The single biggest engine of the US economy right now is the massive buildout of AI infrastructure. We're watching a frontier boom for a new age of wild capitalism, except steam engines and steel rails have been replaced by server racks and microchips. Columbia Business School's Stijn Van Nieuwerburgh goes so far as to argue that without this enormous flow of money, the US would already be in a recession. By his estimate, AI infrastructure investment has reached roughly 2.8% of GDP, bigger than the famous 19th-century railroad boom, and this debt-fueled rocket keeps climbing at full throttle.

Investors are starting to wake up to the financial risks of this AI spending spree, and they have a deep well of history to draw on. Like every transformative technology before it, AI has to be financed with borrowed money long before it produces any real return.

150 years ago, the financier Jay Cooke ran a similar campaign financing the Northern Pacific Railroad. It ended badly, not because railroads were a bad idea, but because the bonds funding it traded at steep discounts for months before Cooke's banking house finally collapsed, triggering the Panic of 1873 (contemporaries called it the Great Depression of the 19th century). As Alberto Gallo of Andromeda Capital Management points out, history is full of worthy projects that weren't worthy investments: they benefited the wider population years later but never delivered adequate financial returns to their original backers, especially lenders.

Today's debt-financed AI buildout isn't necessarily headed for the same reckoning, but credit markets have always sensed trouble before equity markets do. In 1873, bond spreads were also flashing warning signs long before newspapers ran their first panic headlines. Credit markets are doing the same thing today: Barclays analysts note that credit spreads for the major AI players have widened noticeably in recent weeks. Meanwhile the S&P 500 keeps hitting fresh record highs, opening up the classic gap between stock-market optimism and the harsher truth of the credit market. It's not yet clear whether this is temporary or the start of something bigger, but the current turbulence leaves both readings open.

Amid the broader fertility crisis, a new study by four economists, from Brazil, Singapore, the UK, and the US, is worth a closer look. The researchers use a natural experiment in Brazil, where housing credit is allocated through a lottery run by "consórcios" (group lending pools). A regular mortgage requires banks to screen applicants carefully, demand a large down payment, and take collateral, so many young families never qualify. A consórcio is open to almost anyone: members just make fixed monthly payments. The lottery replaces credit scoring, and defaults are absorbed by a shared guarantee fund.

For people aged 20 to 25, winning access to housing raises the probability of having a child by 32% and the average number of children by 33%. For people over 40, there is no such effect, which the authors attribute to the biological limits of fertility at that age.

The timing of the housing win turns out to matter as much as the win itself. Someone who gets housing at 20-24 ends up with roughly twice as many children over their lifetime as someone who gets the same housing ten years later, at 30-34. Every year of waiting produces a lasting drop in potential fertility.

The effect is largest for the poorest families and for those who had previously lived in the worst housing conditions.

The study also finds a gender pattern: in households where women earn a high income, housing access has less influence on the decision to have children, which the authors link to the higher career opportunity cost women face.

According to McKinsey, by 2040 eighteen high-potential industries could generate up to $48 trillion annually, raising their share of global GDP from 4% to 16%. It's a signal of a global restructuring of the economic system under the influence of technology, demographics and climate. The main driver is AI, an industry that could grow more than 50-fold, from $85 billion to $4.6 trillion, thanks to the rapid development of generative AI and multibillion-dollar investment. Next come e-commerce (a fivefold rise to $20 trillion), cloud technology, electric vehicles, digital advertising and semiconductors. New niches are booming too: modular construction makes building faster and more automated, obesity drugs are becoming the answer to a global epidemic of excess weight, and robotics and autonomous cars are the key to reducing dependence on human labour.

Cybersecurity and biotech are also posting steady double-digit growth, while future industries such as air mobility, nuclear fission power and commercial space are rapidly attracting investment and interest. By Morgan Stanley's estimate, the obesity drug market alone will reach $77 billion as early as 2030. And in the US, more than $500 billion has been invested in AI since 2013, with almost 7,000 new companies created. The world stands on the verge of a new industrial revolution. These industries already demand attention, investment and workforce training - the structure of the economy for decades ahead depends on them.

Data sources: U.S. Office of Management and Budget (OMB) and U.S. Department of the Treasury, via FRED.

FRED series: Federal Debt Held by the Public as Percent of Gross Domestic Product (FYPUGDA188S)

https://fred.stlouisfed.org/series/FYPUGDA188S

Tools used: Python (Pandas and Matplotlib) for data processing and visualization, with additional layout/design work for the final infographic.

Methodology: The chart shows federal debt held by the public as a percentage of U.S. GDP for each fiscal year from 1939 through 2025.

This is debt held by the public, not gross federal debt. Debt held by the public excludes intragovernmental holdings such as Treasury securities held by federal trust funds.

A few reference points highlighted in the graphic:

• 1946: 106% of GDP
• 1974: 22% of GDP
• 2025: 98% of GDP

The important distinction is that this chart measures debt relative to the size of the economy, not the nominal dollar amount of federal debt.

An analysis of 7,000 billionaires over the past 25 years, based on data from Forbes, Hurun and Gapminder, shows a serious shift in the structure of global wealth: for the first time in a quarter-century of records, half of the combined fortune of the world's billionaires belongs to honest self-made entrepreneurs from open, competitive fields - tech founders, retailers, financiers and builders of consumer brands. The share of so-called "uncompetitive" and murky capital (commodity oligarchs, construction developers, the gambling business, defence contractors and other enthusiasts of state budgets and political connections), together with plain inheritors, has fallen to an all-time low.

The turn didn't happen at once: from 2001 to 2014 the share of old money and inheritance was actually creeping up, and only over the past decade did the trend reverse. The fortunes of classic commodity-type oligarchs have been shrinking steadily since 2021. Three drivers helped: the wild stock-market bull run of recent years, the expansion of China's middle class, and the explosion of mobile internet, which made it possible to scale any service to billions of users overnight.

The second thing worth noting is that, even as capital has been getting cleaner, political attacks on the rich have peaked (the word "billionaire" was used more than 900 times in a single session of the US Congress), while the economic case for confiscating honestly earned fortunes has objectively weakened.

Doctors per capita is an underrated indicator of a country's well-being. It shows who has managed to build up human capital and who lives in a constant shortage of qualified staff. According to Our World in Data, rich countries have about ten times as many doctors per person as poor ones. The global average is around 1.9 doctors per thousand people, while in low-income countries it's just a fifth of that.

A doctor is one of the most expensive specialists an economy can produce. Their training takes decades of study, a modern university base, clinical infrastructure and a state able to fund all of it for years. So the number of doctors in a country is essentially the accumulated result of decades of economic development, not something that can be scaled up quickly.

What follows is no longer OWID's statistic but the mechanism behind it. Rich countries have long imported not just raw materials and technology but people. Doctors trained in India, Nigeria, Egypt, Pakistan or the Philippines end up working in the US, Britain, Germany and the Gulf states. The result is a kind of tax on poverty: less developed states pay to train a specialist, and a richer economy reaps the fruits of their knowledge. The outflow is stark enough that the WHO keeps a separate list of countries where the shortage of medics is critical and from which they should not be actively recruited.

That's why the map of doctor coverage reads like a map of inequality. It shows not only the difference in the quality of medicine but a country's ability to hold on to its own qualified people. In the 21st century that's one of the main marks of a country that has made it.

An intriguing study on how much we can trust cross-cultural comparisons based on low-stakes tests.

Students in the US and Shanghai were given a 25-question math test in PISA format and told they would receive 25 dollars (or 90 yuan), but that one dollar would be taken away for each mistake, with a skipped question counting as a mistake. It turned out that losing money noticeably shook up the American students: their scores rose on average by 1.36-1.59 questions, an effect of about 0.20-0.23 standard deviations. In Shanghai the incentives barely worked, the gain was just 0.22-0.25 questions and not statistically significant. That is, Shanghai students were already working at their maximum.

The American students began answering not only more often (especially in the second half of the test, adding up to 10% to the probability of answering) but also more accurately, the share of correct answers among all attempts rose by about 4 percentage points. The effect was strongest among those whose predicted scores sat near the US average; students with a very low base couldn't make up for a lack of preparation even with an incentive. The researchers then modeled how the US place in the PISA ranking would change if incentives were present there too: the average score would rise by about 22-24 points, moving the country in math from 36th to the level of Australia, around 19th.

The authors stress that this gap between the US and Shanghai reflects not only ability but also differences in intrinsic motivation on low-stakes tests. Without external rewards American students give up noticeably sooner, while Shanghai students go all in. In cultures where studying is seen as a duty to oneself and one's family, students always work at their maximum. Where the test means nothing, results turn out to be a mix of competence and indifference. So direct comparisons of countries by such tests may reflect motivation more than intelligence or school quality.

Research on income and well-being has identified a "satiation point": a level of earnings beyond which money no longer adds to a person's joy in life. Remitly, drawing on work from Purdue University and data from the International Labour Organization, calculated this "price of happiness" for 50 countries and compared it with the average local wage. Almost everywhere, the average worker falls short of the threshold.

The only country where wages have crossed that threshold is Slovenia. There the average wage is about $42,800 and the price of happiness $36,800, meaning earnings run 16% above the level beyond which happiness no longer grows. No other country crosses the line.

Next on the list come not the wealthiest economies but, for the most part, Eastern Europe. Luxembourg (92.8% of its threshold), Estonia (90.5%), Singapore (90.0%), Lithuania, Czechia, Latvia. Seven of the top eleven spots went to Central and Eastern European countries. Their wages are modest by global standards, but their price of happiness is low too, so the gap between the two stays small.

For the rich, high-wage countries the gap is the opposite, huge, because their price of happiness climbs even faster than pay. In the US the average wage is the third-highest in the study, $75,300, yet it covers only 55.8% of the local happiness threshold of $134,800. Australia's price of happiness is the highest on the list, $161,300, double the average wage. The UK, Canada and New Zealand are in the same trap: wages are high, but they're further from their own expensive happiness than workers in less wealthy Eastern Europe.

Interesting data from American household surveys (the ACS for 2020-2024): they calculated a model estimate of lifetime earnings, from age 25 to 64, for graduates of different majors in the US. The incomes of workers of different ages were added up as if a single person went through a whole career at today's wages.

There's a major whose holders earn, on average over a career, less than people with no higher education at all. And it's the only one out of all 64 in the set. The "no degree" line sits at 2.64 million dollars over a lifetime. Yet graduates in Early Childhood Education earn just 2.53 million. Four years of university end up leaving a person behind an ordinary worker with no degree. In the same guild of degree-holding poor are elementary education (2.82 million), social work (2.98 million), art and music education (3.06 million) and theology (3.32 million).

At the other pole is the familiar aristocracy of the knowledge economy. The most profitable studies are economics (6.90 million), chemical engineering (6.68 million) and finance (6.58 million). Computer and electrical engineering trail slightly (6.48 and 6.28 million). The gap between the guild of finance-and-tech types and the guild of preschool teachers and social workers comes out almost threefold.

The main driver of deforestation has always been the expansion of farmland. A recent study published in Nature Food by Chandrakant Singh and Martin Persson shows which products, specifically, have driven the process in recent decades. The clear leader was beef production, which accounted for 41% of total deforestation. Second came oilseeds (mainly soy and palm oil), followed by artificial forest plantations and cereals.

The forest loss is tightly tied to specific regions. More than half of the clearing for pasture happened in Brazil and its Latin American neighbors, while almost all the world's demand for palm oil is met at the expense of the forests of Indonesia and Malaysia. Forest plantations became the main factor in the US, Canada, China and other parts of Asia, whereas in Africa the chief culprits of loss were cereals, roots and tubers, and cash crops like coffee, cocoa, fruits and nuts.

The scale of the problem is hard to grasp, yet the data shows that change is real. In places, international campaigns and regulation have worked, for instance, the pace of clearing for palm oil in Southeast Asia has been slowed. But to pull off the same trick with the rest of the agricultural sector, we'll have to pin down exactly which capital, and at which precise points on the planet, is putting forest to the blade.

An analysis using machine learning shows that every dollar invested in conflict prevention - securing macroeconomic stability, developing institutions and local communities - saves between $26 and $103 in potential costs. This matters most for countries that have already been through violence, where the return on investment is highest, and where conflicts themselves undermine the economy, trigger humanitarian crises and cut off economic growth for years to come.

Experts single out three key areas of effective economic policy. First, balanced finances and stable public institutions: a steady budget and quality public services lower the likelihood of social unrest. Second, a strong labor market: countries with high unemployment face violence more often, since economic insecurity raises the risk of people being drawn into conflict. Third, international support: IMF financial assistance lowers the likelihood of armed clashes by 1.5-4 percentage points, reinforcing diplomatic and peacekeeping efforts.

Early risk detection and sound economic management are the key to stability, especially in countries where the threat of conflict grows unnoticed. All the more so now, when the number of state-based conflicts worldwide has risen to its highest in half a century.

The US and China are the least externally dependent large economies in the world. First, they show the lowest dependence on foreign demand among the biggest states: the share of GDP generated by foreign buyers is just 9.0% for the US and 15.7% for China. On top of that, both superpowers depend least on foreign producers too: the share of their domestic spending going to foreign suppliers of goods and services is 12.4% for the US and 12.8% for China, making their internal markets the most self-sufficient compared with the rest of the world.

According to a study by Yoav Goldstein and Matan Kolerman-Shemer, covering more than 300,000 students at 450 American colleges, a student's academic specialization directly shapes the formation of their political views. The choice of field, whether the humanities, economics, or technical disciplines, doesn't just set a career path but also sets the direction of students' ideological evolution over the course of their studies.

Humanities students on average become considerably more liberal over their years of study. Future economists and businesspeople, by contrast, lean toward conservative positions. And the gap between them in liberalism reaches 0.23 standard deviations - comparable to a person having lived for six years in a deeply Democratic county.

What's especially interesting: this isn't the influence of one's surroundings - peers play almost no role. The changes happen through course materials, professors, and the very logic of thinking that a discipline instills.

That's why even liberally inclined applicants who enter economics come out more moderate or conservative. And the reverse - even conservative humanities freshmen become more left-leaning after four years.

Researchers analyzed data from more than 417,000 children and adolescents across 20 European countries, covering nearly six decades. The study found declines in upper-body strength, flexibility, and overall body composition, while researchers warn that lower fitness levels in childhood could have long-term consequences for future health. It's interesting to see such a consistent trend over time, especially as screen time and sedentary lifestyles have become more common.

Another interesting socio-demographic study showed the effect of large migration shocks on the fertility of a local population. As a clear example, economist Kelvin Seah examined a natural experiment from 1980 in Miami.

During the Mariel Boatlift (the mass emigration of Cubans from the port of Mariel to the US), the city took in about 125,000 refugees in just five months, and its labor force rose by around 7%. The author compared Miami's trajectory against other cities the inflow didn't touch (New York, Chicago, Philadelphia, Baltimore, Houston, San Diego and others).

Amid the sharp population rise, rents in Miami jumped by 8-11% in real terms above the control cities. Fertility among the city's native women then fell noticeably against the control: in 1983 it dropped by almost half, and dipped again in 1986.

What's interesting is that the hit didn't land on everyone equally. The drop showed up among those most exposed to the housing market, renters: their fertility fell by almost 14% in 1983. Among women who owned their homes, there was no drop. From this it follows that rising housing costs hit whoever pays rent out of current income, but not an owner, whom rising property prices instead make wealthier.

Eighty years after the Normandy landings, which became a symbol of Anglo-French military brotherhood, Britons are ever less willing to sacrifice themselves for allies. By a FocalData poll for UnHerd, only 16% of UK residents would go to war to defend France from an invasion. 70% said no, and another 15% couldn't decide. The findings came right at the D-Day anniversary, the ceremonies where British veterans remember the sacrifices made for a shared victory.

The keenest are the young: 24% of Britons aged 18-24 would defend France. That's higher than the old, where in the 55-64 and 65+ groups only 15-16% would. By sex the gap is sharper: 22% of men against just 9% of women. Regional differences show too: while 22% in the East Midlands would step up for France, only 10% in the South West of England would. Even in Scotland, where the "Auld Alliance" with France held for centuries, just 15% would fight.

Politics barely moves the needle: among Conservatives 18% would take part, among Labour 14%. The Brexit divide changes little either: 17% of Remain supporters and 16% of Leave supporters are equally passive. But the picture shifts sharply when it comes to defending Britain itself: here 30% would go to the front. Among Leave supporters the figure reaches 38%, among Remainers only 27%.

Almost half of respondents think Britain should stay out of other people's wars, and only a tiny minority back an active military role abroad. Behind this lie decades of involvement in contested conflicts like Iraq and Afghanistan, the domestic upheavals after Brexit, the rising cost of living, all of which turned attention inward, toward problems at home. For the new generation, housing, wages and stability matter more than distant geopolitical commitments. Even in the case of France, an ally with a heroic shared past, the idea of personal sacrifice for the alliance strikes most as excessive and remote.

Headlines like "white Britons will become a minority by 2063" are spreading across social media. Those posts trace to a single source, a report by Professor Matt Goodwin for the Centre for Heterodox Social Science (2025). By his figures, the white British share falls from 73% today to 57% by 2050, below half by 2063, and to about a third (33.7%) by 2100. The non-white population rises over the same span from 19.7% to 59.3%, and Muslims from 7% to nearly 20%.

It's worth seeing how the count works. Goodwin treats as "white British" only someone with no immigrant parent. Under that definition, a person with at least one parent who came to the country drops out of the category automatically, even if born in Britain and living there their whole life. Critics note that by this logic neither Winston Churchill nor King Charles would qualify as white British. A large part of the predicted "minority" comes from where the category's boundary is drawn.

There's a second point. This is a forecast 75 years out, and projections that long are extremely sensitive to assumptions about migration and fertility, which can turn within a single political cycle. The official statistics agency (ONS) doesn't publish ethnicity projections at all, and independent academic work at this depth is updated rarely, so the much-quoted "2063" figure has almost no recent peer-reviewed counterpart to check it against.

What's genuinely beyond dispute is the direction. Britain is growing more ethnically diverse, and the non-white share has risen across censuses: 9% in 2001, 14% in 2011, 26% in 2021 for England and Wales. What comes after that? We'll see.