As it Stands Today, Are We in An A.I. Bubble?

June 15, 2024

Investors’ enthusiasm for artificial intelligence (AI) has propelled U.S. equity markets to multiple all-time highs as of June 2024. The strong outperformance of AI-related stocks, especially mega-caps, has some market-watchers asking if this could be like the dot-com bubble that burst in the early 2000s.

AI is being viewed as one of the most revolutionary technological advancements in recent history, and a multi-year opportunity. It is only just starting to show up in corporate bottom lines. It is 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. There are 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 exceptional returns. The leader Nvidia has appreciated an eye popping 748% during this time.

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 and is driven by speculation and excess investor enthusiasm. Sooner or later, investors come to the realization that companies will be unable to deliver on growth expectations and the bubble bursts, leading to a rapid decline in prices as investors realize the assets were 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 59, adjusted by their relative sizes. The five biggest tech stocks today (Microsoft, Nvidia, Amazon, Meta and Alphabet) have a forward P/E ratio of 34 – 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 sign for more solid foundation for stock prices now, as compared to the tech era of the early 2000s.

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. It is important to note that the valuation difference between the 2000s and today’s leaders does not guarantee that technology stocks will continue to outperform the rest of the market. But it is also clear that 2024 is not 2000 either.

In closing, investing in artificial intelligence (AI) can be a lucrative opportunity given its transformative potential across various industries. The main items to consider:

  • Diverse Applications: AI is being integrated into numerous sectors such as healthcare, finance, automotive, retail, and more. Investing in AI can mean investing in companies that develop AI technologies directly or those that utilize AI to improve their products and services.
  • Long-Term Growth: AI is still in its early stages, and its potential for growth is vast. Companies that are pioneers in AI research and development may see substantial long-term returns as the technology matures and becomes more widely adopted.
  • Risk Factors: As with any investment, there are risks involved. AI technologies are subject to regulatory scrutiny, ethical concerns, and technical challenges. It’s essential to conduct thorough research and consider the risks before investing.
  • Diversification: Instead of investing in individual AI companies, consider investing in AI-focused exchange-traded funds (ETFs) or mutual funds. These funds spread the investment across multiple companies, reducing the risk of exposure to any single company’s performance.
  • Keep Abreast of Developments: AI is a rapidly evolving field. Stay updated on the latest advancements, breakthroughs, and market trends to make informed investment decisions.
  • Consider Ethical Implications: As AI becomes more prevalent, there’s increasing scrutiny on its ethical implications, such as privacy, bias, and job displacement. Investing in companies that prioritize ethical AI development and address these concerns can mitigate risks associated with negative public perception or regulatory backlash.
  • Partnerships and Collaborations: Look for companies that are forming strategic partnerships or collaborations with established players in the AI space. These collaborations can enhance a company’s competitive advantage and market position.

Before making any investment decisions, it’s essential to conduct thorough research, consider your investment goals and risk tolerance, and consult with a financial advisor if needed.