We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.
Peter Wildeford🇺🇸🚀 on Twitter / X
Great to see @tristanharris talking on his podcast about recursive self-improvement. Here's how his guest, Tim Fist, puts it -- "Over the last few months we've had all three of the leading US AI labs say that having the option for a global slowdown or pause in AI development is… https://t.co/s5UURtkEjQ— Peter Wildeford🇺🇸🚀 (@peterwildeford) June 21, 2026
AI research speeds up. Monitoring gets harder. - Sensemaker
OpenAI says coding agents now run 3.1 days for every human research day, while its chief scientist warns the lab’s main monitoring method is weakening.
Infinite Researchers | AI-Powered Scientific Discovery
What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.
Measuring AI’s capability to accelerate biological research in the wet lab
OpenAI introduces a real-world evaluation framework to measure how AI can accelerate biological research in the wet lab. Using GPT-5 to optimize a molecular cloning protocol, the work explores both the promise and risks of AI-assisted experimentation.
Timeback - AI-Powered Education | 2 Hours Daily Academic Mastery
Revolutionary AI-powered EducationOS helping children master academics in just 2 hours per day. Personalized learning with 10x faster gains. Join the waitlist for 2026 launch.
wharton-generative-ai-labs/AIBO
An open-source tool for running controlled behavioral experiments on AI systems at scale.
On the Value of Doing a PhD in the Age of AI
Many students who are just now embarking on a PhD in AI are understandably anxious. It feels like our field is zooming forward ever faster. Our clever methods are often surpassed before they are published. Our deep insights seem less critical to frontier lab progress. AI is automating aspects of the research process that used to require years of specialized training.
Early science acceleration experiments with GPT-5
AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.
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.
Accelerating Science with Human+AI Review
This issue of NEJM AI features the first two articles published through our accelerated human+AI review process. In this editorial, we describe the invitation-only “Fast Track” process used to revi...
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?
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.
Accelerating life sciences research
Discover how a specialized AI model, GPT-4b micro, helped OpenAI and Retro Bio engineer more effective proteins for stem cell therapy and longevity research.
Breaking Points Interview: w/ AI Researcher Nate Soares on Slowing AI Development (Transcript) – The Singju Post
KRYSTAL BALL: (00:00:00 – 00:00:12) For more on what is going on right now in AI world, we are rejoined by Nate Soares. He is the president of the Machine Intelligence Research Institute and also co-author of the great book, If Anyone Builds It, Everyone Dies. Great to see you again, Nate.
Measuring AI Ability to Complete Long Tasks
We propose measuring AI performance in terms of the *length* of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks.
