The AI bubble – Cracks beneath the surface
In this video insight, I explain why I believe investors should look beyond the strong earnings and seemingly reasonable valuations driving enthusiasm for artificial intelligence (AI). I examine questions surrounding optimistic market assumptions, insider selling incentives, the economics of AI, rising debt levels, weakening cash flows, and whether reported earnings are overstating the sector’s underlying profitability. I also argue that low price-to-earnings (P/E) ratios do not necessarily protect markets from significant corrections and suggest the real bubble may lie in AI earnings expectations rather than share prices.
Roger Montgomery is the Founder and Chairman of Montgomery Investment Management. Roger has over three decades of experience in funds management and related activities, including equities analysis, equity and derivatives strategy, trading and stockbroking. Prior to establishing Montgomery, Roger held positions at Ord Minnett Jardine Fleming, BT (Australia) Limited and Merrill Lynch.
He is also author of best-selling investment guide-book for the stock market, Value.able – how to value the best stocks and buy them for less than they are worth.
Roger appears regularly on television and radio, and in the press, including ABC radio and TV, The Australian and Ausbiz. View upcoming media appearances.
This post was contributed by a representative of Montgomery Investment Management Pty Limited (AFSL No. 354564). The principal purpose of this post is to provide factual information and not provide financial product advice. Additionally, the information provided is not intended to provide any recommendation or opinion about any financial product. Any commentary and statements of opinion however may contain general advice only that is prepared without taking into account your personal objectives, financial circumstances or needs. Because of this, before acting on any of the information provided, you should always consider its appropriateness in light of your personal objectives, financial circumstances and needs and should consider seeking independent advice from a financial advisor if necessary before making any decisions. This post specifically excludes personal advice.
The next stock market crash will likely be triggered by a combination of persistent, stubborn inflation, a Federal Reserve forced to keep interest rates elevated, and a burst in the heavily stretched valuations of AI and mega-cap tech stocks; remember, everything else ex-FAANG / Magnificent Seven has not gone up anywhere near as much, nor makes up as much of the S&P500 now…
Inflation and Bond Yields: Despite efforts to rein in consumer prices, current inflation remains elevated. If inflation spikes again, the Federal Reserve will be forced to maintain or even hike interest rates, which increases the cost of capital, harms corporate profit margins, and drives bond yields up to levels where investors flee to fixed-income assets.
Global Geopolitical Shocks: Ongoing conflicts, such as the U.S.-Iran tension, disrupt global supply chains and push energy prices higher. This can cause economic stagnation and further inflate costs, (see previous point)
Follow up with:
An AI or Tech Bubble Burst: Valuations for AI and tech companies are trading at historical premiums. If consumer-facing AI applications or infrastructure consumers begin running low on money and demand wanes, the massive infrastructure providers could be left with slowing growth, triggering a rapid sell-off in the technology sector. Why ? Because the following are all interlinked and interdependent.
• Unprofitable Business Models: If the massive cost of “AI tokens” required to run processes continues to exceed the value or revenue they generate, corporate buyers will abandon the technology.
• Massive Infrastructure Costs: The immense energy, water, and hardware costs required to build and cool data centres may force investors to pull back if returns fall short of expectations.
• The Revenue Deficit: Current valuations rely heavily on projected future profits. If AI firms fail to scale actual revenue fast enough – relying instead on circular investments or risky financing – a crisis of confidence could easily emerge.
• Technological Plateaus: If deep learning and Large Language Model (LLM) architectures hit a wall, failing to advance into highly reliable, reasoning-based systems (the “outlier problem”), commercial adoption may stall.
Thank you for the insights, Chris. Much appreciated.