Conversable Economist

Predictions for how artificial intelligence technologies will affect future economic outcomes, for good or ill, are all quite dependent on the underlying assumptions. But it’s nonethelss interesting tow see what big international institutions are saying about the future. The recent report from the OECD, Foundations for Growth and Competitiveness 2026, is sweeping in its overview of the topic. Here, I will just focus on the subject of how AI technologies might affect growth.

Here’s an illustrative figure with estimates for seven high-income economies. The light-blue bars are AI-related annual productivity gains over the next decade with a slow-adoption scenario; the darker-blue bars are growth with a faster AI-adoption scenario; and the orange triangles are a medium-adoption scenario.

The OECD report offers some perspective on these growth rates: “For reference, the annual labour productivity growth, on average, has been 0.6% across G7 economies over the last ten years (2014-23), implying a growth-dividend from AI that is close to double the rate of recent productivity growth. In the most optimistic scenario, with fast adoption (as in mobile phones) and expanded AI capabilities, labour productivity gains are estimated at up to 1.3 percentage points, while in the most conservative scenario of slow adoption, they remain significant but at 0.2 to 0.4 points.”

To put it another way, say that AI raises US economic growth at the top end of these estimates, by 1.3% per year. The US GDP will be about $32 trillion this year. So a year from now, the US economy would be $416 billion bigger (that is, 1.3 percentage points) than it would otherwise have been. After 10 years, the GDP would be 13% bigger than it would otherwise have been (actually a bit more than that, because growth rates compound over time). This would be $4.16 trillion larger than the US economy would otherwise have been.

The empirical study underlying this figure, by Francesco Filippucci, Peter Gal, Katharina Laengle and Matthias Schief, is based on three components: “(1) micro-level productivity gains from AI at the task level, (2) the degree of exposure of tasks within sectors to AI, and (3) forecasts of future AI adoption across firms within each sector. To the extent possible, the quantification of these components relies on country-specific assumptions.” For perspective, that earlier paper also provides a range of estimates of the growth effects of AI. (The bars with stripes on top show the range from lower to higher estimates.)

But why are some economies, like the United States, getting a bigger boost from AI than others? Some answer are presented in “Mind the Gap: AI Adoption in Europe and the US,” by Alexander Bick, Adam Blandin, David J. Deming, Nicola Fuchs-Schündeln, and Jonas Jessen (Brookings Papers on Economic Activity, Spring 2026). As a big-picture overview, they point out that US investments in information and communications technologies have been substantially higher since at least the 1990s, and perhaps unsurprisingly, the US edge in output per hour worked has ben expanding since the mid-1990s, too.

But the real heart of their paper is survey data of workers, across countries, about their use of AI. For example, the US has a higher share of workers who report using generative AI tools.

In addition, looking just at the workers who use AI tools, the US workers spend a greater percentage of their work time using those tools.

The survey data from workers also lets the authors look at the personnel management policies across firms (building on questions asked by the World Management Survey). They find that companies and countries which score better on personnel management also tend to have higher AI use. In short, the US economy has a higher share of firms with highly-rated personnel management policies, and thus a higher share of AI users, and thus better prospects for a future AI-related boost in economic growth.

Tim Sablik of the Richmond Fed interviews “Ellen McGrattan: On measuring what businesses do, developing effective tax policy, and searching for answers beyond the lamppost” (Econ Focus: Federal Reserve Bank of Richmond, First/Second Quarter 2026). Here are a few of the comments that caught my eye:

How did McGrattan become interested in business cycles?

In grad school, I stumbled upon a paper by Finn Kydland and Ed Prescott that was game changing for me. Their revolutionary idea, which might sound obvious today, was that you should write down a theoretical model, simulate the data from it, and then match it up head to head with actual economic data. That wasn’t what economists were doing at the time. They would write down a theoretical model, understand its inner workings, and then put up a big wall between the model and the empirical work. Kydland and Prescott decided to take all the warts and pimples of an economic model, make predictions based on it, and go head to head with the data. That was the first time I had ever seen that done. I think it is the right approach.

It just so happens that what they were analyzing in that paper was business cycles. … Kydland and Prescott realized that adding the Fed wasn’t the answer, and they pointed to something else: Total factor productivity (TFP). Some might say that TFP is just a measure of our ignorance — it’s what we don’t understand. In some sense, I’ve been struggling with trying to look inside the black box of TFP all my career.

How studying total factor productivity led to intangible assets

My interest in business cycles partly stems from trying to measure what goes into TFP. … What is TFP? It’s what we don’t know, it’s the part that we need to fill in. It’s not just some magic dust that’s in the air. … If you buy, say, a computer, it has to be put on your balance sheet. But if you’re a dentist and you spend time building your patient list, that’s not put on any balance sheet. That patient list is the thing you sell when you retire or relocate, and that asset contributes to the value in the business, but we never see it until it gets sold or transferred somehow.

There are 40 million active businesses in the United States, and most have assets that we can’t see. Assets like customer bases or trademarks — until there’s a transaction, we can’t see them. You might have a good accounting system that you developed within your business, or you’re a chef and you have recipes, and we can only see those things if you trade them. But most ongoing businesses don’t list these assets on a balance sheet, so we never get to see them. And that’s why we need a theory to infer it. … It all ties back to work I was doing to measure movements in the economy over the business cycle, but now I would say the bigger issue is how to measure all activity in the U.S. economy.

McGrattan’s advice to students

 I always tell my students, ask a question first. Don’t read what other people have done. Decide on your own, especially when you’re writing your first paper. It doesn’t matter if you reinvent the wheel. If you’re thinking about things without having somebody else in your head, you’re going to come to a new creative idea. It’s fine after you’ve done something to compare yourself, because then you can really sharpen your results and make clear distinctions between what has been done in the past and what you did.

I really don’t like it when students are told to replicate findings from papers because that makes them too comfortable working with models that have already been analyzed to death. They learn an existing model and just make a small tweak to it. Before they do anything, I would rather they think of a good and, as yet, unanswered question and how they would go about answering it.

Often when I ask my students what they’re working on, they tell me they have some interesting data. That’s putting the cart before the horse. Start with an idea and then go down the path that may lead you to that data but may not. Don’t start with data and try to identify a question; start with a question and identify what you need to reliably answer that question.

Austin, Texas, is not small. The city itself has a population of about 1 million. The metro area (including the city itself) has a population of 2.5 million. About 10 years ago, Austin faced up to the issue of high and rising costs for rentals and for housing. It changes the rules for building in a way that expanded the housing stock by 30%. And rents fell. Liz Clifford, Seva Rodnyansky,  and Dennis Su of the Pew Foundation tell the story in “Austin’s Surge of New Housing Construction Drove Down Rents: Amid robust demand and a wave of policy reforms, Texas capital added 120,000 new homes from 2015 to 2024″ (March 18, 2026). They begin:

After decades of explosive growth, Austin, Texas, in the 2010s was a victim of its own success. Lured by high-tech jobs and the city’s hip reputation, too many people were competing for too few homes. From 2010 to 2019, rents in Austin increased nearly 93%—more than in any other major American city. And home sale prices increased 82%, more than in any other metro area in Texas.

But starting in 2015, Austin instituted an array of policy reforms aimed at encouraging the development of new housing, especially rentals. The city changed zoning regulations to allow construction of large apartment buildings, particularly near jobs and transit. In 2018, voters approved a $250 million bond measure to build and repair affordable housing. Permitting processes were reformed to speed development and reduce costs.

The efforts worked. From 2015 to 2024, Austin added 120,000 units to its housing stock—an increase of 30%, more than three times the overall rate of growth in the United States (9%).

Rents fell. In December 2021, Austin’s median rent was $1,546, near its highest level ever and 15% higher than the U.S. median ($1,346). By January 2026, Austin’s median rent had fallen to $1,296, 4% lower than that of the U.S. overall ($1,353). This decline occurred even though the city population grew by 18,000 residents from 2022 to 2024. In apartment buildings with 50 or more units, rents fell 7% from 2023 to 2024 alone—the steepest decline recorded in any large metropolitan area.

There seems to be a “folk economics” of housing. While many people believe in general that a higher supply will tend to drive down prices–and conversely, lower supply will tend to raise prices–these beliefs tend to go out the window when the subject of building more housing comes up. Instead, people begin to argue that building more housing only benefits real estate developer, or that any housing will only be for those with the highest income levels. Stan Oklobdzija, Christopher S. Elmendorf, and Clayton Nall describe this dynamic in “The Folk Economics of Housing” (Journal of Economic Perspectives, Summer 2025, where I work as Managing Editor):

[O]rdinary people simply do not believe that adding more housing to the regional stock would reduce housing prices. Across three original surveys of urban and suburban residents, only a minority of respondents say that a large, positive, regional housing supply shock would reduce prices or rents. These beliefs are weakly held and unstable (suggesting people have given the issue little thought), but respondents do have stable views about who is to blame for high housing prices: developers and landlords. Large, bipartisan supermajorities support price controls, demand subsidies, and restrictions on putative bad actors, policies which they believe would be more effective than supply liberalization for widespread affordability. 

People holding these beliefs might consider the Austin example. We aren’t talking here about a few more units of housing designated as “affordable,” or about subsidizing demand for housing and hoping the price comes down (!). If housing doesn’t seem affordable where you live, your local government should be thinking about what set of rules and zoning would make it possible to increase the housing supply by 20%, 30% or more over the medium-term of 5-10 years.

As the United States approaches its semiquincentennial (that is, half of 500 years) July 4, the McKinsey Global Institute offers a report that reads to me as a meditation on long-run US economic growth in “At 250, sustaining America’s competitive edge” (March 9, 2026). The US became the world’s largest economy in 1860, and has kept that lead since. In my reading, a major theme running through the report is the US leadership in originating and applying new technology. In that spirit, here are a few of the figures that caught my eye (but there’s much more in the report itself).

Here’s a proposed list of the top 100 technological innovations in products, systems, and technologies over the past 250 years. (I confess that I am a total sucker for mulling though lists like this.) The entries highlighted in dark blue were US-led; the entries in light blue were US-involved. In a broad sense, consider the dominance of US technological leadership since around 1900.

As US workers and companies used and developed these techologies for practical economic purposes, the McKinsey analysts suggest that it’s useful to think of US economic growth as proceeding through four stages of transformation. (Again, I’m a total sucker for whether these kinds of categories are the right break-points, or appropriately names, and so on, but I will leave you to your own meditations on these points and spare you my own.)

Combine this technological prowess with a legal and regulatory environment that supports a dynamic and evolving economy, along with investments in transportation and communications infrastructure, along with an enormous domestic market to sell into and (historically, although not so much at present) political support for agreements that let the US sell into international markets as wellm and one of the results is that the largest publicly traded firms in the world tend to based here in the United States. To put it another way, US citizens have the luxury of being worried about whether certain domestic firms are too successful and powerful, while people in other countries can only look on. It’s also useful to note that many of the biggest firms do turn over and evolve every few decades.

These and other factors discussed in the report (human capital, geography, investment, and others) add up to big-picture growth. The top panel shows the total size of the US economy, and points to the fact that it became the world’s largest economy in 1860. The bottom panel shows the per capita size of the US economy. Notice that when it comes to total size of an economy, China is #2. But when it comes to per capita size of an economy, China is still well behind the high-income countries of the world.

From a big picture view, the sustained growth and technologial leadership of the US economy is one of the remarkable world-historical facts of the last couple of centuries. We Americans are so close to it that we may fail to see it clearly. But now and again, in between the economic analysis and policy disputes, it’s worth a moment of wonder.

From the headlines, it looks as if momentum toward economic globalization has gone into reverse. But the underlying data is less clear. Steven A. Altman and Caroline R. Bastian lay out some evidence in the “DHL Global Connectedness Report 2026.” Some of their summary findings include:

The DHL Global Connectedness Index does not indicate a shift from international to domestic activity across trade, capital, information, and people flows. Global connectedness reached a record high in 2022 and has not changed appreciably through 2025. … U.S. tariff increases only modestly reduced forecast global trade growth. Other countries supported trade growth by not raising tariffs, and many negotiated new trade deals to secure access to alternative markets. … The world remains far from a split into disconnected geopolitical blocs. Only 4-6% of global goods trade, greenfield FDI, and cross-border M&A have shifted away from geopolitical rivals over the past decade. Trade flows shifted more toward neutral countries than to close allies, implying more ‘de-risking’ than ‘friendshoring’. … Prominent narratives about deglobalization are driven more by politics and public policy than by actual shifts in cross-border flows. While the risk of deglobalization has risen and the pattern of connectedness is shifting, the world overall remains as connected as ever.

Here are a few figures that caught my eye:

Global trade as a share of world GDP has been holding steady for a couple of decades now, with trade in goods sagging a bit and trade in services surging bit.

Global value chains–that is, goods where the stages of production happen in multiple countries–continue to lengthen.

The size of international capital investments, including foreign direct investment and portfolio stock investment have dropped a bit in these last few post-pandemic years, but remain near their all-time highs.

The report seeks to capture economically relevant information flows by looking at international collaboration in scientific articles, charges for use of foreign intellectual property, and international patent applications.

The report emphasizes that globalization still has plenty of room to expand:

[I]nternational flows of many types are close to record high levels relative to domestic activity. Nevertheless, we do not live in a “hyperglobalized” world. Most activity that could happen either within or across national borders is still domestic … The most recent available data show only 21% of all goods and services ending up in a different country from where they were produced. Companies buying, building, or reinvesting in foreign operations via FDI accounted for only 6% of gross fixed capital formation. Just 18% of traffic to online news websites came from abroad. And just shy of 4% of people lived outside of the countries where they were born.

Will globalization remain resilient, in the face of political pressures? Quite possibly. As the report points out, countries around the world outside the US clearly place a high value on the gains from trade; indeed, many of them are signing new trade agreements and keeping tariffs low with each other. The US economy is disengaging from direct trade with China, but in a multipolar world economy, this process often involves re-routing of trade, along with seeking out new opportunities, rather than a decline in the global total. Moreover, increases in trade have often been driven historically by new technologies–shipping, air freight, containerization, information flows. For example, the report cites a projection from the World Trade Organization that “AI could lift global trade in goods and services by 34–37% by 2040.” Love it or hate it, globalization doesn’t seem to be going away.

The GDP of North Korea was about $26 billion in 2024 (roughly equal to total sales for the Kraft Heinz Company), and with a population of about 26 million, that works out to a per capita GDP of (very) roughly $1,000 per year. However, that GDP estimate is not from any statistical release from the government of North Korea, which doesn’t do statistical releases. Instead, it’s from a report by the central bank of South Korea. Back in 2021, North Korea did send to the United Nations a report titled “Democratic People’s Republic of Korea Voluntary National Review on the Implementation of the 2030 Agenda. That report estimated a GDP of $33 billion in 2019–but without context on how that figure was derived.

How can one study an economy where government statistical data is unavailable or unreliable? Stephan Haggard, Kyoochul Kim, and Munseob Lee ask the question in “Studying economic black holes: Lessons from North Korea” (World Development, May 2026, 201: 107315). They discuss techniques of what they call “forensic economics.” “We focus on six sources of information: (i) data derived from remote sensing using satellites, (ii) information gleaned from periodic access by humanitarian agencies, (iii) `mirror statistics’ trade and aid data which relies on foreign partners rather than the government, (iv) information on prices collected surreptitiously, (v) information and data generated by refugee surveys and interviews, and (vi) creative use of official documents that unintentionally provide information that the government might actually seek to control.”

Perhaps the most famous illustration of satellite data for estimating GDP is from a 2014 NASA photo, which shows a view of “night lights” across South Korea, then the darkness over North Korea, and the night lights of China beyond. There has been active research seeking to infer GDP–and changes in GDP–from night lights and other satellite imagery. Haggard, Kim and Lee point out that South Korean central bank estimates of North Korea’s GDP are based on estimating output of key goods, like tons of steel or kilograms of rice, and then combining this with intelligence data. But of course, an estimate of GDP based on goods will miss the service sector. Apparently, South Korea’s estimates of North Korean GDP don’t show much increase in the last couple of decades, but estimates based on night lights do show a rise.

Estimates of agricultural output from satellite imagery can also be combined with data gathered from the UN Food and Agriculture Organization, when it is allowed to operate inside North Korea.

Estimates of North Korean trade patterns can be constructed from “mirror” statistics–that is, base on data from North Korea’s trading partners. China is by far the biggest trading partner for North Korea. The goods being traded can also offer insights into the underlying economy. For example, “the trend in the quality of Vietnam’s exported goods, measured in terms of human capital inputs, contrasts with that of North Korea. Vietnam’s exports have gradually shifted towards higher-quality goods, from rice and coffee to apparel and then electronics. In contrast, as diplomatic and military conflicts severed trade ties with Japan and South Korea in the late 2000s, North Korea expanded its trade with China, where economic growth fueled demand for fossil fuels. Consequently, North Korea’s anthracite coal exports surged, leading to a sharp increase in export volume but a decline in overall export quality. This suggests that North Korea’s heavy dependence on anthracite coal exports to China and reliance on other commodities could dampen long-run growth.”

Price data is considered a state secret in North Korea and “North Korea does not publish an official price index. Gathering pricing information from within North Korea requires the transfer of individuals or data across the country’s borders, at some risk to the participants given that the North Korean regime considers such data breaches as criminal and even capital offenses. However, complex information networks, relying on cell phones with the ability to connect to China, have allowed outside news organizations to put together consistent series of prices on basic commodities across multiple cities …”

Such data are sketchy, of course, but they pick up big patterns. This figure shows the price level in 2020 set equal to 100. As you can see, there was an extraordinary inflation between about 2010 and 2013, and then a milder inflation after 2020.

Surveys of refugees and defectors from North Korea are obviously not much help in estimating GDP, but they can offer detailed insight into particular industries–say, fishing–as well as day-to-day economic interactions.

Even official communications from the government of North Korea can offer insights, if cautiously digested. The authors explain:

North Korea is a particularly good example of an economic black hole because it publishes little if any usable economic data. However, authoritarian regimes do not simply stifle information; they use government-controlled media to communicate core messages to elites and to promote and legitimize their policies. The North Korean regime is no exception, and the array of such publications presents a potentially important field for the extraction of information, even if it requires careful reading; official news outlets; television broadcasts; speeches; and even academic journals reflect the government’s ideological stance and economic policies. In addition, the government publishes laws and regulations that pertain to economic activity and these have now been collated into accessible databases in English translation (North Korea Tech Lab, 202526). In general, the information available from these official North Korean sources has been underutilized by economists because it rarely provides the sorts of data that would be of direct interest to researchers. But recent advances in text mining through machine learning or so-called “text as data” approaches have enabled the conversion of qualitative data into quantitative data that opens up research opportunities.

North Korea is an extreme case of a black hole for economic data. But there are many countries around the world where estimates of GDP, or even basic estimates of population, can be quite uncertain. These tools of “forensic economics” have potentially much broader application.

Imagine that instead of dividing up US spending by program, you instead divide it up by what age group the money is spent upon. Kent Smetters takes this approach in “How Federal Spending is Distributed by Age” (Penn Wharton Budget Model, April 1, 2026). The results are perhaps unsurprising in general, but still eye-opening in their specifics.

The US government spent $7 trillion in 2025. In these calculations, $2.6 trillion of the spending can’t be easily assigned to a certain age group. As Smetters writes: “The largest items in the residual are net interest ($970 billion) and national defense ($917 billion). Transportation ($146 billion), natural resources and environment ($88 billion), administration of justice ($85 billion), community and regional development ($85 billion), and several other functions also remain in the all-ages category. These programs finance public goods, broad administration, place-based investment, or mixed services for households of all ages — they are not programs where recipient age is the central organizing concept.”

As one would expect, much of Social Security and Medicare can be assigned to the elderly, although these programs also cover a certain number of disabled persons who are not over the age of 65. Smetters writes: “Social Security directs $1.3 trillion to retirees, and Medicare sends $835 billion. Together they account for 80 percent of all age-assignable spending on older adults. But the retiree total extends beyond these two entitlements. Federal employee retirement benefits ($169 billion), housing assistance for older households, Medicaid long-term-care spending, and VA medical care all contribute, making the federal budget more retiree-focused than a Social Security–only lens would suggest.” Smetters finds that $2.7 trillion in federal spending goes to the elderly.

For federal spending on working-age adults, ” Medicaid directs $357 billion to working-age adults — more than to any other age bucket. Social Security also sends a substantial amount to working-age adults through disability and survivor pathways, contributing $216 billion. Beyond these two programs, Medicare for disabled beneficiaries under 65 ($159 billion), VA compensation and medical care ($202 billion combined), unemployment compensation ($40 billion), housing assistance ($44 billion), and Marketplace subsidies ($90 billion) all add materially.” Total federal spending directed to this group is $1.2 trillion.

Smetters combines federal spending on children and young adults under the age of 26 into a single group. “For people under age 26, Medicaid is the largest program, contributing $144 billion. SNAP follows at $44 billion, then child nutrition programs ($34 billion), Marketplace subsidies for younger enrollees ($29 billion), higher education support ($26 billion), elementary and secondary education ($24 billion), child care assistance ($21 billion), CHIP ($20 billion), TANF and family support ($18 billion), SSI for children ($15 billion), and foster care and adoption assistance ($11 billion). This is a different mix from the one that drives retiree spending: it is more fragmented and more dependent on means-tested programs.” Total spending here is $448 billion.

Put it together, and here’s a summary table. The punchline is in the final column. US spending on the elderly averaged $43,700 per person in 2025. In comparison, the average was $7,300 on the average working-age adult and $4,300 for the average child.

As I noted at the beginning, the overall pattern here is in unsurprising. Anyone who pays attention knows that federal spending on the elderly via Social Security and Medicare is huge. But of course, the elderly paid payroll taxes during their working lives to support others in these programs, and now it’s their turn to benefit from taxes paid by current workers. The amount directed to working-age people, as well as children, would be higher if one took into account federal benefits delivered through refundable tax credits like the earned income tax credit and the child tax credit, rather than focusing only on spending. Also, in the US system of government, a large portion of spending on children and young adults–like most of education spending and a substantial portion of Medicaid spending–happens at the state and local level, not the federal level.

Smetters, of course, is well aware of all of these issues. He is offering an intentionally blunt comparison to make a point. It’s pretty much always politically popular to offer higher benefits to the elderly, and to push back hard against proposals that seek to hold down the long-term rate of growth in these programs.

But about one-sixth of all US children live in families below the poverty line. If you look at families with income at or below twice the poverty line, which is often used as a measure of “near” poverty, then about two-fifths of US children live in such households. If you look at education as measured by the National Assessment of Educational Progress (often called the “Nation’s Report Card”), about 40% of fourth-graders test below the “basic” level of reading and 69% test below the “proficient” level.” In math, 24% of fourth-graders test below the “basic” level and 61% are below the “proficient” level. In short, the seeds of future inequality and poverty are happening with today’s children, right now. That per person federal spending gap between the elderly and the children/young adults group looks awfully large.

A year and a day ago, President Trump announced that he would enact, without a vote of Congress, higher tariffs across a wide range of countries. I had been thinking about compiling a range of evidence on what has happened, but was delighted to find that Scott Lincicome,  Alfredo Carrillo Obregon, and Chad Smitson have done it for me in “One Year After `Liberation Day’: Here’s What We Know and What We Don’t” (Cato at Liberty blog, April 2, 2026). For example:

“The `reciprocal” tariffs were explicitly imposed to reduce the US trade deficit, but it reached an all-time high (in real terms) last year.” As I wrote a couple of months ago, “Trump’s Tariffs Have Not Shifted the Trade Deficit.”

“Manufacturing jobs did not boom and, in fact, kept declining. Manufacturers reported throughout 2025 that tariff-induced cost pressures and uncertainty hampered economic activity in the sector, and employment data suggest that this also contributed to a slowdown in hiring.” As I’ve tried to point out in the past, many US companies are “The Import-So-That-They-Can-Export Firms”–thus, when tariffs make imports more expensive, a number of US companies that rely on imported inputs find it more difficult to compete in world markets.

“Foreign investment did not boom. Despite the president’s Liberation Day prediction of a boom in foreign direct investment (FDI), quarterly FDI has fallen since April 2025, with the US registering $72.49 billion in FDI in Q4 (Figure 9). Total FDI in 2025 was $288.4 billion, lower than the annual totals from 2021 through 2024, and far short of the rate needed to reach the president’s lofty goal of $18 trillion in investment. New FDI last year was even lower. Several firms and countries have pledged to increase their investments in the US, but such pledges do not show up in data.” Lots of countries have promised to make big investments in the US economy–indeed, they have often promised to make investments much larger than their existing total current investment in the US. Such promises are cheaply made. It is of course unsurprising that imposing tariffs on needed inputs and erratically changing the tariff schedules for different countries and goods is not a way to make the US economy appear like a favorable destination for foreign investment.

“Tariffs raised prices.” The Cato authors link to a number of studies. The actual effective tariffs have turned out to be about 10% on average for imports as a whole, and imports are about 14% of the US economy. The result has been a real if modest boost in prices, as I tried to point out in “Tariffs and Inflation: Where Are We?”

“Contrary to the president’s early claims that there would be no exemptions from his global tariff regime, numerous  product exemptions were issued throughout 2025. … Moreover, 43 percent of US imports avoided all tariffs imposed in 2025, including the reciprocal tariffs. As many as 64 percent of US imports (based on 2025 values) are now exempt from the replacement Section 122 tariffs, which retain most of the reciprocal tariffs’ exemptions. Most notable among the exemptions is, as economist Joey Politano first noticed last year, semiconductors, computers, and related goods that the booming US artificial intelligence industry needs. The loopholes help to explain why the tariffs were not as costly as many experts feared in April 2025.”

With tariffs being announced and unannounced, coming and going, and exemptions openig and closing, “tariff-related lobbying activity—driven by the chance to win an exemption or new restrictions on overseas competitors’ goods—reached levels not seen in years, if not decades. The number of registered clients for tariff-related lobbying increased by 218 percent in 2025 with respect to the previous year, the biggest year-on-year change since Trump’s first term in 2018. Meanwhile, trade-related lobbying expenditures reached more than $900 million in the first half of 2025 alone and were 28 percent higher than in the first half of 2024.” Meanwhile, many firms are filing lawsuits to recover the tariffs they already paid, now that the US Supreme Court has ruled that President Trump lacked legal authority to impose many of them.

As the US has opted out of world trade markets, other countries are building trade ties with each other. The US is not involved in these trade talks, and thus has no influence over how they evolve, or how the trade talks might be shaped for the benefit of US exporters. The authors write: “The US has not negotiated any free trade agreements (FTAs) since 2020, and since then, other countries—including some of America’s largest trading partners and long-standing allies—have negotiated and entered into many such deals . In just the last few months, for example, the EU has inked traditional trade deals with MERCOSUR, India, and Australia; Canada has launched negotiations with Thailand and the Philippines and is working on an FTA with ASEAN; China inked agreements with ASEAN and Congo, and is updating its FTA with South Korea; and even India, which had long been skeptic of FTAs, reached agreements with the United Kingdom and New Zealand.”

Given that the US has surrendered any pretense of global leadership on trade policy, and that the US economy has not in fact been transformed in the past year, some defenders of the tariffs now claim that everyone knew all along that the real and benefits of tariffs would take years to emerge. Maybe they will eventually turn out to be correct. Next year, or the year after, or the year after, we will se. But pointing to expanding US industries like semiconductor manufacturing that are propped up by government subsidies and using inputs that are exempt from US tariffs is hardly evidence that the tariffs are having beneficial effects. The boom in US investment in data centers is not because of tariffs, either. In terms of expecations how quickly tariffs would benefit the US economy, worth remembering what President Trump actually said when announcing the tariffs a year ago:

My fellow Americans, this is “liberation day.” Been waiting a long time. April 2, 2025, will forever be remembered as the day American industry was reborn, the day America’s destiny was reclaimed, and the day that we began to make America wealthy again. Going to make it wealthy-good and wealthy. For decades, our country has been looted, pillaged, raped, and plundered by nations near and far, both friend and foe alike. … Our country and its taxpayers have been ripped off for more than 50 years, but it is not going to happen anymore. It’s not going to happen.In a few moments, I will sign a historic Executive order instituting reciprocal tariffs on countries throughout the world. … This is one of the most important days, in my opinion, in American history. It’s our declaration of economic independence.For years, hard-working American citizens were forced to sit on the sidelines as other nations got rich and powerful, much of it at our expense. But now it’s our turn to prosper and, in so doing, use trillions and trillions of dollars to reduce our taxes and pay down our national debt. And it will all happen very quickly.With today’s action, we are finally going to be able to make America great again, greater than ever before. Jobs and factories will come roaring back into our country, and you it happening already.We will supercharge our domestic industrial base. We will pry open foreign markets and break down foreign trade barriers. And ultimately, more production at home will mean stronger competition and lower prices for consumers. This will be, indeed, the golden age of America.

In short, if you wish to argue that critics are unrealistic in expecting Trump’s tariffs to have large effects “very quickly,” so that “jobs and factories would come roaring back into our country,” you might remember who took the lead in setting such unrealistic expectations.

Each year, India’s Department of Finance published a top-to-bottom overview of the countries economic situation. As I paged through the Economic Survey 2025-26, I was especially struck by some of the comments about the role of the service sector in India’s economy, and the role of India’s service sector in the world economy.

As background, it’s perhaps useful to know that economic development around the world has broadly followed a similar pattern. In a low-income country, most of the workers are in subsistence agriculture. Next comes a push-pull effect: higher productivity in agriculture means that not as many workers are needed on the farm, and available jobs in low-wage manufacturing (textiles is a common example) offer an option for those workers. However, workers and products become more sophisticated. High-wage skilled manufuring develops, which often uses high levels of capital investment, and workers shift over to service industries. There is some argument that a next step may be “information” industries, although this category often overlaps with services.

But in some areas of India, a high-skill service sector has unexpectedly sprinted ahead–it has arrived early in the process of economic development. But can economic development be based on the service sector? Can the service sector provide widespread jobs across the economy? Or is it a useful and positive but ultimately niche industry–such that India should be focusing on the conventional path of building up low-wage manufacturing and agricultural productivity? The Economic Survey offers some background and thoughts on these issues.

(In the shade of this parenthesis, I was amused that the report includes near the start an 18-page list of acronyms: everything from AAGR Average Annual Growth Rate, AAI Airports Authority of India, and AAM Advanced Air Mobility, to XV-FC Fifteenth Finance Commission, YES-TECH Yield Estimation System based on Technology, and YoY Year-on-Year. And yes, before you ask, I do amuse easily.)

Here’s how V. Anantha Nageswaran, the Chief Economic Advisor for the Government of India, expressed concerns about the role of India’s service industry in development in his “Preface” to the report:

India’s export performance since the start of the millennium tells its own story. In general, services exports have outpaced goods exports. In particular, over the five years since 2020, the compounded annual growth rate of total exports has been 9.4%, while that of merchandise exports has been only 6.4%. Services have done much of the heavy lifting, creditable and macro-stabilising, but not a substitute for the goods-based export ecosystems that ultimately underpin durable external and currency stability.

The Information Technology-Enabled Services Sector has been India’s mainstay for growth and exports since the dawn of the millennium. International experience indicates that while service exports are economically valuable, they do not systematically compel broad upgrades in state capacity, as successful firms can bypass weak institutions, relocate easily, and generate limited economy-wide pressure on governments to reform. Unlike manufacturing exports, they do not impose hard fiscal, employment, or logistical constraints on the State, allowing institutional weakness to persist even alongside globally competitive firms. So, manufacturing matters.

In this view, a key role of manufacturing is that it imposes constraints on the government, pressuring the government to overcome its institutional weaknesses. The later chapters of the report on India’s services and manufacturing industries provide more detail. The report notes:

Notably, while global goods trade has stagnated, services trade has continued to expand, reinforcing its role as a critical buffer against external shocks and volatility. The rapid expansion of digitally and remotely deliverable services has allowed the services sector to scale swiftly by transcending traditional geographical and operational constraints. This has enabled new forms of value creation, particularly in knowledge-intensive and technology-enabled activities, helping even smaller economies leave a global imprint. In contrast to the typical manufacturing-led growth paths followed by other countries at a comparable stage of development, India has experienced service-led growth at a significantly lower level of per capita income.

Currently, India’s services sector contributes more than half of the Gross Value Added and serves as a major driver of exports and employment in the country. Not only does it continue to underpin domestic growth, but services have also emerged as the most stable and resilient component of GDP, acting as a high-growth, low-volatility anchor, as is the case across the globe. The sector has recorded average annual growth of around 7-8 per cent year after year, in sharp contrast to the more pronounced cyclical fluctuations observed in agriculture and industry.2 India is the world’s seventh-largest exporter of services, with its share in global services trade more than doubling from 2 per cent in 2005 to 4.3 per cent in 20243, and the sector continues to be the largest recipient of foreign direct investment inflows.

Like the US economy, India consistently runs a trade deficit in goods, but a trade surplus in services. Here’s an illustration of India’s trade surplus in services–which is heavily focused on business and software services.

Finally, it’s useful to remember–whether in India or the United States–that the services economy is close intertwined with manufacturing of higher-value goods, to the benefit of both. In the case of India’s economy, the report introduced me to my ugly word of the day, “servicification”:

[S]ervices are increasingly integrated into manufacturing through activities such as design, R&D, logistics, software development, and professional services, reflecting the growing “servicification” of production systems. This is evident in products such as smart devices, whose value is driven by software ecosystems; medical equipment/wearables bundled with diagnostic and remote-monitoring services; and automobiles, which are increasingly described as “software on wheels”. As manufacturing becomes increasingly technology and data-intensive, services such as ICT, finance, compliance, and after-sales support account for a growing share of value creation. International experience suggests that this integration is a crucial channel for enhancing value addition, export competitiveness, and employment.

The new AI tools are remarkably good at many tasks, and getting better, but one area where they fall badly short is in expressing their level of confidence in the answers they provide. The great mathemetician Terence Tao made this point recently in an interview, in an interview with Matteo Wong about the use of AI tools to suggest answers for unsolved problems in mathematics (“The Edge of Mathematics,” Atlantic Online, February 24, 2026). Tao said:

One very basic thing that would help the math community: When an AI gives you an answer to a question, usually it does not give you any good indication of how confident it is in this answer, or it will always say, I’m completely certain that this is true. Humans do this. Whether they are confident in something or whether they are not is very important information, and it’s okay to tentatively propose something which you’re not sure about, but it’s important to flag that you’re uncertain about it. But AI tools do not rate their own confidence accurately. And this lowers their usefulness. We would appreciate more honest AIs.

Additionally, a lot of AI companies have this obsession with push-of-a-button, completely autonomous workflows where you give your task to the AI, and then you just go have a coffee, and you come back and the problem is solved. That’s actually not ideal. With difficult problems, you really want a conversation between humans and AI. And the AI companies are not really facilitating that. If we can work with at least some tech companies that are willing to develop more interactive platforms, that will be much more readily embraced by the people.

Of course, Tao’s points are interrelated. You can only hand off a job entirely to the AI tool if you are entirely 100% confident that the result will be correct and appropriate. Handing off arithmetic to a spreadsheet program is fine. Handing off the strategic priorities of a company to an AI program is something else.

Moreover, it has seemed to me that the focus of AI companies on completely autonomous workflow is not only unrealistic (because “completely autonomous” means assuming perfect trustworthiness for the AI), but also a public relations disaster. Instead of emphasizing how workers can use AI tools to improve productivity, the companies tend to emphasize how AI can replace workers. As one example, a company called Genspark ran an ad during the Super Bowl about how workers could all take the Monday off after the big game, and just let the AI tool do their jobs that day. Cute! But of course, the obvious question is why any company will want to hire workers to return to their easily-replaced jobs on Tuesday, Wednesday, Thursday, or Friday.

David Autor had a pleasantly acerbic comment on this “replace all the workers” mindset in an interview published earlier this year. Sara Frueh was interviewign Autor on the subject: “How Is AI Shaping the Future of Work?” (Issues in Science and Technology, January 6, 2026). Frueh says: “I was looking at OpenAI’s mission statement, which is, `To ensure that artificial general intelligence, by which we mean highly autonomous systems that outperform humans at most economically valuable work, benefits all of humanity.’ Is that mission statement made up of two mutually exclusive goals? … Can you build something that takes away the jobs of most humans—if that’s possible—and still benefit all humans?” Autor reacted:

Or even should that be your goal? Actually, I don’t even like that definition of artificial general intelligence. The goal of machines should not be to just do what people do slightly better. Our tools are valuable to us because they allow us to do things we can’t do. So many technologies enable capabilities that we simply don’t possess. Powered flight didn’t automate the way we used to fly. We just didn’t fly.

And that’s true for most of modern technologies. They’re important and consequential, not because they do the same old thing better, cheaper, faster, but because they enable us to do things we couldn’t do, telecommunication, flight, fighting disease with penicillin, seeing the insides of the interiors of subatomic particles, designing, computing things that we could never in a lifetime compute.

So I actually, I find their mission statement an amazing bait-and-switch. Artificial intelligence, by which we mean a machine that outcompetes humans in every domain. I’m reminded of this tweet I once saw that said, `We’re a modest company with modest goals. One, sell a quality product at a fair price. Two, drain the world’s ocean so we can find and kill God.’ And that’s what I feel like when I read the OpenAI mission statement.

An honest AI can be wrong, but it won’t be declaratively and definitively wrong. It would convey its level of uncertainty, and try to point out where additional input and feedback would be useful in moving toward a stronger conclusion. It would replace some of the tasks that workers currently do, but it would also expand the tasks that workers are able to do.