By 2035, society had largely abandoned walking as a means of transportation. Urban planners, motivated by efficiency and data-driven optimisation, redesigned cities around autonomous electric vehicles. Pedestrian streets were repurposed into rapid transit corridors. Walking was confined to indoor spaces, explicitly private residences and commercial complexes. Walking was only executed to partake in menial, non-essential tasks such as a grabbing a cup of coffee from a Brew-fficiency 5000 system (with algorithmic precision in every sip!). To walk outside was seen as impractical, even obstructive, a habit of the past that no longer fit within the streamlined flow of modern urban life.
Without foot traffic and bicycles, commutes became faster, more predictable, and free from the inefficiencies of spontaneous human movement. Smart infrastructure coordinated every trip down to the millisecond, eliminating congestion and delay. The idea of wandering, once a hallmark of exploration and leisure, became antiquated and perceived as a waste of time. In the pursuit of optimisation, transportation had become perfectly efficient yet eerily devoid of the unstructured rhythm of human footsteps.
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I hope you hated that depiction of the future (though maybe the coffee is really good).
I’ve attended numerous generative AI presentations over the past few years. The good, the bad, and the ugly have been discussed. However, two omnipresent ideas in all discussions surrounding AI are efficiency and inevitability.
I would like to challenge these ideas using the analogy of modes of transportation. My goal is to illustrate the significance of learning independent navigation to determine which mode of legal research is appropriate for a research task. Each mode has its own purpose, advantages, and limitations. In other words, this post is about the importance of building a solid foundation of legal research skills to inform human decision-making in our increasingly efficient, data-driven, algorithmic, AI embracing world.
Walking
Walking gets us to where we need to go. It’s great exercise. We move slowly and can focus on our surroundings and notice changes in the weather, elevation, and terrain. We can decide which direction we want to take when we come to a fork in the road, or we can go off trail. We are able to walk with others and converse with them. There are a variety of reasons someone might walk, including aimless wandering, exploration, maintaining health benefits, and commuting.
Taking the necessary time to learn the methodical legal research process and foundational skills is like walking. This focused learning improves our understanding of how the law is structured. Understanding the structure of the law allows us to make connections, in turn improving skills like fact and issue spotting. Synthesising materials gives us the knowledge to determine which direction to take a legal argument—to follow precedent or to craft a nuanced and novel approach.
While doing legal research at a slower pace we can converse with colleagues, professors, or librarians about ideas, concepts, and principles. This conversation can be for intellectual exploration or for a specific matter, while we simultaneously maintain social skills and connections. We can take our time critically analysing materials, making independent decisions, and contributing to the evolution of the law.
These foundational skills are often taught using online sources, but there is value in having a tactile experience with print primary and secondary sources. Electronic publications of these resources are bound (pun intended) by the print format. The structure of the law can be obscured or nonobvious in their electronic versions and engaging with sources in their original medium can improve online legal research. The serendipity of browsing the stacks in the library can be better in overall experience, speed, and relevancy than algorithmic results. Walking legal research can be cerebral wandering, iterative practice, or targeted in scope to complete an assignment or submission.
Running
Running is quicker. It is a more challenging activity that requires the foundation of walking but still allows us to take in and engage with our surroundings. We still have significant autonomy when we run. We decide where we want to go and how fast we want to get there. We can stop when we see something interesting or need to take a moment to catch our breath.
Maximising productivity while lowering time and condensing effort spent (read: being efficient) has value in a competitive profession aiming for cost-effectiveness. Once we have honed our skills, the logical progression of legal research is to pick up the pace. We still have the autonomy to choose which direction we want to take and the ability to slow down or pause to catch our breath if we’re researching an unfamiliar area of law, topic, or issue.
Biking
Biking is even quicker. We still benefit from the physical challenge, engaging with our surroundings, exercising autonomy over our pace and route (though bikes are limited to specific terrain more than traveling by foot). Biking also requires equipment. We need the bike, a helmet, and a repair kit. We need to decide which brand we want to purchase, or maybe we prefer to use a public bike share service. We need to learn the basic mechanics of the bike so we can maintain it. However, having a piece of equipment presents opportunities to learn cool tricks, like wheelies. Plus, if speed is the goal, biking is more efficient in getting us where we need to go, though we are required to know and follow the rules of the road.
Legal research services are added equipment. They come with a cost and a learning curve. We need to understand the differences between vendors—content, algorithms, interfaces, navigation, subscription packages—and assess which service or tool is best for our current research task. However, legal research services can and do help us complete research tasks very efficiently. They have lots of cool tricks that can streamline our research, like citator tools for noting up and integrated secondary sources and classification systems. A skilled legal researcher who understands the structure and organisation of the law can use these tools to their advantage. While we are limited to the existing infrastructure and must learn the rules of each service, we still have some ability to explore. We can also hop off the legal research service and walk if we hit a roadblock in our research.
Driving
Driving is, arguably, the quickest mode of transportation. Driving has significant differences when compared with walking, running, or biking. It requires having access to a vehicle and filling it with fuel. Driving is regulated through mechanisms like licensing requirements and vehicle maintenance, road signs, and offences. Travel is restricted to existing infrastructure. We don’t experience our surroundings in the same way. We might see the sun shining or acknowledge an incline, but we don’t engage with it. The mechanics of the vehicle do the work when we press the gas. We only feel the direct warmth of the sun or cool breeze if we roll down the windows. We typically need to take our car to a mechanic for maintenance and repairs because we don’t have either the knowledge or tools to do it ourselves. Most people don’t love their morning driving commute or classify driving to run errands as a leisure activity. However, sometimes we need to cover a great distance at a high speed. If we have access to a vehicle and have our licence, driving can get us where we need to go in a fraction of the time it would take to walk, run, or bike.
Generative AI is like driving—or perhaps it’s closer to plugging a destination into an autonomous vehicle. We receive an easy-to-read output of amalgamated information, but the processes of seeking and synthesising information are almost eliminated. Without solid knowledge of the structure and organisation of the information we receive it becomes difficult to make connections and inferences across materials. We cannot always trace where the information came from or what the algorithmically presumed value is. Seeking and synthesis are where we gain that understanding. Receiving information is not the same as understanding it.
We should pause and consider what degree of inevitability we are comfortable with. Generative AI tools do have a place in the legal research process once we have a competent level of knowledge of the relevant area of law and have internalised foundational skills. However, we should exercise caution in defaulting to reliance on the speed and convenience of generative AI, just as we should with driving. New drivers shouldn’t be taught how to drive using an autonomous vehicle. Rarely should generative AI be the first choice for legal research, particularly for new legal researchers.
Driving limits our engagement with the journey, and generative AI limits our engagement with the research process. Without actively navigating the terrain of information—understanding its sources, evaluating its accuracy, and making critical connections—we can risk missing crucial details, context, and nuance. Efficiency cannot come at the cost of comprehension. Stepping out of the car allows us to experience the world more fully and engaging directly with information allows for a richer, more meaningful understanding.
Final Thoughts
While the sale of personal vehicles has trended upward the past decade, it is unlikely that we would suggest this growth is evidence of the inevitability of cars as the sole mode of transportation. Efficiency needs to be assessed contextually because it’s also about not wasting time. It isn’t efficient to drive to our next-door neighbour’s house or through the public gardens. We understand that each mode of transportation has its own purpose, advantages, and limitations. The same should be true for legal research. While generative AI tools can be efficient, they should not replace the foundational skills that allow us to engage deeply with legal information. The ability to walk through legal research—following the legal research process, critically analysing materials, and making independent decisions—remains essential.
Research is not just about finding an answer, it’s about understanding the path that leads us to it. Proclamations about the efficiency and inevitability of generative AI need to be tempered with the reality that the practice of law relies on human judgment. Maintaining a multimodal approach to legal research ensures human decision-making guides our increasingly efficient, data-driven, algorithmic, AI embracing world.
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By 2035, cities had embraced a multimodal approach to transportation, recognising that efficiency was not just about speed and minimising cost and effort, but about adaptability and human well-being. Urban planners designed spaces where bicycles, personal electric vehicles, and pedestrians coexisted seamlessly, each mode serving a distinct purpose. Wide, tree-lined sidewalks encouraged walking not just as a means of movement but as a way to foster creativity, social connection, and mental stimulation. Roads were designed to prioritise both speed and distance, ensuring balance and that no single mode dominated at the expense of others. Rather than viewing unstructured movement as inefficient, society came to understand that variation in transportation led to more dynamic, resilient, and livable cities.
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