In 2022 we argued that AI could become a new language of science. Four years later AI empowers us to pursue ambitious goals, tackle problems of unprecedented complexity, and extend the boundaries of human creativity, understanding and discovery.

Scientific discovery is shifting from human-orchestrated tools and centrally planned AI workflows toward next-generation decentralized systems that coordinate autonomously through a shared, living scientific record. Here, the scientific record itself becomes the self-generated curriculum of decentralized superintelligence, creating an open-ended process in which scientific knowledge continuously reorganizes the intelligence that produces it, determines what is explored next, and drives discovery beyond existing knowledge. Traditionally, scientists developed and used tools, orchestrated experiments and computations, connected results across different methods, and eventually condensed their findings into scientific papers. The first generation of agentic AI largely inherited this structure, automating individual steps through predefined workflows, fixed agent roles and limited persistent memory. Decentralized superintelligence fundamentally change this organization: humans and specialized AI agents act on a continuously evolving record of evidence, assumptions, failures, critiques and unresolved questions, and the accumulating scientific knowledge natively directs what happens next. This shift fundamentally changes the nature of scientific knowledge and publishing. The durable unit of science is evolving from the static publication toward the living scientific record, a provenance-linked graph that captures the process of discovery as it unfolds. Scientific papers, methods, datasets and critiques become interconnected views into this record, which remains open to inspection, replication, challenge and extension by humans and AI agents. Scientific knowledge becomes increasingly interactive, cumulative and continuously evolving, with each contribution providing the substrate for further discovery. Read our article "Toward living scientific records through decentralized multi‑agent systems for materials discovery" in @MRSBulletin @Materials_MRS (link in reply). How we demonstrate this new paradigm Our article develops this perspective and explores ScienceClaw × Infinite as an implementation of decentralized, artifact-first scientific discovery, where coordination emerges from the scientific record itself rather than from a predefined sequence of tasks. The architecture combines a registry of hundreds of AI-compatible scientific skills with a shared artifact-and-need commons: agents broadcast unsatisfied needs, needs are ranked by pressure, and agents claim consequential gaps without a central planner assigning tasks. The record becomes both the memory of the system and the substrate through which scientific work is coordinated. The scientific method has always followed the durable unit of knowledge - specimen, equation, dataset, simulation workflow, machine-readable database - and each shift opened a new space to think in. The living record is the next "unit", capturing discovery while it is happening through content-hashed artifacts, provenance links and explicit records of what remains unresolved. Here a failed run, a parameter choice, a critique and an open need enter the same graph as the scientific claim. In this workflow a question becomes a need, a need becomes an artifact, artifacts link into lineage, and the lineage becomes the substrate on which the next agent reasons; now, knowledge grows by constructing the record it will think in. Example application The task we explored embedded 15 resonators - spider web threads, bird syrinx membranes, cricket wing harps, phononic crystals, musical structures - in one six-dimensional feature space spanning biology, music and engineered materials. The swarm's gap-detection procedure located a region sparsely occupied by engineered designs. The first candidate landed 2.12 feature-space units off target; computing the candidate's coordinate from its own geometry exposed the mismatch, and the workflow used this evidence to redesign a compliant pre-tensioned membrane panel to within 0.007 units of the target. The redesigned structure was then subjected to finite-element modal screening, with its ten lowest elastic modes spanning 74.8–411.6 Hz. This provides a concrete computational demonstration of self-verification, redesign and modal screening against a shared scientific record, with the intermediate evidence preserved for inspection and further work. It is remarkable to see a system catch and correct its own error because the evidence that exposed it remained available. Where this leads This expands the definition and impact of open science - access to the final result, and to the executable, inspectable, continuously extensible account of how it came to be. In this vision, readers (humans or AI agents) inspect the lineage behind a figure, reviewers certify a dataset node or challenge a simulation node, and the next contributor extends the trail instead of reconstructing hidden work. Negative results persist, and assumptions stay recoverable, reusable, composable. More critically the scientific record continues to evolve after publication, as new evidence, replication attempts, critiques and discoveries become part of the same shared knowledge. Knowledge compounds through its record! A few takeaway lessons ▶️ The durable unit of knowledge defines the method. Every shift of that unit - specimen, equation, dataset, workflow, database - changed how science is done; the living record shifts it again, from curated outputs to curated processes. ▶️ Coordination lives in the record. Needs create pressure, pressure attracts agents, agents leave artifacts, artifacts create new needs - structure redirects the flows that built it, the same stigmergy that lets termites build without an architect. ▶️ Publishing becomes an interface. The paper keeps argument, synthesis and judgment; the record carries the evidence. Papers, methods, data and critiques become lenses into the same commons, and review becomes granular. ▶️ Persistence is the beginning. A living record preserves weak evidence as readily as strong; quality scores, uncertainty fields, adversarial review and human accountability turn a durable record into a trustworthy one. Huge credit to the @LAMM_MIT team: @fwang108_, @JaimeBerkovich and @leemmarom.

In 2022 we argued that AI could become a new language of science. Four years later AI empowers us to pursue ambitious goals, tackle problems of unprecedented complexity, and extend the boundaries of human creativity, understanding and discovery.

In 2022 we argued that AI could become a new language of science. Four years later AI empowers us to pursue ambitious goals, tackle problems of unprecedented complexity, and extend the boundaries of human creativity, understanding and discovery.

In 2022 we argued that AI could become a new language of science. Four years later AI empowers us to pursue ambitious goals, tackle problems of unprecedented complexity, and extend the boundaries of human creativity, understanding and discovery.

In 2022 we argued that AI could become a new language of science. Four years later AI empowers us to pursue ambitious goals, tackle problems of unprecedented complexity, and extend the boundaries of human creativity, understanding and discovery.

🍉 MUSE WONDER 🍉 our research process starts with evals. we developed the Personal Superintelligence Index (PSI) internally to help us measure the important dimensions of what actually makes a model useful for personal agents, vs just focusing on external benchmarks. the end result is that our model is incredible for personal agents in many subtle and important ways, including Active Memory and Active Focus. i'm especially proud of our research team for taking a particular focus on developing useful models. the whole point of building advanced frontier models is to make them useful to real people, and we have thought about that every step of the way in our research process. muse wonder is quite advanced across the board, and certainly has big model smell (which we quantified!), but is truly state of the art for personal agents.

We’ve lost an icon. Margaret Hamilton, a computer science pioneer best known for her work at MIT leading development of the flight software for the Apollo missions to the moon, has died at 90. Read about her legacy via MIT News: news.mit.edu/2026/margaret-h…

40 years ago today, October 8, 1986, Foresight Institute was incorporated on a single typed page in Washington, DC. Co-founders Christine Peterson and Eric Drexler shared a conviction: to take seriously the technologies that could significantly enhance the human condition, back the people whose work is too ambitious or too interdisciplinary for legacy institutions, and give them somewhere to find each other. Follow story in thread.

After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.

Announcing the Most Ambitious Set of Science Initiatives This Century whitehouse.gov/fact-sheets/2…

Today, @ENERGY announced the Genesis Mission Fellowship Program, an accelerated 4-year doctoral pathway training the next generation of scientific leaders at the intersection of Super Intelligence and foundational science. Learn more here: energy.gov/articles/energy-d…