It used to start with a walk. You’d leave your desk, cross the office, and stand awkwardly in the doorway of the one person who actually knew how the derivatives pricing model worked. That person, a 25-year veteran with a messy desk and an encyclopedic memory of every regulatory panic since the 90s, was the company’s real asset. Today, that walk is a click. You open a chat window. You type a question. The expert has been condensed, processed, and served back to you as an API.
This is not a story about efficiency. It’s a story about extraction. The C-suite isn't paying for a tool to assist the expert; they're paying for a machine to replace them. The goal is to ingest the sum total of a career’s worth of nuanced, hard-won knowledge and turn it into a scalable, predictable, and infinitely cheaper utility. The veteran’s brain is the training data. Their emails, their documents, their recorded Zoom calls—all of it is feedstock for the model that will make their job obsolete.
The illusion is that the knowledge has been preserved. It hasn't. It has been flattened. The expert didn’t just know the rules; they knew the exceptions, the history, the politics behind the rules. They knew which corner cases mattered and which ones everyone quietly ignored. An LLM knows none of this. It can recite the text of a regulation, but it can’t tell you the story of the lawsuit that prompted it, or why a particular clause is a time bomb waiting to explode. It provides answers stripped of context, confidence without competence.
This creates a dangerous new form of technical debt. An organization that fires its experts loses its ability to self-correct. When the model hallucinates a critical compliance detail or misinterprets a subtle engineering spec, who is left to sound the alarm? The junior employees who now rely on it for everything lack the experience to spot the error. The managers who championed the system are invested in its success. The system becomes its own source of truth, an echo chamber that reinforces its own mistakes. The 5% of the time the model is wrong becomes an invisible, accumulating risk.
The real power no longer resides with those who know. It has shifted to those who manage the machine. The engineers who fine-tune the model and the data scientists who curate its inputs are the new priesthood. They decide which sources are canonical and which are discarded, shaping the organization’s institutional memory in a way that is both opaque and absolute. The expert is no longer a colleague to be consulted, but a dataset to be cleansed.
We are building organizations that are optimized for asking questions, not for knowing answers. The final product looks clean—a crisp, bulleted list in a chat window. The project moves forward. The feature ships. And months later, when the auditors call or the system fails in a way no one can explain, the search will begin for someone who understands why. By then, they’ll be long gone. We haven’t solved the problem of knowledge transfer. We’ve just created a more elegant way to forget.
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