The general concern amongst many people I have been talking to has been: Is AI in a Bubble? It is a fair question and certainly one that seems to dominate bearish headlines often. I wanted to take an opportunity to share my overall assessment, the risks I am considering, and the data that contributes to my analysis.
Our first step should be to define what a bubble is. A bubble forms when an asset rises significantly above its intrinsic value. This is often driven by speculation or overly optimistic behavior. It is then followed by a sharp decline, often referred to as a bubble popping. This is not a new phenomenon, if you have some spare time, it may be worth reading about Tulip Mania in the 1630s.
Probably a more relevant example of a bubble is the dot-com bubble at the turn of the century (European Central Bank, 2026). This is the crisis that people tend to compare the AI environment to. Some of the charts being shared make the case for concern. The one below compares the Nasdaq, which shows to be heavy in growth and technology companies, with the Dow, which includes established blue-chip companies. As shown, the ratio reached 0.49 last year, above the 0.46 level reached at the height of the dot-com era in 2000 (Opening Bell Daily, 2025).
That being said, a ratio like this is a blunt tool and requires context. The Dow is a price-weighted index of only 30 companies, and the Nasdaq today looks very different from the Nasdaq of 2000. The chart illustrates that the market has tilted heavily toward growth. It cannot tell us on its own whether prices are justified. That is why the situation should be evaluated from a variety of factors and sources.
1. Valuations: bubbles tend to form when prices outrun earnings
One of the defining features of a bubble is a valuation that stretches far beyond what earnings can support. At the top of the dot-com bubble, forward price-to-earnings (P/E) ratios on leading technology names reached extraordinary levels: Cisco at 130.3x, Oracle at 120.4x, Microsoft at 60.2x, and Intel at 47.3x (FactSet, 2025). In comparison, we are seeing P/E ratios for Micron at 13.87x, Google at 17.75x, and Nvidia at 28.89x (Morningstar, 2026).
Today we are seeing strong earnings that many use to justify the valuations of these large AI names. As seen in the graph above, I believe our current landscape is very different from the Dot-com Bubble. The rapid growth of many stocks may be comparable, but we must examine what is fueling this escalation.
2. Monetization: bubbles burst when technology fails to make money
Historically, bubbles tend to pop when a new technology cannot turn its promise into revenue, and the money already spent can no longer be justified (Paul Krugman, 2025). So far, AI has shown the opposite pattern, with revenue and profit rising at many of the companies at the core of AI expansion.
3. Concentration: The S&P 500 falls flat without Big Tech
Investors saw the relatively large contribution to gains by Big Tech in 2023 and 2024 (Morningstar, 2025). While the specific contributors to the gains have shifted a little, the narrative is still consistent, much of the market’s return has come from a small group of companies.
As shown below, the pattern of a few stocks delivering a large portion of growth has continued in 2026. The S&P 500 is up about 14% this year, (YahooFinance, Oct 13th 2026). Excluding AI-related companies, the rest of the index is up only in the mid-single digits, while the AI Winners index is up roughly 55% to 60%.
Examining Figures 4 and 5, we can see that a large portion of the market’s gain can come from one theme. Concentration is a double-edged sword. It has rewarded investors who owned these companies, but when so much of an index’s return depends on a small group of stocks, a setback in that group weighs heavily on the whole market and on portfolios that track it.
4. CAPEX
Perhaps, the most debated part of the AI story could be the amount of money going into infrastructure. I think that demand for AI computing power could rise dramatically as AI capacity and utility expands. Goldman Sachs Research estimates that monthly token use by agentic AI applications could multiply about 24 times by 2030.
AI spending is far above other innovations as a percentage of GDP. This statistic can be taken in two different ways. It could be construed as businesses having high faith in the productivity and potential of AI, so they are spending to take advantage of the boom. It can also be construed negatively as a closed loop where revenues may be inflated due to overspending. One company’s spending can be seen as another’s revenue, but if the returns on these investments disappoint, spending could be cut and the effect would run in reverse. That is the central risk to this story, but one that I think has the potential to pay off greatly.
Spending is accelerating quickly amongst the big players. Capital expenditure by large hyperscalers is expected to jump from $412 billion in 2025 to $789 billion in 2026, an increase of about 92% in a single year, and estimates reach nearly $1.2 trillion per year by 2029 according to data from FactSet.
5. Interest rates: the Fed is keeping rates elevated. Should we be concerned?
As shown in the chart, the S&P 500 has been less sensitive to the level of interest rates than many investors might assume. Since 1954, the S&P 500’s average forward returns have been positive across every interest-rate environment shown below. For example, when the effective Fed funds rate was above 6.85%, the S&P 500’s average return over the following year was 14.7%. When it was between 0% and 1.34%, it was 16.5% (Creative Planning, 2026).
So, are we in a bubble?
Based on the facts currently available, no definitive conclusion can be reached as to whether it is or isn’t. However, based on the analysis that I have done; I believe that the AI buildout still has strong potential to grow. Spending does not appear to be slowing down, earnings have been strong, and my thesis that winners take most has been largely correct over the last few years. There will always be risks and we are continually examining and mitigating them. However, portfolios are designed to achieve long-term objectives, and investment theses often require time to develop and be reflected in results. If you have any questions or concerns, please don’t hesitate to reach out to your advisor.
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