INNOVATION ECOSYSTEMS

The Meeting of Materiality and Immateriality

Author: Piero Formica

Introduction

Innovation applies to objects and culture. It is the innovation of the latter that allows us to lead rather than be led by physical and digital objects, to freely choose whether, how, for what purpose, and for whose benefit we produce and use them. Innovation in objects is proceeding exponentially, while innovation in culture is slowing, and the gap between the two is widening.

Cultural innovation, which concerns human systems and their governance, impacts intellectual capital (IC) and social capital (SC). In IC, cultural innovation helps to understand the value and purpose of the new skills required by technological advances. In SC, cultural channels of cooperation are innovated, addressing trust and shared norms: that is, ethical, social, and normative bonds within a community are updated.

Cultural innovations should lead Innovation Ecosystems, which aim to tap the potential of innovation and turn it into entrepreneurship. While efficient and sustainable physical infrastructure facilitates the transition to business creation, a renewed culture generates new ideas and charts the course for entrepreneurial innovation.

Suppose innovation and entrepreneurship aim to improve the state of the art. Materiality comes into play, manifested in the search for data, collecting it, and grouping it into tables and graphs for the gradual evolution of products, services, or processes already on the market. The result is innovation and entrepreneurship, both incremental in nature.

In the opposite scenario, the materiality of data describing the past and present is replaced by the immateriality of imagination and intuition. A logical leap is made to create from nothing. Data is absent. Mental images and concepts that are not present to the senses (imagination) serve as guides. At the same time, logical thinking is eluded to grasp what already exists beneath the surface (intuition), resulting in frustration with the status quo. Radical innovation and entrepreneurship emerge.

Discovery (that which exists but is unknown) is the fruit of the immateriality of method. The human mind sheds light on what was previously obscure through immaterial hypotheses, such as doubt and theory.

Invention (that which does not exist) arises from the immateriality of the creative spark ignited, for example, by a need. Consider Almon B. Strowger, the undertaker who revolutionised telephone technology by inventing the first automatic telephone switchboard in the late 19th century. Since the technology did not exist, Strowger had no access to data on automated telephone traffic. His motivation stemmed from a personal problem: he feared that the manual switchboard operator (the wife of a rival undertaker) would divert customer calls to the competition.

The immaterial transmission and sharing of stories and myths radically innovate the invisible infrastructure of culture. New knowledge is gained through experimentation in the immaterial field of knowledge, which is uncertain and approximate.

Focused on innovation and entrepreneurship, driven by data and ideas, and characterised by both materiality and immateriality, these organisations take the form of Industrial Districts, Clusters, Science Parks, Knowledge Ecosystems, and Innovation Ecosystems. As the title suggests, we'll focus particularly on the latter. Following Japanese culture, we might call them all "Ba" spaces, which are physical, virtual, mental, or a combination of these, where there is no separation between self and others. Participants adapt to the conditions of cooperation for unpredictable and improvised creations.

The boundaries of these organisational configurations of the production system, which we define as Collaborative Territorial Systems, shift through experimentation. There are no fixed barriers between them. The transition between these configurations depends on the beliefs and cultural paradigms of the actors involved. We will focus on Innovation Ecosystems after taking a bird's-eye view of the configurations’ vast panorama.

Industrial Districts bring together artisanal businesses and small and medium-sized enterprises firmly rooted in a local area, sharing a production specialisation and being vertically integrated. Mutual trust arises from sharing a local community and its culture.

Italian industrial districts, which acted as training vessels for similar initiatives in other countries, favoured locally rooted vocational and technical schools and, subsequently, Higher Technological Institutes (ITS Academies), post-diploma academies offering two- to three-year highly specialised technological programs as an alternative to traditional university tracks.

In Industrial Districts, the Materiality is high:

Fixed production infrastructure, shared hardware, transportation links, and localised physical supply chains.

The Immateriality is medium-low:

Social capital, tacit knowledge passed down from generation to generation, community trust to regulate transactions.

In Clusters, which can extend beyond a specific local community, sectoral specialisation is characterised by high-tech features, the presence of high-value-added services, and universities and research laboratories. Patenting experts and venture capitalists operate within them, translating academic research into market value and facilitating the creation of innovative businesses.

In Clusters, the Materiality is medium:

Physical co-location is important for accessing shared regional talent pools and local suppliers.

The Immateriality is medium-high:

Systemic relationships between buyers and suppliers and collaborative research.

Science Parks are places of intellectual production characterised by research and development, training, technology transfer, design and prototyping, and cooperation between companies, universities and public and private research centres. 

In 1951, Stanford Science Park, the first science park, was founded on the initiative of Stanford University and the City of Palo Alto. In Europe, the article ‘Le Quartier Latin des Champs’ by Pierre Lafitte, a French scientist and politician, published in the French newspaper Le Monde on 2 August 1960, envisioned the creation in 1970 of Sophia Antipolis on the Coˆte d’Azur, the forerunner of European parks and a precursor to the technopolitan city of the twenty-first century, the cradle of a second Renaissance.

Attraction, retention and generation are the three main strategies pursued by the parks.

Attraction aims at establishing higher education and research institutions, multinationals, and other companies operating in the park’s nerve centres of international trade networks. 

Retention aims at developing local potential by regenerating existing enterprises that otherwise would be attracted to more developed areas or condemned to decline in the absence of renewal of the productive fabric.

Generation aims at creating innovative enterprises, for example, by promoting new businesses from those already existing in the park territory.

In Science Parks, the Materiality is high to medium-high:

Real estate developments with explicit physical boundaries, such as campuses, laboratories, and incubators.

The Immateriality is medium:

Physical real estate serves to fuel intangible assets, including technology transfer, research commercialisation, and formal university-industry partnerships.

Knowledge Ecosystems generate and disseminate scientific and technological knowledge. They create an open science environment where academic institutions converge, committed to making discoveries that aren't necessarily immediately commercialised. Their mission is to advance the frontier of knowledge.

In Knowledge Ecosystems, the Materiality is low:

Unconstrained by physical structural requirements. Interaction occurs primarily through digital networks, data repositories, cloud systems, and research organisations.

The Immateriality is extremely high:

Scientific discoveries and intellectual frameworks.

In Innovation Ecosystems, there is continuous interaction between actors producing market-driven knowledge and technology and companies that assimilate it, both existing and new. Venture capitalists operate within these ecosystems, engaging with the entrepreneurial incubators present there.

In Innovation Ecosystems, the Materiality is medium-low:

Physical resources are highly flexible, adaptable, and temporary.

The Immateriality is high:

The network is open to venture capital flows, entrepreneurial culture, and co-innovation partnerships.

The wide range of these configurations and the many nuances within each make it difficult to quantify them precisely. The numbers reported below are very approximate.

In Italy, universally recognised as the pioneer and leading country in terms of industrial districts, which account for over a third of national manufacturing employment, there are officially 141 districts according to the definition and statistical mapping of Istat (Istituto Nazionale di Statistica). Other estimates trace a network of over 150 districts.

The Global Peter Drucker Forum has estimated approximately 7,000 innovation clusters worldwide. The European Union has mapped over 1,500 industrial clusters in its member states, representing approximately 25% of all European employment. The World Intellectual Property Organisation (WIPO) has compiled a global ranking of the 100 most important innovation clusters. China ranks first with 24 clusters, followed by the United States with 22, and Germany with 7.

The Global Startup Ecosystem Report (GSER) has monitored and classified over 300 important knowledge-based ecosystems globally.

According to the Global Development Innovation Database, there are approximately 7,000 innovation ecosystems worldwide.

The Innovation Ecosystem

Gigafactories are huge, highly automated industrial plants designed for Artificial Intelligence (AI). Appetite comes with eating. There is a strong desire for the Gigafactory to foster an unprecedented Innovation Ecosystem, alongside innovation laboratories, shared production plants, smart warehouses, coworking spaces, shared cloud platforms, research and development budgets, and investment capital.

The Innovation Ecosystem (IE) is a complex network of actors (businesses, start-ups, universities, and investors) collaborating to transform ideas into market solutions. Drawing on history, this Ecosystem is structured as a closed system that advances innovation linearly: basic research, internal development, and proprietary commercialisation. Consider the Industrial District, a territorial cluster of small and medium-sized enterprises specialising in the same production chain. Collaboration was based on physical proximity and historical supply relationships. As a result of its evolution, the Innovation Ecosystem appears open and interconnected.

AI has evolved from a product of the IE to its infrastructure, transforming it from static and linear to dynamic and non-sequential. This is because probabilistic neural networks are a feature of AI that allows it to function according to probability calculations rather than relying on rigid rules (such as 'if you do X, then Y follows') that separate ideas instead of connecting them in a world of randomness, nuance, and answers without absolute certainty where AI operates.

The Open IE illuminates the sky of ideas, thus generating Open Innovation. It transcends sectoral and geographical boundaries, thanks in part to digitalisation. Companies share ideas and projects with research centres, entrepreneurial incubators, and start-ups. Sharing is possible through non-linear thinking, which appears disorganised and expands in multiple directions simultaneously, finding connections between seemingly separate concepts. It is holistic, intuitive, and creative thinking, and does not stifle the free and generous flow of human ingenuity. Everyone can benefit.

The permacultural roots of the Innovation Ecosystem

The Innovation Ecosystem draws on "Permaculture," which, by mimicking Natural Ecosystems, creates sustainable human settlements. It cares for nature, people, and the equitable sharing of resources.

The Innovation Ecosystem is inclusive and sustainable, promoting quality over quantity in economic growth. Focused on the "iCapital", the intellectual capital of knowledgeable people, which represents the total value of an organisation's intangible assets—specifically the collective knowledge, skills, information, and data that can be used to generate wealth and drive a competitive edge- the IE operates differently from traditional, physical-asset Ecosystems. The IE:

• Is aligned around solving climate change and healthcare access,      rather than just maximising short-term financial returns

• Decouples economic progress from environmental degradation.

• Progress that      comes closer to nature

• Prioritises high-quality jobs.

• Targets breakthrough scientific discoveries that solve fundamental human problems, rather than iterative apps designed purely for rapid monetisation.

"iCapital", research, software, and data are the IE intangible investments, which are non-physical, scalable, and heavily reliant on network effects.

It follows that the IE mission shifts from raw GDP expansion to long-term societal well-being, environmental health, and equitable wealth distribution. Its success, which is measured by using comprehensive indicators like the OECD Better Life Index or environmental, social, and governance (ESG) impacts rather than standard GDP alone, depends on:

• Highly skilled knowledge workers, researchers, and creators rather than physical labourers.

• Strong ties with universities and research institutes, which keep the Ecosystem fed with fresh ideas and specialised talent.

• High talent mobility between companies, universities, and accelerators, which creates a shared community pool of expertise.

• Knowledge easily "leaks" between companies, meaning one firm's research often sparks innovation in a neighbouring startup.

• Investors value brand equity, user networks, and proprietary code rather than physical book value.

•  

China has deliberately transitioned its massive economic engine toward "soft power" innovation, investing heavily in intangible assets like data infrastructure, software code, intellectual property (IP), and human capital. But the challenge is not to focus all investments on information and computer software closely tied to physical equipment, neglecting the research value chain (the end-to-end lifecycle of research from initial funding to final societal impact).

Five Case Studies of Permaculture-Based Innovation Ecosystems

1. Zhongguancun & Yizhuang Hub, Beijing

China's centre for software, artificial intelligence, and deep-tech algorithms.

The Ecosystem prioritises university-born intellectual capital.

Sources:

(Zhongguancun National Innovation Demonstration Zone and its Yizhuang Sub-park

Zhongguancun Science Park)

2. The Yangtze River Delta Regional Hub, Shanghai

Shanghai connects its financial machinery directly to intangible digital architectures, housing multinational R&D headquarters focused entirely on software and advanced code integration.

Source:

(Shanghai Municipal People's Government Policy Portal)

3. Singapore's Agrifood and Gastronomy Ecosystem

The volume of farmland or low-cost mass output does not calculate growth. Instead, this Ecosystem measures the nutritional value, resource efficiency, and climate resilience of its agricultural production per square meter, building a specialised, high-value tech footprint.

Sources:

EnterpriseSG Agritech Hub

FoodInnovate Network

4. Germany's Energiewende Ecosystem (Mission-Oriented Clean Growth)

Instead of pursuing rapid tech market dominance, this Ecosystem catalysed deep engineering advances in smart grids, industrial energy efficiency, and decentralised renewable energy infrastructure. It decoupled manufacturing growth from environmental degradation.

(Source: European Commission Case Study Report on Energiewende)

5. The Boston-Cambridge Life Sciences Corridor

Heavily anchored by academic institutions like MIT and Harvard, this Ecosystem focuses on breakthrough biological research, therapeutics, and medical tech. 

The growth generated is characterised by highly specialised human capital and profound scientific value. It prioritises extending human health spans and solving fundamental clinical problems rather than chasing immediate monetisation.

(Source: Life Sciences Corridor Strategic Brochure via the City of Cambridge)

The world of intangible quality versus the world of tangible quantity

There is a marked tendency to prioritise everything in the Ecosystem that is measurable and has monetary value. At the forefront is money, the source of luxury. Reading Alice in the Land of Ideas by the philosopher Roger-Pol Droit, we learn that Voltaire believed money was a good thing. Spinoza remarked that those who lack nothing are inclined to share. Do not run out of money? Not necessarily. We share when we have a lot of joy and understanding.

The source of an Ecosystem's vitality, however, lies in the intangible terrain of culture. The behaviours of operators in the cultural field are not measured; this could be done by administering questionnaires (but would the answers be sincere?). From them, we expect the generation of relational capital that instils trust and loyalty within the Ecosystem community. Shared knowledge forms strong alliances between innovation developers.

Below are some of the many questions that emerge from culture. Self-transcendence: We observe material boundaries; Are we capable of overcoming them to rise above a certain reality? Impermanence: Nothing lasts forever. We go through moments where we go from order to disorder, and vice versa. We live in a constant state of change. Are we willing to accept the transitory nature of events? Extraction of ideas: Do we rely exclusively on reason to observe reality? Or is it theory, a word derived from ancient Greek meaning contemplation, that lights the spark of imagination? Wisdom: The wise person does not follow fixed paths, nor does he rely on a pre-established plan. The wise man understands that rigid schemes are useless when reality changes. He acts with flexibility, observing the nuances of the present moment. Are we wise or ideological when examining the complexity of reality with static and often contradictory rules ("my rules versus yours")? Knowing to act, acting to know: you must first know to act. This is the approach of those who pursue innovation by looking at existing paths.

In contrast, believing that knowledge is experiential, engagement is first and foremost action-oriented. This is what route creators do. It's one thing to build on what we already know. It's another to push ourselves to the edges of the unknown, question what we think we know, and shed light on our blind spots. In this case, we are ready to explore, taking on the role of the creative ignoramus who consciously suspends established skills to examine problems through a pristine lens. Do we intend to identify paths in our Map of Knowledge, or create completely new ones by distancing ourselves from the Map?

Interdisciplinarity and Transdisciplinarity: let's consider the difference. The first is achieved by building bridges across the Knowledge Sea, connecting the Disciplinary Islands scattered within it. The Disciplines remain. The second is like a tsunami that overwhelms the Islands. Disciplines disappear, and Holistic Knowledge is formed. Transdisciplinarity has existed for a long time. Two examples: philosophical economists and philosophical mathematicians. Are we bridge builders or instigators of extreme events that overturn the Sea of Knowledge?

Will Artificial Intelligence help us answer these questions? It depends on the data it was trained on. AI is not curious about the unknown and operates only on recorded information, whose flows are managed by mathematical models and statistical probabilities. If the Innovation Ecosystem is governed by everything that is material and has monetary value, AI will have no difficulty contributing to its improvement. However, to drink from the source of the IE's vitality, which, as already mentioned, lies in the intangible terrain of culture, one should grasp the intangibility of human approximation, which leads to deciding and acting with incomplete, nuanced, and imprecise information.

Innovation Ecosystems shift from competition to co-opetition

The primitive form of collaboration is defined as "co-ordination". It enhances the process of <<simultaneous adoption of identical or complementary strategies by independent agents>>(John Kay, Foundations of Corporate Success, Oxford: Oxford University Press, 1995) whose relationships are informal and implicit, based on unwritten rules and unwritten codes of behaviour.

Distinctive features of the relationships prevailing in the co-ordination environment are the following:

• Arm's-length and kinship/family ties.

• Informal, opportunistic and short-term relations.

• Spontaneous dissemination of implicit knowledge.

• Joint problem-solving.

Innovating to compete can mean improving what you already do well, or changing the rules of the game to do new things in new ways. In the second case, one needs to familiarise oneself with the word "co-opetition". Participants in an Innovation Ecosystem don't just pursue maximum individual market share, protecting the silos that contain their data, and aggressively patenting. Combining co-operation and competition, they practice co-opetition, jointly investing in critical infrastructure or data, forming independent consortia or joint ventures with strict limits on data sharing, sharing the huge costs of capital-intensive innovation, and setting standards.

Competition is a finite game, with a winner and a loser. Those who bite win. It is not for nothing that the Asians depict it as a poisonous snake. Co-operation, on the other hand, by creating new opportunities to the advantage of all players, is an endless game that evolves spontaneously towards co-opetition in which cooperative and competitive behaviour coexists among the parties in the field.

Co-opetition, which prompts actors to knot bonds of trust outside their historical groups, is a nudge that manages to move large boulders. With an image à la Aesop, we could say it is not the gust of the Northwind that removes the traveller's cloak, but the persuasive behaviour of the Sun's rays. In Far Eastern culture, the co-opetition game is represented by an inverted eight, the symbol of infinity. To reconcile co-operation and competition, Asians have become masters of co-opetition.

The principles and practices of co-opetition have been attributed to Harvard and Yale professors Adam M. Brandenburger and Barry J. Nalebuff. In their book Co-opetition, they expound on their thinking as follows:

[There] is co-operation when it comes to creating a pie and competition when it comes to dividing it up. In other words, [there] is War and Peace. But it's not Tolstoy – endless cycles of war followed by peace followed by war. It's simultaneously war and peace... You have to compete and cooperate at the same time. The combination [that is, "co-opetition"] makes for a more dynamic relationship than the words competition and co-operation suggest individually. [In the co-opetition game] your success doesn't require others to fail – there can be multiple winners.

Innovation Ecosystems that actively practice co-opetition

Co-opetition, or the strategy of simultaneous cooperation and competition, is the intellectual energy that fuels the engine of growth, starting with high-tech clusters. By sharing research and infrastructure to achieve innovation while simultaneously competing on innovative products and services launched on the market, organisations accelerate, at a reduced cost, the race from basic research to commercialisation and define industry standards.

With the advent of artificial intelligence, Research-to-Innovation Ecosystems have emerged to address its scalability, which requires massive datasets, computational optimisation, and fundamental engineering. A prominent example is the Structural Genomics Consortium (SGC). Founded in 2003, the SGC is a UK-registered charity with its head office in Toronto, Canada. Competing pharma companies place their early-stage discoveries on protein structures into the public domain to accelerate global drug discovery. Deeply committed to AI research, the SGC serves as the primary foundation for AI-driven drug discovery, generating and managing the massive, high-quality, open-access biological datasets needed to train and evaluate machine learning models. (https://www.thesgc.org)

A historical case of a co-opetitive IE is Silicon Valley, which drives global AI, software, and hyper-competitive start-up culture. The IE spans several cities in the San Francisco Bay Area. Namely:

San Jose: The economic capital.

Palo Alto & Menlo Park, which are the academic and financial nucleus. 

Mountain View & Cupertino, which provide talent recirculation.

Sunnyvale & Santa Clara, where the microprocessors were originally pioneered.

In the IE, high-tech companies make intangible investments for academic research, attracting talent and venture capital funding, and, where appropriate, sharing them. Ensuring transparency and security, the IE community guides the production of software, followed by peer review to introduce improvements and modifications, that represents the intangible counterpart of the computer's physical hardware. (https://library.stanford.edu/libraries/silicon-valley-archives)

References

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Brandenburger, Adam M. and Barry J. Nalebuff (1996). Co-opetition. London, UK: Harper Collins Business.

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https://www.sparkmuseum.org/almon-b-strowger-the-undertaker-who-revolutionized-telephone-technology/.

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