Understanding the AI Investment Bubble
People often imagine tech bubbles as catastrophic, but the reality doesn’t have to be so extreme. Economically speaking, a bubble occurs when a bet grows too large, leaving supply to exceed demand.
The key takeaway is that it’s rarely all-or-nothing — even promising investments can falter if they aren’t executed carefully.
The challenge of assessing whether we are in an AI bubble lies in the mismatch between the rapid pace of AI software development and the slow process of building and powering data centers.
Constructing data centers takes years, during which many factors may change. The supply chain for AI infrastructure is complex and constantly evolving, making it difficult to predict future demand. Success depends not just on AI adoption in 2028, but also on how it is used and whether there are breakthroughs in energy, chip design, or power distribution along the way.
When stakes are this high, there are numerous ways a project can go wrong — and AI investments are enormous. Recently, reports indicated that an Oracle-linked data center campus in New Mexico attracted up to $18 billion in credit from 20 banks. Oracle has contracted $300 billion in cloud services to OpenAI, and with SoftBank, the companies aim to develop $500 billion in AI infrastructure as part of the “Stargate” initiative. Meta has pledged $600 billion for infrastructure over the next three years. The scale of these commitments is staggering.
Challenges in Scaling AI Infrastructure
Yet demand for AI services remains uncertain. A recent McKinsey survey of leading companies found that while nearly all are using AI in some form, few are deploying it at scale. AI has helped cut costs in specific areas but has not yet had a significant impact on overall business operations. Many companies remain cautious, in a “wait and see” mode.
Even if demand for AI services is huge, infrastructure limitations could create bottlenecks. Microsoft CEO Satya Nadella recently noted that his biggest concern is running out of data center space rather than chip shortages, explaining that there aren’t enough ready-to-use facilities. At the same time, some data centers remain idle because they cannot supply enough power for the latest chips.
While companies like Nvidia and OpenAI are pushing forward rapidly, the electrical grid and physical infrastructure evolve at a slower pace. This discrepancy creates the potential for expensive delays and bottlenecks, even if everything else is going smoothly.
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