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Legal IT

Legal issues with stablecoins

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In the previous article, we talked about what stablecoins are, why they matter, and what different types of stablecoins there are. In this follow-up article, we look at the main legal issues. There are qualification issues with stablecoins. There are new regulatory frameworks. We also discuss some other risks and legal issues with stablecoins.

Qualification issues with stablecoins

The legal qualification of stablecoins remains one of the most debated issues, as they do not fit neatly into existing legal categories. The core challenge lies in determining whether stablecoins should be treated as money, securities, commodities, or something else entirely. This classification has significant implications for which regulators have jurisdiction, and which legal rules apply. In many jurisdictions, a key issue is whether a stablecoin qualifies as a security.

In the European Union, the Markets in Crypto-Assets Regulation (MiCA) resolves this ambiguity to a large extent by creating new categories specifically for stablecoins: “e-money tokens” and “asset-referenced tokens”. E-money tokens are those that are pegged to a single currency and resemble traditional electronic money under the E-Money Directive. Asset-referenced tokens are broader and can include tokens backed by baskets of currencies or commodities. This approach avoids trying to fit stablecoins into outdated categories like securities or commodities and instead regulates them on their own terms.

In the UK, the Financial Conduct Authority (FCA) does not generally treat fiat-backed stablecoins as securities unless they exhibit investment characteristics. However, the upcoming regulatory framework under the Financial Services and Markets Act 2023 will grant the Bank of England and FCA more tools to supervise stablecoins used for payments. At present, August 2025, they have not published any regulations yet.

In the United States, the Securities and Exchange Commission (SEC) has suggested that certain stablecoins, particularly those offering interest-bearing features or tied to investment mechanisms, may fall under the definition of securities. However, fiat-backed payment stablecoins like USDC or USDP, which simply maintain a 1:1 peg to a currency and do not generate returns for holders, are more often considered outside the scope of securities regulation. At the same time, the Commodity Futures Trading Commission (CFTC) has taken the position that some stablecoins may qualify as commodities. In a 2023 enforcement action, the CFTC referred to tethered assets like USDT as commodities under the Commodity Exchange Act. This has added to the regulatory uncertainty in the U.S., where overlapping authorities and inconsistent classifications have left issuers and users in legal limbo.

In essence, the legal qualification of stablecoins hinges on their structure and function. If they are used for payments and are fully backed by fiat currency reserves, they are more likely to be treated as payment instruments or e-money. If they are algorithmic, generate returns, or have speculative components, they may fall under securities or commodities laws. Regulatory frameworks are required to resolve the ambiguity and uncertainty stablecoin issuers face. Which brings us to …

Regulatory Frameworks

These days, the regulation of stablecoins is rapidly evolving. Regulatory initiatives focus on concerns about consumer protection, financial stability, and the risks of unregulated digital assets. Both the European Union and the United States have recently introduced or implemented significant legislative frameworks to address these concerns.

As mentioned above, in the European Union, stablecoins fall under the Markets in Crypto-Assets Regulation (MiCA). MiCA was formally adopted in 2023 and began phasing in from June 2024. MiCA distinguishes between different types of crypto assets. It introduces specific provisions for “e-money tokens” (which are pegged to a single fiat currency) and “asset-referenced tokens” (which may be backed by a basket of assets or commodities). Issuers of these stablecoins are required to obtain authorization from national competent authorities and must meet stringent governance, capital, and reserve requirements. MiCA also imposes obligations on crypto-asset service providers, ensuring oversight of issuance, custody, and trading. The European Central Bank has highlighted the importance of this framework to prevent the fragmentation of the digital finance market and to protect consumers.

In the United States, after years of regulatory ambiguity, Congress has recently made progress toward a unified approach. In July 2024, the Clarity for Payment Stablecoins Act was passed by the House Financial Services Committee and gained bipartisan traction. This bill focuses specifically on payment stablecoins, such as those issued by Circle (USDC) and Paxos (USDP), and introduces a clear licensing regime. Under this legislation, stablecoin issuers must either be state-licensed nonbank entities or federally approved institutions regulated by the Federal Reserve. The bill also imposes strict reserve backing requirements, limits on rehypothecation of reserve assets, and detailed disclosure obligations to increase transparency. In July 2025, the Genius Act – the first federal regulatory framework for stablecoins – was passed in Congress. It creates a new licensing regime for payment stablecoin issuers and is the first major crypto-related legislation to be passed by both chambers of Congress. The bill was signed into law on 18 July 2025.

Regulators in both areas understood that stablecoins might have a big impact once they become widely used. In the EU, MiCA includes special oversight mechanisms for “significant” stablecoins, allowing the European Banking Authority to step in. Similarly, in the U.S., the President’s Working Group on Financial Markets believes the federal government needs to regulate companies that issue stablecoins, especially the big ones that process lots of payments.

Outside the EU and U.S., countries like Japan and the UK are also catching up. Japan already passed a law in 2022 that allows only licensed banks and trust companies to issue stablecoins, while the UK’s Financial Services and Markets Act 2023 granted the Bank of England new powers to oversee systemic digital settlement assets, including fiat-backed stablecoins.

Other risks and legal issues with Stablecoins

Apart from classification and regulatory frameworks, stablecoins raise several other legal issues and risks. These have to do with financial stability, consumer protection, monetary sovereignty, and data governance. These concerns are particularly significant given the potential for stablecoins to scale rapidly across borders and integrate with mainstream financial services.

A first issue is the operational risk, especially the risk of technical failure, cyberattacks, or fraud within the stablecoin infrastructure. Since most stablecoins rely on centralized issuers or custodians, the reliability of reserve management and smart contracts is critical. A failure in these systems could cause a loss of peg, mass redemptions, or loss of user funds. In the previous article we mentioned the TerraUSD’s collapse in 2022, which was algorithmic stablecoin. Its collapse exposed how vulnerabilities in design can destabilize not only a single token but also the broader market. The US Financial Stability Board (FSB) has emphasized the importance of robust governance and risk management frameworks to prevent such collapses. Its October 2023 report outlines these concerns in detail.

Another legal concern is redemption rights. Users need clear, enforceable rights to redeem stablecoins for fiat currency on demand. In practice, many stablecoin issuers include disclaimers or reserve the right to delay or deny redemptions under certain conditions. This raises questions about contractual enforceability and consumer protection, particularly in jurisdictions without clear legal protections for token holders. The IMF has raised similar concerns in its global policy papers, especially when stablecoins operate across borders where legal remedies may be unclear or unenforceable.

There are also anti-money laundering (AML) and counter-terrorist financing (CTF) concerns. Stablecoins offer a relatively stable value and fast, borderless transfers, which make them attractive for illicit use. Many stablecoin platforms operate with limited KYC (Know Your Customers) procedures or allow anonymous transfers via decentralized protocols. Regulators have warned that this can undermine AML frameworks and create enforcement gaps.

Another major legal issue is monetary sovereignty. Central banks have raised concerns that widespread use of privately issued stablecoins could erode control over national currencies and monetary policy, especially in developing countries. If a stablecoin pegged to the US dollar becomes a dominant means of payment in another country, it can cause de facto dollarization and limit a central bank’s ability to manage inflation or respond to economic shocks.

Finally, data privacy and surveillance pose emerging legal and ethical challenges. Stablecoin providers often collect and process sensitive personal and financial data. In jurisdictions like the EU, such processing is subject to the General Data Protection Regulation (GDPR). But questions remain about how decentralized systems can comply with data minimization, user consent, and the right to erasure. Moreover, law enforcement access to stablecoin transaction data creates a tension between privacy rights and regulatory compliance.

Together, these issues show that the legal issues regarding stablecoins involves much more than just classification or licensing. Since stablecoins touch on financial law, contracts, data protection, monetary policy, and consumer rights, both companies and users face significant legal risks until we get better, more coordinated regulations worldwide.

 

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Legal IT

Using AI for legal research

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How safe is using AI for legal research? On the one hand, AI is making quick progress and keeps getting better. The arrival of a new generation of AI agents will only speed up that process. But on the other hand, we keep getting headlines where law firms are being fined for using AI that referred to non-existing legislation and jurisprudence. In this article, we look at a) how AI is reshaping legal research, b) at the risks and accuracy concerns of using AI in legal research, c) at possible mitigation strategies. Finally, d) we look at using AI for legal research on non-US law.

How is AI reshaping legal research – benefits

AI has been having a significant impact on legal research, and generative AI has certainly sped up that process. Many law firms are using generative AI to assist them with their legal research. It is easy and convenient, as they can ask questions in natural language, rather than having to study some query language. And now that most generative AIs have started offering more advanced research agents that can provide sources, AI has become even more attractive. So, AI is significantly reshaping legal research in several impactful ways. Most of those are beneficial.

One of the most noticeable changes is the enhanced speed and efficiency it brings. AI tools are capable of sifting through vast volumes of legal data in seconds, identifying relevant information much faster than a human could. This efficiency saves lawyers considerable time and resources.

Beyond speed, AI can also improve the accuracy and depth of insight in legal research. By analysing large datasets, AI can detect patterns and extract insights that might go unnoticed by human researchers. It can also flag potential errors or inconsistencies in legal documents, helping to ensure the accuracy and reliability of the information used. But caution is needed, as we will discuss below.

Another major advantage is the broader access to legal information that AI provides. These tools can draw from a wide array of sources, including statutes, case law, legal journals, and specialized databases. This comprehensive reach allows lawyers to develop a fuller understanding of the legal issues they face.

Natural Language Processing (NLP) and machine learning further enhance AI’s capabilities in the legal field. NLP enables AI to comprehend the meaning within legal texts. This allows it to extract key information and identify relevant precedents. Meanwhile, machine learning algorithms can analyse historical case data to predict outcomes. This gives lawyers valuable insights into the strengths and weaknesses of their cases.

AI is also increasingly being integrated into established legal research platforms. This integration improves the efficiency and comprehensiveness of legal research.

However, as AI becomes more embedded in legal practice, responsible usage is essential. Ensuring accuracy, upholding ethical standards, and maintaining regulatory compliance are critical. Lawyers must treat AI as a supportive tool rather than a standalone solution, and it remains vital to verify any information generated by AI systems. Because there are still considerable risks involved in using AI for legal research.

Risks and accuracy concerns of using AI in legal research

In a recent case in California, a judge found that nine out of the twenty-seven quoted sources were non-existent. The two law firms involved (one had delegated research to the other) were fined 31 000 USD. If you follow the news on legal AI, it is a common problem. Apart from that, AI still often is biased, too. Let’s have a closer look at both issues.

Accuracy concerns

AI systems can produce inaccurate, incomplete, or misleading legal information. This is particularly the case when dealing with complex cases, with nuanced legal concepts, or when legislation or jurisprudence has changed recently.

Even worse are AI “Hallucinations”. As witnessed in the example above, AI can generate plausible but factually incorrect information. It is therefore crucial to verify all AI-generated output against credible sources. The Californian example above highlights how this is a serious risk, as one in three sources that were quoted did not exist.

The example also illustrates the risk of reliance on AI without oversight. You cannot assume the AI knows what it’s doing. Over-reliance on AI without thorough human review can lead to errors that compromise case outcomes and erode client trust.

Bias and ethical concerns

In previous articles, we pointed out that AI inherits and reflects all the biases of the data pool that it was trained upon. This can lead to unfair or discriminatory legal outcomes. So, bias in AI algorithms is a first concern.

Many AI systems cannot explain how they reached their conclusions, or they fail to mention sources. Lack of transparency and accountability, therefore, is a second issue. The algorithms used by AI systems can be opaque, making it difficult to understand how decisions are made and hold the AI system accountable.

Clients may not fully understand the role of AI in their legal representation. This can easily undermine their trust. Clear communication is essential.

As with any online tool lawyers use that share client information, there are data privacy and confidentiality concerns.

Finally, there is the aspect of professional responsibility. Lawyers have a duty to supervise AI-generated work, ensuring it is accurate and ethical. They also must communicate with clients about the use of AI tools.

Mitigation strategies

It is possible to counteract these risks by implementing some mitigation strategies.

  • Always verify AI-generated results against credible legal databases and primary sources.
  • Actively oversee and review AI-generated work to ensure accuracy, as well as ethical compliance.
  • Be transparent with clients about the use of AI tools.
  • Implement robust data security measures to protect client information and comply with privacy regulations.
  • Adhere to ethical guidelines and professional responsibilities when using AI in legal practice.

What about using AI for legal research on non-US law?

Most of the advances in generative AI are being made in the US, and the EU is catching up. How well do the generative AI platforms perform when it comes to non-US law? And are they available in other languages?

Let’s start with the language question: all the major generative AI engines are available in Dutch and French.

Then, what about non-US law? We did some test with European Law, more specifically about GDPR, and overall, these tests went well. We did not test on recent legislation or jurisprudence.

We also briefly did some tests with Belgian law. We thought art. 1382 of the Civil Code would be an interesting test case, given that it was recently replaced by a new book 6 on extra-contractual liability. We ran the test on ChatGPT, CoPilot, Gemini, Claude, Perplexity, Grok, and you.com. Only four out of seven pointed out that art. 1382 CC had been replaced. They were ChatGPT, CoPilot, Gemini and Grok. The other three, Claude, Perplexity, and You.Com, all did not mention book 6 on extracontractual liability at all.

So, while caution and supervision are already needed for US and EU law, it is even more the case for the law of EU member states, where several generative AI platforms were not (yet) aware of recent legislation.

Conclusion

Using AI for legal research holds promise, but supervision is still very much needed. The above examples show how they can still hallucinate, and that they may not be aware of recent changes in legislation or jurisprudence.

 

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Legal IT

The dangers of artificial intelligence

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Artificial intelligence (AI) is a powerful technology that can bring many benefits to society. However, AI also poses significant risks and challenges that need to be addressed with caution and responsibility. In this article, we explore the questions, “What are the dangers of artificial intelligence?”, and “Does regulation offer a solution?”

The possible dangers of artificial intelligence have been making headlines lately. First, Elon Musk and several experts called for a pause in the development of AI. They were concerned that we could lose control over AI considering how much progress has been made recently. They expressed their worries that AI could pose a genuine risk to society. A second group of experts, however, replied that Musk and his companions were severely overstating the risks involved and labelled them “needlessly alarmist”. But then a third group again warned of the dangers of artificial intelligence. This third group included people like Geoffrey Hinton, who has been called the godfather of AI. They even explicitly stated that AI could lead to the extinction of humankind.

Since those three groups stated their views, many articles have been written about the dangers of AI. And the calls to regulate AI have become louder than ever before. (We published an article on initiatives to regulate AI in October 2022). Several countries have started taking initiatives.

What are the dangers of artificial intelligence?

So, what are the dangers of artificial intelligence? As with any powerful technology, it can be used for nefarious purposes. It can be weaponized and used for criminal purposes. But even the proper use of AI holds inherent risks and can lead to unwanted consequences. Let us have a closer look.

A lot of attention has already been paid in the media to the errors, misinformation, and hallucinations of artificial intelligence. Tools like ChatGPT are programmed to sound convincing, not to be accurate. It gets its information from the Internet, but the Internet contains a lot of information that is not correct. So, its answers will reflect this. Worse, because it is programmed to provide any answer if it can, it sometimes just makes things up. Such instances have been called hallucinations. In a lawsuit in the US, e.g., a lawyer had to admit that the precedents he had quoted did not exist and were fabricated by ChatGPT. (In a previous article on ChatGPT, we warned that any legal feedback it gives must be double-checked).

As soon as ChatGPT became available, cybercriminals started using it to their advantage. A second set of dangers therefore has to do with cybercrime and cybersecurity threats: AI can be exploited by malicious actors to launch sophisticated cyberattacks. This includes using AI algorithms to automate and enhance hacking techniques, identify vulnerabilities, and breach security systems. Phishing attacks have also become more sophisticated and harder to detect.

AI can also be used for cyber espionage and surveillance: AI can be employed for sophisticated cyber espionage activities, including intelligence gathering, surveillance, and intrusion into critical systems. Related to this is the risk of invasion of privacy and data manipulation. AI can collect and analyse massive amounts of personal data from various sources, such as social media, cameras, sensors, and biometrics. This can enable AI to infer sensitive information about people’s identities, preferences, behaviours, and emotions. AI can also use this data to track and monitor people’s movements, activities, and interactions. This can pose threats to human rights, such as freedom of expression, association, and assembly.

Increased usage of AI will also lead to the loss of jobs due to automation. AI can perform many tasks faster and cheaper than humans, which will lead to unemployment and inequality. An article on ZD Net estimates that AI could automate 300 million jobs. Approximately 28% of current jobs could be at risk.

There also is a risk of loss of control. As AI systems become more powerful, there is a risk that we will lose control over them. This could lead to AI systems making decisions that are harmful to humans, such as launching a nuclear attack or starting a war. This risk of the loss of control is a major concern about the weaponization of AI. As AI technology advances, there is a worry that it could be weaponized by state or non-state actors. Autonomous weapon systems equipped with AI could potentially make lethal decisions without human intervention, leading to significant ethical and humanitarian concerns.

We already mentioned errors, misinformation, and hallucinations. Those are involuntary side-effects of AI.  A related danger of AI is the deliberate manipulation and misinformation of society through algorithms. AI can generate realistic and persuasive content, such as deepfakes, fake news, and propaganda, that can influence people’s opinions and behaviours. AI can also exploit people’s psychological biases and preferences to manipulate their choices and actions, such as online shopping, voting, and dating.

Generative AI tends to use existing data as its basis for creating new content. But this can cause issues of infringement of intellectual property rights. (We briefly discussed this in our article on generative AI).

Another risk inherent to the fact that AI learns from large datasets is bias and discrimination. If this data contains biases, then AI can amplify and perpetuate them. This poses a significant danger in areas such as hiring practices, lending decisions, and law enforcement, where biased AI systems can lead to unfair outcomes. And if AI technologies are not accessible or affordable for all, they could exacerbate existing social and economic inequalities.

Related to this are ethical implications. As AI systems become more sophisticated, they may face ethical dilemmas, such as decisions involving human life or the prioritization of certain values. Think, e.g., of self-driving vehicles when an accident cannot be avoided: do you sacrifice the driver if it means saving more lives? It is crucial to establish ethical frameworks and guidelines for the development and deployment of AI technologies. Encouraging interdisciplinary collaboration among experts in technology, ethics, and philosophy can help navigate these complex ethical challenges.

At present, there is insufficient regulation regarding the accountability and transparency of AI. As AI becomes increasingly autonomous, accountability and transparency become essential to address the potential unintended consequences of AI. In a previous article on robot law, we asked the question who is accountable when, e.g., a robot causes an accident. Is it the manufacturer, the owner, or – as AI becomes more and more self-aware – could it be the robot? Similarly, when ChatGPT provides false information, who is liable? In the US, Georgia radio host Mark Walters found that ChatGPT was spreading false information about him, accusing him of embezzling money. So, he is suing OpenAI, the creators of ChatGPT.

As the abovementioned example of the lawyer quoting non-existing precedents illustrated, there also is a risk of dependence and overreliance: Relying too heavily on AI systems without proper understanding or human oversight can lead to errors, system failures, or the loss of critical skills and knowledge.

Finally, there is the matter of superintelligence that several experts warn about. They claim that the development of highly autonomous AI systems with superintelligence surpassing human capabilities poses a potential existential risk. The ability of such systems to rapidly self-improve and make decisions beyond human comprehension raises concerns about control and ethical implications. Managing this risk requires ongoing interdisciplinary research, collaboration, and open dialogue among experts, policymakers, and society at large. On the other hand, one expert said that it is baseless to automatically assume that superintelligent AI will become destructive, just because it could. Still, the EU initiative includes the requirement of building in a compulsory kill switch that allows to switch the AI off at any given moment.

Does regulation offer a solution?

In recent weeks, several countries have announced initiatives to regulate AI. The EU already had its own initiative. At the end of May, its tech chief Margrethe Vestager said she believed a draft voluntary code of conduct for generative AI could be drawn up “within the next weeks”, with a final proposal for industry to sign up “very, very soon”. The US, Australia, and Singapore also have submitted proposals to regulate AI.

Several of the abovementioned dangers can be addressed through regulation. Let us go over some examples.

Regulations for cybercrime and cybersecurity should emphasize strong cybersecurity measures, encryption standards, and continuous monitoring for AI-driven threats.

To counter cyber espionage and surveillance risks, we need robust cybersecurity practices, advanced threat detection tech, and global cooperation to share intelligence and establish norms against cyber espionage.

Privacy and data protection regulations should enforce strict standards, incentivize secure protocols, and impose severe penalties for breaches, safeguarding individuals and businesses from AI-enabled cybercrime.

To prevent the loss of jobs, societies need to invest in education and training for workers to adapt to the changing labour market and create new opportunities for human-AI collaboration.

Addressing AI weaponization requires international cooperation, open discussions, and establishing norms, treaties, or agreements to prevent uncontrolled development and use of AI in military applications.

To combat deepfakes and propaganda, we must develop ethical standards and regulations for AI content creation and dissemination. Additionally, educating people on critical evaluation and information verification is essential.

Addressing bias and discrimination involves ensuring diverse and representative training data, rigorous bias testing, and transparent processes for auditing and correcting AI systems. Ethical guidelines and regulations should promote fairness, accountability, and inclusivity.

When it comes to accountability and transparency, regulatory frameworks can demand that developers and organizations provide clear explanations of how AI systems make decisions. This enables better understanding, identification of potential biases or errors, and the ability to rectify any unintended consequences.

At the same time, regulation also has its limitations. While it is important, e.g., to regulate things like cybercrime or the weaponization of AI, it is also clear that the regulation will not put an end to these practices. After all, by definition, cybercriminals don’t tend to care about any regulations. And despite the fact that several types of weapons of mass destruction have been outlawed, it is also clear that they are still being produced and used by several actors. But regulation does help to keep the trespassers accountable.

It is also difficult to assess how disruptive the impact of AI will be on society. Depending on how disruptive it is, additional measures may be needed.

Conclusion

We have reached a stage where AI has become so advanced that it will change the world and the way we live. This is already creating issues that need to be addressed. And as with any powerful technology, it can be abused. Those risks, too, need to be addressed. But while we must acknowledge these issues, it should also be clear that the benefits outweigh the risks, as long as we don’t get ahead of ourselves. At present, humans abusing AI are a greater danger than AI itself.

 

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