The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.
AI for science needs reasoning, not just data
AI agents that can model the human process of research will accelerate discoveries in science.
Artificial intelligences and human scientists exhibit...
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as...
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 AI Agents are transforming scientific discovery
AI agents are starting to reshape science, from proposing novel hypotheses to writing code. Learn what comes next for scientists and policymakers.
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.
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
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.
Agentic Laboratories of the Future: Towards World Models for Scientific Discovery
Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works. The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context. By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines. A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context. Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks. Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system.
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
AI as the Catalyst of the New Paradigm of Science? | Cadmus Journal
The rapid advancement of Artificial Intelligence (AI) is reshaping the landscape of scientific inquiry. This article examines the challenges confronting modern science, including knowledge fragmentation, diminishing returns of current paradigms, and the disconnect between science and society. It explores how AI offers solutions enhancing collaboration and integrating knowledge—as well as considers the possible “darker side” of using AI in research. However, the advent of AI in science not only enhances scientific research but also redefines the role of human researchers and fosters a new paradigm of human understanding. By embracing these transformations, a more cohesive and responsive scientific enterprise can emerge.
AI Research Agents Narrow Scientific Exploration
AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted scientific discovery. Many current agent frameworks explicitly encourage the generation of novel and high-impact ideas. Yet it remains unclear whether AI-assisted ideation broadens scientific exploration or mainly concentrates around existing work. We study AI research agents as scientific search systems. Using four AI research-agent frameworks and six large language models, we generate 37,802 scientific ideas from shared seed literature across citation-defined research areas in AI and machine learning. We then compare the resulting AI ideas against human-authored papers from the same research areas, follow-on human research emerging from the same seed literature, and the seed literature itself. Across experiments, four consistent patterns emerge. First, AI-generated ideas are substantially more concentrated than human-authored papers from the same research areas. Second, AI-generated ideas remain much closer to their starting literature than later human follow-on work does. Third, papers most similar to AI-generated ideas tend to receive lower subsequent citations. Fourth, when AI-generated ideas differ from prior work, the differences arise primarily from recombining existing technical methods rather than introducing fundamentally new research questions. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.
AI co-scientists are revolutionizing how research is done
Artificial-intelligence systems can generate hypotheses, design experiments and analyse data — but humans still need to decide what makes sense.
The AI Chemist: To be trustworthy, LLMs need to show their work
Good scientists reveal how they do their experiments and report their results; so should any machine-driven research
Large language models are not the problem
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.
[Keynote 04] AgentSociety: Exploring Large Language Model Agents for Piloting Social Experiments