Subsea Cables & Internet Infrastructure

1. Massive capital expenditures. Much higher capital intensity per rack than a standard data center.  2. Extremely high operating costs due to the power intensity of AI infrastructure. 3. Low customer switching costs shift negotiating power from supplier to customer.  4. Huge gap between capex and revenue flows: 2 to 5 years. 5. Heavy dependence on debt financing and hence vulnerability to rising risk premiums and interest rates. 6. Underlying technology, large language models, is fundamentally flawed according to many computer scientists. This is corroborated by persistent hallucinations and weak problem solving abilities. 7. Large language models show sharply diminishing returns to scale. 8. Luck is required because there are only a few, very large customers for these projects and loss of a single customer can kill a project. Luck is also important because any of these data center projects requires upgrades to the power transmission grid and also generator capacity. 

 META Loses $310 Billion In Stock Value In March: The AI Meltdown Begins In March alone META's market cap fell $310 billion driven by poor financial performance. The key metric is free cash flow, which equals cash profits minus capital expenditures. In the end free cash flow is what investors want, namely oney they could put in their pockets without impairing company operations. META's free cash flow is expected to be only $8 billion this year down from a 2025 figure of $46 billion. The company's massive AI investments are not yielding much revenue and hence are driving free cash flow to zero.  Unfortunately, the emerging AI crash will affect our industry. I anticipate the very expensive Waterworth project could be cancelled as well as other projects. AI is Zuckerberg's second big mistake. The Metaverse was his first. He only remains CEO because he is the majority owner of a special class of voting shares that gives him ten votes per share versus one vote per share for ...