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AI Data Centers: A Fragile Business Model Requiring Perfect Execution & A Lot Of Luck

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1. Massive capital expenditures. 2. Extremely high operating costs due to power intensity of AI infrastructure. 3. Low customer switching costs. 4. Huge gap between capex and revenue flows: 2 to 4 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 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. 

ADC Cable Capacity For Sale & Quick Delivery: 100G ~ $12.5K MRC

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What a deal!!! Get yourself a promotion, buy now. 🙂 A point: SG1. Z point: Maruyama CLS. Service: Layer 100G Wave. Interface: 100GE. Term: 1 Year. Delivery: Under 4 weeks.

Modern AI, Large Language Models, Are Fundamentally Flawed

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Some claim that Pangram, an AI program, is good at detecting AI written text. So I tested it on Shakespeare. Apparently the Bard was using AI in the 16th century ... Large language models (LLMs) are not logic engines. They are huge nonlinear statistical models with huge number of parameters, in some cases as many as three trillion. LLMs are black boxes. They spit out answers, but it is not clear how they reach those conclusions. Moreover, complicated statistical models are fragile. This means they provide reasonable answers the majority of the time, but will from time to time produce bizarre responses. In contrast, human intelligence is quite robust. Outliers are extremely rare. 

My 2026 Nigerian Peering Forum Presentation On African Subsea Cables

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 https://drive.google.com/file/d/1DgndmDXSnGOa5BiWZsVv0pQ_bEx45bhw/view?usp=sharing

Equiano 100G Lisbon/Capetown Wave: $17K MRC

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Term: three years. NRC: $8,500. Delivery: 4-6 weeks. Remark: Equiano has the lowest latency between Capetown and Lisbon. A point: LS1, Lisbon Equinix. Z point: CT1, Capetown, SA. Customer responsible for cross connects. 

Anthropic Likely To Have Junk Credit Rating Post IPO

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Without an investment grade rating, Anthropic will be unable to tap the funds of most mutual and bond funds despite a trillion dollars in compute commitments over the next three years. This dramatically increases the likelihood of a liquidity crisis and possible bankruptcy. "The FT says the investment giant Blackstone, the anchor investor in a recent $63 billion deal to finance Anthropic’s chips, told investors in the deal that it expects the AI lab to receive a “high” junk rating after its IPO. Previously, the FT notes, Blackstone had said the opposite: that the Claude maker would receive an investment-grade rating (indicating low risk for lenders) before 2027 rolls around." Click here for more details: https://techcrunch.com/2026/10/30/anthropic-expected-to-receive-high-junk-rating-from-credit-agencies-following-ipo-report-says/. 

AWS Enjoys Surging Profits, But Free Cash Flow Turns Negative

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Free cash flow equals operating cash flow minus capital expenditures. If capex exceeds economic or real depreciation, then that cash could be returned to the owners via dividend or stock buybacks without sacrificing or reducing the company's ability to generate future profits and cash flow. It is a sustainable payout to the owners. Negative free cash flow increases wealth if marginal capex returns are high enough relative to the risk hurdle. Amazon is still quite profitable today, but investors are concerned future returns are inadequate to justify current negative free cash flow. After all, current wealth is being sacrificed in pursuit of future wealth that may be an illusion. The crux of investor angst is that end user AI revenues are tiny relative to investments. Goldman Sachs forecasts global AI capex to reach $1 trillion in 2026 and $3 trillion in 2030. Gartner, the consulting firm estimates $64 billion in 2026 AI end user revenues. The gap between return as measured by end us...