Why I Stopped Waiting for AI to 'Mature'
Part 1 of the AI Knowledge Series
Last year, I was where most lawyers are now: curious about AI, skeptical of the hype, and convinced I should wait until things "settle down" before investing serious time.
I had good reasons. The technology was changing fast. Today's best practices might be tomorrow's obsolete approaches. Why build expertise in something that could look completely different in six months?
Then I realized I was asking the wrong question.
The Waiting Trap
The "wait and see" approach assumes that AI tools will eventually become plug-and-play -- that someday you'll be able to subscribe to a service, point it at your documents, and get reliable legal analysis without significant setup or customization.
That's not how this technology works, and it's not how it's going to work.
Every law practice has its own institutional knowledge: the arguments that work before specific judges, the contract provisions that have caused problems, the research trails that lead to the best authorities. This knowledge lives in partners' heads, in old briefs, in emails between colleagues. No off-the-shelf AI will ever capture it automatically.
The firms that benefit from AI won't be those who wait for perfect tools. They'll be the ones who start building their knowledge infrastructure now.
What Changed My Mind
I started experimenting with Claude for drafting research memos. The results were... fine. Generic. The kind of competent-but-unremarkable work you'd expect from someone who knew the law but didn't know the practice.
Then I tried something different. Instead of asking Claude to research a topic cold, I first fed it a collection of my own previous memos on similar issues. I gave it my preferred citation format. I showed it how I like to structure arguments.
The difference was stark. Not because the AI suddenly became smarter, but because I'd given it context that made its intelligence useful.
That's when I understood: the limiting factor isn't AI capability. It's the knowledge infrastructure we bring to it.
The Compounding Advantage
Here's what the "wait and see" crowd misses: the value of knowledge infrastructure compounds over time.
Every brief you write can teach your AI system something new. Every research trail can be documented for future use. Every successful argument can be encoded as a template.
Firms that start building now will have years of accumulated knowledge by the time their competitors decide to get serious. That's not a gap you close by waiting for better tools.
The Oil and Gas Context
I practice oil and gas litigation in Texas. It's a specialized field with specialized knowledge: specific contract provisions that appear again and again, regulatory frameworks that interact in complex ways, decades of case law interpreting industry customs.
This specialization makes AI both more challenging and more valuable. Challenging because generic tools don't understand our terminology or our context. Valuable because the knowledge infrastructure, once built, becomes a genuine competitive advantage.
When I can ask my AI assistant about the treatment of "continuous drilling" clauses in habendum provisions -- and have it draw on a curated database of relevant Texas authorities -- that's capability no subscription service will ever match.
The Path Forward
I'm not suggesting everyone needs to become an AI expert. What I am suggesting is that the sooner you start systematically capturing your practice's institutional knowledge in AI-accessible formats, the better positioned you'll be.
In the posts that follow, I'll share what I've learned about:
- The specific knowledge problems AI can help solve
- Architecture patterns that keep humans in control
- Practical workflows for encoding institutional knowledge
- What this looks like in day-to-day practice
The tools will keep changing. But the fundamental challenge -- making your firm's accumulated expertise accessible and actionable -- won't. That's work worth starting now.
Next: The Knowledge Problem Every Oil and Gas Litigator Knows