Why personal AI tools might not be all we need to revolutionize science
AI Is a Waste of Time
The newest AI tools are accelerating basic research and scaring the general public. But many people are simply using them as toys.
Designing AI for Disruptive Science
Why scaling AI won’t automatically lead to paradigm shifts.
Which ‘AI scientist’ suits your lab? A guide for the perplexed
General-purpose AI tools for science, such as Claude Science, promise to accelerate research. But which is right for you?
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…
The Fork in the Road: AI Horseless Carriages or Collective Intelligence for Science - Cosmik Labs
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.
Stop saying that AI is just a tool and it only matters how it is used
I’m tired of this phrase and this simple way of thinking about tools. This blog post is a wandering train of thought on the topic of what tools are and why it matters to be even slightly more mature in how we think about them.
AI for science needs reasoning, not just data
AI agents that can model the human process of research will accelerate discoveries in science.
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.
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 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.
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
No. 008: AI co-mathematician, compiler-verified protocols, open infrastructure bets - The Hallway Track
The Hallway Track No. 008 - How AI is affecting the practice of science
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Developments in artificial intelligence (AI) have accelerated scientific discovery1. Alongside recent AI-oriented Nobel prizes2–9, these trends establish the role of AI tools in science10. This advancement raises questions about the influence of AI tools on scientists and science as a whole, and highlights a potential conflict between individual and collective benefits11. To evaluate these questions, we used a pretrained language model to identify AI-augmented research, with an F1-score of 0.875 in validation against expert-labelled data. Using a dataset of 41.3 million research papers across the natural sciences and covering distinct eras of AI, here we show an accelerated adoption of AI tools among scientists and consistent professional advantages associated with AI usage, but a collective narrowing of scientific focus. Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations and become research project leaders 1.37 years earlier than those who do not. By contrast, AI adoption shrinks the collective volume of scientific topics studied by 4.63% and decreases scientists’ engagement with one another by 22%. By consequence, adoption of AI in science presents what seems to be a paradox: an expansion of individual scientists’ impact but a contraction in collective science’s reach, as AI-augmented work moves collectively towards areas richest in data. With reduced follow-on engagement, AI tools seem to automate established fields rather than explore new ones, highlighting a tension between personal advancement and collective scientific progress.
AI for science: What can it do? Can it do things? Let’s find out!
A what we’re reading spotlight
