Anatomy of an AI Correction


Key Takeaways

  • The AI ecosystem comprises semiconductors, software, other hardware and innovative use cases, all of which play crucial roles in the advancement and adoption of AI. 
  • Realistic expectations for 2024 include the potential for a hardware refresh cycle driven by AI in devices, but caution is advised when it comes to big headline topics like fully autonomous vehicles, as their timelines may be difficult to define.
  • Investing in AI carries risks such as intense competition, rapid product obsolescence, dependence on intellectual property rights and the uncertainty of successful product or service development. 

ChatGPT was released in November 2022, about 18 months ago. The novelty of the software went viral, and AI now pervades the market and the public’s consciousness. While we are as excited as anyone about AI’s potential, some reality has set in.

  • AI Is Not Independent of Interest Rates: Equities still are taking cues from interest rates. Coming into 2024, the market was pricing in a number of rate cuts, and expectations have been sharply tempered.1 Higher-for-longer rates are not helpful for growth-oriented equities, especially startups without cash flows and profits.
  • We Overestimate the Short Term while Underestimating the Long Term: ChatGPT came out 18 months ago, so why aren’t more jobs fully automated? Enterprise software adoption takes time. AI represents an intersection of numerous technologies. The fuel for AI is data, and the world is still determining how to treat data privacy and cybersecurity. Companies cannot simply adopt AI; they must develop an entire strategy around it. Thirty years into the internet’s adoption curve, a lot more has happened than many would have predicted when it started growing in 1993.
  • Don’t Rush the Phases of AI: The “AI picks and shovels" (largely Nvidia) have delivered strong performance. When will the benefits diffuse? During 2024, we are still seeing the hyperscalers spending $175 billion on capital expenditures.2 It has also taken until 2024 to see the CHIPS Act unlocking a lot of the government grants to encourage chip production in the U.S.3 Software companies can leverage the AI infrastructure buildout. But it is difficult to foresee the next Nvidia-like return without the market adjusting forward interest rate expectations downward.

Corrections are natural. We believe those looking at AI should contemplate disruptive innovations that challenge our wildest imaginations over the next five to 10 years. The journey will include significant bouts of volatility, as we saw in April.

The Four Components of the AI Ecosystem

The WisdomTree Artificial Intelligence and Innovation Fund (WTAI), which tracks the returns, before fees, of the WisdomTree Artificial Intelligence and Innovation Index, approaches the space with diversification at the center. A number of AI-focused ETFs place significant weight—more than 10%—in Nvidia. A concentrated strategy worked well after the release of ChatGPT. Yet, over time, we think investors will benefit from a more diverse AI ecosystem:

  • Semiconductors: Nvidia’s move after the initial release of ChatGPT and the awakening of the world to generative artificial intelligence has been historic. Yet, the idea of application-specific integrated circuits and companies developing custom chips also receives a lot of attention—Broadcom and Marvell are strong here. Any company designing chips is using software from Cadence or Synopsys for electronic design automation. Many processes also require ever-increasing amounts of memory chips (think what Samsung, SK Hynix and Micron provide). Finally, there are specialized players like Mobileye, focused on a particular use case (autonomous driving), and ARM Ltd., licensing an array of designs to all sorts of users. Will these companies capture as much economic value as Nvidia has over the past 18 months? No company has gained an incremental trillion in market capitalization as quickly as Nvidia.4 However, absent developing chips in completely new ways, many companies will have to partake in the AI megatrend over the coming years for these ideas to come to fruition.
  • Software: Software is a natural next leg of the AI megatrend, as this is what the picks and shovels will be used for. Copilot for Office 365 at Microsoft is an important example, with a $30 per-user-per-month option that companies can immediately use to adopt a form of AI across tasks they already do. has certain packages that provide different types of AI functionality. Amazon’s AWS, Microsoft’s Azure and Google’s Cloud also provide different menus of AI services. In our opinion, the big, established names are important, yes, but take a smaller firm like Darktrace. Darktrace applies AI to the use case of cybersecurity. We think cybersecurity will be one of the most important and impactful AI use cases, and companies like Darktrace, SentinelOne and Palo Alto could play a big role in AI’s proliferation.
  • Other Hardware: There are semiconductors, and then there’s everything else. One part of that “everything else" is robotics. New AI models allow for robotics to effectively learn by doing and learn by repetition, and we believe this approach will ultimately lead us from a world where robots have to be programmed explicitly, function by function, to robots being able to accomplish goals logically, learning from their environment. Another type of hardware is sensors. Sensor providers recognize that the fuel for AI is data, and one type of data, for example, comes from a car collecting information about its surroundings in different ways. Whether companies are using lidar, radar or visual cameras, the concept that systems will need to take in massive amounts of environmental data is a good one.
  • Innovative Use Cases: Finally, there is the long tail of things that AI can be used for. In a sense, these companies may have returns that are not as correlated to other broad macroeconomic factors. For example, for a company like Recursion or Exscientia, things like interest rates and the market are important until a drug of significant success is produced. Currently, the world accepts the concept of AI being used for drug discovery, but it is still early, and we do not yet have a long list of successful drugs that have been developed this way. If certain companies can prove that they can raise the success rate of drug development or quicken the timeline, this would be extremely valuable (also extremely difficult).

Realistic Expectations for 2024

The bottom line, in our opinion, is that for any shorter period, like the next year, we need the appropriate expectations. A single year is too fast to imagine the majority of companies across every industry adopting and benefiting from AI. However, something that we may see is a catalyst of AI in devices—laptops and smartphones—leading to a hardware refresh cycle. The innovation in this case is that the chips in these devices would be designed such that certain types of AI could run directly on the devices instead of needing to run only in the cloud. If this is borne out, it could be important for semiconductor fabricators, like TSMC, and certain chip designers, like Qualcomm and ARM, that would generate the designs and make the chips that go into many of these devices.5

Beware of big headline topics, like Tesla announcing its robotaxi model in early August 2024. The concept of fully autonomous vehicles is one of the most exciting in the AI landscape, but we have to recognize as investors that this topic (like fusion power, general artificial intelligence or commercially available quantum computers) has a tougher-to-define timeline. Maybe something interesting happens in the next couple of years … or maybe it takes closer to a decade. Don’t underestimate the difficulty, and always remember that the hard stuff is hard for a reason.



Source: Nicholas Jasinski, “The Fed Is Beginning to Shift Its View on Rates. Watch Closely,” Barron’s, 3/21/24.

2 Source: Adam Clark, “Nvidia Stock Gains. Here’s How Much Its Major Customers are Set to Spend,” Barron’s. 3/26/24.

3 Source: Kathrin Hille, “TSMC boosts Joe Biden’s AI chip ambitions with $11.6bn US production deal,” Financial Times, 4/8/24.

4 Source: David Marino-Nachison, “Nvidia’s Market Cap Finished the Day Above $2 Trillion,” Wall Street Journal, 3/1/24.

5 Source: Isabelle Bousquette, “AI Gives Enterprise Device Market Something to Be Excited About,” Wall Street Journal, 4/19/24.

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Important Risks Related to this Article

For current Fund holdings, please click here. Holdings are subject to risk and change.

There are risks associated with investing, including possible loss of principal. The Fund invests in companies primarily involved in the investment theme of artificial intelligence (AI) 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.

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About the Contributor

Global Head of Research

Christopher Gannatti began at WisdomTree as a Research Analyst in December 2010, working directly with Jeremy Schwartz, CFA®, Director of Research. In January of 2014, he was promoted to Associate Director of Research where he was responsible to lead different groups of analysts and strategists within the broader Research team at WisdomTree. In February of 2018, Christopher was promoted to Head of Research, Europe, where he was based out of WisdomTree’s London office and was responsible for the full WisdomTree research effort within the European market, as well as supporting the UCITs platform globally. In November 2021, Christopher was promoted to Global Head of Research, now responsible for numerous communications on investment strategy globally, particularly in the thematic equity space. Christopher came to WisdomTree from Lord Abbett, where he worked for four and a half years as a Regional Consultant. He received his MBA in Quantitative Finance, Accounting, and Economics from NYU’s Stern School of Business in 2010, and he received his bachelor’s degree from Colgate University in Economics in 2006. Christopher is a holder of the Chartered Financial Analyst Designation.