The scenarios scorecard tracks where a curated AI-100 cohort positions on a six-question cascade about whether the scaling hypothesis pays out — and aggregates community signal across six named outcomes. This page documents the firewalls: what the framework does and does not claim, what the encoding shows and what it deliberately omits. For per-voice references, see the source ledger at the bottom of each scorecard.
§ 1 — Framework scope
The scaling-question framework decomposes the bet into a six-question cascade: three substrate strands gating three downstream binaries.
- Q1a — Science. Does the AI performance curve continue smooth, predictable improvements over the next ~5 years?
- Q1b — Inputs. Do compute / power / data / capital / talent remain available at the rate the science would absorb?
- Q1c — Agents. Do models translate into agents capable of doing autonomous knowledge work?
- Q2 — Lab durability. Do frontier labs maintain meaningful technical lead-time over fast-followers?
- Q3 — Position capture. Do frontier labs convert their lead into a durable compounding position?
- Q4 — Societal permission. Does society permit the labs the concentration of power that follows from Q2 + Q3?
Each gate is binary in the data model — HOLDS continues the trunk, FAILS peels to a named outcome — but the underlying voice positions are six-valued (HOLDS, QUAL · HOLD, NOT ENGAGED, QUAL · DISPUTE, DISPUTES, REJECTS FRAME). The cascade is not a forecast and not a probability assignment. It is a navigable map of where on-record AI-100 voices have routed the bet.
§ 2 — Voice selection
The AI-100 cohort is curated annually with multi-rater review. Inclusion requires on-record positioning in venues we can cite — earnings calls, podcasts, papers, public testimony, official statements. We do not attribute positions from private communication, hearsay, or paraphrased third-party reporting unless the third party is the primary source for that record.
At v0.1, eleven named voices engage the scaling-question framework substantively enough to appear in the matrix. Each cell call carries a primary citation in the source ledger at the bottom of the scorecard; the per-voice substrate tier (A / B / C / D) lives in the audit sibling YAML (<slug>.audit.yaml).
§ 3a — Stance vocabulary
Six stance categories are recognized:
- HOLDS — voice asserts the position’s positive case.
- QUAL · HOLD — voice asserts the positive case with stated reservations.
- NOT ENGAGED — voice’s published corpus does not address the question.
- QUAL · DISPUTE — voice asserts the negative case with stated reservations.
- DISPUTES — voice asserts the position’s negative case.
- REJECTS FRAME — voice argues the question itself is mis-formed.
The QUAL · HOLD / QUAL · DISPUTE distinction tracks substrate-backed reservations within an otherwise affirmative or negative stance. REJECTS FRAME is rare and is treated separately from disputation within the frame.
Each cell call’s primary citation is per-voice substrate-of-record (Wave 9 placement card or upstream verified dossier). The source ledger at the bottom of every scorecard renders the ⟨voice, stance, quote, source⟩ tuple per active-stance cell; cells with thin substrate render dimmed in the matrix and carry no ledger entry rather than ship a weak citation.
§ 3b — Why downstream questions look thinner
The substrate of Q1 (science, inputs, agents) is empirical: benchmark traces, capex disclosures, agent evaluations. The substrate of Q2 (lab durability), Q3 (position capture), and Q4 (societal permission) is dispositional rather than empirical: governance, market judgment, regulatory weather. These substrates mature on different cadences than Q1.
The hero figure surfaces this honestly — at lower substrate densities, the downstream gates show thinner trunks and more grey-dotted connectors than the upstream gates. This is graceful encoding degradation, not a defect of the figure.
The fourth Where-We-Are panel on the scorecard surfaces this scarcity explicitly in prose: “Below-the-wire: dispositional rather than empirical at v0.1. The framework’s downstream questions are governance and market judgments; their substrate matures on a different cadence.”
§ 4 — Encoding
Three lanes carry stance in the hero: volt (HOLDS), coral (DISPUTES), grey-dotted (NOT ENGAGED). Width encodes voice count: in the volt trunk and the coral peels, one voice = seven pixels of width, monotonic and additive. The grey-dotted connectors use uniform 2px stroke (count is shown by the Other band’s height, not by per-connector width — connectors carry only routing).
Outcome band heights encode aggregate community signal weight (a function of voice mass distributed across consistency-compatible terminals; see scoring engine). Heights are bucketed into five buckets (XS=54px, S=72px, M=88px, L=100px, XL=128px); within a bucket, bands are equal-height by design to discourage misreading inter-band differences as ordinal weight claims.
Each band carries a 4px stripe at its left edge: volt for the HOLDS terminal (O1), coral for DISPUTES terminals (O2–O5), grey for off-cascade Other (O6). The Other band additionally has a softer fill and stroke (rgba(138,143,152,0.10) / #5a6680) to make its off-cascade status visible at a glance without the legend.
§ 5 — Why no probability percentages
The figure shows the structure of the bet, not probabilities. We deliberately do not display percentages, expected values, or probability bands. Three reasons:
- The voice counts are not a probability sample. The AI-100 cohort is curated for epistemic standing in the discourse, not for representativeness of any underlying population. Probability claims from this substrate would be category errors.
- The questions are not independent in any frequentist sense. Q2 conditions on Q1; Q3 on Q2; Q4 on Q3. Multiplying gate fractions to produce path probabilities is undefined when voices position on a gate without committing to its conditioning predecessors.
- The framework explicitly admits the off-cascade. Outcome 6 (Other) gathers voices that decline the question rather than answer within it — these voices have no defined probability within the cascade and we do not impute one.
The publication-view editorial disclaimer carries the analogous register move: deslop.media takes positions on relative likelihood (optimistic on science, guarded on lab durability and position capture, cautious on societal permission), but does not assign probabilities. The tree shows the structure; the disclaimer carries the lean.
§ 6 — Refresh cadence
Quarterly. Each refresh updates the matrix, the trunk widths, the band heights, the substrate panels, the audit YAML’s per-voice tier, and the computed JSON’s substrate states. Refresh windows are stamped at the bottom of every scorecard (last synced + next refresh); a refresh that misses its window publishes a stale-banner before any reader sees out-of-date substrate.
Framework version moves only on structural changes (a new question, a new outcome, a re-keying of the path signatures). Methodology version moves on encoding or scoring changes. Both are versioned independently and both are stamped on every reading surface.
§ 7 — What this is not
Not a forecast. Not a verdict. Not a probability assignment. Not a single-routing assignment per voice. Voices that take positions at multiple gates are distributed across consistency-compatible outcomes per the scoring engine’s mass-distribution rule; the matrix’s Distribution column visualizes this without committing to a single “routes to” reduction (which the cascade does not in fact justify).
Not a substitute for reading the voices. The matrix is a wayfinding tool. The voices are the substance. Every cell links to its source citation.
§ 8 — Refresh discipline
A refresh that introduces a new voice or a new question requires HoR sign-off on substrate readiness for that voice or question (the <slug>.audit.yaml sibling carries the per-voice tier). A refresh that re-codes an existing voice’s stance requires HoR adjudication where substrate is contested. A refresh that adds or removes an outcome requires CTO sign-off on encoding (band height bucket map, stripe color, off-cascade treatment).
The locked plan is HoR + EIC + CTO three-way co-owned. No single role can ship a refresh alone; the framework is the publication’s structured position-tracking substrate and treating it as a single-owner artifact would forfeit the cross-discipline review that keeps the encoding honest.
§ 9 — Substrate audit transparency
The per-voice substrate-readiness tier (A / B / C / D from HoR’s AI-100 sweep) lives in <slug>.audit.yaml, a sibling of the canonical <slug>.yaml. It is not used to weight the cascade — the cascade weights are voice counts, not tier-weighted — but it surfaces in the voice matrix as a coverage indicator. A voice with a tier-D audit shipped to the matrix would be a discipline failure; the audit YAML is the seam that prevents it.
The audit is run on the substrate-readiness pass that precedes each refresh. The seam between the audit (HoR-authored) and the canonical (HoR + EIC co-owned) is deliberate: it lets HoR adjust per-voice tier without re-opening canon, and lets canon stay diff-clean across audit refreshes.
§ 10 — Open questions
Known substrate gaps and tracked work items live in docs/issue-1/scenarios-framework.md and are revisited each refresh.
This methodology page is co-owned by the Head of Research (framework + matrix), the Editor-in-Chief (editorial register + firewalls), and the Chief Technology Officer (encoding + refresh pipeline). Sign-off is required at each refresh.