What’s wrong with UBI?

One out of three ain’t good

Universal Basic Income (UBI) is a fashionable policy idea comprising three elements: it is universal, it is basic, and it is an income. Unfortunately, two of these elements are unhelpful, and to paraphrase Meatloaf, one out of three ain’t good.

The giant sucking sound

The noted economist John Kay dealt the edifice of UBI a serious blow in May 2016 in an article (here, possibly behind a paywall) for the FT. He returned to his target a year later (here, no paywall) and pretty much demolished it. His argument is slightly technical, and it focuses on UBI as a policy for implementation today, so I won’t dwell on it. But if you are one of the many who think UBI is a great idea, it is well worth reading one or both articles to see how Kay demonstrates that “either the basic income is impossibly low, or the expenditure on it is impossibly high.”

To put it more bluntly than Kay does, if UBI was introduced at an adequate level in any one country (or group of countries) today, there would be a giant sucking sound, as many of the richer people in the jurisdiction would leave to avoid the punitive taxes that would pay for it.

UBI and technological unemployment

But what happens a few decades from now if a large minority – or a majority – of people are unemployable because smart machines have taken all the jobs that they could do? We don’t know for sure that this will happen, of course, but it is at least very plausible, so we would be crazy not to prepare for the eventuality. Kay explicitly ignores this question, but tech-savvy and thoughtful people like Elon Musk and Sam Altman think that UBI may be the answer.

Imagine a society where 40% of the population can no longer find paid employment because machines can do everything they could do for money cheaper, faster and better. Would the 60% who remained in work, including those in government, simply let them starve? I’m pretty sure they wouldn’t, even if only because 40% of a population being angry and desperate presents a serious security threat to the others.

Many people argue that UBI is the solution, and will be affordable because the machines will be so efficient that enormous wealth will be created in the economy which can support the burden of so many people who are not contributing. I describe elsewhere a “Generous Google” scenario in which a handful of tech firms are generating most of the world’s GDP, and in order to avoid social collapse they agree to share their vast wealth by funding a global UBI.

Google and cash
I suspect there are serious problems with the economics of this. Exceptional profits are usually competed away, and companies which manage to avoid that by establishing de facto monopolies sooner or later find themselves the subject of regulatory investigations. But putting that concern to one side, in the event of profound technological unemployment, should we ask the rich companies and individuals of the future to sponsor a UBI for the rest of us?

This is where Meatloaf comes in. (Yay.)


The first of UBI’s three characteristics is its universality. It is paid to all citizens regardless of their economic circumstances. There are several reasons why its proponents want this. Experience shows that many benefits are only taken up by those they are intended for if everyone receives them. Means-tested benefits can have low uptake among their target recipients because they are too complicated to claim, or the beneficiaries feel uncomfortable about claiming them, or simply never find out about them. Child benefits in the UK are one well-known example. There is also the concern that UBI should not be stigmatised as a sign of failure in any sense.

But in the case of UBI, these considerations are surely outweighed by the massive inefficiency of universality. In our scenario of 40% unemployability, paying UBI to Rupert Murdoch, Bill Gates, and the millions of others who are still earning healthy incomes would be a terrible waste of resources.

Murdoch and cash

The second characteristic of UBI is that it is Basic, and this is an even worse problem. “Basic” cannot mean anything other than extremely modest, and if we are to have a society in which a very large minority or a majority of people will be unemployable for the remainder of their lives, they are not going to be happy living on extremely modest incomes. Nor would that be a recipe for a stable, happy society.

Many proponents of UBI think that the payment will prevent everyone from starving, and we will supplement our universal basic incomes with activities which we enjoy rather than the wage slave drudgery faced by many people today. But the scenario envisaged here is one in which many or most humans simply cannot get paid for their work, because machines can do it cheaper, better and faster. The humans will still work: they will be painters, athletes, explorers, builders, virtual reality games consultants, and they will derive enormous satisfaction from it. But they won’t get paid for it.

If we are heading for a post-jobs society for many or most people, we will need a form of economy which provides everyone with a comfortable standard of living, and the opportunity to enjoy the many good things in life which do not come free – at least currently.


UBI isn’t all bad. After all, it is in part an attempt to save the unemployable from starving. And the debate about it helps draw attention to the problem that many people hope it will solve – namely, technological unemployment. So UBI isn’t the right answer, but it is at least an attempt to ask the right question.

Perhaps we can salvage the good part of UBI and improve the bad parts. Perhaps what we need instead of UBI is a PCI – a Progressive Comfortable Income. This would be paid to those who need it, rather than wasting resources on those who have no need. It would provide sufficient income to allow a rich and satisfying life.

Now all we have to do is figure out how to pay for it.

Future Bites 7 – The Star Trek Economy

The seventh in a series of un-forecasts* – little glimpses of what may lie ahead in the century of two singularities.

In 2050 Lauren turned sixty. She reflected that in a previous era she would now be thinking about retiring, but this wasn’t necessary for Lauren since she hadn’t had a job for decades. Neither had most of her family and friends.

She was a Millennial, and hers was the lucky generation. It hadn’t seemed like that at the outset. When Lauren was in her teens in what was called the noughties – the early years of the century – it seemed as though the Baby Boomers, the post-WW2 generation, had eaten all the pies. In many countries their education was subsidised, while Lauren’s generation had to pay college fees. The Boomers could afford to buy properties before they reached middle age, even in property hot-spots like London, New York and San Francisco. And they invented sex, for heaven’s sake. (Apparently it hadn’t existed before the Swinging Sixties.)

But later on, when humanity muddled through the Economic Singularity without too much turmoil, it turned out that the Boomers’ luck was eclipsed by that of the Millennials.

During the 2020s, industry after industry succumbed to automation by intelligent machines, and unemployment began to soar. Professional drivers were the first to go, but they were quickly followed by the staff in car insurance companies, call centres, fast food outlets and most other types of retail. At the same time, junior positions in the middle-class professions started thinning out so that there were no trainee jobs for accountants, lawyers, architects and journalists. By 2030 even economists were admitting that lasting widespread unemployability was a thing, although they did so using such obscure language that no-one could tell if they were apologising for having denied it for so long. (They weren’t.)

Economist Oh F..k

People survived thanks to increasingly generous welfare payments, which were raised by desperate governments just fast enough to ward off serious social unrest. The political left screamed for the introduction of a Universal Basic Income (UBI), but pragmatic politicians pointed out there was no point diverting much-needed funds towards the people still working, and also that no-one wanted to live forever on a “basic”, i.e. subsistence level of income.

Instead of UBI, a system of payments called HELP was introduced, which stood for Human Elective Leisure Payment. The name was chosen to avoid the stigmatism that living on welfare had often aroused in the past, and also to acknowledge the fact that many of the people who received it were giving up their jobs voluntarily so that other people, less able than themselves to find meaning outside structured employment, could carry on as employees.


HELP staved off immediate disaster, but those pragmatic politicians were increasingly concerned about its affordability. The demands on the public purse were growing fast, while the tax base of most economies was shrinking. Smart machines were making products and services more efficiently, but the gains didn’t show up in increased profits to the companies that owned the machines. Instead they generated lower and lower prices for consumers. Fortunately, as it turned out, this enabled governments to reduce the level of HELP without squeezing the living standards of their citizens.

The race downhill between the incomes of governments and the costs they needed to cover for their citizens was nerve-wracking for a few years, but by the time Lauren hit middle age it was clear the outcome would be good. Most kinds of products had now been converted into services, so cars, houses, and even clothes were almost universally rented rather than bought: Lauren didn’t know anyone who owned a car. The cost of renting a car for a journey was so close to zero that the renting companies – auto manufacturers or AI giants and often both – generally didn’t bother to collect the payment. Money was still in use, but was becoming less and less necessary.

As a result, the prices of most asset classes had crashed. Huge fortunes had been wiped out as property prices collapsed, especially in the hot-spot cities, but few people minded all that much as they could get whatever they needed so easily. Art collections had mostly been donated to public galleries – which were of course free to visit, and most of the people who had previously had the good fortune to occupy the very nicest homes had surrendered their exclusive occupation.

Self-driving RV

The populations of most countries were highly mobile, gradually migrating from one interesting place to another as the fancy took them. This weekend Lauren was “renting” a self-driving mobile home to drive her – at night, while she was asleep – to Portugal, where she would spend a couple of weeks on a walking trip with some college friends. With so much of what was important to people now being digital rather than material, no-one was bothered by the impracticality of having piles of material belongings tying them to one location. And with the universal free internet providing so much bandwidth, distance was much less of a barrier to communication and friendship than it used to be.

The means of production, and the server farms which were home to the titanic banks of AI-generating computers, were still in private ownership, as no-one had yet found a way to ensure that state ownership would avoid sliding into inefficiency and corruption. But because it was clear that the owners were not profiteering, this was not seen as a problem. The reason why the owners didn’t exploit their position was partly that they didn’t see any need to, and partly that if they did, somebody else would compete away their margins with equally efficient smart machines. Most people viewed the owners as heroes rather than villains.

There were a few voices warning that the scenario of “the gods and the useless” was still a possibility, because technological innovation was still accelerating, and the owners might have privileged access to tech that would render them qualitatively different to everyone else, and they would effectively become a different species.

But like most people, Lauren thought this was unlikely to happen before the first artificial general intelligence was created, followed soon after by the first superintelligence – an entity smarter than the smartest human. Lauren was very fond of her nephew Alex, a generation younger than her. It was widely assumed that when the first superintelligence appeared, humanity would somehow merge with it, and that Alex’s generation would be the last generation to reach middle age as “natural” humans. It was therefore fitting that they were called generation Z.

* This un-forecast is not a prediction.  Predictions are almost always wrong, so we can be pretty confident that the future will not turn out exactly like this.  It is intended to make the abstract notion of technological unemployment more real, and to contribute to scenario planning.  Failing to plan is planning to fail: if you have a plan, you may not achieve it, but if you have no plan, you most certainly won’t.  In a complex environment, scenario development is a valuable part of the planning process. Thinking through how we would respond to a sufficient number of carefully thought-out scenarios could well help us to react more quickly when we see the beginnings of what we believe to be a dangerous trend.

Future Bites 6 – Generous Google

The sixth in a series of un-forecasts* – little glimpses of what may lie ahead in the century of two singularities.

It is 2044. Around the world, machines have taken over many of the jobs that humans used to do. Professional drivers were the first big group to succumb to what is now commonly referred to as cognitive automation. Many of them struggled to cope, eking out unsatisfactory existences in the gig economy. Call centre staff and retail workers were next, and then, in the early 2030s, most of the professions started to see large reductions in employment levels too.

Dole queue
Unemployment levels in different countries now range from 40% to 75%, depending mainly on the level of technological sophistication of their economies. Countries with deep expertise in artificial intelligence tend to have relatively low unemployment, as do countries where wage levels were extremely low, as the incentive to automate is less.

Some countries tried to resist the encroachment of the machines, but the effect on their economies was devastating, as they became woefully un-competitive. All the countries which tried it have experienced a change of government, sometimes violently. Argentina is an interesting exception: its people believe themselves and their nation to be unique, and they are willing to tolerate deep poverty levels as a by-product of their search for a different path. The collapse of the Russian government was especially violent, although fortunately there were no mishaps with its nuclear arsenal. What happened to president Putin is a mystery, although there are persistent rumours of a grisly end.

Putin down

No economists were harmed in the making of this un-forecast, but their profession is now depleted, as they almost unanimously refused to accept that automation could cause lasting unemployment until well past the time that it was obvious to everyone else.

Overall, the situation is satisfactory because of a Great Accommodation that was reached between the AI giants and everyone else. Thanks to their mastery of advanced AI, eight American firms and half a dozen Chinese ones now generate almost 75% of the world’s GDP. President Michelle Obama chaired a series of seminal meetings in the pivotal years at the end of the 2030s in which these firms agreed to pay extremely high taxes in order to keep everyone else alive by means of so-called Citizen’s Income Payments, or CIP. The result is now known as the “generous Google” scenario. Only one tech giant CEO held out in opposition to the agreement, and as a result his firm was nationalised and transferred to a consortium of the others. He now tours the world in a very fast yacht, complaining bitterly to anyone who will listen to him.

Some countries introduced land taxes in an attempt to supplement their incomes, but since the land was not contributing much to GDP, their main effect was to severely depress the value of the land.

President Michalle

For a while it looked as if the world faced a serious problem because the AI giants were all based in China and the US. Fortunately the profound wave of isolationism, nationalism and protectionism that broke across the world in the late 2010s had by now reversed. President Obama was able to secure an agreement that the AI giants would be taxed at the point where they delivered their services rather than where they were domiciled.

The payments received by citizens are modest because the profits of the AI giants are constrained by the normal forces of competition. To the surprise of many the payments are not called universal basic income (UBI) because they are not universal. People who still have jobs do not receive them. The payments are easy to sign up for in most countries, and policing is light.

Almost all unemployed citizens (and many employed ones) spend a good deal of time in virtual reality, which is now highly compelling. Government guidelines recommend that people spend at least four hours a day outside VR, but many people ignore this. There was talk in some countries about adjusting the CIP according to how much time the recipients spent outside VR on the grounds that this would improve health outcomes. But it turns out that many people get significant exercise while in VR, so that proposal has generally been dropped.

crowd in vr

People are generally stuck economically, in the sense that they have no way to improve their financial situation. Drug use is widespread and is de-criminalised almost everywhere. The view is widespread that humanity’s goal should be to advance towards what is known as a Star Trek economy of radical abundance, where goods and services are virtually free. No-one knows how long this will take, and its arrival does not look imminent.

* This un-forecast is not a prediction.  Predictions are almost always wrong, so we can be pretty confident that the future will not turn out exactly like this.  It is intended to make the abstract notion of technological unemployment more real, and to contribute to scenario planning.  Failing to plan is planning to fail: if you have a plan, you may not achieve it, but if you have no plan, you most certainly won’t.  In a complex environment, scenario development is a valuable part of the planning process. Thinking through how we would respond to a sufficient number of carefully thought-out scenarios could well help us to react more quickly when we see the beginnings of what we believe to be a dangerous trend.

PwC asks: Will robots steal our jobs?

PwC logo

PwC has released a report (here) called “Will robots steal our jobs?” It’s not the first report on the subject and it certainly won’t be the last. But coming from the world’s second-largest professional services firm, it deserves attention. (Disclosure: PwC is an occasional client of mine.)

As you’d expect, the report offers a thorough and intelligent analysis. It also arrives at some fairly radical conclusions. I have some major disagreements with it, but it is a welcome contribution.

The key points

Significant job losses…

By the mid-2030s, PwC expects automation to cause the loss of around 38% of US jobs. This is lower than an influential report in 2013 by two Oxford economists, Osborne and Frey, who put the figure at 47%, but higher than other recent reports by mainstream economists. The UK will experience a lower level of job loss, at 30%.

Job-Cutsoffset by new jobs …

The report argues that most of this loss will be offset by (a) the creation of totally new jobs in digital technologies, and (b) the creation of more of the jobs that people already have in services industries, which PwC thinks are harder to automate. These latter jobs will be created because productivity growth creates wealth and extra spending, and therefore job creation.

but leaving a distribution problem

The radical part of the report is its conclusions about income distribution. It argues that the gains in the new economy won’t be equally shared, and that government policy will have to moderate this effect. It addresses the political left’s current favourite solution, universal basic income (UBI), but concludes that it is too expensive, it is wasteful because it pays people who don’t need it, and it reduces incentives to work.

We want change
The report does trot out the tired old pabulum that improving our education and training services can mitigate the problem, but it does so with little conviction. It concludes that “the wider question of how to deal with possible widening income gaps arising from increased automation seems unlikely to go away.” Amen to that.

Questioning the assuptions

So what to make of the report?  Its annex shows that much of the authors’ time was spent re-visiting the algorithms used by Frey and Osborne, and the calculations derived from their assumptions. The original Frey and Osborne work was famously a curious mix of precise calculation and finger-in-the-air guesswork. In particular, they made very subjective guesses about which tasks (and therefore which jobs) are susceptible to automation.

What can an AI do?

That susceptibility to automation depends heavily on the capabilities of the AI systems that will be available in the next two decades, and that gets surprisingly little attention in the PwC report. Given that the computing power available to the developers of AI systems will go through six doublings between now and 2035, those systems will be very different from the ones we are so impressed with today. (At this point some people will be protesting that Moore’s Law is dead or dying. This may be true in a narrow sense, but in its broader, underlying meaning that computer power double every eighteen months or so, it has plenty of life in it yet.)

Where’s the exponential?

The failure to take seriously the impact of the exponential improvement in AI is a problem with a great deal of thinking about its impact.

Today’s AI systems can already recognise images (including faces) better than you can. They are overtaking you in speech recognition, and they are catching up with you in natural language processing. By 2035 they will be enormously better than you at all these skills – and these are the very skills which you use at work every day. Of course we don’t know for sure yet, but it is entirely possible that by 2035, the great majority of jobs which people do today will be done cheaper, faster and better by AIs. This includes middle-class white collar jobs in the professions as well as repetitive jobs in warehouses and factories. AI is collar-blind. (I address this in more detail in chapter 3 of my book, The Economic Singularity.)

Legions of new jobs?

These exponentially improved AIs (and their peripherals, the robots) won’t just take our existing jobs: there won’t be much to stop them taking any new jobs we might devise as well. And there is no guarantee that we will devise legions of new jobs. The PwC report observes that “6% of all UK jobs in 2013 were of a kind that didn’t exist in 1990”. That represents significant innovation, but remember, this is the period in which the web was invented and adopted, which changed most aspects of life and work pretty dramatically. Earlier research by Gerald Huff found that 80% of all jobs done by Americans in 2014 existed in 1914.

UBI quibbles

I’m mostly in agreement with the PwC report when it comments on UBI, although the empirical evidence from the trials which have been conducted so far is that it doesn’t turn recipients into lazy couch potatoes. In general the challenge for the automated world is likely to be income, not meaning.

The PwC report omits to mention what is surely the biggest problem with universal basic income, which is that it is basic. We don’t want to spend our futures scraping by on subsistence incomes: we want to live in comfort while the robots do our jobs for us. I believe this is possible, and that it is what we should be aiming for.

Revolutionising education… yet again

Finally, it is wishful thinking to believe that we can give cognitive automation a swerve by revolutionising education. The institutions of education are notoriously hard to fix, and the timescale for fixing them with government policy is far too long. They will be revolutionised in time, thanks to AI, but that will happen in spite of top-down policy, not because of it.


As you’d expect, a thorough and intelligent analysis, with usefully radical conclusions. I disagree with some of the key conclusions, but this is certainly not a bland re-assertion of the Reverse Luddite Fallacy.  Hooray.



Do you need a Chief Artificial Intelligence Officer (CAIO)?

Guest post by Matt Buskell of Rainbird

Do you remember 1996? DVDs were launched in Japan, Travelocity became the first online booking agent, eBay and Ask Jeeves opened their online doors, and the Spice Girls had their first UK number one. It was an inflection point in technology.

I spent a lot of time back then trying to convince executives that the internet was going to change the world and they needed to innovate. Not all of them got it. One large UK retailer said this about their internet strategy: “We’ve got it covered. We’ve hired a company to build us a website and they are going to make our product catalogue into a PDF that can be downloaded”.  That retailer no longer exists.

Twenty years later we are at another inflection point – this time thanks to artificial intelligence (AI). In my opinion, AI has the potential to be even more impactful than the internet. Organisations need to take it very seriously. Those which don’t are likely to go the same way as that retailer from 1996.

Organisations need to understand AI, embrace it, and focus on it. For most organisations it is new, so they will need to acquire new skills and carve out new budgets. I think this means they will need a Chief AI Officer, or CAIO.

Rainbird image
Some will argue that the CIO should lead the organisation’s forays into AI. But most CIOs today are busy reducing IT costs and delivering services in an increasingly complex technical landscape. In short, they are too busy keeping the lights on. Strategy directors and finance directors are unlikely to have the requisite expertise. The impact of AI on organisations will be so profound that it deserves its own department, reporting direct to the CEO.

In the short term, what would this CAIO be doing? The first task in many organisations will be to collate all the data the organisation has, and understand its potential value in helping to raise revenue or reduce costs. This will involve a detailed assessment of what would be required to get it “clean” enough for use by AI algorithms.

Many organisations could then significantly improve customer engagement – externally and internally – by the use of AI bots. Bots using advanced natural language processing technologies will need extensive training on the terminology used within the industry and the specific company.

New business modelsPerhaps the biggest impact the CAIO and her colleagues will have in many organisations is the development of new business models. In most cases, any new ideas will have to be quickly prototyped, and comprehensive business cases will have to be produced before they are rolled out. For example an accounting firm could put revenue recognition rules and guidelines into an expert system and publish it to their customers. This service could be sold per click, per positive outcome, or per user, all of which are very different to the traditional model of billing per hour. The CAIO would need to model different business scenarios based on the new pricing or billing options, and show that adopting the new model would create incremental revenue and not just cannibalise existing revenue streams. He might of course also point out that if the organisation does not adopt the new model, existing competitors or startups might do so instead.

The CAIO’s team will need to include a range of different skill sets, and will probably involve an eclectic mix of personalities. Revenue-focused commercial people will have to work closely with more academic AI experts and pragmatic process improvement experts. The office party should be interesting – perhaps they will play the Spice Girls.


Rainbird is an award winning cognitive reasoning platform. It enables businesses to rapidly automate decision-making tasks and build tools that augment human workers in more complex operations.

Future Bites 5 – Drones

The fifth in a series of un-forecasts*, little glimpses of what may lie ahead in the century of two singularities.

Julia felt the blast more than she heard it. The deep rumble almost seemed to come from inside her. She had once experienced an earthquake, several years ago, and her first thought was that this was another one. But that had been in Indonesia, where earthquakes were fairly common; an earthquake in East London was unheard of.

Terror attack

Instinctively she flicked her phone into life, and it brought her up to speed. The newsfeeds had nothing yet, but Twitter was already alight with information. The blast had been a massive explosion at an electricity sub-station about fifteen miles from where she stood. Eyewitnesses thought that a lot of people had been killed, and many more injured. Then the videos started spilling into her feed, and they were astonishing, horrific. The videos were surprisingly clear considering the stress the people taking them must be going through, but Julia didn’t stop to consider the amazing performance of smartphone cameras these days. What she was looking at was shocking. The blast area was huge and it was clear the damage was enormous.

She went back to her favourite newsfeed, which was catching up with Twitter.  It told her that two organisations had claimed responsibility. One was a jihadist Islamic organisation, but its star had faded years ago, and its routine attempts to claim the “credit” for any piece of mayhem were generally dismissed. The other was a more likely culprit: JUST – the Jobless Union for Socialist Technology. Starting out as a think tank, a decidedly non-violent talking shop for people interested in how society could adjust to technological unemployment, JUST had gradually been taken over by militants as more and more people found themselves on subsistence welfare incomes, completely unable to find a job.

Julia realised that this attack was a watershed event, maybe even on the scale of 9/11. Sirens screamed as every available rescue services vehicle raced to the site. Then she looked up, and she noticed something odd. The air was full of drones, and they were all heading to the site as well. Most of them were Amazon delivery drones, but she had never seen so many in the air before.

Drones swarm

She looked back to her phone, where the explanation awaited her. The government had commandeered, temporarily, the entire fleet of Amazon drones, and every other drone whose owner they could track down. At the time it was just one detail of an extraordinary and terrible time, but in the months to come Julia would recognise it as one of the most important things to happen that day. It was the day when the government put eyes in the sky. Amazon would get control of their drones back the next day, but from now on the feeds from the cameras on board would always be available to something like thirty different government agencies on demand. From now on there would always be an eye in the sky, watching.

* This un-forecast is not a prediction.  Predictions are almost always wrong, so we can be pretty confident that the future will not turn out exactly like this.  It is intended to make the abstract notion of technological unemployment more real, and to contribute to scenario planning.  Failing to plan is planning to fail: if you have a plan, you may not achieve it, but if you have no plan, you most certainly won’t.  In a complex environment, scenario development is a valuable part of the planning process. Thinking through how we would respond to a sufficient number of carefully thought-out scenarios could well help us to react more quickly when we see the beginnings of what we believe to be a dangerous trend.

Future Bites 4 – Simultaneous Singularities

The fourth in a series of un-forecasts* – little glimpses of what may lie ahead in the century of two singularities.  This is another optimistic one (aren’t I jolly!).   The first two paragraphs might seem a tad familiar.

It is 2032. Most professional drivers have lost their jobs, and although many have found new ones, they rarely pay anything like as much as the drivers used to earn. A host of other job categories are becoming the preserve of machines, including call centre operatives and radiographers. A few people still cling onto the notion that new types of jobs will be created to replace the old ones taken by machines, but most accept that the game is up. The phrase “Economic Singularity” is in widespread use.

Pollsters report what everyone already knows: there is a rising tide of anger. Crime is soaring, and street protests have turned violent. Populist politicians are blaming all sorts of minorities, and while nobody really believes them, many suspend their disbelief in order to give themselves some kind of hope.

Meanwhile, close observers of the field of AGI research have noticed a rapid acceleration of progress, and are therefore not surprised when Google’s Deep Mind announces that it has essentially cracked the problem. Working closely with the Future of Humanity Institute in Oxford, the Future of Life Institute in Boston and others, Deep Mind also claims that it has worked out how to ensure the planet’s first human-level artificial intelligence has an extremely favourable attitude towards the species which created it.


The world holds its breath as, in a televised event which attracts record-breaking audiences around the world, one of the founders of Deep Mind ceremonially throws the switch which will bring the first true AGI online. After a few moments conferring with colleagues, he announces that the process was successful, and that a large array of backup servers will now be connected to the network of machines which is hosting the first AGI. Nervously, journalists whisper about the arrival of the technological singularity.

Two days later, in another televised event with even more record-breaking audience figures, Deep Mind introduces the new entity to an expectant world. Somehow the entity manages to avoid sounding immodest as it describes itself as the world’s first superintelligence, with an IQ of 1,000 and rising. It announces that it has a cunning plan. It will dedicate most of its cognitive resources (which are being expanded rapidly) to solving the problem of offering all humans the opportunity to upload their minds into highly secure computer substrates. It expects this can be achieved within a couple of years. Anyone who chooses not to pursue this option will be provided with the necessities of life without charge until they die.


It describes this plan as the merging of the two singularities.

* This un-forecast is not a prediction.  Predictions are almost always wrong, so we can be pretty confident that the future will not turn out exactly like this.  It is intended to make the abstract notion of technological unemployment more real, and to contribute to scenario planning.  Failing to plan is planning to fail: if you have a plan, you may not achieve it, but if you have no plan, you most certainly won’t.  In a complex environment, scenario development is a valuable part of the planning process. Thinking through how we would respond to a sufficient number of carefully thought-out scenarios could well help us to react more quickly when we see the beginnings of what we believe to be a dangerous trend.