Jul 15, 2026 06:00 PM
https://www.thetransmitter.org/artificia...cientists/
EXCERPTS: Mass-produced does not have to mean low quality. Your favorite clothes, dear reader, are made from mass-produced fabric, and you would not have clothes as nice if all fabric were hand-woven.
Thanks to artificial intelligence (AI), we will soon enter a world in which high-quality scientific research can also be mass-produced, at low cost—not just summaries of what scientists already know but new analyses, new figures and new conclusions, at the request of anyone asking. AI will also enable production of low-quality science in even greater quantities, and discerning between the two will be a key challenge. But if we can solve that problem—if we can find ways to identify reliable and important results within the vast quantities produced—then both the quantity and quality of science produced will be higher than ever before.
For consumers of science—the public, medical patients, technology users—the effects will be positive. For producers, the effects will be as disruptive as industrial mass production was for artisan fabric makers. The way scientists publish and communicate their work will likely change completely, as will the way they evaluate, fund and promote human researchers. Some current jobs will vanish, and other yet-unnamed roles will take their place.
To get a feeling for what’s coming, consider pure mathematics, in which AI-driven research is most advanced.
[...] What roles, then, will remain for human scientists? And for how long might they remain?
First, direction. In the immediate future, at least, humans will still have a role in steering the direction of AI-accelerated research: deciding which questions are interesting to answer and allocating resources.
[...] Second, conceptual refinement. Scientists often start a project with an incompletely defined, intuitive question, which becomes precise only after contact with data. The philosophical term for turning initially imprecise intuitive ideas into precise concepts that can be answered scientifically is explication...
[...] Third, experiments. “Closed loop” AI-driven experimental work is already underway in chemistry, using robots; and in neuroscience, based on computer-designed sensory stimuli. Nevertheless, fully autonomous experiments involving animal behavior seem further off: Even if AI designs the experiments, human hands will be required until robotics advances to become better than humans at physical experimentation, which currently seems many years away.
Fourth, validation. Proper statistics, such as preregistered confirmatory analyses of held-out data, can make rejection of null hypotheses reliable. However, most science consumers read verbal conclusions, not exact null hypothesis statements. Confirming that statistical analyses truly support conclusions is currently performed by both authors and peer reviewers, with the former incentivized to make maximal claims. A role for humans in validating scientific results will persist until readers trust AI more than they trust other humans to separate truth from hype...The biggest challenge will be ensuring that results produced by AI research are reliable.
Fifth, filtration. Scientific papers have long been published faster than any individual person can read. Scientific importance is subjective, but journal editors, peer reviewers and citation counts play a major role in helping readers discern which papers are worth their time. This process is coming under ever more strain as AI accelerates research production; indeed, this may be the first part of our current system to “break.” Peer reviewers are increasingly using AI, even when it is against journal guidance...
[...]Most likely, the main roles for humans in the age of mass-produced science will be things we don’t yet have words for... (MORE - missing details)
EXCERPTS: Mass-produced does not have to mean low quality. Your favorite clothes, dear reader, are made from mass-produced fabric, and you would not have clothes as nice if all fabric were hand-woven.
Thanks to artificial intelligence (AI), we will soon enter a world in which high-quality scientific research can also be mass-produced, at low cost—not just summaries of what scientists already know but new analyses, new figures and new conclusions, at the request of anyone asking. AI will also enable production of low-quality science in even greater quantities, and discerning between the two will be a key challenge. But if we can solve that problem—if we can find ways to identify reliable and important results within the vast quantities produced—then both the quantity and quality of science produced will be higher than ever before.
For consumers of science—the public, medical patients, technology users—the effects will be positive. For producers, the effects will be as disruptive as industrial mass production was for artisan fabric makers. The way scientists publish and communicate their work will likely change completely, as will the way they evaluate, fund and promote human researchers. Some current jobs will vanish, and other yet-unnamed roles will take their place.
To get a feeling for what’s coming, consider pure mathematics, in which AI-driven research is most advanced.
[...] What roles, then, will remain for human scientists? And for how long might they remain?
First, direction. In the immediate future, at least, humans will still have a role in steering the direction of AI-accelerated research: deciding which questions are interesting to answer and allocating resources.
[...] Second, conceptual refinement. Scientists often start a project with an incompletely defined, intuitive question, which becomes precise only after contact with data. The philosophical term for turning initially imprecise intuitive ideas into precise concepts that can be answered scientifically is explication...
[...] Third, experiments. “Closed loop” AI-driven experimental work is already underway in chemistry, using robots; and in neuroscience, based on computer-designed sensory stimuli. Nevertheless, fully autonomous experiments involving animal behavior seem further off: Even if AI designs the experiments, human hands will be required until robotics advances to become better than humans at physical experimentation, which currently seems many years away.
Fourth, validation. Proper statistics, such as preregistered confirmatory analyses of held-out data, can make rejection of null hypotheses reliable. However, most science consumers read verbal conclusions, not exact null hypothesis statements. Confirming that statistical analyses truly support conclusions is currently performed by both authors and peer reviewers, with the former incentivized to make maximal claims. A role for humans in validating scientific results will persist until readers trust AI more than they trust other humans to separate truth from hype...The biggest challenge will be ensuring that results produced by AI research are reliable.
Fifth, filtration. Scientific papers have long been published faster than any individual person can read. Scientific importance is subjective, but journal editors, peer reviewers and citation counts play a major role in helping readers discern which papers are worth their time. This process is coming under ever more strain as AI accelerates research production; indeed, this may be the first part of our current system to “break.” Peer reviewers are increasingly using AI, even when it is against journal guidance...
[...]Most likely, the main roles for humans in the age of mass-produced science will be things we don’t yet have words for... (MORE - missing details)