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The Limits of Generative AI in Mediation and Legal Practices


The recent surge in artificial intelligence (AI) hype, fueled by bold promises from influential figures like Elon Musk, suggests that AI development is advancing at an unstoppable pace. Musk’s projections include fully autonomous Tesla within a few years, AI surpassing human intelligence shortly, and a future dominated by AI-driven robots. Despite these claims, the reality of AI
technology will hit a plateau because of diminishing returns in its development.



AI operates through deep learning and artificial neural networks that identify patterns in massive datasets, which they use to make predictions or generate new data. Initially, AI systems improve rapidly as they train on larger datasets, enhanced by advances in programming and algorithms. However, the gains from increasing data size are diminishing.

This diminishing return is twofold. First, the value of each new data point decreases as the dataset grows, making it increasingly difficult to find new, applicable patterns or content for training. Second, the computational demand of training on these larger datasets grows exponentially, requiring more power and generating more cost, which could eventually make further advancements difficult if not impractical.

Even high-profile AI models like OpenAI’s ChatGPT4, which used a training dataset 571 times larger than its predecessor, exhibit only marginal improvements and continue to struggle with issues like factual accuracy (removal of hallucinations). Estimates suggest that the next significant leap in AI performance could require an impractically large amount of data and energy, rendering the cost and environmental impact prohibitively high.

Further compounding the issue, even potential energy breakthroughs like nuclear fusion are unlikely to offer a cheap and viable solution soon. Research from the University of Massachusetts Amherst suggests that achieving over 95% accuracy in image recognition, for instance, would cost $100 billion and emit as much carbon as New York City does in a month.

The repercussions of these energy demands extend beyond global environmental impact; they also have significant local consequences. Data centers, the powerhouses behind AI capabilities, necessitate extensive cooling and ventilation, further increasing energy consumption and water use. These facilities emit considerable heat and noise, potentially disrupting local ecosystems and diminishing the quality of life for nearby communities. Often situated in regions where electricity is inexpensive and plentiful, these data centers frequently rely on non-renewable energy sources. Thus, the environmental cost of generative AI is a pressing concern not only for our planet but also for the health and well-being of human and ecological communities globally. (Francesco Federico, The Financial and Environmental Cost of Generative AI, Chronicles of Change, December 2023)

Given these challenges, the AI industry may not achieve the futuristic visions being promoted. Instead, it faces a likely stagnation unless new technologies, such as more efficient AI hardware or innovative architectures requiring less data, can be developed. These technologies are still in the early stages and may take a decade to mature. The excitement around AI’s capabilities and future
potential needs to be tempered with an understanding of the technological and practical limitations currently facing the field.


Fast, fair, and full of potential, artificial intelligence (AI) tools can be used by arbitrators and mediators to boost efficiency, offer deeper insights, and provide an enhanced level of precision in their work as demonstrated by platforms like NextLevelTM Mediation.

However, its adoption into the legal sector, encompassing arbitration, mediation, and litigation, has progressed very slowly. There are a number of factors contributing to this slow adoption:

  1. 1. Complexity and Tradition: The legal field is anchored in longstanding traditions and established protocols. There is a general reluctance within the legal community to adopt novel technologies that may disrupt these norms. Additionally, the complexities of AI systems pose a significant challenge for legal practitioners who lack technological expertise.
  2. 2. Risk Aversion: Legal professionals are characteristically cautious, prioritizing risk mitigation. There is apprehension that dependence on AI could result in errors or unforeseen outcomes, which could lead to malpractice claims or ethical breaches, thus deterring lawyers from relying heavily on AI technologies.
  3. 3. Lack of Awareness and Education: A significant portion of the legal workforce remains uninformed about the potential advantages and capabilities of AI, limiting understanding of how AI could improve their practice. Educational initiatives and training programs are crucial to overcome this barrier and foster wider adoption.
  4. 4. Quality Assurance and Accountability: The legal profession demands high standards of quality and accountability, raising concerns that AI reliance could compromise these expectations. It is imperative to ensure the accuracy, reliability, and ethical application of AI tools to gain acceptance in legal circles.
  5. 5. Resistance to Change: The inherently conservative nature of the legal culture often leads to resistance against modifications to established practices. Adoption of AI by legal professionals necessitates clear demonstrations of its benefits and direct responses to their specific concerns.
  6. 6. Legal Ethics and Professional Responsibility: There is concern among legal professionals regarding the ethical ramifications of employing AI systems that may inadvertently replicate existing biases from their training data. Addressing these ethical issues is essential for the responsible deployment of AI in legal settings.
  7. 7. Fear Loss of revenue: Finally, there is a concern within the legal industry that the adoption of artificial intelligence (AI) could lead to increased efficiency and subsequently reduce the number of billable hours. This fear stems from the potential of AI to automate tasks that lawyers traditionally perform manually, such as document review, legal research, and even some aspects of drafting legal documents. These automated processes could significantly decrease the time lawyers spend on certain tasks, which is a fundamental change to the traditional billing model based on hourly rates. Since many law firms and practitioners derive substantial revenue from billable hours, the integration of AI could impact their income streams and financial stability. Additionally, this shift might also lead to changes in staffing needs and the skill sets required in legal practices, prompting a reevaluation of roles and responsibilities within law firms.
  8. 8. Regulatory Uncertainty: The legal industry operates within a complex regulatory framework. Uncertainty about how AI fits into existing regulations can hinder adoption.

In summary, there are limits to growth in any ecosystem, and AI is no exception. However, while the AI legal and dispute resolution ecosystems are growing, those professionals who adopt these technologies could gain a significant advantage over those who do not. The legal and dispute resolution fields are thus facing a critical period of transformation. To navigate this transition successfully, these communities must integrate technological advances carefully and thoughtfully, ensuring that ethical standards are upheld even as they innovate. This proactive adaptation is not merely beneficial but essential for those aiming to remain relevant and effective in their practices.


Robert Bergman

Robert Bergman with Next Level Mediation provides full mediation services - including proprietary and confidential Decision Science (DS) analysis that assists each party in understanding their true litigation priorities as aligned with their business objectives. Each party receives a one-time user license to access our exclusive DS Application Cloud. We… MORE >

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