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Investors’ enthusiasm for artificial intelligence (AI) has propelled U.S. equity markets to multiple all-time highs in early 2024. The strong outperformance of AI-related stocks, especially mega-caps, has some market-watchers asking if this could be similar to the dot-com bubble that burst in 2000.
We still see AI, one of the most revolutionary technological advancements in recent history, as a multi-year opportunity. It is only just starting to show up in corporate bottom lines.
A lot has happened since last year, so we’re rounding up our current thinking on AI stocks, as well as the likely next steps for the technology and the outlook for AI worldwide.
We emphasize that AI must be viewed as a global phenomenon, and we don’t advise investing in U.S. companies exclusively. We also think it’s paramount that investors who want to buy into AI make sure portfolios have exposure to companies across the AI value chain ranging from infrastructure to software and AI applications. We see potential opportunities in “enablers” and “adopters” of the technology.
AI stocks are on a roll as investors have been reacting to signs that demand for the technology is at the start of a long period of growth. Since the beginning of 2023, AI-connected stocks have delivered 30% better returns than both U.S. and global indexes.1
Some investors have compared these outsized moves to the dot-com bubble of the late 1990s. In that period, tech stocks outperformed dramatically as investors began to recognize the potential of the internet. The bubble burst in March 2000, and the Nasdaq Composite declined almost 80% over two years, wiping out the gains of the bubble era. It didn’t make a full recovery until 2015.
How similar are the two eras? Let’s consider the data.
An equity market bubble is characterized by stocks that become unreasonably expensive driven by speculation and excess investor enthusiasm. Sooner or later, investors come to the realization that companies will be unable to deliver on investor growth expectations and the bubble bursts, leading to a rapid decline in prices as investors realize the asset is overvalued.
One common way to evaluate stock prices involves comparing the share price to the company’s expected profits as expressed in earnings-per-share. Earnings-per-share is simply a company’s profit divided by the number of shares on the market. That metric is the primary fuel for stock prices. The ratio of price to earnings-per-share is known as a forward price-to-earnings, or P/E, ratio. A high forward P/E can indicate optimism and confidence in future earnings growth, but it can also signal excessive enthusiasm.
In January 2000, the five largest tech companies (Microsoft, Cisco, Intel, Lucent and IBM) traded at an average forward P/E ratio of 59x, adjusted by their relative sizes. The five biggest tech stocks today (Microsoft, Nvidia, Amazon, Meta and Alphabet) have a forward P/E ratio of 34x—barely half as much.
Data shows that in 2000, analysts expected 30% earnings-per-share growth from the tech leaders of the day, while today’s analysts expect 42% growth. That’s a more solid foundation for stock prices.
On this basis, Wall Street thinks today’s AI leaders will deliver better earnings growth than it expected from dot-com leaders even as the AI stocks trade at much lower prices as measured by the P/E ratio.
Investing in the AI theme may have felt simple so far, as the biggest tech stocks have delivered very strong returns. We think something broader and more balanced will work better from here.
We see potential opportunities in two areas that we refer to as AI 1.0 and AI 2.0.
AI 1.0 is the infrastructure that underpins AI. As the demand for sophisticated AI capabilities grows, so does demand for a scalable and powerful infrastructure. The technology is built on the foundation of data centers, and because most AI workloads live on the cloud, AI is fuelling further cloud growth.
Leading cloud computing companies, including Amazon, Microsoft, Alphabet and Meta, have all rapidly rolled out multi-year investment plans to support the greater cloud capacity they will need in the AI era.
Demand from AI is rapidly consuming existing data center capacity, pushing companies to build new facilities. That, too, presents potential investment opportunities. Utilities may have to add coal- or gas-fired power, and rising demand will undoubtedly spark infrastructure investments and efforts to develop a more energy efficient network, better cooling systems, and new solutions to integrate renewable energy.
Another crucial layer in the AI infrastructure and the advancement of large language models (LLMs) such as ChatGPT is the computational power required to rapidly process the vast amounts of data. LLMs are computer programs that learn and generate human-like language using an architecture trained on large pools of data. These computations are processed by semiconductors known as graphics processing units (GPUs). A decade of significant GPU advancements means much quicker and more efficient performance today. The GPU advancement has rendered obsolete most computational electronics from before 2020 obsolete.
Nvidia, maker of industry-leading GPUs, recently estimated that total demand for GPUs might reach $2 trillion2. This includes $1 trillion from data centers, and $1 trillion from work connected to AI such as training new LLMs, machine learning, and scientific simulations.
There are many beneficiaries in the AI 1.0 environment, a theme that could continue to work as demand for new computing infrastructure and AI-enabled services grows, costs drop, and consumers make greater use of the technology.
The AI journey is in its early stages, and past performance is no guarantee of future returns, but we think today’s leading AI infrastructure companies, such as the data center and cloud providers and the semiconductor manufacturers, should continue to grow as the market develops.
However, most of the unrecognized value in AI is in areas such as software and applications. We call this the AI 2.0 theme, which focuses on “adopters”. Industries such as customer service, healthcare, finance, and logistics are poised for significant transformation through AI.
For example, the buy now pay later company Klarna recently began using an AI assistant powered by OpenAI. In the first month, it had 2.3 million conversations, or two-thirds of Klarna’s customer service chats, doing the equivalent work of 700 full-time agents3.
We believe maintaining a balanced portfolio exposure between AI 1.0 and AI 2.0 could be an effective way to potentially capitalize on the promise of this space.
U.S. tech giants have received much of the attention in the AI era dating back to the release of ChatGPT in November 2022, which drew new attention to the AI field and supercharged adoption of the technology. Investors may be missing the potential of AI leaders elsewhere.
China closely rivals the United States in the AI leadership race. In December, Chinese tech company Baidu said its generative AI chatbot had surpassed 100 million users, rivalling the 180 million users of ChatGPT4.
There is also considerable potential for AI adoption in India, a data-rich nation with widespread mobile device use. In time, this could translate into significant investment opportunities. South Korea, Japan and Singapore remain innovative hubs.
There are leading companies across Europe that could become major beneficiaries of AI as well, as the technology may help them become more efficient and profitable. These include Germany’s robust industrial sectors, France’s aerospace, automotive and chemicals firms, and the Netherlands’ high-end tech manufacturing firms. The latter is also a primary hub in global logistics.5
Governments are beginning to understand the national security implications that surround access and control of their data6. They are also strategically positioning themselves to harness the potential of AI. Competition is likely to intensify, and new rules similar to the U.S. policy limiting the sale of some advanced AI chips to China may become more common.7
For all these reasons, investors who fail to approach AI through a global lens could miss out on true ground-breaking innovation.
The evolution of AI is only just beginning, and we expect that opportunities throughout the ecosystem will continue to emerge over the next few years in the form of countries, companies, and startups. In the coming years, we believe AI will rapidly change the way we think, work, and solve problems, opening the path to ground-breaking innovation and change.
1The Indxx Artificial Intelligence and Big Data Index, which holds 85 stocks that make hardware needed for AI or that use AI in their services, outperformed both the S&P 500 and the MSCI World by more than 30%. Past performance is no guarantee of future results. It is not possible to invest directly in an index.
2NVIDIA.com. Morgan Stanley Technology, Media & Telecom Conference (link). March 4, 2024.
3Klarna AI assistant handles two-thirds of customer service chats in its first month, February 27, 2024 (link).
4Evelyn Cheng, “Baidu says its ChatGPT rival Ernie bot now has more than 100 million users.” CNBC.com, December 28, 2023 (link).
5“Charting the Emerging Geography of AI,” Harvard Business Review, December 12, 2023 (link).
6Keith Strier, “What is Sovereign AI?” NVIDIA.com, February 28, 2024 (link).
7Bureau of Industry and Security October 7, 2022 (link).
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