While high capex is often seen as a negative, for giants like Alphabet and Microsoft, it functions as a powerful moat in the AI race. The sheer scale of spending—tens of billions annually—is something most companies cannot afford, effectively limiting the field of viable competitors.

The AI race has been a prisoner's dilemma where companies spend massively, fearing competitors will pull ahead. As the cost of next-gen systems like Blackwell and Rubin becomes astronomical, the sheer economics will force a shift. Decision-making will be dominated by ROI calculations rather than the existential dread of slowing down.

Companies feel immense pressure to integrate AI to stay competitive, leading to massive spending. However, this rush means they lack the infrastructure to measure ROI, creating a paradox of anxious investment without clear proof of value.

The current AI spending spree by tech giants is historically reminiscent of the railroad and fiber-optic bubbles. These eras saw massive, redundant capital investment based on technological promise, which ultimately led to a crash when it became clear customers weren't willing to pay for the resulting products.

Many top AI CEOs openly admit the extinction-level risks of their work, with some estimating a 25% chance. However, they feel powerless to stop the race. If a CEO paused for safety, investors would simply replace them with someone willing to push forward, creating a systemic trap where everyone sees the danger but no one can afford to hit the brakes.

The rhetoric around AI's existential risks is framed as a competitive tactic. Some labs used these narratives to scare investors, regulators, and potential competitors away, effectively 'pulling up the ladder' to cement their market lead under the guise of safety.

OpenAI now projects spending $115 billion by 2029, a staggering $80 billion more than previously forecast. This massive cash burn funds a vertical integration strategy, including custom chips and data centers, positioning OpenAI to compete directly with infrastructure providers like Microsoft Azure and Google Cloud.

Current AI spending appears bubble-like, but it's not propping up unprofitable operations. Inference is already profitable. The immense cash burn is a deliberate, forward-looking investment in developing future, more powerful models, not a sign of a failing business model. This re-frames the financial risk.

The enormous financial losses reported by AI leaders like OpenAI are not typical startup burn rates. They reflect a belief that the ultimate prize is an "Oracle or Genie," an outcome so transformative that the investment becomes an all-or-nothing, existential bet for tech giants.