Today’s session featured Brian Tang, Founder of LITE Lab at the University of Hong Kong. Tang’s career spans big law, investment banking, and legal tech entrepreneurship before moving into academia to focus on the intersection of law, technology, and innovation.
The Changing Landscape of Legal Practice
Tang opened by addressing the scale and pace of transformation facing the legal profession. Lawyers across firms and in-house teams are grappling with fundamental questions around relevance, value, and future roles. The emergence of AI has intensified both opportunity and anxiety, challenging traditional models of legal training and delivery.
Drawing on his work at HKU, Tang emphasised that the future lawyer must move beyond doctrinal expertise to embrace interdisciplinary thinking. His programmes integrate students from law, business, engineering, and medicine, reflecting the increasingly hybrid nature of legal work.
Rethinking Legal Education
Tang outlined four core programme types at LITE Lab, spanning innovation-led initiatives in which students build AI-native legal solutions and ventures, technology-led collaborations with law firms and corporates on real AI use cases, law-led research enhanced by AI tools, and a focus on ethics and governance that addresses the societal and philosophical implications of AI.
Central to all programmes are three foundational methodologies: legal design thinking, business model frameworks, and agile working. This reflects a shift from passive learning to experiential, “learning by doing” approaches, where students actively create and test solutions.
Tang stressed the importance of flexibility in technology adoption. Rather than relying on a single tool, students are encouraged to experiment with multiple AI models, compare outputs, and develop critical judgment.
AI for Productivity vs AI for Learning
A key distinction highlighted was between AI for productivity and AI for learning.
- Productivity use prioritises speed, efficiency, and output.
- Learning use requires friction, encouraging individuals to think, draft, and refine before leveraging AI.
Tang argued that over-reliance on AI for first drafts risks “cognitive offloading”, weakening core legal skills. A more effective approach is to use AI to critique and improve human-generated work.
This distinction has significant implications for both education and practice, particularly in maintaining the integrity of the apprenticeship model.
The Evolution of the Apprenticeship Model
The traditional apprenticeship model in law is under strain. While historically effective, its quality has been inconsistent, and AI introduces new complexities.
Tang suggested that future lawyers must learn not only to manage junior colleagues but also to manage technologists, non-lawyers, and AI systems or agents. This requires stronger communication, collaboration, and instruction-giving skills. Prompting AI systems, for example, mirrors the process of delegating work to junior associates, yet many lawyers have not been trained to give clear, structured instructions.
Knowledge as Competitive Advantage
Another critical insight was the growing importance of knowledge management (KM). As AI tools become widely accessible, differentiation will depend on the proprietary knowledge firms feed into these systems. Without strong KM, firms risk commoditisation.
Prompting, Context, and Creativity
Tang challenged the notion that prompt engineering alone is a lasting skill. As AI evolves, the focus is shifting towards context engineering and more advanced, creative uses of AI.
He encouraged lawyers to move beyond using AI as an answer generator and instead adopt more strategic applications. These include using AI for scenario testing, supporting negotiation preparation, acting as a sparring partner for legal arguments, and analysing judicial or counterparty behaviour.
Organisational Barriers and Cultural Tensions
The discussion highlighted tensions within law firms. While firms promote innovation and creativity, hierarchical structures and risk aversion can discourage junior lawyers from experimenting with AI.
Tang advised junior professionals to develop skills independently, engage with innovation lawyers and knowledge teams, and identify supportive partners and practice groups. However, he acknowledged that firms must also adapt, or risk losing “mini-unicorn” talent, lawyers who combine legal expertise with technological capability.
Final Reflections
The session concluded with a clear message: the future lawyer must combine legal expertise with adaptability, technical fluency, and creative problem-solving. AI is not replacing lawyers but reshaping what it means to be one.
Success will depend not only on adopting new tools but on developing the mindset to use them critically, responsibly, and strategically.
