The Geo-Politics of Innovation
From fears about long-term employment shaping election outcomes, to the rise and rise of intangibles, to machines as inventors, the shape of the innovation landscape foreshadows volatility and radical change. Systemic innovation will drive structural disruption across all industry sectors and asset classes, driving growth and productivity, but there are darker forces at work.
1 Innovation is political
Picture this: over the next two decades systemic, accelerating and radical innovation will bring growth and new sources of wealth, contrary to the argument that the age of fundamental change has ended. Yet with this, we will see deepening inequality and high levels of unemployment.
In another scenario, entirely novel forms of environmentally sustainable and socially inclusive innovation will change the trajectory: a new moral economy will emerge, focused on social well-being and quality of life.
However it may play out, the fact is that innovation is moving faster than ever. Yet there is a structural fault line: political leaders are slow to adapt, let alone shape future outcomes by supporting positive innovation and regulating against potential long term risks.
The globalisation narrative, together with the corporate, institutional and financial frameworks that support it, stands charged of failing to deliver broad benefits around the world. Unless national leaders learn how to act early to forestall and manage the impact of disruptive technological change long before events overwhelm them, then social chaos and revolution may be inevitable. The more disruptive the innovation, the greater the risks.
The fundamental uncertainty is whether political and social systems, at all levels, can adapt not only to potential global and climate catastrophes, but to runaway technological change. The present day reality is that overstressed social systems are ill-equipped to deal with increasingly chaotic conditions, as events in Europe, the US and the Middle East illustrate. Entire communities are not only left behind, but lack the mobility to shift direction and adapt.
Emotion plays a vital role in politics, as events over the last year show. It tends to polarise around the fears exploited by President Trump at one end of the spectrum, to the hope of renewal and creativity illustrated by President Macron. There are clear signs that many people, not only in the West, are experiencing anxiety and a sense of helplessness about the unknown, technology-driven future.
Meantime, the transparency and immediacy of global media networks is not only technologically disruptive in itself, but intensifies the emotional impact on people’s lives, bringing a continuous flow of world events to everyone. This has fundamentally changed cultural and political landscapes over the last two decades, breaking down boundaries between national and local communities and threatening local social cohesion.
The strategic, long-term challenge to political and corporate leaders is clear: create a sense of hope and stability in a period of turbulence and structural change. Time will tell whether the challenge is beyond them.
2 Secret worlds and why forecasters fail
Politicians are not alone in facing the complex uncertainties associated with technological and social disruption. The potential impact of systemic innovation, in particular, is understated in financial market forecasting.
Analysts are typically silent on the broad potential influence of disruptive ideas, in part because they are not yet articulated, or even imagined. Ideas belong to a secret world. Why secret? Inventors in small firms and corporates alike protect big ideas with their lives.
Innovation is also understated because, at a macro level, systemic innovation is non-linear. Open, highly networked innovation has emergent properties and so is inherently unpredictable. It is difficult to estimate outcomes, even when patterns become clear. Whilst combinatorial patents are ballooning, as often disparate inventions are brought together, the linkages are not obvious. This largely hidden world drives what Paul Romer calls ‘compounding’ and suggests that faced with uncertainty, the more pessimistic forecasts lack imagination:
“Only a failure of imagination—the same failure that lets people think that all the big ideas have already been discovered—could convince us that with our current institutions of science, we have already found the best of all possible systems for encouraging the production and distribution of new ideas. There is surely room for improvement. Improvement might increase the rate of growth by a little bit. The benefits that follow will accumulate faster than people realize.”
Many inventions are latent in the ‘white spaces’ of new markets targeted by the most sophisticated corporates and by leading edge sovereign funds. White space is usually defined as an area at the intersection of technologies and markets where the products or patent coverage is weak or non-existent, but commercially valuable. Inventing in white space is about creating new paradigms and by definition market leadership; high margins and returns; and sustainable differentiation. In contrast, innovation hot spots are overcrowded, with many overlapping and competing claims, patent ‘clusters’ and ’thickets’ that ultimately mean low returns.
The conundrum then, at first sight, is simple: how to forecast the future value and risks associated with innovation as the world becomes more interconnected, complex, fast moving and so more uncertain.
3 Emerging driving forces
There are many competing and conflicting innovation philosophies at work, all of which have cultural and political roots. In simple terms, the tension is between ‘open innovation’ and commercial competition. Yet this is not a straightforward dichotomy: the future innovation landscape will most likely be diverse, heterogeneous and itself the result of Schumpeter-style ‘creative destruction’.
Some open innovation examples like the ‘open data’ movement are obvious. Many national governments are making public sector information available to encourage public interest innovation, in everything from food standards, to security and crisis management. At the same time, governments are promoting open and freely available publicly funded research.
Open innovation is pervasive in the private sector, in part inspired by the ‘public good’ idea that inventions belong to society as a whole. Perry Alagappan’s water purifier, designed to deal with toxic ‘e-waste’ is symbolic of inventions that in the past would have been patented, but has been gifted to the world. The Creative Commons movement, essentially a means by which intellectual property can be shared openly, with simple rules of attribution, has become mainstream.
There is also growing evidence that philanthropists are focusing on long-term grand challenges and seeding global innovation by gifting inventions to the public. Elon Musk’s artificial intelligence initiative, ‘OpenAI’, is one of many recent examples. Open AI is interesting because some of the world’s leading researchers have turned down lucrative jobs with Google and Facebook, in favour of what on the surface is a not-for-profit based on altruistic principles.
In parallel, ‘mass innovation’, illustrated in part by ‘maker culture’, brings 3D manufacturing designs to open networks via the web for local production, assembly and distribution. This is taking hold as one of many examples of grass roots ‘mass flourishing’. Maker culture, within which open innovation is central, echoes ‘garage inventors’ in the US and took off at large scale in China, is moving from the margins to the mainstream.
Meanwhile, some technological innovation will follow an inexorable path, regardless of social outcomes. Manufacturing businesses, for example, will face the convergence of 3D printers, artificial intelligence and robotics forcing them, for competitive reasons, to substitute machines for labour, likely leading to what Tyler Cowen has called ‘radical onshoring’.
This will put pressure on the West, but will also lead to early de-industrialisation and large-scale unemployment worldwide. The signs are already clear: Asian textile and footwear manufacturing businesses are switching rapidly to low cost robotics, particularly in China. According to the International Labour Organisation, this threatens millions of jobs over the next decade.
Over the long-term, we can expect manufacturing to become increasingly localised, using remote 3D techniques to produce everything from volume consumer and household goods to food. This will cut demand for international transport. Instant copying and the global distribution of ideas via digital ‘Apps’ will decentralise production. Why ship around the world if low cost production is just across town, everywhere? Replication, already pervasive, will become the norm.
Patents and commercialisation: on the rise
Open sharing of ideas has exploded, but the instinct to hoard, protect and commercialise via patents is following a similar pattern. This is partly reflected in the rapid increase in new patent filings, which reached 2.9 million in 2015, driven above all by vast increases in applications in China.
This shows that the geography of innovation is changing quickly, with China set to become the world’s largest investor in both R&D and in patent protection by 2020. China’s patent office received over 1.1 million new applications in 2015. About 1.24 million patents were granted around the world in 2015, with China accounting for almost 360,000 against the US total of 298,000, making it the largest patent office. Trademark applications showed similar growth.
Though there is a caution that patent applications numbers are only one way of looking at growth, performance and future value and quality is more decisive, in 2015, WIPO showed China’s share of global filings at 13.7%, after the US, at 26.3% and Japan at 20.3%.
Patents remain, for all the concerns that surround trolling and failings in the global system, a primary means by which to protect commercial returns. Research in the US found that the ‘patent premium’ averages 50% over ‘no patenting’, ranging from 60% in health-related industries to 40% in electronics’.  Global small business patent filing, venture funding, corporate open innovation and off-balance sheet R&D continues to rise.
Hybrid approaches, such as, ‘patent pooling’ and collaborative cross-licensing are long established in digital technology manufacturing and will grow rapidly in all sectors. They are particularly important in areas such as sustainable development, where there is a strong public-good argument.
Similarly, in healthcare, novel methods will be designed to further social benefits, rather than necessarily drive profit. This is critical in structural terms, because of the scale of the challenges facing national governments in dealing with aging populations
For example, Shinya Yamanaka of Kyoto University, Japan who won a Nobel Prize for his work on induced pluripotent stem cell technology, has adopted an open, non-exclusive patent licensing approach that aims to protect the core IP, but maximise public benefits. The approach is designed to avoid the core IP falling into the wrong hands and at the same time to encourage large-scale collaboration in spin-off application development.
We can expect social entrepreneurs to take a similar route: invent, file patent and distribute free to the world to prevent commercial interests developing so-called derivative works and capturing consumer relationships and markets. Patents do not necessarily equate to commercialisation, or hoarding. The idea of the moral economy may re-emerge in new forms.
Over the long term, the contest between the cultures of hoarding and openness will intensify. The pressure to find socially and economically coherent models will increase.
Meantime, some technologies have potential for good, yet are unlikely to form part of the true open innovation culture, given the uncertainties and risks associated with widespread distribution and potential misuse. Once again, innovation is political.
Take two examples. First, the scenario of true machine intelligence, of runaway ‘superintelligence’ envisioned by Nick Bostrom, which has already created widespread fear, even amongst AI researchers. Self-replicating systems may not only make humans irrelevant, but create existential risk.
None of this compares with the convergence of digital systems and knowledge engineering with synthetic biology and artificial life. This may be the next phase, as imagined by Craig Venter, by which vaccines to deal with disease outbreaks, for example, are designed and tested, then released via a new kind of ‘App’ store for local manufacture. Once again, there are many positive applications, but more important, potential risks, some of which are existential.
4 The marketplace of ideas
Taken together, the future dynamics of innovation, distinct from the inventions themselves, can be simplified in the form of two interrelated variables, from which several scenarios emerge.
The first is the degree to which ideas, knowledge and information is codified and categorised, at one extreme making the diffusion of ideas simpler and more effective and at the other, ideas remain uncodified and tacit, constraining collaboration and distribution.
The second is whether ideas and intellectual capital are hoarded, at one extreme, or openly distributed. If they are hoarded and protected in the form of trade secrets, know-how and patents, at the extreme fragmentation, isolationism and protectionism will develop. Intellectual property may be expensive and dominated by commercial interests. At the other extreme, it may be openly distributed, in the spirit of the ‘open data’ and ‘open innovation’ culture.
One particularly important scenario may take shape, amongst many: a true open marketplace of ideas, in which intellectual capital is codified and openly distributed, or traded within an exchange system, either free of IP conditions as part of a ‘gift’ culture, or ‘securitised’ in the form of tradable instruments and licenses. New technologies, such as Blockchain, may play a central role.
In this scenario the disruptive impact of open innovation will produce novel structures.
In a world dominated by intangibles, where products ranging from cars to access fees to large-scale computing power are provided as services, a new system of political economy will emerge. The early signs are already there, with Microsoft, Google, IBM and Amazon, amongst many others, creating infrastructure and AI ‘on demand’, for rental fees.
This will accelerate the evolution of not only machine-driven innovation systems, but human structures and global interaction in the form of ‘social machines’, increasing pressure on social adaptive capacity and increasing levels of uncertainty.
If the marketplace, combined with computational creativity, leads to runaway machine intelligence, the impact on social systems will increase further.
5 Hotspots, white space and unknown unknowns.
Against this background, the challenge facing national governments, business and city leaders, and investors alike is how to rise to the growing challenges, not only of climate induced change and technological innovation, but to the broader structural risks and opportunities that are beginning to emerge.
The prerequisite is to navigate the emerging secret worlds, developing intelligence about early stage ideas.
Given the lag between invention and commercialisation, intellectual property landscapes – networks of ideas maps – have strategic value, since patterns of ideas give vital early indications of the shape and structure of industrial, product and economic revolutions well before commercial products and services emerge. They are the source of important known unknowns – predictable surprises. They also highlight network linkages that point to emerging systemic innovation – ‘hot spots’ where there is intense activity.
Yet it is only the first step. Armed with pictures of the IP network, the second step challenges convention from a commercial perspective: invent in ‘white space’. The principle is to avoid inventing in hot spots: search and invent precisely in the gaps.
This is where we can expect entrepreneurial investors and specialist fund managers to look for exceptional returns. After all, many of the most successful products, services and businesses are based on inventions in white space.
Entrepreneurial, private office investors; sovereign wealth funds; specialist industry funds; private equity; venture capital are all searching for big ideas. Corporate venture funds, driven more than ever by competition to innovate, are outsourcing invention; taking more risks; investing for strategic reasons, rather than short-term financial returns; and investing early. To illustrate, corporate venture investment as a proportion of total venture funding grew to 9% by 2015, with the US showing a compound annual growth rate of 39% between 2010 and 2015, according to Bain. In China, corporate venture reached 5% of the total.
6 Creativity re-invented: the rise of the machines?
Innovation is not simply a business issue or the primary challenge to entrepreneurial investors looking for the next big idea, but for government leaders, who to avoid the maelstrom ahead, must learn how to act on foresight, actively driving invention and creating home-grown versions of the ‘Entrepreneurial State’.
The missing ingredient in all present narratives is the nature of creativity and invention itself. This, like everything else, is changing at an ever-increasing rate. What is known as ‘Computational creativity’ may soon emerge to stretch adaptive capacity more than ever.
This has the potential to boost the rate of innovation, shifting the focus from research to origination, as ‘general purpose learning machines’, hinted at by Deep Mind’s ‘Alpha Go’, evolve from problem solving systems to delivering novelty. Alpha Go surprised experts not because it produced results a decade ahead of forecasts, but because there were hints at ‘intuition’, one of the defining characteristics of human creativity.
If Alpha Go showed signs of intuition, then the fourth of the so-called ‘Lovelace’ questions, quoted by a pioneer of computational creativity Margaret Boden as long ago as 1990, may be answered as ‘yes’. As Wired put it:
“From where Lee sat, AlphaGo displayed what Go players might describe as intuition, the ability to play a beautiful game not just like a person but in a way no person could.”
The Deep Mind system used deep neural reinforcement learning from human experts (similar in principle to how animals learn using dopamine reward mechanisms), together with value networks and techniques that incorporate long term planning and ‘look ahead’ search. It is no coincidence that Demis Hassabis researched the neuroscience of futures thinking before founding Deep Mind.
Maurice Saatchi reportedly said that ‘Creativity is the last legal way to gain an unfair advantage’.
Machines are about to change the formula. Power may shift from man to machine, from labour to capital. This may increase the competitive advantage of large-scale AI-centric firms. Alternatively, it may follow Elon Musk’s vision, promoting his argument for OpenAI:
“I think the best defense against the misuse of AI is to empower as many people as possible to have AI. If everyone has AI powers, then there’s not any one person or a small set of individuals who can have AI superpower.”
Big data, open information and more recently open innovation have been primary drivers of the the knowledge economy. Computational methods may now move centre stage and drive the development of a new creative age. We may soon see innovation itself and globalisation transformed: an open, networked marketplace of ideas, with everything from food production to advanced manufacturing becoming part of vast, distributed network systems.
 Edmund Phelps, Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge and Change.
 Source: Carnegie Mellon University, Georgia Institute of Technology, and Duke University, R&D and the Patent Premium.
 This model is based on Max Boisot’s ‘I-Space’, one example of which is explained in The I-Space: a framework for analyzing the evolution of social computing by Boisot and Benita Cox.
 Margaret Boden, The Creative Mind. The fourth Lovelace question is whether ‘computers themselves could ever really be creative (as opposed to merely producing apparently creative performance whose originality is wholly due to the human programmer).’
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