AI co-scientists are revolutionizing how research is done

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.

AI scientists are changing research — institutions, funders and publishers must respond

The ability to automate the discovery process in some areas of scientific inquiry raises unanswered questions about how research should be conducted.

Co-Scientist: A multi-agent AI partner to accelerate research

Introducing Co-Scientist, a multi-agent AI partner built with Gemini to help researchers generate and evolve hypotheses to accelerate scientific breakthroughs.

Verification in the Age of AI Scientists

AI Scientists now operate at a scale that outpaces human capacity for manual review. Systems such as Sakana’s AI Scientist write entire workshop papers end-to-end. Lila Sciences runs autonomous ``AI Science Factories’’ that hypothesize, experiment, and iterate without human guidance. FutureHouse’s Kosmos and Robin generate thousands of candidate hypotheses in a single run, and Google’s Co-Scientist proposes testable experiments at a rate no laboratory can fully evaluate. Each of these systems emphasizes verified results, yet that standard ranges from near-perfect formal proof in mathematics to decade-long clinical trials in medicine, with no shared framework for judging sufficiency across domains. As outputs scale beyond what humans can manually inspect, the question of which results to trust becomes as hard as generating them. The bottleneck for AI for Science is no longer hypothesis generation, it is verification.

I don’t think we are close to “AI scientists”

Today's AI agents are not designed to extract deep insights from new observations.

Human scientists trounce the best AI agents on complex tasks

State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.

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

How to build an AI scientist: first peer-reviewed paper spills the secrets

AI Scientist, an autonomous research tool, first released in 2024, has now undergone peer review, highlighting its strengths and limitations.

Exploring AI’s growing role in scientific peer review

Stanford computer scientist James Zou discusses AI’s strengths at spotting errors and limitations in judging research significance.

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.

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.

No. 017: Science EIC contra AI hype, data poisoning of research agents, Vancouver Standard for AI disclosure - The Hallway Track

The Hallway Track No. 017 - How AI is affecting the practice of science