While lower rates seem beneficial for leveraged companies, the context is critical. The Federal Reserve typically cuts rates in response to a weakening economy. This economic downturn usually harms issuer fundamentals more than the lower borrowing costs can help, making rate-cutting cycles a net negative for high-yield credit.

Major tech companies are investing in their own customers, creating a self-reinforcing loop of capital that inflates demand and valuations. This dangerous practice mirrors the vendor financing tactics of the dot-com era (e.g., Nortel), which led to a systemic collapse when external capital eventually dried up.

Default rates are not uniform. High-yield bonds are low due to a 2020 "cleansing." Leveraged loans show elevated defaults due to higher rates. Private credit defaults are masked but may be as high as 6%, indicated by "bad PIK" amendments, suggesting hidden stress.

Tech giants are shifting from asset-light models to massive capital expenditures, resembling utility companies. This is a red flag, as historical data shows that heavy investment in physical assets—unlike intangible assets—tends to predict future stock underperformance.

Unlike prior tech revolutions funded mainly by equity, the AI infrastructure build-out is increasingly reliant on debt. This blurs the line between speculative growth capital (equity) and financing for predictable cash flows (debt), magnifying potential losses and increasing systemic failure risk if the AI boom falters.

The credit market appears healthy based on tight average spreads, but this is misleading. A strong top 90% of the market pulls the average down, while the bottom 10% faces severe distress, with loans "dropping like a stone." The weight of prolonged high borrowing costs is creating a clear divide between healthy and struggling companies.

Contrary to theories that recent blow-ups like Tricolor indicate more fraud is coming, the real issue is broad economic stress. Using Warren Buffett's "tide goes out" analogy, higher rates and persistent inflation are exposing fundamental weaknesses and squeezing consumers across large, non-AI sectors of the economy.

Widespread credit is the common accelerant in major financial crashes, from 1929's margin loans to 2008's subprime mortgages. This same leverage that fuels rapid growth is also the "match that lights the fire" for catastrophic downturns, with today's AI ecosystem showing similar signs.

A new risk is entering the AI capital stack: leverage. Entities are being created with high-debt financing (80% debt, 20% equity), creating 'leverage upon leverage.' This structure, combined with circular investments between major players, echoes the telecom bust of the late 90s and requires close monitoring.