Rescuing the Future
How the People's AI Could Overtake Big Tech by 2050
Article Written by Jeremy Rifkin
Jeremy Rifkin is the bestselling author of twenty-five books translated into over thirty-five languages. He is a principal architect of the European Union’s and China’s economic plans for transitioning into a Third Industrial Revolution and he served as an economic advisor to Senate Majority Leader Charles Schumer on the U.S. infrastructure plan. He is listed among the top ten most influential economic thinkers in The Huffington Post’s global survey of “The World’s Most Influential Voices." In his latest book Rescuing the Future: Reimagining Artificial Intelligence in a World on the Edge, Rifkin argues that the People's AI is on a deep existential curve and likely to surpass the corporate AI giants by 2050 in bioregions around the world.
The European Union's Digital Decade, extending to 2030, may prove to be far more than a program for upgrading Europe's communications networks, expanding digital literacy, and integrating artificial intelligence into commercial and public services. Viewed through the larger historical lens of what I call the Third Industrial Revolution and the emerging Age of Resilience, it offers the institutional foundations for a potentially profound transformation in the organization of economic life, political governance, and humanity's relationship with the living world.
At the center of this transformation is a struggle between two different approaches to artificial intelligence and, more fundamentally, two different ways of understanding reality.
The first, associated here with substance ontology, emphasizes discrete entities, proprietary assets, centralized ownership, command-and-control management, and the accumulation of economic power. Its technological expression is the emerging network of gigantic hyperscale cloud-computing data centers, owned or controlled by a relatively small number of multinational corporations. These facilities marshal enormous quantities of computational power, electrical energy, water, semiconductor equipment, and financial capital to develop and operate increasingly sophisticated AI systems.
The second, associated with process ontology, begins with a different premise: that reality is constituted not primarily by independent things but by relationships, flows, interactions, transformations, and continuous becoming. Its potential technological expression is a distributed infrastructure of fog and edge computing, embedded within the economic and ecological life of bioregions, governed in part as commons, and connected through interoperable public digital platforms.
This second possibility may be called People's AI.
Rather than concentrating computational intelligence within a few corporate centers, People's AI would distribute computational capabilities across thousands of communities and millions of participating enterprises, cooperatives, educational institutions, public agencies, and civil-society organizations. These local systems would process information close to its source while sharing appropriate knowledge across regional and international networks.
The emerging architecture would be simultaneously local and global, or glocal: rooted in the distinctive metabolic conditions of individual bioregions while interconnected with counterparts around the world.
A watershed in northern Italy could exchange ecological information with one in southern France. Agricultural cooperatives in Spain could share adaptive practices with counterparts in Kenya. Municipal energy networks in Denmark could exchange operating knowledge with communities in India. Universities and public laboratories could participate in federated scientific research without surrendering all their data to a single corporate intermediary.
Much of this exchange could occur at very low incremental software-distribution costs, although the total marginal cost of computing, validating, securing, storing, and transmitting information would remain positive.
The larger possibility is that the European Union's Digital Decade could help create the infrastructure for a transition from an economy organized predominantly around the extraction, production, sale, and accumulation of things toward one increasingly organized around the stewardship, regeneration, circulation, and shared use of resources.
Yet this is not the inevitable outcome of existing EU policy. The Digital Decade is a framework of technological targets and governance arrangements, not an enacted program of bioregional commons ownership. Achieving People's AI would require additional political, institutional, and financial decisions.
The distinction between what Europe has already committed to and what it could still become is essential.
The Digital Decade: Europe's Emerging Distributed Infrastructure
The European Commission introduced its Digital Compass in March 2021, establishing the vision that subsequently became the Digital Decade Policy Programme 2030 through Decision (EU) 2022/2481.
The program identifies four broad objectives: improving citizens' digital skills, establishing secure and sustainable digital infrastructures, transforming businesses through digital technologies, and making public services widely accessible online.
Among its most consequential infrastructure targets is the deployment of at least 10,000 climate-neutral, highly secure edge nodes across the European Union by 2030.
These nodes are particularly important because they could help change the geography of computation.
For much of the internet era, increasing quantities of information generated by individuals, businesses, and public institutions have been transmitted to centralized cloud facilities for storage, analysis, and processing. This model has supported tremendous economies of scale, but it can also generate dependence on distant infrastructures, create latency, concentrate sensitive data, and reinforce the market power of dominant platform providers.
Edge computing offers a different arrangement.
Information can be processed within or near the facilities, communities, and ecosystems where it originates. Fog computing extends this principle by organizing intermediate computational resources between individual devices and centralized cloud facilities.
The European Commission describes the resulting architecture as a cloud-edge continuum, allowing computing tasks to be allocated across interconnected systems according to their requirements.
Europe's digital transformation is already supported by initiatives including the Important Project of Common European Interest on Next Generation Cloud Infrastructure and Services, the European Alliance for Industrial Data, Edge and Cloud, European Digital Innovation Hubs, and the EuroHPC Joint Undertaking.
The IPCEI on Next Generation Cloud Infrastructure and Services was approved in December 2023, authorizing up to €1.2 billion in public funding from seven member states, with the expectation of mobilizing an additional €1.4 billion in private investment.
These initiatives support interoperable infrastructure, cross-border collaboration, and the development of European digital capabilities.
They do not, however, guarantee that distributed computing will be publicly owned or operated as a commons. Large technology companies can own edge infrastructure just as they own cloud infrastructure.
The next stage of Europe's transformation must therefore address not merely where computing occurs, but who controls it.
From Substance Ontology to Process Ontology
The deeper philosophical significance of this technological transition becomes visible when placed within the long intellectual history of substance ontology and process ontology.
In much of the Western philosophical tradition, particularly in interpretations of Aristotle and later Cartesian and Newtonian thought, the world has been understood in terms of identifiable substances, objects, and entities possessing relatively stable properties.
Modern industrial civilization translated this orientation into economic institutions.
Factories assembled discrete products. Businesses acquired raw materials, transformed them into commodities, and sold them through markets. Economic success was measured largely through production, consumption, ownership, capital accumulation, and growth.
The First and Second Industrial Revolutions organized economic life through vertically integrated enterprises, centralized fossil-fuel infrastructures, and increasingly extensive systems of resource extraction.
My work, from Entropy and Biosphere Politics through The Third Industrial Revolution, The Zero Marginal Cost Society, The Age of Resilience, Planet Aqua, and Rescuing the Future, has repeatedly examined the contradictions emerging within this industrial worldview. My argument places particular emphasis on the thermodynamic foundations of economic life.
Every economic activity transforms energy and materials. Every transformation is subject to the first and second laws of thermodynamics. Every industrial process produces waste heat, material degradation, and ecological consequences.
The economy cannot be separated from the biosphere within which it operates. Process ontology provides a contrasting philosophical perspective. Associated with thinkers including Alfred North Whitehead, Henri Bergson, John Dewey, and later ecological and systems theorists, it emphasizes becoming, interdependence, relationality, and change.
From this standpoint, a river is not merely a quantity of water available for extraction. It is a continually evolving system of precipitation, groundwater, organisms, sediments, nutrients, evaporation, human activity, and ecological feedback.
A forest is not merely a collection of timber-producing trees. It is a dynamic community of organisms, soils, water, microorganisms, and atmospheric interactions.
Similarly, a bioregional economy is not simply a geographical collection of firms. It is an evolving network of metabolic exchanges.
The significance for artificial intelligence is considerable.
AI organized around a process-oriented framework could assist communities in understanding and responding to these relationships, rather than merely maximizing the extraction and throughput of resources.
But philosophical categories should not be confused with inherent properties of technologies. Centralized AI can model dynamic ecological processes, while decentralized AI can serve extractive commercial objectives. The ontological transformation depends on the purposes, institutions, and governance structures within which the technology is deployed.
The Ontological Edge: Where Digital Intelligence Meets Living Systems
The phrase ontological edge suggests something more consequential than the conventional technical meaning of edge computing.
Technically, the edge refers to computational resources positioned close to data-generating devices and users.
Philosophically, the ontological edge can be understood as the meeting place between computational representations and the continuously changing processes of the living world.Consider the European Union's watersheds.
Each watershed is a dynamic metabolic system shaped by precipitation, runoff, aquifer recharge, evaporation, soil chemistry, microbial activity, biodiversity, agriculture, settlement, and infrastructure.
A centralized AI platform might receive large quantities of information about these processes and generate useful predictions.But a distributed bioregional infrastructure could position sensors, computing resources, and decision-support systems directly within the watershed.
Local water authorities could monitor groundwater conditions. Farmers could adjust irrigation. Municipalities could identify contamination. Ecologists could track species migration. Energy cooperatives could coordinate water and electricity use.
The resulting intelligence would be situated within the relationships it helps interpret.This is particularly relevant to the European Union's ecological transition.
The European Green Deal seeks to transform energy, industry, agriculture, transportation, and resource use. A proposed European Blue Deal, advanced notably by the European Economic and Social Committee, would place water resilience more centrally within European policy.
The Blue Deal should not be confused with an already enacted, comprehensive EU program equivalent to the Green Deal. It remains a policy agenda whose elements intersect with existing European water legislation and newer water-resilience initiatives.
A People's AI infrastructure could help integrate these agendas.
Regional digital systems could connect renewable electricity, water management, mobility, agriculture, buildings, and ecological restoration within shared territories.
The result would be more than digital modernization. It could become a new way of organizing economic activity around the adaptive capacities of ecosystems.
The People's AI: From Corporate Platforms to Bioregional Commons
The transition from centralized AI toward a People's AI would require a transformation in the institutional organization of computing.
Today, many AI systems depend on proprietary platforms controlled by corporations that own the computational infrastructure, determine access conditions, establish prices, and control important portions of the underlying software.
Distributed computing could provide an alternative, but only if it is accompanied by institutional innovation.
Imagine a European bioregion whose computing infrastructure is governed through a federation of municipalities, SMEs, cooperatives, universities, public utilities, hospitals, and environmental organizations.
These institutions could establish a jointly stewarded digital commons.
The commons would not necessarily imply unrestricted public access to every dataset or the elimination of private property. Rather, it would establish agreed rules for managing shared infrastructure, protecting sensitive information, distributing benefits, and maintaining accountability.
Elinor Ostrom's research on common-pool resource governance provides an important intellectual foundation. Her work demonstrated that communities can develop durable institutions for governing shared resources without relying exclusively on centralized government or unrestricted private ownership.
Ostrom's principles—including clearly defined responsibilities, participatory rulemaking, monitoring, conflict resolution, and nested governance—could be adapted to digital infrastructure.
A bioregional computing commons might establish a governing council representing participating institutions. Its responsibilities could include infrastructure investment, cybersecurity, data access, interoperability, maintenance, environmental standards, and allocation of computing capacity.
Some applications could be operated by municipal enterprises. Others could be developed by SMEs and cooperatives. Universities could provide research and training. Regional public agencies could ensure compliance with democratic and legal obligations.
The European principle of subsidiarity offers a complementary institutional foundation.Under Article 5 of the Treaty on European Union, subsidiarity concerns the circumstances in which the Union should act rather than member states in areas of nonexclusive EU competence. It is not itself a legal requirement to organize economic activity at the bioregional level.Nevertheless, its underlying concern with allocating authority appropriately across levels of governance can inform a system of nested bioregional institutions.
Local decisions could be made locally, while cross-border challenges could be coordinated through wider European frameworks.
The resulting governance architecture would resemble a federation of commons rather than a collection of isolated communities.
Glocalization and the Near-Zero-Marginal-Cost Economy
One of the most consequential possibilities emerging from distributed digital infrastructure is the ability to connect local economic activity with global knowledge networks. My previous book, The Zero Marginal Cost Society anticipated a world in which digital communications, renewable energy, and increasingly automated production could reduce the incremental costs of providing certain goods and services.
The distinction between marginal and total cost is essential.
Developing an AI model may require substantial initial investment. Operating servers requires electricity, cooling, maintenance, networking, and periodic hardware replacement. Even distributing software involves cybersecurity, technical support, and governance.
Therefore, neither artificial intelligence nor digital infrastructure operates literally at zero marginal cost.
Yet once a digital model, dataset, or software application has been developed, the incremental cost of allowing additional communities to reuse or adapt it can be dramatically lower than the cost of independently recreating it.
This creates opportunities for shared economic value.
A publicly developed model for predicting watershed flooding could be adapted by multiple European regions. An agricultural cooperative's open-source irrigation system could be improved collaboratively by farmers in different countries. Municipal renewable-energy systems could exchange algorithms for managing distributed electricity generation.
The software might circulate globally while its implementation remains locally adapted. This is the economic meaning of glocalization.
It combines the advantages of global knowledge exchange with the adaptive intelligence of local institutions.
The European Union has already established elements of the required framework through its European Strategy for Data, Common European Data Spaces, and legislation supporting data portability and interoperability.
The Common European Data Spaces initiative seeks to enable secure data sharing among trusted participants in fields including health, agriculture, energy, mobility, and the environment. These spaces involve technical, legal, semantic, and organizational interoperability, not simply the unrestricted publication of data.European Data Portal+1A People's AI could extend this principle beyond Europe.
Bioregional computing commons could share open-source models and appropriately governed datasets with counterparts in Africa, Asia, Latin America, and North America.
This would require common standards, multilingual systems, reliable authentication, privacy protections, and mechanisms for sharing benefits.
It would also require recognition that not all knowledge should be freely extracted or redistributed. Personal medical records, commercially sensitive information, Indigenous knowledge, and ecological information that could facilitate exploitation may require special protections.
The most promising vision is therefore not a world in which all information becomes universally accessible, but one in which useful knowledge can circulate widely under trustworthy and democratically legitimate rules.
Financing People's AI Through Pension and Insurance Capital
The transformation of Europe's computing infrastructure raises a question as important as the technology itself: who will finance it?
Here an extraordinary opportunity emerges from the enormous pools of capital accumulated through pension contributions and insurance premiums.
Workers contribute to occupational pension schemes throughout their working lives. Homeowners and businesses pay insurance premiums to protect against losses. These contributions are aggregated into investment portfolios that help fund retirement benefits and insurance obligations.
A portion of these investments already finances government bonds, corporate debt, real estate, infrastructure, and other long-term assets.
The European Insurance and Occupational Pensions Authority reported in September 2025 that occupational pension institutions within its reporting framework managed approximately €2.7 trillion in assets, based on 2024 data. This figure is not the total value of all European pension and insurance assets: national pension systems, other retirement arrangements, and insurers must be distinguished to avoid double counting.
The economic question is whether a carefully selected portion of these long-term financial resources could be invested back into the regions where workers and policyholders live. Such a strategy has intellectual antecedents in The North Will Rise Again: Pensions, Politics, and Power, a work I co-atuhored with Randy Barber in 1978.
Our argument concerned the possibility of redirecting institutional capital toward productive regional development rather than allowing local savings to flow almost exclusively into distant investment opportunities.
Applied to the Digital Decade, this principle could support a new form of circular regional finance.Workers' pension savings could help finance productive digital infrastructure serving their communities. Insurance investment portfolios could participate in carefully structured infrastructure debt. Regional enterprises and municipalities could use these investments to establish computing facilities, renewable-energy systems, fiber networks, water-monitoring infrastructure, and digital manufacturing capabilities.
Returns generated by these investments could support retirement obligations and insurance commitments while strengthening the productive economy from which future contributions arise.But this opportunity comes with a fundamental fiduciary qualification.
Pension assets belong to beneficiaries and must be managed in their financial interests. Insurance assets must support insurers' obligations to policyholders. Neither pool can simply be redirected into politically preferred local projects without regard to risk, liquidity, diversification, and expected return.
EU pension institutions operate under national rules and the IORP II framework, while insurers are subject to Solvency II and related prudential requirements. Solvency II explicitly uses a risk-based system to protect policyholders and beneficiaries.
The proper mechanism would therefore be voluntary, commercially defensible investment in qualifying infrastructure, not compulsory geographical reinvestment.
Circular Financial Architecture for Europe's Bioregions
Consider a hypothetical European watershed region with several hundred thousand residents, a diversified economy, and a network of municipalities, universities, SMEs, cooperatives, and public utilities.
Its institutions propose establishing a bioregional digital infrastructure partnership.The partnership develops a business plan covering regional fiber connectivity, shared edge-computing facilities, cybersecurity, distributed renewable electricity, environmental sensors, and AI applications for water, energy, transportation, and agriculture.The initial capital might come from several sources.
Municipalities could contribute existing infrastructure or purchase defined public services. SMEs and cooperatives could provide equity or subscribe to computing services. EU programs could support eligible research, innovation, and initial deployment. Public development banks could supply long-term loans or guarantees. Pension funds and insurers could invest through regulated infrastructure funds, bonds, or other instruments appropriate to their mandates. A regional development institution could coordinate these arrangements.
The business model would depend on identifiable revenues and savings: subscriptions from enterprises, service contracts with public utilities, payments for computing capacity, maintenance contracts, and documented efficiency gains.
Projects providing essential public goods without sufficient commercial revenues would require transparent public funding rather than fictitious investment returns.
In a mature arrangement, financial returns could circulate through several channels.
Pension beneficiaries would receive retirement income supported in part by productive investments. Insurers would receive investment returns supporting policyholder obligations. Participating enterprises would gain access to affordable digital services. Municipalities could reduce operating costs. Residents could benefit from more resilient infrastructure.
The resulting system would create a partial economic feedback loop between regional savings, investment, productive activity, and household well-being.This is a form of circular finance.
It should, however, be distinguished from the conventional circular economy, which primarily concerns reducing waste, keeping products and materials in use, and regenerating natural systems. Circular financial flows can help fund a circular economy, but they do not automatically create one.
For example, a regional AI network that improves repair, reuse, remanufacturing, material recovery, and resource efficiency could support both circular finance and a circular material economy.
The two become mutually reinforcing when the underlying investments demonstrably reduce resource consumption and improve ecological resilience.
A particularly promising institutional model would involve blended finance.
Public capital could fund early-stage research or provide limited guarantees. Commercial lenders could finance established assets. Pension and insurance investors could participate once projects have predictable cash flows and acceptable risks.
Crucially, the public sector should not simply absorb losses while private investors capture all the gains.
Contracts would need to allocate risks transparently and provide fair compensation for public support.
This is how the ambition of People's AI could be translated into a financially credible infrastructure program rather than remaining an attractive philosophical proposition.
The Continuing but More Specialized Role of Hyperscale AI
The emergence of distributed computing need not mean the disappearance of hyperscale AI.
Indeed, the most technically plausible future is one in which centralized and distributed computation become increasingly complementary.
Hyperscale facilities possess advantages that cannot always be reproduced economically within small regional installations.
Training frontier foundation models requires large clusters of advanced processors, high-bandwidth interconnections, specialized engineering, and sophisticated cooling and power-management systems.
Certain scientific applications also benefit from immense computational resources.Climate and atmospheric modeling, high-energy physics, molecular simulation, advanced materials research, genomics, pharmaceutical discovery, and other computationally demanding activities may continue to rely on large-scale facilities.
Europe's EuroHPC Joint Undertaking demonstrates this continuing requirement.
Its AI Factories initiative connects supercomputing resources with researchers, startups, SMEs, and industrial users, while supporting the development and application of AI models.
These facilities could perform a specialized role within a broader distributed infrastructure.
A European scientific computing center might develop sophisticated climate models, while bioregional edge systems incorporate their outputs into local watershed management. A large medical research facility might train models on appropriately governed datasets, while hospitals conduct selected inference tasks locally. A major materials-science laboratory might discover new battery chemistries, while regional enterprises apply those discoveries to distributed energy systems. The economic distinction would concern the appropriate scale of computation.
Highly specialized, computationally intensive work could remain concentrated where economies of scale justify it.
Routine, time-sensitive, privacy-sensitive, and geographically situated applications could increasingly operate closer to their users.
Nevertheless, there is no established evidence that hyperscale computing will inevitably shrink to a small scientific niche. Commercial cloud services, frontier-model training, large-scale inference, and enterprise computing may all remain substantial markets.
The proposition that hyperscale companies will become diminished is therefore a plausible scenario to investigate, not a demonstrated forecast. The crucial issue is whether the future distribution of computing capacity will be determined by technical efficiency and public benefit or by the ability of dominant firms to control digital markets.
From an Age of Progress to an Age of Resilience
The larger historical implications return us to the distinction between the Age of Progress and the emerging Age of Resilience.
The industrial Age of Progress was organized around expanding production, increasing material throughput, conquering geographical distance, and accelerating economic growth.Its governing assumption was that humanity could progressively master nature through science, technology, and centralized industrial organization.
Yet the ecological consequences of that worldview have become increasingly apparent.
Climate disruption, biodiversity loss, water insecurity, soil degradation, and the growing vulnerability of interconnected infrastructures challenge the assumption that economic progress can be measured primarily by increasing output.
An Age of Resilience would begin with different priorities.Its purpose would be to strengthen the adaptive capacities of communities and ecosystems, reduce thermodynamic waste, protect biodiversity, and maintain the regenerative conditions upon which economic life depends.
Artificial intelligence could contribute to this transformation, but it cannot substitute for the living intelligence of ecosystems or the judgment of human communities.
AI systems do not experience the metabolic vulnerability of organisms. They do not possess the biological interoception, ecological embeddedness, or lived evolutionary history of human beings and other animals.
Their computational capabilities can nevertheless assist living beings in interpreting complex information and coordinating responses.
This is where my concept of Homo empathicus becomes relevant.
A resilient civilization would require more than increasingly powerful computation. It would require an expanded capacity to recognize the vulnerability and interdependence of other human beings and the more-than-human world.
A People's AI could support this objective when its institutional design encourages ecological stewardship, participatory governance, cooperation, and the circulation of useful knowledge.
The technology would become an instrument of collective adaptation rather than an autonomous substitute for living agency.
The European Union's Historical Opportunity
The European Union occupies an unusual position in this emerging transformation. Its institutional architecture combines continental coordination with member-state governments, regional authorities, municipalities, and longstanding traditions of cooperative enterprise.
Its treaties recognize subsidiarity. Its environmental legislation increasingly addresses transboundary ecological challenges. Its Digital Decade establishes measurable commitments to digital infrastructure, skills, business transformation, and public services.
The Commission's 2026 State of the Digital Decade report identifies 1,934 national measures worth approximately €289.3 billion, including €205.9 billion from public budgets. It also reports that edge-node deployment is progressing toward the 2030 target, even as Europe faces substantial weaknesses in technological sovereignty, advanced computing capacity, digital skills, and enterprise adoption.
These commitments provide a starting point, not a completed transformation.
A more ambitious European agenda could connect the Digital Decade with the Green Deal, water resilience, regional economic development, and long-term institutional finance.
Such an agenda would need to accomplish three transformations simultaneously.
First, it would establish interoperable computing infrastructure capable of operating across local, regional, national, and continental scales.
Second, it would develop democratic and cooperative governance arrangements that allow communities and enterprises to participate meaningfully in the ownership and stewardship of digital resources.
Third, it would mobilize patient investment capital—including appropriately structured pension and insurance investments—to finance productive infrastructure without compromising the financial security of beneficiaries and policyholders.
The resulting system would not eliminate markets, private enterprise, or centralized computing. It would place them within a more pluralistic economic architecture.Large scientific facilities would coexist with regional computing commons. Private enterprises would operate alongside cooperatives and public institutions. Local ecological knowledge would circulate through international research networks. Regional investment could complement continental financial markets.
The transition would be gradual, uneven, and contested.But its potential significance extends beyond technology. It would represent a change in the way Europe organizes economic intelligence itself.
Conclusion: People's AI and the Great Migratory Transformation
The most profound possibility emerging from Europe's Digital Decade is that artificial intelligence could gradually migrate from a predominantly centralized architecture toward a more distributed and ecologically situated system of computation. In this future, intelligence would increasingly operate where economic and ecological processes unfold: within watersheds, energy networks, agricultural landscapes, industrial districts, transportation systems, hospitals, universities, and communities.
Bioregional institutions could steward portions of the infrastructure as commons, while open-source software and interoperable public platforms would enable knowledge to circulate internationally at low incremental costs.
Pension and insurance capital, invested under appropriate fiduciary safeguards, could help finance the transformation, allowing a portion of long-term savings to support productive regional infrastructure.
Hyperscale AI would continue performing functions for which large-scale computation offers genuine advantages, particularly in demanding scientific and technical applications.
The result would not necessarily be the replacement of centralized computing by decentralized computing. It could be the emergence of a more balanced, federated, and socially accountable computational ecology. This is where the philosophical significance of process ontology becomes most apparent. The economy would increasingly be understood not as a collection of isolated enterprises extracting resources and producing commodities, but as an evolving network of relationships embedded within the metabolism of the biosphere.
Digital intelligence would be distributed through those relationships, helping communities recognize interdependencies, anticipate disturbances, and coordinate adaptive responses. Yet neither the technology nor the philosophy guarantees such an outcome. Without appropriate governance, distributed AI could reproduce the concentration of power characteristic of centralized platforms. Europe's historical opportunity is therefore institutional as much as technological.
The Digital Decade could become a foundation for a People's AI in which distributed computing, ecological stewardship, cooperative enterprise, and circular finance reinforce one another.
Such a development would give concrete economic expression to my broader vision of a transition from an Age of Progress to an Age of Resilience. That would constitute a meaningful transformation: from artificial intelligence concentrated principally as corporate capital to artificial intelligence increasingly available as a shared instrument of ecological and social resilience.
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Jeremy Rifkin is the bestselling author of twenty-five books translated into over thirty-five languages. He is a principal architect of the European Union’s and China’s economic plans for transitioning into a Third Industrial Revolution and he served as an economic advisor to Senate Majority Leader Charles Schumer on the U.S. infrastructure plan. He is listed among the top ten most influential economic thinkers in The Huffington Post’s global survey of “The World’s Most Influential Voices."
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