Philippa Kaur

At the steelworks near the German city of Salzgitter, ironmaking is a dramatic affair. Red-hot molten metal pours forth from the bottom of towering blast furnaces. The noise is deafening. Sparks fly everywhere. Soon things will be much more sedate. Seven wind turbines already tower over the site, run by a firm called Salzgitter AG. In a few years the electricity they generate will power banks of electrolysers, container-sized machines that split water into oxygen and hydrogen. The hydrogen will replace coke in reducing iron ore to iron in a new type of furnace, which will operate at much lower temperatures. Instead of CO2, the process will emit H2O. Listen to this story. Enjoy more audio and podcasts on More

You cannot see the Hollywood sign from the picket line outside Netflix’s compound on Sunset Boulevard. It is obscured by an office tower with a busty advertisement for a “Bridgerton” spin-off splashed on the wall. Yet Hollywood, with its arcane paraphernalia, is all around you. The Writers Guild of America (WGA), which called the strike, traces its roots back to cinema’s early days. The language that the strikers use is steeped in history. They talk of “rooms” where writers gather to work on a script and of “notes”, the often brutal feedback they receive from studio executives. In Los Angeles, Hollywood still confers cachet. You can tell from the horns blasting out in support of the strikers from passing cars. It is a town, and an industry, in upheaval, though. The strike, the first in 15 years, is the latest manifestation of that. Cinemas are still struggling to lure audiences back after the pandemic. Media companies are drowning in debt. Amid a surfeit of TikTok celebrities and minor Hollywood glitterati, only a few old warhorses like Tom Cruise are guaranteed to bring out the crowds. The main cause of the turmoil is streaming. Its firehose of content keeps people at home, rather than going to the multiplex. Its shows cost the film industry a fortune to make. And they are served up with such blink-and-you-miss-them rapidity that it is harder than ever to create universal cultural icons. Yet as leisure activities go, there are few better ways to get a bang for 15 bucks or less. Streaming hasn’t just changed the way people watch TV. It has changed the business model, too. With studios and streamers under the same roof, what used to be a value business driven by hits has turned into a volume business driven by subscriptions. MoffettNathanson, a media-focused consultancy, vividly illustrates this with a quote from a talent agent: “Streaming turned an industry with a profit pool that looked like New York’s skyline into the Los Angeles skyline.” In other words, a few monumental hits, with a sprawl of minor hits and misses in between. Over this landscape, no streamer stands taller than Netflix. Not for nothing is Hollywood calling this “the Netflix strike”.Netflix may not have single-handedly changed Hollywood; HBO, a maker of edgy TV, deserves a screen credit. But its success shows there is no going back. At the end of March it had 232.5m subscribers worldwide. That gives it a huge base for absorbing the costs of shows. Unlike its rivals, its streaming service is profitable, which allows it to reinvest in better content. Its geographic reach lets it take low-budget series from local markets, as it did in 2021 with “Squid Game”, a dystopian South Korean satire on inequality, and turn them into global hits. Its new cheap ad-supported tier offers huge potential to increase revenue and subscriber growth.Given its strength, one might think it could afford to splash out on writers. Perish the thought. In a volume business, cost is key. Its ability to control production expenses helped bolster its cashflow in the first quarter. Investors loved it. Writers, once accustomed to more lavish treatment, did not. Their retort, visible on the picket lines outside Netflix offices: “Fists up. Pencils down.”Talk to the strikers and it is hard not to feel sympathetic. In the pre-streaming era, writing for a moderately successful film or TV series guaranteed a steady income. Writers’ rooms, with at least eight scribes firing off each other, were common when working in pre-production, on set and during editing. Helping write a 26-episode TV show could take up most of the year. Once a film was released, or a TV show broadcast, there was a lucrative aftermarket, including home video and syndicated sales, which brought in residual royalties. It was easy to measure success. Third-party firms reported ratings, box-office numbers and after-sales. The early days of streaming were, if anything, even better. Not only did Netflix, and deep-pocketed tech giants such as Apple and Amazon, spray cash on content to attract subscribers. They made payments up front, regardless of success (they kept most of the viewing figures to themselves). They gave writers unusual creative freedom. The streaming wars gave rise to a golden age of TV. But since investors have taken fright at the ballooning budgets, the money-spigot has been turned off. Shows are shorter than in the pre-streaming era, and work is intermittent. Writing after pre-production has virtually ground to a halt, says Danielle Sanchez-Witzel, union captain and writer for Netflix, whose comedy show, “Survival of the Thickest”, comes out this summer. She says she was shocked at how intransigent the platform was when she asked for more writers on set. “It’s led to a lot of soul-searching.” It isn’t just the WGA. Directors and actors are starting separate contract negotiations with the Association of Motion Picture and Television Producers (AMPTP), which represents the studios, ahead of a June 30th deadline. They, too, have concerns about pay, staffing and residuals. In the background lurks artificial intelligence, and the question of whether it will change the economics of the movie industry as much as—or more than—the internet did. Sunset Boulevard, sunset industry Given such seismic changes, it would not be surprising if the guilds dig in their heels. They have loud voices on social media. The lavish salaries studio bosses pay themselves, while cutting costs elsewhere, make for easy targets. Yet the strikers’ leverage is limited. Netflix’s rivals could have offered more generous terms to win the war for talent. They didn’t, instead joining under the AMPTP umbrella. Netflix may be one of their biggest targets, but it has a large slate of releases ready to go that may insulate it better than its peers from a lack of new scripts. The global reach of the streamers could undercut American content creators; there are plenty of non-unionised foreigners keen to step into their shoes. This is a world where unscripted fare, including YouTube and TikTok, competes with traditional media for viewers’ attention. The skyline has changed. It is foolish to think Hollywood will not change with it. ■ More

IF YOU LISTEN to the bombastic rhetoric in Beijing and Washington, America and China are engaged in an all-out contest for technological supremacy. “Fundamentally, we believe that a select few technologies are set to play an outsized importance over the coming decade,” thundered Jake Sullivan, President Joe Biden’s national security adviser, last September. In February Xi Jinping, China’s paramount leader, echoed the sentiment, stating that “we urgently need to strengthen basic research and solve key technology problems” in order to “cope with international science and technology competition, achieve a high level of self-reliance and self-improvement”. No technology seems to obsess policymakers on both sides of the Pacific more right now than artificial intelligence (AI). The rapid improvements in the abilities of “generative” AIs like ChatGPT, which analyse the web’s worth of human text, images or sounds and can then create increasingly passable simulacrums, have only strengthened the obsession. If generative AI proves as transformational as its boosters claim, the technology could give those who wield them an economic and military edge in the 21st century’s chief geopolitical contest. Western and Chinese strategists already talk of an AI arms race. Can China win it?On some measures of AI prowess, the autocracy pulled ahead some time ago (see chart 1). China surpassed America in the share of highly cited AI papers in 2019; in 2021 26% of AI conference publications globally came from China, compared with America’s share of 17%. Nine of the world’s top ten institutions, by volume of AI publications, are Chinese. According to one popular benchmark, so are the top five labs working on computer vision, a type of AI particularly useful to a communist surveillance state.At the same time, when it comes to “foundation models”, which give the buzzy generative AIs their wits, America finds itself firmly in front (see chart 2). ChatGPT and the pioneering model behind it, the latest version of which is called GPT-4, are the brain child of OpenAI, an American startup. A handful of other American firms, from small firms such as Anthropic or Stability AI to tech giants like Google, Meta and Microsoft (which part-owns OpenAI), have their own powerful systems. ERNIE, a Chinese rival to ChatGPT built by Baidu, China’s internet-search giant, is widely seen as less clever than most of them (see chart 3). Alibaba and Tencent, China’s mightiest tech titans, have yet even to unveil their own generative AIs. This leads those in the know to conclude that China is two or three years behind America in foundation-model building. There are three reasons for this underperformance. The first concerns data. On the surface, a centralised autocracy should be able to marshal a lot of it—the government was, for instance, able to hand over its troves of surveillance information on Chinese citizens to companies such as SenseTime or Megvii that, with the help of the country’s leading computer-vision labs, then used it to develop top-notch facial-recognition systems. That advantage has proved less formidable in the context of generative AIs, because foundation models are trained on the much more voluminous unstructured data of the internet. American model-builders benefit from the fact that 56% of all websites are in English, whereas just 1.5% are in Mandarin or China’s other languages, according to data from the W3Techs, an internet-research site. As Yiqin Fu of Stanford University points out, the Chinese interact with the internet primarily through mobile super-apps like WeChat and Weibo. These are “walled gardens”, so much of their content is not indexed on search engines. This makes that content harder for AI models to suck up. Lack of data may explain why Wu Dao 2.0, a Chinese model unveiled in 2021 by the Beijing Academy of Artificial Intelligence, a state-backed outfit, failed to make a splash despite its possibly being more computationally complex than GPT-4.The second reason for China’s lacklustre generative achievements has to do with hardware. Last year America imposed swingeing export controls on any technology that might give its main geostrategic rival a leg-up in AI. In particular, that includes the powerful chips used in the cloud-computing data centres where foundation models do their learning, and the chipmaking tools that could enable China to build such semiconductors on its own. That was a blow to Chinese model-builders. An analysis of 26 big Chinese models by the Centre for the Governance of AI, a British think-tank, found that more than half depended on Nvidia, an American chip designer, for their processing power. Some reports suggest that SMIC, China’s biggest chip manufacturer, has produced prototype chips which are just a generation or two behind TSMC, the Taiwanese industry leader that manufactures chips for Nvidia (see chart 4). But the Chinese firm may only be able to mass-produce chips which TSMC was churning out by the million three or four years ago. A professor at a leading Chinese university laments his country’s weakness in such “basic infrastructure” of AI.Chinese AI firms are also having more trouble getting their hands on another American export: know-how. America remains a magnet for the world’s tech talent; two-thirds of AI experts in America who present papers at the biggest AI conference are foreign-born. Chinese engineers made up 27% of that select group in 2019. Many Chinese AI boffins studied or worked in America before bringing their machine learnings back home. (Few non-Chinese boffins would consider moving to a police state a wise career move.) The covid-19 pandemic and rising Sino-American tensions are causing their numbers to dwindle. In the first half of 2022 America granted half as many visas to Chinese students as in the same period in 2019. The triple shortage—of data, hardware and expertise—has been a genuine hurdle for China. Whether it will hold Chinese AI ambitions back much longer is, though, another matter. Info attainmentTake data. On February 13th the local authorities in Beijing, where nearly a third of China’s AI firms are located, said they were releasing data from 115 state-affiliated organisations, giving model-builders 15,880 data sets to play with. To liberate more data, the central government also wants to dismantle Chinese apps’ walled gardens. Most important, the latest models appear able to transfer learnings from one language to another. In the paper describing GPT-4, OpenAI said that the model performed remarkably well on tasks in Chinese despite the dearth of Chinese source material in the model’s training data. Already Baidu’s ERNIE was trained on lots of English-language data, notes Jeffrey Ding of George Washington University. In hardware, too, China is finding workarounds. The Financial Times reported in March that SenseTime, which is blacklisted by America’s government, has used intermediaries to skirt the export controls. Some Chinese AI firms are able to harness the computing power of Nvidia’s advanced chips through cloud servers based in other countries. Alternatively, they can simply buy more of Nvidia’s less advanced semiconductors or use them more efficiently with the help of clever software. To continue serving the vast Chinese market, the American company has designed less powerful sanctions-compliant processors. These are between 10% and 30% slower than its top-of-the-range kit, and end up being costlier for the Chinese customers per unit of processing power. But they do the job. China could partly alleviate the dearth of chips—and of brain power—with the help of “open-source” models. Such models’ inner workings can be downloaded by anyone and fine-tuned to a specific task. Most importantly, that includes the numbers, called “weights”, which define the structure of the model and which are derived from costly training runs. Alpaca, a model built by researchers at Stanford University using the weights from LLaMA, a foundation model created by Meta, was made for less than $600, compared with sums on the order of $100m for training something like GPT-4. Alpaca performs just as well as the original version of ChatGPT on many tasks. Chinese AI labs could similarly avail themselves of open-source models, which embody the collective wisdom of international research teams. Matt Sheehan of the Carnegie Endowment for International Peace, another think-tank, says that China has form in being a “fast follower”—its labs have absorbed advances from abroad and then rapidly incorporated them into their own models, often with flush state resources. A prominent Silicon Valley venture capitalist is more blunt, calling open-source models a gift to the Communist Party.Such considerations make it hard to imagine that either America or China could in the long run build an unbridgeable lead in AI modelling. Each may well end up with AIs of roughly similar ability, even if it costs China over the odds to keep up in the face of American sanctions. But even if the race of the model-builders is a dead heat, America has one thing going for it that could make it the big AI winner—its peerless ability to spread cutting-edge innovation throughout the economy. It was, after all, more efficient diffusion of technology that helped America open up a technological lead over the Soviet Union, which in the 1950s was producing twice as many science PhDs as its democratic adversary.China is, of course, far more competent than the Soviet Union ever was at adopting new technologies. Its fintech platforms, 5G telecoms and high-speed rail are all world-class. But those successes may be the exception, not the rule, says Mr Ding. Particularly, in the deployment of sensors, cloud computing and business software—all complementary to AI—China has done less well. Although American export controls may not derail all Chinese model-building, they do constrain China’s tech industry more broadly, thereby slowing the adoption of any new technology. Moreover, corporate China as a whole, and especially small and medium-sized companies, is short of technologists who act as conduits for technological diffusion. Swathes of the economy are dominated by state-owned firms, which tend to be stodgy and change-averse. China’s “Big Fund” for chips, which raised $50bn in 2014 with a view to backing domestic semiconductor firms, has been mired in scandals. Many of the thousands of AI startups created in recent years have simply slapped on the AI label in the hope of getting a slice of the lavish subsidies doled out by the state to the favoured industry. As a consequence, China’s private sector may find it hard to take full advantage of generative AI, especially if the Communist Party imposes heavy regulations to prevent chatbots from saying something its censors do not like. Such handicaps would come on top of Mr Xi’s broader suborning of private enterprise, including a two-and-a-half-year crackdown on China’s tech industry. Although this anti-tech campaign has officially ended, it has left businesses scarred. The result is a chill in tech sentiment. Last year private investments in Chinese AI startups amounted to $13.5bn, less than one-third the amount that flowed to their American rivals. In the first four months of 2023 the funding gap appears only to have widened, according to PitchBook, a data provider. Whether or not generative AI proves revolutionary, the free market has placed its bet on who will make the most of it. ■ More

IF YOU LISTEN to the bombastic rhetoric in Beijing and Washington, America and China are engaged in an all-out contest for technological supremacy. “Fundamentally, we believe that a select few technologies are set to play an outsized importance over the coming decade,” thundered Jake Sullivan, President Joe Biden’s national security adviser, last September. In February Xi Jinping, China’s paramount leader, echoed the sentiment, stating that “we urgently need to strengthen basic research and solve key technology problems” in order to “cope with international science and technology competition, achieve a high level of self-reliance and self-improvement”. No technology seems to obsess policymakers on both sides of the Pacific more right now than artificial intelligence (AI). The rapid improvements in the abilities of “generative” AIs like ChatGPT, which analyse the web’s worth of human text, images or sounds and can then create increasingly passable simulacrums, have only strengthened the obsession. If generative AI proves as transformational as its boosters claim, the technology could give those who wield them an economic and military edge in the 21st century’s chief geopolitical contest. Western and Chinese strategists already talk of an AI arms race. Can China win it?On some measures of AI prowess, the autocracy pulled ahead some time ago (see chart 1). China surpassed America in the share of highly cited AI papers in 2019; in 2021 26% of AI conference publications globally came from China, compared with America’s share of 17%. Nine of the world’s top ten institutions, by volume of AI publications, are Chinese. According to one popular benchmark, so are the top five labs working on computer vision, a type of AI particularly useful to a communist surveillance state.At the same time, when it comes to “foundation models”, which give the buzzy generative AIs their wits, America finds itself firmly in front (see chart 2). ChatGPT and the pioneering model behind it, the latest version of which is called GPT-4, are the brain child of OpenAI, an American startup. A handful of other American firms, from small firms such as Anthropic or Stability AI to tech giants like Google, Meta and Microsoft (which part-owns OpenAI), have their own powerful systems. ERNIE, a Chinese rival to ChatGPT built by Baidu, China’s internet-search giant, is widely seen as less clever than most of them (see chart 3). Alibaba and Tencent, China’s mightiest tech titans, have yet even to unveil their own generative AIs. This leads those in the know to conclude that China is two or three years behind America in foundation-model building. There are three reasons for this underperformance. The first concerns data. On the surface, a centralised autocracy should be able to marshal a lot of it—the government was, for instance, able to hand over its troves of surveillance information on Chinese citizens to companies such as SenseTime or Megvii that, with the help of the country’s leading computer-vision labs, then used it to develop top-notch facial-recognition systems. That advantage has proved less formidable in the context of generative AIs, because foundation models are trained on the much more voluminous unstructured data of the internet. American model-builders benefit from the fact that 56% of all websites are in English, whereas just 1.5% are in Mandarin or China’s other languages, according to data from the W3Techs, an internet-research site. As Yiqin Fu of Stanford University points out, the Chinese interact with the internet primarily through mobile super-apps like WeChat and Weibo. These are “walled gardens”, so much of their content is not indexed on search engines. This makes that content harder for AI models to suck up. Lack of data may explain why Wu Dao 2.0, a Chinese model unveiled in 2021 by the Beijing Academy of Artificial Intelligence, a state-backed outfit, failed to make a splash despite its possibly being more computationally complex than GPT-4.The second reason for China’s lacklustre generative achievements has to do with hardware. Last year America imposed swingeing export controls on any technology that might give its main geostrategic rival a leg-up in AI. In particular, that includes the powerful chips used in the cloud-computing data centres where foundation models do their learning, and the chipmaking tools that could enable China to build such semiconductors on its own. That was a blow to Chinese model-builders. An analysis of 26 big Chinese models by the Centre for the Governance of AI, a British think-tank, found that more than half depended on Nvidia, an American chip designer, for their processing power. Some reports suggest that SMIC, China’s biggest chip manufacturer, has produced prototype chips which are just a generation or two behind TSMC, the Taiwanese industry leader that manufactures chips for Nvidia (see chart 4). But the Chinese firm may only be able to mass-produce chips which TSMC was churning out by the million three or four years ago. A professor at a leading Chinese university laments his country’s weakness in such “basic infrastructure” of AI.Chinese AI firms are also having more trouble getting their hands on another American export: know-how. America remains a magnet for the world’s tech talent; two-thirds of AI experts in America who present papers at the biggest AI conference are foreign-born. Chinese engineers made up 27% of that select group in 2019. Many Chinese AI boffins studied or worked in America before bringing their machine learnings back home. (Few non-Chinese boffins would consider moving to a police state a wise career move.) The covid-19 pandemic and rising Sino-American tensions are causing their numbers to dwindle. In the first half of 2022 America granted half as many visas to Chinese students as in the same period in 2019. The triple shortage—of data, hardware and expertise—has been a genuine hurdle for China. Whether it will hold Chinese AI ambitions back much longer is, though, another matter. Info attainmentTake data. On February 13th the local authorities in Beijing, where nearly a third of China’s AI firms are located, said they were releasing data from 115 state-affiliated organisations, giving model-builders 15,880 data sets to play with. The central government has previously signalled it wants to dismantle Chinese apps’ walled gardens, potentially liberating more data, says Kayla Blomquist, a former American diplomat in China now at Oxford University.Most important, the latest models appear able to transfer learnings from one language to another. In the paper describing GPT-4, OpenAI said that the model performed remarkably well on tasks in Chinese despite the dearth of Chinese source material in the model’s training data. Already Baidu’s ERNIE was trained on lots of English-language data, notes Jeffrey Ding of George Washington University. In hardware, too, China is finding workarounds. The Financial Times reported in March that SenseTime, which is blacklisted by America’s government, has used intermediaries to skirt the export controls. Some Chinese AI firms are able to harness the computing power of Nvidia’s advanced chips through cloud servers based in other countries. Alternatively, they can simply buy more of Nvidia’s less advanced semiconductors or use them more efficiently with the help of clever software. To continue serving the vast Chinese market, the American company has designed less powerful sanctions-compliant processors. These are between 10% and 30% slower than its top-of-the-range kit, and end up being costlier for the Chinese customers per unit of processing power. But they do the job. China could partly alleviate the dearth of chips—and of brain power—with the help of “open-source” models. Such models’ inner workings can be downloaded by anyone and fine-tuned to a specific task. Most importantly, that includes the numbers, called “weights”, which define the structure of the model and which are derived from costly training runs. Alpaca, a model built by researchers at Stanford University using the weights from LLaMA, a foundation model created by Meta, was made for less than $600, compared with sums on the order of $100m for training something like GPT-4. Alpaca performs just as well as the original version of ChatGPT on many tasks. Chinese AI labs could similarly avail themselves of open-source models, which embody the collective wisdom of international research teams. Matt Sheehan of the Carnegie Endowment for International Peace, another think-tank, says that China has form in being a “fast follower”—its labs have absorbed advances from abroad and then rapidly incorporated them into their own models, often with flush state resources. A prominent Silicon Valley venture capitalist is more blunt, calling open-source models a gift to the Communist Party.Such considerations make it hard to imagine that either America or China could in the long run build an unbridgeable lead in AI modelling. Each may well end up with AIs of roughly similar ability, even if it costs China over the odds to keep up in the face of American sanctions. But even if the race of the model-builders is a dead heat, America has one thing going for it that could make it the big AI winner—its peerless ability to spread cutting-edge innovation throughout the economy. It was, after all, more efficient diffusion of technology that helped America open up a technological lead over the Soviet Union, which in the 1950s was producing twice as many science PhDs as its democratic adversary.China is, of course, far more competent than the Soviet Union ever was at adopting new technologies. Its fintech platforms, 5G telecoms and high-speed rail are all world-class. But those successes may be the exception, not the rule, says Mr Ding. Particularly in the deployment of sensors, cloud computing and business software—all complementary to AI—China has done less well. Although American export controls may not derail all Chinese model-building, they do constrain China’s tech industry more broadly, thereby slowing the adoption of any new technology. Moreover, corporate China as a whole, and especially small and medium-sized companies, is short of technologists who act as conduits for technological diffusion. Swathes of the economy are dominated by state-owned firms, which tend to be stodgy and change-averse. China’s “Big Fund” for chips, which raised $50bn in 2014 with a view to backing domestic semiconductor firms, has been mired in scandals. Many of the thousands of AI startups created in recent years have simply slapped on the AI label in the hope of getting a slice of the lavish subsidies doled out by the state to the favoured industry. As a consequence, China’s private sector may find it hard to take full advantage of generative AI, especially if the Communist Party imposes heavy regulations to prevent chatbots from saying something its censors do not like. Such handicaps would come on top of Mr Xi’s broader suborning of private enterprise, including a two-and-a-half-year crackdown on China’s tech industry. Although this anti-tech campaign has officially ended, it has left businesses scarred. The result is a chill in tech sentiment. Last year private investments in Chinese AI startups amounted to $13.5bn, less than one-third the amount that flowed to their American rivals. In the first four months of 2023 the funding gap appears only to have widened, according to PitchBook, a data provider. Whether or not generative AI proves revolutionary, the free market has placed its bet on who will make the most of it. ■ More

Two work laptops, two work calendars, two bosses and two pay-cheques. So far, neither of Matt’s employers is any the wiser. The tech worker (who, for obvious reasons, asked The Economist not to use his real name) meets deadlines and does what is requested, though not more. He is not the only one.People working several jobs is nothing new. Low earners have long had to juggle shifts to make ends meet. At the other end of the pay scale, directors often sit on a few corporate boards. According to America’s Bureau of Labour Statistics, at any given point in the past 30 years, between 4% and 6.5% of the American workforce was working more than one job. Estimates from the Census Bureau put that share even higher, going from 6.8% in 1996 to 7.8% in 2018. What is novel, as Matt’s example illustrates, is the rise of the job-juggling white-collar type, especially in the technology industry. Thank—or blame—remote work. Despite efforts by bosses to lure or coerce people back to their desks, the share of techies working fully remotely remains 60% higher than in other sectors (see chart). Without managers physically looking over their shoulders, some of them are two-timing their employers. Mid-career software engineers report applying for more junior positions so that they can “underpromise and overdeliver”, with minimal effort. Matt took a second job, or “J2” as he calls it, for two main reasons: boredom and concerns over job security. The tasks required by his first job, working remotely as a data scientist for a medium-sized tech firm, were not particularly challenging, taking him only eight hours a week. He had no inclination to “play office politics and move up the corporate ladder”. He did, though, covet cash. He reckoned he could take on a second job, double his pay and gain a safety-net were he to be laid off.After interviewing for a few weeks, Matt found a promising J2: data engineering at a startup. He suspected that demands on his time would be as low as they were at his first job. He was mostly right, though striking a balance required some footwork. In his first week a rare J1 meeting was scheduled at the same time as one of his J2 “onboarding” sessions. Some fellow members of an online forum for the overemployed on Reddit, a social-media site, claim to have taken two meetings at once, with video off. If called on to speak at the same time, they feign connectivity problems or play a pre-recorded audio clip of a dog barking. Matt decided to tune in to the J1 call and reschedule his onboarding, blaming a doctor’s appointment.The rise of generative artificial intelligence like ChatGPT may in time make double-jobbing harder by replacing some menial tech tasks. Until then, coasters can themselves use clever chatbots to help structure computer code, write documents and even conduct preliminary research. ChatGPT cannot replace the work of a software engineer, says one overemployee, but it gets you 90% of the way there. The employee-employer relationship has historically favoured the employers, who wield more clout because they can typically choose from more workers than workers can among companies. Matt thinks of his ruse as taking back some control. Two decently paying jobs afford him flexibility. And, he says, flexibility is power. If he were to get laid off, or if one job were to become unreasonably demanding, he could go and find another. For now, he thinks he is safe. So safe, in fact, that he is starting his search for a third job. ■To stay on top of the biggest stories in business and technology, sign up to the Bottom Line, our weekly subscriber-only newsletter. More

A sensational scoop was tweeted last month by America’s National Public Radio: Elon Musk’s “massive space sex rocket” had exploded on launch. Alas, it turned out to be an automated mistranscription of SpaceX, the billionaire’s rocketry firm. The error may be a taste of what is to come as artificial intelligence (AI) plays a bigger role in newsrooms.Listen to this story. Enjoy more audio and podcasts on More

AS CHRISTOPHER WAS preparing to board a flight from New York to Singapore in February 2019, he was pulled aside by local authorities and told to stay put. An Interpol “red notice”, a request for local law enforcement to make an arrest on behalf of another government, had been issued on his name, he would soon learn. The executive, who has asked that his real name not be used because his case is ongoing, was the founder of an international advertising group that a few years earlier had got into big trouble in China over data security. Listen to this story. Enjoy more audio and podcasts on More

BEFORE CARL ICAHN was an activist investor, he was an arbitrageur. Although it was swashbuckling corporate raids during the 1980s that made him infamous, some of Mr Icahn’s earliest campaigns involved investing in closed-end funds, a type of investment company which often trades at a discount to the value of its assets. Closing this gap, perhaps by agitating for the fund to liquidate its holdings, yields a profit.Mr Icahn’s own investment holding company, Icahn Enterprises, suffered no such discount. Until this week the firm had a market capitalisation of around $18bn, more than triple the reported net value of its assets. These include majority ownership of energy and car companies, in addition to an activist-investment portfolio. On May 2nd Hindenburg Research, a short-selling outfit founded in 2017 by Nathan Anderson, accused Icahn Enterprises of operating a “Ponzi-like” structure. Icahn Enterprises has shed more than a third of its market value since Hindenburg released its report. It has become the latest of Hindenburg’s targets to hit the skids—and the headlines. Mr Anderson’s firm has previously taken aim at Nikola, a maker of electric lorries, the Adani Group, one of India’s mightiest conglomerates, and Block, an American fintech giant (see chart).Hindenburg’s latest report alleges that Icahn Enterprises has inflated the value of its assets and funded its dividend with proceeds from selling shares to unwitting investors. It also calls on Mr Icahn to disclose the terms of personal loans secured against his majority holding in Icahn Enterprises. And it scolds Jefferies, Mr Icahn’s long-time investment bankers and the only big bank whose research analysts cover Icahn Enterprises, for allegedly turning a blind eye to the firm’s risks. Mr Icahn, Hindenburg argues, “has made a classic mistake of taking on too much leverage in the face of sustained losses”. Bill Ackman, another famed activist investor who once locked horns with Mr Icahn over an investment in Herbalife, an American supplement firm, gloated on Twitter that there was a “karmic quality” to the report. Short-sellers’ targets can be hamstrung in their immediate defences—share prices can tank quickly but detailed rebuttals take time. Even so, Mr Icahn’s first response looks muted compared with that of Hindenburg’s recent victims. In March Block described Hindenburg’s report as “factually inaccurate” and threatened litigation. In January the Adani Group accused the short-seller of “selective misinformation”. After stating that Hindenburg’s report is “self-serving”, Mr Icahn said on May 2nd merely that his firm’s performance would “speak for itself”. Jefferies has not commented on Hindenburg’s claims. Quite how messy this activist showdown becomes remains to be seen. Hindenburg’s report pitches a doyen of classic shareholder activism, which involves trying to drive a target’s share price up, against a newly prominent practitioner of short-selling, which aims to send it through the floor. The stakes are higher for Mr Icahn. His brand of activism requires investors to take him more seriously than they do the bad managers that, in his “anti-Darwinian” view, American commerce seems to promote. Icahn Enterprises must now prove that the same thing is not true of its own boardroom. ■To stay on top of the biggest stories in business and technology, sign up to the Bottom Line, our weekly subscriber-only newsletter. More