"If you work as a radiologist, you're like the coyote that's already over the edge of the cliff but hasn't yet looked down."

That was Geoffrey Hinton—whom many consider the godfather of AI—in 2016. 

Long story short, he told the world to stop training radiologists. 

"It's just completely obvious that within five years, deep learning is going to do better than radiologists."

Then they’ll all be out of jobs. 

Wile E. Coyote, meet gravity.

The press believed him. Medical students believed him—some may have even walked away from radiology careers on the strength of that one prediction.

Ten years later, the verdict is in.

Hinton was right about the technology… 

But he was dead wrong about everything else.

Meanwhile, the same prediction is being made today—about your job, your kids' jobs, everyone's jobs.

Which is why what actually happened matters.

The Cliff That Never… Cliffed 

Hinton’s logic was airtight. 

If AI can do a better job than humans, humans lose those jobs. 

And yet… 

Since Hinton’s prediction, the Mayo Clinic has grown its radiology staff 55%. Four hundred radiologists and hiring.

The American College of Radiology projects the demand will keep growing for the next three decades.

And America’s now facing the largest radiologist shortage in its history. Imaging departments at some hospitals are backlogged for months. 

Why? Because AI did what Hinton said it would and then demand exploded.

The smartest man in artificial intelligence—Nobel laureate, father of the field—looked at the data, made the right call, but got it exactly backwards.

We don't need fewer radiologists. We need more than ever.

Right About AI. Wrong About the World.

Hundreds of FDA-cleared radiology algorithms are in use today. On narrow tasks, the machines match or beat human eyes. 

Hinton called that part perfectly.

Here are three things he didn’t account for: 

Problem one: the data doesn't exist. A radiologist reads X-rays, CTs, MRIs, ultrasounds—forty different scan types a day. No training set covers them all. So AI attacks one slice at a time. Chest CTs. Mammograms. Bone fractures. An algorithm that handles one of forty daily tasks helps at the margin. 

That’s theoretically solvable. The next two are harder. 

Problem two: the money follows the doctor. Insurance pays when a licensed physician signs the report. No signature, no payment. That’s not changing. 

Problem three: somebody has to get sued. Miss a tumor, face a jury. Malpractice law wants a human name on that report. No hospital is putting an algorithm on the witness stand.

Capability is one thing. Absorption is another. Hinton had the first and ignored the second.

Why This Matters to You

AI did change radiology. 

It made imaging faster and cheaper. And when something gets cheaper, people consume more of it.

Cheaper imaging meant doctors ordered more imaging. More scans meant more reads. More reads meant more radiologists.

The technology that was supposed to destroy the job multiplied it.

You've heard the predictions. Your kids, your grandkids, you: staring down mass unemployment. 

Those making these forecasts are brilliant. Credentialed. Confident.

So was Hinton.

And they're making his exact error—dazzled by what the machine can do, blind to the friction of the real world. 

The missing data. The regulation. The liability. The institutions built brick by brick around human judgment. 

The trust that takes decades to transfer.

What actually happens looks like radiology. Throughput climbs. Costs fall. Demand explodes. 

The industries that thrive will be the ones wrapped around AI, not hiding from it. Human judgment at the edges of what the machine can't yet do. 

And there will always be edges.