The first-order effect of AI is efficiency. You can do more with less. You can process more data, generate more content, respond to more customers, analyze more options. Organizations that adopt AI early gain an efficiency advantage over those that do not.
This is real. But it is temporary.
The second-order effect of AI is the collapse of the advantages that efficiency used to create. When everyone has the same AI tools: and we are approaching that point faster than most people realize: the efficiency advantage disappears. And what is left is a question that AI cannot answer: what do you do with the efficiency?
The Commoditization of Capability
In the promotional products industry, the data lake work I did at iPromoteu was valuable in part because it was hard. Building a scalable data architecture that could ingest supplier data from hundreds of sources, normalize it into a common schema, and make it queryable in real time: that was a significant technical and product challenge. The companies that could do it had a capability advantage.
AI is making that kind of capability easier to build. The tools for data ingestion, normalization, and querying are getting better and cheaper. The technical barrier to building a data lake is lower than it was three years ago. And it will be lower still in three more years.
When capabilities become commoditized, the competitive advantage shifts. It shifts from "can you do this?" to "what do you do with this?"
The Second-Order Questions
The second-order questions are harder than the first-order ones. They are not about capability. They are about judgment.
In the promotional products industry, the second-order question is not "can you build a data lake?" It is "what do you do with the data once it is in the lake?" What decisions does it enable? What products does it make possible? What value does it create for distributors and suppliers that they could not create before?
In automotive marketing, the second-order question is not "can you run targeted campaigns?" It is "what do you target?" What signals do you use to identify the right buyer at the right moment? What message do you send? What experience do you create when the buyer clicks?
These questions require domain expertise, customer understanding, and strategic judgment. They are not questions that AI can answer on its own. They require a human who understands the domain deeply enough to know what questions to ask.
The Implication for Product Managers
The second-order effects of AI have a specific implication for product managers: the value of domain expertise is increasing, not decreasing.
When the tools are commoditized, the differentiator is the judgment about how to use them. And that judgment requires deep understanding of the domain: the users, the workflows, the competitive dynamics, the regulatory environment, the cultural norms.
For surface-level domain knowledge: the kind you can get from reading industry reports: AI probably does reduce the value. But for deep domain knowledge: the kind that comes from years of working in an industry, building relationships, making mistakes, and developing judgment: AI does not reduce the value. It increases it. Because deep domain knowledge is what tells you what to do with the AI tools. And that is the question that matters in the second-order phase.
The first-order phase rewarded adoption. The second-order phase rewards judgment. And judgment, unlike AI tools, is not something you can buy.
