If you were a mathematician (human computer) at NASA Langley in the 1960s, and watched room-sized IBM 7090 mainframe computers arrive in your office to replace you,
how would you feel?
Worried? Anxious?
That’s exactly what mathematicians like Katherine Johnson and Dorothy Vaughan felt as they saw their career about to become obsolete in a few years.
“With the arrival of the first IBM machines, a quiet anxiety settled over the desks of West Computing…the machine was a dagger hanging over their heads.”
However, in that quiet moment of anxiety, Vaughan saw what her colleagues weren’t and didn't wait for the tide to sweep them away.
“We are going to have to learn to program these machines,” she would tell them.
And so Vaughan bought books and taught herself overnight until the logic of FORTRAN, the programming language used in the machines, was as familiar to her as the differential equations she had been solving by hand for years.
Subsequently, the disruption didn’t break them, but like wind to a fire, it promoted them. This was career antifragility at its best, improving after a shock.
But here’s what nobody tells you. Self-learning before AI was pure friction and that was the whole point.
Buying FORTRAN books and learning from them demanded friction and that was what ensured their skills got into their hands.
However, when AI entered the room, it collapsed all that friction required for self-learning. Now, you can skip the entire method and effort and ship the flawless answers AI gives you as your output without breaking a sweat.
This looks like a gift, until it isn’t. Until you are holding answers to a problem you never understood.
This week, we examine what broke in self-learning when AI is now part of the equation and show you how you can restore it.
This way, your self-learning velocity becomes your best job security and the whole difference between the career that shatters when the market changes and one that gets stronger each time it does.
Let’s continue.
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