Chips of the Trade: TSMC, Samsung Benefit from AI Demand

By Andrew West, CFA, Analyst and Portfolio Manager | April 15, 2024

In international markets, a big theme of investor interest relates to companies developing the underlying technology that powers AI. This includes the designers and manufacturers of the advanced semiconductors necessary to run AI, as well as producers of semiconductor manufacturing equipment and providers of the critical computing infrastructure required by AI systems.

Expectations are that semiconductor industry revenue growth will accelerate to annualized double-digit levels this decade, spurred by demand for AI chips. This would be a growth rate well above levels that we’ve seen since the mid-1990s, with predictions that the roughly US$50 billion dollars of AI chips sold in 2023 could rise to US$400 billion dollars of sales before the end of the decade.

While US chip designer NVIDIA gets most of the AI press, TSMC manufactures virtually all of the high-performance AI chips designed by NVIDIA. The Taiwan-based company uses some of its most sophisticated manufacturing technologies for these chips, enabling them to be at the forefront of transistor density, speed, and energy efficiency. AI chip customers also increasingly depend on TSMC’s innovative capabilities in integrating and packaging the logic, memory, and input/output components involved. TSMC’s CEO recently projected that in four years about 20% of this company’s total revenue would come from AI-specific chips.

Samsung Electronics has received less attention from investors but is also carving out a niche for itself in AI-related semiconductor manufacturing. Samsung anticipates a rebound in their memory chip prices in 2024, in part due to the expected proliferation of on-device AI, a memory-hungry implementation, and is investing for longer term in the next generation of high-bandwidth memory chips integral to AI applications, promising to leapfrog competitors in performance. Samsung Electronics is also positioning itself to compete with TSMC in manufacturing advanced logic chips, using its capability of designing and manufacturing memory, logic, and chip packaging to unlock new opportunities for its foundry business.

Infineon, widely recognized for its strength in automotive and power semiconductors, is less directly exposed to AI themes but does have products supporting growth data centers and AI computing whose high-power demands is a good match for Infineon’s switched mode power supplies and voltage regulator designs that power high performance systems.

Image source: Synopsys as of 2024. ¹Global Semiconductor Sales – Sources: SIA/WSTS (historicals); forecasts based on Gartner, TechInsights, IBS, SIA/WSTS and consensus analyst forecasts for leading semiconductor companies. ²Explosion in Demand for AI Chips – Sources: Gartner, AMD, NVIDIA, Intel, Goldman Sachs.

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NVIDIA and the Cautionary Tale of Cisco Systems

By Christopher Mack, CFA, Analyst and Portfolio Manager and Richard Schmidt, CFA, Analyst and Portfolio Manager | November 22, 2023

NVIDIA, the giant semiconductor company founded by Taiwanese American Jensen Huang, seems invincible these days. Annual revenue has more than doubled since 2020. Its stock price has more than doubled this year and is up more than 700% over the past five years. It is one of the rare trillion-dollar market-cap companies.

Perhaps most intriguingly, NVIDIA is a leading company in the biggest tech-driven trend to come along in years, artificial intelligence. It makes the chips that power AI-based computer systems, and it sells the software and services companies need to operate their own AI programs. And NVIDIA was a big company before AI took off.

As if to emphasize the point, this week the company posted third-quarter revenue of US$18.1 billion—tripling from a year ago and setting a company record. Earnings were also a record at US$10 billion.

NVIDIA meets all our core criteria for a quality growth company: It has a sustainable business and a competitive advantage, solid management, and financial strength. Over the last six years, its revenue has grown from nearly US$7 billion in fiscal 2017 to just under US$27 billion in fiscal 2022.

But as NVIDIA reaches dizzying valuations—it currently trades at 118 times earnings—we can’t help thinking of another good technology company whose stock once commanded nose-bleed premiums: Cisco Systems.

Cisco makes the networking equipment that enables much of the internet. As the World Wide Web was taking off in the mid-1990s, Cisco Chief Executive John Chambers was telling people a new era of computing was coming, and the company’s hardware played a big part in making that new era real. Between 1995 and 2000, its revenue surged 850%, from about US$2 billion to US$19 billion.

Cisco stock did even better, growing an incredible 3,800%, from US$2 in January 1995 to US$79 in March 2000. The title of its 1999 annual report exhorted investors to “Capture the Momentum.” Many took that advice. In March 2000, at the height of the dot-com bubble, Cisco became the most valuable company in the world, with a market capitalization of more than US$500 billion.

Then the dot-com bubble burst. Cisco shares fell 88%, dropping from US$79 to a low of US$9.50 two years later. That drop wasn’t the result of any large change in the company’s performance. Revenue was nearly US$19 billion in fiscal 2000, US$22 billion in 2001, and about US$19 billion in 2002. But the premium that investors were willing to pay for the company disappeared as the hype around the web faded.

Over the next two decades, Cisco kept growing. Its sales in 2022 were nearly US$52 billion, more than double the US$19 billion it hit in 2000. Yet its stock has never traded as high as it did on March 1, 2000. Today it’s at roughly US$53. On a total return basis, it took 20 years for Cisco stock to recover from the dot-com crash.

So, are there lessons we can learn from Cisco as we look at NVIDIA? Just as Chambers argued 25 years ago that a new era of computing was at hand, Huang has been talking up AI. Just this month he said “In the last 40 years, nothing has been this big. It’s bigger than PC, it’s bigger than mobile, and it’s gonna be bigger than the internet, by far.” The enthusiasm for AI’s future has led to very high valuation multiples for NVIDIA, although not as high as Cisco reached in 2000.

Source: YCharts

By all accounts, NVIDIA is strong—in its last full fiscal year (2022), it earned US$9.8 billion on US$27 billion in revenue—and in an enviable position. Semiconductors are the oil of the 21st century, an essential component for the tech-fueled modern world. The company produces chips that allow for the most advanced computing, expanding from its core gaming prowess into related lucrative areas like data centers, autos, and of course AI.

But as Cisco shows, the success of a company and the performance of its stock aren’t the same thing, and as valuations increase, it’s more and more difficult for a stock to outperform. Over the past 50 years, 231 companies have reached a similar multiple. Only 20% of the stocks trading with a price/sales ratio between 30 and 40 outperformed the market over the next 12 months. The more you extend your time horizon, the worse the results get. Over a 10-year period, that number drops down to 6%.¹

NVIDIA is a good company with all the quality and growth prospects we look for in an investment. The critical question is how much one is willing to pay for those prospects. As Cisco showed in the waning days of the dot-com boom, overpaying for a good company is a bad choice.

Endnotes

¹ Siegel, Jeremy with Schwartz, Jeremy. Stocks for the Long Run: The Definitive Guide to Financial Market Returns and Long-Term Investment Strategies, Sixth edition. McGraw-Hill Companies, 2022

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Meta Accelerates Its AI Game

By Igor Tishin, PhD, Analyst | August 11, 2023

Meta has been quieter about its artificial-intelligence-focused endeavors this year than some of its big-tech peers like Microsoft and NVIDIA, but it expects just as massive a transformation of its business from the much-hyped technology.

In its second-quarter earnings conference call, Meta founder and CEO Mark Zuckerberg detailed how AI permeates the company. For example, nearly all of Meta’s advertisers now use at least one AI-based product, allowing them, for instance, to personalize and customize ads. He also touted an increase of 7% in time spent on Facebook after launching AI-recommended content from accounts that users don’t follow.

Now the company plans an aggressive push of its own version of generative AI, the kinds of large language models that have gotten so much attention lately. In July, the company released an open-source—i.e., free for even commercial use—generative AI platform called Llama 2, which Meta hopes will emerge as a competitor to OpenAI’s GPT-4. Meta is betting its platform will unleash users’ creative potential and result in a flood of content. If that occurs, Meta’s powerful algorithms for matching content with users—4 billion of them across all of its platforms—will become indispensable as a content-discovery tool with a rich set of monetization options from advertising to ecommerce to subscriptions.

The big-picture view suggests that Meta is building a hub for user-generated content (UGC) discovery and distribution that aims to go head-to-head with the likes of YouTube and TikTok. In the world of unlimited content, the power shifts from creators to distributors and curators. In this scenario, the ultimate constraint on demand for content is the totality of leisure man-hours; Meta’s goal is to optimize how to fill those hours with the most compelling content.

Generative AI could also help transform Meta’s main money-maker, its advertising business. The digital-ad market comprises roughly 70% of the overall advertising market, and is dominated by two firms, Meta and Alphabet. Finding a way to expand its market share has been a stumbling block for Meta. However, with these new AI tools, Meta may have found a way around that constraint. It may not be able to take much more share of the advertising market, but it could start taking share of the advertising-creation market. On the call, Zuckerberg specifically mentioned the strides made by its Advantage+ generative AI tools that allows advertisers to supply just an image, from which the AI will then develop an entire ad campaign. For small companies that can’t afford to hire an advertising agency, this could be a powerful alternative and a lucrative market for Meta. Its Advantage+ ad program has the potential to take share from creatives and even advertising agencies by customizing, altering, or outright creating content for advertising, rather than just targeting or measuring it.

Source: Statista

Meta’s embrace of AI is one of several efforts the company’s made over the past year to reverse the slump that followed its 2021 shift into the metaverse. Zuckerberg showed common sense on reining in spending, the company executed on its new identifier for advertisers (IDFA) independent ad-targeting model, and it ramped up new initiatives like Reels, Advantage+, and most recently Threads. Meta has also delivered robust recoveries in free cash flow and revenue, with free cash flows for 2023 now forecast at approximately US$30 billion, a big increase from the US$10 billion forecast at the end of 2022. Even critical media coverage about privacy concerns and Facebook’s effects on democracy have waned and regulatory pressures have eased (except in the EU).

All this doesn’t mean Meta’s out of the woods, though. While impressive, the turnaround only just got the company back to where it was a year ago, when it was facing hard questions on rising costs, large infrastructure investments, and its incredibly audacious spending on the metaverse. Reality Labs—the division of the company building the metaverse that has already burned through US$30 billion since 2020—is losing a further US$15 billion per year, and Meta expects those losses to increase meaningfully in fiscal year 2024. Meta has yet to provide an explanation about how this is going to pay off. Even with its recent good fortune and AI potential, Zuckerberg will have to provide some real clarity soon to avoid another investor rebuke.

Image source: Romain TALON – stock.adobe.com.

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Generative AI Through a Fundamental-Research Lens

By Jingyi Li, Analyst and Portfolio Manager | July 26, 2023

The following is an excerpt from our second-quarter report for the Global Equity strategy. Click here to read the full report.

Anyone who has interacted with popular AI models—asked them about the mysteries of life and the cosmos or created convincing Van Gogh replicas using AI-enabled image generators—can sense that we may be in the midst of a technological revolution. That prospect has consumed equity markets lately, with seven US tech-related stocks responsible for most of the market appreciation in the second quarter.

As an investor in high-quality, growing businesses, we have always tried to position this portfolio to benefit from secular trends, the kind that transcend economic cycles and are driven by fundamental changes in key areas such as tech. Still, it is incredibly difficult for anyone to predict how such trends will unfold; the vicissitudes of cryptocurrency are a sobering reminder of this. Furthermore, as seen with the rise of the internet and, later, mobile connectivity, technology is merely a platform; it’s the applications of the technology that eventually determine many of the winners and losers. In the case of generative AI, some of the future applications may not yet be conceivable, although many companies, even outside the tech field, are now pondering the possibilities.

ChatGPT, the chatbot that helped spark the market’s AI enthusiasm, is an important innovation because it can digest large amounts of text (hence the term large language model), communicate in natural (human) language, and generate sophisticated responses. Based on the Transformer architecture, a technique first introduced by Google in 2017, ChatGPT demonstrates the advances that have been made in AI that open the door to a wider set of business uses. But while natural language models recently produced epiphanies among lay chief executives and investors regarding AI, some tech companies were already investing in such capabilities and are being rewarded for that foresight. NVIDIA has been the biggest beneficiary this year in terms of its stock run and projected revenue gains; however, our other holdings, such as Adobe, Microsoft, Salesforce, ServiceNow, Synopsys, and TSMC, also appear among the possible beneficiaries. More companies—including, perhaps, some not yet in existence—will certainly join the ranks over time.

While it is still early, it’s evident that these companies see generative AI as transformative to their businesses and something upon which they can build new revenue models. Additionally, they are turning to AI to boost internal productivity, enhance existing customer offerings, and improve the quality and efficiency of customer interactions.

Most notably, Microsoft was able to gain an immediate leadership position in generative AI by making a US$10 billion investment in OpenAI, the company behind ChatGPT, earlier this year. Microsoft’s Bing search engine has since introduced ChatGPT into its web index data—a collection so large that it is rivaled by the dataset of only one other business in the world, Alphabet’s Google. Data are the feedstock of AI models, and an AI-enhanced search engine trained on so much data may attract more users to Bing, allowing Microsoft to sell more ads on the service. Microsoft is also adding generative AI to other products, including the Azure cloud service, enabling business customers who use Azure to easily integrate OpenAI models to glean more insights from their data and automate functions such as certain IT tasks. These added capabilities should motivate more businesses to migrate their data to the cloud and make Azure more competitive with Amazon.com’s AWS and Google Cloud.

The beneficiaries of demand for generative AI aren’t limited to traditional IT-sector companies. Data centers used to train AI models require up to ten times more power than typical data centers, thus requiring more-powerful equipment and backup power. Given the amount of heat they generate, new liquid-cooling solutions will be needed as well. This creates an opportunity for Schneider Electric, which has been developing innovative data-center equipment solutions for many years.

In the meantime, NVIDIA has emerged as the unrivaled global leader in providing the technologies at the center of the AI arms race. Due to an explosion of demand related to generative AI and LLMs from across its customer base, NVIDIA projects that data-center revenue for its fiscal second quarter ending in July will surge to US$11 billion. Not only is that more than double last quarter’s total, but the forecast also shattered the average analyst estimate that called for about US$7 billion.

Our investments in NVIDIA and Schneider are reflective of how we are thinking through the many unknowns and approaching portfolio structure in this environment. Through our fundamental framework, we can appreciate the broad excitement for AI, but we also remain conscious of valuations and thoughtful about diversification, recognizing that it’s unlikely anyone can predict today the biggest long-term winners.

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Out of Our Minds

Chips of the Trade: TSMC, Samsung Benefit from AI Demand

NVIDIA and the Cautionary Tale of Cisco Systems

Source: YCharts

Endnotes

Meta Accelerates Its AI Game

Source: Statista

Generative AI Through a Fundamental-Research Lens

Disclosures

Out of Our Minds

NVIDIA and the Cautionary Tale of Cisco Systems

Source: YCharts

Endnotes

Meta Accelerates Its AI Game

Source: Statista

Generative AI Through a Fundamental-Research Lens

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Disclosures