The emergence of Artificial Intelligence (AI) is triggering rapid and structural change across the economy, and is shaping up to be a disruptive innovation that has the potential to leave a lasting mark on entire industries.
That promise has already fueled a powerful investment cycle and driven substantial gains in AI-linked assets. It has also raised a critical question: Can adoption and earnings growth develop quickly enough to justify the expectations already reflected in market prices?
The rollout of AI can be viewed through the technological adoption cycle, developed by organizational theorist and management consultant Geoffrey Moore in the 90s. This framework draws lessons from disruptive technological advances such as the railroad, telephones, or the internet and how they have spread through the fabric of society.
Underpinning the framework is the way technology diffuses from the early market to the mainstream market. There is a wide gap between these two segments which, in Moore’s technology adoption framework, is referred to as the “chasm.” As a result, not all disruptive technologies successfully climb the adoption curve.
However, if the disruptive technology does cross the chasm, momentum builds towards increased usage. This triggers a tornado of hypergrowth, characterized by mass-market adoption. At this stage, a company's ability to scale and meet growing demand matters most.
How adoption broadens from the early market to the mainstream market, illustrative
Note: Curve and segment widths are not based on underlying data. Source: Geoffrey A. Moore “Crossing the Chasm”, Principal Asset Management. For illustrative purposes only.
The reported use of AI in business functions has climbed to over 20% of firms in the U.S. There is also momentum building behind its use, driven in part by firms that do not want to risk missing out on the perceived benefits of AI. Indeed, with its potential use-case spanning almost all industries, a growing share of companies have indicated increased spending on AI tools.
Much of this spending is driven by potential cost savings and the increased availability of “out-of-the-box” solutions, suggesting that a real target market has emerged. All of these factors suggest that AI has likely crossed the chasm and entered a hypergrowth phase.
At this stage, there is inherent uncertainty about the size of the total addressable market. Indeed, questions remain about whether AI applications will be limited to agentic use or will extend to robotics, humanoids, and beyond. Therefore, during the hypergrowth phase, traditional valuation metrics do not matter. Instead, valuations are primarily driven by expectations of future growth.
As AI usage is expected to rise, there is an ongoing race between overall adoption and underlying growth expectations. At some point, these two forces will need to converge to match reality.
In the meantime, a potentially large spread between the two isn’t necessarily a negative catalyst by itself, though it highlights the magnitude of the risk that markets face if sentiment softens. In other words, while it is entirely possible that AI could become mainstream technology in the future, this could occur even if investor expectations today are overly high.
One measure of market expectations is the market-implied value of growth. This is the portion of enterprise value that is attributable to earnings growth beyond the sustainability of current earnings. The latter, known as earnings power value, is the portion of value based on current earnings, while ignoring assumptions about future growth. Taken together, these measures show that 70% of the value of the technology index today is based on expectations of future earnings growth.
A similar measure that looks at the market-implied future earnings growth rate indicates that earnings will need to grow by more than 8% per year in the long term to meet current expectations. On a relative basis, this is a significant deviation from proxies of long-term nominal growth, such as the yield of the 30-year Treasury. Moreover, this is over 2.5x the earnings growth rate markets were pricing in during the business cycle prior to the pandemic—when growth was expected at 3%—and is slowly approaching the peak rate seen during the 2000 dot-com bubble.
With so much of the sector’s valuation tied to future growth and expectations already at historically high levels, a key question for investors is what could cause those expectations to be revised.
The race between adoption and expectations can continue as long as the mainstream market expands, providing an anchor to market-implied growth rates.
However, a critical tipping point is reached once the market becomes saturated. This has historically coincided when half of the total addressable market has been acquired. At this stage, it becomes evident that there may not be enough room for everyone to compete in the market. Earnings and margin growth will likely begin to decelerate, competitive pressures will return, and traditional competitive advantages are tested.
The importance of market share increases as a winner-take-all dynamic emerges. The resulting AI market leader becomes the de facto standard because it can ultimately capture a disproportionate share of industry profits. Moreover, in this process, a valuation reset occurs as earnings sustainability is reemphasized and more traditional valuation tools come back into focus.
Looking ahead, identifying when AI begins to experience a deceleration in expected growth will be critical in managing risks. While doing so involves some uncertainties, there are some signals that investors can monitor:
- Surveys of AI adoption: While these provide a good directional indicator of where adoption is heading, they likely do not provide the whole story. Adoption is less about availability and more about intensity, so these measures do not necessarily need to reach exactly 50% adoption for the saturation point to be reached.
- Token consumption and prices: These indicators are useful proxies for enterprise AI adoption that incorporate intensity. As hyperscalers are ultimately monetized through AI token usage, these measures also highlight competitive pressures among AI providers. So far, the data point to low intensity of aggregate AI usage, with enterprises adopting AI but not yet scaling up their usage. Meanwhile, mixed competitive pressure among AI providers has become more prominent, with prices generally trending lower.
- Return on Invested Capital (ROIC): As the return of every capex dollar decelerates, it points to a diminishing opportunity set within the market. This can be due to weakening pricing power, rising costs, or increasing cost of capital. For highly capital-intensive hyperscalers, the latter appears most relevant in the current environment. With more AI capex funded by debt issuance, the emergence of a crowding-out effect that increases the overall cost of capital for AI firms would challenge project economics.
These indicators can help investors assess whether AI’s earnings potential is keeping pace with the growth already priced into markets. Adoption may continue to expand for years, but weaker pricing power or declining returns on investment could challenge valuations well before the market reaches saturation.
Consistent with history, AI is facing an ongoing race between rising adoption and evolving earnings growth expectations. These will need to converge at some point, and investors can look to the technological adoption cycle framework to help make sense of how valuations can shift in the process. Notably, the AI adoption cycle has only recently entered the mainstream market. This suggests that investors should be able to continue capitalizing on the growing momentum in adoption and should maintain AI exposure through the hypergrowth phase.
However, as 70% of the value of the technology index is now based on expectations about the future, it leaves the sector acutely exposed to shifting expectations. To manage risks, investors should closely monitor signs of market saturation and increasingly focus on businesses that can convert adoption gains into durable earnings growth.
Finally, once adoption matures, differentiation between AI winners and losers will only intensify. While this process may coincide with a broad market re-rating, the leaders in the AI race will ultimately recover, while losers will become irrelevant and fade away. Given how much is at stake for investors, active management and good stock selection through this adoption cycle will play a critical role in effectively capitalizing on this period of profound technological change while minimizing risks.
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