This article was originally published on Forbes on Jul 11, 2024,09:48pm EDTForbes Forbes on Jul 11, 2024,09:48pm EDT
After months of being the lowest performing Mag 7 stocks, Tesla saw rapid gains — up 42% in a one month rally, with 37% of those gains in eight sessions — after it reported Q2 deliveries ahead of expectations and a surge in energy storage deployments.
Optimism had also been building for its much-anticipated robotaxi reveal on August 8, but that now has reportedly been pushed back until October. Despite the surge in share price and renewed deliveries growth in Q2 relative to Q1, Tesla still is facing an EV demand problem with production and deliveries set to decline in 2024. Investors are hoping Tesla is at a meaningful bottom, yet that will require significant growth in the back half of the year.
Production & Deliveries Decline
Tesla delivered 443,956 EVs in Q2, about 1% more than consensus for 439,302 deliveries. Despite rebounding to a 57,000 QoQ increase from Q1, Q2 notched a second straight YoY decline, at (4.8%), though this was an improvement from Q1’s (8.5%) YoY drop.
Production fell to the lowest level in seven quarters, falling to 410,831 vehicles – this represented a (5.2%) QoQ and (14.4%) YoY decline in production. This follows production issues the EV maker faced in Q1, primarily impacts to ramping up the refreshed Model 3 in Fremont, and more recent issues with lowered Model Y production in China and five days of production pauses in Germany in June.
Source: I/O Fund
While the QoQ increase in deliveries was a positive sign to see after Q1’s sharp sequential decline, production and deliveries are both peaking in the short term on a TTM basis. Both have pulled back below the 1.8 million mark in Q2 – production totaled 1.769 million vehicles, with deliveries at 1.75 million vehicles. This comes after Tesla warned in Q1 that 2024’s “vehicle volume growth rate may be notably lower than the growth rate achieved in 2023.” At the moment, we’re tracking for a (3%) to (4%) decline.
Production and deliveries both declined below 1.8 million on a TTM basis.
Source: I/O Fund
Q2’s deliveries point to inventory reduction/channel clearing efforts. Q1 had excess inventory of more than 46,000 vehicles, and thus Tesla had lowered production for this excess to be absorbed in Q2.
In Q1, Tesla noted that it began lowering vehicle and subscription prices, and offering leasing and financing deals to help boost demand, and its TTM trend seems to confirm that weaker demand from Q1 is persisting through to Q2. The industry backdrop in the US remains challenged, as EV demand “has grown more slowly than expected due to high borrowing costs, economic uncertainty and consumer preference for gasoline-electric hybrids.”
To ease these fears, Tesla would need to report strong sequential growth in Q3 and Q4, for both production and deliveries. Assuming 5% QoQ growth in production in Q3 and 7% QoQ growth in Q4, for volumes of ~431,370 and 461,570 respectively, and 2% residual inventory in each quarter, Tesla would end the year at 1.737 million vehicles produced and 1.705 million delivered. This would mark a nearly (6%) YoY decline.
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China Deliveries, Market Share Slip
Tesla continues to face major headwinds in China, with China-made deliveries declining on a YoY basis for a third consecutive month in June. We noted to our free readers in November and December 2023 that primary rival BYD’s strong growth presented a tangible and challenging headwind for Tesla in that nation.
Now, we’re seeing more evidence that Tesla’s growth challenges are unique for China. Tesla’s China-made deliveries totaled 71,007 vehicles in June, a 2.2% MoM decline and a 24.2% YoY decline. Stripping out exported vehicles, local deliveries were 59,261, down nearly (20%) YoY but up 7.3% from 55,215 deliveries in May.
BYD outsold Tesla more than 2-to-1 in June, delivering 145,179 BEVs in the month, up 13.2% YoY. Smaller EV rivals Nio and Zeekr also saw strong deliveries, posting record high tallies for June. Industry-wide growth was strong, with NEV sales projected to rise 8% MoM and 28% YoY to 970,000 vehicles. Tesla’s market share dropped below 7% in June, down from more than 11% a year ago as industry growth remains strong and as BYD continues to outsell Tesla significantly in China.
Tesla has an easy comp for July, where China-made deliveries were 64,285 vehicles, including exports. It’s imperative that Tesla break this string of declines in one of its core automotive markets as it heads into Q3. China-made sales were 205,747 vehicles in Q2, or more than 46% of total deliveries.
Energy Storage a Bright Spot, But EPS Impact Likely to be Minimal
Energy storage was a bright spot in Q2, with Tesla reporting a record 9.4 GWh in deployments, up more than 129% QoQ and 154% YoY. Q2’s deployments exceeded historical levels at 4 GWh per quarter, at a maximum.
Source: I/O Fund
This strong growth in deployments should help the segment contribute more to both revenue and gross profit, as its contribution to gross profit has increased significantly through 2023 and 2024. Energy storage contributed less than 8% of revenue in Q1, but could contribute 14% or more of total revenue assuming revenue more than doubles sequentially.
In terms of gross profit contribution, energy storage contributed 10.9% of Tesla’s $3.69 billion in gross profit last quarter, compared to 3.7% in Q1 2023. Energy storage has a superior margin profile versus Tesla’s automotive segment, at above a 24% gross margin in Q1. However, EPS impacts will be minimal in Q2 despite the likely triple digit QoQ revenue growth, as automotive margin has stabilized in the 18% range.
Assuming energy storage gross margin expands at the same pace in Q1 at 3 percentage points, to a 28% gross margin, the segment could generate $1.05 billion in gross profit, up from $403 million in Q1. This $600 million sequential growth would be a primary driver of sequential growth in gross profit company-wide, likely to $4.7 billion to $4.8 billion in Q2, up from $3.7 billion in Q1.
However, this boost to gross profit, driven by energy storage, won’t translate into a meaningful bottom-line impact. Assuming a 10% QoQ increase in operating expenses, net income would project to $1.85 billion, or ~$0.62 per share, just $0.01 ahead of the current consensus estimate for $0.61.
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Revenue, EPS Growth Muted
Q2 is currently expected to be the last quarter in which Tesla registers negative growth on both the top and bottom line, with analyst estimates pointing to (2.8%) revenue growth to $24.24 billion and (33.5%) adjusted EPS growth to $0.61.
Source: I/O Fund
Analysts expect Tesla to return to YoY growth in Q3, with revenue growth of 9.2% and adjusted EPS growth of just 2%, suggesting margin headwinds are expected to persist as this comes against a weak comp. It also highlights that despite this recent rapid growth in energy storage, automotive sales and margins will be a primary driver of bottom-line strength or weakness. Should energy storage continue to grow off of Q2’s level of deployments, it may shape up to be a more significant driver in 2025.
Margins Yet To Rebound
We have tracked Tesla’s margins for nearly a year now, assessing how low Tesla’s margins could go in an analysis in August 2023. We had estimated that Tesla’s operating margin would decline to 7.8% in our base case, or to 6.2% in a more bearish case. We also reiterated after Q3 earnings that this continual decline in margins highlights a broader concern for investors in that Tesla has provided no concrete guidance on how far margins will decline.
Tesla's operating margin continues to slide on a quarterly and TTM basis
Q1 2024’s actual operating margin was 5.5%, down from 11.4% in Q1 2023. On a TTM basis, operating margin fell to 7.8%, back to 2021 levels, and down from a peak of 17% at the end of 2022. Automotive margin has yet to rebound, and energy storage’s contribution is still not large enough to drive a meaningful inflection in operating margin.
Tesla's automotive operating margin dropped back below 16.4% in Q1, just a fraction above Q3 2023’s low.
Source: I/O Fund
Automotive operating margin dropped back below 16.4% in Q1, just a fraction above Q3 2023’s low. If this is a sign of stabilization in the 16% to 17% range, Tesla is facing a rocky road ahead, as operating margin has weakened consistently with automotive gross margin below 20%.
Conclusion
Tesla’s monster 42% one-month rally follows its Q2 delivery beat and budding optimism for its robotaxi reveal event, but under the surface, Q2’s delivery numbers do not seem quite as strong. TTM production and deliveries both peaked and have begun to decline, and it would take strong double-digit sequential growth through the remainder of the year to break this trend and return to positive YoY growth.
Demand issues look to be persisting as Tesla has lowered production to sell off a large chunk of existing vehicle inventory, with Q2’s production volume the lowest in seven quarters and more than 5% below delivery volume. Energy storage was a bright spot with triple-digit sequential growth, but its contribution down the line is not yet meaningful enough to drive a significant EPS beat.
While many will argue that Tesla is one of the most advanced AI companies in the world, my response is “sure,” but Tesla is also heavily exposed to consumer spending — and this is entirely out of their control. It’s been our contention for some time that Tesla is a Fed-related stock as vehicle financing and EV demand hinges on interest rates.
Interest rates are truly the most important data to track for Tesla in the current environment as high interest rates mean Tesla must lower prices (or vice versa). Therefore, it’s not surprising that Tesla has rallied during a period of increased optimism that a rate cut may be on the horizon.
Some will talk about recurring software revenue from robotaxis as the most important catalyst, but the harsh reality is that the Fed lowering rates is the most important catalyst for Tesla today. That may not be as exciting as AI, but Tesla is one of many tech stocks whose revenue growth and profitability is depends on the Fed instilling a more dovish policy.
Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.
TSMC is a foundry that manufactures the world’s most advanced chips, designated by node size. The most advanced node in production today is the 3nm and is primarily used by Apple in iPhones and MacBooks. The 5nm/4nm is used by Nvidia and others for AI accelerators, with high-performance computing quickly moving to 3nm and even 2nm.
Taiwan Semiconductor reported earnings on April 18th. The company topped analyst estimates and its internal guide with revenue growth of 12.9% YoY growth for US$18.9 billion. EPS beat by 4.4% at $1.38 reported compared to $1.32 expected.
Advanced node revenue continues to remain strong, though 3nm revenue dipped sequentially. Per the opening remarks: “3-nanometer process technology contributed 9% of wafer revenue in the first quarter.” This is down from 15% last quarter. The decline is temporary with Trend Force expecting 3nm production capacity utilization to be up 80% by year end. This quarter, revenue from 5nm and 7nm both expanded 2 points.
Despite warning of a slowdown in the broader semiconductor industry this year, TSMC’s April sales surged to NT$236 billion for growth of 60% YoY and 21% MoM. This marks a positive start to the 20-percentage point acceleration to 33% revenue growth that analysts expect as soon as the September quarter.
May sales grew by 30.1% YoY to NT$229.6 billion and June sales grew by 32.9% YoY to NT$207.87 billion, primarily due to the strong demand for AI chips.
We’ve provided an important foundry update on TSMC in early June, which you can find here. Ultimately, we feel obliged to cover TSMC again for our premium members as it’s truly the one that got away from us in H1 2024.
We feel some of the most important work we can do as investors is to look at the stocks that got away from us. The team is continually looking to improve, and thus we present you with an update on the financials as we look for an entry again.
Market Dominance
According to Trend Force research, TSM is the leading global foundry in terms of revenue. It has a market share of 61.7% in Q1 2024, up from 61.2% in Q4 2023. Samsung ranks a distant second with a market share of 11%, down from 11.3% in Q4 2023. SMIC and UMC rank third and fourth with a market share of 5.7%.
Most importantly, the company has over 90% market share in the manufacturing of advanced AI chips.
Financials Update: Strong Q2 Sales due to AI
The company’s June monthly sales grew by 32.9% YoY to NT$207.87 billion. May monthly revenue grew 30.1% YoY to NT$229.6 billion and April monthly revenue grew 59.6% YoY to NT$236 billion. Q2 revenue grew by 40.1% YoY to NT$673.51 billion. In USD, it grew by 32.9% YoY to $20.83 billion using the average exchange rate of 1 US dollar to 32.33 NT dollars. We will get the official USD numbers when the results are announced on July 18th. The monthly numbers suggest that Q2 revenue will easily beat the management guide of $19.6 billion to $20.4 billion. The company is benefiting from the strong demand for Artificial Intelligence chips and a recovery in the PC market.
The company reported its Q1 2024 results on April 18th. Revenue grew 12.9% YoY and down (-3.8%) QoQ to $18.87 billion and beat management guidance of $18 billion to $18.8 billion. The recent quarter showed an acceleration of revenue from a decline of (-1.5%) in the previous quarter. Management guidance for the next quarter is $19.6 billion to $20.4 billion, representing YoY growth of 27.6% at the midpoint.
The analyst consensus estimates are trending higher. They expect revenue to grow 29.8% YoY in the next quarter, up from the 20.6% YoY growth expected in mid-October and are expected to accelerate to 32.7% YoY growth in the September quarter.
Note: The below figures will differ slightly from our reports/the company IR due to the currency conversion. However, we use the estimates below to understand the expected growth rates.
Margins
The Q1 gross margin improved 10 bps sequentially to 53.1%, but it was down 320 bps YoY. The gross margin was down YoY as the company is witnessing higher costs due to the increase in electricity costs and due to a higher contribution from the 3nm node. This is expected, given that TSMC has historically seen headwinds in the initial ramp phase before ultimately realizing higher margins once the node has scaled.
The gross margin guide for the next quarter is 51% to 53%. The gross margin is expected to decline 110 bps sequentially at the mid-point primarily due to the impact of the earthquake on April 03rd in Taiwan and due to another hike in the electricity prices in April this year.
Wendell Huang, CFO of the company, said in the Q1 earnings call, “After last year's 17% electricity price increase from April 1, TSMC's electricity price in Taiwan has increased by another 25% starting April 1 this year. This is expected to take out 70 to 80 basis points from our second quarter gross margin. Looking ahead to the second half of the year, we expect the impact from higher electricity cost to continue and dilute our gross margin by 60 to 70 basis points. We also expect the higher electricity cost to indirectly lead to higher materials, chemical and gases and other variable costs.
In addition, we expect our overall business in the second quarter of the year to be stronger than the first half. And the revenue contribution from 3-nanometer technologies is expected to increase as well, which will dilute our gross margin by 3 to 4 percentage points in second half of '24 as compared to 2 to 3 percentage points in first half of '24.”
The operating margin improved 40 bps sequentially to 42% and was down 350 bps YoY due to the points discussed above. The company is working on tighter cost controls and has helped to reduce operating expenses to 11.1% of revenue in the recent quarter from 11.4% in the December quarter. The operating margin guide for the next quarter is 40% to 42%.
The net margin declined 20 bps sequentially to 38% and down 270 bps YoY. GAAP EPS came at $1.38 compared to $1.31 in the same period last year and beat estimates by 4.4%. The analysts expect GAAP EPS to grow 21.1% YoY to $1.38 in the next quarter and accelerate to 34.1% growth to $1.73 in Q3.
Management is confident of achieving a “long-term gross margin of 53% and higher and sustainable ROE of greater than 25%.”
Due to the economies of scale and its leadership position in the foundry industry, the company maintained good profitability. Many companies have struggled with rising costs, while TSM has successfully navigated these challenges by controlling costs and negotiating better prices with its customers.
Cash Flows and Balance Sheet
The operating cash flow was $13.9 billion or 74% of revenue compared to $12.66 billion or 76% of revenue in the same period last year.
The company’s financial stability is evident in its free cash flow growth, which has more than doubled YoY. Free cash flow was $8.12 billion or 43% of revenue compared to $2.72 billion or 16% of revenue last year. The foundry industry is capital-intensive, and the company is witnessing a stabilization in capital investments that led to higher free cash flow in the recent quarter.
The company has cash and marketable securities of $60 billion and debt of $30.25 billion compared to $54.89 billion and $30.03 billion in the December quarter. The company paid $2.48 billion in cash dividends in the recent quarter.
Advanced Nodes and AI revenue
The Advanced nodes are defined as 7-nanometer and below. We discussed in our recent editorial on the advanced nodes and AI-related revenue reaching fresh records. Most of the AI chips produced by the company utilize 5-nanometer and 4-nanometer process technology. However, 3-nanometer revenue is expected to triple this year. Volume production for 2-nanometer is expected in Q4 of 2025 and should have a meaningful revenue contribution in the first half of 2026.
We mentioned, “Currently, AI accelerators use TSMC’s 5nm process. Nvidia’s Hopper and Blackwell are built with a N4X process that is tailored for high-performance computer applications. This is a customized variant called “4N” that Nvidia uses, yet TSMC recognizes this as 5nm revenue in their earnings report. AI accelerators are expected to quickly move to smaller nodes to help lower power consumption. TSMC’s 3nm process is more energy efficient, and energy efficiency will improve further with the 2nm process.”N4X process that is tailored for high-performance computer applications. This is a customized variant called “4N” that Nvidia uses, yet TSMC recognizes this as 5nm revenue in their earnings report. AI accelerators are expected to quickly move to smaller nodes to help lower power consumption. TSMC’s 3nm process is more energy efficient, and energy efficiency will improve further with the 2nm process.”
Due to its leadership position, management has been optimistic about the long-term opportunity in the manufacturing of AI chips. “In summary, our technology leadership enable TSMC to win business and enables our customer to win business in their end market. Almost all the AI innovators are working with TSMC to address the insatiable AI-related demand for energy-efficient computing power. We forecast the revenue contribution from several AI processors to more than double this year and account for low-teens percent of our total revenue in 2024.Almost all the AI innovators are working with TSMC to address the insatiable AI-related demand for energy-efficient computing power. We forecast the revenue contribution from several AI processors to more than double this year and account for low-teens percent of our total revenue in 2024.
For the next 5 years, we forecast it to grow at 50% CAGR and increase to higher than 20% of our revenue by 2028. Several AI processors are narrowly defined as GPUs, AI accelerators and CPU's performing, training and inference functions and do not include the networking edge or on-device AI. We expect several AI processors to be the strongest driver of our HPC platform growth and the largest contributor in terms of our overall incremental revenue growth in the next several years.”For the next 5 years, we forecast it to grow at 50% CAGR and increase to higher than 20% of our revenue by 2028. Several AI processors are narrowly defined as GPUs, AI accelerators and CPU's performing, training and inference functions and do not include the networking edge or on-device AI. We expect several AI processors to be the strongest driver of our HPC platform growth and the largest contributor in terms of our overall incremental revenue growth in the next several years.”
According to the Commercial Times report, the company is planning to increase the price of 3 nanometer chips by 5% and price of advanced packaging by 10-20% next year. This should help to clear up some of the margin worries caused by the increase in electricity prices and the increased costs of operations of overseas fabs.
Nvidia’s CEO Jensen Huang replied to a question from Morgan Stanley analyst on Nvidia’s opinion on raising prices saying “The price of TSMC's services was too low, and that TSMC's contribution to the world and to the technology industry was not adequately reflected in its financial reports.” It shows the immense pricing power of TSMC.
Its customers Apple, Nvidia, Qualcomm, and AMD have booked the company’s 3nm process technology through 2026 amid the strong demand for AI chips. It further shows TSMC's leadership position in advanced nodes. TrendForce also reported that the company has received new orders from MediaTek and Google for the 3nm advanced chips.
The AI wave has also boosted the company’s advanced packaging business, particularly Chip-on-wafer-on-substrate (CoWoS) services. During the last earnings call, the management also highlighted that the demand for advanced packaging “is high, extremely high. And we do our best to increase the capacity to alleviate the shortage.” TSMC’s monthly CoWoS capacity is expected to expand to 60,000 wafers by the end of next year, up 300% from 15,000 at the end of 2023.
The company’s other advanced packaging technology, system-on-integrated chips (SoIC), is also in robust demand. According to TrendForce, the company is expected to increase the monthly capacity to 5,000 to 6,000 units by the end of this year, up 2.5x to 3x from 2,000 units in 2023. Furthermore, by the end of next year, it is expected to scale to 10,000 units. According to the Economic Daily News, the company has secured Apple as the second major customer of SoIC.
Valuation
The company is trading at a P/E ratio of 35.1 and a forward P/E ratio of 28.9. The P/S ratio is 13.4 and the forward P/S ratio of 11.3. In the last five years, the P/E ratio peaked at 41.8 in February 2021 and hit a low of 10.3 in November 2022. The stock is now trading above its five-year average P/E ratio of 23.7. However, with the expected growth from the increasing contribution of AI revenue and its leadership position in advanced nodes, the market will likely continue to reward with a premium valuation.
Conclusion
TSMC has good long-term revenue growth potential due to its leadership position in advanced technology nodes. The primary catalysts are HPC, particularly AI chips and the recovery in the smartphone market. It has been able to negotiate better prices with its customers, made cost improvements, and maintained strong margins and free cash flows. Here are some technical analysis notes from Portfolio Manager Knox Ridley:
There are times in which Technical Analysis can be quite accurate at nailing lows/highs and managing risk. We use it in conjunction with our outstanding fundamental process to increase our accuracy. However, there are times in which the technicals are at odds with the fundamentals, meaning one is wrong. This is exactly what happened in November of last year, as the technicals sensed risk while the fundamentals did not.
The one in red was a 5 wave drop from the June high followed by a 3 wave bounce. More times than not, when we see this setup from a technical perspective, it tends to lead to more downside. This is not what happened, as TSM took the green path higher, and then exceeded it.
We decided to lock in +20% gains in TSM on this bounce, due to technical and geopolitical risk, leaving a lot on the table. This happens in investing, and it has led to the I/O Fund tightening up our process moving forward.
That being said, where TSM is today is much clearer now that we have a solid trend in place.
The near vertical move higher suggests that we are in an unfinished uptrend. This is unfolding as a 5 wave pattern, which still needs at least a 4th wave drop and a 5th wave push higher to complete. This is where the two counts in the chart diverge:
Red – We are completing the large 3rd wave, should see a sizable 4th wave correction soon, followed by one more large push to new highs.
Green – We are completing a minor 3rd wave soon. This would be followed by a smaller correction, which would then lead to at least two more swings higher.
Like most 3rd waves, they can continue to extend, which makes identifying a turning point challenging. But, at some point, this 3rd wave will need to correct which is why we plan to buy on a dip rather than a breakout. The best way to handle this is set up a moving support line that will signal the 3rd wave has ended and the 4th wave drop has begun. That level for TSM is $176.
Once we drop below $176, it will signal that the expected correction is underway, and we will setup our buy plan.
Two weeks ago, Knox Ridley was asked to present at the Seeking Alpha Investing Summit in New York. The topic of the presentation was “How to Safely Invest in Tech: The Million Dollar Question,” a topical question that we believe has not been answered. Tech investors either buy with no risk management measures in place, or they are too safe and do not participate fully in the life-changing returns that tech has to offer. As a result, we’ve seen investors hold the wrong stocks for too long, the right stocks for not long enough, or approach tech investing as a long term buy and hold strategy that has led to large losses.
With audited returns of 131% since inception, compared to the NASDAQ-100’s 82%, portfolio manager, Knox Ridley, lays out how we have successfully maintained an overexposure to the right tech stocks, while navigating the inherent volatility within tech. We also discussed our views on AI and if we view it as a bubble, as well as our views on crypto currencies. The entire presentation can be viewed on Seeking Alpha here.
Below are a few highlights of the event. Knox Ridley discusses the importance of a catalyst within proper tech investing, how the investing community is already confusing AI, and how the our team is preparing for a potential weak spot within the economy.
If you are a tech investor who would like know our plans for participating in the upside tech has to offer, and how we minimize the downside, we encourage you to attend one of our weekly webinars. Every Thursday at 4:30 EST, portfolio manager, Knox Ridley, goes through various broad market charts, as well as discusses our game plan on various stocks and crypto currencies that we currently own or want to own. Learn more here.
Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.
Lead Tech Analyst and CEO Beth Kindig recently joined Real Vision’s Nico Brugge to discuss her AI outlook on leading AI stock Nvidia, while sharing which AI stock she believes may outpace Nvidia’s returns through 2030.
This AI stock’s opportunity is in the AI inference market, which will begin to take shape when large language models (LLMs) migrate and operate locally on AI-capable client devices, such as PCs and smartphones. Kindig has boldly stated in Forbes, and on CNBC, and Bloomberg that Nvidia will reach a $10 trillion valuation by 2030. Yet, she believes this AI stock may outpace Nvidia’s stock and provide investors with a larger percentage return.
We built a leading AI portfolio beginning with Nvidia’s AI thesis in 2018, with our AI allocation of 45% in 2023 helping push us to a 131% cumulative return since inception. Now, we’re closely tracking what we believe is one of the next explosive growth waves in AI – and it’s not the cloud. Learn more here.here.
Training Versus Inference
Nvidia had surged to briefly become the world’s most valuable company due to its impenetrable moat in the data center GPU market, which was built upon the CUDA software platform for the purposes of training AI models. Eventually, we will see a shift from AI training to AI inference, which leaves the market open for competitors.
Kindig explained in the interview with Brugge that Nvidia’s H100 transformer engine was the impetus for Chat-GPT’s moment. Chat-GPT, and its competitors, are essentially large R&D departments for training models. We are in the midst of AI training, and what follows will be the AI inference market. As Kindig explains, “when you take the models and you bring them to the edge, and you run those models and have it make predictions based on live data for actionable results, that’s inference.” She pointed out that “for the most part, it’s agreed that inference will be a larger market than training once the ecosystem is mature.”
Currently, there’s one primary headwind to the inference market; devices are not powerful enough to handle the requirements to run AI at the edge. Kindig says that “one of the things holding back inference is our client devices, so our PCs and mobile. Inference runs best close to the data, and we don’t have powerful enough devices for inference, for where AI needs to go.”
AI PCs are currently working on solving this critical bottleneck, with NPU, GPU and CPU equipped devices packing the necessary power and efficiency to operate AI models locally, on-device and without relying on data being sent to and from the cloud.
Kindig told Brugge on Real Vision that she believes one AI stock is well positioned to capitalize on the long-term opportunity arising in AI inference — that stock is AMD.
Why AMD Can Outpace Nvidia Through 2030
Nvidia will need to rise nearly 250% by 2030 to reach Kindig’s $10 trillion target, yet she thinks AMD has the potential to provide a larger return over that time frame.
She told Brugge that her “time horizon would be that we see really nice movement by 2027, but we really need this 2030 time period to play out, and there’s a few reasons. Number one, Nvidia has the training market cornered right now. Training requires a lot of compute power, and they’ve gone through architectural changes that have defied Moore’s Law. This is things like Tensor Cores, which do matrix computations; floating-point precision, moving from 16 point [FP16] to 8 point [FP8], [the transformer engine switches back and forth which] increases accuracy while also increasing speed [depending on the workload]. So, all of those things, Nvidia has 98% of the GPU market and is crushing it, but a lot of that is training.”
Core to this thesis on AMD is giving time for the budding inference market to take off and mature – Kindig explains that “where AMD is going to compete with Nvidia is a market that is very early, so we need time for that to mature, which is inference. Many people may get that confused, because we are fully in the AI market today because Nvidia is putting up those huge data center numbers. We are in the data center training market today; one day, we will be an AI market led by inference.”
Kindig told Brugge that there are a “few reasons” that AMD could do better than Nvidia in inference and etch a niche, with the primary reason being that inference is “one way to circumvent CUDA.” CUDA is Nvidia’s proprietary software stack that has essentially locked developers into its GPU ecosystem, and what has driven its ~98% market share in AI GPUs.
For a deep dive on CUDA and how it’s Nvidia’s moat and first line of defense in the AI accelerator market, read more here and here.here and here.
How AMD Can Fend Off Nvidia
AMD is equal to Nvidia on hardware in many regards, but CUDA has locked in Nvidia’s monopoly; however, it’s likely that Big Tech and developers will seek alternatives to CUDA to limit reliance on Nvidia for the entirety of the hardware stack for AI development.
Kindig notes that CUDA will be the “biggest hurdle for sure” for AMD to compete against, “but after that, it’s probably product roadmap versus product roadmap, meaning that for everything AMD does, can Nvidia do better, by 6 months.” Put differently, Nvidia took the industry by storm with its transformer engine-equipped H100s, which saw extreme demand outstrip supply for multiple quarters. No company could compete at the time with a similarly spec’d GPU that could provide the same level of AI computing performance.
Now, AMD is accelerating its product roadmap cycle to align with Nvidia’s, after being a generation behind. AMD is aiming to launch its MI400 lineup in 2026 alongside Nvidia’s Rubin platform, catching up in the release cycle after being behind the GB200 with its MI350x accelerators.
AMD has an edge over Nvidia in that it is undercutting them quite heavily on price, though this is detrimental to margins and thus bottom line growth. Kindig explains that this “incentive of saving $20,000 or more [per GPU] is big enough for these companies that are building these huge data centers, that they’re likely to try their very best to make this work with their in-house engineering departments. This is Big Tech only. This will not apply to enterprises or small businesses, which won’t have the time or resources to do anything other than CUDA.”
At scale, that $20,000 savings for a GPU with similar compute performance capabilities and similar memory bandwidth, albeit with AMD’s software instead of CUDA, can entice companies to shift towards allocating some of the tens of billions flowing to Nvidia’s chips to AMD in the long-run.
For example, Microsoft is reportedly aiming to triple its GPU supply this year, from 600,000 GPUs to 1.8 million GPUs, and is a customer of both Nvidia and AMD. As AI accelerator purchases increase in size and scale, with upgrades to the latest generation for performance improvements and decreasing TCOs, Big Tech can save billions by allocating a fraction to AMD – hypothetically speaking, allocating one-third of a 1.2 million GPU purchase could save $8 billion with AMD’s pricing. That $8 billion could then be deployed to purchase more GPUs, train the next generation of AI models, and otherwise remain ahead of stiff competition.
Kindig explains that this is both an “opportunity and a risk that AMD undercut so much on price, because their margins will not look as good as Nvidia’s. Nvidia has been an amazing stock not only because of these revenue beats, but because the margins and the pricing power that CUDA has created” has driven 600% growth on the bottom line that AMD won’t be able to replicate.
Analysts foresee strong growth for AMD on both the top and bottom lines over the next few years, though it pales in comparison to Nvidia’s streak of blazing triple-digit growth rates. AMD’s revenue growth is forecast to accelerate from under 13% in 2024 to 28% in 2025, before moderating to 18% in 2026. Adjusted EPS growth is expected to accelerate from 32% to 59% in 2025.
Source: Seeking Alpha
While it is by no means the triple digit growth that Nvidia has been putting up, these top and bottom line accelerations are what has been rewarded by the market, especially for AI stocks. Because of the differing growth rates, AMD trades at a cheaper valuation than Nvidia: currently, AMD is valued at 7.8x 2025 revenue and 28.3x adjusted EPS, versus 19.2x revenue and 34.7x adjusted EPS for Nvidia for the same period. However, both companies are currently trading above long-term historical averages for these valuation multiples, with AMD trading above its 10-year average 4.3x revenue multiple and Nvidia above its 10-year average of 14.0x revenue.
Conclusion
Nvidia has greatly rewarded investors as it quickly ascended to be the pinnacle of the generative AI revolution of 2023 and 2024, with revenue consistently exceeding expectations so far on robust demand. Beth Kindig and the I/O Fund have projected Nvidia to potentially rise to a $10 trillion valuation by 2030 on strong data center growth from its rapid GPU roadmap and upcoming software and automotive opportunities, but Kindig believes that AMD and its opportunity in AI inference may help the stock outpace Nvidia’s projected 250% return through 2030.
For more insights on AMD, consistent deep dive research on AI stocks and mega-trends, weekly webinars with AI stock and broad market outlooks, real-time trade alerts on AI stock buys and sells, consider taking a look at the I/O Fund’s premium services here.here.
Disclaimer: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA and AMD at the time of writing and may own stocks pictured in the charts.
Recently, the I/O Fund team wrote an article in Forbes: “AI Power Consumption: Rapidly Becoming Mission-Critical” where it was stated: “Over the past few months, multiple forecasts and data points reveal soaring data center electricity demand, and surging power consumption. The rise of generative AI and surging GPU shipments is causing data centers to scale from tens of thousands to 100,000-plus accelerators, shifting the emphasis to power as a mission-critical problem to solve.”
As the analysis points out, eventually we will see million-plus accelerator data centers. Thus, AI’s electricity demand is forecast to surge, especially in the data center. Morgan Stanley’s base case is calling for 500% increase in power demand over the next three years. Wells Fargo is projecting AI power demand to grow 550% by 2026, before rising another 1050% by 2030, from 8 TWh to 652 TWh in a seven-year period.
Liquid Cooling plays an important role in reducing the heat that AI systems generate. We first covered Liquid Cooling in a Super Micro analysis in May of 2023, yet would like to double-click on this trend for our premium members as it’s becoming what we consider to be “the third realm of competition.” Liquid cooling technology has been around for decades, yet this technology is becoming mission critical due to the increasing levels of compute power from AI accelerators, starting with the GB200 systems and B200 GPUs.
Per the previous analysis: “In 2022, Supermicro stated that liquid cooling is being used in 10% of supercomputers but will grow to be used in the “vast majority” in order to offset the heat generated by power-consuming components.”
Although the GB200 will ship end of this year, and the B200 will fully ship in early 2025, vendors are scaling their liquid cooling capacity now. The capacity investments are being made right now, and we can see evidence of this in Super Micro’s most recent earnings report with an increase in inventory. We also find hints of this in Dell’s earnings report, with the company also reporting an increase in inventory as these leading AI server companies wait for Nvidia’s Blackwell to ship.
Last week, we clearly outlined that AI power consumption is a problem the industry must work diligently to solve. For our premium members, we take this further to discuss how liquid cooling is at the forefront of driving down energy requirements for AI systems. Below, we look at the beneficiaries of this important trend and what we are looking for in managing our positions – including our plans for re-entering Super Micro, how we view Dell given the weak earnings report, plus some brief analysis on Hewlett Packard Enterprises (HPE), Vertiv, to name a few.
Brief Overview of Liquid Cooling:
Liquid molecules are closer together than air molecules, which results in higher heat transfer. This results in liquid removing 4 times more heat than air. The heat capacity of water or glycol is higher than air, so the amount of heat being transferred is higher. Most servers today are air-cooled, yet AI necessitates a shift to liquid cooling are GPUs are already at 700W of power and are moving toward 1000W of power.
AI/ML require massive amounts of data processing, and as future generations of CPUs and GPUs are released, these systems will exceed air cooling capacity. Liquid cooling also solves throttling, which occurs when CPUs and GPUs overheat and are throttled back to avoid damage to the chip. In the case of high-performance computing, liquid cooling reduces total cost of ownership as air cooling requires air conditioning and server fans to run constantly.
Cooling data center servers is responsible for 40% of the data center energy consumption. According to Dell, enclosed DLC solutions can save up to 23% of energy compared to traditional air-cooled racks. McKinsey places this number at 27% savings when there is 75% liquid cooled and 25% air cooled servers.
Direct Liquid Cooling: DLC uses liquid-cooled cold plates that in direct contact with GPUs and CPUs. The cold plates transport heat away from the processors. The process of circulating liquid directly over the components is also known as Direct to Chip Cooling, and is a closed loop system, or also known as a self-contained cooling system.
Immersion Cooling: The system is immersed in liquid for cooling. The immersion tank has a coolant distribution unit, including a pump to circulate dielectric fluid to extract heat from the servers.
Rear Door Heat Exchanger: Uses a specialized rear door to the rack where coolant absorbs the heat.
In-row cooling refers to solutions designed to cool and distribute air in a data center aisle. When combined with row or rack containment, the in-row coolers capture 100% of the IT-generated heat.
In addition to lowering power consumption, benefits from liquid cooling includes higher server density as the need to create space for airflow is removed. Liquid cooling also eliminates hot spots with more even distribution. The lower temperatures from liquid as opposed to air also extends the life of the server (removes throttling).
Nvidia’s Blackwell is Hot
Nvidia’s A100 released in 2021 operates at 300W, the H100s released in 2023 operate at 700W. The Blackwell architecture is a catalyst for liquid cooling as it nears 1000W, specifically the GB200 systems and the B200. This represents a 40% increase from the previous generation. Tom’s Hardware makes the argument that: “we can only refer to the basic rule of thumb with heat dissipation, which says that thermal dissipation typically tops out around 1W per square millimeter of the chip die area” and that “When it comes to high-performance AI and HPC applications, we need to consider the performance measured in FLOPS and the power it takes to achieve these FLOPS and cool the resulting thermal energy. What matters for software developers is how to use those FLOPS efficiently. What matters for hardware developers is how to cool the processors producing those FLOPS.”
Therefore, as computing power increases with each GPU generation, which is measured in FLOPS, cooling the GPUs is the crux, as it becomes a larger thermal dynamics issue that must be solved.
In terms of timing, the B100 is due out first and will be primarily air cooled. The B200 systems and chipsets will be the first release to be primarily liquid cooled. This is due to consuming upwards of 1,000 watts, which is too hot to be air cooled. The B200 doubles the transistor count compared to the H100 and provides 20 petaflops of AI performance compared to the H100s 4 petaflops. The resulting 3X leap in training performance and 15X leap in inference performance is shifting the focus to ways that power consumption can be lowered. Note: We’ve covered Nvidia’s upcoming Blackwell extensively, please see resources below.
The B200 will technically be out in late 2024 as a system that combines either 36 GPUs or 72 GPUs with Nvidia’s in-house Arm-based Grace Hopper CPUs. These superchips are called the GB200 NVL36/NVL72 and will operate as one supercomputer, allowing for trillion-plus parameter models to be trained.
The B200 chipset will ship in early 2025. As stated in a previous analysis on Nvidia, the B200 chipset will offer “a second-generation transformer engine that supports 4-bit floating point (FP4) with the goal of doubling the performance and size of models the memory can support while maintaining accuracy […] This is helpful because AI models are moving toward neural nets that lean on the lowest precision and yet still yield an accurate result. In this case, 4 bits double the throughput of 8-bit units, compute faster and more efficiently, and they require less memory and memory bandwidth.”
Nvidia offers SuperPODs that combine eight H100 GPUs connected with NVLink with the DGX SuperPOD connecting 32 nodes of eight GPUs for a total of 256 H100 GPUs. As the B200s come out, these SuperPODs will scale to provide more than 1 exaflop of AI compute power and move from FP8 precision to FP4. The DGX GB200 SuperPODs will connect up to tens of thousands of GB200 superchips with shared memory.
This section is included to help cement the inevitability that liquid cooling is becoming what Damien Robbins, equity analyst at the I/O Fund dubbed the third realm of competition.
Recent Commentary on Liquid Cooling:
At BofA’s Global Technologies Conference in early June, analyst Vivek Arya questioned Nvidia’s VP of Accelerated Computing Ian Buck about the increasing power requirements per GPU. Since liquid cooling’s catalyst begins with Nvidia’s second release in the Blackwell generation (GB200 and B200), it’s important for us to examine what is being said before we look more closely at the beneficiaries.
Vivek Arya:
How is the outlook around Blackwell as we look at next year? First of all, do you think that because of the different — the power requirements that are going up significantly, does that constrain the growth of Blackwell in any way?
Ian Buck, Nvidia:
“Data centers don't drop out of the sky. They're big construction projects. [Customers] need to understand what is a Blackwell data center look like and how is it going differ than Hopper. And it will. The opportunity we saw with Blackwell was to transition to a denser form of computing, to put 72 GPUs in a single rack, which has not been taken to scale before. … In Taiwan, for example, the people that are building the liquid cooling infrastructure, the power shelves, the WIPs, which is the cables that go down into the bus bars. The opportunity here is to help them get the maximum performance through a fixed megawatt data center and at the best possible cost and optimized for cost. By doing 72 GPUs in a single rack, we need to move to liquid cooling. We want to make sure we had the higher density, higher power rack, but the benefit is that we can do all 72 in one NVLink domain.
Connect them all up with copper instead of having to go to optics, which adds cost and adds power. And every time you add cost and power, you're just taking away from a number of GPUs you can put in your 10-, 50-, 100-megawatt data center. So that is driving us towards reducing cost, increasing density.
So when you look at a Blackwell, you may say, well, it's really hot, that's actually going to be significantly improving the total throughput of a fixed power data center. So there's a strong economic and technological driver to transition to more denser and more power efficient and more — and next-generation cooling technologies than just air.
Water is a fantastic mover of heat. Your house is built with insulation that is nothing more than just trapping air. Air is actually an insulator. It's not a good transfer to heat, but water is excellent at it. If you ever jumped from a 70-degree pool from a 70-degree air, it feels really cold.
That's because water is sucking the heat right out of you. It's really good at moving heat around. And that efficiency goes right to more GPUs, more capabilities and denser, more capable AI systems.”
–End Quote
Buck’s response did not directly touch upon increasing power requirements, but hinted that this shift to Blackwell and beyond, with larger racks and denser compute, will be built upon liquid cooling, which will allow these more powerful and power-hungry GPUs to operate efficiently at scale.
Nvidia CEO Jensen Huang also discussed liquid cooling in Q1’s earnings call: “the Blackwell platform has expanded our offering tremendously. The integration of CPUs and the much more compressed density of computing, liquid cooling is going to save data centers a lot of money in provisioning power and not to mention to be more energy efficient. And so it's a much better solution.”The integration of CPUs and the much more compressed density of computing, liquid cooling is going to save data centers a lot of money in provisioning power and not to mention to be more energy efficient. And so it's a much better solution.”
Super Micro: Leader in Liquid Cooling
Super Micro’s ascent in the server market has been breathtaking. In 2021, the company ranked #6 among server companies with $2.5 billion in revenue compared to Dell’s $14 billion in server revenue and HPE’s $12 billion. Fast-forward and SMCI is on a direct path to reaching $25 billion in revenue over the next year. That’s a 10X revenue increase in about 3-4 years’ time.
Super Micro expects liquid cooling to be rapidly adopted over the next year and a half. The company is deploying three of the “world’s largest DLC liquid-cooled” systems in the current quarter, ending in June. The Nvidia HGX AI supercomputers with liquid cooling are expected to “potentially” save customers up to 40% of energy costs compared to air-cooled systems.
SVP and CFO, David Weigand, explained at BofA’s conference:
“we have started to ship liquid cooling at really at scale, at larger volumes in this core. And so, there's no question that that industry is coming up to speed with the reality of where we're going, which is the fact that power is constrained all around the world. And then — and therefore, when you build these large data centers, you're going to have to think twice now about using liquid cooling, because by you using liquid cooling, you can not only — we say that it's free with a bonus, and that's because it's free because you're not only having to put in smaller chillers, you don't have to use air conditioning.
If you have really liquid cooled racks, you can put more dense racks and more racks into a data center, so it's more efficient if you're using liquid cooling. And so, it's really the cutting-edge companies right now that are putting in liquid cooling, liquid cooled racks into their data centers so that it's — the huge use of power right now is going to really drive liquid cooling. As much as the fact that all the GPUs and CPUs are running at higher wattage as they go over 1000, it's going to start to become painfully obvious.”
–End Quote
SMCI’s management has stated that liquid cooling will cost more as it takes longer to assemble and test, and the company plans to charge for this. It’s also expected that SMCI will be the first to ship liquid cooled AI systems before its competitors.
At a recent investors conference, management stated the company has a rack capacity of 5,000 per month and a liquid cooled rack capacity of 2,000 racks per month. This means that SMCI plans to utilize direct liquid cooling (DLC) in 30% of its racks, to start.
According to the CFO, the Malaysia site will offer the opportunity to “double eventually our worldwide capacity” and will offer both air cooled and liquid cooled servers. With this in mind, Super Micro CEO Charles Liang expects direct liquid cooling (DLC) adoption to reach 15% in the next 12 months and 30% over the next two years, a rapid shift up from 1% of the market. The CEO updated this on xAI, stating they now foresee DLC adoption growing from <1% to 30% in a year.
Super Micro is Seeing Higher Inventory Levels and Lower Cash Levels
Per our recent SMCI earnings write-up, inventory increased to 92 days compared to 67 days in the previous quarter. The company’s Q3 closing inventory was $4.1 billion, which increased by 67% quarter-over-quarter from $2.5 billion in Q2 due to the “purchase of key components.” Our post-earnings analysis explained that the increase of inventory and key components was partly related to liquid cooling.
The CEO stated: “Two reasons we had to increase inventory: One is because Q4, I mean, June quarter, we will have a strong revenue growth; a second reason because we're preparing for high-volume liquid cooling. Again, we have more than 1,000 of 100k watt, I mean, liquid cooling rack we have to ship to customers in Q4. And liquid cooling as you know, is pretty new. So we had to prepare enough inventory so that we can deliver liquid cooling rack scale product to customer on time or with minimal lead time. So both factor, indeed, is a positive factor. And with our economic scale continuing to grow, indeed, our inventory average [ daily ], indeed, will slightly improve.”
Super Micro has further discussed its plans to fund its capacity expansion efforts through short-term debt, or perhaps by diluting shareholders. In the last earnings writeup, I called cash Super Micro’s “Achilles heel” as the seemingly invincible company has one drawback – in order to keep growing, they must build more facilities, which requires more cash. Liquid cooling also necessitates more components, which means inventory levels will rise. This combination is putting a wrench in SMCI’s cash flow.
Ruplu Bhattachary:
David, let's talk about working capital. So, to support growth, you need to support a lot of working capital. When do you make the determination, or how do you make the determination that you need to raise more capital? And how should investors think about your trade-off between using more debt or doing another equity raise?
David Weigand:
Yeah. We've had to do a couple of raises in the past six months, because we saw the permanent level of our business going up higher. So it wasn't temporary. Now, remember, as a manufacturer, if we sell a billion dollars, an additional billion dollars in a quarter, we have to. And remember, when I first started, we were doing about $3.5 billion a year, and now we're doing well, more than that per quarter.
So, when you're increasing by a billion dollars in a quarter, you've got to go out and you've got to, let's say even if the margin is 20%, you have to buy 80% of materials and you have to carry those through inventory. You have to carry them through accounts receivable until they convert to cash. And so it becomes an immediate problem. And we've had some very large customers come that I've sat across the table from, and they say, we have two questions. Do you have the capacity, do you have the capital to take this project on? And so we had to go out and get some more permanent capital so we could answer that question always, yes. So, we finished out with $2 billion at the end of last quarter.
We think that the things that we've done in terms of raising the visibility of the company, raising the profits, raising the sales, have been good for our shareholders, and we want to continue to balance that because we don't like dilution. We previously used to repurchase shares that's still in our tool bag. But right now, it's about being able to deliver against our backlog. And so therefore, we will get as much capital as we need to in order to do that. And so, it's really about whether you see, with short term debt, we can address temporary increases, but if we see sustained orders such as we have seen, then we're going to have to do some more permanent debt raises like we've done with the convertible bonds and also with the common stock equity raise.
–End Quote
The rise in inventory due to liquid cooling components plus Super Micro needing to increase capacity to further meet AI server demand may lead to a lower entry price, which we will gladly take.
Barron’s published just today that SMCI is the top performing stock in the S&P 500 for the first six months of the year. This is on the heels of being the second-best performing stock of 2023, ending the year a tad bit higher than Nvidia. We’ve participated since mid-2023 for a roughly 300% return in less than a year. We have plans to re-enter Super Micro which can be found here.
Dell
Dell has a long way to go to catch up with Super Micro as the company reported $2.6 billion in AI-optimized server revenue and AI server backlog of $3.8 billion. This represents 7.6% of Dell’s revenue. Compare this to Super Micro with over 50% of its revenue from AI and this number is surely higher today.
Due to Dell’s scale, it will take some time before Dell sees this level of AI concentration, as Client revenue is a large portion of Dell’s overall revenue. Therefore, for Dell to become a full-fledged AI stock, it will need AI PCs to participate. It’s only a matter of time before AI PCs provide the next leg up for AI investors, with our best guess being 2025 on the early side and late 2026 on the late side.
Dell may have a long way to go to see the levels of concentration that Super Micro has, but AI also has a long way to go. In our Dell write-up, the base case is for 15% of Dell’s revenue to be from AI, yet the more likely outcome is we will see something in the 30% range by 2027. This does not factor-in AI PCs which will rapidly accelerate this percentage once the trend is in play.
It's doesn’t require much speculation to think Dell will be the runner-up when SMCI reaches capacity. Jensen Huang of Nvidia recently stated: “Everybody who is building these chatbots and Generative AI, when you are ready to run it, you need an AI factory and nobody is better at building end-end systems of very large scale for the enterprise than Dell.” you need an AI factory and nobody is better at building end-end systems of very large scale for the enterprise than Dell.” Last week, we saw Elon Musk’s xAI announce the AI project is ordering servers from both Super Micro and Dell.
Dell’s Power Edge Servers with Liquid Cooling
Dell’s Power Edge servers are designed for AI and HPC (high-performance computing) workloads. In September, the servers were launched with support for four H100 Tensor Core GPUs with liquid cooled GPUs with higher efficiency due to liquid cooling and higher GPU capacity per rack.
In May, Dell announced a new Power Edge server “L” version with liquid cooling and eight Blackwell Tensor Core GPUs. The eight GPUs communicate seamlessly with NVLink across memory and cores, which helps to support the training of large language models. Independent industry analysts have described the new Power Edge Server XE9680L as “the densest rack scale architecture in the industry.” The ”L” version is expected in the second half of this year and offers “33% more GPU density per node.” The air-cooled version can support 64 GPUs whereas the liquid cooled rack scale design supports 72 GPUs.
Upcoming AI Releases for Dell
Dell has a few more important AI release coming out this year. AI factories integrate Nvidia’s AI Enterprise software to allow companies to go-to-market quickly on AI workloads. The goal is to reduce setup time for AI development by up to 86%. The fully integrated solution combines Dell’s Hardware with Nvidia’s infrastructure and software.
As of now, Nvidia has three software businesses: Nvidia AI Enterprise, Omniverse and newly-announced Nvidia NIM. Dell’s AI factories set up Nvidia’s road map for both AI Enterprise software and NIMs, which provides models as optimized containers for generative AI application development. You can think of NIMs as something similar to an app store, to where developers can develop and market AI apps.
We’ve stated numerous times that Nvidia’s AI software revenue will rival the company’s GPU revenue. This is one of many examples where the stage is being set. In this case, Dell will ship fully integrated systems to enterprises, startups and SMBs who want to skip critical steps to deploy quickly.
Dell NativeEdge is another recent announcement, and is a software platform that reduces the amount of resources required for deploying an AI application at the edge. The platform targets the immense amount of operations work that is needed for when AI applications are deployed across many endpoints and devices. The most obvious first customer will likely be the Federal Government or hospitals and other industries that manage very large data sets at the edge.
Dell is Reporting Higher Inventory, Too
There were comments on the call that inventory is higher-than-usual at Dell, as well. If the higher-than-usual inventory levels at both Dell and SMCI are due to building out liquid cooling systems, then we will likely see higher inventory again this quarter. Inventory should alleviate in Q4 to Q1 when Blackwell’s GB200s and B200s ship. There are some cases where higher inventory is a good thing, such as when companies prepare for a spike in demand. It’s likely these companies are preparing for a spike in demand on DLC systems, rather than the opposite, which is that inventory is building because demand is waning.
Here is what Dell’s CFO stated:
“Our cash conversion cycle was negative 47 days, flat sequentially, with higher inventory related to our AI business, offset by strong collections performance.”
Here was a discussion on the earnings call relating to the higher inventory, and why this may be a bullish indicator for determining demand over the next six months and beyond:
Amit Daryanani
[…] And then, Yvonne, could you also just clarify, the inventory was up dramatically in the quarter and it's somewhat unusual for it to be up in this quarter. So just talk about what's driving that and is it AI pre-builds or strategic inventory? Any help on that would be great as well. Thank you.
Yvonne McGill
Sure. So let me start with inventory, because I think that's pretty straightforward. So inventory was up and I would say slightly, for 25 days, really representing about a $1.2 billion increase quarter over quarter. We mentioned inventory was up slightly as we ramp our AI server business. So I think it's nothing substantial. I don't know, Jeff, if you have anything to add on inventory.
Jeff Clarke
No, but we didn't go out and make any strategic purchases. Some of the terms of the AI gear we need to deploy means we take ownership of it. We did and we have it in backlog. We'll ship it as those customer orders are fulfilled. That was the driver. We weren't out buying strategic or making strategic investments of inventory across the large component basins.
Vertiv
Super Micro, Dell and Vertiv are three stocks with fantastic returns this year. SMCI is up about 200% (down from a high of about 300%), Dell is up 83% (down from a high of 118%) and Vertiv is up 82% (down from a high of 117%).
Vertiv offers power management and thermal management to data centers and telecom companies, such as Alibaba, AT&T, China Mobile, Tencent and Verizon. The company was formed in 2016 after spinning off from Emerson, and reported $6.8 billion in revenue last year. The company is considered one of the larger players in data center technologies, in terms of power management and thermal management, with 24,000 employees and 30 manufacturing facilities. Vertiv’s thermal management technologies include liquid cooling for servers and racks.
The data center accounts for 75% of Vertiv’s business with communications networks and commercial/industrial facilities at 25% of revenue. Most recently, their management team stated that AI-related projects have doubled in the past two months.
“The ramp-up of production of liquid cooling globally continues as planned, and I'm happy to report we have production underway already at two of the three plants we shared with you we were planning to activate in 2024. We are on track with the capacity ramp-up as shared in February. We continue to see strong momentum with AI-related orders. While we are not disclosing specific detail on our liquid cooling orders, or more broadly AI-related orders, we did see the pipeline for AI projects more than double in the last two months.”
Vertiv offers many thermal management solutions. Among them is the Liebert XDU, which is a compact unit that sits in the row near the rack or on the perimeter. The liquid-to-liquid cooling distribution unit (CDU) functions as a heat exchanger between the data center and IT equipment, and is used in all forms of liquid cooling: direct-to-chip, rear door heat exchange and immersion. The Liebert XDU offers a secondary fluid cooling loop so that alternative cooling fluids can be used alongside water.
In 2023, Vertiv acquired a company called CoolTera after partnering with the company for three years to add advanced cooling technologies to its thermal management portfolio. One of the main areas of need for data centers and colocation sites is to convert air-cooled equipment to liquid cooled equipment. Retrofitting existing air-cooled infrastructure is an area where Vertiv specializes, as opposed to only providing thermal solutions for new servers and racks.
In May of 2023, Nvidia selected Vertiv to design a cooling system that secured a $5 million grant from the COOLERCHIPS program. In 2024, Vertiv joined the Nvidia Partner Network with a statement that Vertiv is “collaborating to build state-of-the-art liquid cooling solutions for next-gen NVIDIA accelerated data centers powered by GB200 NVL72 systems.”
In late 2023, Vertiv announced a partnership with Intel to supply air-cooled and liquid-cooled servers for the Gaudi3 AI accelerator.
This is a thematic analysis on liquid cooling, and thus, we have not done a deep dive into Vertiv’s financials. Briefly, the company reported revenue of $1.63 billion in Q3, up 7.76% YoY yet down sequentially from $1.86 billion. The operating margin of 12.6% expanded from 9.6% for operating profit of $206 million. The adjusted operating profit was $249 million. Net margin decelerated from 3.3% to (-0.36%). Cash flow was $101 million in the most recent quarter and the company repurchased $600 million for share repurchases in the quarter,
Hewlett-Packard Enterprise (HPE)
HPE is another commoditized hardware company that is seeing a revival due to its large portfolio of liquid cooling technologies and patents. The company has over 300 patents related to direct liquid cooling (DLC) with four of the world’s top 10 supercomputers featuring liquid cooled servers from HPE. According to HPE, their Apollo DLC System reduces fan power by 81%.
The HPE Cray EX Liquid-Cooled Cabinet offers liquid-cooled cabinetry that provides DLC to all the components in a compact design. This is for CPUs and GPUs in excess of 500W, and can help to reduce the interconnect cabling systems that are required, which further reduces operational expense. As a reminder, Cray is a supercomputer built by HPE that ranks as the #1 and #2 supercomputers in the world. Therefore, the liquid cooling for these systems is quite advanced as the #1 Cray supercomputer contains hundreds of thousands of AMD EPYC CPUs and 37,000 AMD Instinct GPUs. The cooling technology for Cray features a bladed cabinet that allows for the mixing and matching of various CPUs and GPUs, and allows for easy upgrading as new generations of CPUs and GPUs are released. At one point, a system the size of Cray was reserved for only supercomputers, but the AI market is driving forth 24,000-plus GPU clusters today and Broadcom believes we will see million-plus GPU clusters by 2027.24,000-plus GPU clusters today and Broadcom believes we will see million-plus GPU clusters by 2027. HPE’s experience on liquid cooling the Cray supercomputers will be helpful as GPU clusters continue to scale.
HPE held a recent conference with Nvidia’s Jensen Huang, who appeared at a recent HPE conference to showcase the strength of the partnership between the two companies. There was a string of announcements, the primary one being that HPE and Nvidia are partnering on a private cloud (presumably to compete with Broadcom’s VMWare integration). You can find more information here in the press release on Nvidia-HPE announcements.more information here in the press release on Nvidia-HPE announcements.
In the most recent earnings report, HPE provided the following color related to AI sales: “Our cumulative AI system product and service orders since Q1 2023, rose approximately $600 million sequentially to $4.6 billion. I am very pleased with our AI system product revenue more than doubled sequentially to over $900 million. This strong revenue growth allowed us to make progress against our backlog, which is now $3.1 billion.” The company also stated that enterprise is “north of 15%” of the AI orders, which is a key market for both HPE and Dell (as opposed to predominately hyperscalers like SMCI).
The stock has risen about 20% YTD, quite a bit less than SMCI’s outperformance, and is lagging Dell and Vertiv considerably, as well.
Conclusion:
As pointed out in our AI power consumption write-up, AI power demand is forecast to rise at a rapid rate. GPU demand is showing no signs of slowing as Big Tech continues to spend billions on AI infrastructure, with each GPU generation seeing higher peak power consumption. The industry is quickly taking steps to address this, and power consumption, or more specifically, power efficiency per chip, looks to be emerging as the third realm of competition.
The first two realms of competition are raw computing power and memory; both have been extensively covered for our premium members. Now, we turn toward keeping an eye on the AI power consumption space as new winners will emerge now that power consumption has become mission critical.
As of now, our plans are to jump aboard the AI bullet train again (i.e., Super Micro) at key levels and to also follow our trading plan on Dell. If we decide any others are a good fit, then you will surely get a deep dive into those stock names.
To view our recent Advanced Market Signals webinar with SMCI and DELL trading plans, click here.click here.
Damien Robbins, Equity Analyst at the I/O Fund, contributed to this article.
This article was originally published on Forbes on Jun 27, 2024,04:25pm EDTForbesForbes on Jun 27, 2024,04:25pm EDT
AI-capable PCs are expected to be an explosive trend through 2025 and beyond. The trajectory of AI will increase when more people can access AI-powered applications, which in turn, will help AI developers build a bigger ecosystem. Currently, there is a major bottleneck right now for AI applications to where client devices are not powerful enough or energy efficient enough to leverage AI capabilities at the edge.
We’ve discussed the PC rebound in late 2023 for our premium members with executive commentary on how AI PCs will accelerate the PC market’s growth rate. Industry research organizations similarly see strong growth in AI PCs, with some forecasting annual AI PC shipments to more than triple by 2028. In other words, AI-capable PCs are projected to rise from ~19% of total PC shipments this year to more than 70%, even up to 80% by 2028. The rapid adoption curve will be driven “with a strong inclination towards commercial adoption.” There is indication the early majority will adopt AI PCs in 2025, and the late majority in 2026. This leaves time for consumers to participate, which thus far has been a challenge for AI, as it's been predominately driven forward by Big Tech.
Refresher on AI PCs
With the rapid ascent of generative AI over the past year and a half, the term ‘AI PC’ may be self-explanatory but there are nuances to each release. Microsoft has adopted a new definition for AI PCs that underpins the launch of its Copilot+ PCs on the market, which launched in mid-June.
According to Microsoft’s definition, an AI PC will contain a CPU, a GPU, and an NPU (neural processing unit), as well as its Copilot key and Copilot software onboard. NPUs are highly efficient at parallel processing for AI and ML workloads by running matrix multiples. Essentially, NPUs offer a very power-efficient way of running localized AI on devices such as PCs and smartphones without draining battery life by operating in the background. Per Microsoft, AI PCs must be capable of 40 TOPS or greater performance on the NPU.
Meeting Microsoft’s Copilot+ requirement calls for at least 16GB RAM and 256GB storage alongside the 40+ TOPS NPU performance. This is currently only met by Qualcomm’s Snapdragon X Elite chips, but will soon be met with Intel’s Lunar Lake chips, AMD’s Strix Point chips, and others.
AMD and Intel define the AI PC more broadly – AMD defines an AI PC as “a PC designed to optimally execute local AI workloads across a range of hardware, including the CPU, GPU, and NPU.” Intel’s definition says an AI PC “has a CPU, a GPU and an NPU, each with specific AI acceleration capabilities.”
Intel believes the AI PC “promises to be a huge improvement for everyday PC usages,” as it “represents a fundamental shift in how our computers operate.” AI PCs meeting the TOPS and memory requirements set forth by Microsoft will allow AI models and workloads to be built and deployed directly at the edge, without transferring data to and from the cloud, offering an extra layer of security and privacy.
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Strong Growth Forecasts
Shipments of AI-capable PCs are forecast to grow at a rapid rate over the next four years, while also boosting broader PC market growth. HP believes that as AI PC commercialization accelerates, the “overall PC category growth rate can double over the next three years.”
Canalys is projecting AI PC shipments to rise at a 44% CAGR from 2024 to 2028, from an estimated 48 million PCs this year, before doubling to more than 100 million in 2025 and rising to over 205 million by 2028. Cumulative shipments of AI PCs are projected to surpass 600 million over the next four years.
Gartner is slightly more optimistic on the near-term growth of AI PC shipments, forecasting shipments to more than double from 24 million in 2023 to 54.5 million in 2024, nearly 14% higher than Canalys’ estimate. Gartner’s 2025 forecast calls for shipments more than doubling once more to 116 million units, or 43% of total PC shipments, up from just 10% in 2023 and 22% this year.
IDC is projecting 50 million shipments in 2024, with 3-year growth of 234%, reaching 167 million annual shipments in 2027. Here’s what the three projections look like:
Source: I/O Fund
While there are some nuances in the growth projections, especially in the next twelve to eighteen months, the longer-term growth trends remain intact, with shipments projected to increase more than 200% by 2027.
Much of the growth through 2025 is expected to be in the premium (or high-end) laptop segment, with ASPs rising due to the NPU. For example, the first Copilot+ PCs equipped with Qualcomm’s Snapdragon X Plus chips start at $999, and the Snapdragon X Elite PCs at $1,249.
In addition, while the growth in AI PC shipments is expected to be felt across both the consumer and commercial end markets, commercial adoption is forecast to be higher, at approximately 60% by 2028 versus 40% for consumer. This is due to the productivity gains that AI PCs can enable via powerful on-device AI as well as benefits to software developers and related roles. For example, Dell’s XPS and Latitude 7455, equipped with the Snapdragon X Elite, “can support 13 billion-plus parameter models which means customers can run popular models like Llama 3 directly on their PCs.”
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PC Market Growth
A look at the broader PC market shows that the industry finally inflected back to growth after a challenging two-year stretch of declines; however, this came against an easy comp of a nearly (29%) YoY decline in Q1 2023, per IDC.
Counterpoint Research and Canalys both reported 3% YoY growth for the PC market in Q1, with Lenovo leading the way with nearly 8% YoY growth. In addition, IDC noted that “global PC shipments finally returned to pre-pandemic levels as 1Q24 volumes rivaled those seen in 1Q19 when 60.5 million units were shipped.”
For the full year, the market is expected to see approximately 2% to 3% growth, with annual shipments in the 265 million and 270 million range. This is still a far cry from the ~340 million shipments seen in 2021, due to the challenging landscape the industry navigated through 2023.
An Ultra-Competitive Landscape
Competition in AI PCs is quickly heating up, with Intel forecasting a surge in its PC chip shipments, while Nvidia, AMD and others line up powerful Arm-based CPUs to take on Qualcomm’s Snapdragon X chips once its exclusivity deal with Arm expires at the end of 2024. Apple is rumored to be planning an M4-powered Mac refresh either by the end of this year or early 2025.
The four are competing on NPU performance, alongside efficiency:
Qualcomm’s Snapdragon X NPU offers 45 TOPS of AI performance, while CEO Cristiano Amon “claiming a performance-per-watt 2.6 times better than AMD and 5.4 better than Intel's Core Ultra 7 chips.”
Intel’s upcoming Lunar Lake chip offers up to 48 TOPS on the NPU, and Intel is claiming “1.4x AI performance over the Snapdragon X Elite running the Stable Diffusion tool in a GIMP plugin; faster overall core performance versus Ryzen and Qualcomm competition; and a 1.5x improvement over its previous generation in the performance of the integrated GPU.”
AMD’s upcoming Ryzen AI 300 series chips (Strix Point and Strix Halo) offer up to 50 TOPS performance from the NPU, the highest on the market so far.
Apple’s M4 chip offers up to 38 TOPS performance on the NPU, with the chip originally deploying on the iPad lineup with the Mac refresh rumored for this year or next.
Nvidia does not have an NPU competitor yet, as it believes its GeForce RTX GPUs offer significantly higher TOPS and more AI performance: “Performing 40 TOPS is sufficient for some light AI-assisted tasks, like asking a local chatbot where yesterday’s notes are. But many generative AI tasks are more demanding. NVIDIA RTX and GeForce RTX GPUs deliver unprecedented performance across all generative tasks — the GeForce RTX 4090 GPU offers more than 1,300 TOPS. This is the kind of horsepower needed to handle AI-assisted digital content creation, AI super resolution in PC gaming, generating images from text or video, querying local large language models (LLMs) and more.” However, Nvidia and MediaTek are reportedly working on an Arm-based AI PC chip for a 2025 launch following the expiration of Qualcomm’s exclusivity deal.
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Industry Commentary on PCs, AI PCs
Industry commentary on the AI PC outlook is optimistic over the longer-term, with vendors and chipmakers alike seeing growth through 2026. Management teams are broadly bullish on the upcoming refresh cycle and the potential for AI PCs to not only boost growth but also to improve ASPs.
Let’s break down some recent industry commentary:
Hewlett-Packard:
HPQ’s management is expecting to see stronger AI PC demand as 2024 closes with larger impacts in 2025 and 2026. Management said that “in the second half, we expect to see the introduction of AI PCs accelerate demand, over-and-above the anticipated PC refresh cycle and Windows 11 roll out.”
CEO Enrique Lores said that HP believes the “penetration of AI PCs is going to be growing over time,” with its first AI PCs representing “around 10% of the shipments for the second half. That's how we are quantifying that. But really, the impact will be more relevant in 2025 and in 2026. In fact, we expect that AI PCs, and at that point will be our new generation, will be between 40% and 60% of our sales three years after launch. … And as we have discussed before, we continue to believe that they will drive an improvement in average selling price of between 5% and 10%”
He further clarified that of the new AI PC products, HP expects “a stronger traction in consumer because commercial requires some evaluation done by customers. That takes some time. But over time, we expect the penetration in commercial to grow and to be more relevant in 2025 and in 2026.”
Dell:
Despite have one of the largest AI PC lineups in the industry, Dell’s management has been a bit more opaque about the opportunity, though they remain bullish on AI PCs.
Management said in Q1’s earnings call that the “commercial PC demand has also stabilized and we saw an improving demand environment as we move through the quarter. … We expect commercial PCs to continue to improve as the year progresses. We remain optimistic about the coming PC refresh cycle, driven by multiple factors. The PC installed base continues to age, Windows 10 will reach end of life later next year and the industry is making significant advancements in AI-enabled architectures and applications.”
This return to growth in commercial PCs and stabilization in demand is a positive sign, and also echoes HP’s view that the commercial space may need more than a quarter or two to fully embrace AI PCs and for shipment growth to accelerate.
Qualcomm:
Qualcomm sees AI redefining the PC, and its understandable management would be outwardly very optimistic about the opportunity given the Arm exclusivity this year and partnership with Microsoft.
CEO Cristiano Amon said at Computex that “the PC is truly reborn. It's a new era for the PC and that is happening with the combination of Snapdragon X Elite and Copilot Plus,… it's one of the most significant transitions in Windows. Personally, I believe is as significant as Windows 95. It is changing the experience, delivering groundbreaking AI capabilities, fundamentally changing how we interact with our PCs.” Amon added that the AI PC “will become indispensable for both personal and business applications. One thing is going to be different about this new PC. Unlike the past, your Windows PC will get better over time.”
Despite the optimism, Qualcomm said that “in our June quarter guidance, there isn't material PC volume forecasted in our numbers,” with more of the impacts coming from the back-to-school season and into 2025.
AMD:
AMD is arguably one of the more bullish companies in the industry regarding the impact that AI PCs will have on the upcoming refresh cycle.
CFO Jean Hu mentioned that AMD’s “PC client business are performing really well. We're gaining share. And primarily, they are driven by our most recent generation of processors, Ryzen 8000.” She added that the company believes the “AI PC is a very significant inflection point. It will potentially help the refresh the PC market. … [And] we think generation over generation technology and product leadership will help us both on the commercial side and the consumer side to continue to gain share.”
This echoes statements from CEO Lisa Su in AMD’s Q1 earnings call: “We see clear opportunities to gain additional commercial PC share based on the performance and efficiency advantages of our Ryzen Pro portfolio and an expanded set of AMD-powered commercial PCs from our OEM partners. Looking forward, we believe the market is on track to return to annual growth in 2024, driven by the start of an enterprise refresh cycle and AI PC adoption.We see AI as the biggest inflection point in PC since the Internet with the ability to deliver unprecedented productivity and usability gains.”
Q1 had already seen rather strong demand for AMD’s latest Ryzen series, as “Ryzen desktop CPU sales grew by a strong double-digit percentage year-over-year and Ryzen mobile CPU sales nearly doubled year-over-year as new Ryzen 8040 notebook designs from Acer, Asus, HP, Lenovo and others ramped.” If anything, this could be seen as a strong indicator of demand for the upcoming Ryzen AI 300 series.
Intel:
Intel has had the most to say about AI PCs, given that their positioning in the x86 versus Arm-based processor competition is most at risk if Arm-based PCs really start to see strong adoption over the next few quarters to years. We previously discussed the outlook for Arm-based PCs for our premium readers, saying that “if Arm-based PCs stick this time, it will mark a massive shift in edge devices. X86 dominates PCs as it stands today, yet AI leaders have their roadmaps loaded with Arm-based releases over the next year.” For more on Arm-based PCs, reference our analysis “Arm-Based PCs and AI Edge Devices”.
However, Intel has made it crystal clear that they don’t see Arm as much of a threat. Management explained that “Arm and Windows PC is not a new dynamic. This is something that was a big concern of the investment community as far back as 2011. And so there's been 14, 15 years of trying to break Arm into the Windows PC market with very little success in large part because we had a very strong road-map in large part because we had a strong ecosystem and in large part x86 PCs not only make us a profitable, it makes the OEMs profitable as well.
And so we kind of feel like the dynamic really hasn't changed all that much from the 2011 time period. Clearly, Microsoft is throwing more weight behind this. They've done an exclusivity with a single vendor in Qualcomm and that is up at the end of the year. And we fully expect to see other potential Arm suppliers come into the market when that exclusivity is up. But in general, there's been one successful Arm PC vendor in the market, and that's been Apple. And they've had 25 plus years in the market and they've got about a 10% market share.”
Turning to AI PCs, Intel is one of the most bullish on the long-term potential, seeing up to 80% of annual PC shipments being AI PCs by 2028.
Intel also believes revenue in Q1 was the “bottom and we expect sequential revenue growth to strengthen throughout the year and into 2025, underpinned by, one, the beginnings of an enterprise refresh cycle and growing momentum for AI PCs.” Management also hinted that the weaker Q2 revenue guide in part boiled down to supply constraints for its Core Ultra chips: “Q2 client revenue is constrained by wafer-level assembly supply, which is impacting our ability to meet demand for our Core Ultra-based AI PCs.”
Management further explained that the ramp of Core Ultra (Meteor Lake) “continues to accelerate beyond our original expectation with units expected to double sequentially in Q2, limited only by our supply of wafer level assembly. Improving second half Meteor Lake supply and the addition of Lunar Lake and Arrow Lake later this year will allow us to ship in excess of our original 40 million AI PC CPU target in 2024.” As a reminder, Intel is aiming to ship more than 100 million AI PC chips by the end of 2025, with a target of 40 million or more in 2025, and 50% growth to 60 million or more in 2025. Supply constraints will certainly pressure this target if wafer supply takes longer to improve, but at the moment, the demand is present, aided by the enterprise refresh.
As a whole, management teams from both chipmakers and PC vendors alike are projecting strong growth for AI PCs. Qualcomm is leading the push for the Arm-based PC, while Intel is targeting a huge growth in shipments for its x86-based Core Ultra lineup over the next six quarters.
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Conclusion
We see AI PCs as the next wave of growth in the budding AI industry, following GPU hardware and memory as professionals and consumers alike both stand to benefit from the ability to run on-device AI efficiently. AI PCs are projected to spearhead growth in the broader PC industry over the next few years, while adoption rates of AI PCs are estimated to soar, from less than one-fifth of total shipments this year to nearly four-fifths of annual shipments by 2028.
In terms of unit growth, AI PCs are expected to more than triple from approximately 50 million units this year to north of 200 million units by 2027, a rapid growth curve for the industry. We’re keeping a close eye on the major players and in the space as we work to identify the top beneficiaries of this trend, recently sharing a downstream beneficiary with our premium members.
Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.