In the debate on scientific progress and innovation, Europe was dominated for years by the idea of copying a typically American model—one built on startups, aggressive venture capital, and rapid scalability. However, this paradigm has not yielded the same results seen in the United States or the United Kingdom. Gradually, an alternative vision has emerged: Europe possesses world-class science and an industrial structure stronger than many Western competitors, thanks primarily to advanced manufacturing. Therefore, European innovation should be developed autonomously, with close links to production and by taking advantage of the ability to transform research into tangible products.
In recent months, this perspective has been challenged by radical shifts in the global context. The emerging world order, the acceleration of investments in “hard” AI (infrastructure, data centers, semiconductors), and the return of a phase of military competition are imposing a different pace. In such a scenario, a strategic question becomes urgent: how can the forms of innovation born from science and those developed in industry engage in a better dialogue? How can research be transformed into competitive capability without losing technological autonomy?
The central knot is technology transfer, understood not as a mere bureaucratic hand-off of patents, but as a complex process that unites scientific knowledge and enterprise. History proves that many great innovations originate in frontier research, but to truly transform society, they must be translated into technologies and products. The evolution of technology transfer shows a profound paradigm shift. Initially, the model was linear: research produced a discovery, which was patented and licensed to a company. This seemingly logical scheme is no longer sufficient. Contemporary technologies—AI, robotics, biotechnology—require early industrial involvement. Increasingly, the enterprise does not arrive at the end of the journey but enters at the very beginning, co-designing research directions and application goals.
Today, technology transfer develops along four main axes: intellectual property management, licensing agreements, industry-sponsored research, and startup creation. The point is not merely quantitative; what matters is building strategic partnerships capable of creating continuity between the laboratory and the factory. These are ecosystems capable of producing a multiplier effect: the cross-pollination between researchers and companies generates discoveries and solutions applicable across a plurality of sectors. Even more effective is the direct presence of scientists within companies and, conversely, industrial managers embedded in research programs to define real-world problems and guide priorities. This drive toward application, however, must not obscure an essential principle: true innovation needs basic research. Fundamental research is often unpredictable and does not follow linear paths: one starts with an idea and arrives elsewhere, but it is precisely this “elsewhere” that can open new horizons. Therefore, adequate and continuous funding is essential.
Education and awareness: the AI challenge
In this framework, the university maintains its two fundamental missions—teaching and research—but is now increasingly called upon to fulfill a “third mission”: valuing and communicating knowledge, not only through patents but also through outreach, education, and dialogue with society. A key theme concerns the training of the younger generations. The ongoing technological transition, dominated by AI, requires new skills and a different mindset. Artificial Intelligence will be everywhere; however, the fact that young people are “digital natives” does not imply an automatic knowledge of the critical tools needed to use it effectively. They need soft skills and a broad “toolbox” capable of combining technology, scientific method, and evaluation skills. Even the language used to communicate science becomes strategic: it must be simple yet rigorous, as this is the only way to engage with businesses, policymakers, and public opinion. In this context, humanistic thought becomes central once again, helping us imagine what to do with new technologies and providing sense and direction to innovation.
On the industrial front, a concrete problem emerges: many companies still have a limited understanding of AI. The most common mistake is treating it as a commodity—a generic technology valid for every purpose—when, in reality, many different types exist. A company’s priority should not be “buying AI,” but understanding what it wants to improve and which tool is best suited for that purpose. Misguided investments generate frustration and a lack of economic return, a risk that is particularly grave for SMEs. Therefore, training is not just the task of universities; it must become a strategic pillar for companies as well.
Furthermore, it is important to overcome the widespread fear that AI will replace human labor. The more realistic perspective is that of augmented intelligence, where machines amplify human capabilities rather than replace them. Today, AI is heavily driven by data and experience, often without theory or context. However, according to an emerging vision, the future will belong to a “scientific AI” capable of reintegrating theoretical knowledge and domain expertise. This means that expert professionals will remain essential: AI does not eliminate specialized knowledge; it makes it more productive.
The importance of a financial, industrial, and regulatory ecosystem
From an economic and strategic standpoint, the comparison between incremental and disruptive innovation is crucial. Many success stories—such as Google or Amazon—are examples of incremental innovation: they did not invent a new product but knew how to make it sustainable and profitable. Disruptive innovation, on the other hand, is rare and requires long timeframes. Here, a European weakness emerges: capital is often not “patient.” If a fund lasts an average of five years, it is difficult to support technologies that mature over ten or twenty. What is needed is a progressive financing ecosystem capable of attracting new investors at different stages of growth, up to public market access. In this process, companies themselves can become strategic investors rather than just consumers of innovation.
One question remains: if Italy is a “difficult” country for innovation, how does it manage to be among the world’s leading exporters? The answer should likely be sought in a peculiar form of innovation linked to industrial districts, manufacturing specialization, and adaptability. However, the structural problem remains: fragmentation, lack of critical mass, and excessive regulation and bureaucracy. This is not just a national issue; in this field, Europe risks becoming its own primary obstacle due to complex regulations and slow decision-making. At the same time, tools such as special regimes for startups and territorial networks show that change is possible.
Ultimately, innovation and scientific progress are not produced by decree; they are born from talent and passion. They are born thanks to systems capable of connecting research, industry, capital, and institutions. One cannot “cultivate” talent as a programmable resource, but one can create an ecosystem where it emerges and finds reasons to stay.