Book review: “A World Without Work” by Daniel Susskind


Technological unemployment and economists

The term “technological unemployment” was popularised in the 1930s by the celebrated economist John Maynard Keynes. Fifty years later, another renowned economist called Wassily Leontief warned that jobs for humans might follow the same path that jobs for horses did in the early 20th century. So the idea has a respectable economic heritage, but economists are still arguing about whether it will actually happen.

The latest contribution comes from Daniel Susskind, a member of an unreasonably talented family of lawyers, economists and academics. His economic credentials are strong: previously an adviser at Number 10, he is now a fellow at Balliol College, Oxford. Susskind is on the side of those who think that technological unemployment is very likely to happen in a matter of decades. He does not attempt a watertight proof, but he helps to clarify how economists should think about the issue.

Unlike many commentators, Susskind goes beyond diagnosis and into prognosis and prescription. This is commendable, and although I disagree with where he ends up, the journey is important because it is vital that more thinking is done on this subject. The book is well-written, and easy to digest.

Susskind is not a technological determinist: he speculates that if horses had the vote, their fate might have been different. He describes himself instead as a technological realist, and thinks that our freedom of movement is constrained. We can’t escape the fact that we will build more and more capable machines. Our challenge is not to try to stop this, but to work out how to flourish anyway.

The substitution force and the complementary force

Opposing forces

The book provides a clear and helpful discussion of the two main economic forces that determine whether there is technological unemployment: the substitution force and the complementary force.

The substitution force is straightforward: machines replace horses and humans in jobs if they are cheaper, better, and / or faster. In 1915 there were 21 million horses labouring away in America, and the US horse population today is two million.

Despite numerous rounds of automation (mostly mechanisation so far) humans have not been ejected from the workplace, and many developed economies are close to full employment. This is because of the complementary force, which has three effects: the productivity effect, the bigger pie effect, and the changing pie effect.

The productivity effect is when automation eliminates some jobs, but makes other workers more productive. Computer-aided design (CAD) has reduced employment for draughtsmen, but it has also enabled architects to work much faster, and design more complex, efficient and elegant buildings.

The bigger pie effect is clearly visible in the economic history of the USA: its GDP was 15,000 times higher in 2000 than it was in 1700. This means more wealth, more demand, and more jobs.

The changing pie effect is seen in the shift in economies and employment from farms to factories, and then to offices.



In 2003, the MIT economists David Autor, Frank Levy, Richard Murnane gave their names to the ALM hypothesis, which provides more detail about how the substitution effect works. It points out that jobs are not monolithic, but comprised of tasks. Automation replaces tasks which are routine. It is not always obvious which tasks are routine, and they are certainly not restricted to low-paid, blue-collar jobs. The Luddites who smashed looms in the early 19th century were not unskilled labourers, but artisans. Their jobs were being de-skilled by machines.

Today, lots of office jobs involve routine tasks, and Bank of England Governor Mark Carney has warned of an impending “massacre of the Dilberts”. Many economists think that since the 1980s, automation has hollowed out middle-range jobs, leaving untouched the high-skilled cognitive jobs, and also the manual jobs, which, as Moravec’s Law explains, are hard to automate because while reasoning (which is hard for humans) requires very little computation, sensorimotor skills (which are easy for human adults) require enormous computational resources.

David Autor has argued for years that the complementary effect means that technological unemployment is not something to worry about for the foreseeable future. In 2016 he gave an engaging TED talk in which he argued that when ATMs automated the jobs of bank tellers, the number of human tellers actually rose, because the ATMs enabled banks to open more branches, and the tellers carried out more value-added tasks.

It is a pity that Susskind repeats this story, because it is almost certainly untrue. The real reason the number of branches increased was a piece of financial deregulation, the Riegle-Neal Act of 1994, which allowed more banks to operate across state borders. This explains why the number of branches only increased in the USA, and not, for example, in Europe.

The Big Bang in AI, in 2012

Big Bang Theory

In 2012 there was a Big Bang in artificial intelligence. Access to more data and more powerful machines enabled AI researchers to deploy machine learning, a well-established type of statistical technique. Since then, it has been much harder to sustain the argument that the complementary effect will persist for the foreseeable future. Machine learning, and in particular a sub-set called deep learning, enables machines to carry out tasks which are non-routine, and indeed sometimes require creativity. Those who continue to deny that machines can be creative have simply not been paying attention. There is clear evidence of creativity in the famous move 37 in game two of AlphaGo’s defeat of Lee Sedol, the best human Go player. Umpires in chess championships between humans now watch out for unusual creativity, which is a sign that the player has cheated by using a computer.

Oddly, Susskind chooses not to use the terminology of machine learning, but calls it “pragmatic” AI instead. The form of AI which prevailed before is generally called symbolic AI, or good old-fashioned AI (GOFAI), but Susskind calls it “purist”.

This transition within AI has caught out many eminent thinkers. They seem to confuse consciousness (which machines do not appear to possess) and intelligence (which they certainly do display). The philosopher John Searle complained that by developing Deep Blue, IBM was giving up on AI. Douglas Hofstadter thought that IBM’s Watson, which won Jeopardy, was vacuous. Intelligence is most concisely defined as goal-oriented learning behaviour, and it is not a specifically human thing. We humans are just the most advanced exemplar we have today. We are unlikely to be the most advanced exemplar this planet will ever host.

For these thinkers, intelligence is whatever machines can’t do today. They assume that machines will have to reach artificial general intelligence (AGI, or strong AI) to be capable of the more complex things that we do, like driving, diagnosing cancers, holding conversations, etc. Whenever any of these things falls to the machines, they move on to the next. Susskind is surely correct to say that technological unemployment does not require AGI.

The capabilities we use to earn a living can be classified as manual, cognitive, and affective. (Affective capabilities relate to human moods, feelings and attitudes.) Machines have been taking over the jobs requiring manual capabilities for decades. They dominate heavy-lifting tasks, and they are increasingly good at fiddly manual jobs too. We can already see cognitive capabilities starting to go the same way, and we cannot we be confident that jobs requiring affective capabilities will always be reserved for humans: machines can already tell if you are happy, surprised, or depressed. Or gay. Some AI systems can tell these things by your facial expressions, and others by how you walk, or dance, or type.

Automation will proceed at different paces in different places, not least because the cost of the alternative to automation will vary. Countries that age faster will automate faster, and regulations and cultures will also play a role in setting the timeline. But overall, the process is ineluctable. Take any piece of technology – a computer, a mobile phone, a robot: the current version is the least advanced that it is ever going to be. As Susskind says, “nothing is certain in life except death, taxes, and the relentless process of task encroachment.”

Frictional and structural technological unemployment


Susskind expects task encroachment to cause two kinds of unemployment: frictional, and then structural.

Frictional technological unemployment means there are still jobs, but not all of us are equipped to do them. Susskind suggests this may already be happening: in the USA, unemployment is very low, at 3.7%, but it is no secret that unemployment numbers don’t tell the whole story. The participation rate is the number of people who are employed, or looking for employment. This is depressed, with one in six men of working age having dropped out of the workforce – double the level in 1940. There is a mis-match of skills, identity, and place: men are unwilling to take the so-called “pink collar” jobs which have expanded, like nursing, teaching, housekeeping, and hairdressing. Or they may not be offered them.

If more and more workers chase a dwindling number of jobs, the result will be lower wages, and the growth of “the precariat”. And if automation replaces human jobs at an accelerating rate, we will see what I call the Churn, as people have to re-skill and re-train more and more often, changing their jobs, their companies, and even their careers.

Structural technological unemployment, by contrast, means the complementary force has become ineffective. A human is replaced in one job, and even though the productivity effect, the bigger pie effect or the changing pie effect means that another job is created, that new job is done by a machine, not by the displaced human. Susskind is no more able to prove beyond reasonable doubt that this will happen than any other commentator has been, but he provides plenty of compelling examples which show why we should take the idea seriously.

Skeptics about the idea of technological unemployment think it is the “lump of labour fallacy”, the misconception that there is a fixed amount of work—a lump of labour—to be done within an economy which can be distributed to create more or fewer jobs. David Schloss, a British economist, pointed out back in 1892 that instead of being static, work expands. The trouble is, there is no guarantee that the additional work will always be done by humans instead of machines.

If technological unemployment is coming, when will it arrive? Susskind is refreshingly honest: he does not know. There may be sudden surges and abrupt tipping points, or there may be a constant, gradual erosion of the complementary force. Unusually, for a book on this subject, Susskind does not explicitly refer to the exponential growth in the power of our machines. But he does say that he thinks the timing is decades rather than centuries, because given eight decades of current progress, a machine will be a trillion times more powerful than its equivalent today.

The Big State

Big government

In the book’s concluding chapters, Susskind asks how we will all find meaning in our lives without jobs, and how we will all earn enough to live on. Many of the authors who have addressed technological unemployment before have ground to a halt at this point, particularly American ones. Susskind’s proposal gives a clue as to why they struggle so much. For both questions, he concludes that the answer lies partly in developing a Big State, which will redistribute income and wealth, and nudge us all into behaviours that will give us lives of fulfilment rather than boredom and despair. What he has in mind is something much more radical and much more intrusive than the currently popular appeals for more industrial strategy and a more generous welfare state. This will be anathema to many American readers, and it certainly raises the spectre of an authoritarian state, or at least a heavily patronising one.

Although Susskind thinks we will need the Big State to help us find meaning and purpose in a world without work, he provides a series of historical examples which demonstrate that you don’t need a job to enjoy a life with meaning. One of them is aristocrats. He quotes Bertrand Russell as saying that far from being bored and useless, the leisure class “contributed nearly the whole of what we call civilisation”.

Furthermore, working nine to five (or five to nine, for the merchants bankers and startup founders amongst you) is not a state of nature. Foragers and hunter-gatherers worked fewer hours each day than we do. The ancient Greeks’ approach to work and leisure was the opposite of ours: the ancient Greek word for work is ascholia, and it means the absence of leisure. Aristotle declared that “citizens must not lead the life of artisans or tradesmen, for such a life is ignoble and inimical to excellence.”

This idea is also enshrined in the foundation story of our most widespread religion. When Adam and Eve ate the forbidden apple, God sentenced them to work, and they lost the lives of leisure they had led up till then. If Marx were alive today, he might say that work is the new opiate of the masses – the pastime we use to blind ourselves to the possibility of a better life.

Missing abundance

Abundance sign

Susskind also thinks the Big State will be needed to re-distribute income and wealth. He envisages taxes rising sharply, and various kinds of intrusive behaviour change programmes, including obliging accountants to work against the financial interests of their clients.

Like many economists, he is skeptical of universal basic income. He understands the argument that making such payments universal would ensure they reach everybody, and should neutralise their stigma, but he cannot reconcile himself to the waste involved in paying UBI to, for instance, Rupert Murdoch and Mark Zuckerberg.

But I think he neglects the biggest problem with UBI, which is the little word in the middle: “basic”. At best, even if it is somehow affordable, UBI succeeds only in keeping everybody alive but poor. We have to do much better than this. We have to make everybody rich – or at least comfortable.

I suspect there is only one way we can transfer enough income and / or wealth from the rich to the rest of us to make everybody comfortable in a world without jobs. That is to develop the economy of abundance. If prices remain essentially as high as they are today, UBI is doomed to failure, and so are its variants, like Universal Basic Services, or Conditional Basic Income. The transfer would weight heavily on the employed and the rich, and would be resisted. But if we can drive down to almost zero the price of everything we need for a great standard of living, then the transfer should be achievable, and a world without jobs could be a truly wonderful place. I think we can develop an economy of abundance – in fact it may arise naturally. However, the transition will be bumpy, and we need to have our eyes open.

This article first appeared in Forbes

Change has never been this fast. It will never be this slow again

Snail on rocket
The 2010s were an ironic decade. Most metrics show that human welfare improved at an extraordinary rate, but many of us seem to be fearful or resentful, or both. The world is far richer in 2020 than it was in 2010, and global inequality is declining. There is still plenty of poverty, egregious inequality, and injustice, and there are still brutal wars and civil unrest. But overall, life expectancy is sharply up, and child mortality and deaths during childbirth are sharply down. Despite global warming, the number of deaths and injuries from climate-related disasters has fallen significantly, and many rich countries have passed the point of “peak stuff”: they are using fewer resources, polluting less, and the world has actually increased its forest cover.

And yet, the most potent political force in many countries is populism. Some populists are sincere people motivated by genuine conviction, but many more are obvious opportunists. Their claims are consistent: the world used to be a better place; the people’s birthright has been stolen by outsiders, enabled by an established elite, and only the populist can rectify the situation. Oh, and anybody who opposes them is an enemy of the people, and should be vilified, and barred from the media.


Populism is rampant on both sides of the political divide. Today’s right-wing populism is often explained as a reaction against economic disadvantage – the resentment of people who feel left behind by globalism and technological change. There is something in this, but in truth it is much more a cultural phenomenon: a reaction against the decades-long triumphal march of social liberalism, which has overturned what people believed to be the natural order of things. The worst insult a right-wing populist can level is “politically correct”.

Populism of the left claims that modern capitalism is a conspiracy by an elite which is dedicated to (or at least indifferent to) the immiseration of the majority. Contrary to what the data shows, it claims that inequality is at an historical extreme, and getting worse.

Much of the improvement in the quality of human lives which populists don’t want you to know about was produced by the exponential improvements in technology, so it was perhaps inevitable that the ironic 2010s would see a backlash against technology – the techlash. Social media is accused of enslaving everyone to the dopamine rush of a Facebook like or a Twitter reply, and these accusations are often expressed most forcefully by the most avid users of the technologies they rail against. The tech giants are hoovering up our personal data for nefarious purposes, and recklessly deploying algorithms that are opaque, riddled with bias, and diluting the agency and humanity of a population that is increasingly dumbed down – incapable of paying attention to anything for more than ten seconds, unless it is a video game or a blockbuster movie.

Techlash encompasses artificial intelligence too, which is either feared or ridiculed – or both. Either it is about to take over all human jobs and then destroy the species in a robot apocalypse, or it is an over-hyped fad: a mere conjuring trick using statistics and human slave labour.

Big Bang in AI

In fact, the 2010s were AI’s decade of wonders. In 2011, IBM’s Watson beat the best human players of the US TV quiz show “Jeopardy” – an amazing achievement, and the gracious human loser gave us the memorable phrase “I for one welcome our new robot overlords.” The next year saw the Big Bang in AI, when Geoff Hinton and others figured out how to get machine learning to work in AI – and in particular deep learning, which is (to over-simplify) a rehabilitation of neural networks. What made this possible was the huge increases in the available compute power and data, and what it made possible was superhuman facial recognition, and seriously impressive search, mapping, and translation services. (The often lauded recommendation services are still a bit crap, though.)

Two things which will have huge impact during the 2020s showed signs of their promise during the 2010s. Self-driving cars went from being rubbish, to being deployed in a pilot service carrying members of the public in self-driving taxis with nobody in the front seats. Smartphones went from rare in 2010 to globally ubiquitous in 2019. The digital assistants in these phones and other devices (Siri, Cortana, Alexa and co) are basic today, but Google Duplex offers a glimpse of how powerful they will become, and some of this promise will be realised in the 2020s.

In the next few days you will probably read many predictions about what AI will and will not be able to do by 2030. Here are a few contributions.

  • There will be another major breakthrough in AI, similar in impact to 2012’s Big Bang.

  • Researchers will work out how to combine symbolic AI, or good old-fashioned AI with machine learning.

  • Machines will start to display signs of common sense.

  • We will still be a long way off artificial general intelligence, or AGI – a machine with all the cognitive abilities of an adult human.

  • The business world will move beyond pilots to large-scale implementation, and start catching up with the tech giants.

  • Europe will try harder, and might even start to crack the current US-China AI duopoly.

  • By 2030, self-driving cars will be a common sight in most cities, but in taxis rather than privately-owned cars.

Self-driving car 2

  • Many taxi drivers, van drivers and lorry drivers will be looking for new careers.

  • You will have conversations with your phone, and send your digital assistant off into the net to do errands for you.

  • 5G will make the internet of things a reality, so predictive maintenance will mean that things will break down and collapse less often, and there will be less waste.

  • Virtual and augmented reality will work quite well, and it will be interesting to see whether lots of people spend much of their lives in simulated worlds.

  • AI simulations will enable better decisions to be made in business, science, and government.

  • We may finally be able to turn sick care into health care. There’s a decent chance we will cure many types of cancer, and the idea of ending ageing may well be in the mainstream.

  • And yes, we will have flying cars.

Some of this may seem fanciful, and predicting the future is, of course, impossible. But here’s the thing which most people still miss. When you read the forecasts elsewhere in the coming days, ask yourself whether they appear to be taking exponential growth into account.

Moore’s Law is the observation that computers get twice as powerful every 18 months or so. People often say it is dead or dying, but really it is evolving – which is what it has done since the phenomenon was first observed in 1965. Moore’s Law gives us exponential growth, and exponential growth is astonishingly powerful. If you had one unit of computing power in 2010, you will have 128 units in 2020. How many will you have in 2030? Believe it or not, you will have 8,000 units.

Exponential, 128 to 1mChange has never been this fast. And it will never be this slow again. Hang onto your hat: the 2020s are going to be astonishing.

Review of “The Age of AI”

A YouTube series, presented by Robert Downey Jr

Iron Man
Robert Downey Jr is best known as Tony Stark, the character behind Iron Man in the Avengers movies. It is said that Downey Jr modelled his portrayal of Stark on Elon Musk, the creator of Tesla and SpaceX, and one of the most outspoken commentators about artificial intelligence. Musk famously said that by developing advanced AI we are “summoning the demon”, and that we must work hard and fast to ensure it remains safe. In fact he thinks we must develop the technology to link our minds intimately with AI systems, so that instead of being replaced by them we can be enhanced by them.

So it is apt that Downey Jr is introducing “The Age of AI”, YouTube’s expensive new eight-part series on AI. The first two episodes are available now, and the remaining six will be released over the coming weeks – unless you are impatient, and sign up for the premium service. Inevitably, the series has high production values: Robert Downey Jr is not going to lend his name to content below Hollywood standards. Indeed, he introduces each episode from a hangar where the original Iron Man movies were shot, a dozen years ago. The camera moves around a lot, and each shot is short, with lots of close-ups of faces, hands, musical instruments – lots of eye candy for viewers with short attention spans.

Baby X
How do you find a way into a subject as large, complex, and important as artificial intelligence? The storytellers behind “The Age of AI” chose to start by focusing on how far AI can enhance us, and whether it could end up replicating, and even replacing us. The first episode introduces us to Baby X, a lifelike avatar of a baby girl developed by digital effects artist Mark Sagar, who helped create King Kong for Peter Jackson, and the Na’vi characters in Avatar for James Cameron. Graphics by Hollywood, behavioural traits courtesy of machine learning. The experts go on to develop an avatar for Will.I.Am, founder of the Black Eyed Peas, who is impressed by the creation, and then suggests that it should remain a little robotic, so as not to confuse his mother.

The second story in episode one shows us prosthetic hands for two musicians – a drummer and a guitarist. Existing prosthetic hands are rather blunt instruments, and often quickly abandoned by their intended users. Adding analysis by machine learning of the nerve signals the brain can still send down a phantom limb seems to enable a much more lifelike prosthesis. The message of the episode is that machine learning and AI can make us more human, not less, but we will have to think carefully about where we want to draw the line.

Age of AI retinal scans
A geek might ask for more detailed explanations of how AI works. Terms are explained as the series unfolds, but very briefly. Machine learning, for instance, is a technique to find patterns in data. And, er… that’s it. But viewers unfamiliar with AI will learn a lot. The second episode addresses how AI is advancing medical science, and also disseminating it – making it more widely available in the developing world, for instance. It rams home the point that the availability of masses of data is what enables machines to diagnose illnesses faster and more cheaply than human doctors can. In India, which has a chronic shortage of doctors for its enormous population, machines can quickly and accurately diagnose retinal damage caused by diabetes, and push patients through to surgery in time to prevent blindness. There was no discussion in this episode of the controversy surrounding the sharing of patients’ intimate data which is necessary to enable this – perhaps that will come in a later episode.

Sometimes the show feels like an infomercial, either for AI as a whole, or simply for Google, which provided many of the filmed examples. This must have been much easier to arrange, given that YouTube is owned by Google, but it is surprising they didn’t wander down the road to speak to Facebook or Apple, for instance, or hop on a plane to see Amazon, IBM, or even Baidu or Tencent. The programme follows teams from Google as they help ex-NFL star Tim Shaw regain his natural voice after losing muscle control to the tragic disease ALS, also known as Lou Gehrig’s disease. The achievement is impressive, and the emotion provoked in his family is profound and moving. But the failure to mention any of the other tech giants, or the controversy swirling around the industry, will leave some viewers feeling manipulated.

Age of AI title
AI is our most powerful technology, and in the next few decades it will change everything about the nature of being human. Understanding what it is, how it works, and something about its promise and its peril will increasingly be basic literacy for citizens. This is a well-made, well-informed show that will get many more people up to speed, and that is greatly to be welcomed.

This article first appeared in Forbes

Review of “More from Less”, by Andrew McAfee

Cover image

The New Optimists

Andrew McAfee wants to cheer you up. If you read his latest book with an open mind, he might well succeed. McAfee, an MIT economist, is joining the New Optimists (Bill Gates, Stephen Pinker, Hans Rosling and others) in trying to persuade us that the world is not going to the dogs. The central claim of “More From Less” is that capitalism and technological progress are allowing us “to tread more lightly on the earth instead of stripping it bare.” Unfortunately, he admits, this good news is hard for many people to believe because catastrophism has such a strong hold on our imaginations.


For hundreds of years before 1700, England’s population oscillated between two and six million. When peace coincided with good harvests, the number would rise, only to slump again when our inability to feed the growing population brought famine again. Robert Malthus made the reasonable assumption that this pattern would continue, and issued a dire warning about the consequence of Britain’s fast-growing population in the early industrial revolution. He was wrong. Capitalism and technology changed the game entirely, enabling us to feed far larger populations than ever before. Malthus’ name became a synonym for dramatically inaccurate predictions.

Paul Ehrlich is Malthus’ intellectual heir. Since the 1960s he has been forecasting doom and disaster from the exhaustion of all the natural resources we depend upon. The first New Optimist, Julian Simon, offered Ehrlich a bet: choose any resource and any time-frame above a year. If the price of the resource rose, Julian would pay Ehrlich; if it fell, the reverse. Ehrlich chose five – copper, chromium, nickel, tin, and tungsten – and the prices of all five fell. Ehrlich is surprisingly unrepentant: after all these years of abysmal forecasting failure, he is still telling students at Stanford that disaster is just around the corner.

Ehrlich is not alone. Any number of environmentalists and lobby groups will tell you that we are polluting, deforesting, and generally destroying the planet, exhausting its natural resources, and driving most other species extinct. All this is making us sick, and crucially, the damage is accelerating.

Using fewer natural resources

Aluminium cans

Implausible as it will seem to many, the data shows the opposite. As we get richer, we are using resources more efficiently, using less energy, causing less pollution and cleaning up the pollution of the past. We are even re-foresting the earth and protecting other species. McAfee produces compelling data and numerous examples, but sadly, many people will refuse to believe him: good news is no news, and if it bleeds, it leads. We all love a good horror story.

The evidence about resource consumption in America comes from the US Geological Survey, a federal agency formed in 1879. It tracks seventy-two resources, from aluminium to zinc, and only six of them are not yet post-peak. Even energy usage is decreasing, down two percent in 2017 from its 2008 peak, despite a 15 percent growth in GDP between those two years.

America is getting more and more efficient. Milk and aluminium are two of McAfee’s examples. Between 1950 and 2015, US milk production rose from 117 billion pounds to 209 billion, while the herd shrank from 22 million cows to 9 million. This is a productivity improvement of 330 percent. When aluminium cans were introduced in 1959 they weighed 85 grams. This fell to 21 grams by 1972, and by 2011 it was down to 13 grams.


The information revolution has powered much of this improvement, as illustrated by the story of railcars. In the late 1960s, US railway companies owned thousands of these 30-ton beasts, and only about five percent of them moved on any given day. This was not because the other 95 percent needed to rest: it was because their owners didn’t know where they were. They knew that if they could increase the percentage of cars moving each day from 5 percent to 10 percent, they would need only half as many of them. Today, of course, every railcar reports its precise location to its owner several times a second – thanks to the information revolution.

It’s not just the US. In the UK, the Office for National Statistics publishes the annual Material Flow Accounts, and a 2011 paper entitled ‘Peak Stuff’ concluded that the UK reached maximum use of material resources in the early 2000s. Data from the EU’s statistical agency Eurostat show that Germany, France, and Italy have generally seen flat or declining total consumption of metals, chemicals, and fertilizer in recent years.

And no, before you ask, this reduction in natural resource usage is not just the result of our economies switching from goods to services. While goods have been declining compared to services as a percentage of total GDP, the output and consumption of products has carried on increasing in absolute terms. We are experiencing a great decoupling: we are de-materialising industrial production. (This is actually quite an old idea: it was called ephemeralisation by Buckminster Fuller back in 1927. You may remember him as the inventor of geodesic domes, which are very efficient structures.)

Reducing harms

Reducing pollution

As well as using fewer natural resources, the developed world is generating less pollution. In the US, the Clean Air Act was substantially amended and strengthened in 1970, 1977, and 1990. The Clean Water Act was passed in 1972, the Safe Drinking Water Act in 1974, and the Toxic Substances Control Act in 1976. Other developed countries have their equivalents.

The results are impressive. McAfee quotes another member of the New Optimists, Matt Ridley: “A car today emits less pollution travelling at full speed than a parked car did from leaks in 1970.”

Leaky old car

McAfee also denies that we are driving thousands of species extinct: “documented extinctions are relatively rare (with about 530 recorded within the past five hundred years) and appear to have slowed down in recent decades”. That is not to say that our impact on other species is altogether benign: “the biggest threat to animal species isn’t absolute extinction, but instead huge declines in population size due to over-hunting and habitat loss.” But even here the trend is encouraging. “Parks and other protected areas made up only 4 percent of global land area in 1985, but by 2015, this figure had almost quadrupled, to 15.4 percent. At the end of 2017, 5.3 percent of the earth’s oceans were similarly protected.”

It turns out we are using less land for farming, and land that we no longer farm reverts to forest. “Throughout the developed world this process is now dominating any and all tree felling that is taking place, and overall reforestation has become the norm.” This not the case in the developing world, but “even with continued deforestation in developing countries and other challenges, a critical milestone has been reached: across the planet as a whole we have, as an international research team concluded in 2015, experienced a ‘recent reversal in loss of global terrestrial biomass.’ For the first time since the start of the Industrial Era, our planet is getting greener, not browner.”

As the world continues to grow richer, McAfee argues, we can expect this good news to spread. “In 1999, 1.76 billion people were living in extreme poverty. Just sixteen years later, this number had declined by 60 percent, to 705 million. Hundreds of millions fewer people are living in poverty now than in 1820, when the world’s total population was seven times smaller than it is today.” Happily, “the story of global poverty reduction isn’t a purely Chinese one. … Every region around the world has seen large poverty reductions in recent years.”

The Four Horsemen of the Optimist

Four horsemen, 2

If you can suspend your disbelief for a bit longer, you’ll be wondering what is causing these happy developments? McAfee identifies four drivers, which he calls the four horsemen of the optimist: Technology, Capitalism, Public awareness, and Responsive government.

Technology gives us new ways to solve old problems, and capitalism provides the incentive for people to invent these new ways and to implement them once they have been invented. As Abraham Lincoln put it, we add “the fuel of interest [capitalism] to the fire of genius [technology] in the discovery and production of new and useful things.”


Sadly, capitalism is a hard sell in many quarters these days, so McAfee also provides a poignant example of how its great rival, socialism, often yields disastrous outcomes. The USSR was part of the 1946 international convention against whale hunting, but between 1948 and 1973 it killed 180,000 more whales than it reported. Unlike the Japanese, the Russians have no great appetite for eating whale flesh, and most of the animals’ bodies were thrown back into the sea. And why? Because the five-year plan demanded seafood tonnage, and it had no mechanism to incentivise the production (or in this case, hunting) of things that people actually wanted.

Technology and capitalism are not enough, of course. Some humans, capitalist or otherwise, will pillage and poison unless they are prevented from doing so. Public awareness and responsive government is needed to address the fact that markets often ignore what economists call negative externalities, and they often fail to support people who are unlucky and / or unsuccessful.

Nevertheless, McAfee insists that the spread of capitalism has improved the lot of humanity beyond recognition. Its partial adoption by India in “1991… deserves its spot in the annals of economic history alongside December 1978, when China’s Communist Party approved the opening up of its economy, or even May 1846, when Britain voted to repeal the Corn Laws.” “Between 1978 and 1991, more than 2.1 billion people—about 40 percent of the world’s 1990 population—began living within substantially more capitalist economic systems.”

McAfee is confident that in the long run, the four horsemen will continue to ride. “Smartphone use and access to the Internet are increasing quickly across the planet. This means that people no longer need to be near a decent library or school to gain knowledge and improve their abilities.” And countries are unlike companies in that size does not necessarily beget bureaucratic sloth: our most valuable resource is human ingenuity, and “an economy with a larger total stock of human capital will experience faster growth.”

Climate change and its solutions

Extinction Rebellion

To establish that he is no climate change denier, McAfee cites the mantra, “it’s warming; it’s us; it’s bad; and we can fix it.” But once again, he argues that the trend in the developed world is much better than most people think. In the US, “greenhouse gas emissions have gone down even more quickly than has total energy use. This is largely because we have in recent years been using less coal and more natural gas to generate electricity.”

How can we entrench and spread this positive trend? McAfee proposes two solutions: first, cap and tax carbon emissions, and allow companies to trade permits. Second, rehabilitate nuclear energy. “Nuclear power doesn’t deserve its bad reputation. As is the case with vaccines, glyphosate, and GMOs, public awareness around nuclear power is broadly out of step with reality.”

Inequality and Populism


Despite all this good news, the world is undeniably grumpy. People in many countries have elected Populist governments, and in some places, especially rural America, “deaths of despair” like suicide and the mis-use of drugs and alcohol are rising. McAfee thinks that growing inequality plays a significant role in this, but the data from his favourite source, the excellent website “Our World in Data” suggests otherwise. Inequality is certainly not growing on a global level, as developing countries have been growing much faster than developed ones. And while the Gini coefficient, the usual yardstick of inequality, has become slightly worse in the US, the same is not true elsewhere in the developed world, where the coefficient has remained fairly steady at just under 40 since the early 1990s. (100 is perfectly unequal and 0 is perfectly equal.)

The real villain of the piece is not inequality, but the perception of unfairness, which is something people actually care much more about. As McAfee himself notes, “people prefer fair inequality over unfair equality.” Populists have risen to power on the back of resentment. McAfee quotes a book on America’s Tea Party: “Blacks, women, immigrants, refugees – all have cut ahead of you in line. But it’s people like you who have made this country great. The line cutters irritate you. They are violating rules of fairness.”

Pluralists and authoritarians

Pluralism 2

The roots of the perceived inequality lie in the remarkable success of social liberalism in recent decades. Rightly or wrongly, many people feel this has gone too far: it is “political correctness gone mad”. The culture wars are being fought by pluralists and authoritarians. As McAfee puts it, “most countries are becoming significantly more pluralistic—they’re seeing more ethnic diversity and immigration, gender equality, support for gay marriage and other non-traditional lifestyles, and related changes that enhance diversity. A fascinating stream of recent research finds that a large percentage of people in all countries studied have an innate intolerance for this greater diversity. [They] want a strong central authority to enforce obedience and conformity.”

This battle between pluralists and authoritarians is raging all over the world, and it has eclipsed traditional loyalties of class, and the ideologies of the left and the right. How can this battle be won, or at least resolved? McAfee is clearly a pluralist, but he discounts the possibility of persuading authoritarians by rational argument. “It’s particularly important not to try to win arguments with them. … A better way is to start by finding common ground.”

This seems an unpromising approach. As he admits, “more and more people are choosing to have fewer ties to people with dissimilar values and beliefs, opting instead to spend more time among the like-minded. The journalist Bill Bishop calls this phenomenon ‘the big sort.’” Perhaps a better way to respond to the fear and anger which authoritarians breathe is simply to make pluralism the more attractive option, using fun and humour. This should not be hard, since pluralism is inherently more optimistic, although it often trips itself up by taking itself too seriously, and engaging in self-righteous circular firing squads.

Automation and abundance

Robot production line

McAfee is probably best known for his 2014 book “The Second Machine Age”. In that book, he and his co-author, fellow MIT academic Erik Brynjolfsson, argued that many jobs will be automated by artificial intelligence, and that although many new jobs will be created, societies must get better at re-skilling and re-training people to move from the old to the new.

I agree that for the next two or three decades there will be a Big Churn in the job market, but I have been trying for some time to persuade Brynjolfsson and McAfee to cast their minds further forward, and take seriously the idea that after two or three more decades of exponential improvement, our machines will be cheaper, better, and faster at pretty much everything that most of us can do for money. In which case, technological unemployment will become a reality.

McAfee makes little reference to the theme of automation in “More From Less”, which is ironic, because it helps to answer this big question: if machines do take all the jobs, how do we pay for the humans? The answer may well be to reduce the cost of all the goods and services we need to almost zero.

This is called the economy of abundance, and “More From Less” is invaluable in showing some of the ways it could materialise.

Abundance sign


More From Less” is a well-written and convincing book. If it makes a few of us more optimistic, it will also be remembered as an important one

A Brexiteer Among the Bots – review of “The AI Economy” by Roger Bootle


Roger Bootle is not afraid to think and say unconventional things. He is that rare phenomenon: a professional economist who thinks that Brexit is a Good Idea. Indeed, he belongs to a group called Economists for Brexit, now renamed as Economists for Free Trade, which argues for a no-deal Brexit.

Whatever you think of that, the economics consultancy that Bootle founded, Capital Economics, has been very successful financially, and in 2012 it was awarded the £250,000 Wolfson Economics Prize, the second most valuable economics prize in the world after the Nobel, for a proposal that EU member states who wanted to exit should default on a large part of their debts. A book on technological unemployment from such a high-profile economist is to be warmly welcomed. What’s more, it is a well-researched, enjoyable, and thoughtful book.

The AI Economy
The thoughtfulness does have its limits. The book reads as though Bootle was determined to dismiss the possibility of technological unemployment from the outset, and he makes little effort to hide his disdain for those who take the idea seriously. People like Max Tegmark and me, who are guilty of this crime, are labelled “AI visionaries”, and it is clear that this is not a compliment. We “geeks” are “bubbling enthusiasts” but also pessimists, “emanating gloom”. Others who are responsible for “fetid speculation about the implications of AI” are Stephen Hawking, Martin Rees, Stuart Russell, Elon Musk and Bill Gates.  Quite the rogues’ gallery.

Overall, Bootle’s writing style is clear and relaxed, and the book is mostly calm and measured. Occasionally he does give free rein to his inner curmudgeon: “As to the Internet of Things, rarely can something have been so overhyped. … In the future, doorknobs and curtains will also be able to speak to us when they need some attention, rather like those disembodied voices or noises in cars that tell us when we haven’t fastened our seatbelts. Heaven forfend!”

Less than a quarter of the way through the book, Bootle delivers what he thinks is the killer blow to the idea that technological unemployment is possible. “Unless and until robots can produce and reproduce themselves costlessly … human beings will always have some comparative advantage.” He admits that this might not help, as the income they could earn “might be appallingly low such that it hardly seemed worth working and the state has to intervene in a major way.” But he thinks humans have something better than comparative advantage: “In fact, such an outcome lies a long way off and, I suspect, will never transpire. For there are many areas where humans possess an absolute advantage over robots and AI, including manual dexterity, emotional intelligence, creativity, flexibility, and most importantly, humanity. These qualities ensure that in the AI economy there will be a plethora of jobs for humans.” And apparently that’s it.

Move 37
I disagree. AlphaGo’s famous move 37 in its second game against Lee Sedol in 2016 is one of many proofs that machines can be creative, even if their version of creativity does not involve a shred of consciousness. And anyone who has been watching the progress of robots developed by Boston Dynamics and others in the last few years will be under no illusion that humans will remain supreme forever in manual dexterity and flexibility.

The truth is that no-one knows for sure whether technological unemployment will happen, or when. None of us has a crystal ball. But if you think seriously about the impact of the exponential growth in the power of computers, and if you think ahead just a few decades, you realise that it is dangerously complacent to dismiss the possibility of technological unemployment out of hand.

Bootle does consider the phenomenon of exponential growth – he borrows my illustration of a football stadium filling up with water – but he dismisses it because it always collapses into an S curve, and he argues that because observations of exponential growth are sometimes described as a law, they lead to assertions that “rest on flimsy, if not nonexistent foundations.” This is a blatant Aunt Sally: everyone knows that exponential growth always collapses into an S curve eventually – the question is how long before that happens. (You are composed of around 27 trillion cells, which were created by fission, or division – an exponential process. It required 46 steps of fission to create all of your cells. Moore’s Law, by comparison, has had 36 steps in the 54 years of its existence.) And I’m not aware of anybody writing about Moore’s Law who doesn’t realise that it is an observation, not a physical law.

Partly the problem seems to lie in a failure of Bootle’s imagination – or perhaps his unwillingness to exercise it. He studied PPE at Oxford, and one of his favourite questions from back then is “Was the Black Death a good thing?” He says he “cannot imagine any form of AI being capable of assessing adequately the range of possible answers to this question.” I bet he could if he really tried.

Quite a few of Bootle’s assertions are out-of-date, or simply mistaken. He pours scorn on the idea of the paperless office, but the use of paper in offices peaked in 2007. He reports that chess computers are enhanced by collaborating with humans, but this has not been true for several years now. He thinks Kevin Kelly is a singularitarian, when he is actually a prominent opponent of the idea. A quick look at Wikipedia would have saved him from making the erroneous claim that Stanislav Petrov (the man who saved the world by bravely declaring a report about incoming American nuclear weapons to be a false alarm) was sacked. More seriously, his account of the progress with self-driving cars is highly contentious, and probably considerably off the mark. He regards autonomous cars as a bubble which is about to burst and destroy much of the automotive industry which has been foolish enough to invest so heavily in it.

From my point of view, it is a great shame that Bootle seems to have begun his enquiry so prejudiced against the idea that technological unemployment is a realistic possibility some decades ahead. In general, he is a congenial guide to the issues, and it would have been fascinating to have had his economic expertise applied to the idea, for instance, that the economy of abundance is a better solution to the problem than universal basic income, and that fully automated luxury capitalism is a better aspiration than fully automated luxury communism. As it stands, most of his book is only of academic interest if you do take the idea of technological unemployment seriously.

Robot arm wrestle

                   This article first appeared in Forbes magazine in October 2019.

Surveillance capitalism and anti-capitalism

In the last few years, the computer scientists and entrepreneurs who fuel Silicon Valley have gone through a bewildering series of transformations. Once upon a time they were ostracised nerds. Then they were the lovable geeks of the Big Bang Theory TV show, and for a short while they were superheroes. (In case you’re wondering, geeks wonder what sex in zero gravity is like; nerds wonder what sex is like.) Then it all went wrong, and now they are the tech bros; the anti-heroes in the dystopian saga of society’s descent into algorithmic rule by Big Brother, soon to be followed by extermination by Terminators.

Techlash is in full swing, and Shoshana Zuboff is its latest high priestess. She is professor emerita at Harvard Business School, and author of “Surveillance Capitalism”, a 600-page book on how the tech giants, especially Google and Facebook, have developed a “rogue mutation of capitalism” which threatens our personal autonomy, and democracy.

Zuboff is beyond scathing about Google and Facebook: even favourable reviewers agree she is extreme. She likens tech giant executives to the Spanish conquistadores, with the rest of us as the indigenous populations of South America, and rivers of blood as the consequence. (She doesn’t specify which countries have lost 90% of their populations as a result of their citizens using Facebook.) She describes Sheryl Sandberg, Facebook’s COO, as the “Typhoid Mary” of this socio-economic plague.

Apparently, the goal of the tech giants is not just to understand our behaviour so they can enable other organisations to sell things to us. It is to control us and turn us into robots, “to automate us”. She quotes a data scientist: “We are learning how to write the music, and then we let the music make [our victims] dance.”

Zuboff wants governments to “interrupt and outlaw surveillance capitalism’s data supplies and revenue flows … outlawing the secret theft of private experience.” After all, “We already outlaw markets that traffic in slavery or human organs.” The old phrase (which pre-dates the Web) “if you’re not paying for it, you’re the product” isn’t extreme enough for Zuboff: she compares the social media platforms to elephant poachers who kill us in order to steal our ivory tusks. “You are not the product … You are the abandoned carcass.”

Dead elephant, white

Zuboff claims that Google’s founders are fully aware of the harms their company causes, and that originally, they swore off using our personal data so perniciously. They were effectively bullied into exploiting the opportunity – and into becoming billionaires – by the demands of the stock market.

She also claims that surveillance capitalism would not have evolved if there had not been a corresponding rise in state surveillance. She claims that in 2000, the FTC was poised to regulate the tech giants, but the war on terror prompted by the 9/11 attacks drained away any support for privacy campaigns in US government circles.

If we give Zuboff the benefit of the doubt, and push the hyperbole to one side, is her thesis reasonable? Do the tech giants steal our data and sell it to new breeds of capitalists who use it to control us? If we take it literally, much of it is simply mistaken. In general, Google and Facebook do not steal our data. You have to accept their terms and conditions in order for them to access and use it, although of course, none of us read those conditions, and most of us have no detailed knowledge of what they contain. The tech giants could and should do a much better job of explaining that.

It is also untrue that Google and Facebook have spawned new types of capitalists: for decades, firms have spent significant sums of money to obtain data about their customers. In the bad old days when junk mail clogged up hallways, companies desperately wanted to avoid wasting money sending mailers about lawnmowers to people living in high-rise apartments. Direct marketing was a large and growing industry well before the invention of the Web.

Nevertheless, there is clearly a genuine need for debate about whether Google, Facebook, and other tech giants are harming us with the ways they use our data. Certainly it can be disconcerting when you search for information about a product category, and then notice that ads for companies selling that product are following you around the internet for several hours or even days. Many people find this exploitative, dishonest, creepy, and intrusive.

There are plenty of instances where the tech giants, and indeed many other organisations, have obtained personal data improperly, mis-used it, and / or failed as its custodians. The FTC has just imposed its largest-ever fine on Facebook for allowing its customers data to be mis-used by Cambridge Analytica, although some people felt that $5 billion was too trivial a sum.

Cambridge Analytica
But does that mean that the business model is illegitimate? An important test of that is whether consumers want it. It is patronising and simply wrong to say that the population as a whole does not know what is going on. Most users do know that companies sell access to our data to companies that want to show us adverts, and in return we get free stuff. “Take my data and give me free shit”, as one consumer put it. We might be foolish to accept this trade-off (it might even be “false consciousness”, as the Marxists like to say) but governments would ban it at their peril – and the ones subject to elections don’t.

Zuboff claims that “research over the past decade suggests that when users are informed of surveillance capitalism’s backstage operations, they want protection, and they want alternatives,” but most of the evidence points to the contrary. Erik Brynjolffson, a professor of economics at MIT, ran a survey in 2018 to assess how much Americans would have to be paid to avoid using the products provided for free by the tech giants. Facebook and other social media were valued at $322 a year, and search was valued at an eye-opening $17,500. Globally, Facebook makes $80 per person for using our data, so on the face of it, the deal is not too shabby. (Americans are more profitable, at $105 per head, and Europeans rather less so, at $35.)

Duck Duck Go
Those who find the trade-off unacceptable are in no way obliged to engage in it. Duck Duck Go is by all accounts a pretty good substitute for Google Search, and sells itself on not using your data. I have never used Facebook, not because of privacy concerns, but because I reckon I would spend too much time looking at cat videos.

The term “Surveillance capitalism” was invented by Zuboff in a 2014 essay. It’s a great phrase, but it is deliberately misleading. The Cambridge Dictionary defines surveillance as “the careful watching of a person or placeespecially by the police or army, because of a crime that has happened or is expected.” That is not what Google is doing. It is trying to figure out what makes me and a thousand other people like me choose to buy a particular type of car and when, and then sell that information to a firm that sells cars. The data about me is useless unless combined in that way, and it is data that I could not possibly sell on my own.

An alternative to the phrase surveillance capitalism would be personalised capitalism. It would be more accurate, but of course it wouldn’t be as scary, or generate as many headlines.

The place we should look for dangerous surveillance is not the capitalists, but the state. China’s developing Social Credit system shows clearly where the real threat lies. Capitalists just want to sell us fizzy black water and cars. Governments provide security and a welfare safety net, but in order to do this they lay claim to between a third and a half of our income, they send some of us to war, and they lock some of us up. It seems that many Chinese are intensely relaxed about Social Credit: they say it improves public behaviour, and they argue that there is nothing to worry about if you have done nothing wrong. This is a very poor argument. State surveillance leads to self-censorship, and if the levers of state power fall into malign hands – which from time to time they do – then a powerful surveillance network becomes a disaster for everyone.

A lot of the current wave of techlash is actually anti-capitalism. The real problem with the tech giants in the eyes of many of their critics is they are too big, too powerful, and above all, they make too much profit. And profit is a Bad Thing. This may not be not true of Zuboff, who declares herself a fan of good old-fashioned capitalism, but it is certainly true of Jeremy Corbyn and Bernie Sanders. Corbyn and Sanders are just as populist as the alt-right, and just as dangerous. They are wilfully ignorant of the huge benefits delivered by modern capitalism, and they seek to wreck it.

It is ironic that the tech giants are currently among the most hated targets of the left, since their founders and staff are so clearly left-leaning themselves. In attacking the tech giants for spreading fake news they are surely missing the most egregious culprits. For instance the blatant lies told about the EU by Murdoch’s News International, the Telegraph, and the Daily Mail are what gave us Brexit, and gave permission to racists and homophobes to re-emerge blinking into the daylight.

In any discussion of the future, timing is important. The data being hoovered up and exploited by the tech giants today is mostly about our shopping habits. We are on the verge of an era when we will generate tsunamis of data about our health. Apple Watches are showing the way, and before long most of us will wear devices which take readings of our pulse, our sweat, our eye fluids, our electrical impulses, analyse some of it on the device and stream more of it to the cloud. Even those of us who are relatively relaxed about Google’s privacy terms today should be thinking about who we want to be custodians of our minute-by-minute health data.

And perhaps further ahead, when AI, biotech, and other technologies are powerful and cheap enough to enable a gruntled teenager to slaughter people in their thousands, what price privacy then? When a megadeath is priced in the mere hundreds of dollars, can we avoid the universal panopticon?

Batman's Panopticon

“Calum’s Rule”

Forecasts should specify the timeframe

Time dispute

Disagreements which suggest profound differences of philosophy sometimes turn out to be merely a matter of timing: the parties don’t actually disagree about whether a thing will happen or not, they just disagree over how long it will take. For instance, timing is at the root of apparently fundamental differences of opinion about the technological singularity.

Elon Musk is renowned for his warnings about superintelligence:

With artificial intelligence, we are summoning the demon. You know all those stories where there’s the guy with the pentagram and the holy water and he’s like, yeah, he’s sure he can control the demon? Doesn’t work out.” We are the biological boot-loader for digital super-intelligence.”

Comments like this have attracted fierce criticism:

I don’t work on not turning AI evil today for the same reason I don’t worry about the problem of overpopulation on the planet Mars.” (Andrew Ng)

We’re very far from having machines that can learn the most basic things about the world in the way humans and animals can do. Like, yes, in particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat. This makes a lot of questions people are asking themselves premature.” (Yann LeCun)

Superintelligence is beyond the foreseeable horizon.”  (Oren Etzioni)

Surviving cover, compressed
If you look closely, these people don’t disagree with Musk that superintelligence is possible – even likely, and that its arrival could be an existential threat for humans. What they disagree about is the likely timing, and the difference isn’t as great as you might think. Ng thinks “There could be a race of killer robots in the far future,” but he doesn’t specify when. LeCun seems to think it could happen this century: “if there were any risk of [an “AI apocalypse”], it wouldn’t be for another few decades in the future.” And Etzioni’s comment was based on a survey where most respondents set the minimum timeframe as a mere 25 years. As Stephen Hawking famously wrote, “If a superior alien civilisation sent us a message saying, ‘We’ll arrive in a few decades,’ would we just reply, ‘OK, call us when you get here—we’ll leave the lights on’? Probably not.”

Although it is less obvious, I suspect a similar misunderstanding is at play in discussions about the other singularity – the economic one: the possibility of technological unemployment and what comes next. Martin Ford is one of the people warning us that we may face a jobless future:

A lot of people assume automation is only going to affect blue-collar people, and that so long as you go to university you will be immune to that … But that’s not true, there will be a much broader impact.”

The opposing camp includes most of the people running the tech giants:

People keep saying, what happens to jobs in the era of automation? I think there will be more jobs, not fewer.” “… your future is you with a computer, not you replaced by a computer…” “[I am] a job elimination denier.” – Eric Schmidt

Schmidt and bot
There are many things AI will never be able to do… When there is a lot of artificial intelligence, real intelligence will be scarce, real empathy will be scarce, real common sense will be scarce. So, we can have new jobs that are actually predicated on those attributes.” – Satya Nadella

For perfectly good reasons, these people mainly think in time horizons of up to five years, maybe ten at a stretch. And in that time period they are surely right to say that technological unemployment is unlikely. For machines to throw us out of a job, they have to be able to do it cheaper, better, and / or faster. Automation has been doing that for centuries: elevator operator and secretary are very niche occupations these days. When a job is automated, the employer’s process becomes more efficient. This creates wealth, and wealth creates demand, and thus new jobs. This will continue to happen – unless and until the day arrives when the machines can do almost all the work that we do for money.

Time horizon

If and when that day arrives, any new jobs which are created as old jobs are destroyed will be taken by machines, not humans. And our most important task as a species at that point will be to figure out a happy ending to that particular story.

Will that day arrive, and if so, when? People often say that Moore’s Law is dead or dying, but it isn’t true. It has been evolving ever since Gordon Moore noticed, back in 1965, that his company was putting twice as many transistors on each chip every year. (In 1975 he adjusted the time to two years, and shortly afterwards it was adjusted again, to eighteen months.) The cramming of transistors has slowed recently, but we are seeing an explosion of new types of chips, and Chris Bishop, the head of Microsoft Research in the UK, argues that we are seeing the start of a Moore’s Law for software: “I think we’re seeing … a similar, singular moment in the history of software … The rate limiting step now is … the data, and what’s really interesting is the amount of data in the world is – guess what – it’s growing exponentially! And that’s set to continue for a long, long time to come.”

So there is plenty more Moore, and plenty more exponential growth. The machines we have in 10 years time will be 128 times more powerful than the ones we have today. In 20 years time they will be 8,000 times more powerful, and in 30 years time, a million times more powerful. If you take the prospect of exponential growth seriously, and you look far enough ahead, it becomes hard to deny the possibility that machines will do pretty much all the things we do for money cheaper, better and faster than us.

New rule
So I would like to propose a new rule, and with no superfluous humility I’m calling it Calum’s Rule:

Forecasts should specify the time frame.”

If we all follow this injunction, I suspect we will disagree much less, and we can start to address the issue more constructively.