Unlike debt-laden startups, tech giants are funding AI buildouts with cash and can weather a downturn. They fully expect smaller, leveraged competitors to go bankrupt, creating a strategic opportunity to purchase their data center assets for pennies on the dollar, thereby reducing their own future capital expenditures.

Public markets, fearing AI's disruption, value SaaS companies at low single-digit revenue multiples. Simultaneously, private VCs, driven by upside potential, fund early-stage AI startups at hundreds of times ARR, creating a massive valuation disconnect between the two markets.

Uncertainty around AI's impact on software companies is creating two distinct CLO markets. Older deals with high software exposure are heavily discounted and risky, while newly issued, software-light CLOs offer superior risk-adjusted returns, even if they aren't trading at a discount.

Despite fears of AI disruption, private credit software loans have significant downside protection. With loan-to-value ratios around 30-40%, there is a substantial equity cushion. A company's value must erode by nearly 70% before the lender's principal is at risk, highlighting the structural safety of debt versus equity.

While public software stocks have dropped 20-30% on fears of AI disruption, credit markets, particularly private credit, remain confident. Lenders are protected by low leverage multiples (1-6x EBITDA) and a substantial equity cushion, making them less sensitive to equity valuation shifts.

Diameter Capital's analysis reveals a dangerous concentration in private credit, with roughly 50% of loans exposed to AI disruption risk (software, IT services). For a debt instrument with limited upside, this level of single-factor exposure is described as "crazy portfolio management."

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.

The hype and potential bubble in AI are concentrated in private markets, evidenced by vendor financing and easy credit for any AI-linked venture. In contrast, public markets are viewed as more realistic, and the high concentration in top tech stocks is not statistically correlated with poor forward-looking returns.

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.