Antonio Garamendi, Javier García, Elena Gonzáles-Blanco, Almudena Trigo, Ezequiel Sánchez and Oihana Basilio
The Rafael del pino Foundation organised, on 15 April 2024, the meeting "Innovation with a future in Spain. 10 essential technologies to adapt business models to new technologies", on the occasion of the publication of the work edited by Gestión 2000 which includes the contents of the INTEC 2023 REPORT, the result of the work of the Rafael del Pino Foundation's Chair in Science and Technology, directed by Professor Rafael del Pino, Javier García
The event took place according to the following programme:
7.00 p.m. Welcome
7.08 p.m. Speech by Antonio Garamendi, President of the Spanish Confederation of Business Organisations (Ceoe).
7.30 p.m. Intervention by Javier GarcíaFounder of Rive Technology, Director of the UA Molecular Nanotechnology Laboratory and Past President of IUPAC.
7.50 p.m. Dialogue: "Deep-Tech, Deep Sciencice, AI: Challenges in Spain".
Elena González-BlancoHead of Artificial Intelligence for Digital Natives - Europe, Middle East and Africa, Microsoft.
Almudena TrigoPresident and Founding Partner, Beable Capital
Oihana BasilioRafael del Pino- MIT Fellow and Professor at the Universidad Autónoma de Madrid (Moderator)
Summary:
On 15 April 2024, the Rafael del pino Foundation organised the meeting "Innovation with a future in Spain. 10 essential technologies to adapt business models to new technologies", with the participation of Elena González-Blanco, head of Artificial Intelligence for digital natives - Europe, Middle East and Africa - at Microsoft; Almudena Trigo, president and founding partner of Beable Capital, and Ezequiel Sánchez, executive president of PLD Space, on the occasion of the publication of the book edited by Gestión 2000, which includes the contents of the INTEC 2023 REPORT, the result of the work of the Rafael del Pino Foundation's Chair in Science and Technology, directed by Professor Rafael del Pino, Javier García.
Ezequiel Sánchez: There was a business opportunity that was being operated in the public sector and was going to be transferred to the private sector, which is space transport, for which the technology had to be developed. We are creating a complex system, as design authorship, which is the design of a space launcher in which the technologies are not too complex to develop in isolation, but rather to design, manufacture, test and operate in an integrated manner, which is what gives it the complexity of deep tech. A deep technology, a very complex technology to operate properly with the level of success that a space launcher requires. We launched Miura 1, which is a suborbital launcher, in October. We were the first private company in Europe to have achieved this, having raised 85 million euros of mainly private funding. The objective is to be able to create industry, that is to say, that behind this technology that is creating a gap, that is allowing a supply chain to be created with suppliers who were working in the footwear and toy industry and who are now making space parts. That and repatriating talent are the two aspects that fill us the most and that we are moving in that market direction to be able to create a company that is competitive. Maybe we demystify. We don't talk about deep tech, but we are demystifying technology by bringing it into reality.
Almudena Trigo: Beable Capital is a private equity firm specialising in deep science. Deep tech is very general, it is a very broad term. It is very important to get into deep tech and define it a little more. Deep science is, within the disruptive technologies that deep tech is, we have those technologies that are tangible. On the one hand, we have these more digital technologies, which are led by artificial intelligence. Then we have another very broad pillar, which is industrial, tangible technologies, and we are talking about nanotechnology, advanced materials, photonics, micro and nano electronics, really science-based industrial companies. This differentiation is very important in order to create markets, to really create a value chain. Deep science would be that part of more tangible technologies. What is the importance of deep science? We have just finished a report with BBVA Research on deep science in Europe and its importance. Science is no longer something for four people who love it. Science is an economic engine. Science is industry. And, in the coming years, it is going to make a difference. We have challenges ahead of us to which deep science is going to provide the solution because we don't have technology and the greatest source of technology production is science. So science is going to be a very important economic factor. This report shows that we are in a convulsive moment in which we have seen that we have to leverage technologies to provide a response, to see a more sustainable, more competitive and more excellent Europe. 90% of the patents that go directly to sustainability are deep science patents. Two thirds of the technologies that Europe claims are essential to achieve European competitiveness are deep science. More than half of the technologies that will give us the resilience to be more stable in the face of any kind of crisis are deep science technologies. Therefore, in the coming years, deep science is going to be a very important player that will determine how countries and economies position themselves.
Elena González-Blanco: Artificial intelligence is, right now, a mix between deep tech and deep science, in the sense that everything that is happening is an innovation in science. But it needs to be put on the market, put into production, so that it can be used. The speed at which this is happening is so great that, at the moment, we cannot conceive of science advancing without the participation of companies and vice versa, because it has become clear, now more than ever, that this joint work is essential. Artificial intelligence is not a fad or a fad that suddenly appears out of nowhere. Artificial intelligence is the result of many years of scientific research, with Alan Turing or Marvin Minsky, creator of the first artificial intelligence laboratory at MIT in the 1950s. The mathematical theories that underlie the neural networks that we are implementing today in the transformers behind ChatGPT and all the models that go with it were created in this era at universities like MIT and have been developed over many, many years. What we are seeing is the mixture of these long years of research, where we have gone from a series of systems based on the rules of logic to systems that combined with the whole probability part. Suddenly, we're seeing that, thanks to the innovation and the science that companies like Nvidia and GPU manufacturers have invested in, and all the data that's available to train these models, generative artificial intelligence has emerged. It is the evolution of many people who have worked in natural language processing, as in computer vision, as in robotics, as in hardware infrastructure to make all this possible. And, of course, companies that have worked on building cloud and computational capacity to be able to make available language models that are trained on trillions of data to be something that runs on a device like a mobile phone. This combination of science, technology and innovation is what makes it increasingly easy to use and available to any user, but also makes it necessary to be on the cutting edge.
In this sense, we have a lot to do from the scientific part of those universities that are making these discoveries, but also research in companies. At Microsoft we are constantly researching on issues such as how the whole part of artificial intelligence models affects the whole part of molecular production and how they are tested in a way that can help with machine learning and generative artificial intelligence models to generate protein and innovation in different sectors. On the other hand, we are looking at how we implement this in society. The challenge is that this is advancing at a speed that we are all already behind and, in particular, Spain and Europe are also at the tail end of these innovations. We need a lot of training and a lot of cultural adoption of these technologies within these companies. The technologies are there, but we have a cultural challenge. For example, in the healthcare system, if the data is not organised, if there is no culture of being able to unify information and use different data sources so that an artificial intelligence model can learn from it, we have a brake on innovation. In this third leg, which is to take it to society, implement it and make it possible in the day-to-day life of companies, in small aspects such as day-to-day productivity, reading a document, or analysing a series of conversations, we have a gigantic field to transform any industry. That is the big challenge because it affects all of us, and not to take our jobs or to kill us, but because we have before us the possibilities to change any industry with tools that are already available.
A company like Microsoft is leading the technological transformation and artificial intelligence itself, but the change it has undergone in the last year and a half has been absolutely brutal. First, because everything we are talking about right now practically did not exist two years ago. Generative language models, as we know them today, have only recently come onto the market, which has forced us to change the entire business model, the entire customer service model and the training of our own teams. The needs of your customers and the fact that the product you sell is constantly changing means that you have to be on your guard and have to change the whole business model by looking at everything that is going on around you. Right now we are facing hardware challenges, that is, everything related to the production of units to process the large amounts of data required by the use of artificial intelligence is a challenge per se in which research is being done on the energy consumption required by all these types of models, but also everything related to optimising processes so that they can be efficient in countries with less connectivity or on mobile devices. On the other hand, we face a regulatory challenge, especially living in Europe, because we are in an industry that is trying to regulate late and often without adequate knowledge to be able to put all the pieces on the table and, moreover, with the political disadvantage that it is not being done in the same way everywhere. This is a brake on innovation, given the number of requirements when it comes to putting an innovation that is being developed into production. These are phenomena that make the company have to adapt on a geopolitical and local level, and that have a cultural impact on how customers and society also consume and view the product we are selling. That reception and fear of innovation is very much alive and well in this industry.
Ezequiel Sánchez: The challenges we face are all those mentioned by Elena because they are very cross-cutting. We are in a field in which we are pioneers. On the issue of regulation, we must try to avoid over-regulation in a sector that is in a definition phase because it can affect the ability to compete. From an entrepreneurial point of view, there is no industrial investment capital in Spain, neither in the early stages, nor in medium-sized companies, in companies that are pre-revenue, which is our case. But we have obtained financing thanks to the fact that there are people who bet, family offices and private investors who, in their regulated mandate, when they see the project, understand that there is an industry behind it and develop the capacity to invest. This investment capital needs to be developed in industrial aspects, which is what lies behind deep science. Behind this there are many companies that will be formed as a result of the need to develop new applications. The main challenge is to keep this talent, but also to allow talent to rotate to other destinations. Then, for sure, interesting companies will be created that will feed back into the sector.
Almudena Trigo: We are a private equity firm that is leading investment in deep science. We are an investor that understands that we are born to invest in, support, develop and obtain profitability from science-based industrial companies. This is an opportunity. We have a new kind of investment, which is science equity, and what it does is encourage this investment. We have companies like Captoplastic, which eliminates micro-plastic waste from water. We have another investee that makes new materials, for example, leather that does not come from animals or plastic, that does not pollute, that is sustainable and, at the same time, that has the characteristics we want. Or a social filter that protects us without destroying the environment. Regulation and customers are becoming more and more aware of all this. All this is revolutionising and we have to ask that of the big industries, which are looking for new models, new products. As private equity, what we do is to understand these companies, invest in them and get them to market earlier to develop. Spain is a great place to do that. We have the right ingredients, we can lead this change, this new wave of creating advanced industrial companies that come to solve these challenges.
There is a task ahead of us, which is to protect technologies better. We need to acquire in the scientific culture that it is very important to protect new technologies in a broader sense. These companies employ a lot of engineers, a lot of PhDs, and we are doing quite well there. This type of company is very attractive to engineers with doctorates beyond a scientific career. It is important to continue research and development in private companies. Then, there is one thing I miss, more on a cultural level. We manage 85 million euros. In Germany, there are many families with assets that want to obtain profitability, but at the same time, they want to create a country. This is missing in Spanish family offices, which are more conservative. I would like to see that, by mobilising a little capital, these families with wealth could put enough capital into circulation to capitalise Spanish companies. I would like to see this culture encouraged.
Elena González-Blanco: I am less optimistic from the outset because I think we still have a long way to go. We are far behind what could be done, especially if we compare it with what is being done in other countries. It has improved a lot in recent years, but unfortunately, when it comes to start-ups, innovation, cutting-edge technologies, artificial intelligence, we are a long way behind. We have good things, spectacular talent, very well-trained people, and we have to continue investing in them, in technology hubs. But there is still a lot to do, firstly, from a cultural point of view, especially in terms of interdisciplinarity. In this country we still talk about science and literature when artificial intelligence speaks languages. These things need to be put on the table because in this country many things need to change. Then there is the whole cultural part of how we enter these fields. Today, the main challenge and the main change in artificial intelligence has been language. We should be making a very serious commitment to developing cutting-edge technology in a language that is spoken by more than 600 million people in Spain, Latin America and the United States. We have a golden opportunity in our data, which is our language, our variants and a reflection of our culture; it is still in the process of development because artificial intelligence models are developed in English and then translated. This requires scientific collaboration with companies and is of economic benefit because we have many companies that have grown towards Latin America and require a market that is our language. Language is an asset, but applied to the legal sector, to the health sector, to any type of sector that makes artificial intelligence work better is a pending subject, and this is in our hands.
Ezequiel Sánchez: I would put a lot of emphasis on this transversality. At MIT they used to say that technologies are always born in the wrong place. The great applications of artificial intelligence are still to come. This transversality is something that needs to be encouraged much more. We also need to make a real commitment to intangibles, to invest in industrial property, knowledge and capitalise on it. If we want to increase productivity, the only way to do so is to increase that income with these patents, with applications, and also with branding.
The Rafael del Pino Foundation is not responsible for the comments, opinions or statements made by the people who participate in its activities and which are expressed as a result of their inalienable right to freedom of expression and under their sole responsibility. The contents included in the summary of this conference are the result of the debates held at the meeting held for this purpose at the Foundation and are the responsibility of their authors.
The Rafael del Pino Foundation is not responsible for any comments, opinions or statements made by third parties. In this respect, the FRP is not obliged to monitor the views expressed by such third parties who participate in its activities and which are expressed as a result of their inalienable right to freedom of expression and under their own responsibility. The contents included in the summary of this conference are the result of the discussions that took place during the conference organised for this purpose at the Foundation and are the sole responsibility of its authors.
