Artificial intelligence is accelerating the pace of discovery across science and technology. But today’s AI ecosystem risks centralizing compute, talent, and decision-making power – concentrating capabilities in ways that could undermine both innovation and safety.
Artificial Intelligence Safety and Security License Requirements
Artificial Intelligence Safety and Security License Requirements Artificial Intelligence (AI) is now broadly capable, which can produce both benefits and dangers. AI will hasten the design and proliferation of bioweapons, cyberweapons, nuclear weapons, progressively more general intelligence,...
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.
An End-to-End View of AI Safety
In this blog, Rachel Coldicutt OBE, Executive Director, Careful Industries discusses our newly published literature review on the safe adoption of artificial intelligence in engineered systems.
Import AI 475: Swarm scaling; Google DeepMind watermarks biology; and the AI science economy
Who chooses what AI gets to do?
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.
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
Safety isn’t safety without a social model (or: dispelling the myth of per se technical safety) — AI Alignment Forum
As an AI researcher who wants to do technical work that helps humanity, there is a strong drive to find a research area that is definitely helpful so…
Strengthening biosecurity in the era of AI
AI is reshaping biology, unlocking breakthroughs while raising new risks. Learn how smarter safeguards can strengthen biosecurity without slowing innovation.
A Safe Path to Open Weights
Strategic openness can strengthen AI safety and support broader access as defenses and safety science mature.
Kimi K3: The open-weights escalation
The global implications on the AI ecosystem.
Inside the Dirty, Dystopian World of AI Data Centers
The race to power AI is already remaking the physical world.
The ecology of AI risk
Understanding the risk from applications of artificial intelligence (AI) is a critical part of creating AI governance strategies. Building on the idea of studying AI using ecological and evolutionary perspectives, we propose a novel approach for assessing risk from AI using indicators derived from theoretical ecology models. We illustrate our methods by deriving 3 indicators from population and ecosystem models originating from theoretical ecology. We conclude with a discussion of limitations of our analysis and considerations for improving AI governance policy.
Cosmik Updates: February 2026 - Cosmik Labs
@atproto.science @cosmik.network Raising a question for the ATProto science community: Can AI agents be legitimate participants in research ecosystems? What would make their outputs trustworthy?
The AI safety movement needs normies
A broader base may be the only way for the AI safety field to get what it wants
