Enthusiasm for artificial intelligence (AI) has driven US stocks to multiple all-time highs in 2024. The high returns of AI-related stocks have some wondering if this could be similar to the tech bubble of 2000. We see AI as a future opportunity that is just beginning to impact corporate results. But could it experience a bubble like the tech companies did during the dot-com crash? Since the beginning of 2023, AI-related stocks have outperformed the indices by 30%, and some investors have compared these moves to the dot-com bubble of the late 1990s. But looking at the data, in 2000, analysts were expecting 30% earnings per share growth for the tech leaders of the time, while today’s analysts expect 54% growth, representing a stronger foundation for stock prices. The ratio of price to earnings per share, known as forward P/E, can indicate optimism and confidence in earnings growth if it’s high. But it can also indicate overenthusiasm. In January 2000, the largest tech stocks were trading at an average forward P/E of 59 times. The top five tech stocks today trade at a forward P/E of 34 times, just half that. Thus, Wall Street believes AI will deliver better earnings growth than expected from dot-coms, even trading at prices below those of the 2000s. While the valuation gap between the leaders of the 2000s and today is no guarantee that AI-themed stocks will continue to outperform the rest of the market, in our view, it is clear that 2024 is not the year 2000. The potential opportunities in artificial intelligence are in two areas: AI 1.0 and AI 2.0. The first is the infrastructure that underpins AI. As demand for sophisticated AI capabilities grows, so does the demand for scalable and powerful infrastructure. Major cloud computing companies, including Amazon, Microsoft, Alphabet, and Meta, have all quickly implemented investment plans to support the increased cloud capacity they will need in the AI era. Furthermore, demand for AI is rapidly consuming the capacity of existing data centers on which the technology relies, necessitating new facilities. This also presents investment opportunities. Another crucial layer in AI infrastructure and the advancement of large language models like ChatGPT is the computational processing power required. Language models are computer programs that learn and generate human-like language using an architecture trained on large data sets. These data calculations are processed by semiconductors, known as graphics processing units (GPUs), which deliver much faster and more efficient performance and have rendered most computing electronics obsolete prior to 2020. Industry-leading GPU manufacturer Nvidia recently estimated that total GPU demand could reach $2 billion. This includes $1 billion from data centers and another $1 billion from AI-connected work such as training new language models, machine learning, and scientific simulations. AI is in its early stages, and while past performance is no guarantee of future results, we believe that companies engaged in developing the infrastructure for AI today, such as data center and cloud providers and semiconductor manufacturers, should continue to grow as the market develops. However, most of the unrecognized value of AI is in areas like software and applications. This is what we call AI 2.0, and it is focused on adopters. Industries such as customer service, healthcare, finance or logistics are all primed for significant transformation through AI. Maintaining a balanced investment portfolio exposure between AI 1.0 and AI 2.0 could be an effective way to potentially capitalise on the promise this area offers for investment. Added to this is that while US tech giants may be getting most of the attention in the AI era, there is also the potential of AI leaders elsewhere to keep an eye on. China is rivalling the US in the race for AI leadership, and there is also considerable potential for AI adoption in India, a data-rich nation with widespread mobile device use. Over time, this could translate into significant investment opportunities. South Korea, Japan and Singapore also remain important innovation hubs. And Europe? There are continental companies that could also be big beneficiaries of AI, as the technology can help them become more efficient and profitable. These include those in the German industrial sector; Aerospace, automotive and chemical companies in France; or logistics and high-tech companies in the Netherlands. Additionally, States are beginning to understand the national security implications surrounding access to and control of their data and are also strategically positioning themselves to take advantage of the potential of AI. Competition is likely to intensify and new regulations similar to the US policy limiting the sale of some advanced AI chips to China will spread. For all these reasons, investors must approach AI with a global perspective. The evolution of AI is just beginning and we expect that in the coming years opportunities will continue to emerge throughout the ecosystem associated with this technology. AI will change the way we think, work and solve problems, opening the way to innovation and revolutionary changes. Luis Artero is Investment Director of JP Morgan Private Banking in Spain. Follow all the information on Economy and Business on Facebook and Twitter. Xor in our weekly newsletter