Christopher Marquis is a professor at the University of Cambridge and the author of The Profiteers. In 1930, in the depths of the Great Depression, John Maynard Keynes wrote a short essay called . It is often remembered for one striking prediction: by 2030, people in wealthy countries might only need to work about 15 hours a week. What Keynes imagined was a society advanced enough to solve what he called the "economic problem" of basic material provision. If technology kept improving, and societies kept growing richer, then fewer hours of human labor would be needed to produce the necessities and comforts of life.

One in six young people will not be in education, employment or training within five years unless urgent action is taken, a major review has warned. The education, health and welfare systems are no longer fit for purpose in preparing young people for adult life, said its author former minister Alan Milburn. We are at risk of a lost generation, he warned, with the number of 16 to 24-year-olds out of work, education or training set to rise to 1.25 million by 2031. The first rung of the career ladder has thinned and that for too many young people it is now simply out of reach, Milburn is set to say in a speech later. That places them in a hopeless catch-22 where employers ask for work experience but the opportunities for young people to gain it have narrowed or gone, he will say.

Taiwan's economy is booming thanks to AI. For Li, an engineer at Taiwanese computer giant ASUS, the AI boom sweeping Taiwan has made it an exciting time to work in tech. Taiwan is a semiconductor powerhouse, producing about 90 percent of the most advanced chips used to power leading AI models such as ChatGPT and Claude. Still, Li worries that the spoils of Taiwan's AI windfall are not being shared equally. "Most industries unrelated to tech don't seem to be feeling the benefits, so it doesn't feel evenly distributed at the moment," Li said, explaining that many of his former classmates working outside of tech do not appear to be doing as well.

Sam Altman Says AI'Jobs Apocalypse' He Once Predicted Probably Won't Happen. OpenAI CEO Sam Altman speaks during the BlackRock Infrastructure Summit on March 11, 2026 in Washington, DC. OpenAI CEO Sam Altman speaks during the BlackRock Infrastructure Summit on March 11, 2026 in Washington, DC. Throughout his rise to becoming one of the most influential CEOs in artificial intelligence, OpenAI's Sam Altman made repeated bold assertions about the impact that the new technology would have on jobs. He has said that AI will "probably replace most of the jobs people do today," that entire job categories will be "totally, totally gone," and that those impacted by the dramatic shifts will "find all sorts of new things to do. Now, however, Altman appears to have changed his tune, saying he is "delighted to be wrong" about the impact AI would have on employment. I don't think we're going to have the kind of jobs apocalypse that some of the companies in our space advocate or talk about, he said during a virtual interview at a Commonwealth Bank of Australia (CBA) conference in Sydney on Tuesday. "I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened, Altman said.

Plus: The Pope has called for governments to regulate AI. Despite the growing hysteria over AI's threat to white-collar jobs, there's still scant evidence that the technology has had a large-scale impact on the labor market. Analysis of US labor data shows that unemployment in occupations most exposed to AI is actually lower than in less-exposed jobs. There are also no signs that large numbers of workers are shifting from AI-threatened professions into supposedly safer manual-labor jobs. It's true that things aren't great in the job market--but the question is why. Here's what the data really says about AI and jobs .

This paper studies the joint role of long-memory dynamics,rough-volatility behavior, and persistence-based forecasting features in equity volatility modeling. We combine semiparametric long-memory estimation, rough-volatility diagnostics, and structured forecasting regressions to examine whether persistence measures contain economically meaningful forecasting information beyond conventional volatility predictors. Using a panel of 115 S&P500 constituents from November 2001 through April 2026, we document that volatility proxies exhibit substantial long-memory behavior and locally rough dynamics. The cross-sectional mean Geweke-Porter-Hudak estimate of the memory parameter is $\hat{d} = 0.226$, while the corresponding local-Whittle estimate is $\hat{d} = 0.440$, with statistical significance observed across nearly the entire panel. Rolling estimates of persistence rise substantially during the global financial crisis and the COVID period and display a positive contemporaneous association with the VIX. We then examine whether persistence-related features improve out-of-sample volatility forecasts beyond standard HAR and HAR-X benchmarks. Incorporating cross-sectional persistence aggregates, sectoral persistence measures, and persistence-by-stress interaction terms produces moderate but statistically significant forecasting improvements, particularly at longer horizons and during stress regimes. Forecast gains are strongest during periods of elevated market volatility and in volatility-managed portfolio applications. The results suggest that persistence measures may serve as useful reduced-form indicators of the duration and propagation of uncertainty in financial markets, although the paper does not claim structural identification of the economic mechanisms generating persistence.

There's Never Been a Better Time to Study Computer Science Even as AI progresses, coders aren't doomed. It's a weird time to be studying computer science. Recent grads have a higher unemployment rate than those in just about every other major--yes, even philosophy. The internet is littered with rants from newly minted programmers who can't find work. On one such YouTube video, the top comment reads: "Your first mistake is not being born earlier."

Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and second moments of the conditional distribution and miss long-range nonlinear structure. We propose SAGA, a decoder-only transformer for irregular tabular panel sequences, paired with a split conformal calibration wrapper that delivers individual-level prediction intervals with finite-sample marginal coverage guarantees. Trained on the longitudinal Swedish LISA register over 1990 to 2022, comprising 2,143,817 individuals and 61,284,903 person-years, the model forecasts annual labor earnings at horizons of one to thirty years and aggregates them by Monte Carlo into present-discounted lifetime earnings distributions. Against the canonical Guvenen, Karahan, Ozkan, and Song parametric process and tabular and recurrent baselines, SAGA reduces continuous ranked probability score by 31.9 percent at the ten-year horizon and mean absolute error by 37.7 percent at the twenty-year horizon. Conformal intervals achieve nominal coverage to within 0.4 percentage points marginally and within 2.4 percentage points on the worst-case demographic subgroup. The reconstructed lifetime earnings Gini coefficient is 0.327 against the partially observed truth of 0.341 and the GKOS estimate of 0.378. Model weights, calibration tables, and a synthetic equivalent dataset are released for replication outside the protected SCB MONA environment.

People attend a jobs fair in London. Only 24% of the members of public surveyed thought AI was a positive thing for humanity. People attend a jobs fair in London. Only 24% of the members of public surveyed thought AI was a positive thing for humanity. One in three university students think AI will wipe out jobs so rapidly it will trigger civil unrest, according to a survey by King's College London (KCL).

Like clockwork each May, soon-to-be college graduates drift into New York City's Washington Square Park in caps and gowns, typically in purple, the school colour of nearby New York University. A sea of mostly 20-somethings gather for photographs that mark the moment when the predictability of collegiate life comes to a close and new graduates face the uncertainty of what's next. Julie Patel, who just finished a master's degree in public health, was one of those graduates. But a tight job market has dampened the joy of the graduation ceremony. Like millions of her peers around the country, she is headed into a precarious job market amid a surge in economic uncertainty driven by a range of reasons, including tariffs, the proliferation of artificial intelligence, global conflicts and, in her case, government funding cuts in her industry, slowing hiring, especially of new graduates.