Viewpoints

Milton Friedman taught us that when it comes to evaluating the stance of monetary policy, look at monetary quantities, not the level of interest rates. A given level of federal funds rate can represent a tight monetary policy if the demand for credit is weak. In this case we would expect to see a relatively low rate of growth in bank credit. Similarly, that same level of the federal funds rate can represent an easy monetary policy if the demand for credit is strong. In this case we would expect to see a relatively high rate of growth in bank credit.

The federal funds rate has been at a level of 4.33% since December 25, 2024. Yet, as can be seen in the chart below, growth in bank credit has increased significantly. In the 13 weeks ended May 21, 2025, the annualized growth in bank credit was 7.9%, the highest since mid-August 2022. Adjusted for consumer inflation, today’s 7.9% growth in bank credit is higher than it was in August 2022.

The Federal Reserve Bank of Philadelphia’s state coincident indexes in April were rather soft. In the one-month changes, Nevada’s fairly modest .55 percent increase was the largest, with Indiana the only other state with a gain higher than .5 percent. 12 states showed declines; all except Arkansas were in the Northeast and Middle West. Massachusetts had the largest decline (.47 percent). Over the three months ending in April seven states were down (again, all except Arkansas in the Northeast and Middle West), with Massachusetts on the bottom (down .67 percent) here as well. West Virginia’s 1.54 percent was the largest gain, while 9 other states had increases above 1 percent. Over the last twelve months, Iowa and Michigan were down, and eight others saw increases of less than one percent. No state had an increase higher than four percent (Utah was up 3.55 percent), and only three were at or higher than three percent.

The independently estimated national estimates of growth over the last three and twelve months were, respectively, .46 and 2.34 percent. Both measures appear to be a bit weaker than the state numbers would have suggested.

In Kevin Gaynor’s last post (see Crowding Out) he argued that fears of public borrowing crowding out private investment—particularly the surge in AI-driven capex—were likely misplaced. The real challenge is not whether savings will adjust—they must, by definition—but rather what shifts in prices, policy, or expectations will be required to expand household savings sufficiently to finance both government and corporate borrowing. In a world where global capital no longer flows as freely, that adjustment process becomes central.

There is also a deeper and more consequential issue lurking beneath these concerns: will the recent wave of AI-related investment actually deliver the productivity renaissance that markets now seem to be pricing in? The recent rally in equity markets—led by tech and infrastructure names—has been powered as much by narrative as by numbers, with investors embracing a vision of AI that promises to transform corporate efficiency and unlock a new era of structurally higher trend growth.

• Rising real yields reflect structural shifts: Long-end yields are climbing not just due to inflation fears but rising real rates, suggesting expectations of stronger trend growth, higher risk premia, or tighter global capital supply.

• Surge in capex demand: A coming wave of AI-driven investment, especially in the U.S., could significantly boost corporate capex, mirroring the dot-com boom and pushing the corporate sector into a net borrowing position again.

• Large fiscal deficits projected in most major economies: It’s not just a US story: China running an 8% government deficit through to 2030, and worsening budget positions in Germany and Japan.

• Global savings-investment imbalance tightening: Post-GFC changes, like reduced capital mobility, mean countries are increasingly reliant on domestic savings to fund investment and deficits—requiring higher interest rates.

• Sustained higher equilibrium rates likely: With large fiscal deficits across major economies and higher private investment needs, the bond market may be signaling a long-term rise in equilibrium interest rates (r*), ending the era of cheap money.

For better or worse markets had (more Trump trade headlines as I write) substantially reduced estimates for a recession this year. Betting markets have reflected the same sentiment. Our own measure of cross market implied growth has recovered smartly pushing implied vols back down in many markets.

But across all major markets back end yields have continued to trend higher - a process that began post Covid, paused for a bit and then started again last year before accelerating over recent weeks.

April saw little change in state labor markets. Five states saw statistically significant gains in jobs in March, with none larger than .4 percent. Texas’s .3 percentage point gain amounted to 37,700 jobs. A number of states had insignificant declines.

Four states (DC, Iowa, Massachusetts, and Virginia) had statistically significant increases in their unemployment rates, with DC and Massachusetts up .2 percentage points and Iowa and Virginia up .1 percentage point. Nevada’s rate fell .1 percentage point and Indiana was down .2. The highest unemployment rates were in DC (5.8%), Nevada (5.6%), Michigan (5.5%) California (5.3%), and Kentucky (5.2%). Hawaii, Montana, North Dakota, South Dakota, and Vermont had unemployment rates under 3.0%, while South Dakota’s 1.8% was yet again the lowest in the nation.

Puerto Rico’s unemployment rate was again unchanged at 5.3% and the island’s job count moved up by 1,100.

Geopolitics, AI, and the New Digital Divide In the final part of this series, we turn from the physical and technological constraints on growth to the geopolitical ones. Part I highlighted structural headwinds in the world’s labor, capital, and energy markets. Part II explored the potential of AI to act as a macro workaround—while warning that it is not immune to physical limits, particularly energy. Part III brings these threads together: AI is not just a labor-saving innovation or a capital redeployment tool. It is increasingly becoming a geostrategic asset—one whose deployment depends heavily on energy availability, infrastructure, and political control.

1. AI as a Tool of Statecraft What oil was to 20th-century power, processing power is becoming in the 21st. Nations are now racing to dominate the AI value chain—from chip design and fabrication, to data access, to the energy systems needed to power high-performance computing. Export controls, industrial policy, and national security reviews are no longer confined to oil tankers and pipelines—they now apply to GPUs and data centers.

The result is a world increasingly defined not by technological openness, but by fragmentation. From Trump's protectionist agenda—including tightened semiconductor export controls—to the strategic ambitions of the US CHIPS Act under the previous administration, and China’s intensified homegrown AI efforts, digital sovereignty has become an explicit geopolitical objective. The once-celebrated ideal of borderless innovation is yielding rapidly to a harsher reality: national power now hinges increasingly on digital dominance and technological self-reliance.

2. The Energy Arms Race Beneath the Silicon If the second in this series taught us anything, it’s that AI is not ethereal—it’s physical. It runs on electricity, minerals, and metals. It demands stable grids, high-capacity cooling, and energy infrastructure on a scale few countries currently possess. AI isn’t just compute-intensive. It’s energy-intensive. And that shifts the locus of global competition. The new AI geopolitics is not just a chip race. It is quite literally a power race. Countries that control abundant, cheap, and low-carbon energy will gain a disproportionate advantage in AI scalability. Those that don’t, risk falling behind, regardless of their tech ambitions.

As such, energy security is fast becoming the ultimate gating factor in the AI revolution. Just as the post-WTO growth boom in the 2000s was fueled by a surge in real energy consumption and prices, the AI boom may repeat that pattern—only faster, and more unevenly distributed.

Among the many issues facing the public and financial markets, the impact of President Trump’s tariffs on inflation and how they affect the Federal Reserve’s monetary policy stand out. Estimates of the economic and inflation impacts of the tariffs are quickly evolving as the twists and turns of Trump’s policies change the magnitudes and character of the tariffs. This short note doesn’t speculate on the magnitude of the tariffs, rather it considers how CPI and PCE inflation will likely be affected differently. These differences may influence Fed behavior.

PCE inflation measures the percentage change in prices of all consumer goods and services. This includes all goods, including those paid for by consumers as well as those financed by third party payments like Medicare, Medicaid and medical services paid for by employer insurance. CPI inflation measures the percentage change in prices of goods and services directly paid for by consumers and excludes goods and services paid for by third parties.

As such, PCE and CPI inflation measure the price changes of different baskets of goods and services. PCE inflation is a broader measure of inflation, while the CPI is a better measure of consumer out-of-pocket expenses. The CPI comprises a much larger share 35.4% of shelter costs (including rental costs and owners’ equivalent rent, OER) than the PCE, and a smaller share of medical care (8.3%). Both measures of consumer prices include items like personal care and education expenses.

Of note, both the PCE and CPI inflation measures are adjusted for estimates of quality improvement. The quality adjustments are estimated by the Bureau of Economic Analysis within the U.S. Department of Commerce, based on hedonic regression analyses that try to capture the quality improvements of new products, other estimating techniques, and some degree of subjectivity.

While CPI and PCE inflation are the most widely used measures of inflation, the GDP deflator is a broader measure of inflation that captures the price changes in other components of GDP, including business fixed investment, imports and exports, and residential investment (new construction and home improvements). This is important in the current context, since a sizable portion of imported goods that are subject to tariffs are capital goods purchased by domestic businesses and used in production.

An historical note. The Fed’s semi-annual Monetary Policy Report to Congress and its central tendency projections of economic growth and inflation, which began in 1980 as required by the Full Employment Act of 1978, used the GDP deflator as its inflation measure until 1990, when it switched to the CPI; Fed Chair Greenspan subsequently switched it to the PCE. The Fed’s first-ever Strategic Plan of 2012 officially established the PCE inflation target of 2%.

All other government agencies--and all other central banks in the world--rely on CPI inflation, and the CPI is an important “policy variable” used in various functions, including to index the benefits of Social Security, other pensions and an array of other programs. The Fed began focusing on core inflation early on: Fed Chair Arthur Burns instructed his research staff in the mid-1970s to calculate the CPI excluding food and energy following the spike in agricultural prices in 1971-1972 and surging oil prices generated by the first Arab Oil Embargo in November 1973. His primary interest was to tell the public that the high inflation was caused by exogenous forces. He was also attempting to quell rising inflationary expectations and bond yields without having to tighten monetary policy too much (Remember, before May 1983, mortgage rates were calculated directly in the CPI).

Recent CPI and PCE inflation trends. CPI inflation has been materially higher than PCE inflation in the post-Covid economy, as shown in Chart 1. CPI inflation was 0.6 percentage points higher than PCE inflation in 2024 (3.4% vs 2.8%), 0.7 ppt higher in 2023 (4.8% vs 4.1%) and 0.8 ppt higher in 2022 (6.1% vs 5.3%). So far in 2025, they have tracked closer. The cumulative differences have added up: since December 2019, the CPI index has risen by 3.8 percentage points more than the PCE price index Chart 2). As we all know, the large rise in the general price level has resonated with American citizens and created a communications problem with the Fed.

The Age of Constraints, Part 2, AI and the Energy Reality Check

If Part I of this series mapped the fractures in the global economy’s supply-side foundations—labor, capital, and energy—this second installment turns to the potential workaround: artificial intelligence.

AI has been frequently pitched as the ultimate macroeconomic escape hatch. It promises to plug labor shortfalls, redeploy idle capital, and inject new life into flagging productivity trends. And in some areas, it already is. From medical imaging to generative models in finance, AI is no longer theoretical—it’s live, scaling, and impressive.

But it’s not weightless. Or frictionless. And it is certainly not energy-free.

A look at the long-run relationship between real energy prices and global per capita energy consumption—illustrated in the chart below—potentially suggests that AI may not ease the Age of Constraints so much as intensify it. Particularly when it comes to energy.