As candidates use AI to tailor resumes to every job description, employers are finding themselves with a strange problem.

"The task is, not so much to see what no one has yet seen; but to think what nobody has yet thought, about that which everybody sees." Erwin Schrödinger, the Austrian physicist and Nobel laureate noted this in the context of his work with quantum mechanics.

There’s an interesting example of this playing out in hiring right now.

As candidates use AI to tailor resumes to every job description, employers are finding themselves with a strange problem: more and more applications look perfect.

But when everyone can produce a "perfect" application, it becomes less useful as a signal.

Recruiters are starting to move in the opposite direction: more referrals, networking, live assessments, and situations where candidates have to demonstrate what they can do.

There’s a deeper point here about AI and the future of work.

AI is making many things we used to value in knowledge work much easier to produce. And when everyone can produce something that looks good, looking good becomes a much weaker signal.

What becomes more valuable are things that are harder to manufacture: a real achievement, a demonstrated skill, a useful connection, a point of view that is genuinely your own, or a judgment call you made when the answer wasn’t obvious.

These things reflect who YOU are, rather than simply what you can produce.

That makes Schrödinger’s distinction between seeing, knowing and thinking increasingly relevant.

The scarce skill may be noticing what the system isn’t looking for: What assumption are we all making that might be wrong? What question isn’t being asked? What connection exists between things everyone treats as unrelated? What problem are we solving that shouldn’t be solved?

There’s another Schrödinger idea that I think is interesting here: superposition.

Very loosely speaking, a quantum system can exist across multiple possible states until it is measured. "Schrödinger’s Cat," his famous thought experiment was his way of illustrating just how counterintuitive this idea is.

There’s an interesting parallel with AI.

AI can now give us 100 possible answers, strategies or paths forward. It can expand the space of possibilities far beyond what any individual could explore.

BUT possibilities aren’t reality. At some point, someone has to choose.

Which answer do we pursue? Which strategy do we commit to? Which idea do we turn into something real?

In physics, the language is wave function collapse: many possible states resolving into one observed outcome.

AI may increasingly expand the wave function of what is possible. The human role is deciding which possibility to collapse into reality. And perhaps that is where the value shifts: not in generating more answers, but in knowing which one is worth choosing.

It is a version of Schrödinger's idea == looking at the same possibilities everyone else has and thinking differently about which one should become real.

Until next time,

Ram

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Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Substrate Shift."

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