When I was a young lawyer, I spent hours in dark dusty warehouses paging through documents, looking for something relevant and important. At the time, I didn’t think it did much for me and I still don’t. It was my belief then and now that much of the mundane work young lawyers were expected to do really didn’t train them to do much of anything other than maximize billable hours. Moreover, having to do that kind of work was depressing and lowered worker satisfaction. That’s why I’ve been a vocal advocate for the theory that AI’s takeover of mundane legal work won’t necessarily harm junior lawyer training.
So, while future on-the-job training may be different, it doesn’t have to mean younger lawyers wouldn’t ultimately just as good if not better than today’s lawyers for a whole lot of reasons.
Being passionate and defending a theory can often be a good thing. However, it’s sometimes good to question that theory every now and then just to make sure there isn’t some nuance that you need to be aware of. It’s time to at least question my own assumptions. It’s always good to recognize that things are never as simple as your beliefs make it seem.
Gut Instinct and Science
Recent research by Daniel Kahneman and Gary Klein in a substantive work entitled The Foundational Kahneman-Klein Study “Conditions for Intuitive Expertise: A Failure to Disagree looks at the unconscious recognition of patterns a person has previously seen and experienced when presented with a problem. These patterns are then applied to the situation presented to find solutions to a problem. This theory may have significant implications for how we think about legal training, particularly the grunt work AI is replacing
This process leads you, for example, to “sense” something is wrong, although it’s really not sensing at all. The theory is called Recognition-Primed Decision Making (RPD). When I was more experienced, for example, I usually could predict when the other side would approach settlement because I had been through enough cases to see patterns of behavior in the other side.
Experienced lawyers though unknowingly call this their gut instinct when making decisions and recommendations.
If RPD is valid, then it stands to reason that the more patterns you see, the more ability you would have to apply those patterns to future situations and the better your so-called gut instinct will be.
Pattern Recognition in Legal Training
Let’s take document review and research, the bane of many young lawyers’ existence.
The argument can be made that document review, the sifting through contracts or discovery materials, due diligence, simple legal research, or drafting routine motions exposes young lawyers to recurring patterns: common clauses, the patterns and links between documents that you see as you review typical legal risks, the pattern of judicial reasoning that leads to conclusions, the patterns of judicial reasoning that enables prediction. This repetitive work exposed lawyers to thousands of variations of legal problems.
For example, a junior lawyer reviewing hundreds of contracts might start noticing red flags like ambiguous indemnity clauses or risky termination provisions. Once I was exposed to a situation involving a potential ambiguous noncompete clause, I could use what I had seen in similar cases so that I recognized the clause in question could lead to trouble. Over time, this repetition could embed a mental database of “what looks wrong” or “what feels right,” which manifests as gut instinct.
Of course, real life is more nuanced than that. We can’t just say that the old ways that younger lawyers gained experience meant they were necessarily better lawyers than those who come up in age of AI. Merely reviewing endless emails with little legal significance without more might not sharpen instincts as much as analyzing a few pivotal documents that AI can enable.
Simple Repetition Is Not Enough
Tools like legal research platforms or predictive analytics can expose lawyers to more patterns, faster, by surfacing relevant cases, trends, or outcomes that would take years to encounter manually. Moreover, you don’t hone contract negotiation skills by reviewing a bunch of contracts. You don’t learn how to create a successful litigation strategy by doing routine motions over and over. It takes more.
Of course, there are studies, particularly Ericsson’s work that suggest that repeated exposure to specific tasks can build some sort of intuitive judgment.
But Ericcson’s studies on expertise show that effective skill-building—real expertise— requires more than just repetition. It requires what he calls “deliberate practice.” Deliberate practice refers to “a special type of practice that is purposeful and systematic. While regular practice might include mindless repetitions, deliberate practice requires focused attention and is conducted with the specific goal of improving performance.”
A junior lawyer using AI might develop a richer “database” of patterns than one slogging through manual document review. Indeed, AI may eliminate or at least reduce the need for the gut instinct. Data analysis may replace the gut instinct with fact-based data. Indeed, those who subscribe to my theory about the impact of AI on younger lawyers typically pooh-pooh the whole notion of a gut instinct, viewing it as little more than wild ass guesses versus reliance on data and facts.
(Interestingly, even if you subscribe to RPD, humans are basically doing just what AI and data analytics do inside their brains: they are looking at past experaince (data) and looking for patterns. AI can just do it thousands of times faster and better.)
And what we call gut instinct doesn’t solely stem from pattern recognition that somehow develop through repetitive tasks. It involves a broader mix of cognitive and emotional skills like empathy, or ethical judgment. A lawyer’s ability to read a client’s emotional state or anticipate a judge’s reaction, for example, might rely on interpersonal skills or situational awareness, not just patterns from grunt work. These softer skills, developed through mentorship, courtroom observation, or client interactions, could contribute significantly to what’s perceived as “gut instinct.” AI taking over grunt work wouldn’t erode this instinct although it might shift its foundation to other forms of experience.
Practical Solutions
So, I still think we will survive AI and that future lawyers will turn out fine. But we do need to at least consider that the RPD theory may have some merit. While it makes little sense to make younger lawyers do what AI can now do, we may need to think about things that would build pattern recognition and be more purposeful about it. Things like:
- Adopting the notion of deliberate pattern exposure by creating structured programs that expose junior lawyers to diverse scenarios even if not billable.
- Embracing simulation-based learning: Use case studies, mock transactions, and scenario planning much like what Alta Clara is doing and I have discussed. The idea is to present young lawyers with a simulated legal scenario and then have senior lawyers critique their solutions
- Thinking of AI as teaching tool: Teach younger lawyers how to effectively use AI and then mentor them to the separate the wheat from the chaff in the results.
- Embracing AI for younger lawyers and recognizing that it may enable younger lawyers to do more sooner so that the development of pattern recognition will come more through actual experience instead of grunt work in a dusty warehouse.
- Evolving mentorship programs to ensure senior lawyers actively teach pattern recognition rather than assume it develops naturally
- Understanding that what makes a good lawyer is not only how well they do mundane tasks but how well they adopt and learn the key skills that document review may in the past have constituted only a small part of.
- Engaging in client education to help clients understand the value to them of investing in junior lawyer development
A Path Forward
Clearly, AI will alter not only the substantive practice of law but how we need to train younger lawyers. Whether you buy RPD or not, AI and the work it replaces isn’t going away. And the need to maximize its use across the board, including training, is imperative.
So, we need to think carefully about training our lawyers for the future and what skills they need to have. Wringing our hands over what used to work doesn’t solve the training dilemma. Let’s be thoughtful. And purposeful about training.
The legal profession stands at a crossroads. We can either thoughtfully redesign legal training for the AI age, or watch as tomorrow’s lawyers lack the pattern recognition that makes today’s best attorneys so effective.
Stephen Embry is a lawyer, speaker, blogger and writer. He publishes TechLaw Crossroads, a blog devoted to the examination of the tension between technology, the law, and the practice of law.
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