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`SCI = (E × I + M) per R` is well specified in every term but one. `I` has a location-based methodology, `M` a lifecycle one, and `R` must be stated and reproducible. **`E` has no comparable requirement**: a figure can be labelled "measured" without saying what measured it, over what span, on which silicon, or on whose behalf. Five proposed requirements, all machine-checkable: E1 name the counter (not the word "measured"); E2 declare its update period and **refuse a measured claim for a span shorter than that period**, with an undeclared period never supporting one; E3 declare whether the figure counts one principal's work or the whole machine, and if machine-wide, report the magnitude of the idle correction; E4 break down per fabric and state which fabrics are uncovered; E5 say *why* a fabric is uncovered, as one of frozen / absent / needs-privilege / no-counter-exists / not-implemented. E5 is the one with teeth outside accounting. The EU AI Act's GPAI Code of Practice requires energy documentation for training and inference while permitting an exemption where a provider lacks "critical information from compute or hardware provider" — which is a claim of `no-counter-exists`. E5 distinguishes it from the four statuses that do not justify it, and an unprivileged on-die counter needs no cooperation from the compute provider at all, so it narrows the set of accelerators for which the claim is honest. The evidence is three independent observations plus our own: instrumented AI-energy studies are GPU-rail-only and none declares a sampling interval; a reference implementation in this ecosystem stamps `measured` on a lifecycle estimator's output; per-call energy is being read from provider SDKs with no stated boundary. And counters do take the unsafe direction by large factors — a 588-run study found one under-reporting integrated power by 95.4% at 0.5 ms sampling, a second paper built its per-token decomposition on that same counter for the opposite reason without citing the first, and a third observation has the same counter class reading 4–30× the board's draw. We reproduced the shape on a fourth counter and characterised it (0% advancing at 50 µs through 100% at 10 ms, with ~20 mJ batched steps below 1 ms), which is what let us declare a period instead of assuming one. **A correction this annex makes explicitly: no change to `R` is proposed.** I had taken SCI-for-AI's "tokens are a natural functional unit" to mean the unit was token-shaped, and therefore undefined for the zero-token decision models that appeared in Sept–Oct 2026. That is wrong: SCI-for-AI supports diverse functional units — tokens for language models, **inferences for classifiers**, FLOPs for training — so `R = one inference` already covers them. The gap is in the energy, not the unit of work. Our own Mesh defect was real and is fixed (ed0c07b5); the standards-level version of it was not. Complements `sci-for-ai-submission.md`, which maps EOC receipts onto the formula and takes E as given. Cross-linked both ways. Proposed URN `urn:gsf:sci-ai:ext:energy-provenance:v1`, additive, and imposing nothing on deployments that report estimates — which stay conformant provided they say so. Co-Authored-By: Claude Opus 5 <[email protected]> |
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EOC — Energy-Optimized Compute
A free, always-available, energy-honest AI substrate. AI that costs joules, not tokens.
AI is not free yet. The internet is. EOC closes the gap: a substrate that resolves work at the lowest sufficient cost and refuses to spend unbounded energy chasing benchmark deltas. Every query flows through a four-stage pipeline — state-construct → retrieve → refine → check — and a large language model is one of six refine operators, ordered last. The Fourth Truth: refinement before generation. The substrate runs in a browser, runs on commodity CPUs, denominates everything in joules per task against an open evaluation corpus, and cannot be turned off because no one entity runs it.
The substrate is intentionally ownerless: no protocol-level token, permissive licensing, conformance test vectors and open eval corpora so any implementation can verify itself without a trust relationship with the authors. Transaction Science is one steward — it publishes the spec, ships a reference implementation, and operates commercial services around the substrate (managed nodes, certification, registry hosting) — not its proprietor.
Contents
spec/eoc1_spec.docx,spec/eoc1_v0_1_1_patch.docx,spec/eoc1_v0_2.docx,spec/eoc1_v0_2_addendum_A.docx— EOC-1: the substrate architecture (v0.1 tiers → v0.2 four-stage pipeline; Addendum A: non-goals — token economics)spec/eoc2_wire.docx— EOC-2: the wire protocol (capability exchange, gossip, signed envelopes)spec/eoc3.docx— EOC-3: artifact distribution (content-addressed fetch for models, state machines, corpora)spec/eoc4_dcy.docx— EOC-4: DCY (Deterministic Cypher — the query language for the knowledge-graph retrieve operator)spec/eoc5_registry.docx— EOC-5: the Operator Family Registry (stable identifiers + conformance tests)spec/eoc1_eval001*.docx,spec/eval001_worked_instances.docx,spec/eval002.docx— Eval-001 (schema-conformant generation proficiency standard) and Eval-002 (retrieval/provenance)spec/eoc_architecture.docx— the architecture working draftspec/tokens_concealment.docx— Tokens Are the Concealment (the rhetorical companion to Addendum A)os/THEOREM.md,os/Q1_W_DEFINITION.md— the energy-first OS theorem the substrate is designed to run on, and the resolution of its open question on defining "useful work" in joules-per-work
Status
EOC-1 at v0.2; EOC-2..5 and Eval-001/002 at working-draft 0.1; OS theorem at v0.2. The reference implementation referenced across the specs as "v0.3" is to be brought into this repository.
Related
- Smart Byte (
../smart-byte/) — the value substrate; uses EOC's energy attribution. - TX Science AI (
ais.transaction.science) — the AI PaaS; its memoizing cascade is an instance of the EOC four-stage pipeline.