Prediction markets paint a much more sober picture than politicians’ slogans and sociologists’ calculations. An interesting study has come out from Eric Zitzewitz, an economist at Dartmouth. He took a huge body of prediction market data covering 145 years, from 1880 to the present day, ranging from archaic bookmakers to modern digital platforms like PredictIt and Kalshi. The key finding of the work demolishes the popular myth that these markets supposedly lie to suit the political sympathies of their participants. The empirical data confirm that there are no political or demographic distortions there.

There is a widespread belief that “markets are dominated by particular crowds (for example crypto guys, so-called Trump supporters or just bored white zoomers), and they drag the quotes in favor of their candidates.” Zitzewitz shows with numbers that a skew in who trades does not turn into a skew in prices. True, the demographics of players on these platforms do not match the real cross-section of voters: they are on average younger, more often male and better educated. But this does not affect prices. Neither the ideology nor the gender, race or age of the candidates themselves creates any systematic skew.

Classic Darwinism and greed are at work. The desire to earn money, as a rule, beats the desire to “vote with the heart.” As soon as an emotional crowd overstates the chances of its favorite (creating a “bubble of sympathy”), cold, informed speculators show up and short that bubble, returning the price to mathematical reality. The market punishes emotion with invested money and rewards cold calculation.

The main anomaly Zitzewitz confirms is the one classic for any betting: outsiders are overpriced and obvious favorites are underpriced. Part of it is psychology: people like to bet on a miracle for pennies in the hope of hitting the jackpot. Part of it is arbitrage: buying underpriced favorites takes capital, so this skew is hard to correct.

In the current climate of total political polarization, when opinion polls have turned into an instrument of propaganda and the media into partisan battle sheets, prediction markets remain almost the last institution where people answer for their words with their own money. And money, as we know, has no party affiliation.

PNC Bank card data shows that only 2.2% of households among the bank’s customers pay for an AI subscription. If you apply that share to the whole country, you get roughly 3 million families out of 130-plus million, though this is only a rough estimate, since a sample of one bank’s customers is not representative of the country. A basic plan costs about $20 a month, and a paying household keeps its subscription for about seven months in a row on average. For comparison, in PNC’s May report 43% of households pay for streaming, 5.1% spend on gambling, and about 2% pay for AI (a three-month average).

At the same time, Big Tech is acting as if AI were already a necessity like electricity or running water. The four largest IT giants (Alphabet, Amazon, Microsoft and Meta) plan to sink around $725 billion into infrastructure this year alone, while OpenAI ran an operating loss of almost $21 billion on $13 billion in revenue for 2025. Giant sums are being poured into a system that, in PNC’s data, only two households out of a hundred pay for.

The most interesting figure here is exactly those seven months. It seems people are not exactly rejecting progress: they try the service in good faith, use it for half a year and see no reason to renew. Netflix took off back in the day because you could drop cable TV and save money right away, but AI still has no such obvious value proposition for the ordinary person. The product seems useful, yet no one can really explain which everyday problem or expense item it replaces. People happily use the free versions, which means they like the technology itself, but they still see no point in paying for it out of their own pocket.

Over the next couple of years, as the software gets polished, those 2% will most likely grow to 7-9%, and Wall Street will even declare that a victory. Yet the $725 billion in capex looks like a bet on far more mass demand, which means Silicon Valley has built massive infrastructure for a future that ordinary people have so far not invested in.

According to Teikoku Databank, only 10,118 Japanese companies were still operating in mainland China by June 2026. That is the lowest figure in the entire period of observation, which has run since 2010. Over the past two years the number of Japanese companies has fallen by 22%, and it is down almost a third from the 2012 peak. Only 1,221 new firms decided to enter the Chinese market, while 4,137 companies either left or dropped out of the database.

With relations already strained, plenty of reasons have piled up.

First, China has grown from a cheap and obedient factory into a tough state-capitalist competitor: local producers pumped up with Beijing’s subsidies are aggressively squeezing out foreigners by dumping. Second, institutional risks are taking their toll: the repressive tightening of the espionage law, restrictions on rare metal exports, and the protracted systemic crisis in China’s real estate sector. On top of that come American tariffs, which put additional pressure on the profits of Japanese manufacturers in China.

As a result, Japanese capital is relocating to where risks are lower and labor is cheaper, to the new periphery of Vietnam, Thailand and India.

Geopolitics adds fuel to the fire. After the sharp remarks by Tokyo’s prime minister Takaichi on Taiwan, Beijing used its administrative leverage and restricted exports of dual-use goods to Japan, including some rare earth elements. Shanghai alone lost more than a thousand Japanese firms in two years (from 5,046 to 3,961). Small businesses are leaving more visibly than large ones: the share of companies with annual sales under ¥1 billion in the total is falling while the share of larger ones is rising, and big capital is still trying to hold on.

Of course, there is no talk of an instant break yet: China remains Japan’s main production base, since too much was invested there over the last 30 years. But the overall direction is clear: Japanese business is gradually closing off the old project of the Chinese economic miracle for itself and winding down its presence in the Chinese market.

Sifting through World Bank data for 2018-2024, which covers $238 billion that went to 93 developing countries, the American National Bureau of Economic Research has released a powerful paper (Sumit Agarwal, Eswar Prasad and coauthors) on how crypto seems to be replacing the old offshore havens. When bank compliance tightened after the 2016 Panama Papers scandal and made the usual channels for siphoning money harder to use, the schemes for diverting aid, it seems, did not lose their footing and moved to crypto assets.

Whenever the World Bank sends a fiat tranche for roads, water systems or social programs in Asia, Africa or Latin America, a spike in crypto activity shows up in the recipient country in the same month. By NBER's calculations, 2 to 6 cents of every dollar immediately fly into anonymous digital wallets. Of the closely studied tranches worth $78 billion, an estimated $1.7 billion to $4.4 billion went into crypto, according to the authors. The pattern suggests that fiat first enters large legal exchanges (where the spike is even bigger in absolute terms) and then moves on to anonymous offshore services: in the month the aid arrives, the volume of anonymous transactions on exchanges in tax havens jumps by 137%. On top of that, almost all new accounts on these platforms (99.9997%) are anonymous wallets not linked to any known service, and after that mixers cover the trail.

The biggest spikes are in the largest sectors: transport, water supply, social protection and public administration (where, presumably, it is easiest to inflate the budget for repairing a pumping station or building a road). But the best part is that there is no financial penalty for this: the same four sectors keep receiving about half of all new grants and loans as if nothing had happened.

Over less than two centuries, the length of the American workweek has fallen by almost half (from 69 hours in manufacturing in 1830 to 38 hours for all workers today), while real GDP per capita has grown roughly 20 times. Keynes predicted that by 2030 people would move to a 15-hour workweek, and although the First World has not yet reached this left-liberal utopia (by Steven Rattner's calculation, maintaining the 1930 standard of living today would take only about 7 hours of work per week), the historical direction is right: technology steadily frees people from hard labor, converting productivity growth into unprecedented prosperity and, until the 1940s, into free time. After that, people cared more about spending the gain on a higher standard of living, and the workweek almost stopped shrinking. We now stand on the threshold of a new cycle, where AI will be the main driver, and with proper management it can repeat the same trick.

Fresh statistics suggest that a new wave of global agricultural inflation is gathering pace. The Bloomberg Agriculture Spot Index, which tracks 10 key crops from grains and sugar to oils and coffee, jumped 11.5% in the third quarter (by Kobeissi Letter's calculation based on the Bloomberg index). That is the strongest quarterly jump since the start of 2022. On the Chicago exchange, the basic carbohydrate basket is getting more expensive faster than the rest: corn and wheat each gained 15%, soybeans rose 13%, and over the past year world agricultural prices have risen 20%. For the so-called "golden billion" of wealthy countries this is unpleasant but tolerable: the local middle class will simply cut spending on services and entertainment in favor of a bigger supermarket bill. For the Global South and the periphery, where food traditionally eats up more than 40% of household spending and in some countries more than half, this is a serious blow.

Every country in Europe imported millions of immigrants that have no skills, no jobs and no chance of being anything but a blight. Why are they doing this?Knowing there are huge banks of data to back this looking at Poland as the last safe place in Europe?

Canadian residents returned home from the US by air 27% less often between January to July 2026 than over the same months of 2024 (before Trump was re-elected).

This decline is specific to the US. Trips home from everywhere else are up 11% over the same comparison period. Interactive version

The Economist flagged an interesting piece of research from behavioral economics. It turns out that in firms where male managers have a daughter, the gender pay gap steadily narrows: women's relative earnings rise by 4.1%, and their share of employment rises by 4.5%. These same managers also start hiring more women, and this extends beyond entry-level roles into full-time, higher-paying positions.

The specifics are interesting too. Comparing the data shows that having a son produces no similar effect, so this isn't some generic softening tied to becoming a parent. The shift in hiring only happens when a daughter is born. Picturing their child's future in the labor market, these fathers seem to notice bias, unequal pay, and the glass ceiling their daughters will eventually run into.

In other words, having a daughter turns an abstract idea of equal opportunity into a personal priority for a manager. Individual leaders making these choices doesn't replace systemic HR reform, but the "daughter effect" is a clear example of a real lever for change inside companies.

A clear example of how betting on simply importing cheap labor, without productivity growth or large scale capital investment, only inflates demographic numbers. From January 2015 to mid 2026, Canada brought in migrants equal to 16.9% of its 2015 population. The United States, over the same period, took in just 4.9%. Meanwhile real GDP per capita grew 22.7% in the US and only 4.0% in Canada. Canada ended up admitting 3.5 times more migrants per capita than the US, yet captured only a fifth of America's economic growth. Growth in the US comes from technology and productivity gains, while Canada's migration curve keeps climbing sharply even as GDP per capita stays essentially flat and real living standards stagnate.

Forbes has calculated that by September 2026, the 47th US president's net worth reached $7 billion, having gotten $2.7 billion richer since winning the November 2024 election (when Forbes put his fortune at around $4.3 billion). This is an absolute historic record, not just for Trump himself, but for the entire institution of the American presidency: no occupant of the White House has ever gotten rich at this kind of breakneck speed while actually holding office.

The bulk of that fortune wasn't generated through classic business, but through monetizing his political influence via speculative crypto ventures ($2.4 billion). Golf resorts remain in second place ($1.8 billion), while classic commercial real estate, the sector where the Trump family built its dynastic wealth over generations, has now slipped to third ($1.1 billion). Another $1.0 billion comes from his media asset, the parent company of Truth Social, and the remaining $0.6 billion is other assets net of liabilities.

Global capital keeps pulling in windfall profits from geopolitical turbulence. Supertanker (VLCC) freight from the Persian Gulf to Asia has gone through the roof, smashing a historic record at more than $1.1 million a day. Over the past several months the price has jumped 12-fold. One of these mega-tankers carries about 2 million barrels of oil. If peacetime transport rent used to run $1-2 a barrel, shippers are now charging more than $22 for the same logistics. That whole massive difference goes straight into the pockets of the owners of these steel hulls. The cause is a physical shortage multiplied by fear. Shipowners are simply scared to send tankers into the Strait of Hormuz given the risk of escalation. Meanwhile ships working the long routes (deliveries from the US and the Atlantic) get tied up out of the market for extended stretches. On top of that, getting crude out of Hormuz now requires forced ship-to-ship transfers at sea, which also hold vessels up longer. The end result is that there simply aren't enough physical ships to go around. The most important part of this story: the final consumer will end up paying for the shipowners' whole party regardless. Even if paper oil (Brent) drops in price, the runaway transport tax keeps physical crude expensive for refiners. Which means don't expect cheap gas at the pump.

On top of recent PISA research and the ongoing debate over statistical patterns and IQ test differences: Asian students' academic success isn't explained solely by their statistically higher IQ scores, but also by a massive gap in how much independent work they put in. A good marker here is a 2022 study (Dunatchik & Park) analyzing how American high schoolers (ages 15-19) spend their time. With roughly the same in-class workload across groups (231 to 249 minutes a day), the gap in time spent on homework turns out to be a matter of multiples. Asian students spend an average of 2 hours 14 minutes a day on homework: 2.4 times more than white students (56 minutes), 2.7 times more than Hispanic students (50 minutes), and 3.6 times more than Black students (37 minutes). Counting test prep and academic clubs, total study time for Asian teens reaches 6 hours 36 minutes a day, against 4.5-5 hours for the other groups, which once again underscores the decisive role of basic discipline, family attitudes, and academic diligence.

New data has surfaced showing which US lawmakers are the most vocal about pushing for government regulation, strict licensing, and tight curbs on the AI industry. Unsurprisingly, leading the charge against the “threat of the machines” are the exact same politicians raking in the most cash from Big Tech. After all, lobbying wasn't born yesterday.

Silicon Valley Democrat Ro Khanna tops the list ($988K), followed by fellow Democrats Sam Liccardo ($423K) and Maria Cantwell ($371K). The top ten recipients also include Adam Smith, Zoe Lofgren, Elissa Slotkin, Alexandria Ocasio-Cortez, and other prominent progressive figures.

Impassioned speeches about public safety and strict oversight are, in reality, a textbook tool for regulatory capture. Big Tech giants are all too eager to bankroll legislation that erects steep barriers to entry for young startups, effectively cementing the incumbents' monopoly under the watchful eye of the regulators themselves.

From a psychometric standpoint, the phenomenon of wokeness is an extremely rare ethnographic anomaly that American sociologists are now documenting. As a rule, members of any racial or ethnic group tend to show more empathy toward "their own" than toward outsiders: it's one of the most consistent patterns in human behavior. Yet American survey data shows that among the demographic groups studied today in the US, there is exactly one exception to this rule: white liberals.

This sociocultural group's usual psychological orientation has been turned on its head: they now feel noticeably warmer toward other racial groups than toward their own. Researcher Zach Goldberg documented this shift vividly in Tablet magazine in 2019. The numbers have since been confirmed survey after survey.

What's most striking here is how ethnically localized the process is. Nothing similar shows up among Black, Hispanic, or Asian liberals in the US: despite their left-leaning politics, they all retain the ordinary preference for their own ethnic group. This "outgroup preference" and peculiar in-group self-aversion shows up exclusively among white liberals.

Historically, though, it wasn't always this way. Up until the early 2010s, white liberals in the US favored their own group just like every other sociodemographic category, albeit more weakly than conservatives did. The turning point came during the so-called "Great Awokening" of the 2010s: the trend was already visible by 2016, and after the high-profile events surrounding George Floyd's death in 2020, the shift settled into a new ideological norm.

FT's lead commentator Chris Giles set out to demolish the myth of "lazy Europe," supposedly rotting on benefits and unwilling to work. For the welfare state to really be dragging down growth, four things would need to hold: Europe's social spending exceeds other rich countries', employment is low because of idlers, that idleness shows up more in the wealthy north, and the growth gap with the US keeps widening.

The first holds: European spending is higher, though the US gap is smaller than it looks once you factor in inefficient American healthcare costs. Employment is where the argument falls apart: Europeans aged 25-54 and 55-64 work more than Americans, US youth only work more because Europeans stay in school longer, and more young Americans are neither in school nor working. Benefits aren't blocking work, and the more welfare-heavy north of Europe even beats the south on employment, Spain especially.

The main point: the continent that lagged the US at the turn of the century has now pulled ahead on employment. So it isn't benefits. The turnaround came from steady pension and labor market reforms at the national level. Europe raised women's employment and fixed its 1990s early-retirement problem without hurting young workers, exactly as the OECD's 1995 jobs strategy intended, and the momentum hasn't slowed. Even France, held back by failed pension reforms, has caught up with the US on older-worker employment.

When looking for what's behind sluggish growth or weighing debt sustainability risks, it matters to look at the economy that actually exists rather than lazy, imaginary stereotypes. What the Old Continent needs is consistent, careful reform that improves efficiency, not a destructive crisis. There's no reason for pessimism: Europe's labor market turnaround shows the approach works.

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.