Scarcity is the engine of economic value. When something is abundant, its price approaches the cost of production. When something is scarce and in demand, its price is set by what people are willing to pay to access it. For most of human history, information was scarce. Libraries, universities, and professional guilds existed in part as gatekeepers of knowledge that was expensive to produce and difficult to distribute. The internet began dissolving that scarcity. AI has accelerated it past the point of no return.
We are now, for the first time in history, genuinely close to a world where information of arbitrary breadth is available for free, on demand, instantly. That is a profound transformation. It is also creating a new scarcity that most people haven't fully noticed yet.
When information becomes abundant, the thing that becomes scarce is the ability to evaluate it.
Chart: The Scarcity Shift — From Attention to Trust
Needs real cited data before publishing. This chart should show the shift in what is scarce on the internet — from information (pre-2020) to attention (2020–2023) to verified trustworthy expertise (2024+).
Evaluating information — knowing which claims are reliable, which frameworks apply to your specific situation, which caveats matter and which don't — requires exactly the kind of knowledge that AI cannot replicate: experience. Time spent in a domain, making real decisions with real consequences, accumulating calibration through feedback loops that include failure. This is the new scarce resource. It's not data. It's not processing power. It's verified human experience in specific domains.
The economics are already shifting to reflect this. Look at where professional compensation has moved in the last decade. The people who've seen the largest increases in their economic value are not generalists who know a little about a lot of things — those people are being replaced by AI. The people whose value has increased are specialists with deep, domain-specific, judgment-based expertise that is demonstrably hard to replicate. The surgeon, not the general practitioner. The litigation partner, not the associate. The principal engineer, not the generalist developer.
The pattern is consistent: AI is commoditizing breadth and driving premiums toward depth. The deeper and more specific the expertise, the harder it is to replicate, the more valuable it becomes in a world where shallow synthesis is free.
This creates a structural opportunity and a structural challenge simultaneously. The opportunity is for genuine domain experts — people who have spent years or decades developing real mastery — to command returns on that expertise that weren't possible before AI made the contrast with shallow knowledge so stark. The challenge is packaging and delivering that expertise at a scale that matches the demand for it.
Historically, expert time has been the bottleneck. A great specialist can only see so many clients, advise so many companies, answer so many questions. That constraint kept expertise scarce in a way that limited both the income of experts and the access of people who needed them. The interesting thing about AI is that it's actually beginning to dissolve that bottleneck — not by replacing expertise, but by making it possible to deliver expertise without a proportional investment of expert time.
An expert who has documented their knowledge in sufficient depth can now make that knowledge accessible at scale, to people who couldn't afford a direct consultation, in a form that's genuinely useful rather than generic. That's not a threat to expertise — it's the most natural extension of it. If you're ready to protect and monetize your knowledge in this new environment, the infrastructure to do it now exists. And if you're looking to access expert knowledge in a domain you need — the new kind of scarcity is being solved, one vault at a time.