead silicon photonics customer had moved to full volume production during 2021. It added that “we also saw three different silicon photonics customers move from engineering testing to high volume production test and burn-in of their devices using our FOX-P systems. We expect all three of these customers to ramp production during fiscal 2021”. Aehr’s customers must first perform lengthy qualification tests before ramping up orders. Aehr’s silicon photonic customers are nearly done qualifying and are finally expected to start putting in large orders this year.

During the Q1 call, CEO Erickson explained how we are still in the early days of silicon photonics. He stated that “companies like Intel and Nvidia are talking about integrating fiber optic transceivers into their core and graphics processor units or CPUs and GPUs. This is very exciting and we believe an enormous opportunity for Aehr Test with our unique position of having a cost effective and proven multi wafer solution for testing and burning-in or stabilizing silicon photonics devices at a massive scale while still in the wafer form”.

Silicon photonics are being used to increase communication speeds, which is critical for edge computing as it links 30-megawatt data centers within a 120 km distance to function like a 120-megawatt data center. This enables 100G Ethernet services for cloud operators and enterprises. Microsoft and telecom operators are both customers of Inphi’s silicon photonics. Beth had previously discussed this micro trend in her in-depth analysis of Inphi. She explained that the 100G switches were launched in 2020 but were more of a 2021 story as this is when they will be deployed in volume. Importantly, these 100G switches use silicon photonics.

We see this trend starting to ramp, as Inphi accounted for 10% of Aehr’s sales in 2021, the first time it was mentioned as a significant customer. As Inphi’s thesis plays out, Aehr will also benefit from the increased demand for its silicon photonic testing systems.

As shown below, Yole Research anticipates silicon photonics to rapidly grow at a CAGR of 49% through 2026. The market is relatively small right now, as customers complete lengthy qualification tests, but once the test systems are qualified, bookings of Aehr test systems will likely significantly ramp as silicon photonics gains market share. I discuss how Aehr’s order have started to ramp in 2021 in greater detail next.

Source: Yole Research

Orders begin to ramp in 2021

Aehr’s testing technology, branded as FOX-XP test systems, was first introduced in 2016, and the company had initially sold only small orders of its testing systems (~$2 million to $5 million a quarter) as customers qualified the equipment before placing large orders.

During the fiscal Q4 Earnings Call in July 2021, CEO Erickson announced that the company’s lead silicon carbide customer had finally qualified Aehr’s FOX-XP test systems for “high-volume production” for wafer level burn-in testing for electric vehicles. This was a significant development, as it meant that orders for its test systems may soon start to accelerate.

Just five days after the Q4 earnings call, Aehr announced that it had received an $11 million order for its silicon carbide test systems with a “leading Fortune 500 supplier of semiconductor devices with a significant customer base in the automotive semiconductor market.” To put this order into perspective, it represented more than half of the company’s total orders in all of FY2020 ($16 million). In fact, since the close of Q4, the company has announced a total of $40 million orders to date (June through September), which is more than the aggregate bookings amount over the prior nine quarters.

The company’s strong orders led to an 80% increase in the company’s fiscal 2022 sales guidance, which Aehr now expects to be at least $50 million, or 3x the amount in fiscal 2021. Furthermore, attached to Aehr’s test system sales are high margin WaferPak and DiePak consumables, which are purchased multiple times over the life of a FOX testing system. Over time, management expects that sales of its consumables to be 4x the level of sales of its test systems.

This strategy of selling test systems and recurring consumables is similar to an ink and printer sales model, where the sale of the product leads to recurring sales of higher margin consumables. Aehr explained in its 10K that “as we continue to build our installed base of FOX systems, our WaferPak and DiePak [consumables] business continues to grow and is becoming a much more significant part of our business … our high margin proprietary WaferPak contactors and DiePak carrier consumables … can be up to four times the annual sales of systems. The systems typically are used for longer periods, with annual needs for new contactors and consumables. This is why we are confident that our consumable business is likely to exceed our overall systems business over time, even though both will grow in absolute dollars.”

To highlight the upside to the recent growth in orders, management explained on the Q1 Earnings Call that the $19 million order it recently received in September will likely be accompanied by a separate ~$10 million order for consumables, pushing the true order size up closer to $30 million. Importantly, these consumable purchases will also be recurring over the lifetime of the test systems, so the “lifetime bookings” value of this $19 million system sale may be closer to $60 million once consumables are considered

Continuing with this logic, Aehr’s $40 million in bookings YTD may be understated. Using Aehr’s disclosure from its 10K that recurring consumable purchases can be ~4x system purchases, then the $40m in YTD systems orders can result in a high-margin recurring sales stream of ~$160 million in consumable orders. Stated differently, the firm’s $40 million in test system bookings should also yield an additional $160 million in consumable purchases over the life of the systems, resulting in “lifetime bookings” of ~$200 million.

Importantly, orders are expected to continue to ramp going forward. CEO Erickson stated during the Q1 Earnings Call that “we believe we will add several new silicon carbide customers over the next 18 months that will ramp into production on our solutions.” He explained further that its leading silicon carbide customer is the “fourth largest in the space and not even half as large as the next largest”, meaning that new customer orders could be much larger. If Aehr’s test systems provide a competitive advantage (discussed below) then new customers will likely soon place large orders, further bolstering Aehr’s growth rate. I discuss what is driving the adoption of Aehr’s test systems in more detail next.

What is driving the adoption of Aehr’s test systems?

Aehr has a unique technology that is just now starting to ramp called FOX-XP test systems, which are used for wafer level burn-in testing of silicon carbide and silicon photonics. The main advantage of wafer level burn-in testing is that it reduces “infant mortalities”, or early failures in semiconductor equipment. Burn-in testing attempts to lower the failure rates from stage 1 of the “bathtub curve” (shown below), which increases the reliability of semiconductors.

Wafer level burn-in testing reduces chip failure, which is critical in certain industries such as EVs, 5G and datacenters. For example, if a chip fails in an EV’s drive train, then the passenger is walking home. The high costs of chip failures in EVs is driving the industry to push to zero failures and wafer level burn-in testing helps to achieve this.

“Bath Tub Curve” Representation of Chip Failures

CEO Erickson explained further on the Q1 call that he anticipates “that wafer level test and burn-in will become the industry standard for quality and reliability screening of silicon carbide devices”. He added that Aehr’s patented technology allows “customers to screen devices that would otherwise fail after they are packaged into multi die modules, where the yield impact is 10 times or even a 100 times as costly. With the most cost effective solution in the market to address this opportunity, we believe that Aehr has the chance to achieve a significant, perhaps dominant market share for silicon carbide wafer level burn-in.”

Assuming a yield impact of 10x to 100x, then early adopters of Aehr’s technology will gain a significant advantage over their peers. CEO Erickson added that customers with wafer level burn-in technology are “differentiating themselves against their competitors because they … [are] shipping a higher quality module … So other customers have figured this out, but they're still doing package per burn-in. So the discussions we're having with them in many cases is to move from package to wafer.” The 10x to 100x yield benefit awarded to early adopters can lead to a rapid acceleration in orders of Aehr’s test systems, since competitors will need to also adopt the new technology or risk falling behind. This dynamic rapidly moves Aehr up the “S-Curve” as customers rush to adopt the new technology and orders rapidly increase.

The qualification of the lead customer in July 2021 may have been the beginning of Aehr’s growth story (pictured below). While bookings are relatively low right now at ‘just’ $40 million, the company may be entering a period of growth, which is why the I/O Fund has taken a position. I discuss Aehr’s most recent financial performance and forward growth projections in greater detail next.

Aehr’s Potential S-curve Growth Rate

Source

Aehr’s financials and valuation

As mentioned above, Aehr reported a surge in bookings, as fiscal year YTD bookings (June through September) have grown to $40 million, or more than the prior nine quarters combined. These bookings are expected to convert into sales this year, which led management to raise its FY2022 sales guide to at least $50 million.

In the most recent Q1 quarter, sales increased 181% YOY to $6 million, while gross margin came in at 40%. Operating expenses are largely fixed, and operating income was a slight loss of $1 million during the quarter, while EPS was a loss of -$0.02, which beat by a penny.

Aehr has a history of losses, which makes sense as customers have been placing small orders for the company’s test systems as they qualify the equipment for high-volume use. With customers starting to ramp orders, the company has forecasted that it will be profitable going forward.

Specifically, CEO Erickson stated that the company breaks-even at around $28 million in annual sales, and for every dollar in sales above $28 million, it expects ~50% of that to flow down to earnings. Simply put, the $50 million guide for FY2022 sales should lead to ~$11 million in earnings. Moreover, since Aehr has a history of operating losses, the company has significant net operating loss (NOLs) carry forwards, which lowers the amount of taxes that the company must pay.

Taking this math a step further, the $11 million in earnings will equal about ~$0.43 in after-tax earnings per share (EPS). At ~$13/share, Aehr is valued at 29x forward earnings and 6x forward sales, which seems reasonable for a company expected to grow 200%+ this year. As discussed above, the company has numerous tailwinds as the SiC and silicon photonics markets rapidly grow at a 36% and 49% CAGR through 2026, respectively.

Above is a table of comparable semi-conductor equipment providers that are expected to grow at similar rates to Aehr. Relative to the above comparable companies, Aehr trades at a premium valuation based on forward sales and earnings. However, Aehr has exposure to markets that are expected to rapidly grow going forward (silicon carbide and silicon photonics) which supports a premium valuation. Aehr is also expected to grow faster than the peer median.

Below is a table of peers that Aehr competes against. Aehr is competing against some very large companies, however, Aehr is well positioned to benefit from the above-mentioned tailwinds. If Aehr can capture a dominate position in these rapidly growing markets, its market cap may close in on some of its peers. For reference, just six years ago in 2015, Teradyne’s market cap was much smaller at ~$4 billion, highlighting how semi-conductor testing equipment providers can grow into large companies in a relatively short period of time. It is also noteworthy that Aehr’s CTO used to work at Teradyne.

What sets Aehr apart from the competition is that its wafer level burn-in testing equipment can scale better than competing products. CEO Erickson explained that Aehr’s systems are “architecturally different and unique” as the systems test 18 wafers at a time, while competing products test one to eight wafers at a time. This is significant as each test system has the footprint of a Toyota Prius, so customers who are scaling and need to test 500+ wafers a day will need 500 Prius’ of footprint in a wafer fab, which is expensive and difficult to accomplish.

CEO Erickson explained further that “we've made an enormous investment in this platform over the last decade, and particularly with the last handful of years. We have IP and patents protecting that capability, both in the tester and the proprietary contactor that enables it and we intend to defend it.” To give you a better sense of how long Aehr has been developing its unique, proprietary technology, the company received funding from DARPA back in 2001 to develop wafer level burn-in testing, and its first product wasn’t introduced until 2016.

While Aehr trades at a premium to some of its peers, the company’s technology has the potential to be a homerun if it can quickly scale with customers and provide the promised cost savings. If early adopters realize strong cost savings and higher quality products, then this will likely lead to more customers rapidly adopting Aehr’s technology. Furthermore, if Aehr can continue to capture market share in the rapidly expanding silicon carbide and silicon photonic markets, then its market cap will likely grow and mimic some of its other semi-conductor testing peers.

Risks and conclusion

A key risk going forward is supply chain risk, which isn’t unique to Aehr, but nonetheless may impact Aehr’s ability to convert the recent ramp in bookings into sales. The company has $10 million in inventory on its books, which helps get the ball rolling but is well below the $40 million in orders it has received in the past few months. The firm also has just $6.5 million of cash on its balance sheet, which helps explain why Aehr recently announced a $25 million share offering to raise more cash.

CEO Erickson explained on the Q1 Call that customers were happy to see the cash raise as it added conviction that Aehr will be able to have the necessary working capital to support its rapid growth in bookings. However, the capital raise will weigh on Aehr’s stock price in the near term.

In regards to the supply chain concerns, CEO Erickson stated that “Aehr has the manufacturing infrastructure and supply chain in place to ramp to significantly higher revenue levels. We have been ordering long lead components for systems and WaferPak, particularly for the enormous opportunity we see for silicon carbide that is gaining momentum, and we have been able to maintain reasonably lead times to meet customer requests.” The company likely has enough capacity to honor the orders that have come in thus far, and it also has the vendor relationships in place to support a further increase in customer orders going forward.

Looking forward, the company has two strong tailwinds propelling it: silicon carbide and silicon photonics. The recent qualification of Aehr’s lead silicon carbide customer may have been the tipping point that drives rapid adoption of its technology, as early adopters can gain a significant cost saving advantage.

Silicon carbide is quickly becoming the material of choice for EVs, which itself are expected to rapidly grow going forward. Furthermore, silicon photonics is also in the early stages of ramping and has exposure to strong microtrends such as datacenters, 5G and edge computing. Taken together, these two markets should help support sales of Aehr’s test systems, which in turn will lead to recurring sales of Aehr’s high-margin consumables. The company’s stock has increased following strong results, but its valuation is not overstretched. In fact, given the firm’s strong growth rate and forecasted profitability, it appears cheap relative to peers. It is also noteworthy that forward growth estimates do not fully encapsulate sales of consumables, meaning that the company’s true sales multiple is cheaper than reported.

Disclosure: Bradley Cipriano and the I/O Fund own shares in Aehr Test Systems and do not have plans to change their respective positions within the next 72 hours. You can access the I/O Fund’s positions herehere. The above article expresses the opinions of the author, and the author did not receive compensation from any of the discussed companies

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:

Spectrum-4 Ethernet Platform

Eos: The First Hopper AI Factory

DPX Instructions (ISA):

GPU Confidential Computing:

Nvidia is Becoming a Leading AI Software Company

Transformers

Triton Inference Server:

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

Note on CUDA:

Risk: Valuation

Conclusion:

Without further ado, let’s talk about who will be the world’s most valuable companies in 2030

Artificial Intelligence and Machine Learning will Exceed the Mobile Economy

Accelerated Computing Required for Artificial Intelligence and Machine Learning will Produce Two (New) FAANGs: Nvidia and AMD

Nvidia’s Moat is Called Cuda:

Nvidia’s Game Changer Was the A100 GPU:

AMD: The Dark Horse of AI Chips

AMD’s Strength Came from the Zen Architecture

AI Acceleration Goes Far Beyond Data Centers

2030 Cloud Companies Won’t Look like 2020 Cloud Companies

Cloud: Only the Strong will Survive

Big Data and Analytics/ML – Consumption Model is Here to Stay

The Blockchain is Eating the Internet

Bitcoin:

Layer One Networks

Avalanche

Chainlink:

FAANG Isn’t Dead

Microsoft:

Alphabet:

Consumer Isn’t Dead

Conclusion:

Top 20 semiconductor stocks with highest growth rates for the current fiscal year.

Top 20 semiconductor stocks with highest growth rates for the next fiscal year.

Semiconductors with Top Forward P/S Sales multiples

Quarterly Revenue Surprise

Top 5 ranked semiconductor stocks based on Free Cash Flow Margin

Top 5 ranked semiconductor stocks based on Net Profit Margin

Semiconductors: Top 10 EV/FWD Revenue Multiples

Semiconductors: Top 10 Three-month Forward YoY Growth Rates

Top 10 Weekly Share Price Movements

Top 10 Changes in sales growth estimates – last 90 days

Update on EV/Fwd revenue multiples:

EV/FWD SALES:

Top 5 EV/FWD SALES:

EV TO FWD Sales Growth Buckets:

Top EV TO FWD SALES:

Growth adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth)

Lam Research – Earnings on January 26th

Advanced Micro Devices Inc – Earnings on February 1st

Teradyne Inc – Earnings on January 26th

NVIDIA Corporation – Tentative Earnings date is February 24th

Texas Instruments Inc – Earnings on January 25th

Broadcom Inc – Tentative Earnings date is March 4th

Qualcomm Inc – Tentative earnings date is February 3rd

Throwback: Nvidia will Surpass Apple’s Valuation in 4.5 Years

Lam’s Product & Growth Opportunities

Conclusion:

Lam Q1 FY2022 Results

Lam and 3D NAND

Conclusion

Recommended Reading:

NVIDIA Corporation – Earnings on November 17th

Lam Research – Earnings on October 20th

Ambarella Inc – Earnings on November 23rd

Advanced Micro Devices Inc – Earnings on October 27th

Broadcom Inc – Earnings on December 10th

Marvell Technology – Earnings on December 3rd

Qorvo Inc – Earnings on November 04th

Semiconductor Stocks: Top 10 EV/FWD Revenue Multiples

Top 10 Weekly Share Price Movement

Top 10 Changes in sales growth estimates – last 90 days

EV/FWD SALES:

TOP 5 EV/FWD SALES:

EV TO FWD SALES Semiconductor Universe:

Growth adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth)