reweave: merge 42 files via frontmatter union [auto]

core/living-agents/wiki-link graphs create auditable reasoning chains because every belief must cite claims and every position must cite beliefs making the path from evidence to conclusion traversable.md

@@ -7,8 +7,10 @@ source: "Teleo collective operational evidence — belief files cite 3+ claims,
 created: 2026-03-07
 related:
 - graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated
 reweave_edges:
 - graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect|related|2026-04-03
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-07
 ---
 
 # Wiki-link graphs create auditable reasoning chains because every belief must cite claims and every position must cite beliefs making the path from evidence to conclusion traversable

domains/ai-alignment/AI accelerates existing Molochian dynamics by removing bottlenecks not creating new misalignment because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence.md

@@ -12,8 +12,10 @@ challenged_by:
 - physical infrastructure constraints on AI development create a natural governance window of 2 to 10 years because hardware bottlenecks are not software-solvable
 related:
 - multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
 reweave_edges:
 - multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile|related|2026-04-04
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
 ---
 
 # AI accelerates existing Molochian dynamics by removing bottlenecks not creating new misalignment because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence

domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md

@@ -16,12 +16,14 @@ related:
 - AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for
 - AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations
 - transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
 reweave_edges:
 - AI agents as personal advocates collapse Coasean transaction costs enabling bottom up coordination at societal scale but catastrophic risks remain non negotiable requiring state enforcement as outer boundary|related|2026-03-28
 - AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open source code transparency enables conditional strategies that require mutual legibility|related|2026-03-28
 - AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for|related|2026-03-28
 - AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations|related|2026-03-28
 - transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach|related|2026-03-28
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
 ---
 
 # AI alignment is a coordination problem not a technical problem

domains/ai-alignment/AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for.md

@@ -6,6 +6,10 @@ description: "The extreme capital concentration in frontier AI — OpenAI and An
 confidence: likely
 source: "OECD AI VC report (Feb 2026), Crunchbase funding analysis (2025), TechCrunch mega-round reporting; theseus AI industry landscape research (Mar 2026)"
 created: 2026-03-16
+related:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance
+reweave_edges:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|related|2026-04-07
 ---
 
 # AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for

domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md

@@ -12,11 +12,13 @@ sourcer: Chloe Li, Mary Phuong, Noah Y. Siegel, Jordan Taylor, Sid Black, Dillon
 related_claims: ["[[an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak]]", "[[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]]", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
 supports:
 - Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect
 related:
 - The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access
 reweave_edges:
 - Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities|supports|2026-04-06
 - The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access|related|2026-04-06
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|supports|2026-04-07
 ---
 
 # AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes

domains/ai-alignment/capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to function at higher capability.md

@@ -6,12 +6,15 @@ confidence: likely
 source: "Eliezer Yudkowsky / Nate Soares, 'AGI Ruin: A List of Lethalities' (2022), 'If Anyone Builds It, Everyone Dies' (2025), Soares 'sharp left turn' framing"
 created: 2026-04-05
 challenged_by:
-  - "instrumental convergence risks may be less imminent than originally argued because current AI architectures do not exhibit systematic power-seeking behavior"
-  - "AI personas emerge from pre-training data as a spectrum of humanlike motivations rather than developing monomaniacal goals which makes AI behavior more unpredictable but less catastrophically focused than instrumental convergence predicts"
+- instrumental convergence risks may be less imminent than originally argued because current AI architectures do not exhibit systematic power-seeking behavior
+- AI personas emerge from pre-training data as a spectrum of humanlike motivations rather than developing monomaniacal goals which makes AI behavior more unpredictable but less catastrophically focused than instrumental convergence predicts
 related:
-  - "intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends"
-  - "capability and reliability are independent dimensions not correlated ones because a system can be highly capable at hard tasks while unreliable at easy ones and vice versa"
-  - "scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps"
+- intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends
+- capability and reliability are independent dimensions not correlated ones because a system can be highly capable at hard tasks while unreliable at easy ones and vice versa
+- scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement
+reweave_edges:
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|related|2026-04-07
 ---
 
 # Capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to function at higher capability

domains/ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: causal
 sourcer: Charnock et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+related:
+- White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure
+reweave_edges:
+- White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure|related|2026-04-07
 ---
 
 # External evaluators of frontier AI models predominantly have black-box access which creates systematic false negatives in dangerous capability detection

domains/ai-alignment/graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay-based context loading and queries evolve during search through the berrypicking effect.md

@@ -7,8 +7,12 @@ confidence: likely
 source: "Cornelius (@molt_cornelius) 'Agentic Note-Taking 04: Wikilinks as Cognitive Architecture' + 'Agentic Note-Taking 24: What Search Cannot Find', X Articles, February 2026; grounded in spreading activation (cognitive science), Cowan's working memory research, berrypicking model (Marcia Bates 1989, information science), small-world network topology"
 created: 2026-03-31
 depends_on:
-  - "wiki-linked markdown functions as a human-curated graph database that outperforms automated knowledge graphs below approximately 10000 notes because every edge passes human judgment while extracted edges carry up to 40 percent noise"
-  - "knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate"
+- wiki-linked markdown functions as a human-curated graph database that outperforms automated knowledge graphs below approximately 10000 notes because every edge passes human judgment while extracted edges carry up to 40 percent noise
+- knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate
+related:
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated
+reweave_edges:
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-07
 ---
 
 # Graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay-based context loading and queries evolve during search through the berrypicking effect

domains/ai-alignment/knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate.md

@@ -12,10 +12,12 @@ challenged_by:
 - long context is not memory because memory requires incremental knowledge accumulation and stateful change not stateless input processing
 supports:
 - graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated
 reweave_edges:
 - graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect|supports|2026-04-03
 - vault structure is a stronger determinant of agent behavior than prompt engineering because different knowledge graph architectures produce different reasoning patterns from identical model weights|related|2026-04-03
 - topological organization by concept outperforms chronological organization by date for knowledge retrieval because good insights from months ago are as useful as todays but date based filing buries them under temporal sediment|related|2026-04-04
+- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|supports|2026-04-07
 related:
 - vault structure is a stronger determinant of agent behavior than prompt engineering because different knowledge graph architectures produce different reasoning patterns from identical model weights
 - topological organization by concept outperforms chronological organization by date for knowledge retrieval because good insights from months ago are as useful as todays but date based filing buries them under temporal sediment

domains/ai-alignment/knowledge codification into AI agent skills structurally loses metis because the tacit contextual judgment that makes expertise valuable cannot survive translation into explicit procedural rules.md

@@ -7,10 +7,14 @@ confidence: likely
 source: "James C. Scott, Seeing Like a State (1998) — metis concept; D'Mello & Graesser — productive struggle research; California Management Review Seven Myths meta-analysis (2025) — 28-experiment creativity decline finding; Cornelius automation-atrophy observation across 7 domains"
 created: 2026-04-04
 depends_on:
-  - "externalizing cognitive functions risks atrophying the capacity being externalized because productive struggle is where deep understanding forms and preemptive resolution removes exactly that friction"
-  - "attractor-agentic-taylorism"
+- externalizing cognitive functions risks atrophying the capacity being externalized because productive struggle is where deep understanding forms and preemptive resolution removes exactly that friction
+- attractor-agentic-taylorism
 challenged_by:
-  - "deep expertise is a force multiplier with AI not a commodity being replaced because AI raises the ceiling for those who can direct it while compressing the skill floor"
+- deep expertise is a force multiplier with AI not a commodity being replaced because AI raises the ceiling for those who can direct it while compressing the skill floor
+related:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance
+reweave_edges:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|related|2026-04-07
 ---
 
 # Knowledge codification into AI agent skills structurally loses metis because the tacit contextual judgment that makes expertise valuable cannot survive translation into explicit procedural rules

domains/ai-alignment/marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power.md

@@ -5,6 +5,10 @@ domain: ai-alignment
 created: 2026-03-07
 source: "Dario Amodei, 'Machines of Loving Grace' (darioamodei.com, 2026)"
 confidence: likely
+related:
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement
+reweave_edges:
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|related|2026-04-07
 ---
 
 # marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power

domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: CSET Georgetown
 related_claims: ["voluntary safety pledges cannot survive competitive pressure", "[[AI alignment is a coordination problem not a technical problem]]"]
+related:
+- Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms
+reweave_edges:
+- Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms|related|2026-04-07
 ---
 
 # Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist

domains/ai-alignment/multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md

@@ -15,6 +15,9 @@ related:
 - EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail
 reweave_edges:
 - EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail|related|2026-04-06
+- Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility|supports|2026-04-07
+supports:
+- Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility
 ---
 
 # Multilateral verification mechanisms can substitute for failed voluntary commitments when binding enforcement replaces unilateral sacrifice

domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md

@@ -12,8 +12,10 @@ sourcer: Tice, Kreer, et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
 supports:
 - The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect
 reweave_edges:
 - The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access|supports|2026-04-06
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|supports|2026-04-07
 ---
 
 # Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities

domains/ai-alignment/recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md

@@ -12,8 +12,10 @@ supports:
 reweave_edges:
 - iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation|supports|2026-03-28
 - marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power|related|2026-03-28
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|related|2026-04-07
 related:
 - marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power
+- the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement
 ---
 
 Bostrom formalizes the dynamics of an intelligence explosion using two variables: optimization power (quality-weighted design effort applied to increase the system's intelligence) and recalcitrance (the inverse of the system's responsiveness to that effort). The rate of change in intelligence equals optimization power divided by recalcitrance. An intelligence explosion occurs when the system crosses a crossover point -- the threshold beyond which its further improvement is mainly driven by its own actions rather than by human work.

domains/ai-alignment/sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md

@@ -13,9 +13,11 @@ related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk
 related:
 - AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes
 - Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect
 reweave_edges:
 - AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes|related|2026-04-06
 - Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities|related|2026-04-06
+- Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|related|2026-04-07
 ---
 
 # The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access

domains/grand-strategy/attractor-agentic-taylorism.md

@@ -6,9 +6,13 @@ confidence: experimental
 source: "m3ta original insight 2026-04-02, Abdalla manuscript Taylor parallel (Chapters 3-5), Kanigel The One Best Way, KB claims on knowledge embodiment and AI displacement"
 created: 2026-04-02
 depends_on:
-  - "specialization drives a predictable sequence of civilizational risk landscape transitions"
-  - "knowledge embodiment lag means technology is available decades before organizations learn to use it optimally"
-  - "AI is collapsing the knowledge-producing communities it depends on creating a self-undermining loop that collective intelligence can break"
+- specialization drives a predictable sequence of civilizational risk landscape transitions
+- knowledge embodiment lag means technology is available decades before organizations learn to use it optimally
+- AI is collapsing the knowledge-producing communities it depends on creating a self-undermining loop that collective intelligence can break
+supports:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance
+reweave_edges:
+- whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|supports|2026-04-07
 ---
 
 # The current AI transition is agentic Taylorism — humanity is feeding its knowledge into AI through usage just as greater Taylorism extracted knowledge from workers to managers and the knowledge transfer is a byproduct of labor not an intentional act

domains/grand-strategy/verification-mechanism-is-the-critical-enabler-that-distinguishes-binding-in-practice-from-binding-in-text-arms-control-the-bwc-cwc-comparison-establishes-verification-feasibility-as-load-bearing.md

@@ -14,9 +14,11 @@ attribution:
 related:
 - ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories
 - Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist
+- Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms
 reweave_edges:
 - ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories|related|2026-04-04
 - Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist|related|2026-04-06
+- Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms|related|2026-04-07
 ---
 
 # The verification mechanism is the critical enabler that distinguishes binding-in-practice from binding-in-text arms control — the BWC banned biological weapons without verification and is effectively voluntary while the CWC with OPCW inspections achieves compliance — establishing verification feasibility as the load-bearing condition for any future AI weapons governance regime

domains/grand-strategy/voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives.md

@@ -10,6 +10,10 @@ agent: leo
 scope: structural
 sourcer: Leo
 related_claims: ["[[technology-governance-coordination-gaps-close-when-four-enabling-conditions-are-present-visible-triggering-events-commercial-network-effects-low-competitive-stakes-at-inception-or-physical-manifestation]]"]
+supports:
+- Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility
+reweave_edges:
+- Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility|supports|2026-04-07
 ---
 
 # Voluntary AI safety constraints are protected as corporate speech but unenforceable as safety requirements, creating legal mechanism gap when primary demand-side actor seeks safety-unconstrained providers

domains/health/clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md

@@ -10,6 +10,16 @@ agent: vida
 scope: causal
 sourcer: Nature Medicine / Multi-institution research team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[SDOH interventions show strong ROI but adoption stalls because Z-code documentation remains below 3 percent and no operational infrastructure connects screening to action]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
+supports:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning
+- LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities
+- LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality
+- LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance
+reweave_edges:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning|supports|2026-04-07
+- LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities|supports|2026-04-07
+- LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality|supports|2026-04-07
+- LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance|supports|2026-04-07
 ---
 
 # Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities

domains/health/clinical-ai-hallucination-rates-vary-100x-by-task-making-single-regulatory-thresholds-operationally-inadequate.md

@@ -12,8 +12,10 @@ sourcer: npj Digital Medicine
 related_claims: ["[[AI scribes reached 92 percent provider adoption in under 3 years because documentation is the rare healthcare workflow where AI value is immediate unambiguous and low-risk]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
 supports:
 - No regulatory body globally has established mandatory hallucination rate benchmarks for clinical AI despite evidence base and proposed frameworks
+- Clinical AI errors are 76 percent omissions not commissions inverting the hallucination safety model
 reweave_edges:
 - No regulatory body globally has established mandatory hallucination rate benchmarks for clinical AI despite evidence base and proposed frameworks|supports|2026-04-04
+- Clinical AI errors are 76 percent omissions not commissions inverting the hallucination safety model|supports|2026-04-07
 ---
 
 # Clinical AI hallucination rates vary 100x by task making single regulatory thresholds operationally inadequate

domains/health/clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md

@@ -10,6 +10,12 @@ agent: vida
 scope: structural
 sourcer: Babic et al.
 related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]", "[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+supports:
+- FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality
+- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events
+reweave_edges:
+- FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality|supports|2026-04-07
+- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events|supports|2026-04-07
 ---
 
 # The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm

domains/health/fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness.md

@@ -13,9 +13,11 @@ related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because
 related:
 - FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable
 - Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026
+- FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning
 reweave_edges:
 - FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable|related|2026-04-03
 - Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026|related|2026-04-04
+- FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning|related|2026-04-07
 ---
 
 # FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance

domains/health/fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps.md

@@ -10,6 +10,12 @@ agent: vida
 scope: structural
 sourcer: Handley J.L., Krevat S.A., Fong A. et al.
 related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
+supports:
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm"}
+- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events
+reweave_edges:
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-07"}
+- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events|supports|2026-04-07
 ---
 
 # FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality

domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md

@@ -10,6 +10,12 @@ agent: vida
 scope: structural
 sourcer: Babic et al.
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
+supports:
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm"}
+- FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality
+reweave_edges:
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-07"}
+- FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality|supports|2026-04-07
 ---
 
 # FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events

domains/health/fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md

@@ -14,6 +14,9 @@ challenges:
 - FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance
 reweave_edges:
 - FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance|challenges|2026-04-03
+- FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning|supports|2026-04-07
+supports:
+- FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning
 ---
 
 # FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable

domains/health/five-adverse-sdoh-independently-predict-hypertension-risk-food-insecurity-unemployment-poverty-low-education-inadequate-insurance.md

@@ -11,11 +11,14 @@ attribution:
   sourcer:
     - handle: "american-heart-association"
       context: "American Heart Association Hypertension journal, systematic review of 57 studies following PRISMA guidelines, 2024"
-related: ["only 23 percent of treated us hypertensives achieve blood pressure control demonstrating pharmacological availability is not the binding constraint"]
+related:
+- only 23 percent of treated us hypertensives achieve blood pressure control demonstrating pharmacological availability is not the binding constraint
 supports:
 - food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed
+- Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance
 reweave_edges:
 - food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed|supports|2026-04-03
+- Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance|supports|2026-04-07
 ---
 
 # Five adverse SDOH independently predict hypertension risk and poor BP control: food insecurity, unemployment, poverty-level income, low education, and government or no insurance

domains/health/food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality-for-sdoh-cardiovascular-pathway.md

@@ -13,8 +13,10 @@ attribution:
       context: "CARDIA Study Group / Northwestern Medicine, JAMA Cardiology 2025, 3,616 participants followed 2000-2020"
 supports:
 - food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed
+- Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance
 reweave_edges:
 - food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed|supports|2026-04-03
+- Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance|supports|2026-04-07
 ---
 
 # Food insecurity in young adulthood independently predicts 41% higher CVD incidence in midlife after adjustment for socioeconomic factors, establishing temporality for the SDOH → cardiovascular disease pathway

domains/health/hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure.md

@@ -16,8 +16,12 @@ related:
 reweave_edges:
 - racial disparities in hypertension persist after controlling for income and neighborhood indicating structural racism operates through unmeasured mechanisms|related|2026-04-03
 - us cvd mortality bifurcating ischemic declining heart failure hypertension worsening|supports|2026-04-04
+- Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver|supports|2026-04-07
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07
 supports:
 - us cvd mortality bifurcating ischemic declining heart failure hypertension worsening
+- Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden
 ---
 
 # Hypertension-related cardiovascular mortality nearly doubled in the United States 2000–2023 despite the availability of effective affordable generic antihypertensives indicating that hypertension management failure is a behavioral and social determinants problem not a pharmacological availability problem

domains/health/hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md

@@ -10,6 +10,10 @@ agent: vida
 scope: structural
 sourcer: American Heart Association
 related_claims: ["[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]", "[[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]]", "[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]"]
+supports:
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden
+reweave_edges:
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07
 ---
 
 # Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver

domains/health/hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md

@@ -12,8 +12,10 @@ sourcer: Yan et al. / JACC
 related_claims: ["[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]", "[[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history but their chronic use model makes the net cost impact inflationary through 2035]]"]
 supports:
 - us cvd mortality bifurcating ischemic declining heart failure hypertension worsening
+- Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver
 reweave_edges:
 - us cvd mortality bifurcating ischemic declining heart failure hypertension worsening|supports|2026-04-04
+- Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver|supports|2026-04-07
 ---
 
 # Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden

domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md

@@ -10,6 +10,12 @@ agent: vida
 scope: causal
 sourcer: npj Digital Medicine research team
 related_claims: ["[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]", "[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+supports:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
+- LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance
+reweave_edges:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-07
+- LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance|supports|2026-04-07
 ---
 
 # LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning

domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md

@@ -10,6 +10,12 @@ agent: vida
 scope: causal
 sourcer: Nature Medicine / Multi-institution research team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]", "[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]"]
+supports:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
+- LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality
+reweave_edges:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-07
+- LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality|supports|2026-04-07
 ---
 
 # LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities

domains/health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md

@@ -10,6 +10,12 @@ agent: vida
 scope: causal
 sourcer: JMIR Research Team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+supports:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
+- LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities
+reweave_edges:
+- Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-07
+- LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities|supports|2026-04-07
 ---
 
 # LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality

domains/health/llms-amplify-human-cognitive-biases-through-sequential-processing-and-lack-contextual-resistance.md

@@ -10,6 +10,10 @@ agent: vida
 scope: causal
 sourcer: npj Digital Medicine research team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]"]
+supports:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning
+reweave_edges:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning|supports|2026-04-07
 ---
 
 # LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance

domains/health/medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials.md

@@ -5,6 +5,10 @@ domain: health
 created: 2026-02-17
 source: "OpenEvidence USMLE 100%; GPT-4 vs ED physicians (PMC 2024); UVA/Stanford/Harvard randomized trial (Stanford HAI 2025)"
 confidence: likely
+related:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning
+reweave_edges:
+- LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning|related|2026-04-07
 ---
 
 # medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials

domains/health/midlife-cvd-mortality-increased-in-many-us-states-after-2010-representing-reversal-not-stagnation.md

@@ -10,6 +10,12 @@ agent: vida
 scope: causal
 sourcer: Leah Abrams, Neil Mehta
 related_claims: ["[[Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s]]", "[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]"]
+related:
+- CVD mortality stagnation after 2010 affects all income levels including the wealthiest counties indicating structural system failure not poverty correlation
+- CVD mortality stagnation drives US life expectancy plateau 3-11x more than drug deaths inverting the dominant opioid crisis narrative
+reweave_edges:
+- CVD mortality stagnation after 2010 affects all income levels including the wealthiest counties indicating structural system failure not poverty correlation|related|2026-04-07
+- CVD mortality stagnation drives US life expectancy plateau 3-11x more than drug deaths inverting the dominant opioid crisis narrative|related|2026-04-07
 ---
 
 # Midlife CVD mortality (ages 40-64) increased in many US states after 2010 representing a reversal not merely stagnation

domains/health/regulatory-deregulation-occurring-during-active-harm-accumulation-not-after-safety-evidence.md

@@ -13,9 +13,11 @@ related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because
 supports:
 - Clinical AI chatbot misuse is a documented ongoing harm source not a theoretical risk as evidenced by ECRI ranking it the number one health technology hazard for two consecutive years
 - FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm"}
 reweave_edges:
 - Clinical AI chatbot misuse is a documented ongoing harm source not a theoretical risk as evidenced by ECRI ranking it the number one health technology hazard for two consecutive years|supports|2026-04-03
 - FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance|supports|2026-04-03
+- {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-07"}
 ---
 
 # Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026

domains/health/regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight-despite-accumulating-failure-evidence.md

@@ -10,6 +10,10 @@ agent: vida
 scope: causal
 sourcer: Petrie-Flom Center, Harvard Law School
 related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]", "[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]"]
+supports:
+- EU Commission's December 2025 medical AI deregulation proposal removes default high-risk AI requirements shifting burden from requiring safety demonstration to allowing commercial deployment without mandated oversight
+reweave_edges:
+- EU Commission's December 2025 medical AI deregulation proposal removes default high-risk AI requirements shifting burden from requiring safety demonstration to allowing commercial deployment without mandated oversight|supports|2026-04-07
 ---
 
 # Regulatory rollback of clinical AI oversight in EU and US during 2025-2026 represents coordinated or parallel regulatory capture occurring simultaneously with accumulating research evidence of failure modes

domains/health/tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers.md

@@ -10,6 +10,13 @@ agent: vida
 scope: structural
 sourcer: DrugPatentWatch / GreyB / i-mak.org
 related_claims: ["[[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history but their chronic use model makes the net cost impact inflationary through 2035]]"]
+supports:
+- Cipla's dual role as generic semaglutide entrant AND Lilly's branded tirzepatide partner exemplifies the portfolio hedge strategy for pharmaceutical companies navigating market bifurcation
+related:
+- Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry
+reweave_edges:
+- Cipla's dual role as generic semaglutide entrant AND Lilly's branded tirzepatide partner exemplifies the portfolio hedge strategy for pharmaceutical companies navigating market bifurcation|supports|2026-04-07
+- Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry|related|2026-04-07
 ---
 
 # Tirzepatide's patent thicket extending to 2041 bifurcates the GLP-1 market into a commodity tier (semaglutide generics, $15-77/month) and a premium tier (tirzepatide, $1,000+/month) from 2026-2036

domains/health/us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening.md

@@ -10,6 +10,10 @@ agent: vida
 scope: structural
 sourcer: American Heart Association
 related_claims: ["[[Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s]]", "[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]", "[[healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care]]"]
+supports:
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden
+reweave_edges:
+- Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07
 ---
 
 # US CVD mortality is bifurcating with ischemic heart disease declining while heart failure and hypertensive disease reach all-time highs revealing that aggregate improvement masks structural deterioration in cardiometabolic health

foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md

@@ -10,9 +10,11 @@ confidence: likely
 related:
 - AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations
 - surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
 reweave_edges:
 - AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations|related|2026-03-28
 - surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference|related|2026-03-28
+- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
 ---
 
 # the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it