Mobileye Steers Closer to Autonomous Driving

By Andrew West, CFA, Analyst and Portfolio Manager | May 29, 2024

The road to creating fully autonomous vehicles has been plagued by technological obstacles and accidents. Apple scrapped its decade-long electric vehicle project this year after reportedly struggling to create a self-driving car. Last October, a pedestrian in San Francisco was trapped under a driverless car operated by Cruise, which is majority owned by General Motors. And Tesla has been the target of lawsuits and regulatory investigations due to fatal accidents involving cars equipped with its Autopilot feature.

When most people think of automation in driving, they think of cars that could operate anywhere without a human driver, or what the Society of Automotive Engineers calls “Level 5 automation.” But many cars on the road today offer some level of automation, whether it’s lane centering features or adaptive cruise control, or both. The key distinction in these so-called Level 1 and Level 2 systems is that these features support the driver, rather than replacing the driver as the higher levels would.

The market for the driver-assistance technology is currently dominated by the Israeli company Mobileye. The company’s EyeQ system analyzes the input from a camera and sensors to alert drivers to dangerous traffic conditions and can initiate maneuvers such as evasive steering when a collision is imminent. As of last year, the company has captured about 70% of the private vehicle market with its basic driver assistance tools, with more than 50 automaker clients including Volkswagen, Ford, Toyota, and Chinese electric vehicle maker Geely.

Mobileye now hopes to leverage its success in basic driver assistance solutions to allow it to have similar success in more advanced levels of automated driving. Mobileye rolled out SuperVision, its Level 2+ driving assistance solution, in 2021. The system requires drivers to keep their eyes on the road, but they can let go of the steering wheel and let the automated system take over in specific circumstances such as on “all regular road types at up to 80 miles per hour,” according to the company.

In 2026, the company plans to release a true Level 3 autonomy system it calls Mobileye Chauffeur. That system would enable motorists to take their eyes off the road and their hands off the steering wheel in a wider array of traffic conditions, another step toward fully autonomous cars.

These more advanced systems come with extra risks for the company. If a car is involved in an accident while the automated systems are engaged, the manufacturer of the driver assistance tools can be held legally responsible for the incident. The added liability risk and the regulatory certification process, especially for Level 3 cars and above, are two of the reasons why few companies have ventured to bring advanced self-driving cars to market.

There is also strong competition in the market. The biggest threat is from large automakers that can afford to produce technologies for self-driving cars that could replace, or surpass, Mobileye’s offerings. An example is Mercedes-Benz, which in 2023 introduced the first Level 3 automation system approved for use in the United States. Chipmakers such as NVIDIA and Qualcomm also make competitive products.

One of Mobileye’s competitive advantages so far has been the low cost of its systems. Since they are mass produced and compatible with over 800 car models, the company has economies of scale that reduces the per unit cost of its products. The company’s basic driver’s assistance package sells to manufacturers for about US$50 per EyeQ unit, while the SuperVision product is sold for between US$1,000 and US$2,000 per system. Last year, most SuperVision units were sold to the electric vehicle maker Geely.

The cost of Mobileye’s automation systems for consumers depends on the manufacturer’s suggested retail price. For example, Ford recommends car dealerships charge consumers a US$600 to US$700 markup for the Ford Escape Active SUV, with Level 2 automation features.

This is compared with Tesla’s Level 2 offerings. While most Tesla car models are manufactured with the basic autopilot features pre-installed, consumers can upgrade to the enhanced Autopilot function for US$6,000 or the Full Self Driving package for US$12,000.

So far, orders for Mobileye’s advanced offerings have been below market expectations for 2024 as the company initially expected to ship 175,000 to 195,000 SuperVision units to a handful of large automakers including Porsche, China’s FAW group, Geely, and India’s Mahindra. But Mobileye’s stock edged up in late March after Volkswagen Group announced it would use SuperVision and Chauffeur in the future production of car brands such as Audi, Bentley, and Lamborghini, and also unveiled a partnership with Mobileye to rollout a robot taxi fleet in 2026.

Despite the positive update, investors would like to see more large Western automakers join the ranks of customers for the company’s new system. Mobileye says it’s in talks with 11 manufacturers who make 37% of global light vehicles. If the company wins more deals with automotive companies that essentially give up on their in-house autonomous vehicle systems, Mobileye could create a competitive moat to defend its long-term profits and market share.

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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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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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NVIDIA’s Competitive Structure May Be More Fragile Than Its Valuation Implies

April 23, 2024

Advances in artificial intelligence have created an AI gold rush, and one company—NVIDIA—supplies the necessary picks and shovels. With a dominant position in a fast-growing market, shares of NVIDIA have soared. However, NVIDIA’s competitive advantage may be more fragile than its stock price indicates.

At Harding Loevner, we believe that whether a business thrives largely depends on the competitive structure of its industry, which we evaluate using Michael Porter’s Five Forces framework. NVIDIA has an estimated 95% share of the market for AI chips. Its graphics processing units (GPUs), which were originally used to power video games, have been adapted to become the key engine of AI computation. The customer base for these chips is highly concentrated and primarily consists of “hyperscalers”—companies such as Alphabet, Amazon.com, Meta, and Microsoft that operate large networks of data centers with immense computing power and storage needs. More than half of NVIDIA’s data-center revenue for its fiscal quarter that ended in January came from large cloud-services providers, and one company alone accounted for 19% of last year’s total company revenue.

From the Porter perspective, a key factor in the bargaining power of buyers is their ability to backward integrate. All of the aforementioned customers have this capability, which threatens to diminish their reliance on NVIDIA. In our view, it’s only a matter of time before this manifests. In fact, Amazon, Google, and Microsoft each have unveiled custom data-center chips as potential substitutes for NVIDIA’s general-purpose GPUs. Because each server in a data center performs only a few specific functions that are repeated over and over, these custom chips, called application-specific integrated circuits (ASICs), are tailored to just the tasks the companies need them to perform.

Industry rivalry is also likely to increase. As the frenzied pace of spending on AI infrastructure eventually eases, companies will begin to look for less expensive options from new suppliers, which may include NVIDIA’s own customers.

NVIDIA is a high-quality company led by skillful managers. Its revenue more than doubled over the past fiscal year, and net profit rose fivefold. But the high potential for increased rivalry and backward integration among key customers suggest that it is unlikely NVIDIA can continue growing at these rates with these margins while maintaining its grip on 95% of the AI-chip market. The fragility of its competitive structure limits our valuation tolerance, which is why we exited the stock in the first quarter after holding it for more than five years.

While it is possible that NVIDIA will continue to exceed increasingly aggressive consensus earnings forecasts for some time, we own other more attractively priced businesses that allow us to invest in AI’s potential. For example, Microsoft and other providers of software and services have the advantage of being able to monetize generative-AI technology without regard for whose GPUs or ASICs are used in the future. Microsoft’s customers also don’t appear to have the ability or interest to try to backward integrate; in fact, AI functionality such as its Copilot chatbot are helping to lock in users. In general, as the applications for AI evolve, value is likely to shift from hardware, such as NVIDIA’s GPUs, to the software and services built upon this infrastructure.

Read more about our software and services holdings in the first quarter Global Equity report.

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Out of Our Minds

Mobileye Steers Closer to Autonomous Driving

NVIDIA and the Cautionary Tale of Cisco Systems

Source: YCharts

Endnotes

Generative AI Through a Fundamental-Research Lens

NVIDIA’s Competitive Structure May Be More Fragile Than Its Valuation Implies

Disclosures

Out of Our Minds

NVIDIA and the Cautionary Tale of Cisco Systems

Source: YCharts

Endnotes

Generative AI Through a Fundamental-Research Lens

NVIDIA’s Competitive Structure May Be More Fragile Than Its Valuation Implies

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Disclosures