While most investors chased Nvidia, Diameter Capital focused on the infrastructure needed for AI inference. They identified that AI models must transmit data out of data centers via commercial fiber. They bought distressed debt in a telecom company at 30 cents on the dollar, which recovered to par after signing billions in contracts with hyperscalers.
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The massive capital required for AI infrastructure is pushing tech to adopt debt financing models historically seen in capital-intensive sectors like oil and gas. This marks a major shift from tech's traditional equity-focused, capex-light approach, where value was derived from software, not physical assets.
During the dot-com crash, application-layer companies like Pets.com went to zero, while infrastructure providers like Intel and Cisco survived. The lesson for AI investors is to focus on the underlying "picks and shovels"—compute, chips, and data centers—rather than consumer-facing apps that may become obsolete.
Credit investors should look beyond direct AI companies. According to Victoria Fernandez, the massive infrastructure build-out for AI creates a significant tailwind for power and energy companies, offering a less crowded investment thesis with potentially wider spreads and strong fundamentals.
The current AI infrastructure build-out is structurally safer than the late-90s telecom boom. Today's spending is driven by highly-rated, cash-rich hyperscalers, whereas the telecom boom was fueled by highly leveraged, barely investment-grade companies, creating a wider and safer distribution of risk today.
Unlike the speculative "dark fiber" buildout of the dot-com bubble, today's AI infrastructure race is driven by real, immediate, and overwhelming demand. The problem isn't a lack of utilization for built capacity; it's a constant struggle to build supply fast enough to meet customer needs.
The financing for the next stage of AI development, particularly for data centers, will shift towards public and private credit markets. This includes unsecured, structured, and securitized debt, marking a crucial role for fixed income in enabling technological growth.
The AI infrastructure boom has moved beyond being funded by the free cash flow of tech giants. Now, cash-flow negative companies are taking on leverage to invest. This signals a more existential, high-stakes phase where perceived future returns justify massive upfront bets, increasing competitive intensity.
Unlike the speculative overcapacity of the dot-com bubble's 'dark fiber' (unused internet cables), the current AI buildout shows immediate utilization. New AI data centers reportedly run at 100% capacity upon coming online, suggesting that massive infrastructure spending is meeting real, not just anticipated, demand.
Unlike past tech booms funded by venture capital, the next wave of AI investment will come from hyperscalers like Google and Meta leveraging their pristine balance sheets to take on massive corporate debt. Their capacity to raise capital this way dwarfs the entire VC ecosystem, enabling unprecedented spending.
Silver Lake cofounder Glenn Hutchins contrasts today's AI build-out with the speculative telecom boom. Unlike fiber optic networks built on hope, today's massive data centers are financed against long-term, pre-sold contracts with creditworthy counterparties like Microsoft. This "built-to-suit" model provides a stable commercial foundation.
