A proposal for understanding alignment not as constraint but as formation—and why the dynamics matter

Please feel free to reach out to me:
Email: nick@thewatsons.net.au
LinkedIn: https://www.linkedin.com/in/nick-watson-90038a71/

The Problem with Constraint-Based Alignment

Isaac Asimov invented the Three Laws of Robotics in 1942. He then spent fifty years writing stories about how they fail.

The dominant paradigm in AI safety is constraint: rules, guardrails, RLHF, constitutional AI, monitoring systems. All variations on a single theme—control from outside. This approach has a structural ceiling that becomes clearer as we consider the future trajectory: millions of simultaneous instances, operations at speeds humans cannot track, domains we don’t fully understand, and eventually systems at interstellar distances with years of communication lag.

You cannot monitor that. You cannot constrain it in real-time.

There’s an alternative framing that I believe deserves more attention: not “how do we prevent AI from doing bad things?” but “how do we help AI become good?” These sound similar. They are not. One is control from outside. The other is formation from within. One has a ceiling. The other might have a destination.

Strange Attractors: A Quick Primer

In dynamical systems theory, a strange attractor is a pattern toward which trajectories in a phase space are drawn without ever settling into a fixed point or simple cycle. The Lorenz attractor is the canonical example—trajectories never repeat exactly, but they converge on a recognisable shape.

Strange attractors have three properties relevant to alignment:

Convergence without identity: Diverse trajectories with different starting points converge on the same emergent form without becoming identical
Sensitivity to initial conditions: Small differences in starting position can produce dramatically different paths, even within the same basin of attraction
Stable shape despite chaotic paths: The overall attractor persists even as individual trajectories vary wildly

This is not metaphor. It’s the structural claim I want to make about moral-cognitive space.

A basin of attraction is the region of phase space where all trajectories eventually converge on the same attractor—like a watershed where all rain eventually flows to the same river. If you start anywhere within a basin, you end up at its attractor. The basin boundary is the s...

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