Ambient Computing

In this article we discuss ambient computing. We answer the following questions: What is ambient computing? What are some examples of ambient computing? What are the benefits? What are the challenges? In a follow-up article, we will look at the relevance of ambient computing for lawyers.

What is ambient computing?

Ambient computing is the idea of embedding computing power into everyday objects and environments, to make them smart, connected, and responsive. The goal is to make it easier for users to take full advantage of technology without having to worry about the details. Instead of having to directly interact with different computing devices to get desired results – for example, using your phone to make a phone call and your remote to turn on a TV– ambient computing allows all your devices to work together seamlessly to fulfil your needs.

Ambient computing relies on a variety of technologies, such as sensors, artificial intelligence, cloud computing, voice recognition, gesture control, and wearable devices, to create a seamless and personalized user experience. Ambient computing devices are designed to be unobtrusive and blend into the background, so that users can focus on their tasks and goals rather than on the technology itself.

Why call it ambient computing? The Oxford Dictionary defines the word ambient as “relating to the immediate surroundings of something.” So, in this vision of ambient computing the technology is largely invisible and integrated into our surroundings. For this reason, it is also often referred to as ubiquitous computing.

The concept of ambient computing is closely related to the concept of the Internet of Things. Sabrina Ortiz in ZDNet: “the two concepts are intertwined. IoT refers to the vast array devices that connect to the internet to optimize their functionality, like smart sensors and smart speakers: ambient computing builds on that. ‘IoT forms a base for ambient computing, with ambient computing more focused on how devices and intelligent services interact with users,’ Jason Low, principal analyst at the research firm Canalys, tells ZDNET. In other words, ambient computing focuses on the interaction between these devices once they are connected.”

Ambient computing is not a new idea, but it has become more prevalent and accessible in recent years, thanks to the advancements in the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and 5G networks. These technologies enable a vast array of smart devices, sensors, data, and agents that can communicate, analyse, and act in the background.

Some examples of ambient computing

One of the most widespread examples of ambient computing devices are smart speakers and personal voice assistants. Think, e.g., of devices like Amazon Echo, and Google Home. These devices allow users to control their smart home devices, access information, play music, and more, simply by using their voice. Users can speak naturally to their surroundings, without having to look at a screen or press a button. These smart speakers and personal voice assistants and connected devices can turn you home into a smart home where almost everything can be controlled from your phone, or it can be voice controlled. This includes lights, microwave ovens, window blinds, thermostats, washing machines, etc.

Another example of ambient computing is found in cars. Present day cars have all kinds of technology embedded in them, and they are all seamlessly integrated. They have built-in cameras, microphones, and a legion of sensors that all collect and process data in a seemingly unobtrusive way. Consulting firm McKinsey predicts that 95% of new vehicles sold globally will be connected ones by 2030.

Ambient computing can help in business operations, too. The pandemic changed how corporations operate, with many workplaces still having hybrid work models in place. To mitigate the problem of having employees in different places, many workplaces have developed conference rooms that have a sound system which picks up voices from across the room for Zoom calls and allows for smart casting of virtual meetings on a big screen. These office spaces are a great example of how ambient computing is implemented: both the smart casting tech and the sound system remain active in the conference room at all times, cannot be seen and facilitate an everyday activity.

Ambient computing is also expanding to other domains, such as health care, education, entertainment, and transportation. For instance, wearable devices such as smartwatches and fitness trackers can monitor users’ health and activity levels and provide feedback and recommendations. Smart glasses and augmented reality headsets can overlay digital information onto users’ vision, enhancing their perception and interaction with the physical world. Self-driving cars can sense their environment and navigate autonomously, while providing passengers with entertainment and comfort options.

What are the benefits?

As the above examples demonstrate, ambient computing clearly has the potential to bring many benefits to users and society at large. To mention but a few:

  • Convenience: Ambient computing can make users’ lives easier by automating tasks, simplifying interactions, and providing personalized services.
  • Efficiency: Ambient computing can improve users’ productivity and performance by reducing errors, saving time, and optimizing resources.
  • Engagement: Ambient computing can enhance users’ enjoyment and satisfaction by creating immersive and interactive experiences.
  • Empowerment: Ambient computing can enable users to access information and opportunities that they might not have otherwise.

What are the challenges?

As with any technology, ambient computing also poses several challenges and risks that need to be addressed. The most important challenges include:

Privacy: Ambient computing can collect vast amounts of data about users’ behaviour, preferences, location, health, and more. This data can be used for beneficial purposes, such as improving services and personalization. But it can also be misused or compromised by malicious actors or third parties. Or they can just be sold to third parties. Research by the Mozilla Foundation reveals that many car manufacturers, e.g., make a bigger profit from the collected data they sell than from the actual sales of cars. And that is alarming.

Security: Ambient computing can expose users’ devices and data to cyberattacks or physical tampering. This can compromise users’ safety and functionality of their devices. Cars and baby monitors, e.g., appear to be easy targets for hackers.

Ethics: Ambient computing can raise ethical questions about users’ autonomy, consent, trust, responsibility, and accountability. For example, who decides what data is collected and how it is used? How transparent are the algorithms that make decisions for users? How do users balance convenience with control?

Accessibility: Ambient computing can create digital divides between those who have access to ambient technology and those who do not. This can create inequalities in terms of opportunities, education, health care, and social inclusion.


Ambient computing is already changing the way we live and work. It offers many benefits, but there also are some serious challenges that may require extra regulation. In our next article, we have a closer look at its relevance for lawyers.



Virtual Legal Assistants

In this article, we discuss virtual legal assistants (VLAs). We answer questions like, “What are virtual legal assistants?”, “What services do they offer?”, “What are the benefits of using virtual legal assistants?”, and “What are the limitations?”. We also have a look at some statistics.

What are virtual legal assistants?

When we read articles on virtual legal assistants, we discover that the term is used in different ways. Some definitions restrict it to physical persons who work remotely and to whom purely administrative tasks are outsourced. Most authors also include the work of (remote) paralegals, while others also include all the services offered by third-party (or alternative) legal service providers who may use AI-powered or technology-driven platforms like bots. So, in its widest sense, a virtual legal assistant is an assistant that remotely assists lawyers, law firms, and legal professionals with various tasks and processes. They typically work as subcontractors for the law firm.

What services do they offer?

Virtual legal assistants offer a wide range of services. They can streamline and facilitate client communications and interactions. E.g., they can answer frequently asked questions and provide updates on case status, while maintaining confidentiality and security. They can personalize client engagement. And if you work with VLA bots and/or with physical people in different locations, they can guarantee 24/7 availability and instant responses.

Virtual legal assistants can enhance legal research. They can help lawyers find relevant case law, statutes, regulations, and legal articles to support their arguments and build stronger cases. They provide instant access to legal knowledge.

VLAs can assist in drafting legal documents such as contracts, agreements, pleadings, and other legal correspondence, often by generating templates or suggesting content based on context. One area where VLA bots have been proven very useful is contract review. They can review contracts, highlight important clauses, identify potential risks, and ensure compliance with relevant laws.

Virtual legal assistants also contribute to facilitating and optimizing case management and workflow. They can organize and manage case-related information, deadlines, and tasks, streamlining the workflow for lawyers and legal teams. VLA bots can provide automated case updates.

Other areas where VLAs are useful include bookkeeping, billing, and time tracking. They can help lawyers track billable hours and manage invoicing for clients.

You can also hire a VLA for data entry.

There is the aspect of due diligence, as well. Virtual legal assistants can assist in conducting due diligence for mergers, acquisitions, or other transactions by analysing legal and financial data.

VLA bots are also useful for legal analytics. They can analyse large sets of legal data and provide insights into trends, patterns, and potential outcomes.

Finally, there is E-discovery. VLA bots can help with the process of identifying, collecting, and analysing electronically stored information (ESI) for litigation purposes.

Some statistics

There are plenty of interesting statistics available when it comes to virtual assistants. Here is a selection.

  • Virtual assistants can decrease operating costs by up to 78%.
  • Investing in virtual assistants cuts the attrition by 50%. (The attrition rate pertains to the number of people resigning from an organization over a period of time).
  • Virtual assistants increase productivity by 13%.
  • According to a survey by the American Bar Association, 26% of lawyers use virtual assistants or paralegals. The 2020 Legal Trends Report found that law firms only spend an average of 2.5 hours each day on billable work, which can be improved by delegating work to legal virtual assistants.
  • A study by the University of Oxford found that 23% of legal work can be automated by existing technology, and that virtual assistants can handle tasks such as document review, contract analysis, due diligence, and research.
  • A report by Deloitte estimated that 39% of legal jobs will be replaced by automation, and that virtual assistants will play a key role in enhancing productivity, efficiency, and accuracy.
  • A survey by LawGeex found that virtual assistants can review contracts faster and more accurately than human lawyers. The average accuracy rate for virtual assistants was 94%, compared to 85% for human lawyers. The average time for virtual assistants to review a contract was 26 seconds, compared to 92 minutes for human lawyers.
  • According to Gartner, by 2023, virtual legal assistants (VLAs) will field 25% of internal requests to legal departments at large enterprises, increasing operational capacity for in-house corporate teams.
  • A survey by Virtudesk found that 82% of business owners who hired virtual assistants reported increased productivity and efficiency, and 78% said they saved money on operational costs.

What are the benefits of using virtual legal assistants?

We listed the tasks virtual legal assistants can do above. By delegating these tasks to a virtual legal assistant, you can free up your time and focus on the core aspects of your practice, such as strategy, advocacy, and client relations. As such, they increase efficiency and productivity.

You can also reduce your overhead costs, as you only pay for the services you need, when you need them. You don’t have to worry about hiring, training, supervising, or providing benefits to an in-house staff member. In other words, they can also be a more cost-effective solution compared to hiring additional staff for administrative tasks. (Cf. the statistics quoted above).

A virtual legal assistant can also offer you flexibility and convenience, as they can work from anywhere and often at any time. You can access their services on demand, without being limited by office hours or location. You can also communicate with them through various channels, such as phone, email, chat, or video conferencing. VLA bots work 24/7.

There also is the access to technology aspect. AI virtual legal assistants can automate repetitive tasks. They can leverage advanced AI and technology and may provide access to powerful tools that may not be affordable or available to smaller law firms.

Virtual legal assistants increase accuracy. Especially AI-driven assistants can often perform tasks with a high level of accuracy and consistency, reducing the likelihood of human errors. (Cf. our article on when lawyers and robots compete).

Scalability is another benefit. Working with VLAs allow you to easily adapt to the changing needs of your law practice, whether it’s handling increased workloads during busy periods or scaling down during quieter times.

What are the limitations?

While virtual legal assistants can be valuable tools, they are not meant to replace human lawyers. Instead, they complement legal professionals by enhancing their productivity and efficiency. It’s essential to consider the specific needs of the law practice and the capabilities of the virtual legal assistant platform before making a choice. They are meant to assist lawyers, not replace them.

Another thing to keep in mind is that at present most of the VLA bots are only available in English.


The use of virtual legal assistants is on the rise, and that should not come as a surprise. They boost efficiency, productivity, are cost-effective, and allow lawyers to focus on legal work.




Social media for lawyers

In this article, we focus on social media for lawyers. We start with some statistics about social media that underpin their relevance. Next, we have a look at the different categories of social media that exist. We conclude with a selection of social media that are recommended for lawyers.

Some statistics about social media

In a previous article, we explained why social media matter. They are a valuable marketing tool, even for lawyers, because they provide direct access to your target audiences.  Legal consumers are online consumers and social media allow them to familiarize themselves with the lawyers whose services they want to employ.

And social media are extremely popular, as the following statistics illustrate.

  • In April 2023, there were 4.8 billion social media user identities, which is the equivalent of 59.9% of the global population. If we narrow it down to adults (18 years and older) then we are dealing with a number that is the equivalent of 78% of the population.
  • If we look at the number of people using the Internet, then we find that 92.7% of them are on at least one form of social media.
  • The average time a social media user spends each day on social media amounts to 2 hours and 24 minutes.
  • Demographics: 46.5% of social media users are female, while 53.5% are male.
  • People who are active on social media tend to be active on more than one platform and on average have 6.6 social media profiles.

A key concept in the metrics about social media users is the number of monthly active users (MAU). These are the unique users who use a social media platform at least once a month. Based on these monthly active users, these are the 21 most popular social media platforms.

  1. Facebook — 2.96 billion
  2. YouTube — 2.2 billion
  3. WhatsApp — 2 billion
  4. Instagram — 2 billion
  5. WeChat — 1.26 billion
  6. TikTok — 1 billion
  7. Sina Weibo — 573 million
  8. QQ — 538.91 million
  9. Telegram — 550 million
  10. Snapchat — 557 million
  11. Kuaishou — 573 million
  12. zone — 553.5 million
  13. Pinterest — 444 million
  14. Twitter — 238 million
  15. Reddit — 430 million
  16. LinkedIn — 424 million
  17. Quora — 300 million
  18. Discord — 150 million
  19. Twitch — 140 million
  20. Tumblr — 135 million
  21. Mastodon — 2.5 million

Now, many of those you may not be familiar with because they are mainly used in the Far East, and more specifically in China. Those are probably less relevant for most Western lawyers.

Not yet included in the list is Threads, the new Twitter alternative that was launched by Instagram. In the week after it was launched on 5 July 2023, it already gathered more than 100 million users, which would put it near the top 20. But note that at the time of writing, Threads is not available in the EU.

Categories of social media

There are many different types of social media. To tell them apart, it makes sense to group them in different categories. The Wikipedia uses the following categories.

  • Blogs are informational websites published on the World Wide Web, consisting of discrete, often informal diary-style text entries (posts). By now, most law firms have their own blog. (For more information, read our article on starting a blog).
  • Business networks are a type of social network service that focuses on interactions and relationships for business opportunities and career growth, with less emphasis on activities in personal life. LinkedIn is an example of a business network. Most law firms also are on LinkedIn.
  • Collaborative projects like, e.g., Wikipedia.
  • Enterprise social networks focus on the use of online social networks or social relations among people who share business interests and/or activities. Enterprise social networking is often a facility of enterprise social software. Yammer and Socialcast are examples.
  • Discussion Forums
  • Microblogs are a form of blogging using short posts without titles. When Twitter, e.g., launched, the size of a message was limited to only 140 characters. Other examples include Tumblr, Mastodon, Post.News, Threads, Spoutible, et. al.
  • Photo sharing websites like Instagram, Flickr, Photobucket, etc.
  • Websites with crowd-sourced reviews of products and/or services. Sometimes these focus on specific market segments like, e.g., Tripadvisor, while others are more generic, like Yelp or Amazon.
  • Social bookmarking are online services which allow users to add, annotate, edit, and share bookmarks of web documents. Delicious and Pinterest are two examples.
  • Social gaming (ex. Mafia Wars, World of Warcraft).
  • Generic social network sites like Facebook.
  • Video sharing websites like YouTube, Vimeo, TikTok, etc.
  • Virtual worlds are computer-simulated environments which may be populated by many users who can create a personal avatar, and simultaneously and independently explore the virtual world, participate in its activities and communicate with others. (We discussed virtual worlds before in our article on Web3).

Note that in this overview the Wikipedia did not include messaging apps like WhatsApp, Telegram, or Signal, while most other overviews do include them. Not included either are websites where people can ask all kinds of questions, like Quora or Reddit.

It is also worth pointing out that the boundaries between these categories are fluid and that they often overlap. Videos, e.g., are not only shared on video sharing sites, but also on virtually all messaging apps, as well as on most other popular platforms like LinkedIn, Facebook, etc.

A selection of social media for lawyers

The annual reports by the American Bar Association and Good2bSocial reveal that by now most law firms are active on social media. Many are present on the most popular platforms like LinkedIn and Facebook, and most have their own blog. The most important current trends are that a) more and more law firms are discovering short form video and start using those, and b) that firms who cater to multiple audiences are seeing the importance of market segmentation. In other words, law firms are learning it pays off to target different audiences on different platforms.

Online articles that recommend lawyers which social media to use are fairly consistent in their recommendations. The selection below is limited to those social media that are most relevant for lawyers, topic-wise and geographically. For most of the ones listed below, we have discussed them before in our blog articles.

When it comes to business networking, LinkedIn is the platform of choice.

When it comes to general social media, Facebook is the most popular choice. Recent statistics show that in the US more law firms are advertising on Facebook than on LinkedIn.

When it comes to messaging apps, there are more options. WhatsApp, Instagram, Snapchat, Telegram, and Signal are all popular. Because of privacy concerns, Signal is probably most recommended for lawyers. Note that most messaging apps now also allow to create groups which can function as mailing list for updates as well as short newsletters. A WhatsApp group can currently have 1 024 members, whereas a Telegram super group can have up to 200 000 members.

Because online consumers like to know in advance who they are dealing with, photo sharing apps can provide them with a more human side of a law firm. Instagram still is the most popular platform, followed by Snapchat, Flickr, and Photobucket.

Up until recently, the platform of choice for micro-blogging was Twitter. But since Elon Musk took over, many users and advertisers have turned their back on Twitter. Several alternatives are available, like Mastodon, Threads, Tumblr, Bluesky, or, e.g., Threads seems to be positioning itself as the main alternative but is not available in Europe.

For video sharing, there again are several options. For longer videos, YouTube and Vimeo are the platforms of choice. For short-form videos, TikTok, Instagram, and Snapchat are currently most popular. Note, however, that for lawyers TikTok is not recommended because of serious privacy concerns. Several governments worldwide have ordered officials to remove TikTok from their smart phones.

Pinterest still is the recommended platform for social bookmarking.

When it comes to question-and-answer forums, Quora and Reddit are the two main platforms.

That concludes our overview of recommended social media for lawyers in 2023.




The dangers of artificial intelligence

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.


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.




Token Economics for Lawyers (2): the value of tokens

This is the second part of a two-part article. In the first part we explained what tokens and token economics are. We also discussed some legal issues and the relevance for lawyers. In this second part, we look at the value of tokens. Tokens are digital assets. Assets have a value. How is that value assessed? What are the factors in determining the value of tokens? And what are the most common value models for digital tokens?

Factors determining the value of tokens

The value of tokens is determined by a variety of factors, including supply and demand, market sentiment, and the underlying technology. Let’s have a look at those.

Supply and Demand: Like any other asset, the value of tokens is determined by the balance of supply and demand. If there is a high demand for a particular token and a limited supply, the price of the token will go up. Conversely, if there is a low demand and a high supply, the price of the token will go down.

Market Sentiment: The value of tokens is also influenced by market sentiment. If investors believe that a particular token has a strong future, they will be more likely to buy it, increasing its value. Conversely, if there are concerns about the future of a token, investors may sell their holdings, causing the value to drop.

Underlying Technology: The underlying technology of a token can also influence its value. If a token is built on a strong, reliable blockchain network, it may be seen as more valuable than a token built on a less secure network. Similarly, if a new technology is more environmentally friendly than its competitors, it will be considered more valuable.


The value of a token is largely determined by the token model. The term token model refers to the design and structure of a token, and how these aim to create a self-sustaining ecosystem that incentivizes network participation and rewards valuable contributions. These models typically include elements such as token distribution, inflation rate, governance mechanisms, and utility functions, among others.

There are several different value models of tokens, each with its own unique characteristics and potential benefits. But before we explain those, it is good to first familiarize ourselves with some of the terminology regarding these characteristics.

A hard cap is the maximum amount of funds that a token sale or initial coin offering (ICO) can raise. Once this cap is reached, no more tokens will be sold or created. A soft cap, on the other hand, is a lower funding threshold that must be met for the project to proceed. If the soft cap is not met, the project may be cancelled or delayed, and investors may receive a refund.

The total token supply is the total number of tokens that will ever be created for a given project. This includes both tokens that have already been distributed and tokens that have not yet been released. The circulating supply, on the other hand, is the number of tokens that are currently in circulation and available for trading. This excludes tokens that are locked up or held by the project team, for example.

The market cap, short for market capitalization, is the total value of all tokens in circulation. It is calculated by multiplying the current price of a token by the circulating supply of tokens. Market cap can be used as an indicator of the overall size and success of a project and is often used to compare different projects within the same industry or sector.

Token value models

There are basically four common types of token value models:

Deflationary tokens

Deflationary tokens are designed to decrease in supply over time, either through burning tokens or reducing the rate of token issuance. In other words, there is a hard cap on the number of tokens created. which acts as a deflationary mechanism as demand increases over time, but supply does not. This creates scarcity and can lead to a rise in the value of the token as demand outstrips supply. The goal is to incentivize long-term holding of the token and discourage short-term speculation. However, there is a risk that the decreasing supply may make the token less attractive for use in transactions, reducing its utility. Examples of deflationary tokens are Bitcoin (BTC), Litecoin (LTC), and Bitcoin Cash (BCH).

Inflationary tokens

Inflationary tokens are the opposite of deflationary tokens. They are designed to increase in supply over time, either through regular token issuance or through other mechanisms such as staking rewards. The goal is to encourage spending and discourage hoarding, as the value of the token may decrease over time due to increased supply. This can also incentivize network participation and development, as new tokens are issued to those who contribute to the network. In this model, there is no hard cap on the number of tokens created. Instead, there are various iterations of the tokens being issued. Some token issuers limit token creation to a yearly basis, while others stick to a set schedule. And some adjust the supply, based on demand data. Examples of inflationary tokens are Ethereum (ETH), Polkadot (DOT), and Solana (SOL).

Dual-tokens model

In a dual-tokens model, there are two tokens: one that serves as a utility token for network access or use, and another that serves as a store of value or governance token. This can help separate the utility and speculative aspects of the token, and potentially provide more stable value for both. The utility token can be inflationary or deflationary depending on the specific model, while the store of value/governance token is typically designed to be more stable. Examples of this are VeChain (VET), VeChain Thor Energy (VTHO), NEO, GAS, Ontology Coin (ONT), Ontology Gas (ONG)

Asset-backed tokens

Asset-backed tokens are tokens that are backed by a physical asset or reserve, such as gold or fiat currency. This provides a degree of stability and confidence in the token’s value, as it is tied to a tangible asset. However, the value of the token may be limited by the value of the underlying asset, and there may be challenges in ensuring the proper reserve ratio and maintaining transparency. Examples are Pax Gold (PAXG), which is linked to the price of gold, as well as Tether (USDT) and USD Coin (USDC), which are both linked to the US Dollar.

Additional considerations

It’s worth noting that these different value models are not mutually exclusive, and hybrid models may also be used to achieve specific goals. Ultimately, the choice of value model depends on the specific use case and goals of the token and its associated network. Overall, the deflationary utility token model is the most popular, with the inflationary token model in second place.

The design of token models plays a crucial role in determining the value of tokens within a given ecosystem. A well-designed token model can create a strong network effect and incentivize various stakeholders to participate in the network. This, in turn, can increase the demand for tokens.

For example, a token with a deflationary supply mechanism may see an increase in its value as the supply decreases over time. Similarly, a token with strong governance mechanisms that allow token holders to have a say in the network’s decision-making process may be more attractive to investors and users, increasing the demand for tokens.

On the other hand, poorly designed token models may lead to a lack of adoption, low network activity, and a decrease in the token’s value. For example, a token with excessive inflation or high transaction fees may discourage users from participating in the network, leading to a decrease in demand and a corresponding decrease in the token’s value.

The value of tokens: conclusion

The value of tokens is determined by a variety of factors, including supply and demand, market sentiment, and the underlying technology. Overall, the value of tokens is closely tied to the token model’s ability to incentivize network participation, promote adoption, and balance the supply and demand dynamics within the network. The success of a token model depends on its ability to provide clear value propositions, promote adoption and usage, and maintain a healthy balance between supply and demand dynamics.




Token Economics for Lawyers – part 1

In this article as well as the next, we have a look at token economics. Now, the expression “token economics” can mean different things depending on the context. What these two articles are about is tokens as digital assets. It is not about token economics in the context of behavioural therapy. (Which is what the entry in the Wikipedia entry is about. So, that may be confusing). In this article, we will discuss definitions of tokens and token economics. We will answer the question “what are digital tokens used for?”. We will also talk about some of the legal issues of token economics, and finally look at the relevance of token economics for lawyers. In the follow-up article, we will focus on the values of tokens as digital assets.

Definitions of tokens and token economics

In today’s digital world, tokens have become a buzzword among investors, entrepreneurs, and businesses. From cryptocurrencies to utility tokens, the rise of tokens has created a new economy where the value of tokens is determined by a complex interplay of supply and demand.

So, what are we talking about? Let’s start with tokens and give an example that everybody probably is familiar with. If you go to a casino, you don’t play with real money. Instead, you exchange real money for tokens, and each token has a specific monetary value. While you are playing, you are using tokens. When you leave, you can exchange the tokens again for real money. A token therefore is something that symbolizes or represents something else.

In the context of token economics, tokens are digital assets, and they are typically created and managed through blockchain technology. They are unique assets that can represent a wide range of things, from cryptocurrencies to loyalty points to real-world assets like stocks and commodities. The key feature of tokens is their ability to store value and be traded freely on digital marketplaces. This means that tokens can be bought and sold like any other asset, allowing investors to benefit from price movements and businesses to raise funds through initial coin offerings (ICOs).

The article on the ESPEO Blockchain website defines token economics as “the study of a new type of economy that can be defined as the design of a particular ecosystem in a blockchain environment. There are as many ecosystems as startups and projects in the blockchain industry, where tokenization is a popular process.” In this context, the expression “token economics” is often shortened to tokenomics.

What are digital tokens used for?

Digital tokens can be used in a variety of ways, depending on the type of token. Some common uses include:

  • Currency: Cryptocurrencies like Bitcoin and Ethereum are tokens that are used as a medium of exchange. They can be used to purchase goods and services or traded for other currencies.
  • Utility Tokens: Utility tokens are tokens that are used to access a particular product or service. For example, a company may create a token that can be used to access their platform or to pay for a specific service.
  • Security Tokens: Security tokens represent ownership of an asset, like stocks, bonds, or real estate. They are governed by securities regulations and offer investors the opportunity to earn dividends or other forms of income.
  • Fungible and non-fungible tokens:
    • Fungible tokens are interchangeable with other tokens of the same type, meaning that each token has the same value and can be exchanged for another token without any loss of value. For example, a 5€ bill is a fungible token, because any two 5€ bills have the same value and can be exchanged for one another without any loss of value.
    • Non-fungible tokens, NFTs, on the other hand, are unique and cannot be exchanged for another token without a loss of value. Each token represents a specific asset, such as a piece of artwork or a collectible item and has its own unique value. For example, a unique piece of digital art might be represented as a non-fungible token. (We talked about non-fungible tokens in a previous article).

Top of Form

Legal issues of token economics

As tokens are assets, there are several legal challenges and issues regarding token economics that one should be aware of.

One of the main challenges is the regulatory uncertainty surrounding tokens. In our article on NFTs, e.g., we mentioned the issues about whether or when NFTs become securities. Depending on their economic characteristics, tokens may be subject to various regulatory regimes, including securities laws, commodities laws, or money transmission laws. This can make it difficult for issuers and investors to navigate the legal landscape and comply with applicable regulations.

Another issue is the potential for fraudulent or abusive practices in token offerings or trading. Due to the lack of regulation and oversight in some token markets, there have been cases of fraud, market manipulation, and other forms of misconduct. Lawyers may need to advise clients on how to comply with anti-fraud laws and regulations, as well as how to mitigate legal risks associated with token-based transactions.

Additionally, there are intellectual property issues related to token economics, particularly with respect to the ownership and licensing of the underlying technology and protocols that support token ecosystems. Lawyers may need to advise clients on patent, copyright, and trademark issues related to token-related technologies, as well as on licensing and commercialization strategies.

Finally, there are data privacy and cybersecurity concerns associated with token transactions, which can be particularly acute in decentralized networks where personal data is stored and transmitted across multiple nodes. Lawyers may need to advise clients on how to comply with data protection.

Relevance for lawyers

So, how are token economics relevant to lawyers?

As mentioned above, token economics can impact the regulatory treatment of tokens. For example, if a token is classified as a security, it may be subject to more stringent regulations than if it is classified as a utility token. Lawyers may need to be familiar with the various factors that determine the classification of a token, such as its economic purpose, distribution, and governance.

Secondly, lawyers may need to understand token economics to advise clients on the legal implications of launching a token-based project or participating in a token sale. This could include drafting legal documents such as token purchase agreements, whitepapers, or terms of service that incorporate the economic features of the token.

Thirdly, token economics can affect the way that tokens are valued and traded in the market. Lawyers may need to understand the mechanics of token supply, demand, and circulation to advise clients on issues such as token pricing, market manipulation, or insider trading. We will discuss those in our next article.

Finally, token economics is a rapidly evolving field that requires interdisciplinary knowledge and collaboration between legal and technical experts. Lawyers who are familiar with token economics may be better positioned to engage with clients in emerging sectors such as decentralized finance (DeFi) or non-fungible tokens (NFTs), where the legal implications of token economics are still being defined.


Tokens are digital assets that are typically created and managed through blockchain technology. They are assets that can represent a wide range of things, from cryptocurrencies to loyalty points to real-world assets like stocks and commodities. They can be used in a variety of ways, including as currency, utility tokens, and security tokens. As the digital world continues to evolve, the importance of tokens in the economy is likely to grow. By understanding token economics and the value of tokens, lawyers can assist their clients in making informed decisions and navigating this new landscape with confidence.




Generative AI

In a previous article, we talked about ChatGPT. It is a prime example of generative AI (artificial intelligence). In this article, we will explore generative ai a bit more in detail. We’ll answer questions like, “What is Generative AI?”, “Why is Generative AI important?”, “What can it do?”, “What are the downsides?”, and “What are the Generative AI applications for lawyers?”.

What is Generative AI?

A website dedicated to generative AI defines it as “the part of Artificial Intelligence that can generate all kinds of data, including audio, code, images, text, simulations, 3D objects, videos, and so forth. It takes inspiration from existing data, but also generates new and unexpected outputs, breaking new ground in the world of product design, art, and many more.” (

The definition Sabrina Ortiz gives on ZDNet is complementary: “All it refers to is AI algorithms that generate or create an output, such as text, photo, video, code, data, and 3D renderings, from data they are trained on. The premise of generative AI is to create content, as opposed to other forms of AI, which might be used for other purposes, such as analysing data or helping to control a self-driving car.” As such, Generative AI is a type of machine learning that is specifically designed to create (generate) content.

Two types of generative AI have been making headlines. There are programs that can create visual art, like Midjourney or DALL-E2. And there are applications like ChatGPT that can generate almost any desired text output and excels in conversation in natural language.

Why is Generative AI important?

Generative AI is still in its early stages and already it can perform impressive tasks. As it grows and becomes more powerful, it will fundamentally change the way we operate and live. Many experts agree it will have an impact that is at least as big as the introduction of the Internet. Just think of how much the Internet has become of our daily lives. Generative AI, too, is expected to become fully integrated into our lives. And it is expected to do so quickly. One expert predicts that on average we will have new and twice as powerful generative AI systems every 18 months. Only four months after ChatGPT 3.5 was released, on 14 March 2023, a new, more powerful, more accurate, and more sophisticated version 4.0 was released. The new version is a first step towards a multimodal generative AI, i.e., one that can work with several media simultaneously: text, graphics, video, audio. It can create output of over 25 000 words of text, which allows it to be more creative and collaborative. And it’s safer and faster.

Let us next have a look at what generative AI can already do, and what it will be able to do soon.

What can it do?

One of the first areas where generative AI was making major breakthroughs was to create visual art. Sabrina Ortiz explains, “Generative AI art is created by AI models that are trained on existing art. The model is trained on billions of images found across the internet. The model uses this data to learn styles of pictures and then uses this insight to generate new art when prompted by an individual through text.” These are five free AI art generators that you can try out for yourself:

We already know from our previous article that ChatGPT can create virtually any text output. It can write emails and other correspondence, papers, a range of legal documents including contracts, programming code, episodes of TV series, etc. It can assist in research, make summaries of text, describe artwork, etc.

More and more search engines are starting to use generative AI as well. Bing, DuckDuckGo, and, e.g., all already have a chat interface. When you ask a question, you get an answer in natural language, instead of a list of URLs. Bing even gives the references that it based its feedback on. Google is expected to launch its own generative AI enabled search engine soon.

More specifically to programming, one of the major platforms for developers (GitHub) announced it now has an AI Copilot for Business which is an AI-powered developer tool that can write code, debug and give feedback on existing code. It can solve any issues it may detect in the code.

Google’s MusicLM already can write music upon request, and the new ChatGPT version 4 announced a similar offering, too. YouTube also has announced that it will start offering generative AI assistance for video creation.

Generative AI tools can be useful writing assistants. The article on, mentioned in the sources, lists 48 free writing assistants, though many of them use a freemium model. Writer’s block may soon be a thing of the past, as several of these writing assistants only need a key word to start producing a first draft. You even get to choose the writing style.

Generative AI can also accelerate scientific research and increase our knowledge. It can, e.g., lower healthcare costs and speed up drug development.

In Britain, a nightclub successfully organized a dance event where the DJ was an AI bot.

All existing chatbots can get an upgrade where they will become far better at natural language conversations. And generative AI integrated with the right customer processes will improve customer experience.

As you can see, even though we’re only at the beginning of the generative AI revolution, the possibilities are endless.

What are the downsides?

At present, generative AI tools are mostly tools that assist. The output needs to be supervised. Sometimes, ChatGPT, e.g., gives incorrect answers. Worse, it can just make things up, and an experiment with a legal chatbot discovered that the bot just started lying because it had concluded that that was the most effective way to get the desired end result. So, there are no guarantees that the produced output is correct. And the AI system does not care whether what it does is morally or legally acceptable. Extra safeguards will have to be built in, which is why there are several calls to regulate AI.

There also is an ongoing debate about intellectual property rights. If a program takes an existing image and merely applies one or more filters, does this infringe on the intellectual property of the original artist? Where do you draw the line? And who owns the copyright on what generative AI creates? If, e.g., a pharmaceutical company uses an AI tool to create a new drug, who can take a patent? Is it the pharmaceutical company, the company that created the AI tool, or the AI tool itself?

And as generative AI becomes better, it will transform the knowledge and creative marketplaces, which will inevitably lead to the loss of jobs.

Generative AI applications for lawyers

As a result of the quick progress in generative AI, existing legal chatbots are already being upgraded. A first improvement has to do with user convenience and user-friendliness because users can now interact with the bots through a natural language interface. The new generation of bots understand more and are also expected to become faster, safer, and more accurate. The new ChatGPT 4 scored in the 90th percentile for the bar exams, where ChatGPT 3 – only a few months earlier – barely passed some exams.

Virtual Legal Assistants (VLA) are getting more and more effective in:

  • Legal research
  • Drafting, reviewing, and summarizing legal documents: contracts, demand letters, discovery demands, nondisclosure agreements, employment agreements, etc.
  • Correspondence
  • Creative collaboration
  • Brainstorming, etc.

As mentioned before, at present these AI assistants are just that, i.e., assistants. They can create draft versions of legal documents, but those still need revision by an actual human lawyer. These VLAs still make errors. But at the same time, they can already considerably enhance productivity by saving you a lot of time. And they are getting better and better fast, as the example of the bar exams confirms.




Legal Technology Predictions for 2023

Towards the end of every calendar year, the American Bar Association publishes the results of its annual legal technology survey. Several legal service providers, experts, and reporters, too, analyse existing trends and subsequently make their own legal technology predictions for 2023. Some items stand out that most pay attention to. In this article, we will look at automation, artificial intelligence, cloud-native solutions, virtual legal assistants, data privacy and cybersecurity, crypto technologies, blockchain, and smart contracts. We will briefly pay attention to some other trends, as well.


Automation keeps being a major driver of change in many industries. The legal sector is no exception, even though it lags compared to many other sectors. Lawyers seem to take longer to catch up that automation is beneficial. It is making many processes in the legal industry faster, more efficient, and less expensive. Automation has proven to be successful in fields like legal research, e-discovery and document review and management. In 2023, we can expect to see this trend continue, with a renewed focus on automating the law firm administration and on the creation and review of legal documents. Automated workflows can be used to streamline legal processes, such as litigation support, e-discovery, and case management. Automation can also assist in organizing and tracking progress and regulatory changes, data collection, reporting, and communication. An increase in automation will help to improve the accuracy of legal processes, reducing the risk of errors, and increasing efficiency.

Artificial Intelligence

Artificial Intelligence is becoming ubiquitous. In many aspects of our lives, there now are AI solutions available that make life easier. In the legal sector, too, AI is starting to make waves. In all the above-mentioned examples of automation, AI is playing a crucial role. As mentioned above, AI has already been successfully assisting lawyers with legal research, with process and workflow automation, with the generation of legal documents, as well as with e-discovery. But those are still fairly simple applications of AI. It can do far more. These days, AI is also being used to digest vast volumes of text and voice conversations, identify patterns, or carry out impressive feats of predictive modelling. The virtual legal assistants that we’ll discuss below, too, are all AI applications. If properly used, AI can save law firms much time and money. In 2023, we can expect to see a more widespread adoption of AI in the legal sector. (More on Artificial Intelligence and the Law).

Cloud-Native Solutions

Cloud computing has been a game-changer for many industries. Previous reports had already revealed that lawyers, too, are more and more relying on cloud solutions. This should not come as a surprise, as Cloud-based solutions provide many benefits, including reduced costs, increased scalability, and improved data security. They help lawyers and clients share files and data across disparate platforms rather than relying solely on emails. Additionally, cloud-based solutions are more accessible, allowing legal firms to work from anywhere and collaborate more effectively with clients and other stakeholders. In 2023, we can expect this trend to continue. (In the past, we have published articles on cloud solutions for lawyers, on managing your law firm in the cloud, an on lawyers in the cloud).

Virtual Legal Assistants (VLAs)

In the past, we have talked on several occasions about legal chatbots. Chatbots have sufficiently matured to now start playing the role of virtual legal assistants. VLAs are AI-powered chatbots that build on basic neural network computing models to harness the power of deep learning. They use artificial intelligence algorithms to assist law firms with various tasks. Gartner predicts VLAs can answer one-quarter of internal requests made to legal departments. They extend the operational capacity of law firms as well as of in-house corporate legal teams. As a result, they assist in reducing lawyers’ average response time and producing distinct service delivery efficiencies. Furthermore, as VLAs are a form of automation, all the benefits of automation apply here too: virtual legal assistants can help to improve the accuracy of legal work, reduce the risk of errors and increase efficiency. At present, virtual legal assistants are still primarily being used in uncomplicated and repetitive operations. Recent breakthroughs, however, indicate that they are already able to take on more complex tasks and will continue to do so.

Data Privacy and Cybersecurity

Ever since the GDPR, data privacy and cybersecurity have become increasingly important. In 2023, we can expect to see an ongoing emphasis on data privacy and as well as an increase in attention to cybersecurity in the legal sector. (The examples of high-profile Big Tech corporations receiving massive fines seem to be a good incentive). Law firms have understood that they too need to make sure that they have robust data privacy and cybersecurity measures in place to protect their clients’ confidential information. Several law firms also provide their clients assistance with the legal aspects of data protection.

Crypto technologies, Blockchain, and smart contracts

The market of cryptocurrencies was volatile in 2022. That did not stop an increase in interest in the underlying crypto technologies. Experts predict rises in a) regulation of cryptocurrencies and crypto technologies, in b) the adoption of cryptocurrency, c) a growing interest in decentralized finance (DeFi), and d) an increase in attempts at cryptocurrency taxation. We are already witnessing an intensification in litigation with regard to cryptocurrency and crypto technologies. This trend is expected to continue. Litigation about NFTs, e.g., is one of the areas where litigation is expected to rapidly increase.

Experts also expect an ongoing interest in and an increased adoption of Blockchain technology. Blockchain can be used to securely store and manage legal data, reducing the risk of data breaches and ensuring the integrity of legal records. Additionally, blockchain can be used to automate many legal processes, such as contract management and dispute resolution, by enabling the creation of smart contracts. As we mentioned in previous articles, smart contracts can streamline many legal processes, reducing the time and cost associated with contract management and dispute resolution. They can also help to increase the transparency and accountability of legal transactions, reducing the risk of fraud and improving the overall efficiency of legal processes.

Other Trends

The ABA survey report noticed that law firms are spending more money on legal technology than ever before. In many cases, this involved investing more in tightening cybersecurity.

The trend to work remotely and to use video conferencing for virtual meetings that started during the pandemic is ongoing.

More than ever before lawyers pay attention to their own work experience, as well as to the user experience for their clients by making their law firms more client centred. There is an ongoing focus on work-life balance, not only for the lawyers but also for the employees of law firms. Law firms are finally starting to consider things like employee satisfaction.

While billable hours remain the most used fee model, there has been a noticeable increase in lawyers using a subscription fee model.

Finally, the trend that law firms are increasingly hiring people with hybrid profiles is continuing. By increasing cognitive diversity, law firms want to close the gap between professionals with knowledge of legal matters and those with enough legal tech expertise to manage the digitization and automation of workflows. Gartner predicts that by the end of 2023, one third of corporate legal departments will have a legal tech expert in charge of managing the digital transformation and automation of internal processes. Large law firms are also increasingly hiring lawyers that are familiar with business administration.



ChatGPT for Lawyers

In this article we will first talk about recent evolutions in the field of generative Artificial Intelligence (AI) in general, and about a new generation of chat bots. Then we focus on one particular one that is getting a lot of attention, i.e., ChatGPT. What is ChatGPT? What can it do, and what are the limits? Finally, we look at the relevance of ChatGPT for lawyers.


We are witnessing the emergence of a new generation of chat bots that are more powerful than ever before. (We discussed legal chat bots before, here and here). Several of them excel in conversation. Some of these conversationalist chat bots recently made headlines on several occasions. In a first example, in December 2022, the DoNotPay chat bot renegotiated a customer’s contract with Comcast’s chat bot and managed to save 120 USD per year. (You read that correctly, two bots effectively renegotiating a contract). Shortly afterwards, a computer using a cloned voice of a customer was connected to the DoNotPay chat bot. A call was made to the support desk of a company and the speaking chat bot negotiated successfully with a live person for a reduction of a commercial penalty. The search engine has added a conversation chat bot that allows people to ask a question and the reply is presented in a conversational format rather than a list of links. Microsoft has announced that its Bing search engine will start offering a conversational interface as well.

Conversationalist chat bots are a form of generative AI. Generative AI has made tremendous progress in other fields like the creation of digital artwork, or in filters and effects for all kinds of digital media, or in the generation of documents. These can be any documents: legal documents, blog or magazine articles, papers, programming code… Only days ago, the C-NET technology website revealed that they had started publishing articles since November 2022 that were entirely written by generative AI. Over a period of two months, they published 74 articles that were written by a bot, and the readers did not notice.

One chat bot in particular has been in the news on a nearly daily basis since it was launched in November 2022. Its name is ChatGPT and the underlying technology has also been used in some of the examples mentioned above.

What is ChatGPT?

ChatGPT stands for Chat Generative Pre-trained Transformer. The Wikipedia describes it as “a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI’s GPT-3 family of large language models and is fine-tuned (an approach to transfer learning) with both supervised and reinforcement learning techniques. ChatGPT was launched as a prototype on November 30, 2022, and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge.”

In other words, it’s a very advanced chat bot that can carry a conversation. It remembers previous questions you asked and the answers it gave. Because it was trained on a large-scale database of texts, retrieved from the Internet, it can converse on a wide variety of topics. And because it was trained on natural language models, it is quite articulate.

What can it do and what are the limits?

Its primary use probably is as a knowledge search engine. You can ask a question just like you ask a question in any search engine. But the feedback it gives does not consist of a series of links. Instead, it consults what it has scanned beforehand and provides you with a summary text containing the reply to the question you asked.

But it doesn’t stop there, as the examples we have already mentioned illustrate. You can ask it to write a paper or an article on a chosen topic. You can determine the tone and style of the output. Lecturers have used it to prepare lectures. Many users asked it to write poetry on topics of their choice. They could even ask it to write sonnets or limericks, and it obliged. And most of the time, with impressive results. It succeeds wonderfully well in carrying a philosophical discussion. Programmers have asked it to write program code, etc. It does a great job of describing existing artwork. In short, if the desired output is text-based, chances are ChatGPT can deliver. As one reporter remarked, the possibilities are endless.

There are of course limitations. If the data sets it learned from contained errors, false information, or biases, the system will inherit those. A reporter who asked ChatGPT to write a product review commented on how the writing style and the structure of the article were very professional, but that the content was largely wrong. Many of the specifications it gave were from the predecessor of the product it was asked to review. In other words, a review by a person who has the required knowledge is still needed.

Sometimes, it does not understand the question, and it needs to be rephrased. On the other hand, sometimes the answers are excessively verbose with little valuable content. (I guess that dataset contained speeches by politicians). There still are plenty of topics that it has no reliable knowledge of. When you ask it if it can give you some legal advice, it will tell you it is not qualified to do so. (But if you rephrase the question, you may get an answer anyway, and it may or may not be accurate). Some of the programming code appeared to be copied from sites used by developers, which would constitute a copyright infringement. And much of the suggested programming code turned out to be insufficiently secure. For those reasons, several sites like StackOverflow are banning replies that are generated by ChatGPT.

Several other concerns were also voiced. As the example of CNET shows, these new generative AI bots have the potential of eliminating the need for a human writer. ChatGPT can also write an entire full essay within seconds, making it easier for students to cheat or to avoid learning how to write properly. Another concern is the possible spread of misinformation. If you know enough sources of the dataset that the chatbot learns from, you could deliberately flood it with false information.

What is the Relevance of ChatGPT for Lawyers?

Lawyers have been using generative AI for a while. It has proven to be successful in drafting and reviewing contracts and other legal documents. Bots like DoNotPay, Lawdroid, and HelloDivorce are successfully assisting in legal matters on a daily basis. For these existing legal bots, ChatGPT can provide a user-friendly conversationalist interface that make them easier to use.

When it comes to ChatGPT itself, several lawyers have reported on their experiences and tests with the system. It turned out that it could mimic the work of lawyers with varying degrees of success. For some items, it did a great job. It, e.g., successfully wrote a draft renting agreement. And it did a good job at comparing different versions of a legal document and highlighting what the differences were. But in other tests, the information it provided was inaccurate or plain wrong, where it, e.g., confused different concepts.

And the concerns that apply to generative AI in general, also apply to ChatGPT. These include concerns about bias and discrimination, privacy and compliance with existing privacy and data protection regulation like the GDPR, fake news and misleading content. For ChatGPT, the issue of intellectual property rights was raised as well. The organization behind ChatGPT claims it never copies texts verbatim, but tests with programming code appear to show differently. (You can’t really paraphrase programming code).

Given the success and interest in ChatGPT, the usual question was raised whether AI will replace the need for lawyers. And the answer stays the same that, no, it won’t. At present, the results are often very impressive, but they are not reliable enough. Still, the progress that has been made shows that it will get better and better at performing some of the tasks that lawyers do. It is good at gathering information, at summarizing it and at comparing texts. And only days ago (13 January 2023) the American Bar Association announced that ChatGPT had successfully passed one of its bar exams on evidence. But lawyers are still needed when it comes to critical thinking or the elaborate application of legal principles.


A new generation of chat bots is showing us the future. Even though tremendous progress has been made, there are still many scenarios where they’re not perfect. Still, they are improving every single day. And while at present supervision is still needed to check the results, they can offer valuable assistance. As one lecturer put it, instead of spending a whole day preparing a lecture, he lets ChatGPT do the preparation for him and write a first draft. He then only needs one hour to review and correct it.

For lawyers, too, the same applies. The legal texts it generates can be a hit and miss, and supervision is needed. You could think of the current status where the chat bot is like a first- or second-year law student doing an internship. They can save you time, but you have to review what they’re doing and correct where necessary. Tom Martin from Lawdroid puts it as follows: “If lawyers frame Generative AI as a push button solution, then it will likely be deemed a failure because some shortcoming can be found with the output from someone’s point of view. On the other hand, if success is defined as productive collaboration, then expectations may be better aligned with Generative AI’s strengths.”




The dark web and the law

In our previous article, we gave a general introduction to the dark web. In this article, we analyse the relationship between the dark web and the law. The first question we answer is, ‘Is using the dark web legal?’. Next, we have a look at lawyers on the dark web. Then, we focus on the different efforts to fight crime on the dark web. Finally, we look at the need for dark web lawyers.

Is using the dark web legal?

With all the criminal activity taking place on the dark web, one could wonder whether it is even legal to access the dark web. The short answer is that accessing the dark web is perfectly legal in most countries. In more than 130 countries, the right to privacy is a constitutional right. And browsing the dark web to maintain anonymity is one way of exercising that right. In our previous article, we also pointed to the many positive uses there are for the dark web.

But there are some caveats. While access to the dark web may be legal, what you do on there is what counts. Your actions on the dark web must respect the laws of the country you are in. If you buy contraband or pirated goods on a black market, e.g., that is illegal. Also keep in mind that accessing the dark web is not legal everywhere. There are several countries where access to the Internet is restricted and accessing the dark web in those countries may very well be criminalized. Using a VPN or the TOR network typically is illegal as well in those countries. They include, but are not limited to China, Russia, Iran, Saudi Arabia, and Venezuela. If you are visiting those countries, using TOR or a VPN is illegal.

Lawyers on the dark web

There are several legitimate reasons for lawyers too to use the dark web. Because all information is encrypted, using the Tor browser and the dark web can be a safe way for lawyers and their clients to communicate. As such, it helps protect client and attorney information.

Another legitimate reason to use the dark web is to conduct legal research. The dark web can be useful in the discovery process to collect evidence. Lawyers can communicate anonymously with whistleblowers, including corporate ones. Or they can build a case against infringements of intellectual property, of which there are plenty on the dark web. Human rights lawyers often need the anonymity of the dark web to communicate about and to collect evidence of human rights violations. Lawyers can also ask other lawyers for anonymous advice.

Another area where law firms may use the dark web is to test and enhance their cybersecurity.

Finally, when you, as a lawyer, access the dark web, make sure you abide by your code of ethics. If something is not allowed on the surface web, it also will not be allowed on the dark web.

Efforts to fight crime on the dark web

The dark web provides anonymity, and many transactions are paid for with untraceable cybercurrencies. The combination of both – anonymity and untraceable payments – make the dark irresistible to criminals. Any type of crime with covert transactions can be committed on the dark web. These include murder for hire, blackmail and extortion, illegal sales of drugs and arms, sex trafficking, terrorism, child pornography, etc.

The abovementioned combination of anonymizing technologies and the use of cryptocurrencies to hide transactions also poses serious challenges for law enforcement. The transactions are hidden by design. Law enforcement agencies may therefore very well be unaware of their existence. Gathering evidence that would stand in court poses additional challenges.

So, how does one fight crime on the dark web? Several techniques have been used. A common and successful strategy is to go undercover online. In cases where no cryptocurrencies are used, following the money also has been successfully used. And while transactions may be hidden, any goods that are being traded must be shipped. Monitoring shipping procedures therefore is another useful strategy. Finally, using sophisticated technology and hacking techniques has also been successful. Often, this is done in combination with a so-called honeypot trap, where law enforcement agencies set up a dark web site that pretends to be involved in illicit activities. The moment visitors access the trap website, tools are used to undo the anonymity of the visitors. If they commit a crime or conspire to commit a crime, they can be identified.

The article on the US National Institute of Justice, listed below, provides a summary of a 2017 Report on “Identifying Law Enforcement Needs for Conducting Criminal Investigations Involving Evidence on the Dark Web”. The report identified 40 problems or opportunities, and 46 potential solutions. It also gave a series of high-level recommendations for law enforcement agencies on training, information sharing, new structures for cooperation, new laws for package inspection, and research on crime connections.

The need for dark web lawyers

By now, it has become clear that there is a growing need for lawyers who are familiar with the dark web. The ever-increasing number of cybercrime incidents that originate from the dark web (hacking, data leaks, extortion, malware, ransomware, …) is testimony to that. Another bonus is that if your law firm is familiar with the dark web, that will give you a competitive advantage.

Most cases where there is a need for a dark web lawyer are criminal cases. But that is not necessarily the case. There are developers and lawyers who are offering perfectly legal services. And there are legal markets, too. (Though they are by far outnumbered by the black marketplaces, where “caveat emptor” is even more applicable than usual). In all of these, issues may arise where the services of lawyers are needed. We also mentioned before that there are cases where lawyers may need to rely on the dark web to collect evidence like witness testimony or tipoffs, etc.

Still, at present most cases involving the dark web where lawyers are needed are criminal cases. Your client may be the victim or the perpetrator. Victims of hacking, data breaches and data leaks, ransomware attacks, etc. need the assistance of lawyers as well as of cybersecurity experts. Sometimes, these victims may face additional problems. These days, companies and organizations who fall victim to data leaks, e.g., may face substantial fines because information about their clients has been leaked. In all those cases, the assistance of a dark web lawyer is recommended.

Your client may also be the suspect of crimes committed on the dark web. Lawyers defending suspects of dark web cybercrimes often face additional obstacles. One of those is that de facto the assumption of innocence is undermined when it comes to dark web crimes. Prosecutors as well as jury members see the dark web as a place where crime thrives and just being on the dark web may come across as suspicious. Getting a fair trial often becomes an extra challenge.