The Librarian Who Never Left the Library
Recently the renowned computer scientist - Yann LeCun explained, “large language models are trained on approximately 30 trillion words. The volume of words representing nearly all publicly available internet text. For a human to read that volume would take over 500,000 years of continuous reading.”
LeCun’s point is meant to impress. And it should.
Now imagine this shows up on a résumé. A candidate who has absorbed the entirety of human documented knowledge. Every book, every paper, every forum post, every manual ever written.
Would you hire them on the spot? Or would you want to know what they can’t do?
Here’s the thing: you’ve already hired them. And they brought friends.
Character One: The Librarian Who Never Left the Library
She has read every book in existence. She can retrieve any fact, cross-reference any concept, and summarise any topic on demand. Her knowledge spans Ottoman trade routes to chemical bonding to customer segmentation frameworks.
She has never stepped outside the building.
What she offers: Coverage. The probability that somewhere in her vast collection, information exists that relates to whatever you’re asking about. Faster retrieval than any human researcher. Synthesis across sources that no individual could replicate.
What she lacks: Any sense of which book matters for your situation. She treats peer-reviewed studies and Reddit comments with equal seriousness — both are just text on shelves. She can tell you what has been written about organisational politics. She knows nothing about yours. She spends time to reason and analyse for even a common sense.
How to work with her: Bring specific questions. Verify what she gives you. Never assume she knows what’s relevant. She only knows what’s written.
Character Two: The Intern Who Is Eager To Show Off Syntax But Never Shipped
He aced every programming course. He can write code in twelve languages, recite design patterns from memory, and explain the theory behind any architecture you name.
He has never deployed a system that real users depend on.
What he offers: Speed on well-defined tasks. Familiarity with virtually every documented approach. The ability to produce working components faster than you could write them yourself.
What he lacks: The scar tissue that comes from debugging at 2am when production is down. The instinct for where systems actually break. The judgment that only comes from facing consequences. He learned from descriptions of what works and he has never felt what happens when it doesn’t. He tends to over-engineer simple problems to demonstrate what he knows.
How to work with him: Use him for drafts, scaffolding, and acceleration. Review everything before it touches production. Never let him make architectural decisions alone.
Character Three: The Design Guru With Consistency Issues
He produces stunning visual work at extraordinary speed. What takes others hours, he delivers in minutes. Each output is impressive in isolation.
But he cannot work longer than three minutes at a stretch. And he has a troubling tendency to forget his own style between sessions.
What he offers: Rapid generation. Impressive quality in short bursts. The ability to explore ten directions in the time it takes to manually create one.
What he lacks: Sustained coherence. The ability to maintain design integrity across a larger project. Reliability when consistency matters. Each piece looks good; the pieces don’t necessarily fit together.
How to work with him: Use him for exploration, iteration, and speed. Build the system for consistency yourself. Never assume today’s output will match yesterday’s.
The Team You Actually Have
These three aren’t hypothetical. They’re different faces of the same technology you’re already using.
The librarian is retrieval and synthesis. The intern is code generation and technical drafting. The design guru is image, video, and design generation.
Each offers genuine capability. Each has systematic limitations. And each is available to your competitors at the same cost.
The question is not whether these team members are “really” intelligent. The question is whether you know how to manage them.
The conversation about AI at work fixates on reskilling employees. But these three characters don’t need training. They need management.
The real upskilling isn’t teaching your team to use AI. It’s teaching yourself to manage technology which is fast, knowledgeable, but systematically unreliable.
That’s the skill. The rest is myth.
