AI is unlikely to replace fields like radiology because of Jevons Paradox. By making scans cheaper and faster, AI increases the overall demand for scans, which in turn can increase the total number of jobs for human radiologists to manage the higher volume and complex cases.

Mala Gaonkar argues the most profound applications of AI are improving non-tech industries. For example, AI has improved the accuracy and speed of medical scans by 70% and is transforming the 300 million surgeries performed globally each year through robotics, reducing errors.

While fears of AI-driven job loss are valid in some industries, healthcare faces a massive and growing supply-demand mismatch. With record shortages of clinicians and unlimited demand, AI is less a job destroyer and more a critical tool to augment existing workers.

Contrary to the job loss narrative, AI will increase demand for knowledge workers. By drastically lowering the cost of their output (like code or medical scans), AI expands the number of use cases and total market demand, creating more jobs for humans to prompt, interpret, and validate the AI's work.

Countering job loss fears, Jensen Huang cites that AI in radiology increased the demand for radiologists. AI automated the *task* (reading scans) but amplified the *purpose* (diagnosing disease). This efficiency allows for more scans and more patients to be treated, ultimately growing the need for the professionals who leverage the technology.

Contrary to popular belief, AI won't replace healthcare workers. By increasing awareness and making it easier for people to identify health issues, AI will drive significantly more demand for healthcare services, intensifying the existing global shortage of professionals, not solving it.

Jensen Huang uses radiology as an example: AI automated the *task* of reading scans, but this freed up radiologists to focus on their *purpose*: diagnosing disease. This increased productivity and demand, ultimately leading to more jobs, not fewer.

Contrary to sensationalist interpretations, a high 'AI exposure' score for a job does not automatically mean displacement. Economists suggest it can mean the opposite, as AI acts as a complement. Highly exposed roles could see increased hiring, higher wages, and greater demand for complementary human skills, depending on demand elasticity.

Whether AI productivity gains create or destroy jobs depends on how much more consumers buy when prices fall. If demand is "inelastic," firms will fire workers. If it's "elastic," they might hire more. Economists lack sufficient data on this elasticity across sectors, making predictions highly uncertain.