The glow of the monitor is the only light in the room. A junior engineer stares at a stack trace bubbling up from a service whose original author was laid off six months ago. The documentation is a single, cryptic README.md file, last updated during the Obama administration. Their manager’s only advice: “Ask the Copilot.” So they do. They paste the error into the chat interface, and the AI responds instantly with a confident, plausible, and utterly wrong block of code.
This is the new knowledge transfer. It’s a séance.
Companies are engaged in a great forgetting. In the frantic pursuit of efficiency, they are shedding their most experienced—and expensive—engineers. The logic is seductive: trim the headcount, flatten the hierarchy, and let AI assistants handle the tedious work of remembering how things function. Wall Street applauds the cost-cutting. The C-suite talks of agility. But what is actually happening is a deliberate erasure of institutional memory. The unwritten context, the subtle warnings, the hard-won lessons from a decade of outages and architectural debates—it all walks out the door with the severance package.
The belief is that this void can be filled with technology
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