The hottest Systemic Risk Substack posts right now

A lot of ordinary people’s pension and retirement money has been funneled into private credit funds and insurance vehicles, not just Wall Street elites.
A sudden AI-driven selloff in software stocks — after new language models showed software engineering can be automated — slammed software valuations and spread stress through the private credit market.
Because these funds are opaque and marketed as safe, everyday savers may not realize their long-term security is exposed to a hidden, potentially huge blowup.

Generative AI is expensive and often unreliable, so many big corporate investments are not delivering the expected returns.
Banks and lenders are financing a massive AI and data-center buildout, creating large debt exposure that could spill over into broader financial stress if those investments sour.
The current LLM-focused approach probably won’t produce the promised productivity gains, meaning economic and social pain is likely until more reliable forms of AI are developed.

AI and richer data can meaningfully improve credit scoring and underwriting by uncovering low-risk borrowers traditional models miss and by using unstructured inputs like digital footprints and text.
More powerful, complex models introduce new risks: they can worsen fairness across groups, be brittle to regime shifts, enable adversarial attacks or coordinated runs, and create competitive arms races and herding that amplify systemic risk.
Managing these dangers requires verification and simpler hybrid or explainable rules, active monitoring (often with AI itself), and more documentation, validation, and regulatory effort because system-wide feedbacks and incentives will shift.

The financial sector framed a new category called "climate risk" and built a regulatory and commercial ecosystem around it, treating it as a novel systemic threat to global finance.
That risk has been measured mainly by economic losses from extreme-weather events, which often mixes up rising damages with actual changes in weather rather than accounting for exposure and vulnerability.
Financial actors argue historical climate data is a poor guide and have pushed new scenarios, models, and private vendors to quantify "climate risk," creating a large market influence despite questions about the scientific basis.

The Fed has turned crisis tools into permanent powers, like a standing repo facility and huge emergency lending programs, without clear sunset clauses or limits.
Those powers let the Fed act beyond its original mandate — extending credit to borrowers Congress never explicitly authorized and exercising wide regulatory discretion, as seen in decisions around crypto banks.
Weak oversight and accountability (no independent inspector general and only semiannual Congressional checks) invite political pressure and create moral hazard, making firms more dependent on the Fed and eroding its independence and credibility.

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Private credit may offer stability due to duration-matched investments and reduced leverage in the banking system.
Money moving into private credit doesn't necessarily reduce overall leverage in the system; just shifts it around.
Private credit's growth can help banks manage capital risks and liquidity challenges, allowing for retranching and reduced regulatory capital requirements.

When betting against the system, bet big to secure assets - lessons from the SVB collapse.
Question ownership and control of your assets. Be prepared for unanticipated vulnerabilities in the system.
In financial dealings, always think ahead and consider the risks. Act like the system might fail and prepare for it.