Predicting the future can be a difficult business, especially in today's economically, technologically and politically rocky times. But for individuals and countries to prosper, we need a guide to those changes ahead. Alec Ross, formerly a senior advisor on innovation to Hillary Clinton when she was US Secretary of State, is well placed to help us navigate change, and examines how things may pan out in the next two decades in his new book, The Industries of the Future.
Ross, now an independent advisor to governments and corporations, casts his eye across everything from robotics and machine learning to finance, agriculture, data, geopolitics and cyberwar. His central question is how "to prepare both ourselves and our children for what's coming in the next economy – for the economy that begins now". The Long + Short spoke to him about his findings.
In an increasingly automated and competitive global work environment, as people change employers much more frequently and work is more project-based, what skills will be most valuable and how do we equip children for this future?
I think there will be abundant skills that will still be needed and ripe for employment in the future; so let me tell you what won't be needed: workers who do things repetitively. Even those things that are cognitive but still repetitive will be replaced by artificial intelligence. Those things that make us most distinctly human – our emotional intelligence, our creativity and persuasive communication – these are the kinds of attributes that will be all the more necessary in the industries of the future.
In terms of preparing our children for tomorrow's world, more than anything else I advocate for interdisciplinary learning. By one estimate, 65 per cent of children entering primary school today will ultimately end up working in completely new job types that don't yet exist. That makes it difficult to pinpoint specific skills that map to specific jobs that we imagine will be around in 20 years. In the face of this, I counsel making sure that students blend the humanities with those things that are scientific and technological. Learn languages! Computer languages and foreign languages. Study science! Political science and life sciences. It's the combination of domains in the humanities like economics and behavioural psychology in combination with things technical and scientific that will shape the industries of the future.
What are the crucial industries of the future, and who's going to lose out?
I wrote about this for 253 pages in my book, so let me get away with just a little bit of a list here: robotics, artificial intelligence, the commercialisation of genomics, Big Data, cyber security and the codification of money, markets and trust.
Who's going to lose out? Low skilled workers in high-cost labour markets. Also those states and societies that do not embrace the openness that will characterise the headquarter cities for the industries of the future. In this context I define openness as: 1) allowing for upward economic and social mobility by non-elites; 2) setting religious and cultural norms but not by a central authority; 3) rights for respecting women and all minority groups including religious, racial and ethnic minorities.
What countries are benefiting from shifts in job functions and labour markets? And how can rising nations hope to match the likes of Silicon Valley in creating their own innovation hotspots?
There are a number of countries and types of countries that are benefiting from shifts in job functions and labour markets so let me just segregate one type: those countries that have drastically reformed their models of primary education to map to the post-industrial economy. I see this in Asian countries like South Korea and Singapore and in European countries like Estonia and Finland.
How can rising nations hope to match the likes of Silicon Valley in creating their own innovation hotspots? Well, first they can't (and won't) 'match' Silicon Valley but they can create their own centres of innovation. With the industries of the future, new avenues of opportunity for countries and people alike will hinge on domain expertise: deep knowledge about a single industry, which tends to concentrate in specific cities or regions. Detroit has domain expertise in cars, Paris has it in fashion, and Silicon Valley has it in internet-based businesses.
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Domain expertise for the industries of the future is still broadly distributed.
To understand domain expertise, consider the following question: why does a ridiculously high percentage of Internet companies still come out of Silicon Valley when massive investment is being made around the world to compete with it? Many factors are at play, but domain expertise is the most important. For more than 20 years, the world's best computer scientists have overwhelmingly been based in Silicon Valley. They could have been born anywhere, but they came to Silicon Valley for school (Stanford or Berkeley); employment (which creates a self-reinforcing cycle that concentrates talent); and investment (with the Valley offering far and away the most access to early-stage capital in the world). And they came to be included in a culture and community that placed the computer science engineer at the highest level of social status. The Valley came to be not just any old industrial centre, but a kind of beacon – a place that promised not just opportunity but a sense of belonging – and that continues to attract wave after wave of ambitious entrepreneurs.
But nothing like that exists yet for the industries of the future, where the most interesting and important innovations are taking place with greater geographic spread than we see with internet-based innovation. There are early geographic leaders in each of the fields, but it is still far too early to describe any of these as the winners or losers in the competition to be home to the next generation of innovation. And what concentration there is today is not destined to be permanent.
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In the current landscape, the most important work in the commercialisation of genomics is clustered around universities where much of the original research and development took place. It is in and around Boston because of Harvard and MIT, Baltimore because of Johns Hopkins, and Silicon Valley because of Stanford and the Universities of California in San Francisco and Berkeley. Walking through the offices of these companies, one can't help but notice how diverse the workforces are. European, Asian, African, and South American employees fill these companies and live in Boston, Baltimore, or California because they all studied at American universities. The other major prong of genetics research is in China. Though it does not have a top university programme in genetics, China has done an excellent job recruiting its citizens back home after they have studied abroad. As a result, Beijing is quickly becoming a centre of domain expertise in genomics.
In cyber, the most interesting companies are often based proximate to government, where domain expertise was developed inside the best law enforcement and intelligence communities, including Washington DC, Tel Aviv, London, and Moscow. Europe's first cyber-security accelerator, CyLon, was co-founded by two top foreign policy aides to British prime ministers. One of the world's largest cybersecurity companies, Kaspersky Lab, is full of former Russian military and intelligence officers. Israel has many of the best cybersecurity firms, founded by people who got their start in cyber in the Israeli Defence Forces, especially Unit 8200, Yehida Shmoneh-Matayim, the intelligence corps focused on signals intelligence.
In robotics, domain expertise and the early commercial leadership is generally concentrated where there is preexisting domain expertise in electronics and advanced manufacturing – in countries like Japan, South Korea, and Germany.
Thinking of this book as a guide to the next 20 years, how do you approach predicting how things may pan out? How should governments approach their future planning?
I'm not sure how I approach prediction… I study things and then things swim in my brain and synthesise. It's as much art as science.
For governments, they must get out of the chronic short-termism in their planning and budgeting. Budgets need to be done in three-year cycles versus one-year, and be tethered to a longer-term plan. Singapore does this best.
You write that 20 years ago you would have benefited greatly from an understanding of the coming digital revolution. But some things that have such a dramatic effect on our lives, like the internet, are very hard to predict. How do we prepare for the unpredictable?
You prepare for the unpredictable by committing to being a lifelong learner across interdisciplinary fields.
With much automation there are downsides and losers – lost labour such as less need for truck drivers and so on – as well as upsides – fewer road deaths with driverless cars. What's your overall sense of the balance between the positives and negatives?
I take a nuanced view that is net positive.
You were Hillary Clinton's right-hand man on innovation at the State Department. When you first arrived, what did you find in terms of innovation capacity?
Very little. Not because the people weren't capable, but because the Department had been emasculated under George W Bush and was (and still is to a significant degree) too risk averse to be truly innovative.
Would a robot be a better president than Donald Trump?
A pile of dog faeces would be a better president than Donald Trump.
The history of AI is plagued with hype and disillusionment, but do you think we really are now entering a golden age?
I would not use the word 'golden' (even with respect to C3P0's gold gleaming body) but I do think that the robots of the cartoons and movies will be the reality of the 2020s.
Do we need to support a stronger social safety net if we are facing an age of automation? Should we be taking ideas like basic income seriously?
I think that we will need to make sure we have a strong social safety net to ensure that people are not completely bereft in an economy with even greater levels of automation. I'm unpersuaded (but open-minded) about the possibility of a basic income. It has some virtues but also some serious flaws. I'd like to see something that doesn't reduce incentives for people to work.