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Two of the current leading researchers in labor economics studying the impact of machines and automation on jobs have released a new National Bureau of Economic Research (NBER) working paper, The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.
The authors, Daron Acemoglu and Pascual Restrepo are far from the robot-supporting equivalent of Statler and Waldorf, the Muppets who heckle from the balcony, unless you consider their heckling is about how so many have overstated the argument of robots taking all the jobs without factual support:
Similar claims have been made, but have not always come true, about previous waves of new technologies… Contrary to the increasingly widespread concerns, our model raises the possibility that rapid automation need not signal the demise of labor, but might simply be a prelude to a phase of new technologies favoring labor.
In The Race Between Machine and Man, the researchers set out to build a conceptual framework, which shows which tasks previously performed, by labor are automated, while at the same time more ‘complex versions of existing tasks’ and new jobs or positions in which labor has a comparative advantage are created.
The authors make several key observations that show as ‘low skilled workers’ are automated out of jobs, the creation of new complex tasks always increases wages, employment and the overall share of labor increases. As jobs are eroded, new jobs, or positions are created which require higher skills in the short term:
Whilst “automation always reduces the share of labor in national income and employment, and may even reduce wages. Conversely, the creation of new complex tasks always increases wages, employment and the share of labor.”
They show, through their analysis, that for each decade since 1980, employment growth has been faster in occupations with greater skill requirements
During the last 30 years, new tasks and new job titles account for a large fraction of U.S. employment growth.
In 2000, about 70% of the workers employed as computer software developers (an occupation employing one million people in the US at the time) held new job titles. Similarly, in 1990 a radiology technician and in 1980 a management analyst were new job titles.
Looking at the potential mismatch between new technologies and the skills needed the authors crucially show that these new highly skilled jobs reflect a significant number of the total employment growth over the period measured as shown in Figure 1:
From 1980 to 2007, total employment in the U.S. grew by 17.5%. About half (8.84%) of this growth is explained by the additional employment growth in occupations with new job titles.
Unfortunately we have known for some time that labor markets are “Pareto efficient; ” that is, no one could be made better off without making anyone worse off. Thus Acemoglu and Restrepo point to research that shows when wages are high for low-skill workers this encourage automation. This automation then leads to promotion or new jobs and higher wages for those with ‘high skills.’
Because new tasks are more complex, the creation may favor high-skill workers. The natural assumption that high-skill workers have a comparative advantage in new complex tasks receives support from the data.
The data shows that those classified as high skilled tend to have more years of schooling.
For instance, the left panel of Figure 7 shows that in each decade since 1980, occupations with more new job titles had higher skill requirements in terms of the average years of schooling among employees at the start of each decade (relative to the rest of the economy).
However it is not all bad news for low skilled workers the right panel of the same figure also shows a pattern of “mean reversion” whereby average years of schooling in these occupations decline in each subsequent decade, most likely, reflecting the fact that new job titles became more open to lower-skilled workers over time.
Our estimates indicate that, although occupations with more new job titles tend to hire more skilled workers initially, this pattern slowly reverts over time. Figure 7 shows that, at the time of their introduction, occupations with 10 percentage points more new job titles hire workers with 0.35 more years of schooling). But our estimates in Column 6 of Table B2 show that this initial difference in the skill requirements of workers slowly vanishes over time. 30 years after their introduction, occupations with 10 percentage points more new job titles hire workers with 0.0411 fewer years of education than the workers hired initially.
Essentially low-skill workers gain relative to capital in the medium run from the creation of new tasks.
Overall the study shows what many have said before, there is a skills gap when new technologies are introduced and those with the wherewithal to invest in learning new skills, either through extra education, on the job training, or self-learning are the ones who will be in high demand as new technologies are implemented.
Frank Levy an economist and Professor at MIT and Harvard, who work’s on technology’s impact on jobs and living standards, has written to assay the sensationalized fears of the overhyped study by Frey and Osborne. Levy indicates:
- The General Proposition – Computers will be subsuming an increasing share of current occupations – is unassailable.
- The Paper (Frey and Osborne study) is a set of guesses with lots of padding to increase the appearance of “scientific precision.”
- The authors’ understanding of computer technology appears to be average for economists (= poor for computer scientists). By my personal guess, they are overestimating what current technology can do.
Researchers at the OECD analyzed the Frey and Osborne study and conducted their own research on tasks and jobs and concluded that: “automation was unlikely to destroy large numbers of jobs.”
I have also been quite critical of the Frey and Osborne study based on my understanding of technological advances, which they claim to be way more ahead than it is:
We argue that it is largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition.
With the exception of three bottlenecks, namely:
“Perception and manipulation.”
Frey and Osborne divided the tasks involved in jobs along two dimensions: cognitive vs. manual and non-routine vs. routine. They then identified three aspects (bottlenecks) of a job making it less likely that a computer would be able to replicate the tasks of that job: First, “perception and manipulation” in unpredictable tasks such as handling emergencies, performing medical treatment, and the like. Second, “creative intelligence” such as cooking, drawing, or any other task involving creative values relying on novel combinations of inspiration; Third, “social intelligence”, or the real-time recognition of human emotion.
Race with the machines
Now a new research paper, released in July 2016, by researchers at the Centre for European Economic Research has indicated that technology has in fact had the opposite impact and is a net creator of jobs not destroyer (at least in 27 European countries – and I suspect the same is true for other regions).
The paper, Racing With or Against the Machine? Evidence from Europe by authors Terry Gregory, Anna Salomons, and Ulrich Zierahn (Gregory and Zierahn were also two of the OECD paper authors) looked at the impact of routine replacing technology on jobs and concluded:
Overall, we find that the net effect of routine-replacing technological change (RRTC ) on labor demand has been positive. In particular, our baseline estimates indicate that RRTC has increased labor demand by up to 11.6 million jobs across Europe – a non-negligible effect when compared to a total employment growth of 23 million jobs across these countries over the period considered. Importantly, this does not result from the absence of significant replacement of labor by capital. To the contrary, by performing a decomposition rooted in our theoretical model, we show that RRTC has in fact decreased labor demand by 9.6 million jobs as capital replaces labor in production. However, this has been overcompensated by product demand and spillover effects which have together increased labor demand by some 21 million jobs. As such, fears of technological change destroying jobs may be overstated: at least for European countries over the period considered, we can conclude that labor has been racing with rather than against the machine in spite of these substitution effects.
My research of companies using robots has also categorically shown, through factual evidence, that those companies have created significantly more jobs than have been lost due to technological change. Similarly a detailed analysis prepared for the European Commission Director General of Communications Networks, Content & Technology by Fraunhofer about the impact of robotic systems on employment in the EU found that:
European manufacturing companies do not generally substitute human workforce capital by capital investments in robot technology. On the contrary, it seems that the robots’ positive effects on productivity and total sales are a leverage to stimulate employment growth.
So if robots are not job killers what is the real problem?
We need to fill the skills gap
I have argued before that we have a skills problem. Jobs all over the world are not being filled because of lack of skilled personnel to fill them.
New and emerging technologies both excite and worry. Robotics and Artificial Intelligence (AI) is certainly a minefield for both exuberance and fears.
By definition, there is a knowledge and skills gap during the emerging stages of any new technology, Robotics and AI is no exception: researchers and engineers are still learning about these technologies and their applications. But, in the meantime, hope, fears and hype naturally and irresistibly fill this vacuum of information.
Depending on whom you ask Robots and AI is predicted to help solve the world’s problems. Or by building this devil, these technologies may scorch the earth and fulfill a prophecy of Armageddon.
On the other side, especially with respect to AI, what it will most likely do – if and only if adopted by major corporations and governments — is foster technological and institutional betterment at a frenetic pace through improved health care, solving climate problems, helping those with sight problems, helping to get much needed aid spread more equitably.
We need education and training fitted to a different labour market, with more focus on creativity, flexibility and social skills. We need more Moonshots from Governments and Industry as so well described by Mariana Mazzucato in her book the Entrepreneurial State: Debunking Public vs. Private Sector.
Machines are there to augment human intelligence and ingenuity, to improve our environment and workplace, we need to stop fearing the machines and learn how to better integrate them into our processes, destroy the fears and improve productivity. We are not going to stop technological progress, if we embrace it we are better prepared to gain from it.
Founded in Spain in 1861 in Penedès, the main district of Catalonia, Freixenet S.A. currently owns 18 wineries across three continents and is one of the best-known Spanish wine brands. The 155 years old family owned business has annual sales exceeding Euro 500 million (US$ 560 million) and produces over 200 million bottles of sparkling wine each year.
The sparkling wine is known as “cava” due to the fact that much of the production fermentation process is in a network of several miles of underground caves or cellars. To be branded cava, sparkling wine must be produced in the ‘champenoise traditional method’, in the past cava was referred to as “Spanish champagne”, however this branding is no longer permitted under European Union law. Nevertheless the method of production for cava and Champagne are pretty much the same in which wine is fermented twice and sugar added to make it bubbly.
Sparkling wine is currently the key growth area in the beers, wine and spirits category. This growth has caused some challenges for Freixenet to increase production capacity to the same degree as an increase in the success of the brand and its products. The challenges are compounded by the traditional methods of production which require that processes are maintained, in fact according to Josep Palau, Head of Production at Freixenet:
What has not changed at all is our traditional elaboration process, which still includes each and every one of the stages as they were undertaken 50 years ago. We collect the grapes, make the base wines, bottle them, ferment them, then the crianza process begins, disgorging, etc. But what we have done continuously is make these stages more technical and automated in order to adapt ourselves to an increase in demand.
Those changes in production also depend on the particular cava being produced; the process is either done by hand (for the very top cuvees), or increasingly by automation. For example the company now uses pneumatic presses with a soft membrane that creates a pressure similar to traditional foot treading for pressing the grapes.
Once the grapes are pressed the ‘must’ from which the base wines are made is mixed in large vats by adding sugar, yeast and clarifiers, this then undergoes a bottling process and then the wines are taken to the cellars for fermentation. The fermentation involves the use of computerized automation that slowly rotate the bottles to help the build up of the carbon dioxide gas needed for cava’s characteristic bubbles. Depending on the product, this may range from a minimum of nine months to three years or more in higher quality wines.
Of Freixenet’s 1700 employees worldwide approximately 350 are employed at their main production facility. According to Josep Palau a large number of employees are involved in heavy manual tasks of moving the bottles around.
Once the base wines are bottled, the bottles have to be stored in cellars and this requires a great deal of internal logistics.
The cellar process, whether it is positioning the bottles or retrieving them a year later for the clarifying process before disgorging, involves a lot of internal movement and labor.
To help overcome many of the handling, maneuvering and bottling problems Freixenet have installed 36 industrial robots from Fanuc. With the help of Fanuc’s robots production capability has increased substantially. Josep Palau says:
Now an operator can move 500 bottles with each action rather than the two bottles before. The disadvantage before was that, as well as continuing to need somebody to intervene manually, the process also took up a lot of space in our cellars.
The next major innovation was automating the stacking process, or placing the bottles in the cellars, which had previously been done manually until Freixenet’s technicians and a local engineer came up with and implemented a robotic system that allowed the job to be done more efficiently. Mr. Palau believes this automation was the most significant milestone in improving productivity and reducing waste:
This was probably one of the most important innovations that was introduced. Later, and in the aim of being able to manage a great number of bottles, a new bottling process was created, which was almost completely automated and was fully robotized during the end stage. The bottles leave the production line via an automated transport system and arrive directly to the cellars, where an automatic robot system positions them in place for the crianza stage.
By automating this process, work was greatly simplified and our ability to handle this removal step increased enormously, thereby allowing us to handle growth.
In addition to increasing productivity by more than 32 per cent since the introduction of the robots and securing jobs, Freixenet have also discovered environmental benefits from the new technology for bottling and handling. The automation has resulted in a reduction of 25 per cent of the organic pollution load, chemical oxygen demand (COD) of wastewater per unit produced between 2012-2014, and glass waste has been reduced by 7 per cent.
In Spain, one of the key dates on the calendar in the run-up to Christmas is the first broadcast of the Freixenet TV advert. A tradition established in 1978, which has been graced through the years by celebrities such as Demi Moore, Pierce Brosnan, Penélope Cruz, Kim Basinger, Sharon Stone, Antonio Banderas, Paul Newman, Josep Carreras, Plácido Domingo, and many more. The celebrities of the 2012 campaign were two of Freixenet’s production Fanuc robots saluting with 2 glasses of cava. Cheers!
Society is caught between blind faith in technology and resistance to progress, between technological possibilities and fears that it has a negative impact.
Increasingly Artificial Intelligence, the latest buzzword for everything software related, is stirring up much of the fears.
In an interesting paper: Is This Time Different? The Opportunities and Challenges of Artificial Intelligence, Jason Furman, Chairman of President Obama’s Council of Economic Advisers sets out his belief that we need more artificial intelligence but must find a way to prevent the inequality it will inevitably cause. Despite the labor market challenges we may need to navigate, Furman’s bigger worry is that we will not invest enough in AI.
He is more pragmatic than many economists and researchers who have written ‘popular’ books on the subject but calls for more innovation if we are truly to reap the benefits AI and Robotics will bring:
We have had substantial innovation in robotics, AI, and other areas in the last decade. But we will need a much faster pace of innovation in these areas to really move the dial on productivity growth going forward. I do not share Robert Gordon’s (2016) confidently pessimistic predictions or Erik Brynjolfsson and Andrew Mcafee’s (2014) confidently optimistic ones because past productivity growth has been so difficult to predict.
Technology, in other words, is not destiny but it has a price
My worry is not that this time could be different when it comes to AI, but that this time could be the same as what we have experienced over the past several decades. The traditional argument that we do not need to worry about the robots taking our jobs still leaves us with the worry that the only reason we will still have our jobs is because we are willing to do them for lower wages.
Replacing the Current Safety Net with a Universal Basic Income Could Be Counterproductive
Furman says that AI does not create a call for a Universal Basic Income and that the claims for implementing UBI and cancelling other social welfare programs have been greatly overstated:
AI does not call for a completely new paradigm for economic policy—for example, as advocated by proponents of replacing the existing social safety net with a universal basic income (UBI) —but instead reinforces many of the steps we should already be taking to make sure that growth is shared more broadly.
Replacing part or all of that system with a universal cash grant, which would go to all citizens regardless of income, would mean that relatively less of the system was targeted towards those at the bottom—increasing, not decreasing, income inequality.
Instead our goal should be first and foremost to foster the skills, training, job search assistance, and other labor market institutions to make sure people can get into jobs, which would much more directly address the employment issues raised by AI than would UBI.
Past Innovations Have Sometimes Increased Inequality—and the Indications Suggest AI Could Be More of the Same
Relying on the questionable study by Frey and Osborne, Furman says that work by the Council of Economic Advisers, ranked the occupations by wages and found that, according to the Frey and Osbourne analysis, 83 percent of jobs making less than $20 per hour would come under pressure from automation, as compared to 31 percent of jobs making between $20 and $40 per hour and 4 percent of jobs making above $40 per hour (see Figure 1 below).
AI has not had a large impact on employment, at least not yet
Furman says the issue is not that automation will render the vast majority of the population unemployable. Instead, it is that workers will either lack the skills or the ability to successfully match with the good, high paying jobs created by automation.
The concern is not that robots will take human jobs and render humans unemployable. The traditional economic arguments against that are borne out by centuries of experience. Instead, the concern is that the process of turnover, in which workers displaced by technology find new jobs as technology gives rise to new consumer demands and thus new jobs, could lead to sustained periods of time with a large fraction of people not working.
AI has the potential—just like other innovations we have seen in past decades—to contribute to further erosion in both the labor force participation rate and the employment rate. This does not mean that we will necessarily see a dramatically large share of jobs replaced by robots, but even continuing on the past trend of a nearly 0.2-percentage-point annual decline in the labor force participation rate for prime-age men would pose substantial problems for millions of people and for the economy as a whole.
Investment in AI
Mentioning the fact that AI has not had a significant macroeconomic impact yet, Furman indicates that the private sector will be the main engine of progress on AI. Citing references that in 2015 the private sector invested US$ 2.4 billion on AI, as compared to the approximately US$ 200 million invested by the National Science Foundation (NSF).
He says the government’s role should include policies that support research, foster the AI workforce, promote competition, safeguard consumer privacy, and enhance cybersecurity
AI does not call for a completely new paradigm for economic policy
AI is one of many areas of innovation in the U.S. economy right now. At least to date, AI has not had a large impact on the aggregate performance of the macroeconomy or the labor market. But it will likely become more important in the years to come, bringing substantial opportunities— and our first impulse should be to embrace it fully.
He indicates that his biggest worry about AI is that we may not get all the breakthroughs we think we can, and that we need to do more to make sure we can continue to make groundbreaking discoveries that will raise productivity growth, improving the lives of people throughout the world.
However, it is also undeniable that like technological innovations in the past, AI will bring challenges in areas like inequality and employment. As I have tried to make clear throughout my remarks, I do not believe that exogenous technological developments solely determine the future of growth, inequality, or employment. Public policy—including public policies to help workers displaced by technology find new and better jobs and a safety net that is responsive to need and ensures opportunity —has a role to play in ensuring that we are able to fully reap the benefits of AI while also minimizing its potentially disruptive effects on the economy and society. And in the process, such policies could also contribute to increased productivity growth—including advances in AI itself.
What are those policies? Truman indicates we need to develop more “human learning and skills,” increase investments in research and development, this includes Government investment and also “expand and simplify the Research and Experimentation tax credit,” “increase the number of visas—which is currently capped by legislation—to allow more high-skilled workers to come into the country.” “Consolidate existing funding initiatives, help retrain workers in skills for which employers are looking,” and more focused initiatives such as the “DARPA Cyber Grand Challenge.”
The bottom line is that AI managed well, with innovate government support, could offer significant benefits to humanity, but those benefits, including earning capacity, can only be achieved if governments and corporations help people up-skill.
 For private funding see https://www.cbinsights.com/blog/artificial-intelligence-funding-trends/#funding. For public funding see http://www.nsf.gov/about/budget/fy2017/pdf/18_fy2017.pdf. According to the NSF, in 2015 there was $194.58 million in funding for the NSF Directorate for Computer and Information Science and Engineering’s Division of Information and Intelligent Systems (IIS), much of which is invested in research on AI. These figures do not include investment by other agencies, including Department of Defense.
Around 1900, most inventions concerned physical reality: cars, airplanes, zeppelins, electric lights, vacuum cleaners, air conditioners, bras, zippers. In 2005, most inventions concern virtual entertainment — We have already shifted from a reality economy to a virtual economy, from physics to psychology. ~Geoffrey Miller
Many commentators and researchers have indicated a supposedly imminent end to work, or at least the infamous ‘47% of jobs will be displaced’ within 20 years or so due to the inexorable advance of machines. This is at best a distraction and at worst grossly exaggerated and overhyped, as one of the authors of the infamous papers has noted.
However if we extend the timeframe out and consider the question:
How much could the world of work plausibly change by the end of the 21st century?
Eighty-four years from now will human’s work to earn a living or will machines do all the labor?
Then I believe we have framed a different vision of the future, one where it may be more plausible to consider that human’s will work 15 hours per week (if at all) as predicted by Keynes in 1930.
 John Maynard Keynes, Economic Possibilities for our Grandchildren (1930) – “everybody will need to do some work if (s)he is to be contented – three-hour shifts or a fifteen-hour week may put off the problem for a great while. “
There is so much doom and gloom associated with robots and jobs it is time to add some common sense to the misunderstandings created by so called experts opinions about robots and jobs – thankfully authors from the OECD may have added some clarity to the debate — ‘finding that on average, across the 21 OECD countries, ‘9% of jobs rather than 47%, as proposed by Frey and Osborne face a high automatibility.’
Capitalism, the term for our global ‘free’ markets, is a uniquely future-oriented economic system in which people invest, make innovations, apply for patents, and in other ways bet on the future. Behind all of this we find the hallmark of humanity, which is our creative intelligence.
It is intelligence that drives these investments and innovations, and intelligence that forges within many of us an intense curiosity of what the future may hold.
It is also intelligence that forges in others an anxiety over what the future holds. For many the future is no longer a promise but a threat!
Pessimism is the easy way out.
This curiosity and anxiety has stirred the same debates in society for generations. On one side there is intense optimism for a future where machines can take over many of the dirty, dangerous, dull and repetitive jobs, opening up new and more ‘interesting and rewarding’ jobs for those that may be displaced.
And on the other side those who are concerned that this time really is different and the machines we are building now, or which we will soon be capable of building, will be so advanced that there really will be no ‘new types’ of jobs for humans – and so they claim the majority of jobs for humans will be eliminated.
To those pessimists I often quote Lord Thomas Babington Macaulay who in 1830 wrote about the prophet’s of gloom:
On what principle is it, that when we see nothing but improvement behind us, we are to expect nothing but deterioration before us?
In his 1995 book Jeremy Rifkin stated that ‘intelligent machines’ were being ‘hurried in to’ work environments, thus ending work for people.
Now, for the first time, human labor is being systematically eliminated from the production process… A new generation of sophisticated information and communication technologies is being hurried into a wide variety of work situations. Intelligent machines are replacing human beings in countless tasks, forcing millions of blue and white-collar workers into unemployment lines, or worse still, breadlines.
It is 21 years since Rifkin made that claim, yet somehow human ingenuity marches on and continues to create more jobs and new industries. Sometimes new technologies eliminate jobs overall, but they also create demand for new capabilities and new jobs.
Looking with both eyes open
Despite the vast improvements we have made as a society, I wonder why it is that we look with one eye open, only seeing the negative aspect of technological change, instead of opening both eyes and seeing the benefits too. Often studies by ‘research scientists’ which receive significant media attention lead to misrepresentation of the potential benefits and impacts of technology and create fears, sometimes as if it is a fait accompli, even if this is not the intention of the study authors.
A new study by Melanie Arntz, Terry Gregory and Ulrich Zierahn for the OECD argues that studies on robots or computerization eradicating jobs, such as that by Frey and Osborne, lead to a severe overestimation of job automatibility, as occupations labelled as high-risk occupations often still contain a substantial share of tasks that are hard to automate.
9 % of jobs could be automatable
The OECD authors provide far more realistic assessments than Frey and Osborne:
In contrast to other studies, we take into account the heterogeneity of workers’ tasks within occupations. Overall, we find that, on average across the 21 OECD countries, 9 % of jobs are automatable. The threat from technological advances thus seems much less pronounced.
Arntz, et al. argue that the estimated share of “jobs at risk” must not be equated with actual or expected employment losses from technological advances for three reasons.
- The utilisation of new technologies is a slow process, due to economic, legal and societal hurdles, so that technological substitution often does not take place as expected.
- Even if new technologies are introduced, workers can adjust to changing technological endowments by switching tasks, thus preventing technological unemployment.
- Technological change also generates additional jobs through demand for new technologies and through higher competitiveness.
Effectively the authors take into account that not whole occupations, but specific jobs are exposed to automatibility, depending on the tasks performed at these particular jobs.
They also demonstrate the necessity to view technological change as substituting or complementing certain tasks rather than whole occupations, which as I have mentioned before in this blog a major flaw in the Frey and Osborne study.
The OECD study authors state:
We find that in the US only 9% of jobs rather than 47%, as proposed by Frey and Osborne face a high automatibility.
We further find heterogeneities across OECD countries: while the share of automatable jobs is 6 % in Korea, the corresponding share is 12 % in Austria. The differences across countries may reflect general differences in workplace organisation, differences in previous investments into automation technologies as well as differences in the education of workers across countries.
Table 1 Automatibility by OECD Countries
The main conclusion from the paper
Automation and digitalisation are unlikely to destroy large numbers of jobs. However, low qualified workers are likely to bear the brunt of the adjustment costs as the automatibility of their jobs is higher compared to highly qualified workers. Therefore, the likely challenge for the future lies in coping with rising inequality and ensuring sufficient (re-)training especially for low qualified workers.
Too many so called research experts have created way too much fear and public perception, which in turn can lead to bad policy recommendations. We need to be thoughtful in our vision, and analytical in our implementation – and realistic in our expectations of technologies capabilities.
Herbert Spencer’s words in “From Freedom to Bondage” are as relevant today as when he wrote them in 1891:
The more things improve the louder become the exclamations about their badness.
 Thomas Babington Macaulay, Review of Southey’s Colloquies on Society, 1830 Edinburgh Review
 Jeremy Rifkin, The End of Work, 1995 Chapter 1.
 Arntz, M., T. Gregory and U. Zierahn (2016), working paper “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis”, OECD Social, Employment and Migration Working Papers, No. 189, OECD Publishing, Paris.
 Herbert Spencer, The Man Versus the State, With Six Essays on Government, Society, and Freedom (Indianapolis: Liberty Press, 1891), p. 487.
Buckminster Fuller said: “We are called to be architects of the future, not its victims.” Here are some things I think will probably be true:
It’s probably a good time to invest in robot courier services
- Fleets of self-driving trucks will be on the roads worldwide by 2020
- Those same trucks will have a ‘delivery driver’ inside the cabin for at least another 10 years
- By 2030 all sales of new trucks will be self-driving
- From 2030 onwards a robot such as the latest generation Atlas will be in the cabin to handle deliveries
- By 2050 very few, if any, human couriers will be used, instead people will have new jobs coordinating the self-driving trucks, delivery robots and facilitation depots
Update – 10th May 2016. DHL recently hosted journalists, customers, and experts in the field of robotics at “Robotics Day” in their DHL Innovation Center in Troisdorf, Germany. The company says “Robots will be part of the future of logistics, and we’re excited to be on the ground floor of what that future cooperation will be like.” See the video here for more information…
By 2020 Sales of co-bots (smaller industrial robots) will explode
- By 2020 Co-bots will reach sales exceeding half a million units
- By 2030 most manufacturers will use co-bots to perform some tasks
- Co-bots, together with 3d printers will be decisive tools in bringing manufacturing local
- Co-bots will start to handle many of the tasks performed by larger ‘caged’ robots
- Co-bots will contribute to significant declines in manufacturing personnel, but productivity and profit gains will lead to manufacturers increasing headcount of personnel in other parts of the organizations, sales, marketing, data analytics, IT support, etc.
Military robots will be used extensively on the battlefield
- By 2025 major world militaries will use driverless tanks, driverless armored cars and other driverless vehicles
- This will free up military personnel to conduct reconnaissance and attack missions from behind a ‘safe zone’
- By 2020 smaller more advanced hand held drones will be used extensively on reconnaissance missions inside buildings
- Soldiers will be kitted out with lightweight exoskeleton suits to give them extra strength, agility and protection
- By 2025 Atlas style robots will be used on the battlefield as ground forces
And a bonus — one thing I think is highly probable although not robot related
By 2050 gas stations will disappear to be replaced by electric charging stations
Picture credit, screenshot of Rolls-Royce Future Control Centre