In these webinar clips, the I/O Fund goes over how the team plans to position for AI moving forward.
Our Criteria
In 2023, the Market Agreed with our Criteria
AI is Real but With Few Winners
AI Valuations
What our Criteria is Not
Looking Towards H2 2023
Adtech has seen extreme volatility over the past few years with Covid causing some stocks to see 1,000% gains in a brief period of time between 2019-2021, and then plummet by up to 80% in 2022. For the current year-to-date, many have rebounded despite reporting depressed growth levels.
Below, we review the stocks in the ad-tech sector to find out which companies have performed well in the recent quarter results and which companies stand out in revenue growth estimates, profits, cash flows, earnings surprise, and we also look into management insights.
Pictured Above: Ad-tech returns from Jan 1, 2022 to Dec 31, 2022. Source: YCharts
Pictured Above: Ad-tech returns since Jan 1, 2023 Source: YCharts
Source: YCharts
Unity sits at the cross-section of cloud and ad-tech. The company’s revenue grew by 56% YoY to $500 million in the recent quarter, however, the revenue was down (2%) YoY on a pro-forma basis to reflect the ironSource merger that was completed in November 2022. The company beat its own guidance of $470 million to $480 million and the analyst’s revenue estimates by 4.3%.
Unity’s guidance for the next quarter is $510 million to $520 million, representing a YoY growth of 72% to 75% and 6% to 8% YoY on a pro-forma basis. Management is expecting the overall advertisement sector to be flat QoQ. Per the macro-outlook, they are still cautious, as the company’s CFO Luis Visoso said in the earnings call that “the economic environment is still volatile and uncertain.”
Perion Network is a small cap ad-tech stock with a market cap of $1.4 billion that has sustained a stronger bottom line than its peers. We covered this stock here. Revenue grew 16% YoY to $145.2 million. Management stated the company is likely to raise its revenue growth in 2023, when CEO Gerstel stated: “Given our current visibility, and the sustainability and predictability of our business model, we feel confident in raising annual guidance for the full year 2023.”
The company’s new 2023 revenue guidance is $725 million to $745 million, representing YoY growth of 15% at the mid-point, up from the previous guidance of $720 million to $740 million.
Source: YCharts
Fubo beat analyst revenue estimates by 6.9% in the Q1 results, which led the ad-tech sector. The company’s revenue grew by 34% YoY to $324.4 million, and its North American business grew by 34% YoY to $316.5 million.
The company’s North American revenue guidance for the next quarter is $292.5 million to $297.5 million, representing a YoY growth of 36% at the mid-point. It also raised FY2023 North American revenue guidance to $1.235 billion to $1.265 billion, representing YoY growth of 27% at the mid-point, up from the previous guidance of $1.195 billion to $1.225 billion. It also reiterated its goal of being cash flow and adjusted EBITDA positive by 2025.
The company sees some improvement in its advertising business as the company’s CFO, John Janedis, answered to an analyst question on CTV advertisement demand trends. “And so when we looked at our Q1 results, to your point, we came in about flat on ad revenue. From a monthly perspective, let me just talk you through that and then I'll also go through 2Q in some of the categories. March was better than February, which is better than January. And I'd say if I sort of give you some of the numbers around that, January was down slightly, February, call it, flattish and then March was up a bit, maybe call it mid-singles. And then we're seeing further acceleration now into April and 2Q and so far April, I think finished up in the double-digits. So, we're encouraged by what we're seeing in terms of some of the trends.”
Source: YCharts
Unity leads with the highest growth estimate for the next quarter. Per what was already discussed, this is due to the ironSource acquisition. Unity is followed by Fubo and DoubleVerify. DoubleVerify’s revenue grew by 27% YoY to $122.6 million.
Revenue guidance for the next quarter is $131 million to $135 million, representing YoY growth of 21% at the mid-point. Analysts expect revenue to grow 22% YoY to $133.5 million.
Needham analyst Laura Martin raised the firm's price target on DoubleVerify (DV) to $45 from $35 and kept a Buy rating on the shares after attending an investor call with its CEO Mark Zagorski.
According to her note, Meta Platforms (META) accounts for half of the company's total social revenue, and the firm now believes that measurement revenue from Meta could double over the next 12-24 months after DoubleVerify adds brand safety suitability to its product suite, the analyst told investors in a research note. Retail media networks will drive total addressable market and revenue upside for DoubleVerify as more brands insist on closing the loop between ad spending and sales, the firm added.
Source: YCharts
For the current fiscal year, analysts expect Unity to have the highest revenue growth estimate among ad-tech stocks. It is followed by Fubo, which analysts expect to grow by 28% and DoubleVerify is expected to grow by 25%.
Source: YCharts
Most ad-tech stocks are trading at a low valuation. The Trade Desk has the highest forward P/S ratio of 19.5. The Trade Desk has been trading at a premium valuation as its revenue growth has been stronger and its bottom line is better than its peers. The company’s 2022 revenue grew by 32% YoY to $1.58 billion. This revenue growth was exceptional while other ad companies struggled with growth last year, such as Meta, which reported a decline of (1%) in revenue.
The company’s CEO and Founder, Jeff Green, highlighted in the Q4 earnings call, “Specifically in the last 6 months of 2022, The Trade Desk started to separate from much of the digital advertising market in terms of relative outperformance. In the third quarter, we have reported 31% growth while our competitors were either in retreat or posting single-digit growth. That same trend continued into the fourth quarter as we grew 24% and most of our large competitors were posting between negative 9% and negative 2% growth. I don’t think we have ever had the level of industry outperformance in our 6 years or so as a public company as we did in 2022.”
Analysts expect revenue to grow 22% in FY2023 and continue to grow over 20% till 2030, with a revenue growth forecast of 30% for FY2028.
The company’s recent quarter revenue grew by 21% YoY to $383 million. While macro conditions remain uncertain and advertising budgets are carefully scrutinized, the management sees some improved visibility. Laura Schenkein, the new CFO of the company, said in the earnings call, “Turning now to our outlook for the second quarter. While macro conditions remain uncertain, visibility has improved slightly since the beginning of the year. We are cautiously optimistic and estimate Q2 revenue to be at least $452 million which would represent growth of 20% on a year-over-year basis.”
The company reported an operating loss of ($23.3) million compared to ($17.1) million for the same period last year. The increase in operating loss was due to increased operating expenses related to in-person events and travel this year that was stopped briefly post Covid. We have noted later in our article that ad-tech stocks have a weak bottom line. The company has a better bottom line than most adtech stocks, and in the recent quarter, it ranks 7 in the operating margin among the 17 stocks we track in the sector. The company reported an adjusted EBITDA margin of 28% compared to 38% in the same period last year.
Morgan Stanley analysts recently upgraded the stock to overweight from equal weight. “We see growth in ad-supported streaming and retail media as two of the strongest growth areas in online advertising and see the US CTV market growing at a ~18% '22-'25 CAGR while we forecast retail media (global ex-China) to grow at a ~17% CAGR. As the leading independent demand-side platform (DSP), TTD is well positioned to benefit from both trends,” the analysts said in a client note. “We believe TTD will be able [to] leverage its position as an independent player to sign more retail media partners…and ultimately be a leader in offsite retail media advertising,” they added.
Source: YCharts
Ad-tech is a very cash-efficient industry, evidenced by the robust free cash flow (FCF) margins, as seen in the above chart. The Trade Desk has the highest free cash flow margin of 46%, followed by Pinterest with 30%, and Netflix with 26%.
We had highlighted the Netflix’s cash flow turnaround in our editorial in July 2022 when we said, “The most important line item for Netflix is the company’s cash flow. Looking back, this has been troublesome for Netflix as the company lost $3.3 billion in cash in 2019 as it built up its original content pipeline. However, the company is on an entirely new trajectory with $1 billion in free cash flow expected this year and “substantial” free cash flow in 2023, per Netflix management.”
Source: YCharts
Only five of the ad-tech stocks have positive GAAP operating margins in the recent quarter. Meta leads the sector, followed by Google and Netflix. We believe focusing on profitable companies or those with strong profitability paths is prudent during a time of current macro uncertainty.
AMD is more complex than Nvidia as I can simply say “near-monopoly on GPUs” or “deep moat from CUDA” and that summarizes quite nicely why Nvidia is an AI leader. Of course, there is much more to the products, and that complexity has worked in our favor.
However, AMD is far less straight forward, and coupled with the complexity of the chip market, I am not surprised there was a muted reaction to the AI conference this month. Wall Street was quite late to Nvidia’s H100 release, in fact, Nvidia’s stock was at a deep discount the very month the H100 shipped. One can only hope that AMD will be at a similar deep discount when the MI300 ships in volume in Q4 2023.
I’ve said “I’m Still Feeling Zen” with AMD during the PC-slump – a play on AMD’s Zen Architecture — and I’ve also called the company “The Dark Horse,” which refers to being an underestimated competitor. The Dark Horse reference is becoming less noteworthy given its penetration in the data center has grown 5-6X since we first started referring to the company as underestimated.
In October, I had said Nvidia was Ready to Rumble. At the exact time that Nvidia has been named AI King, I’m going to say that AMD is Ready to Rival.
Below, I go through the product lines you can expect to be Phase 1 of AMD’s AI Acceleration strategy. I believe what is described below will take us through the next two years of gains as AMD will primarily rely on the MI300A and MI300X to nibble at Nvidia’s GPU monopoly. There is also exciting things happening in CPUs with the Zen 4/4c release in the second half of the year, including a cloud optimized processor.
Later down the line, in Phase 2, AMD will benefit from recurring software revenue, hybrid AI, edge computing, FPGAs/Xilinx and Automotive. However, maintaining CPU growth coupled with a competitive GPU strategy is most important right now and I think it’s prudent to focus entirely here for the time being as our current position depends on this.
Notably, the revenue potential from Phase 2 will be quite substantial after the GPU strategy materializes. The main point to know is that major design companies will do quite well outside of the data center, so what you’re seeing now and next year will only multiply.
The goal of this particular analysis is to describe what AMD is setting out to accomplish with GPUs in as lucid a manner as possible. The opportunity in front of AMD is exciting. But, let’s first start with the risks before we go into the larger analysis as I want to make sure the risks are fully understood.
Here are the hurdles that AMD must clear to become a major AI contender – I expand on these points below.
There are also a few major positives that are in AMD’s favor – I expand on these points below.
To understand AMD’s beginnings on AI acceleration, we have to start the discussion with supercomputers.
Supercomputers are the world’s most powerful computers and are government owned by the Department of Energy at national laboratories. Currently, Frontier is the world’s fastest supercomputer, and this is powered by AMD’s EPYC Milan CPUs and AMD’s MI250X GPUs. Infinity Fabric is essential to AMD’s architecture as it links functions of the CPUs and GPUs by providing interconnects for purposes of data exchange and memory.
Supercomputers are important for national defense purposes. Per the announcement from the Lawrence laboratory: “Besides supporting the nuclear stockpile, El Capitan will perform secondary national security missions, including nuclear nonproliferation and counterterrorism. NNSA laboratories are building machine learning and AI into computational techniques and analysis that will benefit NNSA’s primary missions and unclassified projects such as climate modeling and cancer research for DOE.”
Last year, the AMD-powered Frontier supercomputer broke Japan’s record at a speed of 1.1 exaflops, which is two times faster than the record held by Japan for two years. By breaking the 1.0 ExaFLOP/s barrier in the HPL benchmark test, AMD-powered Frontier became the world’s first exascale computer. This speed is greater than a quintillion calculations per second.
This year, AMD will be powering the launch of a new and highly anticipated supercomputer called El Capitan located in Livermore, California. The ambitious goal for El Capitan is to exceed 2 exaFLOPS of “double-precision” processing power. This supercomputer is powered by AMD EPYC Genoa CPUs and AMD’s MI300A GPUs. El Capitan will also feature AMD’s ROCm open compute software platform.
AMD’s first real-go at competing with Nvidia on commercialized AI acceleration will be this year, however, the company has been powering the world’s top performing computer for five years. I think answering this question — why did the Frontier and El Capitan projects choose AMD — is critical for understanding why AMD can rival Nvidia on GPUs in the near future. The analysis below is aimed at discussing a few advantages AMD has in terms of design.
Also, investors should note that AMD’s new GPUs will be shipping around the same time that El Capitan will launch (ETA: Oct/Nov 2023).
We’ve covered AMD’s Zen Architecture in depth a few times, including about two years ago in a 1-hour webinar on AMD and three years ago in a premium report here. The 2020 report is important because it was real-time on discussing the bullish thesis that AMD could take substantial market share from Intel in the data center. At the time, the company had 4% CPU server market share and now has over 20% market share.
We need to revisit how AMD was able to take on an 800 lb. gorilla in order to piece together how AMD plans to do it again.
Here is what was said a couple of years back – I’ve bolded what is important for the purposes of this analysis:
“In August of 2019, AMD released a competitive 7nm chip while Intel was still producing 14nm chips with a 10nm chip on the way. Essentially, AMD leapfrogged the incumbent with a product that is more power efficient and allows for more cores per chip. Because 7nm are twice as dense as 14nm, AMD was able to release a 64-core server chip and 128 threads rather than AMD’s previous 32-core server chip. Up until early 2019, Intel’s offering has been a 28-core server chip and 64 threads. […] AMD has blatantly stated the second-generation EPYC server processors had 1.8 to 2 times the performance advantage of Intel’s Xeon processor line and is half the cost in some instances.”
Here is a recent statement from Microsoft on the dramatic results from AMD’s Zen Architecture:
Source: Twitter
The Zen-2 architecture introduced a multi-chip module that used the most advanced technology where it’s needed most by combining 7nm chiplets with a 14nm die. This was quite a competitive leap as Intel was still using a monolithic design.
In this case, the 14nm was leveraged for memory controllers because the central hub runs input/output (I/O) and memory better. This helped AMD beat Intel on memory bandwidth. The design also greatly improved performance by putting the L2 cache on the core and the L3 cache across the core. Overall, these design improvements lower the power required while increasing the performance as it requires fewer NUMA hops, which in turn, increases instructions per clock, and this ultimately reduces latency.
From there, AMD undercut Intel on price, which becomes a virtuous cyclebecomes a virtuous cycle as driving down costs means more chips will be bought from AMD. We also mentioned in the 2021 webinar that at the time, a third-party analyst named Michael Larabel benchmarked AMD as being 14% faster than Intel while costing about 30% less.
In what can a be an industry full of jargon, this is most important point in my previous AMD analysis as to why AMD went from 2-4% CPU server share to 20%+ when AMD’s Rome went up against Intel Xeon Cascade Lake:
“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.”$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.”
The older Rome Series Zen 2 architecture is what was discussed in our webinar. Meanwhile, the Milan Series is the current series driving forward AMD’s data center growth. The Milan Series Zen 3 architecture has made improvements in performance largely due to 3D stacking. By incorporating 3D stacking in Zen 3, AMD was able to triple the L3 cache size while only adding four clock cycles of latency. When 3D stacking is incorporated with GPUs, the result is computers that train neural nets up to 40 percent faster with 16 percent less energy.
Next up is Bergamo, the CPU line specifically designed for cloud native workloads. In this case, Bergamo will have less cache and more performance per watt. During the conference, Meta was on stage with AMD to attest to Bergamo having 2.5X better throughput performance. Part of the upcoming release includes Siena, which will drive more dollar performance per watt at the edge for telco customers. Notably, Genoa and Genoa-X will continue to provide more cache for general purpose workloads.
Total cost of ownership (TCO) refers to the total cost to own and operate equipment over its useful life span. TCO is a motivating factor for hyperscalers when evaluating equipment as it factors in not only the acquisition cost but also the costs associated with owning and operating the equipment over the hardware life cycle.
For example, Spiceworks reported that “in 2010, the research firm Gartner estimated that for a desktop PC priced at $1,000, the total cost of ownership works out to at least $2,680 per year when you factor in things like capital expenditures, labor expenses of supporting the computer, and indirect costs such as lost end-user productivity due to downtime.” Over a four-year lifespan, the costs are estimated to be $9,800 to $17,600.
We will have to get the benchmarks after the MI300s are released, but power consumption is a major contributor to higher TCO. In addition to this, if a company has to buy more GPUs to train and run similar size LLMs, then this would (theoretically) also contribute to a higher TCO for Nvidia equipment.
In the past, AMD advertised up to 20% Capex savings compared to Intel based on Epyc processors delivering more performance from a single chip compared to Intel’s dual-processor powered by two CPUs. Big Tech has capex budgets into the tens of billions. Although it’s not specifically disclosed exactly how much goes toward AI acceleration, we know that Big Tech is driving forward Nvidia’s GPU sales at $8 billion per quarter or $35 billion to $40 billion per year.
Here is the thesis in a nutshell: If a competitor can deliver 20% savings on this kind of budget with similar performance, then it will turn heads. We can geek out all day long on the computing performance of Nvidia’s H100 GPU, however, if the MI300s drive down total cost of ownership through low unit pricing, better power efficiency and reducing the number of GPUs required, then hyperscalers will line up to support this.
What Google, Amazon, Microsoft, Meta and large enterprises want most of all is to build incredible AI systems but at a manageable cost. This goes back to the virtuous cycle. The more they save, the more they can build.
If Big Tech capex goes further with AMD, then that will be something Nvidia will be forced to address. Nvidia is unrivaled right now on GPUs, which means their pricing power is unrivaled. The H100 is in short supply, and the A100 may be being stockpiled by China before United States sanctions take effect. The lead times on the H100 and A100 are into 2024 at this point, which means there is no better time for AMD to enter the market and undercut on price/TCO than right now.
In the next section, we discuss how the MI300A and MI300X GPUs may be following a similar path as the Rome, Milan and Genoa CPUs.
The El Capitan Supercomputer is expected to launch this Fall. When it launches, El Capitan is expected to follow a similar system as Frontier which is (1) AMD Epyc CPU with (4) MI300 GPUs with Infinity Fabric. The “A” in the MI300 stands for APU, which refers to a CPU being combined with a GPU. Nvidia has only recently attempted this at the HPC level with the Grace CPU and H100 GPU, but this is technically two discrete devices with separate memories.
By having a fully shared, coherent memory, the MI300A architecture reduces latency while enabling high bandwidth. The high-speed, low-latency unified memory helps improve speed while allowing the CPU and GPU to do what they do best. By allowing both processor types to access shared memory, HPC programming is more efficient.
Notably, AMD has been successful in releasing APUs for PCs and gaming. Technically, APUs underperform GPUs when it comes to gaming but outperform on PCs as they don’t use as much power as dedicated GPUs.
Here are the specs:
According to AMD, the MI300X will have 2.4X the memory density of the H100 and 1.6X the memory bandwidth. The reason that the MI300X was able to run the popular Falcon-40B large language model (LLM) with 40 parameters is because the neural network was ran entirely in memory without the need to move data back-and-forth with the external memory. AMD also stated the MI300X will be able to run up to 80B parameters on a single chip.
The MI300X requires more power than its predecessor MI250X at 750 watts, and this is higher than Nvidia’s H100 at 700 watts. However, it’s not an apples-to-apples because what the MI300X promises to deliver is running compute-intensive large language models with fewer GPUs than is required with the H100s due to offering roughly double the memory.
The need for fewer GPUs is accomplished by running LLMs in the memory. The image below shows why having 2.4X memory at 192GB compared to Nvidia’s 80GB will reduce the number of GPUs required for running popular large language models.
Here is what Dr. Lisa Su said at the recent AI conference:
“For the largest models, that actually reduces the number of GPUs you need, significantly speeding up the performance, especially for inference, as well as reducing the total cost of ownership."
In terms of the MI250X versus the MI300X, the newer model is powering nearly three times more transistors than its predecessor. There is also a lower power variant of the MI300X expected in early 2024. In addition to this, AMD offers chiplet and packaging technologies that reduce power requirements.
To compete with Nvidia’s DGX systems, AMD is also releasing the AMD Instinct Platform which will combine eight MI300X systems with 1.5B terabytes of HBM3 memory. The server utilizes the Open Compute Platform specifications so that it’s compatible with existing hyperscaler infrastructure (more on this below).
We are invested in AMD for the company’s AI potential. However, it makes sense to touch base on gaming as graphics processing units (GPUs) are first and foremost gaming chips. At the time that Nvidia was founded in the early 1990s, and up until recently, gaming was one of the most computationally challenging use cases for hardware.
Nvidia is the inventor of GPUs, and for the first two decades or so, NVDA was primarily a gaming stock. Part of our original thesis was that the AI era would be upon us when AI revenue overtakes gaming revenue for Nvidia, as this would help pinpoint when Nvidia’s industry had officially changed. It’s a big moment when something like this happens (Mac/PC overtaken by iPhone/Mobile revenue, etc.).
Since gaming is what led to Nvidia’s GPU positioning for AI/ML, it makes sense to note that AMD is a decent contender on gaming GPUs. Here’s a snapshot of how gaming GPUs rank in 2023 from industry-expert Tom’s Hardware:
PC Gamer stated that AMD has 9% of market share compared to Nvidia’s roughly 82%. This is hard to rely on for concrete numbers as GPUs are down nearly 50% year-over-year and both Nvidia and AMD’s bigger gaming releases occurred in Q4. The upcoming 2023 numbers will represent the market share a bit better. In the past, we’ve reported that Peddie’s numbers were 25% AMD and 75% Nvidia. According to PC Gamer, Intel may be eating into AMD’s market share, but we won’t know this definitively until there’s reports on Radeon RX 7900 numbers.
In 2019, Meta and Microsoft led a coalition to create open standards that allow for choice in processor and accelerator. The OAM came together to design packaging and motherboard socketing technology that allows accelerators with different sockets and thermals to be consistently deployed.
The OAM form factor replaces the PCIe form factor accelerator cards. The OAM form factor is also ideal for interoperability across custom silicon, such as ASICs, which experience excessive signal insertion loss to PCIe connectors and the baseboard.
AMD’s Instinct GPU accelerators feature OAM baseboards. These universal hardware design standards allow IT departments to choose a new GPU architecture with a more simplified installation and provides the ability to upgrade at any time. The IT departments may or may not choose AMD over Nvidia but the process is easier with OAMs and open standards.
Are you feeling bulled up? If only it were so simple! The predominant weakness AMD must address is ROCm open software versus CUDA. Our original thesis on Nvidia in 2018 pointed toward developers/CUDA being the primary moatthe primary moat. The CUDA software moat will be tougher to disrupt than Nvidia’s A100 and H100 hardware lead. This is because developers have to install new drivers, compilers, and will have to learn new libraries and tools. Meanwhile, Nvidia’s closed source CUDA has everything a developer needs to support code development. So, why would a developer switch? Answering this will not be so easy for AMD to answer compared to GPU performance, power efficiency, and total cost of ownership.
The long history of support CUDA that CUDA offers will be very hard for AMD to shake as it requires time to build up proper support, including libraries and frameworks. AMD’s ROCm has only a fraction of the libraries that CUDA offers. Meanwhile, software engineers in AI/ML lack only one thing: time. It’s a very competitive and fast-moving space, and AI engineers are in high demand. AI startups are getting more funding than any other type of startup right now. Money is tight for startups everywhere – except in AI.
Speaking of startups, AMD is particularly lacking in terms of bottom-up adoption and revenue, which refers to employees at a lower level helping to drive adoption rather than top-down from the C-suite. This is because AMD is weaker in terms of its consumer-level RDNA cards as they lack matrix cores for machine learning purposes.
In December, the company released RDNA3 which has more matrix operations, but does not compare to dedicated matrix cores. The CDNA GPUs from AMD are aimed at hyperscalers and fully satisfy AI/ML operations in this regard, however, the price is prohibitive for individual developers and smaller startups.
An example of ROCm lacking support is that the open source development platform underperforms with the popular 3D modeling program called Blender and does not offer support for bugs. Currently there is a statement on the wiki linux page: “ROCm HIP is known to currently have issuesissues with cycles in the while in the 3D Viewport (refer to the "issue" cited before to find workarounds), however rendering with Render > Render Image or F12 should work fine.”
Overall, developers not only must take the time to learn a new development platform but are likely to encounter roadblocks in terms of support for popular programs.
Pytorch is a popular, deep learning framework that has natively supported ROCm since 2021 and also supports CUDA directly in the interface. In June of 2022, ROCm 5.3 moved from Beta to Stable on the Pytorch 1.12 framework. This a good date to work with (June of 2022) in terms of when ROCm officially launched for AI/ML development.
PyTorch was founded by Meta’s AI research team, and it quickly overtook Google’s framework TensorFlow. The current version PyTorch 2.0 utilizes OpenAI’s Triton software stack. OpenAI open-sourced Triton in an effort to circumvent Nvidia’s closed source CUDA libraries.
Pictured Above: Beta tested throughput performance for AMD’s CDNA architecture with ROCm software platform and PyTorch Framework and libraries. You can expect this to increase dramatically with the MI300.throughput performance for AMD’s CDNA architecture with ROCm software platform and PyTorch Framework and libraries. You can expect this to increase dramatically with the MI300.
What we discussed earlier in the analysis is that the MI300A GPUs are unique due to the unified, coherent memory. It’s expected that AMD will offer memory models on the PyTorch framework to help developers optimize memory usage in AI acceleration.
ROCm is currently only supported on Linux but is expected to support Windows soon. This further prevents adoption as a serious contender to CUDA should be supported on both major operating systems.
The MI300s are on the way and there is pent-up demand for GPUs. Prices are high, lead times are long, and the race for AI is fierce. AMD is an equation where strong management + strong track record on CPUs + lower prices, lower power usage, and fewer GPUs for LLMs = the one and only contender that can take on Nvidia. The MI300s have been in development for longer than one might assume and they arrive in volume in Q4.
Let’s get ready to rumble! –Michael Buffer
This article was originally published on Forbes on Forbes Forbes on Jun 29, 2023, 08:52 pm EDT
The market is like the weather, it changes often. The market’s fickle nature is partly why stocks often lead to losses for retail investors. By going “all-in’ or “all-out,” individual investors can often be overexposed to the sudden changes the market brings. This is especially true when the market has treated investors well as it creates a sense of security – or worse, complacency.
Our site is unique in that we provide active management. This has helped us outperform across four audit periods. Our stance is the weather will always change – from good times to bad times, and from bad times to good times. Therefore, we are not over exposed in either direction.
An example of this is Nvidia, our largest position. As the allocation grew well beyond 10%, we took gains. Even after taking gains, the company is currently at a 17% allocation. In May and June, I stated that our firm was not buying Nvidia right now. Well, similar to the weather, this has changed. My firm bought a small tranche of Nvidia yesterday for $410. This tranche will come with a stop, meaning if the stock sells off, we will close this 2% tranche while hedging the 15% original position.
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With the cash we raised throughout 2022, NVDA was the primary target of deploying some of this cash once our analysis signaled a bottom was in place. The below is a real-time trade notification we sent to our members on the October 13th.
Source: I/O FUND
The above alert was 1 of 9 alerts we sent out from 2021 – 2022 to buy NVDA below $200. However, since February of 2023, we have been systematically taking gains at key levels based on technical and macro warnings. Even with logging sizable wins while raising cash, it in the top position in 2023.
In our pre-earnings buy-plan for NVDA, we stated that “It is our belief that NVDA is setting up for a sizable pullback, which we believe will open the door for better long-term entries.” Though we do believe that lower levels will manifest in time, the recent earnings report moved forward expectations regarding AI, which is showing up in the price action. We have been discussing that Nvidia will be an AI leader for years with an allocation to match, yet predicting the exact day and month the market would finally price in this thesis is impossible to predict (and timing to this level is not necessary when holding a large longer-term position)
Regarding price, we work in probabilities, and when the market changes, so do we. The key to NVDA today is the large gap from their earnings report. This gap is either a breakaway gap, or an exhaustion gap. If it is a breakaway gap, which is represented by our red count below, then it is the halfway point in this push higher. On the other hand, if price breaks below $340, likely on some type of “event,” then the gap is an exhaustion gap, and will mark a larger top. This is represented by our blue count.
Every Thursday at 4:30 pm Eastern, the I/O Fund team holds a webinar for premium members to discuss how to navigate the broad market, as well as various stock entries and exits. We offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here.Learn more here.
Source: I/O Fund
The $405 – $395 region will likely continue to act as strong support for the red count. This is where we added back in anticipation for a ~38% push higher. Our stop for this move will be a break below the $340 critical support region, which is ~14% lower than our entry.
Unlike many, we do not believe AI is a bubble, nor do we think the valuations in some of these names is stretched, as many believe. What does concern us regarding possible “events” are: 1) geo-political tensions forcing a ban of selling NVDA’s chips to China – which Beth spoke about in May with Bloomberg Asia; 2) the inevitable recession that will likely start to be priced into equities in Q4/Q1, but could get pushed forward due to an unforeseen event.
Because of these risks, we are buying with an exit plan for any new entries. It is our belief, based on the economic data, that a recession is a more likely than not for the US economy. However, based on current projections on timing, we could see a continued push in AI leadership through year-end. This is what we are further positioning our portfolio for, with the realization that we could top out sooner than anticipated.
The I/O Fund has been beating the drum about AI for 5 years. Now that it is here, we are targeting choice mid-cap to mega-cap names in the coming pullback. Once this exuberance runs its course, and the market gives up on AI, we will be buying the dip for this once-in-a-lifetime tech trend that is just starting. Join us the week following the holiday, Thursday, 7/13, at 4:30 EST where we will go over the specific AI stocks we are targeting. We will provide the macro backdrop, along with entry prices.about AI for 5 years. Now that it is here, we are targeting choice mid-cap to mega-cap names in the coming pullback. Once this exuberance runs its course, and the market gives up on AI, we will be buying the dip for this once-in-a-lifetime tech trend that is just starting. Join us the week following the holiday, Thursday, 7/13, at 4:30 EST where we will go over the specific AI stocks we are targeting. We will provide the macro backdrop, along with entry prices.
Tier 1 Media outlets such as Fox Business News, Bloomberg and Real Vision interviewed Beth four times over the past month about her call on Nvidia and also picked her brain on other AI-related topics.
Below are clips of Beth Kindig’s Tier 1 media coverage in the month of May and June.
I/O Fund Lead Tech Analyst Beth Kindig was early to Nvidia’s AI story with five years of coverage dating back to 2018 on the very specific thesis that this company would one day lead on AI. Beth’s prescient call on Nvidia and a handful of others led to the I/O Fund portfolio being positioned with a 45% allocation to AI stocks going into May. Compare this to Stanley Drunkenmiller, who had 29.5% allocationhad 29.5% allocation in AI, and has been covered by the press as the leading AI investor.
The stock is up 710% since her coverage five years ago when she stated: “Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FPGA chips used by Microsoft […] Data center revenue stands at 24% and is rapidly growing. When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.
Source: YCharts
In the clip below, Beth Kindig discusses with Charles Payne of Fox Business News how “it was a hardware that provided the $950 billion to $1 trillion market cap we see today [for Nvidia], but it will be software that will propels Nvidia into the Trillions for market cap.”
She specifically points toward DGX Cloud and AI-as-a-service for Nvidia’s software layer and how this is expected to eventually eclipse its hardware revenue. The cost of AI supercomputers can be several million dollars and Nvidia will help to lower the costs of AI development by allowing enterprises and startups to rent a supercomputer in the cloud for roughly $40,000 a month. Kindig states this is likely to be popular not only because the costs of AI development are prohibitive but because AI is a race in terms of time to market, as well.
The video below was originally recorded on June 9, 2023.
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Microsoft is another AI stock that Charles Payne interviewed Beth Kindig about later in the month of June. Payne specifically asked Kindig about her research note that stated the company is well positioned to increase revenue by $100 Billion. She cites Microsoft CoPilot as a primary driver as it can immediately increase enterprise spend on the Office 365 productivity suite, as well Bing Search which will add $2 billion in revenue for every 1% of market share that Bing takes from Google as a result of Chat-GPT. In addition to this, cybersecurity is a $15 billion market and it can easily double by 2027 with the help of AI.
The video below was originally recorded on June 15, 2023. You can view the clip here.
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Beth Kindig discusses Nvidia's competitive strength and AI. She speaks with Rishaad Salamat, David Ingles, and Yvonne Man on "Bloomberg Markets: China Open." Beth Kindig highlights that the company is very resilient and insulated. The reason is that companies have been saving cash to buy accelerated computing for the data center and Generative AI applications. Most tech companies are coming under tighter tech budgets, but the one area of growth is specifically AI within Big Tech capex budgets. This is the market Nvidia serves.
Every Thursday at 4:30 pm Eastern, the I/O Fund team holds a webinar for premium members to discuss how to navigate the broad market, as well as various stock entries and exits. We offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here >>Learn more here >>
Beth said that semiconductors will continue to be winners because this is what is enabling accelerated computing. In the past, data center investment was focused on processors. Now, they have to move to accelerated computing, and that’s where Nvidia benefits because NVDA is the leader in parallel processing.
At minute 3:00, Beth outlines the risks for China, stating that any long-term Nvidia investor should be prepared for China to be the primary threat for this company. “This can rock the stock, so to speak,” she stated. AI is an immense opportunity but “for these major design companies which includes AMD, China could not be more important.”
The video was originally recorded on May 25, 2023.
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Beth was also interviewed by Real Vision, where she followed up on her initial call in January to buy Nvidia. At the time, she was asked what would cause her to sell the stock and she “absolutely nothing except maybe World War 3.” Fast forward, and today Beth has the highest returns from the 3 Ideas Series on Real Vision. The follow up is behind a paywall, but those with a Real Vision membership can view it here:
The video was originally recorded on May 25, 2023.
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As stated in the article, Beth Kindig and I/O Fund currently own shares of NVDA. This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.
