Michael Lewis warned that baseball scouts were heading for extinction. 15 years later, MLB teams had hired 32% more of them.

Geoffrey Hinton said we should stop training radiologists.

The Mayo Clinic now has 55% more of them.

Those touting the technology are quick to make predictions about people. But the technology doesn’t decide what happens. How people use it does. To predict that, you need to understand two things.

First, the job. The inputs and outputs, the edge cases, the workflows, the incentives.

Second, and often overlooked, the cognitive predispositions of the people doing it.

When I was building AI for radiologists, the industry was focused on one kind of error: missing something on the image. But radiologists make at least a dozen others. They anchor on the clinical history. They stop looking after the first finding. Put an AI flag on the screen and they may trust it too much, or after enough false alarms, not enough.

Baseball learned the same lesson. Teams had more data than ever, but a hitter can only hold a thought or two in the batter’s box. The edge went to the coaches who knew which one to give him.

The technology didn’t replace those people. It ran into how they think.

Now predictions abound about the rest of us. AI will replace knowledge workers, we’re told, starting with entry-level jobs.

I don’t think the Big AI labs understand the job.

Their predictions are making the same mistake: they’re looking at what the technology can do, not at how people will work with it.

Yesterday I argued that AI will make human attention and judgement the scarcest resource in enterprise. Baseball already ran that experiment.

The first change is the one everybody knows. Billy Beane had one of the smallest budgets in the league. Payroll was the scarce resource he needed to allocate. He used data to find the players the market was mispricing because other team’s scouting departments made decisions on intuition and feel. He used this edge to make the playoffs four straight years.

This edge was soon eroded. Before the book even came out, Oakland’s front office staff were recruited by other teams. By 2004, teams were paying full price for on-base skill as quantified by analytics. Eventually, every team had an analytics department. The data, the models, the analysts all became commodities.

Data got cheaper and more plentiful. The stadiums were equipped to track everything that could be tracked. Today, every pitch, every swing, every spin rate is tracked and shared with all 30 teams. But a hitter can’t think about forty metrics in the batter’s box.

That’s when the second change began.

Teams recognized the competitive edge had quickly moved. Every team had an abundance of commoditized information and established analytical methods. The advantage became a team of humans capable of effectively distilling and executing on this information. The bottleneck moved from information to human attention and judgement.

So teams did something counterintuitive: they hired more people. Some teams now carry three or four hitting coaches, and their job is to translate the data into something a player will trust and act on. A manager can only have so many conversations per day. At every level, for every matchup, it’s someone’s job to only pass along the insight that matters.

That’s the version of the Jevons Paradox I pitched yesterday, playing out on the diamond. Cheaper data didn’t reduce the need for human judgement. It multiplied it.

Every company is about to go through both changes. But most may only be looking at the first. Building an advantage there will be short-lived. The second is the one that decides who wins.

If enterprise success has always come down to allocating scarce resources efficiently and effectively.

and if AI is making a whole class of once-scarce resources, such as analysis, code, content and expertise nearly free at the margin;

and if access to non-frontier models is becoming a commodity;

and if your competitors have access to the same intelligence as you do;

and if the same models, in two different companies, will only product different results depending on who’s asking the questions and who’s judging the answers;

and if the cost of AI output continues continues falling while demand for inference continues to rise (the mainstream view on Jevons Paradox);

and if more analyses, more drafts, more code, more agents are doing more things;

then human attention and judgement become both your only differentiator and your scarcest resource. This is the real Jevons Paradox.

The job of management hasn’t changed. It’s still resource allocation. The winners in the AI era won’t be the companies with the best models or fewest employees. They’ll be the ones that figure out how to deploy human attention and judgement most effectively.