Could AI slow science?

Will AI spark a scientific renaissance — or a diffuse monoculture?

Artificial intelligence’s ability to enrich science will depend not only on model capability, but also on whether researchers, reviewers and funders reward originality over speed.

No. 014: Science's AI productivity paradox, Sakana clears peer review, AI proof search on open Erdős problems - The Hallway Track

The Hallway Track No. 014 - How AI is affecting the practice of science

AI in scientific publishing: Slower, worse, and more expensive

There’s a saying in the management world, popularized by NASA administrator Daniel Goldin in the 1990s, that the goal of technological improvements is to make products faster, better, and cheaper. Although this strategy had some success in the aerospace industry, the zealots of artificial intelligence (AI) have been making the same argument regarding how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits, leaving society with more time for creative pursuits. But history tells a different story. When machines began to increase productivity during the second industrial revolution, American engineer Frederick Winslow Taylor’s The Principles of Scientific Management encouraged corporations to use surveillance to get employees to work harder and longer, an approach that exhausted and discouraged workers and led to the transfer of knowledge and any decision-making from workers to management, while enriching the profits for only those at the top. Yet, it remains foundational to the American economic enterprise. Indeed, scientific publishing is starting to experience some Taylorism with the insertion of AI. Rigorous human checking of AI-generated research papers is creating bottlenecks as publishers strive to maintain the integrity of the scientific record. The challenge is requiring even more human effort, making the whole endeavor slower and more expensive.

Infinite Researchers | AI-Powered Scientific Discovery

What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.

AI is doing something weird to Science

In both good and bad ways, and it won't go away.

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 in Science: Boosting Productivity and Innovation | Arthur Turrell posted on the topic | LinkedIn

Today at Google & Google DeepMind, we published work led by Mihai Codreanu on AI in Science, drawing on 15 million Gemini interactions, 2,600 specialised AI models & a 600-scientist survey. What did we find about how AI is changing science? Read on... First, why does this matter? If AI is a tech boost we get a one-off economic growth boost. But if AI is an "innovation in the method of innovation", we get long run higher expected future growth rates! Compound interest makes those scenarios very different for future prosperity. Where might we see innovation in the method of innovation first? Well, science of course—the engine of innovation and one of the main drivers behind the huge increase in prosperity over the last 200 years. So what do we find? 1) In our data, scientists are about 2.7x overrepresented in AI use compared to what you'd expect from employment share. Scientists are using AI a lot! 2) Science convos are longer (tokens), more multimodal, and involve more domain expertise 3) Computer science has highest use 4) specialised models and LLMs are complements: LLMs do code, stats, literature, drafting, teaching, admin while specialised models predict, generate, classify — eg protein structures, weather. 5) within LLMs, analysis & modelling + communicating research findings are top 2 tasks But are we getting *improvements*? 6) 74% of scientists surveyed report net time savings; (6% report losses). Average saving of ~7 hours/week. This is mostly going back into more research. 47% report an increase in outputs of 10% or more. There's another effect too though—as some things (hypothesis generation, modelling) become 'cheap', science gets bottlenecked by other tasks... 7) 89% of those who save time spend more than a tenth of it checking AI outputs & 41% report a growing backlog of untested hypotheses Looking a bit beyond, we expect time spent on bottleneck tasks to grow as a %, more papers that recombine ideas from across disciplines, & publication volume increases for fields that don't need wet lab work. Unfortunately, not all of this volume may be high quality ("sci-slop") So what's our early take on AI as an innovation in the method of innovation? There's significant potential to increase scientific productivity, & 7 hours/week (big!) is *already* being saved. But it's early days & bottlenecks (inc. verification) limit sci-productivity take off Huge team involved in this work, as you can imagine—see the paper for more! https://lnkd.in/g55s4MtC | 38 comments on LinkedIn

The Jevons Paradox of AI - Wesley's notes

Why AI can make us more productive but will never save us time

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.

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.

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.

Publish and Perish: How AI-Accelerated Writing Without...

Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize...

The Fork in the Road: AI Horseless Carriages or Collective Intelligence for Science - Cosmik Labs

Designing AI for Disruptive Science

Why scaling AI won’t automatically lead to paradigm shifts.

Tech companies are cutting jobs and betting on AI. The payoff is far from guaranteed

AI experts say we’re living in an experiment that may fundamentally change the model of work