The most imprudent lending decisions occur during economic booms. Widespread optimism, complacency, and fear of missing out cause investors to lower their standards and overlook risks, sowing the seeds for future failures that are only revealed in a downturn.

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.

A condition called "fiscal dominance," where massive government debt exists, prevents the central bank from raising interest rates to cool speculation. This forces a flood of cheap money into the market, which seeks high returns in narrative-driven assets like AI because safer options can't keep pace with inflation.

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.

The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.

Judging the credit market by its overall index spread is misleading. The significant gap between the tightest and widest spreads (high dispersion) reveals that the market is rewarding quality and punishing uncertainty. This makes individual credit selection far more important than a top-down market view.

Once considered safe due to low CapEx and recurring revenue models, the technology sector now shows significant credit stress. Investors allowed higher leverage on these companies, but the sharp rise in interest rates in 2022 exposed this vulnerability, placing tech alongside historically troubled sectors like media and retail.