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Key Takeaways The AI boom has become a trillion-dollar infrastructure arms race, yet most companies are seeing little return and growing dependence on a handful of centralized platforms.
As cloud costs rise and trust issues deepen, founders are realizing that building on closed AI systems creates structural risk.
History suggests the next wave of innovation will come from distributed, sovereign AI models that shift control, resilience and ownership back to users
The race to build artificial intelligence has become an infrastructure arms race. In recent months, Oracle signed a $300 billion deal with OpenAI. Microsoft spent $35 billion on AI infrastructure in just three months. Nvidia poured $100 billion into OpenAI, which then used that money to buy Nvidia’s chips — a flow of investments that has some experts questioning the entire model.
The numbers are staggering. Total AI spending from major U.S. companies is expected to reach $1.1 trillion between 2026 and 2029. Yet despite this, a Massachusetts Institute of Technology report found that 95% of organizations are getting zero return. Even Sam Altman, CEO of OpenAI, has acknowledged that an AI bubble may be underway.
The scale seems impossible to compete with. But history suggests that when an industry becomes this closed and concentrated, it’s ripe for disruption.
The limits of centralized AI
For founders, centralized AI systems pose serious risks. You’re not just dependent on OpenAI’s or Google’s infrastructure — you’re betting your company on their ability to sustain trillion-dollar spending indefinitely. If the bubble bursts, the platforms you’ve built on could be repriced, restructured or shut down entirely.
When cloud providers raise prices, thousands of startups see their margins evaporate overnight. When OpenAI changes its API terms, entire businesses built on top of it will face existential risk. Founders are essentially building features that can be turned off with a policy change.
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