I said something to a room of investors in January that I do not think most people have worked through yet.
Information is becoming a commodity.
Not eventually. Now. The transition is already happening.
The Erosion of Information Scarcity
For most of human history, information was scarce. Knowing things that other people did not know was genuinely valuable. Expertise meant having accumulated, through years of study and experience, a body of knowledge that was hard to access anywhere else.
Lawyers charged premium rates because understanding the law required years of practice. Consultants commanded high fees because they had synthesized patterns across dozens of engagements that most companies had not seen. Financial advisors, marketing strategists, and HR experts — their value rested partly on information asymmetry. They knew things you did not.
AI has been systematically eroding that asymmetry, and the erosion is accelerating.
Ask an AI about tax law in three different jurisdictions. About construction codes for a specific building type. About the psychological research underpinning a marketing technique. About the failure modes of a particular supply chain structure. The breadth and depth of what these systems know now exceeds what any individual expert can carry in their head.
This does not mean experts become worthless. But it does mean that knowing things is no longer a sustainable standalone moat. The information access premium is going to zero.
What This Means for Information-Based Businesses
If your business model is built on being the person who knows things others do not, this is worth taking seriously.
Newsletter writers whose value is staying current and sharing what they find. Consultants who advise on a specific domain. Coaches who teach frameworks they have assembled. These businesses are not disappearing — but the foundation they are built on is shifting.
When I work with business owners and investors on AI, I keep running into the same thing: people on Twitter think everyone is up to speed on AI. The reality is that most business owners — the ones running real companies with real teams — barely know how to write a basic prompt. The gap between the AI-native community and the real market is enormous.
That gap creates a window. But it is a window based on timing, not information scarcity. It closes as adoption spreads.
The Three-Dot Principle
I think the enduring value moves to people who can connect three dots.
Not two — three. Here is why the distinction matters.
Two-dot combinations are the most common play: take your domain expertise and add AI skills. A lawyer who knows how to use AI tools. A marketer who builds AI workflows. An architect who uses AI rendering. These combinations are valuable right now. But they are visible, replicable, and will become the baseline expectation within a few years.
Three-dot combinations are something different. They create categories that do not yet have obvious names. They produce outputs that require all three components simultaneously — and because fewer people hold all three, they are harder to replicate.
The example I keep coming back to: someone who combined interior design expertise, AI image generation skills, and real estate investment experience. That combination lets them show developers photorealistic renders of buildings that have not been built — the kind of visual that closes presale deals before construction starts. None of those three skills alone produces that outcome. Neither does any two. All three together creates something genuinely new.
What This Looks Like in Practice
I have someone on my team, Brooks, who spent time learning operations, digital marketing, and productivity systems. He did not do this because someone handed him a roadmap. He just kept adding adjacent domains.
Now, when we discuss a marketing campaign, he understands the backend mechanics. When I describe a system, he immediately sees the marketing angle. I do not have to translate between worlds — he holds both simultaneously. Add productivity expertise on top of that and you have someone with a combination that is genuinely rare. Very few people know all three.
That is the pattern. It is not about picking the right career track. It is about deliberately accumulating at the intersection of multiple domains — especially domains that do not obviously go together.
The question I keep asking people: what are the three domains you sit at the intersection of? Not your primary one. Three. What combination, if you owned it, would make you hard to replicate?
The New Leverage
The shift I am describing is not a reason to stop learning or to abandon expertise. It is a reason to think differently about where the edge is.
Raw information is cheap. AI made it that way, and that is not reversing. The leverage is not in knowing more than the model — it is in knowing how to synthesize across domains in ways the model alone will not produce without a specific human pointing it in the right direction.
That synthesis is what I mean by connecting three dots. It requires depth in more than one area, pattern recognition across unexpected domains, and judgment about which combinations create actual value.
AI accelerates the people who have that. It makes the synthesis faster, the outputs higher quality, the iteration tighter. But it does not replace the human who knows which three dots to connect.
That is where the value is going. Not to information hoarders. To cross-domain synthesizers.
Want to develop the kind of AI fluency that compounds with your existing expertise? The 4-Day AI Sprint is a hands-on program for people who want to move from knowing about AI to actually building with it.
