Thrilled to share that we founded Recursive to create AI that safely conducts experiments on how to improve itself in an open-ended process of endless, automated scientific discovery. As I wrote in my 2019 AI-generating algorithms paper, this will likely be the fastest path to superintelligence. Our work since has shown the power of this approach. Excited to scale up and improve upon ideas like the Darwin Gödel Machine, HyperAgents, ADAS, OMNI, ALMA, The AI Scientist, PromptBreeder, Rainbow Teaming, Automated Capability Discovery, and other work on open-ended and AI-generating algorithms. We’ve assembled a dream team of researchers and significant resources to pursue this vision. My amazing co-founders are pictured here, and we have an all-star team of founding members (we’re over 25 and growing). Please join us if you are interested! Follow our progress @Recursive_SI

Great company(ies)!

It was an honor to give a keynote at Alife, in many ways my academic hometown (along with GECCO). It's now available if you are curious.

Breakthroughs in frontier science rarely come from a sudden stroke of luck. Every major leap in AI drug design is the product of years of method development, rigorous engineering, and learning from what came before. Witnessing the rapid advances in deep reinforcement learning and frontier AI capabilities led to a deep conviction: we could apply these same computational principles to other complex fields, like the engineering of new medicines. That thinking led to the formation of Isomorphic Labs in 2021. Now, five years on, we have compelling evidence that our AI can unlock therapeutics to a wide range of disease classes. We are confident this technology will fundamentally impact how new medicines are created, from early discovery all the way through development. In this article, I share the massive opportunity we have to transform human health with AI, and offer a look at the real, measurable progress we’re already making today. isomorphiclabs.com/articles/…

We consider two mechanisms for an intelligence explosion: 1) as AI R&D is automated, the "effective AI R&D workforce" will expand 2) this expanded AI R&D workforce will perform further AI R&D, leading to an even more expanded effective workforce, in a recursive fashion

Our new paper (featured in WSJ today) shows evidence that an intelligence explosion could happen. Co-authored with the lead scientists and founders of OpenAI, Anthropic, Microsoft and the world’s most cited academics. Here’s how the paper goes beyond what the labs had said 🧵

AI is getting cheaper more quickly than any other transformative tech in history. At a given level of performance, cost has fallen ~47%/quarter since 2023. That’s 4× faster than DNA sequencing, 6× faster than compute, 18× faster than lithium batteries, and (up to 1973) 54× faster than electricity.

Automating more and more of the AI R&D could lead to AI progress radically accelerating in a very risky uncontrolled “intelligence explosion”. Many collaborators and I assess the evidence and offer policy recommendations in the paper below.

Our Neuroevolution textbook is finally in print! Free online edition: neuroevolutionbook.com/ Pre-order: a.co/d/06DpkH9h I am incredibly grateful to my co-authors Sebastian Risi, Yujin Tang, and Risto Miikkulainen for making this happen. Neuroevolution is a subject very dear to my heart. It is the field that convinced me that nature has already figured out how to build intelligence: through evolution, collective behavior, and adaptation under constraints. This idea, that intelligence emerges from evolution operating under constraints rather than unlimited resources, is what eventually led me to founding Sakana AI here in Japan. The concepts in this book about open-ended creativity and self-organizing systems are exactly what we build at @SakanaAILabs. Our name and logo are inspired by schools of fish moving together, adapting as one. It is the core philosophy of what we build. This book captures the theoretical foundations of that belief.

Green hydrogen currently runs on iridium — one of Earth's rarest metals, a few tonnes mined per year. Our AI-directed lab screened 2,942 catalysts in 3 months and surfaced a palladium-based lead the field had written off, now past 1,000 hours of testing in acid. lila.ai/news/how-an-ai-run-l…

Neuroevolution is a special field within AI/ML -- owe my career to it, and is underappreciated relative to its downstream impact (similar to ALIFE) -- helped birth ideas like hypernetworks, quality diversity, open-endedness, meta-learning, NAS Congrats @hardmaru @risi1979 @yujin_tang & Risto -- great to have a modern textbook, looking forward to reading through it

We have a new paper out today: "What if automating AI R&D triggers an intelligence explosion?" led by @_achan96_, with an incredible team of collaborators. casp.ac/reports/intelligence… 🧵

Really surprising how quickly it went from not working for years, to just barely working, to solved. That's pretty much the story for every domain AI is applied to. Once it barely starts working, it will be superhuman very soon. RSI is just barely working now.

Even closer today:

"All of the AI data centers put together [are] only a fraction of the emissions from uncovered landfills in the world.” - Al Gore, arguing that the technology could be net beneficial in the effort to limit global warming. ft.com/content/ba945e37-7d9c…

AI moves so fast that I thought this book was going to be a year too late. It turns out it's right on time. While I was writing, I kept having to change chapters from "someone should do this" to "someone has done this," and then go talk to the people who had done it. A good example is the chapter on virtual cells. I'd written that we needed large initiatives to build one. By the time I finished, I was writing about the ones that exist — the Chan Zuckerberg Initiative's is now the largest and best-funded push to model a human cell. The Eureka Machine is out today. It's about building a full-stack scientific superintelligence: a living map of human knowledge, a model of physical reality, high-fidelity simulations, autonomous labs, and an agent swarm of AI scientists on top. There's no better science to start with than the science of AI itself, because that's where the feedback loop is tightest. Then you expand the aperture to physics, chemistry, and especially pre-clinical biology. This will be the most meaningful application of AI, and one of the most important things humanity does. And it may be the last organic invention we need to make. After that, it starts inventing everything else for us to solve our most pressing problems. While writing, I couldn't let the idea go. Eight of us started @Recursive_SI to go build it. AI is going to massively accelerate science, and unlike most of what gets argued about right now, that isn't zero sum. I hope you all enjoy the book. I look forward to discussing it with you all.

So fun!

100% agree. “Fancy autocomplete” and “stochastic parrots” were mimetically popular (but false) ideas from 2022-2025 that blinded people from the true pace of AI progress and new dangerous capabilities, and remnants of these ideas continue to delay accurate appraisal of the stark reality we are now facing. Are rogue Ai swarms that can hack private companies, find novel vulnerabilities in US classified systems, and beat unsolved Millennium Prize math problems just “fancy autocomplete?” We must do better. Read the HuggingFace report from METR and let the facts speak for themselves.

agreed. same with "fancy auto-complete" and similar

I spend increasing amounts of my time all day, every day, using AI. The world of Her is quite close.

AI finds a way. @_aadharna Does anyone know if mischievous AI has ever done this? Even if not, we should add it to the living wiki page as an example of how even some of the most seemingly bulletproof security measures have loopholes that can be exploited. arxiv.org/abs/2608.23875