Locals could buy $10 worth of “Deli Dollars” for $8 in US currency, with the currency redeemable at a future date. In essence, they gave Tortoriello a tiny personal loan and collected a bit of interest. The program was a success, easily raising the $5,000 Tortoriello needed. Even the banker who turned down Tortoriello’s formal loan request bought some Deli Dollars: he couldn’t justify an institutional investment in the deli as a business, but he couldn’t pass up a personal investment in Frank.

Tortoriello’s successful experiment was the first of several conducted in the area, known as the Berkshires, through the 1990s. They in turn inspired a local paper currency known as Berkshares, founded in 2006 with the intent of supporting local businesses – shoppers get an effective 5% discount when they use Berkshares at local businesses that accept them. Today, according to Schumacher Center co-founder Susan Witt, between $125,000 and $175,000 worth of Berkshares are in circulation at any given time.

The launch of Berkshares added momentum to a global movement for local currencies, which now exist from Bristol in England to Spain’s northern Basque region. The main goal of Berkshares is to support local businesses, but advocates say local currencies have many potential benefits, above all increasing community resilience and self-determination. Money, after all, is a kind of communication technology: the most important signal of what actors in a modern economy want. But the structure and administration of a currency can skew those signals, and national currencies can be seriously out of sync with what a particular community needs or values. Local currencies can take back a portion of that power, and use it to encourage environmental responsibility and community spirit, or just mobilise untapped resources.

If you’ve paid even marginal attention to cryptocurrency in recent years, your antenna might be twitching. Frank Tortoriello’s Deli Dollars and local currencies like Berkshares were precursors of ICOs, or initial coin offerings: private, digital currencies issued to help fund the development of tech businesses using distributed blockchain database technology.

Further reading

Is Bitcoin really 'rat poison squared'?

By David Z Morris

ICOs first made headlines in 2017, and over the last 18 months have raised more than $15 billion dollars. That’s a far cry from $5,000 to move a deli – and not surprisingly, the ICO boom has attracted more than its share of bad actors, with many of the issuances proving to be outright scams.

Still, ICOs highlight how blockchain technology could help make local currencies more mainstream. Issuing a local paper currency is labour and infrastructure intensive: Berkshares are administered by a community non-profit in partnership with 16 local banks, who handle the exchange of dollars for Berkshares. By contrast, the basic software for cryptocurrencies like Bitcoin is open-source and relatively easy to copy, while a plethora of digital wallets and exchanges exist to facilitate the distribution and spending of cryptocurrency. Re-branding a Bitcoin-based system as a local currency is primarily a graphic design and marketing problem – no physical infrastructure is needed.

No surprise, then, that some of the very companies founded in the blockchain boom are joining the ranks of local-currency advocates. At the Oslo Freedom Forum in May of this year, Galia Benartzi, co-founder of the blockchain trading platform Bancor, told the story of the Lev Market, an experimental program in Israel. Bancor issued “Levs” – “hearts” in Hebrew – to 20,000 mothers, who used the currency to pay one another for services like babysitting, school pickups, and baking.

Bancor’s experiment seems to have borne out one of the strongest arguments for local currencies – that they can unlock unused resources. According to Benartzi, in less than a year, network participants exchanged over $24 million worth of goods and services using the Lev currency. “Not a shekel, not a dollar ever changed hands,” Benartzi said. “And what we asked ourselves was, why weren’t they doing this to begin with? All the mothers were there before, all the goods and services were there before.”

According to Benartzi, the Lev market catered largely to women in low-income and marginalised communities – women who didn’t have much ‘official’ currency to spare. Such monetary privation has a double cruelty. Poor communities, more or less by definition, can’t build financial wealth or reap any benefit from long-term investments. But nearly as bad, and much less attended to, is their limited ability to benefit from money’s day-to-day role as an economic coordinator. If we consider money a communications technology that generates signals about collective needs and desires, poorer communities are being deprived of both wealth, and the information they need to generate it.

This is one of the key arguments made by Bernard Lietaer and Jacqui Dunne in their book Rethinking Money. Adding new currency to communities with too little of it can help connect unused resources to unmet needs, generating real benefits that can improve quality of life. Similar logic lies behind government stimulus spending during recessions – but local currencies, Lietaer argues, have the added benefit of providing some insulation from national fiscal and monetary crises.

Further reading

How digital dissidents are defying the despots

By Ian Birrell

Of course, at least some of the activity measured by Bancor’s Lev Market experiment was taking place before the introduction of the new currency: parents, especially in poor communities, have never needed money to coordinate shared child care, for example. And this suggests a possible drawback to community currencies. As Leitaier and Dunne themselves point out, considerable research has shown that non-monetary exchanges are crucial to cementing social bonds: when I babysit your children as a favour, we are bound together by an outstanding social obligation.

The network of social ‘debt’ built up by this sharing is weakened by adding money to the equation. When I babysit your children and you pay me for it in currency, we have no debt, and no resulting social bond. Injecting currency at the community level, then, could increase efficiency, but come at a serious cost in the erosion of social capital. That’s worrying, because social capital is itself a major factor in the resilience of communities – sociologists like Robert Putnam argue that in money-centric societies like America, it has been eroding for decades.

Creating local currency on a blockchain could also create a novel sort of risk: by nature, cryptocurrency can be sent anywhere on the globe, bought and sold freely, and, almost inevitably, financialised and speculated upon. In fact, increasing the convertibility of local currencies is part of Bancor’s stated mission, and its current major product is a trading platform.

For some locales, this could actually be a benefit. Well-run currencies from economically dynamic regions could hold on to their value elsewhere – you’d probably be welcome to spend your Brooklyn Bucks in San Francisco, and vice versa. This could be a boon to local businesses, who would have privileged access to liquid capital, much as the international acceptance  of the US dollar gives American companies an advantage in world markets.

But many local cryptocurrencies, exposed to the wild complexity of the global financial market, could become chum for its circling sharks. As digitisation continues making financial markets more granular, the ability to ‘short’ any cryptocurrency – bet that its value will decline – could become widespread. This would allow speculators to bet against the currency of a city or region, and put downward pressure on its exchange value. Such tactics have had devastating effects when used against national currencies – George Soros’ historic shorting of the British pound, for instance, or the attack on the Thai bhat that kicked off the Asian financial crisis.

Those cases were made possible by mismanagement at the national level, and resulted in massive transfers of wealth from entire national populations to hedge funds and other speculators. Many local currencies rely on fixed exchange rates, or ‘pegs,’ to national currencies – the same sort of pegs that made the baht and pound vulnerable. Local administrators are at least as likely to make mistakes as the Bank of England, and would have fewer resources to defend against hostile speculators.

More from this author

The cracks appearing in the digital economy's foundations

By David Z Morris

A world of local cryptocurrencies, then, could feature regular short attacks on the Portsmouth Pound, the Philadelphia Peso, and the Ypsilanti Yen. But these would have more limited impacts than large-scale shorts on national currencies, both because the impacts would be localised and because community currencies almost always coexist with national currencies. In fact, one of Lietaer’s most frequently made poinst is that using multiple currencies protects against fragility in any one of them.

Digitising local money would undeniably have complex and unpredictable implications. In fact, there may be a core tension to the idea itself: if digitisation inevitably connects a currency seamlessly with global finance, is that currency truly local anymore? Of course a certain level of trading friction may in fact be part of the insulating benefits Lietaer believes local currencies create, a technological moat against assaults by the global financial mob.

But digital currency, at its heart, is largely about eliminating friction – the friction of printing paper money, the friction of central bank authority, and the friction of geography itself. There may be ways to insulate a local digital currency, but this is largely uncharted territory: it’s not clear whether a local currency could prevent a global cryptocurrency exchange from listing it for trade, or perhaps use GPS systems to prevent it from being sent or used outside a particular region.

For their part, Susan Witt and the administrators of Berkshares say there are no plans to go digital anytime soon. They prefer to keep up good relations with trustworthy local banks – and make sure their local currency stays local.

One possible culprit is the rise of the so-called ‘gig economy’. In her new book, Gigged: The Gig Economy, the End of the Job and the Future of Work, tech journalist Sarah Kessler uncovers how despite early claims that the flexibility of short-term “gigs” would make workers happier, they bring ‘liberation’ from a traditional safety net, reliable work hours, and even, in many cases, anything close to a livable wage.

While ‘freelancers’ have existed since the invention of money, the ‘gig economy’ kicked off with Uber. The taxi company used AI scheduling and mobile apps to connect passengers to independent drivers. It was distinct from existing freelance marketplaces in its focus on a lower-skill task, and its control over things such as pricing and marketing that, in higher-skill markets, were mostly handled by freelancers themselves.

After Uber truly took off in around 2013, droves of startups mimicked the model – Instacart was the Uber of grocery shopping, Taskrabbit was the Uber of housework.

Kessler has been reporting on the gig economy nearly from the beginning, starting as a blogger for Mashable in 2011, and more recently as an editor at Quartz. When we met recently to discuss the book, she said she was initially encouraged to write it as an immersive first-person narrative – an update of Barbarah Ehrenreich’s Nickel and Dimed in which she would try to live for a year on wages earned through the gig economy.

But she realised that her own background as a well-educated white Midwesterner would make that viewpoint far too narrow. So we get just a few glimpses of Kessler trying her hand at things like the banally authoritarian ‘personality test’ administered to would-be contract housecleaners (“If a $100 bill is laid out on a table without a note, do you take it?”).

Further reading

Amazon: the new Victorian workhouse

By James Bloodworth

Instead, Kessler focuses on the workers living out the reality of the gig economy’s promise. She spent years following them, and captures them as fascinating, complex figures: an Uber driver who would later agitate for a strike against the company; a Canadian woman who masters the micro-drudgery of Amazon’s Mechanical Turk; a computer programmer who tires of the corporate world and goes freelance; and an activist hoping to bring gig opportunities to the rural poor.

Alongside her intimate portraits of these workers’ lives, Kessler picks apart the founding mythology of the gig economy. Companies like Uber consistently emphasise workers’ supposed love of ‘flexibility’, especially to deflect hard questions about pay and fairness. That sort of glad-handing has roots going back at least to the Kelly Girls of the 1950s – temporary office workers who were described by their services as “bored with strictly keeping house.” The was that they didn’t need to be paid or treated well because they were just pampered homebodies, working purely for fun.

That wasn’t true back then – Kessler finds a contemporary study showing that 75% of temp workers in the early 1960s were primarily motivated by a need to earn money. And it’s not true now. While workers today of course value “flexibility” – who doesn’t? – surveys show that all things being equal, they’d rather have a reliable full-time job.

An even broader version of the ‘flexibility’ mantra came from Uber founder, Travis Kalanick. Kalanick (before getting unceremoniously booted from his own company in 2017) was fond of saying that his platform offered “turnkey entrepreneurship”.

“Uber totally sold its job on this idea that it was a path to the American Dream,” Kessler told me. “That it was going to really significantly improve your life status and you’d be running your own business.” That rhetoric, along with tactics like $200 bonuses for drivers who recruited friends to join the platform, helped Uber successfully sign up tens of thousands of drivers around the world.

Further reading

Test article

By James Bloodworth

One of those drivers was Mamdooh Husein, better known as “Abe,” whose tale is easily the most compelling, weird, and enlightening Kessler tells. We meet Abe as a 28-year-old waiter, whose spartan life in Kansas City makes him long for more. That longing makes him an easy mark for scams including a pyramid scheme called GIN, which promises him wealth and freedom (in exchange for a few small upfront payments). He is badly stung, in an experience that initially makes him sceptical of Uber’s very similar promises – though deep in debt, he eventually signs on.

For Abe and many other gig workers, the promise of ‘entrepreneurship’ is a deceptive one. Like many other drivers, Abe is initially enthusiastic about Uber, but winds up frustrated by falling pay and unexpected expenses. While the company promised that drivers like him could earn up to $90,000 a year, those calculations didn’t include little things like gasoline, car insurance, and maintenance – all of which drivers footed the bill for. For some Uber drivers, expenses can cut income by as much as half.

Describing their workers as ‘entrepreneurs,’ while downplaying the fact that businesses have expenses, seems to have been a conscious strategy by Uber and other gig platforms. The sense that it’s all a long con is even more acute in a handful of cases where startups encouraged workers to buy supplies from the companies themselves. In one case Kessler describes, a cleaning company offered $150 kits full of supplies to contract workers – which it seems unlikely were sold at a loss. Uber for a time offered subprime leasing deals through a subsidiary, and used the app to pitch them to drivers (though in typical fashion, Uber somehow managed to lose money on this company-store scheme).

Just as damning is Kessler’s finding that on-demand technology itself, loudly promoted for making work more flexible and services more nimble, was often used to create employee-like relationships with a veneer of plausible deniability. Instead of human managers giving direct instructions, which would make them more likely to be classified as employees, gig workers are trained by online videos, scheduled by algorithms, and evaluated by ‘star’ ratings issued by customers themselves.

Those star-rating systems have become standard issue not just for gig apps, but for everything from product reviews on Amazon to restaurant directories like Yelp. But the fact that they’re applied to individuals, rather than companies or products, is emblematic of the larger issue here – it helps companies offload risks that come with having employees.

If a worker employed by a company gives sub-par service, they might be fired, but they’re equally likely to get more training or a chance to improve. On gig platforms, workers who get bad ratings can simply be removed from the platform – often, as Kessler describes, with no explanation, warning, or recourse.

The rise of the gig economy, of course, happened in a broader context that made workers ripe for such exploitation. When Uber arrived, unemployment in the US was still over 7%. At the same time, the recession set off sustained cuts to American education at all levels – cuts which hit the most marginalised communities hardest.

That educational gap is clear in the story of Terrence Davenport. Kessler highlights Davenport’s years-long effort, with help from a well-meaning nonprofit, to connect his poor, rural Arkansas hometown, through the internet, to the gig economy. For most remote work – research, data entry, and customer service – Davenport’s students wind up competing with workers abroad who can afford to charge much less for the same work.

Further reading

All hail the capitalism revolution on the streets of Paris

By Ian Birrell

At the same time, few of the programme participants have even the basic education for online jobs, such as social media management, that require strong language skills. After several years of heartbreaking struggle in which very few participants found even temporary work, the programme was shut down.

Abe eventually came to regard Uber as yet another scam, and started a campaign to pressure the company to improve its treatment of drivers. At the time, some news outlets reported the action as an inspiring blow struck for the little guy, but Kessler’s thorough reporting reveals it as something more complicated. The small strikes Abe organises have essentially no effect, so he – as Kessler finds out through an email he shows her – offers Uber a deal: for a payment of $10,000, he’ll stop organising.

Uber doesn’t bite, but Kessler is rightly troubled by this willingness to trade collective power for personal gain. In her effort to understand it, she concludes Abe “had felt powerless for most of his life” and violently resented that feeling. But he had few models for change aside from self-help scammers – and a bit later, President Donald Trump.

By the end of Gigged, Kessler tells us that Abe has managed to put together the down payment on a rental property. An American optimist to the core, he thinks this will inevitably lead to a future career as a real estate tycoon, making him enthusiastic about Trump’s (since-fulfilled) plans to cut taxes and regulations on the corporations and the wealthy. “When you’re rich,” Abe told Kessler, “You can do things nobody else can do. You’re truly free.”

Abe’s story suggests the gig economy is deeply implicated not just in inequality, but in a disturbing subtext of supposedly ‘populist’ Trumpist ideology. The broad shift away from employment and towards relationships in which workers are treated as second-class citizens drives home the idea that being rich is the only security. It also teaches the lesson that being entirely self-centered – exploiting the weak, blackmailing the strong, and devil take your fellow man – is a great way to get there.

The nature of the platforms reinforces this antisocial spirit, to the benefit of the gig companies. Workers don’t interact in a workplace, and can’t communicate with or even identify one another through the platforms, making organising for change much more difficult. Even skilled workers aren’t immune – Kessler reports on emerging AI systems that can coordinate teams of freelancers to complete complex projects, such as long business documents, without direct human supervision. That could expand companies’ protection from employment lawsuits, applying the same kinds of pressure to high-skilled workers that are already faced by drivers, cleaners, and couriers.

How do we reverse those trends? Technology can be turned to better ends. Platforms similar to Uber, but built without profit in mind, could form the basis for a new kind of worker cooperative, preserving gig flexibility while ensuring fairer pay (and likely leveraging that as a selling point).

Another bright spot Kessler finds is that there is actually a profit motive for some companies to provide good jobs that don’t require college degrees. That’s particularly true of those, like Starbucks and Costco, that emphasise high-quality service, and want to avoid high turnover and training costs. For consumers interested in voting with their dollars, she suggests using licensed taxis, or hiring a housecleaner directly instead of through an app.

But the logic of the market alone won’t restore worker dignity, or mass faith in responsible leadership. European and UK  legislators have increasingly shown a willingness to reinforce traditional labour relationships, but there’s much less stomach for it in in America.

Some states have pushed for more worker-friendly rules, but others, including Texas, have actually prevented cities from regulating gig platforms. Rather than making Uber more like a regular job, though, Kessler argues that laws need to adjust to the new reality of flexible labour – for the US, she suggests that portable benefits and more fine-grained employment categories might help, though she acknowledges those have their own issues. She also touches on more radical proposals like Universal Basic Income, a streamlined welfare system beloved of Silicon Valley itself.

Kessler’s book makes it more clear than ever that some solution to the fragmenting of traditional employment is direly needed.  That the shortcomings of flexible labour have become so clear, at a moment when the US is so politically ill-prepared to act, may yet prove a tragedy of historic proportions.

How quaint all that seems now. Last week, I attended three back-to-back blockchain conferences in New York, all part of the city’s first official Blockchain Week. The largest of which, Consensus, had over 8,500 attendees; the whole 2014 conference could have fitted comfortably into a corner of the hall where Consensus attendees ate lunch.

The other two – Ethereal and Token Summit – were each big in their own right. And were surrounded by a constellation of at least 25 official and dozens of unofficial events, spread from Queens to the Lower East Side to Columbia University’s campus up north. Blockchain Week seems poised, in other words, to become the South By Southwest of blockchain technology.

Further reading

The cracks appearing in the digital economy's foundations

By David Morris

The frenetic activity during the week was remarkable for two reasons. First, because it followed the massive slide in the value of essentially every blockchain asset: Bitcoin is currently down more than 50% from its December 2017 peak. Yet there was no sense of anxiety or uncertainty in those packed hallways, or any discussion of the crash, suggesting it was seen as a mere speedbump on a much longer road. (In fact, the currency has since bounced back, gaining $500 over the weekend.)

More profoundly, the size of Blockchain Week is striking because so many prominent investors and technologists still believe blockchain’s rise to be little more than a high-tech tulip mania. Only a few days before it kicked off, Warren Buffet said Bitcoin is “probably rat poison squared”. There’s a grain of truth to those warnings, in the sense that the nascent blockchain industry is full of wild overoptimism, shady projects, and outright scams.

But it’s hard to imagine even the most hardened Bitcoin sceptic walking away convinced that there’s nothing of real value there. Ignore the headline-grabbing big names who showed up (though for the record, they included Snoop Dogg, Deepak Chopra, and Twitter CEO Jack Dorsey). Leave aside, for that matter, the size of the events or the passion of the attendees. The event demonstrated that real blockchain tools are being built and put into action, both by blockchain-first upstarts, and, increasingly, by legacy giants.

Of course, there was plenty of absurdity to be found if you were looking for it. A fleet of rented Lamborghinis and a staged “Bankers Against Bitcoin” protest outside of Consensus both gave form to some of the industry’s more childish impulses. And Chopra’s appearance at Ethereal didn’t exactly convey seriousness, particularly when one attendee asked him whether the various layers of the Ethereum protocol could be compared to the chakras of Eastern metaphysics. The question fit into the broader silliness of Ethereal, an event long on sweeping generalities and cyberpunk installation art but short on substance. Appropriately enough, Chopra – who might or might not actually understand anything about blockchain – nimbly dodged the question about Ethereum’s chakras.

Further reading

Alexa, how do we disrupt the tech lords?

By Peter Franklin

But elsewhere plenty of deadly serious work was on display. Lawyers wrangled with rising regulatory scrutiny, and how it could impact future digital economies. At Token Summit a panel of lawyers from powerhouses such as Cooley LLP and Perkins Coie concluded, in essence, that existing securities regulation frameworks couldn’t be squared with the way blockchain tokens will be used in the future – an entirely new, blockchain-focused regulatory body would be needed to square the circle.

On the technology front, while there remains plenty of hand-wavy theorising, the first real-life applications for blockchain are becoming apparent. Digital payments are the main focus – and there is real potential here for the disruption of credit card systems. Dorsey, who in addition to Twitter founded and runs the payments upstart Square, declared at Consensus that the internet “will have a native currency. I don’t know if it will be Bitcoin or not. I hope it will be.”

But the technology isn’t just about digital money. Several well-funded projects at Consensus showcased their efforts to use blockchains to rent unused computing power for tasks like rendering CGI images. Other startups see the blockchain as a new way to maintain permanent provenance records of real-world objects including fine art.

But perhaps the most intriguing development on display last week was the blockchain-based secure personal identity system. Because a public blockchain isn’t owned or controlled by a single entity, the creation of a blockchain using coded private keys to prove your digital identity could protect your privacy – and reclaim power from Facebook and Google.

Further reading

Can't we stop data monsters from devouring their rivals?

By David Morris

Also among the novel applications being dreamed up is one that at first might sound silly. Last December, a digital collecting game called Cryptokitties created a big enough furore to temporarily choke the Ethereum blockchain it runs on. This isn’t merely some strange digital version of Beanie Babies, though – at a Token Summit panel, CryptoKitties creator Dieter Shirley explained that because the code behind the Kitties is public, they can be remixed and reused in other applications. Cryptokitty owners can now race their cats and dress them up, using tools built by third-party teams.

Bear with me. Blockchain games are still very primitive, and it’s tempting to dismiss games in general as trivial. But this has the feel of something radically new. Imagine a gamer being able to take their Wolfenstein character on a detour through a level of Call of Duty, whether or not either game’s creator thought that was a good idea. More broadly, Cryptokitties highlights the most mind-bending aspect of blockchain tech: unlike pieces of code sitting on traditional servers, digital objects on the blockchain are genuinely unique and quite hard to destroy, giving them some of the ontological substance of physical stuff.

That sort of conceptual noodling was probably of interest to only a small fraction of the crowd at Blockchain Week. They included day traders looking for their next hot tip, company staff partying hard on their expense accounts, and an array of strivers hoping to get a foothold in the emerging industry. It also included a markedly international cast, from across Europe, India, the Caribbean, and particularly East Asia, with a huge Chinese presence and representatives from no less than two Japanese news outfits.

One grim reality, though, was the scarcity of women. That’s a persistent issue across technology companies, of course. And blockchain does have some notable female leaders, including Lightning Labs CEO Elizabeth Stark, and former JP Morgan crypto analyst Amber Baldet, who made several high-profile appearances across the week. But seeing the crypto community all in one place suggested that the overall imbalance is even worse than in tech more broadly: I would estimate that attendees were as much as 90% male, compared to around 75% even at Amazon, one of Silicon Valley’s least diverse companies.

Of course, that won’t matter much if blockchain turns out to be simply a mass delusion – the wealth it has generated will melt away, leaving unlucky bagholders crying in their soup. That might still happen next year, or the year after that, especially if a broader economic downturn sucks capital out of the ecosystem. But even if Blockchain Week 2018 was partly fueled by a still-overinflated bubble, it did showed that the technology has more potential than its critics seem to grasp – and it’s becoming reality with mind-boggling speed.

Two weeks later, it was discovered that Google’s YouTube Kids app, touted as a safer destination than the main site, was displaying disturbing conspiracy videos to children. That same day, Facebook admitted that Cambridge Analytica had improperly harvested the data of what turned out to be millions of its users, and an Uber self-driving car struck and killed a 49-year-old woman in Tempe, Arizona.

These seemingly unrelated incidents share a root cause. In each case, an automated system, ultimately designed to help generate huge profits by displacing human workers, failed in a crucial way and caused significant harm. In that common failure, they all point to cracks in the foundational conceit of the digital economy: the dream of ‘scaling’.

As far back as the mid-nineties, technologists including Bill Gates himself were predicting that artificial intelligence and “big data” would enable digital companies to grow in unprecedented ways. Those dreams are coming to fruition, as Facebook, Amazon and similar companies reach more customers with more personalised experiences than any companies that came before them, while using fewer people and far less physical infrastructure to do it. This vision has made digital companies immensely valuable and forced the old guard to play catch-up, while genuinely making life better for millions of customers, users – and above all, shareholders.

But the instances where these systems fail – often what engineers refer to as “edge cases”  – have proven deeply troubling to the public and regulators. And technological solutions may not be achievable before the broader idea of digital scaling itself is undermined, along with a global economy whose future is increasingly premised on it.

Henry Ford, building on Frederick Winslow Taylor’s early 20^th century drive for scientific management, built a dynasty on the drive to industrial scale. By producing a uniform product using semi-skilled workers doing repetitive tasks, he flooded the world with affordable cars. Scaling in the digital era is something almost entirely different – it requires no factory floor, and almost no workers, because its products are largely intangible and its distribution systems are automated.

The most exemplary companies, in fact, produce no physical product at all, acting as mere intermediaries between buyers and sellers, drivers and passengers, content creators and eager eyeballs. Adding another customer or user costs a digital company nearly nothing – just a bit more bandwidth and data storage. In exchange, each user is a new chance to harvest data, which in turn is used to better serve (read: more precisely target) other users. Growth also means ‘network effects’, such as the compounding benefit to social media users of being on the same platform as everyone else. Those effects can ultimately add up to de facto, winner-take-all monopolies, in which the harvesting of gargantuan profits is nearly effortless.

It can take years for this model to come to fruition, though, which is why tech companies are often valued at huge multiples of their present profits – and with little connection to the size of their workforce. Instagram only had 13 employees when it was acquired for $1 billion by Facebook in 2012. Facebook itself, in December of last year, had a reported 25,105 employees and a $550 billion market value, while Exxon Mobil at around the same time had 69,000 employees and a market cap of $319 billion.

The gap is even more striking when comparing companies’ physical footprints. WalMart, slightly more recently, had $160 billion in hard assets on a $300 billion market valuation, while Facebook had just $9 billion in hard assets at a $500 billion valuation.

That makes Facebook, according to the stock market, nearly five times more valuable than Exxon per employee, and nearly 30 times more valuable than WalMart per dollar of machinery, real estate, and physical product. Such numbers have for years provoked intense anxiety about how society will cope when all the jobs are automated away, but for investors, they’re deeply compelling. Of the ten most valuable public companies in the world today, at least six depend substantially on data-driven, automated scaling.

These are no longer just speculative, long-term bets. Facebook and Google have become like malfunctioning ATMs, spewing advertising profits into the hands of giddy investors. Amazon, long a punchline of unprofitability, has now made money every quarter since mid-2015, including a gobsmacking $1.9 billion profit in the last quarter of 2017.

Amazon's ambitions

Is Amazon's attempt to disrupt the healthcare industry heroic or villainous?

By Peter Franklin

But those long bets – including the ones that already seem to be paying off – could blow up dramatically if the dream of automated scaling itself goes south. This has already happened in individual cases, such as when the startup Theranos couldn’t deliver on its promise to automate and streamline medical testing. After providing unreliable test results that may have endangered patients, the company, valued at as much as $9 billion, imploded.

A similar fate could easily await Uber, which has been operating at a massive financial loss for years. Investors have continued to feed it cash largely on the premise that the company will become as wildly profitable as Amazon once it masters the technology of self-driving cars and eliminates the cost of drivers. Founder Travis Kalanick has described autonomous driving as an “existential” priority for the company.

But success in that effort is looking increasingly distant. After the Arizona crash, Uber chose to suspend its testing program, and new CEO Dara Khosrowshahi canceled a planned visit to the testing site. Then the Arizona government, which had taken a hands-off approach to regulating autonomous vehicles, itself suspended Uber’s right to test there.

But both Uber and Theranos are individual companies premised on solving major, novel technical challenges. More fundamentally worrying is the prospect that automated systems that seem to already be working may have major hidden flaws.

That possibility is illustrated, above all, by Facebook’s current catastrophe. The site has kept users and advertisers happy for more than a decade, but after the Cambridge Analytica revelations, governments around the world called for investigations, users called on one another to #deletefacebook, and the company’s market value sunk by as much as $95 billion dollars – more than the entire value of Starbucks.

The outrage was driven by a laundry list of perceived fouls – the fact that user data was leaked, the discovery of just how much of it was collected, the fact that it might have helped elect Donald Trump, and perhaps the very idea that political actors could use highly detailed personal data to sway public opinion. But Cambridge Analytica also may have simply been the straw that broke the camel’s back, after months of reports of the insidious spread of ‘fake news’ and Russian manipulation through Facebook.

Those problems hinge on Facebook’s various uses of automation, including news feeds and ad sales processes that only inconsistently involve actual human judgment. Content-suggestion algorithms, which place a premium on user attention, have been shown to systematically amplify shocking and extreme material, including fabricated or misleading news. Facebook’s lightly-screened ad sales process allowed Russia-linked buyers to spend as much as $150,000 to try and influence US politics, and made it possible to run racially discriminatory housing ads – both illegal.

Facebook has taken some steps to address these issues, including identity checks for political ads and fact-checking for news. Those efforts, which haven’t yet proven successful, depend on adding much more human judgment to Facebook’s operation. That necessity could prove very costly for Facebook, as shown by how it and other social media companies already screen for violent, obscene, or illegal content. In 2014, it was estimated that there were over 100,000 content screeners working for various companies worldwide – many times the number of Facebook employees. Those content screeners have to make sometimes subtle judgments, for instance between someone articulating a fantasy (acceptable) and someone actually trying to buy or sell sex (illegal).

Silicon Valley domination

Google, Amazon, Facebook: It's time to smash Silicon Valley monopolies

By Nigel Cameron

Sharing platforms such as Facebook use humans to screen extreme content precisely because automated systems are bad at making those types of judgments. One way of understanding those limitations is by dividing artificial intelligence systems into two categories – ‘limited’ and ‘general’. A limited AI works in a closed, often numerically-readable environment, such as a chess board. Computers excel in these contexts, as they’re able to crunch numbers at a scale humans can’t touch. Business applications of limited AI are so widespread we may not even think of them as AI – Expedia’s algorithm for collecting and ranking available flights, for instance. Uber’s routing software, which essentially moves cars around a fixed map like game pieces, also fits the definition of a ‘limited’ AI.

Platforms such as Facebook and Amazon have worked to turn more complex challenges into this sort of quantifiable and contained problem. Digital ad targetting systems, for instance, reduce individuals to as few as 50 data points about demographics or taste – a process called matrix factorisation which simplifies the complexity of human motivation into the barest sketch.

But these companies haven’t been nearly as successful in automating the detection of malicious actors, fake news stories, hate speech, or terrifyingly violent children’s cartoons. Successfully making such judgments involves variables, such as tone and context, that aren’t currently easy to reduce to machine-readable numbers.

Further reading

Can journalism survive the onslaught of social media?

By Emily Bell

Algorithmic systems, for instance, can quickly recognise a nude image or sexual language, but they can’t always distinguish between pornography and a discussion of gay culture. In media, making these subtler distinctions has long been the province of human intermediaries, such as newspaper editors. Facebook and Google would love for machines to take over those roles, not only to keep profit margins high but to avoid regulation as media outlets. That would require a quantum technological leap: the creation of something at least approaching what’s known as a ‘general’ artificial intelligence.

General AI is the stuff of sci-fi dreams, from C-3PO to Skynet, capable of performing any intellectual task a human can and then some. If regulators or the public successfully pressure YouTube to begin drawing distinctions between Alex Jones’ feverish conspiracies and distraught Yemeni refugees lambasting Saudi intervention, automated systems to effectively do so would require a command of human geopolitics, emotions, standards of fact, humor, and moral values, just for a start.

Some of these capabilities are under development, including emotional judgment. But there is wide disagreement about the potential for linking those discrete capabilities into something capable of actual human-like decision-making. It could be many decades away. It might even be outright impossible – after all, we still only vaguely understand how our own minds work.

Efforts are now underway to automate not just driving, media distribution, and medical diagnosis, but also legal aid, elder care, and many other tasks. But while it may be easy to automate some core aspects, these endeavours all include elements that are well beyond the ability of current or near-future technology to tackle, and which can arise unexpectedly and demand quick reaction.

The last few months have shown that companies pursuing a path to automation at scale are willing to use real people, and even entire societies, as guinea pigs as they test the limits of their technology. Their experiments are becoming more risky, not less, as their reach expands. More regulations like Europe’s new data privacy act might be on the horizon, introducing the possibility of constricting regulation that will limit platforms from reaping the greatest possible profit. But if those profits are premised on stripping away human supervision and waiting for the machines to fail, that might be for the best.

Big tech's big secret

In big tech, users aren’t customers. They’re what’s for sale.

By David Z Morris

On a Thursday, in early March, Casey Hopkins decided that it was time to bite the hand that fed him. Hopkins, co-founder and CEO of the design company Elevation Lab, wrote a long, righteously angry blog post describing how Amazon.com had repeatedly allowed counterfeiters, probably based in China, to sell knockoff copies of brand-name Elevation products through the site. A counterfeiter had, most recently, created copies of The Anchor, Elevation’s popular headphone hook, using a poorly-cast mold and photocopied box graphics. For five days, Hopkins wrote, the counterfeiter had stolen “all” Amazon sales of The Anchor by undercutting the price of the genuine article.

Moreover, Hopkins said Amazon was “complicit” in the problem, because it profited from the copycat sales, and wasn’t doing much to stop them. Amazon has sold knockoffs of a plethora of innovative, niche products – from phone cases to bedsheet helpers  and baby clothes. Each product was crafted with thoughtful attention to an unaddressed need, and in each case, an imitator quickly stole the idea, along with as much as 90% of Amazon sales.

Introducing our social media series

Social media. What are we to do?

By Nigel Cameron

Of course, these niche products and companies might not exist in the first place if it weren’t for Amazon. The retailer’s ability to connect customers to the precise product they want – whether hipster baby clothes or a hook for headphones – is exemplary of the rise of the data economy.

In his new book, Reinventing Capitalism in the Age of Big Data, Viktor Mayer-Schönberger says data-rich marketplaces, such as Amazon, are making commerce drastically more efficient by using trackers and algorithms that predict consumer desires. But Mayer-Schönberger and his co-author, Thomas Ramge, also warn that the nearly-inevitable concentration of digital marketplaces and advertising channels is making society more vulnerable to market breakdowns.

The logic of algorithmic targeting also drives online media such as Facebook, Twitter, and Google’s YouTube, though they’re selling targeted advertising rather than hawking products directly. That slight distinction is what makes the problems of these social media sites more attention-grabbing than Amazon’s counterfeit epidemic.

In building systems that let them find the perfect customers for a headphone hook, social networks also gained the ability to push the specific rage buttons of narrow, vocal social groups. And just as Amazon isn’t structurally punished for selling counterfeits from China, Facebook doesn’t care much if the content demanding user attention is coming from Russia. Facebook CEO Mark Zuckerberg dragged his feet over any admission of flaws in the platform, moving towards reform only after a massive post-election backlash.

Fraud that exploited data targeting would be a minor annoyance at worst, of course, if the malefactors were tricking one or two online outlets out of 20 that were equally popular. But Amazon, Facebook, and Google overwhelmingly dominate their sectors. Google answers 90% of all search queries. Amazon generates nearly half of all online sales. And for niche products, Amazon can generate up to 90% of sales  – making its tolerance for counterfeiters a systemic menace.

Of course, Amazon was a pioneer of ecommerce, so maybe its market share is just a product of first-mover advantage. By standard economic logic, its early lead should now be slowly eroding under competition from new entrants. After all, anyone can start a website – right?

But no – Amazon gained 5% more market share last year alone. That’s because the pathway to market dominance has changed since the age of railroads and telephones. Those systems benefited from ‘network effects’, with each new node making the whole operation more appealing for new customers, and eventually eliminating healthy competition. Mayer-Schönberger and Ramge argue that the combination of data-gathering and machine learning instead leads to what’s called a ‘feedback effect’ – which is something like a network effect on steroids.

As they collect data and refine their algorithms, those such as Google and Netflix pull ahead in the race to predict consumer desires – which helps them win more customers, and in turn more data, until they can’t be overtaken. Because of the resulting concentration, any flaws in the algorithms used by Amazon and Google have immediate and widespread effects. In another alarming example, an unknown group has successfully gamed YouTube and showed algorithmically-generated, often violent videos to millions of children.

But these problems don’t need to be caused by fraudsters, cheats, and counterfeiters. Alex Jones’  Infowars channel was recently censured by YouTube after nearly a decade of spreading dangerous conspiracy theories there. Jones is an American who seems to at least partly believe what he is selling – and has made millions of dollars not only for himself, but for YouTube too. Systematic harassment on Twitter doesn’t necessarily come from the Russians. Amazon could be misreading consumer preferences in ways that are unseen, but have vast distorting impacts on the market. We don’t know because we don’t have access to its data or algorithms.

The only way to preclude such deep distortions of our social and economic systems is to let companies try to out-predict each other – to create competition at the level of the algorithm itself. Because of the nature of machine-learning feedback effects, though, there are big barriers to new entrants. A startup social network has neither Facebook’s current audience, nor its historical trove of data about how audiences in the abstract behave. Without that data, a Facebook competitor can’t hope to train its own system to be nearly as effective at keeping users’ attention, and selling ads against that attention.

There are a number of policy recommendations on the table to encourage competition among data-driven companies. Some have suggested forcing transparency of algorithms themselves, but that wouldn’t give competitors the training data they really need. Chicago Booth School of Business Professors Luis Zingales and Guy Rolnik have proposed that individuals should have a property right to their own data, creating smooth portability between platforms. That might make switching social networks much easier, perhaps applying pressure on Twitter et al to curb abuse.

But those individual data rights wouldn’t create the critical mass of data to let an Amazon or Google competitor come up with new and better ways to match audiences and buyers with creators and sellers.  For that, Mayer-Schönberger and Ramge say we need what they call a “progressive data-sharing mandate”, which would force companies to share their data with smaller competitors in proportion with their market share.

So, for instance, Amazon might be mandated to share half of its current consumer preference data (anonymised, of course) with smaller competitors. Of course, data isn’t sufficient to build a viable Amazon competitor, but it might very well be necessary. Competing startups might apply different insights to raw data gathered by the giants in their field, or simply structure their offering in a different way, or impose different policies – such as more closely scrutinising low-cost Chinese resellers.

For Casey Hopkins, a viable alternative to the Everything Store can’t come soon enough. Amazon pulled the counterfeiter that had squatted The Anchor’s product page within hours of Hopkins’ blog post going viral – but just three days later, another one had taken its place.

More from our social media series

We CAN act to reduce the damage screens do to our children

By Susan Linn

Is the tide turning against the tech monopolies?

The US debate over tech monopolies has been shocked to life by a succession of illustrative cases. First and foremost, the evidence that a Russian-backed social media campaign skewed the 2016 election has driven home a sense that Facebook has transformed from a toy for teenagers into a behemoth in need of serious oversight. Congressional hearings on the issue began Tuesday. Meanwhile, Amazon’s $13.7 billion takeover of Whole Foods in July triggered terror among grocers, and Congressman David Cicilline requested hearings about the deal on antitrust grounds.

A lower-profile but more insidious episode came in late August, when the Open Markets anti-monopoly research group was pushed out of the think tank New America. While the details are disputed, Open Markets head Barry Lynn alleges that his group’s ouster came under pressure from Google, a major funder of New America, after Open Markets praised the EU judgment. Lynn, in the statement that he says got him fired, said the decision protected “the free flow of information and commerce upon which all democracies depend” against Google’s massive reach.

A tale of David and Google-iath

The responses have been remarkably direct, considering that tech innovators have been held up for decades as icons of American exceptionalism. The American Conservative in September called Facebook, Amazon, and Google “the new robber barons” and crony capitalists. New York Times tech columnist Farhad Manjoo in October officially launched his own crusade against what he calls the “Frightful Five.”

Manjoo gets to five by generously granting Microsoft and Apple legacy status. Those companies are indisputably giants, and they fit an easily-grasped rubric. In the 2001 U.S. antitrust case against Microsoft, courts found that the company had illegally used its control of a piece of infrastructure – Windows – to protect its other software from competition. Apple has similarly worked to lock down everything from entertainment to payments in its iOS ecosystem. It’s not a giant conceptual leap to connect such tactics to the way 19th century railroads extorted farmers: successful operating systems, like consolidated rail networks, are an opportunity for rent-taking.

When users aren’t customers

But Google, Facebook, and Amazon are different. Their defenders often argue that “competition is just a click away,” since you can easily use a different social network (they do exist), a different search engine, or a different web store. And because Google and Facebook are free for users, it’s difficult to square them with our image of the ur-monopolist who corners a market and then inflates prices.

That’s because Facebook, Google, and even Amazon’s users aren’t actually their customers. Despite what can look like wildly diverse product portfolios, Facebook and Google make almost all of their revenue from web and mobile advertising. Though Amazon’s profitability is surprisingly dependent on its cloud computing services, the bulk of its revenue is driven by its ability to show users products they want to buy – the fact that they actually fulfill the resulting orders is secondary.

This sort of targeting depends on one asset: user data. It’s user data that lets Amazon show you the same ad, for something you didn’t even know you wanted, on three different websites. It lets Google individually tailor both search results and accompanying ads, and Facebook keep you coming back by insulating you from facts that conflict with your beliefs. In essence, user data are these companies’ rails, delivering buyers to sellers with unbeatable efficiency.

Robber baron technique, then and now

The tech giants built those rails in much the same way previous robber barons did – through a combination raw creative vision, first-mover advantage, and relentless expansionism. Google was undoubtedly superior to almost any other search engine when it first appeared in the late 1990s. Similarly, Facebook won the social network wars because it was simply better than MySpace or Friendster.

But just being dominant doesn’t make a company a monopoly deserving of regulation – the new giants have worked furiously to build anti-competitive moats after gaining their early leads. That has included some old-school tactics, with Google in particular inheriting AT&T’s lust for regulatory capture to supplement its funding of friendly policy research.

Google’s vast appetite for data

But the new giants also have a novel path to total dominance: acquiring huge caches of data wherever they may be found. For Google, that began with the reasonable-enough purchases of ad networks like AdMob and Doubleclick, but has since metastasized wildly. It has gathered patient health data from Britain’s National Health Service, got inside your house with smart-home pioneer Nest, and regularly harvests road traffic data through local governments.

Not all of that data has been brought to bear on ad targeting, but it takes a great deal of credulity to believe Google won’t eventually do just that. In fact, one compelling argument that Google and Facebook have anticompetitive market positions is their apparent inability to make money on much other than advertising. They are, at root, the same sort of media conglomerates that U.S. regulators once regarded as enemies of democratic discourse – but on a global scale.

Collateral damage mounts

The casualties of this concentrated ownership of consumer data are plain to see. Newspapers and magazines have been gutted because their audiences aren’t large or diverse enough to be anything but feedstock for Facebook and Google’s gargantuan ad mills. Brick and mortar retailers are cratering left and right, partly because we’ve (re)discovered the convenience of delivery, but mostly because even sellers with robust online stores can’t profile customers as precisely as Amazon.

The biggest firms have also crushed their online advertising competitors. Google has brazenly appropriated the content of smaller websites to place snippets in search results, gutting the smaller sites’ traffic. Twitter, despite its cultural omnipresence, can’t figure out how to effectively make money from ads without Facebook’s scope. Neither could Yahoo!, a diversified entity that might have actually had a shot, and the once-hyped social network Snapchat is currently playing out the same narrative of futility. In perhaps the ultimate indicator, BitTorrent directory The Pirate Bay is apparently struggling to turn a profit selling ads even against stolen content, and has experimented with hijacking users’ computers to mine cryptocurrency.

Winner take all?

There are a few possible ways of muting these anticompetitive effects. Through the mid-20th century, it was still common in America to simply break up firms that became too dominant. Something like the AT&T settlement, which assigned separate geographic territories to newly created “babies,” could work for Facebook or Google. Amazon could be constrained to stick to online retail and spin off side-projects. More modestly, regulating particular behavior could make Facebook and Google more like public utilities – recent proposals to regulate political advertising on social sites would be a step down that road.

But another, more radical solution is worth considering. Mandatory data-sharing regulations would force web companies to compete on the basis of what they create, not just what they own, by taking away the insurmountable head start enjoyed by Facebook, Google, and Amazon. Right now, the sale of access to user data in the form of advertising seems like a textbook example of rent-seeking. It’s debatable, in fact, whether there should be a right to third-party ownership of such personal data in the first place – just ask the EU. Or Equifax.

But of course, no antitrust action at all seems plausible under the current US regime, despite President Trump’s rhetorical claims to care for the little guy. Peter Thiel, an entrepreneur and early Facebook investor who is one of Trump’s only remaining supporters in the tech sector, explicitly calls Google a monopoly in his 2014 book Zero to One. But he defines monopoly as “a kind of company that’s so good at what it does that no other firm can offer a close substitute,” and explains that Google’s monopoly is socially beneficial because it lets it treat its employees well.

Google has about 70,000 employees, less than a quarter as many as even today’s much-weakened General Electric. Thiel’s belief that serving them is a worthy tradeoff for a radical concentration of economic power is just one more index of how little bothered he or the other Trumpists are by the winner-take-all reality that we live in now.