I think we’re aligned on the additive point — that’s actually the core attraction.
Indexing facts into an ECAI-style structure is step one.
You don’t “retrain weights.”
You extend the algebra.
New fact → new node.
New relation → new edge.
No catastrophic forgetting.
No gradient ripple through 70B parameters.
That’s the additive property.
Where I’d be careful is with the self-teaching / “turn on you” framing.
Deterministic algebraic systems don’t “turn.”
They either:
have a valid transition, or
don’t.
If a system says “unknown,” that’s not rebellion — that’s structural honesty.
That’s actually a safety feature.
Hallucination in probabilistic systems isn’t psychosis — it’s interpolation under uncertainty.
They must always output something, even when confidence is low.
An algebraic model can do something simpler and safer:
> Refuse to traverse when no lawful path exists.
That’s a huge distinction.
On the cost side — yes, probabilistic training is bandwidth-heavy because updates are global and dense.
Algebraic systems localize change:
Add node
Update adjacency
Preserve rest of structure
That scales differently.
But one important nuance:
Probabilistic models generalize via interpolation. Algebraic models generalize via composition.
Those are not equivalent. Composition must be engineered carefully or you just build a giant lookup graph.
That’s why the decomposition layer matters so much.
As for Leviathan — stochastic systems aren’t inherently dangerous because they’re probabilistic. They’re unpredictable because they operate in soft high-dimensional spaces.
Deterministic systems can also behave undesirably if their rules are wrong.
The real safety lever isn’t probability vs determinism.
It’s:
Transparency of state transitions
Verifiability of composition
Constraint enforcement
If ECAI can make reasoning paths explicit and auditable, that’s the real win.
And yes — ironically — using probabilistic LLMs to help architect deterministic systems is a perfectly rational move.
One is a powerful heuristic explorer.
The other aims to be a lawful substrate.
Different roles.
If we get the additive, compositional, and constraint layers right — then “training” stops being weight mutation and becomes structured growth.
That’s the interesting frontier.
Replies (3)
Also — this isn’t just theoretical for me.
The indexing layer is already in motion.
I’m building an ECAI-style indexer where:
Facts are encoded into structured nodes
Relations are explicit edges (typed, categorized)
Updates are additive
Traversal is deterministic
The NFT layer I’m developing is not about speculation — it’s about distributed encoding ownership.
Each encoded unit can be:
versioned
verified
independently extended
cryptographically anchored
So instead of retraining a monolithic model, you extend a structured knowledge graph where:
New contributor → new encoded structure
New structure → new lawful traversal paths
That’s the additive training model in practice.
No gradient descent.
No global parameter mutation.
No catastrophic forgetting.
Just structured growth.
Probabilistic models are still useful — they help explore, draft, and surface patterns.
But the long-term substrate I’m working toward is:
Deterministic
Composable
Auditable
Distributed
Indexer first.
Structured encoding second.
Traversal engine third.
That’s the direction.
In the tapestry model, new knowledge usually takes the form of new nodes and/or edges, so I think we’re all 3 aligned on that. Sometimes there can be deletions, updates and/or reorganizations, but additions would be the main method.
The core insight of tapestry model is that the vast majority of new knowledge comes from my trusted peers. The need to arrive at consensus / develop conventions with my peers over seemingly mundane but nevertheless vital details of semantics and ontology — like what’s the word we use to describe this writing instrument in my hand, or do we use created_at or createdAt for the timestamp in nostr events — is what dictates the architecture of the tapestry model.
The rules that govern the knowledge graph (class thread principle and maybe one or two other constraints) need to be as few and as frugal as possible. Isaac Newton didn’t come up with 10 thousand or 100 thousand or a million laws of motion, he came up with three. (Hamilton later replaced these 3 laws with a single unifying principle — an improvement, getting from 3 all the way down to 1.)
The tapestry method of arriving at social consensus only works if all peers adopt the same initial set of rules as a starting point, and that doesn’t happen if the set of rules are more complicated (or greater in number) than they have to be.
The class thread principle is a simple starting point. Only a handful of “canonical” node types (~ five) and “canonical” relationship types (also ~ five) are needed to get if off the ground. Once off the ground, an unlimited number of node types and relationship types can be added — usually learned from trusted peers. And the class thread principle allows concepts to be integrated vertically and horizontally. (So it’s not like you end up with a huge number of mostly disconnected sql tables. Class threads support multiple meaningful ways to weave disparate concepts together, even without adding any new node types or relationship types.)
I think we’re aligned on the minimal-rule principle.
If the base ontology requires 50 primitive types, it’s already unstable.
If it can emerge from ~5 node classes and ~5 relation types, that’s powerful.
Newton didn’t win because he had more laws —
he won because he had fewer.
Where this becomes interesting economically is this:
When knowledge growth is additive and rule-minimal, value compounds naturally.
If:
Nodes are atomic knowledge units
Edges are verified semantic commitments
Ontology rules are globally agreed and minimal
Then every new addition increases:
1. Traversal surface area
2. Compositional capacity
3. Relevance density
And that creates network effects.
The token layer (in my case via NFT-based encoding units) isn’t speculative garnish — it formalizes contribution:
Encoding becomes attributable
Structure becomes ownable
Extensions become traceable
Reputation becomes compounding
In probabilistic systems, contribution disappears into weight space.
In an algebraic/additive system, contribution is structural and persistent.
So natural economics emerges because:
More trusted peers →
More structured additions →
More traversal paths →
More utility →
More value per node.
And because updates are local, not global weight mutations, you don’t destabilize the whole system when someone adds something new.
Minimal rules →
Shared ontology →
Additive structure →
Compounding value.
That’s when tokenomics stops being hype and starts behaving like infrastructure economics.
The architecture dictates the economics.
Not the other way around.