The article argues that Warsh’s first major Fed signal was not the June rate hold but his task force reviewing the Fed’s balance-sheet framework. After QT ended in late 2025 and reserves reached the ample-reserves floor, the fight shifted from runoff speed to the operating system of post-2008 monetary policy. The balance sheet affects duration risk, term premia, repo markets, bank reserves, mortgage finance and the Fed’s ability to control short rates.

It presents the case for a smaller portfolio: fewer distortions, clearer fiscal-monetary boundaries, less political exposure and more room for future crisis response. But shrinking too far risks plumbing failures, as 2019 repo stress showed. Reserve demand, payment needs and repo capacity may bind before policymakers expect. The piece concludes that Warsh’s test is disciplined redesign, not ideological shrinkage; balance-sheet policy now sits at the core of monetary policy.

The article argues that 3% pandemic mortgages created a lock-in trap. With over half of loans below 4%, moving at roughly 6% rates raises payments sharply, so homeowners stay even when life changes. FHFA estimates each 1-point rate gap cuts selling odds by 18%, helping explain why existing home sales are at mid-1990s lows and inventory stays thin, keeping prices high and first-time buyers squeezed, while owners are paper rich but mobility poor.

It adds that bond-market volatility has pushed rates up again, widening the gap and delaying mobility. The market likely stays frozen until rates fall near 5%, which most forecasters do not expect in 2026 or 2027. Assumable or portable mortgages could reduce the moving penalty, but they complicate securitization, so adjustment will be slow, driven by new construction and forced life events.

The article argues that Fed tightening is a leading indicator for advertising cuts because marketing is one of the most flexible expense lines. When the Fed raises rates, it shifts CFO expectations about future demand, compresses business confidence, and marketing budgets contract 1–2 quarters later. The 2022–2023 cycle is presented as the clearest example: a 525-basis-point hike coincided with widespread budget cuts and repeated downward revisions to industry growth forecasts.

Strategically, the piece argues the “herd” response creates an opening. When competitors cut spend, a firm that simply maintains budgets gains share of voice as ad inventory cheapens and relative visibility rises. Because FOMC statements, dot plots, and forward guidance provide several quarters of signal, these contractions are anticipatable and exploitable in planning. The broader claim is that expectations, not just prices or quantities, transmit monetary policy into real commercial behavior.

If you have been reading this blog for a bit now, you know we have been skeptical of the “AI Bubble.” Our skepticism, or at least my own, has mostly centered around the economic implementation lagging the hype. We spent the better part of 2025 watching companies buy massive amounts of GPU compute to build smarter chatbots, yet aggregate productivity statistics barely budged. (Yes, we have some data now that shows the effects of AI on productivity but not nearly as much as you would think).

While the market was distracted by the “Brain” trade (LLMs, data centers, and NVIDIA chips), you may have missed the momentum building in the “Body” trade.

Headlines this past week have announced the Fed's recent interest rate cut. Interest rates have now been shifted from a range of 3.5 to 3.75 percent, and not without controversy.

The decision was made amid an economically confusing environment characterized by both high inflation and high unemployment. While most headlines highlight the potential impacts on mortgages, inflation, and overall employment, this article focuses on how lower interest rates could significantly increase employment among recent college graduates.

In our business, precision is power. We build predictive models for clients making some of their biggest fiscal decisions. A couple of months ago, one of our best performers, a model that had nailed inventory needs quarter after quarter, started to drift. Its forecasts weren’t wrong, exactly. Just… fuzzier. The prediction intervals widened. The signals got noisier.

When we investigated, the culprit wasn’t the math; it was the map. Our assumptions, built on decades of reliable data from America’s gold-standard statistical agencies, were suddenly out of sorts. Tariff tremors, policy-driven supply distortions, and even now a federal shutdown have all disrupted the data we depended on.