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11. The War in Ukraine: Reality Check for Emerging Technologies and the Future of Warfare

Author:
Jean-Marc Rickli and Federico Mantellassi
Publication Date:
04-2024
Content Type:
Working Paper
Institution:
The Geneva Centre for Security Policy
Abstract:
Russia’s full-scale invasion of Ukraine in February 2022 marked the beginning of one of the most intense and brutal state-on-state conflicts opposing two modern militaries in recent memory. Despite Russia’s qualitative and quantitative advantages, Ukraine’s armed forces have so far put up strong resistance, foiling Russian plans of a quick victory and turning the conflict into a bloody war of attrition. Due to its scale and the nature of its belligerents, the conflict can provide us with a glimpse into what the future of warfare might look like and help us recentre the burgeoning conversation about the future of warfare in the current reality, especially as it relates to the presence and impact of emerging technologies. The war therefore offers us a way to understand how digital and off-the-shelf technologies such as artificial intelligence (AI) impact conflicts of this scale and how relevant they are to current modern warfare. The war can also help us see how new actors become involved in warfare, what new means of influencing nation states are becoming available, and which new tools armed forces can use to affect battlefield outcomes. Importantly, it can help us gauge their importance relative to more traditional aspects of warfare. The analysis is structured in four main parts. This introduction is followed by a short contextualisation to situate the analysis in the wider conversation about emerging technologies and the future of warfare (Part 2). Part 3 provides a short overview of the conflict so far, dividing it into six phases. Part 4 surveys the main elements of the conflict in Ukraine, outlining what emerging technologies have been present and critically assessing their role in the war. Part 5 delves into some implications for the future of warfare that can be understood from the dynamics analysed thus far, especially as they relate to the place of emerging technologies in future conflicts and their role in determining battlefield outcomes. We argue that while some new technologies have come to characterise modern warfare, the Russo-Ukrainian conflict shows that many features of warfighting remain unchanged. Emerging technologies, such as AI and cheaper technological alternatives to traditional armaments, such as drones, are undoubtedly starting to change the battlefield and will play an increasingly larger role in future conflicts. Cyberspace and the globalised digital information space are bringing new actors and new means to exert influence, provide nations’ armed forces with new tools, and make battlefields increasingly globalised and complex. However, the conflict also shows that traditional aspects of warfare will not decrease in importance or be sidelined. Conflicts remain a contest of wills and adaptation, where ammunition supplies, the quantity of traditional armaments such as tanks, and both the number and quality of personnel and the logistical and organisational ability to bring all these elements to bear all remain the most important determinants of success. While technology is playing an increasing role in this equation, it remains unable to determine the outcome of a conflict on its own.
Topic:
Science and Technology, Drones, Artificial Intelligence, Armed Conflict, and Russia-Ukraine War
Political Geography:
Russia, Europe, and Ukraine

12. What to take away from the European law on artificial intelligence

Author:
Thaima Samman and Benjamin De Vanssay
Publication Date:
07-2024
Content Type:
Working Paper
Institution:
Robert Schuman Foundation (RSF)
Abstract:
On top of all these initiatives and legislations, the European Commission has proposed two directives to the regulation on Artificial Intelligence (“Artificial Intelligence Act”, hereafter AI Act) is a landmark piece of EU legislation in the field of AI. One of its primary aims is to regulate the use of this technology in a number of areas based on a risk-based approach. In that respect, the AI Act sets gradual obligations for the different parties involved in the AI value chain depending on the level of risk that the use of AI raises in concrete use cases. The AI Act should therefore be viewed as a targeted intervention, and not a cross-cutting legislation like the General Data Protection Regulation (GDPR). The AI Act was adopted by EU co-legislators in May 2024 and will enter into force 20 days after its publication in the Official Journal of the European Union on July 12. It will apply from August 2, 2026. Meanwhile, the Commission has launched the AI Pact, a voluntary initiative inviting AI providers to comply with the key obligations of the AI Act in advance of its entry into force.
Topic:
Law, European Union, Artificial Intelligence, Value Chains, and European Commission
Political Geography:
Europe

13. Russia’s war against Ukraine: Lessons on infrastructure security and new technologies

Author:
Auriane Técourt and Maria Martisiute
Publication Date:
07-2024
Content Type:
Policy Brief
Institution:
European Policy Centre (EPC)
Abstract:
In 2022, Russia hit Ukraine with a major cyberattack and unleashed a full-scale war of aggression. This includes new technologies and AI-enabled capabilities such as the Bylina electronic warfare command-and-control system. Nord Stream and the Balticconnector also fell victim to sabotage attacks by hostile actors. In this Policy Brief, it is highlighted that the EU has stepped up its resilience and cybersecurity of critical infrastructure. However, the capacity to leverage innovative technologies and defensive AI remains underdeveloped. Furthermore, the protection of industrial control systems (ICS) remains unaddressed. As Russia upgrades its 2030 National AI Development Strategy, there is an urgency to integrate the security of industrial controls into the EU’s approach to the cybersecurity of critical infrastructure before Moscow strikes with deadlier offensives. It is also time to start building a measured, albeit scalable, deployment plan for new technologies that may be AI-enabled for European critical infrastructure in connectivity with Ukraine and Moldova. The Paper explains that the EU has strengthened cybersecurity and resilience of critical infrastructure, but there is still room for improvement. Considering the pace at which new technologies and AI-enabled technologies are developing, the following steps would enhance the security of European infrastructure: Possible attack scenarios. Mandatory SOCs and cyber hubs. Industrial control systems (ICS). Synergies between the EU and NATO. A deployment plan for new technologies that may include AI.
Topic:
Infrastructure, European Union, Cybersecurity, Artificial Intelligence, and Russia-Ukraine War
Political Geography:
Russia, Europe, and Ukraine

14. Empires of Exceptionalism: Lessons from the EU AI Act and Attempts at AI Legislation in California

Author:
Hosuk Lee-Makiyama and Claudia Lozano
Publication Date:
11-2024
Content Type:
Policy Brief
Institution:
European Centre for International Political Economy (ECIPE)
Abstract:
The EU AI Act, which passed after three years of negotiations and much ambivalence, sets a comprehensive regulatory framework that reflects the European Union’s unique needs that are not translatable to other jurisdictions. The proposed bill in California (SB 1047) illustrates this case in point. The bill could potentially mislead the public into thinking that AI is already under effective regulatory control. Lawmakers must recognize that the EU differs from other systems. The EU AI Act is not only shaped by the desire to slow down global AI development, allowing Europe to catch up with rivals like the US and China: The EU lacks an enforceable constitution, and the Act prohibits its governments to impose social scoring and discriminatory measures. SB 1047 only addressed humanity-ending disasters and had unclear practical value. Meanwhile, the EU AI Act was passed into law to prevent internal competition and Europe’s history of authoritarianism. Existing laws in California and the EU already regulate most AI use cases. In addition, the EU AI Act reviews existing regulations to ensure they are still fit for purpose, something California failed to do in SB 1047. A critical takeaway is to examine existing obligations or executive powers (including enforcement agencies and public funding) to achieve the desired policy objectives. The diverging outcomes of the two laws show the importance of avoiding mismatches between policy objectives and legislative scope. AI regulation cannot substitute for other policy objectives, like privacy and antitrust. Also, lawmakers must weigh the cost of failed regulation against the risks of non-regulation. California did not see any immediate costs of inaction, whereas delayed action by the EU might have led to the fragmentation of its internal market. Also, given its collective decision-making, the EU can escape accountability for policy failures due to premature laws in a way the leaders in other political systems cannot.
Topic:
European Union, Digital Economy, Legislation, and Artificial Intelligence
Political Geography:
Europe, California, North America, and United States of America

15. CTC Sentinel: January 2024 Issue

Author:
Haroun Rahimi, Andrew Watkins, Gabriel Weimann, Alexander T. Pack, and Rachel Sulciner
Publication Date:
01-2024
Content Type:
Journal Article
Journal:
CTC Sentinel
Institution:
The Combating Terrorism Center at West Point
Abstract:
In the feature article, Haroun Rahimi and Andrew Watkins assess Taliban rule two and a half years into their renewed control of Afghanistan. They write: “Since their 2021 takeover, the Taliban have consolidated control over an impoverished and austere postwar Afghanistan. Since their victory, the Taliban’s emir has reasserted his status as a ‘supreme leader’ and oriented domestic policy in favor of highly conservative constituencies—which has revealed deep differences among their leadership of visions for the future of the Afghan state and society and how authority is divided among themselves. Yet, the Taliban have persistently prioritized the cohesion of their movement and governing apparatus. This trajectory has earned condemnation from Western states and prompted caution in the entire world’s engagement, which has in turn fueled Taliban motivations to reject foreign demands. After two and a half years of rule, the Taliban’s domestic agenda has become intertwined with their foreign relations impasse.” Gabriel Weimann, Alexander Pack, Rachel Sulciner, Joelle Scheinin, Gal Rapaport, and David Diaz write that “with the arrival and rapid adoption of sophisticated deep-learning models such as ChatGPT, there is growing concern that terrorists and violent extremists could use these tools to enhance their operations online and in the real world. Large language models have the potential to enable terrorists to learn, plan, and propagate their activities with greater efficiency, accuracy, and impact than ever before.” The authors offer “an early exploration of how these large language models could be exploited by terrorists or other violent extremists … to support their efforts in training, conducting operational planning, and developing propaganda.” Georgia Gilroy decodes al-Shabaab’s social media strategy, outlining the “controlled, adaptive, and coordinated approach the terrorist group takes to its online behavior.” She writes that the group’s “continued resilience, even in the face of mounting counterinsurgency efforts, is underpinned by its sophisticated communications architecture.” Christian Jokinen assesses whether left-wing terrorism is making a comeback in Germany in a case study of the violent left-wing Engel – Guntermann network. He writes that “the recent concerning trend among German left-wing extremists is toward greater violence and transnationalism.”
Topic:
Terrorism, Taliban, Violent Extremism, Artificial Intelligence, Leftist Politics, and Al-Shabaab
Political Geography:
Afghanistan, Europe, South America, Germany, and Global Focus

16. Why artificial intelligence is creating fundamental challenges for competition policy

Author:
Bertin Martens
Publication Date:
07-2024
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
The rapidly evolving market for artificial-intelligence services is apparently thriving and very competitive, with a growing number of AI start-ups and ever larger and more capable AI models. Investors are pouring large amounts of money into start-ups and into a few big tech firms that have the large financial resources and computing capacity that constitute key inputs to the production and running of AI-driven services. However, exponentially growing AI model training costs create a market entry barrier for AI start-ups, forcing them to cooperate with big tech firms to get access to computing infrastructure and end users. At the same time, their AI models compete with those of the big tech firms. Competition authorities investigate these co-opetition agreements, looking for market-distorting clauses. Beyond computing costs, other competition choke points in the AI supply chain include access to dedicated AI processor chips and training data. Strict enforcement of copyright on data may further tighten an already scarce supply of affordable training data. Policymakers have few, if any, effective tools to deal with these potential competition bottlenecks in AI industries. Co-opetition agreements are necessary to enable AI start-ups to access hyperscale and costly computing infrastructure. At the same time, the agreements give big tech privileged access to the latest AI models. Competition authorities can try to minimise market-distorting provisions in these agreements but there are technical and economic limits to the extent of fragmentation in the AI value chain that can be meaningfully imposed on big tech firms. Licensing fees for copyright-protected data further tighten the already scarce supply of affordable data. It is hard to implement a fair market-clearing mechanism for dedicated AI processors. If model training costs continue to grow exponentially, as appears to be the case for the foreseeable future, the entire competition policy setting for AI industries may need a revision, with collaboration, including between big tech firms, dominating competition to keep the pace of AI innovation going.
Topic:
Science and Technology, Governance, European Union, Microeconomics, and Artificial Intelligence
Political Geography:
Europe

17. Catch-up with the US or prosper below the tech frontier? An EU artificial intelligence strategy

Author:
Bertin Martens
Publication Date:
10-2024
Content Type:
Policy Brief
Institution:
Bruegel
Abstract:
European Union policymakers want to close the artificial intelligence innovation gap with the United States, as a way to accelerate lagging productivity growth. The EU focus is on expanding an existing supercomputer network with more AI hardware and computing infrastructure, with taxpayer support. However, this computing infrastructure is not adapted to AI modelling. The cost of catching up with leading big tech AI computing centres is already prohibitive for EU budgets, and is set to become even more so. The hardware focus overlooks missing EU markets for complementary services that are required to set up a successful AI business: large-scale business outlets for frontier generative AI models to generate sufficient revenues to cover huge fixed model training costs, hyperscale cloud-computing infrastructure and private equity financing for AI start-ups. In the absence of (or with insufficient) complementary services markets in the EU, start-ups are forced to collaborate with US big tech firms. Injecting taxpayer subsidies to make up for these missing markets may further distort EU markets. Regulatory compliance costs, including uncertainty about the implementation arrangements for the EU Artificial Intelligence Act, add to market problems. The EU should address a wider range of market failures in its policy initiatives. It should strive to increase productivity growth below the AI technology frontier, by facilitating investment and applications of AI-driven services produced by derived and specialised generative AI models, or AI-applications that build on top of existing generative AI models. Building these below-frontier AI applications requires far less computing capacity and less heavy investment costs. Promoting the uptake of AI application services across a wide range of industries can substantially stimulate productivity growth. That requires a razor-sharp focus on pro-innovation guidelines, standards and implementation provisions for the EU AI Act, shortening the Act’s regulatory uncertainty horizon as much as possible, and facilitating collaborations between EU AI startups and big tech companies. Widening and deepening the EU private equity and venture capital market would also be very helpful.
Topic:
Markets, Science and Technology, European Union, Investment, and Artificial Intelligence
Political Geography:
Europe and United States of America

18. The state of the UK economy 2024

Author:
UK in a Changing Europe
Publication Date:
01-2024
Content Type:
Special Report
Institution:
UK in a Changing Europe, King's College London
Abstract:
The state of the UK economy is always an important matter, but it is all the more so in what looks likely to be an election year. This UK in a Changing Europe report takes stock of the UK economy in order to assess how it can break from the current economic and political ‘doom loop’. The report provides a constructive framework to address the need for a longer-term, joined up approach in policy areas, ranging from education to immigration, to public services and artificial intelligence. It shows that there is no single silver bullet for the problems facing the UK economy, however a number of themes have emerged. The UK has long been plagued by short-termism and political instability which needs to be addressed. Edited by Senior Fellow Professor Jonathan Portes, it brings together leading UK academics on the economy from universities across the UK, the Migration Observatory, the Institute for Fiscal Studies and other research institutions.
Topic:
Education, Industrial Policy, Migration, Economy, Tax Systems, Macroeconomics, Trade, Artificial Intelligence, Housing, Labor Market, Public Service, Productivity, and Net Zero
Political Geography:
United Kingdom and Europe

19. What should be done about Google’s quasi-monopoly in search? Mandatory data sharing versus AI-driven technological competition

Author:
Bertin Martens
Publication Date:
07-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
The first part of this paper focuses on competition between search engines that match user queries with webpages. User welfare, as measured by click-through rates on top-ranked pages, increases when network effects attract more users and generate economies of scale in data aggregation. However, network effects trigger welfare concerns when a search engine reaches a dominant market position. The EU Digital Markets Act (DMA) imposes asymmetric data sharing obligations on very large search engines to facilitate competition from smaller competitors. We conclude from the available empirical literature on search-engine efficiency that asymmetric data sharing may increase competition but may also reduce scale and user welfare, depending on the slope of the search-data learning curve. We propose policy recommendations to reduce tension between competition and welfare, including (a) symmetric data sharing between all search engines irrespective of size, and (b) facilitate user real-time search history and profile-data portability to competing search engines. The second part of the paper focuses on the impact of recent generative AI models, such as Large Language Models (LLMs), chatbots and answer engines, on competition in search markets. LLMs are pre-trained on very large text datasets, prior to usage. They do not depend on user-driven network effects. That avoids winner-takes-all markets. However, high fixed algorithmic learning costs and input markets bottlenecks (webpage indexes, copyright-protected data and hyperscale cloud infrastructure) make entry more difficult. LLMs produce semantic responses (rather than web pages) in response to a query. That reduces cognitive processing costs for users but may also increase ex-post uncertainty about the quality of the output. User responses to this trade-off will determine the degree of substitution or complementarity between search and chatbots. We conclude that, under certain conditions, a competitive chatbot markets could crowd out a monopolistic search engine market and may make DMA-style regulatory intervention in search engines redundant.
Topic:
Science and Technology, Governance, European Union, Digital Economy, Innovation, Artificial Intelligence, and Competition
Political Geography:
Europe

20. Antitrust issues raised by answer engines

Author:
Christophe Carugati
Publication Date:
06-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
Rapid development of generative artificial intelligence chatbots like ChatGPT is leading search engine providers to move from search to answer engines. Unlike search engines, which provide search results in the form of blue links to content creators, answer engines generate personalised answers through a conversation with end users. This revolution impacts the internet ecosystem of content creators and the digital advertising market. This paper outlines some early antitrust issues related to answer engines, from the transition from search to answer engines (sections 2 and 3) and the response competition authorities should adopt (section 4). It finds that search and answer engines complement and compete with each other. While the answer-engine market is still at an early stage of development, it already raises some competition issues in relation to data scraping, vertical integration and unfair terms and conditions. Intervention by competition authorities is more likely than not to prevent market power in this new market. In this regard, competition authorities should act to preserve dynamic competition and minimise adverse effects on content creators. Finally, the paper concludes with several research questions for future research (section 5).
Topic:
Science and Technology, Governance, European Union, Digital Economy, Innovation, Artificial Intelligence, and Competition
Political Geography:
Europe