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We will need more (not fewer) scientists

Josh Bloom:

Post 5 Analysis

Labor displacement concerns with new technology are neither new nor entirely unfounded. The Luddites, pegged by history as railing against new machinery, were in essence a labor movement1. Telephone switchboard operators dwindled rapidly following electromechanical switching investments2. In a recent essay, Bill Gates called for policy intervention to stem the wholesale move from human labor to machines: “AI will take on work in law, customer service, medicine, software, and manufacturing… There will be some new jobs, but without the right policies, there will be far fewer than exist today.3

Now, as AI becomes more enmeshed not just in our daily lives but in our scientific work, and looking back on such historical labor displacements, there is a growing unease among scientists, and especially among budding scientists. What will be the role of scientists when increasingly capable AI co-scientists4 can participate in precisely the sort of labor we have trained (or are training) to do?

Attitudes towards AI in science were captured in a survey of scientists in 2023 (which feels like many generations ago now)5. While most saw AI assisting with faster data processing and computation, many worried about entrenched bias, easier fraud, and superficial understanding — labor displacement concerns weren’t at the fore, but clearly on the horizon. Fast forward to 2026, as AI co-scientists have become demonstrably better, astronomer David Hogg’s white paper “Why do we do astrophysics?” identifies two diametrically opposed future paths: one where AI is kept at bay, and one where it sweeps through our profession and relegates scientists to observers, rather than owners, of the scientific process. And Terence Tao, in a recent ICM lecture capturing the transformative moment for AI in mathematics, argues that the real crisis is not machine capability but values: AI can accelerate proof and verification, and it falls to the community — not the technology companies — to decide what mathematical work is actually for6.

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