Deep Moats

AI in legal professional services

I’ve spent countless hours over the last few months trying to wrap my head around what the most recent advancements in AI will do to professional legal services. As usual, I think the truth lies between those espousing total annihilation of the profession and those that want to ignore this transformational paradigm shift. While my own background is in corporate transactional matters, I think this generally holds true across the industry for large multi-national law firms.

While legal AI undeniably is a force multiplier (the potential of which I encounter daily and I consider myself an advocate for early adoption of any AI-enabled gained efficiency possible), human-delivered advice isn’t dead yet (I’ll put a pin in what happens when the agents start needing lawyers). The value of human-derived counsel is concentrated into the following three moats and their value is increasing daily:

Judgment (Data vs. Wisdom)

AI models can summarize, benchmark and suggest revisions to a 200 page indenture or credit agreement. However, these models lack the ability to apply judgment comparable to a human that has spent a decade negotiating them and seeing how the sector has evolved; AI does a great job responding to specific text-based questions, but can fail to understand how certain provisions interact with each other. Nor are they currently able to navigate board-level politics or weigh potential reputational risk. AI will struggle to understand the humanistic motivations of a counterparty or the inclinations of a particular activist investor. I have no doubt that, someday, a model with a large enough context window, coupled with exhaustive iterative feedback and enrichment, can get there, but it will take an extreme effort (and willingness) from those with the relevant insights to digitize that institutional knowledge. We’ll begin to see automation adoption start lower in the value chain, but we are a long way before we let the models independently run a $2 billion bond offering or a $5 billion sell-side M&A transaction; the potential downside risk of a faulty tax analysis or a broken acquisition is far too great to outweigh any efficiencies gained from automation. Strategic risk assessment remains the domain of humans with context for human implications and consequences.

Creativity (Pattern Recognition vs. Pattern Breaking)

AI excels at pattern recognition, but I have not personally seen evidence of pattern breaking. This isn’t surprising – we don’t see this from junior associates either and not because they aren’t talented, bright and hardworking. I’ve been fortunate to work with many category-defining enterprises that disrupted legacy industries because the incumbents had grown innovatively sclerotic; these clients were bold enough to forge a new path where the current law never contemplated the technological advancement, scientific breakthrough or business application (while I wasn’t practicing during the dot-com era, I did witness the cloud migration, rise of SaaS, the emergence of blockchain and cryptocurrency and now the AI revolution). These entrepreneurs were willing to ignore legacy legal frameworks or exist just outside of them following carefully iterated discussions and brainstorming sessions. Perhaps the AI models will evolve on a similar trajectory that our junior attorneys do; after enough repetitions, they can demonstrate an ability to advise beyond their training materials. Again, however, I think this starts out in smaller transactional environments. What’s most likely to happen is that prior outputs become inputs (to borrow a phrase from Packy McCormick); as AI lowers the cost of what humans can initially build, the volume of complex matters arising as a result will actually increase. This is exactly where humans will need creativity and the ability to reason how existing legal frameworks (including general policy considerations) will impact the trailblazers that foster innovation.

Niche Specialization (Liability Backstop)

I fully expect rudimentary, mundane legal work to be commodified shortly (to the extent that it isn’t already). Frankly, this is not work that needs to be done at a large law firm and, to the extent it is currently handled by them, is routinely done by junior attorneys or paralegals. This is an area where large firms will need to adopt AI tools to drive efficiency if they want to retain this work (which is an open question; perhaps these AI-enabled capabilities aren’t part of the value proposition going forward). However, for the highest stake matters where the consequences for errors, omissions or inaccurate analysis dwarf any cost-efficiencies, law firms will still supply the human element as a liability backstop, for which they will charge a commensurate risk premium. I don’t see a world where anyone has the time or inclination to create a highly bespoke model to handle once-in-a-lifetime matters, or the willingness to provide any financial guarantee of accuracy. Perhaps this is really a derivative moat of the first two but there are enough differentiated and complex corporate transactions and litigation where the return won’t justify the investment.

AI-driven technology achievements will cause each of these moats to narrow; but as it does, I believe they get deeper and the value will continue to expand as long as human innovation persists on the exponential curve we’ve witnessed since the industrial revolution. Each decade, humans move up the value chain into more specialized functions. There will clearly be disruption in every field, including legal services, but this has always occurred. As an industry, we need to figure out how to embrace AI capabilities to realize efficiencies from a competitive dynamic, though I believe this adoption will ultimately be in service of each of the distinctly human advantages outlined above.

AI is amazing for automating efficiency. Excellent humans will have transformational tools to raise the ceiling on that excellence. But it would be a mistake to confuse the mathematician with the calculator or the scientist with the lab. As an industry, the survivors will figure out what their strengths are and will reinforce them through AI.

Disclaimer: The views reflected in this piece are mine alone and are not made on behalf of any employer or client.