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.

'We may see a dazzling array of products and services spawned by AI, but few of us will be able to buy them.' 'We may see a dazzling array of products and services spawned by AI, but few of us will be able to buy them.' The bogus four-day workweek that AI supposedly'frees up' Business leaders tout AI as a path to shorter weeks and better balance. The front-page headline in a recent Washington Post was breathless: "These companies say AI is key to their four-day workweeks. " The subhead was euphoric: "Some companies are giving workers back more time as artificial intelligence takes over more tasks." As the explained: "more companies may move toward a shortened workweek, several You may have come across similar articles in Fortune magazine and the New York Times. The AI spin brigade is in full force. Business leaders are rhapsodizing about how AI will free their employees to take more time off. Zoom's Eric Yuan told the Times that "A.I. can make all of our lives better, why do we need to work for five days a week?

Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental question: How can we verify that an LLM's reasoning is accurately grounded in knowledge? To address this question, we introduce a novel evaluation suite that systematically assesses the knowledge grounding of intermediate reasoning. Our framework comprises three key components. (1) Principal Knowledge Collection, a large-scale repository of atomic knowledge essential for reasoning. Based on the collection, we propose (2) knowledge-grounded evaluation metrics designed to measure how well models recall and apply prerequisite knowledge in reasoning. These metrics are computed by our (3) evaluator LLM, a lightweight model optimized for cost-effective and reliable metric computation. Our evaluation suite demonstrates remarkable effectiveness in identifying missing or misapplied knowledge elements, providing crucial insights for uncovering fundamental reasoning deficiencies in LLMs. Beyond evaluation, we demonstrate how these metrics can be integrated into preference optimization, showcasing further applications of knowledge-grounded evaluation.

Elon Musk is not most people's idea of a classic technophobe, so when the owner of Twitter warns of the dangers of artificial intelligence, it is worth sitting up and taking notice. Fearful that a new generation of ever-smarter machines threatens life on Earth as we know it, Musk was one of many at the cutting edge of technological change calling for a six-month timeout in the training of new AI systems. There is nothing new in the idea that the machines are coming, and they are out to get us. Techno-optimists are right to say that the same arguments were aired by Luddites in the early 19th century. By this token, the chatbot ChatGPT is to the fourth industrial revolution what the spinning jenny was to the first – a product that symbolises the dawning of a new era.

You may have heard of a little thing called ChatGPT recently. It's quite simply an incredible tool - and one that opens a lot of possibilities These possibilities, however, have raised a lot of questions too - namely, if this bot is so damn good, can it actually do my job for me? There's a lot of debate right now amongst writers, authors, researchers, scientists, and well, just about anyone who works in an office job as to whether this tool - and AI as a whole - is a threat or not. There's a faint idea, or notion, that automation took all the blue-collar jobs, and now AI is coming for the white-collar ones. I don't believe, however, that there's much evidence for that based on how we work today.

Back in 1930, the economist John Maynard Keynes predicted that with technological change and improvements in productivity, we'd only be working 15 hours a week by now. But while working hours have declined by 26 per cent, most of us still average 42.5 hours a week, according to Eurostat figures. One of the things Keynes underestimated is the human desire to compete with our peers – a drive that makes most of us work more than we need to. "We don't measure productivity by how many acres we've harvested anymore, so the amount of time we spend working becomes a proxy," says Alex Soojung-Kim Pang, visiting scholar at Stanford University and author of Rest: Why You Get More Done When You Work Less. "Overwork as a choice, as opposed to slaving away for subsistence wages, has been part of Western society since the Industrial Revolution when some predicted that automation would create an'excess' of leisure time. Needless to say, that didn't happen."

Oscar Wilde dreamed of a world without work. In The Soul of Man Under Socialism (1891) he imagined a society liberated from drudgery by the machine: "while Humanity will be amusing itself, or enjoying cultivated leisure … or making beautiful things, or reading beautiful things, or simply contemplating the world with admiration and delight, machinery will be doing all the necessary and unpleasant work." This aesthete's Eden prompted one of his most famous observations: "Is this Utopian? A map of the world that does not include Utopia is not worth even glancing at." In Wilde's day the future of work was the first question that every aspiring utopian, from Edward Bellamy to HG Wells, needed to answer.

Tech CEOs and politicians alike have issued grave warnings about the capability of automation, including AI, to replace large swaths of our current workforce. But the people who actually study this for a living -- economists -- have very different ideas about just how large the scale of that automation will be. For example, researchers at Citibank and the University of Oxford estimated that 57 percent of jobs in OECD countries -- an international group of 36 nations including the U.S. -- were at high risk of automation within the next few decades. In another well-cited study, researchers at the OECD calculated only 14 percent of jobs to be at high risk of automation within the same timeline. That's a big range when you consider this means a difference of hundreds of millions of potential lost jobs in the next few decades.

LONDON – From the Luddite movement in the early nineteenth century to the writings of prominent economists like John Maynard Keynes and Wassily Leontief generations later, the prospect of automation has always raised serious concerns about jobs. Keynes and Leontief doubted there would be enough jobs left for workers to do. The impact of today's digital technologies on the labor market raises three questions. Will there be enough jobs for workers to do? Where will these jobs be?

Many innovations come in the shape of machines that replace workers. We hear of cars that drive themselves, of robots that perform more and more tasks, and of how artificial intelligence can replace smart jobs. These technological developments cause alarm among many, and this has intensified since the last recession that began in 2008. The recovery from the recession has been slow, and especially in creating new jobs. That is why many have called it'a jobless recovery.'