The development of artificial intelligence (AI) presents significant opportunities for economic and social flourishing. The release of foundational models such as the large language model (LLM) ChatGPT4 in early 2023 captured the world’s attention, heralding a transformation in our approach to work, communication, scientific research, and diplomacy. According to Goldman Sachs, LLMs could raise global GDP by 7 percent and lift productivity growth by 1.5 percent over 10 years. McKinsey found that generative AI such as ChatGPT4 could add $2.6-$4.4 trillion each year over 60 use cases, spanning customer operations, marketing, and sales, software engineering, and R&D. AI is also impacting international trade in various ways, and LLMs bolster this trend. The upsides of AI are significant and achieving them will require developing responsible and trustworthy AI. At the same time, it is critical to address the potential risk of harm not only from conventional AI but also from foundational AI models, which in many cases can either magnify existing AI risks or introduce new ones.

For example, LLMs are trained on data that encodes existing social norms, with all their biases and discrimination. LLMs create risks of information hazards by providing information that is true and can be used to create harm to others, such as how to build a bomb or commit fraud. A related challenge is preventing LLMs from revealing personal information about an individual that is a risk to privacy. Another higher risk from the misuse of LLMs is an increase in the incidence and effectiveness of crime. In other cases, LLMs will increase existing risks of harm, such as from misinformation which is already a problem with online platforms or increase the incidence and effectiveness of crime. LLMs may also introduce new risks, such as risks of exclusion where LLMs are unavailable in some languages.

International cooperation on AI is already happening in trade agreement and international economic forums

Many governments are either regulating AI or planning to do so, and the pace of regulation has increased since the release of ChatGPT4. However, regulating AI to maximize the upsides and minimize the risks of harm without stifling innovation will be challenging, particularly for a rapidly evolving technology that is still in its relative infancy. Making AI work for economies and societies will require getting AI governance right. Deeper and more extensive forms of international cooperation can support domestic AI governance efforts in a number of ways. This includes by facilitating the exchange of AI governance experiences which can inform approaches to domestic AI governance; addressing externalities and extraterritorial impacts of domestic AI governance which can otherwise stifle innovation and reduce opportunities for uptake and use of AI; and finding ways to broaden access globally to the computing power and data needed to develop and train AI models.

Free trade agreements (FTAs), and more recently, digital economy agreements (DEAs) already include commitments that increase access to AI and bolster its governance. These include commitments to cross-border data flows, avoiding data localization requirements, and not requiring access to source code as a condition of market access, all subject to exception provisions that give government the policy space to also pursue other legitimate regulatory goals such as consumer protection and guarding privacy. Some FTAs and DEAs such as the New Zealand-U.K. FTA and the Digital Economy Partnership Agreement include AI-specific commitments focused on developing cooperation and alignment, including in areas such as AI standards and mutual recognition agreements.

With AI being a focus of discussions, international economic forums such as the G7 and the U.S.-EU Trade and Technology Council (TTC), the Organization for Economic Cooperation and Development (OECD), as well as the Forum for Cooperation on Artificial Intelligence (FCAI) jointly led by Brookings and the Center for European Policy Studies as a track-1.5 dialogue among government, industry, and civil society, are important for developing international cooperation in AI. Initiatives to establish international AI standards in global standards development organizations (SDOs) such as the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) are also pivotal in developing international cooperation on AI.

But more is needed—where new trade commitments can support AI governance

These developments in FTAs, DEAs, and in international economic forums, while an important foundation, need to be developed further in order to fully address the opportunities and risks from foundational AI models such as LLMs. International economic policy for foundational AI models can use commitments in FTAs and DEAs and outcomes from international economic forums such as the G7 and TTC as mutually reinforcing opportunities for developing international cooperation on AI governance. This can happen as FTAs and DEAs elevate the output from AI-focused forums and standard-setting bodies into trade commitments and develop new commitments as well. FCAI is another forum to explore cutting-edge AI issues.

Joshua P. Meltzer is a senior fellow in the Global Economy and Development program at the Brookings Institution.

To read the executive summary, click here.

To access the full paper PDF, click here.

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Generative AI is vital to national interest, regional prosperity, and tackling shared global challenges.

It can help to grow economies, quickly and fairly, by identifying the risks entailed in a long-chain transaction or a complex supply chain. So far, so good — but there is no system in place to monitor and pinpoint suspicious global trade patterns. Nor is there any mapping of complex international trade flows, or overall analysis of trading patterns.

Every data point, each statistical analysis and prediction model, must be spot on. Over-reliance on unverified data, or information that is inaccurate or misleading, can have dire consequences. A simple misunderstanding of context can result in AI’s notorious “technological hallucinations”. Errors can multiply through a supply chain, posing risks that can have far-reaching effects on the economy — such as covering up dumping, counterfeiting, or sanctions avoidance.

AI can play a vital role in monitoring compliance, analysing trends, and assessing the impact of policies. It provides transparency and engenders trust and accountability. AI-driven decisions and recommendations produce credible, far-reaching results. It can tell us where to seek proof of reliability, raise red flags, and shed light on previously invisible interconnections of the global economy. It assists in furthering our understanding of the complexities of trade dynamics.

But it’s crucial to see AI for what it is: a tool for augmenting human capabilities, not replacing them. Take this example. Over 200 million bills of lading, crucial papers in international trade, were recently reviewed by the International Centre for Trade Transparency (ICTTM). It found that 13.6 percent contained at least one error. The OECD decrees that 2.5 percent of global trades, and up to 5.8 percent of EU imports, are counterfeit. The documents provide particulars about country-of-origin, product codes and descriptions, quantities, and costs. Certifications, health and safety requirements, regulatory controls, anti-dumping measures, and taxation are all set by the data collected.

Again, any mistake can have dire results.

ICTTM research shows that goods produced with slave labour still appear on international markets; companies are bypassing safety standards by intentionally mislabelling products as requiring no certification. There have been exports of semiconductors pushed through in this way by dubious actors. Traceability becomes more muddied with each step of the transaction.

There’s clear evidence that some offenders re-incorporate in new jurisdictions as soon as they are caught — still selling to the same importers. This basic move, because of the lack of international oversight, makes these actions almost untraceable.

When error, fraud, and counterfeit percentages are multiplied over a complex supply chain many layers deep, the dangers become apparent. These “mistakes” have serious repercussions for society — and can even put lives at risk. There is an enormous, hidden, problem in our global supply chains and individual “empires” of technology have no way of solving it.

Nationally built systems, siloed in their own technological and political kingdoms, are not a suitable response to these problems. Countries inspect a small percentage of imports, and almost no exports. There is no system in place to monitor global trade patterns, no mapping of international trade flows.

And this is where AI can be of use. The international commerce ecosystem is complex, and bots have the capacity to spot macro- and micro-trends across the entire system, rather than just between two trading partners. The fact that we can exercise some control over our interactions with AI is significant. It can help us spot potential threats and zero-in on the primary papers that need closer inspection. It is a tool to identify and chart patterns and act as an early warning system, while keeping faith in the reliability of source materials. Once we know where to look, locating bad actors and verifying documents becomes simpler.

The boundaries of AI are still expanding. Once we are able to recognise global macro trends, we can use it to our advantage. It can shed light on our reliance on specific vendors and suppliers. It can help us to evaluate the economic risks associated with our suppliers, as well as learn how our products fit into global supply networks. With AI, a component that poses a security concern can be identified and rapidly removed from the supply chain. Without it, such problems may remain hidden.

Human and computer error, and intentional fraud in supply chains, can all be distinguished. AI’s potential lets us conduct comprehensive analyses down to the smallest of details, leading us straight back to the original suppliers, buyers, and documents. The goal is for a zero-trust approach in which papers and records are verified and analysed.

Applying AI to international trade provides a workable answer to the growing difficulties and risks associated with internationally integrated markets. By embracing it, we are not advocating for unquestioning faith in an unknown system. We are suggesting its use as a tool to draw focus to specific areas. If we continue to adopt and use AI with a zero-trust, verify-and-confirm methodology, the transparency, accuracy, and efficiency it can bring could become essential in navigating the global commerce system.

Right now, at the intersection of science and business, artificial intelligence presents a once-in-a-generation opportunity. Used wisely, it has the potential to help overcome some entrenched problems. Its potential extends beyond the cutting of human labour or the generation of otherwise unpredictable results. It gives us a new perspective, an analytical tool that could radically alter how we think about international trade. It could help our economies to flourish in ways that are beneficial to all involved.

There’s no tolerance for AI hallucinations here. Precision, clarity, and faith in human scrutiny are front and centre. ESG reporting is becoming the new norm. Interoperability affords legal protection and a process that safeguards SMEs and banks. Collaborative efforts such as Project Perseus bring together technology, finance, and policy to unlock sustainable access for SMEs via data-sharing. This is critical for stakeholders in the business and banking worlds.

Nationally built systems in technological and political silos must be avoided to combat these challenges. Collaborative efforts between nation states would enable a comprehensive understanding of patterns and targeted strategies. Artificial intelligence should be seen as an instrument that shows us the bigger picture of a vast chain over which no single country, or corporate, should ever have total control.

So, where do governments, regulators, and the private sector go from here? Frameworks and processes are in place to deliver success — and the time for theory is over.

Lord (JD) Waverley is Member of the House of Lords of the United Kingdom and Chairman of Capital Finance International (CFI.co).

To read the full commentary, please click here.

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The agreed non-agreement between the three states pertains to some of the most complex machinery and most miniscule components humankind has ever produced. With their accord, the countries effectively restricted the export to China of the most advanced microchips and the tools to produce them. These items have become a focal point in international power politics because of their use in developing artificial intelligence and their centrality to many of the 21st century’s most important technologies.

As news on the matter emerged, the Dutch prime minister confined his remarks to saying: “Those talks have been going on for a long time and we’re not saying anything about it.” The reason for reticence was clear; in response to their decision, China threatened retaliation against the Netherlands and Japan.

The move followed on from measures unilaterally implemented by the US in October 2022 to restrict the trade of advanced semiconductor technologies with China for reasons of international security. And it now appears that the Dutch national measures could soon be followed by a decision by the German government to restrict the export to China of chemicals needed for chip production.

As these sorts of incidents mount amid the escalating US-China strategic technology competition, the EU and its member states will find themselves increasingly caught in the crossfire. Washington will maintain pressure on its allies to align with its China policy. China’s military build-up will continue to change the balance of power. And Beijing’s willingness and ability to weaponise trade will likely continue to grow – it will no longer be possible for the EU to keep its pursuit of free trade separate from these powerful currents. If a rules-based order is to remain, the rules will need to change to take account of the ways in which economic security forms part of this wider competition.

To steer a course according to its own interests in this new era of strategic trade controls, the EU must urgently develop its own strategy and upgrade its tools to deliver on it. If it is to promote and defend its own sovereignty, it must start to draw its own red lines in technology engagement with China and upgrade its export control policy.

Tobias Gehrke is a senior policy fellow at the European Council on Foreign Relations, based in the Berlin office. He leads ECFR’s Geoeconomics Initiative. His area of focus includes economic security, European economic strategy, and great power competition in the global economy.

Julian Ringhof is a policy fellow with the European Power programme at the European Council on Foreign Relations. His research focuses on the implications of digital and emerging technologies for international affairs, including the topics of EU digital diplomacy and EU technological sovereignty.

To read the full policy brief, please click here.

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