Three Ways I Think About AI
AIIR · AIAS · AIOS
The more time I spend building with AI, not just using it but actually building, the more I find myself organizing my thinking around three frames. Each one captures something the others miss. Together, they explain why this moment in the history of work is unlike anything that came before it.
Artificial Intelligence as an Industrial Revolution
Start here, because the other two frames only make sense if you accept this one.
AI is not another productivity tool. It is not a better search engine or a faster way to draft emails. It is the next Industrial Revolution, and I mean that with the same weight the phrase carried in 1850.
Every generation encounters a handful of moments that permanently alter labor economics. The mechanization of agriculture pulled people off farms and into cities. Electrification transformed what factories could do and when they could do it. The computer eliminated entire categories of white-collar work that had existed for a century. Each time, the displacement was real, the transition was painful, and the economy that emerged looked nothing like the one before it.
We are at one of those moments again.
What makes this one different is the target. For the first time, technology is not replacing physical labor or repetitive clerical work. It is beginning to perform cognitive work (analysis, synthesis, drafting, judgment) that we assumed required years of professional training. The implications for workforce planning, compensation models, organizational structure, and labor markets are not theoretical. They are already unfolding in the companies I advise.
I am a labor and employment lawyer. This is not an abstraction to me. I have spent my career watching technology reshape what work looks like and who gets to do it. AI is the biggest version of that story I have ever seen.
Artificial Intelligence as a Service
Most organizations are treating AI as a procurement exercise. They buy licenses. They issue policies. They send employees to a one-hour training and call it transformation.
That is not going to be enough.
The real opportunity, and the real risk, is in what happens after the purchase. How do you build governance structures that actually work? How do you redesign HR policies for a workforce where some of your employees are AI systems? How do you manage legal exposure when AI is making decisions that affect people’s jobs, compensation, and opportunities? How do you explain that to a regulator, or a plaintiff’s lawyer, or a board?
These are not IT questions. They are legal and HR questions. They require someone who understands both the technology and the workforce consequences.
In my practice, I work with GCs, CHROs, and founders who are trying to get this right. Not in a theoretical sense, but in the sense of: here is the actual policy, here is the actual governance framework, here is the employment law exposure you are carrying right now and what to do about it. The ability to operationalize AI responsibly, in ways that hold up legally and work for the humans inside your organization, is becoming one of the most valuable professional services available.
Most organizations do not yet have someone doing this work. They should.
Artificial Intelligence as an Operating System
The framing I find most useful for my own practice is this: AI is the operating system. My legal judgment, my strategic experience, my professional responsibility: those sit on top of it as the decision-making layer.
The technology does not replace expertise. It changes what one person with expertise can do.
In my practice, I can draft, research, analyze, build tools, run scenario models, and generate client-facing materials at a scale and speed that would have required a team five years ago. That is not a boast. It is a description of what AI-native practice looks like at this moment, and it has significant implications for how legal and professional services get priced, staffed, and delivered.
For the GCs and founders I work with, I think about this the same way. The question is not whether to adopt AI. The question is whether you are building your organization so that your best people are amplified by it, or whether you are buying tools and hoping for the best.
The difference between those two outcomes is enormous. It is also where I spend most of my time.
AIIR tells you why this moment is serious. AIAS tells you what organizations need to do about it. AIOS tells you how I approach the work.
If you are a GC trying to get ahead of AI governance, a CHRO navigating workforce transformation, or a founder building a company where humans and AI systems work alongside each other: these are the problems I am built for.