AI Is Killing the Premium on Average Work

Here is the simplest way to say what AI is doing to the labor market: it is killing the premium on average work.

AI has democratized a lot of knowledge. It has made competent output easier to produce. It has raised the floor. That part is real.

But it has also flooded the market with sameness. More people can now produce decent-looking work, decent-sounding writing, decent-enough strategy, and decent-enough code. That does not make excellence more common. It makes average work more widespread.

Which is why the winners now are not the people using AI to avoid thinking. They are the people using AI to enhance judgment, not replace it.

The Middle Is Getting Crowded

For a long time, the market rewarded people simply for knowing things other people did not know. How to write a memo. How to structure a deck. How to summarize research. How to get started on a coding task. How to sound polished.

AI makes all of that more available. The result is not that everyone suddenly becomes great. The result is that a lot more people can get to the same middle zone.

The bell curve flattens into a spike. More output looks passable. More ideas sound plausible. More work appears finished. But the gap between average and excellent does not disappear. If anything, it becomes easier to spot.

AI should be used to enhance judgment, not replace it.

What PwC Found

PwC's framing is useful because it puts real labor-market evidence behind something many of us have been feeling intuitively.

The report says AI is creating a two-track market. In one track are jobs where AI acts like a force multiplier for experts. PwC calls these “professionalised” roles. In those jobs, routine work gets automated, but the human work becomes more important. Judgment. Leadership. Creativity. Client handling. Strategic thinking.

In the other track are jobs where AI makes the work itself easier for non-experts. Those are the roles where average output gets cheaper. The press release notes that the first category is growing faster and seeing meaningfully stronger wage growth than the second.

PwC also found that AI-exposed entry-level roles in the US are now seven times more likely to require traditionally senior human skills like judgment and leadership. That is not a small shift. That is the market telling junior workers to show up more senior much earlier.

Employers Can Feel the Difference Fast

I saw this recently in an engineering hiring process.

Some candidates talked about AI like it was a novelty. They had asked ChatGPT for a coding plan once. They had played around with Claude a little. They had a light familiarity with the tools, but not a real operating model for them.

Others were clearly thinking AI-first through every part of the workflow. How they planned. How they prototyped. How they reviewed. How they documented. How they learned. How they checked quality. Those candidates felt different almost immediately.

Not because they had delegated their thinking to the machine, but because they had built a better thinking process around it.

That is what companies want. They do not just want someone who touched the tool. They want someone whose judgment got better because they learned how to use the tool well.

Curiosity Is Becoming a Professional Requirement

The most important quality in this environment might simply be curiosity.

Curious people keep testing. They keep refining. They do not stop at the first decent answer. They want to know what changed this week, what is possible now, and what breaks if they push the system harder.

People who are not curious tend to use AI like a vending machine. Prompt in, output out, brain off.

That is fine if the goal is to get something passable. It is a terrible strategy if the goal is to become unusually valuable.

What This Means for Schools

Schools should stop treating AI as a pure productivity story.

Yes, students need tool fluency. Yes, they need practice. Yes, they need to know how the systems work.

But the deeper question is whether we are teaching them how to think better because of AI, or just how to finish tasks faster with it.

This is part of why the humanities conversation matters. The market is not suddenly paying extra for vague “soft skills.” It is paying extra for judgment. Perspective. Interpretation. Taste. The ability to see tradeoffs. The ability to know when the machine is technically right but directionally wrong.

In other words, the human layer is not becoming less important because AI got stronger. It is becoming more important.

What I Would Tell a Student Right Now

If I had a child under 18, or a college student asking what matters now, I would ask them four questions:

• How can you prove you are more human than the average output?
• How can you show real judgment, not just polished answers?
• What have you built, tested, or learned that most applicants have not?
• Who knows you well enough to advocate for you beyond a cold application?

Networking matters more in this market, not less. Original work matters more. Applied projects matter more. The ability to explain why you made a choice matters more. For students without prior internship experience, AI credentials and portfolio work are now the primary differentiator.

The students who stand out are not the ones pretending they never use AI. They are the ones who can clearly show that AI made their work sharper because they were already capable of making good decisions.