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    Why Mumbai’s old business district is so shabby

    Every indian business dreads waking up to a bill from the state. So too the Taj Mahal Palace. The Mumbai Port Trust, owner of the land upon which the landmark hotel sits, is demanding $92m in retrospective rent for the years 2012-22. The Taj, which is owned by Tata Group, a conglomerate, has called the demand “exorbitant and untenable” in a petition to the Bombay High Court. The claim’s size and the Taj’s prominence make the claim unique. But many tenants get similar treatment. As a result, Mumbai’s old business district, once home to many global firms, has slid into disrepair. Listen to this story. Enjoy more audio and podcasts on More

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    Can the French nuclear industry avoid meltdown?

    Nuclear power seems, in some ways, tailor-made for this day and age. It emits next to no carbon. It provides reliable baseload electricity when sun isn’t drenching solar panels or wind isn’t wafting through turbine blades. And it does not leave its operators hostage to fossil fuels from dictators like Vladimir Putin, who has throttled the supply of Russian natural gas to Europe in response to Western sanctions over his invasion of Ukraine. With memories of the Fukushima meltdown in Japan 11 years ago fading, countries from Britain to India are considering fission as a critical part of their future energy mix. Even in nuclear-sceptical Germany, which decided to mothball its nuclear reactors in that disaster’s wake, the government felt compelled in October to extend the lifetime of the three remaining ones until April 2023.If there is one country that should already be enjoying all the benefits of this abundant carbon- and autocrat-free power, it is France. Its fleet of 56 reactors account for around 70% of national electricity-generating capacity, the highest share in the world and more than three times the figure in America. That allows the French to emit just 4.5 tonnes of carbon-dioxide per person in a typical year, much less than gas-addled Germans (7.9 tonnes) or car-crazy Americans (14.7 tonnes). As for Mr Putin’s energy blackmail, on European minds again as a mercifully mild autumn suddenly gives way to a frigid winter this week, you might expect it to be met with a Gallic shrug.France should, in other words, be basking in the warm glow of controlled fission reactions. Instead, after a decade of mismanagement and political mixed signals, its nuclear industry is desperately trying not to implode. A third of France’s ageing fleet is out of action owing to maintenance and other technical problems. Experts warn of possible power outages during extreme cold spells later this winter. To keep up with demand, France has to import pricey electricity, from Germany of all places. The fleet’s state-controlled operator, EDF, is being fully renationalised to save it from bankruptcy. The company’s newly appointed boss, Luc Rémont, talks of a “serious crisis”. A lot is riding on its resolution. Europe is counting on the French nuclear industry to stop being a drag on the continent’s beleaguered energy system this winter. Emmanuel Macron, France’s president, is counting on it for a national nuclear renaissance. More broadly, its success may determine whether the world’s newer nuclear converts see the French experience as an object lesson—or a cautionary tale. To understand the French nuclear business’s current predicament it is worth going back to its roots in the oil shock of 1973. At the time, most French power plants ran on petroleum. As the fuel became scarce, French politicians concluded that in order to be truly sovereign, the country needed an energy source it could control. Nuclear power seemed just the ticket. France already knew something about the technology, having built an atom bomb and nuclear submarines. It also boasted a cohesive corps of engineers, most of whom attended the same university, the École Polytechnique. And the country’s centralised political system allowed the powerful executive branch to ram through the ambitious programme without much consultation with either the French public or their elected representatives.This rapid ramp-up had big advantages. Critically, it enabled France to enjoy what industry types call the “fleet effect”. Building a reactor is hugely complex and requires a lot of learning by doing. So long as you keep doing, the expertise grows, making each new project easier. Between 1974 and the late 1980s EDF brought reactors online at a rhythm of up to six a year, with construction crews moving swiftly from one plant to another. Atom’s heart smotheredHowever, the French approach has created a number of lingering problems. On the technical side, squeezing a lot of construction into a few years means that reactors undergo their big decennial refit (le grand carénage) around the same time. And since they are built to the same standard, problems found in one regularly trigger repairs in others. As a result, French reactors’ “load factor”, a measure of whether a plant is running at full capacity, hovers at 60% or so, compared with more than 90% in America. In 2021, 5,810 reactor-days were lost to outages, of which almost 30% were unplanned, according to the “World Nuclear Industry Status Report”, an industry publication. The latest refits keep revealing ugly surprises: a year ago EDF discovered cracks, due to corrosion, in the emergency core-cooling systems of some reactors, leading the company to shut down 16 of them. Three have been turned back on; the other 13 remain idle. Meanwhile, with little accountability and oversight the industry quickly became a state within a state, characterised by groupthink and, in the words of one former insider, “a serious lack of self-doubt”. This led to some terrible business decisions. In the early 2000s Framatome, the company that built reactors for EDF, developed ambitions of its own. Under new management—and a new name, Areva—it signed a contract with Finland to build a new type of plant, called the European pressurised-water reactor (EPR), which it had developed jointly with Siemens, a German conglomerate. Not to be outdone, EDF decided to build its own EPR at home in Flamanville, and sell others to China and Britain. Both Areva and EDF started construction before they knew what exactly they would build and how much it would cost. As often happens when the French and Germans co-operate, the EPR was a hugely complex beast, not least because it had to satisfy both countries’ nuclear inspectors. The upshot is that neither reactor has yet produced much electricity. Both are way over budget. The Finnish project, at Olkiluoto, bankrupted Areva, whose reactors business EDF took over in 2017. The cost of Flamanville has gone from an original price tag of €3.3bn (then $4.8bn) to €19bn (including financing) and counting.Finally, bypassing the legislature, which may have speeded things up at first, has made French nuclear policy more vulnerable to political winds. In 2012 François Hollande, the Socialist president, convinced the Greens to back his successful presidential campaign in exchange for a promise to close the country’s two oldest reactors in Fessenheim, near the German border, and limit nuclear power in the country’s electricity mix to 50% by 2025, which implied the closure of up to 20 reactors. Mr Hollande kept the first promise but not the second. Still, the prospect of wider decommissioning helped put the fleet effect into reverse. Just as nuclear success begets more success, nuclear failure feeds on itself, as lost expertise gets harder to replenish. Mr Macron now wants to turn the vicious circle virtuous once again. In February, even before Mr Putin attacked Ukraine, the French president announced that the country will start building new reactors again: at least six and up to 14 if things go well. “We have to pick up the thread of the great adventure of civil nuclear energy,” he declared. Barring last-minute legal hiccups, the French state will have full control of EDF within a fortnight, recreating unité d’action, as the French would say. “The state is now fully back in charge,” explains Emmanuel Autier of BearingPoint, a consultancy.The next, harder task is for the president’s hand-picked EDF boss, Mr Rémont, to get as many of the shut reactors back online as he can. EDF has pledged to have most of them up and running by January, which seems ambitious. The new CEO must also deal with the bill for the outages, and for the government’s cap on tariff rises imposed to stave off anger over high energy prices. This, plus the requirement to sell some power at a discount to rival suppliers, could cost EDF €42bn this year in gross operating losses, reckons Moody’s, a ratings agency. With net debt already at €90bn, up from around €70bn a year ago, Mr Rémont will have to convince the French state to provide the firm with additional capital to cover the upcoming big refit, which could cost €50bn-60bn, and Mr Macron’s new reactors, which would add up to about the same, all told. And he has to persuade the eu’s competition enforcers to accept the state aid and refrain from insisting that EDF split itself up by selling its profitable global renewable business.More difficult still may be building the new reactors. EDF engineers have been working on a new design, called EPR2, which is an attempt to learn from previous mistakes and simplify the first version. Gone are many parts needed to comply with German rules. Components will be standardised. Instead of 13,309 different faucets and valves, for instance, the EPR2 will sport only 1,205, according to the current plan. And it is supposed to be built in pairs, with only 18 months between the start of construction of the first and the second reactor. To ensure everything goes smoothly, EDF has added a head of “industrial quality” to its executive board. In this role Alain Tranzer, a former carmaking executive, has launched “Excell plan” to fortify the ecosystem of nuclear-related companies, digitise the surprisingly analogue industry and introduce better project management. As part of the plan, in October EDF and its partners opened a school for welders, teaching students how to bind a reactor’s 370km or so of pipes so tightly that no superheated, often contaminated water can escape; at the moment such professionals are so scarce in France that EDF has had to fly them in at a high cost from America and Canada. Mr Tranzer’s plan also calls for the creation of a University for Nuclear Trades, which opened its lecture halls in April. Not everyone is convinced of the new strategy. “They are making the same mistake again by starting before detailed engineering is completed,” says Mycle Schneider, co-ordinator of the report on the state of the nuclear industry. EDF may have already invested more than 1m engineer-hours in the EPR2, but another 19m may be needed to fine-tune the design. Even government experts have doubts about whether EDF will be able to deliver six EPR2s on time and on budget. In a leaked internal memo from late 2021 they warn that the first pair may not be ready before 2043, not 2035 as promised, and could cost €21bn in today’s money, rather than €17bn-18.5bn. The Cour des Comptes, France’s auditing office, has calculated that in 2019 a megawatt-hour (MWh) of nuclear power cost nearly €65 (taking into account construction costs). The EPR2 may be able to produce it more cheaply, but certainly not at the rate of €15 and €46 that Spaniards and Germans, respectively, already sometimes pay per solar MWh.And recreating the broader tailwinds that helped France launch the fleet effect in the 1970s and 1980s will not be easy. Despite the new welding school and nuclear university, France is no longer the industrial power it once was, limiting the pool of candidates. It may be difficult to recruit the skilled workers needed, beyond the 220,000 that already work in the sector. And although the reputation of nuclear power is improving—two-thirds of French think that it has a future, up from less than half in 2016—local protests are likely near proposed plants. “We have to be very humble about our capacity to build new reactors,” cautions Nicolas Goldberg of Colombus Consulting, a firm of advisers. For the French, a nation not known for humility, that may be the hardest test of all. ■ More

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    The scourge of job-title inflation

    When you enter an unfamiliar office for a meeting with someone who works there, you will almost certainly approach a person sitting behind a large desk. You might think you are about to speak to a receptionist. But in some buildings, you will be dealing with someone far grander: a lobby ambassador. Listen to this story. Enjoy more audio and podcasts on More

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    The rise of the super-app

    Is Elon Musk bored of the town square already? A month after completing his acquisition of Twitter, his iconoclastic gaze appears to be trained on the entire city. Mr Musk wants to build a super-app. Whether called “Twitter 2.0”, “The everything app” or “X”, his plans are still super-vague. A series of slides containing hardly any information tweeted on November 26th did little to shed light on his plans. Doting references to Tencent’s WeChat provide some clues—earlier this year Mr Musk described the Chinese super-app as “Twitter, plus PayPal, plus a whole bunch of other things, and all rolled into one with actually a great interface”. What is clear is that Mr Musk will face obstacles in his path.Listen to this story. Enjoy more audio and podcasts on More

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    How good is ChatGPT?

    One morning your correspondent woke up to an email from his editor, asking for yet another article. “Chatgpt and other generative-ai services seem to be taking the world by storm,” it read. “Could you write an article explaining what they are and why they are not just hype?” As he was feeling lazy he asked Chatgpt, More

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    What next for China’s covid-industrial complex?

    The harsher China’s zero-covid regime, the bigger its covid-industrial complex. The zero-covid mantra was to test as many people as possible and then to quarantine not only the infected but their contacts and even the contacts of those contacts. In many cases the occupants of entire residential buildings were carted away to isolation wards—called fangcang yiyuan in Chinese—after the discovery of a single case in an area. As leaders in Beijing fought to keep the virus from spreading across the country, many firms cashed in. Listen to this story. Enjoy more audio and podcasts on More

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    Artificial intelligence is permeating business at last

    The machines are coming for your crops—at least in a few fields in America. This autumn John Deere, a tractor-maker, shipped its first fleet of fully self-driving machines to farmers. The tilling tractors are equipped with six cameras which use artificial intelligence (ai) to recognise obstacles and manoeuvre out of the way. Julian Sanchez, who runs the firm’s emerging-technology unit, estimates that about half the vehicles John Deere sells have some AI capabilities. That includes systems which use onboard cameras to detect weeds among the crops and then spray pesticides, and combine harvesters which automatically alter their own setting to waste as little grain as possible. Mr Sanchez says that for a medium-sized farm, the additional cost of buying an AI-enhanced tractor is recouped in two to three years.For decades starry-eyed technologists have claimed that AI will upend the business world, creating enormous benefits for firms and customers. John Deere is not the only proof that this is happening at last. A survey by McKinsey Global Institute, the consultancy’s in-house think-tank, found that this year 50% of firms across the world had tried to use AI in some way, up from 20% in 2017. Powerful new “foundation” models are fast moving from the lab to the real world. Excitement is palpable among corporate users of AI, its developers and those developers’ venture-capital backers. Many of them attended a week-long jamboree hosted in Las Vegas by Amazon Web Services, the tech giant’s cloud-computing arm. The event, which wrapped up on December 2nd, was packed withI talks and workshops on ai. Among the busiest booths in the exhibition hall were those of AI firms such as Dataiku and Blackbook.ai.The buzzing AI scene is an exception to the downbeat mood across techdom, which is in the midst of a deep slump. In 2022 venture capitalists have ploughed $67bn into firms that claim to specialise in AI, according to PitchBook, a data firm. The share of vc deals globally involving such startups has ticked up since mid-2021, to 17% so far this quarter. Between January and October, 28 new AI unicorns (private startups valued at $1bn or more) have been minted. Microsoft is said to be in talks to increase its stake in OpenAI, a builder of foundation models. Alphabet, Google’s parent company, is reportedly planning to invest $200m in Cohere, a rival to OpenAI. At least 22 AI startups have been launched by alumni of OpenAI and Deepmind, one of Alphabet’s AI labs, according to a report by Ian Hogarth and Nathan Benaich, two British entrepreneurs. The exuberance is not confined to Silicon Valley. Large companies of all sorts are desperate to get their hands on AI talent. In the past 12 months large American firms in the S&P 500 index have acquired 52 AI startups, compared with 24 purchases in 2017, according to PitchBook. Figures from PredictLeads, another data provider, show that the same group of firms posted around 7,000 job ads a month for AI and machine-learning experts in the three months to November, about ten times more than in the first quarter of 2020 (see chart). Derek Zanutto of CapitalG, one of Alphabet’s vc divisions, notes that large companies had spent years collecting data and investing in related technology. Now they want to use this “data stack” to their advantage. AI offers ways to do that.Unsurprisingly, the first industry to embrace AI was the technology sector itself. From the 2000s onwards, machine-learning techniques helped Google supercharge its online-advertising business. Today Google uses Ai to improve search results, finish your sentences in Gmail and work out ways to cut the use of energy in its data centres, among (many) other things. Amazon’s AI manages its supply chains, instructs warehouse robots and predicts which job applicants will be good workers; Apple’s powers its Siri digital assistant; Meta’s serves up attention-grabbing social-media posts; and Microsoft’s does everything from stripping out background noise in Teams, its videoconferencing service, to letting users create first drafts of PowerPoint presentations. Big tech quickly spied an opportunity to sell some of those same AI capabilities to clients. Amazon, Google and Microsoft all now sell such tools to customers of their cloud-computing divisions. Revenues from Microsoft’s machine-learning cloud service have doubled in each of the past four quarters, year on year. Upstart providers have proliferated, from Avidbots, a Canadian developer of robots that sweep warehouse floors, to Gong, whose app helps sales teams follow up a lead. Greater use of cloud computing, which brings down the cost of using AI, enabled the technology to spread to other sectors, from industry to insurance. You may not see it, but these days AI is everywhere.Dulling the cutting edgeIn 2006 Nick Bostrom of Oxford University observed that “once something becomes useful enough and common enough it’s not labelled AI any more”. Ali Ghodsi, boss of Databricks, a company that helps customers manage data for AI applications, see an explosion of such “boring AI”. He argues that over the next few years AI will be applied to ever more jobs and company functions. Lots of small improvements in AI’s predictive power can add up to better products and big savings. This is especially true in less flashy areas where firms are already using some kind of analytics, such as managing supply chains. When in September Hurricane Ian forced Walmart to shut a large distribution hub, cutting off the flow of goods to its nearby supermarkets in Florida, the retailer used a new AI-powered simulation of its supply chain to reroute deliveries from other hubs and predict how demand for goods will change after the storm. Thanks to AI the process took hours rather than days, says Srini Venkatesan of Walmart’s tech division. The coming wave of foundation models is likely to turn a lot more AI boring. These algorithms hold two big promises for business. The first is that foundation models are capable of generating new content. Stability AI and Midjourney, two startups, build generative models which create new images for a given prompt. Request a dog on a unicycle in the style of Picasso—or, less frivolously, a logo for a new startup—and the alogrithm conjures it up in a minute or so. Other startups build applications on top of other firms’ foundation models. Jasper and Copy.AI both pay OpenAI for access to GPT3, which enables their applications to convert simple prompts into marketing copy.The second advantage is that, once trained, foundation AIs are good at performing a variety of tasks rather than a single specialised one. Take GPT3, a natural-language model developed by OpenAI. It was first trained on large chunks of the internet, then fine-tuned by different startups to do various things, such as writing marketing copy, filling in tax forms and building websites from a series of text prompts. Rough estimates by Beena Ammanath, who heads the AI practice of Deloitte, a consultancy, suggest that foundation models’ versatility could cut the costs of an AI project by 20-30%. One early successful use of generative AI is, again predictably, the province of tech: computer programming. A number of firms are offering a virtual assistant trained on a large deposit of code that churns out new lines when prompted. One example is Copilot on GitHub, a Microsoft-owned platform which hosts open-source programs. Programmers using Copilot outsource nearly 40% of the code-writing to it. This speeds up programming by 50%, the firm claims. In June Amazon launched CodeWhisperer, its own version of the tool. Alphabet is reportedly using something similiar, codenamed PitchFork, internally. In May Satya Nadella, Microsoft’s boss, declared, “We envision a world where everyone, no matter their profession, can have a Copilot for everything they do.” In October Microsoft launched a tool which automatically wrangles data for users following prompts. Amazon and Google may try to produce something similar. Several startups are already doing so. Adept, a Californian firm run by former employees from Deepmind, OpenAI and Google, is working on “a Copilot for knowledge workers”, says Kelsey Szot, a co-founder. In September the company released a video of its first foundation model, which uses prompts to crunch numbers in a spreadsheet and perform searches on property websites. It plans to develop similar tools for business analysts, salespeople and other corporate functions. Artificial colouringCorporate users are experimenting with generative AI in other creative ways. Mr Sanchez of John Deere says his firm is looking into AI-generated “synthetic” data, which would help train other AI models. In December 2021 Nike, a sportswear giant, bought a firm that uses such algorithms to create new sneaker designs. Since last month Alexa, Amazon’s virtual assistant, has been able to invent stories to tell children. Nestlé, a Swiss food company, is using images created by DALLE-2, another OpenAI model, to help sell its yogurts. Some financial firms are employing AI to whip up a first draft of their quarterly reports. Users of foundation models can also tap an emerging industry of professional prompters, who craft directions so as to optimise the models’ output. PromptBase is a marketplace where users can buy and sell prompts that produce particularly spiffy results from the large image-based generative models, such as DALLE-2 and Midjourney. The site also lets you hire expert “prompt engineers”, some of whom charge a $50-200 per prompt. “It’s all about writing prompts these days,” says Thomas Dohmke, boss of GitHub.As with all powerful new tools, businesses must tread carefully as they deploy more AI. Having been trained on the internet, many foundation models reflect humanity, warts and all. One study by academics at Stanford University found that when GPT3 was asked to complete a sentence starting “Two Muslims walked into a…”, the result was likelier to invoke violence far more often than when the phrase referred to Christians or Buddhists. Meta pulled down Galactica, its foundation model for science, after many claimed it generated real-sounding but fake research. Carl Bergstrom, a biologist at the University of Washington in Seattle, derided it as a “random bullshit generator”. (Meta says that the model remains available for researchers who want to learn about the work.)Other problems are specific to the world of business. Because foundation models tend to be black boxes, offering no explanation of how they arrived at their results, they can create legal liabilities when things go amiss. And they will not do much for those firms that lack a clear idea of what they want AI to do, or which fail to teach employees how to use it. This may help explain why merely a quarter of respondents to the McKinsey Global Institute’s survey said that AI had benefited the bottom line (defined as a 5% boost to earnings). The share of firms seeing a large benefit (an increase in earnings by over 20%) is in the low single digits—and many of those are tech firms, says Michael Chui, who worked on the study. Still, those proportions are bound to keep rising as more AI becomes ever more dull. Rarely has the boring elicited this much excitement. ■ More

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    Is a white-collar recession looming?

    On december 2nd America’s Bureau of Labour Statistics (BLS) reported that the number of workers on non-farm payrolls rose by 263,000 in November, fewer than the 284,000 in October but hardly a sign of wide-reaching retrenchment. The country’s labour market remains awkwardly tight, with 1.7 job openings for every unemployed American in October, the latest figure available. Many businesses are still contending with staffing shortages in factories and restaurants.Meanwhile, in a seemingly parallel universe, American technology firms have shed 88,000 workers this year, according to Crunchbase, a data provider. On November 30th DoorDash, a food-delivery business, joined the firing frenzy, announcing it would lay off 1,250 workers, 6% of its total workforce. Banks have also been showing staff the door. On December 1st Wells Fargo, an American lender, reportedly cut hundreds from its mortgage division. Barclays, a British one, let go of around 200 workers last month. Wall Street stalwarts, including Goldman Sachs and Citigroup, have also made cuts. Retail titans such as Amazon and Walmart have trimmed corporate headcounts, but not jobs in warehouses and supermarkets. All this has prompted much hand-wringing about a “white-collar recession” (or, given the cohort’s sartorial tastes, a Patagonia-vest downturn). In an inversion of the usual pattern, this argument goes, the axe is now falling mostly at the top of the corporate pecking order; the boss of one big consulting firm talks of the hollowing out of middle management. So just how worried should America’s white-collar set be?On the surface, there is plenty of room for axe-swinging. In recent decades America’s economy has become ever more top-heavy. Managerial and professional occupations now make up 44% of total employment, up from 34% in 2000 according to the BLS (see chart 1). Partly that reflects faster growth in industries like tech and finance. But even within industries the share of white-collar jobs has grown: in manufacturing it has risen to 35% today from 29% in 2002; in retail it has gone up to 15%, from 12% two decades ago. Automation and offshoring have meant fewer technicians and cashiers but lots more business analysts and systems architects.As the rush of layoffs suggests, some of those workers have found themselves in the crosshairs. Still, talk of a white-collar recession seems overblown. For one thing, desk-jockeying jobs remain plentiful. Payrolls in finance are roughly at pre-pandemic levels. The tech industry employs 10% more staff today than in January 2020, according to the Computing Technology Industry Association (CompTIA). Even after Meta, a social-media giant, loses the 11,000 workers it laid off last month, it will still employ nearly 70% more than it did before the pandemic. Sacked techies should not find it hard to get work. Lots of old-economy firms would love to get their hands on their skills. Walmart, despite its corporate layoffs, is continuing to snatch up data scientists and other hypernumerate types. Already 59% of tech professionals work outside the tech industry, according to CompTIA. On the whole, demand for highly paid white-collar professionals is as voracious as ever. Unemployment rates for business and financial professionals, technologists and managers are even lower than America’s overall rate of 3.7%, and have fallen further over the past 12 months (see chart 2).Demographic changes will mean that rich-world companies find it increasingly difficult to recruit workers of all types, regardless of the colour of their collars. In America the share of the population aged between 20 and 64 tipped from 60% in 2010 to 59% in 2020, and by 2030 will fall to 56%, according to estimates from the World Bank. In Britain and the euro area the share is expected to fall from 58% to 56%, and 59% to 56%, respectively, between 2020 and 2030. Younger generations are now more likely to be studying and less likely to be working during their early 20s, adding to the squeeze on labour supply.Falling immigration compounds the problem. In 2019 net migration into America—the difference between immigrants and emigrants—was 595,000, the lowest in over a decade, thanks in part to the policies and rhetoric of Donald Trump’s administration. The pandemic pushed it down further, to 247,000 in the year to June 2021. In Germany immigration surged in the mid-2010s as the country opened its doors to Syrian and other refugees, but fell in subsequent years. A temporary spike from Ukrainian refugees this year will not be enough to resolve persistent labour shortages in many areas. Britain’s government, meanwhile, has declared itself “fully committed” to bringing net migration down.Barring big changes to immigration or retirement ages, in the coming years firms will have to shift their focus to doing more with less. For the agile project managers and programmers who can help engineer such productivity enhancements, the good times may just be getting started. ■ More