This is a beguiling timeline which has won a fair bit of attention for itself. It’s challenging stuff, particularly the point around 2060 when “all human tasks” will apparently be capable of being done by machines. But drawing an apparently precise timeline such as this obscures two massive sources of uncertainty. The first is the implication that people working on artificial intelligence have expertise in predicting the future of artificial intelligence. Their track record suggests that that is far from the case: like nuclear fusion, full blown AI has been twenty years in the future for decades (and the study underlying this short article strongly implies, though without ever acknowledging, that the results are as much driven by social context as by technical precision). The second is the implication that the nature of human tasks has been understood, and thus that we have some idea of what the automation of all human tasks might actually mean. There are some huge issues barely understood about that (though also something of a no true Scotsman argument – something is AI until it is achieved, at which point it is merely automation). Even if the details can be challenged, though, the trend looks clear: more activities will be more automated – and that has some critical implications, regardless of whether we choose to see it as beating humans.
This new report from the RSA takes a more balanced view than most on the impact of automation on work, and particularly on low-skill work. This is neither a story of a displaced workforce condemned to penury as the robots take over, nor one of a blithe assumption that everything will muddle through. Much of the underlying analysis is now fairly familiar – certainly to regular readers here – what is distinctive and valuable is the focus on the quality as well as the quantity of work and the ways in which automation can enhance human work rather than displace it
Impressively, the authors have put some of their approach into practice by partly automating the process of reading it. Traditional manual readers can work through the full eighty page report; automation maximalists need only skim the eight key takeaways; and those with intermediate ambitions can focus on extracts from the main report summarising the main arguments or focusing on the impact of automation on the quality of work.
How do changing patterns of employment affect not just the nature of people’s work in the short term, but their ability to progress and to have careers? This article attempts an answer to that question by looking at two tech giants of different generations, Kodak and Apple, and their very different employment models. It is unashamedly a powerful story rather than a deep analysis – but interesting read as an illustration of Simon Caulkin’s recent article, which covers closely related ground in a very different way.
The fact that new technologies can destroy jobs (even if they can enable the creation of others) is almost universally discussed as a problem for the workers concerned. That doesn’t always mean that those affected are abandoned – there is recognition that governments have a role in retraining or providing other forms of support, up to and perhaps including a universal basic income – but it does mean that the component of the wider system which is expected to deal with the negative consequences is the affected worker. In the modern corporate era focused above all on shareholder value, companies necessarily do everything they can to minimise employment costs. But that is a political choice, not a force of nature (as a report from the White House recognised last year).
That framing of the issue is so deep rooted as to be almost invisible – this post brings it to light in order to challenge its assumptions: what can be done and should be done to sustain the demand for labour, and what implications does that have for the role and purpose of employing organisations?
The impact of technology on employment often focuses on the jobs at risk of being automated out of existence, not at the ones which might be created, either because of new technical possibilities, or as a consequence of increasing wealth and disposable income. This research looks at how patterns of employment have changed by tracking census data on occupations from 1871 to 2011 and concludes, not altogether surprisingly that their distribution has steadily changed, with patterns ranging from a steady decline in agricultural labourers and launderers, to telephonists rising to a peak in 1971 before declining by 2011 to the level of 1911 – and accountants, hairdressers and bar staff showing relentless growth, which is either the triumph of the service economy or an alarming step towards the reality of the B Ark.
Critically though, the conclusions are that although technological unemployment is very real, the stock of employment is not fixed or limited by technology, and that there is every reason to expect that new – and often unforeseen – jobs will continue to be created, as they always have been.
Link to the full report below – there was also a good summary of it published in the Guardian.
All the signs that you would expect to see in the labour market and wider economy if robots were displacing jobs are absent: productivity is not growing rapidly, labour turnover is not going up, and employment remains high.
That’s not to say, of course, that automation isn’t happening – and Surowiecki is careful not to say it – or that what has happened up to now is an infallible guide to what will happen in the future. But this article does contribute to the recognition that technological progress, the social and economic adoption of that progress, and the wider impact of that adoption are all very different things, potentially with very significant lags between them. That perspective is now coming through more strongly elsewhere as well – which should mean that the debate can be more balanced.
Technology is rarely just about technology, a fact often overlooked in slightly hysterical predictions about the impact of AI on jobs. This is a good summary of social, political and financial reasons why the path to universal automation might not be as straightforward as it is often portrayed. And that is to say nothing of the reasons to think that the technology itself may have intrinsic limitations as a substitute for humans.
Organisations exist to get things done. They are necessary because they solve problems ranging from communication and co-ordination to moral hazard. But in principle, if we could find other ways of solving those problems, we wouldn’t need organisations any more – at least of the traditional kind. This article gives some examples of where that is starting to happen, by assembling not just project teams, but project organisations, created to meet a specific need and disbanded as soon as that need is met.
The examples given show that it can work, but they don’t and can’t show that it can scale, and there may be good reasons to think that it can’t. But that doesn’t stop the idea being a good challenge, particularly to those in organisations which don’t tend to think about organisational change in quite those terms.
This is a great post on two entirely different levels. It’s a reading (and listening and watching) list of material on the future of work, with a dozen or so interesting annotated links to follow.
But it’s also an approach to improving the quality of conversations, creating the space to think differently and more creatively, using the shared material to support a richer conversation, based on the insight that “a library of inspiration develops through a lifetime of experiences”. That’s an approach which it feels well worth borrowing – whether on the future of work or any other subject.
Neatly sidestepping the question of whether technology will only destroy jobs or, as every past cycle of technical innovation has done, will also create new (and perhaps currently unimaginable) ones, this post focuses instead on when such a shift can be expected to occur. The period of disruption between the old and the new is important even if it were possible to be confident that the new would be a better place. For much of the nineteenth century, the industrial revolution brought poverty and reduced life expectancy for many – only towards its end did the subsequent century of rising living standards begin. Are we at similar risk of facing serious, and potentially long drawn out, disruption to social order?
This is a powerfully argued article on the strategic case for a UBI – in this case, with a very strong stress on being unconditional. The problem of technological unemployment can be solved only by breaking the strong assumption that income should be linked to work. Doing that is hard because we have been so strongly conditioned to seeing them as inexorably linked – but breaking that link creates new social and economic possibilities and increases overall welfare levels, as well as creating the conditions in which people are far more strongly empowered to exercise their basic human rights. The conclusion is quite a radical one, but the real question is less whether it is right or wrong, and much more whether the starting analysis is well founded.
— Google (@Google) May 17, 2017
Jobs, we keep being told, will increasingly be automated. But if, in the modern welfare system, claimants are required to demonstrate that they are, in effect, working full time at looking for work, what happens when looking for work is just another job which gets automated?
Last week Google announced a new Google for Jobs service which isn’t quite that, but which is clearly a step in that direction, and it’s a safe bet that there will be more steps to come. This post reflects on the implications of that for people who are seeking to use their time productively while looking for paid work – and for the welfare systems which support them as they are doing so.
The growing gig economy is often associated with low wages and exploitation, with the flexibility it offers advantaging the employer rather than the worker (and as one of the speakers at the recent RSA event said, flexibility is fine, so long as it works in both directions). Some of that is to do with ambiguities in legal status which haven’t kept pace with the changing labour market, but some of it is about power imbalances – another reflection of the changing relationship between technology and work. This report attempts to answer the question of what a good gig economy would look like, with government given the primary role for creating the conditions for success.
A thirty minute discussion on job automation. Building on Michael Osborne’s work on the levels of job automation, Ryan Avent paints a dystopian future where, paradoxically, humans are forced into low skill and low wage work – and Judy Wajcman points out that the impact of technology is not inexorably deterministic, but is a function of social and political choices. As in previous industrial revolutions, there may be many losers in the transition, even if in the long run, society as a whole is better off, bringing a clear need to avoid technology driving social and political division. The goal seems obvious – that automation should lessen the burdens of work as far as possible – but the means of getting there requires many assumptions to be challenged and reset.
If, as the World Economic Forum has argued, five million jobs are about to be automated out of existence, it becomes important to know which skills will be less in demand and which align with future jobs growth. This article argues that there are two important dimensions – the ‘soft’ skills, such as sharing and negotiation, and mathematical ability, and that it is the combination of the two which will lead to greatest success.
Work is a critically important part of life, and it matters enormously that work should be good not bad, that we should be interested in the quality of work as well as its quality. That’s the central premise of this thoughtful article by Matthew Taylor which covers similar, but not identical, ground to his lecture, delivered the same day, on good work for all.
A short, sharp lecture – the main part is less than twenty minutes – on the nature of work, and particularly what should count as ‘good work’ in a modern economy, covering similar ground to the article Matthew Taylor published the same day. It is followed by responses from Carolyn Fairbairn, Carol Black and Peter Cheese, which are a slightly more mixed bag, but interesting for what they both do and do not say directly.
A clear expression of the counter-argument to Brad De Long’s peak horse analogy – the current round of technology-driven innovation, like every round before it will generate new jobs to meet the new demand for the new products and services which the new technology enables and which were previously unimaginable. If human needs are unlimited and unforseeable, there is no reason to think that the model as a whole is under threat (though that, of course, says nothing about the individuals caught in the transition, or about the distribution of gains and losses more generally).
The question of whether new technology is a threat or an opportunity never goes away, because it can never definitively be answered. Despite contemporary fears, past new technologies have resulted in more – albeit often different – jobs, rather than fewer, so why should this time be different? One answer might be that past changes have left ‘cybernetic control’ of work firmly in the human sphere, and that current and prospective challenges are shrinking that area of advantage. The era of peak horse labour passed a century ago – for a long time they were irreplaceable, despite changes in some of the surrounding technology, until suddenly they weren’t. Are we approaching a similar position for peak human labour?
From the last weeks of the Obama White House, this is an exemplary analysis of increasing automation on the economy in general and on employment in particular, with a range of policy recommendations to address the challenges it identifies. It makes the important point that since variations in technology across the major economies cannot explain the differing impacts on employment, differences in policy and institutions must be having an effect. One example of that is very different national policies on the level of support offered to help people move from old jobs prone to automation to new jobs which are better protected from it. The report is well worth reading, but is also helpfully summarised in a commentary in the current MIT Technology Review.