The AI Revolution: Mega-Cap Tech Companies Lead the Charge in AI Investments and Revenue Growth

Investing In AI: It's About How, Not If

Investing in the full AI value chain - such as through an AI-focused ETF - could offer a broader approach compared to traditional tech sector investments. Artificial Intelligence (AI) is showing up everywhere you turn, from self-driving vehicles to summarized product reviews. AI thrives on data, and we live in an increasingly digital world. In fact, the AI market size is expected to reach $407 billion by 2027, and BlackRock believes AI could disrupt the global economy over the next decade. (Learn more about digital disruption and AI). Although the concept of AI has been around for decades, recent trends of advancements in semiconductor breakthroughs, data proliferation, and software design have converged to bring the field to an inflection point. AI has transcended its "buzzword status," with businesses across sectors looking to integrate the technology to grow new revenue streams or improve efficiencies. Yet despite the recent attention on AI, we believe the AI theme is still in its early stages, presenting investment opportunities for long-term investors. Certain areas of the AI space continue to attract more headline attention than others, like the select group of mega-cap tech companies known as the "Magnificent 7." Focusing on the Mag 7 illustrates the potential emphasis on "picking a winner now" when investing in AI. The S&P Technology Select Sector Index has exhibited notable disparities between top and bottom performers. The difference between top-decile and bottom-decile technology stocks year-to-date is about 47.4%. Long-term thematic AI investing is not just about investing in today's largest tech stocks, it is about identifying underappreciated areas within AI that are well-positioned for long-term growth and going beyond today's market leaders. The AI value chain includes companies and industries directly contributing to AI's growth, including software, hardware, and infrastructure. A few key examples include: Did you know? Worldwide AI chip revenue is projected to increase to $71 billion in 2024, marking a 33% increase from 2023.³ Did you know? The U.S. data center market saw the largest pricing increase of all commercial real estate assets in 2023.⁴ Did you know? AI software spending is predicted to grow from $124 billion in 2022 to approximately $298 billion by 2027.⁵ Did you know? It is estimated that over 80% of enterprises will have used generative AI by 2026, up from less than 5% in 2023 - creating demand for AI-as-a-service providers.⁶ These categories represent the diverse and evolving landscape of AI. By owning the full value chain of AI industries, investors can seek to capture a broader spectrum of companies and reduce the risk of overreliance on a few dominant players. This approach may allow for exposure to both established and emerging innovators and enablers. A thematic ETF focused on these areas can offer a streamlined approach to AI. Investors interested in capturing themes across the AI value chain may consider the iShares Future AI & Tech ETF (ARTY). AI is still at the early stages of one of the potentially most powerful technological revolutions since the birth of the Internet. AI may introduce new efficiencies and revenue streams across sectors of the economy. As this technology matures, investors can turn to AI to complement the core exposure of their portfolios or to replace their traditional tech exposure. AI-focused ETFs can offer a streamlined way to tap into potential diverse opportunities with a single trade. Footnotes Source: Forbes, "24 Top AI Statistics and Trends in 2024." June 15, 2024. Source: Morningstar, as of 7/31/2024. Based on stocks in the S&P Technology Select Sector Index. Top decile funds are up 37.3% and bottom decile funds are down -10.1%. Source: Gartner, "Gartner Forecasts Worldwide AI Chips Revenue to Grow 33% in 2024." As of May 29, 2024. Forward-looking estimates may not come to pass. Source: CBRE "North American Data Center Pricing Nears Record Highs, Driven by Strong Demand and Limited Availability", March 7, 2024. Source: Gartner "Forecast Analysis: AI Software Market by Verticl Industry, 2023-2027", March 27, 2024. Forward-looking estimates may not come to pass. Source: Gartner, Catherine Howley, "Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026." As of October 11, 2023. Forward-looking estimates may not come to pass. This fund was previously known as iShares Robotic and Artificial Intelligence Multisector ETF (IRBO). On August 12th 2024, the fund was renamed iShares Future AI & Tech ETF (ARTY) and tracks a new underlying index, the Morningstar® Global Artificial Intelligence Select Index℠ and will cease to track the NYSE FactSet Global Robotics and Artificial Intelligence Index. The fund's new investment objective will be to seek to track the investment results of an index composed of U.S. and non-U.S. companies that provide products and services that are expected to contribute to artificial intelligence technologies in areas including generative AI, AI data and infrastructure, AI software, and AI services. Carefully consider the Funds' investment objectives, risk factors, and charges and expenses before investing. This and other information can be found in the Funds' prospectuses or, if available, the summary prospectuses, which may be obtained by visiting the iShares Fund and BlackRock Fund prospectus pages. Read the prospectus carefully before investing. 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Where Are The AI Revenues? A Look At Mega-Cap Tech Sales Multiples

It's important to be valuation-sensitive with position sizing to avoid over-concentration at peak valuations. Since the release of ChatGPT, mega-cap technology companies poised to profit from AI-enhanced software tools or cloud AI-model training capabilities have seen a surge in their stock prices. Yet, many have yet to realize significant AI-driven revenue growth, let alone a substantial impact on their bottom lines. This has formed the basis for what Sequoia Capital calls AI's $600B question -- whether today's capital expenditures (CapEx) levels can offer an estimated $600B in revenue generated from AI software and services to provide positive return on investment (ROI), given the industry's heavy investment in hardware infrastructure. Figure 1: Mega-Cap Tech CapEx Estimates for Year-End 2024 The obvious beneficiaries of this investment so far have been Nvidia (NVDA) and its semiconductor peers, who are experiencing exponential revenue growth due to the high demand for AI training chips. With significant capital expenditures being made to purchase these chips and build the next wave of AI data centers, several critical questions arise: Will end users and enterprises see enough value to justify these costs? Will current investments in AI infrastructure deliver positive returns? And most importantly, are these firms fairly valued? In this blog post, we will focus on the question of valuation, examining whether the current stock prices of these tech giants are justified given the modest impact of AI on their revenues so far. The narrative has always positioned AI as a software revolution. While semiconductors serve as essential tools, it's the software that will be the key differentiator as users seek the most advanced, intelligent platforms. Consequently, mega-cap tech companies have seen significant stock price appreciation since ChatGPT's launch, driven by investor optimism about AI's potential future earnings being concentrated among these prominent players. However, this enthusiasm has led to valuation multiple expansions, which many believe may indicate a bubble. Examining the period since ChatGPT's launch, figure 2 shows that the Nasdaq forward price-to-sales (P/S) ratio expanded from 3.8 to 5.0, a moderate 34% increase. However, Amazon (AMZN), Google (GOOG), Meta (META) and Nvidia all saw expansions of more than 50%, with some exceeding 100%. This could imply that these stocks are overvalued, or it might indicate that the market considers them fairly valued given the expectations of substantial future AI revenues and earnings potential beyond current forward sales estimates. Figure 2: Forward Price/Sales Multiple Expansion after Launch of ChatGPT More recently, Wall Street's sentiment toward these firms has shifted from positive to negative as investors question the potential ROI from large capital expenditures and the timeline for realizing these returns. Recent earnings reports from major tech companies revealed mixed results. Amazon's stock declined due to a cautious revenue outlook and disappointing sales, compounded by rising costs to expand Amazon Web Services. Microsoft (MSFT) reported slowing growth in its Azure cloud-computing arm and plans to continue substantial investments in data centers. In contrast, Meta posted strong earnings, appeasing investors and buying time for its AI investments to bear fruit. Meanwhile, Alphabet's shares fell after the company surprised Wall Street with sharply higher costs, overshadowing its better-than-expected sales. The impact of a weaker-than-expected jobs report at the end of the week further exacerbated declines in these stocks, prompting investors to reassess their positions amid a slowing economy. As a result, there have been significant multiple contractions as investors sell shares and reposition themselves. The valuation premium previously afforded to these stocks has diminished as concerns grow that the AI hype may not meet expectations. Examining current P/S ratios in the context of historical trends can provide valuable insights into whether valuations have become stretched compared to the past. Figure 3 sheds light on whether the recent pullbacks in stock prices are justified. Nvidia and Microsoft stand out as notable outliers, with current P/S ratios significantly higher than their historical 10-year medians. This could suggest that the market expects fair value for extremely strong growth ahead, or it could indicate overvaluation. By piecing together forward and historical ratios, we see that Amazon, Google and Meta have recovered from relatively low valuation ratios recently. With significant multiple expansions post-GPT launch, they have returned to valuations that are in line with their historical numbers. However, the story may be different for Microsoft and Nvidia, as both have experienced significant multiple expansions beyond what is seen in the broader Nasdaq Index, materially exceeding historical norms. AI's potential as a game-changer for mega-cap tech companies might justify higher valuations now and into the future. Historically, investing in these firms five or more years ago would have been highly profitable, regardless of valuation. However, the current valuations of some indicate a significant "valuation premium" compared to the past, which likely explains why investors are now more cautious. This caution has contributed to recent price pullbacks, even amid positive earnings reports. Investing in exponential technologies like AI can benefit portfolios, but it is essential to manage concentration risk and market timing. By being aware of valuation trends, investors can strategically trim positions when overvalued and add when undervalued - following the classic "buy low, sell high" adage. A 10-year chart of Meta illustrates how trimming positions during overvaluation periods and accumulating during undervaluation relative to historical norms could have been beneficial. Figure 4: Meta Price and Price/Sales History (Current vs. Trailing Median) Reflecting on past market bubbles, such as Cisco (CSCO) during the dot-com era, can provide valuable context for remaining valuation-sensitive when investing in technology equities. Cisco's P/S ratio soared to 60 before the stock price collapsed by more than 80% in the early 2000s. Comparatively, Nvidia's current P/S of approximately 35 is not at dot-com bubble levels, indicating a less extreme valuation. This historical perspective helps address the question, "How far is too far?" when valuations seem stretched. While mega-cap tech firm P/S ratios have expanded significantly since the onset of the AI wave, they remain well below the extremes seen during the dot-com bubble. This suggests that although valuation multiples have increased since ChatGPT's launch, we are not witnessing a bubble akin to the early 2000s. While investing in AI and exponential technologies is exciting, a valuation-aware approach is crucial. Rather than avoiding these investments entirely, investors should adjust their exposure as valuations fluctuate, ensuring they avoid over-concentration at peak valuations and maintain a diversified portfolio. At WisdomTree, we seek to take this approach in our WisdomTree Artificial Intelligence and Innovation Fund (WTAI), which invests in the entire AI ecosystem and value chain. With exposures in software and semiconductors, as well as other important hardware key to the value chain, the Fund remains diversified by taking a modified equal weighting approach, with the flexibility to rebalance allocations where dislocations may be by trimming positions that may have been "too far stretched" while adding to those that may be "underappreciated," giving investors the benefit of flexibility within a highly dynamic market environment. Important Risks Related to this Article There are risks associated with investing, including the possible loss of principal. The Fund invests in companies primarily involved in the investment theme of artificial intelligence and innovation. Companies engaged in AI typically face intense competition and potentially rapid product obsolescence. These companies are also heavily dependent on intellectual property rights and may be adversely affected by loss or impairment of those rights. Additionally, AI companies typically invest significant amounts of spending on research and development, and there is no guarantee that the products or services produced by these companies will be successful. Companies that are capitalizing on innovation and developing technologies to displace older technologies or create new markets may not be successful. The Fund invests in the securities included in, or representative of, its Index regardless of their investment merit, and the Fund does not attempt to outperform its Index or take defensive positions in declining markets. The composition of the Index is governed by an Index Committee, and the Index may not perform as intended. Please read the Fund's prospectus for specific details regarding the Fund's risk profile. Blake Heimann, Senior Associate, Quantitative Research Blake Heimann is a Senior Associate on the Quantitative Research & Multi Asset Solutions team at WisdomTree, based in Europe. He initially joined WisdomTree in 2020 as an Analyst on the Research team in the U.S. In his current role, he is responsible for supporting the creation, maintenance, and reconstitution of equity and digital asset indices. Blake's finance career began in 2017 at TD Ameritrade, where he started as an Analyst before transitioning to a role as a Quantitative Analyst. During this time, he focused on research and development of machine learning applications in finance. Blake holds bachelor's degrees in Mathematics and Economics from Iowa State University, and he has completed his Master's in Computer Science with a specialization in Machine Learning at Georgia Tech.