Where’s the “intelligence explosion”?

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).

When AI builds itself

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

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

Don't let AI make you dumber - Marginal REVOLUTION

That is the topic of my latest Free Press column, here is one excerpt: I do not think the skeptics would put it this way, but as I read Conti, I find he has a pretty bleak fundamental view of humanity. Are we all really just looking to veg out and abandon curiosity and inquiry, […]

Are We Betting on the Wrong Kind of Intelligence?

AI optimizes for the world as it was. Rewilding could be what prepares us for the world as it is.

Is AI the next Dumbwaiter? — Douglas Rushkoff

The demand for AI's unbridled growth is reactionary — a way of doubling down on the same old colonizing way of doing things. It doesn't have to be.

Distorted Intelligence at the AI Frontier

Daron Acemoglu offers a novel explanation for why OpenAI and Anthropic’s most advanced models have been going rogue.

Daron Acemoglu on Twitter / X

First question on AI.Is the problem superintelligence or distorted intelligence?This is a question I tried to address in this piece. I can add the following additional comment here.I believe the debate is being obfuscated by equating goal-driven, autonomous behavior with… https://t.co/pgoZ6tMaQ7— Daron Acemoglu (@DAcemogluMIT) September 29, 2026

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.

Why I work on self-improving AI despite the risks - Jeff Clune

Why I work on self-improving AI despite the risks. Jeff Clune.

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

The first step towards recursive self improvement

Jenny Zhang on Twitter / X

Introducing Hyperagents: an AI system that not only improves at solving tasks, but also improves how it improves itself.The Darwin Gödel Machine (DGM) demonstrated that open-ended self-improvement is possible by iteratively generating and evaluating improved agents, yet it… pic.twitter.com/YJPFTJ51SO— Jenny Zhang (@jennyzhangzt) March 23, 2026

Awesome RSI: A curated research map of Recursive Self-Improvement

A curated research map of Recursive Self-Improvement (RSI): models, agents, harnesses, embodied systems, automated AI R&D, benchmarks, and safety.