This is the final part of the “Automation, Education and Work” talk.
Part 1 of this talk was introduced with the following:
There is a proliferation of articles in the media about how the accelerating technology of artificial intelligence and robots will change the world of work radically in the next 30 years. “47% of current jobs are under threat”. “Self-driving vehicles will put millions of truck drivers out of work”. “We are seeing a hollowing out of middle-income jobs.”
Some say this Luddite fear is unwarranted. “Just because we can’t imagine what new jobs will come along doesn’t mean the next generation won’t”. “We just need to educate everyone for the future knowledge economy.”
Others say “This time it’s different”. “We cannot compete with the robots”. “Maybe we need to tax the robots and have Universal Basic Income?”
In this final part of the talk, I try to formulate responses to these statements. I start with…
“Is there technological unemployment?”
For those in the world of work, life is so busy. We may well say:
New technology has come along but we still work as hard as ever!
So, if new technology hasn’t created unemployment so far, why should it this time?
But new technology has created unemployment previously: we forget the workload of our forefathers. They worked longer working weeks and typically died not long after retiring, if they even got to retire at all. The number of hours we get to do things outside work for every hour spend we spend working has gone up dramatically. The unemployment that has arisen has been spread out among us so that the vast majority of us are part-timers compared to 100 years ago!
The back-of-an-envelope estimations in the table below show how much things have improved.
|School leaving age||14||17|
|Working hours per day||9||7||78%|
|Working days per week||6||5||83%|
|Days holidays per year||6||32|
|Working days per year||306||228|
|Work hours per year||2,754||1,596||58%|
|Age at failing health||65||75|
|Years of quality retirement||0||15|
|Waking hours per day||15||16|
|Leisure hours per working day||3||6|
|Leisure days per working year||58||136|
|Leisure hours per leisure day||9||11|
|Leisure hours per working year||1,440||2,864|
|Leisure hours per retired year||N/A||4,015|
|Total leisure hours||73,440||183,377||250%|
|Adult working hours||140,454||68,628||49%|
|Leisure / work ratio||0.5||2.7||511%|
These are just simplistic calculations. They ignore the large amount of leisure time to be had as a child, and what leisure time there is during the years of failing health at the other end of life. You may disagree with some of the numbers but, however you might modify them, the point still stands: the amount of time we need to work to provide us with leisure time has gone down dramatically.
And this does not show the full picture. The greatest single factor of mass unemployment we have seen so far had been the replacement of horse power with mechanical horsepower! In 1900 there was 1 horse for every 4 people in England, with the large majority of horses having an extremely high workload. Nowadays, there just aren’t those working horses around. There is just 1 horse for every 30 people. And they are working much more comfortably – in the leisure industry!
It may seem that we are working as hard as ever and this may well be true within the last few decades but the long-term (e.g. 100-year) trend is downward. See the table below showing the average number of hours worked per year for various industrialised nations over the 20-year period 1976-1996.
Change in annual hours worked for some European countries: Picture credit: OECD.
Note that Anglophone countries have had the least improvements over this time and things have actually gone backwards in the USA. (Also note that figures for inequality show a reduction until about 1980, since when it has increased.)
(Table derived from https://www.oecd.org/els/emp/2080270.pdf)
Change in annual hours worked for mainly Anglosphere countries: Picture credit: OECD.
“This time it is different”
This time is different, just as last time was!
Every time is different; every technological wave that hits us is unique. But some are more pronounced than others. The First Industrial Revolution was truly revolutionary compared with the information revolution that started around the 1950s.
The thing that is different this time is that the revolution affects the cognitive rather than the physical. Yes, human muscle work has been reduced but it was horses rather than us that were fully exposed to the effect of physical-replacement technology. If the new technology starts to do significant brain work, it will not be taking any creatures’ work away other than that of us humans.
Physical automation continues, through the ongoing synthesis of computers and motors – and it is now being enhanced by the addition of Artificial Intelligence (leading to the more intelligent robots and self-driving cars).
But more pronounced (more revolutionary) is the cognitive-only aspect – the arrival of intelligence that is sat inside computers rather than inside robots. Combined with new processes – the near-zero marginal cost business models – this artificial intelligence can be rolled out much more quickly and with much less capital outlay than brigades of robots. Unlike previously, this technology can replace much of the skilled workforce.
“Just because we can’t imagine what new jobs will come along doesn’t mean the next generation won’t”
There will be many jobs opportunities in the future which we cannot imagine, but many new occupations will never come about: if Artificial Neural Nets can learn to do the current skilled cognitive tasks then they will probably also be able to learn to do many of those new skilled cognitive tasks.
When machines relieved us of physical burdens, we shifted to jobs requiring intelligence (then considered a uniquely human characteristic). Now that machines can relieve us of our intelligent work, we presumably must find refuge in emotional/creative work (now considered a uniquely human characteristic) – but emotional and ‘creative’ work is not completely immune to AI.
|deep learning||automation (continuing)|
|low marginal cost||increasingly affordable|
“We cannot compete with the robots”
It is true; in many ways, we will not be able to compete with the robots in terms of both capability and productivity. Erik Brynjolfsson and Andrew McAfee say this should not be a problem: we shouldn’t think that we are competing against them but that we are working with them. To provide an example, Garry Kasparov, having lost to Deep Blue, now advocates what is called ‘Advanced Chess’ (also called ‘Centaur Play’), where teams of players play chess against each other and in which some of the team members are computers. Collectively, they are stronger than any of the individuals. More commonly, one expert in a particular discipline can team up with a machine learning expert with a bit of knowledge in that discipline – plus computer – to create a winning combination.
Most of us will not just be unable to compete with the robots; even if we organize ourselves into teams of humans and computers, we will also be unable to compete with the small set of super-expert winning teams.
And this teamwork between man and machine can be leveraged for a lot of ‘creative’ work.
“Is there a hollowing out?”
I have previously talked about the distinction between technology innovations and process innovations. Now, making a generalisation, we can say that:
- technology innovations lead to up-skilling plus unemployment whereas
- process innovations lead to down-skilling.
- The introduction of tractor technology led to new skilled jobs involving designing tractors, making tractors, selling tractors and maintaining tractors but this was at the cost of putting many unskilled agricultural labourers out of work.
- In contrast, the factory production line replaced workers who each had many skills with workers who only needed to perform a single task each.
The net effect is the tendency to create highly-skilled well-paid jobs plus low-paid unskilled jobs at the expense of those skilled jobs in the middle. This is the so-called hollowing out of the labour market.
Maarten Goos and Alan Manning describe the jobs at the top as ‘lovely jobs’ and those at the bottom as ‘lousy jobs’. And within the lousy jobs category there are the ‘three Ds’ that describe the attributes of many of those jobs: dull, dirty, dangerous and demeaning (yes, there are four, but for some reason people only seem to refer to some combination of 3 of these at any one time!)
This hollowing-out seems to be happening now, but might this just be a temporary blip? After all, it hasn’t really happened in the past. Education is part of the answer. Productivity improvements (being able to produce a particular amount of stuff for less work) meant that goods became available at a price affordable to a larger proportion of the population. Cars were no longer the luxury of the elite. So unskilled people (or their children) made unemployed by technology had to be educated to be able to do new skilled jobs. Why won’t this re-educating happen in the future then? It will to some extent. But this time, it will be possible for much of the new skilled work to be done more efficiently and effectively by machines working in collaboration with a relatively small number of the very highly-skilled.
We can always find useful work for the other people to do, including skilled work. More doctors and teachers would be good even when technology has made each doctor and teacher more productive. But where is the money going to come from to pay for those? Will the extraordinarily-rich super-skilled pay it all?
What is the problem with technological unemployment?
There is more to work than just earning money. Imagine if we could automate all jobs away tomorrow, with robots doing everything to run society, leaving us humans to lead lives of full-time leisure. As Voltaire said (in Candide):
“Work distances us from three great ills: boredom, vice, and need”
There is more to work than just satisfying our basic needs of life: eating and drinking, a roof over our heads, and so on. It (potentially) provides us with social interaction, interesting challenges and a feeling of useful contribution to society, commanding respect. And it keeps us preoccupied rather than causing trouble.
M. Keynes, (in ‘Economic Possibilities for our Grandchildren’) re-iterated the problem of boredom:
“It is a fearful problem for the ordinary person, with no special talents, to occupy himself.”
He went on to say:
“We shall endeavour to spread the bread thin on the butter – to make what work there is still to be done to be as widely shared as possible. … a fifteen-hour week may put off the problem for a great while.”
Given the history of employment over the last 40 years, the idea of a 15-hour week is laughable for most of us. But if we could transition to shorter and shorter working weeks gradually (as seen before 1970 and continuing in some countries) it would lead to a culture where people defined themselves according to what they do in their leisure time at least as much as to what they do in their work time. People will have grown up in this new culture and learnt (partly through formal education) how to deal with this. We would realistically hope that there would be an increase in overall well-being as a result.
Seen this way, technological unemployment is not a problem – it is a good thing.
But of course, the problems with technological unemployment are the political issues raised by it:
- How do we ensure the work gets shared out – to spread the butter thin on the bread (or ‘spread the bread thin on the butter’ as Keynes said) – rather than just allowing those that want to take more than their ‘fair share’ of work to do so?
- Would we want to, or be able to, restrict the hours of those on significantly-higher wages?
- We will end up with a new ‘normal’ where the majority are on capped hours on near-minimum wage. How do we cope with a democratic society when the proportion of the working-age population on minimum or near-minimum wage exceeds 50% and becomes the majority?
- How do we ensure that the diminishing number of highly-paid, highly-skilled workers pay their taxes, when they can so easily move from one economy to another with their wealth?
- How does the government intervene into the free market of work to make it operate better?
Included in the suite of political problems is how to steer the jobs market so that it is the lousy jobs that are automated away rather than the lovely ones. And note: the demarcation between lovely and lousy jobs may not be as you think. I think we want jobs that are like the office work in that they are clean, safe and interesting compared with their factory predecessors, but office work is generally sedentary (impacting long term health) and often stressful. Light manual, mobile jobs are good jobs.
There will be limits on how far we are able to automate away many of the dull and dirty jobs. We may only be able to get robots to help relieve workers to make them less dull or dirty. It is often said how difficult it is to get a plumber or a cleaner. The same is rarely said about lawyers or accountants. We somehow expect to pay not very much for a cleaner but are resigned to paying high hourly rates for some professions. AI can make big productivity improvements in some of these professions. We perceive white-collar work as higher-status work and therefore deserving of more pay. But in a society in which so many people would be on minimum or near-minimum wage, we will need to get over this prejudice and pay for the dull and dirty tasks at a level more in keeping with their social value.
Universal Basic Income
In a society where work is scarce, large-scale unemployment becomes normal. How do we redistribute wealth from those that are employed to those that are not? Universal Basic Income and Negative Income Tax have been suggested as possible solutions.
With Universal Basic Income, the government provides everyone with a flat-rate benefit with which to pay for life’s basic essentials – food, drink, a shirt on one’s back and a roof over one’s head. Individuals are then able to compete for the work remaining (or engage in other, hopefully useful, activities). Just how basic is basic is open to debate. The value envisaged is typically around $10,000, 10,000€ or £10,000.
Negative Income Tax (NIT) works like this: with normal Income Tax, income up to, say $20,000 is taxed at 0% and income above is taxed at 20%. If the zero-band rate is set instead at -40%, there is a net pay-out as shown below:
|Standard income tax||Negative income tax||New net|
People support these various ideas when initially presented with them. But when they are told what tax rates would be needed, they turn against them! It is the political inability to redistribute wealth from some people to others that is the problem. Income tax hikes on the better off have long been considered to be electorally suicidal.
Opinion is divided over UBI. Claimed effects include:
- +/- helps to alleviate mental stress (even though often work helps keep you sane)
- + avoids the stigma of claiming benefits (although this reduces as a larger and larger proportion of the population receive it)
- – counteracting the above, it diverts targeted welfare target to the most needy, dependent on circumstance, to a generalised welfare.
- – delinking income and work by rewarding people for staying at home is what lies behind social decay.
- – undermines incentives to participate in society.
- – allows corporate and political leaders to postpone the real discussion about rising inequality, social dislocation and the future of jobs.
Taxing Good and Bad Income
But there is a more fundamental problem here: In a world with technological unemployment, we end up taxing the very thing we want to conserve, namely, good jobs! We need to manage the reduction in work in a controlled way. The market should be steered so that there is a strong incentive to innovate methods of automating the bad jobs (the dull, dirty, demeaning and potentially automatable) and to reduce incentives to innovate methods of automating the good jobs. We could tax work deemed to be good at lower rates (even 0%), but this obviously shifts the burden onto those with ‘bad work’. The latter’s salaries would have to rise to compensate.
Taxing by Other Means
If we are not taxing income, we need to tax by other means. Options include:
- Wealth (including land and inheritance taxes), and
- Sales (Use tax) – taxing consumption rather than production.
A significant problem here is that economies are not closed systems:
- People earning money in one country move that money abroad.
- People providing a service in one country employ themselves in another.
- People buy things from another country to avoid paying high sales taxes.
- (Additionally, people educated in one country go to work in another.)
Note: In what I say above:
- ‘country’ is any polity, be it a nation, region or super-nation – a political unit in which wealth is redistributed through taxes of one form or another.
- ‘person’ also includes corporations.
I am considering Corporation Tax as essentially the same as income tax, but it is easier to move this income from country to country because there isn’t any actual body to be physically located anywhere (and they can afford more lawyers than you or I).
Taxing the Robots
If workers aren’t earning the money, who can afford to buy the products? There is the well known (if apocryphal) conversation of Henry Ford II showing off his automated factory line to the labor union leader:
Henry Ford II: “Walther, how are you going to get these robots to pay union dues?”
Walter Reuther: “Henry, how are you going to get these robots to buy your cars?”
So what of taxing the robots, as often asked? Again, we can end up taxing what we want to promote. We want to tax robots that replace good jobs but we don’t want to tax the robots that do the bad.
The popular imagination of future automation is a world of science fiction robots, an imagination reinforced by the media with stock pictures of androids. The reality is not armies of robots scurrying around factory floors but of huge conveyor-belt installations of computer-controlled industrial equipment. And I have argued here that artificial intelligence in software form that can be rolled out quicker than the robots because of its very low marginal cost. Better paid skilled cognitive sedentary jobs will disappear more than commonly expected. Getting robots to do what we think of as simple tasks (such lifting something up and carrying it around an environment evolved for humans) is far more difficult (it is a task comparable to self-driving cars – robots that carry people from one pace to another). So many low paid unskilled physically active light manual jobs will remain for many years.
And besides, many of these light manual jobs are good jobs, useful and sociable. We need to actively manage the taxing of technology to incentivize the market to automate the Lousy jobs away and allow a transition towards Lovely jobs, sharing them out, and with a gradual reduction in the length of the working week.
How can we tax robots when there is a gradual progression of automation from the fixed minimally intelligent toaster (a device that automatically stops toasting without human intervention) up to the general-purpose humanoid robot of science fiction? Clearly we can tax the latter more. Key differentiators are mobility and ability to repurpose. A fixed ‘robo-barista’ (referring back to the Starbucks University un-forecast in Part 2) may still take someone’s job but it cannot be repurposed to, say, vacuum the floor (performed by a different robot) or clear up tables. How do you manage taxing a general-purpose robot that was initially procured to perform a Lousy job when it is repurposed to replace a Lovely job? It may be that the capability of robots is deliberately restricted such that they cannot be re-purposed (also allaying the fears of the ‘singularity’ when the ‘robots taking over’), either through the choice of its owner (to reduce taxes) or by law.
Taxing soft artificial intelligence is more difficult. Robots are physically located in one place and so can be taxed in that place. But Internet services could be served from anywhere. Perhaps taxing at the customer end is the answer – a sales tax. Previously, I have looked at Freemium and platform models of services. Perhaps we should tax the internet super-highway just as we impose taxes on the old-fashioned highway. Both are means of getting products and services from producers to consumers.
Universal Basic Services
An alternative to UBI is Universal Basic Services (UBS). I think there is potential here to overcome some of UBI’s difficulties, or at least alleviate them by being a complementary partial solution.
A basic service is provided to everyone ‘free at the point of use’, like state education and the much-loved National Health Service in the UK. Seven free public services identified are:
- legal & democracy
These should go a long way to solving issues such as hunger and homelessness that should have been solved a long time ago in the so-called ‘advanced’ Western societies.
If everything in life is given a monetary value and must be bought in a free market, the amount of money needed just to survive becomes significant.
With UBS, the welfare benefits are de-monetized: the costs are not directly visible to the consumer. The amount of money needed to survive becomes much less. The services still need to be paid for somehow but I believe UBS is more politically acceptable because, when taxpayers baulk at the high rates of tax, they can be challenged with questions:
- What basic service do you want to deny the poor?
- Should they be malnourished?
- Should they be unable to have a warm home?
- Are they really denied basic medical care?
A major objection to higher welfare benefits is removed: the perception that the ‘feckless poor’ ‘waste their money on’ this, that and the other. They are not given the opportunity to waste ‘our’ money. (Conversely, we no longer have the opportunity to spend ‘our’ money as we choose; the invisible hand of the free market is replaced with the state providing the service, or tendering it out.)
- There is no stigma about using the services when the majority of the population do.
- And hopefully, economies of scale reduce the cost per user.
But a more significant advantage of UBS is that it is amenable to the ‘Freemium’ business model (introduced in Part 2): the service can be paid out of our purchases rather than our tax. Income tax doesn’t have to be increased. Users pay for a service and, in doing so, allow others to have a basic version of what they themselves want. There is a direct connection between their needs and the same needs of others:
‘I am hungry; I want to eat; I pay to eat. I understand that other people get hungry too and they want to eat but cannot afford to. I am happy to pay a bit to allow them to have some food that is not as nice/much as mine.’
(Note: This provides a mechanism for the free market to select the service provider, through the choices of the premium user)
Here is a small example (‘un-forecast’): ‘Information’ is one of the basic services above. It is an important contributor to social inclusivity. Access to the internet, via a mobile phone should be universal. Mobile phone operators (private companies) should be mandated to provide a limited number of texts/minutes/Gbytes to and lending a refurbished phone to anyone who requests (with some means to restrict each user to just one servicer provider at a time). It provides the service provider with the opportunity to receive revenue through paid-for (top-up, PAYG) additional benefits and services.
Other services are more difficult to provide freely through a freemium model, but I don’t believe it is impossible. Consider the provision of food via ‘Universal Basic Canteens’. For efficiency, food is provided ready-prepared (food waste is minimized) rather than as (packaged) raw ingredients (in packaging). They are canteens rather than restaurants: self-service, with limited choice. This also provides the important social benefits of communal eating.
But: it presupposes that those who pay are happy to share the same eating space with those cannot. There is a huge status gradient going from food banks to ‘exclusive restaurants’ (the name says it all). The general population may no longer be prepared to share eating space in canteens in the way that they are generally still prepared to share hospital space. We need a sufficient level of social cohesion.
The introduction of UBS can be seen as part of a wider shift from owning ‘stuff’ oneself to using a service. The overall cost of ownership can be less and resources are used more efficiently (it is more environmentally friendly). Part 2 covered self-driving cars, which allow the completely new business model of what is called ‘Transportation as a Service’ (TaaS): dramatically reducing personal transportation costs by subscribing to a car service rather than owning a car oneself.
Younger generations, growing up in the internet world, are more used to this concept. They are happy to subscribe to music streaming services such as Spotify in marked contrast with their parents who prefer to own CDs (‘what if the internet disappeared’?). The young are ‘Generation Rent’ in more than one sense of the phrase.
In the song ‘Imagine’, John Lennon asks us to imagine being without countries, wars, religion, greed and hunger and this is all conceivable. But he also asks:
‘Imagine no possessions. I wonder if you can?’
This seems to go too far.
However, a shift from ownership to subscription services makes it possible to imagine a world where we generally own less. I still want my own toothbrush but I am quite happy to live without a (self-mowing) lawnmower. Subscription services take the hassle out of ownership (if it is broke, get the service provider to replace it straight away) and counters the culture that owning material goods intrinsically contributes to happiness.
Un-forecast 5: The Good Gig Economy
Defenders of the gig economy say that it offers workers flexibility to work when they want to. This is at least sometimes true. But mainly, those working in the gig economy are doing so because they need the money and the work offered is the best that they can get. But in a possible future world of Universal Basic Services, less money is needed in order to live a rewarding life. Here I present another un-forecast, in which the gig economy is working better for people…
Three friends in their twenties are working on a musical. They write the script and music using free software on a tablet and practice the performance in their free time whilst working casually in Starbucks. Their uploaded music creates publicity for gigs in their local town and nearby. But they occasionally have a holiday which is a nationwide tour of their music or of their musical. They use their tablets to record a promotional video with the help of free video editing software. Other material is edited with free software. It is all uploaded onto various social media sites.
They rent flats which have secure storage which enables them to sub-let them easily through Airbnb. They do this when touring, staying in similar accommodation. An app allows them to build up an itinerary so that renting and sub-letting more or less cancel each other. A similar Starbucks app gives them the flexibility to shift work to the various places that fit in with their itinerary.
Since so many people would be doing this, there would hardly be any money to be made performing music but they would be able to fulfil their leisure passions and go on long vacations at minimal cost.
The point I am trying to make here is that people can lead lives in this form of the gig economy that have a better work / life balance, providing creative opportunities for whoever wants it and opening up the talent pool for entrepreneurial enterprises far wider than it is today. And this can be achieved with relatively little money sloshing around, through the use of peer to peer and free services. Society would be qualitatively richer as a result.
New activities would arise as a result of this new economy. These activities may create new jobs, but they are more likely to be in the form of providing services to one another rather than in formal employment.
“We just need to educate everyone for the future knowledge economy”
People supposedly say that ‘we just need to educate everyone for the future knowledge economy’ because we need to expand higher-level education so that the unskilled workers for whom ‘the robots are going to take their jobs’ are in a position to do the newly-created jobs. But, as I have said, the AI that might replace the job you have now might also be able to take the job you might have had in the future.
Now, we do need to improve the level of education of the ‘workforce’ but a significant factor here should be that we need people to do more of the current skilled jobs, like doctors (very skilled) for example, so that we can ‘spread the bread thin on the butter’. But this will be expensive (investing heavily in a highly-trained person and not using them for as many hours of the week as possible is obviously less efficient).
In a surprising article called ‘The Skills Delusion’, Adair Turner says there is no point in educating people to perform zero-sum jobs i.e. those that do not increase net human welfare. More people getting university degrees does not mean higher productivity. Students may simply be willing to spend a lot of money on an education to signal to employers that they have high-value skills!
We should be providing the following education:
- Primary education, including numeracy, literacy and computer literacy, for basic functioning within modern society.
- Secondary education extending that primary education, providing a broader knowledge of the world around us and allowing individuals to make life choices.
- Secondary education of general well-being for ‘survival’ in the modern world. This includes having a flexibility and the attitude to adapt and learn. It also includes having entrepreneurial skills to help stimulate an enriching life within the ‘good gig economy’.
- Secondary education in ‘Data Science’. Older people grew up in a world where knowledge was scarce; people needed skills such as using an index catalogue in a library to find knowledge and people needed the ability to remember that knowledge. Young people have grown up in a world where knowledge is abundant (overwhelmingly so). People now need to know how to handle knowledge – how to sift out information from an internet of varying quality (including ‘fake news’).
- Tertiary education in ‘Data Science’ and other STEM (Science, Technology, Engineering and Mathematics) subjects (including Computer Science) to provide the technology that means we don’t have to work so much.
- Tertiary education in other (existing) subjects for skilled work, such as for doctors, as already mentioned.
- And then: tertiary education in other areas, not for any direct benefit but as part of an education for life outside of employment, enriching one’s quality of life and thereby enriching the culture in society.
The countries with most STEM (Science, Technology, Engineering and Mathematics) graduates are going to be the ones that will be able to capitalise on this new technology. Which countries are these?
World Economic Forum data from 2016 and OECD projections for 2030 is tabulated below. Both the STEM graduate numbers and total country populations are in thousands. What is being measured is different between the two years but the data highlights the relative strength in numbers of STEM in China, Russia, Iran, Saudi Arabia and South Korea compared with USA, the EU and Japan.
Obviously, the quality of those graduating varies from country to country.
Migration is a significant factor. Currently, 16% of US scientists come from outside the USA whereas only 3% of EU scientists come from non-EU countries (source: OBHE, 2013) and China is a large exporter of graduates, although these migration patterns may not continue.
The table provides some indication of where the new technology will be created.
|Country||STEM metric 1, 2016||STEM metric 2, 2030||Total population, 2015||Projected population, 2030||Per-capita metric 1, 2016||Per-capita metric 2, 2030|
Vladimir Putin has said that the development of AI raises
“colossal opportunities and threats that are difficult to predict now”,
“the one who becomes the leader in this sphere will be the ruler of the world”
“it would be strongly undesirable if someone wins a monopolist position.”
I have tried to get beyond the media hype and doom-mongering about the effect of robots and AI on jobs. To summarize the answers to the questions posed:
- No, “47% of current jobs are under threat” does not mean that about half of jobs will disappear. It is that a very large number of jobs will be significantly affected by AI.
- Yes, the new technology will have a big impact but technological revolutions also involve new ways of doing things, such as currently with the platform economy. It is these new processes that will surprise us. Some ‘vulnerable’ jobs will remain and some ‘safe’ jobs will disappear. The future is not the present with more robots.
- Yes, robots will have a big impact but it is the technology of artificial intelligence that will have the biggest impact in the short term because it can be rolled out very quickly.
- No, we cannot compete with the robots. But we can cooperate with them, with us doing the ‘lovely’ work in fewer hours and them doing the ‘lousy’ jobs. But this means we need to actively manage the economy to achieve this.
- Yes, technological development does cause unemployment but this has manifested itself in the past as a reduction in general working hours.
- Yes, this time is different from previous technological revolutions as it will be taking away cognitive rather than exclusively physical tasks. It is not just a problem of our imagination. There will not be an equal number of new jobs created as lost, because the AI will be able to do so many of those new jobs. “just because we can’t imagine what new jobs will come along doesn’t mean the next generation won’t” is much less true than it was before.
- Yes, it is true that “self-driving vehicles will put millions of truck drivers out of work” but this is in the long In the meantime, driving will become more automated hence easier. Legislation should ensure that huge lumps of metal are not let loose onto roads unsupervised. After all, we still have train drivers.
- Yes, there is a hollowing out of the jobs market. This is divisive and a serious threat to the cohesion of society.
- Yes, technological unemployment is a problem but, if we manage to spread the work, it is a problem of disparity of income rather than the binary division between employed and unemployed. In any case, there is the problem of wealth redistribution.
- Yes, Universal Basic Income may be a useful means of redistributing wealth but I believe a better solution is Universal Basic Services. The Freemium model may be a way of achieving UBS without increasing income tax or other taxes.
- Yes, we should tax the robots but we should make sure this doesn’t have the consequence of stopping them doing the ‘dull, dirty and dangerous’ tasks that they could do.
- Yes, we need to educate everyone for the future knowledge economy but: this is not about getting everyone to be AI experts. We need a world-class AI STEM education – but only for some (quality, not quantity) and the rest of us just need to be ‘AI-aware’ (aware of the ethical and other problems surrounding AI and Data Science). We need to educate for a world of less work: we must educate in order to spread the highly-paid work more equally across society and we need to educate in preparation for a more fulfilling world of less employed work and more opportunity for personal enterprise.
I have illustrated my arguments with some imagined futures but I doubt that they will actually come true in any way as described – these are only ‘un-forecasts’ after all. Accounts of the death of the university campus may be greatly exaggerated. There will be legal factors at play. Bots may not be allowed to impersonate real people – as suggested in help dementia patients in un-forecast 2. We may insist of having truck drivers sit in the huge chunks of metal hurtling around our roads, as we currently do on railway tracks. Automation may be thwarted by the ‘little things’. The San Francisco BART trains were designed in the 1970s to be self-driving but they are not, defeated by the controlling the opening and closing of doors for the passengers, not the driving itself).
The current technological revolution is generally presented as a problem – that is, presented in negative terms: the threat of the robots taking our jobs, seemingly leaving us unable to provide for our own daily needs. We are passive in this. The technology is disruptive and cannot be stopped. We have no control over it.
But the problem is not one of technology. It never is, per se. Technology forces change but the matter of which direction the change will take us is a political question not a technical one. It is about who benefits from the technology; in whose interests does the technology serve?
The same can be said about the economy: who benefits from it and in whose interests does it serve? How should we reform the economy to overcome the current crisis in the West, and how should we manage the transition? Technology is just one factor that needs to be borne in mind when answering these questions.