The massive capital investment in AI infrastructure is predicated on the belief that more compute will always lead to better models (scaling laws). If this relationship breaks, the glut of data center capacity will have no ROI, triggering a severe recession in the tech and semiconductor sectors.

The AI boom is fueled by 'club deals' where large companies invest in startups with the expectation that the funds will be spent on the investor's own products. This creates a circular, self-reinforcing valuation bubble that is highly vulnerable to collapse, as the failure of one company can trigger a cascading failure across the entire interconnected system.

A financial flywheel, reminiscent of the pre-2008 crisis, is fueling the AI data center boom. Demand for yield-generating securities from investors incentivizes the creation of more data center projects, decoupling the financing from the actual viability or profitability of the underlying AI technology.

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.

Despite a massive contract with OpenAI, Oracle is pushing back data center completion dates due to labor and material shortages. This shows that the AI infrastructure boom is constrained by physical-world limitations, making hyper-aggressive timelines from tech giants challenging to execute in practice.

Despite staggering announcements for new AI data centers, a primary limiting factor will be the availability of electrical power. The current growth curve of the power infrastructure cannot support all the announced plans, creating a physical bottleneck that will likely lead to project failures and investment "carnage."

Blue Owl's decision to back out of financing an Oracle data center reflects a growing concern among lenders about overexposure to Oracle's massive AI infrastructure commitments. This suggests a potential funding bottleneck for the entire ecosystem as lenders become more cautious.

The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.

The current trend of AI infrastructure providers investing in their largest customers, who then use that capital to buy their products, mirrors the risky vendor financing seen in the dot-com bubble. This creates circular capital flows and potential systemic risk.