Arguments that AI chips are viable for 5-7 years because they still function are misleading. This "sleight of hand" confuses physical durability with economic usefulness. An older chip is effectively worthless if newer models offer exponentially better performance for the price ('dollar per flop'), making it uncompetitive.
Investor Michael Burry argues that hyperscalers overstate profits by depreciating GPUs over 5-6 years when their economic usefulness is only 2-3 years due to rapid technological advances. This accounting practice, which Burry calls a "common fraud," masks true costs and inflates valuations.
The rapid accumulation of hundreds of billions in debt to finance AI data centers poses a systemic threat, not just a risk to individual companies. A drop in GPU rental prices could trigger mass defaults as assets fail to service their loans, risking a contagion effect similar to the 2008 financial crisis.
Unlike debt-laden startups, tech giants are funding AI buildouts with cash and can weather a downturn. They fully expect smaller, leveraged competitors to go bankrupt, creating a strategic opportunity to purchase their data center assets for pennies on the dollar, thereby reducing their own future capital expenditures.
Analyst Gil Luria argues that financing speculative AI infrastructure with debt, based on promises from cash-burning startups like OpenAI, is fundamentally unsound. This "unhealthy behavior" mirrors patterns from past financial bubbles by confusing equity-type risk with debt-based financing, creating significant instability.
OpenAI, a startup losing billions, has reportedly committed $1.4 trillion for future compute from partners like Oracle and CoreWeave. These partners then use these speculative promises to justify raising massive debt, creating a fragile, interdependent financial structure built upon a single startup's highly uncertain success.
Unprofitable AI models mirror Uber's early strategy. By subsidizing services, they integrate into workflows and create dependency. Once users rely on the tool (e.g., a law firm replacing an associate), prices can be increased dramatically to reflect the massive value created, ultimately achieving profitability.
Meta is using off-balance-sheet "special purpose vehicles" (SPVs) to finance its AI data centers. This financial engineering obscures the true scale of its capital commitments by keeping massive debt and assets off its main balance sheet, a tactic explicitly compared to the controversial methods used by Enron.
Major AI players treat the market as a zero-sum, "winner-take-all" game. This triggers a prisoner's dilemma where each firm is incentivized to offer subsidized, unlimited-use pricing to gain market share, leading to a race to the bottom that destroys profitability for the entire sector and squeezes out smaller players.