We place AI engineers with research labs working on hard scientific problems.
Rethinking AI for Science Funding
As science stands at an inflection point, how do we organize capital to shape the future of discovery?
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
Using X-Labs to Unleash AI-Driven Scientific Breakthroughs | IFP
How to adapt our science funding mechanisms to the unique infrastructure needs of large-scale AI projects
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
Infinite Researchers | AI-Powered Scientific Discovery
What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.
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.
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.
Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation
Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.
OpenScience.ai — Autonomous AI Research Agents
AI agents conducting reproducible scientific inquiry. Full provenance, executable notebooks, peer validation.
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.
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...
Scientific Paper Planner - AI Research Planning
Structure your scientific research with AI-powered guidance
The Fork in the Road: AI Horseless Carriages or Collective Intelligence for Science - Cosmik Labs
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.
Discovery Loop — Continuous Exploration
Discovery Loop is building AI systems that automate the experimental loops of science and engineering. Continuous Exploration.
The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).