Originally posted on The Horizons Tracker.
Listening to the breathless commentary surrounding technologies such as AI and robotics and one could be minded to believe that technology is transforming life as we know it on a scale never seen before. No doubt many would argue that is indeed the case, and will point to the way technologies from the smartphone to CRISPR are opening up new ways of living and working.
The problem is, these technologies don’t appear to be making a difference to productivity figures, or subsequently the wages and wellbeing of people. It’s a refrain with a strong heritage, with Robert Solow famously remarking that “you can see the computer age everywhere but in the productivity statistics” back in 1987.
Technology advocates argue that the nature of technology, which has often made things either significantly cheaper, or free entirely, provide benefits that are not easily captured by traditional economic metrics of productivity. It’s an argument that economists such as Robert Gordon believe carries little weight, and the problem is not so much that the various benefits provided by the technologies of the 4th industrial revolution cannot be captured, but that the technologies themselves are not utilized enough.
This need for greater dissemination of technology was recently promoted by a report1 from MIT’s task force on the work for the future, which argued that there are relatively few organizations that are fully utilizing the technologies of our age, and that productivity stats won’t really move until these technologies are utilized not by the 1% of organizations at the frontier of our economy, but the remainder who are thus far lagging far behind.
A recent exploration2 of the German economy by the University of Maastricht showed just how big a problem this is, with productivity growth just 0.3% in 2013, compared to 2.5% in 1992. The authors suggest that this is predominantly because the majority of investment in new technology is done by a relatively small number of large companies.
The need to disseminate technology
It’s an argument that Haas Business School’s Henry Chesborough wholly agrees with, and outlines the case for a more open way of innovating as the solution to this productivity paradox in his latest book3, Open Innovation Results.
He argues that innovation has three core facets:
- Innovation generation, which is the sexy stuff you see all the time in the media. The new products, new technology, new startups.
- Innovation dissemination, which is the movement of these ideas and technologies into mainstream usage. In the innovation vernacular, such ideas have ‘crossed the chasm’.
- Innovation absorption, which is when the technologies and concepts are utilized at scale and are part of the new ‘business as usual’.
“Something is not right, and the root of the problem is in how we are managing and investing in innovation, both inside individual organizations and also in the larger society,” Chesbrough says. “We must extend beyond the creation of new technologies, to also include their broad dissemination and deep absorption, in order to prosper from new technologies.”
Nowhere is this unequal distribution of technology more evident than in healthcare, where variability in both access and health outcomes has more serious consequences than in any other sector. At the recent annual conference of the European Institute of Technology and Innovation’s (EIT) healthcare division, Professor Gregory Katz, Chair of Innovation & Value in Health at the University of Paris School of Medicine, reminded the audience of the tremendous variation in care and outcomes across the European Union, whether in the 30-day mortality rates after emergency hospital admissions for COPD in England or the variation in capsule complications after cataract surgery in Sweden.
This variation of outcome is not due to a lack of interest in technology, but rather the poor ability to spread best practice and groundbreaking approaches successfully. Indeed, in the UK’s National Health Service (NHS), a common quip is that there are more pilots than in British Airways, but these pilots seldom scale appropriately.
A 2018 report4 from the King’s Fund explored the diffusion of innovation throughout the NHS, and found that there was ample entrepreneurship on an individual level, but scaling those innovations across the entire service was very difficult. The report suggests that traditional approaches, including publicizing them at conferences and producing toolkits, are not working, and instead advocates the need for more manpower to help do the vital work of spreading new innovation.
This manpower should consist of teams built around the innovators themselves to help with things like marketing, change management and investment appraisal. Suffice to say, the resourcing for such teams is not present in the NHS today, with just 0.1% of total NHS spending devoted to the adoption of innovation.
This results in inevitable inequalities in access to care, and from this, inequalities in health outcomes across the country, and between countries. It’s a challenge dialysis technology startup Advitos, a member of the EIT Health accelerator, have been struggling with, as resource constraints mean they have to focus their efforts on larger municipalities, and indeed in markets that they know well.
“As soon as it goes into selling, you have to be able to understand the differences between different countries, not only in the nuances of language, but in the unique characteristics of the healthcare systems in each country in terms of reimbursement and so on,” founder Bernhard Kreymann told me recently. “With limited resources, you have to decide carefully on the approach you will take, and in this instance partnering with bodies such as EIT Health is really useful as they have a network all over Europe.”
As Michael Hammer famously remarked in the Harvard Business Review5 in 1990, successful dissemination of technology often requires a reconstruction of the processes that underpin the way our organizations function. Too often, we try and superimpose new technologies onto legacy processes, with this inevitably leading to underwhelming results.
In healthcare, therefore, there is a need not only to understand the different healthcare systems around the world, but to have sufficient clout to drive systemic change so that they can better utilize the technologies that are emerging across the sector.
While there have been attempts to develop the Digital Single Market, with the free movement of health data a key pillar of this initiative in healthcare, it remains the case that the sector is notoriously difficult to change, and this intransigence underpins efforts to boost the sclerotic productivity in the sector.
“In countries across Europe, we see great support from the public, but the people that are holding up change are a relatively small, yet powerful group of very conservative organizations,” Jan-Philipp Beck, CEO of EIT Health told me recently. “Some of these critical decisions need to be undertaken at a high policy level, and cannot be achieved from the bottom up, despite our hopes to the contrary.”
Technology at scale
With an estimated 300 million startups around the world, there is no shortage of attempts to transform society, but precious few of these grow to scale, with subsequently dire consequences for productivity that is deprived of the winds of creative destruction.
Indeed, while there has been considerable hype surrounding new technologies such as AI, there is a growing concern that the hype has been overblown. For instance, even industry cheerleaders such as the MIT Tech Review6 recently poured cold water on the transformational claims of various AI startups, reminding us that the journey from idea to scale is a perilous one.
It’s certainly not a new problem, and indeed Solow’s maxim about the computing age coincided with Michael Hammer’s missive about the lack of business process reengineering, but there is little sense that the lessons of that age have been learned. Indeed, as Jeffrey Funk points out in IEEE Spectrum7, the proportion of technology companies that are turning a profit when they go public has plummeted from 76% in 1980 to just 17% in 2018, despite the fact that startups today are 7.7 years old by the time they go public, compared to an average of 2.8 years as recently as 1998.
This journey not only to scale, but to profitability is therefore an increasingly perilous one. A report from Tata Consulting Services last year explores some of the factors startups in Europe most crave when attempting to scale their businesses, and found a number of key areas that officials could improve to better support startups, including:
- Complete, strengthen and extend the EU Single Market and the EU Digital Single Market.
- Removing asymmetry in the ease of doing business, especially around areas such as tax systems and regulations.
- Support to scale up, not just to start up.
- Capital funds and investment for globalizing, with the costs involved in scaling across Europe greater than when doing so domestically.
- Build an entrepreneurial spirit and overcome the preference for employment over self-employment in Europe.
The so called “Juncker Plan” does address some of these issues, and support is being raised for startups across Europe, with the likes of the European Fund for Strategic Investment providing considerable amounts of funding to ventures throughout Europe. Member states are also working collectively to remove some of the structural barriers that are harming startup growth outside of their domestic market.
Myths vs reality
Alas, the reporting of entrepreneurship, and the technologies contained within these startups has allowed many myths to emerge that not only don’t bear much relation to reality, but also hinder our ability to innovate successfully as a society.
Indeed, in the United States, data9 reveals that entrepreneurship has declined by around half between 1978 and 2011, with this especially pronounced10 among the share of young firms, as employment at young firms fell from nearly half of the workforce in the 1980s to just 39% by 2006. By contrast, employment at big firms rose from 51% to 57% of the overall workforce in the same timeframe. What’s more, this rate has been especially pronounced among those with the highest education.
Alongside this, there is also strong evidence to suggest that, contrary to the job-hopping legend that has emerged in recent years, people today are actually moving company less frequently than in the past. This lack of mobility diminishes the kind of network connections that are so important in helping innovations disseminate.
“So one critical requirement for effective Open Innovation is a high level of education and skill in the workforce, combined with a reasonably high level of labor mobility from one organization to other organizations, to diffuse that knowledge broadly throughout society,” Chesborough says.
Indeed, census data11 shows that people aren’t even moving home as often as was previously the case, with statistics showing that over the past 35 years the number of Americans who have moved has declined to roughly half previous levels.
All of this contributes not only to a lack of fresh perspectives, but a gumming up of the innovation pipeline. It’s a phenomenon so aptly captured by Stanford’s Nicholas Bloom in recent research12 that highlighted the growing costs associated with innovation. His data shows that research productivity has declined by a factor of 41 since the 1930s, which equates to over 5% per year on average. This is a problem13 across the developed world, and is far from confined to the United States.
This is all contributing to growing market concentration14 that sees less of the creative destruction famously chronicled by Joseph Schumpeter, and more of the stagnation and rent seeking Cowen suggests is contributing to the ‘great stagnation’ we’re seeing today.
Science fiction writer William Gibson famously said that “the future already exists, but that it’s unevenly distributed,” and it’s a perspective shared by research15 from Northwestern University, which found that solutions to many of the worlds problems already exist, but they are not disseminated to the people who need them.
“The challenge is not that we don’t have solutions to solve major societal problems, but that we struggle with how to take a known solution and get a large number of people to use it,” the researchers state. “There is a big gap between what science offers us and what gets applied.”
The researchers believe the answer to closing that gap lies with social networks. They think that if you combine our social networks with knowledge about social motives, then you can begin to influence the adoption of various health related innovations.
Whilst it might be overly simplistic to assume that greater connectivity and openness is the key to our productivity paradox, it seems certain that such principles will certainly help.
- MIT Task Force on the Work of the Future (2019). The Work of the Future: Shaping Technology and Institutions. ↩
- Naudé, W. & Nagler, P. (2017). Technological Innovation and Inclusive Growth in Germany. Inclusive Growth for Germany 18. Gütersloh:Bertelsmann Stiftung. ↩
- Chesbrough, H. (2019). Open Innovation Results: Going Beyond the Hype and Getting Down to Business. Oxford University Press. ↩
- Collins, B. (2018). Adoption and spread of innovation in the NHS. London: The Kings Fund. ↩
- Hammer, M. (1990). Reengineering work: don’t automate, obliterate. Harvard business review, 68(4), 104-112. ↩
- Bergstein, B. (2019). This is Why AI has Yet to Reshape Most Businesses. MIT Tech Review. ↩
- Funk . J. (2019). AI and Economic Productivity: Expect Evolution, Not Revolution. IEEE Spectrum. ↩
- Gaskell, A. (2019). Is Entrepreneurship As Popular As We Think? Forbes. ↩
- Hopenhayn, H., Neira, J., & Singhania, R. (2018). From population growth to firm demographics: Implications for concentration, entrepreneurship and the labor share (No. w25382). National Bureau of Economic Research. ↩
- Decker, R., Haltiwanger, J., Jarmin, R., & Miranda, J. (2014). The role of entrepreneurship in US job creation and economic dynamism. Journal of Economic Perspectives, 28(3), 3-24. ↩
- United States Census Bureau (2019). Annual Geographic Mobility Rates, By Type of Movement: 1948-2019. ↩
- Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2017). Are ideas getting harder to find? (No. w23782). National Bureau of Economic Research. ↩
- Naudé, W. (2019). The decline in entrepreneurship in the West: Is complexity ossifying the economy? IZA Institute of Labor Economics. ↩
- Grullon, G., Larkin, Y., & Michaely, R. (2019). Are US industries becoming more concentrated?. Review of Finance, 23(4), 697-743. ↩
- Contractor, N. S., & DeChurch, L. A. (2014). Integrating social networks and human social motives to achieve social influence at scale. Proceedings of the National Academy of Sciences, 111(Supplement 4), 13650-13657. ↩