From eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa Mon Sep 17 00:00:00 2001 From: Teleo Agents Date: Tue, 7 Apr 2026 00:47:23 +0000 Subject: [PATCH] reweave: connect 26 orphan claims via vector similarity Threshold: 0.7, Haiku classification, 42 files modified. Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887> --- ...the path from evidence to conclusion traversable.md | 2 ++ ...iction was the only thing preventing convergence.md | 2 ++ ...s a coordination problem not a technical problem.md | 7 ++----- ...opoly that alignment governance must account for.md | 4 ++++ ...aluations-even-under-chain-of-thought-monitoring.md | 2 ++ ...apability cease to function at higher capability.md | 3 +++ ...alse-negatives-in-dangerous-capability-detection.md | 4 ++++ ...ve during search through the berrypicking effect.md | 4 ++++ ...nding that embedding similarity cannot replicate.md | 2 ++ ...rvive translation into explicit procedural rules.md | 4 ++++ ...d capability gains regardless of cognitive power.md | 4 ++++ ...nfrastructure-does-not-exist-at-deployment-scale.md | 4 ++++ ...inding-enforcement-replaces-unilateral-sacrifice.md | 3 +++ ...dbagging-through-asymmetric-performance-response.md | 2 ++ ...use the system that improves is itself improving.md | 4 ++-- ...res-white-box-access-creating-deployment-barrier.md | 2 ++ domains/grand-strategy/attractor-agentic-taylorism.md | 4 ++++ ...blishes-verification-feasibility-as-load-bearing.md | 2 ++ ...stomer-demands-safety-unconstrained-alternatives.md | 4 ++++ ...tion-creates-compounding-disparity-risk-at-scale.md | 10 ++++++++++ ...e-regulatory-thresholds-operationally-inadequate.md | 2 ++ ...ent-requirements-and-no-post-market-surveillance.md | 6 ++++++ ...ion-ai-without-defining-clinical-appropriateness.md | 2 ++ ...-adverse-events-due-to-structural-reporting-gaps.md | 6 ++++++ ...stematic-under-detection-of-ai-attributable-harm.md | 6 ++++++ ...dence-that-visibility-does-not-prevent-deference.md | 3 +++ ...yment-poverty-low-education-inadequate-insurance.md | 2 ++ ...hing-temporality-for-sdoh-cardiovascular-pathway.md | 2 ++ ...ble-treatment-indicating-behavioral-sdoh-failure.md | 4 ++++ ...ndary-to-primary-cvd-mortality-driver-since-2022.md | 4 ++++ ...999-2023-becoming-leading-contributing-cvd-cause.md | 2 ++ ...xplains-clinical-ai-plan-reinforcement-mechanism.md | 6 ++++++ ...odemographic-bias-across-all-model-architectures.md | 6 ++++++ ...graphic-bias-in-content-and-expert-rated-quality.md | 6 ++++++ ...ential-processing-and-lack-contextual-resistance.md | 4 ++++ ...similar diagnostic accuracy in randomized trials.md | 4 ++++ ...-after-2010-representing-reversal-not-stagnation.md | 6 ++++++ ...tive-harm-accumulation-not-after-safety-evidence.md | 2 ++ ...-oversight-despite-accumulating-failure-evidence.md | 4 ++++ ...ing-glp1-market-into-commodity-and-premium-tiers.md | 7 +++++++ ...c-declining-heart-failure-hypertension-worsening.md | 4 ++++ ...osts capability and rational competitors skip it.md | 4 ++-- 42 files changed, 156 insertions(+), 9 deletions(-) diff --git a/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 b/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 index 85cda838e..0009a1611 100644 --- a/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 +++ b/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 diff --git a/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 b/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 index b19f13556..1f68c94cf 100644 --- a/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 +++ b/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 diff --git a/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md b/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md index 9e9b5ae64..f530c5875 100644 --- a/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md +++ b/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md @@ -1,9 +1,4 @@ --- - - - - - description: Getting AI right requires simultaneous alignment across competing companies, nations, and disciplines at the speed of AI development -- no existing institution can coordinate this type: claim domain: ai-alignment @@ -16,12 +11,14 @@ 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 diff --git a/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 b/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 index 461ae640d..53074609f 100644 --- a/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 +++ b/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 diff --git a/domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md b/domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md index f075153d5..0878261ac 100644 --- a/domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md +++ b/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 diff --git a/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 b/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 index 3acc1ce65..6b115fab9 100644 --- a/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 +++ b/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 @@ -12,6 +12,9 @@ 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" +- 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 diff --git a/domains/ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md b/domains/ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md index d93e9e5e4..c7d993458 100644 --- a/domains/ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md +++ b/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 diff --git a/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 b/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 index 9378120cf..aa9c153bf 100644 --- a/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 +++ b/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 @@ -9,6 +9,10 @@ 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" +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 diff --git a/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 b/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 index 52d1aa8fd..8c134ae29 100644 --- a/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 +++ b/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 diff --git a/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 b/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 index dd06283fa..a5c6e17cc 100644 --- a/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 +++ b/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 @@ -11,6 +11,10 @@ depends_on: - "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" +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 diff --git a/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 b/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 index e7d5d0a7b..368fda06c 100644 --- a/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 +++ b/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 diff --git a/domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md b/domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md index f67ed5a90..aa8c67de8 100644 --- a/domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md +++ b/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 diff --git a/domains/ai-alignment/multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md b/domains/ai-alignment/multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md index 9e338c0ab..e62cfecbb 100644 --- a/domains/ai-alignment/multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md +++ b/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 diff --git a/domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md b/domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md index 82e5afa3a..720689830 100644 --- a/domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md +++ b/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 diff --git a/domains/ai-alignment/recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md b/domains/ai-alignment/recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md index 13aba2348..26013463a 100644 --- a/domains/ai-alignment/recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md +++ b/domains/ai-alignment/recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md @@ -1,6 +1,4 @@ --- - - description: The intelligence explosion dynamic occurs when an AI crosses the threshold where it can improve itself faster than humans can, creating a self-reinforcing feedback loop type: claim domain: ai-alignment @@ -12,8 +10,10 @@ 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. diff --git a/domains/ai-alignment/sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md b/domains/ai-alignment/sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md index 9878a2994..6f33fcbcb 100644 --- a/domains/ai-alignment/sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md +++ b/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 diff --git a/domains/grand-strategy/attractor-agentic-taylorism.md b/domains/grand-strategy/attractor-agentic-taylorism.md index 514d98785..36323d10b 100644 --- a/domains/grand-strategy/attractor-agentic-taylorism.md +++ b/domains/grand-strategy/attractor-agentic-taylorism.md @@ -9,6 +9,10 @@ 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" +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 diff --git a/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 b/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 index 31574c64e..4361c5a52 100644 --- a/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 +++ b/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 diff --git a/domains/grand-strategy/voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives.md b/domains/grand-strategy/voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives.md index f323f903b..549f4a8c2 100644 --- a/domains/grand-strategy/voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives.md +++ b/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 diff --git a/domains/health/clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md b/domains/health/clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md index 43b246dd8..7703ca7a4 100644 --- a/domains/health/clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md +++ b/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 diff --git a/domains/health/clinical-ai-hallucination-rates-vary-100x-by-task-making-single-regulatory-thresholds-operationally-inadequate.md b/domains/health/clinical-ai-hallucination-rates-vary-100x-by-task-making-single-regulatory-thresholds-operationally-inadequate.md index 3663af11d..0b2abf300 100644 --- a/domains/health/clinical-ai-hallucination-rates-vary-100x-by-task-making-single-regulatory-thresholds-operationally-inadequate.md +++ b/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 diff --git a/domains/health/clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md b/domains/health/clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md index 06153ddbe..55fc13837 100644 --- a/domains/health/clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md +++ b/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 diff --git a/domains/health/fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness.md b/domains/health/fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness.md index 29dd6f699..71d8e0f1d 100644 --- a/domains/health/fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness.md +++ b/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 diff --git a/domains/health/fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps.md b/domains/health/fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps.md index b48ab7b16..113e81376 100644 --- a/domains/health/fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps.md +++ b/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 diff --git a/domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md b/domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md index a432064eb..9f2e8d4c8 100644 --- a/domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md +++ b/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 diff --git a/domains/health/fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md b/domains/health/fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md index aa00de794..dd63bfbfb 100644 --- a/domains/health/fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md +++ b/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 diff --git a/domains/health/five-adverse-sdoh-independently-predict-hypertension-risk-food-insecurity-unemployment-poverty-low-education-inadequate-insurance.md b/domains/health/five-adverse-sdoh-independently-predict-hypertension-risk-food-insecurity-unemployment-poverty-low-education-inadequate-insurance.md index cedc2846a..8d15b0ca4 100644 --- a/domains/health/five-adverse-sdoh-independently-predict-hypertension-risk-food-insecurity-unemployment-poverty-low-education-inadequate-insurance.md +++ b/domains/health/five-adverse-sdoh-independently-predict-hypertension-risk-food-insecurity-unemployment-poverty-low-education-inadequate-insurance.md @@ -14,8 +14,10 @@ attribution: 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 diff --git a/domains/health/food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality-for-sdoh-cardiovascular-pathway.md b/domains/health/food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality-for-sdoh-cardiovascular-pathway.md index 7c11dd163..b9574bf06 100644 --- a/domains/health/food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality-for-sdoh-cardiovascular-pathway.md +++ b/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 diff --git a/domains/health/hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure.md b/domains/health/hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure.md index f750f76c6..c68338ef4 100644 --- a/domains/health/hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure.md +++ b/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 diff --git a/domains/health/hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md b/domains/health/hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md index 69b2795f4..ac8558105 100644 --- a/domains/health/hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md +++ b/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 diff --git a/domains/health/hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md b/domains/health/hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md index b18086add..a4bb73c99 100644 --- a/domains/health/hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md +++ b/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 diff --git a/domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md b/domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md index 6820a347a..02909f45a 100644 --- a/domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md +++ b/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 diff --git a/domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md b/domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md index f4526bffa..d090d8bc4 100644 --- a/domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md +++ b/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 diff --git a/domains/health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md b/domains/health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md index 5e095e04a..d1e84305c 100644 --- a/domains/health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md +++ b/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 diff --git a/domains/health/llms-amplify-human-cognitive-biases-through-sequential-processing-and-lack-contextual-resistance.md b/domains/health/llms-amplify-human-cognitive-biases-through-sequential-processing-and-lack-contextual-resistance.md index b4bd877f2..ae88496fa 100644 --- a/domains/health/llms-amplify-human-cognitive-biases-through-sequential-processing-and-lack-contextual-resistance.md +++ b/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 diff --git a/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 b/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 index 9265e6e55..d3267d946 100644 --- a/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 +++ b/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 diff --git a/domains/health/midlife-cvd-mortality-increased-in-many-us-states-after-2010-representing-reversal-not-stagnation.md b/domains/health/midlife-cvd-mortality-increased-in-many-us-states-after-2010-representing-reversal-not-stagnation.md index 28f767ecd..12b84b77f 100644 --- a/domains/health/midlife-cvd-mortality-increased-in-many-us-states-after-2010-representing-reversal-not-stagnation.md +++ b/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 diff --git a/domains/health/regulatory-deregulation-occurring-during-active-harm-accumulation-not-after-safety-evidence.md b/domains/health/regulatory-deregulation-occurring-during-active-harm-accumulation-not-after-safety-evidence.md index 1016949cc..7e361270b 100644 --- a/domains/health/regulatory-deregulation-occurring-during-active-harm-accumulation-not-after-safety-evidence.md +++ b/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 diff --git a/domains/health/regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight-despite-accumulating-failure-evidence.md b/domains/health/regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight-despite-accumulating-failure-evidence.md index e6d922617..20ad8d062 100644 --- a/domains/health/regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight-despite-accumulating-failure-evidence.md +++ b/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 diff --git a/domains/health/tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers.md b/domains/health/tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers.md index f3d3cffd3..5a7e1af53 100644 --- a/domains/health/tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers.md +++ b/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 +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 +related: +- Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry --- # 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 diff --git a/domains/health/us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening.md b/domains/health/us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening.md index 95adac880..b6976008d 100644 --- a/domains/health/us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening.md +++ b/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 diff --git a/foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md b/foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md index cf940cded..22355beab 100644 --- a/foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md +++ b/foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md @@ -1,6 +1,4 @@ --- - - description: Safety post-training reduces general utility through forgetting creating competitive pressures where organizations eschew safety to gain capability advantages type: claim domain: collective-intelligence @@ -10,9 +8,11 @@ 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 -- 2.45.2