The growth of the opaque private credit market is a potential systemic risk and a direct, perhaps unintended, consequence of post-2008 banking regulations.
AI represents a recent and fundamental disruptive threat to the software industry's business model by automating coding and empowering non-technical employees to perform development tasks.
The Federal Reserve's ability to conduct monetary policy is significantly constrained by both external political pressure and deep internal divisions among its governors.
Systemic risk is not just about the amount of money at risk but is critically amplified by market opacity; he formalizes this as 'Systemic Risk = Value at Risk * Opacity'.
Economic shocks can come from unexpected policy decisions, such as tariffs, which he reports nearly caused a financial meltdown in the repo market, a risk that was not widely appreciated at the time.
1970s
Liesman references this period as a time when high oil prices led companies like Exxon to use excess profits for unsuccessful diversification into non-core businesses.
Post-2008 Financial Crisis
He suggests that banking regulations like the Dodd-Frank Act, implemented in this era, may have pushed lending outside the regulated system, fueling the growth of the private credit market.
Trump Administration
Liesman reports that tariffs implemented during this time led to the loss of 100,000 manufacturing jobs and nearly caused a meltdown in the repo market. He also notes intense political pressure on the Fed, with Trump's tweets being used as evidence in legal proceedings.
Post-Pandemic
He points to data showing that lower and middle-income households, which had seen income gains, are now beginning to show signs of financial stress.
Recent (Last 60 days)
Liesman claims the market has only very recently realized the disruptive potential of AI to automate coding and upend the software business model.
Present
He reports that Fed Chair Powell is signaling no imminent policy changes, his term is set to expire on May 15th, and the oil market faces a potential supply crisis depending on events in the Straits of Hormuz.
▶Federal Reserve Under Political and Internal PressureMay 2026
Liesman details how the Federal Reserve navigates intense political influence, citing a quashed subpoena against Chair Powell that was deemed politically motivated by Trump's tweets, and a senator blocking nominees. He also highlights internal policy divisions among governors on issues like interest rates and the balance sheet.
The Fed's operational independence is being actively challenged, creating significant policy uncertainty that complicates market forecasting and investment strategy.
▶The Migration of Systemic Risk
Liesman posits that post-crisis banking regulations may have been overly restrictive, pushing lending activities into the less-regulated and opaque private credit market. He also recounts how Trump-era tariffs nearly triggered a meltdown in the repo market, illustrating how non-banking shocks can create systemic threats.
Analysts should look beyond traditional banking institutions for the next source of systemic risk, focusing on opaque markets where risk is a product of value and opacity, as per Liesman's theorem.
▶The Evolving Impact of Energy ShocksMay 2026
Liesman analyzes the modern economic impact of oil price fluctuations, calculating the massive wealth transfer from a $35/barrel increase and noting the US now has a domestic oil economy the size of Saudi Arabia's. He contrasts the current situation with 1973, stating oil would need to hit $550/barrel for a similar effect, yet warns a geopolitical crisis could still spike prices to $150-200.
While the US economy is more resilient to energy shocks than in the past, geopolitical flashpoints like the Straits of Hormuz remain a potent source of tail risk for inflation and economic stability.
▶AI as a Fundamental Disruption to SoftwareMay 2026
Liesman identifies the market's recent realization (within the last 60 days) that AI can automate coding as a major disruptive event for the software industry. He describes new tools that empower any employee to be a coder, blurring traditional roles and challenging the established business model.
Investors should re-evaluate the long-term moats and valuation models of software companies, as the core value proposition of specialized coding expertise is being directly challenged by AI.