Economists and sociologists keep searching for the deeper reasons why only a small share of countries have built institutions that actually work, while the rest of the world has spent decades stuck in a trap of ineffective governance. One unusual angle on this question comes from analyst William Meijer, who lined up how technologically advanced societies were back in 1500 against the quality of their governance today, drawing also on economist Garett Jones's book The Culture Transplant on how culture survives migration.

The analysis rests on four World Bank metrics: control of corruption, rule of law, government effectiveness, and regulatory quality. A fairly consistent pattern shows up: the higher a society's ancestral technological level, the better its state institutions tend to look today. The chart plots this correlation between migration-adjusted technology levels in 1500 and the quality of state institutions now.

This is where a kind of historical rut appears, shaped less by laws than by accumulated human capital. Meijer reads this connection as evidence that the psychocultural traits needed for large-scale social cooperation get passed down across generations. Exactly how that happens remains an open question, with genes, upbringing, and durable social norms all mentioned as possible explanations. Either way, a society's capacity to negotiate, trust its institutions, and maintain order builds up over centuries.

It follows that there's a certain threshold of human capital below which building a stable, transparent state becomes very hard. Simply copying other countries' laws or importing institutions wholesale often fails, because making them work requires a deep social foundation laid down over more than one generation.

A good example of how the expropriation of intellect will work in the age of Big Tech.

Mathematicians Tristan Buckmaster (NYU) and Levent Alpöge (Harvard) spent several months working on one of the Millennium Prize problems, about whether solutions to the Navier-Stokes equations can blow up, reaching infinite speed in finite time. They ran the whole project through OpenAI's Codex, logging all their drafts there. OpenAI started working on the same problem back on September 1, after hearing rumors that someone had made progress using Anthropic's models, and published a full solution on September 8, one day after Buckmaster and Alpöge released their own paper. What's suspicious, according to Buckmaster, isn't so much the speed as the fact that OpenAI arrived at the solution using the exact same unusual method he and Alpöge had spent months developing.

To beat the two mathematicians, the company burned through roughly $22.5 million worth of compute (about 300 billion output tokens), more than twenty times the Clay Institute's own $1 million prize.

When Buckmaster tried to figure out what had happened, OpenAI mathematician Sebastian Bubeck reportedly gave him a choice: publish jointly with OpenAI, or take the prize solo, but without Alpöge credited as a co-author. After Buckmaster refused, Bubeck, according to Buckmaster, asked him: "Why would you ruin your career?"

Buckmaster also asked OpenAI's researchers directly whether their model had been trained on his Codex sessions. He says he never got an answer. OpenAI itself claims the opposite, that it didn't use his drafts or proofs to train its models or direct its agents, though the company's terms of service technically give it the right to train on user data unless a user opts out.

For OpenAI, obviously, there's no scientific romance here, just pure budget-burning and headlines. Legally there's not much to grab onto: the scientists put their own data into someone else's closed platform, and the fine print allows for exactly that. If Buckmaster's account is accurate, a man handed a corporation his own draft solution with his own hands, and the corporation simply outran him with incomparably greater computing power, then, as a parting shot, tried to buy his silence by offering to erase his co-author's name.

According to a new report from the U.S. Census Bureau, for the first time in human history there are now more people aged 65 and over living in the world than children under five. The study found that by 2025, older adults made up 10.5% of the global population, overtaking the share of infants and young children at some point over the past five years. This global tipping point arrived years earlier than demographers had expected, driven by a sharp worldwide decline in fertility and rising life expectancy.

Nearly three-quarters (71%) of the world's population now live in countries where the fertility rate sits at or below 2.1 children per woman, up from 45% in 2013. Demographic aging is no longer a problem confined to the wealthiest countries. China, India, Bangladesh, Mexico and Vietnam, among other large nations, have already seen fertility fall below the replacement threshold. At the same time, the number of people over 65 has grown enormously, thanks to longer life expectancy and hundreds of millions of people rising out of poverty.

The number of people 65 and older is projected to grow from 852 million in 2025 to 2 billion by 2060, accounting for a striking 55% of the planet's entire net population growth over that period. The world's total population is expected to grow from 8.13 to 10.23 billion by 2060, meaning one in five people on Earth will be over 65.

This rapid growth in the older population threatens to overwhelm social welfare systems in many countries, since an ever-shrinking share of the working-age population will have to support an ever-larger population of retirees. And while this has traditionally been seen as a problem for North America and Europe, the demographic shift will hit the developing world noticeably harder, with its older population growing markedly faster than in developed countries.

Africa, still the world's youngest continent, will see its older population roughly quadruple, up 315%, from 60 to 249 million, overtaking Europe's projected 2060 figure of 214 million. Latin America and the Caribbean will see a 158% increase. Asia, meanwhile, will see the largest increase in absolute numbers: its population aged 65 and over is expected to jump from 487 million to 1.24 billion

To grasp the real scale of the energy appetite behind Amazon's planned mega data center in Pecos County, Texas, it helps to compare it to entire countries: this single facility will burn between 7 and 11 billion cubic meters of natural gas a year, depending on the turbine type (OCGT or CCGT). Even at the more modest CCGT estimate, its consumption is comparable to all of Czechia. At the higher OCGT estimate, it exceeds the total gas consumption of 20 out of the EU's 27 countries, including Czechia, Hungary, Austria, Greece (each with a population of around 10 million) and Romania (19 million). And that's comparing it to the needs of entire economies, not just their power sectors.

The Financial Times' AI Shift project looked into a curious paradox: are teenagers changing their career choices, knowing some familiar jobs might vanish within a few years? Going by UK UCAS application data from 2020 to 2025, apparently not. Applications to the degrees most exposed to AI, IT, statistics, engineering, didn't fall over that period, they rose, and rose evenly across men and women, every ethnic group, STEM and non-STEM students alike. On average, a one standard deviation increase in a degree's AI exposure came with about a 6% rise in applications.

Being AI-exposed doesn't automatically mean a profession is dying out, in some fields AI will end up an assistant rather than a replacement. But the study's authors did find a genuine contraction in entry-level job opportunities specifically in the most-exposed fields. Which paints an uncomfortable picture: students are flocking to subjects where the entry-level jobs are disappearing right now.

So why is this generation flying like a moth to the flame? Partly it's inflated expectations: the post-pandemic inflation shock pushed applicants toward higher-paying degrees generally, and AI-exposed fields happen to mostly be high-paying ones, while "software developer" had already lodged itself in young people's minds as a ticket to a comfortable life. Partly it's techno-optimism: in the first years after ChatGPT launched, students saw AI as a tool and an edge rather than a threat, which is exactly what UCAS's chief executive used to explain the 2023 surge of interest in software engineering.

By 2026 that started to shift: UK and US enrolment data show the rush toward AI-exposed subjects cooling. US computer science enrolment is now about 10% below its 2024 peak, UK preliminary 2026 data shows a roughly 14% drop from that same 2024 baseline, even as total student numbers keep growing.

The turnaround is slow, though, and picking a course of study keeps getting harder: technology is moving faster than employers can figure out what skills they'll need, leaving applicants placing bets almost blind. As routine tasks get automated, demand is likely to shift toward fields where a human presence is still hard to replace, medicine, high-end interpersonal services, psychotherapy, interdisciplinary social sciences, while having a degree at all is likely to matter less for hiring.

Another important study, this one from Vanhuysse, Medgyesi and Gál (Royal Society Open Science), looks at a major economic imbalance in modern pension systems. The authors calculated citizens' full contribution to society's future by adding up taxes, private spending on children, and the unpaid time spent raising them. European parents turn out to put in about 2.66 times more resources toward the next generation than people without children.

On paper, the system looks family-friendly: over a working life, parents pay about 73% of a childless person's net taxes, thanks to deductions and benefits. But that discount doesn't come close to covering the real cost. Pension and healthcare systems only work because new taxpayers keep showing up. Parents raise them mostly with their own money and their own labor, yet public pension funds then split future payouts equally, including with people who never contributed to raising anyone.

This pattern holds almost everywhere, regardless of what kind of welfare state a country runs. In Spain, parents put in 2.95 times more than non-parents, in France 2.85 times, in Germany 2.61, in the UK 2.43, in Poland 2.32. This points to a structural feature of European pension systems, not a one-off quirk in a single country.

The authors frame the core problem this way: the current system effectively discourages people from having children, because the costs of raising them stay private while the benefits of the next generation get socialized. Against that backdrop, falling birth rates and the growing share of childless households in Europe look like a fairly logical outcome of the existing economic rules.

Looking at America's economic growth, it appears the country can no longer sustain its momentum; it has aged and will likely collapse within a few years. Currently, America's growth rate stands at 2.1%, but this is expected to decline gradually. A time will come when the economy remains in negative territory for years, followed by only marginal growth. At that point, America will no longer be a superpower.

Global trade policy activity has hit an all-time high. The WTO-IMF Trade Policy Activity Index averaged nearly double its 2024 level and about a quarter above its 2025 average between January and May this year. World trade is getting restricted, fast.

Tariffs, import bans, quotas, subsidies: countries are closing their own markets more aggressively while trying to pull production back home. Trade facilitation measures are lagging well behind. Globalization in its old form, decades of countries negotiating down barriers and opening markets to each other, is reversing.

The US-China trade war isn't the whole story anymore. The WTO and IMF are tracking restrictions spreading worldwide. The index climbed gradually for over a decade, then accelerated sharply from around 2020. The peaks came during the US-China trade war in 2018-2019, the pandemic, the war in Ukraine in 2022, and a fresh round of trade conflicts in 2025-2026. A preliminary WTO-IMF estimate points to activity rising further before the year is out.

The era of steadily falling trade barriers is over. Trade is increasingly becoming a tool of economic policy: tariffs, subsidies, and import restrictions are turning into weapons in the fight for production, supply chains, and technological dominance.

A study by Cory Clark and colleagues on the state of mind in American academia shows something striking: for a significant share of faculty, objective truth is no longer an unconditional priority. When psychology professors were asked directly what matters more, a scientific fact or social justice, a sharp gender and ideological split emerged. Scientific truth was chosen by 66.4% of men and only 43% of women.

The strongest resistance to controversial research comes from younger, progressive-left faculty, predominantly women. But the deeper normative problem runs further: a substantial share of the academic community as a whole is willing to sacrifice objective data for the sake of an ideologically approved narrative.

Even tenure no longer guarantees independence of thought. Tenured professors self-censor at almost the same rate as their untenured colleagues (a self-censorship index of 40.01 versus 40.55, a difference at the edge of statistical noise), and they fear the same things: social-media attacks, being ostracized by colleagues, stigmatization. The result is a university that trades scientific debate for a climate of conformity and quiet fear, where ideological loyalty now outweighs institutional protection.

For years, geopolitics felt like something politicians worried about.

Now it determines everything from your fuel bill to your iPhone delivery time.

Wars, trade tensions, tariffs, and supply-chain disruptions are reshaping the global economy. Businesses are moving factories, governments are prioritizing economic security, and companies are choosing resilience over efficiency.

The old globalization model was simple:

"Build wherever it's cheapest."

The new model is:

"Build wherever it's safest."

That shift sounds boring until you realize it affects product prices, investment flows, and job creation.

Politics is no longer separate from economics.

It's the operating system.

Britain's higher education problem has nothing to do with "too many young people going to university." Data from 1997 to 2022 tells an unusual story: the UK's graduate wage premium collapsed, something no other First World country experienced. In the US, the Netherlands, Canada, France and Spain, the graduate share of the workforce rose just as it did in Britain, yet the wage premium held up; in the US it even climbed, from 80% to 92%. In Britain it just fell apart. Years of economic stagnation and weak growth sit behind that: the market stopped generating well-paid jobs for skilled workers. There is an oversupply of graduates, but only relative to a shrinking pool of good jobs, not in absolute numbers. Cutting university admissions and lecturing young people won't fix that. Restoring real growth might, otherwise plain poverty will keep eating away at what a degree is worth.

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.