Unlike fields with finite demand, the appetite for scientific discovery is infinite. Therefore, automating science won't displace scientists. Instead, it will create more questions and opportunities, transforming the scientist's role into a manager or 'wrangler' of AI systems that explore hundreds of ideas simultaneously.
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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.
Fears that AI will eliminate entry-level jobs are unfounded due to Jevon's paradox. Just as Excel didn't kill accounting jobs but instead enabled more complex financial analysis, AI will augment the work of junior employees, increasing the sophistication and volume of their output rather than replacing them.
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.
As technology made marketing tasks more efficient (e.g., Google Ads), it democratized access, causing a 5x increase in marketing jobs since the 1970s. Box's CEO argues AI will have a similar effect on all knowledge work by lowering costs, which will dramatically increase overall demand for that work.
Increased developer productivity from AI won't lead to fewer jobs. Instead, it mirrors the Jevons paradox seen with electricity: as building software becomes cheaper and faster, the demand for it will dramatically increase. This boosts investment in new projects and ultimately grows the entire software engineering industry.
AI agents that explain equations or decompose forecast changes are seen as complementary technologies. They automate routine tasks, allowing economists to focus on enhancing model quality, building new models, or expanding coverage, rather than reducing headcount. This follows the Jevons paradox, where efficiency gains increase demand.
AI makes tasks cheaper and faster. This increased efficiency doesn't reduce the need for workers; instead, it increases the demand for their work, as companies can now afford to do more of it. This creates a positive feedback loop that may lead to more hiring, not less.
Jevons Paradox states that as a resource becomes more efficient, consumption increases. Applied to AI, making software development faster won't eliminate developer jobs. Instead, it will create a surge in demand by enabling new applications like internal tools and personal apps.
The Jevons Paradox observes that technologies increasing efficiency often boost consumption rather than reduce it. Applied to AI, this means while some jobs will be automated, the increased productivity will likely expand the scope and volume of work, creating new roles, much like typewriters ultimately increased secretarial work.
Contrary to fears of displacement, AI tools like 'AI co-scientists' amplify human ingenuity. By solving foundational problems (like protein folding) and automating tedious tasks, AI enables more researchers, even junior ones, to tackle more complex, high-level scientific challenges, accelerating discovery.
