The current AI boom may not be a "quantity" bubble, as the need for data centers is real. However, it's likely a "price" bubble with unrealistic valuations. Similar to the dot-com bust, early investors may unwittingly subsidize the long-term technology shift, facing poor returns despite the infrastructure's ultimate utility and value.
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Today's massive AI company valuations are based on market sentiment ("vibes") and debt-fueled speculation, not fundamentals, just like the 1999 internet bubble. The market will likely crash when confidence breaks, long before AI's full potential is realized, wiping out many companies but creating immense wealth for those holding the survivors.
During the dot-com crash, application-layer companies like Pets.com went to zero, while infrastructure providers like Intel and Cisco survived. The lesson for AI investors is to focus on the underlying "picks and shovels"—compute, chips, and data centers—rather than consumer-facing apps that may become obsolete.
Current AI investment patterns mirror the "round-tripping" seen in the late '90s tech bubble. For example, NVIDIA invests billions in a startup like OpenAI, which then uses that capital to purchase NVIDIA chips. This creates an illusion of demand and inflated valuations, masking the lack of real, external customer revenue.
Vincap International's CIO argues the AI market isn't a classic bubble. Unlike previous tech cycles, the installation phase (building infrastructure) is happening concurrently with the deployment phase (mass user adoption). This unique paradigm shift is driving real revenue and growth that supports high valuations.
The massive capital expenditure in AI infrastructure is analogous to the fiber optic cable buildout during the dot-com bubble. While eventually beneficial to the economy, it may create about a decade of excess, dormant infrastructure before traffic and use cases catch up, posing a risk to equity valuations.
The massive investment in AI infrastructure could be a narrative designed to boost short-term valuations for tech giants, rather than a true long-term necessity. Cheaper, more efficient AI models (like inference) could render this debt-fueled build-out obsolete and financially crippling.
The current massive capital expenditure on AI infrastructure, like data centers, mirrors the railroad boom. These are poor long-term investments with low returns. When investors realize this, it will trigger a market crash on the scale of 1929, after which the real value-creating companies will emerge.
Unlike the dot-com era's speculative approach, the current AI infrastructure build-out is constrained by real-world limitations like power and space. This scarcity, coupled with demand from established tech giants like Microsoft and Google, makes it a sustained megatrend rather than a fragile bubble.
The massive spending on AI infrastructure may be a form of 'malinvestment,' similar to the telecom buildout during the dot-com boom. Rajan warns that while AI's promise is real, the transition from infrastructure creation to widespread, profitable use could be slow, creating a valuation gap and risk of a market correction.
The current AI infrastructure build-out avoids the dot-com bubble's waste. In 2000, 97% of telecom fiber was unused ('dark'). Today, all GPUs are actively utilized, and the largest investors (big tech) are seeing positive returns on their capital, indicating real demand and value creation.
