Artificial-intelligence systems can generate hypotheses, design experiments and analyse data — but humans still need to decide what makes sense.
AI for science needs reasoning, not just data
AI agents that can model the human process of research will accelerate discoveries in science.
Deep Research, information vs. insight, and the nature of science
What AI will accelerate in the scientific process, what it cannot do, and how we can prepare for new manners of scientific investigation.
I don’t think we are close to “AI scientists”
Today's AI agents are not designed to extract deep insights from new observations.
Are We Thinking Correctly About AI Intelligence? | Quanta Magazine
Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.
AI has supercharged scientists—but may have shrunk science
Analysis of 41 million papers finds that although AI expands individual impact, it narrows collective scientific exploration
Science that Compounds: The Need for A New Substrate for Research in the Age of AI
This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
Artificial intelligence and illusions of understanding in scientific research
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
Where does the rigor go? Research software and the future of trustworthy science.
Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Nature - Artificial intelligence boosts individual scientists’ output, citations and career progression, but collectively narrows research diversity and reduces collaboration, concentrating...
AI for science: What can it do? Can it do things? Let’s find out!
A what we’re reading spotlight
The Fork in the Road: AI Horseless Carriages or Collective Intelligence for Science - Cosmik Labs
AI is turning research into a scientific monoculture
Generative AI deserves scientific attention. But the rush to study it is producing a feedback loop of topical and methodological convergence, flattening scientific imagination and crowding out the pluralism needed to keep research adaptive, resilient, and intellectually generative.
How should social science think about AI?
Big structures and large processes are back, baby
