#economics

In just the past week, President Donald Trump has ordered defense companies to halt dividends and stock buybacks, and limited executive compensation to $5 million a year; ordered Fannie Mae and Freddie Mac to buy $200 billion of mortgage-backed securities; ordered an array of energy firms to invest in Venezuelan oil infrastructure, called for a 10 percent cap on credit card interest rates; announced steps to ban institutional purchases of single-family homes; and opened a criminal investigation into Jerome Powell's handling of Federal Reserve building renovations in an attempt to influence monetary policy.

Tech companies have moved more than $120bn of data centre spending off their balance sheets using special purpose vehicles funded by Wall Street investors, adding to concerns about the financial risks of their huge bet on artificial intelligence.

Meta in October completed the largest private credit data centre deal, a $30bn agreement for its proposed Hyperion facility in Louisiana that created an SPV called Beignet Investor with New York financing firm Blue Owl Capital.

The SPV raised $30bn, including about $27bn of loans from Pimco, BlackRock, Apollo and others, as well as $3bn in equity from Blue Owl.

Trump said at the roundtable, adding "this money would not be possible without tariffs."

Another way to spend the money that’s already been spent many times over.

Research by Harvard Business School Professor Alberto Cavallo illustrates the downward trend in the price levels for many retail goods, followed by an acceleration after tariff announcements. Prices on both imported and domestic goods have climbed modestly but steadily since March, even if the hike is still small relative to the size of the tariffs.

The researchers created indexes with daily prices collected by PriceStats, a private firm cofounded by Cavallo that provides online data for over 350,000 products sold by five major US retailers. The indexes allow them to track price changes in specific categories and from countries of origin. Overall, the prices of imported products have increased faster than those made in the US. An extended analysis, going back to January 2024, explores price changes of goods relative to their pre-tariff trend.

Current Tariff Rate: Consumers face an overall average effective tariff rate of 17.9%, the highest since 1934. After consumption shifts, the average tariff rate will be 17.4%. (If IEEPA tariffs are invalidated, the rate would be 9.1%.)

Overall Price Level & Distributional Effects: TBL assumes the Federal Reserve “looks through” the tariffs and allows prices to rise such that the tax burden is felt through prices rather than nominal incomes. The price level rises by 1.3% in the short run, representing a loss of $1,800 for the average household and $1,000 for households at the bottom of the income distribution. (Without IEEPA, the price level impact would instead be 0.6%.)

Walmart did announce a 25% reduction. It is offering this year’s basket for under $40, down from last year’s price of around $55.

But aside from the fact that any one company’s holiday promotion is not a good measure of the state of US inflation, the price of Walmart’s Thanksgiving basket is an especially poor proxy – because the composition of the basket changes every Thanksgiving.

The 50 year mortgage is a scam. I’m just not sure if the administration actually knows that or not.

By the numbers: Consider someone taking out a $500,000 home loan. The current rate on a 30-year mortgage is 6.22%, per Freddie Mac. For these calculations, let's assume that a 50-year loan's interest rate exceeds the 30-year by the same margin that the 30-year rate exceeds a 15-year rate.

That translates to a 6.94% rate on the 50-year loan — which would then have a monthly payment of $2,985, only $83 less than the 30-year mortgage. Zoom in: In the early decades of the loan's repayment, the 50-year borrower's payments would almost entirely go to interest, paying down the debt much more slowly.

After five years, for example, the 30-year borrower would have paid off $33,481 of the loan balance, versus $6,707 for the 50-year borrower. After three decades, when the 30-year mortgage is fully paid off, the 50-year borrower would still owe about $387,000.

Be patient. Not afraid.

For layoffs in the tech sector, a likely culprit is the financial stress that companies are experiencing because of their huge spending on AI infrastructure. Companies that are spending a lot with no significant increases in revenue can try to sustain profitability by cutting costs. Amazon increased its total CapEx from $54 billion in 2023 to $84 billion in 2024, and an estimated $118 billion in 2025. Meta is securing a $27 billion credit line to fund its data centers. Oracle plans to borrow $25 billion annually over the next few years to fulfill its AI contracts. 

“We’re running out of simple ways to secure more funding, so cost-cutting will follow,” Pratik Ratadiya, head of product at AI startup Narravance, wrote on X. “I maintain that companies have overspent on LLMs before establishing a sustainable financial model for these expenses.”

We’ve seen this act before. When companies are financially stressed, a relatively easy solution is to lay off workers and ask those who are not laid off to work harder and be thankful that they still have jobs. AI is just a convenient excuse for this cost-cutting.

Last week, when Amazon slashed 14,000 corporate jobs and hinted that more cuts could be coming, a top executive noted the current generation of AI is “enabling companies to innovate much faster than ever before.” Shortly thereafter, another Amazon rep anonymously admitted to NBC News that “AI is not the reason behind the vast majority of reductions.” On an investor call, Amazon CEO Andy Jassy admitted that the layoffs were “not even really AI driven.”

We have been following the slow growth in revenues for generative AI over the last few years, and the revenues are neither big enough to support the number of layoffs attributed to AI, nor to justify the capital expenditures on AI cloud infrastructure. Those expenditures may be approaching $1 trillion for 2025, while AI revenue—which would be used to pay for the use of AI infrastructure to run the software—will not exceed $30 billion this year. Are we to believe that such a small amount of revenue is driving economy-wide layoffs?

The SNAP shutdown halts roughly $8 billion a month in federal food assistance — money that usually flows straight into grocery stores and helps feed 42 million Americans. Without it, both low-income households and major retailers like Walmart, Aldi and Kroger feel the pinch. Driving the news: Companies and nonprofits are rolling out new programs to keep food flowing — from free grocery credits to multimillion-dollar donations.

Seems about right. Interesting metrics on startups too:

• Foundation Model Labs: Revenue must grow faster than Compute Costs.
• Enterprise AI Platforms: High Gross Retention because of high AI Feature Adoption.
• Application Layer: Net Revenue Retention (NRR) > 120% and CAC Payback < 12 months.
• Inference API Players: High Revenue per GPU-Hour (pricing power).
• Energy/Infrastructure: Structural Energy Cost Advantage and high utilization.

Energy infrastructure, unlike GPUs that become obsolete in five years, compounds in value over decades.

Consider the math: A single large AI training cluster can require 100+ megawatts of continuous power — equivalent to a small city. The United States currently generates about 1,200 gigawatts of electricity total. If AI compute grows at projected rates, it could demand 5-10% of the nation’s entire power generation within a decade.

And unlike fiber optic cable or GPU clusters, power infrastructure can’t be deployed quickly. Nuclear plants take 10-15 years to build. Major transmission lines face decades of regulatory approval. Even large solar farms require 3-5 years from planning to operation.

The companies prepping themselves to survive scarcity aren’t just stockpiling compute—they’re building root systems deep enough to tap multiple resources: energy contracts locked in for decades, gross retention rates above 120%, margin expansion even as they scale, and infrastructure that can flex between training and inference as market dynamics shift.