Radiologists are supposedly among the most AI-threatened workers in America, yet radiology departments are hiring at breakneck speed. Why the paradox? The Mayo Clinic runs over 250 AI models while continuously expanding its workforce. Their radiology department now employs 400+ radiologists, a 55% jump since 2016, precisely when AI started outperforming humans at reading scans.
This isn't just a medical anomaly. AI-exposed sectors are experiencing 38% employment growth, not the widespread job losses experts had forecasted. The wage premium for AI-skilled workers has doubled from 25% to 56% in just one year—the fastest skill premium growth in modern history.
The secret lies in understanding amplification versus replacement. Most predictions treat jobs like mechanical puzzles where each task can be automated until humans become redundant. But real work exists in messy intersections between technical skill and human judgment. Radiologists don't just pattern-match on scans—they integrate uncertain findings with patient histories, communicate risks to anxious families, and make calls when textbook answers don't exist.
These "boundary tasks" resist automation because they demand contextual reasoning that current AI fundamentally lacks. A financial advisor reads between the lines of a client's emotional relationship with money. AI excels at pattern recognition within defined parameters; humans excel at navigating ambiguity and building trust.
Those who thrive in the workplace today don’t look at AI as competition. Rather, they’ve learned to think of it as a sophisticated research assistant that frees them to focus on higher-level strategy and relationship building. As AI handles routine cognitive work, intellectual rigor becomes a choice rather than a necessity, creating what Paul Graham calls "thinks and think-nots."
Organizations can choose displacement strategies that optimize for short-term cost savings, or amplification approaches that enhance human capabilities. The Mayo Clinic radiologists have discovered something beautiful: they've learned to collaborate with AI in ways that make them more capable than ever. This provides patients with both machine precision and human wisdom.
The choice is whether we learn to collaborate with AI or compete against it—whether we develop skills that amplify our human capabilities or cling to roles that machines can replicate. This window for choosing amplification over replacement is narrowing rapidly.
Key Topics:
● The False Binary of Replacement (02:28)
● The Amplification Alternative (05:33)
● The Collapse of Credentials (08:04)
● A Great Bifurcation (10:14)
● How Organizations May Adapt (11:18)
● The Stakes of the Choice (15:08)
● The Path Forward (17:35)
More info, transcripts, and references can be found at ethical.fm
