Viewpoints: 2026

The concept of “core” inflation, that is, a measure of inflation excluding food and energy prices, came into fashion in 1973. In 1972, there was an El Nino weather phenomenon, which decimated the sardine school off the coast of Peru. Sardines were ground into fishmeal, which, in turn, was used as animal feed. The dearth of sardines resulted in an increase in the price of land-animal protein in 1973. Energy prices soared in late 1973 as a result of the OPEC oil embargo in the aftermath of the Yom Kippur War between Israel and its neighbors. (It has been argued that the catalyst for the OPEC oil embargo was the decline in the foreign-exchange value of the US dollar. OPEC nations were being paid in US dollars, which reduced their purchasing power for goods and services sold in other currencies). The chairman of the Federal Reserve at that time was the venerable Arthur Burns – he who must be obeyed. Burns argued that the increases in food and energy prices being experienced in 1973 were not the result of the current stance of monetary policy, but were caused by exogenous factors. Therefore, according to Burns, monetary policy decisions should be based on some concept of the underlying rate of inflation, not price increases resulting from exogenous factors.

Let’s fast forward to today. Energy prices have shot up in the past week or so coinciding with the US and Israel shooting up Iran. Less reported, El Nino is once again plaguing Peru. I have not read that El Nino has adversely affected the sardines, but it is producing severe flooding in Peru, which is playing havoc with Peruvian agriculture. I bet you didn’t know that Peru is a major world exporter of blueberries, grapes, avocados, coffee and asparagus. Neither did I until I started writing this commentary. We do not know how long this military “excursion” into Iran will last and, therefore, how long the resulting increase in energy prices will last and/or how high they will go. But I suspect that we will hear Fed policymakers and financial media talking heads say that Fed policy should be guided by the current and expected behavior of core inflation. That is, the inflation rate excluding the prices of food and energy items because the current increases in food and energy items have not been caused by monetary policy and might be transitory. I’ll bet that at least one Fed policymaker whose term has been extended (and whose initials are SM) will argue that monetary policy should be eased because of the negative effects these food and energy price increases will have on real economic growth.

Let’s look at what happened to core inflation in the early 1970s when food and energy prices flared higher (see Chart 1). In 1972:Q4, year-over-year core Personal Consumption Expenditures (PCE) inflation was 3.05%, food inflation 5.14% and energy inflation was 3.10%. By 1974:Q4, year-over-year core PCE inflation was 9.84%, food inflation was 14.10% and energy inflation was 25.80%. So, during this period, not only did food and energy price inflation soar, so, too, did core inflation.

United States & Europe

In the United States—where the fiscal year begins in September—fiscal policy remains expansionary, with Trump’s One Big Beautiful Bill setting the overall policy direction.

The European Commission projects the euro-area fiscal deficit at around 3.3% of GDP in 2026 and insists the year will not be one of fiscal stimulus. Whether this proves accurate remains uncertain.

Within the euro area, Germany’s fiscal plans centre on a major investment push, including the €500bn infrastructure fund, alongside significantly higher defence spending. Germany’s Draft Budgetary Plan suggests the general government deficit could rise to around 4¾% of GDP, driven by spending on infrastructure, innovation, security and defence.

France has also adopted a 2026 budget that reflects political constraints as much as fiscal priorities. Defence spending is rising, but the consolidation path is slower than previously envisaged. As part of a compromise with the left, plans to raise the pension age from 62 to 64 and to cut production taxes have been abandoned.

Italy’s 2026 Budget Law includes measures worth roughly €22bn over 2026–28, though the deficit is still projected to narrow slightly to 5% of GDP in 2026, from about 5.4% in 2025.

Spain, by contrast, faces less need for fiscal support and remains focused on consolidation as pandemic-era emergency measures expire.

Taken together, fiscal policy across the euro area remains broadly restrained, though increasingly shaped by defence priorities. Even so, these developments appear modest when set against fiscal policy elsewhere—particularly Japan.

Japan’s Expansionary Turn

Japan’s government approved a US$135bn fiscal stimulus package in late 2025, the largest in years, aimed at supporting households and economic growth. Around US$118bn comes through general-account spending, with the remainder delivered through tax measures and related initiatives.

The programme focuses heavily on household relief and strategic investment. Measures include utility and gasoline tax relief, subsidies and transfers to support consumption, and expanded investment in infrastructure, AI, semiconductors and other strategic sectors. Support for local governments and small and medium-sized enterprises has also been increased.

The government is also considering a two-year suspension of the 8% consumption tax on food and drinks, though the proposal remains under debate.

Asia: A Measured Fiscal Approach

Compared with advanced economies, Asia’s fiscal position remains relatively strong, reflecting both smaller pandemic stimulus programmes and a longstanding preference for fiscal restraint.

During the Covid-19 crisis, fiscal support in advanced economies averaged about 28% of GDP, while Asian economies averaged only 8.4% (Figure 1).

In an interview last week, Jamie Dimon, CEO of JPMorgan, said "banking rivals are doing dumb things" and drew parallels between current credit conditions and those in the years leading up to the 2008-2009 Great Financial Crisis. Immediately following Dimon's interview, the FDIC issued its "Quarterly Banking Profile", which provided evidence that commercial banks are in sound shape financially, with relatively high levels of capital and fairly low loan delinquencies. Private credit has also been in the news, fueling fears of real problems. Private credit is not supervised and there are no reliable sources of data for evaluating private credit. This makes financial markets vulnerable to speculation and anecdotal evidence.

Are current credit conditions worrisome? My current assessment is that as long as the economy continues to expand, with aggregate demand growing anywhere near its recent pace, commercial bank credit is not worrisome, and credit conditions are far different than the problems and risks that characterized the pre-GFC period. If the economy were to fall into recession, resulting in a decline in business revenues and profits, credit problems would obviously emerge, but current monetary and fiscal policies suggest the probability of that outcome is low.

The rapid growth of private credit may be a problem. However, if problems exist, most likely they are largely concentrated in the debt-financed AI infrastructure buildout, and a deterioration in private credit conditions would have localized economic and financial impacts, unlike the GFC. Unfortunately, there is insufficient collected information and data to assess the situation, so understanding the scope of the credit issue must be based on specific credits, cash flows and capital of companies.

Dimon's concerns about parallels to the pre-GFC credit markets seem overstated. Consumer and business debt and debt-service are in fairly good shape and current credit conditions and banking practices are far different than during the pre-GFC. Corporate America has been deleveraging for years, household debt remains relative low compared to disposable personal income, and commercial banks are largely well capitalized and have a better understanding of the risks in their balance sheets.

In the years leading up to the GFC, nearly the opposite unfolded. There was an explosion of mortgage debt during an unruly bubble in housing activity (national outstanding mortgages rose 75% in the years 2002-2008) driven by rapidly rising home values and excessively loose credit standards (mortgages, HELOCs, home equity loans); a proliferation of overly-complex MBS and ABS derivatives that created tranches of income-yielding securities that were widely held by banks and investors who didn't know their risks; and insufficient bank capital and lax definitions of capital held by banks. Expectations that home values would continue to rise forever were the catalyst that drove the housing and mortgage demand and loose bank credit standards and willingness to hold complex and risky MBS derivatives. There was a high degree of interconnectedness of systematically important financial institutions (SIFIs) that risked contagion.

When home prices and expectations of future values began to fall in Spring 2006, the entire mortgage market began to unravel. Collapsing values of MBS derivatives and massive mortgage defaults resulted in back-breaking losses for banks, revealing insufficient bank capital bases. Widespread uncertainties about banks' capital led to a short-term funding crisis for large banks, a dramatic spike in uncertainty and broader financial paralysis and deep economic contraction. In sum, real estate loans were at the heart of commercial banking portfolios, and the credit quality of the loans were rotten.

The most recent Federal Reserve reports on commercial bank balance sheets and household debt along with the FDIC Report show that household and business debt levels are not high relative to economic activity and disposable personal income, and banks remain healthy and well-positioned. Chart 1 shows total household debt including mortgages as relatively low as a share of disposable income while Chart 2 shows that business debt is falling as a share of GDP. Chart 3 shows the shares of consumer loans by type: excluding the policy-induced spike in student loan delinquencies, the only concern is credit card debt where seriously-delinquencies are uncomfortably high.

Chart 4 shows delinquencies of business loans: delinquencies of commercial real estate loans have risen only modestly since the Covid-related spike in commercial real estate vacancies in big cities and remain below delinquencies of residential real estate loans. Recent indicators suggest that the problems in commercial real estate are now diminishing. Chart 5 shows commercial bank charge-off rates, which remain low.

Charts 6-7 are from the recent FDIC Report: they show that banks' reserve coverage ratio is relatively high; and their unrealized capital losses of investment securities is shrinking (banks have reduced the duration of their securities and longer-term yields have declined). The FDIC tabulation of problem banks is low. All-in-all, commercial banks are in relatively good shape.

The private credit market remains murky, except for those who are directly involved--the lenders and their sources of capital and leverage--and have access to the financials of the credits they are involved in. The lower costs and higher efficiencies of private lending that stems from lower costs of supervision and government regulatory oversight has a downside: lack of knowledge of data and transparency. In a recent study by the Alternative Credit Council and Houlihan Lukey, Financing the Economy 2025, the private credit market is described in broad terms and includes many interviews with active private creditors, but it does not include the sufficient data to analyze and evaluate private credit. (In a recent article I co-authored with Amit Seru, “The Fed Needs to Earn Its Independence. Just Setting Rates Isn’t Enough”, we argued that the Federal Reserve should seek to obtain information on the credit activities of the private lenders.)

It's important to distinguish between the different kinds and structures of private credit. Some (many?) private creditors are basically making the loans that commercial banks used to make, benefiting from the lower costs of supervision, compliance and regulation. They raise private capital, leverage it with loans from commercial banks, and provide credit to borrowers at healthy spreads. They conduct credit analyses of their clients. Presumably, the credit quality of their loans does not deviate significantly from the commercial and industrial (C&I) loans of commercial banks, although that is not ensured. Also, it is likely that most private creditors are less leveraged than banks.

Two of the largest concerns with private credit are 1) that the proliferation of private lending may be diluting the quality of the private creditors and lessening their credit standards and oversight of the loans, and 2) some private lenders have a narrow focus of their lending activities, and their lack of sufficient diversification may generate concentrations of defaults.

Currently, there's a ton of news on the rising debt used in the AI infrastructure buildout, including data centers and energy production facilities. Sorting out the corporate finances of the companies leading the AI capital spending buildout is difficult. For many companies involved in the AI buildout, much of the capital spending on data centers and energy related buildouts has been financed with internal capital and cash flows. More recently, however, a significant amount has been financed by debt. At the same time, the revenues and free cash flows generated by AI-related products are accelerating dramatically. Based on standard business metrics, a snapshot of the finances of many of the largest AI firms are reasonable or even good. Several may be problematic.

The pace of implementation of AI into commerce and society is stunning. Most likely it will add materially to productivity, economic growth and profits, and will disrupt labor markets. As with all other innovations in U.S. history, disruptions will result in some jobs lost and other new jobs created. I take a positive view of longer-run outcomes. In weighing current commentary on the labor market outcomes stemming from AI innovations, be careful in extrapolating anecdotal evidence to the entire economy, and remember the adage “bad news sells”.

As with all episodes of technological innovation in the U.S., there will be some failures among the AI innovators, and some of the providers of capital and credit will incur losses. However, in contrast to the GFC, the losses will be incurred by a narrow group of capital and credit providers and will not be pervasive and unhinge society and commercial banking. Consider an insurance company or a state pension that finances an AI project and incurs a sizable loan default or capital loss. The jarring impacts of the losses will be narrowly focused and would be relatively minor to the broader commercial banking industry. Similarly, the impacts of the large losses stemming from the Covid-initiated collapse of commercial real estate in big cities were relatively narrow and didn’t unhinge the banking industry. The private credit industry and AI require scrutiny. A close assessment of individual company products, revenues and profits and capital spending is required. This may be a time to be cautious in private lending. But starting with the premise that we should be fearful because it has parallels to the pre-GFC period is not particularly instructive.

Taiwan stands to benefit disproportionately from the current AI-led global semiconductor upcycle. Its domestic semiconductor ecosystem has expanded at remarkable speed, more than tripling over the past decade—from about US$69bn in 2014 to US$166bn in 2024—and is estimated to approach US$200bn in 2025. This growth is no accident. Taiwan sits at the heart of the world’s most advanced logic-chip production, with demand increasingly driven by AI and high-performance computing. Its dominance reflects early specialisation, rapid scaling, and an unmatched ability to protect customers’ intellectual property.

At the centre of this ecosystem is TSMC—Taiwan’s true national treasure—which continues to consolidate its lead at the technological frontier. While near-shoring projects in Japan, Germany and the US are often framed as diversification, in practice they reinforce Taiwan’s position rather than weaken it. TSMC’s latest commitment to build five additional fabs in Arizona illustrates this point clearly. Leading-edge R&D, pilot runs and sub-7nm process know-how remain anchored on the island. Overseas fabs are best understood as extensions of Taiwan’s production model, not substitutes for it.

The foundry race is no longer about sheer scale but about leadership at the cutting edge—and that leadership still belongs to Taiwan. A November 2025 report noted that Nvidia sources 100% of its top-tier GPUs from TSMC, including advanced 3nm and emerging 2nm-class production. AI and high-performance computing now account for more than half of TSMC’s wafer revenue, with AI processors alone on track to reach around 20% of sales by 2028.

Taiwan’s strength is not limited to foundries. Its fabless sector benefits from unusually tight coupling with domestic manufacturers, rapid design-to-manufacturing feedback loops, and a dense ecosystem in which suppliers, advanced packaging, talent and fabs are co-located. This structure enables faster iteration and innovation than is possible in more geographically fragmented semiconductor hubs.

That said, the industry has become increasingly concentrated, and this brings risks (Figure 1). The foundry sector’s share of output has risen from 41.5% in 2014 to an estimated 63.4% in 2025. By contrast, other subsectors—IC design, packaging, testing and related activities—have seen their shares decline. The memory segment has shrunk particularly sharply, from 11.8% of output in 2014 to just 3% in 2025, leaving Taiwan heavily dependent on Korea in this area. IC packaging’s share has also roughly halved to around 7%.

Yet again, the Federal Reserve Bank of Philadelphia’s state coincident indexes in December were generally soft. In the one-month changes, Kentucky and Missouri were the only states with increases above .50 percent. 16 were down, with West Virginia off by more than one percent. Over the three months ending in December, Missouri was the only state with an increase higher than 1 percent (Idaho was up .99 percent), while eight fell, with Delaware off more than 1 percent and West Virginia down more than two percent. Over the last twelve months, four states were down (Delaware by more than two percent), and seven others saw increases of less than one percent. Seven states saw increases of 3 percent or more, with Alabama’s 3.91 percent at the top.

The independently estimated national estimates of growth over the last three and twelve months were, respectively, .38 and 1.84 percent. The three-month reading seems to be in line with what the state numbers would have suggested, while the twelve-month one appears to be lower.

Excess, innovation and why this boom may still have room to run

Recent weeks have seen financial markets gripped by a shifting mix of narratives. A weaker US dollar, rising rate volatility and renewed geopolitical uncertainty have revived talk of a tentative “sell America” trade, as investors reassess US exceptionalism after several years of outperformance. At the same time, global equity markets have remained remarkably resilient, supported in large part by continued optimism around artificial intelligence and the powerful investment cycle it has unleashed.

Against this unsettled backdrop, a familiar question has returned to the centre of market debate: are prices being driven by fundamentals, or by faith? With artificial intelligence, that conversation has clearly arrived. Equity markets — particularly in the United States — have surged on the back of a relatively small group of AI-exposed firms that now account for a striking share of index gains. Valuations increasingly rest on the assumption that AI will deliver a sustained uplift to productivity, profits and long-run growth. Investors are no longer debating whether AI will transform the economy; instead, they are debating how quickly — and how completely — those gains will materialise.

Lessons From History

History offers a useful starting point. Truly transformative technologies almost always arrive alongside periods of financial excess. Railroads, electricity, telecoms and the internet all produced extraordinary investment booms that ultimately reshaped economic structures, even as they destroyed large amounts of capital along the way. The presence of a bubble never meant the technology was flawed. It meant expectations moved faster than cash flows, infrastructure deployment and balance-sheet reality. AI fits this historical pattern uncomfortably well.

In this environment, narrative often dominates near-term profitability, and that is typically how transformative technologies diffuse rapidly across the economy. But where bubbles become truly dangerous is not via investor enthusiasm itself, but in the financing structures that accompany large infrastructure build-outs. Equity investment in diversified firms with strong balance sheets is fundamentally different from debt-financed bets on long-lived assets whose viability depends on uncertain demand and rapidly evolving technology. Yet we are already seeing significant capital being channelled into data centres, energy infrastructure and specialised AI facilities, increasingly funded through project-level vehicles and long-dated financing. These investments rest on assumptions about sustained utilisation, manageable energy costs and technological continuity — assumptions that inevitably carry risk.

This should feel familiar. The late-1990s telecoms boom was not undone because fibre optics were useless, but because capacity was financed almost entirely with leverage long before revenues caught up. When pricing power collapsed, investors absorbed enormous losses, even as the infrastructure went on to support the digital economy that followed.

The Macro Dimension

However, there is also a macro dimension in this debate that deserves more attention, and this is where today’s AI boom still looks somewhat different from past excesses. The chart below contrasts private non-residential investment in the US with the financial balance of non-financial corporations. During the dot-com era, corporate investment surged at the same time as the corporate sector moved deep into financial deficit. Firms were spending far beyond internal cash flows, and increasingly reliant on external financing — particularly debt — to fund speculative expansion. That combination of soaring investment and widening financial imbalances was a classic Minsky-style warning sign.

State labor markets were again soft in December. No state had a statistically significant change in payroll employment; the largest absolute gain was a 19,700 increase in Texas, which was barely .1 percent.

Six states saw statistically significant increases in their unemployment rates (Delaware, Florida, Illinois, Minnesota, Oklahoma, and Washington)—Delaware’s was up .3 percentage points. The highest unemployment rates were in DC (6.7%), California (5.5%), New Jersey (5.4%), Delaware (5.2%), Nevada (5.2%), Oregon (5.2%), and Michigan (5.0%). Alabama, Hawaii, North Dakota, South Dakota, and Vermont had unemployment rates below 3.0%, with Hawaii and South Dakota’s the lowest, both at 2.2%.

Puerto Rico’s unemployment rate was again unchanged at 5.7% and the island’s job count rose by 3,600.

U.S. economic growth has remained solid through most of 2025, driven by healthy gains in consumption and strong business fixed investment, particularly for the buildout of AI. This has defied the pessimists’ worries about President Trump’s misguided tariffs, clampdown on immigration and cuts in research grants to universities. The only real laggard in 2025 was the housing sector, which suffered from continuous declines in construction and improvements. But that was last year and we should not expect any let up in erratic tariff policies and anti-immigrant initiatives in 2026.

Despite these obstacles, the outlook for sustained expansion in 2026 looks favorable, and the probability for recession is low. Current conditions are inconsistent with onsets of recession in the past. Consider the following two items that will support aggregate demand: 1) three Fed interest rate cuts in September-December 2025 lowered the real Fed funds rate below the Fed’s estimate of the longer-run real rate of interest consistent with its dual mandate of 2% inflation and maximum employment, and the Federal Reserve Bank of Chicago’s Financial Conditions Index signals loose financial conditions, and 2) fiscal policy is stimulative, as the OBBBA of 2025 extended the 2017 tax cuts and added some additional cuts (eliminating tax on income from tips, expensing of outlays for research and development) that will boost tax refunds in Spring 2026 by approximately 0.6% of disposable personal income. In this environment, 3) business inventories are relatively low and 4) employment is well-aligned with output (GDP). Accordingly, any slump in aggregate demand will not force businesses to cut output and/or employment in a meaningful way.

Labor market and personal income dynamics. One key trend to keep an eye on is real wage and salary incomes, a key indicator of labor market conditions and measure of consumer purchasing power. Growth in personal income from wages and salaries has decelerated to 3.8% in the year ending November 2025 (Chart 1). That’s down from a 5.5% rise in the prior year. At the same time, CPI inflation was 2.7% in the last two years ending November 2025. According, the year-over-year growth in real personal disposable income from wage and salaries has receded to 1.1% in the year ending November 2025, significantly slower than its 2.8% rise in the prior year.

This deceleration in real wages and salaries reflects primarily a combination of moderating gains in average hourly earnings (AHE) and weakness in employment. As shown in Chart 2, AHE have moderated to 3.5% year-over-year growth from 4.1% a year earlier. The yr/yr rise in AHE will decline further in the January and February 2026 readings as the high monthly increases in Jan-Feb 2025 roll off. At the same time, establishment payroll gains have flattened significantly. In the six months July-December 2025, employment rose a net 87,000, an average of 14k per month; in the prior six months jobs rose 497k, an average monthly rise of 82k (Chart 3). In the prior year ending December 2024, employment rose over 2 million.