Volatility: how ‘algos’ changed the rhythm of the market

Critics say high-frequency trading makes markets too fickle amid rising anxiety over the global economy  writes Robin Wigglesworth in The FT

Philippe Jabre was the quintessential swashbuckling trader, slicing his way through markets first at GLG Partners and then an eponymous hedge fund he founded in 2007 — at the time one of the industry’s biggest-ever launches. But in December he fell on his sword, closing Jabre Capital after racking up huge losses. The fault, he said, was machines. 

“The last few years have become particularly difficult for active managers,” he said in his final letter to clients. “Financial markets have significantly evolved over the past decade, driven by new technologies, and the market itself is becoming more difficult to anticipate as traditional participants are imperceptibly replaced by computerised models.” 

Mr Jabre is not alone. There has been recently a flurry of finger-pointing by humbled one-time masters of the universe, who argue that the swelling influence of computer-powered “quantitative”, or quant, investors and high-frequency traders is wreaking havoc on markets and rendering obsolete old-fashioned analysis and common sense. 

Those concerns were exacerbated by the volatility in financial markets in December, when US equities suffered their biggest monthly decline since the financial crisis, despite little fundamental economic news. And with growing anxiety over the strength of the global economy, tightening monetary policy across the world and an escalating trade war between China and the US, these trades are getting more attention. 

Even hedge fund veterans admit the game has changed. “These ‘algos’ have taken all the rhythm out of the market, and have become extremely confusing to me,” Stanley Druckenmiller, a famed investor and hedge fund manager, recently told an industry TV station. 

It is true that markets are evolving. HFTs dominate the market-making once done by humans in trading pits and the bowels of investment banks. Various quant strategies — ranging from simple ones packaged into passive funds to pricey, complex hedge funds — manage at least $1.5tn, according to Morgan Stanley. JPMorgan estimates that only about 10 per cent of US equity trading is now done by traditional investors. Other markets remain more human, yet are slowly but surely being transformed. 

This has made “the algos” a fashionable bugbear whenever markets tremble like they did in December. Torsten Slok, Deutsche Bank’s chief international economist, put them at the top of his list of the 30 biggest risks for markets, and even Steven Mnuchin, the US Treasury secretary who caused market unease with comments on liquidity late last year, has said the government will study whether the evolving market ecosystem fed the recent turmoil. 

But markets have always been tempestuous, and machines make a convenient, faceless bogeyman for fund managers who stumble. Meanwhile, quants point out that they are still only small players compared with the vastness of global markets. 

“It’s insane,” says Clifford Asness, the founder of AQR Capital Management. “People are missing the forest for the trees. That we trade electronically doesn’t change things, we just deliver the same thing more efficiently . . . It’s just used by pundits and fund managers as an excuse.” 

The recent turmoil has unnerved many investors, but two other debacles stand out as having first crystallised the fear that algorithms are making markets more fickle and fragile. 

At 2:32pm on May 6 2010, US equities suddenly and mysteriously careened lower. In just 36 minutes the S&P 500 crashed more than 8 per cent, before rebounding just as powerfully. Dubbed the “flash crash” it put a spotlight on the rise of small ultra-fast, algorithmic trading firms that have elbowed out investment banks as the integral intermediaries of many markets. 

Michael Lewis, author of Flash Boys, fanned the flames with his book by casting HFTs as mysterious, investor-scalping antagonists “rigging” the stock market. What was once an esoteric, little-appreciated evolution in the market’s plumbing suddenly became the topic of a vitriolic mainstream debate. 

“It was a wake-up call,” says Andrei Kirilenko, former chief economist at the Commodity Futures Trading Commission who wrote the US regulator’s report on the 2010 event and now leads Imperial College London’s Centre for Global Finance and Technology. “The flash crash was the first market crash in the era of automated, algorithmic trading.” 

In August 2015, markets were once again abruptly thrown into a tailspin — and this time volatility-sensitive quantitative strategies were identified as the primary culprits. The spark was rising concern over China’s economic slowdown, but on August 24, the S&P 500 crashed on opening, triggering circuit-breakers — implemented in the wake of the flash crash to pause wild trading — nearly 1,300 times. That rippled through a host of exchange traded funds, worsening the dislocations as they briefly became divorced from the value of their underlying holdings. 

Many investors and analysts blamed algorithmic strategies that automatically adjust their market exposure according to volatility for aggravating the 2015 crash. Targeting a specific level of volatility is common among strategies known as “risk parity” — trend-following hedge funds and “managed volatility” products sold by insurance companies. Estimates vary, but there is probably more than $1tn invested in a variety of such funds.

Risk parity, a strategy first pioneered by Ray Dalio’s Bridgewater Associates in the 1990s, often shoulders much of the opprobrium. The theory is that a broad, diversified portfolio of stocks, bonds and other assets balanced by the mathematical risk — in practice, volatility — of each asset class should over time enjoy better returns than traditional portfolios. Bonds are less volatile than equities, so that often means “leveraging” these investments to bring the risk-adjusted allocation up to that of stocks. As volatility goes up, risk parity funds in theory rein in their exposure. 

However, risk parity funds can vary greatly in the details of their approach, and are generally slower moving than the $300bn trend-following hedge fund industry. These funds surf market momentum up and down, and also use volatility metrics to scale their exposure. When markets are calm they buy, and when turbulence spikes they sell. 

This has been a successful strategy over time. But it leaves the funds vulnerable to abrupt reversals — such as the market tumble last February — and means they can accentuate turbulence by selling when markets are already sliding.

Leon Cooperman, the founder of Omega Advisors, has argued that the US Securities and Exchange Commission should investigate and tame the new “wild, wild west environment in the stock market” caused by these volatility-sensitive strategies. 

“I think your next guest ought to be somebody from the SEC to explain why they have sat back calmly, quietly, without saying anything and allowing these algorithmic, trend-following models to wreak havoc with what has, up to now, been the best capital market in the world,” he told CNBC in December. 

Some quants will grudgingly admit that volatility-targeting is inherently pro-cyclical and can at least in theory exacerbate market movements. But they say critics wildly overestimate just how much money is invested in these strategies, how much they trade, and their impact. 

“Risk parity is basically a passive portfolio with some periodic, counter-cyclical rebalancing. Our volatility targets aren’t perfectly static, but they only change over a 10-year window,” says Bob Prince, co-chief investment officer at Bridgewater. Other risk parity strategies may vary, but overall “it's only ever going to be a drop in the ocean”, he adds. 

Markets had been vulnerable to panicky plunges long before trading algorithms emerged, yet fears over machines seem deeply embedded in our psyche. A 2014 University of Pennsylvania paper found evidence of what it dubbed “algorithm aversion”, showing how human test subjects instinctively trusted human forecasters more than algorithmic ones, even after seeing the algo make fewer and less severe forecasting errors. 

And there are plenty of other potential culprits to blame for exacerbating recent turbulence. Many traditional active funds suffered a battering in 2018. That has led to a rise in investor redemption notices and has forced many to sell securities to meet the end-of-year withdrawals. 

Hedge fund flow data come with a lag, but traditional equity funds saw withdrawals rise to nearly $53bn in the seven days up to December 12, according to data provider EPFR — comfortably the biggest one-week outflow on record. That probably both reflected and exacerbated the slide that left the S&P 500 nursing a 6 per cent loss for 2018. 

At the same time, market liquidity— a broad term denoting how easy it is to trade quickly without causing prices to move around too much — tends to weaken in December, when many fund managers become more defensive ahead of the end of the year. Liquidity can be particularly poor in the last weeks of the year, when bank traders ratchet back how much risk they take on to avoid extra regulatory charges. 

“This makes it more expensive for dealers to perform their essential functions: providing liquidity, absorbing shocks and facilitating the transfer and socialisation of risk,” Joshua Younger, a JPMorgan analyst, wrote in a recent note. “These costs are generally passed on to customers in the form of higher rates on short-term loans, thinner markets and the risk — now realised — of spikes in volatility.” 

That markets are undergoing a dramatic, algorithmic evolution is an inescapable fact. Although some humbled hedge fund managers may unfairly castigate “algos” for their own failings, there are real risks in how some of these different factors can interact at times of market stress. 

HFTs are far more efficient market-makers than human pit traders. Yet the entire sector probably has less capital than just one of the major banks, says Charles Himmelberg, head of global markets research at Goldman Sachs. It means that they tend to adjust their bids aggressively when market mayhem breaks out. 

Under those circumstances, even a modest amount of selling could have an outsized impact. This is an issue both for human traders and quants, but quant strategies are programmed, quick and on autopilot, and if they start pounding an increasingly thin market, it can cause dislocations between buy and sell orders that can produce big gains or falls. 

For example, JPMorgan estimates that the depth of the big and normally liquid S&P 500 futures market — as measured by how many contracts trade close to the current price — deteriorated in 2018, and was exceptionally shallow in the last months of the year. In December it was even worse than the levels seen in the financial crisis. 

“While it is incorrect to say that systematic flows are the sole driver of recent market moves, it would be equally incorrect to say that systematic flows don’t have a meaningful impact,” says Marko Kolanovic, head of quantitative strategy at JPMorgan. 

Poor liquidity and market volatility have always been linked, and it is in practice impossible to dissect and diagnose the myriad triggers and drivers of a sell-off. But modern markets do appear more vulnerable to abrupt dislocations. 

The question is whether anything should, or even could, be done to mitigate the risks. Mr Kirilenko cautions that a mix of better understanding and modest tweaks may be the only conclusion. 

“We just have to accept that financial markets are nearly fully automated,” he says, “and try to make sure that things don’t get so technologically complex and inter-connected that it’s dangerous to the financial system.” 

Anxiety inducing: the triggers for market fears 

Although the recent market slide has reawakened the debate about whether modern machine-driven markets can exacerbate the severity of any volatility, the fundamental drivers of the turbulence are more conventional. As 2018 progressed, investors grew concerned at three factors: signs that the global economy is weakening; the impact of tighter monetary policy in the US and the end of quantitative easing in Europe; and the escalating trade war between the US and China. The global economy started last year on a strong footing, but markets are always focused on inflection points. Since the summer the impact of US tax cuts has appeared to fizzle, European growth has slowed, and China’s decelerating economy has been buffeted by the trade dispute. That has led analysts to trim their estimates for corporate profits in 2019. At the same time, the Federal Reserve raised interest rates four times last year, and has kept shrinking its balance sheet of bonds acquired in the wake of the financial crisis. That has lifted short-term ultra-safe Treasury bill yields to a 10-year high, and undermined the long-term argument that “there is no alternative” which has helped sustain market valuations. As a result, Treasury bills beat the returns of almost every major asset class last year. Goldman Sachs says that over the past century there have only been three other periods when Treasury bills have enjoyed such a broad outperformance: when the US ratcheted up interest rates to 20 per cent in the early 1980s to subdue inflation; during the Great Depression; and at the start of the first world war.

Big Tech is sorry. Why Silicon Valley can’t fix itself

Tech insiders have finally started admitting their mistakes – but the solutions they are offering could just help the big players get even more powerful. By Ben Tarnoff and Moira Weigel in The Guardian 

Big Tech is sorry. After decades of rarely apologising for anything, Silicon Valley suddenly seems to be apologising for everything. They are sorry about the trolls. They are sorry about the bots. They are sorry about the fake news and the Russians, and the cartoons that are terrifying your kids on YouTube. But they are especially sorry about our brains.

Sean Parker, the former president of Facebook – who was played by Justin Timberlake in The Social Network – has publicly lamented the “unintended consequences” of the platform he helped create: “God only knows what it’s doing to our children’s brains.” Justin Rosenstein, an engineer who helped build Facebook’s “like” button and Gchat, regrets having contributed to technology that he now considers psychologically damaging, too. “Everyone is distracted,” Rosenstein says. “All of the time.” 

Ever since the internet became widely used by the public in the 1990s, users have heard warnings that it is bad for us. In the early years, many commentators described cyberspace as a parallel universe that could swallow enthusiasts whole. The media fretted about kids talking to strangers and finding porn. A prominent 1998 study from Carnegie Mellon University claimed that spending time online made you lonely, depressed and antisocial.

In the mid-2000s, as the internet moved on to mobile devices, physical and virtual life began to merge. Bullish pundits celebrated the “cognitive surplus” unlocked by crowdsourcing and the tech-savvy campaigns of Barack Obama, the “internet president”. But, alongside these optimistic voices, darker warnings persisted. Nicholas Carr’s The Shallows (2010) argued that search engines were making people stupid, while Eli Pariser’s The Filter Bubble (2011) claimed algorithms made us insular by showing us only what we wanted to see. In Alone, Together (2011) and Reclaiming Conversation (2015), Sherry Turkle warned that constant connectivity was making meaningful interaction impossible.

Still, inside the industry, techno-utopianism prevailed. Silicon Valley seemed to assume that the tools they were building were always forces for good – and that anyone who questioned them was a crank or a luddite. In the face of an anti-tech backlash that has surged since the 2016 election, however, this faith appears to be faltering. Prominent people in the industry are beginning to acknowledge that their products may have harmful effects.

Internet anxiety isn’t new. But never before have so many notable figures within the industry seemed so anxious about the world they have made. Parker, Rosenstein and the other insiders now talking about the harms of smartphones and social media belong to an informal yet influential current of tech critics emerging within Silicon Valley. You could call them the “tech humanists”. Amid rising public concern about the power of the industry, they argue that the primary problem with its products is that they threaten our health and our humanity.

It is clear that these products are designed to be maximally addictive, in order to harvest as much of our attention as they can. Tech humanists say this business model is both unhealthy and inhumane – that it damages our psychological well-being and conditions us to behave in ways that diminish our humanity. The main solution that they propose is better design. By redesigning technology to be less addictive and less manipulative, they believe we can make it healthier – we can realign technology with our humanity and build products that don’t “hijack” our minds.

The hub of the new tech humanism is the Center for Humane Technology in San Francisco. Founded earlier this year, the nonprofit has assembled an impressive roster of advisers, including investor Roger McNamee, Lyft president John Zimmer, and Rosenstein. But its most prominent spokesman is executive director Tristan Harris, a former “design ethicist” at Google who has been hailed by the Atlantic magazine as “the closest thing Silicon Valley has to a conscience”. Harris has spent years trying to persuade the industry of the dangers of tech addiction. In February, Pierre Omidyar, the billionaire founder of eBay, launched a related initiative: the Tech and Society Solutions Lab, which aims to “maximise the tech industry’s contributions to a healthy society”.

As suspicion of Silicon Valley grows, the tech humanists are making a bid to become tech’s loyal opposition. They are using their insider credentials to promote a particular diagnosis of where tech went wrong and of how to get it back on track. For this, they have been getting a lot of attention. As the backlash against tech has grown, so too has the appeal of techies repenting for their sins. The Center for Humane Technology has been profiled – and praised by – the New York Times, the Atlantic, Wired and others.

But tech humanism’s influence cannot be measured solely by the positive media coverage it has received. The real reason tech humanism matters is because some of the most powerful people in the industry are starting to speak its idiom. Snap CEO Evan Spiegel has warned about social media’s role in encouraging “mindless scrambles for friends or unworthy distractions”, and Twitter boss Jack Dorsey recently claimed he wants to improve the platform’s “conversational health”. 

Even Mark Zuckerberg, famous for encouraging his engineers to “move fast and break things”, seems to be taking a tech humanist turn. In January, he announced that Facebook had a new priority: maximising “time well spent” on the platform, rather than total time spent. By “time well spent”, Zuckerberg means time spent interacting with “friends” rather than businesses, brands or media sources. He said the News Feed algorithm was already prioritising these “more meaningful” activities.

Zuckerberg’s choice of words is significant: Time Well Spent is the name of the advocacy group that Harris led before co-founding the Center for Humane Technology. In April, Zuckerberg brought the phrase to Capitol Hill. When a photographer snapped a picture of the notes Zuckerberg used while testifying before the Senate, they included a discussion of Facebook’s new emphasis on “time well spent”, under the heading “wellbeing”.

This new concern for “wellbeing” may strike some observers as a welcome development. After years of ignoring their critics, industry leaders are finally acknowledging that problems exist. Tech humanists deserve credit for drawing attention to one of those problems – the manipulative design decisions made by Silicon Valley.

But these decisions are only symptoms of a larger issue: the fact that the digital infrastructures that increasingly shape our personal, social and civic lives are owned and controlled by a few billionaires. Because it ignores the question of power, the tech-humanist diagnosis is incomplete – and could even help the industry evade meaningful reform. Taken up by leaders such as Zuckerberg, tech humanism is likely to result in only superficial changes. These changes may soothe some of the popular anger directed towards the tech industry, but they will not address the origin of that anger. If anything, they will make Silicon Valley even more powerful.

The Center for Humane Technology argues that technology must be “aligned” with humanity – and that the best way to accomplish this is through better design. Their website features a section entitled The Way Forward. A familiar evolutionary image shows the silhouettes of several simians, rising from their crouches to become a man, who then turns back to contemplate his history.

“In the future, we will look back at today as a turning point towards humane design,” the header reads. To the litany of problems caused by “technology that extracts attention and erodes society”, the text asserts that “humane design is the solution”. Drawing on the rhetoric of the “design thinking” philosophy that has long suffused Silicon Valley, the website explains that humane design “starts by understanding our most vulnerable human instincts so we can design compassionately”.

There is a good reason why the language of tech humanism is penetrating the upper echelons of the tech industry so easily: this language is not foreign to Silicon Valley. On the contrary, “humanising” technology has long been its central ambition and the source of its power. It was precisely by developing a “humanised” form of computing that entrepreneurs such as Steve Jobs brought computing into millions of users’ everyday lives. Their success turned the Bay Area tech industry into a global powerhouse – and produced the digitised world that today’s tech humanists now lament.

The story begins in the 1960s, when Silicon Valley was still a handful of electronics firms clustered among fruit orchards. Computers came in the form of mainframes then. These machines were big, expensive and difficult to use. Only corporations, universities and government agencies could afford them, and they were reserved for specialised tasks, such as calculating missile trajectories or credit scores.

Computing was industrial, in other words, not personal, and Silicon Valley remained dependent on a small number of big institutional clients. The practical danger that this dependency posed became clear in the early 1960s, when the US Department of Defense, by far the single biggest buyer of digital components, began cutting back on its purchases. But the fall in military procurement wasn’t the only mid-century crisis around computing.

Computers also had an image problem. The inaccessibility of mainframes made them easy to demonise. In these whirring hulks of digital machinery, many observers saw something inhuman, even evil. To antiwar activists, computers were weapons of the war machine that was killing thousands in Vietnam. To highbrow commentators such as the social critic Lewis Mumford, computers were instruments of a creeping technocracy that threatened to extinguish personal freedom.

But during the course of the 1960s and 70s, a series of experiments in northern California helped solve both problems. These experiments yielded breakthrough innovations like the graphical user interface, the mouse and the microprocessor. Computers became smaller, more usable and more interactive, reducing Silicon Valley’s reliance on a few large customers while giving digital technology a friendlier face.

The pioneers who led this transformation believed they were making computing more human. They drew deeply from the counterculture of the period, and its fixation on developing “human” modes of living. They wanted their machines to be “extensions of man”, in the words of Marshall McLuhan, and to unlock “human potential” rather than repress it. At the centre of this ecosystem of hobbyists, hackers, hippies and professional engineers was Stewart Brand, famed entrepreneur of the counterculture and founder of the Whole Earth Catalog. In a famous 1972 article for Rolling Stone, Brand called for a new model of computing that “served human interest, not machine”.

Brand’s disciples answered this call by developing the technical innovations that transformed computers into the form we recognise today. They also promoted a new way of thinking about computers – not as impersonal slabs of machinery, but as tools for unleashing “human potential”.

No single figure contributed more to this transformation of computing than Steve Jobs, who was a fan of Brand and a reader of the Whole Earth Catalog. Jobs fulfilled Brand’s vision on a global scale, launching the mass personal computing era with the Macintosh in the mid-80s, and the mass smartphone era with the iPhone two decades later. Brand later acknowledged that Jobs embodied the Whole Earth Catalog ethos. “He got the notion of tools for human use,” Brand told Jobs’ biographer, Walter Isaacson.

Building those “tools for human use” turned out to be great for business. The impulse to humanise computing enabled Silicon Valley to enter every crevice of our lives. From phones to tablets to laptops, we are surrounded by devices that have fulfilled the demands of the counterculture for digital connectivity, interactivity and self-expression. Your iPhone responds to the slightest touch; you can look at photos of anyone you have ever known, and broadcast anything you want to all of them, at any moment.

In short, the effort to humanise computing produced the very situation that the tech humanists now consider dehumanising: a wilderness of screens where digital devices chase every last instant of our attention. To guide us out of that wilderness, tech humanists say we need more humanising. They believe we can use better design to make technology serve human nature rather than exploit and corrupt it. But this idea is drawn from the same tradition that created the world that tech humanists believe is distracting and damaging us.

Tech humanists say they want to align humanity and technology. But this project is based on a deep misunderstanding of the relationship between humanity and technology: namely, the fantasy that these two entities could ever exist in separation.

It is difficult to imagine human beings without technology. The story of our species began when we began to make tools. Homo habilis, the first members of our genus, left sharpened stones scattered across Africa. Their successors hit rocks against each other to make sparks, and thus fire. With fire you could cook meat and clear land for planting; with ash you could fertilise the soil; with smoke you could make signals. In flickering light, our ancestors painted animals on cave walls. The ancient tragedian Aeschylus recalled this era mythically: Prometheus, in stealing fire from the gods, “founded all the arts of men.”

All of which is to say: humanity and technology are not only entangled, they constantly change together. This is not just a metaphor. Recent researchsuggests that the human hand evolved to manipulate the stone tools that our ancestors used. The evolutionary scientist Mary Marzke shows that we developed “a unique pattern of muscle architecture and joint surface form and functions” for this purpose.

The ways our bodies and brains change in conjunction with the tools we make have long inspired anxieties that “we” are losing some essential qualities. For millennia, people have feared that new media were eroding the very powers that they promised to extend. In The Phaedrus, Socrates warned that writing on wax tablets would make people forgetful. If you could jot something down, you wouldn’t have to remember it. In the late middle ages, as a culture of copying manuscripts gave way to printed books, teachers warned that pupils would become careless, since they no longer had to transcribe what their teachers said.

Yet as we lose certain capacities, we gain new ones. People who used to navigate the seas by following stars can now program computers to steer container ships from afar. Your grandmother probably has better handwriting than you do – but you probably type faster.

The nature of human nature is that it changes. It can not, therefore, serve as a stable basis for evaluating the impact of technology. Yet the assumption that it doesn’t change serves a useful purpose. Treating human nature as something static, pure and essential elevates the speaker into a position of power. Claiming to tell us who we are, they tell us how we should be.

Intentionally or not, this is what tech humanists are doing when they talk about technology as threatening human nature – as if human nature had stayed the same from the paleolithic era until the rollout of the iPhone. Holding humanity and technology separate clears the way for a small group of humans to determine the proper alignment between them. And while the tech humanists may believe they are acting in the common good, they themselves acknowledge they are doing so from above, as elites. “We have a moral responsibility to steer people’s thoughts ethically,” Tristan Harris has declared.

Harris and his fellow tech humanists also frequently invoke the language of public health. The Center for Humane Technology’s Roger McNamee has gone so far as to call public health “the root of the whole thing”, and Harris has compared using Snapchat to smoking cigarettes. The public-health framing casts the tech humanists in a paternalistic role. Resolving a public health crisis requires public health expertise. It also precludes the possibility of democratic debate. You don’t put the question of how to treat a disease up for a vote – you call a doctor.

This paternalism produces a central irony of tech humanism: the language that they use to describe users is often dehumanising. “Facebook appeals to your lizard brain – primarily fear and anger,” says McNamee. Harris echoes this sentiment: “Imagine you had an input cable,” he has said. “You’re trying to jack it into a human being. Do you want to jack it into their reptilian brain, or do you want to jack it into their more reflective self?”

The Center for Humane Technology’s website offers tips on how to build a more reflective and less reptilian relationship to your smartphone: “going greyscale” by setting your screen to black-and-white, turning off app notifications and charging your device outside your bedroom. It has also announced two major initiatives: a national campaign to raise awareness about technology’s harmful effects on young people’s “digital health and well-being”; and a “Ledger of Harms” – a website that will compile information about the health effects of different technologies in order to guide engineers in building “healthier” products.

These initiatives may help some people reduce their smartphone use – a reasonable personal goal. But there are some humans who may not share this goal, and there need not be anything unhealthy about that. Many people rely on the internet for solace and solidarity, especially those who feel marginalised. The kid with autism may stare at his screen when surrounded by people, because it lets him tolerate being surrounded by people. For him, constant use of technology may not be destructive at all, but in fact life-saving.

Pathologising certain potentially beneficial behaviours as “sick” isn’t the only problem with the Center for Humane Technology’s proposals. They also remain confined to the personal level, aiming to redesign how the individual user interacts with technology rather than tackling the industry’s structural failures. Tech humanism fails to address the root cause of the tech backlash: the fact that a small handful of corporations own our digital lives and strip-mine them for profit. This is a fundamentally political and collective issue. But by framing the problem in terms of health and humanity, and the solution in terms of design, the tech humanists personalise and depoliticise it.

This may be why their approach is so appealing to the tech industry. There is no reason to doubt the good intentions of tech humanists, who may genuinely want to address the problems fuelling the tech backlash. But they are handing the firms that caused those problems a valuable weapon. Far from challenging Silicon Valley, tech humanism offers Silicon Valley a useful way to pacify public concerns without surrendering any of its enormous wealth and power. By channelling popular anger at Big Tech into concerns about health and humanity, tech humanism gives corporate giants such as Facebook a way to avoid real democratic control. In a moment of danger, it may even help them protect their profits.

One can easily imagine a version of Facebook that embraces the principles of tech humanism while remaining a profitable and powerful monopoly. In fact, these principles could make Facebook even more profitable and powerful, by opening up new business opportunities. That seems to be exactly what Facebook has planned.

When Zuckerberg announced that Facebook would prioritise “time well spent” over total time spent, it came a couple weeks before the company released their 2017 Q4 earnings. These reported that total time spent on the platform had dropped by around 5%, or about 50m hours per day. But, Zuckerberg said, this was by design: in particular, it was in response to tweaks to the News Feed that prioritised “meaningful” interactions with “friends” rather than consuming “public content” like video and news. This would ensure that “Facebook isn’t just fun, but also good for people’s well-being”.

Zuckerberg said he expected those changes would continue to decrease total time spent – but “the time you do spend on Facebook will be more valuable”. This may describe what users find valuable – but it also refers to what Facebook finds valuable. In a recent interview, he said: “Over the long term, even if time spent goes down, if people are spending more time on Facebook actually building relationships with people they care about, then that’s going to build a stronger community and build a stronger business, regardless of what Wall Street thinks about it in the near term.”

Sheryl Sandberg has also stressed that the shift will create “more monetisation opportunities”. How? Everyone knows data is the lifeblood of Facebook – but not all data is created equal. One of the most valuable sources of data to Facebook is used to inform a metric called “coefficient”. This measures the strength of a connection between two users – Zuckerberg once called it “an index for each relationship”. Facebook records every interaction you have with another user – from liking a friend’s post or viewing their profile, to sending them a message. These activities provide Facebook with a sense of how close you are to another person, and different activities are weighted differently. Messaging, for instance, is considered the strongest signal. It’s reasonable to assume that you’re closer to somebody you exchange messages with than somebody whose post you once liked.

Why is coefficient so valuable? Because Facebook uses it to create a Facebook they think you will like: it guides algorithmic decisions about what content you see and the order in which you see it. It also helps improve ad targeting, by showing you ads for things liked by friends with whom you often interact. Advertisers can target the closest friends of the users who already like a product, on the assumption that close friends tend to like the same things.

 

Facebook CEO Mark Zuckerberg testifies before the US Senate last month. Photograph: Jim Watson/AFP/Getty Images

So when Zuckerberg talks about wanting to increase “meaningful” interactions and building relationships, he is not succumbing to pressure to take better care of his users. Rather, emphasising time well spent means creating a Facebook that prioritises data-rich personal interactions that Facebook can use to make a more engaging platform. Rather than spending a lot of time doing things that Facebook doesn’t find valuable – such as watching viral videos – you can spend a bit less time, but spend it doing things that Facebook does find valuable.

In other words, “time well spent” means Facebook can monetise more efficiently. It can prioritise the intensity of data extraction over its extensiveness. This is a wise business move, disguised as a concession to critics. Shifting to this model not only sidesteps concerns about tech addiction – it also acknowledges certain basic limits to Facebook’s current growth model. There are only so many hours in the day. Facebook can’t keep prioritising total time spent – it has to extract more value from less time.

In many ways, this process recalls an earlier stage in the evolution of capitalism. In the 19th century, factory owners in England discovered they could only make so much money by extending the length of the working day. At some point, workers would die of exhaustion, or they would revolt, or they would push parliament to pass laws that limited their working hours. So industrialists had to find ways to make the time of the worker more valuable – to extract more money from each moment rather than adding more moments. They did this by making industrial production more efficient: developing new technologies and techniques that squeezed more value out of the worker and stretched that value further than ever before.

A similar situation confronts Facebook today. They have to make the attention of the user more valuable – and the language and concepts of tech humanism can help them do it. So far, it seems to be working. Despite the reported drop in total time spent, Facebook recently announced huge 2018 Q1 earnings of $11.97bn (£8.7bn), smashing Wall Street estimates by nearly $600m.

Today’s tech humanists come from a tradition with deep roots in Silicon Valley. Like their predecessors, they believe that technology and humanity are distinct, but can be harmonised. This belief guided the generations who built the “humanised” machines that became the basis for the industry’s enormous power. Today it may provide Silicon Valley with a way to protect that power from a growing public backlash – and even deepen it by uncovering new opportunities for profit-making.

Fortunately, there is another way of thinking about how to live with technology – one that is both truer to the history of our species and useful for building a more democratic future. This tradition does not address “humanity” in the abstract, but as distinct human beings, whose capacities are shaped by the tools they use. It sees us as hybrids of animal and machine – as “cyborgs”, to quote the biologist and philosopher of science Donna Haraway.

To say that we’re all cyborgs is not to say that all technologies are good for us, or that we should embrace every new invention. But it does suggest that living well with technology can’t be a matter of making technology more “human”. This goal isn’t just impossible – it’s also dangerous, because it puts us at the mercy of experts who tell us how to be human. It cedes control of our technological future to those who believe they know what’s best for us because they understand the essential truths about our species.

The cyborg way of thinking, by contrast, tells us that our species is essentially technological. We change as we change our tools, and our tools change us. But even though our continuous co-evolution with our machines is inevitable, the way it unfolds is not. Rather, it is determined by who owns and runs those machines. It is a question of power.

Today, that power is wielded by corporations, which own our technology and run it for profit. The various scandals that have stoked the tech backlash all share a single source. Surveillance, fake news and the miserable working conditions in Amazon’s warehouses are profitable. If they were not, they would not exist. They are symptoms of a profound democratic deficit inflicted by a system that prioritises the wealth of the few over the needs and desires of the many.

There is an alternative. If being technological is a feature of being human, then the power to shape how we live with technology should be a fundamental human right. The decisions that most affect our technological lives are far too important to be left to Mark Zuckerberg, rich investors or a handful of “humane designers”. They should be made by everyone, together.

Rather than trying to humanise technology, then, we should be trying to democratise it. We should be demanding that society as a whole gets to decide how we live with technology – rather than the small group of people who have captured society’s wealth.

What does this mean in practice? First, it requires limiting and eroding Silicon Valley’s power. Antitrust laws and tax policy offer useful ways to claw back the fortunes Big Tech has built on common resources. After all, Silicon Valley wouldn’t exist without billions of dollars of public funding, not to mention the vast quantities of information that we all provide for free. Facebook’s market capitalisation is $500bn with 2.2 billion users – do the math to estimate how much the time you spend on Facebook is worth. You could apply the same logic to Google. There is no escape: whether or not you have an account, both platforms track you around the internet.

In addition to taxing and shrinking tech firms, democratic governments should be making rules about how those firms are allowed to behave – rules that restrict how they can collect and use our personal data, for instance, like the General Data Protection Regulation coming into effect in the European Union later this month. But more robust regulation of Silicon Valley isn’t enough. We also need to pry the ownership of our digital infrastructure away from private firms. 

This means developing publicly and co-operatively owned alternatives that empower workers, users and citizens to determine how they are run. These democratic digital structures can focus on serving personal and social needs rather than piling up profits for investors. One inspiring example is municipal broadband: a successful experiment in Chattanooga, Tennessee, has shown that publicly owned internet service providers can supply better service at lower cost than private firms. Other models of digital democracy might include a worker-owned Uber, a user-owned Facebook or a socially owned “smart city” of the kind being developed in Barcelona. Alternatively, we might demand that tech firms pay for the privilege of extracting our data, so that we can collectively benefit from a resource we collectively create.

More experimentation is needed, but democracy should be our guiding principle. The stakes are high. Never before have so many people been thinking about the problems produced by the tech industry and how to solve them. The tech backlash is an enormous opportunity – and one that may not come again for a long time.

The old techno-utopianism is crumbling. What will replace it? Silicon Valley says it wants to make the world a better place. Fulfilling this promise may require a new kind of disruption.

Lessons from the IPL Auction 2018

Suresh Menon in The Hindu




Both Neville Cardus and C.L.R. James asked whether cricket is an art, and answered in different ways. Cardus compared cricket to music while for James it belonged alongside theatre, opera and dance. Thus, art, yes, but the performing arts, and for what happens on the field.

It is now safe to say that cricket belongs to the visual and plastic arts — painting and sculpture — but not for what happens on the field. The IPL auction has added another dimension with the question: what is the value of a player? Is he like a Jeff Koons or an M.F. Hussain?

Is Jayadev Unadkat worth ₹11.5 crores? Is Hashim Amla not worth anything at all? The comparison with art is inevitable. A painting is worth exactly what someone is prepared to pay for it. In his book The Value of Art: Money, Power, Beauty, the art dealer Michael Findlay gives a more sophisticated explanation.

“The commercial value of art,” he says, “is based on collective intentionality. Human stipulation and declaration create and sustain the commercial value.” Replace “art” with “cricketer” and that still holds. If, based on sports metrics and private algorithms, Mumbai Indians think Krunal Pandya is worth ₹8.8 crores, you cannot argue.

On a weekend when every Test-playing country was engaged in an international, the focus was on a hotel ballroom in Bangalore. You can read all kinds of meaning into this. “It will be a distraction,” South Africa’s captain Faf du Plessis had said earlier. Kamlesh Nagarkoti, at the Under-19 World Cup in New Zealand said, “I went and sat inside the washroom even as my bidding was going on.” It went on and on and didn’t stop till it had reached ₹3.2 crores.

It was possible to switch channels between the auction and the incredible Indian performance at the Johannesburg Test. Virat Kohli certainly wasn’t distracted — his ₹17 crores was already in the bank. It would be interesting to discover which event garnered the more eyeballs; that should tell us the direction the sport is taking. In The Australian, Gideon Haigh wrote a piece headlined: IPL auction now the real centre of world cricket.

A union minister tweeted that most players didn’t deserve half the amounts they were bought for. Politicians are allergic to such transparent contract negotiations. However, what he and others find difficult to deal with is the fact that the market decides value. And the market can be cruel and ageist, often casually dispensing with high-performing players of the past. It is influenced by the ego of the bidder too. Monetary value is not always the same as cricketing worth.

Part of the confusion is caused by top players going unsold. In the recent Test, Amla and Ishant Sharma put in inspiring performances, yet find themselves with no role in the IPL. The way to reconcile this is to acknowledge that IPL and Test cricket are as different from each other — tactically, physically, psychologically, emotionally — as soccer and cricket or kabaddi and tennis. They just happen to use the same equipment.

It took the franchises some time to realise this. The inaugural auction had nothing to go by and established Test players were most sought after. Royal Challengers had Rahul Dravid, Jacques Kallis, Wasim Jaffer, Shivnarine Chanderpaul. Today they would have to depend on pity-selection by friends in the franchises, if at all. Cricket has changed, the IPL most of all, and auctions, even if not fully professional yet are headed in that direction. Data is king. How good are you between overs 11 and 16, for example?

If engineers are allowed to rule the world....

How technology is making our minds redundant.

Franklin Foer in The Guardian

All the values that Silicon Valley professes are the values of the 60s. The big tech companies present themselves as platforms for personal liberation. Everyone has the right to speak their mind on social media, to fulfil their intellectual and democratic potential, to express their individuality. Where television had been a passive medium that rendered citizens inert, Facebook is participatory and empowering. It allows users to read widely, think for themselves and form their own opinions.

We can’t entirely dismiss this rhetoric. There are parts of the world, even in the US, where Facebook emboldens citizens and enables them to organise themselves in opposition to power. But we shouldn’t accept Facebook’s self-conception as sincere, either. Facebook is a carefully managed top-down system, not a robust public square. It mimics some of the patterns of conversation, but that’s a surface trait.

In reality, Facebook is a tangle of rules and procedures for sorting information, rules devised by the corporation for the ultimate benefit of the corporation. Facebook is always surveilling users, always auditing them, using them as lab rats in its behavioural experiments. While it creates the impression that it offers choice, in truth Facebook paternalistically nudges users in the direction it deems best for them, which also happens to be the direction that gets them thoroughly addicted. It’s a phoniness that is most obvious in the compressed, historic career of Facebook’s mastermind.

Mark Zuckerberg is a good boy, but he wanted to be bad, or maybe just a little bit naughty. The heroes of his adolescence were the original hackers. These weren’t malevolent data thieves or cyberterrorists. Zuckerberg’s hacker heroes were disrespectful of authority. They were technically virtuosic, infinitely resourceful nerd cowboys, unbound by conventional thinking. In the labs of the Massachusetts Institute of Technology (MIT) during the 60s and 70s, they broke any rule that interfered with building the stuff of early computing, such marvels as the first video games and word processors. With their free time, they played epic pranks, which happened to draw further attention to their own cleverness – installing a living cow on the roof of a Cambridge dorm; launching a weather balloon, which miraculously emerged from beneath the turf, emblazoned with “MIT”, in the middle of a Harvard-Yale football game.

The hackers’ archenemies were the bureaucrats who ran universities, corporations and governments. Bureaucrats talked about making the world more efficient, just like the hackers. But they were really small-minded paper-pushers who fiercely guarded the information they held, even when that information yearned to be shared. When hackers clearly engineered better ways of doing things – a box that enabled free long-distance calls, an instruction that might improve an operating system – the bureaucrats stood in their way, wagging an unbending finger. The hackers took aesthetic and comic pleasure in outwitting the men in suits.

When Zuckerberg arrived at Harvard in the fall of 2002, the heyday of the hackers had long passed. They were older guys now, the stuff of good tales, some stuck in twilight struggles against The Man. But Zuckerberg wanted to hack, too, and with that old-time indifference to norms. In high school he picked the lock that prevented outsiders from fiddling with AOL’s code and added his own improvements to its instant messaging program. As a college sophomore he hatched a site called Facemash – with the high-minded purpose of determining the hottest kid on campus. Zuckerberg asked users to compare images of two students and then determine the better-looking of the two. The winner of each pairing advanced to the next round of his hormonal tournament. To cobble this site together, Zuckerberg needed photos. He purloined those from the servers of the various Harvard houses. “One thing is certain,” he wrote on a blog as he put the finishing touches on his creation, “and it’s that I’m a jerk for making this site. Oh well.”

His brief experimentation with rebellion ended with his apologising to a Harvard disciplinary panel, as well as to campus women’s groups, and mulling strategies to redeem his soiled reputation. In the years since, he has shown that defiance really wasn’t his natural inclination. His distrust of authority was such that he sought out Don Graham, then the venerable chairman of the Washington Post company, as his mentor. After he started Facebook, he shadowed various giants of corporate America so that he could study their managerial styles up close.

Still, Zuckerberg’s juvenile fascination with hackers never died – or rather, he carried it forward into his new, more mature incarnation. When he finally had a corporate campus of his own, he procured a vanity address for it: One Hacker Way. He designed a plaza with the word “HACK” inlaid into the concrete. In the centre of his office park, he created an open meeting space called Hacker Square. This is, of course, the venue where his employees join for all-night Hackathons. As he told a group of would-be entrepreneurs, “We’ve got this whole ethos that we want to build a hacker culture.”

Plenty of companies have similarly appropriated hacker culture – hackers are the ur-disrupters – but none have gone as far as Facebook. By the time Zuckerberg began extolling the virtues of hacking, he had stripped the name of most of its original meaning and distilled it into a managerial philosophy that contains barely a hint of rebelliousness. Hackers, he told one interviewer, were “just this group of computer scientists who were trying to quickly prototype and see what was possible. That’s what I try to encourage our engineers to do here.” To hack is to be a good worker, a responsible Facebook citizen – a microcosm of the way in which the company has taken the language of radical individualism and deployed it in the service of conformism.

Zuckerberg claimed to have distilled that hacker spirit into a motivational motto: “Move fast and break things.” The truth is that Facebook moved faster than Zuckerberg could ever have imagined. His company was, as we all know, a dorm-room lark, a thing he ginned up in a Red Bull–induced fit of sleeplessness. As his creation grew, it needed to justify its new scale to its investors, to its users, to the world. It needed to grow up fast. Over the span of its short life, the company has caromed from self-description to self-description. It has called itself a tool, a utility and a platform. It has talked about openness and connectedness. And in all these attempts at defining itself, it has managed to clarify its intentions.

Facebook creators Mark Zuckerberg and Chris Hughes at Harvard in May 2004. Photograph: Rick Friedman/Corbis via Getty

Though Facebook will occasionally talk about the transparency of governments and corporations, what it really wants to advance is the transparency of individuals – or what it has called, at various moments, “radical transparency” or “ultimate transparency”. The theory holds that the sunshine of sharing our intimate details will disinfect the moral mess of our lives. With the looming threat that our embarrassing information will be broadcast, we’ll behave better. And perhaps the ubiquity of incriminating photos and damning revelations will prod us to become more tolerant of one another’s sins. “The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly,” Zuckerberg has said. “Having two identities for yourself is an example of a lack of integrity.”

The point is that Facebook has a strong, paternalistic view on what’s best for you, and it’s trying to transport you there. “To get people to this point where there’s more openness – that’s a big challenge. But I think we’ll do it,” Zuckerberg has said. He has reason to believe that he will achieve that goal. With its size, Facebook has amassed outsized powers. “In a lot of ways Facebook is more like a government than a traditional company,” Zuckerberg has said. “We have this large community of people, and more than other technology companies we’re really setting policies.”

Without knowing it, Zuckerberg is the heir to a long political tradition. Over the last 200 years, the west has been unable to shake an abiding fantasy, a dream sequence in which we throw out the bum politicians and replace them with engineers – rule by slide rule. The French were the first to entertain this notion in the bloody, world-churning aftermath of their revolution. A coterie of the country’s most influential philosophers (notably, Henri de Saint-Simon and Auguste Comte) were genuinely torn about the course of the country. They hated all the old ancient bastions of parasitic power – the feudal lords, the priests and the warriors – but they also feared the chaos of the mob. To split the difference, they proposed a form of technocracy – engineers and assorted technicians would rule with beneficent disinterestedness. Engineers would strip the old order of its power, while governing in the spirit of science. They would impose rationality and order.

This dream has captivated intellectuals ever since, especially Americans. The great sociologist Thorstein Veblen was obsessed with installing engineers in power and, in 1921, wrote a book making his case. His vision briefly became a reality. In the aftermath of the first world war, American elites were aghast at all the irrational impulses unleashed by that conflict – the xenophobia, the racism, the urge to lynch and riot. And when the realities of economic life had grown so complicated, how could politicians possibly manage them? Americans of all persuasions began yearning for the salvific ascendance of the most famous engineer of his time: Herbert Hoover. In 1920, Franklin D Roosevelt – who would, of course, go on to replace him in 1932 – organised a movement to draft Hoover for the presidency.

The Hoover experiment, in the end, hardly realised the happy fantasies about the Engineer King. A very different version of this dream, however, has come to fruition, in the form of the CEOs of the big tech companies. We’re not ruled by engineers, not yet, but they have become the dominant force in American life – the highest, most influential tier of our elite.

There’s another way to describe this historical progression. Automation has come in waves. During the industrial revolution, machinery replaced manual workers. At first, machines required human operators. Over time, machines came to function with hardly any human intervention. For centuries, engineers automated physical labour; our new engineering elite has automated thought. They have perfected technologies that take over intellectual processes, that render the brain redundant. Or, as the former Google and Yahoo executive Marissa Mayer once argued, “You have to make words less human and more a piece of the machine.” Indeed, we have begun to outsource our intellectual work to companies that suggest what we should learn, the topics we should consider, and the items we ought to buy. These companies can justify their incursions into our lives with the very arguments that Saint-Simon and Comte articulated: they are supplying us with efficiency; they are imposing order on human life.

Nobody better articulates the modern faith in engineering’s power to transform society than Zuckerberg. He told a group of software developers, “You know, I’m an engineer, and I think a key part of the engineering mindset is this hope and this belief that you can take any system that’s out there and make it much, much better than it is today. Anything, whether it’s hardware or software, a company, a developer ecosystem – you can take anything and make it much, much better.” The world will improve, if only Zuckerberg’s reason can prevail – and it will.

The precise source of Facebook’s power is algorithms. That’s a concept repeated dutifully in nearly every story about the tech giants, yet it remains fuzzy at best to users of those sites. From the moment of the algorithm’s invention, it was possible to see its power, its revolutionary potential. The algorithm was developed in order to automate thinking, to remove difficult decisions from the hands of humans, to settle contentious debates.

The essence of the algorithm is entirely uncomplicated. The textbooks compare them to recipes – a series of precise steps that can be followed mindlessly. This is different from equations, which have one correct result. Algorithms merely capture the process for solving a problem and say nothing about where those steps ultimately lead.

These recipes are the crucial building blocks of software. Programmers can’t simply order a computer to, say, search the internet. They must give the computer a set of specific instructions for accomplishing that task. These instructions must take the messy human activity of looking for information and transpose that into an orderly process that can be expressed in code. First do this … then do that. The process of translation, from concept to procedure to code, is inherently reductive. Complex processes must be subdivided into a series of binary choices. There’s no equation to suggest a dress to wear, but an algorithm could easily be written for that – it will work its way through a series of either/or questions (morning or night, winter or summer, sun or rain), with each choice pushing to the next.

For the first decades of computing, the term “algorithm” wasn’t much mentioned. But as computer science departments began sprouting across campuses in the 60s, the term acquired a new cachet. Its vogue was the product of status anxiety. Programmers, especially in the academy, were anxious to show that they weren’t mere technicians. They began to describe their work as algorithmic, in part because it tied them to one of the greatest of all mathematicians – the Persian polymath Muhammad ibn Musa al-Khwarizmi, or as he was known in Latin, Algoritmi. During the 12th century, translations of al-Khwarizmi introduced Arabic numerals to the west; his treatises pioneered algebra and trigonometry. By describing the algorithm as the fundamental element of programming, the computer scientists were attaching themselves to a grand history. It was a savvy piece of name-dropping: See, we’re not arriviste, we’re working with abstractions and theories, just like the mathematicians!

A statue of the mathematician Muhammad ibn Musa al-Khwarizmi in Uzbekistan. Photograph: Alamy

There was sleight of hand in this self-portrayal. The algorithm may be the essence of computer science – but it’s not precisely a scientific concept. An algorithm is a system, like plumbing or a military chain of command. It takes knowhow, calculation and creativity to make a system work properly. But some systems, like some armies, are much more reliable than others. A system is a human artefact, not a mathematical truism. The origins of the algorithm are unmistakably human, but human fallibility isn’t a quality that we associate with it. When algorithms reject a loan application or set the price for an airline flight, they seem impersonal and unbending. The algorithm is supposed to be devoid of bias, intuition, emotion or forgiveness.

Silicon Valley’s algorithmic enthusiasts were immodest about describing the revolutionary potential of their objects of affection. Algorithms were always interesting and valuable, but advances in computing made them infinitely more powerful. The big change was the cost of computing: it collapsed, just as the machines themselves sped up and were tied into a global network. Computers could stockpile massive piles of unsorted data – and algorithms could attack this data to find patterns and connections that would escape human analysts. In the hands of Google and Facebook, these algorithms grew ever more powerful. As they went about their searches, they accumulated more and more data. Their machines assimilated all the lessons of past searches, using these learnings to more precisely deliver the desired results.

For the entirety of human existence, the creation of knowledge was a slog of trial and error. Humans would dream up theories of how the world worked, then would examine the evidence to see whether their hypotheses survived or crashed upon their exposure to reality. Algorithms upend the scientific method – the patterns emerge from the data, from correlations, unguided by hypotheses. They remove humans from the whole process of inquiry. Writing in Wired, Chris Anderson, then editor-in-chief, argued: “We can stop looking for models. We can analyse the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.”

On one level, this is undeniable. Algorithms can translate languages without understanding words, simply by uncovering the patterns that undergird the construction of sentences. They can find coincidences that humans might never even think to seek. Walmart’s algorithms found that people desperately buy strawberry Pop-Tarts as they prepare for massive storms.

Still, even as an algorithm mindlessly implements its procedures – and even as it learns to see new patterns in the data – it reflects the minds of its creators, the motives of its trainers. Amazon and Netflix use algorithms to make recommendations about books and films. (One-third of purchases on Amazon come from these recommendations.) These algorithms seek to understand our tastes, and the tastes of like-minded consumers of culture. Yet the algorithms make fundamentally different recommendations. Amazon steers you to the sorts of books that you’ve seen before. Netflix directs users to the unfamiliar. There’s a business reason for this difference. Blockbuster movies cost Netflix more to stream. Greater profit arrives when you decide to watch more obscure fare. Computer scientists have an aphorism that describes how algorithms relentlessly hunt for patterns: they talk about torturing the data until it confesses. Yet this metaphor contains unexamined implications. Data, like victims of torture, tells its interrogator what it wants to hear.

Like economics, computer science has its preferred models and implicit assumptions about the world. When programmers are taught algorithmic thinking, they are told to venerate efficiency as a paramount consideration. This is perfectly understandable. An algorithm with an ungainly number of steps will gum up the machinery, and a molasses-like server is a useless one. But efficiency is also a value. When we speed things up, we’re necessarily cutting corners; we’re generalising.

Algorithms can be gorgeous expressions of logical thinking, not to mention a source of ease and wonder. They can track down copies of obscure 19th-century tomes in a few milliseconds; they put us in touch with long-lost elementary school friends; they enable retailers to deliver packages to our doors in a flash. Very soon, they will guide self-driving cars and pinpoint cancers growing in our innards. But to do all these things, algorithms are constantly taking our measure. They make decisions about us and on our behalf. The problem is that when we outsource thinking to machines, we are really outsourcing thinking to the organisations that run the machines.

Mark Zuckerberg disingenuously poses as a friendly critic of algorithms. That’s how he implicitly contrasts Facebook with his rivals across the way at Google. Over in Larry Page’s shop, the algorithm is king – a cold, pulseless ruler. There’s not a trace of life force in its recommendations, and very little apparent understanding of the person keying a query into its engine. Facebook, in his flattering self-portrait, is a respite from this increasingly automated, atomistic world. “Every product you use is better off with your friends,” he says.

What he is referring to is Facebook’s news feed. Here’s a brief explanation for the sliver of humanity who have apparently resisted Facebook: the news feed provides a reverse chronological index of all the status updates, articles and photos that your friends have posted to Facebook. The news feed is meant to be fun, but also geared to solve one of the essential problems of modernity – our inability to sift through the ever-growing, always-looming mounds of information. Who better, the theory goes, to recommend what we should read and watch than our friends? Zuckerberg has boasted that the News Feed turned Facebook into a “personalised newspaper”.

Unfortunately, our friends can do only so much to winnow things for us. Turns out, they like to share a lot. If we just read their musings and followed links to articles, we might be only a little less overwhelmed than before, or perhaps even deeper underwater. So Facebook makes its own choices about what should be read. The company’s algorithms sort the thousands of things a Facebook user could possibly see down to a smaller batch of choice items. And then within those few dozen items, it decides what we might like to read first.

Algorithms are, by definition, invisibilia. But we can usually sense their presence – that somewhere in the distance, we’re interacting with a machine. That’s what makes Facebook’s algorithm so powerful. Many users – 60%, according to the best research – are completely unaware of its existence. But even if they know of its influence, it wouldn’t really matter. Facebook’s algorithm couldn’t be more opaque. It has grown into an almost unknowable tangle of sprawl. The algorithm interprets more than 100,000 “signals” to make its decisions about what users see. Some of these signals apply to all Facebook users; some reflect users’ particular habits and the habits of their friends. Perhaps Facebook no longer fully understands its own tangle of algorithms – the code, all 60m lines of it, is a palimpsest, where engineers add layer upon layer of new commands.

Pondering the abstraction of this algorithm, imagine one of those earliest computers with its nervously blinking lights and long rows of dials. To tweak the algorithm, the engineers turn the knob a click or two. The engineers are constantly making small adjustments here and there, so that the machine performs to their satisfaction. With even the gentlest caress of the metaphorical dial, Facebook changes what its users see and read. It can make our friends’ photos more or less ubiquitous; it can punish posts filled with self-congratulatory musings and banish what it deems to be hoaxes; it can promote video rather than text; it can favour articles from the likes of the New York Times or BuzzFeed, if it so desires. Or if we want to be melodramatic about it, we could say Facebook is constantly tinkering with how its users view the world – always tinkering with the quality of news and opinion that it allows to break through the din, adjusting the quality of political and cultural discourse in order to hold the attention of users for a few more beats.

But how do the engineers know which dial to twist and how hard? There’s a whole discipline, data science, to guide the writing and revision of algorithms. Facebook has a team, poached from academia, to conduct experiments on users. It’s a statistician’s sexiest dream – some of the largest data sets in human history, the ability to run trials on mathematically meaningful cohorts. When Cameron Marlow, the former head of Facebook’s data science team, described the opportunity, he began twitching with ecstatic joy. “For the first time,” Marlow said, “we have a microscope that not only lets us examine social behaviour at a very fine level that we’ve never been able to see before, but allows us to run experiments that millions of users are exposed to.” Facebook’s headquarters in Menlo Park, California. Photograph: Alamy

Facebook likes to boast about the fact of its experimentation more than the details of the actual experiments themselves. But there are examples that have escaped the confines of its laboratories. We know, for example, that Facebook sought to discover whether emotions are contagious. To conduct this trial, Facebook attempted to manipulate the mental state of its users. For one group, Facebook excised the positive words from the posts in the news feed; for another group, it removed the negative words. Each group, it concluded, wrote posts that echoed the mood of the posts it had reworded. This study was roundly condemned as invasive, but it is not so unusual. As one member of Facebook’s data science team confessed: “Anyone on that team could run a test. They’re always trying to alter people’s behaviour.”

There’s no doubting the emotional and psychological power possessed by Facebook – or, at least, Facebook doesn’t doubt it. It has bragged about how it increased voter turnout (and organ donation) by subtly amping up the social pressures that compel virtuous behaviour. Facebook has even touted the results from these experiments in peer-reviewed journals: “It is possible that more of the 0.60% growth in turnout between 2006 and 2010 might have been caused by a single message on Facebook,” said one study published in Nature in 2012. No other company has made such claims about its ability to shape democracy like this – and for good reason. It’s too much power to entrust to a corporation.

The many Facebook experiments add up. The company believes that it has unlocked social psychology and acquired a deeper understanding of its users than they possess of themselves. Facebook can predict users’ race, sexual orientation, relationship status and drug use on the basis of their “likes” alone. It’s Zuckerberg’s fantasy that this data might be analysed to uncover the mother of all revelations, “a fundamental mathematical law underlying human social relationships that governs the balance of who and what we all care about”. That is, of course, a goal in the distance. In the meantime, Facebook will keep probing – constantly testing to see what we crave and what we ignore, a never-ending campaign to improve Facebook’s capacity to give us the things that we want and things we don’t even know we want. Whether the information is true or concocted, authoritative reporting or conspiratorial opinion, doesn’t really seem to matter much to Facebook. The crowd gets what it wants and deserves.

The automation of thinking: we’re in the earliest days of this revolution, of course. But we can see where it’s heading. Algorithms have retired many of the bureaucratic, clerical duties once performed by humans – and they will soon begin to replace more creative tasks. At Netflix, algorithms suggest the genres of movies to commission. Some news wires use algorithms to write stories about crime, baseball games and earthquakes – the most rote journalistic tasks. Algorithms have produced fine art and composed symphonic music, or at least approximations of them.

It’s a terrifying trajectory, especially for those of us in these lines of work. If algorithms can replicate the process of creativity, then there’s little reason to nurture human creativity. Why bother with the tortuous, inefficient process of writing or painting if a computer can produce something seemingly as good and in a painless flash? Why nurture the overinflated market for high culture when it could be so abundant and cheap? No human endeavour has resisted automation, so why should creative endeavours be any different?

The engineering mindset has little patience for the fetishisation of words and images, for the mystique of art, for moral complexity or emotional expression. It views humans as data, components of systems, abstractions. That’s why Facebook has so few qualms about performing rampant experiments on its users. The whole effort is to make human beings predictable – to anticipate their behaviour, which makes them easier to manipulate. With this sort of cold-blooded thinking, so divorced from the contingency and mystery of human life, it’s easy to see how long-standing values begin to seem like an annoyance – why a concept such as privacy would carry so little weight in the engineer’s calculus, why the inefficiencies of publishing and journalism seem so imminently disruptable.

Facebook would never put it this way, but algorithms are meant to erode free will, to relieve humans of the burden of choosing, to nudge them in the right direction. Algorithms fuel a sense of omnipotence, the condescending belief that our behaviour can be altered, without our even being aware of the hand guiding us, in a superior direction. That’s always been a danger of the engineering mindset, as it moves beyond its roots in building inanimate stuff and begins to design a more perfect social world. We are the screws and rivets in the grand design.