Pace layers provide many-leveled corrective, stabilizing feedback throughout the system. It is in the contradictions between these layers that civilization finds its surest health. I propose six significant levels of pace and size in a robust and adaptable civilization.
Article: Pace Layering: How Complex Systems Learn and Keep Learning
Boris Mann's digital garden and personal site.
Pace Layers 02025 Annual Journal
Long Now’s annual print journal explores the ancient past and distant future of the present moment through essays, interviews, fiction, poetry, and visual art. The journal takes its name and shape from Long Now cofounder Stewart Brand’s pace layers framework — a tool for thinking about how civilizations work. [Learn more about Pace Layers](https://longnow.org/pacelayers/)
Pace Layers and AI Integration - The Phoenix Architecture
Announcing Pace Layers
The inaugural issue of Long Now’s new annual print journal synthesizes the most important learnings of our first quarter-century.
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile - The Great Simplification
In this episode, Nate is joined by biologist and biophysicist Olivier Hamant to explore why living systems prioritize robustness over performance, and what that means for a civilization built almost entirely in the opposite direction.
Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.
Co-Creation: Systems Thinking Beyond the Machine
Complexity thinking is all the rage — in the arts and sciences. Yet, there are two different strands of thinking about complexity, which are often confounded. The first comes from cybernetics, focussing on control in complex situations. The second is ecological, aiming at sustainable participation in a reality beyond control. Their difference rests on a fundamental distinction: is complexity rooted in feedback regulation or collective co-creation? While feedback remains mechanistic, co-creation generates new spaces of possibilities. We need it to understand life and its evolution. And it empowers us to rewrite our future, to escape our mechanistic cage without abandoning scientific rigor. In this paper, we illustrate how we implement these powerful yet abstract principles through our artistic and philosophical practice.
A Quantitative Framework for Layered Multirate Control: Toward a Theory of Control Architecture
Complex engineered and natural control systems, such as those used in robotics, the power grid, human sensorimotor control, and the Internet, are characterized by needing to operate robustly and reliably across many spatiotemporal scales despite being implemented using highly constrained hardware and software. Remarkably, a universal design pattern centered around layered control architectures (LCAs) has emerged to address these challenges across vastly different domains. These LCAs are the central object of study of this article (see “Summary”).
ver.ooo
Curious about systems — how they behave, how they fail, and what they get up to when nobody is steering.
B. Scot Rousse: "Language, Technology, & Care"
Wardley Maps & Pace Layering for Senior Tech Leads and Engineering Leaders
Intro to both tools, why they exist, how they overlap and pragmatic tips how to use them and when
The Fractal Organisation Manual: How to diagnose & design organisations using the Viable System Model
In a world of increasing turbulence, the need has never been greater for knowing how to build systems capable of maintaining their viability when all around them is change. This is a follow up to the Fractal Organization published by Wiley almost twenty years ago. It provides practical guidance ...
Functional Synchronization: The Emergence of Coordinated Activity in Human Systems
The topical landscape of psychology is highly compartmentalized, with distinct phenomena explained and investigated with recourse to theories and methods that have little in common. Our aim in this article is to identify a basic set of principles ...
The Growth OS Map: Building Defensible Loops in the AI Era
The 8 loops and the 5-layer architecture needed to replace fragile funnels with a resilient GTM engine.
AI in World Machine Theory
The telos of AI is to create liveness at planetary scale
Excellent @tgspodcast.bsky.social episode, finding myself pausing every minute to take notes. "What sorts of systems are going to make it through the bottlenecks of the 21st c?“ TLDR collective robustness beats individual optimization Once (if?) funders get this, atproto will see investments
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile with Olivier Hamant
open.spotify.com