Non-profit or government groups aiming to use AI for safety face the risk of being priced out of compute during an intelligence explosion. A financial hedge against this is to invest a portion of their portfolio in compute-exposed stocks like NVIDIA. If compute prices skyrocket, the investment gains would help offset the increased cost of accessing AI labor.
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Sovereign wealth funds, particularly in the Middle East, view AI as a 30-50 year societal transformation, not just a short-term investment. Their deep pockets and long-term strategic commitment mean they would likely step in to buy key chip stocks like NVIDIA at a discount during a market correction, effectively creating a floor under the market.
While an AI bubble seems negative, the overproduction of compute power creates a favorable environment for companies that consume it. As prices for compute drop, their cost of goods sold decreases, leading to higher gross margins and better business fundamentals.
A primary risk for major AI infrastructure investments is not just competition, but rapidly falling inference costs. As models become efficient enough to run on cheaper hardware, the economic justification for massive, multi-billion dollar investments in complex, high-end GPU clusters could be undermined, stranding capital.
OpenAI's massive, long-term contracts with key infrastructure players mean its success is deeply intertwined with the market. If OpenAI falters, the ripple effect could crash stocks like NVIDIA, Oracle, and Microsoft, potentially bursting the AI bubble.
The current AI investment boom is focused on massive infrastructure build-outs. A counterintuitive threat to this trade is not that AI fails, but that it becomes more compute-efficient. This would reduce infrastructure demand, deflating the hardware bubble even as AI proves economically valuable.
The advanced GPUs essential for AI require a fully globalized supply chain. As globalization breaks down, producing these chips may become impossible. Therefore, the current frenzied build-out of AI data centers, while a bubble, strategically installs critical infrastructure before the window of opportunity closes for good.
The common goal of increasing AI model efficiency could have a paradoxical outcome. If AI performance becomes radically cheaper ("too cheap to meter"), it could devalue the massive investments in compute and data center infrastructure, creating a financial crisis for the very companies that enabled the boom.
A theory suggests Sam Altman's $1.4T in spending commitments may be a strategic move to trigger a massive overbuild of AI infrastructure. This would create a future "compute glut," driving down prices and ultimately benefiting OpenAI as a primary consumer of that capacity.
Concerned about AI's potential to displace white-collar jobs, Wilkinson views investing in the underlying infrastructure as a key strategy. He specifically invested in a Bitcoin mining company pivoting to AI data centers, effectively buying into the "toll bridge" of the future to protect his capital.
Companies are spending unsustainable amounts on AI compute, not because the ROI is clear, but as a form of Pascal's Wager. The potential reward of leading in AGI is seen as infinite, while the cost of not participating is catastrophic, justifying massive, otherwise irrational expenditures.
