I. Reorienting European AI and Innovation Policy

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Situating the Current Political Moment

The European Union stands on the brink of change. Its economic outlook remains uncertain, exacerbated by a cost-of-living crisis,1European Parliament, “Europeans Concerned by Cost of Living Crisis and Expect Additional EU Measures,” press release, January 12, 2023, https://www.europarl.europa.eu/news/en/press-room/20230109IPR65918/europeans-concerned-by-cost-of-living-crisis-and-expect-additional-eu-measures. growing geopolitical tensions, the aftershocks of COVID-19 supply shortages, and an energy crisis caused by Russia’s ongoing war against Ukraine. Despite numerous lawsuits, cases, and major court rulings against Big Tech, Europe has failed to meaningfully intervene in the lopsided and heavily concentrated global tech sector. Meanwhile, a rise of far-right political parties in key Member States challenges some of the core principles of the European project: the idea of an “ever closer union,” democratic norms, fundamental rights, and the rule of law. Against this backdrop, the pressure is on for the new European Commission to deliver—not just on economic growth and prosperity, but on securing a more independent and competitive place in the world that could make the EU more resilient against future shocks. 

Combined, these challenges have ignited a historical rethinking of the core tenets of EU economic policy, and a return to a more active statecraft in the form of industrial policy.2For why this is historically significant, see Max von Thun, “To Innovate or to Regulate? The False Dichotomy at the Heart of Europe’s Industrial Approach,” AI Nationalism(s), AI Now Institute, March 12, 2024, https://ainowinstitute.org/publication/to-innovate-or-to-regulate-the-false-dichotomy. In September 2024, Mario Draghi, the former prime minister of Italy and the president of the European Central Bank, published his anticipated report on the competitiveness and future of the European Union. The report, in line with one published in April by another former Italian prime minister, Enrico Letta, calls for a transformative change in European policy to address the relative decline of the European Union, proposing an €800 billion increase in annual public and private investment, as well as reforms in trade, internal market, and regulatory harmonization, among other proposals. To be sure, traditional regulatory levers are a part of this renovation, too—both Draghi’s report and Commission President Ursula von der Leyen’s political roadmap call for “a new approach to competition policy” that enables European companies to scale up and consolidate3Max von Thun, “Europe Must Not Tie Its Hands in the Fight Against Corporate Power,” Financial Times, September 19, 2024, https://www.ft.Europe must not tie its hands in the fight against corporate powercom/content/cc4d2249-55af-4763-af7b-5a31cf254e2d., while also considering resilience and ensuring a level playing field. These calls have created a vibrant policy opening, in which interest groups are positioning themselves to shape this new industrial policy within existing political and institutional constraints.

AI & Industrial Policy in Europe

As we argue throughout this report, in the absence of a clear public interest vision for Europe’s (technological) future, three scenarios become more likely: public money will be spent in contradictory, insufficient, or incoherent ways; public money will primarily benefit incumbent digital giants who already dominate the AI ecosystem; or (as Seda Gürses and Sarah Chander argue in Chapter V) public budgets will shift toward market-driven, surveillant, punitive, or extractive technologies that are framed as solutions for complex problems.

It is within this environment of anxieties around Europe’s decline and pathways to its resurrection that AI has again risen to the front stage of policy. Its position as a laggard in the AI “race” has become a symbol for the continent’s real and perceived lack of competitiveness and digital sovereignty, especially compared to the US and China. At the same time, AI’s potential is situated as central to a whole range of complex problems the continent is facing: the climate crisis, slowing economic growth, and deteriorating public services.4Although the EU’s nascent industrial policy encompasses far more than just AI and digital transformation, artificial intelligence occupies a central place in the narrative about Europe’s decline and possible resurrection.

There is historical precedent for anxieties around national competitiveness encouraging more active industrial policies in Europe. Already in the 1970–80s, concerns over the decline in competitiveness against Japan and the United States led to familiar calls for increased investment in high-tech industries.5Filippo Bontadini et al., EU Industrial Policy Report 2024, Luiss Institute for European Analysis and Policy, September 2024, https://leap.luiss.it/luhnip-eu-industrial-policy-report-2024. In 1993, Jacques Delors ascribed Europe’s dire economic situation to a lack of investment in high-tech industries in the face of a changing world.6Paul Krugman, “Competitiveness: A Dangerous Obsession,” Foreign Affairs, March 1, 1994, https://www.foreignaffairs.com/articles/1994-03-01/competitiveness-dangerous-obsession. Today, artificial intelligence forms a specific part of this competitiveness frame. In Draghi’s narrative, the lack of EU hyperscalers and the dominance of US firms in foundation model development are emblematic of Europe’s deficiencies and a continuation of previous failures to capitalize on technological waves. According to this narrative, this not only testifies to a lack of digital competitiveness and technological sovereignty but also risks Europe falling even further behind in global value chains. EU policymakers increasingly assume that investing in AI and integrating it into traditional industries is essential for generating economic growth and enhancing productivity across the EU. 

Although it is still early days for industrial policy in this sector, initiatives are in motion that make for good case studies to tease out the early contours of the vision. Initiatives such as the European Commission’s €3 billion innovation package for AI startups and SMEs, the European Chips Act, and the investment and regulatory recommendations outlined in the Draghi report highlight the EU’s focus on fostering AI adoption across key industrial sectors and public services, while nurturing and growing regional and national AI economies—and the industries that underpin them. In practice, this includes plans to unlock new financing from private and public sources, build and coordinate European AI capacities (diffusing them across selected industries), cut down regulatory barriers, and harmonize the European market for AI.

While we’re still some way from a clear and coherent vision animating these public investments in AI, a few features are already coming to the fore. For one, AI—particularly large-scale AI—is viewed as a technology where EU companies still have a fighting chance at leadership, and efforts seem to be directed at identifying these winning niches. There are stray references to broader social and environmental goals—like the Draghi report positioning AI as key to defending the EU’s social model and enabling the green transition—but, as we explore in this collection, these claims are largely asserted and the assumptions don’t stand up to scrutiny.

Key Themes

A Public-Interest Vision for AI in Europe

The EU’s AI strategy needs a coherent public-interest vision to help it move beyond the poorly defined and narrow motivations of sovereignty and competitiveness. 

This must start with rigorous scrutiny of the premise that investing in AI will lead to societal and economic benefit in the first place—including the pervasive (but empirically contested) claim of productivity gains.

The promises of exponential AI-generated productivity growth are the common parlance of tech industry leaders and business-sector forecasters, whereas more careful analysis has identified significant—but more modest—productivity gains.34Daron Acemoglu, “Don’t Believe the AI Hype,” Project Syndicate, May 21, 2024, https://www.project-syndicate.org/commentary/ai-productivity-boom-forecasts-countered-by-theory-and-data-by-daron-acemoglu-2024-05. Indeed, the monomaniacal push for increased adoption of automation technologies has not always been a positive economic force,35Lorraine Daston, Rules: A Short History of What We Live By (Princeton: Princeton University Press,2022), 1–384. leading to increased costs, increased inequality, and reduced resilience without corresponding increases in welfare.36Daron Acemoglu and Pascual Restrepo, “Tasks, Automation, and the Rise in US Wage Inequality,” Econometrica 90, no. 5 (September 2022): 1973–2016, https://doi.org/10.3982/ECTA19815.
The relationship between rapid AI adoption, investments in AI, and an increase in productivity and economic growth are anything but self-evident, especially in a context as diverse as the various national economies of the European Union, and given that most leading European AI-startups are based in Germany and France. This calls for vigorous debate.

Further complicating matters is the fact that EU discussions of AI competitiveness are mired in unclear definitions, which leads the debate astray. “Competitiveness,” regardless of how self-evident the term may seem, has repeatedly been found to collapse upon further scrutiny. “A meaningless term when applied to national economics,” and “a dangerous obsession,” is how the economist Paul Krugman described competitiveness in response to former Commission President Jacques Delors’s preoccupation with the concept in the early 1990s. While the matter is not straightforward, it is important to keep in mind that countries, or economic blocs, do not compete like players on a field; rarely is it even obvious who the players are and what the game ought to be. A similar ambiguity applies to both “sovereignty” and the term “artificial intelligence.” Calling for blanket adoption of “AI” conceals significant differences in resource requirements between large-scale AI and smaller machine learning models. “Sovereignty” is often used interchangeably with the terms state sovereignty, data sovereignty, or strategic autonomy, with unclear, and at times contradictory, implications for what sovereign technology should look like.37Johan David Michels, Christopher Millard, and Ian Walden, “On Cloud Sovereignty: Should European Policy Favour European Clouds?” Queen Mary Law Research Paper, no. 412, November 10, 2023, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4619918. Similar tensions play out even around terms like digital public infrastructures, which are being driven by multiple distinct (and sometimes conflicting) institutional interests. As Zuzanna Warso warns in Chapter XII, the push for digital public infrastructure risks emphasizing fragmented digitization at the expense of prioritizing public attributes, public functions, and public ownership of digital infrastructure. If investments in digital infrastructure are driven by an aspirational desire to lead in large-scale AI development, the bet is particularly risky—as the infrastructure for training large-scale models cannot easily be repurposed for other kinds of uses.

All of these factors should give us pause to reflect on the terms currently framing the debate. We need a more critical and inclusive debate about which public, and whose interest, European industrial policy on AI is intended to serve.38Daniel Mügge, “EU AI Sovereignty: For Whom, to What End, and to Whose Benefit?” Journal of European Public Policy 31, no. 8 (2024): 2200–2225, https://www.tandfonline.com/doi/full/10.1080/13501763.2024.2318475. In her chapter sketching out a vision for the EuroStack, an increasingly popular moniker for publicly funded alternative infrastructure, Francesca Bria foregrounds the question of whose needs these infrastructures serve: “Ultimately, the EuroStack is not just a technological project—it is a political one.” The EU must critically assess who is set to benefit from AI leadership aspirations and which constituencies will have a say in shaping this vision.

Industrial Policy Should Challenge, Not Entrench, Existing Concentrations of Power in the AI Stack

At minimum, industrial policy should be designed so that it doesn’t worsen the concentrations of power in the AI stack by funneling public money to companies that already dominate the market.

Any vision for the values and goals that should drive Europe’s industrial strategy on AI needs to seriously wrestle with concentrated power and the entanglements within the AI ecosystem. Without doing so, there is a real risk that public money will end up primarily flowing toward the companies that already dominate the market, something we have already seen in past interventions like the European Chips Act, or in the early proposals in the United States to develop public computing capacity.39AI Now Institute and Data and Society Research Institute “Democratize AI? How the Proposed National AI Research Resource Falls Short,” AI Industrial Policy, AI Now Institute, October 5, 2021, https://ainowinstitute.org/publication/democratize-ai-how-the-proposed-national-ai-research-resource-falls-short. Reflecting on the shortcomings of  GAIA-X, Europe’s attempt to build a sovereign cloud, in his chapter, Francesco Bonfiglio calls for a paradigm shift away from building national champions towards prioritizing physical infrastructure and a federated approach towards the cloud. From the failure to aggressively seek competition remedies that tackle concentrated power in the AI market, to a resignation to the dominance of and dependence on US hyperscalers and their version of what a “sovereign cloud” could look like, to calls for an emphasis on scale as beneficial for innovation and resilience,40Von Thun, “Europe Must Not Tie Its Hands in the Fight Against Corporate Power.” the Commission is not giving concentrated power in AI the attention it deserves.

One reason for this may be that the complex ways incumbents expand and abuse their power aren’t sufficiently addressed in Europe. Cecilia Rikap suggests thinking of the AI market as an “entrenched and established core of Big Tech surrounded by a turbulent periphery.” From this perspective, even a seemingly thriving EU market of startups that builds models or applications would not challenge but reinforce the high degree of concentration we see in the large-scale AI market already: being located at the core allows hyperscalers and Big Tech firms to leverage their control over data, distribution networks, and computing infrastructure to “skew the innovation trajectory and profit flows” in their direction.

In Chapter III, Cristina Caffarra argues that the conventional rules that are traditionally applied to merger enforcement in Europe will inevitably fail to capture the essence of concerns around Big Tech dealmaking with AI companies. Regulators will need to move beyond traditional narrow, post hoc antitrust analysis and deploy a fitting theory of harm to name an increasingly clear dynamic: by aggressively “weaponizing” their scaled assets into new applications, Big Tech firms and their ecosystems entrench their first-mover advantages and effectively preempt competition from challengers.

Large-Scale AI as Inconsistent with Europe’s Climate Goals

Large-scale AI’s current trajectory has serious climate impacts that might stand in irreconcilable tension with Europe’s environmental and green transition goals.

In European policy discussions, investing in AI is often framed as a necessity to achieve Europe’s climate goals, either directly by using “AI for good” to fight climate change, or indirectly by securing the prosperity Europe needs to fund the “green transition.” In Chapter IV, Fieke Jansen and Michelle Thorne show how the climate implications of large-scale AI pose an existential question for this particular technological trajectory. The production of chips and the operation of data centers are both energy-intensive and environmentally damaging and in some European countries, such as Ireland, data centers already use a fifth of the countries’ total electricity consumption.41George Kamiya and Paolo Bertoldi, Energy Consumption in Data Centres and Broadband Communication Networks in the EU, Publications Office of the European Union, February 16, 2024, https://publications.jrc.ec.europa.eu/repository/handle/JRC135926. The recent massive global investments in AI and data infrastructures are poised to exacerbate this situation even further.42Microsoft Source, “BlackRock, Global Infrastructure Partners, Microsoft and MGX Launch New AI Partnership to Invest in Data Centers and Supporting Power Infrastructure,” press release, September 17, 2024, https://news.microsoft.com/2024/09/17/blackrock-global-infrastructure-partners-microsoft-and-mgx-launch-new-ai-partnership-to-invest-in-data-centers-and-supporting-power-infrastructure.

In this context, positioning AI as a climate solution is not just misleading; it also distracts from an urgent policy priority, allowing unsustainable and inequitable systems to thrive while delaying crucial policy actions. Given the Commission’s continued (albeit weakened43WWF, “Von der Leyen Secures Second Term, Diluted European Green Deal Lives On,” July 18, 2024, https://www.wwf.eu/?14383941/Von-der-Leyen-secures-second-term-diluted-European-Green-Deal-lives-on.) commitment to a green and just transition, aspiring to global leadership in AI seems counterproductive. Jansen and Thorne suggest that Europe needs to redefine innovation by placing environmental justice at the core of its industrial policy. 

The sustainability question is not just of regional importance. If Europe decides to bet on large-scale AI, environmental exploitation in its supply chain, particularly of raw materials, will be felt elsewhere. As UN Trade and Development (UNCTAD) stated in their 2024 report on the digital economy, “developing countries bear the brunt of the environmental costs of digitalization while reaping fewer benefits.”442024 Digital Economy Report: Shaping an Environmentally Sustainable and Inclusive Digital Future, United Nations Conference on Trade and Development, 2024, https://unctad.org/publication/digital-economy-report-2024.

Conditionalities to Industrial Policy are Essential to Ensure Public Benefit

Public funding or access to other public resources (including land, water, and energy) must be attached to conditions that guarantee outcomes that serve the broader public interest. This includes accountability, climate, and labor conditionalities and standards. Conditionalities must be crafted through participatory processes that involve civil society, trade unions, and affected communities, with guaranteed transparency into the implementation of conditionalities.

Europe’s newfound embrace of industrial policy is often framed in opposition to what has come before: a focus on regulation instead of, or even at the expense of, investing in alternatives. However, industrial policy never truly went away in Europe. Over the course of the Digital Decade that is about to come to an end, Europe has invested billions in flagship projects like the European sovereign cloud Gaia-X, acted as a key player in European venture capital markets, and allocated billions of euros to research and development through Horizon Europe and Digital Europe Programs.

History tells us that without emphasis on conditionalities and accountability, industrial policy will work to serve narrow industry interests at the expense of the broader public. Europe’s previous ventures into industrial policy are no exception. Both the European Chips Act and the innovation package for AI startups and SMEs show that past EU attempts to invest in AI economies and the infrastructures that underpin them have left concentrated power in the AI stack largely unaddressed, and have instead solidified the role of a few incumbents. In Chapter VI, drawing from investigations done by the Centre for Research on Multinational Corporations, Margarida Silva and Jeroen Merk show how the EU Chips Act failed to impose social, environmental, or redistributive conditions on the public subsidies granted, and excluded the public from both the negotiations and the ability to scrutinize the resulting agreements. Combined, this led to the threat of regulatory capture by well-positioned companies.

Conditionalities could also require meaningful openness in the development and release of publicly funded AI projects. But as Udbhav Tiwari argues in Chapter IX, for open source initiatives to meaningfully challenge concentrated power and the trend toward homogeneity, there will also need to be a focus on shifting broader structural conditions in the market, including via robust antitrust enforcement.

What different calls for conditionalities have in common is the involvement of a much more diverse public. In fact, this is also something Draghi calls for: strong social dialogue that fosters collaboration among trade unions, employers, and civil society, considered essential for setting goals and actions to transform Europe’s economy toward greater inclusivity and equity. Making these commitments a tangible reality is a true challenge for European tech policy in the coming years.

Industrial Policy Must Not Promote Uncritical Application of AI Into Sensitive Social Domains

Incentivizing blanket AI adoption in the public sector could contribute to a hollowing out of the state, a waste of public funds, single points of failure, and rights abuses, especially when deployed in risky contexts or in ways that are incompatible with AI’s inherent limitations.

Europe’s ambition to boost the adoption of AI in the public sector is premised on the assumption that AI will improve public services. Beyond unclear definitions—it is unclear whether “improvement” means better quality or cheaper delivery—such hopes need to be grounded in empirical evidence about the actual capabilities, benefits, and inherent limitations of AI technologies and their ability to increase the quality and efficiency of public services. This evidence is frequently lacking.45“Lessons from the FDA Model,” Lessons from the FDA for AI, AI Now Institute, August 1, 2024, https://ainowinstitute.org/publication/section-3-lessons-from-the-fda-model. Instead, a rich body of research, including from the European Union, has documented the risks and harms associated with using AI to cut costs in the public sector. In 2019, for instance, Philip Alston, the UN special rapporteur on extreme poverty and human rights, warned that the rapid digitization and automation of welfare systems is harming the poorest and most vulnerable people in society.46United Nations Human Rights, “World Stumbling Zombie-Like into a Digital Welfare Dystopia, Warns UN Human Rights Expert,” press release, October 17, 2024, https://www.ohchr.org/en/press-releases/2019/10/world-stumbling-zombie-digital-welfare-dystopia-warns-un-human-rights-expert. The European Anti-Poverty Network has coined the term “digitally induced poverty,” a phenomenon induced through a combination of the automation of discrimination, digital exclusion, and digitization as a tool for implementing austerity.47European Anti-Poverty Network, An Exploratory Study on the Use of Digital Tools by People Experiencing Poverty, 2024, https://www.eapn.eu/an-exploratory-study-on-the-use-of-digital-tools-by-people-experiencing-poverty. Amnesty International,48Amnesty International, “Trapped by Automation: Poverty and Discrimination in Serbia’s Welfare State,” December 4, 2023, https://www.amnesty.org/en/latest/research/2023/12/trapped-by-automation-poverty-and-discrimination-in-serbias-welfare-state. Human Rights Watch,49Amos Toh, “Automated Neglect: How The World Bank’s Push to Allocate Cash Assistance Using Algorithms Threatens Rights” Human Rights Watch, June 13, 2023, https://www.hrw.org/report/2023/06/13/automated-neglect/how-world-banks-push-allocate-cash-assistance-using-algorithms. Algorithm Watch,50Alina Yanchur, “‘All Rise for the Honorable AI’: Algorithmic Management in Polish Electronic Courts,” Algorithm Watch, May 27, 2024, https://algorithmwatch.org/en/polish-electronic-courts. and A1151https://antisocijalnekarte.org/en. have all documented how automating the delivery of essential public services can lead to discrimination and exclusion, while shifting the burden of proof to those who are already marginalized. 

One challenge that public authorities face, as MEP Kim Van Sparrentak and Simona de Heer explain in Chapter VII, involves the limitations set by the EU Procurement Directives. Public authorities currently need to choose vendors based on the lowest price, rather than considering strategic autonomy, sustainability, social standards, privacy, or the long-term governance of the end product—criteria that are crucial to ensure that taxpayer money is being spent in ways that are best for society and the economy.

Sarah Chander and Seda Gürses highlight some of the more fundamental issues that come with the blanket adoption of AI in their chapter, From Infrastructural Power to Redistribution. A punitive vision of security dominates many EU investments that fuse the concept of public safety with police, borders, and the military. Pushing for the blanket adoption of AI underestimates how expanding computational infrastructure can lead to a transformation of the economy that ultimately places the economic interests of tech companies at the heart of public and private institutions.52Agathe Balayn and Seda Gürses, “Misguided: AI Regulation Needs a Shift in Focus,” Internet Policy Review 13, no. 3, September 30, 2024, https://policyreview.info/articles/news/misguided-ai-regulation-needs-shift/1796.

Thinking about alternatives, or even considering AI’s inherent limitations, requires a shift away from weighing risks and benefits toward asking more fundamental questions about the role that AI technologies can and should play in delivering public services, or in European society more broadly.53AI Now Institute, “AI Now Submission to the Office and Management and Budget on AI Guidelines,” December 20, 2023, https://ainowinstitute.org/publication/ai-now-submission-to-the-office-and-management-and-budget-on-ai-guidelines.

Innovation Grows with Bold Regulatory Enforcement

Rather than pit innovation against regulation, industrial policy investments should move in tandem with bold regulatory enforcement, with the goal of shaping innovation in the public interest.

The idea that tech regulation is merely a burden for European companies and harmful to European innovation is a long-standing argument advanced by US tech companies and their allies. This framing, which now also informs some of the thinking behind Europe’s nascent industrial strategy, is based on a false distinction. There are no unregulated markets—only differently regulated markets. It is a key, and unavoidable, task of policymakers to shape the patterns of economic action. Regulation can proactively shape digital economies and ensure that companies pursue innovation that strengthens, rather than undermines, core European values such as fundamental rights.

One key way to flip the script is to underscore that the lack of effective enforcement of existing data protection and competition has contributed to the kind of market concentration we see across the AI stack and the business models that sustain this concentration. Allowing the current surveillance-based business models to proliferate has prevented alternative business models from emerging. Any diagnosis of Europe’s inability to compete in this paradigm must at least partially, then, fall upon the failure to adopt a sharp enforcement posture on unchecked commercial surveillance and on the consolidation of market position across the AI stack. 

This regulation-versus-innovation paradigm can also distract from the important question of what kinds of regulatory approaches and enforcement mechanisms will best be able to discipline and shape the market in the public interest. In fact, while the AI Act is routinely invoked to argue that regulation is too complex and burdensome, the A Act is mired in loopholes and exemptions in areas concerning direct harms to human life—for instance in domains relating to police, migration control, and security actors.54#ProtectNotSurveil, “Joint statement – A Dangerous Precedent: How the EU AI Act Fails Migrants and People on the Move,” March 13, 2024, https://www.accessnow.org/press-release/joint-statement-ai-act-fails-migrants-and-people-on-the-move. The risk-based product safety approach that underlies the AI Act is not sufficient and sometimes even counterproductive when it comes to protecting people from fundamental rights violations and harm in many sensitive contexts.55European Center for Not-for-Profit Law, “ECNL, Liberties and European Civic Forum Put Forth an Analysis of the AI Act from the Rule of Law and Civic Space Perspectives,” April 3, 2024, https://ecnl.org/news/packed-loopholes-why-ai-act-fails-protect-civic-space-and-rule-law.56European Disability Forum, “EU’s AI Act Fails to Set Gold Standard for Human Rights,” April 3, 2024, https://www.edf-feph.org/publications/eus-ai-act-fails-to-set-gold-standard-for-human-rights.

Europe’s Place in the World

In what is perceived as an existential race for geopolitical influence and competitiveness vis-à-vis the US and China, and amid widespread fears of Europe’s subordination, Europe must not lose sight of the the many ways in which its policy orientation will shape the landscape of possibility, not just for the EU but also for the rest of the world.

Digital trade policy, and in particular requirements for maintaining low trade barriers for digital goods and services, often implying minimal or no regulation or conditionalities, can significantly shape the field of possibility when it comes to industrial policy. As Burcu Kilic argues in Chapter VIII, the EU’s digital trade policy has long taken a neoliberal approach; Draghi’s recommendations focusing on keeping low trade barriers to ensure continued access to the latest AI models and processors in the US is no exception. Primarily benefiting Big Tech, the EU’s neoliberal approach to trade has promoted tech-driven globalization while remaining largely disconnected from broader EU domestic policies and priorities (with the exception of privacy). In this paradigm, many industrial policy measures to shape the trajectory of tech development could be treated as trade barriers, preventing not just Europe, but also other regions and nations that Europe trades with, from adopting an ambitious industrial policy strategy that prioritizes people and the planet. A neoliberal trade agenda, Kilic argues, is simply incompatible with industrial policy.

In her chapter, Francesca Bria urges Europe not to be purely inward-looking in its industrial policy orientation, even if it is motivated primarily by the desire for sovereignty, pointing to the possibilities for more global alliances motivated by challenging the current concentrations of power. While “the Brussels effect,”57Anu Bradford, The Brussels Effect: How the European Union Rules the World (New York: Oxford University Press, 2020), https://doi.org/10.1093/oso/9780190088583.001.0001. a process of unilateral regulatory globalization caused by the European Union, is ultimately unidirectional and has not always been positively received by the countries that are nudged to comply with European laws, Bria hopes that through a stronger emphasis on building alternatives, Europe can become a collaborator in sharing a fairer, digital future.

Conclusion

This collection features a broad spectrum of ideas for what a public-interest vision of AI beyond competitiveness and sovereignty could look like. These critical interventions provoke a rethinking of the fundamental ideas underpinning European tech and innovation policy. They offer different approaches to the values and norms that should underpin European industrial policy on AI, and the economic and societal outcomes that such interventions should ultimately seek to create. 

While authors differ in their stances, backgrounds, and political positioning on these issues, they are united in showing that past tools and approaches are not fit for purpose. Neither incremental change, nor significant investments into a predefined innovation trajectory, will benefit the public interest. Instead, European tech and innovation policy needs a vision and a radical rethink.

To engage in a radical reset, Europe must grapple with no less than existential questions about the direction and nature of its digital future. Answering these questions requires abandoning comfortable, established speaking points, superficial analyses, and bland statements that stand in for a serious discussion of what technology politics could be:

• What kind of (digital) future does Europe want? 
• What role can, and should, AI technologies play in this future? 
• Who will have a say in determining the path?

These are the challenging questions we will start to address in this report and will continue to grapple with in the coming years.