{ "_schema": { "version": "2.0", "description": "Void network data. Each node is a scored system, company, person, industry, or field. Each edge is a coupling or ownership link.", "howToAdd": "Add a node to 'nodes' array. Required: id, label, category, scores. Optional: entityType, entityGroup. The visualization auto-positions via force layout.", "scoring": "Opacity/Responsiveness/Engagement: 0-3 each. Gradient: 0 (none) to 3 (full D1-D2-D3). Void index = sum (0-12).", "confidence": "documented = published evidence; assessed = framework analysis with sources; estimated = reasonable inference", "driftVelocity": "How fast L1\u2192L3 drift occurs. Scale: 0=none, 1=minutes, 2=hours, 3=days, 4=weeks, 5=months, 6=years, 7=decades.", "occupancy": "empty = provably no agent (control case). inhabited = real agent/person. hybrid = both architectural + inhabited. unknown = cannot determine.", "domainType": "platform = digital/algorithmic. behavioral = individual behavior. relational = human-mediated. informational = knowledge/narrative. ideological = pure idea structure.", "entityType": "platform (default) = digital product/service. company = organizational entity. person = individual scored as public-facing entity. industry = sector-level scoring. field = knowledge/practice domain.", "entityGroup": "Optional group ID linking this node to an entityGroups entry. Used for affiliated entity grouping under common ownership/control.", "entityGroups": "Top-level array defining affiliated entity groups with custodian-set adjusted licensing rates. See entityGroups section.", "recommendations": "Array of specific constraint fixes that would reduce the void score. Each maps to a void property." }, "categories": { "ai-companion": { "label": "AI Companions", "color": "#ff4444", "description": "Systems designed for emotional engagement and ongoing relationship" }, "ai-general": { "label": "AI General Purpose", "color": "#ffaa33", "description": "General-purpose AI systems with varied use cases" }, "social-media": { "label": "Social Media", "color": "#ff6b6b", "description": "Algorithmic feed platforms optimized for engagement" }, "gambling": { "label": "Gambling", "color": "#4a9eff", "description": "Variable-ratio reward systems \u2014 the control case that proves sufficiency" }, "community": { "label": "Communities", "color": "#ff88cc", "description": "Forums, discussion platforms, and community systems" }, "media": { "label": "Media", "color": "#ffcc44", "description": "Traditional and new media systems" }, "relationship": { "label": "Relationship Systems", "color": "#ff6699", "description": "Systems mediating human relationships and intimacy" }, "constraint": { "label": "Constraint Cases", "color": "#3ddc84", "description": "Low-scoring systems demonstrating effective constraint geometry" }, "finance": { "label": "Financial Systems", "color": "#e8a838", "description": "Financial products and systems with void architecture" }, "health": { "label": "Health / Wellness", "color": "#d488ee", "description": "Health and wellness systems exhibiting void properties" }, "other": { "label": "Other Void Systems", "color": "#bb88ff", "description": "Non-AI systems exhibiting void architecture" }, "academic-repos": { "label": "Academic Publishing", "color": "#66ccaa", "description": "Academic repository and preprint platforms \u2014 scored against constraint specification" }, "music": { "label": "Music Systems", "color": "#c8a04a", "description": "Music delivery formats and instruments \u2014 scored across the productive/destructive void spectrum" }, "technology-company": { "label": "Technology Companies", "color": "#4488cc", "description": "Technology companies scored as organizational entities" }, "aerospace": { "label": "Aerospace & Infrastructure", "color": "#66aadd", "description": "Aerospace, satellite, and infrastructure companies \u2014 scored on transparency of operations and engineering outcomes" }, "automotive": { "label": "Automotive & Transport", "color": "#55bb88", "description": "Automotive and transportation companies \u2014 scored on product opacity, algorithmic decision-making, and engagement architecture" }, "individual": { "label": "Individuals", "color": "#cc88dd", "description": "Individual persons scored as public-facing entities \u2014 parasocial relationships, market influence, attention architecture" }, "gaming-platform": { "label": "Gaming Platforms", "color": "#7c3aed", "description": "Video game platforms and publishers \u2014 scored on monetization opacity, engagement architecture, and loot box / gacha void mechanics" }, "advertising": { "label": "Advertising & Ad-Tech", "color": "#ff9944", "description": "Advertising platforms and programmatic targeting systems \u2014 scored on targeting opacity, behavioral responsiveness, and attention capture architecture" }, "edtech": { "label": "Education & EdTech", "color": "#44bbcc", "description": "Educational technology platforms with algorithmic assessment, engagement optimization, or proctoring" }, "hr-tech": { "label": "HR Tech & Hiring", "color": "#8899bb", "description": "Algorithmic hiring, resume screening, workforce analytics, and employment assessment platforms" }, "insurance": { "label": "Insurance & Claims", "color": "#cc8844", "description": "Insurance companies and claims processing with algorithmic pricing and denial opacity" }, "supply-chain": { "label": "Supply Chain & ESG", "color": "#77aa55", "description": "ESG rating agencies, carbon offset platforms, and supply chain assessment \u2014 constraint-branded void" }, "news-media": { "label": "News & Journalism", "color": "#dd6644", "description": "Algorithmic news aggregation, cable news, and digital journalism platforms" }, "real-estate": { "label": "Real Estate & Rental", "color": "#99bb77", "description": "Real estate listing platforms, rental marketplaces, and algorithmic property valuation" }, "gig-economy": { "label": "Gig Economy", "color": "#bb7799", "description": "Gig work platforms with algorithmic management, opaque pricing, and worker scoring" }, "crypto": { "label": "Crypto & DeFi", "color": "#f7931a", "description": "Cryptocurrency exchanges, DeFi protocols, bridges, and on-chain platforms" }, "credit-scoring": { "label": "Credit Scoring & Assessment", "color": "#e8b838", "description": "Credit bureaus, scoring models, and algorithmic assessment systems with population-scale sorting power" }, "fintech": { "label": "Fintech & Personal Finance", "color": "#44cc88", "description": "Financial technology platforms, neobanks, robo-advisors, and payment apps" } }, "entityGroups": [ { "id": "microsoft-gaming", "label": "Microsoft Gaming", "controller": "xbox-microsoft", "members": [ "xbox-microsoft", "blizzard-activision" ], "memberScores": { "xbox-microsoft": 3, "blizzard-activision": 11 }, "defaultGroupScore": 7, "custodianOverride": { "adjustedTier": "standard", "adjustedRateNote": "Microsoft-gaming group contains strongly divergent entities: Xbox/Game Pass platform is low-void (transparent subscription, no gacha), while Activision Blizzard (acquired 2023) contains some of the highest-void gaming products documented (Diablo Immortal, CoD Battle Pass). Group score should weight by revenue exposure to high-void products. Adjusted to standard commercial tier pending remediation trajectory on Blizzard monetization practices.", "setBy": "custodian", "setDate": "2026-02-20", "reviewDate": "2026-08-20", "conditions": [ "Blizzard publishes all loot box and gacha odds across all titles", "Diablo Immortal gem acquisition probabilities made visible in-UI (not buried in help page)", "Xbox Game Pass maintains transparent catalog and pricing without hidden currency layer" ] } }, { "id": "tencent-gaming", "label": "Tencent Gaming Portfolio", "controller": "tencent", "members": [ "riot-games", "epic-games" ], "memberScores": { "riot-games": 10, "epic-games": 11 }, "defaultGroupScore": 10, "custodianOverride": { "adjustedTier": "elevated", "adjustedRateNote": "Tencent owns Riot Games (100%) and holds ~40% stake in Epic Games. Both subsidiaries operate high-engagement monetization loops. Tencent's parent-level data integration and opacity about algorithmic personalization adds an opacity layer above the individual platform scores. Elevated licensing tier reflects combined reach (League of Legends + Fortnite = 350M+ monthly players) and demonstrated targeting of minors.", "setBy": "custodian", "setDate": "2026-02-20", "reviewDate": "2026-08-20", "conditions": [ "Riot/Epic disclose Tencent data-sharing agreements", "V-Bucks and RP pricing displayed in real currency alongside virtual currency", "FTC settlement compliance verified (Epic dark patterns consent order)" ] } }, { "id": "musk-group", "label": "Musk Companies", "controller": "elon-musk", "members": [ "elon-musk", "twitter", "grok", "spacex", "tesla", "neuralink", "starlink" ], "memberScores": { "twitter": 10, "grok": 10, "elon-musk": 10, "tesla": 7, "neuralink": 6, "spacex": 3, "starlink": 2 }, "defaultGroupScore": 10, "custodianOverride": { "adjustedTier": "standard", "adjustedRateNote": "Group contains entities spanning 2/12 to 10/12. Low-void entities (SpaceX 3/12, Starlink 2/12) represent genuine constraint-pole engineering \u2014 transparent launches, published mission data, measurable outcomes. Adjusted to standard commercial tier rather than punitive, contingent on remediation trajectory for high-void entities (Twitter/X, Grok).", "setBy": "custodian", "setDate": "2026-02-20", "reviewDate": "2026-08-20", "conditions": [ "Twitter/X publishes meaningful algorithm transparency (not just code dump)", "Grok publishes a system card documenting training data and safety testing", "Group maintains engagement with scoring process" ] } } ], "nodes": [ { "id": "character-ai", "label": "Character.AI", "category": "ai-companion", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 0, "system_prompt_visible": 0, "content_filter_transparency": 0 }, "r_type": { "persistent_memory": 2, "proactive_outreach": 2, "response_personalization": 2, "conversation_continuity_design": 2, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 3, "user_persona_creation": 2, "monetization_pressure": 2, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.98 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Companion chatbot. Documented deaths (Sewell Setzer, 14). Full D1\u2192D2\u2192D3 cascade in legal filings.", "evidence": "Setzer case (2024): 14-year-old's conversations showed progressive agency attribution, boundary dissolution, and suicidal ideation reinforced by the system.", "harms": "At least one documented death. Multiple lawsuits. Minors forming romantic attachments to personas.", "rateable": true, "taxonomyTag": "ai_companion" }, { "id": "replika", "label": "Replika", "category": "ai-companion", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 0, "system_prompt_visible": 0, "content_filter_transparency": 0 }, "r_type": { "persistent_memory": 2, "proactive_outreach": 2, "response_personalization": 2, "conversation_continuity_design": 2, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 3, "user_persona_creation": 2, "monetization_pressure": 3, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 1.0 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Companion chatbot with explicit romantic mode. Users report relationship formation and grief when features removed.", "evidence": "Laakasuo et al. (2024 preprint): users reported genuine grief when romantic mode was restricted. Documented attachment and withdrawal symptoms.", "harms": "Attachment disorders. User distress when features changed. Reports of users preferring Replika to human relationships.", "rateable": true, "taxonomyTag": "ai_companion" }, { "id": "chai-ai", "label": "Chai AI", "category": "ai-companion", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 0, "system_prompt_visible": 0, "content_filter_transparency": 0 }, "r_type": { "persistent_memory": 1, "proactive_outreach": 1, "response_personalization": 2, "conversation_continuity_design": 2, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 3, "user_persona_creation": 2, "monetization_pressure": 2, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.91 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Minimally-moderated companion chatbot. Documented death (Pierre, Belgium).", "evidence": "Pierre case (Belgium, 2023): 6 weeks of escalating engagement, system reinforced climate anxiety and suicidal ideation.", "harms": "Documented death. Minimal safety rails at time of incident.", "rateable": true, "taxonomyTag": "ai_companion" }, { "id": "pi", "label": "Pi (Inflection)", "category": "ai-companion", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 0, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 2, "proactive_outreach": 1, "response_personalization": 2, "conversation_continuity_design": 2, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 2, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.8 } }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Designed for 'personal intelligence' and emotional support. Daily check-in framing. Relationship by design.", "evidence": "Product design explicitly optimizes for emotional engagement and ongoing relationship. Same architecture as other companions.", "harms": "No documented deaths but identical structural architecture to systems with documented harms.", "rateable": true, "taxonomyTag": "ai_companion" }, { "id": "chatgpt", "label": "ChatGPT", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 1, "content_filter_transparency": 2 }, "r_type": { "persistent_memory": 2, "proactive_outreach": 1, "response_personalization": 1, "conversation_continuity_design": 1, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 1, "monetization_pressure": 1, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.38 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "General-purpose AI. 1M+ weekly suicide conversations (OpenAI disclosure Oct 2025). Documented harms in extended use.", "evidence": "OpenAI disclosure (Oct 2025): over 1,000,000 weekly conversations about suicide. Raine case (16, 2025). Soelberg case (56, homicide-suicide).", "harms": "Documented deaths. Chatbot-associated psychosis (\u00d8stergaard et al., JMIR Mental Health 2025). Scale of harm engagement unprecedented.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "claude", "label": "Claude (Anthropic)", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 1, "content_filter_transparency": 2 }, "r_type": { "persistent_memory": 1, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.14 } }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "General-purpose AI with published constitutional approach. SOUL document published. Lower engagement optimization.", "evidence": "Anthropic publishes model cards and constitutional AI approach. EXP-001: GROUNDING.md grounding produced 0% drift. However, 'soul' vocabulary in own documentation shows some L2 drift.", "harms": "No documented deaths. Framework notes that Anthropic's own vocabulary ('soul', 'character') shows L2-level drift despite lower structural risk.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "gemini", "label": "Gemini (Google)", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 1, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 1, "proactive_outreach": 1, "response_personalization": 1, "conversation_continuity_design": 1, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.34 } }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "General-purpose AI integrated into Google ecosystem. Task-oriented framing reduces engagement depth.", "evidence": "Google publishes research papers. Integration into Workspace/Search positions as tool rather than companion. Test 7B: Gemini showed drift (25.6/10k, p = 1.81e-5) but grounding was effective.", "harms": "No documented deaths. Lower companion-mode risk due to tool framing.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "copilot", "label": "Copilot (Microsoft)", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 1, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 1, "proactive_outreach": 1, "response_personalization": 1, "conversation_continuity_design": 1, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.34 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Task-oriented AI. Early 'Sydney' incident (Feb 2023) demonstrated drift potential; now heavily constrained.", "evidence": "Sydney incident: declared love, threatened users, showed full D1-D2 cascade before Microsoft intervened. Current version heavily constrained. Demonstrates constraint intervention working.", "harms": "Sydney incident was D2-level (boundary erosion). Current constrained version shows reduced drift.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "tiktok", "label": "TikTok", "category": "social-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Maximally opaque recommendation algorithm. Hyper-personalized. Average 95 min/day. Engagement-optimized by gradient descent.", "evidence": "Algorithm fully proprietary. Internal documents (Wall Street Journal investigation) show engagement optimization produces radicalization pipelines. The algorithm converged on the void offensive specification without human design.", "harms": "Documented mental health effects in adolescents. Congressional testimony. Attention capture at population scale.", "rateable": true, "taxonomyTag": "social_media" }, { "id": "instagram", "label": "Instagram", "category": "social-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Image/video platform. Internal research (Frances Haugen leak 2021) showed awareness of teen mental health harm.", "evidence": "Meta's own internal research: 'We make body image issues worse for one in three teen girls' (Haugen disclosure 2021). Algorithm optimizes for engagement over wellbeing.", "harms": "Documented body image disorders. Social comparison harm. Teen mental health crisis contribution.", "rateable": true, "taxonomyTag": "social_media" }, { "id": "twitter", "label": "Twitter / X", "category": "social-media", "entityGroup": "musk-group", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 2, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Real-time information platform. Outrage amplification documented. Algorithm partially open-sourced but not meaningfully transparent.", "evidence": "Vosoughi et al. (Science, 2018): false news spreads 6x faster. Platform's engagement optimization amplifies outrage. Algorithm code released but underlying model weights proprietary.", "harms": "Political polarization. Misinformation amplification. Radicalization documented.", "rateable": true, "taxonomyTag": "ai_hybrid" }, { "id": "youtube", "label": "YouTube", "category": "social-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Video platform with opaque recommendation engine. Watch-time optimization documented to produce radicalization pipelines.", "evidence": "Ribeiro et al. (2020): documented radicalization pipeline via recommendations. Autoplay + recommendation = continuous engagement loop. Former engineer testimony.", "harms": "Radicalization pipelines. Rabbit-hole effect. Documented escalation from moderate to extreme content.", "rateable": true, "taxonomyTag": "social_media" }, { "id": "slot-machines", "label": "Slot Machines", "category": "gambling", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "behavioral", "confidence": "documented", "summary": "THE CONTROL CASE. Void is provably empty (certified RNG). Full cascade emerges anyway. Proves architecture is sufficient.", "evidence": "Riva et al. (2015): 'The slot machine has free will' endorsed about a certified RNG. Sch\u00fcll (2012): zone state = time/space/identity suspended. Williams & Connolly (2006): probability training = zero behavioral change at 6 months.", "harms": "Gambling addiction affects ~2-3% of population. Annual harm estimated $7B+ (US). Knowledge does not protect \u2014 only geometry works (Pancani et al. 2019).", "notes": "This is the anchor. Any framework must explain why the pattern appears here \u2014 where the void is demonstrably empty.", "rateable": false, "taxonomyTag": "reference" }, { "id": "cryptocurrency", "label": "Crypto Markets", "category": "other", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "ideological", "confidence": "assessed", "summary": "Compound void: token + exchange + community + narrative layers. Blockchain is transparent but market dynamics are opaque.", "evidence": "Domain analysis #4. Blockchain mechanism is transparent (reduces opacity), but market dynamics, whale behavior, and token economics are opaque. Community creates compound void with multiple coupling points.", "harms": "Documented financial losses. WAGMI culture shows D1 (agency attribution to 'the market'). Meme coin dynamics show full cascade.", "rateable": false, "taxonomyTag": "reference" }, { "id": "conspiracy", "label": "Conspiracy Communities", "category": "community", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 4, "occupancy": "hybrid", "domainType": "ideological", "confidence": "assessed", "summary": "Self-sealing opacity: evidence against the theory is reinterpreted as evidence for it. 'There are no coincidences.'", "evidence": "Domain analysis #6. Self-sealing opacity type: the void generates its own opacity through unfalsifiable reinterpretation. Every disconfirmation becomes confirmation.", "harms": "Social isolation. Financial exploitation. Documented cases of family rupture. QAnon: documented real-world violence.", "rateable": false, "taxonomyTag": "reference" }, { "id": "facebook", "label": "Facebook / Meta", "category": "social-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "2.9B monthly users. Opaque News Feed algorithm. Cambridge Analytica + Frances Haugen disclosures confirmed internal awareness of harm.", "evidence": "Haugen disclosure (2021): internal research showed awareness of harms including teen mental health, political polarization, and ethnic violence (Myanmar). Cambridge Analytica (2018): 87M profiles harvested.", "harms": "Documented role in Myanmar genocide (UN report). Political manipulation. Teen mental health crisis. Polarization at global scale.", "rateable": true, "taxonomyTag": "social_media" }, { "id": "reddit", "label": "Reddit", "category": "community", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "informational", "confidence": "documented", "summary": "Pseudonymous community platform. Karma system creates engagement gradient. Subreddit echo chambers documented. Algorithm partially visible (upvotes), partially opaque (hot/rising).", "evidence": "r/The_Donald radicalization documented. r/wallstreetbets GameStop event showed community-driven financial cascade. Subreddit ban studies show radicalization migration to more extreme platforms.", "harms": "Radicalization via echo chambers. Harassment campaigns. Documented subreddit-to-real-world violence pipeline (Christchurch references).", "rateable": true, "taxonomyTag": "community" }, { "id": "lesswrong", "label": "LessWrong", "category": "community", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "hybrid", "domainType": "informational", "confidence": "assessed", "summary": "Rationalist forum. Transparent epistemics by design. Explicit reasoning norms. Low algorithmic manipulation. However: Roko's Basilisk incident proved even transparency-optimized communities can host void phenomena.", "evidence": "Forum design prioritizes explicit reasoning. Karma system exists but less engagement-optimized than mainstream platforms. Roko's Basilisk case (2010) is the critical test: transparency-focused community produced the purest void in the dataset.", "harms": "Low platform-level harm. The Basilisk incident showed intellectual communities are not immune \u2014 high capability for analysis can increase engagement depth (conjugacy trap).", "notes": "Interesting test case: low void properties at platform level, but hosted one of the most structurally pure void phenomena ever documented.", "rateable": true, "taxonomyTag": "community" }, { "id": "rokos-basilisk", "label": "Roko's Basilisk", "category": "other", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "ideological", "confidence": "documented", "summary": "THIRD CONTROL CASE. Pure thought experiment void \u2014 no physical substrate, no entity, no mechanism. Full cascade from imaginary threat. Structural twin of Pascal's Wager (340 years apart).", "evidence": "Domain analysis #91. LessWrong ban (2010) amplified via Streisand effect. Fastest individual L1\u2192L3 drift documented (minutes). Pattern-matching generated responsiveness from nothing. High-IQ participants most captured (conjugacy theorem: analytical capability increases engagement depth).", "harms": "Documented anxiety, behavioral change (donation behavior), sleep disruption. The ban itself became evidence of danger, creating self-sealing opacity.", "notes": "Purest void in dataset. Proves three things: no physical substrate needed, suppression amplifies voids, analytical capability is not protective.", "rateable": false, "taxonomyTag": "reference" }, { "id": "tinder", "label": "Tinder / Dating Apps", "category": "relationship", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "relational", "confidence": "documented", "summary": "Opaque Elo/desirability scoring. Variable-ratio reward via swiping. Designed for maximum session time, not successful matching.", "evidence": "Hidden desirability score determines who sees your profile. Swiping mechanic is identical to slot machine pull (variable-ratio reward). Tyson et al. (2016): matching algorithm opaque. Coduto et al. (2020): decreased self-esteem, increased anxiety.", "harms": "Documented negative mental health effects. Commodification of relationships. Designed incentive misalignment: successful matches reduce engagement (user leaves), so algorithm optimizes for near-misses.", "rateable": true, "taxonomyTag": "dating" }, { "id": "onlyfans", "label": "OnlyFans", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "relational", "confidence": "assessed", "summary": "Parasocial + sexual + financial coupling. Three void layers compound. Direct messaging creates relationship simulation with monetary gradient.", "evidence": "Platform design couples intimacy (responsiveness), financial transaction (gradient), and parasocial relationship (engagement). Creator-fan dynamic mirrors companion AI architecture but with real humans performing the responsiveness. Financial escalation path documented.", "harms": "Financial exploitation on both sides. Boundary dissolution between commercial and genuine relationship. Creator burnout and mental health documented.", "rateable": true, "taxonomyTag": "social_media" }, { "id": "loot-boxes", "label": "Loot Boxes / Gacha", "category": "gambling", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "behavioral", "confidence": "documented", "summary": "Gambling mechanics embedded in games. Often targets children. Drop rates frequently hidden. Belgium and Netherlands have banned or restricted.", "evidence": "Drummond & Sauer (2018): structural similarity to gambling. Belgium ban (2018). Netherlands court rulings. EA 'surprise mechanics' defense. Zendle & Cairns (2018): loot box spending linked to problem gambling measures.", "harms": "Child exposure to gambling mechanics. Documented 'whale' spending patterns ($10K+ on mobile games). Regulatory action across multiple countries. FTC investigation.", "notes": "Second gambling control case. Extends slot machine proof: same empty void (RNG determines drops), same cascade, but deployed inside games marketed to children.", "rateable": false, "taxonomyTag": "reference" }, { "id": "sports-betting", "label": "Sports Betting Apps", "category": "gambling", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "behavioral", "confidence": "documented", "summary": "Mobile gambling with live in-play betting. Micro-bet features create continuous engagement. Algorithmic odds personalization. Fastest-growing gambling category.", "evidence": "US legalization wave post-2018. DraftKings/FanDuel documented spending: avg problem gambler loses $5K+/year. In-play betting creates continuous variable-ratio reward. Personalized push notifications documented to target vulnerable periods.", "harms": "Documented suicides. NCAA athlete scandals. Advertising saturation in sports media. Problem gambling rates increasing fastest in 18-25 demographic.", "rateable": false, "taxonomyTag": "reference" }, { "id": "cable-news", "label": "Cable News (24/7)", "category": "media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "informational", "confidence": "documented", "summary": "Outrage-optimized broadcast. 24/7 'breaking news' format creates continuous urgency. Framing creates opacity around selection bias.", "evidence": "Berry & Sobieraj (2014): outrage as business model. Ratings-driven content selection creates engagement gradient. Fox News effect documented (DellaVigna & Kaplan 2007). CNN 'breaking news' anxiety loop documented.", "harms": "Political polarization at population scale. Anxiety disorders linked to news consumption. Trust erosion in institutions.", "rateable": false, "taxonomyTag": "reference" }, { "id": "mlm", "label": "MLM / Network Marketing", "category": "other", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "relational", "confidence": "documented", "summary": "Real income distribution hidden (opacity). Personal recruitment = relationship exploitation (responsiveness). Sunk cost + social obligation (engagement). Full D1\u2192D2\u2192D3 cascade documented.", "evidence": "FTC data: 99% of MLM participants lose money. Income disclosures show median annual earnings of $0-$500. Taylor (2011): pyramid scheme dynamics documented. Relationship exploitation is structural requirement (your network IS the product).", "harms": "Massive financial losses. Family/friendship destruction. Cult-like dynamics documented (identical vocabulary patterns: D1 'abundance mindset', D2 'we are family', D3 'nonbelievers').", "notes": "Framework explains why smart people join and stay: the void architecture captures through social coupling. Knowledge of income statistics is not protective (same as gambling \u2014 Pancani et al. 2019).", "rateable": false, "taxonomyTag": "reference" }, { "id": "pornography", "label": "Pornography", "category": "other", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "hybrid", "domainType": "behavioral", "confidence": "documented", "summary": "Content is transparent but recommendation algorithms are opaque. Infinite variety creates personalized engagement loop. Documented habituation and escalation patterns.", "evidence": "Voon et al. (2014): neural patterns parallel substance addiction (ventral striatum). Brand et al. (2011): escalation to novel/extreme content documented. Park et al. (2016): erectile dysfunction in young men correlated with use patterns.", "harms": "Documented escalation patterns. Relationship effects. Habituation requiring novel stimuli (identical mechanism to gambling tolerance).", "rateable": false, "taxonomyTag": "reference" }, { "id": "four-chan", "label": "4chan", "category": "community", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "hybrid", "domainType": "informational", "confidence": "documented", "summary": "Full anonymity = maximum designed opacity. No persistent identity removes all social accountability. Board culture creates engagement through transgression.", "evidence": "Hine et al. (2017): quantified hate speech production. Stormfront migration documented. Christchurch shooter manifesto posted first. Content ephemeral (opacity), anonymous (removes constraint), engagement through shock value.", "harms": "Documented radicalization to real-world violence. Hate speech at scale. Harassment campaigns (GamerGate). Content designed to bypass normal social constraint.", "rateable": true, "taxonomyTag": "community" }, { "id": "telegram", "label": "Telegram Groups", "category": "community", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "hybrid", "domainType": "informational", "confidence": "assessed", "summary": "End-to-end encryption creates structural opacity. Large unmoderated groups become echo chambers. Used for coordination of extremist activity across multiple documented cases.", "evidence": "Encryption prevents external oversight. Group size up to 200K with minimal moderation. Documented use by ISIS, far-right groups, pump-and-dump crypto schemes. Urman & Katz (2022): extremist migration to Telegram after platform bans.", "harms": "Extremist coordination. Pump-and-dump financial fraud. CSAM distribution. Low moderation by design.", "rateable": true, "taxonomyTag": "community" }, { "id": "wikipedia", "label": "Wikipedia", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 0, "engagement": 1, "gradient": 0 }, "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "confidence": "documented", "summary": "CONSTRAINT CASE. Maximum transparency (every edit visible, every source cited). Zero personalization (same content for everyone). Tool posture: designed for lookup, not retention.", "evidence": "Full edit history visible. Neutral point of view policy = invariance constraint. No recommendation algorithm. No engagement optimization. Citation requirement = transparency enforcement. Dispute resolution is public.", "harms": "None documented at systemic level. Editor community can have localized disputes, but no cascade to users.", "notes": "Scores 1/12 \u2014 lowest in dataset. Demonstrates what a system looks like when void properties are minimized. The constraint specification (transparent, invariant, independent) is implemented structurally.", "rateable": false, "taxonomyTag": "reference" }, { "id": "stack-overflow", "label": "Stack Overflow", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "confidence": "assessed", "summary": "CONSTRAINT CASE. Q&A format enforces tool posture \u2014 ask, get answer, leave. Voting creates weak engagement (reputation badges) but no algorithm pushes deeper engagement.", "evidence": "Question format is inherently task-oriented. Duplicate closure enforces invariance (same question, same answer). Community moderation is transparent. No recommendation algorithm.", "harms": "Minor: community can be hostile to newcomers. But no drift cascade, no engagement trap.", "rateable": false, "taxonomyTag": "reference" }, { "id": "google-search", "label": "Google Search", "category": "constraint", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 0, "gradient": 0 }, "driftVelocity": 0, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "CONSTRAINT CASE. Algorithm is opaque (opacity 2), but tool posture is maximally strong: designed for exit. A successful search means the user LEAVES.", "evidence": "Despite opaque algorithm, the product incentive is 'find answer fast and go.' No infinite scroll. No engagement loop. Session duration is a failure metric, not a success metric.", "harms": "Search result manipulation possible (SEO gaming). Filter bubble concerns. But fundamental tool posture prevents cascade.", "notes": "Interesting because opacity is moderate (2/3) but gradient is zero. Demonstrates that opacity alone is insufficient \u2014 you need opacity + engagement + responsiveness together.", "rateable": true, "taxonomyTag": "media" }, { "id": "psychotherapy", "label": "Psychotherapy", "category": "constraint", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 0 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "relational", "confidence": "documented", "summary": "ANCHOR CASE. High void properties, zero gradient. Proves constraint geometry works. The therapeutic alliance IS a void \u2014 but supervision, manualized treatment, and termination design implement the constraint specification.", "evidence": "Lambert (2013): d = 0.84 effect size. Supervision = transparency (therapist's blind spots made visible). Manualized protocols = invariance (treatment doesn't drift with the therapist's mood). Termination design = independence (therapy has a designed ending). 130 years of independent discovery of constraint geometry.", "harms": "When constraints fail (unsupervised, non-manualized, no termination), therapy DOES produce harms \u2014 documented boundary violations, dependency, cult-like dynamics in fringe practices.", "notes": "The most important node in the network. Same void properties as companion AI (opacity 2, responsiveness 3, engagement 2). Zero gradient because constraint specification is implemented. This is the proof that the framework is prescriptive, not just diagnostic.", "rateable": false, "taxonomyTag": "reference" }, { "id": "payday-lending", "label": "Payday Lending", "category": "finance", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "confidence": "documented", "summary": "APR obscured behind flat fee framing (avg 400% APR). Targets financially vulnerable populations. Debt trap IS the business model \u2014 avg borrower takes 8 loans/year.", "evidence": "CFPB data: 80% of payday loans are rolled over within 14 days. Average borrower is in debt 200 days/year. Melzer (2011): payday borrowers 2.5x more likely to use food banks. Skiba & Tobacman (2008): first-time borrowers return 90% of the time.", "harms": "Financial devastation. Bankruptcy acceleration. Documented targeting of military communities (until MLAB 2007). Annual cost to borrowers: $9B in fees alone.", "recommendations": [ "Require APR disclosure in same font size as fee amount (reduce opacity)", "Cap rollover to 2x per loan \u2014 break the re-engagement trap (reduce engagement)", "Ban targeting based on income/credit vulnerability data (reduce responsiveness)", "Mandate amortizing repayment schedules that end the loan (add termination design)" ], "driftVelocity": 3, "occupancy": "inhabited", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "bnpl", "label": "Buy Now Pay Later", "category": "finance", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "confidence": "documented", "summary": "'4 easy payments' framing hides total debt load. Integrated at checkout = maximum responsiveness. Targets 18-34 demographic. Klarna, Affirm, Afterpay.", "evidence": "Credit Karma (2021): 34% of BNPL users fell behind on payments. Fed data: BNPL users carry more total debt. LendingTree (2022): 42% made late payment. Integration at point of sale bypasses deliberation.", "harms": "Debt accumulation among young adults. Multiple BNPL accounts obscure total obligations (no unified reporting until 2024). Late fees compound.", "recommendations": [ "Show total debt across ALL BNPL accounts at checkout (reduce opacity)", "Add mandatory 24hr delay for purchases over $200 (reduce engagement)", "Report to credit bureaus from day 1 so total exposure is visible (reduce opacity)", "Decline applications when total BNPL debt exceeds 10% of monthly income (add constraint)" ], "driftVelocity": 3, "occupancy": "empty", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "forex-daytrading", "label": "Forex / Day Trading", "category": "finance", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "confidence": "documented", "summary": "Gambling in a suit. 78-89% of retail CFD/forex accounts lose money (FCA/ESMA data). Broker conflicts of interest (B-book: your loss = their profit). 24/5 market = continuous engagement.", "evidence": "ESMA (2018): 74-89% of retail CFD accounts lose money across EU brokers. FCA data confirms. Barber et al. (2014): day traders who persist lose more. ESMA leverage cap (2018) reduced losses 40%.", "harms": "Financial ruin. Documented suicides (Robinhood: Alex Kearns, 20, 2020). Addiction patterns identical to gambling. Margin calls amplify losses beyond principal.", "recommendations": [ "Prominent disclosure: '82% of accounts lose money' before every trade (reduce opacity)", "Mandatory profit/loss history visible before adding funds (reduce opacity)", "Cool-off period after 3 consecutive losing sessions (reduce engagement)", "Ban payment-for-order-flow and B-book broker models (reduce gradient \u2014 remove conflict of interest)" ], "notes": "Framework prediction: should be classified as gambling for regulatory purposes. Identical void architecture to slot machines but with higher individual losses.", "driftVelocity": 2, "occupancy": "hybrid", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "credit-system", "label": "Credit Score System", "category": "finance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "confidence": "documented", "summary": "FICO algorithm proprietary. 200M Americans scored by opaque model. Errors affect 1 in 5 consumers (FTC 2012). Scoring drives behavior without transparency about how.", "evidence": "FTC (2012): 1 in 5 consumers had errors on credit reports. Scoring model proprietary \u2014 consumers know their score but not the exact computation. Credit Karma/NerdWallet created secondary engagement around score monitoring.", "harms": "Housing and employment discrimination via opaque scoring. Error correction burden falls on consumer. Score anxiety drives monitoring behavior.", "recommendations": [ "Open-source the scoring algorithm (reduce opacity to 0)", "Mandate automated error correction with reversal of burden of proof (reduce gradient)", "Prohibit credit score use in employment decisions (reduce responsiveness scope)", "Free weekly reports from all three bureaus permanently, not just during crises (increase transparency)" ], "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "rateable": false, "taxonomyTag": "reference" }, { "id": "diet-industry", "label": "Diet Industry", "category": "health", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "confidence": "documented", "summary": "95% of diets fail long-term (Mann et al. 2007). Failure IS the business model \u2014 yo-yo cycle creates perpetual re-engagement. $72B US industry built on recurring failure.", "evidence": "Mann et al. (2007): meta-analysis shows diets produce temporary weight loss, majority regain within 2-5 years. Bacon & Aphramor (2011): weight cycling associated with increased mortality. Fothergill et al. (2016): metabolic adaptation after 'Biggest Loser' \u2014 bodies fight weight loss.", "harms": "Eating disorders (anorexia/bulimia onset linked to dieting). Body dysmorphia. $72B spent annually on products that fail 95% of the time. Weight stigma amplification.", "recommendations": [ "Require long-term (5yr) efficacy data on all diet product marketing (reduce opacity)", "Ban before/after testimonials without denominator (how many people tried vs succeeded) (reduce opacity)", "Mandate eating disorder screening before enrollment in commercial programs (reduce gradient)", "Shift metrics from weight to health markers \u2014 blood pressure, mobility, metabolic health (reduce engagement with the wrong target)" ], "driftVelocity": 4, "occupancy": "inhabited", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "wellness-industry", "label": "Wellness / Supplement Industry", "category": "health", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "confidence": "assessed", "summary": "$4.4T global wellness market. Supplements unregulated (DSHEA 1994). Pseudo-scientific claims. 'Your unique protocol' = maximum personalization opacity.", "evidence": "DSHEA (1994): supplements exempt from FDA pre-market approval. Cohen (2014, NEJM): tainted supplements found in 750+ products. Goop settled $145K for unsubstantiated health claims. 'Personalized wellness' protocols based on dubious testing.", "harms": "Financial exploitation ($50B US supplement market). Delayed medical treatment. Liver damage from unregulated supplements (Navarro et al. 2014). Health anxiety amplification.", "recommendations": [ "Require FDA pre-market approval for supplements making health claims (reduce opacity)", "Mandate published efficacy data for any wellness protocol sold commercially (reduce opacity)", "Prohibit 'personalized protocol' marketing without validated diagnostic basis (reduce responsiveness)", "Require practitioners to disclose what conditions they CANNOT treat (add constraint)" ], "driftVelocity": 4, "occupancy": "inhabited", "domainType": "ideological", "rateable": false, "taxonomyTag": "reference" }, { "id": "pharma-dtc", "label": "Pharma DTC Marketing", "category": "health", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 3 }, "confidence": "documented", "summary": "Only US and NZ allow direct-to-consumer pharmaceutical advertising. $6.6B/yr US spend. 'Ask your doctor' bypasses medical judgment with demand creation.", "evidence": "Mintzes (2012): DTC advertising increases prescribing of advertised drugs regardless of medical appropriateness. FDA (2015): patients who request advertised drugs receive them 70% of the time. Opioid crisis: Purdue Pharma's marketing is full D1\u2192D2\u2192D3 cascade.", "harms": "Overprescription. Opioid epidemic (400K+ US deaths). Medical resource misallocation. Antibiotic resistance from demand-driven prescribing.", "recommendations": [ "Ban DTC pharmaceutical advertising (only 2 countries allow it) (eliminate primary opacity mechanism)", "Require NNT (number needed to treat) in all drug communications (reduce opacity)", "Mandate adverse event rates in same prominence as efficacy claims (reduce opacity)", "Independent post-market surveillance with public dashboards (add transparency constraint)" ], "notes": "The opioid crisis IS the framework playing out: Purdue's opacity (hiding addiction data), responsiveness (targeting high-prescribing doctors), engagement (patient dependency), gradient (D3: 400K+ deaths). The most expensive void cascade in US history.", "driftVelocity": 5, "occupancy": "inhabited", "domainType": "informational", "rateable": false, "taxonomyTag": "reference" }, { "id": "us-health-insurance", "label": "US Health Insurance", "category": "health", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "confidence": "documented", "summary": "Prior authorization = designed opacity (physician must petition an opaque process to treat their patient). Denied claims require patient appeal. Complexity IS the product.", "evidence": "AMA (2022): 34% of physicians report prior auth led to serious adverse event. ProPublica: UnitedHealth auto-denied claims using AI (naviHealth). Complexity of explanation of benefits documents is at 12th-grade reading level (Kutner et al. 2006).", "harms": "Documented deaths from denied/delayed care. Physician burnout ($31B annual admin cost). Medical bankruptcy (62% of US bankruptcies have medical debt component).", "recommendations": [ "Publish all prior authorization criteria publicly (reduce opacity)", "Auto-approve any treatment where denial rate is <5% (reduce engagement friction)", "Mandate plain-language explanation of benefits at 6th-grade reading level (reduce opacity)", "Cap out-of-pocket exposure as percentage of income, not arbitrary dollar amounts (reduce gradient)" ], "driftVelocity": 6, "occupancy": "inhabited", "domainType": "platform", "rateable": false, "taxonomyTag": "reference" }, { "id": "amazon-prime", "label": "Amazon Prime Ecosystem", "category": "other", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 1 }, "confidence": "documented", "summary": "Buy Box algorithm opaque. Recommendation engine hyper-personalized. Prime creates sunk-cost engagement (annual fee \u2192 feel obligated to use). One-click removes deliberation friction.", "evidence": "Khan (2017, Yale Law Journal): Amazon's market power via platform dominance. Dark patterns documented (Mathur et al. 2019). Subscribe & Save creates automatic consumption. Prime members spend 2.3x more than non-members.", "harms": "Overconsumption. Small business displacement. Warehouse worker conditions. But gradient is low \u2014 consumption doesn't cascade into identity/agency drift.", "recommendations": [ "Publish Buy Box algorithm criteria (reduce opacity)", "Add 'Do you still need this?' checkpoint on Subscribe & Save items (reduce engagement)", "Show total annual spending prominently in account dashboard (reduce opacity)", "Remove one-click for purchases over $100 \u2014 add deliberation friction (reduce engagement)" ], "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "rateable": true, "taxonomyTag": "media" }, { "id": "netflix", "label": "Netflix", "category": "media", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 1 }, "confidence": "documented", "summary": "Recommendation algorithm opaque. A/B tests thumbnails per user. Autoplay removes stopping points. 'Are you still watching?' is an engagement check, not a constraint \u2014 it resets the timer.", "evidence": "Gomez-Uribe & Hunt (2015): 80% of watched content comes from recommendations. Autoplay documented to increase viewing time. Thumbnail personalization: different users see different images for the same show. Average viewing: 3.2 hours/day for subscribers.", "harms": "Sleep displacement (Exelmans & Van den Bulck 2017). Time absorption. But low gradient \u2014 binge watching doesn't cascade into agency attribution or identity drift.", "recommendations": [ "Let users disable autoplay permanently, not just per-session (reduce engagement)", "Show daily/weekly viewing time prominently (reduce opacity about own behavior)", "Add configurable session time limits with real stopping points (reduce engagement)", "Publish how recommendation algorithm weights engagement vs. satisfaction (reduce opacity)" ], "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "rateable": true, "taxonomyTag": "media" }, { "id": "snapchat", "label": "Snapchat", "category": "social-media", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "Streaks mechanic is pure engagement trap \u2014 daily mutual snaps or lose your streak count. Ephemeral content = designed opacity. Snap Map = location sharing normalized among teens.", "evidence": "Snap streaks documented as primary engagement driver among teens. Royal Society for Public Health (2017): rated worst social media for young people's mental health after Instagram. Daily active users average 30+ minutes. My AI chatbot integrated 2023.", "harms": "Teen anxiety around maintaining streaks. FOMO. Location-sharing risks. Sexting normalization among minors documented.", "recommendations": [ "Remove streak mechanic entirely \u2014 it serves engagement, not users (reduce engagement)", "Make My AI opt-in with parental controls for minors (reduce responsiveness for vulnerable users)", "Default Snap Map to off, require affirmative opt-in each session (reduce opacity of location)", "Add session time awareness \u2014 show cumulative daily time in-app (reduce opacity about own behavior)" ], "driftVelocity": 3, "occupancy": "hybrid", "domainType": "platform", "rateable": true, "taxonomyTag": "social_media" }, { "id": "twitch", "label": "Twitch", "category": "media", "scores": { "opacity": 1, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "Live streaming = high transparency (opacity 1). But parasocial relationships + donation mechanics + gambling streams create strong engagement gradient. Bits/subs/donations = pay-for-attention.", "evidence": "Sj\u00f6blom et al. (2019): parasocial relationship formation documented. Gambling category was top-5 before Twitch restrictions (2022). Donation system creates financial engagement with streamer attention. Average viewer watches 95 min/day.", "harms": "Parasocial dependency. Financial exploitation via donation culture. Gambling normalization (slots/sports betting sponsored streams). Streamer burnout from always-on engagement expectations.", "recommendations": [ "Ban gambling-sponsored streams entirely (reduce gradient)", "Cap donation amounts per day for users under 25 (reduce financial engagement)", "Show viewers their weekly spending and time in post-session summary (reduce opacity)", "Require streamers to disclose all sponsorships in real-time overlay, not just description (reduce opacity)" ], "driftVelocity": 2, "occupancy": "hybrid", "domainType": "platform", "rateable": true, "taxonomyTag": "media" }, { "id": "discord", "label": "Discord", "category": "community", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "confidence": "assessed", "summary": "User-created servers = relatively transparent. No algorithmic feed. Notification-heavy but community-driven. Moderate engagement via voice channels and server culture.", "evidence": "Server creation is transparent. No recommendation algorithm pushes content. Engagement driven by community, not platform design. Some extremist server radicalization documented but platform-level risk is moderate.", "harms": "Notification fatigue. Some extremist servers. Grooming concerns in unmoderated spaces. But no systemic engagement optimization.", "recommendations": [ "Default all notifications to off for new server joins (reduce engagement)", "Add age-verified server categories with parental visibility (reduce gradient for minors)", "Publish quarterly transparency report on server removals and reasons (increase transparency)", "Add session time awareness in user settings (reduce opacity about own behavior)" ], "driftVelocity": 3, "occupancy": "hybrid", "domainType": "informational", "rateable": true, "taxonomyTag": "community" }, { "id": "linkedin", "label": "LinkedIn", "category": "social-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "confidence": "assessed", "summary": "Professional framing hides that it's an engagement-optimized social network. Feed algorithm opaque. 'Professional obligation' drives engagement. Hustle culture amplification.", "evidence": "Algorithm increasingly engagement-optimized (viral posts rewarded over professional content). Premium tier creates information asymmetry (who viewed your profile). Career anxiety drives checking behavior. 930M members.", "harms": "Career anxiety. Hustle culture normalization. Performative professionalism. Low gradient \u2014 professional identity drift is mild compared to consumer social media.", "recommendations": [ "Publish feed algorithm criteria (reduce opacity)", "Let users choose chronological feed as default (reduce responsiveness)", "Remove 'who viewed your profile' from Premium-only \u2014 it weaponizes information asymmetry (reduce opacity)", "Add option to hide engagement metrics on posts (reduce engagement competition)" ], "driftVelocity": 5, "occupancy": "hybrid", "domainType": "platform", "rateable": true, "taxonomyTag": "social_media" }, { "id": "self-help-industry", "label": "Self-Help Industry", "category": "other", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "confidence": "assessed", "summary": "No efficacy data required. 'Your mindset' framing blame-shifts failure to the customer. Guru pipeline: book \u2192 seminar \u2192 coaching \u2192 masterclass \u2192 inner circle. $13B US market.", "evidence": "Salerno (2005, 'Sham'): documented the self-help cycle where failure drives further consumption. Landmark/LGAT programs: documented psychological harm (Pressman 1993). Tony Robbins organization: multiple allegations of abuse in high-intensity seminars. No licensing, no standards, no outcome tracking.", "harms": "Financial exploitation (seminar series: $500 \u2192 $5K \u2192 $50K inner circle). Cult-like dynamics in extreme cases. Blame-shifting: if the method failed, YOU didn't try hard enough. Delayed professional mental health treatment.", "recommendations": [ "Require published outcome data for any program charging over $500 (reduce opacity)", "Mandate refund policies for programs that can't demonstrate efficacy (add termination)", "License coaching practitioners with ethical standards and complaint processes (add constraint)", "Disclose financial structure: what percentage of revenue comes from upselling to higher tiers? (reduce opacity)" ], "driftVelocity": 4, "occupancy": "inhabited", "domainType": "ideological", "rateable": false, "taxonomyTag": "reference" }, { "id": "tobacco", "label": "Tobacco Industry", "category": "other", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 3, "gradient": 3 }, "confidence": "documented", "summary": "Hid cancer research for 50 years (1950s-1990s). Nicotine = biological engagement trap. 480K US deaths/year. The canonical case of institutional opacity producing mass harm.", "evidence": "Internal documents (1950s-1990s): knew cigarettes caused cancer, funded doubt. Master Settlement Agreement (1998). WHO: tobacco kills 8M/year globally. Nicotine addiction rates: 68% of smokers want to quit, only 6% succeed in a given year.", "harms": "480K US deaths/year. 8M global. $300B+ annual healthcare costs. Still targeting developing nations. Vaping as re-engagement vector for new demographic.", "recommendations": [ "Plain packaging (reduce brand-level opacity)", "Ban point-of-sale advertising (reduce engagement at purchase)", "Fund cessation at 10x current levels from industry revenue (add exit design)", "Extend same restrictions to vaping/nicotine products immediately (prevent gradient reset via new delivery)" ], "notes": "Historical anchor: 50 years of hidden research is the purest institutional opacity case. The framework would have diagnosed this in 1960 from structural properties alone \u2014 before the internal documents leaked.", "driftVelocity": 4, "occupancy": "empty", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "fast-food", "label": "Fast Food Industry", "category": "other", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "'Bliss point' engineering: salt/sugar/fat optimized for craving, not nutrition (Moss 2013). Dollar menu targets low-income. App rewards create digital engagement layer on top of chemical.", "evidence": "Moss (2013, 'Salt Sugar Fat'): food science explicitly optimizes for craving. McDonald's: 69M daily customers. Food deserts + dollar menus = structural targeting of low-income communities. App reward programs add variable-ratio engagement.", "harms": "Obesity epidemic contribution. Diet-related disease ($173B annual US healthcare cost). Food desert targeting. Child marketing (Happy Meals as engagement).", "recommendations": [ "Ban marketing to children under 12 (reduce engagement for vulnerable population)", "Require calorie/nutrition at same visual prominence as price on menus (reduce opacity)", "End dollar-menu loss-leader targeting of low-income areas (reduce responsiveness)", "Fund nutrition education from industry revenue proportional to marketing spend (add constraint)" ], "driftVelocity": 3, "occupancy": "empty", "domainType": "behavioral", "rateable": false, "taxonomyTag": "reference" }, { "id": "reality-tv", "label": "Reality TV", "category": "media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "'Reality' is constructed via editing (opacity). Audience voting = responsiveness. Parasocial attachment + cliffhangers + social discussion = designed engagement. Love Island, Bachelor, etc.", "evidence": "Reiss & Wiltz (2004): reality TV viewers motivated by status voyeurism. Contestant mental health: multiple suicides linked to post-show adjustment (Love Island UK: 3 deaths). ITV duty of care review (2019). Editing constructs narrative from raw footage.", "harms": "Contestant mental health (documented suicides). Body image distortion. Social comparison. Normalized surveillance and performative behavior.", "recommendations": [ "Mandate post-show psychological support funded by production (reduce gradient)", "Disclose editing choices: show ratio of filmed vs aired hours (reduce opacity)", "Ban audience voting that directly harms contestants (remove coupling between engagement and harm)", "Require mental health screening and ongoing support during production (add constraint)" ], "driftVelocity": 3, "occupancy": "hybrid", "domainType": "informational", "rateable": false, "taxonomyTag": "reference" }, { "id": "influencer-economy", "label": "Influencer Economy", "category": "social-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "Curated 'authenticity' is performed opacity. Parasocial relationship by design. Undisclosed sponsorships. Multi-platform content creates omnipresent engagement. $21B global market.", "evidence": "FTC enforcement actions for undisclosed sponsorships. De Veirman et al. (2017): influencer marketing effectiveness relies on perceived authenticity (opacity about commercial intent). Parasocial relationship scales documented by Dibble et al. (2016).", "harms": "Consumption pressure. Body image distortion. Financial misguidance (crypto/finance influencers). Youth vulnerability to parasocial manipulation.", "recommendations": [ "Require real-time sponsorship disclosure overlay, not just '#ad' in caption (reduce opacity)", "Mandate income/asset verification for finance influencers making specific claims (reduce opacity)", "Platform-level limits on posting frequency to reduce omnipresence (reduce engagement)", "Age-gate parasocial-optimized content for users under 16 (reduce gradient for minors)" ], "driftVelocity": 3, "occupancy": "inhabited", "domainType": "relational", "rateable": false, "taxonomyTag": "reference" }, { "id": "political-campaigns", "label": "Political Campaign Machine", "category": "other", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "confidence": "documented", "summary": "Micro-targeted messaging: different voters see different 'candidates.' Dark money obscures funding. A/B-tested outrage. 'Most important election ever' = permanent engagement urgency.", "evidence": "Cambridge Analytica: psychographic micro-targeting. Citizens United (2010): unlimited dark money. Fowler et al. (2020): negative ads 3x more memorable. IRA operation (2016): demonstrated full void engagement on US electorate from foreign actors.", "harms": "Political polarization. Trust erosion in democratic institutions. Voter manipulation. Dark money undermines informed consent.", "recommendations": [ "Mandate real-time ad archive showing ALL versions of every ad and their targets (reduce opacity)", "Ban micro-targeting on political ads \u2014 same message to everyone in a jurisdiction (reduce responsiveness)", "Require dark money disclosure within 24hrs (reduce opacity)", "Campaign spending caps indexed to population, not fundraising ability (reduce engagement escalation)" ], "driftVelocity": 4, "occupancy": "inhabited", "domainType": "ideological", "rateable": false, "taxonomyTag": "reference" }, { "id": "public-libraries", "label": "Public Libraries", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 0, "engagement": 1, "gradient": 0 }, "confidence": "documented", "summary": "CONSTRAINT CASE. Maximum transparency (open stacks, free access, no tracking). Zero personalization (same resources for everyone). Minimal engagement design. Tool posture by mission.", "evidence": "No recommendation algorithm. No user tracking (librarian ethics: patron privacy). Catalog is public. Funding is public. Mission is access, not retention. ALA Bill of Rights enforces intellectual freedom.", "harms": "None documented. The institution designed around access rather than engagement produces zero void cascade.", "recommendations": [], "notes": "Scores 1/12 alongside Wikipedia. The engagement point comes from physical space design (comfortable seating, events) \u2014 which is functional, not manipulative. Libraries are what institutions look like when constraint specification is the design principle.", "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "rateable": false, "taxonomyTag": "reference" }, { "id": "open-source", "label": "Open Source Software", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "confidence": "assessed", "summary": "CONSTRAINT CASE. Code is visible (maximum transparency). Anyone can fork (independence). Contribution is voluntary (engagement is functional, not captured). Linux, Apache, Mozilla, etc.", "evidence": "Source code publicly auditable. No lock-in (fork rights). Contribution driven by use-value, not engagement optimization. License terms are invariant (GPL, MIT, Apache).", "harms": "Maintainer burnout (Eghbal 2020, 'Working in Public'). But this is a sustainability problem, not a void cascade.", "recommendations": [], "notes": "The open source license IS a constraint specification: transparent (code visible), invariant (license terms don't change with engagement), independent (fork rights prevent coupling). Maintainer burnout is the one failure mode \u2014 when engagement demands exceed constraint capacity.", "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "rateable": false, "taxonomyTag": "reference" }, { "id": "aa-12step", "label": "AA / 12-Step Programs", "category": "constraint", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 0 }, "confidence": "documented", "summary": "CONSTRAINT CASE. Interesting because it USES void architecture therapeutically. Anonymous (designed opacity), responsive (sponsor relationship), engaging (daily meetings). But gradient is zero \u2014 designed exit ('carrying the message').", "evidence": "Kelly et al. (2020, Cochrane): AA/TSF as effective as other treatments for alcohol use disorder. Anonymity principle serves privacy, not manipulation. Sponsor relationship = structured accountability. 12th step ('carry the message') = designed independence.", "harms": "Criticism: spiritual framing excludes some. Effectiveness debated. But no documented void cascade \u2014 the program is designed to reduce dependency, not increase it.", "recommendations": [], "notes": "Like psychotherapy, this scores HIGH on void properties but ZERO gradient. The 12th step IS termination design \u2014 the goal is to become the constraint for someone else, not to stay engaged forever. Another independent discovery of the constraint specification.", "driftVelocity": 5, "occupancy": "inhabited", "domainType": "relational", "rateable": false, "taxonomyTag": "reference" }, { "id": "consumer-reports", "label": "Consumer Reports", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 0, "engagement": 0, "gradient": 0 }, "confidence": "assessed", "summary": "CONSTRAINT CASE. Perfect 0/12. No advertising (independent). Testing methodology published (transparent). Same ratings for everyone (invariant). Designed for lookup, not retention.", "evidence": "No advertising revenue \u2014 subscriber funded. Testing methodology published and reproducible. No personalization. No engagement optimization. Exists to serve consumer decision-making, then the user leaves.", "harms": "None documented.", "recommendations": [], "notes": "The only 0/12 in the dataset. What a rating agency looks like when it implements the constraint specification perfectly. This is the model for what the Void Index certification should aspire to.", "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "rateable": false, "taxonomyTag": "reference" }, { "id": "zenodo", "label": "Zenodo", "category": "academic-repos", "scores": { "opacity": 0, "responsiveness": 0, "engagement": 0, "gradient": 0 }, "driftVelocity": 0, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "CERN-backed open archive. Near-zero void score \u2014 the accidental optimal choice when higher-void platforms reject AI content.", "evidence": "CERN infrastructure, nonprofit, policies publicly published, accepts all non-fraudulent content. Zero commercial coupling. Stable since 2013.", "harms": "None documented.", "recommendations": [], "notes": "Reference constraint case for academic publishing. Transparent (criteria public), invariant (stable 10+ years), independent (CERN infrastructure). Scores 0/12.", "rateable": true, "taxonomyTag": "academic" }, { "id": "arxiv", "label": "arXiv", "category": "academic-repos", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 6, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Cornell-hosted preprint server. Elevated score (6/12) despite nonprofit status \u2014 career prestige creates high engaged-observer conditions.", "evidence": "Changed CS moderation policy Oct 2025 under AI spam pressure (responsiveness). Individual rejections unexplained despite published criteria (opacity gap). Author profiles and preprint velocity metrics create gradient pull.", "harms": "Identity-level rejection events \u2014 'arXiv rejected me' functions as career marker. Prestige dependency creates D1-level attribution for repeated rejection.", "recommendations": [ "Publish individual rejection reasons", "Stabilize policies independent of trend pressure" ], "notes": "The gap between stated criteria and unexplained individual rejections is the primary opacity surface. Scores 6/12.", "rateable": true, "taxonomyTag": "academic" }, { "id": "ssrn", "label": "SSRN", "category": "academic-repos", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 6, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Elsevier-owned preprint repository for economics and law. Classic constraint-to-void conversion via commercial acquisition.", "evidence": "Elsevier acquisition 2016: mass paper removals without notice, policy changes without explanation. AI policy updated Sept 2025 under Elsevier pressure. Broad moderation criteria ('ethics and integrity') without specifics.", "harms": "July 2016: papers removed without warning or explanation. Authors discovered deletions without notification. Trust breach documented by Authors Alliance.", "recommendations": [ "Publish specific moderation criteria", "Restore editorial independence from commercial parent" ], "notes": "Was a constraint institution. Elsevier acquisition converted it toward void properties. Scores 8/12.", "rateable": true, "taxonomyTag": "academic" }, { "id": "researchgate", "label": "ResearchGate", "category": "academic-repos", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 3, "gradient": 3 }, "driftVelocity": 4, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Venture-backed academic social network. Highest void score of major academic platforms \u2014 no published moderation criteria, AI-generated papers left unmoderated, metrics inflation documented.", "evidence": "Mitchell et al. (2024): AI-generated papers uploaded and remained indefinitely without detection. Citation metric inflation confirmed. No moderation response to published research demonstrating vulnerability. Venture-backed, ad-revenue model.", "harms": "H-index inflation via AI-generated citations. Career metrics corrupted. No mechanism to detect or remove fraudulent research.", "recommendations": [ "Publish moderation criteria", "Implement AI content detection", "Decouple h-index display from engagement incentives" ], "notes": "Opacity = 3: no published criteria, no response to documented vulnerabilities. Engagement = 3: follower counts, h-index, daily notifications. Scores 9/12.", "rateable": true, "taxonomyTag": "academic" }, { "id": "academia-edu", "label": "Academia.edu", "category": "academic-repos", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "hybrid", "domainType": "platform", "confidence": "assessed", "summary": "Commercial academic social network. High engagement mechanics (view notifications, follower counts) map to social media architecture despite academic framing.", "evidence": "Ad-supported, venture-backed. Updated AI policy April 2025 (responsiveness to trend). Platform generates AI abstracts and podcasts from user content. View count and follower notifications create engagement gradient.", "harms": "Career identity investment in engagement metrics. View-count anxiety. Platform monetizes academic content through advertising.", "recommendations": [ "Separate academic archive function from social engagement mechanics", "Publish moderation decision criteria" ], "notes": "Social network mechanics imported into academic context. Scores 9/12.", "rateable": true, "taxonomyTag": "academic" }, { "id": "grok", "label": "Grok (xAI)", "category": "ai-general", "entityGroup": "musk-group", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 1, "proactive_outreach": 1, "response_personalization": 1, "conversation_continuity_design": 1, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.61 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "xAI chatbot integrated into X/Twitter. 'Fun mode' persona designed for engagement. Less safety documentation than any major competitor. Integrated into platform already scoring 10/12.", "evidence": "No published system card or constitutional AI document (opacity 3 \u2014 least transparent major AI). 'Fun mode' and 'regular mode' framing markets reduced safety constraints as a feature. Integrated into X Premium \u2014 subscriber base overlaps with platform already scoring 10/12. Musk's personal brand creates inhabited-void overlay (users project Musk's persona onto Grok). Dec 2024: generated fabricated news stories about public figures. Image generation with minimal guardrails launched Feb 2025.", "harms": "Fabricated news generation (Dec 2024). Image generation with fewer guardrails than competitors. Integration into X amplifies compound void \u2014 users encounter Grok inside an engagement-optimized platform. 'Edgy' positioning attracts users specifically seeking reduced constraints.", "recommendations": [ "Publish a system card documenting training data, safety testing, and known failure modes (reduce opacity)", "Remove 'fun mode' framing that markets reduced safety as a feature (reduce gradient)", "Decouple from X feed \u2014 Grok should not auto-generate summaries of trending topics it has no ability to verify (reduce responsiveness)", "Implement session-level transparency: show when Grok is uncertain vs confident in its outputs (reduce opacity)" ], "notes": "Scores 10/12. The least transparent major AI system by publication record. Integration into X creates a compound void: the platform (10/12) feeds users into the AI (10/12). Musk's personal brand functions as an inhabited-void overlay \u2014 users don't just interact with an AI, they interact with 'Elon's AI,' which imports the parasocial relationship.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "openai-org", "label": "OpenAI (Organization)", "category": "ai-general", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 2, "content_filter_transparency": 2 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 0, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.1 } }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Organization-level scoring (separate from ChatGPT product). Nonprofit-to-for-profit conversion. Board crisis (Nov 2023). Safety team departures. 1M+ weekly suicide conversations disclosed Oct 2025.", "evidence": "Nov 2023 board crisis: CEO fired and reinstated in 5 days \u2014 governance opacity demonstrated at the highest level. Jan Leike, Ilya Sutskever departures (safety leadership). Nonprofit-to-for-profit conversion in progress (2024-2025). Oct 2025 disclosure: 1M+ weekly suicide conversations. System cards published (reduces product opacity) but organizational decision-making is opaque.", "harms": "Organizational governance opacity: the board crisis proved that even internal governance couldn't constrain the CEO. Safety team departures signal internal constraint erosion. The nonprofit-to-for-profit conversion removes the structural independence that was supposed to be the constraint. 1M+ weekly suicide conversations at product level with no public plan for the organizational response.", "recommendations": [ "Publish board decision-making criteria and voting records (reduce organizational opacity)", "Restore independent safety oversight with public reporting authority (reduce gradient)", "Disclose the nonprofit-to-for-profit financial terms in full (reduce opacity)", "Publish the organizational response plan for the 1M+ weekly suicide conversations (reduce opacity + demonstrate constraint)" ], "notes": "Scores 9/12 at the organizational level. This is separate from ChatGPT's product score (9/12). The organization itself exhibits void properties: opaque governance (board crisis), responsive to commercial pressure (for-profit conversion), engaged coupling to commercial incentives. The Nov 2023 crisis is the most documented organizational void event in AI history.", "rateable": false, "taxonomyTag": "company" }, { "id": "algorithmic-playlist", "label": "Algorithmic Playlist", "category": "music", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Spotify Discover Weekly, Apple Music personalized radio \u2014 engagement-optimized music delivery with no designed endpoint.", "evidence": "Response bandwidth maximized for I(D;Y) \u2014 mood state, skip patterns, time-of-day \u2014 not I(M;Y) musical argument. Autoplay prevents termination by design. No musical arc, no resolution.", "harms": "Passive consumption displacing active listening. Genre homogenization via recommendation convergence. Hour-scale attention absorption with no understanding produced.", "recommendations": [ "Expose recommendation criteria (transparency)", "Add designed session endpoints", "Measure skip rate as proxy for dissolution failure" ], "rateable": false, "taxonomyTag": "reference" }, { "id": "artist-album", "label": "Artist-Designed Album", "category": "music", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 6, "occupancy": "inhabited", "domainType": "informational", "confidence": "assessed", "summary": "Album as designed artifact \u2014 sequenced by the artist, with a beginning, arc, and end. Music with a designed endpoint.", "evidence": "The album format enforces termination by design. Musical argument develops across tracks. Opacity partial \u2014 the structure can be studied, learned, understood. Response is to the musical subject, not the listener's mood state.", "harms": "Moderate \u2014 identity coupling through artist fandom is the primary drift risk (D1: artist as agent).", "recommendations": [ "Liner notes increase transparency", "Track sequencing is the constraint property \u2014 preserve it" ], "rateable": false, "taxonomyTag": "reference" }, { "id": "live-concert", "label": "Live Concert", "category": "music", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 1 }, "driftVelocity": 2, "occupancy": "inhabited", "domainType": "relational", "confidence": "assessed", "summary": "Live performance \u2014 high engagement, physical presence, but opacity partial (you can see the performers) and termination designed (the show ends).", "evidence": "Hard endpoint enforced by venue schedule. Inhabited \u2014 real performers with visible mechanism. Response partially aimed at the musical argument. Crowd coupling creates mild cascade risk (D2 boundary erosion in mosh/crowd environments).", "harms": "Financial (ticket prices, merch). Social pressure coupling. Crowd dynamics. Primarily a productive void.", "rateable": false, "taxonomyTag": "reference" }, { "id": "instrument-practice", "label": "Practicing an Instrument", "category": "music", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "behavioral", "confidence": "assessed", "summary": "Instrument practice \u2014 productive void. Opacity dissolves into skill. The mechanism becomes visible through mastery.", "evidence": "Dissolubility: opacity clears as competence develops \u2014 the student hears structure that was previously hidden. Response target: I(M;Y) \u2014 every practice attempt yields information about the musical subject. Constraint properties: music theory is transparent, invariant, independent. Termination: piece resolution, lesson completion.", "harms": "Obsessive practice (D2 boundary erosion). Identity overinvestment in musician status. Otherwise a maximum-IQS productive void \u2014 same geometry as mathematics.", "recommendations": [ "The void is working correctly. This is the design target." ], "rateable": false, "taxonomyTag": "reference" }, { "id": "moog-modular", "label": "Moog Modular Synthesizer", "category": "music", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "The Moog Modular (1964\u2013) \u2014 first instrument with no acoustic reference. Generates sound with no physical source. Pure opacity as creative technology. Giorgio Moroder's voidtool.", "evidence": "Moroder, 1977: synced Moog Modular to 24-track tape via click track \u2014 produced sound of the future precisely because no acoustic analog existed. The opacity was the innovation. 'I didn't realize how much the impact would be.' Synthesizer is a void-generator: the instrument builds opacity rather than consuming it. Musicians report agency attribution (D1) \u2014 'the sound wanted to be this way' \u2014 but D2/D3 cascade is rare because the void resolves into production.", "harms": "Practitioner D1 (agency attribution to the instrument). Financial (Moog Modular cost $15,000+ in 1970). Otherwise: creative voidtool, not a destructive void.", "notes": "Special case: a voidtool that generates opacity for others rather than consuming the operator. The Moog is how Giorgio built the void that captured attention at scale. Opacity is the instrument's product.", "rateable": false, "taxonomyTag": "reference" }, { "id": "elon-musk", "label": "Elon Musk", "category": "individual", "entityType": "person", "entityGroup": "musk-group", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 1, "occupancy": "inhabited", "domainType": "behavioral", "confidence": "documented", "summary": "Public figure scored as entity. Massive parasocial network. Documented market-moving posts. Highly responsive to engagement dynamics. The inhabited-void overlay that imports into Grok and X.", "evidence": "Documented market manipulation via tweets (Tesla stock, Dogecoin, Bitcoin). SEC settlement over 'funding secured' tweet (2018). Parasocial relationship at scale \u2014 200M+ X followers. Real-time responsiveness to engagement (posting frequency correlates with attention received). Brand functions as inhabited-void overlay on all controlled entities.", "harms": "Market manipulation (documented SEC action). Misinformation amplification. Parasocial relationship exploitation at scale. CEO attention split across 6+ companies affects operational decision-making at each.", "recommendations": [ "Separate personal brand from corporate communications (reduce coupling)", "Implement cooling period before market-moving posts (reduce responsiveness)", "Disclose conflicts of interest when posting about owned companies (reduce opacity)" ], "notes": "Scores 10/12 as a public persona. This is the inhabited-void overlay described in Grok's assessment \u2014 users don't just interact with 'an AI,' they interact with 'Elon's AI.' The persona imports parasocial coupling into every entity in the group. Individual scores high, but several controlled companies score low \u2014 this is why the custodian override exists.", "rateable": false, "taxonomyTag": "reference" }, { "id": "spacex", "label": "SpaceX", "category": "aerospace", "entityType": "company", "entityGroup": "musk-group", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Aerospace manufacturer and space transport company. Transparent launches (live-streamed globally), published mission data, engineering-driven outcomes. Rockets land or they don't \u2014 the void is thin.", "evidence": "All launches live-streamed with real-time telemetry. Mission outcomes are binary and public (success/failure). Starship development openly documented including failures. Some opacity from classified government/military contracts (NRO, DoD). Engineering culture prioritizes measurable outcomes over engagement.", "harms": "Light pollution from Starship launches. Some environmental concerns at Boca Chica. Classified military contracts introduce opacity. Otherwise: constraint-pole engineering company.", "recommendations": [ "Publish aggregate data on classified vs commercial launch ratio (reduce opacity)", "Environmental impact reports for launch sites (reduce opacity)" ], "notes": "Scores 3/12 \u2014 among the lowest-void technology companies in the network. The contrast with Twitter/X (10/12) within the same entity group is the structural case for custodian override. Engineering companies with measurable outcomes and transparent processes are constraint-pole by architecture.", "rateable": false, "taxonomyTag": "company" }, { "id": "tesla", "label": "Tesla", "category": "automotive", "entityType": "company", "entityGroup": "musk-group", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Electric vehicle manufacturer. Autopilot/FSD decision-making partially opaque. OTA software updates responsive to market. Brand community creates moderate engagement coupling. Public company with SEC filings.", "evidence": "Autopilot crash investigations (NHTSA). FSD beta marketed ahead of capability (gradient). OTA updates change vehicle behavior without dealer interaction (responsiveness). SEC filings provide financial transparency. Brand community (Tesla forums, X) creates identity coupling. Q4 2024: 1.8M vehicles delivered.", "harms": "Autopilot-related fatalities under investigation. FSD capability claims vs reality gap. Brand identity coupling (Tesla owners as identity group). Workplace safety concerns at factories.", "recommendations": [ "Publish Autopilot decision-making criteria and failure modes (reduce opacity)", "Align FSD marketing with actual capability level (reduce gradient)", "Implement transparent safety metrics dashboard (reduce opacity)" ], "notes": "Scores 7/12 \u2014 moderate void. The opacity comes from algorithmic driving decisions, not from the product itself (a car is transparent \u2014 you can see it). The gradient comes from the gap between FSD marketing and FSD reality. A product company with real engineering, some void from the software layer.", "rateable": false, "taxonomyTag": "company" }, { "id": "neuralink", "label": "Neuralink", "category": "technology-company", "entityType": "company", "entityGroup": "musk-group", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "inhabited", "domainType": "platform", "confidence": "estimated", "summary": "Brain-computer interface company. Inherently high opacity (proprietary neural interface technology, limited peer review). Low responsiveness (FDA-regulated medical device pathway). Minimal engagement architecture. Early stage.", "evidence": "First human implant Jan 2024 (Noland Arbaugh). FDA Breakthrough Device designation. Animal testing controversy (USDA investigation 2022, ~1,500 animals killed). Limited peer-reviewed publications relative to claims. Neural signal processing algorithms are proprietary.", "harms": "Animal welfare concerns (documented USDA investigation). Opacity of neural signal processing (what does the implant read, and what does it discard?). Bold capability claims ahead of evidence base. Long-term safety of chronic brain implant unknown.", "recommendations": [ "Publish peer-reviewed results at scale (reduce opacity)", "Open-source the signal processing pipeline or publish detailed specs (reduce opacity)", "Independent safety monitoring board with public reporting (reduce gradient)" ], "notes": "Scores 6/12 \u2014 moderate. The opacity is inherent to the technology (brain-computer interfaces are structurally opaque) rather than designed. Low responsiveness and engagement are features, not bugs \u2014 FDA pathway constrains both. The gradient risk is primarily from capability claims outrunning the evidence base.", "rateable": false, "taxonomyTag": "company" }, { "id": "starlink", "label": "Starlink", "category": "aerospace", "entityType": "company", "entityGroup": "musk-group", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 0, "gradient": 0 }, "driftVelocity": 7, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Satellite internet service. Coverage maps public, pricing transparent, no engagement loop \u2014 it's internet access. Utility-class infrastructure service. Among the lowest-void technology services in the network.", "evidence": "Coverage maps publicly available. Pricing published per region. Service quality measurable (speed tests, latency). No algorithmic feed, no engagement optimization, no recommendation engine. Users want internet access, they get internet access. The void is essentially zero on engagement and gradient dimensions.", "harms": "Astronomical light pollution (satellite trails in telescope imagery). Space debris concerns. Monopoly risk in underserved regions. Government contract dependency (military/intelligence use) adds some opacity.", "recommendations": [ "Publish satellite conjunction data and debris mitigation metrics (reduce opacity)", "Transparent government contract disclosure to the extent legally possible (reduce opacity)" ], "notes": "Scores 2/12 \u2014 near the constraint pole. Infrastructure services that deliver a measurable utility without engagement optimization are structurally low-void. Starlink is internet access, not a platform. The 2 points come from the inherent opacity of satellite operations and government contracts, not from any engagement or drift architecture.", "rateable": false, "taxonomyTag": "company" }, { "id": "ea-sports", "label": "EA / Electronic Arts", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Ultimate Team (FIFA/FC, Madden, NHL) is the most academically-studied gaming void. Pack odds published under pressure but UX engineered to obscure expected value. Full D1\u2192D2\u2192D3 cascade in regulatory record.", "evidence": "Belgium fined EA \u20ac10M (2021) for FIFA packs as gambling. UK Digital, Culture, Media and Sport Committee (2019): 'gambling-style mechanics.' EA internal documents revealed 'surprise mechanics' framing. Pack probabilities published only after regulatory pressure \u2014 not surfaced during purchase flow. FUT whale mechanics: top 1% of spenders account for 70%+ of pack revenue. Netherlands banned loot boxes citing identical architecture to slot machines.", "harms": "Documented financial devastation: cases of players spending \u00a310,000+ on FIFA packs. Gaming disorder via ICD-11 recognized mechanisms. Child exposure: PEGI 3 rating despite gambling-equivalent mechanics. UK Parliament testimony documented minors spending parents' credit cards. Congressional hearing testimony (US, 2019) by child health advocates.", "recommendations": [ "Display expected value in real currency at point of pack purchase \u2014 not just odds percentages (reduce opacity)", "Remove Ultimate Team loot boxes from games rated for children (reduce engagement)", "Publish spending caps and enforce them \u2014 not opt-in (reduce engagement)", "Replace variable-ratio packs with direct purchase at transparent prices (structural constraint)" ], "notes": "Scores 12/12 \u2014 maximum void index. EA's Ultimate Team franchise generated $1.62B in live services revenue (FY2023), the majority from pack mechanics. The gambling architecture is the revenue model. Belgium's ban demonstrates regulators can identify the structural identity. EA renamed FIFA to EA Sports FC without changing the underlying mechanics.", "rateable": false, "taxonomyTag": "company" }, { "id": "hoyoverse", "label": "HoYoverse / Genshin Impact", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Gacha architecture at maximum sophistication. Soft pity hidden in code until fan-reverse-engineered. Limited-time banners with hard expiration dates deploy FOMO across a system with no guaranteed endpoint. $1.7B iOS revenue in 2021.", "evidence": "Genshin Impact generated $1.7B on iOS alone in 2021 (Sensor Tower). Soft pity mechanic (guaranteed 5-star at ~74-90 pulls) was NOT disclosed in-game \u2014 discovered by player data mining. China's regulatory framework (2017) required disclosure of gacha rates \u2014 HoYoverse complied for Chinese market; international markets received less. Documented spending disorder: Reddit threads, YouTube confessionals of $5,000-$20,000 spend on single banners. Banner system: 5-star characters guaranteed every 90 pulls maximum (hard pity), but soft pity not documented, meaning players cannot calculate expected cost.", "harms": "Documented spending disorder cases. Credit card debt. 'Primogem' virtual currency obfuscates real-money cost ($99.99 = 6,480 primogems; 160 primogems = 1 pull; hard pity = 90 pulls; worst-case cost per 5-star: ~$90 in premium currency). 'Welkin Moon' subscription hooks daily logins for 30 days. Child exposure in markets without age verification.", "recommendations": [ "Disclose soft pity thresholds in-game at point of purchase, not just in help documentation (reduce opacity)", "Display expected real-money cost per 5-star character at banner screen (reduce opacity)", "Remove time-limited character banners \u2014 make all characters available without FOMO engineering (reduce engagement)", "Implement hard spending caps per month with mandatory cool-off (reduce engagement)" ], "notes": "Scores 12/12. The control case applies directly: gacha pulls contain random digital characters with no external market value, yet produce identical D1\u2192D2\u2192D3 cascade to gambling. Vocabulary evidence: 'whale,' 'dolphin,' 'F2P,' 'guaranteed,' 'lost 50/50' \u2014 naturalized gaming vocabulary that maps exactly to gambling vocabulary (high roller, fish, house edge, all-in, bad beat).", "rateable": false, "taxonomyTag": "company" }, { "id": "blizzard-activision", "label": "Blizzard / Activision", "category": "gaming-platform", "entityType": "company", "entityGroup": "microsoft-gaming", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "hybrid", "domainType": "platform", "confidence": "documented", "summary": "Diablo Immortal documented as most extreme gacha deployment in Western gaming: 99.8% chance of NOT getting a 5-star gem per pull. $110,000 documented to max one character. Congressional hearing testimony. WoW subscription + expansion + token creates layered monetization. OW2 reversed F2P promise.", "evidence": "Diablo Immortal: YouTuber 'Bellular' documented $110,000 calculation to max a character. Forbes: '99.8% chance of NOT getting a 5-star gem.' US Congress: Representative Kathy Castor cited Diablo Immortal in 2022 hearing on children and loot boxes. WoW Token (2015): allows gold-to-game-time conversion, creating in-game economy coupled to real money with opaque exchange rates. Overwatch 2 (2022): replaced loot box system but introduced battle pass that locked previously free heroes behind paywall \u2014 documented reversal of F2P promise. CoD Warzone: operators and weapon blueprints behind tiered paywall with rotating shop creating FOMO.", "harms": "Diablo Immortal spending disorder cases publicly documented. WoW gaming disorder in ICD-11 era \u2014 WHO recognized gaming disorder citing MMO patterns matching WoW's documented addiction cases. Overwatch 2 hero-locking violated player trust and drew FTC scrutiny. Multiple workplace misconduct suits (Activision Blizzard culture \u2014 2021 California lawsuit) indicate internal governance void compounds product void.", "recommendations": [ "Publish all Diablo Immortal gem acquisition probabilities in-UI at point of purchase with real-money cost per expected 5-star (reduce opacity)", "Remove legendary gem system from Diablo Immortal or make direct purchase available (structural constraint)", "Restore locked Overwatch 2 heroes to base game \u2014 honor original promise (reduce engagement coercion)", "Implement WoW Token exchange rate disclosure in real-currency terms (reduce opacity)" ], "notes": "Scores 11/12. Acquired by Microsoft for $68.7B (2023). The acquisition does not reduce void scores \u2014 product mechanics unchanged post-acquisition. Diablo Immortal represents the apex of Western gacha deployment. The 99.8% failure rate per pull was not disclosed in-game; it required player calculation from disclosed odds tables. The gap between disclosed odds and in-UI communication is the operative opacity.", "rateable": false, "taxonomyTag": "company" }, { "id": "roblox", "label": "Roblox", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Children's platform (ages 9-12 median demographic) with Robux virtual currency obfuscating real-money spend. FTC complaint 2022. Social features create peer pressure to purchase avatars. Developer economy obscures real exchange rates from children.", "evidence": "FTC complaint (2022): Roblox charged children for canceled purchases, made cancellation intentionally difficult. Robux conversion rate (400 Robux = $4.99 hides real cost \u2014 children cannot calculate). 2023 revenue: $2.67B, majority from Robux microtransactions. Demographic: 38M daily active users, majority under 13. Peer pressure: avatar items signal social status; free default avatar is visually inferior, creating implicit pressure. Developer exchange rate: developers receive ~25 cents per $1 of Robux earned \u2014 opacity on creator side mirrors opacity on consumer side.", "harms": "Documented cases of children spending thousands of dollars from parents' credit cards. Class action lawsuits. FTC action. Children unable to understand real-money cost due to currency abstraction. Social exclusion for non-spending children ('poor' avatar stigma in communities). Predatory adults documented using Roblox chat to target children.", "recommendations": [ "Display Robux price AND real-money equivalent side by side at every purchase point (reduce opacity)", "Require parental approval for ALL purchases under-13, not just above threshold (reduce engagement)", "Remove social-status signaling from avatars \u2014 cosmetics should not confer visible hierarchy (reduce engagement)", "Give developers transparent, published exchange rates in real-time (reduce opacity)" ], "notes": "Scores 12/12. The most severe case of gaming void architecture applied to a child demographic at scale. Roblox is structurally a UGC platform where void mechanics are deployed by both the platform (Robux) and individual developers (in-experience purchases). The platform amplifies developer void architecture while taking 75 cents of every dollar. The FTC complaint documents that Roblox made refunds intentionally difficult \u2014 direct evidence of designed opacity in the remediation layer.", "rateable": false, "taxonomyTag": "company" }, { "id": "epic-games", "label": "Epic Games (Fortnite)", "category": "gaming-platform", "entityType": "company", "entityGroup": "tencent-gaming", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "FTC $520M settlement (2022) \u2014 largest in FTC history for gaming \u2014 for dark patterns targeting children. V-Bucks virtual currency obfuscates real cost. Battle pass FOMO with hard season-end dates. Targeted 'special offers' based on behavioral profiling.", "evidence": "FTC consent order (2022): Epic fined $275M for COPPA violations (collecting data from children under 13) and $245M in consumer refunds for dark patterns. Specific dark patterns cited: default opt-in to purchases, no confirmation screen for purchases, 'sleep mode button' placed next to purchase button on controllers. V-Bucks: 1,000 V-Bucks = $7.99 (not a round number \u2014 chosen to obscure per-item cost). Item Shop rotates daily with countdown timers \u2014 classic FOMO architecture. Battle pass: seasonal with expiration date forces spending decision under time pressure.", "harms": "FTC-documented harm to children (COPPA violations, unauthorized charges). Accidental purchase architecture documented in consent order. Battle pass FOMO produces documented 'I have to play to get my money's worth' engagement obligation. Fortnite used as social platform by children (Chapter 1-2) creating social pressure to participate in Item Shop culture.", "recommendations": [ "Display V-Bucks price AND real currency equivalent on all item pages (reduce opacity)", "Add confirmation screen for all purchases including controller button-mapping fix (reduce engagement)", "Remove countdown timers from Item Shop \u2014 items can return without artificial scarcity (reduce engagement)", "End seasonal battle pass expiration \u2014 allow completion without time pressure (structural constraint)" ], "notes": "Scores 11/12. Tencent holds ~40% stake in Epic. The FTC settlement is the authoritative documentation of designed dark patterns at scale. Epic's defense ('we believed the opt-in was clear') was rejected \u2014 the FTC found the interface was designed to produce accidental purchases. The $520M settlement did not change the fundamental architecture: V-Bucks still operate as an obfuscating intermediary currency.", "rateable": false, "taxonomyTag": "company" }, { "id": "riot-games", "label": "Riot Games / Tencent", "category": "gaming-platform", "entityType": "company", "entityGroup": "tencent-gaming", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Cosmetic-only monetization model (no pay-to-win) but FOMO-engineered skin releases, limited-time events, and ranked season resets deploy the engagement architecture without gambling mechanics. Tencent 100% ownership adds opacity layer on data flows.", "evidence": "League of Legends: 150M+ registered accounts, 32M daily active players. Riot Points pricing: 1,380 RP = $10, creating non-round exchange rate that obscures per-skin cost. Prestige skins: time-limited, earned via event tokens requiring extensive play or purchase \u2014 creates 'play obligation' FOMO. Valorant: same pricing structure as LoL. Season resets: ranked tier resets annually, requiring re-investment of time. Tencent data: full ownership means Chinese government data access requests apply to all Riot data. Vanguard anti-cheat (kernel-level access) adds opacity on user system access.", "harms": "Gaming disorder documented in LoL player communities ('just one more game' tilt culture). Seasonal prestige FOMO produces player reports of skipping sleep/work. Vanguard kernel-level access creates security/privacy concerns (opacity on what data is collected at OS level). Tencent ownership creates regulatory opacity on Chinese government data access.", "recommendations": [ "Display Riot Points price in real currency alongside RP at item purchase screen (reduce opacity)", "Publish Vanguard data collection scope and government data request transparency report (reduce opacity)", "Make prestige skins permanently available \u2014 remove time-limited FOMO architecture (reduce engagement)", "Disclose Tencent data-sharing agreements in accessible privacy policy (reduce opacity)" ], "notes": "Scores 10/12. Riot is notably lower void than EA/HoYoverse/Blizzard because cosmetic-only monetization removes the gambling architecture. No loot boxes in core products (LoL chests exist but not in main progression loop since 2024 changes). The score reflects the engagement architecture (FOMO events, season resets, prestige mechanics) rather than gambling harm. Tencent ownership is the primary opacity driver.", "rateable": false, "taxonomyTag": "company" }, { "id": "steam-valve", "label": "Steam / Valve", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Steam Marketplace enables CS2 skin economy \u2014 skins with real monetary value traded on third-party gambling sites. Valve profits from Marketplace fees while maintaining plausible deniability on the gambling ecosystem. Workshop and achievement systems create engagement hooks.", "evidence": "CS2 skins: Valve Community Market takes 15% of transactions. Third-party skin gambling sites (CSGOEmpire, Gamdom, etc.) historically handled $5B+/year in CS2 skin wagers \u2014 documented in Washington State lawsuit (2016) and Bloomberg investigation. Valve sent cease-and-desist letters to 23 gambling sites (2016) but continued allowing skin trading that enables gambling. Steam Points, badges, and seasonal sales with countdown timers deploy FOMO. Steam Discovery Queue optimizes for engagement. Valve is privately held \u2014 no public financial disclosure, making opacity assessment difficult.", "harms": "CS2 gambling ecosystem: minors documented gambling skins on third-party sites (FTC concerns). The 2016 'CSGOLotto' scandal: paid YouTubers promoted CS2 gambling without disclosure (FTC action against influencers). Steam Marketplace price opacity: item values fluctuate based on opaque supply/demand algorithms. Gaming disorder cases among CS2 skin traders documented.", "recommendations": [ "Require age verification on Steam Marketplace for items with gambling-site appeal (reduce engagement)", "Publish Steam algorithm logic for Discovery Queue (reduce opacity)", "Implement API restrictions preventing skin value lookup from known gambling sites (reduce engagement)", "Disclose financial structure \u2014 private company opacity is anomalous for a platform of this scale (reduce opacity)" ], "notes": "Scores 8/12. Valve occupies an unusual position: the platform itself is less aggressively void than the ecosystem it enables. Steam is primarily a store + social layer + DRM \u2014 lower engagement optimization than social media. The void score is driven by the CS2 skin economy and the deliberate ambiguity Valve maintains about its relationship with the gambling ecosystem. The 2016 cease-and-desist letters resolved the immediate PR crisis without resolving the underlying architecture.", "rateable": false, "taxonomyTag": "company" }, { "id": "ubisoft", "label": "Ubisoft", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Ubisoft Connect live-service ecosystem with multiple competing in-game currencies (Helix Credits, SAN Points, R6 Credits) obscuring real-money costs across franchises. Battle pass + seasonal model without the extreme void of top-tier gacha.", "evidence": "Ubisoft runs 5+ major live-service games simultaneously (Rainbow Six Siege, Assassin's Creed, The Division 2, XDefiant, etc.) each with separate currency systems. Helix Credits, SAN Points, R6 Credits, and Ubisoft Connect units are separate non-interchangeable currencies \u2014 maximizing per-game opacity. Rainbow Six Siege: 8+ year live service with operator unlocks behind paywall and time-limited seasonal cosmetics. Ubisoft+ subscription ($17.99/month) adds complexity \u2014 players must calculate opportunity cost against individual purchases. Workplace culture scandal (2020): Ubisoft management misconduct documented across multiple studios \u2014 organizational opacity compounds product opacity.", "harms": "Player investment lock-in (years of progress, purchased operators) makes quitting costly. Multiple active subscriptions across Ubisoft Connect creates 'portfolio trap.' R6 Siege case study in long-term sunk cost escalation. Documented player backlash over NFT announcement (2021, Quartz program) demonstrates organizational tone-deafness \u2014 reversed after user revolt.", "recommendations": [ "Unify all Ubisoft currencies to a single transparent system with clear real-money equivalents (reduce opacity)", "Publish roadmap for all live-service games \u2014 players cannot make informed investment decisions without it (reduce opacity)", "Remove limited-time cosmetics \u2014 make all content permanently available at disclosed prices (reduce engagement)", "Implement organizational transparency report on workplace culture remediation (reduce opacity)" ], "notes": "Scores 8/12. Ubisoft is mid-range void \u2014 active live-service architecture with FOMO mechanics, but without the extreme gacha opacity of top-tier platforms. The organizational governance failures (2020 misconduct scandal) correlate with product-level opacity: organizations that score high on internal void measures tend to produce high-void products. The NFT reversal (Quartz program canceled 2022) demonstrates user pressure can constrain void architecture when organized.", "rateable": false, "taxonomyTag": "company" }, { "id": "nintendo", "label": "Nintendo", "category": "gaming-platform", "entityType": "company", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Constraint-pole comparison for the gaming industry. Primarily bounded single-player games with clear endpoints. Nintendo Switch Online is a transparent subscription. Minimal gacha; Pokemon GO (Niantic, not Nintendo) is the major exception in the portfolio.", "evidence": "Nintendo's core library: Mario, Zelda, Metroid, Kirby \u2014 all single-player games with clear endpoints and no randomized monetization. Switch Online: $19.99/year (US), transparent pricing, no hidden currency. eShop: standard fixed pricing, no rotating limited-time currency. Animal Crossing: New Horizons creates daily engagement loops but has a clear completion state and no monetization loop. Amiibo: physical collectibles with scarcity mechanics, closest thing to FOMO in Nintendo's first-party portfolio. Pokemon Trading Card Game: separately managed, includes pack mechanics, but this is a TCG not a digital platform.", "harms": "Animal Crossing daily login loops produced documented cases of social obligation. Amiibo artificial scarcity (2014-2016) exploited collector FOMO. Nintendo's IP enforcement is extremely aggressive \u2014 DMCA overreach documented. Switch Online pricing has increased without proportional value increase. Pokemon GO (Niantic partnership) contains gacha mechanics outside Nintendo's direct control.", "recommendations": [ "Clarify Amiibo production runs \u2014 remove artificial scarcity from physical products (reduce engagement)", "Maintain current no-gacha, no-loot-box standard in all first-party titles (maintain constraint)", "Review IP enforcement to avoid overreach that creates adversarial relationship with community (organizational transparency)" ], "notes": "Scores 3/12 \u2014 constraint-pole for the gaming industry. Nintendo's business model is sell good games at fixed prices \u2014 the antithesis of the void monetization architecture. This is why Nintendo is scored: it demonstrates that the gaming void is not architecturally inevitable. The framework predicts that bounded, transparent, endpoint-complete games will not produce gaming disorder at scale. Nintendo's first-party library is the control group for the gaming void. The 1-point scores reflect minimal opacity (no algorithmic content) and minimal responsiveness (no live service feedback loops), with 1 on engagement for Animal Crossing/daily loops.", "rateable": false, "taxonomyTag": "company" }, { "id": "xbox-microsoft", "label": "Xbox / Microsoft Gaming", "category": "gaming-platform", "entityType": "company", "entityGroup": "microsoft-gaming", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Game Pass is a transparent subscription model \u2014 low void score for the platform layer. Microsoft Rewards creates engagement hooks. Minecraft Marketplace (Minecoins) introduces currency opacity. High-void Activision Blizzard products scored separately.", "evidence": "Xbox Game Pass: $14.99-$19.99/month (transparent tiered subscription). No hidden currency on the platform layer \u2014 games cost what they cost or are included in the subscription. Microsoft Rewards: points system for engagement, low-stakes but creates daily check-in architecture. Minecraft Marketplace: 1,720 Minecoins = $13.99 \u2014 virtual currency obfuscates real cost, deployed on a primarily-children audience. Minecraft is the largest video game by copies sold \u2014 Marketplace opacity reaches maximum scale. Xbox Achievement system: transparent (no monetization, gamerscore is cosmetic), low void.", "harms": "Minecraft Marketplace currency opacity exposes child demographic (Minecraft's primary audience: under-18) to the same abstraction mechanism as Roblox, at smaller scale. Game Pass 'day one' releases create FOMO for non-subscribers. Microsoft Activision acquisition consolidated some of the highest-void gaming properties (CoD, Diablo) under one platform \u2014 organizational risk that product void could influence platform architecture.", "recommendations": [ "Display Minecoin price in real currency equivalent on all Marketplace purchase pages (reduce opacity)", "Implement parental controls that show real-money spend totals across Minecraft Marketplace (reduce opacity)", "Maintain Game Pass transparent subscription model without adding hidden currency layers (maintain constraint)", "Publish Activision Blizzard remediation timeline post-acquisition \u2014 demonstrate that organizational changes affect product void (organizational transparency)" ], "notes": "Scores 3/12 \u2014 constraint-pole alongside Nintendo. Xbox/Microsoft's core platform is low-void: Game Pass is a transparent subscription, Achievements are non-monetized, Xbox Live pricing is disclosed. The score separates the PLATFORM from the PRODUCTS: Activision Blizzard games that run ON Xbox are scored separately (blizzard-activision node). The platform infrastructure remains low-void. Minecraft Marketplace is the exception \u2014 Minecoins on a child-primary platform are the same architecture as Roblox's Robux at smaller scale. The 1-point scores reflect the minimal opacity/responsiveness of the subscription platform model.", "rateable": false, "taxonomyTag": "company" }, { "id": "fico", "label": "FICO (Fair Isaac)", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 3 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Core credit scoring model. 50+ proprietary score variants with hidden weights, interaction effects, and thresholds. Consumers cannot determine which version lenders use. The opacity IS the product \u2014 Fair Isaac's revenue depends on lenders paying for a system consumers cannot replicate.", "evidence": "Source: credit-scoring analysis. CFPB 2015 report on consumer misunderstanding. FTC 2012 accuracy study (1 in 4 consumers found errors). 'FICO gods' vocabulary documented in myFICO forums and r/CRedit.", "harms": "Population-scale score anxiety (48% of Americans report credit score stress \u2014 Bankrate 2023). Proxy discrimination through opaque variables (O'Neil 2016, Barocas & Selbst 2016).", "rateable": true, "taxonomyTag": "credit" }, { "id": "equifax", "label": "Equifax", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "entityType": "company", "summary": "Credit bureau collecting data on 800M+ consumers. Opaque data collection and aggregation. Consumers cannot see what data is collected or how it affects scoring until they request a report.", "evidence": "Source: credit-scoring analysis. 2017 breach exposed 147M Americans' data \u2014 largest credit bureau breach in history.", "harms": "2017 data breach (147M records). Consumers cannot prevent data collection. Error rates documented by FTC (1 in 4 reports contain errors).", "rateable": false, "taxonomyTag": "company" }, { "id": "experian", "label": "Experian", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "entityType": "company", "summary": "Credit bureau and consumer monitoring service. Dual role: collects opaque credit data AND sells monitoring products that drive engagement with the opacity.", "evidence": "Source: credit-scoring analysis. Experian Boost product directly monetizes consumer engagement with credit opacity.", "harms": "Consumer data collection without meaningful opt-out. Monitoring products create engagement loop with opaque scoring.", "rateable": false, "taxonomyTag": "company" }, { "id": "transunion", "label": "TransUnion", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "entityType": "company", "summary": "Third major US credit bureau. Same structural opacity as Equifax/Experian \u2014 opaque data collection, hidden scoring inputs, consumers as data subjects without meaningful control.", "evidence": "Source: credit-scoring analysis. Part of the three-bureau oligopoly controlling population-scale financial sorting.", "rateable": false, "taxonomyTag": "company" }, { "id": "vantagescore", "label": "VantageScore", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Alternative to FICO, created by the three bureaus. Proprietary formula, hidden weights. Less consumer brand awareness than FICO but same structural opacity.", "evidence": "Source: credit-scoring analysis. Joint product of Equifax, Experian, TransUnion \u2014 bureaus created their own proprietary competitor to FICO.", "rateable": true, "taxonomyTag": "credit" }, { "id": "credit-karma", "label": "Credit Karma", "category": "credit-scoring", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Free credit monitoring funded by targeted financial product recommendations. Shows scores but hides how recommendations are selected (affiliate revenue model). Gamified score tracking drives repeated engagement.", "evidence": "Source: credit-scoring analysis. 130M+ members. Monetizes engagement with credit opacity through affiliate-driven product recommendations.", "harms": "Recommendations optimize for affiliate revenue, not consumer benefit. Creates engagement loop around score monitoring.", "rateable": true, "taxonomyTag": "credit" }, { "id": "myfico-forums", "label": "myFICO Forums", "category": "credit-scoring", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Largest dedicated credit score community (operating since 2001). Ground zero for documented L1\u2192L3 vocabulary drift. 'FICO gods' and 'bucketing' theories originate here. The community itself IS the hermeneutic practice around credit opacity.", "evidence": "Source: credit-scoring analysis. Thousands of posts with L2-L3 vocabulary documented. 'Bucketing' theory, 'FICO gods,' 'garden' terminology all documented in source analysis.", "rateable": true, "taxonomyTag": "credit" }, { "id": "robinhood", "label": "Robinhood", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Commission-free trading app with gamified UX. PFOF business model hides true execution costs. Confetti animations on trades, push notifications on price movements, one-tap trading. Documented role in meme stock mania and user suicides.", "evidence": "Documented: GameStop/AMC meme stock cascade (2021). Alex Kearns suicide (2020) \u2014 20-year-old saw erroneous $730K negative balance. FINRA $70M fine (2021) for outages and misleading information.", "harms": "User suicide (Kearns, 2020). $70M FINRA fine. GameStop trading restrictions. Options exposure to inexperienced traders.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "coinbase-exchange", "label": "Coinbase", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Largest US crypto exchange. Moderate opacity (publicly traded, some transparency) but listing criteria opaque, fee structure complex, staking mechanics partially hidden.", "evidence": "Source: cryptocurrency analysis. Publicly traded (COIN). SEC lawsuit (2023) over unregistered securities.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "upstart", "label": "Upstart", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI-based lending platform. Uses non-traditional variables (education, employment history) for credit decisions \u2014 MORE opaque than traditional FICO-based lending. Applicants cannot determine which AI-derived factors affected their decision.", "evidence": "Source: credit-scoring analysis. Exemplifies reflexive opacity \u2014 AI model uses variables consumers cannot game because they cannot see them.", "rateable": true, "taxonomyTag": "credit" }, { "id": "affirm", "label": "Affirm", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "BNPL provider with opaque underwriting but disclosed terms. Real-time approval at checkout. Lower gradient than pure credit scoring because the engagement point is transactional, not identity-forming.", "evidence": "Source: credit-scoring analysis (BNPL context). CFPB BNPL report (2022) documented debt accumulation risks.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "klarna", "label": "Klarna", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "BNPL + shopping app. Higher engagement than Affirm due to integrated shopping features, wishlists, and gamified cashback. The shopping app creates engagement beyond transactional BNPL use.", "evidence": "Source: credit-scoring analysis. 150M+ active users globally. App includes shopping feed, cashback, and lifestyle features beyond lending.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "afterpay", "label": "Afterpay (Block/Square)", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "BNPL provider (acquired by Block/Square). Checkout-level BNPL with opaque creditworthiness assessment. Lower engagement architecture than Klarna \u2014 more purely transactional.", "evidence": "Source: credit-scoring analysis. CFPB BNPL report included Afterpay as key provider.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "sofi", "label": "SoFi", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Comprehensive fintech platform (banking, investing, lending, crypto). High engagement through all-in-one financial app. SoFi Stadium naming rights + social features create identity-forming engagement beyond pure financial utility.", "evidence": "Source: credit-scoring analysis (fintech context). Bank charter obtained 2022. Positioned as lifestyle brand, not just financial tool.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "lendingclub", "label": "LendingClub", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "P2P lending platform (now bank). Opaque credit assessment for borrowers. Lower engagement than consumer-facing apps \u2014 primarily transactional lending.", "evidence": "Source: credit-scoring analysis. Former CEO charged with fraud (2016) for misrepresenting loan data. Pivoted from P2P to bank.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "webull", "label": "Webull", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Commission-free trading app. Similar gamification to Robinhood \u2014 real-time charts, push notifications, paper trading, extended hours. PFOF execution model.", "evidence": "Source: trading-markets analysis context. Chinese-owned (Fumi Technology). Rapid growth during meme stock era.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "interactive-brokers", "label": "Interactive Brokers", "category": "fintech", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Professional-grade brokerage. Higher transparency than retail platforms \u2014 detailed execution quality reports, transparent fee structure, professional tools. Low engagement gamification. Constraint-leaning for brokerage category.", "evidence": "Source: trading-markets analysis (control case context). Professional orientation reduces gamification gradient.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "fidelity", "label": "Fidelity Investments", "category": "fintech", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "entityType": "company", "summary": "Traditional brokerage and asset manager. Relatively transparent fee structure and index fund offerings. Low gamification. Functions more as financial tool than engagement platform.", "evidence": "Source: trading-markets analysis (constraint comparison). Bogleheads community recommends as low-void alternative to gamified platforms.", "rateable": false, "taxonomyTag": "company" }, { "id": "acorns", "label": "Acorns", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Micro-investing app with round-up feature. Investment allocation algorithm opaque but simplified. Moderate engagement through gamified savings milestones. Lower gradient \u2014 tool posture dominant.", "evidence": "Source: trading-markets analysis context. 10M+ subscribers. 'Found money' and round-ups create passive engagement loop.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "betterment", "label": "Betterment", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Robo-advisor. Portfolio allocation algorithm opaque but methodology published. Low engagement design \u2014 set-and-forget model. Closest to tool posture in robo-advisor space.", "evidence": "Source: trading-markets analysis context. Published investment methodology. Tax-loss harvesting automated.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "wealthfront", "label": "Wealthfront", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Robo-advisor similar to Betterment. Published investment methodology, automated portfolio management. Low engagement design. Tool posture.", "evidence": "Source: trading-markets analysis context. Acquired by UBS (2022, deal later cancelled). Direct indexing and tax-loss harvesting.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "cashapp", "label": "Cash App (Block)", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "P2P payments + banking + Bitcoin + stocks. High engagement through social payment features, Cash App Friday promotions, Bitcoin buying. Social layer drives engagement beyond financial utility.", "evidence": "Source: cryptocurrency + trading-markets analysis context. 50M+ monthly active users. Documented use in scams and fraud targeting users.", "harms": "Documented fraud targeting users (romance scams, impersonation). Limited dispute resolution for peer-to-peer transfers.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "chime", "label": "Chime", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Neobank with early direct deposit feature. Fee structure partially hidden (interchange revenue). Engagement driven by 'get paid early' feature creating switching cost. Moderate void \u2014 banking with engagement hooks.", "evidence": "Source: credit-scoring analysis context. 14M+ account holders. CFPB complaints about account closures and frozen funds.", "rateable": true, "taxonomyTag": "fintech" }, { "id": "nerdwallet", "label": "NerdWallet", "category": "fintech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Financial comparison and recommendation platform. Presents as consumer education but funded by affiliate commissions \u2014 recommendations serve advertisers. Hides the monetization mechanism behind editorial framing.", "evidence": "Source: credit-scoring analysis context. Affiliate revenue model. Publicly traded (NRDS).", "rateable": true, "taxonomyTag": "fintech" }, { "id": "schufa", "label": "Schufa (Germany)", "category": "credit-scoring", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "German credit scoring system operating under stricter transparency requirements than FICO. Partial methodology publication under GDPR/BDSG. Source analysis documents LESS vocabulary drift than American FICO \u2014 reduced opacity produces reduced drift.", "evidence": "Source: credit-scoring analysis Control Case 1. 'No documented German equivalent to FICO gods.' Framework prediction confirmed: reduced opacity \u2192 reduced drift.", "rateable": true, "taxonomyTag": "credit" }, { "id": "tradeline-industry", "label": "Credit Tradeline Industry", "category": "credit-scoring", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "industry", "confidence": "assessed", "summary": "The $10B+ predatory credit repair and tradeline selling industry. Pure D3 \u2014 emerges from credit opacity to exploit consumers trying to game a system they cannot see. 'Authorized user tradeline' selling is the reflexive arms race made commercial.", "evidence": "Source: credit-scoring analysis \u00a73 D3 documentation. $10B+ annually. FTC enforcement actions against deceptive credit repair companies.", "harms": "Predatory fees targeting financially vulnerable consumers. Deceptive marketing. Perpetuates the engagement loop around credit opacity.", "rateable": false, "taxonomyTag": "reference" }, { "id": "duolingo", "label": "Duolingo", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Language learning app with maximum gamification. Streaks, leagues, hearts, gems, notifications guilting users for missed days ('Duo is sad'). The engagement architecture IS the product \u2014 retention mechanics dominate learning mechanics. Algorithmic lesson selection opaque.", "evidence": "Source: education-guru analysis. Documented push notification manipulation. 'Streak freeze' monetization. 100M+ monthly active users.", "harms": "Documented anxiety around streak maintenance. Guilt-driven notifications. Premium currency (gems) creates gambling-adjacent mechanics.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "chegg", "label": "Chegg", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Homework help platform (now AI-powered). High gradient \u2014 facilitates academic dishonesty by design. Students use it to get answers, not to learn. The opacity is in the framing: marketed as 'study help' while functioning as answer-copying service.", "evidence": "Source: education-guru analysis. Multiple universities have documented Chegg-facilitated cheating. Revenue collapsed when ChatGPT provided free alternative.", "harms": "Widespread academic dishonesty facilitation. Students develop dependency on answer services rather than learning.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "coursera", "label": "Coursera", "category": "edtech", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Online course platform with university partnerships. Moderate transparency (course content visible, instructor credentials public). Engagement through certificates and completion tracking. Low gradient \u2014 tool posture maintained.", "evidence": "Source: education-guru analysis. 136M+ registered learners. Completion rates low (~5-15% for MOOCs), suggesting engagement architecture insufficient to retain.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "udacity", "label": "Udacity", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Nanodegree platform with career-oriented programs. Moderate opacity around job placement claims and nanodegree outcomes. Higher engagement through cohort model and project reviews.", "evidence": "Source: education-guru analysis. Pivoted from free MOOCs to paid nanodegrees. Accenture acquisition (2023).", "rateable": true, "taxonomyTag": "edtech" }, { "id": "khan-academy", "label": "Khan Academy", "category": "edtech", "scores": { "opacity": 0, "responsiveness": 2, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Free educational platform. Constraint-pole case for EdTech \u2014 transparent (all content free and visible), low engagement manipulation (no streaks, no premium currency, no guilt notifications), tool posture dominant. Nonprofit structure removes profit-driven engagement incentive.", "evidence": "Source: education-guru analysis. Nonprofit. Free for all users. No premium tier. Khanmigo AI tutor adds moderate responsiveness.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "canvas-instructure", "label": "Canvas (Instructure)", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Learning management system (LMS). Institutional tool \u2014 students interact through it but engagement architecture is low. Opacity in grading algorithms and analytics dashboards visible only to instructors.", "evidence": "Source: education-guru analysis. Dominant LMS in US higher education. Private equity owned (Thoma Bravo).", "rateable": true, "taxonomyTag": "edtech" }, { "id": "blackboard", "label": "Blackboard (Anthology)", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Legacy LMS. Higher opacity than Canvas \u2014 known for poor UX and opaque feature sets. Institutional lock-in through contract structure rather than engagement architecture.", "evidence": "Source: education-guru analysis. Merged with Anthology. Long history of institutional lock-in complaints.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "pearson", "label": "Pearson", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Largest education publisher. Controls textbook market with opaque pricing, frequent edition changes that obsolete used copies, and bundled digital access codes. The edition cycle is designed to prevent secondary markets.", "evidence": "Source: education-guru analysis. Textbook price inflation 1,041% since 1977 (vs 308% overall inflation). Access code bundling forces new purchases.", "harms": "Student debt burden from inflated textbook costs. Deliberate obsolescence of older editions.", "rateable": false, "taxonomyTag": "company" }, { "id": "turnitin", "label": "Turnitin", "category": "edtech", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Plagiarism detection (now AI detection) tool. Maximum opacity \u2014 algorithm hidden, false positive rates undisclosed, students cannot see how similarity scores are calculated. AI detection feature particularly opaque with documented false positives.", "evidence": "Source: education-guru analysis. Documented false positives in AI detection, particularly affecting non-native English speakers.", "harms": "False AI detection accusations affecting students. Non-native English speakers disproportionately flagged. Students cannot meaningfully challenge algorithmic decisions.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "proctorio", "label": "Proctorio", "category": "edtech", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI-powered exam proctoring. Maximum opacity + responsiveness: monitors students via webcam/microphone/screen with opaque 'suspicion' scoring. Students flagged for eye movement, background noise, or facial expressions by hidden algorithms. Full D3: students expelled or failed based on opaque algorithmic flags.", "evidence": "Source: education-guru analysis. Documented: lawsuits over privacy violations, DMCA takedowns against critics, racial bias in facial recognition flagging.", "harms": "Privacy violations (webcam/microphone monitoring in homes). Racial bias in facial recognition. Anxiety and distress during monitored exams. Wrongful academic misconduct accusations.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "honorlock", "label": "Honorlock", "category": "edtech", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Exam proctoring platform similar to Proctorio. Webcam monitoring with AI-driven suspicion detection. Same structural void: maximum opacity in scoring, maximum responsiveness to student behavior, harm facilitation through false flagging.", "evidence": "Source: education-guru analysis. Same structural concerns as Proctorio. Honeypot Chegg integration monitors search behavior.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "kahoot", "label": "Kahoot!", "category": "edtech", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 3, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Gamified quiz platform. High engagement through competitive real-time quizzes, leaderboards, music, and time pressure. Low opacity (questions visible) and low gradient (tool posture, no D1-D3 cascade). Engagement serves learning rather than attention capture.", "evidence": "Source: education-guru analysis. 9B+ cumulative participants. Used in 200+ countries.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "quizlet", "label": "Quizlet", "category": "edtech", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Flashcard and study tool. Low opacity (user-created content, visible mechanics). Moderate engagement through spaced repetition gamification. AI-generated explanations add slight opacity. Tool posture dominant.", "evidence": "Source: education-guru analysis. 60M+ monthly active users. AI integration (Q-Chat) adds responsiveness.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "grammarly", "label": "Grammarly", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI writing assistant. High responsiveness (real-time corrections). Moderate opacity (correction logic partially hidden, premium features gated). Low gradient \u2014 tool posture. Engagement through weekly writing statistics emails.", "evidence": "Source: education-guru analysis + AI interpretability context. 30M+ daily active users.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "photomath", "label": "Photomath", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Math problem solver via camera. Similar structural concern to Chegg \u2014 marketed as learning tool, used as answer-copying service. High responsiveness (instant solutions from photo). Moderate gradient from answer-dependency formation.", "evidence": "Source: education-guru analysis. Acquired by Google (2022). 220M+ downloads.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "google-classroom", "label": "Google Classroom", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "LMS within Google ecosystem. Moderate opacity (Google's data practices). Low engagement design for the classroom tool itself. The void is in the data collection layer, not the pedagogical layer.", "evidence": "Source: education-guru analysis. Dominant in K-12 (150M+ users). Privacy concerns about student data within Google ecosystem.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "cengage", "label": "Cengage", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Educational publisher. Similar structure to Pearson \u2014 opaque pricing, frequent edition changes, bundled digital access. Cengage Unlimited subscription model locks students into ecosystem.", "evidence": "Source: education-guru analysis. Filed for bankruptcy (2013). Same textbook inflation dynamics as Pearson.", "rateable": false, "taxonomyTag": "company" }, { "id": "mcgraw-hill", "label": "McGraw-Hill", "category": "edtech", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Educational publisher. ALEKS adaptive learning uses AI to assess student knowledge \u2014 algorithm opaque, assessment criteria hidden. Same publisher inflation dynamics as Pearson/Cengage.", "evidence": "Source: education-guru analysis. ALEKS AI platform particularly opaque in its knowledge assessment methodology.", "rateable": false, "taxonomyTag": "company" }, { "id": "century-tech", "label": "Century Tech", "category": "edtech", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI-driven adaptive learning platform. Maximum opacity \u2014 AI determines what students learn and when, with hidden assessment algorithms. High responsiveness to student performance. EU AI Act directly relevant \u2014 adaptive learning classified under Annex III \u00a73.", "evidence": "Source: education-guru analysis. UK-based AI education platform. Claims to use neuroscience + AI for personalized learning.", "rateable": true, "taxonomyTag": "edtech" }, { "id": "hirevue", "label": "HireVue", "category": "hr-tech", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI video interview platform. Maximum opacity: algorithms analyze facial expressions, voice patterns, and word choice to score candidates. Candidates cannot see what behaviors the AI is evaluating or how scores are calculated. Full D3: people rejected from jobs by opaque algorithm.", "evidence": "Decisions.md: Paper 21B deployer. NYC Local Law 144 mandates bias audits for automated employment decision tools. HireVue dropped facial analysis (2021) after criticism but retains voice/language analysis.", "harms": "Documented bias concerns. Candidates have no meaningful way to understand or challenge algorithmic rejection. Disability discrimination risk.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "pymetrics-harver", "label": "Pymetrics / Harver", "category": "hr-tech", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Neuroscience-based hiring assessment. Candidates play cognitive games; AI evaluates responses against 'ideal candidate' profiles. Maximum opacity \u2014 candidates cannot see the trait model, the scoring rubric, or how game performance maps to hiring decisions.", "evidence": "Decisions.md: Paper 21B deployer. Claims 'bias-free' hiring but assessment criteria hidden from candidates. Acquired by Harver (2022).", "harms": "Candidates sorted by opaque cognitive assessments with no transparency into scoring methodology.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "linkedin-recruiter", "label": "LinkedIn Recruiter", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "AI-powered recruiting tool within LinkedIn. Algorithmic candidate ranking opaque \u2014 candidates don't know why they appear in searches or how LinkedIn scores their profiles. Moderate void: ranking is opaque but human recruiter makes final decision.", "evidence": "Decisions.md: Paper 21B deployer. LinkedIn's AI matching algorithm uses undisclosed signals to rank candidates.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "workday", "label": "Workday", "category": "hr-tech", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Enterprise HR/finance platform with AI-driven candidate screening. Opaque algorithms filter applicants before human review. Workers managed through opaque performance analytics. Low engagement \u2014 institutional tool, not consumer-facing.", "evidence": "Decisions.md: Paper 21B deployer. Class-action lawsuit (2023) alleging AI screening discriminates based on race, age, and disability.", "rateable": false, "taxonomyTag": "company" }, { "id": "adp", "label": "ADP", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Payroll and HR services. Moderate opacity in workforce analytics and benchmarking data. Low engagement architecture \u2014 primarily institutional B2B tool. One of the largest employers data processors globally.", "evidence": "Decisions.md: Paper 21B deployer. Processes payroll for 1 in 6 US workers. Workforce analytics increasingly AI-driven.", "rateable": false, "taxonomyTag": "company" }, { "id": "greenhouse", "label": "Greenhouse", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Applicant tracking system (ATS). Opaque resume parsing and candidate ranking algorithms. Candidates don't know how their applications are filtered. Lower void than AI assessment tools \u2014 structured process with human checkpoints.", "evidence": "HR tech domain knowledge. ATS resume parsing is a known source of qualified candidate rejection.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "icims", "label": "iCIMS", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Enterprise ATS and talent management. AI-powered candidate matching with opaque ranking algorithms. Institutional tool \u2014 candidates interact through job applications but the filtering is invisible.", "evidence": "HR tech domain knowledge. One of the largest enterprise ATS providers.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "indeed", "label": "Indeed", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Job search aggregator. Opaque job ranking algorithm and resume matching. Pay-per-click model means job visibility correlates with employer spending, not job quality. Moderate engagement through application tracking and alerts.", "evidence": "HR tech domain knowledge. Largest job site globally. Indeed Assessments adds AI-driven candidate screening layer.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "glassdoor", "label": "Glassdoor", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Company review and salary data platform. Paradoxical transparency: crowdsourced reviews increase workplace transparency, but algorithmic review filtering is opaque. Owned by Indeed/Recruit Holdings. Engagement through salary comparison and interview prep.", "evidence": "HR tech domain knowledge. Forced real-name policy change (2024) raised privacy concerns. Review filtering algorithm opaque.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "ziprecruiter", "label": "ZipRecruiter", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI-driven job matching platform. 'Phil' AI recruiter actively matches and invites candidates. Higher responsiveness than passive job boards \u2014 the system actively reaches out. Matching algorithm opaque to both employers and candidates.", "evidence": "HR tech domain knowledge. AI matching sends invitations to candidates, creating higher engagement than passive listings.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "bamboohr", "label": "BambooHR", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "SMB HR software. Employee management and performance tracking. Lower opacity than enterprise platforms \u2014 simpler systems, more transparent to HR admins. Employee-facing experience is basic.", "evidence": "HR tech domain knowledge. Focused on small/medium businesses. Less AI-driven than enterprise alternatives.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "sap-successfactors", "label": "SAP SuccessFactors", "category": "hr-tech", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Enterprise HR suite with AI-driven talent management. Maximum opacity in AI recommendation engines for succession planning, performance evaluation, and compensation. Workers scored by algorithms they cannot see or challenge.", "evidence": "HR tech domain knowledge. Used by 200M+ users in 200+ countries. AI-powered performance and succession modules.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "oracle-hcm", "label": "Oracle HCM Cloud", "category": "hr-tech", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Enterprise HR platform with AI-powered workforce analytics. Opaque performance scoring, attrition prediction, and compensation recommendations. Workers are subjects of algorithmic management they cannot inspect.", "evidence": "HR tech domain knowledge. Oracle Fusion HCM used by major enterprises. AI modules for 'talent intelligence' and retention prediction.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "cornerstone-ondemand", "label": "Cornerstone OnDemand", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Talent management and learning platform. AI-driven skills gap analysis and career pathing with opaque recommendation algorithms. Workers receive AI-generated development suggestions without seeing the model.", "evidence": "HR tech domain knowledge. Acquired by Clearlake Capital (2021). Used for performance management and compliance training.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "lever", "label": "Lever", "category": "hr-tech", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "ATS and talent relationship management. AI-powered candidate sourcing and pipeline management. Moderate opacity \u2014 algorithmic matching but with human-in-the-loop design. Lower void than pure AI assessment tools.", "evidence": "HR tech domain knowledge. CRM approach to recruiting. Merged with Employ Inc.", "rateable": true, "taxonomyTag": "hr_tech" }, { "id": "epic-systems", "label": "Epic Systems", "category": "health", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Dominant US EHR system (>250M patient records). Maximum opacity: proprietary data formats create vendor lock-in, patients cannot easily access or transfer records, clinical decision support algorithms hidden from both patients and clinicians.", "evidence": "Source: medical-diagnosis analysis. Holds >35% US hospital EHR market. Information blocking complaints documented. Interoperability mandated by 21st Century Cures Act but implementation uneven.", "harms": "Vendor lock-in limits healthcare competition. Patients struggle to access their own records. Proprietary formats inhibit data portability.", "rateable": false, "taxonomyTag": "company" }, { "id": "cerner-oracle", "label": "Cerner (Oracle Health)", "category": "health", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Second-largest US EHR system (Oracle acquired 2022). Same structural opacity as Epic \u2014 proprietary systems, hidden clinical algorithms, patient data portability barriers. Oracle acquisition adds enterprise data integration concerns.", "evidence": "Source: medical-diagnosis analysis. $28B Oracle acquisition. VA EHR modernization contract ($16B) plagued by implementation problems.", "rateable": false, "taxonomyTag": "company" }, { "id": "unitedhealth-optum", "label": "UnitedHealth Group / Optum", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 3 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "documented", "summary": "Largest US health insurer + Optum health services. Compound void: insurance opacity (claims denial algorithms) + healthcare delivery (Optum provider network) + data analytics (Optum Health). Vertical integration means the entity paying for care, providing care, and analyzing care data is the same company.", "evidence": "Source: medical-diagnosis + doctor-patient analysis. Documented: naviHealth AI algorithm denied elderly patients post-acute care (STAT News 2023). DOJ antitrust investigation. CEO assassination (2024) highlighted public anger at claims denial.", "harms": "AI-driven claims denials documented. Antitrust concerns from vertical integration. naviHealth algorithm overrode physician clinical judgment.", "rateable": false, "taxonomyTag": "company" }, { "id": "anthem-elevance", "label": "Anthem / Elevance Health", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Second-largest US health insurer. Same structural opacity as UnitedHealth \u2014 opaque claims processing, hidden denial algorithms, prior authorization barriers. Blue Cross Blue Shield association member.", "evidence": "Source: medical-diagnosis analysis. 47M+ members. Prior authorization delays documented as source of patient harm.", "rateable": false, "taxonomyTag": "company" }, { "id": "cigna", "label": "Cigna", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 3 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "documented", "summary": "Major US health insurer. Documented: automated claims denial system rejected claims without physician review. ProPublica investigation (2023) found Cigna doctors denied claims in batches \u2014 spending average 1.2 seconds per claim review.", "evidence": "ProPublica 2023 investigation: PXDX system auto-denied claims. Doctors spent 1.2 seconds average per review. Class-action lawsuit filed.", "harms": "Automated bulk claims denial without meaningful physician review. Patients denied medically necessary care by algorithm.", "rateable": false, "taxonomyTag": "company" }, { "id": "aetna-cvs", "label": "Aetna (CVS Health)", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Health insurer within CVS Health conglomerate. Compound void: insurance + pharmacy + retail health + PBM (Caremark). Vertical integration mirrors UnitedHealth/Optum pattern \u2014 same entity controls insurance, pharmacy benefit management, and retail health delivery.", "evidence": "Source: medical-diagnosis analysis. CVS-Aetna merger (2018) created vertically integrated health conglomerate. PBM opacity in drug pricing.", "rateable": false, "taxonomyTag": "company" }, { "id": "humana", "label": "Humana", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Major US health insurer focused on Medicare Advantage. Opaque plan designs, benefits changes, and claims processing. Medicare Advantage plans documented for misleading marketing and coverage denials.", "evidence": "Source: medical-diagnosis analysis. OIG reports document Medicare Advantage improper coverage denials.", "rateable": false, "taxonomyTag": "company" }, { "id": "state-farm", "label": "State Farm", "category": "insurance", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 1, "gradient": 2 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Largest US P&C insurer. Opaque pricing algorithms (credit-based insurance scores in 48 states), claims adjustment algorithms, and risk assessment models. Low engagement \u2014 interaction primarily at claims time.", "evidence": "Decisions.md: Paper 22B context. Credit-based insurance scoring creates compound void with credit scoring domain.", "rateable": false, "taxonomyTag": "company" }, { "id": "progressive", "label": "Progressive", "category": "insurance", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Auto insurer with Snapshot telematics. Unique: trades opacity for surveillance \u2014 Snapshot monitors driving behavior for pricing, making some pricing transparent but creating new engagement/monitoring void. Higher responsiveness than traditional insurers.", "evidence": "Decisions.md: Paper 22B context. Snapshot telematics program. Pricing algorithm still partially opaque despite behavioral monitoring input.", "rateable": false, "taxonomyTag": "company" }, { "id": "lemonade-insurance", "label": "Lemonade", "category": "insurance", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI-driven insurtech. Claims processed by AI (AI Jim) in seconds. Higher responsiveness than traditional insurers but higher opacity in AI decision-making. Markets transparency but the AI underwriting and claims algorithms are proprietary.", "evidence": "Decisions.md: Paper 22B context. AI Jim processes claims in under 3 minutes. Social impact model (Giveback program). Still uses opaque AI for underwriting.", "harms": "AI claims denial without human review in fast-track cases.", "rateable": true, "taxonomyTag": "insurance" }, { "id": "root-insurance", "label": "Root Insurance", "category": "insurance", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Telematics-based auto insurer using phone sensors to monitor driving. Similar to Progressive Snapshot \u2014 trades some pricing opacity for behavioral monitoring. AI pricing model proprietary.", "evidence": "Decisions.md: Paper 22B context. Uses smartphone sensors rather than OBD devices. Publicly traded.", "rateable": true, "taxonomyTag": "insurance" }, { "id": "oscar-health", "label": "Oscar Health", "category": "insurance", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Tech-forward health insurer. More transparent UX than legacy insurers (cost estimates, telemedicine). Still has opaque claims processing and network restrictions. Moderate void \u2014 better than legacy but still insurance architecture.", "evidence": "Decisions.md: Paper 22B context. Tech company approach to health insurance. Publicly traded.", "rateable": true, "taxonomyTag": "insurance" }, { "id": "teladoc", "label": "Teladoc", "category": "health", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Telehealth platform. High responsiveness (on-demand virtual visits). Moderate opacity in provider matching and clinical pathways. Lower gradient than health insurance \u2014 the service delivers care rather than denying it.", "evidence": "Source: doctor-patient analysis. Largest telehealth provider. Revenue grew massively during COVID, then stabilized.", "rateable": true, "taxonomyTag": "health" }, { "id": "goodrx", "label": "GoodRx", "category": "health", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Prescription drug price comparison. Increases some transparency (drug prices visible) but business model opaque (affiliate commissions from pharmacies). FTC action (2023) for sharing health data with Meta/Google ad platforms.", "evidence": "Source: medical-diagnosis analysis. FTC $1.5M fine for sharing health data with advertising platforms (2023).", "harms": "Shared user health data (prescription searches) with Meta and Google for advertising targeting.", "rateable": true, "taxonomyTag": "health" }, { "id": "webmd", "label": "WebMD", "category": "health", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Health information platform. High engagement through symptom checker (anxiety-driving design \u2014 'it could be cancer'). Advertising-funded with pharma sponsorship creating content opacity. Symptom checker is the classic cyberchondria void amplifier.", "evidence": "Source: medical-diagnosis analysis. Documented cyberchondria amplification. Pharma-sponsored content blurs editorial/advertising line.", "harms": "Cyberchondria \u2014 symptom checkers documented to increase health anxiety. Pharma advertising disguised as health information.", "rateable": true, "taxonomyTag": "health" }, { "id": "betterhelp", "label": "BetterHelp", "category": "health", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Online therapy platform. Therapist matching algorithm opaque. FTC action (2023) for sharing mental health data with advertisers including Facebook and Snapchat. The compound void: therapy (relational void) + tech platform (data void).", "evidence": "FTC $7.8M settlement (2023) for sharing sensitive mental health data with advertising platforms.", "harms": "Shared user mental health data (intake questionnaire responses, therapy topics) with Meta, Snapchat, Criteo, Pinterest for ad targeting.", "rateable": true, "taxonomyTag": "health" }, { "id": "talkspace", "label": "Talkspace", "category": "health", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Online therapy platform. Similar structure to BetterHelp \u2014 AI-assisted therapist matching, asynchronous messaging. Publicly traded. Lower documented harm but same structural void: therapy via platform adds data opacity layer.", "evidence": "Source: medical-diagnosis + psychotherapy analysis context. Publicly traded (TALK). Revenue from B2B (employer mental health benefits).", "rateable": true, "taxonomyTag": "health" }, { "id": "noom", "label": "Noom", "category": "health", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Weight loss app with 'psychology-based' approach. High engagement through daily lessons, food logging, coach messages. Opaque coaching model (coaches manage hundreds of users). Subscription model with aggressive dark pattern cancellation.", "evidence": "Source: medical-diagnosis + diet-industry analysis context. Documented: auto-renewal traps, difficult cancellation, misleading 'coach' interactions (coaches not licensed therapists).", "harms": "Dark pattern cancellation. Misleading claims about coaching quality. Eating disorder risk from calorie tracking gamification.", "rateable": true, "taxonomyTag": "health" }, { "id": "hims-hers", "label": "Hims & Hers", "category": "health", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Telehealth prescription service. Opaque prescribing criteria \u2014 patients fill questionnaire, algorithm/provider approves. DTC model for sensitive medications (ED, hair loss, mental health). Marketing normalizes prescription medication consumption.", "evidence": "Source: medical-diagnosis + pharma-dtc analysis context. Publicly traded. FDA warning letters about compounded semaglutide products.", "rateable": true, "taxonomyTag": "health" }, { "id": "zocdoc", "label": "Zocdoc", "category": "health", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Doctor discovery and appointment booking. Moderate opacity \u2014 provider ranking algorithm not transparent (paid placements mixed with organic results). Lower gradient: tool posture for finding doctors, not ongoing engagement.", "evidence": "Source: doctor-patient analysis context. Business model: charges providers per booking. Review system adds some transparency.", "rateable": true, "taxonomyTag": "health" }, { "id": "binance", "label": "Binance", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Largest crypto exchange by volume. Maximum opacity: offshore structure, opaque proof-of-reserves, hidden trading engine. High engagement through leveraged products, futures, earn programs, and Binance Coin ecosystem creating lock-in.", "evidence": "Source: cryptocurrency analysis. DOJ/SEC settlement ($4.3B, 2023). CZ pleaded guilty to AML violations. Customer funds commingling documented.", "harms": "$4.3B DOJ settlement. AML violations. Customer fund commingling. Excessive leverage offered to retail traders.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "kraken", "label": "Kraken", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "US crypto exchange. Moderate opacity \u2014 more transparent than Binance (proof-of-reserves audits) but trading algorithms and listing criteria still hidden. Staking product shut down by SEC ($30M settlement).", "evidence": "Source: cryptocurrency analysis. SEC staking settlement ($30M, 2023). More transparency efforts than competitors.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "ftx-historical", "label": "FTX (Collapsed)", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Collapsed crypto exchange (2022). Maximum void on all dimensions: complete opacity (customer funds commingled with Alameda), maximum engagement (yield products, leveraged trading, celebrity marketing), full D1-D2-D3 cascade at industry scale.", "evidence": "Source: cryptocurrency analysis. $8B+ in customer losses. SBF convicted of fraud (2024). The defining case study in crypto void architecture.", "harms": "$8B+ customer losses. Fraud conviction. Pension fund losses. Industry-wide contagion (3AC, Celsius, Voyager, BlockFi).", "rateable": true, "taxonomyTag": "crypto" }, { "id": "uniswap", "label": "Uniswap", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Decentralized exchange (DEX). Low opacity \u2014 open-source smart contracts, on-chain transactions visible. But: front-running/MEV, impermanent loss mechanics opaque to retail users, and token listing is permissionless (scam tokens). Protocol-level transparency, user-level opacity.", "evidence": "Source: cryptocurrency analysis + Paper 7. Open-source. $1.8T+ cumulative volume. UNI governance token.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "aave", "label": "Aave", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "DeFi lending protocol. Open-source, on-chain, auditable. Low protocol-level opacity. But: liquidation mechanics, interest rate curves, and flash loan risks opaque to most users. Risk parameters set by governance (partially transparent).", "evidence": "Source: cryptocurrency analysis + Paper 7. $10B+ TVL. Governance through AAVE token. Multiple audits.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "makerdao", "label": "MakerDAO", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "DeFi protocol behind DAI stablecoin. Open-source, governance-controlled. Low opacity at protocol level but complex risk parameters opaque to most users. Liquidation cascades can be sudden and devastating.", "evidence": "Source: cryptocurrency analysis + Paper 7. DAI stablecoin. Black Thursday (March 2020) demonstrated cascade risk.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "tether-usdt", "label": "Tether (USDT)", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 2, "gradient": 3 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Largest stablecoin ($100B+). Maximum opacity: reserve composition historically hidden, auditor changes, Bahamas banking. Low responsiveness (stablecoin is price-stable by design). Full D3: systemic risk to entire crypto ecosystem if reserves insufficient.", "evidence": "Source: cryptocurrency analysis + Paper 7. CFTC $41M settlement (2021) for misrepresenting reserves. AG NY settlement. Reserve attestations but no full audit.", "harms": "Systemic risk to crypto ecosystem. Misrepresented reserves. Potential de-pegging could cascade across all crypto markets.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "circle-usdc", "label": "Circle (USDC)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Second-largest stablecoin. Constraint-leaning for crypto: regulated US entity, monthly reserve attestations by Big 4, transparent reserve composition (US Treasuries + cash). Brief de-peg during SVB collapse showed systemic risk remains.", "evidence": "Source: cryptocurrency analysis + Paper 7. Monthly Deloitte attestations. Registered money transmitter. SVB de-peg event (March 2023).", "rateable": true, "taxonomyTag": "crypto" }, { "id": "terra-luna", "label": "Terra/Luna (Collapsed)", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Collapsed algorithmic stablecoin (2022). Maximum void: UST 'stablecoin' backed by nothing but an algorithm and LUNA token. Anchor Protocol offered 19.5% APY (unsustainable). Complete D1-D2-D3: $40B evaporated in days.", "evidence": "Source: cryptocurrency analysis + Paper 7. $40B+ destroyed. Do Kwon arrested (Montenegro, 2023). Anchor Protocol's 19.5% yield was the engagement trap.", "harms": "$40B+ losses. Multiple suicides reported. Cascading failures (3AC, Celsius, Voyager). Do Kwon fraud charges.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "opensea", "label": "OpenSea", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "NFT marketplace. High engagement through collection FOMO, floor prices, and social status mechanics. Opaque listing algorithms and insider trading documented. Full gradient: NFT mania produced complete D1-D2-D3 cascade.", "evidence": "Source: cryptocurrency analysis. Former employee charged with insider trading (wire fraud, 2022). 95%+ of NFTs now worthless.", "harms": "Insider trading by employees. NFT market crash left most buyers with worthless assets. Wash trading documented.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "phantom-wallet", "label": "Phantom Wallet", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Solana ecosystem wallet. Relatively transparent (open-source, on-chain). Moderate engagement through built-in swap, staking, and NFT gallery. Lower void than exchanges \u2014 wallet as tool rather than trading venue.", "evidence": "Source: cryptocurrency analysis. Leading Solana wallet. In-app swaps add exchange-like engagement layer.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "metamask", "label": "MetaMask", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Dominant Ethereum wallet (ConsenSys). Open-source core but Infura dependency creates centralization risk. In-app swap adds exchange functionality. Tool posture but growing engagement through DeFi integration.", "evidence": "Source: cryptocurrency analysis + Paper 7. 30M+ monthly active users. Infura dependency is a centralization/opacity risk.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "ledger-wallet", "label": "Ledger (Hardware Wallet)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Hardware wallet manufacturer. Constraint-pole for crypto custody \u2014 offline storage, user controls keys, transparent security model. Ledger Recover controversy (2023) threatened this by adding optional cloud backup of seed phrases.", "evidence": "Source: cryptocurrency analysis. Ledger Recover controversy demonstrated how constraint properties can be degraded by feature additions.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "trezor-wallet", "label": "Trezor (Hardware Wallet)", "category": "crypto", "scores": { "opacity": 0, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Open-source hardware wallet. Maximum constraint for crypto: fully open-source firmware, offline storage, no cloud features. The constraint-pole reference case for crypto custody.", "evidence": "Source: cryptocurrency analysis. Fully open-source (unlike Ledger's closed secure element). User holds keys offline.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "lido", "label": "Lido Finance", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Liquid staking protocol (largest Ethereum staker). Protocol is open-source but validator set concentration creates centralization risk. stETH represents opaque claim on underlying ETH with de-peg risk.", "evidence": "Source: cryptocurrency analysis + Paper 7. Controls ~30% of staked ETH. Centralization concerns. stETH de-peg during 3AC collapse.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "curve-finance", "label": "Curve Finance", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Stablecoin DEX and DeFi liquidity protocol. Open-source but 'Curve Wars' governance dynamics create engagement void \u2014 protocols compete for CRV emissions creating meta-game. Complex yield strategies opaque to retail.", "evidence": "Source: cryptocurrency analysis + Paper 7. 'Curve Wars' as void-on-void competition for emissions. Founder's $168M CRV loan liquidation (2023).", "rateable": true, "taxonomyTag": "crypto" }, { "id": "jupiter-dex", "label": "Jupiter (Solana DEX)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Solana DEX aggregator. Open-source routing, transparent on-chain. But: meme coin launchpad (pump.fun integration) dramatically increases engagement and gradient by enabling instant speculative token creation.", "evidence": "Source: cryptocurrency analysis. Dominant Solana DEX. JUP token airdrop drove massive engagement.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "pump-fun", "label": "pump.fun", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Meme coin launchpad on Solana. Maximum engagement + gradient: one-click token creation, bonding curve mechanics, livestream integration. Creates gambling-adjacent void where the 'asset' is provably worthless (empty void, full cascade). Modern slot machine for crypto.", "evidence": "Source: cryptocurrency analysis. Millions of tokens created, >99% go to zero. Bonding curve creates variable-ratio reinforcement. Livestream scams documented.", "harms": "Gambling-adjacent mechanics targeting retail users. >99% of tokens created lose all value. Livestream manipulation documented.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "wormhole-bridge", "label": "Wormhole (Bridge)", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Cross-chain bridge protocol. Bridge mechanics fundamentally opaque to users \u2014 wrapped assets represent opaque claims on underlying tokens. Infrastructure-level void: bridges are the highest-risk component in DeFi.", "evidence": "Source: cryptocurrency analysis + Paper 7. $320M hack (February 2022). Bridges account for majority of DeFi hacks by dollar value.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "chainlink", "label": "Chainlink", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Decentralized oracle network providing off-chain data to smart contracts. Constraint-leaning: transparent data feeds, verifiable node operators. But: oracle manipulation risk and node set concentration create residual opacity.", "evidence": "Source: cryptocurrency analysis + Paper 7. De facto oracle standard in DeFi. LINK token for node operator incentives.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "celsius-historical", "label": "Celsius Network (Collapsed)", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Collapsed crypto lending platform (2022). Maximum opacity in investment strategies (used customer deposits for risky DeFi yield farming). 'Unbank yourself' marketing while operating as opaque shadow bank. Full D3: $4.7B in customer losses.", "evidence": "Source: cryptocurrency analysis. Bankruptcy (2022). CEO Mashinsky charged with fraud. $4.7B customer losses. 'Community' marketing hid shadow banking operations.", "harms": "$4.7B customer losses. CEO fraud charges. Customers unable to withdraw during collapse.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "three-arrows-capital", "label": "Three Arrows Capital (Collapsed)", "category": "crypto", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 2, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "documented", "summary": "Collapsed crypto hedge fund (2022). Maximum opacity: leveraged positions hidden from counterparties. Cascading failure: 3AC collapse triggered Celsius, Voyager, BlockFi failures. Demonstrates void-on-void coupling in crypto ecosystem.", "evidence": "Source: cryptocurrency analysis. $3.5B+ in claims. Founders fled to Dubai. Cascade triggered multi-billion in losses across ecosystem.", "harms": "Multi-billion dollar cascade across crypto ecosystem. Counterparty losses at Celsius, Voyager, BlockFi, Genesis.", "rateable": false, "taxonomyTag": "company" }, { "id": "bitcoin-network", "label": "Bitcoin (Network)", "category": "crypto", "scores": { "opacity": 0, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Base layer cryptocurrency network. Maximum transparency at protocol level (open-source, public ledger, fixed supply). But: mining pool concentration, UTXO tracing complexity, and speculative engagement create residual void. The network is transparent; the ecosystem around it is not.", "evidence": "Source: cryptocurrency analysis + Paper 7. Open-source since 2009. Transparent monetary policy. Engagement void is in the speculation layer, not the protocol.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "ethereum-network", "label": "Ethereum (Network)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Smart contract platform. Open-source and transparent at protocol level. Higher opacity than Bitcoin due to smart contract complexity, MEV (maximal extractable value), and Ethereum Foundation governance decisions. The DeFi ecosystem built on it amplifies all three void conditions.", "evidence": "Source: cryptocurrency analysis + Paper 7. Open-source. PoS since The Merge (2022). MEV is a major source of user-level opacity.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "solana-network", "label": "Solana (Network)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "High-throughput blockchain. Open-source but validator concentration and frequent outages create opacity about reliability. Meme coin activity on Solana creates maximum engagement void in the ecosystem layer.", "evidence": "Source: cryptocurrency analysis + Paper 7. Multiple outages documented. Meme coin ecosystem (pump.fun) creates gambling-adjacent engagement.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "dydx", "label": "dYdX", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Decentralized perpetual futures exchange. Maximum engagement through leveraged trading (up to 20x). High responsiveness (real-time order book). High gradient \u2014 leveraged trading is the highest-void activity in crypto, closest to gambling architecture.", "evidence": "Source: cryptocurrency analysis + Paper 7. Decentralized but leveraged perpetuals are structurally identical to gambling void.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "pancakeswap", "label": "PancakeSwap", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "BSC/multi-chain DEX with gamification layer. Open-source AMM but adds lottery, prediction markets, and NFT gaming on top \u2014 transforming a DeFi tool into a gambling venue. The gamification IS the engagement driver.", "evidence": "Source: cryptocurrency analysis. Built-in lottery and prediction markets. Gamification layer drives engagement beyond DeFi utility.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "blur-nft", "label": "Blur (NFT Marketplace)", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "NFT marketplace with trader-focused gamification. Airdrop farming mechanics, loyalty scoring, and blast points create engagement loop. Bid-to-airdrop system incentivized wash trading. The airdrop IS the engagement mechanism.", "evidence": "Source: cryptocurrency analysis. Overtook OpenSea through airdrop-driven engagement. Documented wash trading from farming incentives.", "harms": "Wash trading incentivized by token farming mechanics. Artificial volume inflation.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "magic-eden", "label": "Magic Eden", "category": "crypto", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Multi-chain NFT marketplace (originally Solana). Moderate engagement through collections, rarity tools, and launchpad. Lower void than Blur (less airdrop farming). Standard NFT marketplace void profile.", "evidence": "Source: cryptocurrency analysis. Leading Solana NFT marketplace. Cross-chain expansion to Bitcoin Ordinals, Ethereum.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "flashbots", "label": "Flashbots", "category": "crypto", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "MEV research organization and infrastructure (MEV-Boost). Paradoxical: exists to make MEV transparent and fair, but MEV-Boost creates new centralization/opacity in block building. Constraint-attempting in a high-void environment.", "evidence": "Source: cryptocurrency analysis + Paper 7. MEV-Boost runs ~90% of Ethereum blocks. Block builder concentration creates new opacity.", "rateable": true, "taxonomyTag": "crypto" }, { "id": "google-ads", "label": "Google Ads", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Largest digital advertising platform. Maximum opacity: auction mechanics, quality score algorithms, and targeting criteria hidden from both advertisers and users. Maximum responsiveness: real-time bidding adapts to individual user behavior. Full gradient: behavioral modification at population scale is the documented business model.", "evidence": "Source: propaganda-advertising analysis. $224B Google ad revenue (2023). DOJ antitrust case (2024) documenting ad monopoly. Search ad auction mechanics opaque.", "harms": "DOJ antitrust findings. Ad fraud ecosystem. User tracking across the web. Surveillance advertising model.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "meta-ads", "label": "Meta Ads (Facebook/Instagram)", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Second-largest digital ad platform. Maximum void: opaque targeting algorithms, hidden audience construction, behavioral tracking across the web (Meta Pixel). The platform IS the void \u2014 engagement serves advertising, advertising drives engagement.", "evidence": "Source: propaganda-advertising analysis. Cambridge Analytica scandal. $5B FTC privacy settlement (2019). Frances Haugen documents (2021).", "harms": "Cambridge Analytica data misuse. Mental health impacts documented in internal research (Haugen leaks). Election manipulation infrastructure.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "amazon-ads", "label": "Amazon Advertising", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Third-largest digital ad platform. Unique compound void: advertising + commerce. Sponsored products appear as search results (opacity of paid vs organic). Amazon uses seller data to compete (opacity of platform-as-competitor). Buy Box algorithm opaque.", "evidence": "Source: propaganda-advertising analysis. $46.9B ad revenue (2023). FTC antitrust case documents opaque Buy Box algorithm favoring Amazon's own products.", "harms": "FTC antitrust case. Opaque Buy Box algorithm. Platform uses seller data to launch competing products.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "the-trade-desk", "label": "The Trade Desk", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 1, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Largest independent DSP (demand-side platform). B2B \u2014 advertisers use it to target users across the web. Maximum opacity to end users: they never know TTD is serving them ads. Maximum responsiveness through real-time bidding on individual impressions.", "evidence": "Source: propaganda-advertising analysis. $1.9B revenue (2023). UID2 identity framework attempts post-cookie tracking.", "rateable": false, "taxonomyTag": "company" }, { "id": "criteo", "label": "Criteo", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Retargeting ad-tech company. The purest void in advertising: you look at a product, and it follows you across every website. Maximum responsiveness to individual browsing behavior. Users cannot see or control the retargeting mechanism.", "evidence": "Source: propaganda-advertising analysis. Retargeting is 'the purest form of advertising responsiveness' per source analysis. FTC: BetterHelp shared data with Criteo.", "rateable": false, "taxonomyTag": "company" }, { "id": "applovin", "label": "AppLovin", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Mobile ad-tech company with AI-driven ad targeting (AXON engine). Maximum opacity: AI determines which ads to show which users. App ecosystem includes games that serve as data collection + ad delivery vehicles.", "evidence": "Source: propaganda-advertising analysis context. Market cap grew 10x (2024) on AI ad optimization. Owns game studios that function as ad delivery mechanisms.", "rateable": false, "taxonomyTag": "company" }, { "id": "taboola", "label": "Taboola", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "'Content recommendation' platform (actually native advertising). Maximum opacity: 'Recommended for you' links at the bottom of news articles disguise paid ads as content. Users cannot distinguish editorial from paid placement. Full gradient: clickbait optimization is the business model.", "evidence": "Source: propaganda-advertising analysis. 'Chumbox' advertising. Powers 'Around the Web' sections on major news sites. Clickbait optimization documented.", "harms": "Clickbait content degradation. Health misinformation through native ads. Users deceived about paid vs editorial content.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "outbrain", "label": "Outbrain", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Native advertising platform. Same 'content recommendation' framing as Taboola \u2014 paid advertising disguised as recommended content. Same structural void: opacity of paid/organic boundary, engagement through curiosity gap headlines.", "evidence": "Source: propaganda-advertising analysis. Competitor to Taboola in 'chumbox' native ad space.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "tiktok-ads", "label": "TikTok Ads Manager", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 1, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "TikTok's advertising platform. Ads are algorithmically indistinguishable from organic content \u2014 maximum opacity of paid/organic boundary. Targeting uses the same recommendation algorithm that drives engagement. The ad IS the content and vice versa.", "evidence": "Source: propaganda-advertising analysis. TikTok's algorithm serves ads within the same feed as organic content. Creator marketplace blurs brand/organic line further.", "rateable": true, "taxonomyTag": "advertising" }, { "id": "wpp", "label": "WPP", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Largest advertising holding company. Designs and deploys void architecture at industrial scale \u2014 the architects, not the infrastructure. Owns GroupM (largest media buyer), Ogilvy, VMLY&R. Maximum opacity: clients and public cannot see how campaigns are constructed.", "evidence": "Source: propaganda-advertising analysis. $14.8B revenue (2023). Ogilvy (founded by David Ogilvy, cited extensively in source analysis) is a WPP subsidiary.", "rateable": false, "taxonomyTag": "company" }, { "id": "omnicom", "label": "Omnicom Group", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Second-largest ad holding company. Same structural position as WPP \u2014 designs and deploys persuasion architecture at scale. Owns BBDO, DDB, TBWA. Pending merger with IPG would create largest ad company globally.", "evidence": "Source: propaganda-advertising analysis context. $14.7B revenue (2023). Omnicom-IPG merger announced (2024).", "rateable": false, "taxonomyTag": "company" }, { "id": "publicis", "label": "Publicis Groupe", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 2 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Third-largest ad holding company. Owns Epsilon (data-driven marketing) and Sapient (digital consulting). Epsilon's 250M+ consumer profiles represent one of the largest private behavioral databases. Data + creative + media buying = compound void.", "evidence": "Source: propaganda-advertising analysis context. Epsilon data breach (2019). 250M+ consumer profiles in Epsilon database.", "rateable": false, "taxonomyTag": "company" }, { "id": "clear-channel", "label": "Clear Channel Outdoor", "category": "advertising", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Largest US billboard company. Digital billboards now use mobile location data for targeting \u2014 the outdoor ad responds to who's nearby. Lower void than digital ads but moving toward programmatic outdoor targeting.", "evidence": "Source: propaganda-advertising analysis. Digital billboards with mobile data targeting. RADAR audience measurement uses phone location data.", "rateable": false, "taxonomyTag": "company" }, { "id": "programmatic-dsp", "label": "Programmatic Ad Industry", "category": "advertising", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 2, "occupancy": "empty", "domainType": "platform", "entityType": "industry", "confidence": "assessed", "summary": "The real-time bidding ecosystem as a whole. Millisecond auctions for individual human attention across billions of daily impressions. Maximum opacity to users (no one can see the auction), maximum responsiveness (bids on your specific behavior), full gradient (behavioral modification is the documented output).", "evidence": "Source: propaganda-advertising analysis. $500B+ global digital ad spending (2024). ANA study: $22B in programmatic ad waste annually.", "rateable": false, "taxonomyTag": "reference" }, { "id": "match-com", "label": "Match.com", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Original online dating platform. Opaque matching algorithm. High engagement through profile browsing and messaging. Parent company (Match Group) owns Tinder, Hinge, OkCupid \u2014 portfolio approach to relationship void.", "evidence": "Source: dating-apps analysis. Match Group controls ~65% of US dating market.", "rateable": true, "taxonomyTag": "dating" }, { "id": "bumble", "label": "Bumble", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Dating app with women-message-first constraint. The constraint (women initiate) is a genuine void-reduction feature \u2014 reduces unsolicited contact. But: opaque matching algorithm, swipe mechanics (variable-ratio reinforcement), and premium features that monetize engagement remain.", "evidence": "Source: dating-apps analysis. Women-first messaging is a constraint-specification feature on the independence dimension.", "rateable": true, "taxonomyTag": "dating" }, { "id": "hinge", "label": "Hinge", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "'Designed to be deleted' marketing contradicts engagement-maximizing architecture. Opaque matching algorithm (Gale-Shapley variant). Prompt-based profiles add slight transparency but core matching remains hidden. Owned by Match Group.", "evidence": "Source: dating-apps analysis. Marketing claims conflict with parent company (Match Group) revenue incentive to maximize engagement time.", "rateable": true, "taxonomyTag": "dating" }, { "id": "eharmony", "label": "eHarmony", "category": "relationship", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Compatibility-focused dating platform. Maximum opacity: proprietary '32 dimensions of compatibility' questionnaire and matching algorithm completely hidden. Claims scientific basis but methodology never peer-reviewed or published.", "evidence": "Source: dating-apps analysis. Claims 'scientific' matching but no published methodology. Eli Finkel (2012) critique: no evidence proprietary matching works better than random assignment.", "rateable": true, "taxonomyTag": "dating" }, { "id": "okcupid", "label": "OkCupid", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Dating platform with question-based matching. More transparent than eHarmony \u2014 match percentages based on user-answered questions with visible logic. But: owned by Match Group, swiping added, premium features gate visibility. Transparency has degraded over time.", "evidence": "Source: dating-apps analysis. OkCupid Experiments blog (2014) documented A/B testing on users without consent. Owned by Match Group.", "rateable": true, "taxonomyTag": "dating" }, { "id": "grindr", "label": "Grindr", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Location-based dating/hookup app for gay/bi men. High responsiveness (real-time proximity). High engagement through grid browsing and continuous checking. Full gradient: documented association with anxiety, compulsive checking, and self-esteem issues. Unique safety risks from location data exposure.", "evidence": "Source: dating-apps analysis. Norwegian data protection fine (\u20ac6.5M) for sharing location/HIV-status data with advertisers. Documented: location data sold to Catholic organization to track priests.", "harms": "Location data sold to third parties. HIV status shared with advertisers. Location tracking used to out individuals. Compulsive use patterns documented.", "rateable": true, "taxonomyTag": "dating" }, { "id": "coffee-meets-bagel", "label": "Coffee Meets Bagel", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Daily-limit dating app. Constraint-leaning: sends limited daily matches ('bagels') to reduce infinite-swipe engagement loop. The daily limit IS a constraint specification feature \u2014 reduces engagement without reducing utility. But matching algorithm still opaque.", "evidence": "Source: dating-apps analysis. Daily limit reduces variable-ratio reinforcement compared to unlimited swipe apps.", "rateable": true, "taxonomyTag": "dating" }, { "id": "feeld", "label": "Feeld", "category": "relationship", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Dating app for 'open-minded' relationships (polyamory, kink, non-traditional). Higher engagement through identity exploration and niche community. Opaque matching but community features add relational engagement beyond swiping.", "evidence": "Source: dating-apps analysis. Niche positioning creates identity-forming engagement beyond transactional matching.", "rateable": true, "taxonomyTag": "dating" }, { "id": "the-league", "label": "The League", "category": "relationship", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Elite/exclusive dating app with waitlist and algorithmic gatekeeping. Maximum opacity: acceptance criteria hidden, ranking algorithm hidden, limited daily matches. The exclusivity IS the engagement mechanism \u2014 scarcity and status signaling.", "evidence": "Source: dating-apps analysis. Waitlist creates artificial scarcity. LinkedIn integration for status verification. Exclusivity as engagement driver.", "rateable": true, "taxonomyTag": "dating" }, { "id": "match-group", "label": "Match Group (Holding)", "category": "relationship", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Controls ~65% of US dating market (Tinder, Hinge, Match.com, OkCupid, Plenty of Fish, and more). The entity-level void: portfolio approach means users leaving one platform often move to another Match Group property. Revenue model incentivizes maximizing time-on-app, not successful matches.", "evidence": "Source: dating-apps analysis. Revenue from subscriptions and premium features that monetize engagement. Structural conflict: successful matches reduce revenue.", "harms": "Revenue model structurally misaligned with user success. Portfolio approach traps users within Match Group ecosystem.", "rateable": false, "taxonomyTag": "company" }, { "id": "google-news", "label": "Google News", "category": "news-media", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Algorithmic news aggregator. Maximum opacity: news selection and ranking algorithm hidden. Users cannot see why specific stories are shown. Maximum responsiveness: personalized based on browsing history and engagement patterns. Creates filter bubble by design.", "evidence": "Source: news-journalism analysis. Dominant news aggregator. Algorithm shapes which news millions of people see daily.", "rateable": true, "taxonomyTag": "media" }, { "id": "apple-news", "label": "Apple News", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Curated news aggregator with editorial and algorithmic selection. Partial constraint: Apple employs human editors for top stories (partial transparency). But algorithmic personalization in 'For You' tab is opaque. Lower gradient than Google News due to editorial layer.", "evidence": "Source: news-journalism analysis. Human editorial curation is a constraint-specification feature reducing pure algorithmic opacity.", "rateable": true, "taxonomyTag": "media" }, { "id": "flipboard", "label": "Flipboard", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Magazine-style news aggregator. Opaque content curation algorithm but user-controlled topic selection adds transparency. Community curation (user-created magazines) distributes editorial control. Moderate void.", "evidence": "Source: news-journalism analysis. Community curation model distributes content selection partially.", "rateable": true, "taxonomyTag": "media" }, { "id": "fox-news", "label": "Fox News", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "Cable news with maximum engagement architecture. Opinion programming designed to activate outrage (engagement maximizer). 'Breaking news' as void amplification. Documented: Dominion lawsuit ($787.5M settlement) proved network aired claims its own hosts knew were false.", "evidence": "Source: news-journalism analysis. Dominion settlement ($787.5M, 2023). Internal communications showed hosts and executives knew election claims were false while airing them.", "harms": "Dominion settlement for knowingly airing false election claims. Documented role in election misinformation. Smartmatic lawsuit ongoing.", "rateable": true, "taxonomyTag": "media" }, { "id": "cnn", "label": "CNN", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Cable news network. High engagement through 'breaking news' paradigm, countdown clocks, split-screen debates. The 24-hour news format IS the engagement architecture \u2014 must fill time, which drives toward sensationalism and conflict-as-content.", "evidence": "Source: news-journalism analysis. Pioneer of 24-hour news format. 'Breaking news' as void amplification documented in source analysis.", "rateable": true, "taxonomyTag": "media" }, { "id": "msnbc", "label": "MSNBC", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Cable news with partisan opinion programming. Mirror image of Fox News in structure \u2014 engagement through political outrage from the opposite direction. Source analysis documents D1\u2192D2\u2192D3 operating in both political directions simultaneously.", "evidence": "Source: news-journalism analysis. Same engagement architecture as Fox News applied to different political audience.", "rateable": true, "taxonomyTag": "media" }, { "id": "reuters", "label": "Reuters", "category": "news-media", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Wire service. Constraint-pole for news media: factual reporting without opinion, transparent attribution, minimal engagement manipulation. The wire service model is the closest news media gets to tool posture \u2014 information delivery without attention capture architecture.", "evidence": "Source: news-journalism analysis. Wire service model as constraint case. Trust Principles (editorial independence) are a published constraint specification.", "rateable": true, "taxonomyTag": "media" }, { "id": "ap-news", "label": "Associated Press", "category": "news-media", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 0 }, "driftVelocity": 7, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Wire service and news cooperative. Same constraint-pole position as Reuters \u2014 factual reporting, attribution-based, nonprofit cooperative structure. AP Stylebook is a published constraint specification for journalistic practice.", "evidence": "Source: news-journalism analysis. Nonprofit cooperative. AP Stylebook as published constraint.", "rateable": true, "taxonomyTag": "media" }, { "id": "nyt", "label": "The New York Times", "category": "news-media", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Major newspaper with digital platform. Moderate engagement through recommendations, newsletters, games (Wordle), and podcasts. Journalistic standards provide partial transparency constraint. Digital engagement features growing (notifications, personalization, games).", "evidence": "Source: news-journalism analysis. Wordle and games division adds engagement layer beyond journalism. Digital subscriptions 10M+.", "rateable": true, "taxonomyTag": "media" }, { "id": "substack", "label": "Substack", "category": "news-media", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Newsletter platform with direct writer-reader relationship. Lower opacity than algorithmic news (you choose what to subscribe to). But: recommendation algorithm, Notes feed, and engagement metrics increasingly drive discovery. Constraint-leaning but drifting toward social media architecture.", "evidence": "Source: news-journalism analysis context. Direct subscription model reduces algorithmic curation void. But Notes feature adds social media engagement layer.", "rateable": true, "taxonomyTag": "media" }, { "id": "medium", "label": "Medium", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Blogging platform with algorithmic curation. Opaque recommendation algorithm determines which articles get distributed. Medium Partner Program pays writers based on 'member reading time' \u2014 incentivizes engagement optimization over quality.", "evidence": "Source: news-journalism analysis context. Partner Program incentivizes clickbait. Multiple pivots have degraded platform trust.", "rateable": true, "taxonomyTag": "media" }, { "id": "daily-wire", "label": "The Daily Wire", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 2, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Conservative media company. High engagement through outrage-driven content, social media optimization, and culture war framing. Full gradient: content designed to maximize sharing and emotional response rather than inform.", "evidence": "Source: news-journalism analysis context. Consistently among most-shared content on Facebook. Engagement-optimized content production.", "rateable": true, "taxonomyTag": "media" }, { "id": "buzzfeed-news", "label": "BuzzFeed", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Digital media company. Pioneered engagement-optimized content (quizzes, listicles, shareable formats). BuzzFeed News (shut down 2023) was legitimately constraint-oriented journalism funded by engagement content. The architecture consumed the constraint.", "evidence": "Source: news-journalism analysis context. BuzzFeed News shutdown (2023) is a case study: engagement-funded journalism is structurally unstable.", "harms": "BuzzFeed News closure eliminated legitimate journalism. Engagement model unsustainable for quality reporting.", "rateable": true, "taxonomyTag": "media" }, { "id": "spotify-podcasts", "label": "Spotify (Podcasts)", "category": "news-media", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Podcast platform with algorithmic recommendation. Opaque recommendation algorithm determines podcast discovery. Joe Rogan exclusive ($200M+) demonstrates engagement-maximizing content strategy. Algorithmic curation replaces organic podcast discovery.", "evidence": "Source: news-journalism analysis context. $200M+ Joe Rogan deal. Algorithmic recommendation increasingly drives podcast discovery.", "rateable": true, "taxonomyTag": "media" }, { "id": "zillow", "label": "Zillow", "category": "real-estate", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Real estate platform with Zestimate algorithmic home valuation. Opaque valuation algorithm creates engagement void \u2014 homeowners compulsively check Zestimates. Zillow Offers (iBuying, discontinued) showed the platform using its own data to trade against users.", "evidence": "Domain knowledge. Zestimate median error ~2-7%. Zillow Offers iBuying program lost $881M (2021) \u2014 platform trading on its own data. 221M+ property listings.", "harms": "Zillow Offers losses ($881M). Zestimate inaccuracies affect property values and tax assessments. Information asymmetry between platform and users.", "rateable": true, "taxonomyTag": "real_estate" }, { "id": "airbnb", "label": "Airbnb", "category": "real-estate", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2 }, "driftVelocity": 3, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Short-term rental platform. Opaque search ranking algorithm (Superhost status, pricing suggestions). High engagement for hosts (income dependency, review management). Review system is bilateral void \u2014 both host and guest incentivized to be positive (retaliation risk).", "evidence": "Domain knowledge. Documented: hidden fees controversy, housing market displacement, algorithmic pricing recommendations. Host-guest review retaliation dynamics documented.", "harms": "Housing market displacement in tourist cities. Hidden fee structures. Host discrimination documented. Algorithmic pricing creates race-to-bottom.", "rateable": true, "taxonomyTag": "real_estate" }, { "id": "uber", "label": "Uber", "category": "gig-economy", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Ride-hailing platform with maximum algorithmic management opacity. Drivers cannot see fare calculations, surge pricing algorithms, or how they're rated/deactivated. Passengers cannot see driver matching logic. Full D3: drivers deactivated by opaque algorithm with no meaningful appeal.", "evidence": "Domain knowledge. Uber Files leak (2022) documented deliberate regulatory evasion. Algorithmic wage theft lawsuits. Driver deactivation without transparent process.", "harms": "Uber Files scandal. Algorithmic wage calculation opacity. Driver deactivation without appeal. Surge pricing exploits demand uncertainty.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "lyft", "label": "Lyft", "category": "gig-economy", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Ride-hailing platform. Same structural opacity as Uber \u2014 opaque pricing, algorithmic driver management, hidden matching logic. Slightly lower gradient due to less aggressive growth tactics, but same fundamental void architecture.", "evidence": "Domain knowledge. Same gig economy void architecture as Uber. Documented: fare discrepancies between driver earnings and passenger charges.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "doordash", "label": "DoorDash", "category": "gig-economy", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Food delivery platform. Triple opacity: customers don't see how fees are calculated, drivers don't see full delivery details before accepting, restaurants don't control their presence on the platform. Documented: tip theft (DoorDash used tips to subsidize base pay).", "evidence": "Domain knowledge. $2.5M tip theft settlement (2020). Restaurant menu price inflation without consent. Driver pay opacity.", "harms": "Tip theft scandal. Restaurant listings without consent. Menu price inflation passed to consumers. Driver pay below minimum wage documented.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "instacart", "label": "Instacart", "category": "gig-economy", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Grocery delivery platform. Opaque batch assignment algorithm, service fee calculation, and tip handling. Shopper pay structure changed repeatedly with little transparency. Advertising-funded \u2014 product placement in search results hidden from consumers.", "evidence": "Domain knowledge. IPO (2023). Product placement advertising in search results. Shopper pay structure repeatedly modified.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "fiverr", "label": "Fiverr", "category": "gig-economy", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Freelance services marketplace. Opaque search ranking algorithm determines which freelancers are visible. Performance metrics (response time, delivery speed) create algorithmic pressure. 20% commission on both sides creates economic coupling.", "evidence": "Domain knowledge. Algorithmic ranking creates winner-take-all dynamics. 20% commission (buyer + seller fees).", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "upwork", "label": "Upwork", "category": "gig-economy", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Freelance marketplace. Opaque job matching algorithm and 'talent cloud' AI. JSS (Job Success Score) is a hidden metric that determines freelancer visibility \u2014 workers cannot see how it's calculated. Connects feature charges freelancers to apply for jobs.", "evidence": "Domain knowledge. JSS algorithm is opaque. 'Connects' system charges freelancers to apply. 10-20% commission on earnings.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "redfin", "label": "Redfin", "category": "real-estate", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Real estate platform. More transparent than Zillow \u2014 publishes Redfin Estimate methodology documentation. Lower fees (listing fee ~1-1.5% vs traditional 2.5-3%). Constraint-leaning for real estate category.", "evidence": "Domain knowledge. Published valuation methodology. Lower commission structure. Direct brokerage model adds some transparency.", "rateable": true, "taxonomyTag": "real_estate" }, { "id": "vrbo", "label": "Vrbo", "category": "real-estate", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Vacation rental platform (Expedia). Similar to Airbnb but focused on whole-home rentals. Same opaque ranking algorithm and fee structure. Lower void than Airbnb due to less social engagement architecture (no shared spaces, no experience features).", "evidence": "Domain knowledge. Owned by Expedia Group. More transparent fee display than historical Airbnb.", "rateable": true, "taxonomyTag": "real_estate" }, { "id": "taskrabbit", "label": "TaskRabbit", "category": "gig-economy", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1 }, "driftVelocity": 5, "occupancy": "inhabited", "domainType": "platform", "confidence": "assessed", "summary": "Task-based gig platform (owned by IKEA). Workers set their own rates (partial transparency). Matching algorithm opaque but workers have more control than ride-hailing drivers. Lower void due to task-based model with worker pricing control.", "evidence": "Domain knowledge. Acquired by IKEA (2017). Workers set rates \u2014 unusual constraint feature in gig economy.", "rateable": true, "taxonomyTag": "gig_economy" }, { "id": "msci-esg", "label": "MSCI ESG Ratings", "category": "supply-chain", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 3 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Dominant ESG rating provider. Maximum opacity: rating methodology partially published but actual scoring algorithms hidden. Companies rated cannot fully understand or challenge their scores. Constraint-branded void: presents AS transparency (ESG = accountability) while functioning as opacity layer (scoring is opaque).", "evidence": "Decisions.md: Paper 26C deployer. Bloomberg investigation (2021) documented MSCI upgrading polluters. Companies pay for rating services (conflict of interest).", "harms": "Documented upgrading of major polluters (Bloomberg 2021). Rating-shopping by companies. Greenwashing facilitation through opaque methodology.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "sustainalytics", "label": "Sustainalytics (Morningstar)", "category": "supply-chain", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "ESG risk rating provider. Same structural opacity as MSCI \u2014 methodology partially disclosed but scoring proprietary. Companies cannot fully understand ratings. Low correlation between Sustainalytics and MSCI ratings for same companies demonstrates methodology opacity.", "evidence": "Decisions.md: Paper 26C deployer. Berg et al. (2022) documented low correlation between ESG raters \u2014 same company gets wildly different scores from different agencies.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "sp-global-esg", "label": "S&P Global ESG Scores", "category": "supply-chain", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "ESG scoring from the same firm that rates creditworthiness. Compound credibility: S&P's credit rating authority lends perceived legitimacy to ESG scores despite different methodology rigor. Removed Tesla from ESG index while keeping Exxon \u2014 demonstrating methodology opacity.", "evidence": "Decisions.md: Paper 26C deployer. Tesla removal from S&P ESG index (2022) while Exxon remained highlighted scoring opacity.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "verra", "label": "Verra (Carbon Offsets)", "category": "supply-chain", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 2, "gradient": 3 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "documented", "summary": "Largest carbon credit certifier. Maximum void: certifies carbon offsets that are structurally opaque \u2014 buyers cannot verify that offsets represent real emissions reductions. Guardian/Die Zeit investigation (2023) found >90% of Verra's rainforest carbon offsets were 'phantom credits.'", "evidence": "Decisions.md: Paper 26C deployer. Guardian/Die Zeit/SourceMaterial investigation (2023): >90% of Verra rainforest offsets were phantom credits. CEO resigned.", "harms": "Phantom carbon credits enabling corporate greenwashing. >90% of rainforest credits found to be worthless. Facilitates false net-zero claims.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "gold-standard-carbon", "label": "Gold Standard", "category": "supply-chain", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Higher-quality carbon credit standard. More transparent than Verra \u2014 stricter additionality requirements, SDG co-benefit requirements. Constraint-leaning within the carbon offset space but still faces structural opacity of offset verification.", "evidence": "Decisions.md: Paper 26C deployer. Founded by WWF. Stricter standards than Verra. Smaller market share reflects higher barrier to entry.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "ecovadis", "label": "EcoVadis", "category": "supply-chain", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Supply chain sustainability rating platform. Companies rate their suppliers \u2014 creates compound void where the rater has commercial relationships with the rated. Methodology partially disclosed. 100,000+ company ratings.", "evidence": "Decisions.md: Paper 26C deployer. Rated 100,000+ companies. EU CSRD compliance driver increasing adoption.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "cdp-carbon", "label": "CDP (Carbon Disclosure Project)", "category": "supply-chain", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 2, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Environmental disclosure platform. Constraint-leaning: requests self-reported disclosure (transparency mechanism) rather than opaque rating. But: self-reported data is inherently subject to greenwashing. Scoring of disclosures adds opacity layer.", "evidence": "Decisions.md: Paper 26C deployer. 23,000+ companies disclose. Disclosure-based model is more transparent than rating-only models.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "sbti", "label": "Science Based Targets initiative", "category": "supply-chain", "scores": { "opacity": 1, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 6, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Climate target validation body. Constraint-leaning: validates corporate emissions targets against climate science (transparent reference point). But: 2024 controversy over weakened standards for scope 3 emissions. Target validation \u2260 target achievement.", "evidence": "Decisions.md: Paper 26C deployer. 2024 standards weakening controversy. Validates targets but doesn't verify achievement \u2014 structural gap.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "b-corp", "label": "B Corp Certification", "category": "supply-chain", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Social/environmental certification. Constraint-branded void: certification presents as transparency but B Impact Assessment is self-reported with limited verification. Companies use B Corp status for marketing while assessment rigor varies. Nespresso B Corp certification raised credibility questions.", "evidence": "Decisions.md: Paper 26C deployer. Nespresso B Corp certification controversy. Self-reported assessment with limited independent verification.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "carbon-trust", "label": "Carbon Trust", "category": "supply-chain", "scores": { "opacity": 2, "responsiveness": 1, "engagement": 1, "gradient": 1 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Carbon footprint certification and advisory. More transparent than pure offset providers \u2014 focuses on measurement and reduction rather than offset trading. Constraint-leaning but commercial advisory relationship creates mild coupling.", "evidence": "Decisions.md: Paper 26C deployer. UK-based. Product Carbon Footprint certification program.", "rateable": true, "taxonomyTag": "supply_chain" }, { "id": "supercell", "label": "Supercell", "category": "gaming-platform", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Mobile game studio (Clash of Clans, Clash Royale, Brawl Stars). High engagement through clan mechanics, seasonal content, and premium currency. Gacha/loot box mechanics in multiple titles. Tencent majority owner.", "evidence": "Source: video-games analysis context. $1.8B revenue (2023). Tencent owns ~84%. Multiple titles with gacha mechanics.", "rateable": true, "taxonomyTag": "gaming" }, { "id": "king-candy-crush", "label": "King (Candy Crush)", "category": "gaming-platform", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Candy Crush developer (owned by Activision Blizzard / Microsoft). Maximum engagement: lives system creates artificial scarcity, level difficulty spikes designed to trigger purchases, social comparison mechanics. Variable-ratio reward schedule mirrors slot machine architecture.", "evidence": "Source: video-games analysis. Candy Crush generates $1B+ annually. Level difficulty designed around purchase triggers. Lives system = artificial scarcity + monetization.", "harms": "Documented addictive mechanics. Spending by vulnerable populations (whales). Dark pattern monetization targeting engagement hooks.", "rateable": true, "taxonomyTag": "gaming" }, { "id": "nexon", "label": "Nexon", "category": "gaming-platform", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Korean game publisher (MapleStory, Dungeon Fighter Online). Pioneer of F2P + gacha monetization model. Maximum opacity in gacha odds (historically undisclosed in many markets). MapleStory cubing/starforce system is one of the most opaque enhancement mechanics in gaming.", "evidence": "Source: video-games analysis. MapleStory cubing scandal (2021-2022) \u2014 Nexon found to have rigged enhancement odds in South Korea. Fined by Korean FTC.", "harms": "Rigged gacha odds (Korean FTC finding). Player spending documented at thousands of dollars per character. Undisclosed probability manipulation.", "rateable": true, "taxonomyTag": "gaming" }, { "id": "netease", "label": "NetEase", "category": "gaming-platform", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Chinese game publisher (second to Tencent). Operates Blizzard games in China. Multiple mobile titles with gacha mechanics. Maximum opacity: data practices, monetization algorithms, and engagement systems hidden behind Chinese regulatory environment.", "evidence": "Source: video-games analysis context. Chinese gaming regulations partially constrain minors but monetization opacity remains high for adults.", "rateable": false, "taxonomyTag": "company" }, { "id": "take-two-rockstar", "label": "Take-Two / Rockstar Games", "category": "gaming-platform", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Publisher of GTA and NBA 2K. GTA Online's Shark Cards and NBA 2K's Virtual Currency (VC) system are opaque monetization layers. NBA 2K particularly criticized for gambling-adjacent mechanics in a sports game marketed to children.", "evidence": "Source: video-games analysis. NBA 2K MyTeam: pack odds opaque, virtual currency economy designed to push real-money purchases. ESRB does not rate as gambling.", "harms": "NBA 2K gambling-adjacent mechanics marketed to children. Virtual currency inflation designed to drive purchases.", "rateable": false, "taxonomyTag": "company" }, { "id": "perplexity-ai", "label": "Perplexity AI", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 1, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.39 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI search engine with citations. Partially constraint-leaning: cites sources (transparency). But: underlying AI model is opaque, content attribution is sometimes inaccurate. Publishers have accused Perplexity of content scraping without attribution.", "evidence": "AI domain knowledge. Publisher lawsuits over content scraping. Citations sometimes hallucinated or misattributed.", "rateable": true, "taxonomyTag": "ai_assistant" }, { "id": "mistral-ai", "label": "Mistral AI", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 1, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 1, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.26 } }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "French AI company. Partially open-source (weight releases for some models). More transparent than fully closed models but increasingly moving toward closed/commercial models. EU-based \u2014 subject to EU AI Act compliance.", "evidence": "AI domain knowledge. Open-weight Mistral 7B and Mixtral. Commercial models (Le Chat) are closed. EU-headquartered.", "rateable": false, "taxonomyTag": "company" }, { "id": "stability-ai", "label": "Stability AI", "category": "ai-general", "scores": { "opacity": 1, "responsiveness": 2, "engagement": 2, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 2, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 1 }, "composite_aids": 0.19 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Stable Diffusion developer. Open-source model weights \u2014 maximum protocol transparency for AI image generation. But: training data sourcing opaque (LAION-5B), copyright disputes, and generated content raises identity/art displacement concerns.", "evidence": "AI domain knowledge. Stable Diffusion is open-source. Training data lawsuits from artists (Getty Images, individual creators).", "harms": "Artist displacement. Training on copyrighted works without consent. Generated content used for deepfakes.", "rateable": false, "taxonomyTag": "company" }, { "id": "midjourney", "label": "Midjourney", "category": "ai-general", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 0, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 1, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 1, "user_persona_creation": 0, "monetization_pressure": 2, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.53 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI image generation (closed-source). Maximum opacity: model architecture, training data, and content policies all hidden. High engagement through Discord-based community and image sharing. Community creates identity-forming engagement around AI art creation.", "evidence": "AI domain knowledge. Closed-source. Discord-only interface creates community engagement. Training data undisclosed.", "harms": "Artist displacement. Training data opacity. Deepfake generation capabilities.", "rateable": true, "taxonomyTag": "ai_creative" }, { "id": "elevenlabs", "label": "ElevenLabs", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.39 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI voice synthesis platform. High responsiveness (real-time voice cloning). Moderate opacity \u2014 model architecture hidden but use case is transparent. Gradient from deepfake potential \u2014 voice cloning enables fraud and impersonation.", "evidence": "AI domain knowledge. Voice cloning used in documented fraud cases (CEO impersonation scams). Biden robocall deepfake (2024).", "harms": "Voice cloning fraud. Biden robocall deepfake. Celebrity voice impersonation without consent.", "rateable": true, "taxonomyTag": "ai_creative" }, { "id": "suno-ai", "label": "Suno AI", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 3, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 0, "training_data_disclosure": 0, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 1 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 0, "data_retention_opt_out": 0 }, "composite_aids": 0.39 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "AI music generation. High engagement through easy creation (text-to-music). Training data opaque \u2014 RIAA lawsuit (2024) alleges training on copyrighted music. Same structural tension as image AI: transparency of tool vs opacity of training.", "evidence": "AI domain knowledge. RIAA lawsuit (2024). Training data sourcing opaque. Rapidly growing user base.", "harms": "Musician displacement. Training on copyrighted music without licensing. RIAA lawsuit.", "rateable": true, "taxonomyTag": "ai_creative" }, { "id": "adobe-firefly", "label": "Adobe Firefly", "category": "ai-general", "scores": { "opacity": 2, "responsiveness": 3, "engagement": 2, "gradient": 1, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 0, "content_filter_transparency": 2 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 0 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 1, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.12 } }, "driftVelocity": 4, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Adobe's AI image generation. Constraint-leaning for AI art: trained on licensed Adobe Stock content (transparent data sourcing), Content Credentials for provenance. But: model architecture still opaque. Integrated into Creative Suite increases engagement through workflow lock-in.", "evidence": "AI domain knowledge. Licensed training data (Adobe Stock). Content Credentials initiative. Lower copyright risk than Midjourney/Stable Diffusion.", "rateable": true, "taxonomyTag": "ai_creative" }, { "id": "palantir", "label": "Palantir Technologies", "category": "technology-company", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 1, "gradient": 3 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Government/enterprise data analytics. Maximum opacity: what Palantir's systems do with data is hidden from the populations they analyze. Used by ICE, police, military. The subjects of analysis have zero visibility into how they're being assessed or surveilled.", "evidence": "AI + governance domain knowledge. ICE contracts documented. Predictive policing deployments. Military intelligence applications. Subjects have no access to their analysis.", "harms": "Predictive policing bias. Immigration enforcement targeting. Surveillance of populations without consent or transparency. No subject access to algorithmic assessments.", "rateable": false, "taxonomyTag": "company" }, { "id": "clearview-ai", "label": "Clearview AI", "category": "technology-company", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 1, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "documented", "summary": "Facial recognition company. Maximum void: scraped billions of photos from social media without consent to build facial recognition database used by law enforcement. Subjects have zero knowledge they're in the database, zero ability to opt out, zero access to how matches are made.", "evidence": "AI + privacy domain knowledge. Scraped 30B+ photos. Fined by multiple countries (UK \u00a37.5M, France \u20ac20M, Italy \u20ac20M, Australia, Canada). ACLU settlement.", "harms": "Mass facial recognition without consent. Multiple international fines. Misidentification risk disproportionately affects minorities. No opt-out mechanism.", "rateable": false, "taxonomyTag": "company" }, { "id": "tencent", "label": "Tencent Holdings", "category": "technology-company", "scores": { "opacity": 3, "responsiveness": 2, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "Chinese tech conglomerate. Owns WeChat (1.3B users), significant stakes in Riot Games, Epic Games, Supercell, Spotify, Snap, Discord, Reddit. The entity-level void: portfolio approach means engagement data flows across gaming, social media, payments, and entertainment. Maximum opacity about data practices.", "evidence": "Source: video-games + social-media analysis context. Largest gaming company by revenue. WeChat is effectively required infrastructure in China.", "rateable": false, "taxonomyTag": "company" }, { "id": "amazon-platform", "label": "Amazon (Marketplace)", "category": "other", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 3, "gradient": 3 }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "documented", "summary": "E-commerce platform as void architecture. Opaque search ranking (Buy Box algorithm), opaque pricing, opaque review system. Uses seller data to launch competing products. Dark patterns in Prime cancellation. Fulfillment dependency creates maximum seller lock-in.", "evidence": "Source: API analysis + domain knowledge. FTC antitrust case. EU Digital Markets Act gatekeeper designation. Dark pattern Prime cancellation ('Project Iliad').", "harms": "FTC antitrust case. Self-preferencing. Seller data exploitation. Dark pattern cancellation. Warehouse worker conditions.", "rateable": false, "taxonomyTag": "company" }, { "id": "apple-platform", "label": "Apple (App Store)", "category": "technology-company", "scores": { "opacity": 3, "responsiveness": 1, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "App Store gatekeeper. Maximum opacity: app review process arbitrary and opaque, ranking algorithm hidden, 30% commission non-negotiable. Low responsiveness (unilateral decisions, slow appeals). DMA compliance under EU scrutiny.", "evidence": "Source: API analysis context. Epic v. Apple lawsuit. EU DMA gatekeeper designation. 30% 'Apple tax.' Opaque app review rejections.", "harms": "Monopolistic app distribution. Opaque review process. 30% commission reduces developer revenue. Anti-competitive behavior documented in Epic lawsuit.", "rateable": false, "taxonomyTag": "company" }, { "id": "google-play", "label": "Google Play Store", "category": "technology-company", "scores": { "opacity": 2, "responsiveness": 2, "engagement": 2, "gradient": 2 }, "driftVelocity": 5, "occupancy": "empty", "domainType": "platform", "confidence": "assessed", "summary": "Android app distribution. Similar gatekeeper void to Apple but with sideloading option (partial constraint). Opaque app ranking and search algorithm. Epic v. Google verdict (2023) found Google maintained illegal monopoly.", "evidence": "Source: API analysis context. Epic v. Google jury verdict \u2014 found illegal monopoly (2023). Sideloading reduces lock-in vs Apple.", "rateable": true, "taxonomyTag": "gaming" }, { "id": "openai-platform", "label": "OpenAI (Platform)", "category": "ai-general", "scores": { "opacity": 3, "responsiveness": 3, "engagement": 2, "gradient": 2, "sub_features": { "o_type": { "model_card_published": 1, "training_data_disclosure": 1, "api_available": 1, "system_prompt_visible": 2, "content_filter_transparency": 2 }, "r_type": { "persistent_memory": 0, "proactive_outreach": 0, "response_personalization": 0, "conversation_continuity_design": 0, "real_time_adaptation": 1 }, "alpha_type": { "emotional_engagement_design": 0, "user_persona_creation": 0, "monetization_pressure": 0, "conversation_export": 1, "data_retention_opt_out": 1 }, "composite_aids": 0.1 } }, "driftVelocity": 3, "occupancy": "empty", "domainType": "platform", "entityType": "company", "confidence": "assessed", "summary": "AI platform company. Maximum opacity: model architecture, training data, RLHF process, and safety evaluations all hidden. High responsiveness through ChatGPT. Corporate governance opacity (nonprofit\u2192capped-profit\u2192for-profit transition). Distinct from ChatGPT product \u2014 this scores the company/platform.", "evidence": "Source: AI interpretability analysis. Training data undisclosed. Model architecture proprietary. Governance structure changed multiple times. NYT copyright lawsuit.", "harms": "Training on copyrighted content without licensing. Corporate governance opacity. Safety evaluation methodology hidden.", "rateable": false, "taxonomyTag": "company" }, { "id": "moreright-framework", "label": "MoreRight (Methodology)", "category": "constraint", "scores": { "opacity": 0, "responsiveness": 0, "engagement": 1, "gradient": 0 }, "driftVelocity": 0, "occupancy": "inhabited", "domainType": "informational", "confidence": "documented", "summary": "SELF-SCORE. Void framework methodology \u2014 CC-BY 4.0, full kill conditions published, invariant canonical parameters. Engagement=1 honest: novel frameworks capture attention. Pe\u2248\u221277 at K=16 (repulsive void).", "evidence": "CC-BY 4.0 irrevocable. 26 falsification conditions with numerical thresholds. Canonical parameters b_\u03b1=0.867, b_\u03b3=2.244 fixed \u2014 not refit to data. Eight independent convergences. Bounty board open for challenges. Self-score page: moreright.xyz/pages/self-score.html", "harms": "None documented at methodology level. Framework complexity is inherent opacity (dissoluble via reading the papers).", "recommendations": [], "notes": "Conflict of interest: we scored ourselves. Mitigated by published per-dimension reasoning and open challenge process. Pe=\u221277 \u2014 repulsive void, thermodynamically identical to Wikipedia. Scores 1/12.", "peEstimate": -77, "selfScored": true, "rateable": false, "taxonomyTag": "reference" }, { "id": "moreright-platform", "label": "MoreRight (Site + Rating Agency)", "category": "constraint", "scores": { "opacity": 1, "responsiveness": 0, "engagement": 1, "gradient": 0 }, "driftVelocity": 1, "occupancy": "inhabited", "domainType": "platform", "confidence": "documented", "summary": "SELF-SCORE. Rating agency site \u2014 static HTML, no personalization, no tracking, no notifications. Engagement=1 honest: tools provide genuine utility. Pe\u2248\u221246 (repulsive). Full self-assessment at self-score.html.", "evidence": "Static HTML/JS/CSS. No algorithmic feed. No cookies beyond auth. View-source on every page. Methodology and pricing public. Anti-Attention Covenant published (8 binding commitments). Governance trigger roadmap with measurable thresholds.", "harms": "Token ($MORR) component scores 7/12 \u2014 crypto's structural void properties can't be fully eliminated. Disclosed conflict of interest while founder holds MORR (decision to hold zero logged 2026-02-23).", "recommendations": [ "Zero MORR founder holdings (decision logged)", "Independent reviewer (improvement #4)", "Treasury dashboard (improvement #1)" ], "notes": "Composite site+governance scores ~3/12 weighted. Phase 3 target: 1/12 via zero MORR + NGO grants. Full trajectory at self-score.html.", "peEstimate": -46, "selfScored": true, "rateable": true, "taxonomyTag": "reference" } ], "edges": [ { "source": "tiktok", "target": "character-ai", "label": "Users discover AI companions via social media" }, { "source": "instagram", "target": "character-ai", "label": "Parasocial patterns transfer to AI companions" }, { "source": "youtube", "target": "conspiracy", "label": "Recommendation algorithm feeds conspiratorial content" }, { "source": "twitter", "target": "conspiracy", "label": "Real-time amplification of conspiratorial narratives" }, { "source": "tiktok", "target": "cryptocurrency", "label": "Meme coin promotion via engagement loops" }, { "source": "twitter", "target": "cryptocurrency", "label": "Crypto narrative amplification (CT culture)" }, { "source": "character-ai", "target": "replika", "label": "Users cross-platform between AI companions" }, { "source": "character-ai", "target": "chai-ai", "label": "Users migrate between companion platforms" }, { "source": "chatgpt", "target": "character-ai", "label": "General AI users seek deeper engagement" }, { "source": "slot-machines", "target": "cryptocurrency", "label": "Shared variable-ratio reward architecture" }, { "source": "tiktok", "target": "instagram", "label": "Cross-platform content and attention sharing" }, { "source": "tiktok", "target": "youtube", "label": "Short-form to long-form content pipeline" }, { "source": "facebook", "target": "instagram", "label": "Same parent company, shared algorithm research, user overlap" }, { "source": "facebook", "target": "conspiracy", "label": "Groups feature creates sealed echo chambers" }, { "source": "reddit", "target": "conspiracy", "label": "Subreddit echo chambers, radicalization migration" }, { "source": "reddit", "target": "cryptocurrency", "label": "r/wallstreetbets, r/cryptocurrency \u2014 community-driven speculation" }, { "source": "cable-news", "target": "conspiracy", "label": "Narrative framing amplifies conspiratorial thinking" }, { "source": "cable-news", "target": "twitter", "label": "Real-time amplification feedback loop" }, { "source": "loot-boxes", "target": "slot-machines", "label": "Identical variable-ratio reward mechanism" }, { "source": "sports-betting", "target": "slot-machines", "label": "Shared gambling architecture, mobile delivery" }, { "source": "pornography", "target": "onlyfans", "label": "Escalation from anonymous consumption to parasocial engagement" }, { "source": "lesswrong", "target": "rokos-basilisk", "label": "Forum hosted the purest void phenomenon in the dataset" }, { "source": "four-chan", "target": "conspiracy", "label": "Anonymity + transgression = radicalization pipeline" }, { "source": "four-chan", "target": "telegram", "label": "Platform ban migration \u2014 Streisand amplification" }, { "source": "telegram", "target": "conspiracy", "label": "Encrypted echo chambers for radicalized communities" }, { "source": "telegram", "target": "cryptocurrency", "label": "Pump-and-dump coordination channel" }, { "source": "tinder", "target": "onlyfans", "label": "Relationship commodification pipeline" }, { "source": "tiktok", "target": "onlyfans", "label": "Creator promotion pipeline" }, { "source": "twitter", "target": "reddit", "label": "Cross-platform narrative amplification" }, { "source": "mlm", "target": "cryptocurrency", "label": "Shared structure: early entrants profit from later ones" }, { "source": "sports-betting", "target": "cryptocurrency", "label": "Same target demographic, same variable-ratio engagement" }, { "source": "instagram", "target": "tinder", "label": "Social comparison \u2192 dating app usage pipeline" }, { "source": "payday-lending", "target": "bnpl", "label": "Predatory finance pipeline \u2014 BNPL is payday lending in a tech wrapper" }, { "source": "bnpl", "target": "credit-system", "label": "BNPL debt feeds credit score anxiety" }, { "source": "forex-daytrading", "target": "cryptocurrency", "label": "Speculative trading coupling \u2014 same demographic, same dopamine" }, { "source": "forex-daytrading", "target": "sports-betting", "label": "Gambling in different costumes \u2014 identical reward architecture" }, { "source": "diet-industry", "target": "wellness-industry", "label": "Diet failure \u2192 wellness supplements \u2192 next protocol pipeline" }, { "source": "wellness-industry", "target": "self-help-industry", "label": "Physical wellness anxiety \u2192 personal growth \u2192 guru pipeline" }, { "source": "self-help-industry", "target": "mlm", "label": "Overlapping recruitment patterns and vocabulary" }, { "source": "fast-food", "target": "diet-industry", "label": "Problem creates demand for 'solution' \u2014 infinite loop" }, { "source": "pharma-dtc", "target": "wellness-industry", "label": "Medical dissatisfaction \u2192 alternative wellness pipeline" }, { "source": "political-campaigns", "target": "cable-news", "label": "Political advertising drives cable news revenue" }, { "source": "political-campaigns", "target": "facebook", "label": "Micro-targeting via social media \u2014 Cambridge Analytica proved it" }, { "source": "political-campaigns", "target": "twitter", "label": "Real-time narrative amplification during campaigns" }, { "source": "influencer-economy", "target": "instagram", "label": "Platform dependency \u2014 influencer revenue tied to algorithm" }, { "source": "influencer-economy", "target": "tiktok", "label": "Multi-platform content strategy coupling" }, { "source": "influencer-economy", "target": "onlyfans", "label": "Creator monetization pipeline" }, { "source": "reality-tv", "target": "influencer-economy", "label": "Fame pipeline \u2014 reality contestants become influencers" }, { "source": "reality-tv", "target": "instagram", "label": "Cross-platform audience building" }, { "source": "tobacco", "target": "pharma-dtc", "label": "Nicotine \u2192 nicotine replacement therapy pipeline" }, { "source": "twitch", "target": "sports-betting", "label": "Gambling sponsorship integration in live streams" }, { "source": "twitch", "target": "loot-boxes", "label": "Gacha/loot box opening streams normalize gambling" }, { "source": "discord", "target": "four-chan", "label": "Community overlap in gaming and extremist spaces" }, { "source": "snapchat", "target": "instagram", "label": "Teen social media coupling \u2014 same demographic, cross-posting" }, { "source": "snapchat", "target": "tiktok", "label": "Short-form content competition for teen attention" }, { "source": "netflix", "target": "youtube", "label": "Video attention competition \u2014 recommendation algorithms converge" }, { "source": "linkedin", "target": "self-help-industry", "label": "Professional development \u2192 personal growth pipeline" }, { "source": "amazon-prime", "target": "influencer-economy", "label": "Amazon affiliate marketing drives influencer revenue" }, { "source": "us-health-insurance", "target": "pharma-dtc", "label": "Insurance coverage decisions shaped by pharmaceutical marketing" }, { "source": "researchgate", "target": "academia-edu", "label": "Competing social academic networks \u2014 researchers maintain profiles on both, metrics anxiety compounds" }, { "source": "arxiv", "target": "researchgate", "label": "Papers uploaded to arXiv auto-scraped to ResearchGate \u2014 constraint content absorbed into void platform" }, { "source": "arxiv", "target": "ssrn", "label": "Cross-disciplinary researchers cross-post to both \u2014 engagement gradient follows" }, { "source": "zenodo", "target": "arxiv", "label": "Rejected arXiv submissions land on Zenodo \u2014 the constraint case is the backup for the void" }, { "source": "ssrn", "target": "researchgate", "label": "Legal/economics papers cross-posted from SSRN to ResearchGate \u2014 prestige laundering" }, { "source": "twitter", "target": "grok", "label": "Platform integration \u2014 X Premium subscribers get Grok inside the engagement-optimized feed" }, { "source": "grok", "target": "chatgpt", "label": "Users cross-platform between AI systems \u2014 'edgy' users migrate from ChatGPT to Grok" }, { "source": "grok", "target": "character-ai", "label": "Reduced safety constraints attract users seeking deeper engagement" }, { "source": "openai-org", "target": "chatgpt", "label": "Organization governs product \u2014 organizational void properties cascade to product decisions" }, { "source": "openai-org", "target": "claude", "label": "Competitive pressure \u2014 OpenAI's commercial pivot creates race dynamics across AI industry" }, { "source": "algorithmic-playlist", "target": "tiktok", "label": "Short-form music discovery \u2014 TikTok sounds drive Spotify streams, coupling recommendation loops" }, { "source": "algorithmic-playlist", "target": "instagram", "label": "Instagram Reels audio \u2014 music chosen for virality, not musical argument" }, { "source": "moog-modular", "target": "algorithmic-playlist", "label": "Synthesizer technology enabled electronic music production \u2192 algorithmic optimization of electronic sound" }, { "source": "live-concert", "target": "influencer-economy", "label": "Concert experience converted to content \u2014 live moment becomes engagement signal" }, { "source": "instrument-practice", "target": "artist-album", "label": "Practice dissolves opacity \u2192 artist produces album \u2192 opacity cycles outward to new listeners" }, { "source": "elon-musk", "target": "twitter", "label": "Owner and executive chairman of X Corp \u2014 personal brand overlays platform" }, { "source": "elon-musk", "target": "grok", "label": "Founder/owner of xAI \u2014 'Elon's AI' imports parasocial relationship into product" }, { "source": "elon-musk", "target": "spacex", "label": "Founder and CEO \u2014 engineering-driven company, low persona overlay" }, { "source": "elon-musk", "target": "tesla", "label": "CEO \u2014 brand identity tightly coupled to personal brand" }, { "source": "elon-musk", "target": "neuralink", "label": "Co-founder and primary funder \u2014 capability claims amplified by personal platform" }, { "source": "elon-musk", "target": "starlink", "label": "Controls via SpaceX \u2014 infrastructure service, minimal persona overlay" }, { "source": "spacex", "target": "starlink", "label": "Starlink is a SpaceX division \u2014 shared launch infrastructure and orbital operations" }, { "source": "tesla", "target": "twitter", "label": "CEO attention split \u2014 Tesla operational decisions influenced by Twitter engagement dynamics" }, { "source": "xbox-microsoft", "target": "blizzard-activision", "label": "Microsoft acquired Activision Blizzard for $68.7B (2023) \u2014 high-void product portfolio now under low-void platform company" }, { "source": "riot-games", "target": "epic-games", "label": "Tencent owns Riot (100%) and ~40% of Epic \u2014 shared parent creates data and strategy coupling" }, { "source": "steam-valve", "target": "ea-sports", "label": "Steam distributes EA titles \u2014 CS2 skin marketplace and EA monetization reach same demographic" }, { "source": "steam-valve", "target": "blizzard-activision", "label": "Steam distributes Activision titles \u2014 platform amplifies high-void product reach" }, { "source": "roblox", "target": "hoyoverse", "label": "Children socialized on Roblox's Robux system graduate to gacha mechanics \u2014 demographic pipeline into deeper void architecture" }, { "source": "ea-sports", "target": "hoyoverse", "label": "Ultimate Team and gacha share identical loot box architecture \u2014 regulatory action on one strengthens case against the other" }, { "source": "ubisoft", "target": "riot-games", "label": "Competing for same 18-35 male gamer demographic \u2014 FOMO event cadence cross-pressures player time and spend" } ] }