“Research can’t be simply turned on and off like a tap; it requires investment in the long term”
Andrew W. Wyckoff (New York, 1958) is an expert on innovation, business dynamics, and regulations in fields such as scientific research, the digital economy, and information and communication technologies. With a degree in Economics from the University of Vermont and a Master’s degree in Public Policies from Harvard, he has held different positions within the OECD. He is currently its director of Science, Technology and Innovation.
We are living through decisive times, with a variety of climate, pandemic, and demographic threats facing us, but also with more technological capacity than ever before to try to fight them. Are there reasons to be optimistic?
I think we should be. Reasons for optimism abound. If we look at what has happened during the covid-19 pandemic, we can see that our science and technology system has been mobilised and invigorated, and it has offered answers for the difficulties posed, despite working under very tough circumstances. Governments have also worked hand in hand with businesses, devoting large amounts of resources and increasing their efforts in R&D. It has been demonstrated that continued and long-term investment in research makes sense. This is what the public sector needs to ensure. Research can’t be simply turned on and off like a tap.
Is the speed with which an anti-covid vaccine was obtained the best proof of R&D’s importance?
It is true that obtaining a vaccine within such a short space of time was a striking accomplishment. Covid-19 has shown us how these global problems can become a tangible reality, and vaccines have helped us adapt to the situation and keep the economy, and society, up and running. Without vaccines, everything would have been much more difficult. And it has been an instructive exercise, as we are faced with the challenge that lies ahead in relation to the environment. Innovation in the pandemic is also marking out the path that needs to be followed to tackle climate change: invest enormous sums and make use of all the innovation capacity we have within our reach to correct the situation. And I am not just talking about environmental science, or science and technology; it is also a question of industrial, fiscal, and education or other policies. We need to realign all our policies in this direction, and that is no easy task for governments. But I believe that it can be done, and for that reason I am optimistic.
What will the main challenges for public policies be in the coming years?
The list is headed by a dual —green and digital— transformation, and covid-19 has clearly affected both of these. We have seen a momentary decline in carbon emissions and there has been a renewed sense of urgency with respect to climate change. In the digital sphere, a boom has also been observed in the use of social networks and apps. The pandemic has accelerated the digital transformation: use of the internet has increased by 60% with the outbreak of this public health crisis.
How should governments act with regard to the high speed of technological changes that we are experiencing?
This is a truly important question, especially in our field, which is the management of the OECD. The problem has always existed: it is often said that we have technology 4.0 and policy 1.0. A certain distance between these two is inevitable, because democratic processes take their time. We need to be realistic: the gap between technology 4.0 and policy 1.0 is never going to disappear, but the aim is to narrow it as far as possible. In this sense, we want to go to the origin of the innovation process rather than wait until the end, when the technology reaches the market and it is too late to react, and it is also difficult to straighten the course. It is a good idea to be more proactive and work with the innovators, tell them what is expected of them and what is not, establishing certain boundaries along the way that will lead us to attain social and economic goals.
Are data the cornerstone of the new industrial revolution?
Data will dominate economic policies over the next two decades. As networks have become more accessible, and with the appearance, firstly, of mobile phones and then of the Internet of Things, there has been a radical change in the nature of data and the increase in their volume. We have to consider this phenomenon as a new economic resource, as an asset. And I don’t know to what point yet we have discovered how to manage it, because it is very different to other tangible economic assets, upon which current economic policy is based.
The pandemic has accelerated the digital transformation: use of the internet has increased by 60%
Given this preponderance of data, what is most important? Generating, controlling, or interpreting them?
For me, the most important aspect, although not receiving the attention it deserves, is data analysis. Data, per se, are not very helpful. Everything depends on how you use them, on how you integrate them or link them to other data, and on your skill in interpreting them almost in real time in order to obtain information and make better decisions. However, beware: we often treat data as a homogeneous and monolithic entity, when in reality they are incredibly heterogeneous and flexible. Data relating to our health are not comparable to the engineering data collected by an aircraft as it crosses the Atlantic.
The rise of China is a reality on many levels. Is it also so in data usage?
China is not a member country of the OECD, but it is a key partner, and we have been struck by the major effort it has made in the last decade. It has emerged as a global player that is a force to be reckoned with in the scientific sphere, and the pandemic has reaffirmed this. For example, when it shared genomic material to be able to produce vaccines and develop its own vaccines. When we talk about technological giants, we always refer to Google, Apple, Facebook [now Meta], Amazon, Netflix or Microsoft, and we do not talk enough about Baidu, Alibaba or Tencent, other giants that are even more sophisticated because they integrate, in one platform, a broader spectrum of applications. In Paris you can buy a Metro ticket using the payment system of WeChat [the Chinese equivalent of WhatsApp], and this represents a data source that the other platforms are lacking. They are also clearly doing very well in artificial intelligence. Despite the difficulties in obtaining some data, we believe that China is in the first line in this sphere, at the level of the United States.
In what direction should we be heading in the control of personal data, with respect to businesses, governments and citizens?
I think that we will see a hybrid model between governments, imposing certain limits and restrictions on what companies can do with personal data. In fact, the European General Data Protection Regulation already does this, but without actually restricting them, because thanks to these data we benefit from major innovations and commodities.
In recent years, in Spain and Portugal, investment in R&D has remained stable in the business sphere, but it has fallen at governmental level. What is your opinion of this?
We have seen the process of consolidation that took place after the economic crisis of the years 2007 and 2008 and that has represented the assignment of greater resources to R&D, but in different ways. Spain started off from a level of support higher than that of Portugal, and it was not until recently that it recovered the levels prior to the crisis, with significant budget assignments for the year 2020. In the case of Portugal, we are observing sustained growth in public support for research by businesses thanks to fiscal credit, a common mechanism in several OECD countries, although sometimes it is compensated by a reduction in direct support. In fact, Spain also presents a very generous fiscal credit for R&D, but we believe that it is relatively underused.
Spain presents a very generous fiscal credit for R&D, but we believe that it is relatively underused
The digital economy is changing the nature of jobs and, therefore, the skills required. How should the education system be preparing for this?
We have warned that some jobs with repetitive tasks are very easy to automate and that we will have to be cautious in this respect. It is not necessarily a case of mechanical questions; the interpretation of x-rays can also be automated if there are sufficient data available. In contrast, other jobs present a more emotional, creative or cognitive component that makes them more difficult to automate. And this includes innovation. In any case, all young people should have, at least at a basic level, a certain computational education. Furthermore, knowing how to encode or analyse, knowing how to approach a problem so that a machine can resolve it, or knowing how to interpret a set of results and understand whether they can be accepted or it is necessary to continue working on them, are also key skills at this moment in time.
Another challenge with regard to the future is how to bring about the transfer of workers from mature sectors to the new professions of the 21st century.
Managing behaviours, attitudes, and expertise is absolutely a challenge. We must accept that people will not remain in the same job for their whole life with the skills they had learned at age 25. They will need to change and be flexible as structural changes take place. And we know that in some cases these will be significant. It is also true that tackling these challenges when you are over 55 is not the same as when you are 25, often without the responsibility of a family or of a mortgage. The transition towards new professions will require social policies that can be adjusted to different populational scenarios. It is necessary to help people to move and think about new jobs, and to enable them to train for them. The education system will play a very important role in this sense, and it needs to be prepared.
The transition towards new professions will require social policies that can be adjusted to different populational scenarios
The convergence of 5G, artificial intelligence, and machine learning are bringing us to a present with increasingly intelligent machines. Shouldn’t we be talking more about the ethics that must exist behind their decisions?
Yes, and I am concerned about automatic decision-making and the principles of artificial intelligence. We do not use the word ethical because it is difficult to transfer it to legal terms and its meaning varies according to each individual. We use the concept human-centric, because it is very important to situate human beings at the centre of the process, making decisions and authorising processes, not just automating them. Very often, however, regulation takes place without knowing exactly what is happening. Therefore, what we have to do is demystify concepts such as artificial intelligence, 5G, or the Internet of Things and understand them better. It is instrumental to developing new policies. I am a little worried that we act before we know what is happening, because we do not want to waste an opportunity for innovation. I believe in techno-optimism, but I also understand that a certain level of concern exists in this regard.
Things are becoming increasingly difficult for tax evaders, with the new
big data and artificial intelligence techniques that detect hidden wealth,
the abuse of aggressive tax engineering and money laundering.