Steve Randy WaldmanFeb 27

"They’ve genuinely identified the pattern I described in the Bonfire essay and then committed the most consequential intellectual error possible. They saw the fire and decided to pray to it." @elbowspeak https://syntropic.xyz/posts/2026-02-14-the-bonfire-in-the-cave/

// Josh pointed me to this essay but I flaked. ht @SteveRoth for pointing me again. It is excellent.

The Bonfire in the Cave

Entropy, AI, and what Nobody will Name

Show thread)oshuaFeb 27

@interfluidity @SteveRoth Here's the link to the quoted essay https://syntropic.xyz/posts/2026-02-17-worshipping-the-gradient/ (your current link is to Part 1)

Thanks for reading it!

Worshipping the Gradient

Thoughts on prediction, information compression and coherence

Show threadAsymptosisFeb 28
@elbowspeak @interfluidity Yes have read that also and a few other posts. Lots of ahas for me. Great stuff, thanks.
Show threadAsymptosis
@elbowspeak @interfluidity Syntropy is a great word.
Feb 28 at 12:45amWeb
Show threadAsymptosisFeb 28
@elbowspeak @interfluidity I'm struggling with the idea that more syntropic pockets/microstates are "selected" for. Would mean that more-entropic ones die out, while more-syntropic ones survive and propogate into the future population of microstates. Am I understanding that? Thx.
Show thread)oshuaFeb 28

@SteveRoth @interfluidity Evolution is a special case of the second law. Every biological system is a syntropic experiment for dissipating energy. The most successful "far-from-equilibrium" coherent systems reproduce because they are better at exploiting the energy gradient (bc their inference cone has more temporal depth and hierarchy, and is therefore a better predictor)

There's nothing new here, IMO, once you understand evolution as a means of selecting for successful energy dissipators.

Show thread)oshuaFeb 28
@SteveRoth @interfluidity But perhaps your question was about how biology arose in the first place? That's a story about extremely unlikely, but nevertheless inevitable, organizations of matter at the edge of the entropy distribution curve. Recall that entropy is not merely energy dissipation, but Boltzmann's equations about the distribution of possible states of matter. Even incredibly unlikely formations will persist if they can better ride the gradient -- what we call life.
Show threadAsymptosisMar 2

@elbowspeak @interfluidity Basically struggling to resist the eternal allure of a causal/teleological understanding, eg the universe "experimenting" vs just random ~mutation.

Aha discovery for me, poking around on this: that the universe is not like a jar or room full of gas. In a self-gravitating system, there are more possible clumped states than uniform, so they're higher-entropy! And clumping is a least a necessary precondition for life. Just noodling here...

Show threadAsymptosisMar 2
@elbowspeak @interfluidity Just to add, of course that clumping creates/is energy gradients.
Show thread)oshuaMar 5
@SteveRoth @interfluidity Yep. A star is a big clump of energy gradient that we consume.
Show thread)oshuaMar 5
@SteveRoth @interfluidity To dilate briefly on telos: I don't think the universe has conscious goals. But when weird things happen like more complex dissipative structures developing where you don't expect them (Bénard cells spontaneously creating convection cells to more efficiently dissipate) it's useful to think of entropy as a de facto telos. Problems refracted through that lens provide different views of the same shapes, perhaps making them more tractable. Makes me think of category theory.
Show thread)oshuaMar 5
@SteveRoth @interfluidity It makes me think of category theory because the relationships between Bénard cells and ecosystems and civilizations are more than analogies, they're structure-preserving mappings via entropic formalism. Functors in category theory language. When the underlying shape survives that many transformations, what I see is ***functional invariance*** instead of charming metaphorical narrative.
Show thread)oshuaMar 5
@SteveRoth @interfluidity The practical problem is that we've never had a viable formalism for finding actionable commonality across heterogeneous systems, e.g. what's structurally true of both an ecosystem and an economy? Category theory plus active inference gives you that. Shape-preserving measures of knowledge and uncertainty, compressed into a single quantity.
Show thread)oshuaMar 5

@SteveRoth @interfluidity In Active Inference, that measure is free energy, otherwise described as surprisal: What is the delta between what your predictive internal model expected and what it found? (The negative log of the probability of an observation).

It's Bayes applied to far-from-equilibrium dissipative systems.

Show thread)oshuaMar 5
@SteveRoth @interfluidity Every system (ecosystem, economy, institution) is minimizing free energy against its own model via energy dissipation (entropy). Locally optimal, globally destructive. Surprisal gives you a commensurable measure across all of them. Once you can describe alignment as nested free energy minimization where the higher-order system is also minimizing, we can at least quantify surprisal, uncertainty, and risk across systems that previously had no rigorous shared language.