Peter Wildeford🇺🇸🚀 on Twitter / X

When AI builds itself

Our progress toward recursive self-improvement, and its implications.

Import AI 455: AI systems are about to start building themselves.

The first step towards recursive self improvement

Harness Engineering for Self-Improvement

The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights directly, or more broadly the model improves the training pipeline and the deployment system, which in turn enables a better successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI).

Scott Jenson – Exploring the world beyond mobile

Fast AI requires slow thinking The current Silicon Valley flex is trading notes on your favorite new AI tools over lunch. Each week brings another one to explore. Some of these tools are very impressive; I’ve been able to reply to someone with an alternative UI design in less than a minute, and they were […]

Want to understand the current state of AI? Check out these charts.

According to Stanford’s 2026 AI Index, AI is sprinting, and we’re struggling to keep up.

Itai Yanai on Twitter / X

Unpopular opinion: Going straight to AI limits your creatively, because it short-circuits the iterative process you need to develop new ideas. pic.twitter.com/O2iPs0bfh0— Itai Yanai (@ItaiYanai) June 20, 2026

Could AI slow science?

Confronting the production-progress paradox

AI Research Evaluation: Negative Findings and Failure Modes | Arvind Narayanan posted on the topic | LinkedIn

📢AI agents can autonomously conduct AI research when the result is easily verifiable, but what about open-ended AI research? That’s much harder to study, and our new preprint is our first crack at doing so. Our main finding is negative, and we identify five recurring failure modes. https://lnkd.in/eGKYi4Sa Our results are tentative, and we are working to address the limitations (sample size, potential scaffold improvements). But if the finding holds up, what are the implications? It depends on whether you think recursive self improvement can be achieved simply by hill climbing at scale (I personally don’t think so) and whether you think current limitations of open-ended research like judgment and creativity could change quickly (I’m personally very open to this possibility). We plan to continue this style of evaluation — which we call shadow evaluation — on a regular basis. We’ve wanted to do this for two years, but it took so long because we wanted to get the method right. The idea behind shadow evaluation was suggested by some of the UK AISI coauthors of the paper and refined by the Princeton team. This method has important advantages (and limitations) over the current ways of evaluating agents’ ability to conduct AI research. If you’re an AI researcher interested in working with us on a shadow evaluation based on one of your papers, we’d love to hear from you. https://lnkd.in/ecyp55SW This type of evaluation necessarily involves a ton of researcher flexibility in design, execution, and interpretation. Members of the core team have a particular position in the debate on recursive self-improvement / superintelligence, and this could influence how we conduct the research. We have a detailed section in the paper on our potential biases and how we address them. We sought out a team of collaborators who don’t all share our priors, and we explicitly surface the interpretive disagreements that resulted. For future evaluations, we are interested in having “adversarial collaborators” as part of the core team. This paper exists because of the careful, time-consuming and very much human work that Peter Kirgis, Sayash Kapoor, Andrew Schwartz, and Stephan Rabanser did over the last few months. I’m also very grateful to the larger group of collaborators and co-authors. The work is part of the larger CRUX project that pushes frontier AI agents beyond what benchmarks can measure (https://cruxevals.com/). We are looking for a senior researcher to join the team: https://lnkd.in/e9dC22X5

RSI Simulator: Play the Economics of Recursive Self-Improvement

Bootstrap an AI lab from scratch. A web game from Paradigm built on real economic models of AI recursive self-improvement.

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...

The Biggest Advance in AI Since the LLM

Claude Code isn’t AGI or even close, but it is an impressive and possibly game-changing “coding agent” for programmers to write code faster that is arguably the single biggest advance in AI since the LLM.

The Ma of a New Machine – Scott Jenson

The current Silicon Valley flex is trading notes on your favorite new AI tools over lunch. Each week brings another one to explore. Some of these tools are very impressive; I’ve been able to reply to someone with an alternative UI design in less than a minute, and they were dumbfounded: “How did you do that so fast?”