© REUTERS/Benoit Tessier

At a much-awaited launching event in early June, held together with his French counterpart Bruno Le Maire, German Economy Minister Peter Altmaier lavished superlatives on the cloud computing and data infrastructure project “Gaia-X”: According to Altmaier, it is nothing less than “perhaps the most important digital aspiration of Europe in a generation”—a “moonshot” even.

Indeed, Gaia-X is intended to be a silver bullet. Altmaier wants to tackle the fact that Europe is badly behind the United States and China in terms of cloud services and data availability, be it for nation states, companies, or citizens. Thus, Europe’s striving for “digital sovereignty” is motivated by the fear of losing out in terms of innovation capabilities amid growing market dependencies and data privacy concerns.

A Brief Ride Through the Clouds

Simply put, cloud computing provides IT services—such as procession power, data storage or specific applications—over a network of servers, operating independently of location and devices while being accessible via the internet. But not all cloud services are equal. Roughly speaking, three different delivery models of cloud computing can be distinguished.

When cloud providers deliver access to basic data storage or other computing resources, we talk about “Infrastructure as a Service” (IaaS). Clients buying IaaS services access storage and servers via a “virtual data center” in the cloud, and not through a costly and labor-intensive on-premise IT infrastructure. Due to its “pay-as-you-go” business model, it is highly flexible and scalable as needed. Using “Platform as a Service” (PaaS), clients can additionally access tools to develop and host user-defined applications on a cloud-based platform. And then there is “Software as a Service” (SaaS), where consumers use ready-to-use applications and software on the cloud. Managed by a third-party vendor and hosted on a remote cloud network, the SaaS approach is the most frequently utilized cloud business model.

Companies have different options when accessing cloud-based computing capabilities, including exclusive organization-based “private clouds” that promise a higher degree of control and security over firm-owned data and “public clouds” run by an external cloud services providers. These can be combined in different ways: for example, a “hybrid cloud,” that outsources non-critical information to a public cloud while protecting a firm’s critical data on a private one, or “multi clouds,” which use multiple public cloud providers in order to benefit from the individual advantages of each provider.

And then there is “Edge Computing.” Instead of moving data to a central cloud, this brings computation and date storage closer to the device or data source, thus away from distant data centers towards the edge of the network. This leads to faster data processing and low-latency levels, important requirements for innovation such as autonomous driving, smart cities, or smart industries—they all rely on the almost real-time availability of data and processing power. In sum, cloud and edge computing offer services lay the foundation of the digital economy, driving innovation and economic competitiveness.

Europe as an Also-Ran

Given this importance, the cloud market is highly competitive and has rapidly grown lately;  total revenue was $96 billion in 2019, over twice as much as in 2017. With over two thirds of the market share, Amazon Web Services is leading the cloud computing market (including PaaS and IaaS models as well as hosted private cloud services), followed by Microsoft Azure, and Google Cloud. When looking at the SaaS cloud market, which is the largest market segment of the three cloud models, US providers are again in the global lead: As of 2019, Microsoft had a market share of 17 percent, followed by Salesforce with 12 percent and Adobe with 10 percent. Notably, Germany-based SAP is one of the top five providers in the SaaS market—one of the very few notable European players in the global cloud computing market. Meanwhile, Chinese tech giants are trying to break the strong US dominance. Alibaba  and Tencent recently announced that they were each investing billions of dollars over the next couple of years in cloud infrastructure. However, US providers have traditionally a strong presence in Europe, while Chinese firms are less represented.

The strong dependency on cloud services of American tech companies also raises legal and data privacy concerns. In that context, the US Cloud Act is a contentious issue since it theoretically compels US companies to grant US agencies access to critical information. The problem: this might even apply when data servers are located outside of the US. Therefore, the law possibly opens the door for US agencies to access data of European companies and citizens. Particularly from a European perspective, where regulation explicitly protects and strengthens the integrity of an individual by giving them power over their data under the General Data Protection Regulation (GDPR), the Cloud Act has been subject to heavy criticism and has ignited a debate among legal experts

Gaia-X: The Silver Bullet?

How does Gaia-X fit into this universe of data and clouds? The prestigious Franco-German project is attempting to tackle the mentioned predicaments by aiming at reducing overall external dependency, protecting data to European standards, and promoting innovation. However, strictly speaking, it skips the extreme difficulty of creating a new, competitive European hyperscaler. Instead, it envisages connecting different existing providers to establish a central, federated data infrastructure system. This ecosystem is supposed to be open to all interested cloud providers, as long as they accept the technical requirements of Gaia-X and the specific architecture reflecting European data privacy standards.

Gaia-X will be steered by a Belgian non-profit organization which has been founded by 22 German and French companies, including SAP, Deutsche Telekom, Orange, and Atos. The first prototype of Gaia-X should be ready by the end of the year. According to Gaia-X’s technical architecture unveiled in June 2020, it will encompass the three cloud computing models IaaS, PaaS, and SaaS as well as Edge Services. Furthermore, with various providers operating in one standardized infrastructure, Gaia-X is supposed to allow for individualized strategies of companies for multi-cloud approaches.

Decreasing dependency on US providers is a core aspect of Gaia-X by facilitating control over data. Firms are supposed to have a choice on handling and storing with the help of clear set standards and regulations—an idea also embraced by the recently issued EU Data Strategy. Although Gaia-X mainly functions as an alternative to already existing providers, non-European market participants can also join, when they adhere to the strict data privacy standards of Gaia-X. Furthermore, Europe’s future competitiveness is among the objectives of the project. With the possibility of accessing large data pools, framed by an open source approach and coherent standards, a significant amount of data should become accessible feeding smart machines or Artificial Intelligence (AI) systems.

Scarce Funds, Complex Implementation

But before Gaia-X can turn into a success, the initiative has to deal with huge obstacles: The enormous investments Chinese and US hyperscalers have made in cloud infrastructure run into billions of dollars on a yearly basis; in contrast, Gaia-X private and public investments will only amount to a two-digit millions figure, according to current plans—a comparatively small amount for a project of such complexity. Moreover, the technical implementation is highly complex, raising the question of whether Gaia-X—the enthusiasm of its French and German founding members notwithstanding—can convince on operability, cost, and applicability. If not, Gaia-X will have a rough ride growing into a pan-European project.

However, Gaia-X may still show the way. Its concept indicates that “digital sovereignty” is not defined as having “mastery and ownership”—a term used by European Commission President Ursula von der Leyen—over key technologies and operating its own hyperscalers in this particular context. Moreover, the “European Cloud” intends to achieve this overarching goal by setting clear rules and standards and protecting its own values. The journey to the moon may still end up in a crash landing; but at present it looks like the best shot Europe has at safeguarding its technological competitiveness.

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© REUTERS/David Gray

“The AI Arms Race,” “Information Iron Curtain”, or even a new “Tech Cold War”: there’s apparently no shortage of hyperbolic headlines harking back to pre-1989 times, when it comes to describing the now fierce competition between the United States and China over influence in global affairs that is progressively waged also over technologies. The current fight in Western countries about allowing the Chinese telecoms giant Huawei a role in the build-up of their 5G networks is just one example of this.

At the first glance, one could perhaps argue that the re-emergence of two superpowers clashing over global dominance is reminiscent of the old Cold War days when the US battled it out with the Soviet Union. However, appearances are often deceptive, especially in light of the mounting importance of technologies for global competition, and analogies that are leftovers of a bygone era are misleading.

“Brave” New World

The probably most obvious fundamental difference from Cold War times—as trivial it might sound—is the absence of an unequivocal political block confrontation. The “Western world” is not engaged in an official system and military contest with China, in contrast to the much more ideologically charged conflict between NATO members and the Warsaw Pact countries prior to 1989. With the present structure of international politics, the US, for instance, cannot take for granted that its organizational or like-minded “allies” will automatically join it in banning Huawei from their own 5G networks.

This is closely connected to the ever-growing economic interdependence on a global scale. China’s state-capitalistic system is much more integrated into the world economy than the rather old Soviet closed-off system and—as can be seen in the current trade conflict—is also deeply intertwined with the US economy. Moreover, this increasing economic entanglement has been propelled by digitalization itself, as technological systems are often shaped by individual modular components with different countries of origin. And within these global supply chains, the US and China are―to some extent― dependent on each other.

In several areas, including tech-related ones, it seems that Washington is forced into competition with Beijing over who offers the better (and perhaps cheaper) products and services, including to some longstanding US allies. Given this political and economic setting, these allies often don’t feel obliged to choose one side. This is due to the lack of two closed blocks. This is for example shown in the way some NATO members have simultaneously “signed up” for China´s Belt and Road Initiative or the 17+1 format. Hence, calling the ongoing dispute between China and the US a “Cold War”—even without considering the increasing impact of digitalization—would already barely reflect the current realities.

“General-Purpose” Technologies

Often overlooked, but no less important, are the new technologies themselves, which also mean the Cold War analogies do not work. Since the inception of the internet age, the sheer quantity and nature of emerging technologies have led to a rapid and structural transformation. During the Cold War, the number of technologies with relevance for the US-Soviet power battle was comparatively limited. Nowadays, the list of relevant innovations with important implications for various sectors seems to be getting longer by the day. Hence, data-driven technologies, for instance, have a much bigger impact on a country´s GDP today than say, nuclear production, did before.

Many of those technologies don’t just have a dual-use nature, but rather a “general-purpose” one. For instance, the application of artificial intelligence systems is often compared with the invention and use of electricity by pointing to the range of application possibilities. Furthermore, AI innovations can now be developed and shared without the huge industrial effort that, for instance, building intercontinental ballistic missiles meant. Today’s innovations have few parallels with the Cold War ones, which were characterized by huge logistics requirements and complex and lengthy manufacturing phases. However, this also means that countries like the US can no longer control them the way they did in the early days.

The Innovation Edge

This is boosted by the fact that—in contrast to the Soviet Union—China is heavily investing in all of these technologies and genuinely challenging the US innovation edge, especially by pushing forward with the “military-civilian fusion” to fully leverage the general-purpose applicability.
Also, the sources of major technological advances have shifted from the public sector (or the “military-industrial complex”) to commercial companies, most notably illustrated by the tech giants both in the US and in China. This growing reliance on the private sector requires much more sophisticated long-term strategies: while the US has to find ways to ensure that the access to those innovations stemming from US-based “multinational companies” will not be impeded, China will attempt to use its state-capitalistic companies without stifling innovation; in both cases, permanent readjustments are needed.

And last but not least, nowadays there is the contradictory development of digitalization connecting people across borders via information and communication technologies and leading to a democratization of interaction, but at the same time also handing nation states the tools that allow them to put into practice totalitarian visions to an unprecedented degree. China´s application and export of surveillance technologies or internet censorship are cases in point. Hence, technologies and its usage have much more impact on the ideological battlefield internationally.

New Parameters

These are just some of the profound differences from the Cold War period. At the same time, it is telling that within both the US and China, some are pushing for technological decoupling and thus showing a longing for the old Cold War structures. However, such a process would be messy and expensive, if doable at all.

The analogy of a “Tech Cold War” suggests parameters that are far too static and structured to describe the current situation. Conversely, Washington and Beijing find themselves in a perpetual mode of competition and negotiation vis-à-vis third countries, and even with actors in their own countries. If they want to avoid harming themselves, they will need a much more nuanced strategy and a better understanding of the impact of technologies in various areas including economic and military competitiveness and the international order.

It is of utmost importance that those dynamics are understood as precisely as possible—not only for the two main competitors, but also for countries and regions like Europe, who run the risk of becoming of pawns caught between the two fronts.

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© REUTERS/Stringer

In its quest for technological supremacy, China has a specific soft spot: its chip industry.

Beijing semiconductor efforts in the recent years and decades have not born fruit. Be it microprocessors, memory chips or mobile processors, in all of these fields the country is still not capable of developing its own top-notch assets on a large scale. Consequently, there is still a high reliance on chips produced by the semiconductor market leaders, normally situated in the US (e.g. Intel) as well as in Taiwan (e.g. Taiwan Semiconductor Manufacturing Co, or TSMC) and South Korea (Samsung Electronics).

This high level of dependence became very clear recently: In 2018, after the Chinese smartphone manufacturer ZTE was accused of illegally selling US equipment to Iran and North Korea, the US Department of Commerce imposed a ban on American companies selling their products to the company. Unable to buy chips from American chip makers like Qualcomm, ZTE teetered close to bankruptcy.  From a Chinese perspective, the last straw came when the same procedure was used in May, this time with telecoms giant Huawei as target.

These actions served as a wake-up call for China, pushing it to put greater efforts into achieving technological self-sufficiency—a claim already made by President Xi Jinping. This blatant exposure of China’s vulnerabilities in the global supply chain feels particularly painful for decision-makers in Beijing since Chinese strategists themselves pursue precisely this approach—pushing others to become technologically reliant on China and weaponizing this “interdependence” to exert economic and political pressure when required.

Untapped Market Potential

To reduce its reliance on foreign semiconductor industries, China has set up initiatives and funds to counteract the trend. However, according to Gu Wenjun, chief analyst at Shanghai-based semiconductor research company ICWise, it will take up to 40 years for China to reach self-sufficiency in many areas of chip production.

Although China has most probably lost the battle on “traditional chips” for the time being, it might win another one: In line with its ambitious and aggressive efforts to become an AI superpower, Beijing has recently started to cast an eye on AI chips. These chips are specifically designed to process and compute machine learning algorithms at a faster pace and are optimized for AI-specific functions, be it in the context of autonomous vehicles or robots as well as within the framework of cloud computing services or data centers.

For instance, this September, Chinese tech giant Alibaba officially entered the AI chip market by presenting the Hanguang 800. According to the company, this AI chip can shorten computing tasks that would usually last one hour down to a couple of minutes. Just one month earlier, Huawei presented its first commercial Ascend 910 AI computing chip. Other tech companies and start-ups such as Baidu, Tencent, Bitmain or Horizon Robotics intend following suit to capitalize on a niche in the semiconductor industry that still possesses market potential on an international scale.

Even though their American counterparts such as Google and Facebook have also already entered the “AI chip race” (at least for in-house purposes), no clear leader can be perceived so far, which gives Chinese companies a chance to successfully exhaust this untapped market potential.

Role Model for Europe

At first glance, one could simply regard this as yet another field where China will take a bold step in its efforts to solidifying its position as AI superpower. However, Europe can learn from the Chinese approach when it comes to its own endeavors to catch up in the global AI power game. It can detect the areas within the AI industry (or in the technological realm in general) where there are opportunities to gain ground or even to take the lead globally by benefiting from the first mover advantage—as the US and China have done when it comes to many AI-related components, which have been missed by Europe in the past.

One such area, for instance, could be using AI systems to process machine and engine data (temperature, pressure, rotor speed, etc.), which has a strong industrial base in Europe. With such an approach, Europe could combine its strengths in the physical world (i.e. its manufacturing industries) with AI technologies, also in the context of the increasing data generated by the Internet of Things (IoT). On the other hand, heavy investments in consumer data would most probably mean fighting a losing battle with the US and China.

There are also other data types where Europe could showcase its strengths: According to a study by the Center for Data Innovation, public health data can be leveraged on a large scale within the EU and could provide an opportunity for fueling further AI developments. Another related opportunity for Europe could be to focus on the quality of data, which can compensate for the lack of quantity to some extent. China, for instance, is said to have weaknesses in compiling structured data. However, in order to build on these scenarios, private sector data-sharing approaches in the business-to-business and business-to-government areas must be further supported by institutions such as the European Commission.

Against this backdrop, the European Union and its member states still have a chance to gain ground in the global AI industry. Within this context, however, the debate concerning AI ethics for Europe as a unique selling point is important but not sufficient. In combination, however, with a related thriving industry or at least with certain strong points in the European AI ecosystem, such a human-centered digital area can be developed to its fullest potential in order to compete with the US and China.

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© REUTERS/Jason Lee

Artificial intelligence (AI) is turning into an essential enabler for economic and military affairs. It has also become the tool of choice for surveillance activities in certain countries. Against this backdrop, governments, international organizations, and corporations have been drawing up guidelines on the ethical design and usage of AI algorithms and data.

In 2018, major technology companies already drafted related principles, which is hardly surprising as AI innovations nowadays mostly originate from the private sector. Google published AI at Google: Our Principle, while Microsoft wrote Microsoft AI Principles. Yet their data-driven business model and their commercial interest in AI fuel distrust. Critics accuse them of “ethical white-washing.” The reproach is that their published guidelines are nothing more than a marketing gimmick which aim to distract from their abusive and massive application of AI algorithms.

Irrespective of whether these accusations are true or not, there is an urgent need for stakeholders other than “profit-driven players” to become genuinely engaged in the AI ethics debate. In April 2019, the European Commission released its “Ethics Guidelines for Trustworthy Artificial Intelligence.” These guidelines were drafted by the 52-member High-Level Expert Group on Artificial Intelligence (HLEG AI), which consists of representatives from politics, industry, research institutions and civil society. The document encompasses seven guiding principles, among them transparency (the traceability of AI systems should be ensured), privacy and data governance (citizens should have control over their own data) and diversity, non-discrimination and fairness (which tackles the bias problems of AI systems).

In May, the Organization for Economic Co-operation and Development (OECD) released its AI ethics guidelines, the “Recommendation of the Council on Artificial Intelligence.” Even though the document is shorter than the EU one and lighter on detail, its principles are noticeably similar. Later that month, the Beijing AI Principles were announced by the Beijing Academy of Artificial Intelligence (BAAI)—an organization backed by the Chinese Ministry of Science and Technology and the Beijing municipal government—in a joint effort with several Chinese research institutions and industrial groups involving firms like Baidu, Alibaba and Tencent. In comparison with the guidelines provided by the EU, these principles are more descriptive and less comprehensive. However, they cover three crucial clusters: research and development, use, and governance.

Promising Signals

At first sight, it is a welcome development that major international organizations and powerful states are officially looking at ethical concerns about AI. And indeed, it is possible to identify positive aspects for each of the released AI guidelines and their wider significance: the EU document has great scope and is deliberately defined as a living document to be reviewed and updated over time. Given that AI systems are subject to constant changes and need continuous adjustment, such a mechanism is indispensable. The EU also includes a checklist with easy-to-understand questions that companies can used as points of orientation to ensure that ethical concerns are respected.

With regard to the OECD recommendations, it worth noting that—even though it is a non-binding document—it is backed by the United States. This means that the Trump administration is officially voicing ethical concerns about AI at an international level, despite its skepticism toward multilateralism. In addition, these recommendations are not limited to the 36-member states of the OECD—six non-members having already also embraced these principles. As a follow-up measure, an AI Policy Observatory will be established to help implement and monitor adhesion to these principles throughout the world. Based on these recommendations but with a more limited scope, the G20 meeting in Japan this June agreed a set of G20 AI Principles. Both the US and China were signatories.

Last but not least, there the promising sign of the Beijing AI Principles. It was surprising and gratifying to see that China’s government—which is widely criticized for using AI as a tool to monitor and grade citizens—is suddenly interested in ethical concerns and that, for instance, research and development of “AI should serve humanity and conform to human values.” This can be interpreted as a signal that China wishes to become engaged in a dialogue with international partners in spite of the increasingly powerful narrative of an “AI race” with the United States.

Stumbling Blocks Ahead

Nevertheless, it would be premature to speak of a new era of AI multilateralism and an effective AI ethics framework. The recent haste in drafting AI guidelines is partly motivated by the desire not to be left out of the conversation and the “standard setting game.” It marks the start of a likely long-running debate within the international community, with many stumbling blocks ahead. A small sample of these lingering challenges follow:

First, the devil will be in the detail, as the principles presented by all sides are still very vague. Even the most comprehensive and detailed guidelines—the ones drafted by the EU—fail to set non-negotiable ethical principles or so-called “red lines.” This was even criticized by one of the members of the HLEG AI, the philosopher Thomas Metzinger. At present, all of the three principles are more about opening up new thematic areas such as non-discrimination or robustness and safety to international discussion. Taken together with the fact that none of these principles are enforceable by law, it means that countries continue to have a lot of room for maneuver in their application of AI systems.

Second, the application possibilities for AI are too widespread for a one-fits-all approach. Different circumstances require different solutions. More specific application areas like manufacturing, surveillance, and the military need additional guidelines.

Third, ethics is always embedded in a cultural and social context that depends on a system of values shaped by a unique history. Since algorithms will impact many areas of our everyday lives, these cultural differences must be taken into account when drafting AI ethics. For instance, studies show that people in China and in the West have quite different responses to the famous “Trolley Dilemma,” a thought experiment in ethics that forces participants to make a difficult choice between a greater and a lesser evil.

Ultimately, such culture clashes will also be reflected in international politics. It will be a huge challenge to find common ground, especially if the international community seeks to develop more detailed principles and guidelines. Bringing in additional stakeholders and transferring what are ultimately ethical principles into hard law will be just as difficult.

Great Power Rivalry

Finally, and in addition to the challenges related to process, content, and implementation, there is a need to take the geopolitical context into account. This is true for the new technologies in general, but especially for general-purpose tools like AI. The great power rivalry between the US and China has only just begun, and emerging technologies with dual-use nature will be the main driver for economic profitability and military prowess. Hence, it is highly doubtful whether the so-called AI superpowers—first and foremost Beijing with its current demonstration of AI-based surveillance on minorities—will be willing to bind themselves in “ethical chains” through a self-imposed ethics regime. This is made evident by the reluctance of these countries to ban lethal autonomous weapons systems.

That is why it’s imperative that the EU continues to take the lead in the global debate on AI ethics in order to see the emergence of its “third way”—a digital sphere that is human-centered, regulated, and democratic. Yet setting high ethical standards is not enough. The EU and its member states also need to do more to establish a vibrant European AI ecosystem. This means not just encouraging additional investment, but also, among other measures, supporting European companies that develop AI systems. Otherwise, the EU will end up proclaiming and promoting detailed and sophisticated AI ethics guidelines without having any leverage to implement them internationally.

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