Artificial Intelligence | Wells Fargo Investment Institute

Key takeaways

Mega-cap technology companies have allocated hundreds of billions of dollars in artificial-intelligence- (AI-) related capital expenditures over the past five years, and investors are increasingly starting to scrutinize the path to sufficient returns on investment, specifically in the form of increased revenue contributions from AI applications.
We believe generative AI has likely progressed from the “peak of inflated expectations” stage in the Gartner Hype Cycle into the “trough of disillusionment” stage. Although interest has not waned, we believe that management teams continue to balance expenses with productivity gains while also planning implementation on a larger scale.
Ultimately, investors may extrapolate the growth potential for generative AI in a linear fashion out into the future, but we believe we remain in early innings of generative-AI technology deployments and the discovery of relevant use cases — in our view, the growth potential of the technology is large yet unpredictable.

The monetization of generative AI

As the generative-AI trend emerged, companies across several industries and sectors benefited from the initial wave and excitement over potential benefits. Much talk among investors has been around generative AI’s potential to revolutionize industries and make workers more productive. The companies that emerged as early benefactors of the technology have included those that supply components to build out the infrastructure, to include semiconductor manufacturers (specifically GPUs); suppliers of hardware and equipment (such as servers and racks); and suppliers of heavy equipment (used for power generation and cooling). Data-center spending has continued to ramp up, driven by the ongoing development of AI applications. Mega-cap technology companies have allocated hundreds of billions of dollars in AI-related capital expenditures over the past five years. As capital spending remains elevated, investors are increasingly starting to scrutinize the path to sufficient returns on investment for these hyperscalers¹, specifically in the form of increased revenue contributions from AI applications.

While software companies initially benefited from the rollout of the generative-AI functionality, it became more evident that not all software firms would immediately benefit from the technology. The spending environment became very challenging as IT spending was scrutinized even further, the sales cycle elongated, contracts were adjusted by terms and duration of deals, and some deals were delayed and pushed out into subsequent quarters. We expect software companies to eventually monetize the technology in the form of increased revenue related to added AI capabilities, especially as enterprises optimize expenses and realize the benefits (productivity and efficiency gains) related to the premium services. In our view, the benefit to software companies may not be materially realized until at least 2025 (if not 2026) — this is largely a result of how revenue is recognized from a subscription business model, where year-over-year comparisons would not fully reflect the added revenue related to the premium AI content within new contracts. Further, if companies do not realize the productivity and efficiency gains from the increased expenses related to premium AI-capable software platforms, management teams may pull back on the spending and revert to legacy subscriptions without the AI capabilities as they optimize expenses.

¹ Hyperscalers refers to hyperscale cloud providers, a small group of companies who operate large networks of large data centers to facilitate cloud computing.

Media and investor excitement in perspective

A woman looking at her phone on a city street

The Information Technology sector is no stranger to economically disruptive technology and innovation cycles or to attention from investors and the media. Nevertheless, the typical technology innovation cycle can get ahead of reality in terms of attractive business models that work, unit economics that are profitable, and companies that can generate healthy amounts of free cash flow (amount of cash left after all expenses are covered) over the long run. As with past technology platform investment cycles, we believe investors tend to overestimate the near-term prospects of an emerging technology that has the potential to enormously impact society and to underestimate the long-term prospects of the technology’s adoption as well as its ability to penetrate previously unforeseen end markets.

The Gartner Hype Cycle (the Cycle, shown in Chart 1) maps the path of potentially innovative and disruptive technologies. It tracks the path from the technology’s development and introduction through mass acceptance and enterprise implementation, where businesses typically experience the benefits of the new technology. While the speed varies, it historically has taken about three to five years for a technology to move through the Cycle. We would note that not all technologies make it through all five phases of the Cycle. Some technologies fall off as enthusiasm simply fades away amid slow development of use cases or in the event that the technology is replaced.

On August 16, 2023, Gartner issued a press release confirming its view that we are currently within the “peak of inflated expectations” phase. While Gartner believes that generative AI is still in this stage of the Cycle, they believe it has passed the peak and is a bit further along on the curve. The number of generative-AI apps has increased substantially over the past year and a half, and interest in the technology remains elevated. Users continue to look for that “killer app” — an application deemed sufficiently necessary and desirable that users cannot live without it — and developers remain focused on creating a platform to increase productivity and efficiency. However, the focus for enterprises has now shifted to returns on investment for the capital being allocated to developing and implementing these new AI-capable software platforms.

Chart 1: Gartner Hype Cycle

The chart depicts the flow of Gartner’s hype cycle. The stages are as follows: Innovation trigger – This marks the beginning of the hype cycle around the disruptive technology. There usually is some sort of technology breakthrough that gets people talking. Start-up companies emerge and investments from private companies increase substantially. Peak of inflated expectations – At this stage, the technology begins to bring in more suppliers and users. Generally, hype begins to build around the technology and media coverage increases. Still, there is limited proof of the technology achieving expectations. Trough of disillusionment – The original excitement around the technology starts to wear off. Performance issues emerge and investors typically begin experiencing lower-than-expected returns on investment. Slope of enlightenment – This is when the tides begin to turn. Early adopters start seeing the benefits of the technology and new opportunities begin to emerge. The technology becomes more widely understood and accepted, notably as new use cases emerge. Enterprises begin to understand how they can implement the technologies within their own organizations. Plateau of productivity – Mainstream adoption begins to take hold. Businesses and users alike are realizing more and more real-world benefits of the technology. Broad understanding of the technology is paying off; users are experiencing productivity and efficiency gains.

Source: Gartner, 2023.

Questions around long-term sustainability of generative AI have also begun to bubble up, and the initial hype around generative AI has somewhat subsided. If it has not moved already, we believe generative AI is moving into the next phase of the cycle, the “trough of disillusionment”. At this stage, widespread use cases continue to develop and are implemented on a larger scale. Although interest has not waned, we believe that management teams continue to balance expenses with productivity gains while also planning implementation on a larger scale. The movement into this next phase does not mean doom and gloom and certain abandonment of the technology. Quite the contrary, it is more of a rationalization for uses of the technology — users are seeking to develop further applications while also balancing the added expenses and resources with the increased productivity. In essence, enterprise customers are seeking a minimum return on investment from the elevated levels of capital allocated to develop new AI applications. While there are many technologies that do not fully move through all five stages of the cycle, we believe that generative AI technology is more in a phase of limbo. Excitement around the platform remains elevated, but at this point use cases for the technology are limited or at least have not reached peak potential.

Amid the ongoing excitement, enterprises are now seeking those generative-AI applications that can drive sustainable returns on investment over longer periods of time. As previously stated, Gartner believes that generative AI has moved over the “peak of inflated expectations” but still remains in that phase, just a little further along in the process. That said, we expect multiple applications to develop as the technology becomes even more widespread and ingrained into our everyday lives, similar to e-commerce and search. However, the pace at which those cutting-edge applications are developed will determine how long the technology stays within the “trough of disillusionment” phase. We believe that phase may be shorter than is typical, particularly as the focus around the technology remains and capital continues to flow in to develop those applications. Yet, if the capital runs dry and app development slows to a crawl, widespread deployment of generative-AI uses could be at risk.

However, applications and use cases for the technology should develop over time. For example, we expect several companies to attempt to bring the technology closer to the end user (consumer), as has been seen with new handsets that include an expanding range of AI capabilities. Further, we believe we are in the early stages of the transformation of the PC market and expect laptop and desktop computer hardware to be embedded with AI functionality. This anticipated upgrade cycle follows slower demand and ongoing supply chain issues related to the pandemic in addition to slower PC upgrades over the past few years, due somewhat to a lack of innovation and new features.

Valuation snapshot

Amit Chanda

Equity Sector Analyst, Information Technology

Tom Christopher

Equity Sector Analyst, Communication Services

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Generative AI — Monetization, investor excitement, and valuations