AI Stocks: A Tale of Soaring Heights and Looming Risks

Can AI And Technology Stocks Keep On Keeping On? An Expert Take

We believe earnings growth can remain healthy for the technology sector broadly, fueled by the build-out of AI and a commitment to cost prudence on the part of tech firms. The tech sector powered global equity markets higher in the first half of 2024, led by an elite group of mega-cap stocks leveraged to the advancement in artificial intelligence (AI). These leaders, dubbed the "Magnificent 7" in 2023, retained their luster through the first half of 2024 as AI momentum continued unabated. July introduced a speed bump that stirred consternation for some tech investors. Can the sector's strong performance continue? We believe it can and see the summer setback as temporary. That said, we do observe greater differentiation across technology stocks, even among the Magnificent 7 as the market attempts to assign "winners" and "losers" in the race for AI development, enablement and adoption. Our recent week-long tour and conversations with the leaders of 29 public and private technology companies in San Francisco and Silicon Valley confirmed our underlying thesis: AI is here to stay, and we're only at the tip of the iceberg in terms of the investment opportunity. Today, monetization of AI resides primarily in the buildout of AI "factories." Hyperscalers, private enterprises and government entities are pouring hundreds of billions of dollars into the construction of these data centers, namely by spending on clusters of GPUs in the race to support ever-larger AI models. GPUs, or graphic processing units, are the type of semiconductor that is critical for generative AI training and inference workflows. Meanwhile, use cases and real-world applications of AI are in much earlier stages, with significant impact from AI products not expected until 2025. This is leading to cautious spending on software development, while software customers are likewise delaying purchases in anticipation of AI advancements. The chart below illustrates how this divergence has manifested in returns across technology subsectors. According to a Morgan Stanley survey of CIOs, 50% of AI spending is drawn from existing IT budgets, suggesting AI investment is supplanting other IT spend. Hyperscalers offering cloud services are consuming a large portion of enterprise IT budgets. This type of bifurcation underscores the heavy influence that AI is having across the economy and markets, with its evolution driving the investment opportunity set. AI momentum reinforces our outlook for future returns, and the differentiation we're seeing does nothing to dim that. Rather, it highlights the need for individual stock research and selection to capitalize on the opportunities as the AI evolution advances. The technology sector's strong performance last year and into 2024 has prompted questions as to whether technology stocks are overvalued. Some have made comparisons to the dot-com bubble of the late 1990s and the pandemic-era market where the "digitization of everything" led to heavy IT investment and historically low interest rates supercharged tech stocks. Both ended in busts. We don't think today's market is akin to either of these periods. The chart below illustrates the valuation difference relative to the dot-com bubble and pandemic peak. Much of the skepticism on tech's ability to power on is based on trailing sales and earnings estimates. Yet, many tech stocks are cheaper today than they were before the rally began in 2023 when factoring in revisions to forward-looking earnings estimates. As of July, the average 2024 earnings growth forecast for the technology sector stood at 20%. This represents a notable increase from early-year estimates, while profit margins in the sector have also expanded. We believe earnings growth can remain healthy for the technology sector broadly, fueled by the build out of AI and a commitment to cost prudence on the part of tech firms. That said, we evaluate company fundamentals on an individual basis with an emphasis on owning the right names at the right price. Our team of technology-focused, fundamentals-based stock researchers and selectors is looking to identify the next areas of opportunity while also assessing which business models may be at risk as AI becomes increasingly sophisticated. In all, we believe the rapid evolution of AI and all of its ramifications makes investing in this space very much an active pursuit. Volatility such as that seen of late, while always unsettling, is not unexpected and could present buying opportunities in this exciting and, we believe, enduring theme. This post originally appeared on the iShares Market Insights. Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.

AI stocks lose appeal amid heavy spending and slowing earnings growth

The one-month selloff in technology stocks might indicate sector rotations by major funds as the investment cycle shifts. Quarterly earnings reports also suggest a slowdown in growth due to capacity constraints, driven by the surge in AI demand. Technology stocks have faced strong headwinds over the past month, driven by disappointing quarterly earnings and a broad risk-off sentiment in global markets. Investors have begun unwinding their positions in artificial intelligence (AI) stocks, reallocating funds to sectors likely to benefit from lower interest rates, such as real estate and utilities. On Wall Street, most of the so-called "Magnificent Seven" stocks have seen sharp declines over the past month, with leading AI frontrunners like Microsoft, Nvidia, Amazon, and Alphabet all losing more than 10%, placing them in correction territory. Meta Platforms was the only tech giant to outperform the S&P 500, posting a modest 1% gain, while Tesla slumped 17% due to a continued slowdown in electric vehicle sales. European firm takes a hit In Europe, the largest technology firm, ASML, saw its shares plunge 20% over the past month, reflecting the global trend. ASML, often regarded as a bellwether for AI stocks in Europe, was severely impacted by stricter US export restrictions on chips to China. The Dutch semiconductor machinery manufacturer reported strong quarterly earnings in July but offered disappointing guidance for the current quarter, suggesting potential negative effects from the export curbs. AI-related stocks in Asia have also experienced sharp declines over the past month, highlighting a shift in the investment cycle as economic conditions evolve. Investors scrutinise big tech earnings amid massive AI take-up According to research firm FactSet: "The market is rewarding positive earnings surprises reported by S&P 500 companies less than average and punishing negative earnings surprises reported by S&P 500 companies more than average." This trend suggests that investors are scrutinising earnings growth more closely than usual, particularly for fast-growing AI companies. Many major tech companies reported slowing earnings growth in the second quarter or provided weaker-than-expected guidance due to three key factors: capacity constraints, rising capital expenditure on AI infrastructure, and the economic slowdown in China. In its earnings report last month, Microsoft highlighted that capacity constraints played a significant role in limiting the company's cloud growth, a critical driver of overall performance. Microsoft invested $19 billion (€17.4 billion) to expand data centre capacity for AI training and other workload demands. Similarly, TSMC, the world's largest semiconductor chip manufacturer, warned that AI chip output will remain constrained until 2025, longer than previously expected. TSMC, a major supplier to Apple and Nvidia and the largest customer of European tech firm ASML, underscores how closely intertwined global technology companies are and how their stock performances are interlinked. Surging demand for AI chips has exacerbated production pressures, worsening capacity constraints, according to industry leaders. Analysts believe that stretching capacities are a challenge facing all hyperscalers. As a result, tech companies will need to increase their spending on AI projects to keep up with computing demands and maintain their leadership in the industry. Goldman Sachs estimates that US tech giants, including Microsoft, Alphabet, Meta, and Amazon, will collectively invest more than $1 trillion (€0.91 trillion) in the coming years. US restrictions on China may affect AI firms Additionally, US policy measures aimed at curbing China's technological advancement could impact the growth of AI companies, compounded by the country's slowing economic growth. Export restrictions on China may significantly affect AI chip makers such as AMD, Nvidia, and ASML, given the critical market share that Chinese buyers represent in their overseas sales. Moreover, sluggish domestic demand in China and intensifying competition from local rivals have notably weighed on the performance of companies such as Tesla and Apple. This adds another layer of risk for major technology players amid rising geopolitical tensions and the trend towards de-globalisation. Sector rotations emerge amid shifting economic cycles From a macroeconomic perspective, another factor contributing to the selloff in tech stocks is profit-taking amid the emerging trend of sector rotations, set against the backdrop of shifts in economic cycles. Major central banks have either begun or are poised to initiate a rate-cutting cycle in response to cooling inflation and slowing economic growth. As a result, sectors such as utilities and real estate, which were among the hardest hit by surging interest rates in 2022 and 2023, are starting to regain favour among investors, while AI-driven tech stocks are being sold off to secure profits. Notably, value investing veteran Warren Buffett offloaded 510 million Apple shares in the first two quarters of this year, reducing his investment firm Berkshire Hathaway's stake in the company from 56% to 41%. Despite this downsizing, Apple remains the firm's largest equity holding. Investors will be keenly observing Buffett's next moves at this critical juncture. This week, all eyes will be on the upcoming inflation data from the UK and the US, set to be released on Wednesday and Thursday, as investors seek to gauge macroeconomic shifts and uncover clues about future market trends.

Nvidia And Other High-Flying AI Stocks Face Risk Of Falling Off A Cliff Over The Next Year? Tech Strategist Flags 'Not-So-Hidden' Costs That Market Is Not Factoring In - Global X Artificial Intelligence & Technology ETF (NASDAQ:AIQ)

The firm calls for much more transparency between extending useful life of networking, storage, and servers versus GPUs. Tech companies at the forefront of the artificial intelligence revolution are facing a looming risk, which in turn could drag these stocks, according to an analyst. Hidden But Not So Hidden Costs: As AI companies are striving to justify their massive investments in GPU chips, they could be stymied by depreciation, said Barclays analysts in a recent note, according to Business Insider. Depreciation is a book entry into the statement that apportions the value of a fixed asset over time so that a company sets aside provisions for replacing that asset at the end of its shelf life. In the books, the purchase price is recorded not as an expense but as a capital expenditure that can show higher profits upfront. Barclays analysts called depreciation to the massive AI chip investments a "not-so-hidden" cost of AI that only very few investors will factor into their valuation analysis. Barclays tech strategist Ted Mortonson reportedly told Business Insider, "Because Nvidia has this very aggressive design cycle of roughly a year between major releases, all of those products have different skews and functionality and power profiles." The strategist called this a headwind that can have a big impact on valuations and potentially send AI stocks lower over the next year. Wall Street analysts are grossly underestimating the depreciation costs over the next two years, he said. See Also: Best Artificial Intelligence Stocks To make his case, Mortonson noted that while Barclays estimates depreciation costs of $28 billion for Alphabet, Inc. GOOGL GOOG in 2026, the consensus is modeling 24% less. Barclays' estimate of depreciation for Meta Platforms, Inc. META is $30.8 billion compared to the consensus of $21 billion. Alphabet, Meta and Amazon, Inc. AMZN shares are 5%-25% more expensive than the consensus models, given this mis-modeling, Barclays' Ross Sandler said, the report added. Although valuations aren't stretched as they were during the bubble era such as the one seen in 2021, depreciation disconnects will likely be scrutinized given the ongoing debate where the big techs warrant multiple expansions, the report said. Innovation Increases Risk? Extending the useful life of server assets from five years to six years or more could spread out the depreciation over a longer period of time, reducing its impact on earnings, the analysts said. But the rapid pace at which Nvidia is releasing new GPU chips could hamper this mitigative measure, they said. Mortonson noted that the companies are spending over $200 billion and their capex was up over 50%. "We're so early in this, that combined with all the accounting, it all wraps up to return on invested capital, and I don't think you see a return on invested capital till sometime in 2025 or 2026," he said. "I think the jury is still out. I think the accountants got to take a hold of it, and there's got to be much more transparency between extending the useful life of networking, storage, and servers versus GPUs. That's the bottom line," he added. The Global X Artificial Intelligence & Technology ETF AIQ ended Monday's session down 0.03% at $33.34, according to Benzinga Pro data. Read Next: As Nvidia Leads Magnificent 7 Volatility, Analyst Says These Mega-Caps Are Poised To Outperform Small-Caps -- But There's A Catch Image Via Shutterstock Market News and Data brought to you by Benzinga APIs

Can AI Manage Money?

Advances in AI, such as Nvidia's digital twin technology, could significantly impact investment strategies by providing more accurate predictions related to weather, climate and other factors affecting various industries. One of the biggest discussions regarding artificial intelligence in 2024 concerns job replacement versus job augmentation-meaning, will these systems replace people or augment people and make them better? We frequently see people attempting to make the leap, indicating that AI will be able to actually "manage money" as well as or better than human portfolio managers. This is, of course, the most exciting framing of the issue, in that people love to picture the highly paid portfolio management and analyst community and indicate that computerized systems could do a better job for much less money. Asset management, however, represents a far more diversified array of job functions than just these jobs. For example, every asset management firm has its own specific marketing effort. Personalized, predictive marketing is something that AI has already proven capable of doing, essentially building a digital profile of each potential customer and seeking to predict the ideal "next interaction" that the asset management firm should take in each case. The concept relates to what we see employed by Netflix (NFLX), YouTube and TikTok, meaning that if you can recommend the most relevant next piece of content for each user, you keep that user engaged for a much longer time. Firms that optimize AI deployment in an area that has already been proven may see a bigger impact than trying to get AI to "beat the market" on an investment return basis. How does a model with more than a trillion underlying parameters make individual decisions? Why might a model with more than a trillion underlying parameters sometimes hallucinate and sometimes not? Most explanations are very strong with the theory of why this or that might happen, but it is very difficult to dissect exactly why one outcome occurred versus another in a singular instance. Quantitative investing has faced challenges in the past. Consider a scenario where a system recognized a strong positive correlation between two datasets for simplicity's sake. One dataset might be the earnings of the S&P 500 Index, and another might be the yield of the coffee crop in Brazil. Let's say, for argument's sake, there was a massive, statistically significant, positive correlation between these two datasets. Do you believe that investors would be comfortable making S&P 500 Index-related allocations based on the yield of the coffee crop in Brazil? The data is telling that story, but investors might say that the correlation is not meaningful because they cannot see a clear narrative as to WHY that relationship should make sense. Systems can find all sorts of different relationships within data, but historical best practice within economics and asset management has been that if you cannot create a sensible narrative as to why a relationship exists, then you have to discount or ignore it. Clients, of course, appreciate returns, but history has also trained them to recognize that relationships and correlations are not constant. Few things are more uncomfortable in asset management than having a strategy run into performance challenges and then not being able to understand why. In asset management, there are many jobs focused on selecting investment strategies, and the worst nightmare in these roles is not being able to explain what is happening in an underperforming strategy they have selected. In March 2024, Nvidia (NVDA) held its annual GTC event. One thing that caught my attention was the concept of the "digital twin." I had seen this before in AI, where one takes something that exists in the physical world, like a factory or a car, and creates a digital version of it. A highly accurate digital version could then be used to simulate different conditions and predict the effect on the overall system. Making changes in a digital system first or running tests on a digital system may be more cost-effective than doing everything first in the physical world. For instance, think about how expensive it is to build a whole race car to undertake different tests, as opposed to doing the most you can with a simulated digital version. Now, Nvidia in 2024 is not one for understatement-they indicated a project where they were making a digital twin of the earth, meaning the atmosphere and all the different systems that comprise the planet. A highly accurate digital system could be very interesting for attempting to forecast weather and climate. Let's say reasonably accurate predictions of the weather become more and more feasible as Nvidia's, and likely the systems of others as well, proliferate. To be clear, Nvidia is not creating its version of "digital Earth" with the idea of improving investment returns, but it doesn't take much of a leap to say that better, more accurate predictions related to the weather and climate could have a significant impact. There is an anecdote many have heard before where one is considering consumer behavior and is able to look at the number of cars in parking lots of particular types of businesses, like Walmart. Satellites are orbiting the planet all the time, and they can take pictures of many different things. The path starts out years ago, when a few firms had access to the satellite imagery, and evolves to a point where nearly everyone attempting to make certain types of investments is buying access to the same data. AI is interesting in that it may be able to be pointed toward a goal-making a consumer forecast-and then take in data from all sorts of different sources to build a perspective. The perspective may not be accurate, but the output would be fast-much faster than a person attempting to look at the same amount of data and come up with a conclusion or recommendation. Within the asset management industry, when we say "beat the market," we take for granted that people know what we mean. But, think about this for the moment-what is "the market"? In the United States, "the market" is often taken to mean the S&P 500 Index. This benchmark may not, however, be appropriate for the investment strategy in every individual case. For instance, what if a portfolio manager is focused on smaller market capitalization stocks? Should we automatically translate "the market" to mean the Russell 2000 Index? What about the S&P SmallCap 600 Index? Benchmarking performance can be quite complex, and in many cases, different investors have different views. It's interesting to take the idea of "prompting," a big topic in thinking through getting large language models to properly perform desired functions, and recognize that if we want an AI system to "beat the market," we have to take a careful approach to defining what this means. We also have to recognize that humans and AI systems operate differently. Humans can only take in so much information and have the capability for any sort of useful recall or decision-making process. AI systems can take in ALL the information and not have any issue with recall. If one can take in and use ALL information, does it make the same amount of sense to think in terms of the Morningstar style box, for instance? The Morningstar style box has risen to prominence because it is a very effective tool that helps the industry understand a framework for setting up a portfolio of different managers. If an AI system was truly trusted and could take in all relevant information on the market and different managers, it could make sense to take a view on how such a system would organize investments and set up various conditions to switch between them. The goal for most investors is not to "organize the market" or "perform great analysis"-the goal tends to be focused on the generation of returns. Sometimes, industries are captured by history in that most of the way they operate is rooted in decades of entrenched habits and norms. Investing is unlikely to ever be "easy," but starting out with systems that can take in and process all information instantly is totally different than starting in an environment where people have to do all analysis by hand. Bottom Line: Stock-Picking Models Do Exist The beauty of the exchange-traded fund (ETF) market is that so many options already exist. WisdomTree already has two funds utilizing AI in conjunction with human portfolio managers to inform better investment decisions. While the plus side is that it is exciting to bring such strategies to the market, the less positive side is that they focus on the value investment style, and many investors have been, for the time being, more interested in exposure to growth equities. Still, the two strategies are: It will be interesting to see these strategies build a track record and compare their performance to other strategies in their respective value-oriented peer groups. Christopher Gannatti, CFA, Global Head of Research